Skip to main content

Some notes on Python imports and project organization

One majorly annoying thing for Pythonistas is the search path that the import command uses. When I was a beginner and wanted to get cracking I'd simply add whatever I was working on to PYTHONPATH, not wanting to get bogged down in silly details (that's what we were using Python for, right?).

I learned my lesson when I "forked" one of my projects (by duplicating the directory - give me a break, I wasn't born learning version control systems) and spent several hours trying to figure out why changes to a file I was making did not show up when I ran the code - Python, of course, was picking up the old version of the module in the original directory.

My current strategies, in somewhat random order, for project organization and testing are:

Project organization


ProjectName
   |--------->Readme
   |--------->setup.py
   |--------->docs
   |--------->mainmodule
   |             |------->__init__.py  (empty)
   |             |-------> root level modules 
   |             |-------> submodule1
   |             |    |---------------> __init__.py (empty)
   |             |    |----> sub modules
   |             |-------> submodule2
   |                  |---------------> __init__.py (empty)
   |                  |----> sub modules
   |
   |--------->tests
                 |------->__init__.py  import sys; sys.path.append('../mainmodule')
                 |-------> root level module tests 
                 |-------> submodule1
                 |    |---------------> __init__.py (empty)
                 |    |----> sub module tests
                 |-------> submodule2
                      |---------------> __init__.py (empty)
                      |----> sub module tests

An important lesson is a) not to forget the __init__.py files and b) to add relevant paths to sys.path in __init__.py when needed.

Using this directory structure allows me to use sphinx for the documentation (the source root goes in conf.py in docs) and allows me to invoke nosetests by doing

nosetests tests

and the path appending going on in tests/__init__.py allows me to import any part of the module in any test script just as I would from a different application after the application has been installed using setup.py

Comments

Popular posts from this blog

A note on Python's __exit__() and errors

Python's context managers are a very neat way of handling code that needs a teardown once you are done. Python objects have do have a destructor method ( __del__ ) called right before the last instance of the object is about to be destroyed. You can do a teardown there. However there is a lot of fine print to the __del__ method. A cleaner way of doing tear-downs is through Python's context manager , manifested as the with keyword. class CrushMe: def __init__(self): self.f = open('test.txt', 'w') def foo(self, a, b): self.f.write(str(a - b)) def __enter__(self): return self def __exit__(self, exc_type, exc_val, exc_tb): self.f.close() return True with CrushMe() as c: c.foo(2, 3) One thing that is important, and that got me just now, is error handling. I made the mistake of ignoring all those 'junk' arguments ( exc_type, exc_val, exc_tb ). I just skimmed the docs and what popped out is that you need to return True or...

Remove field code from Word document

e.g. before submitting a MS, or hand manipulating some formatting because Word does things (like cross-references) so half-assed [from here ] Select all the text (CTRL-A) Press Ctrl+Shift+F9 Editing to remove anonymous comments that only contain thanks. I really appreciate the thanks, but it makes it harder to find comments that carry pertinent information. I'm also going to try and paste informative comments in the body of the post to make them easier to find.

h5py and multiprocessing

The HDF5 format has been working awesome for me, but I ran into danger when I started to mix it with multiprocessing. It was the worst kind of danger: the intermittent error. Here are the dangers/issues in order of escalation (TL;DR is use a generator to feed data from your file into the child processes as they spawn. It's the easiest way. Read on for harder ways.) An h5py file handle can't be pickled and therefore can't be passed as an argument using pool.map() If you set the handle as a global and access it from the child processes you run the risk of racing which leads to corrupted reads. My personal runin was that my code sometimes ran fine but sometimes would complain that there are NaNs or Infinity in the data. This wasted some time tracking down. Other people have had this kind of problem [ 1 ]. Same problem if you pass the filename and have the different processes open individual instances of the file separately. The hard way to solve this problem is to sw...