def compare_mea_speeds():
"""Compare the speeds of the three mea implementations"""
plt.figure(figsize=(14, 4))
panel1 = plt.axes([0.07, 0.11, .4, .8])
panel2 = plt.axes([0.55, 0.11, .4, .8])
# longest = max(data[0]) + data[1])
fast_times = []
slow_times = []
slower_times = []
num_points = []
for x in range(10, 200, 10):
size = x
prob_matrix, shortest_future_col_per_row = create_random_prob_matrix(col=size, row=size)
points = np.count_nonzero(prob_matrix)
num_points.append(points)
_, time = time_it(mea_slow, prob_matrix, shortest_future_col_per_row)
slow_times.append(time)
print(time, time/points)
_, time = time_it(mea_slower, prob_matrix, shortest_future_col_per_row)
slower_times.append(time)
print(time, time/points)
_, time = time_it(maximum_expected_accuracy_alignment, prob_matrix, shortest_future_col_per_row)
fast_times.append(time)
print(time, time/points)
# plot number of points vs time
handle1, = panel1.plot(num_points, fast_times, color='black')
handle2, = panel1.plot(num_points, slow_times, color='blue')
handle3, = panel1.plot(num_points, slower_times, color='red')
slowest_time = max(slower_times + slow_times)
most_points = max(num_points)
panel1.set_xlim(0, most_points)
panel1.set_ylim(0, slowest_time)
panel1.set_xlabel('Number of Points')
panel1.set_ylabel('Time (s)')
panel1.legend([handle1, handle2, handle3], ["Fast", "Slow", "Slower"], loc='upper left')
panel1.set_title('Time (s) vs Number of points ')
fast_ratio = [x/y for x, y in zip(fast_times, num_points)]
slow_ratio = [x/y for x, y in zip(slow_times, num_points)]
slower_ratio = [x/y for x, y in zip(slower_times, num_points)]
handle1, = panel2.plot(num_points, fast_ratio, color='black')
handle2, = panel2.plot(num_points, slow_ratio, color='blue')
handle3, = panel2.plot(num_points, slower_ratio, color='red')
panel2.set_xlim(0, max(num_points))
panel2.set_ylim(0, max(slower_ratio))
panel2.set_xlabel('Number of Points')
panel2.set_ylabel('Time/ number of points (s/point)')
panel2.legend([handle1, handle2, handle3], ["Fast", "Slow", "Slower"], loc='upper left')
panel2.set_title('Time(s)/Data Points vs Number of points ')
plt.show()
def test_time_it(self):
"""Test time_it function"""
with captured_output() as (_, _):
def add(x, y):
return x + y
_, _ = time_it(add, 1, 2)
with self.assertRaises(AssertionError):
time_it(1, 1, 2)
def test_generate_buildAlignments4(self):
kmers = get_kmers(6, alphabet="ATGC")
data_files = [self.alignments_path]
data, time = time_it(multiprocess_make_kmer_assignment_tables,
data_files, kmers, {"t", "c"}, 0.0, False, True,
10, 8)
with tempfile.TemporaryDirectory() as temdir:
output_file = os.path.join(temdir, "built_alignment.tsv")
data2, time2 = time_it(generate_top_n_kmers_from_sa_output,
data_files, temdir, output_file, 10, "ACGT",
6, 0.0, 8, False, True, False, True)
# get kmers associated with each sample
num_lines = len(list(open(output_file)))
print(time2, time)
self.assertEqual(len(data.index), num_lines)
self.assertLess(time2, time)
def test_binary_search_exact_match(self):
with captured_output() as (_, _):
for x in range(100, 1000, 10):
test_list = list(range(x))
time_list = []
for _ in range(100):
find_number = np.random.randint(0, x)
index, time = time_it(binary_search, test_list,
find_number)
time_list.append(time)
self.assertEqual(test_list[index], find_number)
def test_binary_search_no_match(self):
with captured_output() as (_, _):
for x in range(10, 100, 10):
test_list = list(range(x))
time_list = []
for _ in range(100):
find_number = np.random.randint(0, x) + 0.5
index, time = time_it(binary_search, test_list,
find_number, False)
time_list.append(time)
if index == x - 1:
self.assertTrue(test_list[index] < find_number)
elif index == 0:
self.assertTrue(find_number < test_list[index + 1])
else:
self.assertTrue(
test_list[index] < find_number < test_list[index +
1])
args.positions_file)
step_size = 10000
step_number = 0
with open(args.positions_file, "r") as fh:
while True:
out_name = args.output_file + str(step_number) + "tmp"
execute = "samtools view -b -o {} {}".format(out_name, args.bam)
positions = " "
counter = 0
for line in fh:
split_line = line.split()
chromosome = split_line[0]
position = split_line[1]
positions += chromosome + ":" + position + "-" + position + " "
counter += 1
if counter > 10000:
break
if positions == " " or step_number > 10:
break
execute += positions
check_call(execute.split())
check_call(
f"samtools rmdup -s {out_name} {args.output_file+str(step_number)}"
.split())
os.remove(out_name)
step_number += 1
if __name__ == '__main__':
print(time_it(main)[1])
print("Per-genomic-site confusion matrix", file=log_file)
print(cmh.confusion_matrix(), file=log_file)
all_data.append(chr_strand_data)
print("All Chromosomes both strands:", file=log_file)
print("Per-call confusion matrix", file=log_file)
print(print_confusion_matrix(tps, fps, fns, tns),
file=log_file)
plot_confusion_matrix(tps, fps, fns, tns,
normalize=True,
output_path=os.path.join(output_dir,
"all_calls_confusion_matrix.png"),
title="All calls CpG "
"Normalized Confusion Matrix")
all_data = pd.concat(all_data)
label_data = all_data.loc[:, ['C_label', "E_label"]]
prediction_data = all_data.loc[:, ['C', "E"]]
label_data.rename(columns={'C_label': 'C', "E_label": "E"}, inplace=True)
cmh = ClassificationMetrics(label_data, prediction_data)
cmh.plot_roc("E", os.path.join(output_dir, "per_genomic_site_all_chromosomes_roc.png"))
cmh.plot_precision_recall("E", os.path.join(output_dir,
"per_genomic_site_all_chromosomes"
"precision_recall.png"))
print("Per-genomic-site confusion matrix", file=log_file)
print(cmh.confusion_matrix(), file=log_file)
if __name__ == '__main__':
print(time_it(main))
def main():
args = parse_args()
filter_bed = FilterBed()
filters = []
if args.filter_by_percentage is not None:
filter_bed.set_filter_by_percentage(
*[float(x) for x in args.filter_by_percentage])
filters.append(filter_bed.filter_by_percentage_min_min_max_max)
if args.filter_by_coverage is not None:
filter_bed.set_filter_by_coverage(
*[float(x) for x in args.filter_by_coverage])
filters.append(filter_bed.filter_by_coverage_min_max)
filter_bed.chain_logic(*filters)
kmers = get_kmer_counts_from_reference_given_bed(
args.reference,
args.methyl_bed,
k=args.kmer_length,
param_filter=filter_bed.function,
check_base=args.check_base)
print(kmers)
with open(os.path.join(args.output, "kmer_counts.pkl"), 'wb') as fh:
pickle.dump(kmers, fh)
if __name__ == '__main__':
_, time = time_it(main)
print(time, "seconds")
