def chromatogram_figures(chroma):
figures = []
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 = svguri_plot(plot,
bbox_inches='tight',
height=5,
width=9,
svg_width="100%")
figures.append(chroma_svg)
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 = svguri_plot(adduct_plot,
bbox_inches='tight',
height=5,
width=9,
svg_width="100%")
figures.append(adduct_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 = svguri_plot(charge_separating_plot,
bbox_inches='tight',
height=5,
width=9,
svg_width="100%")
figures.append(charge_separation)
return figures
def chromatogram_figures(chroma):
figures = []
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 = svguri_plot(
plot, bbox_inches='tight', height=5, width=9, svg_width="100%")
figures.append(chroma_svg)
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(
"mass_shift-Separated\nExtracted Ion Chromatogram", fontsize=24)
mass_shift_separation = svguri_plot(
mass_shift_plot, bbox_inches='tight', height=5, width=9, svg_width="100%")
figures.append(mass_shift_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 = svguri_plot(
charge_separating_plot, bbox_inches='tight', height=5, width=9,
svg_width="100%")
figures.append(charge_separation)
return figures
def site_specific_abundance_plots(self, glycoprotein):
axes = OrderedDict()
for glyco_type in glycoprotein.glycosylation_types:
for site in sorted(glycoprotein.glycosylation_sites_for(glyco_type)):
spanning_site = glycoprotein.site_map[glyco_type][site]
if len(spanning_site) == 0:
continue
bundle = BundledGlycanComposition.aggregate(spanning_site)
if len(bundle) == 0:
continue
ax = figax()
AggregatedAbundanceArtist(
bundle, ax=ax, colorizer=glycan_colorizer_type_map[glyco_type]).draw()
ax.set_title("%s Glycans\nat Site %d" % (glyco_type.name, site + 1,), fontsize=18)
axes[site, glyco_type] = svguri_plot(ax, bbox_inches='tight')
return axes
def site_specific_abundance_plots(self, glycoprotein):
axes = OrderedDict()
for glyco_type in glycoprotein.glycosylation_types:
for site in sorted(
glycoprotein.glycosylation_sites_for(glyco_type)):
spanning_site = glycoprotein.site_map[glyco_type][site]
if len(spanning_site) == 0:
continue
bundle = BundledGlycanComposition.aggregate(spanning_site)
ax = figax()
AggregatedAbundanceArtist(
bundle,
ax=ax,
colorizer=glycan_colorizer_type_map[glyco_type]).draw()
ax.set_title("%s Glycans\nat Site %d" % (
glyco_type.name,
site,
),
fontsize=18)
axes[site, glyco_type] = svguri_plot(ax, bbox_inches='tight')
return axes
def make_template_stream(self):
template_obj = self.env.get_template("overview.templ")
ads = serialize.AnalysisDeserializer(
self.database_connection._original_connection,
analysis_id=self.analysis_id)
self.glycan_chromatograms = gcs = ads.load_glycan_composition_chromatograms(
)
# und = ads.load_unidentified_chromatograms()
self.unidentified_chromatograms = und = ChromatogramFilter(
ads.query(serialize.UnidentifiedChromatogram).filter(
serialize.UnidentifiedChromatogram.analysis_id ==
self.analysis_id).all())
if len(gcs) == 0:
self.log(
"No glycan compositions were identified. Skipping report building"
)
templ = Template('''
<html>
<style>
body {
font-family: sans-serif;
}
</style>
<body>
<h3>No glycan compositions were identified</h3>
</body>
</html>
''')
return templ.stream()
summary_plot = summaries.GlycanChromatographySummaryGraphBuilder(
filter(lambda x: x.score > self.threshold, gcs + und))
lcms_plot, composition_abundance_plot = summary_plot.draw(min_score=5)
try:
lcms_plot.ax.legend_.set_visible(False)
except AttributeError:
# The legend may not have been created
pass
lcms_plot.ax.set_title("Glycan Composition\nLC-MS Aggregated EICs",
fontsize=24)
fig = lcms_plot.ax.figure
fig.set_figwidth(fig.get_figwidth() * 2.)
fig.set_figheight(fig.get_figheight() * 2.)
composition_abundance_plot.ax.set_title(
"Glycan Composition\nTotal Abundances", fontsize=24)
composition_abundance_plot.ax.set_xlabel(
composition_abundance_plot.ax.get_xlabel(), fontsize=14)
def resolve_key(key):
match = gcs.find_key(key)
if match is None:
match = und.find_key(key)
return match
template_stream = (template_obj.stream(
analysis=ads.analysis,
lcms_plot=svguri_plot(lcms_plot.ax,
bbox_inches='tight',
patchless=True,
svg_width="100%"),
composition_abundance_plot=svguri_plot(
composition_abundance_plot.ax,
bbox_inches='tight',
patchless=True,
svg_width="100%"),
glycan_chromatograms=gcs,
unidentified_chromatograms=und,
resolve_key=resolve_key))
return template_stream
def make_template_stream(self):
template_obj = self.env.get_template("overview.templ")
ads = serialize.AnalysisDeserializer(
self.database_connection._original_connection,
analysis_id=self.analysis_id)
self.glycan_chromatograms = gcs = ads.load_glycan_composition_chromatograms()
# und = ads.load_unidentified_chromatograms()
self.unidentified_chromatograms = und = ChromatogramFilter(
ads.query(serialize.UnidentifiedChromatogram).filter(
serialize.UnidentifiedChromatogram.analysis_id == self.analysis_id).all())
if len(gcs) == 0:
self.log("No glycan compositions were identified. Skipping report building")
templ = Template('''
<html>
<style>
body {
font-family: sans-serif;
}
</style>
<body>
<h3>No glycan compositions were identified</h3>
</body>
</html>
''')
return templ.stream()
summary_plot = summaries.GlycanChromatographySummaryGraphBuilder(
filter(lambda x: x.score > self.threshold, gcs + und))
lcms_plot, composition_abundance_plot = summary_plot.draw(min_score=5)
try:
lcms_plot.ax.legend_.set_visible(False)
except AttributeError:
# The legend may not have been created
pass
lcms_plot.ax.set_title("Glycan Composition\nLC-MS Aggregated EICs", fontsize=24)
fig = lcms_plot.ax.figure
fig.set_figwidth(fig.get_figwidth() * 2.)
fig.set_figheight(fig.get_figheight() * 2.)
composition_abundance_plot.ax.set_title("Glycan Composition\nTotal Abundances", fontsize=24)
composition_abundance_plot.ax.set_xlabel(
composition_abundance_plot.ax.get_xlabel(), fontsize=14)
def resolve_key(key):
match = gcs.find_key(key)
if match is None:
match = und.find_key(key)
return match
template_stream = (template_obj.stream(
analysis=ads.analysis, lcms_plot=svguri_plot(
lcms_plot.ax, bbox_inches='tight', patchless=True,
svg_width="100%"),
composition_abundance_plot=svguri_plot(
composition_abundance_plot.ax, bbox_inches='tight', patchless=True,
svg_width="100%"),
glycan_chromatograms=gcs,
unidentified_chromatograms=und,
resolve_key=resolve_key
))
return template_stream