def family_exemplar_structs(rfid,
refseq_method = None,
sp_method = None,
aff_type = None):
suboptimals = rutils.family_suboptimals(rfid)
c2 = rutils.cluster_2(spairs, ungapped_ref)
arr = rutils.rna_draw(ungapped_ref.seq,
rutils.pairs_stk(sp,len(ungapped_ref)),
'name' )
raise Exception()
affinities, ss = rutils.affinity_matrix(spairs, aff_type = aff_type)
aff_shape, ss_shape = rutils.affinity_matrix(spairs, aff_type = 'easy', ss_multiplier = .5)
pca_vecs = mlab.PCA(affinities).project(affinities)
pca_vecs_shape = mlab.PCA(aff_shape).project(aff_shape)
inds = compute_clusters(aff_shape, ss_shape)
exemplars = list(set(inds))
import compbio.utils.colors as mycolors
ct = mycolors.getct(len(exemplars))
import matplotlib.pyplot as plt
f = plt.gcf()
plt.clf()
for idx0, embeddings in enumerate([pca_vecs, pca_vecs_shape]):
ax = f.add_subplot('21{0}'.format(idx0 +1))
lims =[ [min(embeddings[:,0]),max(embeddings[:,0])],
[min(embeddings[:,1]),max(embeddings[:,1])] ]
lims += [-.5,.5] *squeeze(diff(lims,1))[:,newaxis]
ax.set_xlim(lims[0])
ax.set_ylim(lims[1])
print sum(embeddings)
for idx, embedding in enumerate(embeddings):
if mod(idx,1) != 0: continue
sp = spairs[idx]
arr = rutils.rna_draw(ungapped_ref.seq,
rutils.pairs_stk(sp,len(ungapped_ref)),
'name' )
struct_emb = arr + embedding[0:2]
#plt.plot(*struct_emb.T)
pkw = {'color':ct[exemplars.index(inds[idx])],
'lw':8 if idx in inds else 1,
'alpha': 1 if idx in inds else .2}
lc = rplots.show_rna(embedding, arr, pkw = pkw)
#exemplar_structs = [spairs[e] for e in set(inds)]
raise Exception()
return pca_vecs, exemplar_structs
def show_rna_structs(xvals, yvals, structs, energies, pfracs,
rname, rtype,ns,rfid,figsize,colors,
seq, n, selection_type,vert_idxs):
verts = rutils.struct_verts([structs['structs'][i] for i in vert_idxs]
,seq,rfid)
f = myplots.fignum(3,figsize)
ax = f.add_subplot(111)
myplots.padded_limits(ax, xvals, yvals, .2)
for vi, v in enumerate(verts):
i = vert_idxs[vi]
dims = [30]
shadow_width = 10
pkw0 = {'linewidth':shadow_width,
'color':'white',
'alpha':1,
'zorder':1.1}
rplots.show_rna([xvals[i],yvals[i]], v,
dims = dims,
pkw = pkw0)
pkw0 = {'linewidth':shadow_width,
'color':'white',
'alpha':.8,
'zorder':vi+2}
rplots.show_rna([xvals[i],yvals[i]], v,
dims = dims,
pkw = pkw0)
pkw1 = {'linewidth':2,
'color':colors[i],
'zorder':vi+2}
rplots.show_rna([xvals[i],yvals[i]], v,
dims = dims, pkw = pkw1)
ax.set_ylabel('mutation score')
ax.set_xlabel('free energy (-kCal)')
ax.annotate('''Suboptimal foldings, positioned by energy and
a mutation based evolutionary score.
Color indicates a second score from paired BL.''' , [0,1],xycoords ='axes fraction',
xytext = [10,-10], textcoords='offset pixels',
va = 'top')
f.savefig(figfile.format('{3}_frac_silent_{0}_{1}{2}'.\
format(rname,selection_type,n,rtype)))
return vert_idxs
def show_rna_structs(xvals, yvals, structs, energies, pfracs, rname, rtype, ns,
rfid, figsize, colors, seq, n, selection_type, vert_idxs):
verts = rutils.struct_verts([structs['structs'][i] for i in vert_idxs],
seq, rfid)
f = myplots.fignum(3, figsize)
ax = f.add_subplot(111)
myplots.padded_limits(ax, xvals, yvals, .2)
for vi, v in enumerate(verts):
i = vert_idxs[vi]
dims = [30]
shadow_width = 10
pkw0 = {
'linewidth': shadow_width,
'color': 'white',
'alpha': 1,
'zorder': 1.1
}
rplots.show_rna([xvals[i], yvals[i]], v, dims=dims, pkw=pkw0)
pkw0 = {
'linewidth': shadow_width,
'color': 'white',
'alpha': .8,
'zorder': vi + 2
}
rplots.show_rna([xvals[i], yvals[i]], v, dims=dims, pkw=pkw0)
pkw1 = {'linewidth': 2, 'color': colors[i], 'zorder': vi + 2}
rplots.show_rna([xvals[i], yvals[i]], v, dims=dims, pkw=pkw1)
ax.set_ylabel('mutation score')
ax.set_xlabel('free energy (-kCal)')
ax.annotate('''Suboptimal foldings, positioned by energy and
a mutation based evolutionary score.
Color indicates a second score from paired BL.''', [0, 1],
xycoords='axes fraction',
xytext=[10, -10],
textcoords='offset pixels',
va='top')
f.savefig(figfile.format('{3}_frac_silent_{0}_{1}{2}'.\
format(rname,selection_type,n,rtype)))
return vert_idxs