Skip to main content

Installing python and friends on a mac

  1. Get the latest python interpreter and some packages from here [from a pointer on python.org]
  2. To get matplotlib installed you will need pytz and dateutil, both found on the above mentioned page [pythonmac.org].
  3. SciPy is notably missing - get the source and the fortran compiler from here and compile as acc to the instructions.
  4. There are several IDEs, I'm giving eclipse and xcode a whirl. This [showmedo] is an excellent video tutorial on how to get PyDev for eclipse all set up. Also see this for PyDev installation. PyDev has a really annoying nag screen now. I'm stopping using it.
  5. Eclipse does not play sensibly with matplotlib - I'm using ipython to actually run the scripts and eclipse for the editing environment. Eclipse beats Xcode because eclipse will layout your methods and classes for browsing (like pyscripter).
  6. Marxy has some scripts for Python on Xcode here.
  7. ipython should be installed from source as detailed here.
Labels:

Comments

  1. NickCJune 3, 2008 at 1:11 PM

    so were you able to get MatPlotLib,NumPy and SciPy all to work with 2.5 on the Mac? I tried MacPython.org, Fink and MacPorts and in all cases couldn't get it all working with 2.5. In the end I chose to stick with MacPorts and roll back to Python2.4, no problems since, but that doesn't mean I wouldn't prefer to be using 2.5.

    ReplyDelete
  2. Kaushik GhoseJune 3, 2008 at 5:13 PM

    Yes. I'm on Mac OS 10.5. I got it to work using the steps I wrote down here

    ReplyDelete

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