This Python script is designed for converting raw astronomical simulation data from a proprietary format into a CSV file format for easier data analysis and visual representation. The script detects the presence of neutrino data, adjusts the data reading process accordingly, and translates complex simulation outputs into structured data tables. Additionally, it contains functionality for extracting specific data points from the CSV to generate histograms, facilitating a visual representation of the simulation's findings. The script utilizes libraries such as pandas for data manipulation and matplotlib for plotting, demonstrating an interdisciplinary approach that combines astrophysics, data science, and software engineering.
import matplotlib.pyplot as plt import numpy as np import pandas as pd import os import math class convert: def __init__(self, rawfilename): self.rawfilename = rawfilename self.trktocsv() self.rawfilename = rawfilename[:-4] + '.csv' self.csvtohist() def isfloat(self, val): try: float(val) return True except ValueError: return False def rightname(self, val): retval = True if self.isfloat(val)==False: if val[0].isnumeric()==False: retval = False return retval def trktocsv(self): filename = self.rawfilename[:-4] file1=open(self.rawfilename,'r') lines = file1.readlines() Neutrino=False for line in lines: if line[1:9]=='Neutrino': Neutrino=True if Neutrino==False: df = pd.read_table(self.rawfilename,sep='\s+',skiprows=13,names=['Model #', 'shells', 'AGE', 'log(L/Lsun)', 'log(R/Rsun)', 'log(g)', 'log(Teff)', 'Mconv. core', 'Mconv. env.', 'Rconv. env.','M He core', 'Xenv', 'Zenv']) namearray = ['Model #', 'shells', 'AGE', 'log(L/Lsun)', 'log(R/Rsun)', 'log(g)', 'log(Teff)', 'Mconv. core', 'Mconv. env.', 'Rconv. env.','M He core', 'Xenv', 'Zenv', 'Luminosity: ppI', 'Luminosity: ppII', 'Luminosity: ppIII', 'Luminosity: CNO', 'Luminosity: triple-alpha', 'Luminosity: He-C', 'gravity', 'neutrinos (old)', '% Grav. eng.', 'Itot', 'Central: log(T)', 'Central: log(RHO)', 'Central: log(P)', 'Central: BETA', 'Central: ETA', 'Central: X', 'Central: Z', 'Central: H shell midpoint', 'Central: H shell mass', 'Central: T and rho at base of c.z.', 'Central Abundances: He3', 'Central Abundances: C12', 'Central Abundances: C13', 'Central Abundances: N14', 'Central Abundances: N15', 'Central Abundances: O16', 'Central Abundances: O17', 'Central Abundances: O18', 'Surface Abundances: He3', 'Surface Abundances: C12', 'Surface Abundances: C13', 'Surface Abundances: N14', 'Surface Abundances: N15', 'Surface Abundances: O16', 'Surface Abundances: O17','Surface Abundances: O18'] dataarray = np.empty(shape=(len(df)//5,len(namearray))) for i in range(len(df.columns)): for j in range((len(df)//5 * 5)): self.rightname(df[df.columns[i]][j])==True if self.isfloat(df[df.columns[i]][j])==True: if j%5 == 0: dataarray[j//5,i] = df[df.columns[i]][j] elif j%5 == 1: if i <= 9: dataarray[j//5,13+i] = df[df.columns[i]][j] elif j%5 == 2: if i <= 9: dataarray[j//5,23+i] = df[df.columns[i]][j] elif j%5 == 3: if i <= 7: dataarray[j//5,33+i] = df[df.columns[i]][j] else: if i <= 7: dataarray[j//5,41+i] = df[df.columns[i]][j] else: if j%5 == 0: dataarray[j//5,i] = 0 elif j%5 == 1: if i <= 9: dataarray[j//5,13+i] = 0 elif j%5 == 2: if i <= 9: dataarray[j//5,23+i] = 0 elif j%5 == 3: if i <= 7: dataarray[j//5,33+i] = 0 else: if i <= 7: dataarray[j//5,41+i] = 0 df2 = pd.DataFrame(dataarray,columns=namearray) df2.to_csv('{}.csv'.format(filename)) if Neutrino==True: df = pd.read_table(self.rawfilename,sep='\s+',skiprows=14,names=['Model #', 'shells', 'AGE', 'log(L/Lsun)', 'log(R/Rsun)', 'log(g)', 'log(Teff)', 'Mconv. core', 'Mconv. env.', 'Rconv. env.','M He core', 'Xenv', 'Zenv']) namearray = ['Model #', 'shells', 'AGE', 'log(L/Lsun)', 'log(R/Rsun)', 'log(g)', 'log(Teff)', 'Mconv. core', 'Mconv. env.', 'Rconv. env.','M He core', 'Xenv', 'Zenv', 'Luminosity: ppI', 'Luminosity: ppII', 'Luminosity: ppIII', 'Luminosity: CNO', 'Luminosity: triple-alpha', 'Luminosity: He-C', 'gravity', 'neutrinos (old)', '% Grav. eng.', 'Itot', 'Central: log(T)', 'Central: log(RHO)', 'Central: log(P)', 'Central: BETA', 'Central: ETA', 'Central: X', 'Central: Z', 'Central: H shell midpoint', 'Central: H shell mass', 'Central: T and rho at base of c.z.', 'Central Abundances: He3', 'Central Abundances: C12', 'Central Abundances: C13', 'Central Abundances: N14', 'Central Abundances: N15', 'Central Abundances: O16', 'Central Abundances: O17', 'Central Abundances: O18', 'Surface Abundances: He3', 'Surface Abundances: C12', 'Surface Abundances: C13', 'Surface Abundances: N14', 'Surface Abundances: N15', 'Surface Abundances: O16', 'Surface Abundances: O17','Surface Abundances: O18', 'Neutrinos: pp', 'Neutrinos: pep', 'Neutrinos: hep', 'Neutrinos: Be7', 'Neutrinos: B8', 'Neutrinos: N13', 'Neutrinos: O15', 'Neutrinos: F17','Neutrinos: Cl37 flux', 'Neutrinos: Ga71 flux'] dataarray = np.empty(shape=(len(df)//6,len(namearray))) for i in range(len(df.columns)): for j in range((len(df)//6 *6)): if self.isfloat(df[df.columns[i]][j])==True: if j%6 == 0: dataarray[j//6,i] = df[df.columns[i]][j] elif j%6 == 1: if i <= 9: dataarray[j//6,13+i] = df[df.columns[i]][j] elif j%6 == 2: if i <= 9: dataarray[j//6,23+i] = df[df.columns[i]][j] elif j%6 == 3: if i <= 7: dataarray[j//6,33+i] = df[df.columns[i]][j] elif j%6 == 4: if i <= 7: dataarray[j//6,41+i] = df[df.columns[i]][j] else: if i<= 9: dataarray[j//6,49+i] = df[df.columns[i]][j] else: if j%6 == 0: dataarray[j//6,i] = 0 elif j%6 == 1: if i <= 9: dataarray[j//6,13+i] = 0 elif j%6 == 2: if i <= 9: dataarray[j//6,23+i] = 0 elif j%6 == 3: if i <= 7: dataarray[j//6,33+i] = 0 elif j%6 == 4: if i <= 7: dataarray[j//6,41+i] = 0 else: if i<= 9: dataarray[j//6,49+i] = 0 df2 = pd.DataFrame(dataarray,columns=namearray) df2.to_csv('{}.csv'.format(filename)) def eformat(self, f, prec, exp_digits): s = "%.*e"%(prec, f) mantissa, exp = s.split('e') # add 1 to digits as 1 is taken by sign +/- return "%se%+0*d"%(mantissa, exp_digits+1, int(exp)) def csvtohist(self): filename = self.rawfilename[:-4] #idxglb = [1,2,3,4,5,6,7,8] #hdrglb = ['version_number','compiler','build','MESA_SDK_version','math_backend','date','burn_min1','burn_min2'] #datglb = [hdrglb,["15140","gfortran","10.2.0","x86_64-linux-20.12.1","CRMATH","20210325",str(eformat(5, 16, 3)),str(eformat(1000, 16, 3))]] #dfglb = pd.DataFrame(datglb, columns = idxglb) idxloc = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22] hdrloc= ['star_age','star_mass','star_mdot','he_core_mass','c_core_mass','log_L','log_LH','log_LHe','log_Teff','log_R','log_g','surface_h1','surface_he3','surface_he4','surface_c12','surface_o16','log_center_T','log_center_Rho','center_gamma','center_h1','center_he4','center_c12'] datloc=[hdrloc] dp = pd.read_csv(self.rawfilename) lendp = len(dp) star_mass = lendp listzip = [[]*22]*lendp for i in range(lendp): listzip[i] = [(dp['AGE'].values[i])*1e9,dp['Mconv. env.'].values[0],0,dp['M He core'].values[i],0, dp['log(L/Lsun)'].values[i],np.log10((dp['Luminosity: ppI'].values[i] + dp['Luminosity: ppII'].values[i] + dp['Luminosity: ppIII'].values[i] + dp['Luminosity: CNO'].values[i])/(3.839e33) + 1e-10), np.log10((dp['Luminosity: triple-alpha'].values[i])/(3.839e33) + 1e-10),dp['log(Teff)'].values[i],dp['log(R/Rsun)'].values[i],dp['log(g)'].values[i],dp['Xenv'].values[i],dp['Surface Abundances: He3'].values[i],1 - dp['Xenv'].values[i] - dp['Zenv'].values[i],dp['Surface Abundances: C12'].values[i], dp['Surface Abundances: O16'].values[i], dp['Central: log(T)'].values[i],dp['Central: log(RHO)'].values[i],0,dp['Central: X'].values[i],1 - dp['Central: X'].values[i] - dp['Central: Z'].values[i],dp['Central Abundances: C12'].values[i]] #listzip = list(zip(dp['AGE'],lendp*[dp['Mconv. env.'].values[0]],lendp*[0],dp['M He core'],lendp*[0], dp['log(L/Lsun)'],np.log10(dp['Luminosity: ppI'] + dp['Luminosity: ppII'] + dp['Luminosity: ppIII'] + dp['Luminosity: CNO'] + 1e-10), np.log10(dp['Luminosity: triple-alpha'] + 1e-10),dp['log(Teff)'],dp['log(R/Rsun)'],dp['log(g)'],dp['Xenv'],dp['Surface Abundances: He3'],1 - dp['Xenv'] - dp['Zenv'],dp['Surface Abundances: C12'], dp['Surface Abundances: O16'], dp['Central: log(T)'],dp['Central: log(RHO)'],lendp*[0],dp['Central: X'],1 - dp['Central: X'] - dp['Central: Z'],dp['Central Abundances: C12'])) #listzip1 = [['']*len(listzip[0])]*len(listzip) for i in range(len(listzip)): for j in range(len(listzip[i])): try: listzip[i][j] = str(self.eformat(listzip[i][j], 16, 3)) except: listzip[i][j] = listzip[i-1][j] dfloc=pd.DataFrame(listzip, columns = hdrloc,dtype=str) with open('locidx.track', 'w') as fp: fp.write(' 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 \n') with open('loc.track', 'w') as fp: dfloc.to_string(fp, col_space=40, index=False) with open('locidx.track') as fp: data2 = fp.read() with open('loc.track') as fp: data3 = fp.read() with open('{}.data'.format(filename), 'w') as fp: fp.write(' 1 2 3 4 5 6 7 8\n') fp.write(' version_number compiler build MESA_SDK_version math_backend date burn_min1 burn_min2\n') fp.write(' "15140" "gfortran" "10.2.0" "x86_64-linux-20.12.1" "CRMATH" "20210328" 5.0000000000000000E+001 1.0000000000000000E+003\n') fp.write('\n') fp.write(data2) fp.write(data3) os.remove('locidx.track') os.remove('loc.track')