workbook.py

import os
import shutil
import numpy as np
import obonet
import pandas as pd
import torch
import torch_geometric.datasets as datasets
import torch_geometric.data as data
import torch_geometric.transforms as transforms
import networkx as nx
from torch_geometric.utils.convert import to_networkx
import CONSTANTS
from Classes.Diamond import Diamond
from Classes.Fasta import Fasta
from Classes.Interpro import Interpro, create_indicies
from Classes.STRING import STRING
from Utils import count_proteins, get_proteins_from_fasta, pickle_load, pickle_save
import urllib.request
from Bio import SeqIO





train_fasta = "/home/fbqc9/Workspace/DATA/uniprot/train_sequences.fasta"
query = "/home/fbqc9/Workspace/DATA/uniprot/test_fasta.fasta"


def get_proteins_from_fasta(fasta_file):
    proteins = list(SeqIO.parse(fasta_file, "fasta"))
    proteins = [i.id for i in proteins]
    return proteins


test = set(get_proteins_from_fasta(query))


seqs = []

input_seq_iterator = SeqIO.parse(train_fasta, "fasta")
for pos, record in enumerate(input_seq_iterator):

    if record.id in test:
        pass
    else:
        seqs.append(record)

SeqIO.write(seqs, "test_fasta", "fasta")

exit()

for i in test:
    print(i)
    exit()


exit()


x  = sorted_terms = pickle_load(CONSTANTS.ROOT_DIR+"/mf/sorted_terms")

print(x[1794])


exit()
data = pickle_load(CONSTANTS.ROOT_DIR+"/{}/terms_stats".format("bp"))
max_threshold = 5 # 30 # 100 #70000
min_threshold = 0 # 0 #30 #100
crew = []
for i in data:
    if data[i][0] > min_threshold and data[i][0] <= max_threshold:
        crew.append(i)

print(len(crew))



exit()



'''print(count_proteins("/home/fbqc9/Workspace/DATA/uniprot/test_fasta.fasta"))

exit()

x = pickle_load("/home/fbqc9/Workspace/DATA/test/t3/test_proteins")
keep = set()
for i in x:
    keep.update(x[i])
    print(i, len(x[i]))
print(len(keep))
exit()

x = torch.load("/bmlfast/frimpong/shared_function_data/esm2_t48/A1Z9J1.pt")

print(x['representations_48'].shape)


exit()
input_seq_iterator = SeqIO.parse("/home/fbqc9/Workspace/DATA/uniprot/train_sequences.fasta", "fasta")
for pos, record in enumerate(input_seq_iterator):
    if len(record.seq) > 4000:
        print(record.id)
        exit



exit()'''


prediction = torch.tensor([[0.1, 0.5, 1.0, 1, 0, 1, 1, 1, 1, 1], [0, 1, 1, 0, 0, 0, 1, 0, 1, 1]])
truth = torch.tensor([[1, 0, 1, 0, 1, 0, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1, 0, 1, 0, 1]])
prediction = prediction > 0.5

print(prediction)
conf = prediction/truth

print(conf)

exit()
c = prediction / truth


poi = torch.tensor([[0.8, 0.5, 1.3, 1.5, 2.0, 1.1, 1.2, 1.3, 1.4, 1.5], 
                    [0.6, 1.2, 1.4, 0.7, 0.1, 0.9, 1.0, 0.4, 1.2, 1.7]])


true_positives_indicies  = (c == 1)

a = torch.masked_select(poi, true_positives_indicies)

print(a)
print(true_positives_indicies)
exit()


true_positives = torch.sum(c == 1).item()
false_positives = torch.sum(c == float('inf')).item()
true_negatives = torch.sum(torch.isnan(c)).item()
false_negatives = torch.sum(c == 0).item()


recall = true_positives / (1.0 * (true_positives + false_negatives))
precision = true_positives / (1.0 * (true_positives + false_positives))
fscore = 2 * precision * recall / (precision + recall)

print(precision, recall, fscore)

exit()
diff = real-predicted
print('diff: ',diff)

# Correct is 0 
# FP is -1 
# FN is 1
print('Correctly classified: ', np.where(diff == 0)[0])
print('Incorrectly classified: ', np.where(diff != 0)[0])
print('False positives: ', np.where(diff == -1)[0])
print('False negatives: ', np.where(diff == 1)[0])


exit()





train = list(pickle_load(CONSTANTS.ROOT_DIR + "{}/{}_proteins".format("bp", "train")))
valid = list(pickle_load(CONSTANTS.ROOT_DIR + "{}/{}_proteins".format("bp", "validation")))
tot = set(train + valid)
print(len(tot))

test_group1 = pickle_load(CONSTANTS.ROOT_DIR + "test/test_proteins")['LK_bp']
test_group2 = pickle_load(CONSTANTS.ROOT_DIR + "test/test_proteins")['NK_bp']
test_group = set(test_group1+ test_group2)
print(len(test_group1 + test_group2))

print(len(test_group.intersection(tot)))

exit()


x = torch.randn(3, 4)

mask = x.ge(0.5)

print(x)
print(mask)

a = torch.masked_select(x, mask, dim=1)

print(a)

exit()




x = pickle_load("tst_labesl")

for i in x:
    for j in x[i]:
        assert(sum(x[i][j]) > 0)



exit()



onts = ['bp', 'cc', 'mf']
print("Generating labels")
labels = {}

test_groundtruth = pickle_load(CONSTANTS.ROOT_DIR + "test/groundtruth")
test_group = pickle_load(CONSTANTS.ROOT_DIR + "test/test_proteins")

all = {
    'cc': test_group['LK_cc'] + test_group['NK_cc'],
    'mf': test_group['LK_mf'] + test_group['NK_mf'],
    'bp': test_group['LK_bp'] + test_group['NK_bp']
}

res = {}
for ont in onts:
    res[ont] = {}
    prots = all[ont]
    for prot in prots:
        prot_terms = test_groundtruth[prot]
        terms = pickle_load(CONSTANTS.ROOT_DIR+"/{}/sorted_terms".format(ont))
        tmp = []
        for t in terms:
            if t in prot_terms:
                tmp.append(1)
            else:
                tmp.append(0)

    
        res[ont][prot] = tmp
pickle_save(res, "tst_labesl")
exit()


x = pickle_load("evaluation/results/tale_out")


for i in x:
    for j in x[i]:
        print(i, j, x[i][j])
    exit()



exit()



def format_input(fasta_file):
    seqs = []
    input_seq_iterator = SeqIO.parse(fasta_file, "fasta")
    for pos, record in enumerate(input_seq_iterator):

        if len(record.seq) < 30:
            pass
        else:
            seqs.append(record)

    SeqIO.write(seqs, "evaluation/predictions/atgo/test_fasta", "fasta")


format_input("evaluation/predictions/tale/test_fasta")


exit()


go_path=CONSTANTS.ROOT_DIR + "/obo/go-basic.obo"
go_graph = obonet.read_obo(open(go_path, 'r'))


exp = [(i, len(nx.ancestors(go_graph, i)), len(nx.descendants(go_graph, i))) for i in go_graph]

exp = [i[0]  for i in exp if i[1] == 0]

print(exp)


exit()

shared = set(os.listdir("/bmlfast/frimpong/shared_function_data/esm_msa1b/"))
print(len(shared))

home = set(os.listdir("/home/fbqc9/esm_msa1b/"))
print(len(home))

diff = home.difference(shared)
print(len(diff))


for pos, i in enumerate(diff):
    print("{}/{}".format(pos, len(diff)))
    src = "/home/fbqc9/esm_msa1b/{}".format(i)
    des = "/bmlfast/frimpong/shared_function_data/esm_msa1b/"
    shutil.copy(src, des) 
exit()






src = torch.arange(1, 16).reshape((3, 5))

index = torch.tensor([[3, 0, 1, 2]])


des = torch.zeros(3, 5, dtype=src.dtype)


final = des.scatter_(1, index, src)

print(src)
print(index)
print(final)

exit()




import torch, gc
gc.collect()
torch.cuda.empty_cache()
exit()





from Bio import SeqIO
from Bio.Seq import Seq
from Bio.SeqRecord import SeqRecord

data = {'A0A7P0Z4A0', 'A6JB73', 'A0A7U3JVZ2', 'Q7LYX6', 'Q1LW14', 'A0A7I2YQ95', 'A0A1X7SBU8', 'H0RND5', 'A1Z8K2',
        'B6K6Z7', 'Q8JI29', 'Q6FY42', 'F1RCY7', 'A0A7P0T9I2', 'D6W8J5', 'A0A8M2BHQ8', 'A0A6Q8PGN5', 'D6W891', 'F1Q554',
        'A0A8I5KRG4', 'A0A8I5KR98', 'A0A0R4IJ94', 'D6RCB1', 'P19931', 'A0A7P0TAS2', 'A0A804HLJ9', 'C9JGT8',
        'A0A6Q8PFX2', 'A0A8I5KUP2', 'A0A7I2V3T6', 'Q7KUL3', 'A0A7I2V2H9', 'Q5A1D5', 'Q9I9N0', 'A0A8M1PAC4',
        'A0A8M3B1J1', 'A8DYC4', 'D3Z6V5', 'P59773', 'A0A8M3AUU7', 'A0A1D8PTL5', 'Q9XB18', 'D2A5R8', 'F8W4I7', 'P32369',
        'A0A7I2V307', 'A8WFS3', 'A5WUE8', 'P45524', 'A0A1D8PFG4', 'Q9VXK9', 'A0A8I5KXM7', 'B6K054', 'A0A7I2V5N2',
        'E9PUN2', 'A0A8M9PX23', 'Q9VFB9', 'A0A804HLI8', 'Q59XB0', 'B6JUV7', 'O42926', 'A0A7P0T9N9', 'K7ERH5',
        'A0A7I2V4J2', 'A0A2H0ZMV6', 'A0A0R4IZR5', 'P0C218', 'Q5AGM0', 'Q7KVC9', 'B1H1M3', 'A0A7I2V5W3', 'A0A8I5QKL1',
        'A0A7P0Z4Q5', 'A0A0R4IBL7', 'A0A7I2PIU1', 'Q9CPZ1', 'A8KC22', 'Q6DGM9', 'A0A1W2PR02', 'A0A8I5QJW9',
        'A0A8I5KXH8', 'A0A2R8Y705', 'A1B2F3', 'A0A8I5KYI5', 'A7MBU2', 'A0A7L8Y648', 'A6JLV3', 'A0A024R7K8', 'Q8SYA4',
        'A0A023GQA5', 'A0A804HJV4', 'A3KPG7', 'Q59L72', 'A0A0A0MSG8', 'Q7ZVT2', 'A0A7P0TAJ0', 'Q46820', 'R4GRT4',
        'A0A7G6J4N4', 'A0A804HKJ3', 'A0A286YEV3', 'Q59NP5', 'B0S6J3', 'A0A8M9PMS3', 'Q5ANA3', 'Q568T7', 'D6WHK0',
        'A0A804HI51', 'A0A8I5KSV3', 'A0A7P0T870', 'Q1RM47', 'J3KSZ1', 'A0A6Q8PGI1', 'F1RC43', 'Q59KZ1', 'A0A804HIN4',
        'A0A7I2V2E6', 'A0A7P0T8N5', 'Q5F0G5', 'B6K329', 'E9PNQ0', 'M0R1V3', 'A0A6Q8PFH6', 'P83722', 'Q6PIS1',
        'A0A7I2V4X8', 'A0A087WZB5', 'A0A8I5KUB2', 'D6WQX0', 'Q54I20', 'B6K0R2', 'D6W7U6', 'A0A8I5KT73', 'A0A7I2V412',
        'A0A7P0Z4K1', 'C9JP48', 'I3NI02', 'A0A1D6P520', 'A0A8I5KUM3', 'B6K4P1', 'Q873W7', 'Q5ABE2', 'G1UAZ9',
        'A0A7P0T984', 'A3KMT2', 'A0A8I5KYV8', 'A0A804HL01', 'P71242', 'A0A7P0TAK3', 'E7EQB5', 'A0A7I2V4G5',
        'A0A8I5KXU4', 'A0A804HIK6', 'E7ESK4', 'A0A7P0T975', 'A0A8I5KSY5', 'A0A8M2B975', 'Q5ADT1', 'A0A8V0YY16',
        'A0A7P0T843', 'Q8R1T4', 'B3DND5', 'Q7K1D7', 'Q59NW5', 'Q6FKB1', 'A0A8M9PHR9', 'A0A087X033', 'A0A8I5KUC2',
        'A0A7P0TA65', 'A0A8I5KQ45', 'B0R0T1', 'A0A7P0T922', 'B4FQV3', 'A0A804CC06', 'A0A142F121', 'A0A7P0TB51',
        'Q6DGL0', 'A0A068CNX1', 'A0A0S2Z4R0', 'Q568K9', 'A0A7P0TA34', 'A0A7P0T9T8', 'A0A7P0Z489', 'C9IZ13',
        'A0A804HJC4', 'A0A3Q7HJG4', 'B4F7D0', 'Q5A1Z5', 'A0A8M3AU53', 'A0A1B0GUL0', 'H3BMM2', 'B0EXP6', 'A0A804HJ20',
        'E5KTA5', 'V5XKC3', 'H7C1L9', 'Q9D7H3', 'A0A1D8PHB8', 'A0A6Q8PGP3', 'Q9VJZ4', 'A0A8M2BBM0', 'Q9VP52', 'Q5FWI3',
        'F6NRK1', 'M0QX13', 'A0A0R4IVC0', 'P26672', 'J2EKT7', 'H3BUS8', 'P64610', 'A0A8N7TE51', 'A0A7P0T9L0',
        'A0A0A0MTM3', 'B6K141', 'P75977', 'H0Y765', 'A0A7I2V428', 'A0A8I5KXJ3', 'A0A804HIB8', 'A0A8I5KRE7', 'Q7ZW11',
        'A0A804HLB3', 'A0A804HKA7', 'P37127', 'A0A6Q8PH24', 'A0A7P0TBF8', 'A6JM95', 'A7B1V0', 'Q9W0Q3', 'A0A8M3AZ07',
        'A0A0D9SFF6', 'D3Z8L5', 'A0A0D1C8C8', 'A0A7I2V450', 'E9QG33', 'B7Z317', 'A0A7I2V3W8', 'A0A1L8I2C5', 'Q5AQ47',
        'Q5QPQ1', 'K7ES00', 'A0A804CCZ3', 'A0A7P0Z437', 'Q59LY1', 'A0A5P3XKL4', 'A0A6Q8PHA5', 'A0A7I2PJZ2', 'I3L4Z6',
        'Q4V3Y0', 'Q5A6U1', 'A0A8M1NDD8', 'A0A804HJD4', 'A0A7P0T895', 'A0A804HJH8', 'P30015', 'A0A2R8QCP0', 'Q9VNN7',
        'A6K8N6', 'Q3U214', 'A0A7I2YQA4', 'A0A804HKT3', 'Q6DGQ8', 'Q8R123', 'A0A7P0T9U7', 'P73429', 'A0A7I2V3W6',
        'B7Z0A0', 'A0A8M3B0U8', 'Q9DGJ3', 'A0A7P0TAE3', 'H0Y9T6', 'A0A8C8KBU6', 'Q1RLY5', 'A0A8I5KY07', 'A0A1W2PRP7',
        'F6YB23', 'Q7T011', 'A0A7I2YQU0', 'A0A8I5KYP7', 'A0A0R4IVL9', 'T0S324', 'C9J8F8', 'P0ADK0', 'A0A804HIJ5',
        'P07657', 'B3DI91', 'H0YJ45', 'A0A8V1APS5', 'B6K5G8', 'Q562E2', 'Q5AED0', 'E9PIZ0', 'A2R6H0', 'Q5AMH1',
        'A0A8M2BJK9', 'A0A7P0T8G6', 'A0A804HK37', 'A1A5R1', 'Q802U2', 'A0A7I2V474', 'Q8N999', 'D2A1M9', 'A0A6Q8PFF6',
        'P0CU04', 'A2AB23', 'H0YJQ1', 'K7EK07', 'A0A804CDA6', 'P31037', 'Q535D9', 'A0A087WZU3', 'A0A8I5KZC0',
        'A0A7P0T8S8', 'P52496', 'A0A7P0Z4D9', 'A0A7I2V4R5', 'A0A8M9Q3K9', 'A0A804HJ96', 'Q5SZA1', 'Q9VYM2',
        'A0A3P3ZKM0', 'Q9VB54', 'F1QRD8', 'Q5VJM5', 'B6K5C1', 'A1A5X3', 'A0A8M2B201', 'E5RFV8', 'A6HQ90', 'A0A804HJ75',
        'A4QP04', 'Q9VE58', 'A0A7I2V4W4', 'A6IPG1', 'A0A2R8YCS4', 'A0A494C0H2', 'B6JZ25', 'A0A8I6AJI9', 'B7Z7V6',
        'A0A7I2V2E0', 'J3QLP2', 'A0A7I2V5C6', 'Q9VXZ0', 'A0A7I2V2W5', 'Q1LVA5', 'P33354', 'P07908', 'Q5A1M4',
        'A0A804HHU2', 'Q5AHB1', 'A0A7I2V370', 'A0A6Q8PH57', 'Q6PBS7', 'A0A804HKE7', 'A0A2H0ZD43', 'A0A6Q8PG59',
        'A0A6Q8PGY4', 'Q7SZ56', 'K7EKW2', 'Q9WIK7', 'A0A804HIT3', 'A0A8M9PQ80', 'H3BU93', 'D6WX36', 'A0A8I5KVP8',
        'A0A804HLC5', 'Q6ZM67', 'C9JY76', 'D6WYU0', 'Q76QY7', 'Q6NYB4', 'A0A7I2ST14', 'D6WRB8', 'Q5QPQ3', 'A0A7I2YQB2',
        'A6I0Z0', 'E7EVN1', 'A0A286Y937', 'A6KKB6', 'A0A8M2B1Q3', 'Q59P88', 'A0A7P0T8X1', 'F1QL05', 'Q91X85', 'Q1ZXH9',
        'A0A7I2V347', 'A0A8M9PN25', 'H0YCR2', 'Q567I9', 'A0A7P0TB54', 'A0A1D8PEB0', 'A0A7P0Z4A2', 'A0A6Q8PHQ1',
        'Q9VUB1', 'A0A6Q8PFJ0', 'A0A219YHC2', 'Q9VL63', 'A0A0A8J9V7', 'B0BN92', 'A0A8I5KWR6', 'Q8YZR0', 'A0A8M9QPY5',
        'A0A7I2V3Z1', 'A0A6P3HVI0', 'Q08CD0', 'A0A1D8PIP5', 'A0A8I5KT14', 'A0A7P0T937', 'A0A7I2V629', 'Q504N2',
        'A0A6Q8PF02', 'B6JWS3', 'A0A8I5QJL1', 'E7FFG5', 'A6HV62', 'K7EJA3', 'A0A804HKH9', 'P26434', 'O67434', 'Q641M3',
        'A0A2Z5QL08', 'A0A7I2V327', 'A0A7I2YQD9', 'H7C1B3', 'Q59UP6', 'A0A8M3AXB8', 'A0A2R8RYU9', 'A0A7I2V3H5',
        'A0A7I2V3U5', 'A0A804HKJ9', 'Q6P5M3', 'C9J4U4', 'Q8BI22', 'Q9VGP1', 'B1AKM6', 'Q5JRP2', 'A0A7I2V4C8',
        'A0A8I5KTR5', 'P22274', 'A0A8M2BEF6', 'Q9VNI4', 'A0A6Q8PGY7', 'A0A8I5KP10', 'A0A6Q8PH04', 'A6IKE5', 'Q7ZUQ1',
        'Q59RR3', 'A0A7P0T9Z6', 'Q5A0L9', 'Q59XY9', 'E5RG18', 'E7F5V3', 'A0A1D5RMS5', 'Q5A5P9', 'Q8WPU1', 'P10035',
        'F5H3P1', 'M0QXD1', 'A0A087WYQ1', 'Q5A985', 'Q5ANL6', 'Q54GY1', 'A0A6Q8PHP5', 'O92435', 'H0YJ05', 'Q5ZU83',
        'A0A7P0Z4G8', 'A0A7P0TBG1', 'Q59FH4', 'Q9VF81', 'A0A7P0T868', 'A0A7P0Z4A4', 'P07385', 'A0A8I5QJI3',
        'A0A804HJT1', 'A0A8M1RLP2', 'Q8JGS4', 'A0A7I2YQE9', 'A0A6Q8PG37', 'Q5AG97', 'A0A1W2PPJ0', 'B6K517', 'Q7SYM9',
        'A0A8I5KQZ6', 'E7F6Z2', 'A0A7P0T8S7', 'D6RFX8', 'A0A804HJ25', 'A0A2R8Y4Y0', 'D6RBK7', 'Q20754', 'Q7K1C0',
        'Q803W6', 'Q8WPU2', 'A0A6Q8PHB3', 'A0A7I2V649', 'Q9VNM9', 'E7FDV8', 'A0A8I5KSD7', 'A0A7I2V4Y9', 'K7EP01',
        'A0A7I2YQD1', 'Q6FY28', 'A0A7P0TBH8', 'A0A7I2YQ90', 'A0A8I5KRB8', 'A0A804HJG1', 'A0A8I5KYU4', 'Q95ST0',
        'A6HL67', 'F1QMN6', 'A0A8I5KQX1', 'P21839', 'A0A7I2V461', 'A0A8M9Q8W7', 'A0A8M9QGN3', 'H7C0M7', 'B9NAE4',
        'A0A8N7TD97', 'A0A6Q8PHC0', 'B6K1D4', 'E9PR18', 'Q6DHQ4', 'A0A8M3B3Q2', 'A6HU01', 'B6K5C2', 'A0A7I2V2W2',
        'A0A384MR27', 'E9PSF2', 'A0A7I2YQK6', 'A0A8M3AX14', 'A0A087WUE2', 'A0A8I5QKW7', 'A0A7I2PK44', 'A0A804HL95',
        'A6IZN2', 'D6R964', 'A0A7P0T997', 'A0A8M3AZ17', 'Q7ZXP8', 'Q1ZXB8', 'A0A8M1P379', 'Q9VBQ3', 'Q80UP8',
        'A0A8I5KPV0', 'F5HGQ8', 'A0A6Q8PH88', 'Q59U81', 'H2J4R4', 'J3KRR1', 'A0A0R4IBX9', 'A0A8I5KVA2', 'A0A7P0Z497',
        'P54192', 'D7ELL9', 'F1QSJ2', 'A0A8I5QL21', 'A0A0G2L7I0', 'C0HM65', 'A0A7I2V2Y6', 'A0A8I5KVV1', 'Q96SJ8',
        'P23906', 'A0A7P0T9Y7', 'A0A8J9GB59', 'A0A0A0MTP0', 'A0A8I5KTN1', 'A0A6Q8PGZ3', 'A0A804HL40', 'Q9Y7W4',
        'A0A8V0ZLT4', 'D6X2L8', 'S5RVW8', 'A0A3S5IBH4', 'B5DFC7', 'A0A7I2YQN9', 'F1QDA5', 'O44989', 'A6JFC1',
        'A0A8D1U8F1', 'B3DGJ7', 'O28126', 'S4R3L2', 'A0A6Q8PHK3', 'P77509', 'A0A7I2V5M7', 'A0A7I2YQI1', 'Q6DG11',
        'F1QSZ7', 'D4AB34', 'A0A7P0TAQ3', 'A0A7I2V2S7', 'A6HTM5', 'E7F662', 'Q9VJG4', 'B0EXP7', 'A0A7I2YQN6', 'O33125',
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        'A0A219YHF3', 'A0A8I5ZL71', 'Q4V8S6', 'A6IE38', 'A0A0R4IGP6', 'D4AA91', 'Q9DFG8', 'A0A804HKS3', 'A0A8I5QKL9',
        'A0A8I5KPC6', 'A0A8I5KVU2', 'Q4FZZ9', 'A0A2R8RMX4', 'Q7K4B6', 'A0A7P0T8N0', 'P13649', 'Q1LWP9', 'Q6NY85',
        'A0A804HJN3', 'A0A8M6YTH1', 'A0A8M2BBE5', 'A0A7P0T9F7', 'A0A7I2V4F2', 'Q9W3N7', 'O35929', 'P9WHX3', 'Q59QW5',
        'A0A6Q8PHH9', 'V9HW48', 'A0A3B3IU23', 'A0A8I6AIU7', 'E5RHU3', 'A0A8M1P8Y1', 'A0A7I2V2X3', 'D4A2E9', 'B6K4Y8',
        'A0A8M1P874', 'B0QY60', 'A1Z7Z8', 'Q7ZTY5', 'Q55EX7', 'E9PBI9', 'A0A7P0T900', 'A0A8I5KRU1', 'A0A6Q8PG14',
        'H0Y8U8', 'A0A8I5QL42', 'A0A499FJ41', 'O16787', 'A0A8I5KUS2', 'Q6FY41', 'Q54K33', 'A0A804HIF5', 'D6X1Z6',
        'A6KSS8', 'A0A0G2JWY7', 'A0A8I5KR47', 'B6K4X5', 'A4QNW4', 'A0A6Q8PG02', 'Q8I942', 'A0A7I2V4N4', 'P12234',
        'A0A804HJ61', 'A0A7I2V4X5', 'E7EXE0', 'E5RQL4', 'A0A6Q8PG75', 'Q5VJM6', 'A0A2R8QK81', 'Q8U3D2', 'A0A8I5KWA9',
        'A0A0U2N547', 'P0DW65', 'Q8WWA1', 'A0A8I5KV76', 'A0A804HKQ1', 'A0A804HJZ1', 'F8VSJ1', 'A0A0A0MRK1',
        'A0A8I5QJV9', 'Q9VWI0', 'A0A8I5KS87', 'A6IMJ6', 'A0A7I2V4X1', 'A0A8I5KZB7', 'Q9U4F8', 'B6ZIV2', 'A0A804HK76',
        'Q553C0', 'A0A140VKA2', 'A0A804F6T5', 'A0A7I2V3E8', 'A0A0S2Z3M4', 'Q9VWS2', 'P37650', 'A0A7I2V5S2', 'F1QRC8',
        'A0A7P0TAS3', 'A0A7I2V556', 'A0A8M9P612', 'Q59X67', 'A0A2R8QT05', 'A0A804HLD4', 'Q5T4E8', 'E9PAV9', 'R4GNC9',
        'A0A8I5KY61', 'A0A7P0TBM9', 'A0A1D8PDM8', 'Q5T0C7', 'A0A1D8PIC1', 'A0A6Q8PHJ9', 'C9JS37', 'A0A6Q8PHK0',
        'Q5BIV4', 'A0A096LP25', 'Q8R1G1', 'A0A7I2V4L7', 'A0A7P0Z4F9', 'A0A8I5KYZ5', 'A0A7P0T855', 'F1R833', 'Q5AL03',
        'Q5A1E3'}

print(len(data))
def get_sequences(fasta_file):
    collected = set()
    for seq_record in SeqIO.parse(fasta_file, "fasta"):
        if "|" in seq_record.id:
            id = seq_record.id.split("|")[1]
        else:
            id = seq_record.id

        if id in data:
            collected.add(id)

    return collected


collected = get_sequences(CONSTANTS.ROOT_DIR + "uniprot/{}".format('uniprot_sprot.fasta'))
data = data.difference(collected)
print(len(data))

collected = get_sequences(CONSTANTS.ROOT_DIR + "uniprot/down/{}".format('uniprot_sprot.fasta'))
data = data.difference(collected)
print(len(data))

collected = get_sequences(CONSTANTS.ROOT_DIR + "uniprot/{}".format('uniprot_gt_1022.fasta'))
data = data.difference(collected)
print(len(data))

collected = get_sequences(CONSTANTS.ROOT_DIR + "uniprot/{}".format('old.fasta'))
data = data.difference(collected)
print(len(data))

collected = get_sequences(CONSTANTS.ROOT_DIR + "uniprot/{}".format('train_sequences.fasta'))
data = data.difference(collected)
print(len(data))


exit()


_data = pickle_load("cc_string_comps").key_val
data = torch.load("cc_string_comp.pt")
data = data.cpu()

res = dict((v,k) for k,v in _data.items())


for i, j in enumerate(data):
    print(i, res[i], j)
    exit()






exit()
obsolete_terms = {'Q7T2I8', 'Q5BLG6', 'A0A0R4IWQ3', 'F1REJ2',  'Q6IQ94', 'F1RCG7', 'Q7T2I9'}

x = Fasta("/home/fbqc9/Workspace/DATA/uniprot/rem_trm/combined.fasta")

x.reformat(output="testerall/combined.fasta")
x.fastas_from_fasta(out_dir="testerall")


exit()



a = pickle_load(CONSTANTS.ROOT_DIR + "{}/all_proteins".format('mf'))
b = pickle_load(CONSTANTS.ROOT_DIR + "{}/all_proteins".format('cc'))
c = pickle_load(CONSTANTS.ROOT_DIR + "{}/all_proteins".format('bp'))


print(len(a))
print(len(b))
print(len(c))

exit()


for record in SeqIO.parse("Q54801.a3m", "fasta"):
    print(record)
    exit()



# return [(record.description, remove_insertions(str(record.seq))) ]



exit()


x = Diamond()

grp = x.create_pytorch_graph()



x = STRING()

strg = x.create_pytorch_graph()

exit()

# for i in strg:
#     print(i, strg[i])
#     exit()


# exit()

filt = pickle_load(CONSTANTS.ROOT_DIR+"/{}/terms_stats".format('cc'))
term_indicies = []
for i in filt:
    term_indicies.append((i, filt[i][5]))
term_indicies.sort(key=lambda x: x[1])

terms = pickle_load(CONSTANTS.ROOT_DIR+"/{}/sorted_terms".format('cc'))



for i in zip(term_indicies, terms):
    assert(i[0][0] == i[1])

exit()

x = Diamond()

grp = x.create_pytorch_graph()

exit()
nodes = torch.randn(1, 2, 4)
edges = torch.randn(1, 2, 4, 8)
# mask = torch.ones(1, 128).bool()

print(nodes)

print(edges)

exit()
all_fastas = os.listdir("/bmlfast/frimpong/fastas/")
all_fastas = set([i.split(".")[0] for i in all_fastas])
print(len(all_fastas))

exit()
from Classes.Fasta import Fasta
fasta_path = "/home/fbqc9/Workspace/TransFun2/DataGen/output_test"
x = Fasta(fasta_path=fasta_path)
x.fastas_from_fasta(out_dir="/bmlfast/frimpong/fastas")

exit()

# x =  Interpro()
# features, categories, category_count = x.get_interpro_ohe_data()


# count_filter = {0:set(), 3:set(), 5: set(), 20:set(), 50:set(), 100:set()}
# for i in count_filter:
#     for j in category_count:
#         if category_count[j] >= i:
#             count_filter[i].add(j)

# pickle_save(count_filter, CONSTANTS.ROOT_DIR + "interpro/count_filter")
# exit()


# x = pickle_load(CONSTANTS.ROOT_DIR + "interpro/count_filter")

# # for i in x:
# #     print(i, len(x[i]))
# x = x[100]

# interpro_stats = pickle_load(CONSTANTS.ROOT_DIR + "interpro/int_stats_pos")

# indicies = []

# for i in x:
#     indicies.append(interpro_stats[i][1])

# pickle_save(indicies, CONSTANTS.ROOT_DIR + "interpro/indicies")


# exit()

# rows = np.random.choice(100, 500)
# cols = np.random.choice(100, 500)
# edges = torch.tensor([rows, cols])

# graph = data.Data(edge_index=edges)

# cluster = data.ClusterData(graph, 5)

# clusterloader = data.ClusterLoader(cluster)

# for i in clusterloader:
#     print(i)

# x = pickle_load(CONSTANTS.ROOT_DIR + "interpro/categories")
# pot = {}
# for i, j in enumerate(x):
#     pot[j] = i

# x = pickle_load(CONSTANTS.ROOT_DIR + "interpro/int_stats_134359")


# tmp = {}
# for i in x:
#     tmp[i] = (x[i], pot[i])

# pickle_save(tmp, CONSTANTS.ROOT_DIR + "interpro/int_stats_pos")


# interpro_stats = pickle_load(CONSTANTS.ROOT_DIR + "interpro/int_stats_pos")
# print(interpro_stats)
# levs = [3, 5, 10, 15, 20, 25, 30, 50, 100, 150, 200]

# interpro_indicies = {}
# for lev in levs:
#     _tmp = [interpro_stats[i][1] for i in interpro_stats if interpro_stats[i][0] >= lev]
#     interpro_indicies[lev] = _tmp
#     print(lev, len(_tmp))

# pickle_save(interpro_indicies, CONSTANTS.ROOT_DIR + "interpro/interpro_indicies")


# interpro_indicies = pickle_load(CONSTANTS.ROOT_DIR + "interpro/interpro_indicies")


# # exit()
# ct = 0
# x = pickle_load(CONSTANTS.ROOT_DIR + "interpro/int_stats_pos")
# for i in x:
#     print(i, x[i])
#     ct = ct +1

#     if ct > 100:
#         break

# exit()
# # filtered_ip = {key: value for key, value in x.items() if value >=5}

# stats = {5:0, 10:0, 50:0, 100:0}
# for i in x:
#     if x[i] < 5:
#         stats[5] = stats[5] + 1
#     elif x[i] < 10:
#         stats[10] = stats[10] + 1
#     elif x[i] < 50:
#         stats[50] = stats[50] + 1
#     else:
#         stats[100] = stats[100] + 1

# for i in stats:
#     print(i, stats[i])


# exit()

# # Interpro Data
# # interpro = Interpro()
# # interpro_data, interpro_sig = interpro.get_interpro_ohe_data()

# # x = pd.DataFrame.from_dict(interpro_data, orient='index', columns=interpro_sig)


# # writer = pd.ExcelWriter('output.xlsx', engine='openpyxl')
# # x.describe(percentiles=[.1, .2, .3, .4, .5, .6, .7, .8, .9]).to_excel(writer, sheet_name='interpro')
# # writer.save()
# # print(interpro_data.head())



# # exit()

# # filtered_interpro_indicies = []
# # for pos, key in enumerate(interpro_sig):
# #     if key in filtered_ip:
# #         filtered_interpro_indicies.append(pos)

# # pickle_save(filtered_interpro_indicies, "filtered_interpro_indicies")

# # assert len(filtered_interpro_indicies) == len(filtered_ip)

# x = pickle_load("filtered_interpro_indicies")
# print(len(x))


# all_files = os.listdir("/home/fbqc9/Workspace/DATA/data/processed")
# all_files = set([i.split(".")[0] for i in all_files])

# af = os.listdir("/home/fbqc9/Workspace/Data/AF")
# af = set([i.split("-")[1] for i in af])

# rem = all_files - af

# print("{} remaining".format(len(rem)))

# found, notfound = 0, 0 
# for i in rem:
#     try:
#         urllib.request.urlretrieve("https://alphafold.ebi.ac.uk/files/AF-{}-F1-model_v4.pdb".format(i), "/home/fbqc9/AFD/AF-{}-F1-model_v4.pdb".format(i))
#         found = found + 1
#         print("{} found".format(found))
#     except urllib.error.HTTPError:
#         notfound = notfound + 1
#         print("{} not found".format(i))
    

# train = set(get_proteins_from_fasta("test_rem"))

# x = Fasta(fasta_path="test_rem")
# x.fastas_from_fasta(out_dir="rem_test_fastas")
# exit()
# from Bio.Seq import Seq
# from Bio import SeqIO, SwissProt
# from Bio.SeqRecord import SeqRecord

# train = set(get_proteins_from_fasta("/home/fbqc9/Workspace/DATA/cafa5/Train/train_sequences.fasta"))

# test = set(get_proteins_from_fasta("/home/fbqc9/Workspace/DATA/cafa5/Test_Target/testsuperset.fasta"))


# notfound = set([])
# diff = test.difference(train) 

# for i in diff:
#     if os.path.isfile("/home/fbqc9/Workspace/DATA/AF/AF-{}-F1-model_v4.pdb.gz".format(i)):
#         pass
#     else:   
#         notfound.add(i)

# print(len(notfound))

# new_seq = []
# for seq_record in SeqIO.parse("test_rem", "fasta"):
#     if seq_record.id in notfound:
#         new_seq.append(seq_record)

# SeqIO.write(new_seq, 'test_rem_omega', "fasta")

# x = set(get_proteins_from_fasta("test_rem"))

# print(len(x))

# gen1 = os.listdir("/home/fbqc9/a3ms")
# gen1 = set([i.split(".")[0] for i in gen1])

# gen2 = os.listdir("/bmlfast/frimpong/a3ms")
# gen2 = set([i.split(".")[0] for i in gen2])

# gen = gen1.union(gen2)
# print(len(gen))

# rem = list(x - gen)
# print(len(rem))



# # pickle_save(rem, "/home/fbqc9/rem_prot_names")

# # generated = os.listdir("/home/fbqc9/esm2_t48")
# # generated = set([i.split(".")[0] for i in  generated])
# # print(len(generated))



# # from Bio.Seq import Seq
# # from Bio import SeqIO, SwissProt
# # from Bio.SeqRecord import SeqRecord

# # new_seq = []
# # added = set()
# # for seq_record in SeqIO.parse("testsuperset.fasta", "fasta"):
# #     if seq_record.id in generated:
# #         pass
# #     else:
# #         if not seq_record.id in added:
# #             new_seq.append(SeqRecord(id=seq_record.id, seq=seq_record.seq, description=""))
# #             added.add(seq_record.id)

# # SeqIO.write(new_seq, 'test_rem', "fasta")

# # print(count_proteins("test_rem"))

# min_max_map = {
#     'cc': (100000, 1500),
#     'mf': (100000 , 1500),
#     'bp': (100000, 2200)
# }

# cc_prot = set()
# mf_prot = set()
# bp_prot = set()


# x = set(['GO:0005575', 'GO:0008150', 'GO:0110165', 'GO:0003674', 'GO:0005622', 'GO:0009987', 'GO:0043226', 'GO:0043229', 'GO:0005488', 'GO:0043227', 'GO:0005737', 'GO:0043231', 'GO:0005515', 'GO:0065007', 'GO:0050789', 'GO:0050794', 'GO:0050896', 'GO:0008152', 'GO:0032501', 'GO:0005634', 'GO:0032502', 'GO:0071704', 'GO:0048856', 'GO:0016020', 'GO:0003824', 'GO:0044237', 'GO:0044238', 'GO:0007275', 'GO:0006807', 'GO:0071944', 'GO:0071840', 'GO:0019222', 'GO:0016043', 'GO:0048518', 'GO:0005829', 'GO:0043170', 'GO:0048731', 'GO:0060255', 'GO:0051716', 'GO:0005886', 'GO:0032991', 'GO:0043228', 'GO:0043232', 'GO:0031974', 'GO:0043233', 'GO:0070013', 'GO:0048519', 'GO:0048522', 'GO:0080090', 'GO:0031323', 'GO:0042221', 'GO:1901564', 'GO:0051171', 'GO:0006950', 'GO:0010468', 'GO:0051179', 'GO:0048869', 'GO:0031981', 'GO:0030154', 'GO:0012505', 'GO:0048513', 'GO:0097159', 'GO:0009653', 'GO:1901363', 'GO:0048523', 'GO:1901360', 'GO:0051234', 'GO:0034641', 'GO:0007154', 'GO:0009605', 'GO:0009889', 'GO:0006725', 'GO:0006810', 'GO:0009058', 'GO:0031326', 'GO:0046483', 'GO:0005654', 'GO:0019219', 'GO:1901576', 'GO:0048583', 'GO:0003676', 'GO:0010556', 'GO:0048468', 'GO:0006996', 'GO:0044249', 'GO:0016740', 'GO:0023052', 'GO:0051252', 'GO:0009893', 'GO:0019538', 'GO:0010033', 'GO:0006139', 'GO:0005739', 'GO:2001141', 'GO:1903506', 'GO:0006355', 'GO:0010604', 'GO:0007165', 'GO:0005576', 'GO:0000003', 'GO:0007399', 'GO:0009888', 'GO:0010646', 'GO:0023051', 'GO:0044260', 'GO:0044085', 'GO:0051239', 'GO:0016787', 'GO:0022414', 'GO:0050793', 'GO:0065008', 'GO:0031090', 'GO:0031325', 'GO:0009892', 'GO:0051173', 'GO:0044281', 'GO:0090304', 'GO:0031982', 'GO:0051641', 'GO:0070887', 'GO:0140096', 'GO:0042995', 'GO:0009628', 'GO:0120025', 'GO:0010605', 'GO:0022607', 'GO:0044419', 'GO:0009966', 'GO:1901700', 'GO:0051128', 'GO:0043412', 'GO:0033554', 'GO:0030054', 'GO:0003677', 'GO:0009607', 'GO:0009790', 'GO:0009056', 'GO:0010467', 'GO:0005856', 'GO:0043207', 'GO:0051707', 'GO:0003006', 'GO:0036211', 'GO:1902494', 'GO:0042802', 'GO:0005783', 'GO:0033036', 'GO:0043167', 'GO:0022008', 'GO:0071702', 'GO:0051172', 'GO:0031324', 'GO:0097708', 'GO:0031410', 'GO:1901575', 'GO:0009891', 'GO:0006793', 'GO:0032879', 'GO:1901566', 'GO:0005615', 'GO:0031328', 'GO:0045935', 'GO:0006796', 'GO:0009719', 'GO:0009887', 'GO:0051246', 'GO:0048584', 'GO:0048699', 'GO:0044248', 'GO:0044271', 'GO:0060429', 'GO:0048646', 'GO:0016070', 'GO:0005215', 'GO:0031975', 'GO:0010557', 'GO:0006357', 'GO:0030182', 'GO:0065009', 'GO:0051649', 'GO:0051254', 'GO:0006629', 'GO:0016491', 'GO:0022857', 'GO:0071310', 'GO:0003008', 'GO:0043565', 'GO:0070727', 'GO:0009059', 'GO:0030030', 'GO:0042592', 'GO:0031967', 'GO:1901362', 'GO:0098588', 'GO:0006082', 'GO:0008104', 'GO:0006952', 'GO:0043436', 'GO:0120036', 'GO:1902680', 'GO:0032504', 'GO:1903508', 'GO:0045893', 'GO:0003723', 'GO:0005694', 'GO:0051049', 'GO:0005794', 'GO:0016772', 'GO:0019953', 'GO:0002376', 'GO:0007049', 'GO:0019752', 'GO:0009890', 'GO:0140513', 'GO:0019899', 'GO:0007417', 'GO:0048585', 'GO:0031327', 'GO:0071705', 'GO:0098772', 'GO:0140110', 'GO:0044255', 'GO:2000026', 'GO:0007010', 'GO:0003690', 'GO:0048666', 'GO:0080134', 'GO:0022402', 'GO:0051240', 'GO:0010558', 'GO:0072359', 'GO:0010647', 'GO:0045595', 'GO:0023056', 'GO:0015630', 'GO:0009791', 'GO:0045934', 'GO:0035295', 'GO:0048609', 'GO:0018130', 'GO:0010941', 'GO:0000902', 'GO:0019438', 'GO:1901135', 'GO:0099080', 'GO:0009725', 'GO:0098542', 'GO:0030312', 'GO:0045202', 'GO:1990837', 'GO:0046907', 'GO:0048870', 'GO:0043005', 'GO:0051253', 'GO:1902531', 'GO:0007166', 'GO:0042127', 'GO:0032989', 'GO:0009536', 'GO:0051094', 'GO:0007423', 'GO:0048598', 'GO:0033993', 'GO:1901701', 'GO:0016301', 'GO:0007389', 'GO:0040007', 'GO:0005773', 'GO:0043933', 'GO:0071495', 'GO:0010648', 'GO:0007276', 'GO:0009967', 'GO:0023057', 'GO:0036094', 'GO:0005730', 'GO:0098796', 'GO:0043067', 'GO:0050790', 'GO:0009792', 'GO:1902679', 'GO:0048729', 'GO:1903507', 'GO:0043169', 'GO:0045892', 'GO:0016788', 'GO:0002682', 'GO:0003002', 'GO:0016192', 'GO:0098590', 'GO:0060322', 'GO:0005929', 'GO:0016773', 'GO:1901698', 'GO:0140535', 'GO:0003700', 'GO:0001067', 'GO:0000904', 'GO:0007610', 'GO:0000976', 'GO:0031175', 'GO:0014070', 'GO:0060089', 'GO:0043168', 'GO:0005102', 'GO:0042981', 'GO:0032101', 'GO:0009617', 'GO:0006259', 'GO:0045944', 'GO:0019637', 'GO:0038023', 'GO:0046872', 'GO:0033043', 'GO:0009968', 'GO:0007420', 'GO:0051726', 'GO:0022412', 'GO:0035556', 'GO:1901565', 'GO:0010035', 'GO:0016477', 'GO:0010629', 'GO:0070161', 'GO:0065003', 'GO:0048878', 'GO:0050877', 'GO:0008610', 'GO:1990904', 'GO:0044877', 'GO:0010628', 'GO:0009991', 'GO:0009057', 'GO:0034645', 'GO:0006396', 'GO:0031399', 'GO:0010243', 'GO:0015075', 'GO:0055085', 'GO:0002009', 'GO:0051130', 'GO:0051247', 'GO:0044283', 'GO:0031984', 'GO:0048880', 'GO:0044093', 'GO:0035239', 'GO:0051241', 'GO:0040011', 'GO:0070925', 'GO:0007017', 'GO:0030234', 'GO:0015318', 'GO:0051174', 'GO:0019220', 'GO:0048589', 'GO:1990234', 'GO:0043603', 'GO:0034654', 'GO:0006508', 'GO:0061458', 'GO:0040008', 'GO:0048608', 'GO:0031667', 'GO:0032990', 'GO:0044087', 'GO:0022603', 'GO:0043230', 'GO:0006974', 'GO:0140677', 'GO:0065010', 'GO:0040012', 'GO:0000278', 'GO:0000785', 'GO:0048858', 'GO:0120039', 'GO:0007507', 'GO:0048812', 'GO:0043009', 'GO:1903561', 'GO:0045184', 'GO:0051093', 'GO:0004672', 'GO:0150063', 'GO:0001654', 'GO:0009314', 'GO:0048667', 'GO:0006811', 'GO:0042330', 'GO:0097367', 'GO:0048568', 'GO:0006955', 'GO:0140640', 'GO:0005975', 'GO:0070062', 'GO:0009507', 'GO:0008324', 'GO:0061061', 'GO:0007267', 'GO:0099081', 'GO:0009894', 'GO:0060341', 'GO:0099512', 'GO:0005815', 'GO:0046983', 'GO:0004888', 'GO:0005768', 'GO:0032787', 'GO:0051050', 'GO:0036477', 'GO:0016310', 'GO:0030029', 'GO:0000166', 'GO:1901265', 'GO:0030097', 'GO:0060548', 'GO:0006935', 'GO:0042325', 'GO:0005911', 'GO:1901137', 'GO:0061564', 'GO:0015031', 'GO:1901702', 'GO:0031347', 'GO:0019725', 'GO:0009986', 'GO:0005740', 'GO:0022890', 'GO:1903047', 'GO:0008092', 'GO:0007281', 'GO:0030855', 'GO:0003729', 'GO:0030036', 'GO:0000323', 'GO:0060284', 'GO:0010564', 'GO:2000145', 'GO:0007409', 'GO:0051248', 'GO:0044265', 'GO:0006812', 'GO:1901615', 'GO:0055086', 'GO:0031966', 'GO:0001932', 'GO:0002684', 'GO:0030424', 'GO:0050776', 'GO:0032880', 'GO:0010608', 'GO:0009416', 'GO:0043069', 'GO:0008284', 'GO:0031344', 'GO:0099503', 'GO:0033365', 'GO:0008233', 'GO:0043604', 'GO:0008219', 'GO:0006468', 'GO:0045597', 'GO:0034660', 'GO:0000226', 'GO:0097435', 'GO:0120035', 'GO:0043085', 'GO:0017076', 'GO:0009266', 'GO:0000122', 'GO:0001944', 'GO:0016071', 'GO:1902533', 'GO:0031329', 'GO:0043066', 'GO:0042175', 'GO:0006979', 'GO:0051276', 'GO:0030334', 'GO:0022803', 'GO:0048732', 'GO:0015267', 'GO:0031012', 'GO:0012506', 'GO:0071496', 'GO:0098827', 'GO:0090066', 'GO:0012501', 'GO:0050801', 'GO:0061024', 'GO:0140657', 'GO:0044297', 'GO:0030659', 'GO:0099568', 'GO:0051129', 'GO:0044403', 'GO:0042803', 'GO:0016829', 'GO:0090596', 'GO:0051640'])
# print(len(x))


# filt = pickle_load(CONSTANTS.ROOT_DIR+"/{}/terms_stats".format('mf'))
# maxi, mini = min_max_map['mf']
# for i in filt:
#     if filt[i][0] < maxi and filt[i][0] > mini:
#         mf_prot.add(i)

# filt = pickle_load(CONSTANTS.ROOT_DIR+"/{}/terms_stats".format('cc'))
# maxi, mini = min_max_map['cc']
# for i in filt:
#     if filt[i][0] < maxi and filt[i][0] > mini:
#         cc_prot.add(i)


# filt = pickle_load(CONSTANTS.ROOT_DIR+"/{}/terms_stats".format('bp'))
# maxi, mini = min_max_map['bp']
# for i in filt:
#     if filt[i][0] < maxi and filt[i][0] > mini:
#         bp_prot.add(i)


# mf_diff = x.intersection(mf_prot)
# print(len(mf_prot), len(mf_diff))

# cc_diff = x.intersection(cc_prot)
# print(len(cc_prot), len(cc_diff))

# bp_diff = x.intersection(bp_prot)
# print(len(bp_prot), len(bp_diff))

# #print(len(prot))

# exit()

# tren = set(pickle_load("/home/fbqc9/Workspace/DATA/all_proteins_cafa"))

# test = set(pickle_load("/home/fbqc9/Workspace/DATA/test_proteins"))

# test = list(test - tren)

# test = ["O76358", "Q20967", "Q9US39", "P48459", "Q17334", "P47089", "P90820", "O74851", "Q53G44", "P40449", "G5EC68", "Q8NIP8", "P11006", "Q09304", "O42944", "P34696", "P33046"]


# for pos, i in enumerate(test):

#     y = tmp = torch.load(CONSTANTS.ROOT_DIR + "data/processed2/{}.pt".format(i))

#     x = tmp = torch.load("/home/fbqc9/esm_msa1b/{}.pt".format(i))['representations_12'].squeeze(0)

#     y['esm_msa1b'].x = x
    
#     torch.save(y, CONSTANTS.ROOT_DIR + "data/processed2/{}.pt".format(i))



# exit()

# diamond = Diamond(session='test')
# diamond_data = diamond.get_graph(_format='list')

# for i in diamond_data:
#     print(i, diamond_data[i])
#     exit()



# interpro

# interpro = Interpro(ont='mf')
# interpro_data, interpro_sig, category_count = interpro.get_interpro_ohe_data()
# number_of_signatures = len(interpro_sig)
# print("Number of signatures", number_of_signatures)


# exit()


# String = STRING()
# data = String.get_string_neighbours(ontology='mf', recreate=True, confidence=0.8)
# String = STRING()
# data = String.get_string_neighbours(ontology='cc', recreate=True, confidence=0.8)
# String = STRING()
# data = String.get_string_neighbours(ontology='bp', recreate=True, confidence=0.8)

# print()

# # data = data[["Uniprot protein 1", "Uniprot protein 2", "Combined score"]]
# # print(data["Combined score"].describe().apply(lambda x: format(x, 'f')))


# exit()



# cat_counts = pickle_load(CONSTANTS.ROOT_DIR + "interpro/category_count_{}".format("mf"))
# cats = pickle_load(CONSTANTS.ROOT_DIR + "interpro/categories_{}".format("mf"))
# ind = pickle_load(CONSTANTS.ROOT_DIR + "interpro/indicies_{}".format("mf"))

# filt = [i for i, j in cat_counts.items() if j > 5]


# for i, j in enumerate(cats):
#     if j == 'IPR047580':
#         print(i, j)

# for i in ind[5]:
#     if 24531 == i:
#         print("yes")
# # print(cats)
# # print(ind)

# exit()


# esm2_t48 = torch.load("/home/fbqc9/esm2_t48/{}.pt".format("Q6BJD8"))['mean_representations'][48].view(1, -1).cpu()
# print(esm2_t48)
# exit()


# ROOT = "/home/fbqc9/Workspace/DATA/"
# String = STRING(session='test')
# neighbours = String.get_string_neighbours()

# for i in neighbours:
#     print(i, neighbours[i])
#     exit()


df1 = pd.DataFrame(

    {

        "A": ["A0", "A1", "A2", "A3"],

        "B": ["B0", "B1", "B2", "B3"],

        "C": ["C0", "C1", "C2", "C3"],

        "D": ["D0", "D1", "D2", "D3"],

    },

    index=[0, 1, 2, 3],

)

df2 = pd.DataFrame(

    {

        "A": ["A4", "A5", "A6", "A7"],

        "B": ["B4", "B5", "B6", "B7"],

        "C": ["C4", "C5", "C6", "C7"],

        "D": ["D4", "D5", "D6", "D7"],

    },

    index=[4, 5, 6, 7],

)

df4 = pd.DataFrame(

    {

        "B": ["B2", "B3", "B6", "B7"],

        "D": ["D2", "D3", "D6", "D7"],

        "F": ["F2", "F3", "F6", "F7"],

    },

    index=[2, 3, 6, 7],

)


print(df1)
print(df2)
print(df4)

result = pd.concat([df1, df2, df4], axis=1, join="outer")

print(result)


exit()

from torch_geometric.nn import global_add_pool, global_mean_pool, global_max_pool
from models import net_utils

x = torch.tensor(np.array([[1, 2, 3], [4, 5, 6]]))
print('x', x)

y = torch.tensor(np.array([[7, 8, 9], [10, 11, 12]]))
print('y', y)


# z = torch.cat([x, y], 0)

# print(z)
# print(z.shape)

z = torch.cat([x, y], 0)

# print(z)
# print(z.shape)


# z = torch.stack([x, y], 1)#.reshape(4,3)
print(z)
print(z.shape)

out_shape = x.shape[0]
print(out_shape)
batches = torch.arange(out_shape)
batches = torch.cat((batches, batches), 0)
print('batches', batches)

_z = z

z = net_utils.get_pool(pool_type='mean')(_z, batches)
print(z.shape)
print(z)

z = net_utils.get_pool(pool_type='max')(_z, batches)
print(z.shape)
print(z)

z = net_utils.get_pool(pool_type='add')(_z, batches)
print(z.shape)
print(z)