def plot_clusters(model,
SX,
angles,
phi_scaler,
psi_scaler,
fragment,
output_writer=None,
output_dir=None):
ramachandran_surface()
X = SX.todense()
# Start from 10 to avoid blacks
colors = np.linspace(10, 255, model.n_clusters)
for k, color in zip(range(model.n_clusters), colors):
col = cm.spectral(int(color))
my_members = model.labels_ == k
# cluster_center = model.cluster_centers_[k]
plt.scatter(x=phi_scaler.inverse_transform(X[my_members, 0]),
y=psi_scaler.inverse_transform(X[my_members, 1]),
c=col,
s=30,
marker='o',
alpha=0.5)
plt.title('Clusters for {} (inertia: {:.2})'.format(
fragment, model.inertia_))
if output_writer:
output_writer.savefig(dpi=150)
if output_dir:
plt.savefig(
os.path.join(output_dir, 'clustering', fragment + '.cluster.svg'),
format='svg',
dpi=300,
)
plt.close()
def plot(self, index=None):
from tests import plot_whiskers
from pylab import cm
cmap = lambda i: cm.spectral(i / float(len(self._paths)))
if index is None:
for i, p in enumerate(self._paths):
plot_whiskers([e for e in p if e], color=cmap(i))
else:
p = list(self._paths)[index]
plot_whiskers([e for e in p if e], color=cmap(index))
def plot(self, index = None):
from tests import plot_whiskers
from pylab import cm
cmap = lambda i: cm.spectral( i/float(len(self._paths)) )
if index is None:
for i,p in enumerate(self._paths):
plot_whiskers( [e for e in p if e], color = cmap(i) )
else:
p = list(self._paths)[index]
plot_whiskers( [e for e in p if e], color = cmap(index) )
def compute_pairwise_conflict_paths(a, b, prune):
"""
a and b are each tuples of (whisker, hitindex), as returned from CollisionTable.next()
Returns a set of tuples.
"""
bnda, bndb = trace_overlap(a, b)
la, ma, ra = breakout(a[0], bnda)
lb, mb, rb = breakout(b[0], bndb)
ownership = {a[0]: (la, ma, ra), b[0]: (lb, mb, rb)}
if not ma or not mb or prune(ma) or prune(mb):
ownership = {a[0]: None, b[0]: None}
pset = set()
return pset, ownership
pset = [[la, ma, ra], [la, ma, mb, rb], [lb, mb, ma, ra], [lb, mb, rb]]
#prune
for p in pset:
for i, w in enumerate(p):
if w and prune(w):
p[i] = None
#transform to set of tuples
pset = set([tuple(e) for e in pset])
#0. Path's must have legititmate middle nodes.
for p in list(pset):
if len(p) == 3 and p[1] is None:
pset.remove(p)
elif len(p) == 4 and (p[1] is None or p[2] is None):
pset.remove(p)
#reduction
hasfullpath = False
# 1. Remove if can't get from left to right
for p in list(pset):
if p[0] is None and p[-1] is None:
pset.discard(p)
if p[0] is not None and p[-1] is not None:
hasfullpath = True
# 2. if only overlap, return composite of overlap
if len(pset
) == 0: #no endpoints - this happens for middle-only overlaps
m = max((ma, mb), key=lambda w: w.scores.mean())
ownership = {a[0]: (None, m, None), b[0]: (None, m, None)}
#ownership = {a[0]:None, b[0]:None}
pset = set(((None, m, None), )) #return best scoring
return pset, ownership
# 3. if there is at least one path from left to right, remove paths that
# don't
if hasfullpath:
for p in list(pset):
if p[0] is None or p[-1] is None:
pset.discard(p)
else:
# 4. if not, path's should not begin or end on merged middles
for p in list(pset):
if len(p) == 4 and (p[0] is None or p[-1] is None):
pset.discard(p)
if 0:
from tests import plot_whiskers
from pylab import cm, clf, show
cmap = lambda i: cm.spectral(i / float(len(pset)))
for i, p in enumerate(pset):
plot_whiskers([e for e in p if e], color=cmap(i))
show()
return pset, ownership
def compute_pairwise_conflict_paths( a, b, prune ):
"""
a and b are each tuples of (whisker, hitindex), as returned from CollisionTable.next()
Returns a set of tuples.
"""
bnda,bndb = trace_overlap(a,b)
la,ma,ra = breakout(a[0],bnda)
lb,mb,rb = breakout(b[0],bndb)
ownership = {a[0]:(la,ma,ra), b[0]:(lb,mb,rb)}
if not ma or not mb or prune(ma) or prune(mb):
ownership = {a[0]:None, b[0]:None}
pset = set()
return pset, ownership
pset = [ [ la, ma, ra ],
[ la, ma, mb, rb ],
[ lb, mb, ma, ra ],
[ lb, mb, rb ] ]
#prune
for p in pset:
for i,w in enumerate(p):
if w and prune(w):
p[i] = None
#transform to set of tuples
pset = set( [tuple(e) for e in pset] )
#0. Path's must have legititmate middle nodes.
for p in list(pset):
if len(p)==3 and p[1] is None:
pset.remove(p)
elif len(p)==4 and ( p[1] is None or p[2] is None ):
pset.remove(p)
#reduction
hasfullpath = False
# 1. Remove if can't get from left to right
for p in list(pset):
if p[0] is None and p[-1] is None:
pset.discard(p)
if p[0] is not None and p[-1] is not None:
hasfullpath = True
# 2. if only overlap, return composite of overlap
if len(pset)==0: #no endpoints - this happens for middle-only overlaps
m = max( (ma,mb), key = lambda w: w.scores.mean() )
ownership = {a[0]:(None,m,None), b[0]:(None,m,None)}
#ownership = {a[0]:None, b[0]:None}
pset = set( ((None,m,None),) ) #return best scoring
return pset, ownership
# 3. if there is at least one path from left to right, remove paths that
# don't
if hasfullpath:
for p in list(pset):
if p[0] is None or p[-1] is None:
pset.discard(p)
else:
# 4. if not, path's should not begin or end on merged middles
for p in list(pset):
if len(p)==4 and ( p[0] is None or p[-1] is None ):
pset.discard(p)
if 0:
from tests import plot_whiskers
from pylab import cm, clf, show
cmap = lambda i: cm.spectral( i/float(len(pset)) )
for i,p in enumerate(pset):
plot_whiskers([e for e in p if e], color = cmap(i))
show()
return pset,ownership
