Comparing objects and sequences

Python’s unittest package often fails to give very useful feedback when comparing long sequences or chunks of text. It also has trouble dealing with objects that don’t natively support comparison. The functions and classes described here alleviate these problems.

The compare function

The compare() function can be used as a replacement for assertEqual(). It raises an AssertionError when its parameters are not equal, which will be reported as a test failure:

>>> from testfixtures import compare
>>> compare(1, 2)
Traceback (most recent call last):
 ...
AssertionError: 1 != 2

However, it allows you to specify a prefix for the message to be used in the event of failure:

>>> compare(1, 2, prefix='wrong number of orders')
Traceback (most recent call last):
 ...
AssertionError: wrong number of orders: 1 != 2

This is recommended as it makes the reason for the failure more apparent without having to delve into the code or tests.

You can also optionally specify a suffix, which will be appended to the message on a new line:

>>> compare(1, 2, suffix='(Except for very large values of 1)')
Traceback (most recent call last):
 ...
AssertionError: 1 != 2
(Except for very large values of 1)

The expected and actual value can also be explicitly supplied, making it clearer as to what has gone wrong:

>>> compare(expected=1, actual=2)
Traceback (most recent call last):
 ...
AssertionError: 1 (expected) != 2 (actual)

The real strengths of this function come when comparing more complex data types. A number of common python data types will give more detailed output when a comparison fails as described below:

sets

Comparing sets that aren’t the same will attempt to highlight where the differences lie:

>>> compare(set([1, 2]), set([2, 3]))
Traceback (most recent call last):
 ...
AssertionError: set not as expected:

in first but not second:
[1]

in second but not first:
[3]

dicts

Comparing dictionaries that aren’t the same will attempt to highlight where the differences lie:

>>> compare(dict(x=1, y=2, a=4), dict(x=1, z=3, a=5))
Traceback (most recent call last):
 ...
AssertionError: dict not as expected:

same:
['x']

in first but not second:
'y': 2

in second but not first:
'z': 3

values differ:
'a': 4 != 5

lists and tuples

Comparing lists or tuples that aren’t the same will attempt to highlight where the differences lie:

>>> compare([1, 2, 3], [1, 2, 4])
Traceback (most recent call last):
 ...
AssertionError: sequence not as expected:

same:
[1, 2]

first:
[3]

second:
[4]

namedtuples

When two namedtuple() instances are compared, if they are of the same type, the description given will highlight which elements were the same and which were different:

>>> from collections import namedtuple
>>> TestTuple = namedtuple('TestTuple', 'x y z')
>>> compare(TestTuple(1, 2, 3), TestTuple(1, 4, 3))
Traceback (most recent call last):
 ...
AssertionError: TestTuple not as expected:

same:
['x', 'z']

values differ:
'y': 2 != 4

generators

When two generators are compared, they are both first unwound into tuples and those tuples are then compared.

The generator helper is useful for creating a generator to represent the expected results:

>>> from testfixtures import generator
>>> def my_gen(t):
...     i = 0
...     while i<t:
...         i += 1
...         yield i
>>> compare(generator(1, 2, 3), my_gen(2))
Traceback (most recent call last):
 ...
AssertionError: sequence not as expected:

same:
(1, 2)

first:
(3,)

second:
()

Warning

If you wish to assert that a function returns a generator, say, for performance reasons, then you should use strict comparison.

strings and unicodes

Comparison of strings can be tricky, particularly when those strings contain multiple lines; spotting the differences between the expected and actual values can be hard.

To help with this, long strings give a more helpful representation when comparison fails:

>>> compare("1234567891011", "1234567789")
Traceback (most recent call last):
 ...
AssertionError:
'1234567891011'
!=
'1234567789'

Likewise, multi-line strings give unified diffs when their comparison fails:

>>> compare("""
...         This is line 1
...         This is line 2
...         This is line 3
...         """,
...         """
...         This is line 1
...         This is another line
...         This is line 3
...         """)
Traceback (most recent call last):
 ...
AssertionError:
--- first
+++ second
@@ -1,5 +1,5 @@

         This is line 1
-        This is line 2
+        This is another line
         This is line 3

Such comparisons can still be confusing as white space is taken into account. If you need to care about whitespace characters, you can make spotting the differences easier as follows:

>>> compare("\tline 1\r\nline 2"," line1 \nline 2", show_whitespace=True)
Traceback (most recent call last):
 ...
AssertionError:
--- first
+++ second
@@ -1,2 +1,2 @@
-'\tline 1\r\n'
+' line1 \n'
 'line 2'

However, you may not care about some of the whitespace involved. To help with this, compare() has two options that can be set to ignore certain types of whitespace.

If you wish to compare two strings that contain blank lines or lines containing only whitespace characters, but where you only care about the content, you can use the following:

compare('line1\nline2', 'line1\n \nline2\n\n',
        blanklines=False)

If you wish to compare two strings made up of lines that may have trailing whitespace that you don’t care about, you can do so with the following:

compare('line1\nline2', 'line1 \t\nline2   \n',
        trailing_whitespace=False)

objects

Even if your objects do not natively support comparison, when they are compared they will be considered identical if they are of the same type and have identical attributes. Take this instances of this class as an example:

from datetime import datetime

class MyObject(object):
    def __init__(self, name):
        self.name = name
    def __repr__(self):
        return '<MyObject>'

If the attributes and type of instances are the same, they will be considered equal:

>>> compare(MyObject('foo'), MyObject('foo'))

However, if their attributes differ, you will get an informative error:

>>> compare(MyObject('foo'), MyObject('bar'))
Traceback (most recent call last):
 ...
AssertionError: MyObject not as expected:

attributes differ:
'name': 'foo' != 'bar'

While comparing .name: 'foo' != 'bar'

This type of comparison is also used on objects that make use of __slots__.

Recursive comparison

Where compare() is able to provide a descriptive comparison for a particular type, it will then recurse to do the same for the elements contained within objects of that type. For example, when comparing a list of dictionaries, the description will not only tell you where in the list the difference occurred, but also what the differences were within the dictionaries that weren’t equal:

>>> compare([{'one': 1}, {'two': 2, 'text':'foo\nbar\nbaz'}],
...         [{'one': 1}, {'two': 2, 'text':'foo\nbob\nbaz'}])
Traceback (most recent call last):
 ...
AssertionError: sequence not as expected:

same:
[{'one': 1}]

first:
[{'text': 'foo\nbar\nbaz', 'two': 2}]

second:
[{'text': 'foo\nbob\nbaz', 'two': 2}]

While comparing [1]: dict not as expected:

same:
['two']

values differ:
'text': 'foo\nbar\nbaz' != 'foo\nbob\nbaz'

While comparing [1]['text']:
--- first
+++ second
@@ -1,3 +1,3 @@
 foo
-bar
+bob
 baz

This also applies to any comparers you have provided, as can be seen in the next section.

Providing your own comparers

When using compare() frequently for your own complex objects, it can be beneficial to give more descriptive output when two objects don’t compare as equal.

Note

If you are reading this section as a result of needing to test objects that don’t natively support comparison, or as a result of needing to infrequently compare your own subclasses of python basic types, take a look at Comparison objects as this may well be an easier solution.

As an example, suppose you have a class whose instances have a timestamp and a name as attributes, but you’d like to ignore the timestamp when comparing:

from datetime import datetime

class MyObject(object):
    def __init__(self, name):
        self.timestamp = datetime.now()
        self.name = name

To compare lots of these, you would first write a comparer:

def compare_my_object(x, y, context):
    if x.name == y.name:
        return
    return 'MyObject named %s != MyObject named %s' % (
        context.label('x', repr(x.name)),
        context.label('y', repr(y.name)),
        )

Then you’d register that comparer for your type:

from testfixtures.comparison import register
register(MyObject, compare_my_object)

Now, it’ll get used when comparing objects of that type, even if they’re contained within other objects:

>>> compare([1, MyObject('foo')], [1, MyObject('bar')])
Traceback (most recent call last):
 ...
AssertionError: sequence not as expected:

same:
[1]

first:
[<MyObject ...>]

second:
[<MyObject ...>]

While comparing [1]: MyObject named 'foo' != MyObject named 'bar'

From this example, you can also see that a comparer can indicate that two objects are equal, for compare()’s purposes, by returning None:

>>> MyObject('foo') == MyObject('foo')
False
>>> compare(MyObject('foo'), MyObject('foo'))

You can also see that you can, and should, use the context object passed in to add labels to the representations of the objects being compared if the comparison fails:

>>> compare(expected=MyObject('foo'), actual=MyObject('bar'))
Traceback (most recent call last):
 ...
AssertionError: MyObject named 'foo' (expected) != MyObject named 'bar' (actual)

It may be that you only want to use a comparer or set of comparers for a particular test. If that’s the case, you can pass compare() a comparers parameter consisting of a dictionary that maps types to comparers. These will be added to the global registry for the duration of the call:

>>> compare(MyObject('foo'), MyObject('bar'),
...         comparers={MyObject: compare_my_object})
Traceback (most recent call last):
 ...
AssertionError: MyObject named 'foo' != MyObject named 'bar'

A full list of the available comparers included can be found below the API documentation for compare(). These make good candidates for registering for your own classes, if they provide the necessary behaviour, and their source is also good to read when wondering how to implement your own comparers.

You may be wondering what the context object passed to the comparer is for; it allows you to hand off comparison of parts of the two objects currently being compared back to the compare() machinery, it also allows you to pass options to your comparison function.

For example, you may have an object that has a couple of dictionaries as attributes:

from datetime import datetime

class Request(object):
    def __init__(self, uri, headers, body):
        self.uri = uri
        self.headers = headers
        self.body = body

When your tests encounter instances of these that are not as expected, you want feedback about which bits of the request or response weren’t as expected. This can be achieved by implementing a comparer as follows:

def compare_request(x, y, context):
    uri_different = x.uri != y.uri
    headers_different = context.different(x.headers, y.headers, '.headers')
    body_different = context.different(x.body, y.body, '.body')
    if uri_different or headers_different or body_different:
        return 'Request for %r != Request for %r' % (
            x.uri, y.uri
        )

Note

A comparer should always return some text when it considers the two objects it is comparing to be different.

This comparer can either be registered globally or passed to each compare() call and will give detailed feedback about how the requests were different:

>>> compare(Request('/foo', {'method': 'POST'}, {'my_field': 'value_1'}),
...         Request('/foo', {'method': 'GET'}, {'my_field': 'value_2'}),
...         comparers={Request: compare_request})
Traceback (most recent call last):
 ...
AssertionError: Request for '/foo' != Request for '/foo'

While comparing .headers: dict not as expected:

values differ:
'method': 'POST' != 'GET'

While comparing .headers['method']: 'POST' != 'GET'

While comparing .body: dict not as expected:

values differ:
'my_field': 'value_1' != 'value_2'

While comparing .body['my_field']: 'value_1' != 'value_2'

As an example of passing options through to a comparer, suppose you wanted to compare all decimals in a nested data structure by rounding them to a number of decimal places that varies from test to test. The comparer could be implemented and registered as follows:

from decimal import Decimal
from testfixtures.comparison import register

def compare_decimal(x, y, context):
     precision = context.get_option('precision', 2)
     if round(x, precision) != round(y, precision):
         return '%r != %r when rounded to %i decimal places' % (
             x, y, precision
         )

register(Decimal, compare_decimal)

Now, this comparer will be used for comparing all decimals and the precision used will be that passed to compare():

>>> expected_order = {'price': Decimal('1.234'), 'quantity': 5}
>>> actual_order = {'price': Decimal('1.236'), 'quantity': 5}
>>> compare(expected_order, actual_order, precision=1)
>>> compare(expected_order, actual_order, precision=3)
Traceback (most recent call last):
 ...
AssertionError: dict not as expected:

same:
['quantity']

values differ:
'price': Decimal('1.234') != Decimal('1.236')

While comparing ['price']: Decimal('1.234') != Decimal('1.236') when rounded to 3 decimal places

If no precision is passed, the default of 2 will be used:

>>> compare(Decimal('2.006'), Decimal('2.009'))
>>> compare(Decimal('2.001'), Decimal('2.009'))
Traceback (most recent call last):
 ...
AssertionError: Decimal('2.001') != Decimal('2.009') when rounded to 2 decimal places

Ignoring __eq__

Some objects, such as those from the Django ORM, have pretty broken implementations or __eq__. Since compare() normally relies on this, it can result in objects appearing to be equal when they are not.

Take this class, for example:

class OrmObj(object):
    def __init__(self, a):
        self.a = a
    def __eq__(self, other):
        return True
    def __repr__(self):
        return 'OrmObj: '+str(self.a)

If we compare normally, we erroneously understand the objects to be equal:

>>> compare(actual=OrmObj(1), expected=OrmObj(2))

In order to get a sane comparison, we need to both supply a custom comparer as described above, and use the ignore_eq parameter:

def compare_orm_obj(x, y, context):
    if x.a != y.a:
        return 'OrmObj: %s != %s' % (x.a, y.a)
>>> compare(actual=OrmObj(1), expected=OrmObj(2),
...         comparers={OrmObj: compare_orm_obj}, ignore_eq=True)
Traceback (most recent call last):
...
AssertionError: OrmObj: 2 != 1

Strict comparison

If is it important that the two values being compared are of exactly the same type, rather than just being equal as far as Python is concerned, then the strict mode of compare() should be used.

For example, these two instances will normally appear to be equal provided the elements within them are the same:

>>> TypeA = namedtuple('A', 'x')
>>> TypeB = namedtuple('B', 'x')
>>> compare(TypeA(1), TypeB(1))

If this type difference is important, then the strict parameter should be used:

>>> compare(TypeA(1), TypeB(1), strict=True)
Traceback (most recent call last):
 ...
AssertionError: A(x=1) (<class '__main__.A'>) != B(x=1) (<class '__main__.B'>)

Comparison objects

Another common problem with the checking in tests is that you may only want to make assertions about the type of an object that is nested in a data structure, or even just compare a subset of an object’s attributes. TextFixtures provides the Comparison class to help in situations like these.

Comparisons will appear to be equal to any object they are compared with that matches their specification. For example, take the following class:

class SomeClass:

    def __init__(self, x, y):
       self.x, self.y = x, y

When a comparison fails, the Comparison will not equal the object it was compared with and its representation changes to give information about what went wrong:

>>> from testfixtures import Comparison as C
>>> c = C(SomeClass, x=2)
>>> print(repr(c))
<C:...SomeClass>x: 2</C>
>>> c == SomeClass(1, 2)
False
>>> print(repr(c))

<C(failed):...SomeClass>
attributes in actual but not Comparison:
'y': 2

attributes differ:
'x': 2 (Comparison) != 1 (actual)
</C>

Note

assertEqual() has regressed in Python 3.4 and will now truncate the text shown in assertions with no way to configure this behaviour. Use compare() instead, which will give you other desirable behaviour as well as showing you the full output of failed comparisons.

Types of comparison

There are several ways a comparison can be set up depending on what you want to check.

If you only care about the class of an object, you can set up the comparison with only the class:

>>> C(SomeClass) == SomeClass(1, 2)
True

This can also be achieved by specifying the type of the object as a dotted name:

>>> import sys
>>> C('types.ModuleType') == sys
True

Alternatively, if you happen to have an object already around, comparison can be done with it:

>>> C(SomeClass(1,2)) == SomeClass(1,2)
True

If you only care about certain attributes, this can also easily be achieved with the strict parameter:

>>> C(SomeClass, x=1, strict=False) == SomeClass(1, 2)
True

The above can be problematic if you want to compare an object with attributes that share names with parameters to the Comparison constructor. For this reason, you can pass the attributes in a dictionary:

>>> compare(C(SomeClass, {'strict':3}, strict=False), SomeClass(1, 2))
Traceback (most recent call last):
 ...
AssertionError:
<C(failed):...SomeClass>
attributes in Comparison but not actual:
'strict': 3
</C> != <...SomeClass...>

Gotchas

  • If the object being compared has an __eq__ method, such as Django model instances, then the Comparison must be the first object in the equality check.

    The following class is an example of this:

    class SomeModel:
        def __eq__(self,other):
            if isinstance(other,SomeModel):
                return True
            return False
    

    It will not work correctly if used as the second object in the expression:

    >>> SomeModel() == C(SomeModel)
    False
    

    However, if the comparison is correctly placed first, then everything will behave as expected:

    >>> C(SomeModel)==SomeModel()
    True
    
  • It probably goes without saying, but comparisons should not be used on both sides of an equality check:

    >>> C(SomeClass) == C(SomeClass)
    False
    

Round Comparison objects

When comparing numerics you often want to be able to compare to a given precision to allow for rounding issues which make precise equality impossible.

For these situations, you can use RoundComparison objects wherever you would use floats or Decimals, and they will compare equal to any float or Decimal that matches when both sides are rounded to the specified precision.

Here’s an example:

from testfixtures import compare, RoundComparison as R

compare(R(1234.5678, 2), 1234.5681)

Note

You should always pass the same type of object to the RoundComparison object as you intend compare it with. If the type of the rounded expected value is not the same as the type of the rounded value being compared against it, a TypeError will be raised.

Range Comparison objects

When comparing orderable types just as numbers, dates and time, you may only know what range a value will fall into. RangeComparison objects let you confirm a value is within a certain tolerance or range.

Here’s an example:

from testfixtures import compare, RangeComparison as R

compare(R(123.456, 789), Decimal(555.01))

Note

RangeComparison is inclusive of both the lower and upper bound.

String Comparison objects

When comparing sequences of strings, particularly those comping from things like the python logging package, you often end up wanting to express a requirement that one string should be almost like another, or maybe fit a particular regular expression.

For these situations, you can use StringComparison objects wherever you would use normal strings, and they will compare equal to any string that matches the regular expression they are created with.

Here’s an example:

from testfixtures import compare, StringComparison as S

compare(S('Starting thread \d+'),'Starting thread 132356')

Differentiating chunks of text

TextFixtures provides a function that will compare two strings and give a unified diff as a result. This can be handy as a third parameter to assertEqual() or just as a general utility function for comparing two lumps of text.

As an example:

>>> from testfixtures import diff
>>> print(diff('line1\nline2\nline3',
...            'line1\nlineA\nline3'))
--- first
+++ second
@@ -1,3 +1,3 @@
 line1
-line2
+lineA
 line3