def get_context_data(self, **kwargs):
context = super().get_context_data(**kwargs)
assays = AssayExperiment.objects.all().prefetch_related(
'protein__family__parent__parent__parent', 'protein__family')
lig_count_dict = {}
assays_lig = list(AssayExperiment.objects.all().values(
'protein__family__parent__parent__parent__name').annotate(
c=Count('ligand', distinct=True)))
for a in assays_lig:
lig_count_dict[
a['protein__family__parent__parent__parent__name']] = a['c']
target_count_dict = {}
assays_target = list(AssayExperiment.objects.all().values(
'protein__family__parent__parent__parent__name').annotate(
c=Count('protein__family', distinct=True)))
for a in assays_target:
target_count_dict[
a['protein__family__parent__parent__parent__name']] = a['c']
prot_count_dict = {}
proteins_count = list(Protein.objects.all().values(
'family__parent__parent__parent__name').annotate(
c=Count('family', distinct=True)))
for pf in proteins_count:
prot_count_dict[
pf['family__parent__parent__parent__name']] = pf['c']
classes = ProteinFamily.objects.filter(
slug__in=['001', '002', '003', '004', '005',
'006']) #ugly but fast
proteins = Protein.objects.all().prefetch_related(
'family__parent__parent__parent')
ligands = []
for fam in classes:
if fam.name in lig_count_dict:
lig_count = lig_count_dict[fam.name]
target_count = target_count_dict[fam.name]
else:
lig_count = 0
target_count = 0
prot_count = prot_count_dict[fam.name]
ligands.append({
'name': fam.name,
'num_ligands': lig_count,
'avg_num_ligands': lig_count / prot_count,
'target_percentage': target_count / prot_count * 100,
'target_count': target_count
})
lig_count_total = sum([x['num_ligands'] for x in ligands])
prot_count_total = Protein.objects.filter(
family__slug__startswith='00').all().distinct('family').count()
target_count_total = sum([x['target_count'] for x in ligands])
lig_total = {
'num_ligands': lig_count_total,
'avg_num_ligands': lig_count_total / prot_count_total,
'target_percentage': target_count_total / prot_count_total * 100,
'target_count': target_count_total
}
#Elegant solution but kinda slow (6s querries):
"""
ligands = AssayExperiment.objects.values(
'protein__family__parent__parent__parent__name',
'protein__family__parent__parent__parent',
).annotate(num_ligands=Count('ligand', distinct=True))
for prot_class in ligands:
class_subset = AssayExperiment.objects.filter(
id=prot_class['protein__family__parent__parent__parent']).values(
'protein').annotate(
avg_num_ligands=Avg('ligand', distinct=True),
p_count=Count('protein')
)
prot_class['avg_num_ligands']=class_subset[0]['avg_num_ligands']
prot_class['p_count']=class_subset[0]['p_count']
"""
context['ligands_total'] = lig_total
context['ligands_by_class'] = ligands
context['release_notes'] = ReleaseNotes.objects.all()[0]
tree = PhylogeneticTreeGenerator()
class_a_data = tree.get_tree_data(
ProteinFamily.objects.get(name='Class A (Rhodopsin)'))
context['class_a_options'] = deepcopy(tree.d3_options)
context['class_a_options']['anchor'] = 'class_a'
context['class_a_options']['leaf_offset'] = 50
context['class_a_options']['label_free'] = []
context['class_a'] = json.dumps(class_a_data.get_nodes_dict('ligands'))
class_b1_data = tree.get_tree_data(
ProteinFamily.objects.get(name__startswith='Class B1 (Secretin)'))
context['class_b1_options'] = deepcopy(tree.d3_options)
context['class_b1_options']['anchor'] = 'class_b1'
context['class_b1_options']['branch_trunc'] = 60
context['class_b1_options']['label_free'] = [
1,
]
context['class_b1'] = json.dumps(
class_b1_data.get_nodes_dict('ligands'))
class_b2_data = tree.get_tree_data(
ProteinFamily.objects.get(name__startswith='Class B2 (Adhesion)'))
context['class_b2_options'] = deepcopy(tree.d3_options)
context['class_b2_options']['anchor'] = 'class_b2'
context['class_b2_options']['label_free'] = [
1,
]
context['class_b2'] = json.dumps(
class_b2_data.get_nodes_dict('ligands'))
class_c_data = tree.get_tree_data(
ProteinFamily.objects.get(name__startswith='Class C (Glutamate)'))
context['class_c_options'] = deepcopy(tree.d3_options)
context['class_c_options']['anchor'] = 'class_c'
context['class_c_options']['branch_trunc'] = 50
context['class_c_options']['label_free'] = [
1,
]
context['class_c'] = json.dumps(class_c_data.get_nodes_dict('ligands'))
class_f_data = tree.get_tree_data(
ProteinFamily.objects.get(name__startswith='Class F (Frizzled)'))
context['class_f_options'] = deepcopy(tree.d3_options)
context['class_f_options']['anchor'] = 'class_f'
context['class_f_options']['label_free'] = [
1,
]
context['class_f'] = json.dumps(class_f_data.get_nodes_dict('ligands'))
class_t2_data = tree.get_tree_data(
ProteinFamily.objects.get(name='Taste 2'))
context['class_t2_options'] = deepcopy(tree.d3_options)
context['class_t2_options']['anchor'] = 'class_t2'
context['class_t2_options']['label_free'] = [
1,
]
context['class_t2'] = json.dumps(
class_t2_data.get_nodes_dict('ligands'))
return context
def get_context_data (self, **kwargs):
context = super().get_context_data(**kwargs)
assays = AssayExperiment.objects.all().prefetch_related('protein__family__parent__parent__parent', 'protein__family')
classes = ProteinFamily.objects.filter(slug__in=['001', '002', '003', '004', '005', '006']) #ugly but fast
proteins = Protein.objects.all().prefetch_related('family__parent__parent__parent')
ligands = []
for fam in classes:
lig_count = len(assays.filter(protein__family__parent__parent__parent=fam).distinct('ligand'))
prot_count = len(proteins.filter(family__parent__parent__parent=fam).distinct('family'))
target_count = len(assays.filter(protein__family__parent__parent__parent=fam).distinct('protein__family'))
ligands.append({
'name': fam.name,
'num_ligands': lig_count,
'avg_num_ligands': lig_count/prot_count,
'target_percentage': target_count/prot_count*100,
'target_count': target_count
})
lig_count_total = sum([x['num_ligands'] for x in ligands])
prot_count_total = len(proteins.distinct('family'))
target_count_total = sum([x['target_count'] for x in ligands])
lig_total = {
'num_ligands': lig_count_total,
'avg_num_ligands': lig_count_total/prot_count_total,
'target_percentage': target_count_total/prot_count_total*100,
'target_count': target_count_total
}
#Elegant solution but kinda slow (6s querries):
"""
ligands = AssayExperiment.objects.values(
'protein__family__parent__parent__parent__name',
'protein__family__parent__parent__parent',
).annotate(num_ligands=Count('ligand', distinct=True))
for prot_class in ligands:
class_subset = AssayExperiment.objects.filter(
id=prot_class['protein__family__parent__parent__parent']).values(
'protein').annotate(
avg_num_ligands=Avg('ligand', distinct=True),
p_count=Count('protein')
)
prot_class['avg_num_ligands']=class_subset[0]['avg_num_ligands']
prot_class['p_count']=class_subset[0]['p_count']
"""
context['ligands_total'] = lig_total
context['ligands_by_class'] = ligands
context['release_notes'] = ReleaseNotes.objects.all()[0]
tree = PhylogeneticTreeGenerator()
class_a_data = tree.get_tree_data(ProteinFamily.objects.get(name='Class A (Rhodopsin)'))
context['class_a_options'] = deepcopy(tree.d3_options)
context['class_a_options']['anchor'] = 'class_a'
context['class_a_options']['leaf_offset'] = 50
context['class_a_options']['label_free'] = []
context['class_a'] = json.dumps(class_a_data.get_nodes_dict('ligands'))
class_b1_data = tree.get_tree_data(ProteinFamily.objects.get(name__startswith='Class B1 (Secretin)'))
context['class_b1_options'] = deepcopy(tree.d3_options)
context['class_b1_options']['anchor'] = 'class_b1'
context['class_b1_options']['branch_trunc'] = 60
context['class_b1_options']['label_free'] = [1,]
context['class_b1'] = json.dumps(class_b1_data.get_nodes_dict('ligands'))
class_b2_data = tree.get_tree_data(ProteinFamily.objects.get(name__startswith='Class B2 (Adhesion)'))
context['class_b2_options'] = deepcopy(tree.d3_options)
context['class_b2_options']['anchor'] = 'class_b2'
context['class_b2_options']['label_free'] = [1,]
context['class_b2'] = json.dumps(class_b2_data.get_nodes_dict('ligands'))
class_c_data = tree.get_tree_data(ProteinFamily.objects.get(name__startswith='Class C (Glutamate)'))
context['class_c_options'] = deepcopy(tree.d3_options)
context['class_c_options']['anchor'] = 'class_c'
context['class_c_options']['branch_trunc'] = 50
context['class_c_options']['label_free'] = [1,]
context['class_c'] = json.dumps(class_c_data.get_nodes_dict('ligands'))
class_f_data = tree.get_tree_data(ProteinFamily.objects.get(name__startswith='Class F (Frizzled)'))
context['class_f_options'] = deepcopy(tree.d3_options)
context['class_f_options']['anchor'] = 'class_f'
context['class_f_options']['label_free'] = [1,]
context['class_f'] = json.dumps(class_f_data.get_nodes_dict('ligands'))
class_t2_data = tree.get_tree_data(ProteinFamily.objects.get(name='Taste 2'))
context['class_t2_options'] = deepcopy(tree.d3_options)
context['class_t2_options']['anchor'] = 'class_t2'
context['class_t2_options']['label_free'] = [1,]
context['class_t2'] = json.dumps(class_t2_data.get_nodes_dict('ligands'))
return context