arieb / python-testdata Goto Github PK

CentOS 6.5
Python 2.6.6
Installed with pip install python-testdata

Example DictFactory code errors with the following:

/usr/bin/python /home/corey/PycharmProjects/test.py
Traceback (most recent call last):
File "/home/corey/PycharmProjects/test.py", line 14, in
for user in Users().generate(10): # let say we only want 10 users
File "/usr/lib/python2.6/site-packages/testdata/dictionary.py", line 27, in init
self._child_factories = deepcopy(self._child_factory_tree)
File "/usr/lib64/python2.6/copy.py", line 189, in deepcopy
y = _reconstruct(x, rv, 1, memo)
File "/usr/lib64/python2.6/copy.py", line 338, in _reconstruct
state = deepcopy(state, memo)
File "/usr/lib64/python2.6/copy.py", line 162, in deepcopy
y = copier(x, memo)
File "/usr/lib64/python2.6/copy.py", line 255, in _deepcopy_dict
y[deepcopy(key, memo)] = deepcopy(value, memo)
File "/usr/lib64/python2.6/copy.py", line 189, in deepcopy
y = _reconstruct(x, rv, 1, memo)
File "/usr/lib64/python2.6/copy.py", line 334, in _reconstruct
value = deepcopy(value, memo)
File "/usr/lib64/python2.6/copy.py", line 162, in deepcopy
y = copier(x, memo)
File "/usr/lib64/python2.6/copy.py", line 255, in _deepcopy_dict
y[deepcopy(key, memo)] = deepcopy(value, memo)
File "/usr/lib64/python2.6/copy.py", line 189, in deepcopy
y = _reconstruct(x, rv, 1, memo)
File "/usr/lib64/python2.6/copy.py", line 338, in _reconstruct
state = deepcopy(state, memo)
File "/usr/lib64/python2.6/copy.py", line 162, in deepcopy
y = copier(x, memo)
File "/usr/lib64/python2.6/copy.py", line 255, in _deepcopy_dict
y[deepcopy(key, memo)] = deepcopy(value, memo)
File "/usr/lib64/python2.6/copy.py", line 189, in deepcopy
y = _reconstruct(x, rv, 1, memo)
File "/usr/lib64/python2.6/copy.py", line 323, in _reconstruct
y = callable(*args)
File "/usr/lib64/python2.6/copy_reg.py", line 93, in newobj
return cls.new(cls, *args)
TypeError: instancemethod expected at least 2 arguments, got 0

Process finished with exit code 1

class Accounts(testdata.DictFactory):
    id = testdata.CountingFactory(10)
    first = testdata.FakeDataFactory('firstName')
    last = testdata.FakeDataFactory('lastName')
    address = testdata.FakeDataFactory('address')
    age = testdata.RandomInteger(10, 30)

Accounts().generate(10)

raises this exception:

tdata/dictionary.py", line 27, in __init__
    self._child_factories = deepcopy(self._child_factory_tree)
AttributeError: 'Accounts' object has no attribute '_child_factory_tree'

If you're running out of ideas: http://www.mockaroo.com/ has some more factory types ;)

Using Python 3.5.2 and 3.6.1 shows this error:

Traceback (most recent call last):
File "./testdata-gen.py", line 13, in
for user in Users().generate(4): # Generate 4 users
File "/anaconda/lib/python3.5/site-packages/testdata/dictionary.py", line 27, in init
self._child_factories = deepcopy(self._child_factory_tree)
AttributeError: 'Users' object has no attribute '_child_factory_tree'

$ python --version
Python 3.5.2 :: Anaconda 4.2.0 (x86_64)

Hello, is this project still active? Should I use another library? Do you need support?

Hi,
Can't get this to install on windows. I get the following:

File "C:\Python27\Lib\distutils\util.py", line 126, in convert_path

raise ValueError, "path '%s' cannot end with '/'" % pathname

ValueError: path './' cannot end with '/'

Hey,
I created some new number classes and wanted to share them. I might keep on working on some more.
With these classes it's possible to add / multiply multiple dependent fields and to create a RandomFloat where the maximum is dependent on another field.

class RelativeNumberAddFields(DependentField):
    """
    Returns a number relative to another number field.

    Example:
    >>> import testdata
    >>> class Foo(testdata.DictFactory):
    ...     a = testdata.CountingFactory(1)
            b = testdata.CountingFactory(5)
            c = testdata.CountingFactory(10)
    ...     d = testdata.RelativeNumberAdd('a', 'b', 'c')
    >>> [i for i in Foo().generate(3)]
    [{'a': 1, 'b': 5, 'c': 10, 'd': 16},
     {'a': 2, 'b': 6, 'c': 11, 'd': 19},
     {'a': 3, 'b': 7, 'c': 12, 'd': 22}]
    """
    def __init__(self, *args):
        super(RelativeNumberAddFields, self).__init__(args)
        self._args = args
    
    def __call__(self):
        super(RelativeNumberAddFields, self).__call__()
        result = 0
        for value in self._args:
            result += self.depending_fields[value]
        return result

class RelativeNumberMultiplyFields(DependentField):
    """
    Returns a number relative to another number field.

    Example:
    >>> import testdata
    >>> class Foo(testdata.DictFactory):
    ...     a = testdata.CountingFactory(1)
            b = testdata.CountingFactory(5)
            c = testdata.CountingFactory(10)
    ...     d = testdata.RelativeNumberAdd('a', 'b', 'c')
    >>> [i for i in Foo().generate(3)]
    [{'a': 1, 'b': 5, 'c': 10, 'd': 50},
     {'a': 2, 'b': 6, 'c': 11, 'd': 132},
     {'a': 3, 'b': 7, 'c': 12, 'd': 252}]
    """
    def __init__(self, *args):
        super(RelativeNumberMultiplyFields, self).__init__(args)
        self._args = args
    
    def __call__(self):
        super(RelativeNumberMultiplyFields, self).__call__()
        result = 1
        for value in self._args:
            result = result * self.depending_fields[value]
        return result

class ConditionalMaxRandomFloat(DependentField):
    """
    Returns a Float between `minimum` and `maximum`
    """
    def __init__(self, minimum, maximum):
        super(ConditionalMaxRandomFloat, self).__init__([maximum])
        self._maximum = maximum
        self._minimum = minimum

    def __call__(self):
        super(ConditionalMaxRandomFloat, self).__call__()
        return (random.random() * (self.depending_fields[self._maximum] - self._minimum)) + self._minimum

To make debugging easier, I like to generate OrderedDict objects, so my eyes can easily scan to where various fields "should be" and do rapid visual inspection. The randomizing effect of dict objects makes this harder.

So I suggest an OrderedDictFactory subclass to DictFactory.

Also, I did not see a quick way to interrogate a DictFactory object as to what fields it would be generating. That too would be handy.

The 1.0.5 code has a different fake-factory requirement in setup.py for the PyPi versus GitHub version.

In the PyPi version it has:
'fake-factory == 0.2'

In the GitHub version it has:
'fake-factory == 0.3.2'