I am trying to unit test a method that reads the lines from a file and process it.
with open([file_name], 'r') as file_list:
for line in file_list:
# Do stuff
I tried several ways described on another questions but none of them seems to work for this case. I don't quite understand how python uses the file object as an iterable on the lines, it internally use file_list.readlines() ?
This way didn't work:
with mock.patch('[module_name].open') as mocked_open: # also tried with __builtin__ instead of module_name
mocked_open.return_value = 'line1\nline2'
I got an
AttributeError: __exit__
Maybe because the with statement have this special attribute to close the file?
This code makes file_list a MagicMock. How do I store data on this MagicMock to iterate over it ?
with mock.patch("__builtin__.open", mock.mock_open(read_data="data")) as mock_file:
Best regards
The return value of mock_open (until Python 3.7.1) doesn't provide a working __iter__ method, which may make it unsuitable for testing code that iterates over an open file object.
Instead, I recommend refactoring your code to take an already opened file-like object. That is, instead of
def some_method(file_name):
with open([file_name], 'r') as file_list:
for line in file_list:
# Do stuff
...
some_method(file_name)
write it as
def some_method(file_obj):
for line in file_obj:
# Do stuff
...
with open(file_name, 'r') as file_obj:
some_method(file_obj)
This turns a function that has to perform IO into a pure(r) function that simply iterates over any file-like object. To test it, you don't need to mock open or hit the file system in any way; just create a StringIO object to use as the argument:
def test_it(self):
f = StringIO.StringIO("line1\nline2\n")
some_method(f)
(If you still feel the need to write and test a wrapper like
def some_wrapper(file_name):
with open(file_name, 'r') as file_obj:
some_method(file_obj)
note that you don't need the mocked open to do anything in particular. You test some_method separately, so the only thing you need to do to test some_wrapper is verify that the return value of open is passed to some_method. open, in this case, can be a plain old mock with no special behavior.)
Related
first of all, sorry if this has been answered before but I have been looking all over the place and could not find anything.
I have a function that does this (in summary):
def func(path):
with open(path) as f:
json.load(f)
I want to unit test it and what I would like is that when I use open as context manager, f is the name of the argument I passed to open. The issue I am having is that I cannot mock the enter method in the proper module, it breaks everytime. This is whay I am doing so far:
def setUp(self):
# Setup the content of the config files for the tests
json.load = MagicMock(side_effect=file_content)
# Opening a file returns the name of the file
builtins.open = MagicMock(side_effect=lambda x: x) #1
builtins.open.__enter__ = MagicMock(side_effect=builtins.open.return_value) #2
the MagicMock number 1 does what I expect it to do, when I run open("test") it returns "test". Nonetheless I cannot mock enter to return whatever I passed to open, it always fails with AttributeError: __enter__. I have tried also doing
builtins.open.return_value.__enter__ = MagicMock...
with no luck. Can anyone think on a way that this can be achieved? I have seen how to mock magic methods and I have thought I was comfortable with unittest mocking but in this case I cannot find a fitting solution. Thanks!
I got it!
I had to define the side_effect dinamically but seems to be working fine. This is what I did:
def setUp(self):
# Setup the content of the config files for the tests
json.load = MagicMock(side_effect=file_content)
# Opening a file returns the name of the file
def get_mock_context(filename):
mock_context = MagicMock()
mock_context.__enter__.return_value = filename
mock_context.__exit__.return_value = False
return mock_context
builtins.open = MagicMock(side_effect=get_mock_context)
In this way, the returned value is a mock that has the __enter__ and __exit__ methods returning exactly the filename I passed to the call of open.
I'm trying to understand if there is there a difference between these, and what that difference might be.
Option One:
file_obj = open('test.txt', 'r')
with file_obj as in_file:
print in_file.readlines()
Option Two:
with open('test.txt', 'r') as in_file:
print in_file.readlines()
I understand that with Option One, the file_obj is in a closed state after the with block.
I don't know why no one has mentioned this yet, because it's fundamental to the way with works. As with many language features in Python, with behind the scenes calls special methods, which are already defined for built-in Python objects and can be overridden by user-defined classes. In with's particular case (and context managers more generally), the methods are __enter__ and __exit__.
Remember that in Python everything is an object -- even literals. This is why you can do things like 'hello'[0]. Thus, it does not matter whether you use the file object directly as returned by open:
with open('filename.txt') as infile:
for line in infile:
print(line)
or store it first with a different name (for example to break up a long line):
the_file = open('filename' + some_var + '.txt')
with the_file as infile:
for line in infile:
print(line)
Because the end result is that the_file, infile, and the return value of open all point to the same object, and that's what with is calling the __enter__ and __exit__ methods on. The built-in file object's __exit__ method is what closes the file.
These behave identically. As a general rule, the meaning of Python code is not changed by assigning an expression to a variable in the same scope.
This is the same reason that these are identical:
f = open("myfile.txt")
vs
filename = "myfile.txt"
f = open(filename)
Regardless of whether you add an alias, the meaning of the code stays the same. The context manager has a deeper meaning than passing an argument to a function, but the principle is the same: the context manager magic is applied to the same object, and the file gets closed in both cases.
The only reason to choose one over the other is if you feel it helps code clarity or style.
There is no difference between the two - either way the file is closed when you exit the with block.
The second example you give is the typical way the files are used in Python 2.6 and newer (when the with syntax was added).
You can verify that the first example also works in a REPL session like this:
>>> file_obj = open('test.txt', 'r')
>>> file_obj.closed
False
>>> with file_obj as in_file:
... print in_file.readlines()
<Output>
>>> file_obj.closed
True
So after the with blocks exits, the file is closed.
Normally the second example is how you would do this sort of thing, though.
There's no reason to create that extra variable file_obj... anything that you might want to do with it after the end of the with block you could just use in_file for, because it's still in scope.
>>> in_file
<closed file 'test.txt', mode 'r' at 0x03DC5020>
If you just fire up Python and use either of those options, the net effect is the same if the base instance of Python's file object is not changed. (In Option One, the file is only closed when file_obj goes out of scope vs at the end of the block in Option Two as you have already observed.)
There can be differences with use cases with a context manager however. Since file is an object, you can modify it or subclass it.
You can also open a file by just calling file(file_name) showing that file acts like other objects (but no one opens files that way in Python unless it is with with):
>>> f=open('a.txt')
>>> f
<open file 'a.txt', mode 'r' at 0x1064b5ae0>
>>> f.close()
>>> f=file('a.txt')
>>> f
<open file 'a.txt', mode 'r' at 0x1064b5b70>
>>> f.close()
More generally, the opening and closing of some resource called the_thing (commonly a file, but can be anything) you follow these steps:
set up the_thing # resource specific, open, or call the obj
try # generically __enter__
yield pieces from the_thing
except
react if the_thing is broken
finally, put the_thing away # generically __exit__
You can more easily change the flow of those subelements using the context manager vs procedural code woven between open and the other elements of the code.
Since Python 2.5, file objects have __enter__ and __exit__ methods:
>>> f=open('a.txt')
>>> f.__enter__
<built-in method __enter__ of file object at 0x10f836780>
>>> f.__exit__
<built-in method __exit__ of file object at 0x10f836780>
The default Python file object uses those methods in this fashion:
__init__(...) # performs initialization desired
__enter__() -> self # in the case of `file()` return an open file handle
__exit__(*excinfo) -> None. # in the case of `file()` closes the file.
These methods can be changed for your own use to modify how a resource is treated when it is opened or closed. A context manager makes it really easy to modify what happens when you open or close a file.
Trivial example:
class Myopen(object):
def __init__(self, fn, opening='', closing='', mode='r', buffering=-1):
# set up the_thing
if opening:
print(opening)
self.closing=closing
self.f=open(fn, mode, buffering)
def __enter__(self):
# set up the_thing
# could lock the resource here
return self.f
def __exit__(self, exc_type, exc_value, traceback):
# put the_thing away
# unlock, or whatever context applicable put away the_thing requires
self.f.close()
if self.closing:
print(self.closing)
Now try that:
>>> with Myopen('a.txt', opening='Hello', closing='Good Night') as f:
... print f.read()
...
Hello
[contents of the file 'a.txt']
Good Night
Once you have control of entry and exit to a resource, there are many use cases:
Lock a resource to access it and use it; unlock when you are done
Make a quirky resource (like a memory file, database or web page) act more like a straight file resource
Open a database and rollback if there is an exception but commit all writes if there are no errors
Temporarily change the context of a floating point calculation
Time a piece of code
Change the exceptions that you raise by returning True or False from the __exit__ method.
You can read more examples in PEP 343.
Is remarkable that with works even if return or sys.exit() is called inside (that means __exit__ is called anyway):
#!/usr/bin/env python
import sys
class MyClass:
def __enter__(self):
print("Enter")
return self
def __exit__(self, type, value, trace):
print("type: {} | value: {} | trace: {}".format(type,value,trace))
# main code:
def myfunc(msg):
with MyClass() as sample:
print(msg)
# also works if uncomment this:
# sys.exit(0)
return
myfunc("Hello")
return version will show:
Enter
Hello
type: None | value: None | trace: None
exit(0) version will show:
Enter
Hello
type: <class 'SystemExit'> | value: 0 | trace: <traceback object at 0x7faca83a7e00>
I am trying to unit test a piece of code:
def _parse_results(self, file_name):
results_file = open(file_name)
results_data = list(csv.reader(results_file))
index = len(results_data[1])-1
results_file.close()
return float(results_data[1][index])
by using mock_open like so:
#mock.patch('path.open', mock.mock_open(read_data='test, test2, test3, test4'))
def test_parse_results(self):
cut = my_class(emulate=True)
self.assertEqual(VAL, cut._parse_results('file'))
The problem I am running into is that I do not get any data when running csv.reader. If I run results_file.readlines() I get 'test, test2, test3, test4' which means that mock_open is working properly. But when I run csv.reader(results_file) I lose all the data.
This is because mock_open doesn't implement every feature that a file has, and notably not some of the ones that csv needs.
mock_open implements the methods read(), readline() and readlines(), and works both as a function and when called as a context manager (https://docs.python.org/3/library/unittest.mock.html#mock-open), whereas csv.reader works with…
any object which supports the iterator protocol and returns a string each time its __next__() method is called — file objects and list objects are both suitable
— https://docs.python.org/3/library/csv.html#csv.reader
Note that mock_open doesn't implement the __next__() method, and doesn't raise StopIteration when the end is reached, so it won't work with csv.reader.
The solution, as #Emily points out in her answer, is to turn the file into a list of its lines. This is possible because mock_open implements readlines(), and the resulting list is suitable for reading into csv.reader as the documentation says.
This really got me too, and was a nightmare to pinpoint. To use your example code, this works
results_data = list(csv.reader(results_file.read()))
and this works
results_data = list(csv.reader(results_file.readlines()))
but this doesn't work
results_data = list(csv.reader(results_file))
using Python 3.4.
It seems counter to the documented interface of csv.reader so maybe an expert can elaborate on why.
I have written a small web application, and with each request I should open and read a JSON file. I am using pickledb for this purpose.
What concerns me about it, is that the library passes open() as a parameter for the json.load() function . So it got me thinking ..
When I write code like this:
with open("filename.json", "rb") as json_data:
my_data = json.load(json_data)
or
json_data = open("filename.json", "rb")
my_data = json.load(json_data)
json_data.close()
I am pretty sure that the file handle is being closed.
But when I open it this way :
my_data = json.load(open("filename.json", "rb"))
The docs say that json.load() is expecting a .read()-supporting file-like object containing a JSON document.
So the question is, will this handle stay open and eat more memory with each request? Who is responsible for closing the handle in that case?
Close method of the file will be called when object is destroyed, as json.load expects only read method on input object.
What happens depends on garbage collection implementation then. You can read more in Is explicitly closing files important?
Generally speaking it's a good practice to take care of closing the file.
I tried to somehow fake file-like object with read() and close() methods, and stick it into json.load(). Then I observed, that close() is not being called upon leaving context. Hence, I would recommend to close the file object explicitly. Anyway, doc says that the loading method expects read() method, but does not say it expects close() method on the object.
In test.json:
{ "test":0 }
In test.py:
import json
class myf:
def __init__(self):
self.f = None
#staticmethod
def open(path, mode):
obj = myf()
obj.f = open(path, mode)
return obj
def read(self):
print ("READING")
return self.f.read()
def close(self):
print ("CLOSING")
return self.f.close()
def mytest():
s = json.load(myf.open("test.json","r"))
print (s)
mytest()
print("DONE")
Output:
$> python test.py
READING
{u'test': 0}
DONE
$>
It used to be in Python (2.6) that one could ask:
isinstance(f, file)
but in Python 3.0 file was removed.
What is the proper method for checking to see if a variable is a file now? The What'sNew docs don't mention this...
def read_a_file(f)
try:
contents = f.read()
except AttributeError:
# f is not a file
substitute whatever methods you plan to use for read. This is optimal if you expect that you will get passed a file like object more than 98% of the time. If you expect that you will be passed a non file like object more often than 2% of the time, then the correct thing to do is:
def read_a_file(f):
if hasattr(f, 'read'):
contents = f.read()
else:
# f is not a file
This is exactly what you would do if you did have access to a file class to test against. (and FWIW, I too have file on 2.6) Note that this code works in 3.x as well.
In python3 you could refer to io instead of file and write
import io
isinstance(f, io.IOBase)
Typically, you don't need to check an object type, you could use duck-typing instead i.e., just call f.read() directly and allow the possible exceptions to propagate -- it is either a bug in your code or a bug in the caller code e.g., json.load() raises AttributeError if you give it an object that has no read attribute.
If you need to distinguish between several acceptable input types; you could use hasattr/getattr:
def read(file_or_filename):
readfile = getattr(file_or_filename, 'read', None)
if readfile is not None: # got file
return readfile()
with open(file_or_filename) as file: # got filename
return file.read()
If you want to support a case when file_of_filename may have read attribute that is set to None then you could use try/except over file_or_filename.read -- note: no parens, the call is not made -- e.g., ElementTree._get_writer().
If you want to check certain guarantees e.g., that only one single system call is made (io.RawIOBase.read(n) for n > 0) or there are no short writes (io.BufferedIOBase.write()) or whether read/write methods accept text data (io.TextIOBase) then you could use isinstance() function with ABCs defined in io module e.g., look at how saxutils._gettextwriter() is implemented.
Works for me on python 2.6... Are you in a strange environment where builtins aren't imported by default, or where somebody has done del file, or something?