Skip to main content

Parameter files in Python for Python programs

I often write and use Python programs that require input parameters to tell them what to do. When there are a few parameters I usually pass them through the command line (my current favorite framework for command line argument parsing is the amazing docopt module). When there are many parameters, however, command line arguments can get cumbersome.

In the past I have looked to configuration markup languages (YAML comes to mind) and have also used microsoft excel format files (using the excellent xlrd package). Using proprietary formats makes my stomach upset, but there are no good solutions for the odf counterparts.

Recently I have started to experiment with storing parameter files in python and reading them into my code.

__import__(filename) works and puts the parameters into a separate name space, so you can access them as filename.x, filename.y etc. However, the file needs to be in the module search path, and it feels a bit of a misuse.

A better solution, I find, is to use execfile to read the parameter file into the code. There are no path restrictions, execfile IS meant to execute code (so no misuse there) and the variables from the Python file are loaded into a separate dictionary so they are contained and do not spill all over your code.

#Main file
pars = {}
execfile('params.py', {}, pars)

With params.py looking like 

a = 43
b = 'a string'
c = {'d': 23}

We get:

pars --> {'a': 43, 'b': 'a string', 'c': {'d': 23}}

We can put in comments in the file and structure however we need which I find very convenient.

execfile has disappeared in Python 3 but you can use

with open(filename) as f:
    exec(compile(f.read(), filename, "exec"))

UPDATE: However, see a funny issue with execfile here.
Labels:

Comments

Post a Comment

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...
Post a Comment
Read more

Store numpy arrays in sqlite

Use numpy.getbuffer (or sqlite3.Binary ) in combination with numpy.frombuffer to lug numpy data in and out of the sqlite3 database: import sqlite3, numpy r1d = numpy.random.randn(10) con = sqlite3.connect(':memory:') con.execute("CREATE TABLE eye(id INTEGER PRIMARY KEY, desc TEXT, data BLOB)") con.execute("INSERT INTO eye(desc,data) VALUES(?,?)", ("1d", sqlite3.Binary(r1d))) con.execute("INSERT INTO eye(desc,data) VALUES(?,?)", ("1d", numpy.getbuffer(r1d))) res = con.execute("SELECT * FROM eye").fetchall() con.close() #res -> #[(1, u'1d', <read-write buffer ptr 0x10371b220, size 80 at 0x10371b1e0>), # (2, u'1d', <read-write buffer ptr 0x10371b190, size 80 at 0x10371b150>)] print r1d - numpy.frombuffer(res[0][2]) #->[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] print r1d - numpy.frombuffer(res[1][2]) #->[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] Note that for work where data ty...
Post a Comment
Read more