Instruction:
Here you have a function in Python 3, explain what it does
def on_right_click_listctrl(self, event):
        g_index = event.GetIndex()

        if self.Data[self.s]['measurement_flag'][g_index] == 'g':
            self.mark_meas_bad(g_index)
        else:
            self.mark_meas_good(g_index)

        if self.data_model == 3.0:
            self.con.tables['measurements'].write_magic_file(dir_path=self.WD)
        else:
            pmag.magic_write(os.path.join(
                self.WD, "magic_measurements.txt"), self.mag_meas_data, "magic_measurements")

        self.recalculate_current_specimen_interpreatations()

        if self.ie_open:
            self.ie.update_current_fit_data()
        self.calculate_high_levels_data()
        self.update_selection()

Response: right click on the listctrl toggles measurement bad


Instruction:
Explain what the following Python 3 code does
def onSelect_specimen(self, event):
        self.selected_meas = []
        self.select_specimen(str(self.specimens_box.GetValue()))
        if self.ie_open:
            self.ie.change_selected(self.current_fit)
        self.update_selection()

Response: update figures and text when a new specimen is selected


Instruction:
Explain what the following Python 3 code does
def on_enter_specimen(self, event):
        new_specimen = self.specimens_box.GetValue()
        if new_specimen not in self.specimens:
            self.user_warning(
                "%s is not a valid specimen with measurement data, aborting" % (new_specimen))
            self.specimens_box.SetValue(self.s)
            return
        self.select_specimen(new_specimen)
        if self.ie_open:
            self.ie.change_selected(self.current_fit)
        self.update_selection()

Response: When the specimen is entered it makes that specimen the current specimen and the specimen is selected


Instruction:
Explain what the following Python 3 code does
def get_new_PCA_parameters(self, event):

        tmin = str(self.tmin_box.GetValue())
        tmax = str(self.tmax_box.GetValue())
        if tmin == "" or tmax == "":
            return

        if tmin in self.T_list and tmax in self.T_list and \
           (self.T_list.index(tmax) <= self.T_list.index(tmin)):
            return

        PCA_type = self.PCA_type_box.GetValue()
        if PCA_type == "line":
            calculation_type = "DE-BFL"
        elif PCA_type == "line-anchored":
            calculation_type = "DE-BFL-A"
        elif PCA_type == "line-with-origin":
            calculation_type = "DE-BFL-O"
        elif PCA_type == "Fisher":
            calculation_type = "DE-FM"
        elif PCA_type == "plane":
            calculation_type = "DE-BFP"
        coordinate_system = self.COORDINATE_SYSTEM
        if self.current_fit:
            self.current_fit.put(self.s, coordinate_system, self.get_PCA_parameters(
                self.s, self.current_fit, tmin, tmax, coordinate_system, calculation_type))
        if self.ie_open:
            self.ie.update_current_fit_data()
        self.update_GUI_with_new_interpretation()

Response: calculate statistics when temperatures are selected and PCA type is changed


Instruction:
How would you implement a function in Python 3 that
selects the current fit of the current entry in the fit combobox and sets the current fit to the fit of the GUI

Response:
def on_select_fit(self, event):
        """
        Picks out the fit selected in the fit combobox and sets it to the
        current fit of the GUI then calls the select function of the fit to
        set the GUI's bounds boxes and alter other such parameters

        Parameters
        ----------
        event : the wx.ComboBoxEvent that triggers this function

        Alters
        ------
        current_fit, fit_box selection, tmin_box selection, tmax_box
        selection
        """
        fit_val = self.fit_box.GetValue()
        if self.s not in self.pmag_results_data['specimens'] or not self.pmag_results_data['specimens'][self.s] or fit_val == 'None':
            self.clear_boxes()
            self.current_fit = None
            self.fit_box.SetStringSelection('None')
            self.tmin_box.SetStringSelection('')
            self.tmax_box.SetStringSelection('')
        else:
            try:
                fit_num = list(
                    map(lambda x: x.name, self.pmag_results_data['specimens'][self.s])).index(fit_val)
            except ValueError:
                fit_num = -1
            self.pmag_results_data['specimens'][self.s][fit_num].select()
        if self.ie_open:
            self.ie.change_selected(self.current_fit)


Instruction:
Create a Python 3 function for
allowing the entering of new fit names in the fit combobox

Response:
def on_enter_fit_name(self, event):
        """
        Allows the entering of new fit names in the fit combobox

        Parameters
        ----------
        event : the wx.ComboBoxEvent that triggers this function

        Alters
        ------
        current_fit.name
        """
        if self.current_fit == None:
            self.on_btn_add_fit(event)
        value = self.fit_box.GetValue()
        if ':' in value:
            name, color = value.split(':')
        else:
            name, color = value, None
        if name in [x.name for x in self.pmag_results_data['specimens'][self.s]]:
            print('bad name')
            return
        self.current_fit.name = name
        if color in list(self.color_dict.keys()):
            self.current_fit.color = self.color_dict[color]
        self.update_fit_boxes()
        self.plot_high_levels_data()


Instruction:
Given the following Python 3 function, write the documentation
def on_save_interpretation_button(self, event):

        if self.current_fit:
            self.current_fit.saved = True
            calculation_type = self.current_fit.get(self.COORDINATE_SYSTEM)[
                'calculation_type']
            tmin = str(self.tmin_box.GetValue())
            tmax = str(self.tmax_box.GetValue())

            self.current_fit.put(self.s, 'specimen', self.get_PCA_parameters(
                self.s, self.current_fit, tmin, tmax, 'specimen', calculation_type))
            if len(self.Data[self.s]['zijdblock_geo']) > 0:
                self.current_fit.put(self.s, 'geographic', self.get_PCA_parameters(
                    self.s, self.current_fit, tmin, tmax, 'geographic', calculation_type))
            if len(self.Data[self.s]['zijdblock_tilt']) > 0:
                self.current_fit.put(self.s, 'tilt-corrected', self.get_PCA_parameters(
                    self.s, self.current_fit, tmin, tmax, 'tilt-corrected', calculation_type))

        # calculate high level data
        self.calculate_high_levels_data()
        self.plot_high_levels_data()
        self.on_menu_save_interpretation(event)
        self.update_selection()
        self.close_warning = True

Response: save interpretation to pmag_results_table


Instruction:
Can you implement a function in Python 3 that
functions that is called when the user adds a new interpretation to the current specimen and creates a new interpretation for the current specimen

Response:
def on_btn_add_fit(self, event):
        """
        add a new interpretation to the current specimen

        Parameters
        ----------
        event : the wx.ButtonEvent that triggered this function

        Alters
        ------
        pmag_results_data
        """
        if self.auto_save.GetValue():
            self.current_fit = self.add_fit(self.s, None, None, None, saved=True)
        else:
            self.current_fit = self.add_fit(self.s, None, None, None, saved=False)
        self.generate_warning_text()
        self.update_warning_box()

        if self.ie_open:
            self.ie.update_editor()

        self.update_fit_boxes(True)
        # Draw figures and add  text
        self.get_new_PCA_parameters(event)


Instruction:
Can you tell what is the following Python 3 function doing
def on_btn_delete_fit(self, event):
        self.delete_fit(self.current_fit, specimen=self.s)

Response: Delete the current interpretation from the log


Instruction:
Can you generate the documentation for the following Python 3 function
def do_auto_save(self):
        if not self.auto_save.GetValue():
            if self.current_fit:
                if not self.current_fit.saved:
                    self.delete_fit(self.current_fit, specimen=self.s)

Response: Delete current fit if auto_save == False.


Instruction:
Write a Python 3 script to
update figures and text when a next button is selected

Response:
def on_next_button(self, event):
        """
        update figures and text when a next button is selected
        """
        self.do_auto_save()
        self.selected_meas = []
        index = self.specimens.index(self.s)
        try:
            fit_index = self.pmag_results_data['specimens'][self.s].index(
                self.current_fit)
        except KeyError:
            fit_index = None
        except ValueError:
            fit_index = None
        if index == len(self.specimens)-1:
            index = 0
        else:
            index += 1
        # sets self.s calculates params etc.
        self.initialize_CART_rot(str(self.specimens[index]))
        self.specimens_box.SetStringSelection(str(self.s))
        if fit_index != None and self.s in self.pmag_results_data['specimens']:
            try:
                self.current_fit = self.pmag_results_data['specimens'][self.s][fit_index]
            except IndexError:
                self.current_fit = None
        else:
            self.current_fit = None
        if self.ie_open:
            self.ie.change_selected(self.current_fit)
        self.update_selection()


Instruction:
Can you tell what is the following Python 3 function doing
def main():
    if "-h" in sys.argv:
        print(main.__doc__)
        return
    infile = pmag.get_named_arg("-f", "measurements.txt")
    dir_path = pmag.get_named_arg("-WD", ".")
    infile = pmag.resolve_file_name(infile, dir_path)
    fmt = pmag.get_named_arg("-fmt", "svg")
    save_plots = False
    interactive = True
    if "-sav" in sys.argv:
        interactive = False
        save_plots = True
    experiments = pmag.get_named_arg("-e", "")
    ipmag.chi_magic(infile, dir_path, experiments, fmt, save_plots, interactive)

Response:
NAME
        chi_magic.py

    DESCRIPTION
        plots magnetic susceptibility as a function of frequency and temperature and AC field

    SYNTAX
        chi_magic.py [command line options]

    OPTIONS
        -h prints help message and quits
        -f FILE, specify measurements format file, default "measurements.txt"
        -T IND, specify temperature step to plot -- NOT IMPLEMENTED
        -e EXP, specify experiment name to plot
        -fmt [svg,jpg,png,pdf] set figure format [default is svg]
        -sav save figure and quit


Instruction:
Implement a Python 3 function for
naming thellier_magic_redo.py DESCRIPTION Calculates paleointensity parameters for thellier-thellier type data using bounds stored in the "redo" file SYNTAX thellier_magic_redo [command line options] OPTIONS -h prints help message -usr USER: identify user, default is "" -fcr CRIT, set criteria for grading -f IN: specify input file, default is magic_measurements.txt -fre REDO: specify redo file, default is "thellier_redo" -F OUT: specify output file, default is thellier_specimens.txt -leg: attaches "Recalculated from original measurements; supercedes published results. " to comment field -CR PERC TYPE: apply a blanket cooling rate correction if none supplied in the er_samples.txt file PERC should be a percentage of original (say reduce to 90%) TYPE should be one of the following: EG (for educated guess); PS (based on pilots); TRM (based on comparison of two TRMs) -ANI: perform anisotropy correction -fsa SAMPFILE: er_samples.txt file with cooling rate correction information, default is NO CORRECTION -Fcr CRout: specify pmag_specimen format file for cooling rate corrected data -fan ANIFILE: specify rmag_anisotropy format file, default is rmag_anisotropy.txt -Fac ACout: specify pmag_specimen format file for anisotropy corrected data default is AC_specimens.txt -fnl NLTFILE: specify magic_measurments format file, default is magic_measurements.txt -Fnl NLTout: specify pmag_specimen format file for non-linear trm corrected data default is NLT_specimens.txt -z use z component differenences for pTRM calculation INPUT a thellier_redo file is Specimen_name Tmin Tmax (where Tmin and Tmax are in Centigrade)

Response:
def main():
    """
    NAME
        thellier_magic_redo.py

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

    SYNTAX
        thellier_magic_redo [command line options]

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

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


Instruction:
Given the following Python 3 function, write the documentation
def main(command_line=True, **kwargs):
# initialize some stuff
    noave=0
    inst=""
    samp_con,Z='1',""
    missing=1
    demag="N"
    er_location_name="unknown"
    citation='This study'
    args=sys.argv
    meth_code="LP-NO"
    specnum=-1
    MagRecs=[]
    version_num=pmag.get_version()
    Samps=[] # keeps track of sample orientations
    DIspec=[]
    MagFiles=[]

    user=""
    mag_file=""
    dir_path='.'
    ErSamps=[]
    SampOuts=[]

    samp_file = 'er_samples.txt'
    meas_file = 'magic_measurements.txt'

    #
    # get command line arguments
    #

    if command_line:
        if '-WD' in sys.argv:
            ind = sys.argv.index('-WD')
            dir_path=sys.argv[ind+1]
        if '-ID' in sys.argv:
            ind = sys.argv.index('-ID')
            input_dir_path = sys.argv[ind+1]
        else:
            input_dir_path = dir_path
        output_dir_path = dir_path
        if "-h" in args:
            print(main.__doc__)
            return False
        if '-F' in args:
            ind=args.index("-F")
            meas_file = args[ind+1]
        if '-Fsa' in args:
            ind = args.index("-Fsa")
            samp_file = args[ind+1]
            #try:
            #    open(samp_file,'r')
            #    ErSamps,file_type=pmag.magic_read(samp_file)
            #    print 'sample information will be appended to ', samp_file
            #except:
            #    print samp_file,' not found: sample information will be stored in new er_samples.txt file'
            #    samp_file = output_dir_path+'/er_samples.txt'
        if '-f' in args:
            ind = args.index("-f")
            mag_file= args[ind+1]
        if "-spc" in args:
            ind = args.index("-spc")
            specnum = int(args[ind+1])
        if "-ncn" in args:
            ind=args.index("-ncn")
            samp_con=sys.argv[ind+1]
        if "-loc" in args:
            ind=args.index("-loc")
            er_location_name=args[ind+1]
        if "-A" in args: noave=1
        if "-mcd" in args:
            ind=args.index("-mcd")
            meth_code=args[ind+1]
        if "-lat" in args:
            ind=args.index("-lat")
            site_lat=args[ind+1]
        if "-lon" in args:
            ind=args.index("-lon")
            site_lon=args[ind+1]

    if not command_line:
        dir_path = kwargs.get('dir_path', '.')
        input_dir_path = kwargs.get('input_dir_path', dir_path)
        output_dir_path = dir_path
        meas_file = kwargs.get('meas_file', 'magic_measurements.txt')
        mag_file = kwargs.get('mag_file')
        spec_file = kwargs.get('spec_file', 'er_specimens.txt')
        samp_file = kwargs.get('samp_file', 'er_samples.txt')
        site_file = kwargs.get('site_file', 'er_sites.txt')
        site_lat = kwargs.get('site_lat', 0)
        site_lon = kwargs.get('site_lon', 0)
        specnum = kwargs.get('specnum', 0)
        samp_con = kwargs.get('samp_con', '1')
        er_location_name = kwargs.get('er_location_name', '')
        noave = kwargs.get('noave', 0) # default (0) means DO average
        meth_code = kwargs.get('meth_code', "LP-NO")

    print(samp_con)
    # format variables
    mag_file = os.path.join(input_dir_path,mag_file)
    meas_file = os.path.join(output_dir_path,meas_file)
    spec_file = os.path.join(output_dir_path,spec_file)
    samp_file = os.path.join(output_dir_path,samp_file)
    site_file = os.path.join(output_dir_path,site_file)
    if specnum!=0:specnum=-specnum
    if "4" in samp_con:
        if "-" not in samp_con:
            print("naming convention option [4] must be in form 4-Z where Z is an integer")
            return False, "naming convention option [4] must be in form 4-Z where Z is an integer"
        else:
            Z=samp_con.split("-")[1]
            samp_con="4"
    if "7" in samp_con:
        if "-" not in samp_con:
            print("option [7] must be in form 7-Z where Z is an integer")
            return False, "naming convention option [7] must be in form 7-Z where Z is an integer"
        else:
            Z=samp_con.split("-")[1]
            samp_con="7"

    # parse data
    data=open(mag_file,'r').readlines() # read in data from file
    comment=data[0]
    line=data[1].strip()
    line=line.replace("=","= ")  # make finding orientations easier
    rec=line.split() # read in sample orientation, etc.
    er_specimen_name=rec[0]
    ErSpecRec,ErSampRec,ErSiteRec={},{},{} # make a  sample record
    if specnum!=0:
        er_sample_name=rec[0][:specnum]
    else:
        er_sample_name=rec[0]
    if len(ErSamps)>0: # need to copy existing
       for samp in ErSamps:
           if samp['er_sample_name']==er_sample_name:
               ErSampRec=samp  # we'll ammend this one
           else:
               SampOuts.append(samp) # keep all the others
    if int(samp_con)<6:
        er_site_name=pmag.parse_site(er_sample_name,samp_con,Z)
    else:
        if 'er_site_name' in list(ErSampRec.keys()):er_site_name=ErSampREc['er_site_name']
        if 'er_location_name' in list(ErSampRec.keys()):er_location_name=ErSampREc['er_location_name']
    az_ind=rec.index('a=')+1
    ErSampRec['er_sample_name']=er_sample_name
    ErSampRec['er_sample_description']=comment
    ErSampRec['sample_azimuth']=rec[az_ind]
    dip_ind=rec.index('b=')+1
    dip=-float(rec[dip_ind])
    ErSampRec['sample_dip']='%7.1f'%(dip)
    strike_ind=rec.index('s=')+1
    ErSampRec['sample_bed_dip_direction']='%7.1f'%(float(rec[strike_ind])+90.)
    bd_ind=rec.index('d=')+1
    ErSampRec['sample_bed_dip']=rec[bd_ind]
    v_ind=rec.index('v=')+1
    vol=rec[v_ind][:-3]
    date=rec[-2]
    time=rec[-1]
    ErSampRec['magic_method_codes']=meth_code
    if 'er_location_name' not in list(ErSampRec.keys()):ErSampRec['er_location_name']=er_location_name
    if 'er_site_name' not in list(ErSampRec.keys()):ErSampRec['er_site_name']=er_site_name
    if 'er_citation_names' not in list(ErSampRec.keys()):ErSampRec['er_citation_names']='This study'
    if 'magic_method_codes' not in list(ErSampRec.keys()):ErSampRec['magic_method_codes']='SO-NO'

    ErSpecRec['er_specimen_name'] = er_specimen_name
    ErSpecRec['er_sample_name'] = er_sample_name
    ErSpecRec['er_site_name'] = er_site_name
    ErSpecRec['er_location_name'] = er_location_name
    ErSpecRec['er_citation_names']='This study'

    ErSiteRec['er_site_name'] = er_site_name
    ErSiteRec['er_location_name'] = er_location_name
    ErSiteRec['er_citation_names']='This study'
    ErSiteRec['site_lat'] = site_lat
    ErSiteRec['site_lon']= site_lon

    SpecOuts.append(ErSpecRec)
    SampOuts.append(ErSampRec)
    SiteOuts.append(ErSiteRec)
    for k in range(3,len(data)): # read in data
      line=data[k]
      rec=line.split()
      if len(rec)>1: # skip blank lines at bottom
        MagRec={}
        MagRec['measurement_description']='Date: '+date+' '+time
        MagRec["er_citation_names"]="This study"
        MagRec['er_location_name']=er_location_name
        MagRec['er_site_name']=er_site_name
        MagRec['er_sample_name']=er_sample_name
        MagRec['magic_software_packages']=version_num
        MagRec["treatment_temp"]='%8.3e' % (273) # room temp in kelvin
        MagRec["measurement_temp"]='%8.3e' % (273) # room temp in kelvin
        MagRec["measurement_flag"]='g'
        MagRec["measurement_standard"]='u'
        MagRec["measurement_number"]='1'
        MagRec["er_specimen_name"]=er_specimen_name
        if rec[0]=='NRM':
            meas_type="LT-NO"
        elif rec[0][0]=='M' or rec[0][0]=='H':
            meas_type="LT-AF-Z"
        elif rec[0][0]=='T':
            meas_type="LT-T-Z"
        else:
            print("measurement type unknown")
            return False, "measurement type unknown"
        X=[float(rec[1]),float(rec[2]),float(rec[3])]
        Vec=pmag.cart2dir(X)
        MagRec["measurement_magn_moment"]='%10.3e'% (Vec[2]) # Am^2
        MagRec["measurement_magn_volume"]=rec[4] # A/m
        MagRec["measurement_dec"]='%7.1f'%(Vec[0])
        MagRec["measurement_inc"]='%7.1f'%(Vec[1])
        MagRec["treatment_ac_field"]='0'
        if meas_type!='LT-NO':
            treat=float(rec[0][1:])
        else:
            treat=0
        if meas_type=="LT-AF-Z":
            MagRec["treatment_ac_field"]='%8.3e' %(treat*1e-3) # convert from mT to tesla
        elif meas_type=="LT-T-Z":
            MagRec["treatment_temp"]='%8.3e' % (treat+273.) # temp in kelvin
        MagRec['magic_method_codes']=meas_type
        MagRecs.append(MagRec)
    MagOuts=pmag.measurements_methods(MagRecs,noave)
    pmag.magic_write(meas_file,MagOuts,'magic_measurements')
    print("results put in ",meas_file)
    pmag.magic_write(samp_file,SpecOuts,'er_specimens')
    pmag.magic_write(samp_file,SampOuts,'er_samples')
    pmag.magic_write(samp_file,SiteOuts,'er_sites')
    return True, meas_file

Response: This function is the main entry point for the magic_measurements. py script.


Instruction:
Create a Python 3 function to
input x_segment, y_segment, x_mean, y_mean, n, lab_dc_field, steps_Arai output: x_err, y_err, x_tag, y_tag, b, b_sigma, specimen_b_beta, y_intercept, x_intercept, x_prime, y_prime, delta_x_prime, delta_y_prime, f_Coe, g_Coe, g_lim, specimen_q, specimen_w, count_IZ, count_ZI, B_lab, B_anc, B_anc_sigma, specimen_int

Response:
def York_Regression(x_segment, y_segment, x_mean, y_mean, n, lab_dc_field, steps_Arai):
    """
    input: x_segment, y_segment, x_mean, y_mean, n, lab_dc_field, steps_Arai
    output: x_err,  y_err, x_tag, y_tag, b, b_sigma, specimen_b_beta, y_intercept,
            x_intercept, x_prime, y_prime, delta_x_prime, delta_y_prime, f_Coe,
            g_Coe, g_lim, specimen_q, specimen_w, count_IZ, count_ZI, B_lab, B_anc,
            B_anc_sigma, specimen_int
    """
    x_err = x_segment - x_mean
    y_err = y_segment - y_mean
    york_b = -1* numpy.sqrt(old_div(sum(y_err**2), sum(x_err**2)) )  # averaged slope

    b = numpy.sign(sum(x_err * y_err)) * numpy.std(y_segment, ddof=1)/numpy.std(x_segment, ddof=1) # ddof is degrees of freedom
    if b == 0:
        york_b = 1e-10
    else:
        york_b = b

    york_sigma= numpy.sqrt( old_div((2 * sum(y_err**2) - 2*york_b* sum(x_err*y_err)), ( (n-2) * sum(x_err**2) )) )
    if york_sigma == 0: # prevent divide by zero
        york_sigma = 1e-10
    beta_Coe = abs(old_div(york_sigma,york_b))
    # y_T is the intercept of the extrepolated line
    # through the center of mass (see figure 7 in Coe (1978))
    y_T = y_mean - (york_b* x_mean)
    x_T = old_div((-1 * y_T), york_b)  # x intercept
    # # calculate the extrarpolated data points for f and fvds
    x_tag = old_div((y_segment - y_T ), york_b) # returns array of y points minus the y intercept, divided by slope
    y_tag = york_b*x_segment + y_T

    # intersect of the dashed square and the horizontal dahed line  next to delta-y-5 in figure 7, Coe (1978)
    x_prime = old_div((x_segment+x_tag), 2)
    y_prime = old_div((y_segment+y_tag), 2)

    delta_x_prime = abs(max(x_prime) - min(x_prime)) # TRM length of best fit line
    delta_y_prime = abs(max(y_prime) - min(y_prime)) # NRM length of best fit line

    f_Coe = old_div(delta_y_prime, abs(y_T))
    if delta_y_prime:
        g_Coe =  1 - (old_div(sum((y_prime[:-1]-y_prime[1:])**2), delta_y_prime ** 2))  # gap factor
    else:
        g_Coe = float('nan')
    g_lim = old_div((float(n) - 2), (float(n) - 1))
    q_Coe = abs(york_b)*f_Coe*g_Coe/york_sigma
    w_Coe = old_div(q_Coe, numpy.sqrt(n - 2))
    count_IZ = steps_Arai.count('IZ')
    count_ZI = steps_Arai.count('ZI')
    B_lab = lab_dc_field * 1e6
    B_anc = abs(york_b) * B_lab # in microtesla
    B_anc_sigma = york_sigma * B_lab
    specimen_int = -1* lab_dc_field * york_b # in tesla
    specimen_int_sigma = york_sigma * lab_dc_field
    return {'x_err': x_err, 'y_err': y_err, 'x_tag': x_tag, 'y_tag': y_tag,
            'specimen_b': york_b, 'specimen_b_sigma': york_sigma, 'specimen_b_beta': beta_Coe,
            'y_int': y_T, 'x_int': x_T, 'x_prime': x_prime, 'y_prime': y_prime,
            'delta_x_prime': delta_x_prime, 'delta_y_prime': delta_y_prime, 'specimen_f': f_Coe,
            'specimen_g': g_Coe, 'specimen_g_lim': g_lim, 'specimen_q': q_Coe, 'specimen_w': w_Coe,
            'count_IZ': count_IZ, 'count_ZI': count_ZI, 'B_lab': B_lab, 'B_anc': B_anc,
            'B_anc_sigma': B_anc_sigma, 'specimen_int': specimen_int, 'specimen_int_sigma': specimen_int_sigma}


Instruction:
How would you explain what the following Python 3 function does
def get_vds(zdata, delta_y_prime, start, end):
    vector_diffs = []
    for k in range(len(zdata)-1): # gets diff between two vectors
        vector_diffs.append(numpy.sqrt(sum((numpy.array(zdata[k+1]) - numpy.array(zdata[k]))**2) ))
    last_vector = numpy.linalg.norm(zdata[-1])
    vector_diffs.append(last_vector)
    vds = sum(vector_diffs)
    f_vds = abs(old_div(delta_y_prime, vds)) # fvds varies, because of delta_y_prime, but vds does not.
    vector_diffs_segment = vector_diffs[start:end]
    partial_vds = sum(vector_diffs_segment)
    max_diff = max(vector_diffs_segment)
    GAP_MAX = old_div(max_diff, partial_vds) #
    return {'max_diff': max_diff, 'vector_diffs': vector_diffs, 'specimen_vds': vds,
            'specimen_fvds': f_vds, 'vector_diffs_segment': vector_diffs_segment,
            'partial_vds': partial_vds, 'GAP-MAX': GAP_MAX}

Response: gets vds and f_vds and GAP_MAX from the data array


Instruction:
Here you have a function in Python 3, explain what it does
def get_SCAT_box(slope, x_mean, y_mean, beta_threshold = .1):
    # if beta_threshold is -999, that means null
    if beta_threshold == -999:
        beta_threshold = .1
    slope_err_threshold = abs(slope) * beta_threshold
    x, y = x_mean, y_mean
    # get lines that pass through mass center, with opposite slope
    slope1 =  slope + (2* slope_err_threshold)
    line1_y_int = y - (slope1 * x)
    line1_x_int = -1 * (old_div(line1_y_int, slope1))
    slope2 = slope - (2 * slope_err_threshold)
    line2_y_int = y - (slope2 * x)
    line2_x_int = -1 * (old_div(line2_y_int, slope2))
        # l1_y_int and l2_x_int form the bottom line of the box
        # l2_y_int and l1_x_int form the top line of the box
#    print "_diagonal line1:", (0, line2_y_int), (line2_x_int, 0), (x, y)
#    print "_diagonal line2:", (0, line1_y_int), (line1_x_int, 0), (x, y)
#    print "_bottom line:", [(0, line1_y_int), (line2_x_int, 0)]
#    print "_top line:", [(0, line2_y_int), (line1_x_int, 0)]
    low_bound = [(0, line1_y_int), (line2_x_int, 0)]
    high_bound = [(0, line2_y_int), (line1_x_int, 0)]
    x_max = high_bound[1][0]#
    y_max = high_bound[0][1]
    # function for low_bound
    low_slope = old_div((low_bound[0][1] - low_bound[1][1]), (low_bound[0][0] - low_bound[1][0])) #
    low_y_int = low_bound[0][1]
    def low_bound(x):
        y = low_slope * x + low_y_int
        return y
    # function for high_bound
    high_slope = old_div((high_bound[0][1] - high_bound[1][1]), (high_bound[0][0] - high_bound[1][0])) # y_0-y_1/x_0-x_1
    high_y_int = high_bound[0][1]
    def high_bound(x):
        y = high_slope * x + high_y_int
        return y
    high_line = [high_y_int, high_slope]
    low_line = [low_y_int, low_slope]
    return low_bound, high_bound, x_max, y_max, low_line, high_line

Response: returns information about a SCAT box


Instruction:
Create a Python 3 function for
determining if a particular point falls within a box

Response:
def in_SCAT_box(x, y, low_bound, high_bound, x_max, y_max):
    """determines if a particular point falls within a box"""
    passing = True
    upper_limit = high_bound(x)
    lower_limit = low_bound(x)
    if x > x_max or y > y_max:
        passing = False
    if x < 0 or y < 0:
        passing = False
    if y > upper_limit:
        passing = False
    if y < lower_limit:
        passing = False
    return passing


Instruction:
Can you tell what is the following Python 3 function doing
def get_SCAT_points(x_Arai_segment, y_Arai_segment, tmin, tmax, ptrm_checks_temperatures,
                    ptrm_checks_starting_temperatures, x_ptrm_check, y_ptrm_check,
                    tail_checks_temperatures, tail_checks_starting_temperatures,
                    x_tail_check, y_tail_check):
    """returns relevant points for a SCAT test"""
    points = []
    points_arai = []
    points_ptrm = []
    points_tail = []
    for i in range(len(x_Arai_segment)): # uses only the best_fit segment, so no need for further selection
        x = x_Arai_segment[i]
        y = y_Arai_segment[i]
        points.append((x, y))
        points_arai.append((x,y))

    for num, temp in enumerate(ptrm_checks_temperatures): #
        if temp >= tmin and temp <= tmax: # if temp is within selected range
            if (ptrm_checks_starting_temperatures[num] >= tmin and
                    ptrm_checks_starting_temperatures[num] <= tmax): # and also if it was not done after an out-of-range temp
                x = x_ptrm_check[num]
                y = y_ptrm_check[num]
                points.append((x, y))
                points_ptrm.append((x,y))

    for num, temp in enumerate(tail_checks_temperatures):
        if temp >= tmin and temp <= tmax:
            if (tail_checks_starting_temperatures[num] >= tmin and
                    tail_checks_starting_temperatures[num] <= tmax):
                x = x_tail_check[num]
                y = y_tail_check[num]
                points.append((x, y))
                points_tail.append((x,y))
           # print "points (tail checks added)", points
    fancy_points = {'points_arai': points_arai, 'points_ptrm': points_ptrm, 'points_tail': points_tail}
    return points, fancy_points

Response: returns relevant points for a SCAT test


Instruction:
Make a summary of the following Python 3 code
def get_SCAT(points, low_bound, high_bound, x_max, y_max):
    # iterate through all relevant points and see if any of them fall outside of your SCAT box
    SCAT = True
    for point in points:
        result = in_SCAT_box(point[0], point[1], low_bound, high_bound, x_max, y_max)
        if result == False:
           # print "SCAT TEST FAILED"
            SCAT = False
    return SCAT

Response: runs SCAT test and returns boolean


Instruction:
Create a Python 3 function to
run SCAT test and returns Pass or Fail

Response:
def fancy_SCAT(points, low_bound, high_bound, x_max, y_max):
    """
    runs SCAT test and returns 'Pass' or 'Fail'
    """
    # iterate through all relevant points and see if any of them fall outside of your SCAT box
    # {'points_arai': [(x,y),(x,y)], 'points_ptrm': [(x,y),(x,y)], ...}
    SCAT = 'Pass'
    SCATs = {'SCAT_arai': 'Pass', 'SCAT_ptrm': 'Pass', 'SCAT_tail': 'Pass'}
    for point_type in points:
        #print 'point_type', point_type
        for point in points[point_type]:
            #print 'point', point
            result = in_SCAT_box(point[0], point[1], low_bound, high_bound, x_max, y_max)
            if not result:
               # print "SCAT TEST FAILED"
                x = 'SCAT' + point_type[6:]
                #print 'lib point type', point_type
                #print 'xxxx', x
                SCATs[x] = 'Fail'
                SCAT = 'Fail'
    return SCAT, SCATs


Instruction:
Write a Python 3 function for
getting the FRAC of a single element in a list of vector diffs

Response:
def get_FRAC(vds, vector_diffs_segment):
    """
    input: vds, vector_diffs_segment
    output: FRAC
    """
    for num in vector_diffs_segment:
        if num < 0:
            raise ValueError('vector diffs should not be negative')
    if vds == 0:
        raise ValueError('attempting to divide by zero. vds should be a positive number')
    FRAC = old_div(sum(vector_diffs_segment), vds)
    return FRAC


Instruction:
Can you generate the documentation for the following Python 3 function
def get_R_corr2(x_avg, y_avg, x_segment, y_segment): #
    xd = x_segment - x_avg # detrend x_segment
    yd = y_segment - y_avg # detrend y_segment
    if sum(xd**2) * sum(yd**2) == 0: # prevent divide by zero error
        return float('nan')
    rcorr = old_div(sum((xd * yd))**2, (sum(xd**2) * sum(yd**2)))
    return rcorr

Response: Compute R_corr2 based on average of two segments.


Instruction:
How would you implement a function in Python 3 that
returns the R_det2 of the last two values of the y_prime and y_avg

Response:
def get_R_det2(y_segment, y_avg, y_prime):
    """
    takes in an array of y values, the mean of those values, and the array of y prime values.
    returns R_det2
    """
    numerator = sum((numpy.array(y_segment) - numpy.array(y_prime))**2)
    denominator = sum((numpy.array(y_segment) - y_avg)**2)
    if denominator: # prevent divide by zero error
        R_det2 = 1 - (old_div(numerator, denominator))
        return R_det2
    else:
        return float('nan')


Instruction:
Can you tell what is the following Python 3 function doing
def get_b_wiggle(x, y, y_int):
    if x == 0:
        b_wiggle = 0
    else:
        b_wiggle = old_div((y_int - y), x)
    return b_wiggle

Response: returns instantaneous slope from the ratio of NRM lost to TRM gained at the ith step


Instruction:
Can you write a function in Python 3 where it
gets Z from Arai plot

Response:
def get_Z(x_segment, y_segment, x_int, y_int, slope):
    """
    input: x_segment, y_segment, x_int, y_int, slope
    output: Z (Arai plot zigzag parameter)
    """
    Z = 0
    first_time = True
    for num, x in enumerate(x_segment):
        b_wiggle = get_b_wiggle(x, y_segment[num], y_int)
        z = old_div((x * abs(b_wiggle - abs(slope)) ), abs(x_int))
        Z += z
        first_time = False
    return Z


Instruction:
Create a Python 3 function to
calculate the Zstar of a set of segments.

Response:
def get_Zstar(x_segment, y_segment, x_int, y_int, slope, n):
    """
    input: x_segment, y_segment, x_int, y_int, slope, n
    output: Z* (Arai plot zigzag parameter (alternate))
    """
    total = 0
    first_time = True
    for num, x in enumerate(x_segment):
        b_wiggle = get_b_wiggle(x, y_segment[num], y_int)
        result = 100 * ( old_div((x * abs(b_wiggle - abs(slope)) ), abs(y_int)) )
        total += result
        first_time = False
    Zstar = (old_div(1., (n - 1.))) * total
    return Zstar


Instruction:
Can you create a Python 3 function that
gets the normalized points

Response:
def get_normed_points(point_array, norm): # good to go
    """
    input: point_array, norm
    output: normed array
    """
    norm = float(norm)
    #floated_array = []
    #for p in point_array: # need to make sure each point is a float
        #floated_array.append(float(p))
    points = old_div(numpy.array(point_array), norm)
    return points


Instruction:
Write a Python 3 script for
getting xy array from x_segment y_segment

Response:
def get_xy_array(x_segment, y_segment):
    """
    input: x_segment, y_segment
    output: xy_segment, ( format: [(x[0], y[0]), (x[1], y[1])]
    """
    xy_array = []
    for num, x in enumerate(x_segment):
        xy_array.append((x, y_segment[num]))
    return xy_array


Instruction:
How would you explain what the following Python 3 function does
def main():
    if '-h' in sys.argv:
        print(main.__doc__)
        sys.exit()
    if '-i' in sys.argv:
        file=input("Enter file name: ")
        f=open(file,'r')
    elif '-f' in sys.argv:
        ind=sys.argv.index('-f')
        file=sys.argv[ind+1]
        f=open(file,'r')
    else:
        f=sys.stdin
    ofile = ""
    if '-F' in sys.argv:
        ind = sys.argv.index('-F')
        ofile= sys.argv[ind+1]
        out = open(ofile, 'w + a')
    data=f.readlines()
    dat=[]
    sum=0
    for line in data:
        rec=line.split()
        dat.append(float(rec[0]))
        sum+=float(float(rec[0]))
    mean,std=pmag.gausspars(dat)
    outdata = len(dat),mean,sum,std,100*std/mean
    if ofile == "":
        print(len(dat),mean,sum,std,100*std/mean)
    else:
        for i in outdata:
            i = str(i)
            out.write(i + " ")

Response:
NAME
        stats.py

    DEFINITION
        calculates Gauss statistics for input data

    SYNTAX
        stats [command line options][< filename]

    INPUT
        single column of numbers

    OPTIONS
        -h prints help message and quits
        -i interactive entry of file name
        -f input file name
        -F output file name

    OUTPUT
      N, mean, sum, sigma, (%)
      where sigma is the standard deviation
      where % is sigma as percentage of the mean
      stderr is the standard error and
      95% conf.=  1.96*sigma/sqrt(N)


Instruction:
Create a Python 3 function to
name magic_select.py DESCRIPTION picks out records and dictitem options saves to magic_special file SYNTAX magic_select.py [command line optins] OPTIONS -h prints help message and quits -f FILE: specify input magic format file -F FILE: specify output magic format file -dm : data model (default is 3.0, otherwise use 2.5) -key KEY string [T,F,has, not, eval,min,max] returns records where the value of the key either: matches exactly the string (T) does not match the string (F) contains the string (has) does not contain the string (not) the value equals the numerical value of the string (eval) the value is greater than the numerical value of the string (min) the value is less than the numerical value of the string (max) NOTES for age range: use KEY: age (converts to Ma, takes mid point of low, high if no value for age. for paleolat: use KEY: model_lat (uses lat, if age<5 Ma, else, model_lat, or attempts calculation from average_inc if no model_lat.) returns estimate in model_lat key EXAMPLE: # here I want to output all records where the site column exactly matches "MC01" magic_select.py -f samples.txt -key site MC01 T -F select_samples.txt

Response:
def main():
    """
    NAME
        magic_select.py

    DESCRIPTION
        picks out records and dictitem options saves to magic_special file

    SYNTAX
        magic_select.py [command line optins]

    OPTIONS
        -h prints help message and quits
        -f FILE: specify input magic format file
        -F FILE: specify output magic format file
        -dm : data model (default is 3.0, otherwise use 2.5)
        -key KEY string [T,F,has, not, eval,min,max]
           returns records where the value of the key either:
               matches exactly the string (T)
               does not match the string (F)
               contains the string (has)
               does not contain the string (not)
               the value equals the numerical value of the string (eval)
               the value is greater than the numerical value of the string (min)
               the value is less than the numerical value of the string (max)
      NOTES
         for age range:
             use KEY: age (converts to Ma, takes mid point of low, high if no value for age.
         for paleolat:
             use KEY: model_lat (uses lat, if age<5 Ma, else, model_lat, or attempts calculation from average_inc if no model_lat.) returns estimate in model_lat key

      EXAMPLE:
         # here I want to output all records where the site column exactly matches "MC01"
         magic_select.py -f samples.txt -key site MC01 T -F select_samples.txt

    """
    dir_path = "."
    flag = ''
    if '-WD' in sys.argv:
        ind = sys.argv.index('-WD')
        dir_path = sys.argv[ind+1]
    if '-h' in sys.argv:
        print(main.__doc__)
        sys.exit()
    if '-f' in sys.argv:
        ind = sys.argv.index('-f')
        magic_file = dir_path+'/'+sys.argv[ind+1]
    else:
        print(main.__doc__)
        print('-W- "-f" is a required option')
        sys.exit()
    if '-dm' in sys.argv:
        ind = sys.argv.index('-dm')
        data_model_num=sys.argv[ind+1]
        if data_model_num!='3':data_model_num=2.5
    else : data_model_num=3
    if '-F' in sys.argv:
        ind = sys.argv.index('-F')
        outfile = dir_path+'/'+sys.argv[ind+1]
    else:
        print(main.__doc__)
        print('-W- "-F" is a required option')
        sys.exit()
    if '-key' in sys.argv:
        ind = sys.argv.index('-key')
        grab_key = sys.argv[ind+1]
        v = sys.argv[ind+2]
        flag = sys.argv[ind+3]
    else:
        print(main.__doc__)
        print('-key is required')
        sys.exit()
    #
    # get data read in
    Data, file_type = pmag.magic_read(magic_file)
    if grab_key == 'age':
        grab_key = 'average_age'
        Data = pmag.convert_ages(Data,data_model=data_model_num)
    if grab_key == 'model_lat':
        Data = pmag.convert_lat(Data)
        Data = pmag.convert_ages(Data,data_model=data_model_num)
    #print(Data[0])
    Selection = pmag.get_dictitem(Data, grab_key, v, flag, float_to_int=True)
    if len(Selection) > 0:
        pmag.magic_write(outfile, Selection, file_type)
    else:
        print('no data matched your criteria')


Instruction:
Explain what the following Python 3 code does
def main():
#
    dir_path = "."
    version_num = pmag.get_version()
    verbose = pmagplotlib.verbose
    args = sys.argv
    ipar, ihext, ivec, iboot, imeas, isite, iplot, vec = 0, 0, 0, 1, 1, 0, 1, 0
    hpars, bpars, PDir = [], [], []
    CS, crd = '-1', 's'
    nb = 1000
    fmt = 'pdf'
    ResRecs = []
    orlist = []
    outfile, comp, Dir, gtcirc, PDir = 'rmag_results.txt', 0, [], 0, []
    infile = 'rmag_anisotropy.txt'
    if "-h" in args:
        print(main.__doc__)
        sys.exit()
    if '-WD' in args:
        ind = args.index('-WD')
        dir_path = args[ind+1]
    if '-n' in args:
        ind = args.index('-n')
        nb = int(args[ind+1])
    if '-usr' in args:
        ind = args.index('-usr')
        user = args[ind+1]
    else:
        user = ""
    if '-B' in args:
        iboot, ihext = 0, 1
    if '-par' in args:
        ipar = 1
    if '-x' in args:
        ihext = 1
    if '-v' in args:
        ivec = 1
    if '-sit' in args:
        isite = 1
    if '-P' in args:
        iplot = 0
    if '-f' in args:
        ind = args.index('-f')
        infile = args[ind+1]
    if '-F' in args:
        ind = args.index('-F')
        outfile = args[ind+1]
    if '-crd' in sys.argv:
        ind = sys.argv.index('-crd')
        crd = sys.argv[ind+1]
        if crd == 'g':
            CS = '0'
        if crd == 't':
            CS = '100'
    if '-fmt' in args:
        ind = args.index('-fmt')
        fmt = args[ind+1]
    if '-sav' in args:
        plots = 1
        verbose = 0
    else:
        plots = 0
    if '-gtc' in args:
        ind = args.index('-gtc')
        d, i = float(args[ind+1]), float(args[ind+2])
        PDir.append(d)
        PDir.append(i)
    if '-d' in args:
        comp = 1
        ind = args.index('-d')
        vec = int(args[ind+1])-1
        Dir = [float(args[ind+2]), float(args[ind+3])]
#
# set up plots
#
    if infile[0] != '/':
        infile = dir_path+'/'+infile
    if outfile[0] != '/':
        outfile = dir_path+'/'+outfile
    ANIS = {}
    initcdf, inittcdf = 0, 0
    ANIS['data'], ANIS['conf'] = 1, 2
    if iboot == 1:
        ANIS['tcdf'] = 3
        if iplot == 1:
            inittcdf = 1
            pmagplotlib.plot_init(ANIS['tcdf'], 5, 5)
        if comp == 1 and iplot == 1:
            initcdf = 1
            ANIS['vxcdf'], ANIS['vycdf'], ANIS['vzcdf'] = 4, 5, 6
            pmagplotlib.plot_init(ANIS['vxcdf'], 5, 5)
            pmagplotlib.plot_init(ANIS['vycdf'], 5, 5)
            pmagplotlib.plot_init(ANIS['vzcdf'], 5, 5)
    if iplot == 1:
        pmagplotlib.plot_init(ANIS['conf'], 5, 5)
        pmagplotlib.plot_init(ANIS['data'], 5, 5)
# read in the data
    data, ifiletype = pmag.magic_read(infile)
    for rec in data:  # find all the orientation systems
        if 'anisotropy_tilt_correction' not in rec.keys():
            rec['anisotropy_tilt_correction'] = '-1'
        if rec['anisotropy_tilt_correction'] not in orlist:
            orlist.append(rec['anisotropy_tilt_correction'])
    if CS not in orlist:
        if len(orlist) > 0:
            CS = orlist[0]
        else:
            CS = '-1'
        if CS == '-1':
            crd = 's'
        if CS == '0':
            crd = 'g'
        if CS == '100':
            crd = 't'
        if verbose:
            print("desired coordinate system not available, using available: ", crd)
    if isite == 1:
        sitelist = []
        for rec in data:
            if rec['er_site_name'] not in sitelist:
                sitelist.append(rec['er_site_name'])
        sitelist.sort()
        plt = len(sitelist)
    else:
        plt = 1
    k = 0
    while k < plt:
        site = ""
        sdata, Ss = [], []  # list of S format data
        Locs, Sites, Samples, Specimens, Cits = [], [], [], [], []
        if isite == 0:
            sdata = data
        else:
            site = sitelist[k]
            for rec in data:
                if rec['er_site_name'] == site:
                    sdata.append(rec)
        anitypes = []
        csrecs = pmag.get_dictitem(
            sdata, 'anisotropy_tilt_correction', CS, 'T')
        for rec in csrecs:
            if rec['anisotropy_type'] not in anitypes:
                anitypes.append(rec['anisotropy_type'])
            if rec['er_location_name'] not in Locs:
                Locs.append(rec['er_location_name'])
            if rec['er_site_name'] not in Sites:
                Sites.append(rec['er_site_name'])
            if rec['er_sample_name'] not in Samples:
                Samples.append(rec['er_sample_name'])
            if rec['er_specimen_name'] not in Specimens:
                Specimens.append(rec['er_specimen_name'])
            if rec['er_citation_names'] not in Cits:
                Cits.append(rec['er_citation_names'])
            s = []
            s.append(float(rec["anisotropy_s1"]))
            s.append(float(rec["anisotropy_s2"]))
            s.append(float(rec["anisotropy_s3"]))
            s.append(float(rec["anisotropy_s4"]))
            s.append(float(rec["anisotropy_s5"]))
            s.append(float(rec["anisotropy_s6"]))
            if s[0] <= 1.0:
                Ss.append(s)  # protect against crap
            # tau,Vdirs=pmag.doseigs(s)
            ResRec = {}
            ResRec['er_location_names'] = rec['er_location_name']
            ResRec['er_citation_names'] = rec['er_citation_names']
            ResRec['er_site_names'] = rec['er_site_name']
            ResRec['er_sample_names'] = rec['er_sample_name']
            ResRec['er_specimen_names'] = rec['er_specimen_name']
            ResRec['rmag_result_name'] = rec['er_specimen_name'] + \
                ":"+rec['anisotropy_type']
            ResRec["er_analyst_mail_names"] = user
            ResRec["tilt_correction"] = CS
            ResRec["anisotropy_type"] = rec['anisotropy_type']
            if "anisotropy_n" not in rec.keys():
                rec["anisotropy_n"] = "6"
            if "anisotropy_sigma" not in rec.keys():
                rec["anisotropy_sigma"] = "0"
            fpars = pmag.dohext(
                int(rec["anisotropy_n"])-6, float(rec["anisotropy_sigma"]), s)
            ResRec["anisotropy_v1_dec"] = '%7.1f' % (fpars['v1_dec'])
            ResRec["anisotropy_v2_dec"] = '%7.1f' % (fpars['v2_dec'])
            ResRec["anisotropy_v3_dec"] = '%7.1f' % (fpars['v3_dec'])
            ResRec["anisotropy_v1_inc"] = '%7.1f' % (fpars['v1_inc'])
            ResRec["anisotropy_v2_inc"] = '%7.1f' % (fpars['v2_inc'])
            ResRec["anisotropy_v3_inc"] = '%7.1f' % (fpars['v3_inc'])
            ResRec["anisotropy_t1"] = '%10.8f' % (fpars['t1'])
            ResRec["anisotropy_t2"] = '%10.8f' % (fpars['t2'])
            ResRec["anisotropy_t3"] = '%10.8f' % (fpars['t3'])
            ResRec["anisotropy_ftest"] = '%10.3f' % (fpars['F'])
            ResRec["anisotropy_ftest12"] = '%10.3f' % (fpars['F12'])
            ResRec["anisotropy_ftest23"] = '%10.3f' % (fpars['F23'])
            ResRec["result_description"] = 'F_crit: ' + \
                fpars['F_crit']+'; F12,F23_crit: '+fpars['F12_crit']
            ResRec['anisotropy_type'] = pmag.makelist(anitypes)
            ResRecs.append(ResRec)
        if len(Ss) > 1:
            if pmagplotlib.isServer:
                title = "LO:_"+ResRec['er_location_names'] + \
                    '_SI:_'+site+'_SA:__SP:__CO:_'+crd
            else:
                title = ResRec['er_location_names']
                if site:
                    title += "_{}".format(site)
                title += '_{}'.format(crd)
            ResRec['er_location_names'] = pmag.makelist(Locs)
            bpars, hpars = pmagplotlib.plot_anis(
                ANIS, Ss, iboot, ihext, ivec, ipar, title, iplot, comp, vec, Dir, nb)
            if len(PDir) > 0:
                pmagplotlib.plot_circ(ANIS['data'], PDir, 90., 'g')
                pmagplotlib.plot_circ(ANIS['conf'], PDir, 90., 'g')
            if verbose and plots == 0:
                pmagplotlib.draw_figs(ANIS)
            ResRec['er_location_names'] = pmag.makelist(Locs)
            if plots == 1:
                save(ANIS, fmt, title)
            ResRec = {}
            ResRec['er_citation_names'] = pmag.makelist(Cits)
            ResRec['er_location_names'] = pmag.makelist(Locs)
            ResRec['er_site_names'] = pmag.makelist(Sites)
            ResRec['er_sample_names'] = pmag.makelist(Samples)
            ResRec['er_specimen_names'] = pmag.makelist(Specimens)
            ResRec['rmag_result_name'] = pmag.makelist(
                Sites)+":"+pmag.makelist(anitypes)
            ResRec['anisotropy_type'] = pmag.makelist(anitypes)
            ResRec["er_analyst_mail_names"] = user
            ResRec["tilt_correction"] = CS
            if isite == "0":
                ResRec['result_description'] = "Study average using coordinate system: " + CS
            if isite == "1":
                ResRec['result_description'] = "Site average using coordinate system: " + CS
            if hpars != [] and ihext == 1:
                HextRec = {}
                for key in ResRec.keys():
                    HextRec[key] = ResRec[key]   # copy over stuff
                HextRec["anisotropy_v1_dec"] = '%7.1f' % (hpars["v1_dec"])
                HextRec["anisotropy_v2_dec"] = '%7.1f' % (hpars["v2_dec"])
                HextRec["anisotropy_v3_dec"] = '%7.1f' % (hpars["v3_dec"])
                HextRec["anisotropy_v1_inc"] = '%7.1f' % (hpars["v1_inc"])
                HextRec["anisotropy_v2_inc"] = '%7.1f' % (hpars["v2_inc"])
                HextRec["anisotropy_v3_inc"] = '%7.1f' % (hpars["v3_inc"])
                HextRec["anisotropy_t1"] = '%10.8f' % (hpars["t1"])
                HextRec["anisotropy_t2"] = '%10.8f' % (hpars["t2"])
                HextRec["anisotropy_t3"] = '%10.8f' % (hpars["t3"])
                HextRec["anisotropy_hext_F"] = '%7.1f ' % (hpars["F"])
                HextRec["anisotropy_hext_F12"] = '%7.1f ' % (hpars["F12"])
                HextRec["anisotropy_hext_F23"] = '%7.1f ' % (hpars["F23"])
                HextRec["anisotropy_v1_eta_semi_angle"] = '%7.1f ' % (
                    hpars["e12"])
                HextRec["anisotropy_v1_eta_dec"] = '%7.1f ' % (hpars["v2_dec"])
                HextRec["anisotropy_v1_eta_inc"] = '%7.1f ' % (hpars["v2_inc"])
                HextRec["anisotropy_v1_zeta_semi_angle"] = '%7.1f ' % (
                    hpars["e13"])
                HextRec["anisotropy_v1_zeta_dec"] = '%7.1f ' % (
                    hpars["v3_dec"])
                HextRec["anisotropy_v1_zeta_inc"] = '%7.1f ' % (
                    hpars["v3_inc"])
                HextRec["anisotropy_v2_eta_semi_angle"] = '%7.1f ' % (
                    hpars["e12"])
                HextRec["anisotropy_v2_eta_dec"] = '%7.1f ' % (hpars["v1_dec"])
                HextRec["anisotropy_v2_eta_inc"] = '%7.1f ' % (hpars["v1_inc"])
                HextRec["anisotropy_v2_zeta_semi_angle"] = '%7.1f ' % (
                    hpars["e23"])
                HextRec["anisotropy_v2_zeta_dec"] = '%7.1f ' % (
                    hpars["v3_dec"])
                HextRec["anisotropy_v2_zeta_inc"] = '%7.1f ' % (
                    hpars["v3_inc"])
                HextRec["anisotropy_v3_eta_semi_angle"] = '%7.1f ' % (
                    hpars["e12"])
                HextRec["anisotropy_v3_eta_dec"] = '%7.1f ' % (hpars["v1_dec"])
                HextRec["anisotropy_v3_eta_inc"] = '%7.1f ' % (hpars["v1_inc"])
                HextRec["anisotropy_v3_zeta_semi_angle"] = '%7.1f ' % (
                    hpars["e23"])
                HextRec["anisotropy_v3_zeta_dec"] = '%7.1f ' % (
                    hpars["v2_dec"])
                HextRec["anisotropy_v3_zeta_inc"] = '%7.1f ' % (
                    hpars["v2_inc"])
                HextRec["magic_method_codes"] = 'LP-AN:AE-H'
                if verbose:
                    print("Hext Statistics: ")
                    print(
                        " tau_i, V_i_D, V_i_I, V_i_zeta, V_i_zeta_D, V_i_zeta_I, V_i_eta, V_i_eta_D, V_i_eta_I")
                    print(HextRec["anisotropy_t1"], HextRec["anisotropy_v1_dec"], HextRec["anisotropy_v1_inc"], HextRec["anisotropy_v1_eta_semi_angle"], HextRec["anisotropy_v1_eta_dec"],
                          HextRec["anisotropy_v1_eta_inc"], HextRec["anisotropy_v1_zeta_semi_angle"], HextRec["anisotropy_v1_zeta_dec"], HextRec["anisotropy_v1_zeta_inc"])
                    print(HextRec["anisotropy_t2"], HextRec["anisotropy_v2_dec"], HextRec["anisotropy_v2_inc"], HextRec["anisotropy_v2_eta_semi_angle"], HextRec["anisotropy_v2_eta_dec"],
                          HextRec["anisotropy_v2_eta_inc"], HextRec["anisotropy_v2_zeta_semi_angle"], HextRec["anisotropy_v2_zeta_dec"], HextRec["anisotropy_v2_zeta_inc"])
                    print(HextRec["anisotropy_t3"], HextRec["anisotropy_v3_dec"], HextRec["anisotropy_v3_inc"], HextRec["anisotropy_v3_eta_semi_angle"], HextRec["anisotropy_v3_eta_dec"],
                          HextRec["anisotropy_v3_eta_inc"], HextRec["anisotropy_v3_zeta_semi_angle"], HextRec["anisotropy_v3_zeta_dec"], HextRec["anisotropy_v3_zeta_inc"])
                HextRec['magic_software_packages'] = version_num
                ResRecs.append(HextRec)
            if bpars != []:
                BootRec = {}
                for key in ResRec.keys():
                    BootRec[key] = ResRec[key]   # copy over stuff
                BootRec["anisotropy_v1_dec"] = '%7.1f' % (bpars["v1_dec"])
                BootRec["anisotropy_v2_dec"] = '%7.1f' % (bpars["v2_dec"])
                BootRec["anisotropy_v3_dec"] = '%7.1f' % (bpars["v3_dec"])
                BootRec["anisotropy_v1_inc"] = '%7.1f' % (bpars["v1_inc"])
                BootRec["anisotropy_v2_inc"] = '%7.1f' % (bpars["v2_inc"])
                BootRec["anisotropy_v3_inc"] = '%7.1f' % (bpars["v3_inc"])
                BootRec["anisotropy_t1"] = '%10.8f' % (bpars["t1"])
                BootRec["anisotropy_t2"] = '%10.8f' % (bpars["t2"])
                BootRec["anisotropy_t3"] = '%10.8f' % (bpars["t3"])
                BootRec["anisotropy_v1_eta_inc"] = '%7.1f ' % (
                    bpars["v1_eta_inc"])
                BootRec["anisotropy_v1_eta_dec"] = '%7.1f ' % (
                    bpars["v1_eta_dec"])
                BootRec["anisotropy_v1_eta_semi_angle"] = '%7.1f ' % (
                    bpars["v1_eta"])
                BootRec["anisotropy_v1_zeta_inc"] = '%7.1f ' % (
                    bpars["v1_zeta_inc"])
                BootRec["anisotropy_v1_zeta_dec"] = '%7.1f ' % (
                    bpars["v1_zeta_dec"])
                BootRec["anisotropy_v1_zeta_semi_angle"] = '%7.1f ' % (
                    bpars["v1_zeta"])
                BootRec["anisotropy_v2_eta_inc"] = '%7.1f ' % (
                    bpars["v2_eta_inc"])
                BootRec["anisotropy_v2_eta_dec"] = '%7.1f ' % (
                    bpars["v2_eta_dec"])
                BootRec["anisotropy_v2_eta_semi_angle"] = '%7.1f ' % (
                    bpars["v2_eta"])
                BootRec["anisotropy_v2_zeta_inc"] = '%7.1f ' % (
                    bpars["v2_zeta_inc"])
                BootRec["anisotropy_v2_zeta_dec"] = '%7.1f ' % (
                    bpars["v2_zeta_dec"])
                BootRec["anisotropy_v2_zeta_semi_angle"] = '%7.1f ' % (
                    bpars["v2_zeta"])
                BootRec["anisotropy_v3_eta_inc"] = '%7.1f ' % (
                    bpars["v3_eta_inc"])
                BootRec["anisotropy_v3_eta_dec"] = '%7.1f ' % (
                    bpars["v3_eta_dec"])
                BootRec["anisotropy_v3_eta_semi_angle"] = '%7.1f ' % (
                    bpars["v3_eta"])
                BootRec["anisotropy_v3_zeta_inc"] = '%7.1f ' % (
                    bpars["v3_zeta_inc"])
                BootRec["anisotropy_v3_zeta_dec"] = '%7.1f ' % (
                    bpars["v3_zeta_dec"])
                BootRec["anisotropy_v3_zeta_semi_angle"] = '%7.1f ' % (
                    bpars["v3_zeta"])
                BootRec["anisotropy_hext_F"] = ''
                BootRec["anisotropy_hext_F12"] = ''
                BootRec["anisotropy_hext_F23"] = ''
                # regular bootstrap
                BootRec["magic_method_codes"] = 'LP-AN:AE-H:AE-BS'
                if ipar == 1:
                    # parametric bootstrap
                    BootRec["magic_method_codes"] = 'LP-AN:AE-H:AE-BS-P'
                if verbose:
                    print("Boostrap Statistics: ")
                    print(
                        " tau_i, V_i_D, V_i_I, V_i_zeta, V_i_zeta_D, V_i_zeta_I, V_i_eta, V_i_eta_D, V_i_eta_I")
                    print(BootRec["anisotropy_t1"], BootRec["anisotropy_v1_dec"], BootRec["anisotropy_v1_inc"], BootRec["anisotropy_v1_eta_semi_angle"], BootRec["anisotropy_v1_eta_dec"],
                          BootRec["anisotropy_v1_eta_inc"], BootRec["anisotropy_v1_zeta_semi_angle"], BootRec["anisotropy_v1_zeta_dec"], BootRec["anisotropy_v1_zeta_inc"])
                    print(BootRec["anisotropy_t2"], BootRec["anisotropy_v2_dec"], BootRec["anisotropy_v2_inc"], BootRec["anisotropy_v2_eta_semi_angle"], BootRec["anisotropy_v2_eta_dec"],
                          BootRec["anisotropy_v2_eta_inc"], BootRec["anisotropy_v2_zeta_semi_angle"], BootRec["anisotropy_v2_zeta_dec"], BootRec["anisotropy_v2_zeta_inc"])
                    print(BootRec["anisotropy_t3"], BootRec["anisotropy_v3_dec"], BootRec["anisotropy_v3_inc"], BootRec["anisotropy_v3_eta_semi_angle"], BootRec["anisotropy_v3_eta_dec"],
                          BootRec["anisotropy_v3_eta_inc"], BootRec["anisotropy_v3_zeta_semi_angle"], BootRec["anisotropy_v3_zeta_dec"], BootRec["anisotropy_v3_zeta_inc"])
                BootRec['magic_software_packages'] = version_num
                ResRecs.append(BootRec)
            k += 1
            goon = 1
            while goon == 1 and iplot == 1 and verbose:
                if iboot == 1:
                    print("compare with [d]irection ")
                print(
                    " plot [g]reat circle,  change [c]oord. system, change [e]llipse calculation,  s[a]ve plots, [q]uit ")
                if isite == 1:
                    print("  [p]revious, [s]ite, [q]uit, <return> for next ")
                ans = input("")
                if ans == "q":
                    sys.exit()
                if ans == "e":
                    iboot, ipar, ihext, ivec = 1, 0, 0, 0
                    e = input("Do Hext Statistics  1/[0]: ")
                    if e == "1":
                        ihext = 1
                    e = input("Suppress bootstrap 1/[0]: ")
                    if e == "1":
                        iboot = 0
                    if iboot == 1:
                        e = input("Parametric bootstrap 1/[0]: ")
                        if e == "1":
                            ipar = 1
                        e = input("Plot bootstrap eigenvectors:  1/[0]: ")
                        if e == "1":
                            ivec = 1
                        if iplot == 1:
                            if inittcdf == 0:
                                ANIS['tcdf'] = 3
                                pmagplotlib.plot_init(ANIS['tcdf'], 5, 5)
                                inittcdf = 1
                    bpars, hpars = pmagplotlib.plot_anis(
                        ANIS, Ss, iboot, ihext, ivec, ipar, title, iplot, comp, vec, Dir, nb)
                    if verbose and plots == 0:
                        pmagplotlib.draw_figs(ANIS)
                if ans == "c":
                    print("Current Coordinate system is: ")
                    if CS == '-1':
                        print(" Specimen")
                    if CS == '0':
                        print(" Geographic")
                    if CS == '100':
                        print(" Tilt corrected")
                    key = input(
                        " Enter desired coordinate system: [s]pecimen, [g]eographic, [t]ilt corrected ")
                    if key == 's':
                        CS = '-1'
                    if key == 'g':
                        CS = '0'
                    if key == 't':
                        CS = '100'
                    if CS not in orlist:
                        if len(orlist) > 0:
                            CS = orlist[0]
                        else:
                            CS = '-1'
                        if CS == '-1':
                            crd = 's'
                        if CS == '0':
                            crd = 'g'
                        if CS == '100':
                            crd = 't'
                        print(
                            "desired coordinate system not available, using available: ", crd)
                    k -= 1
                    goon = 0
                if ans == "":
                    if isite == 1:
                        goon = 0
                    else:
                        print("Good bye ")
                        sys.exit()
                if ans == 'd':
                    if initcdf == 0:
                        initcdf = 1
                        ANIS['vxcdf'], ANIS['vycdf'], ANIS['vzcdf'] = 4, 5, 6
                        pmagplotlib.plot_init(ANIS['vxcdf'], 5, 5)
                        pmagplotlib.plot_init(ANIS['vycdf'], 5, 5)
                        pmagplotlib.plot_init(ANIS['vzcdf'], 5, 5)
                    Dir, comp = [], 1
                    print("""
                      Input: Vi D I to  compare  eigenvector Vi with direction D/I
                             where Vi=1: principal
                                   Vi=2: major
                                   Vi=3: minor
                                   D= declination of comparison direction
                                   I= inclination of comparison direction""")
                    con = 1
                    while con == 1:
                        try:
                            vdi = input("Vi D I: ").split()
                            vec = int(vdi[0])-1
                            Dir = [float(vdi[1]), float(vdi[2])]
                            con = 0
                        except IndexError:
                            print(" Incorrect entry, try again ")
                    bpars, hpars = pmagplotlib.plot_anis(
                        ANIS, Ss, iboot, ihext, ivec, ipar, title, iplot, comp, vec, Dir, nb)
                    Dir, comp = [], 0
                if ans == 'g':
                    con, cnt = 1, 0
                    while con == 1:
                        try:
                            print(
                                " Input:  input pole to great circle ( D I) to  plot a great circle:   ")
                            di = input(" D I: ").split()
                            PDir.append(float(di[0]))
                            PDir.append(float(di[1]))
                            con = 0
                        except:
                            cnt += 1
                            if cnt < 10:
                                print(
                                    " enter the dec and inc of the pole on one line ")
                            else:
                                print(
                                    "ummm - you are doing something wrong - i give up")
                                sys.exit()
                    pmagplotlib.plot_circ(ANIS['data'], PDir, 90., 'g')
                    pmagplotlib.plot_circ(ANIS['conf'], PDir, 90., 'g')
                    if verbose and plots == 0:
                        pmagplotlib.draw_figs(ANIS)
                if ans == "p":
                    k -= 2
                    goon = 0
                if ans == "q":
                    k = plt
                    goon = 0
                if ans == "s":
                    keepon = 1
                    site = input(" print site or part of site desired: ")
                    while keepon == 1:
                        try:
                            k = sitelist.index(site)
                            keepon = 0
                        except:
                            tmplist = []
                            for qq in range(len(sitelist)):
                                if site in sitelist[qq]:
                                    tmplist.append(sitelist[qq])
                            print(site, " not found, but this was: ")
                            print(tmplist)
                            site = input('Select one or try again\n ')
                            k = sitelist.index(site)
                    goon, ans = 0, ""
                if ans == "a":
                    locs = pmag.makelist(Locs)
                    if pmagplotlib.isServer:  # use server plot naming convention
                        title = "LO:_"+locs+'_SI:__'+'_SA:__SP:__CO:_'+crd
                    else:  # use more readable plot naming convention
                        title = "{}_{}".format(locs, crd)
                    save(ANIS, fmt, title)
                    goon = 0
        else:
            if verbose:
                print('skipping plot - not enough data points')
            k += 1
#   put rmag_results stuff here
    if len(ResRecs) > 0:
        ResOut, keylist = pmag.fillkeys(ResRecs)
        pmag.magic_write(outfile, ResOut, 'rmag_results')
    if verbose:
        print(" Good bye ")

Response: This function is the main function of the anisotropy data plot. It is called by pmag. py to generate the appropriate data structure for the base class and the main function of the anisotropy data.


Instruction:
Make a summary of the following Python 3 code
def main():
    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)

Response:
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


Instruction:
Implement a function in Python 3 to
name is the main function for the module. It is the main function for the module. It is used to convert dec inc pairs to x y pairs using equal area projection.

Response:
def main():
    """
    NAME
        di_eq.py

    DESCRIPTION
      converts dec, inc pairs to  x,y pairs using equal area projection
      NB: do only upper or lower  hemisphere at a time: does not distinguish between up and down.

    SYNTAX
        di_eq.py [command line options] [< filename]

    OPTIONS
        -h  prints help message and quits
        -f FILE, input file
    """
    out=""
    UP=0
    if '-h' in sys.argv:
        print(main.__doc__)
        sys.exit()
    if '-f' in sys.argv:
        ind=sys.argv.index('-f')
        file=sys.argv[ind+1]
        DI=numpy.loadtxt(file,dtype=numpy.float)
    else:
        DI = numpy.loadtxt(sys.stdin,dtype=numpy.float)  # read from standard input
    Ds=DI.transpose()[0]
    Is=DI.transpose()[1]
    if len(DI)>1: #array of data
       XY=pmag.dimap_V(Ds,Is)
       for xy in XY:
           print('%f %f'%(xy[0],xy[1]))
    else: # single data point
       XY=pmag.dimap(Ds,Is)
       print('%f %f'%(XY[0],XY[1]))


Instruction:
Here you have a function in Python 3, explain what it does
def spline_interpolate(x1, y1, x2):
    sp = Spline(x1, y1)
    return sp(x2)

Response: Interpolate a function at a set of points x1 y1 at a set of points x2.


Instruction:
Explain what the following Python 3 code does
def logspline_interpolate(x1, y1, x2):
    sp = Spline(log(x1), log(y1))
    return exp(sp(log(x2)))

Response: Given a function at a set of points x1 y1 interpolate to a set of points x2 evaluate it at points x2.


Instruction:
Explain what the following Python 3 code does
def linear_interpolate(x1, y1, x2):
    li = LinInt(x1, y1)
    return li(x2)

Response: Interpolate a function at a set of points x1 y1 at a set of points x2.


Instruction:
How would you code a function in Python 3 to
evaluate the interpolant of the logarithmic term.

Response:
def call(self, x):
        """
        Evaluate the interpolant, assuming x is a scalar.
        """
    # if out of range, return endpoint
        if x <= self.x_vals[0]:
            return self.y_vals[0]
        if x >= self.x_vals[-1]:
            return self.y_vals[-1]

        pos = numpy.searchsorted(self.x_vals, x)

        h = self.x_vals[pos]-self.x_vals[pos-1]
        if h == 0.0:
            raise BadInput

        a = old_div((self.x_vals[pos] - x), h)
        b = old_div((x - self.x_vals[pos-1]), h)
        return a*self.y_vals[pos-1] + b*self.y_vals[pos]