mattjj

val source = CSVFile("pubmed-oa-subset.csv") ~> IDColumn(1);

val tokenizer = {
  SimpleEnglishTokenizer() ~>            // tokenize on space and punctuation
  CaseFolder() ~>                        // lowercase everything
  WordsAndNumbersOnlyFilter() ~>         // ignore non-words and non-numbers
  MinimumLengthFilter(3)                 // take terms with >=3 characters
}

val text = {

mattjj

from __future__ import division
import numpy as np
na = np.newaxis
from matplotlib import pyplot as plt

def adj_path(n):
    a = np.zeros((n,n))
    a.flat[1::a.shape[0]+1] = a.flat[a.shape[0]::a.shape[0]+1] = 1
    return a

mattjj

#!/bin/bash
curl -s "http://online.wsj.com/mdc/public/page/2_3020-tips-${1}.html?mod=mdc_pastcalendar" \
| tr '^M' '\n' \
| grep -E '<td class="(([pn]?num)|(text))">' \
| sed -E 's/<[^>]*>([^<]*)<.*/\1/' \
| awk '(NR%6==0){print p; p=""; next}{p=p "\t" $0;}' \
| sed "s/^/${1}/"

mattjj

#!/bin/bash
usage() { echo "usage: ${0##*/} commandstring cachefilename"; }
if [ $# -ne 2 ]; then usage; exit 0; fi

if [ -e "$2" ]
then
    cat "$2"
else
    eval "$1" | tee $2
fi

mattjj

from __future__ import division
import numpy as np

def unscented_transform(mu,Sigma,alpha,kappa,beta=2):
    n = mu.shape[0]
    lmbda = alpha**2*(n+kappa)-n

    points = np.empty((2*n+1,n))
    mean_weights = np.empty(2*n+1)
    cov_weights = np.empty(2*n+1)

mattjj

;; the lagrangian for two unconstrained masses in gravity
;; (this is just 1/2 m v^2 - mgh from high school)
(define ((L-ms-in-g m-tuple g) local)
 (let ((v-tuple (velocities local))
       (q-tuple (coordinate local)))
   (let ((momenta (uptuple-map (lambda (mi vi) (* (/ 1 2) mi (norm vi))) m-tuple v-tuple))
         (potentials (uptuple-map (lambda (mi qi) (* mi g (ref qi 1))) m-tuple q-tuple)))
     (let ((T (uptuple-reduce + momenta))
           (V (uptuple-reduce + potentials)))
       (- T V)))))

mattjj

from __future__ import division
import numpy as np

import sys
sys.path.append('/Users/mattjj/Dropbox/work/datta/renderer')
from MouseData import MouseData
m = MouseData('/Users/mattjj/Dropbox/work/datta/renderer/data/mouse_mesh_low_poly3.npz')
translations = m.joint_translations

def inv(E):

mattjj

from __future__ import division
import numpy as np
np.seterr(divide='ignore')
from matplotlib import pyplot as plt
plt.ioff()

import pyhsmm
pyhsmm.internals.states.use_eigen()
from pyhsmm.util.text import progprint_xrange

mattjj

set PATH "$HOME"/bin /opt/local/Library/Frameworks/Python.framework/Versions/Current/bin/\
 /opt/local/bin /opt/local/sbin $PATH
set CDPATH . "$HOME" $CDPATH

set fish_greeting ""

function fish_user_key_bindings
    bind \e\[1\;9A 'history-token-search-backward'
end

mattjj

from __future__ import division
import numpy as np
from numpy.lib.stride_tricks import as_strided as ast

def chunk_data(data,window_size,overlap_size=0,flatten_inside_window=True):
    assert data.ndim == 1 or data.ndim == 2
    if data.ndim == 1:
        data = data.reshape((-1,1))

    # get the number of overlapping windows that fit into the data