def generate_chromatogram(n=5, twin=None):
if twin is None:
twin = (0, 60)
t = np.linspace(twin[0], twin[1], 300)
peak_locs = twin[1] * np.random.random(n)
peak_ws = 0.2 + 0.8 * np.random.random(n)
peak_hs = 0.2 + 0.8 * np.random.random(n)
y = np.zeros(len(t))
for peak_loc, peak_w, peak_h in zip(peak_locs, peak_ws, peak_hs):
y += gaussian(t, x=peak_loc, w=peak_w, h=peak_h)
y += np.random.normal(scale=0.01, size=len(t))
return TimeSeries(y, t, ['X'])
def read_peaks(db, filename, ftype='isodat'):
if ftype is None:
with open(filename, 'r') as f:
header = f.readline()
if 'd 13C/12C[per mil]vs. VPDB' in header:
ftype = 'isodat'
else:
ftype = 'amdis'
if ftype == 'amdis':
delim = '\t'
cvtr = {'name': 'name',
'p-s-time': 'rt',
'p-s-area': 'area'}
elif ftype == 'isodat':
delim = ','
cvtr = {'name': 'peak nr.',
'p-s-time': 'rt[s]',
'p-s-area': 'area all[vs]',
'p-s-width': 'width[s]',
'p-s-d13c': 'd 13c/12c[per mil]vs. vpdb',
'p-s-d18o': 'd 18o/16o[per mil]vs. vsmow'}
headers = None
mapping = defaultdict(list)
ref_pk_info = {}
get_val = lambda line, cols, key: line.split(delim)[cols.index(key)]
with open(filename, 'r') as f:
for line in f:
if bool(re.match('filename' + delim, line, re.I)) \
or headers is None:
headers = line.lower().split(',')
continue
fn = get_val(line, headers, 'filename')
if ftype == 'amdis':
# AMDIS has '.FIN' sufffixes and other stuff, so
# munge Filename to get it into right format
CMP_LVL = 2
fn = op.splitext('/'.join(fn.split('\\')[-CMP_LVL:]))[0]
# find if filtered filename overlaps with anything in the db
for dt in db.children_of_type('file'):
if fn in '/'.join(dt.rawdata.split(op.sep)):
break
else:
continue
info = {}
# load all the predefined fields
for k in cvtr:
info[k] = get_val(line, headers, cvtr[k])
# create peak shapes for plotting
if ftype == 'isodat':
rt = float(info['p-s-time']) / 60.
width = float(info['p-s-width']) / 60.
t = np.linspace(rt - width, rt + width)
data = []
for ion in ['44', '45', '46']:
area = float(get_val(line, headers, \
'rarea ' + ion + '[mvs]')) / 60.
#bgd = float(get_val(line, headers, \
# 'bgd ' + ion + '[mv]'))
height = float(get_val(line, headers, \
'ampl. ' + ion + '[mv]'))
# save the height at 44 into the info for linearity
if ion == '44':
info['p-s-ampl44'] = height
# 0.8 is a empirical number to make things look better
data.append(gaussian(t, x=rt, w=0.5 * area / height, \
h=height))
# save info if this is the main ref gas peak
if info['name'].endswith('*'):
ref_pk_info[dt] = info
ts = TimeSeries(np.array(data).T, t, [44, 45, 46])
else:
ts = TimeSeries(np.array([np.nan]), np.array([np.nan]), [''])
mapping[dt] += [Peak(info, ts)]
# do drift correction
if ftype == 'isodat':
for dt in mapping:
ref_pks = []
hgt44 = ref_pk_info[dt]['p-s-ampl44']
d18o = float(ref_pk_info[dt]['p-s-d18o'])
d13c = float(ref_pk_info[dt]['p-s-d13c'])
for pk in mapping[dt]:
# if the d18o and height are similar, it's a ref peak
if abs(pk.info['p-s-ampl44'] - hgt44) < 10. and \
abs(float(pk.info['p-s-d18o']) - d18o) < 2.:
ref_pks.append(pk)
# get out the Dd13C values and times for the ref gas peaks
d13cs = [float(pk.info['p-s-d13c']) for pk in ref_pks]
Dd13cs = np.array(d13cs) - d13c
rts = [float(pk.info['p-s-time']) for pk in ref_pks]
# try to fit a linear model through all of them
p0 = [d13cs[0], 0]
errfunc = lambda p, x, y: p[0] + p[1] * x - y
try:
p, succ = leastsq(errfunc, p0, args=(np.array(rts), Dd13cs))
except:
p = p0
# apply the linear model to get the Dd13C linearity correction
# for a given time and add it to the value of this peak
for pk in mapping[dt]:
pk.info['p-s-d13c'] = str(-errfunc(p, float(pk.info['p-s-time']), \
float(pk.info['p-s-d13c'])))
# save everything
with db:
for dt in mapping:
dt.children += mapping[dt]