My recommendations for Python projects
How I debug failing Python tests.
This blog post is the second in a series I am writing, covering methods of simple parallelism. The following posts cover more convenient methods, as well as some things that should be considered. Parallelism methods Basics and introduction Function objects If I’ve skipped your favourite method of parallelism, feel free to tweet me or add a comment on the tracking issue informing me. This method was brought to my attention by Tom Marsh, and is a nice alternative to using functools.
I often get asked “how can I parallelise my Python code?". I’ve come up with this simple cheat sheet to explain it. I will only explain the most common method of parallel problems here: embarrassingly parallel problems. This blog post is the first in a series I am writing, covering methods of simple parallelism. The following posts cover more convenient methods, as well as some things that should be considered.
Numpy has the ability to mask arrays and ignore their values for certain computations, called “masked arrays”. They contain a .mask attribute which is a boolean array, True where the value should be masked and False otherwise. Numpy also comes with a suite of functions which can handle this masking naturally. Typically for a function in the np. namespace, there is a masked-array-aware version under the np.ma. namespace: np.median => np.
pymysql Defaults to autocommit=False connection = pymysql.connect(user='user', db='test') cursor = connection.cursor() cursor.execute('insert into test (value) values (10)') connection.close() connection = pymysql.connect(user='user', db='test') cursor = connection.cursor() cursor.execute('select value from test') # =>  To commit changes to the database, #commit() must be called: connection = pymysql.connect(user='user', db='test') cursor = connection.cursor() cursor.execute('insert into test (value) values (10)') # Call the commit line connection.commit() connection.close() connection = pymysql.connect(user='user', db='test') cursor = connection.
I used to have two simple shell aliases for IPython: alias ipy=ipython alias pylab='ipython --pylab' These were separated for a couple of reasons: The pylab mode of IPython was deprecated, for good reason. It “infects” the global namespace with all matplotlib and numpy functions. It breaks two entries in the famous “Zen of Python”: Explicit is better than implicit. Namespaces are one honking great idea – let’s do more of those!
For interpolation in python, scipy includes the interpolateackage containing (amongst other things) interp1d for simple interpolation. The function does not however perform extrapolation; if the interpolator is asked for a value outside the original range it will raise an exception. To get around this, the interpolator contains a .x parameter which contains the original x values used to construct itself. A boolean index can then be used to reject inputoints which fall outside of this range:
So IPython has updated to version 2.0. The full changelog can be found here and to summarise the key points: interactive widgets for the notebook directory navigation in the notebook dashboard persistent URLs for notebooks a new modal user interface in the notebook a security model for notebooks I want to discuss a few of these and my thoughts on how they make IPython notebook finally an incredibly powerful tool for research, more so than before, and how I may be finally switching from simple scripts to using the notebook full time (with a major caveat!
I was trying to play with PyMC3 and as per usual with code under heavy development the tutorials were out of date, and the code wouldn’t run. When I say “out of date” in fact the code ran but no valid numbers were produced. The API seemed to be consistent though. I managed to get the tutorial to run by installing the following: Theano==0.6.0 pymc==3.0 scipy==0.13.3 PyMC3 was installed from git from the pymc3 branch as follows: