Skip to main content

Objects, dictionaries, shallow and deep copies

objects have their attributes stored in a dictionary accessible by doing objectname.__dict__ use copy module to make deepcopies of objects copy.deepcopy()

Also see .

Comments

Popular posts from this blog

Python: Multiprocessing: passing multiple arguments to a function

Write a wrapper function to unpack the arguments before calling the real function. Lambda won't work, for some strange un-Pythonic reason.


import multiprocessing as mp def myfun(a,b): print a + b def mf_wrap(args): return myfun(*args) p = mp.Pool(4) fl = [(a,b) for a in range(3) for b in range(2)] #mf_wrap = lambda args: myfun(*args) -> this sucker, though more pythonic and compact, won't work p.map(mf_wrap, fl)

Flowing text in inkscape (Poster making)

You can flow text into arbitrary shapes in inkscape. (From a hint here).

You simply create a text box, type your text into it, create a frame with some drawing tool, select both the text box and the frame (click and shift) and then go to text->flow into frame.

UPDATE:

The omnipresent anonymous asked:
Trying to enter sentence so that text forms the number three...any ideas?
The solution:
Type '3' using the text toolConvert to path using object->pathSize as necessaryRemove fillUngroupType in actual text in new text boxSelect the text and the '3' pathFlow the text

Calculating confidence intervals: straight Python is as good as scipy.stats.scoreatpercentile

UPDATE:
I would say the most efficient AND readable way of working out confidence intervals from bootstraps is:

numpy.percentile(r,[2.5,50,97.5],axis=1)

Where r is a n x b array where n are different runs (e.g different data sets) and b are the individual bootstraps within a run. This code returns the 95% CIs as three numpy arrays.


Confidence intervals can be computed by bootstrapping the calculation of a descriptive statistic and then finding the appropriate percentiles of the data. I saw that scipy.stats has a built in percentile function and assumed that it would work really fast because (presumably) the code is in C. I was using a simple minded Python/Numpy implementation by first sorting and then picking the appropriate percentile data. I thought this was going to be inefficient timewise and decided that using scipy.stats.scoreatpercentile was going to be blazing fast because
It was native C It was vectorized - I could compute the CIs for multiple bootstrap runs at the same time …