Source code for bin.alphaDist

#!/usr/bin/env python3
import os,sys,seaborn,numpy,re,math,matplotlib,argparse
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit

[docs]def isfloat(value): """ Check if variable is of float type or not, return boolean value. This function is mainly use in the :func:`~AlphaDist.makeatomlist` method from the :class:`AlphaDist` class to loop through every line of the local `atom.dat` file and store all the atomic data. Examples -------- >>> isfloat('13.9854AZ') False >>> isfloat('13.9854') True """ try: float(value) return True except ValueError: return False
[docs]def getshift(explist,left,right,slope,random=False): """ Calculate velocity shift do to distortion depending on model used. Parameters ---------- left : :py:class:`float` Minimum wavelength of the fitting region right : :py:class:`float` Maximum wavelength of the fitting region slope : :py:class:`float` Slope of the long-range distortion model random : :py:class:`bool` (default: False) Whether random slope should be calculated Returns ------- shift : :py:class:`float` Velocity shift due to long-range distortion effect. """ # Initialise distortion shift value shift = 0 # Calculate central wavelength of fitting region middle = (left+right)/2 # Initialize total shift and exposure time parameter sumshift = sumcount = 0 # Loop in the list of UVES exposures until break is called for l in range(len(explist)): # Define starting wavelength wbeg = float(explist['WMIN'][l]) # Define central wavelength (in Angstrom) from value in the list of settings (in nm) cent = float(explist['WMID'][l]) # Define ending wavelength (in Angstrom) from value in the list of settings (in nm) wend = float(explist['WMAX'][l]) if wbeg < left and right < wend: sumshift = sumshift + numpy.sqrt(float(explist['EXPTIME'][l])) * slope * (middle-cent) sumcount = sumcount + numpy.sqrt(float(explist['EXPTIME'][l])) shift = sumshift / sumcount return '{0:.4f}'.format(float(shift)/1000.)
[docs]class DistModel: ''' Create distortion model. Parameters ---------- fort13 : :py:class:`str` Path to input Voigt profile model explist : :py:class:`str` Path to exposure list. Examples -------- >>> alphaDist model --explist xshooter_exposures.dat --model finished.13 ''' def __init__(self,fort13,explist,slope=1,output=None): explist = numpy.genfromtxt(explist,names=True,skip_header=1,dtype=object) ions = numpy.loadtxt(fort13,delimiter='\n',dtype=str) trans = [] flag = 0 for line in ions: if '*' in line and flag==1: break elif '*' in line: flag = 1 else: trans.append((float(line.split()[2])+float(line.split()[3]))/2) fig = plt.figure(figsize=(7,4),dpi=300,frameon=False) fig.patch.set_alpha(0.0) plt.subplots_adjust(left=0.12, right=0.95, bottom=0.12, top=0.95, hspace=0.05, wspace=0.1) ax = plt.subplot(211) # Distortion models and fitting regions exp1 = numpy.empty((0,4)) exp2 = numpy.empty((0,4)) for k in range(len(explist)): wbeg = float(explist['WMIN'][k]) cent = float(explist['WMID'][k]) wend = float(explist['WMAX'][k]) time = float(explist['EXPTIME'][k]) if cent not in [float(j) for j in exp1[:,2]]: color = 'blue' if explist['BRANCH'][k]=='VIS' else 'red' exp1 = numpy.vstack((exp1,[color,wbeg,cent,wend])) exp2 = numpy.vstack((exp2,[time,wbeg,cent,wend])) for k in range (len(exp1)): color = exp1[k,0] wastart = float(exp1[k,1]) wacent = float(exp1[k,2]) waend = float(exp1[k,3]) x = numpy.arange(wastart,waend,1) y = slope*(x-wacent) ax.plot(x,y,color=color,lw=1.5,zorder=3) ax.axvline(x=wastart,color='black',zorder=2,lw=1,ls='dotted') ax.axvspan(wastart,waend,facecolor='yellow',alpha=0.1,zorder=1) ax.axvline(x=waend,color='black',zorder=2,lw=1,ls='dotted') ax.scatter(wacent,0,color='black',marker='d',edgecolors='none',s=25,zorder=4) ax.axhline(y=0,ls='dotted',color='black') plt.setp(ax.get_xticklabels(), visible=False) plt.ylabel('Velocity shift (m/s)',size=10) for i in range(len(trans)): ax.axvline(x=trans[i],ymin=0.85,ymax=0.9,color='red',lw=1) # Correction function ax = plt.subplot(212,sharex=ax,sharey=ax) for k in range (len(exp1)): wastart = float(exp1[k,1]) wacent = float(exp1[k,2]) waend = float(exp1[k,3]) ax.axvline(x=wastart,color='black',zorder=2,lw=1,ls='dotted') ax.axvspan(wastart,waend,facecolor='yellow',alpha=0.1) ax.axvline(x=waend,color='black',zorder=2,lw=1,ls='dotted') xmin = min([float(wmin) for wmin in explist['WMIN']]) xmax = max([float(wmax) for wmax in explist['WMAX']]) x,y = numpy.arange(xmin,xmax,1),[] for wa in x: vdist,texp = [],[] for i in range (len(exp2)): if exp2[i,1] < wa < exp2[i,3]: vdist.append(slope*(wa-exp2[i,2])) texp.append(numpy.sqrt(exp2[i,0])) if vdist!=[]: y.append(sum(numpy.array(texp)*numpy.array(vdist))/sum(numpy.array(texp))) else: y.append(None) ax.plot(x,y,color='black',lw=1.5,zorder=1) ax.axhline(y=0,ls='dotted',color='black') for i in range(len(trans)): ax.axvline(x=trans[i],ymin=0.85,ymax=0.9,color='red',lw=1) plt.xlabel('Wavelength ($\mathrm{\AA}$)',size=12) plt.ylabel('Velocity shift (m/s)',size=12) plt.show() if output==None else plt.savefig(output) plt.close(fig)
[docs]class AlphaDist: ''' Main method to execute all distortion calculations. Parameters ---------- fort13 : :py:class:`str` Path to input Voigt profile model explist : :py:class:`str` Path to exposure list. distmin : :py:class:`float` Minimum slope to compute. Default is -1. distmax : :py:class:`float` Maximum slope to compute. Default is 1. distmid : :py:class:`float` Starting slope value. Default is 0 distsep : :py:class:`float` Interval between consecutive slope values. Default is 0.1. Examples -------- From executable: >>> alphaDist --explist xshooter_exposures.dat --model finished.13 ''' def __init__(self,fort13,explist,distmin=-1,distmax=1,distmid=0,distsep=0.1): local = os.getcwd() # Add both inputs into self object self.fort13 = fort13.split('/')[-1] self.explist = numpy.genfromtxt(explist,names=True,skip_header=1) self.path = local+'/'+'/'.join(fort13.split('/')[:-1]) # Move to model's directory os.chdir(self.path) # Define slope list slope1 = numpy.arange(distmid,distmax+distsep,+distsep) slope2 = numpy.arange(distmid,distmin-distsep,-distsep) distlist = numpy.hstack((slope1,slope2)) # Extract path where fort.13 is located # Store atomic data list self.makeatomlist(self.path+'/atom.dat') # Create data folder in distortion repository #os.system('mkdir -p distortion/data') # Loop through all slope values for self.slope in distlist: # Move to default path os.chdir(self.path) # Create string for slope folder name self.distortion = '0.000' if round(self.slope,3)==0 else \ str('%.3f'%self.slope).replace('-','m') if self.slope<0 else \ 'p'+str('%.3f'%self.slope) # Create distortion slope directory os.system('mkdir -p distortion/'+self.distortion) os.system('ln -s ../../data distortion/%s/'%self.distortion) print('> Fit distortion '+self.distortion) # Prepare distorted model self.create_model(distmid,distsep) # Move to distortion slope directory os.chdir(self.path+'/distortion/'+self.distortion) # Create initial fort.13 model self.write_model() # Fit the model self.fit_system() # Convert fort.26 output to fort.13 format self.convert26to13() os.chdir(local)
[docs] def makeatomlist(self,atompath): """ Store data from atom.dat """ self.atom = numpy.empty((0,6)) atomdat = numpy.loadtxt(atompath,dtype='str',delimiter='\n') for element in atomdat: l = element.split() i = 0 if len(l[0])>1 else 1 species = l[0] if len(l[0])>1 else l[0]+l[1] wave = 0 if len(l)<i+2 else 0 if isfloat(l[i+1])==False else l[i+1] f = 0 if len(l)<i+3 else 0 if isfloat(l[i+2])==False else l[i+2] gamma = 0 if len(l)<i+4 else 0 if isfloat(l[i+3])==False else l[i+3] mass = 0 if len(l)<i+5 else 0 if isfloat(l[i+4])==False else l[i+4] alpha = 0 if len(l)<i+6 else 0 if isfloat(l[i+5])==False else l[i+5] if species not in ['>>','<<','<>','__']: self.atom = numpy.vstack((self.atom,[species,wave,f,gamma,mass,alpha]))
[docs] def atominfo(self,atomID): """ Find transition in atom list and extract information. Parameters ---------- atomID : :py:class:`string` Name of the transition, written as ion_wavelength. """ target = [0,0,0,0,0] atomID = atomID.split('_') for i in range(len(self.atom)): element = self.atom[i,0] wavelength = self.atom[i,1] oscillator = self.atom[i,2] gammavalue = self.atom[i,3] qcoeff = self.atom[i,5] if (len(atomID)>1 and element==atomID[0] and abs(float(wavelength)-float(atomID[1]))<abs(float(target[1])-float(atomID[1]))) \ or (len(atomID)==1 and element==atomID[0]): target = [element,wavelength,oscillator,gammavalue,qcoeff] if target==[0,0,0,0,0]: print(atomID,'not identifiable...') quit() return target
[docs] def create_model(self,distmid,distsep): """ Create distortion model from original fort.13. """ # Store header.dat file read_head = numpy.loadtxt('header.dat',dtype='str',delimiter='\n',ndmin=1) # Read original fort.13 file read_fort = open(self.fort13,'r') # If slope different than first slope, overwrite fort.13 if self.slope!=distmid: # Calculate slope of previous step i = self.slope - distsep if self.slope > distmid else self.slope + distsep # Define name of distortion folder slope = '0.000' if round(i,3)==0 else str('%.3f'%i).replace('-','m') if i<0 else 'p'+str('%.3f'%i) # Read last fort.13 fit read_fort = open(self.path+'/distortion/'+slope+'/fort_fit.13','r') # Store fort.13 in array read_fort = [line.strip() for line in read_fort] # Initialise array to store original fort.13 header fort_header_old = numpy.empty((0,8)) # Initialise array to store content of original fort.13 fort_content_old = numpy.empty((0,8)) # Loop through each line in stored fort.13 i = flag = 0 while i < len(read_fort): # Check if empty line occurs after list of components if flag==2 and (read_fort[i]=='' or (read_fort[i].split()[0]=='>>' and read_fort[i].split('!')[0].split()[-1]=='1')): # End the loop break # Check line corresponds to an asterisks if read_fort[i]=='*': # Increment flag value flag = flag+1 # Jump to next line i = offset = i + 1 # If flag is 1 and line not commented, read fitting regions information if flag==1 and read_fort[i][0]!='!': # Split line by spaces vals = read_fort[i].replace('!',' ').split() # Check if region data is copied in data folder #if os.path.exists(self.path+'/distortion/data/%s'%vals[0].split('/')[-1])==False: # # Copy data associated to fitting region # os.system('cp %s %s/distortion/data/'%(vals[0],self.path)) # Extract and redefine path to data file val0 = 'data/'+vals[0].split('/')[-1] val1 = int(vals[1]) val2 = '%.2f'%float(vals[2]) val3 = '%.2f'%float(vals[3]) val4 = vals[4].split('=')[0]+'='+str('%5.8f'%float(vals[4].split('=')[1])) val5 = read_head[i-offset].split()[0] val6 = '' if len(read_head[i-offset].split())==1 else read_head[i-offset].split()[1] val7 = '' if len(read_head[i-offset].split())==1 else read_head[i-offset].split()[2] fort_header_old = numpy.vstack((fort_header_old,[val0,val1,val2,val3,val4,val5,val6,val7])) if flag==2 and read_fort[i][0]!='!': vals = read_fort[i].split() val0 = vals[0]+' '+vals[1] if len(vals[0])==1 else vals[0] k = 1 if len(vals[0])==1 else 0 val1 = '%.5f'%float(vals[k+1][:-2]+re.compile(r'[^\d.-]+').sub('',vals[k+1][-2:])) val1 = val1+" ".join(re.findall("[a-zA-Z]+",vals[k+1][-2:])) zabs = float(vals[k+2][:-2]+re.compile(r'[^\d.-]+').sub('',vals[k+2][-2:])) val2 = '%.7f'%zabs val2 = val2+" ".join(re.findall("[a-zA-Z]+",vals[k+2][-2:])) val3 = '%.4f'%float(vals[k+3][:-2]+re.compile(r'[^\d.-]+').sub('',vals[k+3][-2:])) val3 = val3+" ".join(re.findall("[a-zA-Z]+",vals[k+3][-2:])) div = 10**(-6) if 'E-0' in vals[k+4] else 1 val4 = '0.000' if '*' in vals[k+4] else '%.3f'%(float(vals[k+4][:-2]+re.compile(r'[^\d.-]+').sub('',vals[k+4][-2:]))/div) val4 = val4+" ".join(re.findall("[a-zA-Z]+",vals[k+4][-2:])) val5 = '%.2f'%float(vals[k+5][:-2]+re.compile(r'[^\d.-]+').sub('',vals[k+5][-2:])) val5 = val5+" ".join(re.findall("[a-zA-Z]+",vals[k+5][-2:])) val6 = '%.2E'%float(vals[k+6][:-2]+re.compile(r'[^\d.-]+').sub('',vals[k+6][-2:])) val6 = val6+" ".join(re.findall("[a-zA-Z]+",vals[k+6][-2:])) val7 = str(int(float(vals[k+7][:-2]+re.compile(r'[^\d.-]+').sub('',vals[k+7][-2:])))) val7 = val7+" ".join(re.findall("[a-zA-Z]+",vals[k+7][-2:])) fort_content_old = numpy.vstack((fort_content_old,[val0,val1,val2,val3,val4,val5,val6,val7])) i = i + 1 # Store selected fort.13 content, and shift components fort_content_new = numpy.empty((0,8)) for i in range (len(fort_content_old)): fort_content_new = numpy.vstack((fort_content_new,fort_content_old[i])) self.model = 'turbulent' if 1 in numpy.array(fort_content_old[:,5],dtype=float) else 'thermal' # Store selected fort.13 header, and shifts store_shift = [] fort_header_new = numpy.empty((0,8)) for i in range (len(fort_header_old)): self.armflag = 'null' trans = fort_header_old[i,-3] wrest = float(self.atominfo(trans)[1]) shift = getshift(self.explist,float(fort_header_old[i,2]),float(fort_header_old[i,3]),self.slope) fort_header_new = numpy.vstack((fort_header_new,fort_header_old[i])) store_shift.append(shift) # Implement fix shift values in fort arrays if self.distortion not in ['','0.000']: for p in range (len(store_shift)): shift = ['>>','1.00000FF','0.0000000FF',store_shift[p]+'FF','0.000FF','0.00','0.00E+00',p+1] fort_content_new = numpy.vstack((fort_content_new,shift)) self.fort_header_new = fort_header_new self.fort_content_new = fort_content_new
[docs] def write_model(self): """ Write input distortion model in fort_ini.13 file. """ write_header = open('header.dat','w') write_fort = open('fort_ini.13','w') write_fort.write(' *\n') for i in range (len(self.fort_header_new)): datalength = [len(self.fort_header_new[k,0]) for k in range (len(self.fort_header_new))] datalength = "{0:<"+str(max(datalength))+"}" write_fort.write(datalength.format(self.fort_header_new[i,0])+' ') write_fort.write('{0:>5}'.format(self.fort_header_new[i,1])+' ') write_fort.write('{0:>10}'.format('%.2f'%float(self.fort_header_new[i,2]))+' ') write_fort.write('{0:>10}'.format('%.2f'%float(self.fort_header_new[i,3]))+' ') write_fort.write('{0:<17}'.format(self.fort_header_new[i,4])+' ! ') write_fort.write('{0:<15}'.format(self.fort_header_new[i,5])+' ') write_header.write('{0:<15}'.format(self.fort_header_new[i,5])+' ') if self.fort_header_new[i,6]!='': write_fort.write('{0:<15}'.format(self.fort_header_new[i,6])+' ') write_header.write('{0:<15}'.format(self.fort_header_new[i,6])+' ') write_fort.write('{0:<15}'.format(self.fort_header_new[i,7])) write_header.write('{0:<15}'.format(self.fort_header_new[i,7])) write_header.write('\n') write_fort.write('\n') write_fort.write(' *\n') for i in range (len(self.fort_content_new)): val = self.fort_content_new[i] write_fort.write(' '+'{0:<5}'.format(val[0])+' ') write_fort.write('{0:>5}'.format(val[1].split('.')[0])+'.'+'{0:<7}'.format(val[1].split('.')[1])+' ') write_fort.write('{0:>3}'.format(val[2].split('.')[0])+'.'+'{0:<9}'.format(val[2].split('.')[1])+' ') write_fort.write('{0:>4}'.format(val[3].split('.')[0])+'.'+'{0:<6}'.format(val[3].split('.')[1])+' ') write_fort.write('{0:>5}'.format(val[4].split('.')[0])+'.'+'{0:<6}'.format(val[4].split('.')[1])+' ') write_fort.write('{0:>5}'.format(val[5].split('.')[0])+'.'+'{0:<2}'.format(val[5].split('.')[1])+' ') write_fort.write(' %s '%val[6]) write_fort.write('{0:>2}'.format(val[7])+' ! ') write_fort.write('{0:>4}'.format(i+1)) write_fort.write('\n') write_header.close() write_fort.close()
[docs] def fit_system(self): """ Fit original fort.13 distortion model. """ # Create alias to atom.dat if os.path.exists('atom.dat'): os.system('rm atom.dat') os.system('ln -s ../../atom.dat') # Create alias to vp_setup.dat if os.path.exists('vp_setup.dat'): os.system('rm vp_setup.dat') os.system('ln -s ../../vp_setup.dat') # Prepare model folders and run VPFIT os.environ['ATOMDIR']='./atom.dat' os.environ['VPFSETUP']='./vp_setup.dat' opfile = open('fitcommands','w') opfile.write('f\n\n\nfort_ini.13\n') for line in self.fort_header_new: if '.fits' in line[0]: opfile.write('\n') opfile.write('n\nn\n') opfile.close() os.system('vpfit < fitcommands > termout')
[docs] def convert26to13(self): """ Create fort_fit.13 after fitting fort_ini.13 completed. """ flag26 = flag18 = 0 final = open('fort_fit.13','w') final.write(' *\n') line26 = numpy.loadtxt('fort.26',dtype='str',delimiter='\n') for i in range(len(line26)): if 'Stats:' in line26[i]: flag26 = 1 if line26[i][0:2]!='%%': break else: final.write(line26[i].replace('%% ','')+'\n') final.write(' *\n') line18 = numpy.loadtxt('fort.18',dtype='str',delimiter='\n') for i in range(len(line18)-1,0,-1): if 'statistics for whole fit:' in line18[i]: flag18 = 1 if 'chi-squared' in line18[i]: chisq = '%.4f'%float(line18[i].split('(')[1].split(',')[0]) chisqnu = '%.3f'%float(line18[i].split('(')[0].split(':')[1]) ndf = '%.0f'%float(line18[i].split(')')[0].split(',')[1]) print(' | chisq=%s, ndf=%s, chisq_nu=%s'%(chisq,ndf,chisqnu)) a = i + 2 break for i in range(a,len(line18)): if len(line18[i])==1: break final.write(line18[i]+'\n') final.close()
[docs]class PlotCurve: """ Plot chi-square curves. Parameters ---------- thermal : :py:class:`str` Directory path of thermal model turbulent : :py:class:`str` Directory path of turbulent model distmin : :py:class:`float` Minimum slope to compute. Default is -1. distmax : :py:class:`float` Maximum slope to compute. Default is 1. distsep : :py:class:`float` Interval between consecutive slope values. Default is 0.1. xmin : :py:class:`float` Minimum slope of fitting range xmax : :py:class:`float` Maximum slope of fitting range output : :py:class:`str` Output figure filename Examples -------- From executable: >>> alphaDist --curve --output curve --thermal thermal/ --turbulent turbulent/ --distmin -1.5 --distmax 1 --distsep 0.1 --xmin -0.5 --xmax0.5 --output plot_curves From python script: >>> import alpha >>> alpha.PlotCurve(thermal='thermal/',turbulent='turbulent/',distmin=-1.5,distmax=1,distsep=0.1,xmin=-0.5,xmax=0.5) """ def __init__(self,thermal=None,turbulent=None,distmin=-1,distmax=1,distsep=0.1,xmin=None,xmax=None,output=None): slope1 = numpy.arange(0,distmax+distsep,+distsep) slope2 = numpy.arange(0,distmin-distsep,-distsep) distlist = numpy.hstack((slope1,slope2)) fitres = self.extract_results(thermal,turbulent,distlist) self.plot_curves(fitres,xmin,xmax,output,plot_range=[distmin,distmax])
[docs] def extract_results(self,thermal,turbulent,distlist): """ Extract chi-square from results """ start = True for i in distlist: self.stats = {} self.alpha = {} # Calculate string name of distortion folder slope = '0.000' if round(i,3)==0 else str('%.3f'%i).replace('-','m') if '-' in str(i) else 'p'+str('%.3f'%i) # Extract thermal fit results self.readfort26('ther',thermal+'/distortion/'+slope+'/fort.26') # Extract turbulent fit results self.readfort26('turb',turbulent+'/distortion/'+slope+'/fort.26') # Determine MoM coefficients mom_df,mom_chisq = None,None ther_chisq,ther_df,ther_n = self.stats['ther'] turb_chisq,turb_df,turb_n = self.stats['turb'] if ther_chisq!=None and turb_chisq!=None: k = ther_n - ther_df ther_AICc = ther_chisq + 2*k + 2*k*(k+1)/(ther_n-k-1) k = turb_n - turb_df turb_AICc = turb_chisq + 2*k + 2*k*(k+1)/(turb_n-k-1) csmin = min([ther_AICc,turb_AICc]) k1 = math.exp(-(ther_AICc-csmin)/2) k2 = math.exp(-(turb_AICc-csmin)/2) k = k1 + k2 k1 = k1/k k2 = k2/k mom_df = k1 * ther_df + k2 * turb_df mom_chisq = k1 * ther_chisq + k2 * turb_chisq if start==True: length = 7*(len(self.alpha)+1) fitres = numpy.empty((0,length)) start = False results = [round(i,3),ther_df,ther_chisq,turb_df,turb_chisq,mom_df,mom_chisq] for label in self.alpha.keys(): mom_alpha,mom_error = None,None zabs,ther_alpha,ther_error,turb_alpha,turb_error = self.alpha[label] if ther_alpha!=None and turb_alpha!=None: mom_alpha = k1 * ther_alpha + k2 * turb_alpha mom_error = numpy.sqrt(k1*ther_error**2 + k2*turb_error**2 + k1*ther_alpha**2 + k2*turb_alpha**2 - mom_alpha**2) results.extend([zabs,ther_alpha,ther_error,turb_alpha,turb_error,mom_alpha,mom_error]) # Check dimensionality of results imiss = abs(len(results)-fitres.shape[1]) if len(results)>fitres.shape[1]: fitres = numpy.hstack((fitres,numpy.reshape([None]*len(fitres)*imiss,(len(fitres),imiss)))) if len(results)<fitres.shape[1]: results.extend([None]*imiss) fitres = numpy.vstack((fitres,results)) return fitres
[docs] def readfort26(self,model,fortpath): """ Extract fitting results from fort.18 output. """ if os.path.exists(fortpath): daoaun = 1. vpsetup = fortpath.replace(fortpath.split('/')[-1],'vp_setup.dat') for line in numpy.loadtxt(vpsetup,dtype=str,delimiter='\n',comments='!'): if 'daoaun'in line: daoaun = float(line.split()[1]) flag = 0 fort26 = numpy.loadtxt(fortpath,dtype='str',delimiter='\n') for i in range(len(fort26)-1,0,-1): # Check if ion is one letter is define offset index accordingly s = 1 if len(fort26[i].split()[0])==1 else 0 # Determine absorption redshift zabs = float(re.compile(r'[^\d.-]+').sub('',fort26[i].split()[1+s])) if 'Stats:' in fort26[i]: n = float(fort26[i].split()[4]) df = float(fort26[i].split()[5]) chisq_nu = float(fort26[i].split()[3]) chisq = chisq_nu * df self.stats[model]=[chisq,df,n] break # Check if component is alpha anchor if 'q' in str(fort26[i].split()[7+s]): label = " ".join(re.findall("[a-zA-Z]+",fort26[i].split()[7+s][-2:])) zabs = float(fort26[i].split()[1+s][:-2]) daoa = float(fort26[i].split()[7+s].split('q')[0])*daoaun/1e-5 error = float(fort26[i].split()[8+s].split('q')[0])*daoaun/1e-5 if label not in self.alpha.keys(): if model=='ther': self.alpha[label] = [zabs,daoa,error,None,None] if model=='turb': self.alpha[label] = [zabs,None,None,daoa,error] if label in self.alpha.keys(): if model=='ther': self.alpha[label][1:3] = [daoa,error] if model=='turb': self.alpha[label][-2:] = [daoa,error] else: self.stats[model]=[None,None,None]
[docs] def order_list(self,x,y,yerr=None): """ Order distortion results in slope order and remove duplicate """ order = numpy.argsort(x) x,y = x[order],y[order] yerr = None if yerr is None else yerr[order] idxs = [i for i in range(len(x)-1) if x[i]==x[i+1]] x = numpy.delete(x,idxs) y = numpy.delete(y,idxs) yerr = None if yerr is None else numpy.delete(yerr,idxs) return x,y,yerr
[docs] def plot_curves(self,fitres,xmin,xmax,output,plot_range): """ Do scatter plot of both chi-square and da/a results versus distortion slope """ nrows = fitres.shape[1]/7 plt.rc('font', size=2, family='sans-serif') plt.rc('axes', labelsize=10, linewidth=0.2) plt.rc('legend', fontsize=10, handlelength=10) plt.rc('xtick', labelsize=7) plt.rc('ytick', labelsize=7) plt.rc('lines', lw=0.2, mew=0.2) plt.rc('grid', linewidth=0.5) fig = plt.figure(figsize=(9,2.1*nrows),frameon=False,dpi=300) plt.style.context('seaborn-darkgrid') plt.style.use('seaborn') #plt.subplots_adjust(left=0.1, right=0.97, bottom=0.07, top=0.97, hspace=0.2, wspace=0.2) slopes = fitres[:,0] model = ['Thermal','Turbulent','Method of Moments'] for j in range(len(model)): chisq = fitres[:,2+2*j] idxs = numpy.where(chisq!=None)[0] x,y,_ = self.order_list(slopes[idxs],chisq[idxs]) dx = (max(slopes)-min(slopes))/20 allchisq = numpy.vstack((fitres[:,2],fitres[:,4],fitres[:,6])).T idxs = numpy.where(allchisq!=None) ymin = 0 if len(y)==0 else min(y)-1 if min(y)==max(y) else allchisq[idxs].min()#min(y) ymax = 1 if len(y)==0 else max(y)+1 if min(y)==max(y) else allchisq[idxs].max()#max(y) dy = (ymax-ymin)/10 ax = plt.subplot(nrows,3,1+j,xlim=[min(slopes)-dx,max(slopes)+dx],ylim=[ymin-dy,ymax+3*dy]) if len(y)>0: ax.errorbar(x,y,fmt='o',ms=4,markeredgecolor='none',ecolor='grey',alpha=0.7,color='black') if len(y)>1: fitmin = xmin if xmin==None else float(xmin) if isfloat(xmin) else float(xmin.split(':')[j]) fitmax = xmax if xmax==None else float(xmax) if isfloat(xmax) else float(xmax.split(':')[j]) self.fit_parabola(x,y,fitmin,fitmax,ymin,ymax,plot_range) y_formatter = matplotlib.ticker.ScalarFormatter(useOffset=False) ax.yaxis.set_major_formatter(y_formatter) ax.set_title(model[j]) plt.setp(ax.get_xticklabels(), visible=False) if j==0: ax.set_ylabel(r'$\chi^2_\mathrm{abs}$') else: plt.setp(ax.get_yticklabels(), visible=False) # Plot alpha systems in redshift order order = [] for k in range(int(nrows)-1): temp = numpy.array(fitres[:,7+7*k],dtype=float) order.append(numpy.nanmean(temp)) for num,k in enumerate(numpy.argsort(order)): temp = numpy.array(fitres[:,7+7*k],dtype=float) zabs = numpy.nanmean(temp) alpha = fitres[:,8+7*k+2*j] error = fitres[:,9+7*k+2*j] idxs = numpy.where(alpha!=None)[0] x,y,yerr = self.order_list(slopes[idxs],alpha[idxs],error[idxs]) allalpha,allerror = [],[] for i in numpy.hstack((fitres[:,8+7*k],fitres[:,8+7*k+2],fitres[:,8+7*k+4])): if i!=None: allalpha.append(i) for i in numpy.hstack((fitres[:,9+7*k],fitres[:,9+7*k+2],fitres[:,9+7*k+4])): if i!=None: allerror.append(i) ymin = 0 if len(y)==0 else min(y)-1 if min(y)==max(y) else min(allalpha)-max(allerror) ymax = 1 if len(y)==0 else max(y)+1 if min(y)==max(y) else max(allalpha)+max(allerror) dy = (ymax-ymin)/10 ax = plt.subplot(nrows,3,4+3*num+j,xlim=[min(slopes)-dx,max(slopes)+dx],ylim=[ymin,ymax+dy]) if len(y)>0: ax.errorbar(x,y,yerr=yerr,fmt='o',ms=4,markeredgecolor='none',ecolor='grey',alpha=0.7,color='black',lw=0.5) if len(y)>1: self.fit_linear(x,y,yerr,fitmin,fitmax,ymin,ymax,plot_range) y_formatter = matplotlib.ticker.ScalarFormatter(useOffset=False) ax.yaxis.set_major_formatter(y_formatter) if j==0: ax.set_ylabel(r'$\Delta\alpha/\alpha$ $(10^{-5})$'+'\n'+r'for $z_\mathrm{abs}=%.4f$'%zabs) else: plt.setp(ax.get_yticklabels(), visible=False) if num==nrows-2: ax.set_xlabel(r'Distortion slope (m/s/$\mathrm{\AA}$)') else: plt.setp(ax.get_xticklabels(), visible=False) plt.tight_layout() plt.show() if output==None else plt.savefig(output) plt.close(fig)
[docs] def fit_parabola(self,x,y,xmin,xmax,ymin,ymax,plot_range): """ Fit parabola to the chi-square curves. """ # Determine in-window label position xpos = numpy.average(plot_range) ypos = ymax+0.1*(ymax-ymin) # Define fitting range imin = 0 if xmin==None else numpy.where(x>=xmin)[0][0] imax = -1 if xmax==None else numpy.where(x<=xmax)[0][-1]+1 x,y = x[imin:imax],y[imin:imax] x = numpy.array(x,dtype=float) y = numpy.array(y,dtype=float) # Execute parabolic fit A = numpy.vander(x,3) (coeffs, residuals, rank, sing_vals) = numpy.linalg.lstsq(A,y,rcond=None) f = numpy.poly1d(coeffs) # Define fitting variables xfit = numpy.arange(-100,100,0.0001) imid = abs(f(xfit)-min(f(xfit))).argmin() isig = abs(f(xfit)-(min(f(xfit))+1)).argmin() xmid = xfit[imid] xsig = abs(xfit[isig]-xfit[imid]) self.xm1sig = xmid-xsig self.xp1sig = xmid+xsig plt.plot(xfit,f(xfit),c='red',lw=1) print('Slope: {0:>8}+/-{1:<8}'.format('%.4f'%xmid,'%.4f'%xsig)) #print('Chisq: {0:>12}'.format(self.residuals)) plt.axvline(x=self.xm1sig,ls='dotted',color='blue',lw=1) plt.axvline(x=xmid,ls='dashed',color='red',lw=1) plt.axvline(x=self.xp1sig,ls='dotted',color='blue',lw=1) t1 = plt.text(xpos,ypos,r'$\chi^2_\mathrm{min}$ at %.4f $\pm$ %.4f'%(xmid,xsig), color='red',fontsize=10,ha='center') t1.set_bbox(dict(color='white', alpha=0.7, edgecolor=None)) self.slope = xmid self.slope_error = xsig
[docs] def fit_linear(self,x,y,yerr,xmin,xmax,ymin,ymax,plot_range): """ Do linear fit to da/a vs. distortion slope curves. """ # Determine in-window label position xpos = numpy.average(plot_range) ypos = ymax # Define fitting range imin = 0 if xmin==None else numpy.where(x>=xmin)[0][0] imax = -1 if xmax==None else numpy.where(x<=xmax)[0][-1]+1 x,y,yerr = x[imin:imax],y[imin:imax],yerr[imin:imax] # Execute parabolic fit x = numpy.array(x,dtype=float) y = numpy.array(y,dtype=float) yerr = numpy.array(yerr,dtype=float) def func(func,a,b): return a + b*x pars,cov = curve_fit(func,x,y,sigma=yerr) # Define fitting variables xfit = numpy.arange(-100,100,0.001) yfit = pars[0] + pars[1]*xfit plt.plot(xfit,yfit,color='red',lw=1) imid = abs(xfit-self.slope).argmin() imin = abs(xfit-(self.slope-self.slope_error)).argmin() imax = abs(xfit-(self.slope+self.slope_error)).argmin() plt.axvline(x=self.xm1sig,ls='dotted',color='blue',lw=1) plt.axvline(x=xfit[imid],ls='dashed',color='red',lw=1) plt.axvline(x=self.xp1sig,ls='dotted',color='blue',lw=1) plt.axhline(y=yfit[imid],ls='dashed',color='red',lw=1) plt.axhline(y=yfit[imax],ls='dotted',color='blue',lw=1) plt.axhline(y=yfit[imin],ls='dotted',color='blue',lw=1) alpha = yfit[imid] alpha_stat = numpy.average(yerr) alpha_syst = abs(yfit[imax]-yfit[imid]) t1 = plt.text(xpos,ypos, r'$\Delta\alpha/\alpha$ = %.4f $\pm$ %.4f $\pm$ %.4f'%(yfit[imid],alpha_stat,alpha_syst), color='red',fontsize=10,ha='center',va='top') t1.set_bbox(dict(color='white', alpha=0.7, edgecolor=None)) print('Alpha: {0:>8}+/-{1:<6}+/-{2:<6}'.format('%.4f'%alpha,'%.4f'%alpha_stat,'%.4f'%alpha_syst))
if __name__=="__main__": # Define arguments parser = argparse.ArgumentParser(description='Alpha Distortion Estimator') parser.add_argument('operation',help='Operation to perform') parser.add_argument('--xmin',default=None,help='Minimum slope of fitting range') parser.add_argument('--xmax',default=None,help='Maximum slope of fitting range') parser.add_argument('--distmin',default=-1,type=float,help='Minimum slope to compute') parser.add_argument('--distmax',default=1,type=float,help='Maximum slope to compute') parser.add_argument('--distmid',default=0,type=float,help='Starting slope value') parser.add_argument('--distsep',default=0.1,type=float,help='Interval between consecutive slope values') parser.add_argument('--explist',help='Exposure list. Must contain column EXPTIME, WMIN, WMAX and WMID.') parser.add_argument('--model',help='Input fort.13') parser.add_argument('--output',help='Output figure filename') parser.add_argument('--thermal',default='./',type=str,help='Directory path where to do the calculations') parser.add_argument('--turbulent',default='./',type=str,help='Directory path where to do the calculations') parser.add_argument('--slope',default=1,type=str,help='Distortion slope') args = parser.parse_args() # Check what to do if args.operation=='model': DistModel(args.model,args.explist,args.slope,args.output) if args.operation=='fit': AlphaDist(args.model,args.explist,args.distmin,args.distmax,args.distmid,args.distsep) if args.operation=='curve': PlotCurve(args.thermal,args.turbulent,args.distmin,args.distmax,args.distsep,args.xmin,args.xmax,args.output) if args.operation=='getshift': explist = numpy.genfromtxt(args.explist,names=True,skip_header=1,dtype=object) print(getshift(explist,args.xmin,args.xmax,float(args.slope)))