log_yld=[] out_yld=[] cur_dir=[] # Arrary of the directory locations of each *.out file tcount=[] shift = -0.031 for root, dirs, files in os.walk("."): dirs[:]=[d for d in dirs if d in targets] lcount=0 for filename in [f for f in files if f.endswith(".out")]: fn = os.path.join(root, filename) #print fn # Create 'output' file containing sputtering data for each meteorite class. # The energy, fluence step, nElem x yld, and tot_yld arrays are given for # nIncident ions. out_yld.append(SDTrimSP_readSput.read_outfile(fn)) lcount+=1 if lcount>0: # Label should contain the names of the meteorite classes, e.g. "Mars for l in range(lcount): cur_dir.append(root) tcount.append(lcount) for filename in [f for f in files if f.endswith(".log")]: fn = os.path.join(root, filename) #print fn # Create independent 'log' files for each simulation file # containing meta data (specific meteorite + ion combination) log_yld.append(SDTrimSP_readSput.read_logfile(fn)) print cur_dir
log_yld = [] sput_yld = [] esput = [] trfile = [] n_eng = 0 path = askdirectory() logfiles = path + "/*.log" datfiles = path + "/*.dat" path_name = find_between(path, 'data') #print path_name # first read log data files to get full simulation description for filename in glob.glob(logfiles): #print 'The file is {0}'.format(filename) log_yld.append(SDTrimSP_readSput.read_logfile(filename)) if log_yld[n_eng].label[4] in tarlist: continue else: tarlist.append(log_yld[n_eng].label[4]) n_eng += 1 print tarlist # Call read functions for each data file type for filename in glob.glob(datfiles): if 'E0_31' in filename: continue # File with variation in elemental concentration vs. timestep(+?) elif 'E0_33' in filename: # File with variation in elemetal sputter yield vs. timestep #print filename
log_yld = [] out_yld = [] cur_dir = [] # Arrary of the directory locations of each *.out file tcount = [] shift = -0.031 for root, dirs, files in os.walk("."): dirs[:] = [d for d in dirs if d in targets] lcount = 0 for filename in [f for f in files if f.endswith(".out")]: fn = os.path.join(root, filename) #print fn # Create 'output' file containing sputtering data for each meteorite class. # The energy, fluence step, nElem x yld, and tot_yld arrays are given for # nIncident ions. out_yld.append(SDTrimSP_readSput.read_outfile(fn)) lcount += 1 if lcount > 0: # Label should contain the names of the meteorite classes, e.g. "Mars for l in range(lcount): cur_dir.append(root) tcount.append(lcount) for filename in [f for f in files if f.endswith(".log")]: fn = os.path.join(root, filename) #print fn # Create independent 'log' files for each simulation file # containing meta data (specific meteorite + ion combination) log_yld.append(SDTrimSP_readSput.read_logfile(fn)) print cur_dir
log_yld=[] sput_yld=[] esput=[] trfile=[] n_eng=0 path = askdirectory() logfiles = path+"/*.log" datfiles = path+"/*.dat" path_name=find_between(path, 'data') #print path_name # first read log data files to get full simulation description for filename in glob.glob(logfiles): #print 'The file is {0}'.format(filename) log_yld.append(SDTrimSP_readSput.read_logfile(filename)) if log_yld[n_eng].label[4] in tarlist: continue else: tarlist.append(log_yld[n_eng].label[4]) n_eng+=1 print tarlist # Call read functions for each data file type for filename in glob.glob(datfiles): if 'E0_31'in filename: continue # File with variation in elemental concentration vs. timestep(+?) elif 'E0_33' in filename: # File with variation in elemetal sputter yield vs. timestep #print filename
cont = 1 nr=1 # Specify whether or not to include experimental and SRIM results # 1 == yes; 0 == no incl_expt=0 incl_srim=0 # ----- Import and plot experimental data ----- if incl_expt==1: #print "Please identify the experimental data file." # show an "Open" dialog box and return the path to the selected file #root.withdraw() #efn = askopenfilename() efn='ExpData.csv' expt_yld=SDTrimSP_readSput.read_exptfile(efn) ion_target_pairs=list(set(expt_yld.label[0])) target= list(set(expt_yld.label[2])) plot_expt = SDTrimSP_plotSput.plot_sputExpt(expt_yld, genClass) # ----- Import and plot SRIM data ----- if incl_srim==1: srim_yld=[] srimfn=[] srimfiles = "./SRIM/*.srim" for filename in glob.glob(srimfiles): if 'Thiel' not in filename: srimfn.append(filename) #print filename srim_yld.append(SDTrimSP_readSput.read_exptfile(filename)) met_class='srim'
cont = 1 nr = 1 # Specify whether or not to include experimental and SRIM results # 1 == yes; 0 == no incl_expt = 0 incl_srim = 0 # ----- Import and plot experimental data ----- if incl_expt == 1: #print "Please identify the experimental data file." # show an "Open" dialog box and return the path to the selected file #root.withdraw() #efn = askopenfilename() efn = 'ExpData.csv' expt_yld = SDTrimSP_readSput.read_exptfile(efn) ion_target_pairs = list(set(expt_yld.label[0])) target = list(set(expt_yld.label[2])) plot_expt = SDTrimSP_plotSput.plot_sputExpt(expt_yld, genClass) # ----- Import and plot SRIM data ----- if incl_srim == 1: srim_yld = [] srimfn = [] srimfiles = "./SRIM/*.srim" for filename in glob.glob(srimfiles): if 'Thiel' not in filename: srimfn.append(filename) #print filename srim_yld.append(SDTrimSP_readSput.read_exptfile(filename)) met_class = 'srim'