##// END OF EJS Templates
check whether all engines are at HWM in a few places...
check whether all engines are at HWM in a few places avoids calling maybe_run on the entire queue when we know that no tasks can be run. This caused an expensive O(N) operation every time an engine just became not-full, which is every time a task finishes in the default case of HWM=1.

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dtexample.py
164 lines | 2.8 KiB | text/x-python | PythonLexer
"""Simple example using doctests.
This file just contains doctests both using plain python and IPython prompts.
All tests should be loaded by nose.
"""
from IPython.utils.py3compat import doctest_refactor_print
def pyfunc():
"""Some pure python tests...
>>> pyfunc()
'pyfunc'
>>> import os
>>> 2+3
5
>>> for i in range(3):
... print i,
... print i+1,
...
0 1 1 2 2 3
"""
return 'pyfunc'
@doctest_refactor_print
def ipfunc():
"""Some ipython tests...
In [1]: import os
In [3]: 2+3
Out[3]: 5
In [26]: for i in range(3):
....: print i,
....: print i+1,
....:
0 1 1 2 2 3
Examples that access the operating system work:
In [1]: !echo hello
hello
In [2]: !echo hello > /tmp/foo
In [3]: !cat /tmp/foo
hello
In [4]: rm -f /tmp/foo
It's OK to use '_' for the last result, but do NOT try to use IPython's
numbered history of _NN outputs, since those won't exist under the
doctest environment:
In [7]: 'hi'
Out[7]: 'hi'
In [8]: print repr(_)
'hi'
In [7]: 3+4
Out[7]: 7
In [8]: _+3
Out[8]: 10
In [9]: ipfunc()
Out[9]: 'ipfunc'
"""
return 'ipfunc'
def ranfunc():
"""A function with some random output.
Normal examples are verified as usual:
>>> 1+3
4
But if you put '# random' in the output, it is ignored:
>>> 1+3
junk goes here... # random
>>> 1+2
again, anything goes #random
if multiline, the random mark is only needed once.
>>> 1+2
You can also put the random marker at the end:
# random
>>> 1+2
# random
.. or at the beginning.
More correct input is properly verified:
>>> ranfunc()
'ranfunc'
"""
return 'ranfunc'
def random_all():
"""A function where we ignore the output of ALL examples.
Examples:
# all-random
This mark tells the testing machinery that all subsequent examples should
be treated as random (ignoring their output). They are still executed,
so if a they raise an error, it will be detected as such, but their
output is completely ignored.
>>> 1+3
junk goes here...
>>> 1+3
klasdfj;
>>> 1+2
again, anything goes
blah...
"""
pass
@doctest_refactor_print
def iprand():
"""Some ipython tests with random output.
In [7]: 3+4
Out[7]: 7
In [8]: print 'hello'
world # random
In [9]: iprand()
Out[9]: 'iprand'
"""
return 'iprand'
@doctest_refactor_print
def iprand_all():
"""Some ipython tests with fully random output.
# all-random
In [7]: 1
Out[7]: 99
In [8]: print 'hello'
world
In [9]: iprand_all()
Out[9]: 'junk'
"""
return 'iprand_all'