### Using PCA in MDP

I found mdp on sourceforge. I needed to do PCA for some of my simulations. The mdp.pca function is easy to handle. The input is a m x n matrix, n is the dimension of the space, and m are the observations (rows = observations, cols = dimensions. The doc string is confused as to this point)

The output is a matrix also m x n, but the vectors have been transformed, so that the first column is along the direction with greatest variance etc.

An example is below:

`import pylab as mimport matplotlib.axes3d as m3import mdpx1 = m.rand(10,3)x2 = m.rand(10,3) + m.array([[2.,-2.,0.]])x = m.concatenate((x1,x2))y = mdp.pca(x)fig = m.figure()ax = m3.Axes3D(fig)ax.scatter3D(x[:10,0].squeeze().T, x[:10,1].squeeze().T, x[:10,2].squeeze().T, marker = '^')ax.scatter3D(x[10:,0].squeeze().T, x[10:,1].squeeze().T, x[10:,2].squeeze().T, marker = 'o')m.axis('scaled')m.axes([.7, .8, .2, .2])m.plot(y[:10,0], y[:10,1],'k^')m.plot(y[10:,0], y[10:,1],'b.')m.axis('scaled')m.title('2 D')m.axes([.7, .6, .2, .2])m.plot(y[:10,0],'k^')m.plot(y[10:,0],'b.')m.axis('scaled')m.title('1 D')`

1. For anyone reading this, the code needs to be updated for the latest version of matplotlib:
line 2 should be:
import mpl_toolkits.mplot3d as m3

also add m.show() at the end to display the results.

### 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

### 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 …

### Drawing circles using matplotlib

Use the pylab.Circle command

import pylab #Imports matplotlib and a host of other useful modules cir1 = pylab.Circle((0,0), radius=0.75, fc='y') #Creates a patch that looks like a circle (fc= face color) cir2 = pylab.Circle((.5,.5), radius=0.25, alpha =.2, fc='b') #Repeat (alpha=.2 means make it very translucent) ax = pylab.axes(aspect=1) #Creates empty axes (aspect=1 means scale things so that circles look like circles) ax.add_patch(cir1) #Grab the current axes, add the patch to it ax.add_patch(cir2) #Repeat pylab.show()