Span(location=-math.log10(0.05 / len(source.data['index'])), dimension='width', render_mode='css',
line_color='darkblue')
]
)
labels = np.arange(10, 100, 10) / 100
labels = np.append([0.001, 0.01, 0.05], labels)
coords = -np.log10(labels)
coords = coords.tolist()
# plot.yaxis[0].ticker = bokeh.models.tickers.FixedTicker(ticks=coords)
y_ticker = bokeh.models.tickers.FixedTicker(ticks=coords)
plot.ygrid[0].ticker = y_ticker
plot.set(x_range=Range1d(-1.1, 1.1),
y_range=Range1d(min(source.data['y']), 10 ** 3)
)
sliders = VBox(
p_value_text,
HBox(p_value, p_mult),
HBox(left_limit, right_limit))
button_box = VBox(
HBox(*buttons[:3]),
HBox(*buttons[3:])
)
save_filename = bokeh.models.TextInput()
save_button = bokeh.models.Button(label='Save')
input_box = HBox(
def plotTracks(preds,tag,n=3,title=None,width=820,height=None,
seqdepot=None,bcell=None,exp=None):
"""Plot epitopes as parallel tracks"""
from bokeh.models import Range1d,HoverTool,FactorRange,Grid,GridPlot,ColumnDataSource
from bokeh.plotting import Figure
alls=1
if title == None:
title=tag
for m in preds:
alls += len(preds[m].data.groupby('allele'))
if height==None:
height = 130+10*alls
yrange = Range1d(start=0, end=alls+3)
plot = Figure(title=title,title_text_font_size="11pt",plot_width=width,
plot_height=height, y_range=yrange,
y_axis_label='allele',
tools="xpan, xwheel_zoom, resize, hover, reset, save",
background_fill="#FAFAFA",
toolbar_location="below")
h=3
if bcell != None:
plotBCell(plot, bcell, alls)
if seqdepot != None:
plotAnnotations(plot,seqdepot)
if exp is not None:
plotExp(plot, exp)
#plotRegions(plot)
#lists for hover data
#we plot all rects at once
x=[];y=[];allele=[];widths=[];clrs=[];peptide=[]
predictor=[];position=[];score=[];leg=[]
l=80
for m in preds:
pred = preds[m]
cmap = mpl.cm.get_cmap(colormaps[m])
df = pred.data
sckey = pred.scorekey
pb = pred.getPromiscuousBinders(data=df,n=n)
if len(pb) == 0:
continue
l = pred.getLength()
grps = df.groupby('allele')
alleles = grps.groups.keys()
if len(pb)==0:
continue
c=colors[m]
leg.append(m)
for a,g in grps:
b = pred.getBinders(data=g)
b = b[b.pos.isin(pb.pos)] #only promiscuous
b.sort('pos',inplace=True)
scores = b[sckey].values
score.extend(scores)
pos = b['pos'].values
position.extend(pos)
x.extend(pos+(l/2.0)) #offset as coords are rect centers
widths.extend([l for i in scores])
clrs.extend([c for i in scores])
y.extend([h+0.5 for i in scores])
alls = [a for i in scores]
allele.extend(alls)
peptide.extend(list(b.peptide.values))
predictor.extend([m for i in scores])
h+=1
source = ColumnDataSource(data=dict(x=x,y=y,allele=allele,peptide=peptide,
predictor=predictor,position=position,score=score))
plot.rect(x,y, width=widths, height=0.8,
#x_range=Range1d(start=1, end=seqlen+l),
color=clrs,line_color='gray',alpha=0.7,source=source)
hover = plot.select(dict(type=HoverTool))
hover.tooltips = OrderedDict([
("allele", "@allele"),
("position", "@position"),
("peptide", "@peptide"),
("score", "@score"),
("predictor", "@predictor"),
])
seqlen = pred.data.pos.max()+l
plot.set(x_range=Range1d(start=0, end=seqlen+1))
plot.xaxis.major_label_text_font_size = "8pt"
plot.xaxis.major_label_text_font_style = "bold"
plot.ygrid.grid_line_color = None
plot.yaxis.major_label_text_font_size = '0pt'
plot.xaxis.major_label_orientation = np.pi/4
#js,html = embedPlot(plot)
script, div = embedPlot(plot)
return script, div
