def analyse_cliques(cliques):
"""
:param cliques: list of cliques
:return: prints an anlysis
"""
coh_seqs = read_multi_fastas('/home/labs/fleishman/jonathaw/no_backup/designs/multi_docs_15Oct/reclique_18Nov/stabilisation/all_stabilised/all_j_st_cohs.fasta', '_st.A')
doc_seqs = read_multi_fastas('/home/labs/fleishman/jonathaw/no_backup/designs/multi_docs_15Oct/reclique_18Nov/stabilisation/all_stabilised/all_j_st_docs.fasta', '_st.B')
cliques_by_charges = parse_cliques_lists('/home/labs/fleishman/jonathaw/no_backup/designs/multi_docs_15Oct/reclique_18Nov/stabilisation/all_stabilised/cliques_2_1.txt', remove='_st')
original_names = parse_name_translation('/home/labs/fleishman/jonathaw/no_backup/designs/multi_docs_15Oct/recliques_4Nov/clique_6_pdbs/mini_diagonal_11Nov/minidiagonal_pdbs/translate_names.txt')
clqs_by_len = {k: [] for k in range(1, 8)}
for clq in cliques:
clqs_by_len[len(clq)].append(clq)
designs_in_all_clqs = []
for length in range(10, 6, -1):
if length not in clqs_by_len.keys():
continue
for clq in clqs_by_len[length]:
coh_diffs, doc_diffs, doc_diffs_symm, doc_bb_diffs = [], [], [], []
print('\n\n\nfor clq', clq)
for mem1 in clq:
designs_in_all_clqs.append(mem1)
wt_doc = original_names[mem1[1]+'.pdb.gz'][10:14]
res1 = Result(mem1, coh_seqs[mem1[0]], doc_seqs[mem1[1]], 1, j=True, doc_wt=wt_doc)
res1_doc_symm = switch_symm_changer(res1.doc_switch)
for mem2 in clq:
if mem1 != mem2:
wt_doc = original_names[mem2[1]+'.pdb.gz'][10:14]
res2 = Result(mem2, coh_seqs[mem2[0]], doc_seqs[mem2[1]], 1, j=True, doc_wt=wt_doc)
doc_bb_diffs.append(are_docs_from_diff_clusters(res1.doc_wt, res2.doc_wt))
coh_diffs.append(switches_differ({}, res1.coh_switch, res2.coh_switch))
doc_diffs.append(switches_differ({}, res1.doc_switch, res2.doc_switch))
doc_diffs_symm.append(switches_differ({}, res1_doc_symm, res2.doc_switch))
print('results', res1)
print('results', res2)
print('docs diff', switches_differ({}, res1.doc_switch, res2.doc_switch))
print('doc symm diff', switches_differ({}, res1_doc_symm, res2.doc_switch))
print('doc BB dif', are_docs_from_diff_clusters(res1.doc_wt, res2.doc_wt))
print('coh diff', switches_differ({}, res1.coh_switch, res2.coh_switch))
N
print('for clq %r found the following results:' % clq)
print('doc_bb_diffs', doc_bb_diffs)
print('doc_diffs', doc_diffs)
print('doc_diffs_symm', doc_diffs_symm)
print('coh_diffs', coh_diffs)
print('total', sum([1 for a in doc_bb_diffs if a] + doc_diffs + doc_diffs_symm + coh_diffs))
all_cohs = list(set([a[0] for a in designs_in_all_clqs]))
all_docs = list(set([a[1] for a in designs_in_all_clqs]))
print('these are all the cohs i need: %s, total %i' % (', '.join(all_cohs), len(all_cohs)))
print('these are all the docs i need: %s, total %i' % (', '.join(all_docs), len(all_docs)))
print('LONGEST CLIQUES FOUND ARE %i' % max([len(clq) for clq in cliques]))
coh_doc_purples = creat_coh_doc_purples()
for clq in clqs_by_len[max(list(clqs_by_len.keys()))]:
print('clq', clq)
cohs = [a[0] for a in clq]
docs = [a[1] for a in clq]
df = pd.DataFrame(index=docs, columns=cohs)
for coh in cohs:
for doc in docs:
df[coh][doc] = coh_doc_purples[coh][doc]
show_prediction_heat_map(df)
def draw_heatmap_for_chosen_designs(cliques):
chosen_desins = ['j5517', 'j4518', 'j3622', 'j3983', 'j1647', 'j4286', 'j4653', 'j5711', 'j5093', 'j3626', 'j4398', 'j829', 'j5106', 'j1526']
coh_doc_purples = creat_coh_doc_purples()
df = pd.DataFrame(index=chosen_desins, columns=chosen_desins)
for coh in chosen_desins:
for doc in chosen_desins:
df[coh][doc] = coh_doc_purples[coh][doc]
print(df)
show_prediction_heat_map(df)
