def eval_lpdf(stan_binary, data, parameters):
# Get temp directory
tmpdirec = tempfile.TemporaryDirectory()
tmpdir = tmpdirec.name
# create data file
data_file = os.path.join(tmpdir, "data.R")
io.rdump(data_file, data)
# Create continuation file
param_file = os.path.join(tmpdir, "param.R")
io.rdump(param_file, parameters)
# Run it for one iteration
out_file = os.path.join(tmpdir, "out.csv")
run = subprocess.run([
stan_binary, "sample", "num_warmup=0", "num_samples=1", "adapt",
"engaged=0", "data", f"file={data_file}", f"init={param_file}",
"output", f"file={out_file}"
],
capture_output=True)
assert run.returncode == 0
# Parse the results
lp = io.parse_csv(out_file)['lp__'][0]
return lp
def viz_mean_excitability(sid, rid):
regpos, w, obsmask, surfaces, contacts = read_structural_data(sid, rid)
vlines, vmeshes, vcontacts = viz_structure(regpos, w, surfaces, contacts)
# Load results
nreg = regpos.shape[0]
res = io.parse_csv([
f"run/solo/INC/vep/id{sid:03d}/output/r{rid:02d}_all/chain_{chain}.csv"
for chain in [1, 2]
])
cinf = res['c']
# cmean = np.mean(cinf, axis=0)
# pexc = np.mean(cinf > 2.0, axis=0)
scalar = np.percentile(cinf, 50, axis=0)
# Regions
cmap = 'plasma'
# vmin = np.min(scalar)
# vmax = np.max(scalar)
vmin, vmax = -2, 2
vpoints = []
for i in range(nreg):
if not obsmask[i]:
vpoints.append(
vp.Sphere(regpos[i],
r=4,
c=vp.colorMap(scalar[i], cmap, vmin, vmax)))
else:
vpoints.append(
vp.Cube(regpos[i],
side=6,
c=vp.colorMap(scalar[i], cmap, vmin, vmax)))
vbar = vp.Points(regpos, r=0.01).pointColors(scalar,
cmap=cmap,
vmin=vmin,
vmax=vmax)
vbar.addScalarBar(horizontal=True, pos=(0.8, 0.02))
def slider(widget, event):
percentile = widget.GetRepresentation().GetValue()
scalar = np.percentile(cinf, percentile, axis=0)
for i in range(nreg):
vpoints[i].color(vp.colorMap(scalar[i], cmap, vmin, vmax))
vplotter = vp.Plotter(axes=0)
vplotter.addSlider2D(slider,
0.,
100.,
value=50.0,
pos=3,
title="Percentile")
vplotter.show(vpoints, vlines, vmeshes, vcontacts, vbar)
def get_data(surgeries, onset):
rows = []
for subject, surgery in surgeries.items():
res_dir = f"run/solo/{onset}/vep/{subject}/output/"
rec_dirs = glob.glob(os.path.join(res_dir, "r*_all/"))
for rec_dir in rec_dirs:
rid = int(rec_dir[-7:-5])
indata = io.rload(f"run/solo/{onset}/vep/{subject}/input/r{rid:02d}_all.R")
reg_sz = list(indata['reg_sz'].astype(int))
reg_ns = list(indata['reg_ns'].astype(int))
reg_obs = reg_sz + reg_ns
resfiles = [os.path.join(rec_dir, f"chain_{chain}.csv") for chain in CHAINS]
statuses = [os.path.join(rec_dir, f"chain_{chain}.status") for chain in CHAINS]
res = io.parse_csv([r for r, s in zip(resfiles, statuses)
if int(open(s).read().strip())])
pez = np.mean(res['c'] > EZ_THRESHOLD, axis=0)
resec = np.array(surgery['resection'])
is_resected = np.array(resec) > RESEC_THRESHOLD
if not np.any(is_resected):
is_resected[np.argmax(resec)] = True
for i in range(NREG):
rows.append(OrderedDict(
onset=onset,
subject=subject,
rid=rid,
region=i,
observed=(i in reg_obs),
seizing=(i in reg_sz),
fracsz=(len(reg_sz) / len(reg_obs)),
pez=pez[i],
resection=resec[i],
is_resected=is_resected[i],
engel=surgery['engel']
))
df = pd.DataFrame(rows)
dfg = df.groupby(['onset', 'subject', 'region']).agg(
{'observed': 'any', 'seizing': 'any', 'fracsz': 'first',
'pez': 'mean', 'resection': 'first', 'is_resected': 'first',
'engel': 'first'}).reset_index()
return dfg
def viz_excitability(sid, rid):
regpos, w, obsmask, surfaces, contacts = read_structural_data(sid, rid)
vlines, vmeshes, vcontacts = viz_structure(regpos, w, surfaces, contacts)
# Load results
nreg = regpos.shape[0]
res = io.parse_csv([
f"run/solo/INC/vep/id{sid:03d}/output/r{rid:02d}_all/chain_{chain}.csv"
for chain in [1, 2]
])
cinf = res['c']
ctr = 2.0
pexc = np.mean(cinf > ctr, axis=0)
# Regions
cmap = 'Reds'
vmin, vmax = 0, 0.15
# vmin, vmax = -2, 0
vpoints = []
for i in range(nreg):
if not obsmask[i]:
vpoints.append(
vp.Sphere(regpos[i],
r=4,
c=vp.colorMap(pexc[i], cmap, vmin, vmax)))
else:
vpoints.append(
vp.Cube(regpos[i],
side=6,
c=vp.colorMap(pexc[i], cmap, vmin, vmax)))
vbar = vp.Points(regpos, r=0.01).pointColors(pexc,
cmap=cmap,
vmin=vmin,
vmax=vmax)
vbar.addScalarBar(horizontal=True, pos=(0.8, 0.02))
def cslider(widget, event):
ctr = widget.GetRepresentation().GetValue()
pexc = np.mean(cinf > ctr, axis=0)
for i in range(nreg):
vpoints[i].color(vp.colorMap(pexc[i], cmap, vmin, vmax))
vplotter = vp.Plotter(axes=0)
vplotter.addSlider2D(cslider, -3.0, 3.0, value=2.0, pos=3, title="c")
vplotter.show(vpoints, vlines, vmeshes, vcontacts, vbar)
def viz_seizure(sid, rid):
regpos, w, obsmask, surfaces, contacts = read_structural_data(sid, rid)
vlines, vmeshes, vcontacts = viz_structure(regpos, w, surfaces, contacts)
# Load results
nreg = regpos.shape[0]
res = io.parse_csv([
f"run/solo/INC/vep/id{sid:03d}/output/r{rid:02d}_all/chain_{chain}.csv"
for chain in [1, 2]
])
tinf = res['t']
t = 0.0
psz = np.mean(tinf < t, axis=0)
# Regions
cmap = 'bwr'
vmin, vmax = 0, 1
vpoints = []
for i in range(nreg):
if not obsmask[i]:
vpoints.append(
vp.Sphere(regpos[i],
r=4,
c=vp.colorMap(psz[i], cmap, vmin, vmax)))
else:
vpoints.append(
vp.Cube(regpos[i],
side=6,
c=vp.colorMap(psz[i], cmap, vmin, vmax)))
vbar = vp.Points(regpos, r=0.01).pointColors(psz,
cmap=cmap,
vmin=vmin,
vmax=vmax)
vbar.addScalarBar(horizontal=True, pos=(0.8, 0.02))
def tslider(widget, event):
t = widget.GetRepresentation().GetValue()
psz = np.mean(tinf < t, axis=0)
for i in range(nreg):
vpoints[i].color(vp.colorMap(psz[i], cmap, vmin, vmax))
vplotter = vp.Plotter(axes=0)
vplotter.addSlider2D(tslider, 0, 90.0, value=0.0, pos=3, title="t")
vplotter.show(vpoints, vlines, vmeshes, vcontacts, vbar)
def make_video(sid, rid, video_file):
regpos, w, obsmask, surfaces, contacts = read_structural_data(sid, rid)
vlines, vmeshes, vcontacts = viz_structure(regpos, w, surfaces, contacts)
# Load results
res = io.parse_csv([
f"run/solo/INC/vep/id{sid:03d}/output/r{rid:02d}_all/chain_{chain}.csv"
for chain in [1, 2]
])
tinf = res['t']
t = 0.0
psz = np.mean(tinf < t, axis=0)
nreg = regpos.shape[0]
# Regions
cmap = 'bwr'
vpoints = []
for i in range(nreg):
if not obsmask[i]:
vpoints.append(
vp.Sphere(regpos[i], r=4, c=vp.colorMap(psz[i], cmap, 0, 1)))
else:
vpoints.append(
vp.Cube(regpos[i], side=6, c=vp.colorMap(psz[i], cmap, 0, 1)))
vbar = vp.Points(regpos, r=0.01).pointColors(psz,
cmap=cmap,
vmin=0,
vmax=1)
vbar.addScalarBar(horizontal=True, pos=(0.8, 0.02))
vtext = vp.Text2D(f"t = {t:4.1f} s", pos=0, s=2, c='black')
center = np.mean(regpos, axis=0)
dist = 2.5 * (np.max(regpos[:, 1]) - np.min(regpos[:, 1]))
# Video -------------------------------------------------------
vplotter = vp.Plotter(axes=0,
interactive=0,
offscreen=True,
size=(1800, 1800))
nframes = 3000
vplotter += vpoints
vplotter += vlines
vplotter += vmeshes
video = vp.Video(name=video_file, duration=90)
ratios = np.array([30, 3, 5, 30, 5, 3, 30, 10])
frames = (nframes * ratios / np.sum(ratios)).astype(int)
# Run and pause
animate(vplotter,
video,
frames[0],
vpoints,
tinf,
pos=center + dist * np.r_[0, 0, 1],
foc=center,
viewup=(0, 1, 1),
prange=(0, 45),
time=lambda p: p)
animate(vplotter,
video,
frames[1],
vpoints,
tinf,
pos=center + dist * np.r_[0, 0, 1],
foc=center,
viewup=(0, 1, 1),
time=45.)
## Fly around
pos = lambda angle: center + dist * np.array(
[0, -np.sin(angle), np.cos(angle)])
animate(vplotter,
video,
frames[2],
vpoints,
tinf,
pos=pos,
foc=center,
viewup=(0, 1, 1),
prange=(0, np.pi / 2),
time=45.,
endpoint=False)
pos = lambda angle: center + dist * np.array(
[-np.sin(angle), -np.cos(angle), 0])
animate(vplotter,
video,
frames[3],
vpoints,
tinf,
pos=pos,
foc=center,
viewup=(0, 0, 1),
prange=(0, 2 * np.pi),
time=45.)
pos = lambda angle: center + dist * np.array(
[0, -np.sin(angle), np.cos(angle)])
animate(vplotter,
video,
frames[4],
vpoints,
tinf,
pos=pos,
foc=center,
viewup=(0, 1, 1),
prange=(np.pi / 2, 0),
time=45.,
startpoint=False)
# Pause + run + pause
animate(vplotter,
video,
frames[5],
vpoints,
tinf,
pos=center + dist * np.r_[0, 0, 1],
foc=center,
viewup=(0, 1, 1),
time=45.)
animate(vplotter,
video,
frames[6],
vpoints,
tinf,
pos=center + dist * np.r_[0, 0, 1],
foc=center,
viewup=(0, 1, 1),
prange=(45, 90),
time=lambda p: p)
animate(vplotter,
video,
frames[7],
vpoints,
tinf,
pos=center + dist * np.r_[0, 0, 1],
foc=center,
viewup=(0, 1, 1),
time=90.)
video.close()