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'
