def update2():
gpcr_val = gpcr1.value
lipid2_val = lipid2.value
radius2_val = radius2.value
resname = df[(df.Protein == str(gpcr_val))
& (df.Lipids == str(lipid2_val)) &
(df.Radius == float(radius2_val))].ResName.to_list()
resid = df[(df.Protein == str(gpcr_val))
& (df.Lipids == str(lipid2_val)) &
(df.Radius == float(radius2_val))].ResID.to_list()
residues = []
for rn, ri in zip(resname, resid):
residues.append("{}-{}".format(rn, ri))
# options = [str(x) for x in options]
residue2.options = residues
if residue2.value not in residues:
residue2.value = residues[0]
f2 = select_dfGPCR()[1]
xt, yt = radar_patch(f2, theta, centre)
xi, yi = star_curv(xt + xt[:1], yt + yt[:1])
circle2.data = dict(xt=xt, yt=yt)
patch2.data = dict(xi=xi, yi=yi)
def radarApp(doc):
nonlocal df
df = df.copy()
for col in df.columns:
if col.endswith('Error'):
del df[col]
gpcrs = list(df.Protein.unique())
lipids = list(df.Lipids.unique())
radius = list(df.Radius.unique())
radius = [str(r) for r in radius]
# Normalize [0-1] all contact parameters with respect to each other.
for cmn in list(df.columns)[:-5]:
for lipid in lipids:
for radius_value in radius:
radius_value = float(radius_value)
dfmin = df[(df.Lipids == lipid)
& (df.Radius == radius_value)][cmn].min()
dfmax = df[(df.Lipids == lipid)
& (df.Radius == radius_value)][cmn].max()
lip_index = list(df[(df.Lipids == lipid)
& (df.Radius == radius_value)].index)
df.loc[lip_index, cmn] = df[(df.Lipids == lipid) & (
df.Radius == radius_value)][cmn].apply(
lambda x: (x - dfmin) / (dfmax - dfmin))
df = df.fillna(0)
# 1 unique set of residues - works also if there's only 1 set available.
resname = df[(df.Protein == gpcrs[0]) & (df.Lipids == lipids[0]) &
(df.Radius == float(radius[0]))].ResName.to_list()
resid = df[(df.Protein == gpcrs[0]) & (df.Lipids == lipids[0]) &
(df.Radius == float(radius[0]))].ResID.to_list()
residues = []
for rn, ri in zip(resname, resid):
residues.append("{}-{}".format(rn, ri))
import random
# Create Input controls
gpcr1 = Select(title="Protein",
value=gpcrs[0],
options=gpcrs,
width=100)
gpcr2 = Select(title="Protein",
value=gpcrs[0],
options=gpcrs,
width=100)
lipid1 = Select(title="Lipid",
value=lipids[0],
options=lipids,
width=100)
lipid2 = Select(title="Lipid",
value=lipids[0],
options=lipids,
width=100)
residue1 = Select(title="Residue",
value=random.choice(residues),
options=residues,
width=120)
residue2 = Select(title="Residue",
value=random.choice(residues),
options=residues,
width=120)
radius1 = Select(title="Radius",
value=radius[0],
options=radius,
width=100)
radius2 = Select(title="Radius",
value=radius[0],
options=radius,
width=100)
# num_vars = 6
# cols_to_display = [x for x in list(df.columns) if not x.endswith('Error')]
# num_vars = len(cols_to_display)
num_vars = len(list(df.columns)[:-5])
centre = 0.5
theta = np.linspace(0, 2 * np.pi, num_vars, endpoint=False)
theta += np.pi / 2
verts = unit_poly_verts(theta, centre)
xv = [v[0] for v in verts]
yv = [v[1] for v in verts]
f1 = df[(df.Protein == str(gpcr1.value))
& (df.Lipids == str(lipid1.value)) &
(df.ResID == int(str(residue1.value).split("-")[1])) &
(df.Radius == float(radius1.value)
)].loc[:, list(df.columns)[:num_vars]].to_numpy().reshape(
num_vars, )
f2 = df[(df.Protein == str(gpcr2.value))
& (df.Lipids == str(lipid2.value)) &
(df.ResID == int(str(residue2.value).split("-")[1])) &
(df.Radius == float(radius2.value)
)].loc[:, list(df.columns)[:num_vars]].to_numpy().reshape(
num_vars, )
flist = [f1, f2]
p = figure(plot_height=400,
plot_width=800,
x_range=(-0.3, 1.3),
y_range=(-0.3, 1.3),
tools="")
cmap = RdBu[num_vars]
colors = cmap[:1] + cmap[-1:]
patch1 = ColumnDataSource(data=dict(xi=[], yi=[]))
circle1 = ColumnDataSource(data=dict(xt=[], yt=[]))
xt, yt = radar_patch(f1, theta, centre)
xi, yi = star_curv(xt + xt[:1], yt + yt[:1])
patch = p.patch(x="xi",
y="yi",
fill_alpha=0.15,
fill_color=colors[0],
line_color=colors[0],
source=patch1)
circle = p.circle(x='xt',
y='yt',
size=10,
alpha=0.5,
line_color=colors[0],
fill_color=colors[0],
source=circle1)
patch2 = ColumnDataSource(data=dict(xi=[], yi=[]))
circle2 = ColumnDataSource(data=dict(xt=[], yt=[]))
xt, yt = radar_patch(f2, theta, centre)
xi, yi = star_curv(xt + xt[:1], yt + yt[:1])
patch = p.patch(x="xi",
y="yi",
fill_alpha=0.15,
fill_color=colors[1],
line_color=colors[1],
source=patch2)
circle = p.circle(x='xt',
y='yt',
size=10,
alpha=0.5,
line_color=colors[1],
fill_color=colors[1],
source=circle2)
# Draw grid so the plot looks like a polar graph.
text_label = list(df.columns)[:num_vars] + ['']
p.circle(x=0.5,
y=0.5,
size=20,
fill_alpha=0,
line_color='black',
line_dash='dashed',
line_alpha=0.2)
for size in [x * 100 for x in range(1, 10)]:
p.circle(x=0.5,
y=0.5,
size=size,
fill_alpha=0,
line_color='black',
line_dash='dashed',
line_alpha=0.2)
line = np.linspace(-2, 2, 100)
p.line(line, (line * 0.58) + 0.21,
line_color='black',
line_dash='dashed',
line_alpha=0.2)
p.line(line, (line * (-0.58)) + 0.79,
line_color='black',
line_dash='dashed',
line_alpha=0.2)
p.line([0.5] * 100,
line,
line_color='black',
line_dash='dashed',
line_alpha=0.2)
# Hide axes.
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = None
p.xaxis.major_tick_line_color = None
p.xaxis.minor_tick_line_color = None
p.yaxis.major_tick_line_color = None
p.yaxis.minor_tick_line_color = None
p.xaxis.major_label_text_font_size = '0pt'
p.yaxis.major_label_text_font_size = '0pt'
p.axis.visible = None
p.toolbar.logo = None
p.toolbar_location = None
# Draw the hexagon with labels attached.
p.line(x=xv + [centre], y=yv + [1], color='black')
xv[1] = xv[1] - 0.13
xv[2] = xv[2] - 0.1
xv[-2] = xv[-2] + 0.16
xv[-1] = xv[-1] + 0.1
# yv[3] = yv[3] - 0.06
label_source = ColumnDataSource({
'x': xv + [centre],
'y': yv + [1],
'text': text_label
})
labels = LabelSet(x="x",
y="y",
text="text",
source=label_source,
level='glyph',
text_align="center")
p.add_layout(labels)
def select_dfGPCR():
gpcr1_val, gpcr2_val = gpcr1.value, gpcr2.value
lipid1_val, lipid2_val = lipid1.value, lipid2.value
res1_val, res2_val = str(residue1.value).split("-")[1], str(
residue2.value).split("-")[1]
f1 = df[(df.Protein == str(gpcr1_val))
& (df.Lipids == str(lipid1_val)) &
(df.ResID == int(res1_val)) &
(df.Radius == float(radius1.value)
)].loc[:, list(df.columns)[:num_vars]].to_numpy().reshape(
num_vars, )
f2 = df[(df.Protein == str(gpcr2_val))
& (df.Lipids == str(lipid2_val)) &
(df.ResID == int(res2_val)) &
(df.Radius == float(radius2.value)
)].loc[:, list(df.columns)[:num_vars]].to_numpy().reshape(
num_vars, )
return (f1, f2)
def update1():
gpcr_val = gpcr1.value
lipid1_val = lipid1.value
radius1_val = radius1.value
resname = df[(df.Protein == str(gpcr_val))
& (df.Lipids == str(lipid1_val)) &
(df.Radius == float(radius1_val))].ResName.to_list()
resid = df[(df.Protein == str(gpcr_val))
& (df.Lipids == str(lipid1_val)) &
(df.Radius == float(radius1_val))].ResID.to_list()
residues = []
for rn, ri in zip(resname, resid):
residues.append("{}-{}".format(rn, ri))
residue1.options = residues
if residue1.value not in residues:
residue1.value = residues[0]
f1 = select_dfGPCR()[0]
xt, yt = radar_patch(f1, theta, centre)
xi, yi = star_curv(xt + xt[:1], yt + yt[:1])
circle1.data = dict(xt=xt, yt=yt)
patch1.data = dict(xi=xi, yi=yi)
def update2():
gpcr_val = gpcr1.value
lipid2_val = lipid2.value
radius2_val = radius2.value
resname = df[(df.Protein == str(gpcr_val))
& (df.Lipids == str(lipid2_val)) &
(df.Radius == float(radius2_val))].ResName.to_list()
resid = df[(df.Protein == str(gpcr_val))
& (df.Lipids == str(lipid2_val)) &
(df.Radius == float(radius2_val))].ResID.to_list()
residues = []
for rn, ri in zip(resname, resid):
residues.append("{}-{}".format(rn, ri))
# options = [str(x) for x in options]
residue2.options = residues
if residue2.value not in residues:
residue2.value = residues[0]
f2 = select_dfGPCR()[1]
xt, yt = radar_patch(f2, theta, centre)
xi, yi = star_curv(xt + xt[:1], yt + yt[:1])
circle2.data = dict(xt=xt, yt=yt)
patch2.data = dict(xi=xi, yi=yi)
controls1 = [gpcr1, lipid1, residue1, radius1]
controls2 = [gpcr2, lipid2, residue2, radius2]
for control1 in controls1:
control1.on_change('value', lambda attr, old, new: update1())
for control2 in controls2:
control2.on_change('value', lambda attr, old, new: update2())
inputs1 = column([gpcr1, lipid1, residue1, radius1])
inputs2 = column([gpcr2, lipid2, residue2, radius2])
l = layout([[inputs1, p, inputs2]])
update1()
update2()
doc.add_root(l)
doc.title = "Radar App"
doc.theme = Theme(json=yaml.load("""
attrs:
Figure:
toolbar_location: above
height: 500
width: 800
Grid:
grid_line_dash: [6, 4]
grid_line_color: black
""",
Loader=yaml.FullLoader))