def process_data_to_dso(self, nmr_data, nmr_ppms, sample_labels, experiment_name):
print("Processing spectra to dso...")
sample_n = len(sample_labels)
ppm_n = len(nmr_ppms)
dso = DataSet(size=(sample_n, ppm_n))
for n, nd in enumerate(nmr_data):
print("Spectra %s" % sample_labels[n])
dso.data[n, :] = nd
dso.labels[0][n] = sample_labels[n]
dso.labels[1] = [str(ppm) for ppm in nmr_ppms]
dso.scales[1] = [float(ppm) for ppm in nmr_ppms]
dso.name = experiment_name
return dso
def process_data_to_dso(self, nmr_data, nmr_ppms, sample_labels,
experiment_name):
print("Processing spectra to dso...")
sample_n = len(sample_labels)
ppm_n = len(nmr_ppms)
dso = DataSet(size=(sample_n, ppm_n))
for n, nd in enumerate(nmr_data):
print("Spectra %s" % sample_labels[n])
dso.data[n, :] = nd
dso.labels[0][n] = sample_labels[n]
dso.labels[1] = [str(ppm) for ppm in nmr_ppms]
dso.scales[1] = [float(ppm) for ppm in nmr_ppms]
dso.name = experiment_name
return dso
def load_bml_datafile(self, data_path, target, name):
dso = DataSet()
# Read in data for the graphing metabolite, with associated value (generate mean)
reader = csv.reader(utils.nonull(open(data_path, 'rb')),
delimiter='\t',
dialect='excel')
for row in reader:
if row and row[0] == 'metabolite': # Look for the top row
break
else:
return
samples = row[1:-2] # Sample identities
samples = [sample[8:-1] for sample in samples]
xdim = 0
ydim = len(samples)
raw_data = []
metabolites = []
for row in reader:
xdim += 1
metabolites.append(row[0])
raw_data.append([float(i) for i in row[1:-2]])
dso = DataSet(size=(ydim, xdim))
dso.labels[1] = metabolites
dso.data = np.array(raw_data).T
dso.name = name
dso.description = 'Imported from FIMA (%s)' % name
return dso
def load_bml_datafile(self, data_path, target, name):
dso = DataSet()
# Read in data for the graphing metabolite, with associated value (generate mean)
reader = csv.reader(utils.nonull(open(data_path, 'rb')), delimiter='\t', dialect='excel')
for row in reader:
if row and row[0] == 'metabolite': # Look for the top row
break
else:
return
samples = row[1:-2] # Sample identities
samples = [sample[8:-1] for sample in samples]
xdim = 0
ydim = len(samples)
raw_data = []
metabolites = []
for row in reader:
xdim += 1
metabolites.append(row[0])
raw_data.append([float(i) for i in row[1:-2]])
dso = DataSet(size=(ydim, xdim))
dso.labels[1] = metabolites
dso.data = np.array(raw_data).T
dso.name = name
dso.description = 'Imported from FIMA (%s)' % name
return dso
def load_datafile(self, filename):
# Determine if we've got a csv or peakml file (extension)
#self.data.o['output'].empty()
dso = DataSet()
# Read data in from peakml format file
xml = et.parse(filename)
# Get sample ids, names and class groupings
sets = xml.iterfind('header/sets/set')
midclass = {}
classes = set()
measurements = []
masses = {}
for aset in sets:
id = aset.find('id').text
mids = aset.find('measurementids').text
for mid in self.decode(mids):
midclass[mid] = id
measurements.append(mid)
classes.add(id)
# We have all the sample data now, parse the intensity and identity info
peaksets = xml.iterfind('peaks/peak')
quantities = defaultdict(dict)
all_identities = []
for peakset in peaksets:
# Find metabolite identities
annotations = peakset.iterfind('annotations/annotation')
identities = False
for annotation in annotations:
if annotation.find('label').text == 'identification':
identities = annotation.find('value').text.split(', ')
all_identities.extend(identities)
break
if identities:
# PeakML supports multiple alternative metabolite identities,currently we don't so duplicate
# We have identities, now get intensities for the different samples
chromatograms = peakset.iterfind(
'peaks/peak') # Next level down
for chromatogram in chromatograms:
mid = chromatogram.find('measurementid').text
intensity = float(chromatogram.find('intensity').text)
mass = float(chromatogram.find('mass').text)
# Write out to each of the identities table (need to buffer til we have the entire list)
for identity in identities:
quantities[mid][identity] = intensity
# Write out to each of the identities table (need to buffer til we have the entire list)
for identity in identities:
masses[identity] = mass
# Sort the identities/masses into consecutive order
# Quantities table built; class table built; now rearrange into dso
dso.empty((len(measurements), len(all_identities)))
dso.labels[0] = measurements
dso.classes[0] = [midclass[mid] for mid in measurements]
dso.labels[1] = all_identities
db_hmdbids = self.m.db.unification['HMDB']
dso.entities[1] = [
db_hmdbids[hmdbid] if hmdbid in db_hmdbids else None
for hmdbid in all_identities
]
dso.scales[1] = [float(masses[i]) for i in all_identities]
for mid, identities in list(quantities.items()):
for identity, intensity in list(identities.items()):
r = measurements.index(mid)
c = all_identities.index(identity)
dso.data[r, c] = intensity
dso.name = os.path.basename(filename)
dso.description = 'Imported PeakML file'
self.set_name(dso.name)
return {'output': dso}
def load_datafile(self, filename):
# Determine if we've got a csv or peakml file (extension)
#self.data.o['output'].empty()
dso = DataSet()
# Read data in from peakml format file
xml = et.parse(filename)
# Get sample ids, names and class groupings
sets = xml.iterfind('header/sets/set')
midclass = {}
classes = set()
measurements = []
masses = {}
for aset in sets:
id = aset.find('id').text
mids = aset.find('measurementids').text
for mid in self.decode(mids):
midclass[mid] = id
measurements.append(mid)
classes.add(id)
# We have all the sample data now, parse the intensity and identity info
peaksets = xml.iterfind('peaks/peak')
quantities = defaultdict(dict)
all_identities = []
for peakset in peaksets:
# Find metabolite identities
annotations = peakset.iterfind('annotations/annotation')
identities = False
for annotation in annotations:
if annotation.find('label').text == 'identification':
identities = annotation.find('value').text.split(', ')
all_identities.extend(identities)
break
if identities:
# PeakML supports multiple alternative metabolite identities,currently we don't so duplicate
# We have identities, now get intensities for the different samples
chromatograms = peakset.iterfind('peaks/peak') # Next level down
for chromatogram in chromatograms:
mid = chromatogram.find('measurementid').text
intensity = float(chromatogram.find('intensity').text)
mass = float(chromatogram.find('mass').text)
# Write out to each of the identities table (need to buffer til we have the entire list)
for identity in identities:
quantities[mid][identity] = intensity
# Write out to each of the identities table (need to buffer til we have the entire list)
for identity in identities:
masses[identity] = mass
# Sort the identities/masses into consecutive order
# Quantities table built; class table built; now rearrange into dso
dso.empty((len(measurements), len(all_identities)))
dso.labels[0] = measurements
dso.classes[0] = [midclass[mid] for mid in measurements]
dso.labels[1] = all_identities
db_hmdbids = self.m.db.unification['HMDB']
dso.entities[1] = [db_hmdbids[hmdbid] if hmdbid in db_hmdbids else None for hmdbid in all_identities]
dso.scales[1] = [float(masses[i]) for i in all_identities]
for mid, identities in list(quantities.items()):
for identity, intensity in list(identities.items()):
r = measurements.index(mid)
c = all_identities.index(identity)
dso.data[r, c] = intensity
dso.name = os.path.basename(filename)
dso.description = 'Imported PeakML file'
self.change_name.emit(dso.name)
return {'output': dso}
