make test_performance work with pre-3.6; add JOSS badge

README.md

@@ -4,6 +4,7 @@
 [![Codacy Badge](https://api.codacy.com/project/badge/Grade/b2c0baa52b604e39a09ed108ac2f53ee)](https://www.codacy.com/app/jdidion/xphyle?utm_source=github.com&utm_medium=referral&utm_content=jdidion/xphyle&utm_campaign=Badge_Grade)
 [![Documentation Status](https://readthedocs.org/projects/xphyle/badge/?version=latest)](http://xphyle.readthedocs.io/en/latest/?badge=latest)
 [![DOI](https://zenodo.org/badge/71260678.svg)](https://zenodo.org/badge/latestdoi/71260678)
+[![JOSS](http://joss.theoj.org/papers/10.21105/joss.00255/status.svg)](http://joss.theoj.org/papers/10.21105/joss.00255)
 
 # xphyle: extraordinarily simple file handling
 

tests/test_performance.py

@@ -1,7 +1,10 @@
 """Self-contained performance tests.
 """
+from bisect import bisect
 from contextlib import contextmanager
 import gzip
+from itertools import accumulate
+from random import random, randint
 import time
 from xphyle.utils import read_lines
 from xphyle.paths import TempDir
@@ -24,6 +27,26 @@ def __exit__(self, exception_type, exception_value, traceback):
             duration=self.duration,
             **self.msg_args))
 
+def choices(population, weights=None, *, cum_weights=None, k=1):
+    """Return a k sized list of population elements chosen with replacement.
+    If the relative weights or cumulative weights are not specified,
+    the selections are made with equal probability.
+    
+    This function is borrowed from the python 3.6 'random' package.
+    """
+    if cum_weights is None:
+        if weights is None:
+            _int = int
+            total = len(population)
+            return [population[_int(random() * total)] for i in range(k)]
+        cum_weights = list(accumulate(weights))
+    elif weights is not None:
+        raise TypeError('Cannot specify both weights and cumulative weights')
+    if len(cum_weights) != len(population):
+        raise ValueError('The number of weights does not match the population')
+    total = cum_weights[-1]
+    return [population[bisect(cum_weights, random() * total)] for i in range(k)]
+
 def perftest(name, text_generator, num_iter=10):
     # generate a big text
     msg = """
@@ -62,7 +85,6 @@ def test_lorem_ipsum():
 
 @pytest.mark.perf
 def test_fastq():
-    from random import randint, choices
     def generate_fastq(seqlen=100):
         num_records = randint(100000, 500000)
         qualspace = list(chr(i + 33) for i in range(60))