def show_images_get():
dataset = bottle.request.params.get('dataset', app.paths.iterkeys().next())
formula = bottle.request.params.get('formula', '')
tolerance = float(bottle.request.params.get('tolerance', 5.0))
resolution = float(bottle.request.params.get('resolution', 1e5))
selected_adduct = bottle.request.params.get('adduct', 'H')
hs_removal = bottle.request.GET.get('hs_removal', False)
k = int(bottle.request.params.get('npeaks', 4))
if hs_removal == 'on':
hs_removal = True
pts = float(bottle.request.params.get('pts', 10))
cutoff = float(bottle.request.params.get('pyisocalc_cutoff', 1e-3))
adducts = ['H', 'K', 'Na']
isotope_patterns = {}
for adduct in adducts:
sf = pyisocalc.SumFormulaParser.parse_string(formula + adduct)
raw_pattern = pyisocalc.isodist(sf, cutoff)
fwhm = raw_pattern.get_spectrum()[0][0] / resolution
pattern = pyisocalc.apply_gaussian(raw_pattern, fwhm, pts, exact=True)
mzs, intensities = map(np.array, pattern.get_spectrum(source='centroids'))
if len(mzs) > k:
order = intensities.argsort()[::-1]
mzs = mzs[order][:k]
intensities = intensities[order][:k]
order = mzs.argsort()
mzs = mzs[order]
intensities = intensities[order]
datacube = app.get_datacube(dataset, mzs, tolerance)
if hs_removal:
for img in datacube.xic:
if len(img) > 0:
pc = np.percentile(img, 99)
img[img > pc] = pc
chaos = measure_of_chaos(datacube.xic_to_image(0), 30, overwrite=False)
iso_corr = isotope_pattern_match(datacube.xic, intensities)
img_corr = 1.0 # return 1 if there's a single peak
if len(intensities[1:]) > 1:
img_corr = isotope_image_correlation(datacube.xic, weights=intensities[1:])
stats = {'measure of chaos': chaos,
'image correlation score': img_corr,
'isotope pattern score': iso_corr}
isotope_patterns[adduct] = (mzs, intensities, stats)
return bottle.template('show_images', hs_removal=hs_removal,
isotope_patterns=isotope_patterns, formula=formula, selected_adduct=selected_adduct,
pretty_formula=re.sub(r"(\d+)", r"<sub>\1</sub>", formula),
resolution=resolution, tol=tolerance, datasets=app.paths.keys(),
npeaks=k, selected_dataset=dataset)
def calculate_isotope_patterns(sum_formulae,adduct,isocalc_resolution,isocalc_do_centroid = True, charge=1):
### Generate a mz list of peak centroids for each sum formula with the given adduct
mz_list={}
for n, sum_formula in enumerate(sum_formulae):
sf = pyisocalc.SumFormulaParser.parse_string(str(sum_formula + adduct))
raw_pattern = pyisocalc.isodist(sf, cutoff=1e-4, charge=charge)
mz = raw_pattern.get_spectrum()[0][0]
# if mz < 200 or mz > 2000:
# continue
fwhm = mz / isocalc_resolution # TODO: resolution = resolution(mz)
isotope_ms = pyisocalc.apply_gaussian(raw_pattern, fwhm, exact=False)
if not sum_formula in mz_list:
mz_list[sum_formula] = {}
mzs, intensities = isotope_ms.get_spectrum(source='centroids')
order = intensities.argsort()[::-1][:5]
mz_list[sum_formula][adduct] = (mzs[order], intensities[order])
return mz_list
def generate_patterns(formulas_fn, resolution_func, mz_range):
mz_min, mz_max = mz_range
patterns = {}
adducts = ['H', 'K', 'Na']
formulae = [s.strip() for s in open(formulas_fn).readlines()]
for f in formulae:
for a in adducts:
sf = pyisocalc.SumFormulaParser.parse_string(f + a)
raw_pattern = pyisocalc.isodist(sf, cutoff=1e-4, charge=1)
mz = raw_pattern.get_spectrum()[0][0]
if mz < mz_min or mz > mz_max:
continue
fwhm = mz / resolution_func(mz)
mzs, intensities = pyisocalc.apply_gaussian(raw_pattern, fwhm, exact=False).get_spectrum(source="centroids")
mzs = np.array(mzs)
intensities = np.array(intensities)
order = np.argsort(intensities)[::-1]
patterns[(f, a)] = (mzs[order], intensities[order])
return patterns
