def pandas_scalability_test():
"""
@brief: check if time is linearly proportional to matrix size
"""
gen_record_large = ld.load_bio_files(
["Data/mitochondrion.1.1.genomic.fna"])
gen_record = ld.load_bio_files(["Data/species_bold_own_genbank.fasta"])
primer_pairs = ld.load_csv_file("Data/P&PP.csv")
max_len = 0
key = None
for gen_key in gen_record_large:
leng = len(gen_record_large[gen_key])
if (leng > max_len):
max_len = leng
key = gen_key
primer = primer_pairs[4].f
#largest_matrix len= 2M*primer_len
gen = gen_record_large[key]
time1 = time.time()
result_matrix = m.MATCH_TABLE.loc[gen, primer]
elapsedTime_l = ((time.time() - time1))
#len = 658*primer_len
gen = gen_record["ACEA563-14_Aphis_gossypii_BOLD"]
len_s = len(gen)
time1 = time.time()
result_matrix = m.MATCH_TABLE.loc[gen, primer]
elapsedTime_s = ((time.time() - time1))
print(int(elapsedTime_l * (10**9) / max_len),
int(elapsedTime_s * (10**9) / len_s))
return
def restore_template():
gen_record = ld.load_bio_files(["Data/species_bold_own_genbank.fasta"])
#gen_record = {"AGB001-11_Salticus_scenicus_BOLD": gen_record["AGB001-11_Salticus_scenicus_BOLD"]};
primer_pairs = ld.load_csv_file("Data/P&PP.csv")
#primer_pairs = [primer_pairs[5]]
template, discarded, rs, cs = m.compute_gen_matching(
5, 5, primer_pairs, gen_record, 0)
header = [
"primerPair", "fastaid", "primerF", "primerR", "mismFT", "mismRT",
"amplicon", "F_pos", "mismFT_loc", "mismFT_type", "mismFT_base",
"R_pos"
]
m.store_matching_results("Test_data/test1.csv",
template,
header=TEMPLATE_HEADER)
templateR = ld.load_template("Test_data/test1.csv")
templateR, discarded, rs, cs = ld.restore_template(templateR, gen_record,
primer_pairs, 10)
i.save_matching_info("Test_data/test2",
templateR,
discarded,
rs,
cs,
header=TEMPLATE_HEADER)
return
def check_if_multiple_alignments_are_frequent():
gen_record = ld.load_bio_files(["Data/species_bold_own_genbank.fasta"])
primer_pairs = ld.load_csv_file("Data/P&PP.csv")
gen_alignment_list = m.compute_gen_matching(5, 5, primer_pairs, gen_record)
for gm in gen_alignment_list:
matching_list = gm.get_matching_list()
for al_list in matching_list:
alignments = al_list.get_list()
if (len(alignments > 1)):
print(al_list.gen.id, al_list.primer_pair.id, len(alignments))
return
def load_gen_record(gen_file, check_integrity, check_uppercase,
hanging_primers):
gen_record = None
try:
gen_record = ld.load_bio_files(gen_file,
writable=check_integrity
or check_uppercase or hanging_primers,
check_uppercase=check_uppercase)
logging.info("Genome record file loaded!")
except:
logging.error("At loading gen record")
if (check_integrity): gen_record = ld.remove_bad_gens(gen_record)
return gen_record
def matching_test():
init_logger()
set_verbosity(True)
gen_record = ld.load_bio_files(["test_input/sbog_test.fasta"])
#gen_record = split(gen_record, 0.005)
#gen_record = {"AGB001-11_Salticus_scenicus_BOLD": gen_record["AGB001-11_Salticus_scenicus_BOLD"]};
primer_pairs = ld.load_csv_file("test_input/PP.csv")
#primer_pairs = {"6":primer_pairs["6"]}
output = "Test_data/output"
header = [i for i in range(len(TEMPLATE_HEADER))]
#template, discarded, rs, cs = m.compute_gen_matching(5, 5, primer_pairs, gen_record, output)
#m.store_matching_results("Test_data/test1.csv", template, header=TEMPLATE_HEADER)
#i.save_matching_info("TEST", output, template, header, discarded, rs, cs)
close_logger()
print(gen_record["ACEA1016-14_Aphis_spiraecola_BOLD"][550:550 + 23])
print(''.join(primer_pairs["10"].r.seq))
return
def test_all_pairs():
#"primerPair","id","fastaid","organism","subgrup","primerF","primerR","mismFT","mismRT","mismTT","mismF3","mismR3","mismT3","long"
trusted_results = pd.read_csv(
"Test_data/mismatches_allPrimers_allMitochondria.csv", sep=',')
global_check = {"amplicon": 1, "missf": 1, "missr": 1}
check = {"amplicon": 1, "missf": 1, "missr": 1}
gen_record = ld.load_bio_files(["Data/mitochondrion.1.1.genomic.fna"],
writable=True)
gen_record = split(gen_record, 0.01)
primer_pairs = ld.load_csv_file("Data/P&PP.csv")
result = m.compute_gen_matching(5,
5,
primer_pairs,
gen_record,
0,
hanging_primers=True)
gen_matching_table = result[0]
header = TEMPLATE_HEADER
correct_alignments = pd.DataFrame(columns=header)
info = {
"total_gens": len(gen_record),
"matches_skipped": 0,
"alignments_processed": 0,
"multiple_alignment_cases": 0,
"better_alignments": 0
}
for index in gen_matching_table.index:
genid = gen_matching_table.iloc[index].loc["fastaid"]
pp = gen_matching_table.iloc[index].loc["primerPair"]
target = trusted_results.loc[trusted_results['fastaid'].str.contains(
genid[4:-1])]
target = target.loc[target['primerPair'] == int(pp)]
if (target.empty):
#print("Target empty, skipping this gen...")
info["matches_skipped"] += 1
#Add them to not checked list
else:
pp = primer_pairs[pp]
info["alignments_processed"] += 1
#amplicon
amplicon = gen_matching_table.iloc[index].loc["amplicon"]
if (amplicon < pp.min_amplicon or amplicon > pp.max_amplicon):
print(genid)
print("PRIMER PAIR: ", pp.id)
print("Amplicon outside range")
print(amplicon, pp.min_amplicon, pp.max_amplicon)
global_check["amplicon"] = 0
check["amplicon"] = 0
if (amplicon != target['long'].iat[0]):
print(genid)
print("PRIMER PAIR: ", pp.id)
print("Amplicon not matching")
global_check["amplicon"] = 0
check["amplicon"] = 0
fm = target['mismFT'].iat[0]
rm = target['mismRT'].iat[0]
#fm
if (gen_matching_table.iloc[index].loc["mismFT"] > fm):
print(genid)
print("PRIMER PAIR: ", pp.id)
print("Bad forward matching")
global_check["missf"] = 0
check["missf"] = 0
#rm
if (gen_matching_table.iloc[index].loc["mismRT"] > rm):
print(genid)
print("PRIMER PAIR: ", pp.id)
print("Bad reverse matching")
global_check["missr"] = 0
check["missr"] = 0
if 0 not in check.values(
): #if everything is correct check if the result found is better
correct_alignments.loc[correct_alignments.shape[
0]] = gen_matching_table.loc[index]
if (gen_matching_table.iloc[index].loc["mismFT"] +
gen_matching_table.iloc[index].loc["mismRT"] <
fm + rm):
info["better_alignments"] += 1
else:
info["better_alignments"] += 0
#correct_alignment_list.append(al)
for info_key in info:
print(info_key, info[info_key])
sum_check = 0
for c in global_check:
sum_check += check[c]
if (sum_check == len(check)):
print("SUCCESS!")
else:
print("TEST FAILED")
correct_alignments.to_csv("Test_data/correct_alignments.csv",
index_label="id")
"""
store_results("Test_data/better_alignments.csv", better_alignment_list, header)
store_results("Test_data/not_tested_alignments.csv", not_tested_alignment_list, header)
ld.store_matching_results("Test_data/full_alignments.csv", gen_alignment_list, header)
"""
return
def check_uppercase():
gen_record = ld.load_bio_files(["Data/sbog_test.fasta"],
check_uppercase=True)
print(gen_record["ACEA1016-14_Aphis_spiraecola_BOLD"])