def add_cluster_centroids_to_axes(dictdata,ax,annotate=True,n_clusters=None,nodiamonds=False,write_to_file=None,k_means=False,fontsize_clusterlabel=14,**kwargs):
from ClusterPoints import get_clusterpoints
print(n_clusters)
if n_clusters is not None:
print(n_clusters)
centroids , sizedict, sizedict_normalized,labels,n_clusters = get_clusterpoints(dictdata,n_clusters=n_clusters,k_means=k_means)
else:
print(n_clusters)
centroids , sizedict, sizedict_normalized,labels,n_clusters = get_clusterpoints(dictdata,k_means=k_means)
if not nodiamonds:
im2 = ax.scatter(centroids[:, 0], centroids[:, 1],centroids[:,2],marker='o', s=110, linewidths=3,
color='black',alpha=1.0)
if annotate:
i=0
for k in sizedict_normalized:
print("k ",k,sizedict_normalized[k])
#print("k centroids ",k,centroids[k,:])
if k<0: continue
#print(sizedict_normalized[k])
label = "{:0.2f}".format(sizedict_normalized[k])
label2 = "{:.1f},\n{:.1f},\n{:.1f}".format(centroids[k, 0], centroids[k, 1],centroids[k,2])
label3 = sizedict[k]
#ax.text(centroids[k, 0]+0.2+0.01*i, centroids[k, 1]+0.2,centroids[k,2]+0.2,label,fontsize=11)
#ax.text(centroids[k, 0]+0.23, centroids[k, 1]+0.23,centroids[k,2]+0.45,label2,fontsize=8)
ax.text(centroids[k, 0]+0.23, centroids[k, 1]+0.23,centroids[k,2]+0.4,label3,fontsize=fontsize_clusterlabel)
i+=1
def clusters_to_dict(dictdata,n_clusters=None):
from ClusterPoints import get_clusterpoints
if n_clusters is not None:
centroids , sizedict, sizedict_normalized = get_clusterpoints(dictdata,n_clusters=n_clusters)
else:
centroids , sizedict, sizedict_normalized = get_clusterpoints(dictdata)
cdict={}
for k in sizedict_normalized:
cdict['centroid_id']=k
cdict['dimensions']=centroids.shape[1]
cdict['n_clusters']=centroids.shape[0]
cdict['center']=centroids[k,:]
cdict['size']=sizedict[k]
cdict['size_norm']=sizedict_normalized[k]
cdict['xfield']=info['xfield']
cdict['yfield']=info['yfield']
cdict['zfield']=info['zfield']
return cdict
def plot3d_slice_topN(collection,keys=None,outfile=None,show=False,cluster=False,color_field='cfield',cmap_field='cfield',dim3d=True,dim2d=False,topNs=[200],use_topN=True,**kwargs):
figsize = (5*len(topNs),4*len(collection))
fig = plt.figure(figsize=figsize)
markers = ['o', 'o','o']
print("keys",keys)
if topNs is None:
topNs = [200]#10%
count=1
cols = len(topNs)
rows = len(collection)
vmin = -40
vmax = -25
for i,org_dictdata in enumerate(collection):
for topN in topNs:
sorteddict = sortdict_by_field(org_dictdata)
if not dim2d:
ax = fig.add_subplot(rows,cols,count,projection='3d')
im1 = ax.scatter( sorteddict['xfield'][:topN] , sorteddict['yfield'][:topN], sorteddict['zfield'][:topN],c=sorteddict[cmap_field][:topN] , cmap=plt.cm.RdPu_r,vmin=vmin, vmax=vmax, alpha=1.0, edgecolors='w',lw=0,s=25,marker=markers[i])
if cluster:
sorteddict_mod={}
for ckey in sorteddict:
sorteddict_mod[ckey]={}
sorteddict_mod[ckey]['vals'] = sorteddict[ckey][:topN]
if topN>15:
from ClusterPoints import get_clusterpoints
centroid_clusters, counts_cluster,_,labels,dbscan_clusters = get_clusterpoints(sorteddict_mod,k_means=False)
add_cluster_centroids_to_axes(sorteddict_mod,ax,annotate=True,n_clusters=3,nodiamonds=True)
if not keys is None:
setup_axes(ax,key=keys[i],ticksize=12,labelsize=10,view={'elev':17, 'azim':-37},view_abs=True,key_fontsize=15)
else:
setup_keys(ax,ticksize=15)
label = '<{}'.format(topN)
delta_ticks['substrate_ca_no_super_rmsd']=0.5
limits , ticks, limits_r, ticks_r = get_axes_ticks()
ax.set_yticks(ticks_r["yfield"])
ax.set_xticks(ticks_r['xfield'])
ax.set_zticks(ticks['zfield'][::2])
if info['xfield']=='substrate_ca_no_super_rmsd':
print('setting new limits')
ax.set_xticks(ticks['xfield'])
ax.set(xlim = [2.0,0.0] , ylim = [6.5,2.5] ,zlim = limits['zfield'])
ax.text(limits['xfield'][0]+0.5,6.5-0.3,limits['zfield'][1]-0.5,label,fontsize=14)
ax.xaxis.set_rotate_label(True)
ax.yaxis.set_rotate_label(True)
count+=1
plt.tight_layout()
if not outfile is None:
plt.savefig(outfile,transparent=True,dpi=dpi[runtype])
if show:
plt.show()
plt.close()
def slice_and_cluster(sorteddict,thr,k_means=False,clus2d=False):
topN = get_slice(sorteddict,thr)
print(thr,topN)
sorteddict_mod={}
for ckey in sorteddict:
print(ckey)
sorteddict_mod[ckey]={}
sorteddict_mod[ckey]['vals'] = sorteddict[ckey][:topN] #must do because of how i hav it setup :( NH
if topN>15:
from ClusterPoints import get_clusterpoints
if k_means:
#not tested for 2d option
centroids , sizedict, sizedict_normalized, labels, distances= get_clusterpoints(sorteddict_mod,n_clusters=3,distances=True,k_means=k_means)
return sort_clusters_by_field(sorteddict,centroids , sizedict, sizedict_normalized, labels, distances=distances)
else:
#listfromdf = np.array([ combined_df[labels_df[info['xfield']]] , combined_df[labels_df[info['yfield']]], combined_df[labels_df[info['zfield']]] ])
#print(listfromdf.shape)
centroids , sizedict, sizedict_normalized,labels,n_clusters,core_samples_ = get_clusterpoints(sorteddict_mod,k_means=k_means,core_samples=True)
return sort_clusters_by_field(sorteddict_mod,centroids , sizedict, sizedict_normalized, labels, core_samples=core_samples_,sortorder=['interaction_energy'])
else:
return None
def plot3d_slice_and_compare_n(collection,keys=None,n=3,thresholds=None,outfile=None,show=False,cluster=False,size=None,color_field='cfield',cmap_field='cfield',dim3d=True,dim2d=False,**kwargs):
figsize = (12,4*len(collection))
if not size is None:
figsize=size
fig = plt.figure(figsize=figsize)
markers = ['o', 'o','o']
print("keys",keys)
if thresholds is None:
thresholds=[-32,-30,-27]
#sort threshold
thresholds.sort(reverse=True) #least negative to most negative
count=1
cols = len(thresholds)
rows = len(collection)
for i,org_dictdata in enumerate(collection):
sorteddict = sortdict_by_field(org_dictdata)
for j,thr in enumerate(thresholds):
topN = get_slice(sorteddict,thr)
print(thr,topN)
vmax=-10
vmin=-35
if cmap_field=='z2field':
vmax=3.0
vmin=0.4
if not dim2d:
ax = fig.add_subplot(rows,cols,count,projection='3d')
im1 = ax.scatter( sorteddict['xfield'][:topN] , sorteddict['yfield'][:topN], sorteddict['zfield'][:topN],c=sorteddict[cmap_field][:topN] , cmap=plt.cm.RdPu_r,vmin=vmin, vmax=vmax, alpha=1.0, edgecolors='w',lw=0,s=25,marker=markers[i])
if cluster:
sorteddict_mod={}
for ckey in sorteddict:
sorteddict_mod[ckey]={}
sorteddict_mod[ckey]['vals'] = sorteddict[ckey][:topN] #must do because of how i hav it setup :( NH
if topN>15:
from ClusterPoints import get_clusterpoints
centroid_clusters, counts_cluster,_,labels,dbscan_clusters = get_clusterpoints(sorteddict_mod,k_means=False)
print('DBSCAN', dbscan_clusters, counts_cluster,centroid_clusters)
add_cluster_centroids_to_axes(sorteddict_mod,ax,annotate=True,n_clusters=3,nodiamonds=True) #max(dbscan_clusters-1,1)
set_colors = plt.cm.rainbow(np.linspace(0,1,dbscan_clusters))
clusters = list(set(labels))
point_colors={}
for ik,key in enumerate(clusters):
point_colors[key] = set_colors[ik]
colors = [point_colors[t] for t in labels]
for ic,col in enumerate(colors):
if labels[ic]==-1:
colors[ic]='black'
print(len(sorteddict['xfield'][:topN]),len(colors))
#im1 = ax.scatter( sorteddict['xfield'][:topN] , sorteddict['yfield'][:topN], sorteddict['zfield'][:topN],c=colors, alpha=1.0, edgecolors='w',lw=0,s=30,marker='o')
if not keys is None:
setup_axes(ax,key=keys[i],ticksize=12,labelsize=10,view={'elev':17, 'azim':-37},view_abs=True,key_fontsize=15)
else:
setup_keys(ax,ticksize=15)
label = '<{}'.format(thr)
delta_ticks['substrate_ca_no_super_rmsd']=0.5
limits , ticks, limits_r, ticks_r = get_axes_ticks()
ax.set_yticks(ticks_r["yfield"])
ax.set_xticks(ticks_r['xfield'])
ax.set_zticks(ticks['zfield'][::2])
if info['xfield']=='substrate_ca_no_super_rmsd':
print('setting new limits')
ax.set_xticks(ticks['xfield'])
ax.set(xlim = [2.0,0.0] , ylim = [6.5,2.5] ,zlim = limits['zfield'])
ax.text(limits['xfield'][0]+0.5,6.5-0.3,limits['zfield'][1]-0.5,label,fontsize=14)
ax.xaxis.set_rotate_label(True)
ax.yaxis.set_rotate_label(True)
if dim2d:
ax = fig.add_subplot(rows,cols,count)
vmin=-40
vmax=-20
im1 = ax.scatter( sorteddict['yfield'][:topN], sorteddict['zfield'][:topN],c=sorteddict[cmap_field][:topN] , cmap=plt.cm.RdPu_r,vmin=vmin, vmax=vmax, alpha=1.0, edgecolors='w',lw=0,s=25,marker=markers[i])
if cluster:
sorteddict_mod={}
for ckey in sorteddict:
sorteddict_mod[ckey]={}
sorteddict_mod[ckey] = sorteddict[ckey][:topN] #must do because of how i hav it setup :( NH
if topN >15:
add_cluster_centroids_to_axes_from_list([sorteddict_mod['yfield'],sorteddict['zfield']],ax,annotate=True,n_clusters=3,nodiamonds=True)
label = '<{}'.format(thr)
limits , ticks, limits_r, ticks_r = get_axes_ticks()
ax.set_yticks(ticks["zfield"][::2])
ax.set_xticks(ticks['yfield'][::2])
ax.set(xlim = limits['yfield'] ,ylim = limits['zfield'])
ax.text(limits['yfield'][1]-1.5,limits['zfield'][1]-0.5,label,fontsize=20)
ax.text(limits['yfield'][0]+0.1,limits['zfield'][1]-0.5,keys[i],fontsize=20)
count+=1
plt.tight_layout()
if not outfile is None:
plt.savefig(outfile,transparent=True,dpi=dpi[runtype])
if show:
plt.show()
else:
plt.show()