def details_for(analysis_uuid, chromatogram_id):
view = get_view(analysis_uuid)
with view:
snapshot = view.get_items_for_display()
with snapshot.bind(view.session):
model = view.analysis.parameters.get('scoring_model')
chroma = snapshot[chromatogram_id].convert(chromatogram_scoring_model=model)
plot = SmoothingChromatogramArtist(
[chroma], colorizer=lambda *a, **k: 'green', ax=figax()).draw(
label_function=lambda *a, **k: "", legend=False).ax
plot.set_title("Aggregated\nExtracted Ion Chromatogram", fontsize=24)
chroma_svg = report.svguri_plot(
plot, bbox_inches='tight', height=5, width=9, svg_width="100%")
glycan_composition = normalize_composition(chroma.glycan_composition)
membership = [neigh.name for neigh in view.neighborhoods if neigh(glycan_composition)]
mass_shift_separation = ""
if len(chroma.mass_shifts) > 1:
mass_shifts = list(chroma.mass_shifts)
labels = {}
rest = chroma
for mass_shift in mass_shifts:
with_mass_shift, rest = rest.bisect_mass_shift(mass_shift)
labels[mass_shift] = with_mass_shift
mass_shift_plot = SmoothingChromatogramArtist(
labels.values(),
colorizer=lambda *a, **k: 'green', ax=figax()).draw(
label_function=lambda *a, **k: tuple(a[0].mass_shifts)[0].name,
legend=False).ax
mass_shift_plot.set_title(
"Adduct-Separated\nExtracted Ion Chromatogram", fontsize=24)
mass_shift_separation = report.svguri_plot(
mass_shift_plot, bbox_inches='tight', height=5, width=9, svg_width="100%")
charge_separation = ""
if len(chroma.charge_states) > 1:
charge_separating_plot = ChargeSeparatingSmoothingChromatogramArtist(
[chroma], ax=figax()).draw(
label_function=lambda x, *a, **kw: str(tuple(x.charge_states)[0]), legend=False).ax
charge_separating_plot.set_title("Charge-Separated\nExtracted Ion Chromatogram", fontsize=24)
charge_separation = report.svguri_plot(
charge_separating_plot, bbox_inches='tight', height=5, width=9,
svg_width="100%")
return render_template(
"/view_glycan_search/detail_modal.templ", chromatogram=chroma,
chromatogram_svg=chroma_svg, mass_shift_separation_svg=mass_shift_separation,
charge_chromatogram_svg=charge_separation,
logitscore=logitsum(chroma.score_components()),
membership=membership)
def details_for_unidentified(analysis_uuid, chromatogram_id):
view = get_view(analysis_uuid)
with view:
snapshot = view.get_items_for_display()
with snapshot.bind(view.session):
chroma = snapshot.unidentified_id_map[chromatogram_id].convert()
plot = SmoothingChromatogramArtist(
[chroma], colorizer=lambda *a, **k: 'green',
ax=figax()).draw(label_function=lambda *a, **k: "",
legend=False).ax
plot.set_title("Aggregated\nExtracted Ion Chromatogram",
fontsize=24)
chroma_svg = report.svguri_plot(plot,
bbox_inches='tight',
height=5,
width=9,
svg_width="100%")
adduct_separation = ""
if len(chroma.adducts) > 1:
adducts = list(chroma.adducts)
labels = {}
rest = chroma
for adduct in adducts:
with_adduct, rest = rest.bisect_adduct(adduct)
labels[adduct] = with_adduct
adduct_plot = SmoothingChromatogramArtist(
labels.values(),
colorizer=lambda *a, **k: 'green',
ax=figax()).draw(label_function=lambda *a, **k: tuple(a[
0].adducts)[0].name,
legend=False).ax
adduct_plot.set_title(
"Adduct-Separated\nExtracted Ion Chromatogram",
fontsize=24)
adduct_separation = report.svguri_plot(adduct_plot,
bbox_inches='tight',
height=5,
width=9,
svg_width="100%")
charge_separation = ""
if len(chroma.charge_states) > 1:
charge_separating_plot = ChargeSeparatingSmoothingChromatogramArtist(
[chroma],
ax=figax()).draw(legend=False,
label_function=lambda x, *a, **kw: str(
tuple(x.charge_states)[0])).ax
charge_separating_plot.set_title(
"Charge-Separated\nExtracted Ion Chromatogram",
fontsize=24)
charge_separation = report.svguri_plot(charge_separating_plot,
bbox_inches='tight',
height=5,
width=9,
svg_width="100%")
return render_template("/view_glycan_search/detail_modal.templ",
chromatogram=chroma,
chromatogram_svg=chroma_svg,
adduct_separation_svg=adduct_separation,
charge_chromatogram_svg=charge_separation,
logitscore=logitsum(
chroma.score_components()),
membership=[])
try:
matplotlib.use("agg")
except Exception:
pass
from matplotlib import pyplot as plt, rcParams
import numpy as np
from scipy import stats
from glycan_profiling.scoring.chromatogram_solution import logitsum
rcParams['figure.figsize'] = 12, 8
rng = np.random.RandomState(500)
simulation_data = (rng.uniform(size=(100000, 5)).T)
simulation_scores = logitsum(simulation_data)
dist = stats.norm(loc=np.mean(simulation_scores),
scale=np.std(simulation_scores))
plt.hist(simulation_scores, bins='fd', density=1)
plt.vlines(0,
0,
.11,
color='black',
linestyle='--',
label='Never Considered (p = %0.3f)' % (1 - dist.cdf(0)))
plt.vlines(5,
0,
.11,
color='red',
linestyle='--',