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?
Related
I want to test whether an object can be serialized using joblib(!). Something like:
assert pickle.dumps(my_obj)
seems to be the way using pickle but joblib doesn't provide .dumps. I tried to do:
with tempfile.TemporaryFile("wb") as f:
assert joblib.dump(my_obj, f)
But this fails because joblib.dump returns None in this case (although according to the doc it should return something which evaluates to True).
What would be the equivalent if I'm using joblib?
According to the source, nothing is returned if you pass in a file object, only if you pass in a file name. https://github.com/joblib/joblib/blob/master/joblib/numpy_pickle.py#L510
So using a named temp file and passing on the name should do the trick.
Running the code and doing the assert on the file size seems also a valid strategy.
While it is simple to search by using help for most methods that have a clear help(module.method) arrangement, for example help(list.extend), I cannot work out how to look up the method .readline() in python's inbuilt help function.
Which module does .readline belong to? How would I search in help for .readline and related methods?
Furthermore is there any way I can use the interpreter to find out which module a method belongs to in future?
Don't try to find the module. Make an instance of the class you want, then call help on the method of that instance, and it will find the correct help info for you. Example:
>>> f = open('pathtosomefile')
>>> help(f.readline)
Help on built-in function readline:
readline(size=-1, /) method of _io.TextIOWrapper instance
Read until newline or EOF.
Returns an empty string if EOF is hit immediately.
In my case (Python 3.7.1), it's defined on the type _io.TextIOWrapper (exposed publicly as io.TextIOWrapper, but help doesn't know that), but memorizing that sort of thing isn't very helpful. Knowing how to figure it out by introspecting the specific thing you care about is much more broadly applicable. In this particular case, it's extra important not to try guessing, because the open function can return a few different classes, each with different methods, depending on the arguments provided, including io.BufferedReader, io.BufferedWriter, io.BufferedRandom, and io.FileIO, each with their own version of the readline method (though they all share a similar interface for consistency's sake).
From the text of help(open):
open() returns a file object whose type depends on the mode, and
through which the standard file operations such as reading and writing
are performed. When open() is used to open a file in a text mode ('w',
'r', 'wt', 'rt', etc.), it returns a TextIOWrapper. When used to open
a file in a binary mode, the returned class varies: in read binary
mode, it returns a BufferedReader; in write binary and append binary
modes, it returns a BufferedWriter, and in read/write mode, it returns
a BufferedRandom.
See also the section of python's io module documentation on the class hierarchy.
So you're looking at TextIOWrapper, BufferedReader, BufferedWriter, or BufferedRandom. These all have their own sets of class hierarchies, but suffice it to say that they share the IOBase superclass at some point - that's where the functions readline() and readlines() are declared. Of course, each subclass implements these functions differently for its particular mode - if you do
help(_io.TextIOWrapper.readline)
you should get the documentation you're looking for.
In particular, you're having trouble accessing the documentation for whichever version of readline you need, because you can't be bothered to figure out which class it is. You can actually call help on an object as well. If you're working with a particular file object, then you can spin up a terminal, instantiate it, and then just pass it to help() and it'll show you whatever interface is closest to the surface. Example:
x = open('some_file.txt', 'r')
help(x.readline)
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.)
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'm using python 2.7 and delving into TDD. I'm trying to test a simple function that uses the csv module and returns a csv.reader object. I want to test that the correct type of object is being returned with the assertIsInstance test however I'm having trouble figuring out how to make this work.
#!/usr/bin/python
import os, csv
def importCSV(fileName):
'''importCSV brings in the CSV transaction file to be analyzed'''
try:
if not(os.path.exists("data")):
os.makedirs("data")
except(IOError):
return "Couldn't create data directory!"
try:
fileFullName = os.path.join("data", fileName)
return csv.reader(file(fileFullName))
except(IOError):
return "File not found!"
The test currently looks like this....
#!/usr/bin/python
from finaImport import finaImport
import unittest, os, csv
class testImport(unittest.TestCase):
'''Tests for importing a CSV file'''
def testImportCSV(self):
''' Test a good file and make sure importCSV returns a csv reader object '''
readerObject = finaImport.importCSV("toe")
self.assertTrue(str(type(readerObject))), "_csv.reader")
I really don't think wrapping "toe" in a str and type function is correct. When I try something like...
self.assertIsInstance(finaImport.importCSV("toe"), csv.reader)
It returns an error like...
TypeError: isinstance() arg2 must be a class, type, or tuple of classes and types
Help???
self.assertTrue(str(type(readerObject)), "_csv.reader")
I don't think that your first test (above) is so bad (I fixed a small typo there; you had an extra closing parenthesis). It checks that the type name is exactly "_csv.reader". On the other hand, the underscore in "_csv" tells you that this object is internal to the csv module. In general, you shouldn't be concerned about that.
Your attempt at the assertIsInstance test is flawed in that csv.reader is a function object. If you try it in the REPL, you see:
>>> import csv
>>> csv.reader
<built-in function reader>
Often, we care less about the type of an object and more about whether it implements a certain interface. In this case, the help for csv.reader says:
>>> help(csv.reader)
... The returned object is an iterator. ...
So, you could do the following test (instead or in addition to your other one):
self.assertIsInstance(readerObject, collections.Iterator)
You'll need a import collections for that, of course. And, you might want to test that the iterator returns lists of strings, or something like this. That would allow you to use something else under the hood later and the test would still pass.