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From: Michael R. Crusoe <michael.crusoe@gmail.com>
Subject: convert scripts to Python3
--- a/src/scripts/mkarv
+++ b/src/scripts/mkarv
@@ -1,6 +1,6 @@
-#!/usr/bin/env python
+#!/usr/bin/python3
+
-from __future__ import print_function
import argparse
import collections
@@ -301,10 +301,10 @@ def add_fraction_of_reads(fragment_lengt
Given a map of fragment lengths to read counts, calculate the fraction of all reads represented at each fragment length.
"""
total_reads = 0.0
- for fragment_length, count in fragment_length_counts.items():
+ for fragment_length, count in list(fragment_length_counts.items()):
total_reads += count
- for fragment_length, count in fragment_length_counts.items():
+ for fragment_length, count in list(fragment_length_counts.items()):
if total_reads < 1:
fraction = 0
else:
@@ -322,7 +322,7 @@ def prepare_fragment_length_counts(fragm
"""
# adjust the distribution to the requested maximum fragment length
- prepared_counts = collections.defaultdict(long)
+ prepared_counts = collections.defaultdict(int)
for fragment_length, count, fraction_of_total_reads in fragment_length_counts:
if fragment_length <= max_fragment_length:
prepared_counts[fragment_length] = count
@@ -336,16 +336,16 @@ def construct_fragment_length_reference(
"""
Construct a reference fragment length density distribution from all metrics in `data`.
"""
- metrics = data['metrics'].values()
+ metrics = list(data['metrics'].values())
metrics_count = len(metrics)
- reference_distribution = collections.defaultdict(long)
+ reference_distribution = collections.defaultdict(int)
for m in metrics:
- for fragment_length, [count, fraction_of_total_reads] in m['fragment_length_counts'].items():
+ for fragment_length, [count, fraction_of_total_reads] in list(m['fragment_length_counts'].items()):
if fragment_length <= max_fragment_length:
reference_distribution[fragment_length] += count
- for fragment_length, count in reference_distribution.items():
+ for fragment_length, count in list(reference_distribution.items()):
reference_distribution[fragment_length] /= metrics_count
add_fraction_of_reads(reference_distribution)
@@ -382,7 +382,7 @@ def calculate_fragment_length_distance(m
nonreference_cdf = make_cdf(metrics['fragment_length_counts'])
- diff = list(map(lambda x: x[0] - x[1], zip(nonreference_cdf, reference_cdf)))
+ diff = list([x[0] - x[1] for x in zip(nonreference_cdf, reference_cdf)])
if diff:
distance = (abs(max(diff)) > abs(min(diff))) and max(diff) or min(diff)
@@ -404,7 +404,7 @@ def add_fragment_length_distances(data,
loaded_distribution = {}
try:
- loaded_distribution = {int(k): int(v) for k, v in json.load(open_maybe_gzipped(reference)).items()}
+ loaded_distribution = {int(k): int(v) for k, v in list(json.load(open_maybe_gzipped(reference)).items())}
except:
with open(reference, 'rb') as f:
dialect = csv.Sniffer().sniff(f.read(1024))
@@ -425,8 +425,8 @@ def add_fragment_length_distances(data,
logging.warn('Reference distribution lacks value at fragment length {}; assigning zero'.format(fragment_length))
# adjust the distribution to the requested maximum fragment length
- cleaned_distribution = collections.defaultdict(long)
- for fragment_length, [count, fraction_of_all_reads] in fragment_length_reference['distribution'].items():
+ cleaned_distribution = collections.defaultdict(int)
+ for fragment_length, [count, fraction_of_all_reads] in list(fragment_length_reference['distribution'].items()):
if fragment_length <= max_fragment_length:
cleaned_distribution[fragment_length] = count
@@ -443,15 +443,15 @@ def add_fragment_length_distances(data,
def calculate_reference_peak_metrics(data):
reference_peak_metrics = {
- 'source': 'calculated from ' + ', '.join(sorted([m['name'] for m in data['metrics'].values()])),
+ 'source': 'calculated from ' + ', '.join(sorted([m['name'] for m in list(data['metrics'].values())])),
'cumulative_fraction_of_hqaa': [],
'cumulative_fraction_of_territory': []
}
- cfh = zip(*[metrics['peak_percentiles']['cumulative_fraction_of_hqaa'] for name, metrics in sorted(data['metrics'].items())])
- reference_peak_metrics['cumulative_fraction_of_hqaa'] = map(lambda x: sum(x) / len(x), cfh)
+ cfh = list(zip(*[metrics['peak_percentiles']['cumulative_fraction_of_hqaa'] for name, metrics in sorted(data['metrics'].items())]))
+ reference_peak_metrics['cumulative_fraction_of_hqaa'] = [sum(x) / len(x) for x in cfh]
- cft = zip(*[metrics['peak_percentiles']['cumulative_fraction_of_territory'] for name, metrics in sorted(data['metrics'].items())])
- reference_peak_metrics['cumulative_fraction_of_territory'] = map(lambda x: sum(x) / len(x), cft)
+ cft = list(zip(*[metrics['peak_percentiles']['cumulative_fraction_of_territory'] for name, metrics in sorted(data['metrics'].items())]))
+ reference_peak_metrics['cumulative_fraction_of_territory'] = [sum(x) / len(x) for x in cft]
return reference_peak_metrics
@@ -512,12 +512,12 @@ PERCENTAGES = {
def prepare_for_viewer(data):
- for name, metrics in data.items():
+ for name, metrics in list(data.items()):
del metrics['peaks']
del metrics['peaks_fields']
metrics['percentages'] = {}
- for numerator, denominator in PERCENTAGES.items():
+ for numerator, denominator in list(PERCENTAGES.items()):
key = '{}__{}'.format(numerator, denominator)
if metrics[denominator] == 0:
if metrics[numerator] == 0:
--- a/src/scripts/srvarv
+++ b/src/scripts/srvarv
@@ -1,4 +1,4 @@
-#!/usr/bin/env python
+#!/usr/bin/python3
import argparse
import os
@@ -7,8 +7,8 @@ try:
import http.server as httpserver
import socketserver
except:
- import SimpleHTTPServer as httpserver
- import SocketServer as socketserver
+ import http.server as httpserver
+ import socketserver as socketserver
if __name__ == '__main__':
@@ -28,7 +28,7 @@ if __name__ == '__main__':
instance = os.path.abspath(args.instance)
os.chdir(args.instance)
- print('Serving ataqv web viewer instance from {} at http://localhost:{}'.format(instance, args.port))
+ print(('Serving ataqv web viewer instance from {} at http://localhost:{}'.format(instance, args.port)))
try:
httpd.serve_forever()
except KeyboardInterrupt:
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