btel

#!/usr/bin/python
import subprocess

import sys
import time
import argparse

parser = argparse.ArgumentParser()
parser.add_argument("team1")
parser.add_argument("team2")

btel

{
    "a": 1, 
    "nested_dictionary": {
        "c": 1, 
        "d": 2
    }, 
    "b": 2, 
    "list": [
        1, 
        2, 

btel

Only in inkscape-0.48.4-patched/: patch-clang.diff
diff -crB inkscape-0.48.4/src/2geom/sbasis.h inkscape-0.48.4-patched/src/2geom/sbasis.h
*** inkscape-0.48.4/src/2geom/sbasis.h	Fri Jul  8 20:25:09 2011
--- inkscape-0.48.4-patched/src/2geom/sbasis.h	Fri Nov  8 00:42:01 2013
***************
*** 75,84 ****
          return d[i];
      }
      Linear& operator[](unsigned i) { return d.at(i); }
!     Linear const* begin() const { return (Linear const*)&*d.begin();}

btel

btel

#!/usr/bin/python


import svgutils.transform as sg
import sys 
import os

#create new SVG figure
fig = sg.SVGFigure("15cm", "10cm")

btel

@jit(double[:,:2](double[:], double[:], double, double, double))
def lif_model(ge, gi, dt=dt, vreset=0, vthresh=-40*mV):
    v = vreset
    mem_pot = np.zeros((len(ge), 2))
    Ee, Ei = 0, -100
    El = -60
    gl = 1/60
    C = 250
    for i in range(len(ge)):
        dv = (-gl*(v-El) - ge[i]*(v-Ee) - gi[i]*(v-Ei))*dt/C

btel

#!/bin/bash

python $*

fname="${@: -1}"

width=`identify -format %w $fname`
gitversion=`git rev-parse --short HEAD`
origin_name=`basename $(git remote show -n origin | grep Fetch | cut -d: -f2-)`
branch=`git rev-parse --abbrev-ref HEAD`

btel

#!/bin/bash
TMPDIR=$(mktemp -d /tmp/git-latexdiff.XXXXXX)
latexdiff "$1" "$2" > $TMPDIR/diff.tex
pdflatex -interaction nonstopmode -output-directory $TMPDIR $TMPDIR/diff.tex
pdflatex -interaction nonstopmode -output-directory $TMPDIR $TMPDIR/diff.tex
evince $TMPDIR/diff.pdf
rm -rf $TMPDIR

btel

When improving the performance of plotting high-dimensional data using 
faceted scatter plots, I noticed that much of time was spent on the axis 
creation (even 50%!).

On my machine creating 20x20 array of subplots without actually plotting 
anything takes about 11 seconds (for comparison plotting 5000 points on 
all of them takes only 0.6s!):

import matplotlib
matplotlib.interactive(True)

btel

#from github.com/gdetor/SPySort

def get_jitter(self, evts, center, centerD, centerDD):
    """ Estimates the jitter given an event or a matrix of events where
    individual events form the rows, a median event and the first two
    derivatives of the latter.

    **Parameters**

    evts : double (array)