### 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
1. It was native C
2. It was vectorized - I could compute the CIs for multiple bootstrap runs at the same time
3. It could pick out multiple percentiles (low and high ends) at the same time.
Funnily enough, my crude measurements showed that the dumb implementation using numpy.sort is just as fast as the builtin one. Well, silly me: it turns out that scipy.stats.scoreatpercentile calls scipy.stats.mquantiles which simply does numpy.sort. I guess I should have thought of that, since sorting is the real bottle neck in this operation and numpy.sort is as efficient as you can get since that's implemented in C.

```python test.py | grep 'function calls'
38 function calls (36 primitive calls) in 0.001 seconds
12 function calls in 0.001 seconds
38 function calls (36 primitive calls) in 0.001 seconds
12 function calls in 0.001 seconds
38 function calls (36 primitive calls) in 0.876 seconds
17 function calls in 0.705 seconds
38 function calls (36 primitive calls) in 0.814 seconds
17 function calls in 0.704 seconds
```

``````import pylab, cProfile, scipy.stats as ss

def conf_int_scipy(x, ci=0.95):
low_per = 100*(1-ci)/2.
high_per = 100*ci + low_per
mn = x.mean()
cis = ss.scoreatpercentile(x, [low_per, high_per])
return mn, cis

def conf_int_native(x, ci=0.95):
ci2 = (1-ci)*.5
low_idx = int(ci2*x.size)
high_idx = int((1-ci2)*x.size)
x.sort()
return x.mean(), x[low_idx], x[high_idx]

def conf_int_scipy_multi(x, ci=0.95):
low_per = 100*(1-ci)/2.
high_per = 100*ci + low_per
mn = x.mean(axis=0)
cis = ss.scoreatpercentile(x, [low_per, high_per],axis=0)
return mn, cis

def conf_int_native_multi(x, ci=0.95):
ci2 = (1-ci)*.5
low_idx = int(ci2*x.shape[1])
high_idx = int((1-ci2)*x.shape[1])
mn = x.mean(axis=1)
xs = pylab.sort(x)
cis = pylab.empty((mn.size,2),dtype=float)
cis[:,0] = xs[:,low_idx]
cis[:,1] = xs[:,high_idx]
return mn, cis

r = pylab.randn(10000)
cProfile.run('conf_int_scipy(r)')
cProfile.run('conf_int_native(r)')

r = pylab.randn(10000)
cProfile.run('conf_int_scipy(r)')
cProfile.run('conf_int_native(r)')

r = pylab.randn(1000,10000)
cProfile.run('conf_int_scipy_multi(r)')
cProfile.run('conf_int_native_multi(r)')

r = pylab.randn(1000,10000)
cProfile.run('conf_int_scipy_multi(r)')
cProfile.run('conf_int_native_multi(r)')
``````

### 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:

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

### 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)

### Pandas panel = collection of tables/data frames aligned by index and column

Pandas panel provides a nice way to collect related data frames together while maintaining correspondence between the index and column values:

import pandas as pd, pylab #Full dimensions of a slice of our panel index = ['1','2','3','4'] #major_index columns = ['a','b','c'] #minor_index df = pd.DataFrame(pylab.randn(4,3),columns=columns,index=index) #A full slice of the panel df2 = pd.DataFrame(pylab.randn(3,2),columns=['a','c'],index=['1','3','4']) #A partial slice df3 = pd.DataFrame(pylab.randn(2,2),columns=['a','b'],index=['2','4']) #Another partial slice df4 = pd.DataFrame(pylab.randn(2,2),columns=['d','e'],index=['5','6']) #Partial slice with a new column and index pn = pd.Panel({'A': df}) pn['B'] = df2 pn['C'] = df3 pn['D'] = df4 for key in pn.items: print pn[key] -> output …