kstrauser

"""
Requirements:

* A Wordpress Blog
* Ghost export file (json).
* Python Packages: python-wordpress-xmlrpc

  >>> pip install python-wordpress-xmlrpc

WARNING:

kstrauser

>>> import mock
>>> m = mock.Mock()
>>> m.assert_called_once()  # Old version of Mock? This may not be what you expect.
<Mock name='mock.assert_called_once()' id='4558624144'>

>>> assert_called_once = mock.Mock.assert_called_once  # Doesn't exist. Now you know!
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: type object 'Mock' has no attribute 'assert_called_once'

kstrauser

from itertools import count

def fizz():
    for i in count(1):
        yield '' if i % 3 else 'fizz'

def buzz():
    for i in count(1):
        yield '' if i % 5 else 'buzz'

kstrauser

Keybase proof

I hereby claim:

• I am kstrauser on github.
• I am ks (https://keybase.io/ks) on keybase.
• I have a public key whose fingerprint is 19CF F48E 5C06 16B4 4A15 1E80 54D9 906A 294B 7FF3

To claim this, I am signing this object:

kstrauser

#!/usr/bin/env python

"""
Build wheels of all currently installed packages (as listed by "pip freeze")
"""

import glob
import importlib
from subprocess import call

kstrauser

I use this decorator in unittest modules to verify that a particularly-named object exists:

def existsin(namespace):
    """Given a namespace, this decorator asserts that an object with
    the same name as the function it's wrapping (minus ) exists in that
    namespace"""
    def wrapper(namedunittest):
        assert namedunittest.__name__.startswith('test_'), \
            "The wrapped '%s' function doesn't look like a unit test" % namedunittest.__name__

kstrauser

def substrcomparatormaker(column, i, j=None):
    """Return a comparator that tests for the substring of 'column'
    from 'i' to 'j', as specified with Python slice values.  This
    means setting the start column to i + 1 because Python is 0-based
    and SQL is 1-based, and setting the length to j - i.

    Please don't create new tables that use this! It's only for legacy,
    unnormalized data where multiple values are lumped together in a single
    column.