def get(self):
args = self.request.arguments
defaultargs = get_args(args)
form = ControlsForm()
path = defaultargs['path']
current_name = defaultargs['name']
if not os.path.exists(path):
msg = help_msg()
self.render('sequence.html', form=form, status=0, name='', msg=msg, path=path)
return
names = web.get_file_lists(path)
if current_name == '': current_name = names[0]
form.path.data = path
form.name.choices = [(i,i) for i in names]
form.name.data = current_name
form.cutoff.data = defaultargs['cutoff']
form.n.data = defaultargs['n']
form.cutoff_method.data = defaultargs['cutoff_method']
form.kind.data = defaultargs['kind']
form.view.data = defaultargs['view']
preds = web.get_predictors(path, current_name)
#alleles = web.get_alleles(preds)
data = web.get_binder_tables(preds, **defaultargs)
tables = web.dataframes_to_html(data, classes='tinytable sortable')
tables = web.tabbed_html(tables)
#info = web.dict_to_html(web.get_results_info(preds[0]))
info=''
kind = defaultargs['kind']
if kind == 'grid':
seqhtml = web.sequence_to_html_grid(preds, classes="gridtable")
div = '<div class="scrolled">%s</div>' %seqhtml
script = ''
elif kind == 'text':
seqhtml = web.create_sequence_html(preds, classes="seqtable")
div = '<div class="scrolled">%s</div>' %seqhtml
script = ''
else:
plots = web.create_figures(preds, **defaultargs)
if len(plots) > 0:
grid = gridplot(plots, ncols=1, merge_tools=True, sizing_mode='scale_width',
toolbar_options=dict(logo=None))
script, div = components(grid)
else:
script = ''; div = ''
links = []
for k in data.keys():
defaultargs['pred'] = k
links.append(self.get_url(defaultargs, link=k))
self.render('sequence.html', script=script, div=div, form=form, tables=tables,
msg='', info=info, name=current_name, status=1, links=links, path=path)
def get(self):
args = self.request.arguments
form = SummaryForm()
defaultargs = {'savepath': '', 'deletecached': 1}
for k in defaultargs:
if k in args:
defaultargs[k] = args[k][0].decode("utf-8")
savepath = defaultargs['savepath'].strip()
deletecached = defaultargs['deletecached']
#if usecached == 1:
# print ('using cached results')
if not os.path.exists(savepath):
msg = help_msg()
self.render('global.html',
form=form,
msg=msg,
savepath=savepath,
status=0)
data = web.get_summary_tables(savepath, **defaultargs)
df = pd.concat(data).reset_index()
plot = plotting.bokeh_summary_plot(df)
plots = [plot]
if len(plots) > 0:
grid = gridplot(plots,
ncols=1,
merge_tools=True,
sizing_mode='scale_width',
toolbar_options=dict(logo=None))
script, div = components(grid)
else:
script = ''
div = ''
for k in data:
data[k] = web.column_to_url(
data[k], 'name', '/sequence?savepath=%s&name=' % savepath)
#convert dfs to html
tables = web.dataframes_to_html(data, classes='tinytable sortable')
#put tables in tabbed divs
tables = web.tabbed_html(tables)
form.savepath.data = savepath
self.render('global.html',
form=form,
tables=tables,
msg='',
script=script,
div=div,
status=1,
savepath=savepath)
def get(self):
args = self.request.arguments
form = ControlsForm()
defaultargs = {'path':'','cutoff':5,'cutoff_method':'rank',
'view':'promiscuous','n':2,'cached':1}
for k in defaultargs:
if k in args:
defaultargs[k] = args[k][0]
path = defaultargs['path'].strip()
view = defaultargs['view']
usecached = defaultargs['cached']
if usecached == 1:
print ('using cached results')
if not os.path.exists(path):
msg = help_msg()
self.render('global.html', form=form, msg=msg, path=path, status=0)
preds = web.get_predictors(path)
data = {}
if view == 'summary':
for P in preds:
if P.data is None: continue
seqs = web.get_sequences(P)
#tables = web.sequences_to_html_table(seqs, classes="seqtable")
pb = P.promiscuousBinders(**defaultargs)
#print (pb)
#cl = analysis.find_clusters(b, min_binders=2)
x = pb.groupby('name').agg({'peptide':np.size,
P.scorekey:np.median}).reset_index()
x = x.rename(columns={'peptide':'binders'})
x = x.merge(seqs, on='name', how='right')
x = web.column_to_url(x, 'name', '/sequence?path=%s&name=' %path)
data[P.name] = x
else:
data = web.get_binder_tables(preds, **defaultargs)
#add url to prot/seq name
for k in data:
data[k] = web.column_to_url(data[k], 'name', '/sequence?path=%s&name=' %path)
#convert dfs to html
tables = web.dataframes_to_html(data, classes='tinytable sortable')
#put tables in tabbed divs
tables = web.tabbed_html(tables)
form.path.data = path
form.cutoff.data = defaultargs['cutoff']
form.n.data = defaultargs['n']
form.cutoff_method.data = defaultargs['cutoff_method']
form.view.data = view
self.render('global.html', form=form, tables=tables, msg='', status=1, path=path)
def get(self):
args = self.request.arguments
form = NeoForm()
args = get_args(args,
defaults={
'savepath': '',
'view': 'final',
'sample': None
})
savepath = args['savepath']
view = args['view']
sample = args['sample']
if not os.path.exists(savepath):
self.no_data_render(form, msg='no such path')
return
labels = pd.read_csv(os.path.join(savepath, 'sample_labels.csv'),
index_col=0)
samples = list(labels.index)
if sample == None:
sample = samples[0]
print(view)
#get results data
data = OrderedDict()
plots = []
preds = []
s = sample
if sample == 'all':
s = samples[0]
for p in base.predictors:
f = os.path.join(savepath, 'results_%s_%s.csv' % (s, p))
if os.path.exists(f):
preds.append(p)
if sample == 'all':
#get all results together
for p in preds:
data['summary'] = labels
res = []
for sample in samples:
fname = os.path.join(savepath,
'binders_%s_%s.csv' % (sample, p))
b = pd.read_csv(fname, index_col=0)
res.append(b)
res = pd.concat(res).reset_index()
x = pd.pivot_table(res,
index=['name', 'peptide'],
columns=['label'],
values='score')
data[p] = x
else:
#get table for variants
variant_file = os.path.join(savepath,
'variant_effects_%s.csv' % sample)
if not os.path.exists(variant_file):
self.no_data_render(form, msg='no such file %s' % variant_file)
return
variants = pd.read_csv(variant_file, index_col=0)
#data['variants'] = variants
for p in preds:
binder_file = fname = os.path.join(
savepath, 'binders_%s_%s.csv' % (sample, p))
if not os.path.exists(binder_file):
resfile = os.path.join(savepath,
'results_%s_%s.csv' % (sample, p))
res = pd.read_csv(resfile, index_col=0)
#get binders here if new cutoff used
else:
pb = pd.read_csv(binder_file)
pb = pb.drop('label', 1)
#top genes with most peptides?
t = pb['name'].value_counts()[:20]
bar = plotting.bokeh_vbar(
t,
title='top genes with peptide binders: %s' % p,
color='#41b6c4')
plots.append(bar)
self.add_links(pb)
#limit table size!
data[p] = pb[:1000]
x = variants.variant_class.value_counts()
pie = plotting.bokeh_pie_chart(x,
radius=.25,
title='variant classes',
height=150)
plots.append(pie)
#test = plotting.bokeh_test(height=200)
v = variants
x = v.chr.value_counts().sort_index()
bar2 = plotting.bokeh_vbar(x,
title='variants per chromosome',
color='#225ea8')
plots.append(bar2)
tables = web.dataframes_to_html(data, classes='sortable')
tables = web.tabbed_html(tables)
if len(plots) > 0:
grid = gridplot(plots,
ncols=1,
nrows=3,
merge_tools=True,
sizing_mode='scale_width',
toolbar_options=dict(logo=None))
script, div = components(grid)
else:
script = ''
div = ''
form.savepath.data = savepath
samples.append('all')
form.sample.choices = [(i, i) for i in samples]
form.sample.data = sample
self.render('neoepitope.html',
status=1,
savepath=savepath,
links='',
form=form,
script=script,
div=div,
tables=tables)