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 = 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 add_links(self, df):
web.column_to_url(
df, 'name', 'http://cancer.sanger.ac.uk/cosmic/gene/analysis?ln=')
web.column_to_url(df, 'transcript_id', 'https://www.ensembl.org/id/')