python pca on leptograpsus data

crabs.dat

sp sex index FL RW CL CW BD
B M  1  8.1  6.7 16.1 19.0  7.0
B M  2  8.8  7.7 18.1 20.8  7.4
B M  3  9.2  7.8 19.0 22.4  7.7
B M  4  9.6  7.9 20.1 23.1  8.2
B M  5  9.8  8.0 20.3 23.0  8.2
B M  6 10.8  9.0 23.0 26.5  9.8
B M  7 11.1  9.9 23.8 27.1  9.8
B M  8 11.6  9.1 24.5 28.4 10.4
B M  9 11.8  9.6 24.2 27.8  9.7
B M 10 11.8 10.5 25.2 29.3 10.3
B M 11 12.2 10.8 27.3 31.6 10.9
B M 12 12.3 11.0 26.8 31.5 11.4
B M 13 12.6 10.0 27.7 31.7 11.4
B M 14 12.8 10.2 27.2 31.8 10.9
B M 15 12.8 10.9 27.4 31.5 11.0
B M 16 12.9 11.0 26.8 30.9 11.4
B M 17 13.1 10.6 28.2 32.3 11.0
B M 18 13.1 10.9 28.3 32.4 11.2
B M 19 13.3 11.1 27.8 32.3 11.3
B M 20 13.9 11.1 29.2 33.3 12.1
B M 21 14.3 11.6 31.3 35.5 12.7
B M 22 14.6 11.3 31.9 36.4 13.7
B M 23 15.0 10.9 31.4 36.4 13.2
B M 24 15.0 11.5 32.4 37.0 13.4
B M 25 15.0 11.9 32.5 37.2 13.6
B M 26 15.2 12.1 32.3 36.7 13.6
B M 27 15.4 11.8 33.0 37.5 13.6
B M 28 15.7 12.6 35.8 40.3 14.5
B M 29 15.9 12.7 34.0 38.9 14.2
B M 30 16.1 11.6 33.8 39.0 14.4
B M 31 16.1 12.8 34.9 40.7 15.7
B M 32 16.2 13.3 36.0 41.7 15.4
B M 33 16.3 12.7 35.6 40.9 14.9
B M 34 16.4 13.0 35.7 41.8 15.2
B M 35 16.6 13.5 38.1 43.4 14.9
B M 36 16.8 12.8 36.2 41.8 14.9
B M 37 16.9 13.2 37.3 42.7 15.6
B M 38 17.1 12.6 36.4 42.0 15.1
B M 39 17.1 12.7 36.7 41.9 15.6
B M 40 17.2 13.5 37.6 43.9 16.1
B M 41 17.7 13.6 38.7 44.5 16.0
B M 42 17.9 14.1 39.7 44.6 16.8
B M 43 18.0 13.7 39.2 44.4 16.2
B M 44 18.8 15.8 42.1 49.0 17.8
B M 45 19.3 13.5 41.6 47.4 17.8
B M 46 19.3 13.8 40.9 46.5 16.8
B M 47 19.7 15.3 41.9 48.5 17.8
B M 48 19.8 14.2 43.2 49.7 18.6
B M 49 19.8 14.3 42.4 48.9 18.3
B M 50 21.3 15.7 47.1 54.6 20.0
B F  1  7.2  6.5 14.7 17.1  6.1
B F  2  9.0  8.5 19.3 22.7  7.7
B F  3  9.1  8.1 18.5 21.6  7.7
B F  4  9.1  8.2 19.2 22.2  7.7
B F  5  9.5  8.2 19.6 22.4  7.8
B F  6  9.8  8.9 20.4 23.9  8.8
B F  7 10.1  9.3 20.9 24.4  8.4
B F  8 10.3  9.5 21.3 24.7  8.9
B F  9 10.4  9.7 21.7 25.4  8.3
B F 10 10.8  9.5 22.5 26.3  9.1
B F 11 11.0  9.8 22.5 25.7  8.2
B F 12 11.2 10.0 22.8 26.9  9.4
B F 13 11.5 11.0 24.7 29.2 10.1
B F 14 11.6 11.0 24.6 28.5 10.4
B F 15 11.6 11.4 23.7 27.7 10.0
B F 16 11.7 10.6 24.9 28.5 10.4
B F 17 11.9 11.4 26.0 30.1 10.9
B F 18 12.0 10.7 24.6 28.9 10.5
B F 19 12.0 11.1 25.4 29.2 11.0
B F 20 12.6 12.2 26.1 31.6 11.2
B F 21 12.8 11.7 27.1 31.2 11.9
B F 22 12.8 12.2 26.7 31.1 11.1
B F 23 12.8 12.2 27.9 31.9 11.5
B F 24 13.0 11.4 27.3 31.8 11.3
B F 25 13.1 11.5 27.6 32.6 11.1
B F 26 13.2 12.2 27.9 32.1 11.5
B F 27 13.4 11.8 28.4 32.7 11.7
B F 28 13.7 12.5 28.6 33.8 11.9
B F 29 13.9 13.0 30.0 34.9 13.1
B F 30 14.7 12.5 30.1 34.7 12.5
B F 31 14.9 13.2 30.1 35.6 12.0
B F 32 15.0 13.8 31.7 36.9 14.0
B F 33 15.0 14.2 32.8 37.4 14.0
B F 34 15.1 13.3 31.8 36.3 13.5
B F 35 15.1 13.5 31.9 37.0 13.8
B F 36 15.1 13.8 31.7 36.6 13.0
B F 37 15.2 14.3 33.9 38.5 14.7
B F 38 15.3 14.2 32.6 38.3 13.8
B F 39 15.4 13.3 32.4 37.6 13.8
B F 40 15.5 13.8 33.4 38.7 14.7
B F 41 15.6 13.9 32.8 37.9 13.4
B F 42 15.6 14.7 33.9 39.5 14.3
B F 43 15.7 13.9 33.6 38.5 14.1
B F 44 15.8 15.0 34.5 40.3 15.3
B F 45 16.2 15.2 34.5 40.1 13.9
B F 46 16.4 14.0 34.2 39.8 15.2
B F 47 16.7 16.1 36.6 41.9 15.4
B F 48 17.4 16.9 38.2 44.1 16.6
B F 49 17.5 16.7 38.6 44.5 17.0
B F 50 19.2 16.5 40.9 47.9 18.1
O M  1  9.1  6.9 16.7 18.6  7.4
O M  2 10.2  8.2 20.2 22.2  9.0
O M  3 10.7  8.6 20.7 22.7  9.2
O M  4 11.4  9.0 22.7 24.8 10.1
O M  5 12.5  9.4 23.2 26.0 10.8
O M  6 12.5  9.4 24.2 27.0 11.2
O M  7 12.7 10.4 26.0 28.8 12.1
O M  8 13.2 11.0 27.1 30.4 12.2
O M  9 13.4 10.1 26.6 29.6 12.0
O M 10 13.7 11.0 27.5 30.5 12.2
O M 11 14.0 11.5 29.2 32.2 13.1
O M 12 14.1 10.4 28.9 31.8 13.5
O M 13 14.1 10.5 29.1 31.6 13.1
O M 14 14.1 10.7 28.7 31.9 13.3 
O M 15 14.2 10.6 28.7 31.7 12.9
O M 16 14.2 10.7 27.8 30.9 12.7
O M 17 14.2 11.3 29.2 32.2 13.5
O M 18 14.6 11.3 29.9 33.5 12.8
O M 19 14.7 11.1 29.0 32.1 13.1
O M 20 15.1 11.4 30.2 33.3 14.0
O M 21 15.1 11.5 30.9 34.0 13.9
O M 22 15.4 11.1 30.2 33.6 13.5
O M 23 15.7 12.2 31.7 34.2 14.2
O M 24 16.2 11.8 32.3 35.3 14.7
O M 25 16.3 11.6 31.6 34.2 14.5
O M 26 17.1 12.6 35.0 38.9 15.7
O M 27 17.4 12.8 36.1 39.5 16.2
O M 28 17.5 12.0 34.4 37.3 15.3
O M 29 17.5 12.7 34.6 38.4 16.1
O M 30 17.8 12.5 36.0 39.8 16.7
O M 31 17.9 12.9 36.9 40.9 16.5
O M 32 18.0 13.4 36.7 41.3 17.1
O M 33 18.2 13.7 38.8 42.7 17.2
O M 34 18.4 13.4 37.9 42.2 17.7
O M 35 18.6 13.4 37.8 41.9 17.3
O M 36 18.6 13.5 36.9 40.2 17.0
O M 37 18.8 13.4 37.2 41.1 17.5
O M 38 18.8 13.8 39.2 43.3 17.9
O M 39 19.4 14.1 39.1 43.2 17.8
O M 40 19.4 14.4 39.8 44.3 17.9
O M 41 20.1 13.7 40.6 44.5 18.0
O M 42 20.6 14.4 42.8 46.5 19.6
O M 43 21.0 15.0 42.9 47.2 19.4
O M 44 21.5 15.5 45.5 49.7 20.9
O M 45 21.6 15.4 45.7 49.7 20.6
O M 46 21.6 14.8 43.4 48.2 20.1
O M 47 21.9 15.7 45.4 51.0 21.1
O M 48 22.1 15.8 44.6 49.6 20.5
O M 49 23.0 16.8 47.2 52.1 21.5
O M 50 23.1 15.7 47.6 52.8 21.6
O F  1 10.7  9.7 21.4 24.0  9.8
O F  2 11.4  9.2 21.7 24.1  9.7
O F  3 12.5 10.0 24.1 27.0 10.9
O F  4 12.6 11.5 25.0 28.1 11.5
O F  5 12.9 11.2 25.8 29.1 11.9
O F  6 14.0 11.9 27.0 31.4 12.6
O F  7 14.0 12.8 28.8 32.4 12.7
O F  8 14.3 12.2 28.1 31.8 12.5
O F  9 14.7 13.2 29.6 33.4 12.9
O F 10 14.9 13.0 30.0 33.7 13.3
O F 11 15.0 12.3 30.1 33.3 14.0
O F 12 15.6 13.5 31.2 35.1 14.1
O F 13 15.6 14.0 31.6 35.3 13.8
O F 14 15.6 14.1 31.0 34.5 13.8
O F 15 15.7 13.6 31.0 34.8 13.8
O F 16 16.1 13.6 31.6 36.0 14.0
O F 17 16.1 13.7 31.4 36.1 13.9
O F 18 16.2 14.0 31.6 35.6 13.7
O F 19 16.7 14.3 32.3 37.0 14.7
O F 20 17.1 14.5 33.1 37.2 14.6
O F 21 17.5 14.3 34.5 39.6 15.6
O F 22 17.5 14.4 34.5 39.0 16.0
O F 23 17.5 14.7 33.3 37.6 14.6
O F 24 17.6 14.0 34.0 38.6 15.5
O F 25 18.0 14.9 34.7 39.5 15.7
O F 26 18.0 16.3 37.9 43.0 17.2
O F 27 18.3 15.7 35.1 40.5 16.1
O F 28 18.4 15.5 35.6 40.0 15.9
O F 29 18.4 15.7 36.5 41.6 16.4
O F 30 18.5 14.6 37.0 42.0 16.6
O F 31 18.6 14.5 34.7 39.4 15.0
O F 32 18.8 15.2 35.8 40.5 16.6
O F 33 18.9 16.7 36.3 41.7 15.3
O F 34 19.1 16.0 37.8 42.3 16.8
O F 35 19.1 16.3 37.9 42.6 17.2
O F 36 19.7 16.7 39.9 43.6 18.2
O F 37 19.9 16.6 39.4 43.9 17.9
O F 38 19.9 17.9 40.1 46.4 17.9
O F 39 20.0 16.7 40.4 45.1 17.7
O F 40 20.1 17.2 39.8 44.1 18.6
O F 41 20.3 16.0 39.4 44.1 18.0
O F 42 20.5 17.5 40.0 45.5 19.2
O F 43 20.6 17.5 41.5 46.2 19.2
O F 44 20.9 16.5 39.9 44.7 17.5
O F 45 21.3 18.4 43.8 48.4 20.0
O F 46 21.4 18.0 41.2 46.2 18.7
O F 47 21.7 17.1 41.7 47.2 19.6
O F 48 21.9 17.2 42.6 47.4 19.5
O F 49 22.5 17.2 43.0 48.7 19.8
O F 50 23.1 20.2 46.2 52.5 21.1

pca.py

#!/usr/bin/env python
# encoding: utf-8

import sys
import os
import numpy as np
import pylab as plt
from scipy import stats

with open('crabs.dat') as f:
    lines = f.readlines()

orig_lines = [l.strip().split() for l in lines[1:]]
lines = [[float(val) for val in line[3:]] for line in orig_lines]

dat = np.array(lines)
dat = stats.stats.zscore(dat)
eigvals, eigvecs = np.linalg.eig(np.cov(dat.T))
proj = np.dot(eigvecs, dat.T)

colors = {'OF': 'r', 'OM': 'b', 'BF': 'r', 'BM': 'b'}

plt.figure()
for i, p in enumerate(proj.T):
    plt.plot(p[0], p[1], '.', color=colors[''.join(orig_lines[i][0:2])])