def _checkbox_group(source, subgroup_col):
checkbox_title = Div(text=str(subgroup_col).title() + ": ")
checkbox_labels = sorted(set(source.data[subgroup_col]))
actives = list(range(len(checkbox_labels)))
checkboxes = CheckboxGroup(
labels=checkbox_labels,
active=actives,
inline=True,
name="subgroup_checkbox",
)
checkboxes.js_on_change(
"active",
CustomJS(code="source.change.emit();", args={"source": source}))
return Row(checkbox_title, checkboxes, name="subgroup_widget")
for(var i=0;i<pc1.length; i++){
if(tissue[i]==f){
x.push(pc1[i]);
y.push(pc2[i]);
}
}
data1['comp 0']=x;
data1['comp 1']=y;
source.change.emit();
""")
p = makePlot(source)
selection = CheckboxGroup(labels=dataCats, active=[0, 1])
selection.js_on_change('active', callback)
controls = WidgetBox(selection)
layout = row(controls, p)
tab = Panel(child=layout, title='test')
tabs = Tabs(tabs=[tab])
#curdoc().add_root(tabs)
show(tabs)
#cancer_selection = CheckboxGroup(labels=list(___), active = [0,1])
#
#show(p)
#show(widgetbox(cancer_selection))
#
#
#def makePlot(data):
Sytsq+=yt[i]*yt[i];
Sexpsq+=exp[i]*exp[i];
}
TSR=(95*Sytexp-Sexp*Syt)/Math.sqrt(95*Sytsq-Syt*Syt)/Math.sqrt(95*Sexpsq-Sexp*Sexp)
TSR=Math.round(TSR*100)/100
corlabTS.text='TS R='.concat(TSR.toString())
source.trigger('change');
""")
cbox.js_on_change('active', cb)
slider.js_on_change('value', cb_slider)
rb_group.js_on_change('active', cb_ff)
rb_group2.js_on_change('active', cb_struct)
div = Div(text="""<b>Figure: Comparison of H-SASA profiles calculated with different parameters with experimental cleavage frequencies. Top strand (TS) of <i>S. cerevisiae</i> centromeric nucleosome with 601TA DNA sequence.</b>
<br><br>
This interactive plot allows to explore dependence of H-SASA profiles on different paramters. Correlation coefficient is interactively displayed in the bottom left corner.
Use contols above to choose between following paramters:<br><br>
<b>Structure used for calculations</b>: X-H nuclear - orginal X-ray derived structure, hydrogen atoms added via REDUCE program with nuclear distances for X-H bond length;
X-H electron - original X-ray derived structure, hydrogen atoms added via REDUCE program with electron cloud distances for X-H bond length;
MD average - average profile for 50 frames spaced 1 ns apart from molecular dynamics simulations with CHARMM36 force field with NAMD.
<br><br>
<b>Radii of atoms used for SASA calculations</b>: FreeSASA Default - default radii in FreeSASA program; CHARMM36-rmin - van der Waals radii (Rmin paramter) of atoms as defined by CHARMM36 force field;
AMBER10-rmin - van der Waals radii (Rmin paramter) of atoms as defined by AMBER10 force field.
<br><br>
<b>Contributions of deoxyribose hydrogen atoms</b>: SASA of deoxyribose hydrogen atoms selected will be included in calculation of H-SASA profile.
title='Depth Cutoff')
slider_af = Slider(start=0,
end=100,
step=1,
value=1,
title='Allele Frequency Cutoff')
slider_af.js_on_change(
'value',
sliderCallback(source_sample, depth_sample, slider, slider_af,
syngroup, unique_longitudinal_muts))
slider.js_on_change(
'value',
sliderCallback(source_sample, depth_sample, slider, slider_af,
syngroup, unique_longitudinal_muts))
syngroup.js_on_change(
'active',
sliderCallback(source_sample, depth_sample, slider, slider_af,
syngroup, unique_longitudinal_muts))
ose.js_on_change(
'value',
sliderCallback2(source_sample, depth_sample, slider, slider_af,
syngroup, ose, unique_longitudinal_muts))
# When mousing over Bokeh plot, allele frequency updated to user input.
if (user_af != -123):
slider_af.value = user_af
## g.js_on_event(events.PlotEvent, sliderCallback(source_sample, depth_sample, slider, slider_af, syngroup))
genome_plot.js_on_event(
events.MouseEnter,
sliderCallback(source_sample, depth_sample, slider, slider_af,
syngroup, unique_longitudinal_muts))
def show_projection(alg, selected_ids, dim_red, directionality):
filename = "static/data/anchs_"
title = "2D Projection - Key Features"
samples = 7468
if (not alg):
samples = 3114
filename = "static/data/changes_"
title = "2D Projection - Changes"
filename += dim_red
if (not directionality):
filename += "_dir_False"
filename += ".csv"
fp = open(filename, 'r', encoding='utf-8')
X = np.zeros((samples, 2))
ids = []
category = []
ft_selected_ids = []
for i in range(samples):
roww = fp.readline().split(',')
# print(roww)
ids.append(int(roww[0]))
X[i][0] = float(roww[3])
X[i][1] = float(roww[4])
if int(roww[0]) in selected_ids:
ft_selected_ids.append(1)
else:
ft_selected_ids.append(0)
if roww[2] == "TP":
category.append(0)
elif roww[2] == "TN":
category.append(1)
elif roww[2] == "FP":
category.append(2)
elif roww[2] == "FN":
category.append(3)
# percentage.append(float(roww[4]))
fp.close()
for p in range(1, 2):
x = []
y = []
for i in range(samples):
x.append(X[i][0])
y.append(X[i][1])
#################
#################
##### bokeh #####
#################
#################
color_opt = [
"rgb(27, 158, 119)", "rgb(217, 95, 2)", "rgb(27, 158, 119)",
"rgb(217, 95, 2)", "gray"
]
alpha_opt = [0.6, 0.05]
line_opt = [
"rgb(27, 158, 119)", "rgb(217, 95, 2)", "black", "black", "gray"
]
if (len(ft_selected_ids) > 0):
alpha_opt = [0.7, 0.02]
colors = []
line_color = []
fill_alpha = []
line_alpha = []
for k in range(samples):
if ft_selected_ids[k]:
colors.append(color_opt[category[k]])
line_color.append(line_opt[category[k]])
fill_alpha.append(alpha_opt[0])
line_alpha.append(alpha_opt[0])
else:
colors.append(color_opt[-1])
line_color.append(line_opt[-1])
fill_alpha.append(alpha_opt[-1])
line_alpha.append(alpha_opt[-1])
output_file('2d_changes_map.html')
s1 = ColumnDataSource(data=dict(x=x,
y=y,
ids=ids,
category=category,
colors=colors,
fill_alpha=fill_alpha,
line_alpha=line_alpha,
line_color=line_color,
ft_selected_ids=ft_selected_ids))
hover = HoverTool(tooltips=""" """)
# help_b = HelpTool(help_tooltip = """ """)
wheel_zoom = WheelZoomTool()
lasso_select = LassoSelectTool()
p1 = figure(
tools=[hover, lasso_select, "reset", "tap", wheel_zoom, "pan"],
toolbar_location="right",
toolbar_sticky=False,
title=title,
width=390,
height=390)
# p1.circle('x', 'y', source=s1, size=7.3, fill_alpha = 'fill_alpha', line_alpha = 'line_alpha', fill_color = 'colors', line_color = 'line_color',
# nonselection_fill_alpha=alpha_opt[-1],
# nonselection_fill_color=color_opt[-1],
# nonselection_line_color=color_opt[-1],
# nonselection_line_alpha=alpha_opt[-1]
# )
p1.title.text_font_size = '10pt'
p1.title.align = 'center'
p1.toolbar.active_scroll = wheel_zoom
p1.toolbar.active_drag = lasso_select
p1.axis.visible = False
lasso_select.select_every_mousemove = False
# CheckboxGroup to select categories
category_selection = CheckboxGroup(labels=["TP", "TN", "FP", "FN"],
active=[0, 1, 2, 3])
selection_callback = CustomJS(args=dict(source=s1),
code="""
source.change.emit();
""")
category_selection.js_on_change('active', selection_callback)
# Define the custom filter to return the indices, compare against values in source.data
js_filter = CustomJSFilter(args=dict(
category_selection=category_selection, source=s1),
code="""
var indices = [];
for (var i = 0; i <= source.data['category'].length; i++){
if (category_selection.active.includes(source.data['category'][i])) {
indices.push(i)
}
}
return indices;
""")
s1.callback = CustomJS(code="""
var lasso_ids = cb_obj.selected['1d'].indices;
//console.log(lasso_ids);
var ft_selected_ids = cb_obj.data['ft_selected_ids'];
var ids = cb_obj.data['ids'];
//console.log(ft_selected_ids);
var aggregation_ids = [];
for (i=0; i<ft_selected_ids.length; i++){
if (ft_selected_ids[i] == 1 && lasso_ids.includes(i)){
//console.log(ids[i]);
aggregation_ids.push(ids[i]);
}
}
if (!(aggregation_ids && aggregation_ids.length)) {
aggregation_ids = [-1];
}
console.log(aggregation_ids);
//parent.makeBokehRequest(aggregation_ids);
parent.makeBokehRequest2(aggregation_ids);
""")
# Use the filter in a view
view = CDSView(source=s1, filters=[js_filter])
p1.circle('x',
'y',
source=s1,
view=view,
size=7.3,
fill_alpha='fill_alpha',
line_alpha='line_alpha',
fill_color='colors',
line_color='line_color',
nonselection_fill_alpha=alpha_opt[-1],
nonselection_fill_color=color_opt[-1],
nonselection_line_color=color_opt[-1],
nonselection_line_alpha=alpha_opt[-1])
layout = column(p1, category_selection)
# show(layout)
# grid = gridplot([[p1, None]], merge_tools=False)
# # show(grid)
html = file_html(layout, CDN, "my_plot")
html = html.replace("auto;", "0px;")
fp = open("static/html/projection_file_raw.html", 'w')
fp.write(html)
fp.close()
# show_projection(False, list(range(7468)), "pca", True )
