def main(): """ NAME goprinc.py DESCRIPTION calculates Principal components from dec/iinc data INPUT FORMAT takes dec/inc as first two columns in space delimited file SYNTAX goprinc.py [options] [< filename] OPTIONS -h prints help message and quits -i for interactive filename entry -f FILE, specify input file -F FILE, specifies output file name < filename for reading from standard input OUTPUT tau_1 V1_Dec, V1_Inc, tau_2 V2_Dec V2_Inc, tau_3 V3_Dec V3_Inc, N """ if len(sys.argv) > 0: if '-h' in sys.argv: # check if help is needed print main.__doc__ sys.exit() # graceful quit if '-f' in sys.argv: ind=sys.argv.index('-f') file=sys.argv[ind+1] f=open(file,'rU') data=f.readlines() elif '-i' in sys.argv: # ask for filename file=raw_input("Enter file name with dec, inc data: ") f=open(file,'rU') data=f.readlines() else: # data=sys.stdin.readlines() # read in data from standard input ofile = "" if '-F' in sys.argv: ind = sys.argv.index('-F') ofile= sys.argv[ind+1] out = open(ofile, 'w + a') DIs= [] # set up list for dec inc data for line in data: # read in the data from standard input if '\t' in line: rec=line.split('\t') # split each line on space to get records else: rec=line.split() # split each line on space to get records DIs.append((float(rec[0]),float(rec[1]))) # ppars=pmag.doprinc(DIs) output = '%7.5f %7.1f %7.1f %7.5f %7.1f %7.1f %7.5f %7.1f %7.1f %i' % (ppars["tau1"],ppars["dec"],ppars["inc"],ppars["tau2"],ppars["V2dec"],ppars["V2inc"],ppars["tau3"],ppars["V3dec"],ppars["V3inc"],ppars["N"]) if ofile == "": print output else: out.write(output+'\n')
def adj_dec(df,hole): cores=df.core.unique() adj_dec_df=pd.DataFrame(columns=df.columns) core_dec_corr={} for core in cores: core_df=df[df['core']==core] di_block=core_df[['dir_dec','dir_inc']].values ppars=pmag.doprinc(di_block) if ppars['inc']>0: # take the antipode ppars['dec']=ppars['dec']-180 core_dec_corr[core]=ppars['dec'] core_df['adj_dec']=(core_df['dir_dec']-ppars['dec'])%360 core_df['dir_dec']=(core_df['adj_dec']+90)%360 # set mean normal to 90 for plottingh adj_dec_df=pd.concat([adj_dec_df,core_df]) adj_dec_df.fillna("",inplace=True) adj_dec_df.drop_duplicates(inplace=True) adj_dec_df.to_csv(hole+'/'+hole+'_dec_adjusted.csv',index=False) print ('Adjusted Declination DataFrame returned') return adj_dec_df,core_dec_corr
def adj_dec(df,hole): """ takes an archive measurement DataFrame and adjusts the average declination by hole to 90 for "normal" Parameters __________ df : Pandas DataFrame data frame of archive measurement data hole : str IODP hole Returns _______ adj_dec_df : DataFrame adjusted declination data frame core_dec_adj : dict dictionary of cores and the average declination """ cores=df.core.unique() adj_dec_df=pd.DataFrame(columns=df.columns) core_dec_adj={} for core in cores: core_df=df[df['core']==core] di_block=core_df[['dir_dec','dir_inc']].values ppars=pmag.doprinc(di_block) if ppars['inc']>0: # take the antipode ppars['dec']=ppars['dec']-180 core_dec_adj[core]=ppars['dec'] core_df['adj_dec']=(core_df['dir_dec']-ppars['dec'])%360 core_df['dir_dec']=(core_df['adj_dec']+90)%360 # set mean normal to 90 for plottingh adj_dec_df=pd.concat([adj_dec_df,core_df]) adj_dec_df.fillna("",inplace=True) adj_dec_df.drop_duplicates(inplace=True) adj_dec_df.to_csv(hole+'/'+hole+'_dec_adjusted.csv',index=False) print ('Adjusted Declination DataFrame returned') return adj_dec_df,core_dec_adj
def main(): """ NAME foldtest_magic.py DESCRIPTION does a fold test (Tauxe, 2010) on data INPUT FORMAT pmag_specimens format file, er_samples.txt format file (for bedding) SYNTAX foldtest_magic.py [command line options] OPTIONS -h prints help message and quits -f sites formatted file [default for 3.0 is sites.txt, for 2.5, pmag_sites.txt] -fsa samples formatted file -fsi sites formatted file -exc use criteria to set acceptance criteria (supported only for data model 3) -n NB, set number of bootstraps, default is 1000 -b MIN, MAX, set bounds for untilting, default is -10, 150 -fmt FMT, specify format - default is svg -sav saves plots and quits -DM NUM MagIC data model number (2 or 3, default 3) OUTPUT Geographic: is an equal area projection of the input data in original coordinates Stratigraphic: is an equal area projection of the input data in tilt adjusted coordinates % Untilting: The dashed (red) curves are representative plots of maximum eigenvalue (tau_1) as a function of untilting The solid line is the cumulative distribution of the % Untilting required to maximize tau for all the bootstrapped data sets. The dashed vertical lines are 95% confidence bounds on the % untilting that yields the most clustered result (maximum tau_1). Command line: prints out the bootstrapped iterations and finally the confidence bounds on optimum untilting. If the 95% conf bounds include 0, then a pre-tilt magnetization is indicated If the 95% conf bounds include 100, then a post-tilt magnetization is indicated If the 95% conf bounds exclude both 0 and 100, syn-tilt magnetization is possible as is vertical axis rotation or other pathologies """ if '-h' in sys.argv: # check if help is needed print(main.__doc__) sys.exit() # graceful quit kappa = 0 dir_path = pmag.get_named_arg("-WD", ".") nboot = int(float(pmag.get_named_arg("-n", 1000))) # number of bootstraps fmt = pmag.get_named_arg("-fmt", "svg") data_model_num = int(float(pmag.get_named_arg("-DM", 3))) if data_model_num == 3: infile = pmag.get_named_arg("-f", 'sites.txt') orfile = 'samples.txt' site_col = 'site' dec_col = 'dir_dec' inc_col = 'dir_inc' tilt_col = 'dir_tilt_correction' dipkey, azkey = 'bed_dip', 'bed_dip_direction' crit_col = 'criterion' critfile = 'criteria.txt' else: infile = pmag.get_named_arg("-f", 'pmag_sites.txt') orfile = 'er_samples.txt' site_col = 'er_site_name' dec_col = 'site_dec' inc_col = 'site_inc' tilt_col = 'site_tilt_correction' dipkey, azkey = 'sample_bed_dip', 'sample_bed_dip_direction' crit_col = 'pmag_criteria_code' critfile = 'pmag_criteria.txt' if '-sav' in sys.argv: plot = 1 else: plot = 0 if '-b' in sys.argv: ind = sys.argv.index('-b') untilt_min = int(sys.argv[ind+1]) untilt_max = int(sys.argv[ind+2]) else: untilt_min, untilt_max = -10, 150 if '-fsa' in sys.argv: orfile = pmag.get_named_arg("-fsa", "") elif '-fsi' in sys.argv: orfile = pmag.get_named_arg("-fsi", "") if data_model_num == 3: dipkey, azkey = 'bed_dip', 'bed_dip_direction' else: dipkey, azkey = 'site_bed_dip', 'site_bed_dip_direction' else: if data_model_num == 3: orfile = 'sites.txt' else: orfile = 'pmag_sites.txt' orfile = pmag.resolve_file_name(orfile, dir_path) infile = pmag.resolve_file_name(infile, dir_path) critfile = pmag.resolve_file_name(critfile, dir_path) df = pd.read_csv(infile, sep='\t', header=1) # keep only records with tilt_col data = df.copy() data = data[data[tilt_col].notnull()] data = data.where(data.notnull(), "") # turn into pmag data list data = list(data.T.apply(dict)) # get orientation data if data_model_num == 3: # often orientation will be in infile (sites table) if os.path.split(orfile)[1] == os.path.split(infile)[1]: ordata = df[df[azkey].notnull()] ordata = ordata[ordata[dipkey].notnull()] ordata = list(ordata.T.apply(dict)) # sometimes orientation might be in a sample file instead else: ordata = pd.read_csv(orfile, sep='\t', header=1) ordata = list(ordata.T.apply(dict)) else: ordata, file_type = pmag.magic_read(orfile) if '-exc' in sys.argv: crits, file_type = pmag.magic_read(critfile) SiteCrits = [] for crit in crits: if crit[crit_col] == "DE-SITE": SiteCrits.append(crit) #break # get to work # PLTS = {'geo': 1, 'strat': 2, 'taus': 3} # make plot dictionary if not set_env.IS_WIN: pmagplotlib.plot_init(PLTS['geo'], 5, 5) pmagplotlib.plot_init(PLTS['strat'], 5, 5) pmagplotlib.plot_init(PLTS['taus'], 5, 5) if data_model_num == 2: GEOrecs = pmag.get_dictitem(data, tilt_col, '0', 'T') else: GEOrecs = data if len(GEOrecs) > 0: # have some geographic data num_dropped = 0 DIDDs = [] # set up list for dec inc dip_direction, dip for rec in GEOrecs: # parse data dip, dip_dir = 0, -1 Dec = float(rec[dec_col]) Inc = float(rec[inc_col]) orecs = pmag.get_dictitem( ordata, site_col, rec[site_col], 'T') if len(orecs) > 0: if orecs[0][azkey] != "": dip_dir = float(orecs[0][azkey]) if orecs[0][dipkey] != "": dip = float(orecs[0][dipkey]) if dip != 0 and dip_dir != -1: if '-exc' in sys.argv: keep = 1 for site_crit in SiteCrits: crit_name = site_crit['table_column'].split('.')[1] if crit_name and crit_name in rec.keys() and rec[crit_name]: # get the correct operation (<, >=, =, etc.) op = OPS[site_crit['criterion_operation']] # then make sure the site record passes if op(float(rec[crit_name]), float(site_crit['criterion_value'])): keep = 0 if keep == 1: DIDDs.append([Dec, Inc, dip_dir, dip]) else: num_dropped += 1 else: DIDDs.append([Dec, Inc, dip_dir, dip]) if num_dropped: print("-W- Dropped {} records because each failed one or more criteria".format(num_dropped)) else: print('no geographic directional data found') sys.exit() pmagplotlib.plot_eq(PLTS['geo'], DIDDs, 'Geographic') data = np.array(DIDDs) D, I = pmag.dotilt_V(data) TCs = np.array([D, I]).transpose() pmagplotlib.plot_eq(PLTS['strat'], TCs, 'Stratigraphic') if plot == 0: pmagplotlib.draw_figs(PLTS) Percs = list(range(untilt_min, untilt_max)) Cdf, Untilt = [], [] plt.figure(num=PLTS['taus']) print('doing ', nboot, ' iterations...please be patient.....') for n in range(nboot): # do bootstrap data sets - plot first 25 as dashed red line if n % 50 == 0: print(n) Taus = [] # set up lists for taus PDs = pmag.pseudo(DIDDs) if kappa != 0: for k in range(len(PDs)): d, i = pmag.fshdev(kappa) dipdir, dip = pmag.dodirot(d, i, PDs[k][2], PDs[k][3]) PDs[k][2] = dipdir PDs[k][3] = dip for perc in Percs: tilt = np.array([1., 1., 1., 0.01*perc]) D, I = pmag.dotilt_V(PDs*tilt) TCs = np.array([D, I]).transpose() ppars = pmag.doprinc(TCs) # get principal directions Taus.append(ppars['tau1']) if n < 25: plt.plot(Percs, Taus, 'r--') # tilt that gives maximum tau Untilt.append(Percs[Taus.index(np.max(Taus))]) Cdf.append(float(n) / float(nboot)) plt.plot(Percs, Taus, 'k') plt.xlabel('% Untilting') plt.ylabel('tau_1 (red), CDF (green)') Untilt.sort() # now for CDF of tilt of maximum tau plt.plot(Untilt, Cdf, 'g') lower = int(.025*nboot) upper = int(.975*nboot) plt.axvline(x=Untilt[lower], ymin=0, ymax=1, linewidth=1, linestyle='--') plt.axvline(x=Untilt[upper], ymin=0, ymax=1, linewidth=1, linestyle='--') tit = '%i - %i %s' % (Untilt[lower], Untilt[upper], 'Percent Unfolding') print(tit) plt.title(tit) if plot == 0: pmagplotlib.draw_figs(PLTS) ans = input('S[a]ve all figures, <Return> to quit \n ') if ans != 'a': print("Good bye") sys.exit() files = {} for key in list(PLTS.keys()): files[key] = ('foldtest_'+'%s' % (key.strip()[:2])+'.'+fmt) pmagplotlib.save_plots(PLTS, files)
def main(): """ NAME find_EI.py DESCRIPTION Applies series of assumed flattening factor and "unsquishes" inclinations assuming tangent function. Finds flattening factor that gives elongation/inclination pair consistent with TK03. Finds bootstrap confidence bounds SYNTAX find_EI.py [command line options] OPTIONS -h prints help message and quits -f FILE specify input file name -nb N specify number of bootstraps - the more the better, but slower!, default is 1000 -sc uses a "site-level" correction to a Fisherian distribution instead of a "study-level" correction to a TK03-consistent distribution. Note that many directions (~ 100) are needed for this correction to be reliable. -fmt [svg,png,eps,pdf..] change plot format, default is svg -sav saves the figures and quits INPUT dec/inc pairs, delimited with space or tabs OUTPUT four plots: 1) equal area plot of original directions 2) Elongation/inclination pairs as a function of f, data plus 25 bootstrap samples 3) Cumulative distribution of bootstrapped optimal inclinations plus uncertainties. Estimate from original data set plotted as solid line 4) Orientation of principle direction through unflattening NOTE: If distribution does not have a solution, plot labeled: Pathological. Some bootstrap samples may have valid solutions and those are plotted in the CDFs and E/I plot. """ fmt, nb = 'svg', 1000 plot = 0 if '-h' in sys.argv: print(main.__doc__) sys.exit() # graceful quit elif '-f' in sys.argv: ind = sys.argv.index('-f') file = sys.argv[ind + 1] else: print(main.__doc__) sys.exit() if '-nb' in sys.argv: ind = sys.argv.index('-nb') nb = int(sys.argv[ind + 1]) if '-sc' in sys.argv: site_correction = True else: site_correction = False if '-fmt' in sys.argv: ind = sys.argv.index('-fmt') fmt = sys.argv[ind + 1] if '-sav' in sys.argv: plot = 1 data = numpy.loadtxt(file) upper, lower = int(round(.975 * nb)), int(round(.025 * nb)) E, I = [], [] PLTS = {'eq': 1, 'ei': 2, 'cdf': 3, 'v2': 4} pmagplotlib.plot_init(PLTS['eq'], 6, 6) pmagplotlib.plot_init(PLTS['ei'], 5, 5) pmagplotlib.plot_init(PLTS['cdf'], 5, 5) pmagplotlib.plot_init(PLTS['v2'], 5, 5) pmagplotlib.plotEQ(PLTS['eq'], data, 'Data') if plot == 0: pmagplotlib.drawFIGS(PLTS) ppars = pmag.doprinc(data) Io = ppars['inc'] n = ppars["N"] Es, Is, Fs, V2s = pmag.find_f(data) if site_correction: Inc, Elong = Is[Es.index(min(Es))], Es[Es.index(min(Es))] flat_f = Fs[Es.index(min(Es))] else: Inc, Elong = Is[-1], Es[-1] flat_f = Fs[-1] pmagplotlib.plotEI(PLTS['ei'], Es, Is, flat_f) pmagplotlib.plotV2s(PLTS['v2'], V2s, Is, flat_f) b = 0 print("Bootstrapping.... be patient") while b < nb: bdata = pmag.pseudo(data) Esb, Isb, Fsb, V2sb = pmag.find_f(bdata) if b < 25: pmagplotlib.plotEI(PLTS['ei'], Esb, Isb, Fsb[-1]) if Esb[-1] != 0: ppars = pmag.doprinc(bdata) if site_correction: I.append(abs(Isb[Esb.index(min(Esb))])) E.append(Esb[Esb.index(min(Esb))]) else: I.append(abs(Isb[-1])) E.append(Esb[-1]) b += 1 if b % 25 == 0: print(b, ' out of ', nb) I.sort() E.sort() Eexp = [] for i in I: Eexp.append(pmag.EI(i)) if Inc == 0: title = 'Pathological Distribution: ' + '[%7.1f, %7.1f]' % (I[lower], I[upper]) else: title = '%7.1f [%7.1f, %7.1f]' % (Inc, I[lower], I[upper]) pmagplotlib.plotEI(PLTS['ei'], Eexp, I, 1) pmagplotlib.plotCDF(PLTS['cdf'], I, 'Inclinations', 'r', title) pmagplotlib.plotVs(PLTS['cdf'], [I[lower], I[upper]], 'b', '--') pmagplotlib.plotVs(PLTS['cdf'], [Inc], 'g', '-') pmagplotlib.plotVs(PLTS['cdf'], [Io], 'k', '-') if plot == 0: pmagplotlib.drawFIGS(PLTS) print("Io Inc I_lower, I_upper, Elon, E_lower, E_upper") print('%7.1f %s %7.1f _ %7.1f ^ %7.1f: %6.4f _ %6.4f ^ %6.4f' % (Io, " => ", Inc, I[lower], I[upper], Elong, E[lower], E[upper])) ans = input("S[a]ve plots - <return> to quit: ") if ans != 'a': print("\n Good bye\n") sys.exit() files = {} files['eq'] = 'findEI_eq.' + fmt files['ei'] = 'findEI_ei.' + fmt files['cdf'] = 'findEI_cdf.' + fmt files['v2'] = 'findEI_v2.' + fmt pmagplotlib.saveP(PLTS, files)
def main(): """ NAME foldtest_magic.py DESCRIPTION does a fold test (Tauxe, 2010) on data INPUT FORMAT pmag_specimens format file, er_samples.txt format file (for bedding) SYNTAX foldtest_magic.py [command line options] OPTIONS -h prints help message and quits -f sites formatted file [default for 3.0 is sites.txt, for 2.5, pmag_sites.txt] -fsa samples formatted file -fsi sites formatted file -exc use criteria to set acceptance criteria (supported only for data model 3) -n NB, set number of bootstraps, default is 1000 -b MIN, MAX, set bounds for untilting, default is -10, 150 -fmt FMT, specify format - default is svg -sav saves plots and quits -DM NUM MagIC data model number (2 or 3, default 3) OUTPUT Geographic: is an equal area projection of the input data in original coordinates Stratigraphic: is an equal area projection of the input data in tilt adjusted coordinates % Untilting: The dashed (red) curves are representative plots of maximum eigenvalue (tau_1) as a function of untilting The solid line is the cumulative distribution of the % Untilting required to maximize tau for all the bootstrapped data sets. The dashed vertical lines are 95% confidence bounds on the % untilting that yields the most clustered result (maximum tau_1). Command line: prints out the bootstrapped iterations and finally the confidence bounds on optimum untilting. If the 95% conf bounds include 0, then a pre-tilt magnetization is indicated If the 95% conf bounds include 100, then a post-tilt magnetization is indicated If the 95% conf bounds exclude both 0 and 100, syn-tilt magnetization is possible as is vertical axis rotation or other pathologies """ if '-h' in sys.argv: # check if help is needed print(main.__doc__) sys.exit() # graceful quit kappa = 0 dir_path = pmag.get_named_arg("-WD", ".") nboot = int(float(pmag.get_named_arg("-n", 1000))) # number of bootstraps fmt = pmag.get_named_arg("-fmt", "svg") data_model_num = int(float(pmag.get_named_arg("-DM", 3))) if data_model_num == 3: infile = pmag.get_named_arg("-f", 'sites.txt') orfile = 'samples.txt' site_col = 'site' dec_col = 'dir_dec' inc_col = 'dir_inc' tilt_col = 'dir_tilt_correction' dipkey, azkey = 'bed_dip', 'bed_dip_direction' crit_col = 'criterion' critfile = 'criteria.txt' else: infile = pmag.get_named_arg("-f", 'pmag_sites.txt') orfile = 'er_samples.txt' site_col = 'er_site_name' dec_col = 'site_dec' inc_col = 'site_inc' tilt_col = 'site_tilt_correction' dipkey, azkey = 'sample_bed_dip', 'sample_bed_dip_direction' crit_col = 'pmag_criteria_code' critfile = 'pmag_criteria.txt' if '-sav' in sys.argv: plot = 1 else: plot = 0 if '-b' in sys.argv: ind = sys.argv.index('-b') untilt_min = int(sys.argv[ind + 1]) untilt_max = int(sys.argv[ind + 2]) else: untilt_min, untilt_max = -10, 150 if '-fsa' in sys.argv: orfile = pmag.get_named_arg("-fsa", "") elif '-fsi' in sys.argv: orfile = pmag.get_named_arg("-fsi", "") if data_model_num == 3: dipkey, azkey = 'bed_dip', 'bed_dip_direction' else: dipkey, azkey = 'site_bed_dip', 'site_bed_dip_direction' else: if data_model_num == 3: orfile = 'sites.txt' else: orfile = 'pmag_sites.txt' orfile = pmag.resolve_file_name(orfile, dir_path) infile = pmag.resolve_file_name(infile, dir_path) critfile = pmag.resolve_file_name(critfile, dir_path) df = pd.read_csv(infile, sep='\t', header=1) # keep only records with tilt_col data = df.copy() data = data[data[tilt_col].notnull()] data = data.where(data.notnull(), "") # turn into pmag data list data = list(data.T.apply(dict)) # get orientation data if data_model_num == 3: # often orientation will be in infile (sites table) if os.path.split(orfile)[1] == os.path.split(infile)[1]: ordata = df[df[azkey].notnull()] ordata = ordata[ordata[dipkey].notnull()] ordata = list(ordata.T.apply(dict)) # sometimes orientation might be in a sample file instead else: ordata = pd.read_csv(orfile, sep='\t', header=1) ordata = list(ordata.T.apply(dict)) else: ordata, file_type = pmag.magic_read(orfile) if '-exc' in sys.argv: crits, file_type = pmag.magic_read(critfile) SiteCrits = [] for crit in crits: if crit[crit_col] == "DE-SITE": SiteCrits.append(crit) #break # get to work # PLTS = {'geo': 1, 'strat': 2, 'taus': 3} # make plot dictionary if not set_env.IS_WIN: pmagplotlib.plot_init(PLTS['geo'], 5, 5) pmagplotlib.plot_init(PLTS['strat'], 5, 5) pmagplotlib.plot_init(PLTS['taus'], 5, 5) if data_model_num == 2: GEOrecs = pmag.get_dictitem(data, tilt_col, '0', 'T') else: GEOrecs = data if len(GEOrecs) > 0: # have some geographic data num_dropped = 0 DIDDs = [] # set up list for dec inc dip_direction, dip for rec in GEOrecs: # parse data dip, dip_dir = 0, -1 Dec = float(rec[dec_col]) Inc = float(rec[inc_col]) orecs = pmag.get_dictitem(ordata, site_col, rec[site_col], 'T') if len(orecs) > 0: if orecs[0][azkey] != "": dip_dir = float(orecs[0][azkey]) if orecs[0][dipkey] != "": dip = float(orecs[0][dipkey]) if dip != 0 and dip_dir != -1: if '-exc' in sys.argv: keep = 1 for site_crit in SiteCrits: crit_name = site_crit['table_column'].split('.')[1] if crit_name and crit_name in rec.keys( ) and rec[crit_name]: # get the correct operation (<, >=, =, etc.) op = OPS[site_crit['criterion_operation']] # then make sure the site record passes if op(float(rec[crit_name]), float(site_crit['criterion_value'])): keep = 0 if keep == 1: DIDDs.append([Dec, Inc, dip_dir, dip]) else: num_dropped += 1 else: DIDDs.append([Dec, Inc, dip_dir, dip]) if num_dropped: print( "-W- Dropped {} records because each failed one or more criteria" .format(num_dropped)) else: print('no geographic directional data found') sys.exit() pmagplotlib.plot_eq(PLTS['geo'], DIDDs, 'Geographic') data = np.array(DIDDs) D, I = pmag.dotilt_V(data) TCs = np.array([D, I]).transpose() pmagplotlib.plot_eq(PLTS['strat'], TCs, 'Stratigraphic') if plot == 0: pmagplotlib.draw_figs(PLTS) Percs = list(range(untilt_min, untilt_max)) Cdf, Untilt = [], [] plt.figure(num=PLTS['taus']) print('doing ', nboot, ' iterations...please be patient.....') for n in range( nboot ): # do bootstrap data sets - plot first 25 as dashed red line if n % 50 == 0: print(n) Taus = [] # set up lists for taus PDs = pmag.pseudo(DIDDs) if kappa != 0: for k in range(len(PDs)): d, i = pmag.fshdev(kappa) dipdir, dip = pmag.dodirot(d, i, PDs[k][2], PDs[k][3]) PDs[k][2] = dipdir PDs[k][3] = dip for perc in Percs: tilt = np.array([1., 1., 1., 0.01 * perc]) D, I = pmag.dotilt_V(PDs * tilt) TCs = np.array([D, I]).transpose() ppars = pmag.doprinc(TCs) # get principal directions Taus.append(ppars['tau1']) if n < 25: plt.plot(Percs, Taus, 'r--') # tilt that gives maximum tau Untilt.append(Percs[Taus.index(np.max(Taus))]) Cdf.append(float(n) / float(nboot)) plt.plot(Percs, Taus, 'k') plt.xlabel('% Untilting') plt.ylabel('tau_1 (red), CDF (green)') Untilt.sort() # now for CDF of tilt of maximum tau plt.plot(Untilt, Cdf, 'g') lower = int(.025 * nboot) upper = int(.975 * nboot) plt.axvline(x=Untilt[lower], ymin=0, ymax=1, linewidth=1, linestyle='--') plt.axvline(x=Untilt[upper], ymin=0, ymax=1, linewidth=1, linestyle='--') tit = '%i - %i %s' % (Untilt[lower], Untilt[upper], 'Percent Unfolding') print(tit) plt.title(tit) if plot == 0: pmagplotlib.draw_figs(PLTS) ans = input('S[a]ve all figures, <Return> to quit \n ') if ans != 'a': print("Good bye") sys.exit() files = {} for key in list(PLTS.keys()): files[key] = ('foldtest_' + '%s' % (key.strip()[:2]) + '.' + fmt) pmagplotlib.save_plots(PLTS, files)
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 sites, samples, specimens, or measurements OPTIONS -h prints help message and quits -f FILE: specify input magic format file from magic, default='sites.txt' supported types=[measurements, specimens, samples, sites] -fsp FILE: specify specimen file name, (required if you want to plot measurements by sample) default='specimens.txt' -fsa FILE: specify sample file name, (required if you want to plot specimens by site) default='samples.txt' -fsi FILE: specify site file name, default='sites.txt' -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 """ # initialize some default variables FIG = {} # plot dictionary FIG['eqarea'] = 1 # eqarea is figure 1 plotE = 0 plt = 0 # default to not plotting verbose = pmagplotlib.verbose # extract arguments from sys.argv if '-h' in sys.argv: print(main.__doc__) sys.exit() dir_path = pmag.get_named_arg_from_sys("-WD", default_val=os.getcwd()) pmagplotlib.plot_init(FIG['eqarea'],5,5) in_file = pmag.get_named_arg_from_sys("-f", default_val="sites.txt") full_in_file = os.path.join(dir_path, in_file) plot_by = pmag.get_named_arg_from_sys("-obj", default_val="all").lower() spec_file = pmag.get_named_arg_from_sys("-fsp", default_val="specimens.txt") samp_file = pmag.get_named_arg_from_sys("-fsa", default_val="samples.txt") site_file = pmag.get_named_arg_from_sys("-fsi", default_val="sites.txt") if plot_by == 'all': plot_key = 'all' elif plot_by == 'sit': plot_key = 'site' elif plot_by == 'sam': plot_key = 'sample' elif plot_by == 'spc': plot_key = 'specimen' else: plot_key = 'all' if '-c' in sys.argv: contour = 1 else: contour = 0 if '-sav' in sys.argv: plt = 1 verbose = 0 if '-ell' in sys.argv: plotE = 1 ind = sys.argv.index('-ell') ell_type = sys.argv[ind+1] ell_type = pmag.get_named_arg_from_sys("-ell", "F") dist = ell_type.upper() # if dist type is unrecognized, use Fisher if dist not in ['F', 'K', 'B', 'BE', 'BV']: dist = 'F' if dist == "BV": FIG['bdirs'] = 2 pmagplotlib.plot_init(FIG['bdirs'],5,5) crd = pmag.get_named_arg_from_sys("-crd", default_val="g") if crd == "s": coord = "-1" elif crd == "t": coord = "100" else: coord = "0" fmt = pmag.get_named_arg_from_sys("-fmt", "svg") dec_key = 'dir_dec' inc_key = 'dir_inc' tilt_key = 'dir_tilt_correction' #Dir_type_keys=['','site_direction_type','sample_direction_type','specimen_direction_type'] # fnames = {"specimens": spec_file, "samples": samp_file, 'sites': site_file} contribution = nb.Contribution(dir_path, custom_filenames=fnames, single_file=in_file) # the object that contains the DataFrame + useful helper methods: table_name = list(contribution.tables.keys())[0] data_container = contribution.tables[table_name] # the actual DataFrame: data = data_container.df # uses sample infile to add temporary site_name # column to the specimen table data_container = contribution.tables[table_name] data = data_container.df if (plot_key != "all") and (plot_key not in data.columns): contribution.propagate_location_to_measurements() contribution.propagate_location_to_specimens() # add tilt key into DataFrame columns if it isn't there already if tilt_key not in data.columns: data.loc[:, tilt_key] = None 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": # return all where plot_key is not blank if plot_key not in data.columns: print('Can\'t plot by "{}". That header is not in infile: {}'.format(plot_key, in_file)) return plots = data[data[plot_key].notnull()] plotlist = plots[plot_key].unique() # grab unique values else: plotlist.append('All') for plot in plotlist: if verbose: print(plot) if plot == 'All': # plot everything at once plot_data = data else: # pull out only partial data plot_data = data[data[plot_key] == plot] DIblock = [] GCblock = [] # SLblock, SPblock = [], [] title = plot mode = 1 k = 0 if dec_key not in plot_data.columns: print("-W- No dec/inc data") continue # get all records where dec & inc values exist plot_data = plot_data[plot_data[dec_key].notnull() & plot_data[inc_key].notnull()] if plot_data.empty: continue # this sorting out is done in get_di_bock #if coord == '0': # geographic, use records with no tilt key (or tilt_key 0) # cond1 = plot_data[tilt_key].fillna('') == coord # cond2 = plot_data[tilt_key].isnull() # plot_data = plot_data[cond1 | cond2] #else: # not geographic coordinates, use only records with correct tilt_key # plot_data = plot_data[plot_data[tilt_key] == coord] # get metadata for naming the plot file locations = data_container.get_name('location', df_slice=plot_data) site = data_container.get_name('site', df_slice=plot_data) sample = data_container.get_name('sample', df_slice=plot_data) specimen = data_container.get_name('specimen', df_slice=plot_data) # make sure method_codes is in plot_data if 'method_codes' not in plot_data.columns: plot_data['method_codes'] = '' # get data blocks DIblock = data_container.get_di_block(df_slice=plot_data, tilt_corr=coord, excl=['DE-BFP']) #SLblock = [[ind, row['method_codes']] for ind, row in plot_data.iterrows()] # get great circles great_circle_data = data_container.get_records_for_code('DE-BFP', incl=True, use_slice=True, sli=plot_data) if len(great_circle_data) > 0: gc_cond = great_circle_data[tilt_key] == coord GCblock = [[float(row[dec_key]), float(row[inc_key])] for ind, row in great_circle_data[gc_cond].iterrows()] #SPblock = [[ind, row['method_codes']] for ind, row in great_circle_data[gc_cond].iterrows()] 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 len(DIblock) == 0 and len(GCblock) == 0: if verbose: print("no records for plotting") continue #sys.exit() 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 list(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 list(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=old_div(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 list(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=old_div(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 list(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 list(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 list(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 list(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) for key in list(FIG.keys()): files = {} filename = pmag.get_named_arg_from_sys('-fname') if filename: # use provided filename filename+= '.' + fmt elif pmagplotlib.isServer: # use server plot naming convention filename='LO:_'+locations+'_SI:_'+site+'_SA:_'+sample+'_SP:_'+specimen+'_CO:_'+crd+'_TY:_'+key+'_.'+fmt else: # use more readable naming convention filename = '' for item in [locations, site, sample, specimen, crd, key]: if item: item = item.replace(' ', '_') filename += item + '_' if filename.endswith('_'): filename = filename[:-1] filename += ".{}".format(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) if plt: pmagplotlib.saveP(FIG,files) continue if verbose: pmagplotlib.drawFIGS(FIG) ans=input(" S[a]ve to save plot, [q]uit, Return to continue: ") if ans == "q": sys.exit() if ans == "a": pmagplotlib.saveP(FIG,files) continue
def main(): """ NAME foldtest.py DESCRIPTION does a fold test (Tauxe, 2010) on data INPUT FORMAT dec inc dip_direction dip SYNTAX foldtest.py [command line options] OPTIONS -h prints help message and quits -f FILE file with input data -F FILE for confidence bounds on fold test -u ANGLE (circular standard deviation) for uncertainty on bedding poles -b MIN MAX bounds for quick search of percent untilting [default is -10 to 150%] -n NB number of bootstrap samples [default is 1000] -fmt FMT, specify format - default is svg -sav save figures and quit INPUT FILE Dec Inc Dip_Direction Dip in space delimited file OUTPUT PLOTS Geographic: is an equal area projection of the input data in original coordinates Stratigraphic: is an equal area projection of the input data in tilt adjusted coordinates % Untilting: The dashed (red) curves are representative plots of maximum eigenvalue (tau_1) as a function of untilting The solid line is the cumulative distribution of the % Untilting required to maximize tau for all the bootstrapped data sets. The dashed vertical lines are 95% confidence bounds on the % untilting that yields the most clustered result (maximum tau_1). Command line: prints out the bootstrapped iterations and finally the confidence bounds on optimum untilting. If the 95% conf bounds include 0, then a post-tilt magnetization is indicated If the 95% conf bounds include 100, then a pre-tilt magnetization is indicated If the 95% conf bounds exclude both 0 and 100, syn-tilt magnetization is possible as is vertical axis rotation or other pathologies Geographic: is an equal area projection of the input data in OPTIONAL OUTPUT FILE: The output file has the % untilting within the 95% confidence bounds nd the number of bootstrap samples """ kappa = 0 fmt, plot = 'svg', 0 nb = 1000 # number of bootstraps min, max = -10, 150 if '-h' in sys.argv: # check if help is needed print main.__doc__ sys.exit() # graceful quit if '-F' in sys.argv: ind = sys.argv.index('-F') outfile = open(sys.argv[ind + 1], 'w') else: outfile = "" if '-f' in sys.argv: ind = sys.argv.index('-f') file = sys.argv[ind + 1] DIDDs = numpy.loadtxt(file) else: print main.__doc__ sys.exit() if '-fmt' in sys.argv: ind = sys.argv.index('-fmt') fmt = sys.argv[ind + 1] if '-sav' in sys.argv: plot = 1 if '-b' in sys.argv: ind = sys.argv.index('-b') min = int(sys.argv[ind + 1]) max = int(sys.argv[ind + 2]) if '-n' in sys.argv: ind = sys.argv.index('-n') nb = int(sys.argv[ind + 1]) if '-u' in sys.argv: ind = sys.argv.index('-u') csd = float(sys.argv[ind + 1]) kappa = (81. / csd)**2 # # get to work # PLTS = {'geo': 1, 'strat': 2, 'taus': 3} # make plot dictionary pmagplotlib.plot_init(PLTS['geo'], 5, 5) pmagplotlib.plot_init(PLTS['strat'], 5, 5) pmagplotlib.plot_init(PLTS['taus'], 5, 5) pmagplotlib.plotEQ(PLTS['geo'], DIDDs, 'Geographic') D, I = pmag.dotilt_V(DIDDs) TCs = numpy.array([D, I]).transpose() pmagplotlib.plotEQ(PLTS['strat'], TCs, 'Stratigraphic') if plot == 0: pmagplotlib.drawFIGS(PLTS) Percs = range(min, max) Cdf, Untilt = [], [] pylab.figure(num=PLTS['taus']) print 'doing ', nb, ' iterations...please be patient.....' for n in range( nb): # do bootstrap data sets - plot first 25 as dashed red line if n % 50 == 0: print n Taus = [] # set up lists for taus PDs = pmag.pseudo(DIDDs) if kappa != 0: for k in range(len(PDs)): d, i = pmag.fshdev(kappa) dipdir, dip = pmag.dodirot(d, i, PDs[k][2], PDs[k][3]) PDs[k][2] = dipdir PDs[k][3] = dip for perc in Percs: tilt = numpy.array([1., 1., 1., 0.01 * perc]) D, I = pmag.dotilt_V(PDs * tilt) TCs = numpy.array([D, I]).transpose() ppars = pmag.doprinc(TCs) # get principal directions Taus.append(ppars['tau1']) if n < 25: pylab.plot(Percs, Taus, 'r--') Untilt.append(Percs[Taus.index( numpy.max(Taus))]) # tilt that gives maximum tau Cdf.append(float(n) / float(nb)) pylab.plot(Percs, Taus, 'k') pylab.xlabel('% Untilting') pylab.ylabel('tau_1 (red), CDF (green)') Untilt.sort() # now for CDF of tilt of maximum tau pylab.plot(Untilt, Cdf, 'g') lower = int(.025 * nb) upper = int(.975 * nb) pylab.axvline(x=Untilt[lower], ymin=0, ymax=1, linewidth=1, linestyle='--') pylab.axvline(x=Untilt[upper], ymin=0, ymax=1, linewidth=1, linestyle='--') tit = '%i - %i %s' % (Untilt[lower], Untilt[upper], 'Percent Unfolding') print tit print 'range of all bootstrap samples: ', Untilt[0], ' - ', Untilt[-1] pylab.title(tit) outstring = '%i - %i; %i\n' % (Untilt[lower], Untilt[upper], nb) if outfile != "": outfile.write(outstring) files = {} for key in PLTS.keys(): files[key] = ('foldtest_' + '%s' % (key.strip()[:2]) + '.' + fmt) if plot == 0: pmagplotlib.drawFIGS(PLTS) ans = raw_input('S[a]ve all figures, <Return> to quit ') if ans != 'a': print "Good bye" sys.exit() pmagplotlib.saveP(PLTS, files)
def main(): """ NAME foldtest.py DESCRIPTION does a fold test (Tauxe, 2010) on data INPUT FORMAT dec inc dip_direction dip SYNTAX foldtest.py [command line options] OPTIONS -h prints help message and quits -f FILE file with input data -F FILE for confidence bounds on fold test -u ANGLE (circular standard deviation) for uncertainty on bedding poles -b MIN MAX bounds for quick search of percent untilting [default is -10 to 150%] -n NB number of bootstrap samples [default is 1000] -fmt FMT, specify format - default is svg -sav save figures and quit INPUT FILE Dec Inc Dip_Direction Dip in space delimited file OUTPUT PLOTS Geographic: is an equal area projection of the input data in original coordinates Stratigraphic: is an equal area projection of the input data in tilt adjusted coordinates % Untilting: The dashed (red) curves are representative plots of maximum eigenvalue (tau_1) as a function of untilting The solid line is the cumulative distribution of the % Untilting required to maximize tau for all the bootstrapped data sets. The dashed vertical lines are 95% confidence bounds on the % untilting that yields the most clustered result (maximum tau_1). Command line: prints out the bootstrapped iterations and finally the confidence bounds on optimum untilting. If the 95% conf bounds include 0, then a post-tilt magnetization is indicated If the 95% conf bounds include 100, then a pre-tilt magnetization is indicated If the 95% conf bounds exclude both 0 and 100, syn-tilt magnetization is possible as is vertical axis rotation or other pathologies Geographic: is an equal area projection of the input data in OPTIONAL OUTPUT FILE: The output file has the % untilting within the 95% confidence bounds nd the number of bootstrap samples """ kappa=0 fmt,plot='svg',0 nb=1000 # number of bootstraps min,max=-10,150 if '-h' in sys.argv: # check if help is needed print(main.__doc__) sys.exit() # graceful quit if '-F' in sys.argv: ind=sys.argv.index('-F') outfile=open(sys.argv[ind+1],'w') else: outfile="" if '-f' in sys.argv: ind=sys.argv.index('-f') file=sys.argv[ind+1] DIDDs=numpy.loadtxt(file) else: print(main.__doc__) sys.exit() if '-fmt' in sys.argv: ind=sys.argv.index('-fmt') fmt=sys.argv[ind+1] if '-sav' in sys.argv:plot=1 if '-b' in sys.argv: ind=sys.argv.index('-b') min=int(sys.argv[ind+1]) max=int(sys.argv[ind+2]) if '-n' in sys.argv: ind=sys.argv.index('-n') nb=int(sys.argv[ind+1]) if '-u' in sys.argv: ind=sys.argv.index('-u') csd=float(sys.argv[ind+1]) kappa=(81. / csd)**2 # # get to work # PLTS={'geo':1,'strat':2,'taus':3} # make plot dictionary pmagplotlib.plot_init(PLTS['geo'],5,5) pmagplotlib.plot_init(PLTS['strat'],5,5) pmagplotlib.plot_init(PLTS['taus'],5,5) pmagplotlib.plot_eq(PLTS['geo'],DIDDs,'Geographic') D,I=pmag.dotilt_V(DIDDs) TCs=numpy.array([D,I]).transpose() pmagplotlib.plot_eq(PLTS['strat'],TCs,'Stratigraphic') if not set_env.IS_WIN: if plot==0:pmagplotlib.draw_figs(PLTS) Percs=list(range(min,max)) Cdf,Untilt=[],[] pylab.figure(num=PLTS['taus']) print('doing ',nb,' iterations...please be patient.....') for n in range(nb): # do bootstrap data sets - plot first 25 as dashed red line if n%50==0:print(n) Taus=[] # set up lists for taus PDs=pmag.pseudo(DIDDs) if kappa!=0: for k in range(len(PDs)): d,i=pmag.fshdev(kappa) dipdir,dip=pmag.dodirot(d,i,PDs[k][2],PDs[k][3]) PDs[k][2]=dipdir PDs[k][3]=dip for perc in Percs: tilt=numpy.array([1.,1.,1.,0.01*perc]) D,I=pmag.dotilt_V(PDs*tilt) TCs=numpy.array([D,I]).transpose() ppars=pmag.doprinc(TCs) # get principal directions Taus.append(ppars['tau1']) if n<25:pylab.plot(Percs,Taus,'r--') Untilt.append(Percs[Taus.index(numpy.max(Taus))]) # tilt that gives maximum tau Cdf.append(float(n) / float(nb)) pylab.plot(Percs,Taus,'k') pylab.xlabel('% Untilting') pylab.ylabel('tau_1 (red), CDF (green)') Untilt.sort() # now for CDF of tilt of maximum tau pylab.plot(Untilt,Cdf,'g') lower=int(.025*nb) upper=int(.975*nb) pylab.axvline(x=Untilt[lower],ymin=0,ymax=1,linewidth=1,linestyle='--') pylab.axvline(x=Untilt[upper],ymin=0,ymax=1,linewidth=1,linestyle='--') tit= '%i - %i %s'%(Untilt[lower],Untilt[upper],'Percent Unfolding') print(tit) print('range of all bootstrap samples: ', Untilt[0], ' - ', Untilt[-1]) pylab.title(tit) outstring= '%i - %i; %i\n'%(Untilt[lower],Untilt[upper],nb) if outfile!="":outfile.write(outstring) files={} for key in list(PLTS.keys()): files[key]=('foldtest_'+'%s'%(key.strip()[:2])+'.'+fmt) if plot==0: pmagplotlib.draw_figs(PLTS) ans= input('S[a]ve all figures, <Return> to quit ') if ans!='a': print("Good bye") sys.exit() pmagplotlib.save_plots(PLTS,files)
def main(): """ NAME foldtest_magic.py DESCRIPTION does a fold test (Tauxe, 2010) on data INPUT FORMAT pmag_specimens format file, er_samples.txt format file (for bedding) SYNTAX foldtest_magic.py [command line options] OPTIONS -h prints help message and quits -f pmag_sites formatted file [default is pmag_sites.txt] -fsa er_samples formatted file [default is er_samples.txt] -fsi er_sites formatted file -exc use pmag_criteria.txt to set acceptance criteria -n NB, set number of bootstraps, default is 1000 -b MIN, MAX, set bounds for untilting, default is -10, 150 -fmt FMT, specify format - default is svg -sav saves plots and quits OUTPUT Geographic: is an equal area projection of the input data in original coordinates Stratigraphic: is an equal area projection of the input data in tilt adjusted coordinates % Untilting: The dashed (red) curves are representative plots of maximum eigenvalue (tau_1) as a function of untilting The solid line is the cumulative distribution of the % Untilting required to maximize tau for all the bootstrapped data sets. The dashed vertical lines are 95% confidence bounds on the % untilting that yields the most clustered result (maximum tau_1). Command line: prints out the bootstrapped iterations and finally the confidence bounds on optimum untilting. If the 95% conf bounds include 0, then a pre-tilt magnetization is indicated If the 95% conf bounds include 100, then a post-tilt magnetization is indicated If the 95% conf bounds exclude both 0 and 100, syn-tilt magnetization is possible as is vertical axis rotation or other pathologies """ kappa=0 nb=1000 # number of bootstraps min,max=-10,150 dir_path='.' infile,orfile='pmag_sites.txt','er_samples.txt' critfile='pmag_criteria.txt' dipkey,azkey='sample_bed_dip','sample_bed_dip_direction' fmt='svg' plot=0 if '-WD' in sys.argv: ind=sys.argv.index('-WD') dir_path=sys.argv[ind+1] if '-h' in sys.argv: # check if help is needed print main.__doc__ sys.exit() # graceful quit if '-n' in sys.argv: ind=sys.argv.index('-n') nb=int(sys.argv[ind+1]) if '-fmt' in sys.argv: ind=sys.argv.index('-fmt') fmt=sys.argv[ind+1] if '-sav' in sys.argv:plot=1 if '-b' in sys.argv: ind=sys.argv.index('-b') min=int(sys.argv[ind+1]) max=int(sys.argv[ind+2]) if '-f' in sys.argv: ind=sys.argv.index('-f') infile=sys.argv[ind+1] if '-fsa' in sys.argv: ind=sys.argv.index('-fsa') orfile=sys.argv[ind+1] elif '-fsi' in sys.argv: ind=sys.argv.index('-fsi') orfile=sys.argv[ind+1] dipkey,azkey='site_bed_dip','site_bed_dip_direction' orfile=dir_path+'/'+orfile infile=dir_path+'/'+infile critfile=dir_path+'/'+critfile data,file_type=pmag.magic_read(infile) ordata,file_type=pmag.magic_read(orfile) if '-exc' in sys.argv: crits,file_type=pmag.magic_read(critfile) for crit in crits: if crit['pmag_criteria_code']=="DE-SITE": SiteCrit=crit break # get to work # PLTS={'geo':1,'strat':2,'taus':3} # make plot dictionary pmagplotlib.plot_init(PLTS['geo'],5,5) pmagplotlib.plot_init(PLTS['strat'],5,5) pmagplotlib.plot_init(PLTS['taus'],5,5) GEOrecs=pmag.get_dictitem(data,'site_tilt_correction','0','T') if len(GEOrecs)>0: # have some geographic data DIDDs= [] # set up list for dec inc dip_direction, dip for rec in GEOrecs: # parse data dip,dip_dir=0,-1 Dec=float(rec['site_dec']) Inc=float(rec['site_inc']) orecs=pmag.get_dictitem(ordata,'er_site_name',rec['er_site_name'],'T') if len(orecs)>0: if orecs[0][azkey]!="":dip_dir=float(orecs[0][azkey]) if orecs[0][dipkey]!="":dip=float(orecs[0][dipkey]) if dip!=0 and dip_dir!=-1: if '-exc' in sys.argv: keep=1 for key in SiteCrit.keys(): if 'site' in key and SiteCrit[key]!="" and rec[key]!="" and key!='site_alpha95': if float(rec[key])<float(SiteCrit[key]): keep=0 print rec['er_site_name'],key,rec[key] if key=='site_alpha95' and SiteCrit[key]!="" and rec[key]!="": if float(rec[key])>float(SiteCrit[key]): keep=0 if keep==1: DIDDs.append([Dec,Inc,dip_dir,dip]) else: DIDDs.append([Dec,Inc,dip_dir,dip]) else: print 'no geographic directional data found' sys.exit() pmagplotlib.plotEQ(PLTS['geo'],DIDDs,'Geographic') data=numpy.array(DIDDs) D,I=pmag.dotilt_V(data) TCs=numpy.array([D,I]).transpose() pmagplotlib.plotEQ(PLTS['strat'],TCs,'Stratigraphic') if plot==0:pmagplotlib.drawFIGS(PLTS) Percs=range(min,max) Cdf,Untilt=[],[] pylab.figure(num=PLTS['taus']) print 'doing ',nb,' iterations...please be patient.....' for n in range(nb): # do bootstrap data sets - plot first 25 as dashed red line if n%50==0:print n Taus=[] # set up lists for taus PDs=pmag.pseudo(DIDDs) if kappa!=0: for k in range(len(PDs)): d,i=pmag.fshdev(kappa) dipdir,dip=pmag.dodirot(d,i,PDs[k][2],PDs[k][3]) PDs[k][2]=dipdir PDs[k][3]=dip for perc in Percs: tilt=numpy.array([1.,1.,1.,0.01*perc]) D,I=pmag.dotilt_V(PDs*tilt) TCs=numpy.array([D,I]).transpose() ppars=pmag.doprinc(TCs) # get principal directions Taus.append(ppars['tau1']) if n<25:pylab.plot(Percs,Taus,'r--') Untilt.append(Percs[Taus.index(numpy.max(Taus))]) # tilt that gives maximum tau Cdf.append(float(n)/float(nb)) pylab.plot(Percs,Taus,'k') pylab.xlabel('% Untilting') pylab.ylabel('tau_1 (red), CDF (green)') Untilt.sort() # now for CDF of tilt of maximum tau pylab.plot(Untilt,Cdf,'g') lower=int(.025*nb) upper=int(.975*nb) pylab.axvline(x=Untilt[lower],ymin=0,ymax=1,linewidth=1,linestyle='--') pylab.axvline(x=Untilt[upper],ymin=0,ymax=1,linewidth=1,linestyle='--') tit= '%i - %i %s'%(Untilt[lower],Untilt[upper],'Percent Unfolding') print tit pylab.title(tit) if plot==0: pmagplotlib.drawFIGS(PLTS) ans= raw_input('S[a]ve all figures, <Return> to quit \n ') if ans!='a': print "Good bye" sys.exit() files={} for key in PLTS.keys(): files[key]=('foldtest_'+'%s'%(key.strip()[:2])+'.'+fmt) pmagplotlib.saveP(PLTS,files)
def main(): """ NAME eqarea_ell.py DESCRIPTION makes equal area projections from declination/inclination data and plot ellipses SYNTAX eqarea_ell.py -h [command line options] INPUT takes space delimited Dec/Inc data OPTIONS -h prints help message and quits -f FILE -fmt [svg,png,jpg] format for output plots -sav saves figures and quits -ell [F,K,B,Be,Bv] plot Fisher, Kent, Bingham, Bootstrap ellipses or Boostrap eigenvectors """ FIG={} # plot dictionary FIG['eq']=1 # eqarea is figure 1 fmt,dist,mode,plot='svg','F',1,0 sym={'lower':['o','r'],'upper':['o','w'],'size':10} plotE=0 if '-h' in sys.argv: print(main.__doc__) sys.exit() if not set_env.IS_WIN: pmagplotlib.plot_init(FIG['eq'],5,5) if '-sav' in sys.argv:plot=1 if '-f' in sys.argv: ind=sys.argv.index("-f") title=sys.argv[ind+1] data=numpy.loadtxt(title).transpose() 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 '-fmt' in sys.argv: ind=sys.argv.index("-fmt") fmt=sys.argv[ind+1] DIblock=numpy.array([data[0],data[1]]).transpose() if len(DIblock)>0: pmagplotlib.plot_eq_sym(FIG['eq'],DIblock,title,sym) #if plot==0:pmagplotlib.draw_figs(FIG) else: print("no data to plot") sys.exit() 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 list(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)>3: fpars=pmag.fisher_mean(nDIs) for key in list(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)>3: fpars=pmag.fisher_mean(rDIs) if pmagplotlib.verbose:print("mode ",mode) for key in list(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 list(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 list(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 len(nDIs)<10 and len(rDIs)<10: print('too few data points for bootstrap') sys.exit() if dist=='BE': print('Be patient for bootstrap...') if len(nDIs)>=10: BnDIs=pmag.di_boot(nDIs) Bkpars=pmag.dokent(BnDIs,1.) if pmagplotlib.verbose:print("mode ",mode) for key in list(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)>=10: BrDIs=pmag.di_boot(rDIs) Bkpars=pmag.dokent(BrDIs,1.) if pmagplotlib.verbose:print("mode ",mode) for key in list(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': print('Be patient for bootstrap...') vsym={'lower':['+','k'],'upper':['x','k'],'size':5} if len(nDIs)>5: BnDIs=pmag.di_boot(nDIs) pmagplotlib.plot_eq_sym(FIG['bdirs'],BnDIs,'Bootstrapped Eigenvectors',vsym) if len(rDIs)>5: BrDIs=pmag.di_boot(rDIs) if len(nDIs)>5: # plot on existing plots pmagplotlib.plot_di_sym(FIG['bdirs'],BrDIs,vsym) else: pmagplotlib.plot_eq(FIG['bdirs'],BrDIs,'Bootstrapped Eigenvectors',vsym) if dist=='B': if len(nDIs)> 3 or len(rDIs)>3: pmagplotlib.plot_conf(FIG['eq'],etitle,[],npars,0) elif len(nDIs)>3 and dist!='BV': pmagplotlib.plot_conf(FIG['eq'],etitle,[],npars,0) if len(rDIs)>3: pmagplotlib.plot_conf(FIG['eq'],etitle,[],rpars,0) elif len(rDIs)>3 and dist!='BV': pmagplotlib.plot_conf(FIG['eq'],etitle,[],rpars,0) #if plot==0:pmagplotlib.draw_figs(FIG) if plot==0:pmagplotlib.draw_figs(FIG) # files={} for key in list(FIG.keys()): files[key]=title+'_'+key+'.'+fmt if pmagplotlib.isServer: black = '#000000' purple = '#800080' titles={} titles['eq']='Equal Area Plot' FIG = pmagplotlib.add_borders(FIG,titles,black,purple) pmagplotlib.save_plots(FIG,files) elif plot==0: ans=input(" S[a]ve to save plot, [q]uit, Return to continue: ") if ans=="q": sys.exit() if ans=="a": pmagplotlib.save_plots(FIG,files) else: pmagplotlib.save_plots(FIG,files)
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 sites, samples, specimens, or measurements OPTIONS -h prints help message and quits -f FILE: specify input magic format file from magic, default='sites.txt' supported types=[measurements, specimens, samples, sites] -fsp FILE: specify specimen file name, (required if you want to plot measurements by sample) default='specimens.txt' -fsa FILE: specify sample file name, (required if you want to plot specimens by site) default='samples.txt' -fsi FILE: specify site file name, default='sites.txt' -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 """ # initialize some default variables FIG = {} # plot dictionary FIG["eqarea"] = 1 # eqarea is figure 1 plotE = 0 plt = 0 # default to not plotting verbose = pmagplotlib.verbose # extract arguments from sys.argv if "-h" in sys.argv: print main.__doc__ sys.exit() dir_path = pmag.get_named_arg_from_sys("-WD", default_val=os.getcwd()) pmagplotlib.plot_init(FIG["eqarea"], 5, 5) in_file = pmag.get_named_arg_from_sys("-f", default_val="sites.txt") full_in_file = os.path.join(dir_path, in_file) plot_by = pmag.get_named_arg_from_sys("-obj", default_val="all").lower() spec_file = pmag.get_named_arg_from_sys("-fsp", default_val="specimens.txt") samp_file = pmag.get_named_arg_from_sys("-fsa", default_val="samples.txt") site_file = pmag.get_named_arg_from_sys("-fsi", default_val="sites.txt") if plot_by == "all": plot_key = "all" elif plot_by == "sit": plot_key = "site" elif plot_by == "sam": plot_key = "sample" elif plot_by == "spc": plot_key = "specimen" else: plot_key = "all" if "-c" in sys.argv: contour = 1 else: contour = 0 if "-sav" in sys.argv: plt = 1 verbose = 0 if "-ell" in sys.argv: plotE = 1 ind = sys.argv.index("-ell") ell_type = sys.argv[ind + 1] ell_type = pmag.get_named_arg_from_sys("-ell", "F") dist = ell_type.upper() # if dist type is unrecognized, use Fisher if dist not in ["F", "K", "B", "BE", "BV"]: dist = "F" if dist == "BV": FIG["bdirs"] = 2 pmagplotlib.plot_init(FIG["bdirs"], 5, 5) crd = pmag.get_named_arg_from_sys("-crd", default_val="g") if crd == "s": coord = "-1" elif crd == "t": coord = "100" else: coord = "0" fmt = pmag.get_named_arg_from_sys("-fmt", "svg") dec_key = "dir_dec" inc_key = "dir_inc" tilt_key = "dir_tilt_correction" # Dir_type_keys=['','site_direction_type','sample_direction_type','specimen_direction_type'] # fnames = {"specimens": spec_file, "samples": samp_file, "sites": site_file} contribution = nb.Contribution(dir_path, custom_filenames=fnames, single_file=in_file) # the object that contains the DataFrame + useful helper methods: table_name = contribution.tables.keys()[0] data_container = contribution.tables[table_name] # the actual DataFrame: data = data_container.df # uses sample infile to add temporary site_name # column to the specimen table data_container = contribution.tables[table_name] data = data_container.df if (plot_key != "all") and (plot_key not in data.columns): data = contribution.propagate_name_down(plot_key, table_name) # add tilt key into DataFrame columns if it isn't there already if tilt_key not in data.columns: data.loc[:, tilt_key] = None 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": # return all where plot_key is not blank if plot_key not in data.columns: print 'Can\'t plot by "{}". That header is not in infile: {}'.format(plot_key, in_file) return plots = data[data[plot_key].notnull()] plotlist = plots[plot_key].unique() # grab unique values else: plotlist.append("All") for plot in plotlist: if verbose: print plot if plot == "All": # plot everything at once plot_data = data else: # pull out only partial data plot_data = data[data[plot_key] == plot] DIblock = [] GCblock = [] # SLblock, SPblock = [], [] title = plot mode = 1 k = 0 if dec_key not in plot_data.columns: print "-W- No dec/inc data" continue # get all records where dec & inc values exist plot_data = plot_data[plot_data[dec_key].notnull() & plot_data[inc_key].notnull()] if plot_data.empty: continue # this sorting out is done in get_di_bock # if coord == '0': # geographic, use records with no tilt key (or tilt_key 0) # cond1 = plot_data[tilt_key].fillna('') == coord # cond2 = plot_data[tilt_key].isnull() # plot_data = plot_data[cond1 | cond2] # else: # not geographic coordinates, use only records with correct tilt_key # plot_data = plot_data[plot_data[tilt_key] == coord] # get metadata for naming the plot file locations = data_container.get_name("location", df_slice=plot_data) site = data_container.get_name("site", df_slice=plot_data) sample = data_container.get_name("sample", df_slice=plot_data) specimen = data_container.get_name("specimen", df_slice=plot_data) # make sure method_codes is in plot_data if "method_codes" not in plot_data.columns: plot_data["method_codes"] = "" # get data blocks DIblock = data_container.get_di_block(df_slice=plot_data, tilt_corr=coord, excl=["DE-BFP"]) # SLblock = [[ind, row['method_codes']] for ind, row in plot_data.iterrows()] # get great circles great_circle_data = data_container.get_records_for_code("DE-BFP", incl=True, use_slice=True, sli=plot_data) if len(great_circle_data) > 0: gc_cond = great_circle_data[tilt_key] == coord GCblock = [[float(row[dec_key]), float(row[inc_key])] for ind, row in great_circle_data[gc_cond].iterrows()] # SPblock = [[ind, row['method_codes']] for ind, row in great_circle_data[gc_cond].iterrows()] 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, 90.0, "g") if len(DIblock) == 0 and len(GCblock) == 0: if verbose: print "no records for plotting" continue # sys.exit() 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 > 90.0: 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 * 90.0) # Beta inc npars.append(fpars["alpha95"]) # gamma npars.append(fpars["dec"] + 90.0) # Beta dec npars.append(0.0) # 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 * 90.0) # Beta inc rpars.append(fpars["alpha95"]) # gamma rpars.append(fpars["dec"] + 90.0) # Beta dec rpars.append(0.0) # 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, 1.0) 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, 1.0) 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) for key in FIG.keys(): files = {} filename = pmag.get_named_arg_from_sys("-fname") if filename: filename += "." + fmt else: 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) if plt: pmagplotlib.saveP(FIG, files) continue if verbose: pmagplotlib.drawFIGS(FIG) 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) continue
def main(): """ NAME foldtest_magic.py DESCRIPTION does a fold test (Tauxe, 2010) on data INPUT FORMAT pmag_specimens format file, er_samples.txt format file (for bedding) SYNTAX foldtest_magic.py [command line options] OPTIONS -h prints help message and quits -f pmag_sites formatted file [default is pmag_sites.txt] -fsa er_samples formatted file [default is er_samples.txt] -fsi er_sites formatted file -exc use pmag_criteria.txt to set acceptance criteria -n NB, set number of bootstraps, default is 1000 -b MIN, MAX, set bounds for untilting, default is -10, 150 -fmt FMT, specify format - default is svg -sav saves plots and quits OUTPUT Geographic: is an equal area projection of the input data in original coordinates Stratigraphic: is an equal area projection of the input data in tilt adjusted coordinates % Untilting: The dashed (red) curves are representative plots of maximum eigenvalue (tau_1) as a function of untilting The solid line is the cumulative distribution of the % Untilting required to maximize tau for all the bootstrapped data sets. The dashed vertical lines are 95% confidence bounds on the % untilting that yields the most clustered result (maximum tau_1). Command line: prints out the bootstrapped iterations and finally the confidence bounds on optimum untilting. If the 95% conf bounds include 0, then a pre-tilt magnetization is indicated If the 95% conf bounds include 100, then a post-tilt magnetization is indicated If the 95% conf bounds exclude both 0 and 100, syn-tilt magnetization is possible as is vertical axis rotation or other pathologies """ kappa = 0 nb = 1000 # number of bootstraps min, max = -10, 150 dir_path = '.' infile, orfile = 'pmag_sites.txt', 'er_samples.txt' critfile = 'pmag_criteria.txt' dipkey, azkey = 'sample_bed_dip', 'sample_bed_dip_direction' fmt = 'svg' plot = 0 if '-WD' in sys.argv: ind = sys.argv.index('-WD') dir_path = sys.argv[ind + 1] if '-h' in sys.argv: # check if help is needed print(main.__doc__) sys.exit() # graceful quit if '-n' in sys.argv: ind = sys.argv.index('-n') nb = int(sys.argv[ind + 1]) if '-fmt' in sys.argv: ind = sys.argv.index('-fmt') fmt = sys.argv[ind + 1] if '-sav' in sys.argv: plot = 1 if '-b' in sys.argv: ind = sys.argv.index('-b') min = int(sys.argv[ind + 1]) max = int(sys.argv[ind + 2]) if '-f' in sys.argv: ind = sys.argv.index('-f') infile = sys.argv[ind + 1] if '-fsa' in sys.argv: ind = sys.argv.index('-fsa') orfile = sys.argv[ind + 1] elif '-fsi' in sys.argv: ind = sys.argv.index('-fsi') orfile = sys.argv[ind + 1] dipkey, azkey = 'site_bed_dip', 'site_bed_dip_direction' orfile = dir_path + '/' + orfile infile = dir_path + '/' + infile critfile = dir_path + '/' + critfile data, file_type = pmag.magic_read(infile) ordata, file_type = pmag.magic_read(orfile) if '-exc' in sys.argv: crits, file_type = pmag.magic_read(critfile) for crit in crits: if crit['pmag_criteria_code'] == "DE-SITE": SiteCrit = crit break # get to work # PLTS = {'geo': 1, 'strat': 2, 'taus': 3} # make plot dictionary pmagplotlib.plot_init(PLTS['geo'], 5, 5) pmagplotlib.plot_init(PLTS['strat'], 5, 5) pmagplotlib.plot_init(PLTS['taus'], 5, 5) GEOrecs = pmag.get_dictitem(data, 'site_tilt_correction', '0', 'T') if len(GEOrecs) > 0: # have some geographic data DIDDs = [] # set up list for dec inc dip_direction, dip for rec in GEOrecs: # parse data dip, dip_dir = 0, -1 Dec = float(rec['site_dec']) Inc = float(rec['site_inc']) orecs = pmag.get_dictitem(ordata, 'er_site_name', rec['er_site_name'], 'T') if len(orecs) > 0: if orecs[0][azkey] != "": dip_dir = float(orecs[0][azkey]) if orecs[0][dipkey] != "": dip = float(orecs[0][dipkey]) if dip != 0 and dip_dir != -1: if '-exc' in sys.argv: keep = 1 for key in list(SiteCrit.keys()): if 'site' in key and SiteCrit[key] != "" and rec[ key] != "" and key != 'site_alpha95': if float(rec[key]) < float(SiteCrit[key]): keep = 0 print(rec['er_site_name'], key, rec[key]) if key == 'site_alpha95' and SiteCrit[ key] != "" and rec[key] != "": if float(rec[key]) > float(SiteCrit[key]): keep = 0 if keep == 1: DIDDs.append([Dec, Inc, dip_dir, dip]) else: DIDDs.append([Dec, Inc, dip_dir, dip]) else: print('no geographic directional data found') sys.exit() pmagplotlib.plotEQ(PLTS['geo'], DIDDs, 'Geographic') data = numpy.array(DIDDs) D, I = pmag.dotilt_V(data) TCs = numpy.array([D, I]).transpose() pmagplotlib.plotEQ(PLTS['strat'], TCs, 'Stratigraphic') if plot == 0: pmagplotlib.drawFIGS(PLTS) Percs = list(range(min, max)) Cdf, Untilt = [], [] pylab.figure(num=PLTS['taus']) print('doing ', nb, ' iterations...please be patient.....') for n in range( nb): # do bootstrap data sets - plot first 25 as dashed red line if n % 50 == 0: print(n) Taus = [] # set up lists for taus PDs = pmag.pseudo(DIDDs) if kappa != 0: for k in range(len(PDs)): d, i = pmag.fshdev(kappa) dipdir, dip = pmag.dodirot(d, i, PDs[k][2], PDs[k][3]) PDs[k][2] = dipdir PDs[k][3] = dip for perc in Percs: tilt = numpy.array([1., 1., 1., 0.01 * perc]) D, I = pmag.dotilt_V(PDs * tilt) TCs = numpy.array([D, I]).transpose() ppars = pmag.doprinc(TCs) # get principal directions Taus.append(ppars['tau1']) if n < 25: pylab.plot(Percs, Taus, 'r--') Untilt.append(Percs[Taus.index( numpy.max(Taus))]) # tilt that gives maximum tau Cdf.append(old_div(float(n), float(nb))) pylab.plot(Percs, Taus, 'k') pylab.xlabel('% Untilting') pylab.ylabel('tau_1 (red), CDF (green)') Untilt.sort() # now for CDF of tilt of maximum tau pylab.plot(Untilt, Cdf, 'g') lower = int(.025 * nb) upper = int(.975 * nb) pylab.axvline(x=Untilt[lower], ymin=0, ymax=1, linewidth=1, linestyle='--') pylab.axvline(x=Untilt[upper], ymin=0, ymax=1, linewidth=1, linestyle='--') tit = '%i - %i %s' % (Untilt[lower], Untilt[upper], 'Percent Unfolding') print(tit) pylab.title(tit) if plot == 0: pmagplotlib.drawFIGS(PLTS) ans = input('S[a]ve all figures, <Return> to quit \n ') if ans != 'a': print("Good bye") sys.exit() files = {} for key in list(PLTS.keys()): files[key] = ('foldtest_' + '%s' % (key.strip()[:2]) + '.' + fmt) pmagplotlib.saveP(PLTS, files)
def main(): """ NAME find_EI.py DESCRIPTION Applies series of assumed flattening factor and "unsquishes" inclinations assuming tangent function. Finds flattening factor that gives elongation/inclination pair consistent with TK03. Finds bootstrap confidence bounds SYNTAX find_EI.py [command line options] OPTIONS -h prints help message and quits -f FILE specify input file name -n N specify number of bootstraps - the more the better, but slower!, default is 1000 -sc uses a "site-level" correction to a Fisherian distribution instead of a "study-level" correction to a TK03-consistent distribution. Note that many directions (~ 100) are needed for this correction to be reliable. -fmt [svg,png,eps,pdf..] change plot format, default is svg -sav saves the figures and quits INPUT dec/inc pairs, delimited with space or tabs OUTPUT four plots: 1) equal area plot of original directions 2) Elongation/inclination pairs as a function of f, data plus 25 bootstrap samples 3) Cumulative distribution of bootstrapped optimal inclinations plus uncertainties. Estimate from original data set plotted as solid line 4) Orientation of principle direction through unflattening NOTE: If distribution does not have a solution, plot labeled: Pathological. Some bootstrap samples may have valid solutions and those are plotted in the CDFs and E/I plot. """ fmt,nb='svg',1000 plot=0 if '-h' in sys.argv: print(main.__doc__) sys.exit() # graceful quit elif '-f' in sys.argv: ind=sys.argv.index('-f') file=sys.argv[ind+1] else: print(main.__doc__) sys.exit() if '-n' in sys.argv: ind=sys.argv.index('-n') nb=int(sys.argv[ind+1]) if '-sc' in sys.argv: site_correction = True else: site_correction = False if '-fmt' in sys.argv: ind=sys.argv.index('-fmt') fmt=sys.argv[ind+1] if '-sav' in sys.argv:plot=1 data=numpy.loadtxt(file) upper,lower=int(round(.975*nb)),int(round(.025*nb)) E,I=[],[] PLTS={'eq':1,'ei':2,'cdf':3,'v2':4} pmagplotlib.plot_init(PLTS['eq'],6,6) pmagplotlib.plot_init(PLTS['ei'],5,5) pmagplotlib.plot_init(PLTS['cdf'],5,5) pmagplotlib.plot_init(PLTS['v2'],5,5) pmagplotlib.plot_eq(PLTS['eq'],data,'Data') # this is a problem #if plot==0:pmagplotlib.draw_figs(PLTS) ppars=pmag.doprinc(data) Io=ppars['inc'] n=ppars["N"] Es,Is,Fs,V2s=pmag.find_f(data) if site_correction: Inc,Elong=Is[Es.index(min(Es))],Es[Es.index(min(Es))] flat_f = Fs[Es.index(min(Es))] else: Inc,Elong=Is[-1],Es[-1] flat_f = Fs[-1] pmagplotlib.plot_ei(PLTS['ei'],Es,Is,flat_f) pmagplotlib.plot_v2s(PLTS['v2'],V2s,Is,flat_f) b=0 print("Bootstrapping.... be patient") while b<nb: bdata=pmag.pseudo(data) Esb,Isb,Fsb,V2sb=pmag.find_f(bdata) if b<25: pmagplotlib.plot_ei(PLTS['ei'],Esb,Isb,Fsb[-1]) if Esb[-1]!=0: ppars=pmag.doprinc(bdata) if site_correction: I.append(abs(Isb[Esb.index(min(Esb))])) E.append(Esb[Esb.index(min(Esb))]) else: I.append(abs(Isb[-1])) E.append(Esb[-1]) b+=1 if b%25==0:print(b,' out of ',nb) I.sort() E.sort() Eexp=[] for i in I: Eexp.append(pmag.EI(i)) if Inc==0: title= 'Pathological Distribution: '+'[%7.1f, %7.1f]' %(I[lower],I[upper]) else: title= '%7.1f [%7.1f, %7.1f]' %( Inc, I[lower],I[upper]) pmagplotlib.plot_ei(PLTS['ei'],Eexp,I,1) pmagplotlib.plot_cdf(PLTS['cdf'],I,'Inclinations','r',title) pmagplotlib.plot_vs(PLTS['cdf'],[I[lower],I[upper]],'b','--') pmagplotlib.plot_vs(PLTS['cdf'],[Inc],'g','-') pmagplotlib.plot_vs(PLTS['cdf'],[Io],'k','-') if plot==0: print('%7.1f %s %7.1f _ %7.1f ^ %7.1f: %6.4f _ %6.4f ^ %6.4f' %(Io, " => ", Inc, I[lower],I[upper], Elong, E[lower],E[upper])) print("Io Inc I_lower, I_upper, Elon, E_lower, E_upper") pmagplotlib.draw_figs(PLTS) ans = "" while ans not in ['q', 'a']: ans= input("S[a]ve plots - <q> to quit: ") if ans=='q': print("\n Good bye\n") sys.exit() files={} files['eq']='findEI_eq.'+fmt files['ei']='findEI_ei.'+fmt files['cdf']='findEI_cdf.'+fmt files['v2']='findEI_v2.'+fmt pmagplotlib.save_plots(PLTS,files)
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 plt=0 if '-sav' in sys.argv: plt=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 plt: pmagplotlib.saveP(FIG,files)
def main(): """ NAME fishqq.py DESCRIPTION makes qq plot from dec,inc input data INPUT FORMAT takes dec/inc pairs in space delimited file SYNTAX fishqq.py [command line options] OPTIONS -h help message -f FILE, specify file on command line -F FILE, specify output file for statistics -sav save and quit [saves as input file name plus fmt extension] -fmt specify format for output [png, eps, svg, pdf] OUTPUT: Dec Inc N Mu Mu_crit Me Me_crit Y/N where direction is the principal component and Y/N is Fisherian or not separate lines for each mode with N >=10 (N and R) """ fmt,plot='svg',0 outfile="" if '-h' in sys.argv: # check if help is needed print main.__doc__ sys.exit() # graceful quit elif '-f' in sys.argv: # ask for filename ind=sys.argv.index('-f') file=sys.argv[ind+1] f=open(file,'rU') data=f.readlines() if '-F' in sys.argv: ind=sys.argv.index('-F') outfile=open(sys.argv[ind+1],'w') # open output file if '-sav' in sys.argv: plot=1 if '-fmt' in sys.argv: ind=sys.argv.index('-fmt') fmt=sys.argv[ind+1] DIs,nDIs,rDIs= [],[],[] # set up list for data for line in data: # read in the data from standard input if '\t' in line: rec=line.split('\t') # split each line on space to get records else: rec=line.split() # split each line on space to get records DIs.append([float(rec[0]),float(rec[1])]) # append data to Inc # split into two modes ppars=pmag.doprinc(DIs) # get principal directions for rec in DIs: angle=pmag.angle([rec[0],rec[1]],[ppars['dec'],ppars['inc']]) if angle>90.: rDIs.append(rec) else: nDIs.append(rec) # if len(rDIs) >=10 or len(nDIs) >=10: D1,I1=[],[] QQ={'unf1':1,'exp1':2} pmagplotlib.plot_init(QQ['unf1'],5,5) pmagplotlib.plot_init(QQ['exp1'],5,5) if len(nDIs) < 10: ppars=pmag.doprinc(rDIs) # get principal directions Drbar,Irbar=ppars['dec']-180.,-ppars['inc'] Nr=len(rDIs) for di in rDIs: d,irot=pmag.dotilt(di[0],di[1],Drbar-180.,90.-Irbar) # rotate to mean drot=d-180. if drot<0:drot=drot+360. D1.append(drot) I1.append(irot) Dtit='Mode 2 Declinations' Itit='Mode 2 Inclinations' else: ppars=pmag.doprinc(nDIs) # get principal directions Dnbar,Inbar=ppars['dec'],ppars['inc'] Nn=len(nDIs) for di in nDIs: d,irot=pmag.dotilt(di[0],di[1],Dnbar-180.,90.-Inbar) # rotate to mean drot=d-180. if drot<0:drot=drot+360. D1.append(drot) I1.append(irot) Dtit='Mode 1 Declinations' Itit='Mode 1 Inclinations' Mu_n,Mu_ncr=pmagplotlib.plotQQunf(QQ['unf1'],D1,Dtit) # make plot Me_n,Me_ncr=pmagplotlib.plotQQexp(QQ['exp1'],I1,Itit) # make plot #print Mu_n,Mu_ncr,Me_n, Me_ncr if outfile!="": # Dec Inc N Mu Mu_crit Me Me_crit Y/N if Mu_n<=Mu_ncr and Me_n<=Me_ncr: F='Y' else: F='N' outstring='%7.1f %7.1f %i %5.3f %5.3f %5.3f %5.3f %s \n'%(Dnbar,Inbar,Nn,Mu_n,Mu_ncr,Me_n,Me_ncr,F) outfile.write(outstring) else: print 'you need N> 10 for at least one mode' sys.exit() if len(rDIs)>10 and len(nDIs)>10: D2,I2=[],[] QQ['unf2']=3 QQ['exp2']=4 pmagplotlib.plot_init(QQ['unf2'],5,5) pmagplotlib.plot_init(QQ['exp2'],5,5) ppars=pmag.doprinc(rDIs) # get principal directions Drbar,Irbar=ppars['dec']-180.,-ppars['inc'] Nr=len(rDIs) for di in rDIs: d,irot=pmag.dotilt(di[0],di[1],Drbar-180.,90.-Irbar) # rotate to mean drot=d-180. if drot<0:drot=drot+360. D2.append(drot) I2.append(irot) Dtit='Mode 2 Declinations' Itit='Mode 2 Inclinations' Mu_r,Mu_rcr=pmagplotlib.plotQQunf(QQ['unf2'],D2,Dtit) # make plot Me_r,Me_rcr=pmagplotlib.plotQQexp(QQ['exp2'],I2,Itit) # make plot if outfile!="": # Dec Inc N Mu Mu_crit Me Me_crit Y/N if Mu_r<=Mu_rcr and Me_r<=Me_rcr: F='Y' else: F='N' outstring='%7.1f %7.1f %i %5.3f %5.3f %5.3f %5.3f %s \n'%(Drbar,Irbar,Nr,Mu_r,Mu_rcr,Me_r,Me_rcr,F) outfile.write(outstring) files={} for key in QQ.keys(): files[key]=file+'_'+key+'.'+fmt if pmagplotlib.isServer: black = '#000000' purple = '#800080' titles={} titles['eq']='Equal Area Plot' EQ = pmagplotlib.addBorders(EQ,titles,black,purple) pmagplotlib.saveP(QQ,files) elif plot==1: pmagplotlib.saveP(QQ,files) else: pmagplotlib.drawFIGS(QQ) ans=raw_input(" S[a]ve to save plot, [q]uit without saving: ") if ans=="a": pmagplotlib.saveP(QQ,files)
def main(): """ NAME eqarea_ell.py DESCRIPTION makes equal area projections from declination/inclination data and plot ellipses SYNTAX eqarea_ell.py -h [command line options] INPUT takes space delimited Dec/Inc data OPTIONS -h prints help message and quits -f FILE -fmt [svg,png,jpg] format for output plots -sav saves figures and quits -ell [F,K,B,Be,Bv] plot Fisher, Kent, Bingham, Bootstrap ellipses or Boostrap eigenvectors """ FIG = {} # plot dictionary FIG['eq'] = 1 # eqarea is figure 1 fmt, dist, mode, plot = 'svg', 'F', 1, 0 sym = {'lower': ['o', 'r'], 'upper': ['o', 'w'], 'size': 10} plotE = 0 if '-h' in sys.argv: print(main.__doc__) sys.exit() pmagplotlib.plot_init(FIG['eq'], 5, 5) if '-sav' in sys.argv: plot = 1 if '-f' in sys.argv: ind = sys.argv.index("-f") title = sys.argv[ind + 1] data = numpy.loadtxt(title).transpose() 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 '-fmt' in sys.argv: ind = sys.argv.index("-fmt") fmt = sys.argv[ind + 1] DIblock = numpy.array([data[0], data[1]]).transpose() if len(DIblock) > 0: pmagplotlib.plotEQsym(FIG['eq'], DIblock, title, sym) if plot == 0: pmagplotlib.drawFIGS(FIG) else: print("no data to plot") sys.exit() 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 list(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) > 3: fpars = pmag.fisher_mean(nDIs) for key in list(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 = old_div(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) > 3: fpars = pmag.fisher_mean(rDIs) if pmagplotlib.verbose: print("mode ", mode) for key in list(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 = old_div(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 list(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 list(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 len(nDIs) < 10 and len(rDIs) < 10: print('too few data points for bootstrap') sys.exit() if dist == 'BE': print('Be patient for bootstrap...') if len(nDIs) >= 10: BnDIs = pmag.di_boot(nDIs) Bkpars = pmag.dokent(BnDIs, 1.) if pmagplotlib.verbose: print("mode ", mode) for key in list(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) >= 10: BrDIs = pmag.di_boot(rDIs) Bkpars = pmag.dokent(BrDIs, 1.) if pmagplotlib.verbose: print("mode ", mode) for key in list(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': print('Be patient for bootstrap...') vsym = {'lower': ['+', 'k'], 'upper': ['x', 'k'], 'size': 5} if len(nDIs) > 5: BnDIs = pmag.di_boot(nDIs) pmagplotlib.plotEQsym(FIG['bdirs'], BnDIs, 'Bootstrapped Eigenvectors', vsym) if len(rDIs) > 5: BrDIs = pmag.di_boot(rDIs) if len(nDIs) > 5: # plot on existing plots pmagplotlib.plotDIsym(FIG['bdirs'], BrDIs, vsym) else: pmagplotlib.plotEQ(FIG['bdirs'], BrDIs, 'Bootstrapped Eigenvectors', vsym) 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) if plot == 0: pmagplotlib.drawFIGS(FIG) if plot == 0: pmagplotlib.drawFIGS(FIG) # files = {} for key in list(FIG.keys()): files[key] = title + '_' + key + '.' + 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) elif plot == 0: ans = input(" S[a]ve to save plot, [q]uit, Return to continue: ") if ans == "q": sys.exit() if ans == "a": pmagplotlib.saveP(FIG, files) else: pmagplotlib.saveP(FIG, files)
def main(): """ NAME scalc.py DESCRIPTION calculates Sb from VGP Long,VGP Lat,Directional kappa,Site latitude data SYNTAX scalc -h [command line options] [< standard input] INPUT takes space delimited files with PLong, PLat,[kappa, N_site, slat] OPTIONS -h prints help message and quits -f FILE: specify input file -c cutoff: specify VGP colatitude cutoff value -k cutoff: specify kappa cutoff -v : use the VanDammme criterion -a: use antipodes of reverse data: default is to use only normal -C: use all data without regard to polarity -b: do a bootstrap for confidence -p: do relative to principle axis NOTES if kappa, N_site, lat supplied, will consider within site scatter OUTPUT N Sb Sb_lower Sb_upper Co-lat. Cutoff """ coord,kappa,cutoff="0",0,90. nb,anti,boot=1000,0,0 all=0 n=0 v=0 spin=1 coord_key='tilt_correction' if '-h' in sys.argv: print main.__doc__ sys.exit() if '-f' in sys.argv: ind=sys.argv.index("-f") in_file=sys.argv[ind+1] f=open(in_file,'rU') lines=f.readlines() else: lines=sys.stdin.readlines() if '-c' in sys.argv: ind=sys.argv.index('-c') cutoff=float(sys.argv[ind+1]) if '-k' in sys.argv: ind=sys.argv.index('-k') kappa=float(sys.argv[ind+1]) if '-n' in sys.argv: ind=sys.argv.index('-n') n=int(sys.argv[ind+1]) if '-a' in sys.argv: anti=1 if '-C' in sys.argv: cutoff=180. # no cutoff if '-b' in sys.argv: boot=1 if '-v' in sys.argv: v=1 if '-p' in sys.argv: spin=0 # # # find desired vgp lat,lon, kappa,N_site data: # A,Vgps,slats,Pvgps=180.,[],[],[] for line in lines: if '\t' in line: rec=line.replace('\n','').split('\t') # split each line on space to get records else: rec=line.replace('\n','').split() # split each line on space to get records vgp={} vgp['vgp_lon'],vgp['vgp_lat']=rec[0],rec[1] Pvgps.append([float(rec[0]),float(rec[1])]) if anti==1: if float(vgp['vgp_lat'])<0: vgp['vgp_lat']='%7.1f'%(-1*float(vgp['vgp_lat'])) vgp['vgp_lon']='%7.1f'%(float(vgp['vgp_lon'])-180.) if len(rec)==5: vgp['average_k'],vgp['average_nn'],vgp['average_lat']=rec[2],rec[3],rec[4] slats.append(float(rec[4])) else: vgp['average_k'],vgp['average_nn'],vgp['average_lat']="0","0","0" if 90.-(float(vgp['vgp_lat']))<=cutoff and float(vgp['average_k'])>=kappa and int(vgp['average_nn'])>=n: Vgps.append(vgp) if spin==0: # do transformation to pole ppars=pmag.doprinc(Pvgps) for vgp in Vgps: vlon,vlat=pmag.dotilt(float(vgp['vgp_lon']),float(vgp['vgp_lat']),ppars['dec']-180.,90.-ppars['inc']) vgp['vgp_lon']=vlon vgp['vgp_lat']=vlat vgp['average_k']="0" S_B= pmag.get_Sb(Vgps) A=cutoff if v==1: thetamax,A=181.,180. vVgps,cnt=[],0 for vgp in Vgps:vVgps.append(vgp) # make a copy of Vgps while thetamax>A: thetas=[] A=1.8*S_B+5 cnt+=1 for vgp in vVgps:thetas.append(90.-(float(vgp['vgp_lat']))) thetas.sort() thetamax=thetas[-1] if thetamax<A:break nVgps=[] for vgp in vVgps: if 90.-(float(vgp['vgp_lat']))<thetamax:nVgps.append(vgp) vVgps=[] for vgp in nVgps:vVgps.append(vgp) S_B= pmag.get_Sb(vVgps) Vgps=[] for vgp in vVgps:Vgps.append(vgp) # make a new Vgp list SBs,Ns=[],[] if boot==1: print 'please be patient... bootstrapping' for i in range(nb): # now do bootstrap BVgps=[] for k in range(len(Vgps)): ind=random.randint(0,len(Vgps)-1) random.jumpahead(int(ind*1000)) BVgps.append(Vgps[ind]) SBs.append(pmag.get_Sb(BVgps)) SBs.sort() low=int(.025*nb) high=int(.975*nb) print len(Vgps),'%7.1f %7.1f %7.1f %7.1f '%(S_B,SBs[low],SBs[high],A) else: print len(Vgps),'%7.1f %7.1f '%(S_B,A) if len(slats)>2: stats= pmag.gausspars(slats) print 'mean lat = ','%7.1f'%(stats[0])
def main(): """ NAME scalc_magic.py DESCRIPTION calculates Sb from pmag_results files SYNTAX scalc_magic -h [command line options] INPUT takes magic formatted pmag_results table pmag_result_name must start with "VGP: Site" must have average_lat if spin axis is reference OPTIONS -h prints help message and quits -f FILE: specify input results file, default is 'pmag_results.txt' -c cutoff: specify VGP colatitude cutoff value -k cutoff: specify kappa cutoff -crd [s,g,t]: specify coordinate system, default is geographic -v : use the VanDammme criterion -a: use antipodes of reverse data: default is to use only normal -C: use all data without regard to polarity -r: use reverse data only -p: do relative to principle axis -b: do bootstrap confidence bounds OUTPUT: if option -b used: N, S_B, lower and upper bounds otherwise: N, S_B, cutoff """ in_file='pmag_results.txt' coord,kappa,cutoff="0",1.,90. nb,anti,spin,v,boot=1000,0,1,0,0 coord_key='tilt_correction' rev=0 if '-h' in sys.argv: print(main.__doc__) sys.exit() if '-f' in sys.argv: ind=sys.argv.index("-f") in_file=sys.argv[ind+1] if '-c' in sys.argv: ind=sys.argv.index('-c') cutoff=float(sys.argv[ind+1]) if '-k' in sys.argv: ind=sys.argv.index('-k') kappa=float(sys.argv[ind+1]) if '-crd' in sys.argv: ind=sys.argv.index("-crd") coord=sys.argv[ind+1] if coord=='s':coord="-1" if coord=='g':coord="0" if coord=='t':coord="100" if '-a' in sys.argv: anti=1 if '-C' in sys.argv: cutoff=180. # no cutoff if '-r' in sys.argv: rev=1 if '-p' in sys.argv: spin=0 if '-v' in sys.argv: v=1 if '-b' in sys.argv: boot=1 data,file_type=pmag.magic_read(in_file) # # # find desired vgp lat,lon, kappa,N_site data: # # # A,Vgps,Pvgps=180.,[],[] VgpRecs=pmag.get_dictitem(data,'vgp_lat','','F') # get all non-blank vgp latitudes VgpRecs=pmag.get_dictitem(VgpRecs,'vgp_lon','','F') # get all non-blank vgp longitudes SiteRecs=pmag.get_dictitem(VgpRecs,'data_type','i','T') # get VGPs (as opposed to averaged) SiteRecs=pmag.get_dictitem(SiteRecs,coord_key,coord,'T') # get right coordinate system for rec in SiteRecs: if anti==1: if 90.-abs(float(rec['vgp_lat']))<=cutoff and float(rec['average_k'])>=kappa: if float(rec['vgp_lat'])<0: rec['vgp_lat']='%7.1f'%(-1*float(rec['vgp_lat'])) rec['vgp_lon']='%7.1f'%(float(rec['vgp_lon'])-180.) Vgps.append(rec) Pvgps.append([float(rec['vgp_lon']),float(rec['vgp_lat'])]) elif rev==0: # exclude normals if 90.-(float(rec['vgp_lat']))<=cutoff and float(rec['average_k'])>=kappa: Vgps.append(rec) Pvgps.append([float(rec['vgp_lon']),float(rec['vgp_lat'])]) else: # include normals if 90.-abs(float(rec['vgp_lat']))<=cutoff and float(rec['average_k'])>=kappa: if float(rec['vgp_lat'])<0: rec['vgp_lat']='%7.1f'%(-1*float(rec['vgp_lat'])) rec['vgp_lon']='%7.1f'%(float(rec['vgp_lon'])-180.) Vgps.append(rec) Pvgps.append([float(rec['vgp_lon']),float(rec['vgp_lat'])]) if spin==0: # do transformation to pole ppars=pmag.doprinc(Pvgps) for vgp in Vgps: vlon,vlat=pmag.dotilt(float(vgp['vgp_lon']),float(vgp['vgp_lat']),ppars['dec']-180.,90.-ppars['inc']) vgp['vgp_lon']=vlon vgp['vgp_lat']=vlat vgp['average_k']="0" S_B= pmag.get_Sb(Vgps) A=cutoff if v==1: thetamax,A=181.,180. vVgps,cnt=[],0 for vgp in Vgps:vVgps.append(vgp) # make a copy of Vgps while thetamax>A: thetas=[] A=1.8*S_B+5 cnt+=1 for vgp in vVgps:thetas.append(90.-(float(vgp['vgp_lat']))) thetas.sort() thetamax=thetas[-1] if thetamax<A:break nVgps=[] for vgp in vVgps: if 90.-(float(vgp['vgp_lat']))<thetamax:nVgps.append(vgp) vVgps=[] for vgp in nVgps:vVgps.append(vgp) S_B= pmag.get_Sb(vVgps) Vgps=[] for vgp in vVgps:Vgps.append(vgp) # make a new Vgp list SBs=[] if boot==1: for i in range(nb): # now do bootstrap BVgps=[] if i%100==0: print(i,' out of ',nb) for k in range(len(Vgps)): random.seed() ind=random.randint(0,len(Vgps)-1) BVgps.append(Vgps[ind]) SBs.append(pmag.get_Sb(BVgps)) SBs.sort() low=int(.025*nb) high=int(.975*nb) print(len(Vgps),'%7.1f _ %7.1f ^ %7.1f %7.1f'%(S_B,SBs[low],SBs[high],A)) else: print(len(Vgps),'%7.1f %7.1f '%(S_B,A))
def main(): """ NAME plotdi_e.py DESCRIPTION plots equal area projection from dec inc data and cones of confidence (Fisher, kent or Bingham or bootstrap). INPUT FORMAT takes dec/inc as first two columns in space delimited file SYNTAX plotdi_e.py [command line options] OPTIONS -h prints help message and quits -i for interactive parameter entry -f FILE, sets input filename on command line -Fish plots unit vector mean direction, alpha95 -Bing plots Principal direction, Bingham confidence ellipse -Kent plots unit vector mean direction, confidence ellipse -Boot E plots unit vector mean direction, bootstrapped confidence ellipse -Boot V plots unit vector mean direction, distribution of bootstrapped means """ dist = 'F' # default distribution is Fisherian mode = 1 EQ = {'eq': 1} if len(sys.argv) > 0: if '-h' in sys.argv: # check if help is needed print main.__doc__ sys.exit() # graceful quit if '-i' in sys.argv: # ask for filename file = raw_input("Enter file name with dec, inc data: ") dist = raw_input( "Enter desired distrubution: [Fish]er, [Bing]ham, [Kent] [Boot] [default is Fisher]: " ) if dist == "": dist = "F" if dist == "Boot": type = raw_input( " Ellipses or distribution of vectors? [E]/V ") if type == "" or type == "E": dist = "BE" else: dist = "BE" else: # if '-f' in sys.argv: ind = sys.argv.index('-f') file = sys.argv[ind + 1] else: print 'you must specify a file name' print main.__doc__ sys.exit() if '-Bing' in sys.argv: dist = 'B' if '-Kent' in sys.argv: dist = 'K' if '-Boot' in sys.argv: ind = sys.argv.index('-Boot') type = sys.argv[ind + 1] if type == 'E': dist = 'BE' elif type == 'V': dist = 'BV' EQ['bdirs'] = 2 pmagplotlib.plot_init(EQ['bdirs'], 5, 5) else: print main.__doc__ sys.exit() pmagplotlib.plot_init(EQ['eq'], 5, 5) # # get to work f = open(file, 'r') data = f.readlines() # DIs = [] # set up list for dec inc data DiRecs = [] pars = [] nDIs, rDIs, npars, rpars = [], [], [], [] mode = 1 for line in data: # read in the data from standard input DiRec = {} rec = line.split() # split each line on space to get records DIs.append((float(rec[0]), float(rec[1]), 1.)) DiRec['dec'] = rec[0] DiRec['inc'] = rec[1] DiRec['direction_type'] = 'l' DiRecs.append(DiRec) # split into two modes ppars = pmag.doprinc(DIs) # get principal directions for rec in DIs: 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 title = "Bingham confidence ellipse" bpars = pmag.dobingham(DIs) for key in bpars.keys(): if key != 'n': print " ", key, '%7.1f' % (bpars[key]) if key == 'n': 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': title = "Fisher confidence cone" if len(nDIs) > 3: fpars = pmag.fisher_mean(nDIs) print "mode ", mode for key in fpars.keys(): if key != 'n': print " ", key, '%7.1f' % (fpars[key]) if key == 'n': 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) > 3: fpars = pmag.fisher_mean(rDIs) print "mode ", mode for key in fpars.keys(): if key != 'n': print " ", key, '%7.1f' % (fpars[key]) if key == 'n': 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': title = "Kent confidence ellipse" if len(nDIs) > 3: kpars = pmag.dokent(nDIs, len(nDIs)) print "mode ", mode for key in kpars.keys(): if key != 'n': print " ", key, '%7.1f' % (kpars[key]) if key == 'n': 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)) print "mode ", mode for key in kpars.keys(): if key != 'n': print " ", key, '%7.1f' % (kpars[key]) if key == 'n': 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.) print "mode ", mode for key in Bkpars.keys(): if key != 'n': print " ", key, '%7.1f' % (Bkpars[key]) if key == 'n': 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.) print "mode ", mode for key in Bkpars.keys(): if key != 'n': print " ", key, '%7.1f' % (Bkpars[key]) if key == 'n': 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']) title = "Bootstrapped confidence ellipse" elif dist == 'BV': if len(nDIs) > 5: pmagplotlib.plotEQ(EQ['eq'], nDIs, 'Data') BnDIs = pmag.di_boot(nDIs) pmagplotlib.plotEQ(EQ['bdirs'], BnDIs, 'Bootstrapped Eigenvectors') if len(rDIs) > 5: BrDIs = pmag.di_boot(rDIs) if len(nDIs) > 5: # plot on existing plots pmagplotlib.plotDI(EQ['eq'], rDIs) pmagplotlib.plotDI(EQ['bdirs'], BrDIs) else: pmagplotlib.plotEQ(EQ['eq'], rDIs, 'Data') pmagplotlib.plotEQ(EQ['bdirs'], BrDIs, 'Bootstrapped Eigenvectors') pmagplotlib.drawFIGS(EQ) ans = raw_input('s[a]ve, [q]uit ') if ans == 'q': sys.exit() if ans == 'a': files = {} for key in EQ.keys(): files[key] = 'BE_' + key + '.svg' pmagplotlib.saveP(EQ, files) sys.exit() if len(nDIs) > 5: pmagplotlib.plotCONF(EQ['eq'], title, DiRecs, npars, 1) if len(rDIs) > 5 and dist != 'B': pmagplotlib.plotCONF(EQ['eq'], title, [], rpars, 0) elif len(rDIs) > 5 and dist != 'B': pmagplotlib.plotCONF(EQ['eq'], title, DiRecs, rpars, 1) pmagplotlib.drawFIGS(EQ) ans = raw_input('s[a]ve, [q]uit ') if ans == 'q': sys.exit() if ans == 'a': files = {} for key in EQ.keys(): files[key] = key + '.svg' pmagplotlib.saveP(EQ, files)
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 plt = 0 if '-sav' in sys.argv: plt = 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: # get all records with this dec_key not blank Decs = pmag.get_dictitem(odata, dec_key, '', 'F') if len(Decs) > 0: break for inc_key in Inc_keys: # get all records with this inc_key not blank Incs = pmag.get_dictitem(Decs, inc_key, '', 'F') 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] = '' # get all records matching specified coordinate system cdata = pmag.get_dictitem(Incs, tilt_key, coord, 'T') if coord == '0': # geographic # get all the blank records - assume geographic udata = pmag.get_dictitem(Incs, tilt_key, '', 'T') 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: # get all records with this name_key not blank Names = pmag.get_dictitem(cdata, name_key, '', 'F') if len(Names) > 0: break for dir_type_key in Dir_type_keys: # get all records with this direction type Dirs = pmag.get_dictitem(cdata, dir_type_key, '', 'F') 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.plot_eq(FIG['eqarea'], DIblock, title) else: pmagplotlib.plot_eq_cont(FIG['eqarea'], DIblock) else: pmagplotlib.plot_net(FIG['eqarea']) if len(GCblock) > 0: for rec in GCblock: pmagplotlib.plot_circ(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.plot_eq_sym( FIG['bdirs'], BnDIs, 'Bootstrapped Eigenvectors', sym) if len(rDIs) > 5: BrDIs = pmag.di_boot(rDIs) if len(nDIs) > 5: # plot on existing plots pmagplotlib.plot_di_sym(FIG['bdirs'], BrDIs, sym) else: pmagplotlib.plot_eq( FIG['bdirs'], BrDIs, 'Bootstrapped Eigenvectors') if dist == 'B': if len(nDIs) > 3 or len(rDIs) > 3: pmagplotlib.plot_conf(FIG['eqarea'], etitle, [], npars, 0) elif len(nDIs) > 3 and dist != 'BV': pmagplotlib.plot_conf(FIG['eqarea'], etitle, [], npars, 0) if len(rDIs) > 3: pmagplotlib.plot_conf(FIG['eqarea'], etitle, [], rpars, 0) elif len(rDIs) > 3 and dist != 'BV': pmagplotlib.plot_conf(FIG['eqarea'], etitle, [], rpars, 0) if verbose: pmagplotlib.draw_figs(FIG) # files = {} locations = locations[:-1] for key in FIG.keys(): if pmagplotlib.isServer: # use server plot naming convention filename = 'LO:_'+locations+'_SI:_'+site+'_SA:_'+sample + \ '_SP:_'+specimen+'_CO:_'+crd+'_TY:_'+key+'_.'+fmt else: # use more readable plot naming convention filename = '' for item in [locations, site, sample, specimen, crd, key]: if item: item = item.replace(' ', '_') filename += item + '_' if filename.endswith('_'): filename = filename[:-1] filename += ".{}".format(fmt) files[key] = filename if pmagplotlib.isServer: black = '#000000' purple = '#800080' titles = {} titles['eq'] = 'Equal Area Plot' FIG = pmagplotlib.add_borders(FIG, titles, black, purple) pmagplotlib.save_plots(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.save_plots(FIG, files) if plt: pmagplotlib.save_plots(FIG, files)
def main(): """ NAME scalc.py DESCRIPTION calculates Sb from VGP Long,VGP Lat,Directional kappa,Site latitude data SYNTAX scalc -h [command line options] [< standard input] INPUT takes space delimited files with PLong, PLat,[kappa, N_site, slat] OPTIONS -h prints help message and quits -f FILE: specify input file -c cutoff: specify VGP colatitude cutoff value -k cutoff: specify kappa cutoff -v : use the VanDammme criterion -a: use antipodes of reverse data: default is to use only normal -C: use all data without regard to polarity -b: do a bootstrap for confidence -p: do relative to principle axis NOTES if kappa, N_site, lat supplied, will consider within site scatter OUTPUT N Sb Sb_lower Sb_upper Co-lat. Cutoff """ coord, kappa, cutoff = "0", 0, 90. nb, anti, boot = 1000, 0, 0 all = 0 n = 0 v = 0 spin = 1 coord_key = 'tilt_correction' if '-h' in sys.argv: print main.__doc__ sys.exit() if '-f' in sys.argv: ind = sys.argv.index("-f") in_file = sys.argv[ind + 1] f = open(in_file, 'rU') lines = f.readlines() else: lines = sys.stdin.readlines() if '-c' in sys.argv: ind = sys.argv.index('-c') cutoff = float(sys.argv[ind + 1]) if '-k' in sys.argv: ind = sys.argv.index('-k') kappa = float(sys.argv[ind + 1]) if '-n' in sys.argv: ind = sys.argv.index('-n') n = int(sys.argv[ind + 1]) if '-a' in sys.argv: anti = 1 if '-C' in sys.argv: cutoff = 180. # no cutoff if '-b' in sys.argv: boot = 1 if '-v' in sys.argv: v = 1 if '-p' in sys.argv: spin = 0 # # # find desired vgp lat,lon, kappa,N_site data: # A, Vgps, slats, Pvgps = 180., [], [], [] for line in lines: if '\t' in line: rec = line.replace('\n', '').split( '\t') # split each line on space to get records else: rec = line.replace( '\n', '').split() # split each line on space to get records vgp = {} vgp['vgp_lon'], vgp['vgp_lat'] = rec[0], rec[1] Pvgps.append([float(rec[0]), float(rec[1])]) if anti == 1: if float(vgp['vgp_lat']) < 0: vgp['vgp_lat'] = '%7.1f' % (-1 * float(vgp['vgp_lat'])) vgp['vgp_lon'] = '%7.1f' % (float(vgp['vgp_lon']) - 180.) if len(rec) == 5: vgp['average_k'], vgp['average_nn'], vgp['average_lat'] = rec[ 2], rec[3], rec[4] slats.append(float(rec[4])) else: vgp['average_k'], vgp['average_nn'], vgp[ 'average_lat'] = "0", "0", "0" if 90. - (float(vgp['vgp_lat'])) <= cutoff and float( vgp['average_k']) >= kappa and int(vgp['average_nn']) >= n: Vgps.append(vgp) if spin == 0: # do transformation to pole ppars = pmag.doprinc(Pvgps) for vgp in Vgps: vlon, vlat = pmag.dotilt(float(vgp['vgp_lon']), float(vgp['vgp_lat']), ppars['dec'] - 180., 90. - ppars['inc']) vgp['vgp_lon'] = vlon vgp['vgp_lat'] = vlat vgp['average_k'] = "0" S_B = pmag.get_Sb(Vgps) A = cutoff if v == 1: thetamax, A = 181., 180. vVgps, cnt = [], 0 for vgp in Vgps: vVgps.append(vgp) # make a copy of Vgps while thetamax > A: thetas = [] A = 1.8 * S_B + 5 cnt += 1 for vgp in vVgps: thetas.append(90. - (float(vgp['vgp_lat']))) thetas.sort() thetamax = thetas[-1] if thetamax < A: break nVgps = [] for vgp in vVgps: if 90. - (float(vgp['vgp_lat'])) < thetamax: nVgps.append(vgp) vVgps = [] for vgp in nVgps: vVgps.append(vgp) S_B = pmag.get_Sb(vVgps) Vgps = [] for vgp in vVgps: Vgps.append(vgp) # make a new Vgp list SBs, Ns = [], [] if boot == 1: print 'please be patient... bootstrapping' for i in range(nb): # now do bootstrap BVgps = [] for k in range(len(Vgps)): ind = random.randint(0, len(Vgps) - 1) random.jumpahead(int(ind * 1000)) BVgps.append(Vgps[ind]) SBs.append(pmag.get_Sb(BVgps)) SBs.sort() low = int(.025 * nb) high = int(.975 * nb) print len(Vgps), '%7.1f %7.1f %7.1f %7.1f ' % (S_B, SBs[low], SBs[high], A) else: print len(Vgps), '%7.1f %7.1f ' % (S_B, A) if len(slats) > 2: stats = pmag.gausspars(slats) print 'mean lat = ', '%7.1f' % (stats[0])
def main(): """ NAME scalc_magic.py DESCRIPTION calculates Sb from pmag_results files SYNTAX scalc_magic -h [command line options] INPUT takes magic formatted pmag_results table pmag_result_name must start with "VGP: Site" must have average_lat if spin axis is reference OPTIONS -h prints help message and quits -f FILE: specify input results file, default is 'pmag_results.txt' -c cutoff: specify VGP colatitude cutoff value -k cutoff: specify kappa cutoff -crd [s,g,t]: specify coordinate system, default is geographic -v : use the VanDammme criterion -a: use antipodes of reverse data: default is to use only normal -C: use all data without regard to polarity -r: use reverse data only -p: do relative to principle axis -b: do bootstrap confidence bounds OUTPUT: if option -b used: N, S_B, lower and upper bounds otherwise: N, S_B, cutoff """ in_file = 'pmag_results.txt' coord, kappa, cutoff = "0", 1., 90. nb, anti, spin, v, boot = 1000, 0, 1, 0, 0 coord_key = 'tilt_correction' rev = 0 if '-h' in sys.argv: print(main.__doc__) sys.exit() if '-f' in sys.argv: ind = sys.argv.index("-f") in_file = sys.argv[ind + 1] if '-c' in sys.argv: ind = sys.argv.index('-c') cutoff = float(sys.argv[ind + 1]) if '-k' in sys.argv: ind = sys.argv.index('-k') kappa = float(sys.argv[ind + 1]) if '-crd' in sys.argv: ind = sys.argv.index("-crd") coord = sys.argv[ind + 1] if coord == 's': coord = "-1" if coord == 'g': coord = "0" if coord == 't': coord = "100" if '-a' in sys.argv: anti = 1 if '-C' in sys.argv: cutoff = 180. # no cutoff if '-r' in sys.argv: rev = 1 if '-p' in sys.argv: spin = 0 if '-v' in sys.argv: v = 1 if '-b' in sys.argv: boot = 1 data, file_type = pmag.magic_read(in_file) # # # find desired vgp lat,lon, kappa,N_site data: # # # A, Vgps, Pvgps = 180., [], [] VgpRecs = pmag.get_dictitem(data, 'vgp_lat', '', 'F') # get all non-blank vgp latitudes VgpRecs = pmag.get_dictitem(VgpRecs, 'vgp_lon', '', 'F') # get all non-blank vgp longitudes SiteRecs = pmag.get_dictitem(VgpRecs, 'data_type', 'i', 'T') # get VGPs (as opposed to averaged) SiteRecs = pmag.get_dictitem(SiteRecs, coord_key, coord, 'T') # get right coordinate system for rec in SiteRecs: if anti == 1: if 90. - abs(float(rec['vgp_lat'])) <= cutoff and float( rec['average_k']) >= kappa: if float(rec['vgp_lat']) < 0: rec['vgp_lat'] = '%7.1f' % (-1 * float(rec['vgp_lat'])) rec['vgp_lon'] = '%7.1f' % (float(rec['vgp_lon']) - 180.) Vgps.append(rec) Pvgps.append([float(rec['vgp_lon']), float(rec['vgp_lat'])]) elif rev == 0: # exclude normals if 90. - (float(rec['vgp_lat'])) <= cutoff and float( rec['average_k']) >= kappa: Vgps.append(rec) Pvgps.append([float(rec['vgp_lon']), float(rec['vgp_lat'])]) else: # include normals if 90. - abs(float(rec['vgp_lat'])) <= cutoff and float( rec['average_k']) >= kappa: if float(rec['vgp_lat']) < 0: rec['vgp_lat'] = '%7.1f' % (-1 * float(rec['vgp_lat'])) rec['vgp_lon'] = '%7.1f' % (float(rec['vgp_lon']) - 180.) Vgps.append(rec) Pvgps.append( [float(rec['vgp_lon']), float(rec['vgp_lat'])]) if spin == 0: # do transformation to pole ppars = pmag.doprinc(Pvgps) for vgp in Vgps: vlon, vlat = pmag.dotilt(float(vgp['vgp_lon']), float(vgp['vgp_lat']), ppars['dec'] - 180., 90. - ppars['inc']) vgp['vgp_lon'] = vlon vgp['vgp_lat'] = vlat vgp['average_k'] = "0" S_B = pmag.get_Sb(Vgps) A = cutoff if v == 1: thetamax, A = 181., 180. vVgps, cnt = [], 0 for vgp in Vgps: vVgps.append(vgp) # make a copy of Vgps while thetamax > A: thetas = [] A = 1.8 * S_B + 5 cnt += 1 for vgp in vVgps: thetas.append(90. - (float(vgp['vgp_lat']))) thetas.sort() thetamax = thetas[-1] if thetamax < A: break nVgps = [] for vgp in vVgps: if 90. - (float(vgp['vgp_lat'])) < thetamax: nVgps.append(vgp) vVgps = [] for vgp in nVgps: vVgps.append(vgp) S_B = pmag.get_Sb(vVgps) Vgps = [] for vgp in vVgps: Vgps.append(vgp) # make a new Vgp list SBs = [] if boot == 1: for i in range(nb): # now do bootstrap BVgps = [] if i % 100 == 0: print(i, ' out of ', nb) for k in range(len(Vgps)): random.seed() ind = random.randint(0, len(Vgps) - 1) BVgps.append(Vgps[ind]) SBs.append(pmag.get_Sb(BVgps)) SBs.sort() low = int(.025 * nb) high = int(.975 * nb) print(len(Vgps), '%7.1f _ %7.1f ^ %7.1f %7.1f' % (S_B, SBs[low], SBs[high], A)) else: print(len(Vgps), '%7.1f %7.1f ' % (S_B, A))
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 sites, samples, specimens, or measurements OPTIONS -h prints help message and quits -f FILE: specify input magic format file from magic, default='sites.txt' supported types=[measurements, specimens, samples, sites] -fsp FILE: specify specimen file name, (required if you want to plot measurements by sample) default='specimens.txt' -fsa FILE: specify sample file name, (required if you want to plot specimens by site) default='samples.txt' -fsi FILE: specify site file name, default='sites.txt' -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 """ # initialize some default variables FIG = {} # plot dictionary FIG['eqarea'] = 1 # eqarea is figure 1 plotE = 0 plt = 0 # default to not plotting verbose = pmagplotlib.verbose # extract arguments from sys.argv if '-h' in sys.argv: print(main.__doc__) sys.exit() dir_path = pmag.get_named_arg_from_sys("-WD", default_val=".") pmagplotlib.plot_init(FIG['eqarea'],5,5) in_file = pmag.get_named_arg_from_sys("-f", default_val="sites.txt") in_file = pmag.resolve_file_name(in_file, dir_path) if "-WD" not in sys.argv: dir_path = os.path.split(in_file)[0] #full_in_file = os.path.join(dir_path, in_file) plot_by = pmag.get_named_arg_from_sys("-obj", default_val="all").lower() spec_file = pmag.get_named_arg_from_sys("-fsp", default_val="specimens.txt") samp_file = pmag.get_named_arg_from_sys("-fsa", default_val="samples.txt") site_file = pmag.get_named_arg_from_sys("-fsi", default_val="sites.txt") if plot_by == 'all': plot_key = 'all' elif plot_by == 'sit': plot_key = 'site' elif plot_by == 'sam': plot_key = 'sample' elif plot_by == 'spc': plot_key = 'specimen' else: plot_by = 'all' plot_key = 'all' if '-c' in sys.argv: contour = 1 else: contour = 0 if '-sav' in sys.argv: plt = 1 verbose = 0 if '-ell' in sys.argv: plotE = 1 ind = sys.argv.index('-ell') ell_type = sys.argv[ind+1] ell_type = pmag.get_named_arg_from_sys("-ell", "F") dist = ell_type.upper() # if dist type is unrecognized, use Fisher if dist not in ['F', 'K', 'B', 'BE', 'BV']: dist = 'F' if dist == "BV": FIG['bdirs'] = 2 pmagplotlib.plot_init(FIG['bdirs'],5,5) crd = pmag.get_named_arg_from_sys("-crd", default_val="g") if crd == "s": coord = "-1" elif crd == "t": coord = "100" else: coord = "0" fmt = pmag.get_named_arg_from_sys("-fmt", "svg") dec_key = 'dir_dec' inc_key = 'dir_inc' tilt_key = 'dir_tilt_correction' #Dir_type_keys=['','site_direction_type','sample_direction_type','specimen_direction_type'] # fnames = {"specimens": spec_file, "samples": samp_file, 'sites': site_file} contribution = nb.Contribution(dir_path, custom_filenames=fnames, single_file=in_file) try: contribution.propagate_location_to_samples() contribution.propagate_location_to_specimens() contribution.propagate_location_to_measurements() except KeyError as ex: pass # the object that contains the DataFrame + useful helper methods: table_name = list(contribution.tables.keys())[0] data_container = contribution.tables[table_name] # the actual DataFrame: data = data_container.df if plot_key != "all" and plot_key not in data.columns: print("-E- You can't plot by {} with the data provided".format(plot_key)) return # add tilt key into DataFrame columns if it isn't there already if tilt_key not in data.columns: data.loc[:, tilt_key] = None 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": # return all where plot_key is not blank if plot_key not in data.columns: print('Can\'t plot by "{}". That header is not in infile: {}'.format(plot_key, in_file)) return plots = data[data[plot_key].notnull()] plotlist = plots[plot_key].unique() # grab unique values else: plotlist.append('All') for plot in plotlist: if verbose: print(plot) if plot == 'All': # plot everything at once plot_data = data else: # pull out only partial data plot_data = data[data[plot_key] == plot] DIblock = [] GCblock = [] # SLblock, SPblock = [], [] title = plot mode = 1 k = 0 if dec_key not in plot_data.columns: print("-W- No dec/inc data") continue # get all records where dec & inc values exist plot_data = plot_data[plot_data[dec_key].notnull() & plot_data[inc_key].notnull()] if plot_data.empty: continue # this sorting out is done in get_di_bock #if coord == '0': # geographic, use records with no tilt key (or tilt_key 0) # cond1 = plot_data[tilt_key].fillna('') == coord # cond2 = plot_data[tilt_key].isnull() # plot_data = plot_data[cond1 | cond2] #else: # not geographic coordinates, use only records with correct tilt_key # plot_data = plot_data[plot_data[tilt_key] == coord] # get metadata for naming the plot file locations = data_container.get_name('location', df_slice=plot_data) site = data_container.get_name('site', df_slice=plot_data) sample = data_container.get_name('sample', df_slice=plot_data) specimen = data_container.get_name('specimen', df_slice=plot_data) # make sure method_codes is in plot_data if 'method_codes' not in plot_data.columns: plot_data['method_codes'] = '' # get data blocks DIblock = data_container.get_di_block(df_slice=plot_data, tilt_corr=coord, excl=['DE-BFP']) #SLblock = [[ind, row['method_codes']] for ind, row in plot_data.iterrows()] # get great circles great_circle_data = data_container.get_records_for_code('DE-BFP', incl=True, use_slice=True, sli=plot_data) if len(great_circle_data) > 0: gc_cond = great_circle_data[tilt_key] == coord GCblock = [[float(row[dec_key]), float(row[inc_key])] for ind, row in great_circle_data[gc_cond].iterrows()] #SPblock = [[ind, row['method_codes']] for ind, row in great_circle_data[gc_cond].iterrows()] 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 len(DIblock) == 0 and len(GCblock) == 0: if verbose: print("no records for plotting") continue #sys.exit() 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 list(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 list(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=old_div(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 list(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=old_div(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 list(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 list(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 list(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 list(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) for key in list(FIG.keys()): files = {} filename = pmag.get_named_arg_from_sys('-fname') if filename: # use provided filename filename+= '.' + fmt elif pmagplotlib.isServer: # use server plot naming convention filename='LO:_'+locations+'_SI:_'+site+'_SA:_'+sample+'_SP:_'+specimen+'_CO:_'+crd+'_TY:_'+key+'_.'+fmt elif plot_key == 'all': filename = 'all' if 'location' in plot_data.columns: locs = plot_data['location'].unique() loc_string = "_".join([loc.replace(' ', '_') for loc in locs]) filename += "_" + loc_string filename += "_" + crd + "_" + key filename += ".{}".format(fmt) else: # use more readable naming convention filename = '' # fix this if plot_by is location , for example use_names = {'location': [locations], 'site': [locations, site], 'sample': [locations, site, sample], 'specimen': [locations, site, sample, specimen]} use = use_names[plot_key] use.extend([crd, key]) for item in use: #[locations, site, sample, specimen, crd, key]: if item: item = item.replace(' ', '_') filename += item + '_' if filename.endswith('_'): filename = filename[:-1] filename += ".{}".format(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) if plt: pmagplotlib.saveP(FIG,files) continue if verbose: pmagplotlib.drawFIGS(FIG) ans=input(" S[a]ve to save plot, [q]uit, Return to continue: ") if ans == "q": sys.exit() if ans == "a": pmagplotlib.saveP(FIG,files) continue
def main(): """ NAME plotdi_e.py DESCRIPTION plots equal area projection from dec inc data and cones of confidence (Fisher, kent or Bingham or bootstrap). INPUT FORMAT takes dec/inc as first two columns in space delimited file SYNTAX plotdi_e.py [command line options] OPTIONS -h prints help message and quits -i for interactive parameter entry -f FILE, sets input filename on command line -Fish plots unit vector mean direction, alpha95 -Bing plots Principal direction, Bingham confidence ellipse -Kent plots unit vector mean direction, confidence ellipse -Boot E plots unit vector mean direction, bootstrapped confidence ellipse -Boot V plots unit vector mean direction, distribution of bootstrapped means """ dist='F' # default distribution is Fisherian mode=1 EQ={'eq':1} if len(sys.argv) > 0: if '-h' in sys.argv: # check if help is needed print main.__doc__ sys.exit() # graceful quit if '-i' in sys.argv: # ask for filename file=raw_input("Enter file name with dec, inc data: ") dist=raw_input("Enter desired distrubution: [Fish]er, [Bing]ham, [Kent] [Boot] [default is Fisher]: ") if dist=="":dist="F" if dist=="Boot": type=raw_input(" Ellipses or distribution of vectors? [E]/V ") if type=="" or type=="E": dist="BE" else: dist="BE" else: # if '-f' in sys.argv: ind=sys.argv.index('-f') file=sys.argv[ind+1] else: print 'you must specify a file name' print main.__doc__ sys.exit() if '-Bing' in sys.argv:dist='B' if '-Kent' in sys.argv:dist='K' if '-Boot' in sys.argv: ind=sys.argv.index('-Boot') type=sys.argv[ind+1] if type=='E': dist='BE' elif type=='V': dist='BV' EQ['bdirs']=2 pmagplotlib.plot_init(EQ['bdirs'],5,5) else: print main.__doc__ sys.exit() pmagplotlib.plot_init(EQ['eq'],5,5) # # get to work f=open(file,'r') data=f.readlines() # DIs= [] # set up list for dec inc data DiRecs=[] pars=[] nDIs,rDIs,npars,rpars=[],[],[],[] mode =1 for line in data: # read in the data from standard input DiRec={} rec=line.split() # split each line on space to get records DIs.append((float(rec[0]),float(rec[1]),1.)) DiRec['dec']=rec[0] DiRec['inc']=rec[1] DiRec['direction_type']='l' DiRecs.append(DiRec) # split into two modes ppars=pmag.doprinc(DIs) # get principal directions for rec in DIs: 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 title="Bingham confidence ellipse" bpars=pmag.dobingham(DIs) for key in bpars.keys(): if key!='n':print " ",key, '%7.1f'%(bpars[key]) if key=='n':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': title="Fisher confidence cone" if len(nDIs)>3: fpars=pmag.fisher_mean(nDIs) print "mode ",mode for key in fpars.keys(): if key!='n':print " ",key, '%7.1f'%(fpars[key]) if key=='n':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)>3: fpars=pmag.fisher_mean(rDIs) print "mode ",mode for key in fpars.keys(): if key!='n':print " ",key, '%7.1f'%(fpars[key]) if key=='n':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': title="Kent confidence ellipse" if len(nDIs)>3: kpars=pmag.dokent(nDIs,len(nDIs)) print "mode ",mode for key in kpars.keys(): if key!='n':print " ",key, '%7.1f'%(kpars[key]) if key=='n':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)) print "mode ",mode for key in kpars.keys(): if key!='n':print " ",key, '%7.1f'%(kpars[key]) if key=='n':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.) print "mode ",mode for key in Bkpars.keys(): if key!='n':print " ",key, '%7.1f'%(Bkpars[key]) if key=='n':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.) print "mode ",mode for key in Bkpars.keys(): if key!='n':print " ",key, '%7.1f'%(Bkpars[key]) if key=='n':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']) title="Bootstrapped confidence ellipse" elif dist=='BV': if len(nDIs)>5: pmagplotlib.plotEQ(EQ['eq'],nDIs,'Data') BnDIs=pmag.di_boot(nDIs) pmagplotlib.plotEQ(EQ['bdirs'],BnDIs,'Bootstrapped Eigenvectors') if len(rDIs)>5: BrDIs=pmag.di_boot(rDIs) if len(nDIs)>5: # plot on existing plots pmagplotlib.plotDI(EQ['eq'],rDIs) pmagplotlib.plotDI(EQ['bdirs'],BrDIs) else: pmagplotlib.plotEQ(EQ['eq'],rDIs,'Data') pmagplotlib.plotEQ(EQ['bdirs'],BrDIs,'Bootstrapped Eigenvectors') pmagplotlib.drawFIGS(EQ) ans=raw_input('s[a]ve, [q]uit ') if ans=='q':sys.exit() if ans=='a': files={} for key in EQ.keys(): files[key]='BE_'+key+'.svg' pmagplotlib.saveP(EQ,files) sys.exit() if len(nDIs)>5: pmagplotlib.plotCONF(EQ['eq'],title,DiRecs,npars,1) if len(rDIs)>5 and dist!='B': pmagplotlib.plotCONF(EQ['eq'],title,[],rpars,0) elif len(rDIs)>5 and dist!='B': pmagplotlib.plotCONF(EQ['eq'],title,DiRecs,rpars,1) pmagplotlib.drawFIGS(EQ) ans=raw_input('s[a]ve, [q]uit ') if ans=='q':sys.exit() if ans=='a': files={} for key in EQ.keys(): files[key]=key+'.svg' pmagplotlib.saveP(EQ,files)
def main(): """ NAME fishqq.py DESCRIPTION makes qq plot from dec,inc input data INPUT FORMAT takes dec/inc pairs in space delimited file SYNTAX fishqq.py [command line options] OPTIONS -h help message -f FILE, specify file on command line -F FILE, specify output file for statistics -sav save and quit [saves as input file name plus fmt extension] -fmt specify format for output [png, eps, svg, pdf] OUTPUT: Dec Inc N Mu Mu_crit Me Me_crit Y/N where direction is the principal component and Y/N is Fisherian or not separate lines for each mode with N >=10 (N and R) """ fmt,plot='svg',0 outfile="" if '-h' in sys.argv: # check if help is needed print(main.__doc__) sys.exit() # graceful quit elif '-f' in sys.argv: # ask for filename ind=sys.argv.index('-f') file=sys.argv[ind+1] f=open(file,'r') data=f.readlines() if '-F' in sys.argv: ind=sys.argv.index('-F') outfile=open(sys.argv[ind+1],'w') # open output file if '-sav' in sys.argv: plot=1 if '-fmt' in sys.argv: ind=sys.argv.index('-fmt') fmt=sys.argv[ind+1] DIs,nDIs,rDIs= [],[],[] # set up list for data for line in data: # read in the data from standard input if '\t' in line: rec=line.split('\t') # split each line on space to get records else: rec=line.split() # split each line on space to get records DIs.append([float(rec[0]),float(rec[1])]) # append data to Inc # split into two modes ppars=pmag.doprinc(DIs) # get principal directions for rec in DIs: angle=pmag.angle([rec[0],rec[1]],[ppars['dec'],ppars['inc']]) if angle>90.: rDIs.append(rec) else: nDIs.append(rec) # if len(rDIs) >=10 or len(nDIs) >=10: D1,I1=[],[] QQ={'unf1':1,'exp1':2} pmagplotlib.plot_init(QQ['unf1'],5,5) pmagplotlib.plot_init(QQ['exp1'],5,5) if len(nDIs) < 10: ppars=pmag.doprinc(rDIs) # get principal directions Drbar,Irbar=ppars['dec']-180.,-ppars['inc'] Nr=len(rDIs) for di in rDIs: d,irot=pmag.dotilt(di[0],di[1],Drbar-180.,90.-Irbar) # rotate to mean drot=d-180. if drot<0:drot=drot+360. D1.append(drot) I1.append(irot) Dtit='Mode 2 Declinations' Itit='Mode 2 Inclinations' else: ppars=pmag.doprinc(nDIs) # get principal directions Dnbar,Inbar=ppars['dec'],ppars['inc'] Nn=len(nDIs) for di in nDIs: d,irot=pmag.dotilt(di[0],di[1],Dnbar-180.,90.-Inbar) # rotate to mean drot=d-180. if drot<0:drot=drot+360. D1.append(drot) I1.append(irot) Dtit='Mode 1 Declinations' Itit='Mode 1 Inclinations' Mu_n,Mu_ncr=pmagplotlib.plot_qq_unf(QQ['unf1'],D1,Dtit) # make plot Me_n,Me_ncr=pmagplotlib.plot_qq_exp(QQ['exp1'],I1,Itit) # make plot #print Mu_n,Mu_ncr,Me_n, Me_ncr if outfile!="": # Dec Inc N Mu Mu_crit Me Me_crit Y/N if Mu_n<=Mu_ncr and Me_n<=Me_ncr: F='Y' else: F='N' outstring='%7.1f %7.1f %i %5.3f %5.3f %5.3f %5.3f %s \n'%(Dnbar,Inbar,Nn,Mu_n,Mu_ncr,Me_n,Me_ncr,F) outfile.write(outstring) else: print('you need N> 10 for at least one mode') sys.exit() if len(rDIs)>10 and len(nDIs)>10: D2,I2=[],[] QQ['unf2']=3 QQ['exp2']=4 pmagplotlib.plot_init(QQ['unf2'],5,5) pmagplotlib.plot_init(QQ['exp2'],5,5) ppars=pmag.doprinc(rDIs) # get principal directions Drbar,Irbar=ppars['dec']-180.,-ppars['inc'] Nr=len(rDIs) for di in rDIs: d,irot=pmag.dotilt(di[0],di[1],Drbar-180.,90.-Irbar) # rotate to mean drot=d-180. if drot<0:drot=drot+360. D2.append(drot) I2.append(irot) Dtit='Mode 2 Declinations' Itit='Mode 2 Inclinations' Mu_r,Mu_rcr=pmagplotlib.plot_qq_unf(QQ['unf2'],D2,Dtit) # make plot Me_r,Me_rcr=pmagplotlib.plot_qq_exp(QQ['exp2'],I2,Itit) # make plot if outfile!="": # Dec Inc N Mu Mu_crit Me Me_crit Y/N if Mu_r<=Mu_rcr and Me_r<=Me_rcr: F='Y' else: F='N' outstring='%7.1f %7.1f %i %5.3f %5.3f %5.3f %5.3f %s \n'%(Drbar,Irbar,Nr,Mu_r,Mu_rcr,Me_r,Me_rcr,F) outfile.write(outstring) files={} for key in list(QQ.keys()): files[key]=file+'_'+key+'.'+fmt if pmagplotlib.isServer: black = '#000000' purple = '#800080' titles={} titles['eq']='Equal Area Plot' EQ = pmagplotlib.add_borders(EQ,titles,black,purple) pmagplotlib.save_plots(QQ,files) elif plot==1: pmagplotlib.save_plots(QQ,files) else: pmagplotlib.draw_figs(QQ) ans=input(" S[a]ve to save plot, [q]uit without saving: ") if ans=="a": pmagplotlib.save_plots(QQ,files)
def main(): """ NAME goprinc.py DESCRIPTION calculates Principal components from dec/iinc data INPUT FORMAT takes dec/inc as first two columns in space delimited file SYNTAX goprinc.py [options] [< filename] OPTIONS -h prints help message and quits -i for interactive filename entry -f FILE, specify input file -F FILE, specifies output file name < filename for reading from standard input OUTPUT tau_1 V1_Dec, V1_Inc, tau_2 V2_Dec V2_Inc, tau_3 V3_Dec V3_Inc, N """ if len(sys.argv) > 0: if '-h' in sys.argv: # check if help is needed print main.__doc__ sys.exit() # graceful quit if '-f' in sys.argv: ind = sys.argv.index('-f') file = sys.argv[ind + 1] f = open(file, 'rU') data = f.readlines() elif '-i' in sys.argv: # ask for filename file = raw_input("Enter file name with dec, inc data: ") f = open(file, 'rU') data = f.readlines() else: # data = sys.stdin.readlines() # read in data from standard input ofile = "" if '-F' in sys.argv: ind = sys.argv.index('-F') ofile = sys.argv[ind + 1] out = open(ofile, 'w + a') DIs = [] # set up list for dec inc data for line in data: # read in the data from standard input if '\t' in line: rec = line.split('\t') # split each line on space to get records else: rec = line.split() # split each line on space to get records DIs.append((float(rec[0]), float(rec[1]))) # ppars = pmag.doprinc(DIs) output = '%7.5f %7.1f %7.1f %7.5f %7.1f %7.1f %7.5f %7.1f %7.1f %i' % ( ppars["tau1"], ppars["dec"], ppars["inc"], ppars["tau2"], ppars["V2dec"], ppars["V2inc"], ppars["tau3"], ppars["V3dec"], ppars["V3inc"], ppars["N"]) if ofile == "": print output else: out.write(output + '\n')