def setUp(self):
# box peak
info = {}
trace = Trace(np.ones(10), np.arange(10), name=1)
baseline = Trace([0, 9], [0, 0], name=1)
c = PeakComponent(info, trace, baseline)
self.peak = Peak('box', components=c)
class TestBoxPeak(unittest.TestCase):
def setUp(self):
# box peak
info = {}
trace = Trace(np.ones(10), np.arange(10), name=1)
baseline = Trace([0, 9], [0, 0], name=1)
c = PeakComponent(info, trace, baseline)
self.peak = Peak('box', components=c)
def test_area(self):
assert self.peak.area() == self.peak.area(1)
class TestBoxPeak(unittest.TestCase):
def setUp(self):
# box peak
info = {}
trace = Trace(np.ones(10), np.arange(10), name=1)
baseline = Trace([0, 9], [0, 0], name=1)
c = PeakComponent(info, trace, baseline)
self.peak = Peak('box', components=c)
def test_area(self):
assert self.peak.area() == self.peak.area(1)
def read_amdis_list(db, filename):
def get_val(line, cols, key):
return line.split('\t')[cols.index(key)]
cmp_lvl = 2 # number of directory levels to compare
# TODO: does this work for agilent files only?
mapping = defaultdict(list)
with open(filename, 'r') as f:
cols = f.readline().split('\t')
for line in f:
filename = get_val(line, cols, 'FileName')
fn = op.splitext('/'.join(filename.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 = {}
info['name'] = get_val(line, cols, 'Name')
info['p-s-time'] = get_val(line, cols, 'RT')
info['p-s-area'] = get_val(line, cols, 'Area')
ts = Chromatogram(np.array([np.nan]), np.array([np.nan]), [''])
mapping[dt] += [Peak(info, ts)]
with db:
for dt in mapping:
dt.children += mapping[dt]
def setUp(self):
# box peak
info = {}
trace = Trace(np.ones(10), np.arange(10), name=1)
baseline = Trace([0, 9], [0, 0], name=1)
c = PeakComponent(info, trace, baseline)
self.peak = Peak('box', components=c)
def setUp(self):
# box peak
info = {}
v = [
0.043, 0.067, 0.094, 0.117, 0.131, 0.131, 0.117, 0.094, 0.067,
0.043
]
trace = Trace(v, np.arange(10), name=1)
baseline = Trace([0, 9], [0, 0], name=1)
c = PeakComponent(info, trace, baseline)
self.peak = Peak('gaussian', components=c)
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 = {}
def get_val(line, cols, key):
return 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 = Chromatogram(np.array(data).T, t, [44, 45, 46])
else:
ts = Chromatogram(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]
def errfunc(p, x, y):
return p[0] + p[1] * x - y
try:
p, succ = leastsq(errfunc, p0, args=(np.array(rts), dd13cs))
except Exception:
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]
