def get_filter_result(request):
if request.is_ajax():
term_count = int(request.POST["term_count"])
filter_terms = []
response_data = {}
for i in range(1,term_count+1):
ttype = str(request.POST["filter_type_"+str(i)])
tkey = str(request.POST["filter_key_"+str(i)])
topp = str(request.POST["filter_terms_op_"+str(i)])
tvalue = []
if ttype == "range" :
tvalue.append(str(request.POST["filter_value_from_"+str(i)]))
tvalue.append(str(request.POST["filter_value_to_"+str(i)]))
elif ttype == "multichoice" :
tvalue.append(request.POST.getlist("filter_multi_value_"+str(i)))
elif ttype == "text_search" :
tvalue.append(str(request.POST["filter_value_"+str(i)]))
term = FilterTerm(
term_type = ttype ,
term_key = tkey ,
term_value = tvalue
)
filter_terms.append(term)
etype = str(request.POST["effect_type"])
if etype == "color" :
evalue = str(request.POST["effect_color"])
elif etype == "scale" :
evalue = str(request.POST["effect_scale"])
elif etype == "hide" :
evalue = ""
effect = VisualEffect( effect_type = etype , effect_value = evalue)
filter_rule = FilterRule(terms = filter_terms ,terms_op = topp ,vis_effect = effect)
compounds = Compound.objects(compound_group = request.session.get("collection_id")).filter(filter_rule.get_query())
Clusters = Cluster.objects(collection = request.session.get("collection_id") , nodes__in=compounds)
if(len(Clusters)>0) :
response_data['cluster_percent'] = [{"id":str(c.id) , "percent" : float(c.filtered_percentage(compounds))} for c in Clusters]
response_data['result'] = 'Success'
response_data['message'] = 'Compounds Fields'
#response_data['field_names'] = []
#for f in field_names :
# response_data['field_names'].append(f)
response_data['filterd_compound'] = [ c.name for c in compounds ]
return HttpResponse(json.dumps(response_data), mimetype="application/json")
return HttpResponse({}, mimetype="application/json")
def calculate_clusters_eden(collection):
compounds = Compound.objects(compound_group=collection)
# call EdenUtil Cluster
clusters_file = EdenUtil.cluster(
EdenUtil.sdf_to_gspan(
CompoundCollection.generateTmpSDF(compounds,
'tmpcollection').name))
clusters = []
# create a cluster for each line
for eden_cluster in clusters_file:
eden_nodes = eden_cluster.split(" ")
eden_nodes = [x for x in eden_nodes if (x != '' and x != '\n')]
cluster = Cluster(
title="Cluster_" + str(eden_nodes[0]),
collection=collection,
nodes=[compounds[int(node)] for node in eden_nodes],
centriod=compounds[int(eden_nodes[0])],
density=0.0,
Color="#EF0000")
clusters.append(cluster)
cluster.save()
clusters_file.close()
def calc_clusters(request):
if request.method == 'POST':
form = CalcClusterForm(request.POST)
if form.is_valid():
compound_group = CompoundCollection.objects().with_id(
str(request.POST["collection_id"]))
compounds = Compound.objects(
compound_group=str(request.POST["collection_id"]))
#input_text = "<("
#for compound in compounds :
# input_text += compound.mol + "\n"
#input_text += ")"
input_text = "/home/mjs/Test/approved.gspan.gz"
Cluster.calcualte_clusters(compound_group, input_text, "EDEN")
# handle_uploaded_file(request.FILES['file'])
return HttpResponseRedirect('/cluster/')
else:
form = CalcClusterForm()
return render_to_response(
'calc_clusters.html', {
"view_titel": "Calculate Clusters",
"form": form
},
context_instance=RequestContext(request)) # Create your views here.
def calc_new_mol(request) :
if request.is_ajax():
selected_compounds = request.GET["selected_compounds"]
compounds = Compound.objects({"name__in":selected_compounds})
new_comp = EdenUtil.molcule_design(EdenUtil.sdf_to_gspan(CompoundCollection.generateTmpSDF(compounds,"tmpsdf").name))
response_data = {}
response_data["new_comp" ] = new_comp
response_data['result'] = 'Success'
response_data['message'] = 'New Molcule Designed'
return HttpResponse(json.dumps(response_data), mimetype="application/json")
def get_coordinate(request):
if request.is_ajax():
# pdb.set_trace()
mdsres = MDSRes.objects().with_id(request.session.get("mds_id"))
Clusters = []
compound_group = CompoundCollection.objects().with_id(request.session.get("collection_id"))
if(mdsres is not None) :
data = numpy.fromstring(mdsres.data.read())
d = len(data)
datamd = numpy.reshape(data, (d/3,3))
compounds = Compound.objects(compound_group = request.session.get("collection_id"))
if mdsres.simmatrix.method == "FP" :
arena = chemfp.load_fingerprints(mdsres.simmatrix.compound_group.fp_file)
compounds = arena.ids
elif mdsres.simmatrix.method == "eden" :
compounds = [compound.name for compound in compounds]
elif mdsres.simmatrix.method == "pre_cluster_eden" or mdsres.simmatrix.method == "pre_cluster_eden_pca" :
Clusters = Cluster.objects(collection = request.session.get("collection_id"))
Clusters = [{"nodes" : [node.name for node in cluster.nodes] ,"id":str(cluster.id) ,"centriod" : cluster.centriod.name ,"density" :len(cluster.nodes)} for cluster in Clusters]
compounds = Cluster.get_clusters_centriod(compound_group)
compounds = [compound.name for compound in compounds]
elif(mdsres is None) :
pcares = PCARes.objects().with_id(request.session.get("mds_id"))
data = numpy.fromstring(pcares.data.read())
d = len(data)
datamd = numpy.reshape(data, (d/3,3))
Clusters = Cluster.objects(collection = request.session.get("collection_id"))
Clusters = [{"nodes" : [node.name for node in cluster.nodes] ,"id":str(cluster.id) ,"centriod" : cluster.centriod.name ,"density" :len(cluster.nodes)} for cluster in Clusters]
compounds = Cluster.get_clusters_centriod(compound_group)
compounds = [compound.name for compound in compounds]
# the order of the compounds is not correct !!! make sure to fix (arena.ids[idx] from the fingerprint file)
response_data = {}
response_data['result'] = 'Success'
response_data['message'] = 'Compounds Coordinate'
response_data['coord'] = datamd.tolist()
response_data["clusters"] = Clusters
response_data['comps'] = []
for compound in compounds :
compitem = {
"name" :compound
}
response_data['comps'].append(compitem)
return HttpResponse(json.dumps(response_data), mimetype="application/json")
return HttpResponse({}, mimetype="application/json")
def get_comp_field_values(request) :
if request.is_ajax():
compounds = Compound.objects(compound_group = request.session.get("collection_id")).item_frequencies(request.GET["field"])
response_data = {}
response_data['result'] = 'Success'
response_data['message'] = 'Compounds Fields'
response_data['field_values'] = list(compounds)
pprint.pprint(compounds)
return HttpResponse(json.dumps(response_data), mimetype="application/json")
return HttpResponse({}, mimetype="application/json")
def get_comp_field_names(request) :
if request.is_ajax():
comp = Compound.objects(compound_group = request.session.get("collection_id"))
response_data = {}
response_data['result'] = 'Success'
response_data['message'] = 'Compounds Fields'
response_data['field_names'] = []
response_data['dfield_names'] = []
if comp :
response_data['field_names'] = comp[0].get_model_fields() + comp[0].get_model_dfields()
response_data['dfield_names'] = comp[0].get_model_dfields()
return HttpResponse(json.dumps(response_data), mimetype="application/json")
return HttpResponse({}, mimetype="application/json")
def calc_knn_eden(self):
# Calculate and Store KNN
print "\n Calculating KNN ... \n"
compounds = Compound.objects(compound_group=self.compound_group)
tmpsdffile = CompoundCollection.generateTmpSDF(compounds, "comps_eden")
knn = EdenUtil.nearest_neighbor(EdenUtil.sdf_to_gspan(tmpsdffile.name))
for knn_entry in knn:
knn_item = KnnItem(
simmatrix=self,
mol_id=str(compounds[int(knn_entry['indxs'].pop(0))].name),
neighbors=[{
"mol_id":
str(compounds[int(x)].name),
"val":
float(knn_entry['knn_vals'][knn_entry['indxs'].index(x)])
} for x in knn_entry['indxs']])
knn_item.save()
def calc_knn_eden(self) :
# Calculate and Store KNN
print "\n Calculating KNN ... \n"
compounds = Compound.objects(compound_group=self.compound_group)
tmpsdffile = CompoundCollection.generateTmpSDF(compounds,"comps_eden")
knn = EdenUtil.nearest_neighbor(EdenUtil.sdf_to_gspan(tmpsdffile.name))
for knn_entry in knn :
knn_item = KnnItem(
simmatrix = self
,mol_id = str(compounds[int(knn_entry['indxs'].pop(0))].name)
,neighbors = [
{
"mol_id":str(compounds[int(x)].name)
,"val":float(knn_entry['knn_vals'][knn_entry['indxs'].index(x)])
} for x in knn_entry['indxs']
]
)
knn_item.save()
def get_comp_details(request) :
if request.is_ajax():
mdsres = MDSRes.objects().with_id(request.session.get("mds_id"))
compound = Compound.objects(compound_group = request.session.get("collection_id"),name = str(request.GET["name"]))
if(mdsres) :
knn_items = KnnItem.objects(simmatrix = mdsres.simmatrix ,mol_id = compound[0].name)
else :
knn_items = KnnItem.objects(compound_group = request.session.get("collection_id") ,mol_id = compound[0].name)
#arena = chemfp.load_fingerprints(mdsres.simmatrix.fp)
#knearest = arena.knearest_tanimoto_search_fp(arena[arena.ids.index(compound[0].name)][1],k=int(request.GET["k"]),threshold=float(request.GET["t"])/100.00)
threshold= float(request.GET["t"])/100.00
k=int(request.GET["k"]) if (int(request.GET["k"])>0) else 30
if len(knn_items) > 0 :
neighbors = [ {"mol_id":str(ki.get("mol_id")),"val":str(ki.get("val"))} for ki in knn_items[0].neighbors if float(ki.get("val") >= threshold )]
else :
neighbors = []
compitem = {
"name" :compound[0].name ,
"generic_name" :compound[0].generic_name ,
"mol_weight" : compound[0].mol_weight ,
"formula" : compound[0].formula ,
"inchi" : compound[0].inchi ,
"exact_mass" : compound[0].exact_mass ,
"smiles" : compound[0].smiles ,
"ring_count" : compound[0].ring_count ,
"mol" : compound[0].mol ,
"neighbors" : neighbors[:k]
}
response_data = {}
response_data['result'] = 'Success'
response_data['message'] = 'Compounds Coordinate'
response_data['compound'] = compitem
#response_data['neighbors'] = knearest.get_ids_and_scores()
return HttpResponse(json.dumps(response_data), mimetype="application/json")
return HttpResponse({}, mimetype="application/json")
def distance_matrix_eden(self) :
comps = Compound.objects(compound_group=self.compound_group)
n = comps.count()
distances = numpy.ones((n, n), numpy.float64)
# mmfile = self.compound_group.mol_file.read()
# tmpsdffile = open('/tmp/'+self.compound_group.title+'.sdf', 'wb+')
# tmpsdffile.write(mmfile)
# tmpsdffile.close()
tmpsdffile = CompoundCollection.generateTmpSDF(comps,"comps_eden")
# Calcuate Distances using EDeN
sm_output = EdenUtil.similarty_matrix(EdenUtil.sdf_to_gspan(tmpsdffile.name))
distances = 1 - sm_output
#######################################
self.data.new_file()
self.data.write(distances.tostring())
self.data.close()
self.save(validate=False,cascade=False)
self.calc_knn_eden()
def detailed_view(request) :
if request.method == 'GET':
mdsres = MDSRes.objects().with_id(request.session.get("mds_id"))
compound = Compound.objects(compound_group = request.session.get("collection_id"),name = str(request.GET["name"]))
threshold= 0/100.00
k=int(10)
neighbors = []
if(mdsres) :
knnitems = KnnItem.objects(simmatrix = mdsres.simmatrix ,mol_id = compound[0].name)
else :
knnitems = KnnItem.objects(compound_group = request.session.get("collection_id") ,mol_id = compound[0].name)
if(len(knnitems)>0) :
neighbors = [ {"mol_id":str(ki.get("mol_id")),"val":str(ki.get("val"))} for ki in knnitems[0].neighbors if float(ki.get("val") >= threshold )]
print(compound[0]._data)
compitem = {
"name" :compound[0].name ,
"generic_name" :compound[0].generic_name ,
"mol_weight" : compound[0].mol_weight ,
"formula" : compound[0].formula ,
"inchi" : compound[0].inchi ,
"exact_mass" : compound[0].exact_mass ,
"smiles" : compound[0].smiles ,
"ring_count" : compound[0].ring_count ,
"mol" : compound[0].mol ,
"neighbors" : neighbors[:k]
}
response_data = {}
response_data['dfield_values'] = [ {"field":f ,"val":compound[0]._data[f]} for f in compound[0].get_model_dfields()]
print response_data['dfield_values']
response_data['result'] = 'Success'
response_data['message'] = 'Compounds Coordinate'
response_data['compound'] = compitem
return render_to_response('detailed_view.html',{"view_titel":"Detailed View","response":response_data},context_instance=RequestContext(request))
return render_to_response('detailed_view.html',{"view_titel":"Detailed View"},context_instance=RequestContext(request))
def calc_clusters(request):
if request.method == 'POST':
form = CalcClusterForm(request.POST)
if form.is_valid():
compound_group = CompoundCollection.objects().with_id(str(request.POST["collection_id"]))
compounds = Compound.objects(compound_group = str(request.POST["collection_id"]))
#input_text = "<("
#for compound in compounds :
# input_text += compound.mol + "\n"
#input_text += ")"
input_text = "/home/mjs/Test/approved.gspan.gz"
Cluster.calcualte_clusters(compound_group,input_text,"EDEN")
# handle_uploaded_file(request.FILES['file'])
return HttpResponseRedirect('/cluster/')
else:
form = CalcClusterForm()
return render_to_response('calc_clusters.html',{"view_titel":"Calculate Clusters","form":form},context_instance=RequestContext(request)) # Create your views here.
def distance_matrix_eden(self):
comps = Compound.objects(compound_group=self.compound_group)
n = comps.count()
distances = numpy.ones((n, n), numpy.float64)
# mmfile = self.compound_group.mol_file.read()
# tmpsdffile = open('/tmp/'+self.compound_group.title+'.sdf', 'wb+')
# tmpsdffile.write(mmfile)
# tmpsdffile.close()
tmpsdffile = CompoundCollection.generateTmpSDF(comps, "comps_eden")
# Calcuate Distances using EDeN
sm_output = EdenUtil.similarty_matrix(
EdenUtil.sdf_to_gspan(tmpsdffile.name))
distances = 1 - sm_output
#######################################
self.data.new_file()
self.data.write(distances.tostring())
self.data.close()
self.save(validate=False, cascade=False)
self.calc_knn_eden()
def calculate_clusters_eden(collection) :
compounds = Compound.objects(compound_group = collection)
# call EdenUtil Cluster
clusters_file = EdenUtil.cluster(EdenUtil.sdf_to_gspan(CompoundCollection.generateTmpSDF(compounds,'tmpcollection').name))
clusters = []
# create a cluster for each line
for eden_cluster in clusters_file :
eden_nodes = eden_cluster.split(" ")
eden_nodes = [x for x in eden_nodes if (x != '' and x != '\n')]
cluster = Cluster(
title = "Cluster_" + str(eden_nodes[0])
,collection = collection
,nodes = [compounds[int(node)] for node in eden_nodes]
,centriod = compounds[int(eden_nodes[0])]
,density = 0.0
,Color = "#EF0000"
)
clusters.append(cluster)
cluster.save()
clusters_file.close()
def calc_pos(request):
if request.method == 'POST':
# Calculate The Similarity Matrix
#Calculate Finger Prints
calc_method = str(request.POST["method"])
cg = CompoundCollection.objects().with_id(str(request.POST['collection_id']))
if calc_method == "FP" :
print "\n Calculating Simmatrix (FingerPrints) ... \n "
sm = SimilarityMatrix(compound_group = cg , method=calc_method)
sm.distance_matrix(0.0)
elif calc_method == "eden" :
print "\n Calculating Simmatrix (EDeN) ...\n "
sm = SimilarityMatrix(compound_group = cg,method=calc_method)
sm.distance_matrix_eden()
elif calc_method == "pre_cluster_eden" :
Cluster.calculate_clusters_eden(cg)
sm = SimilarityMatrix(compound_group = cg,method=calc_method)
sm.distance_matrix_cluster_centriods()
if calc_method == "pre_cluster_eden_pca" :
# Calculate PCA
if(len(Cluster.objects(collection = cg))<1):
print "\n Calculating Clusters ... \n "
Cluster.calculate_clusters_eden(cg)
# Calculate and Store KNN
compounds = Compound.objects(compound_group=cg)
tmpsdffile = CompoundCollection.generateTmpSDF(compounds,"comps_eden")
print "\n Calculating KNN ... \n"
knn = EdenUtil.nearest_neighbor(EdenUtil.sdf_to_gspan(tmpsdffile.name))
for knn_entry in knn :
knn_item = KnnItem(
compound_group = cg
,mol_id = str(compounds[int(knn_entry['indxs'].pop(0))].name)
,neighbors = [
{
"mol_id":str(compounds[int(x)].name)
,"val":float(knn_entry['knn_vals'][knn_entry['indxs'].index(x)])
} for x in knn_entry['indxs']
]
)
knn_item.save()
print "\n Calculating PCA ... \n "
pca = PCARes(compound_group=cg, title=str(request.POST['pos_title']))
res = pca.PerformPCA(collection=Cluster.get_clusters_centriod(compound_group=cg)).tostring()
pca.data.new_file()
pca.data.write(res)
pca.data.close()
pca.save()
else :
# Calculate MDS
print "\n Calculating MDS ... \n "
res = MDSRes(title = str(request.POST['pos_title']),simmatrix = sm,max_iter =300,eps = 1e-6) ;
data = numpy.fromstring(sm.data.read())
d = math.sqrt(len(data))
datamd = numpy.reshape(data, (d,d))
res.data.new_file()
res.data.write(res.runMDS(simmatrix = datamd).tostring())
res.data.close()
res.save()
response_data = {}
response_data['result'] = 'Success'
response_data['message'] = 'Calculate Position Results'
return HttpResponseRedirect('/space_explorer/')
def test_add_meta(request):
CC = CompoundCollection.objects().with_id(request.session["collection_id"])
CC.add_meta_info()
compounds = Compound.objects()
return render_to_response('view_file.html',{"view_titel":"Home",'compounds':compounds},context_instance=RequestContext(request))