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@jterrace
jterrace / distribute_chips.py
Created Sep 20, 2018
Programmatically determine how to distribute your chips for Take Your Chances game
View distribute_chips.py
import sys
DICE_SIDES = 6
distribution = [0] * DICE_SIDES
for d1 in range(1, DICE_SIDES + 1):
for d2 in range(1, DICE_SIDES + 1):
delta = abs(d1 - d2)
distribution[delta] += 1
distribution = [
@jterrace
jterrace / git-annex-gcs.sh
Last active Jun 21, 2020
An example of how to use Google Cloud Storage with git-annex
View git-annex-gcs.sh
# Initialize git and git-annex
$ mkdir annex-gcs-test
$ cd annex-gcs-test/
$ git init
Initialized empty Git repository in /Users/jterrace/annex-gcs-test/.git/
$ git annex init "my machine"
init my machine ok
(Recording state in git...)
# Set up AWS credentials
@jterrace
jterrace / disk-vs-internet.py
Created Aug 30, 2012
Creates a graph comparing disk read throughput to Internet speeds over time
View disk-vs-internet.py
import matplotlib.pyplot as plt
import numpy as np
from matplotlib import rc
rc('text', usetex=True)
rc('font', family='sans-serif')
rc('font', size='16')
# source: http://download.broadband.gov/plan/fcc-omnibus-broadband-initiative-(obi)-technical-paper-broadband-performance.pdf
@jterrace
jterrace / debug.py
Created Jul 19, 2012
Drops python to a debugger on unhandled exceptions
View debug.py
"""Import this module to make python drop to the debugger
when an undhandled exception is raised.
Original script from:
http://code.activestate.com/recipes/65287/
"""
import sys
__old_excepthook__ = sys.excepthook
@jterrace
jterrace / xvfb
Created Jun 11, 2012
xvfb init script for Ubuntu
View xvfb
XVFB=/usr/bin/Xvfb
XVFBARGS=":1 -screen 0 1024x768x24 -ac +extension GLX +render -noreset"
PIDFILE=/var/run/xvfb.pid
case "$1" in
start)
echo -n "Starting virtual X frame buffer: Xvfb"
start-stop-daemon --start --quiet --pidfile $PIDFILE --make-pidfile --background --exec $XVFB -- $XVFBARGS
echo "."
;;
stop)
@jterrace
jterrace / lang-llvm.js
Created Jun 1, 2012
LLVM plugin for google-code-prettify
View lang-llvm.js
/**
* @fileoverview
* Registers a language handler for LLVM.
*
*
* To use, include prettify.js and this file in your HTML page.
* Then put your code in an HTML tag like
* <pre class="prettyprint lang-llvm">(my LLVM code)</pre>
*
*
@jterrace
jterrace / shaper.sh
Created Feb 23, 2012
A utility script for traffic shaping with tc
View shaper.sh
#!/bin/bash
#
# shaper.sh
# ---------
# A utility script for traffic shaping using tc
#
# Usage
# -----
# shape.sh start - starts the shaper
# shape.sh stop - stops the shaper
@jterrace
jterrace / fuzzymath.py
Created Feb 14, 2012
A collection of fuzzy floating point utility functions for Python
View fuzzymath.py
import math
def almostEqual(a, b, rtol=1.0000000000000001e-05, atol=1e-08):
"""Checks if the given floats are almost equal. Uses the algorithm
from numpy.allclose."""
return math.fabs(a - b) <= (atol + rtol * math.fabs(b))
def floor(v):
"""Returns the floor of the given number, unless it is equal to its
ceiling (within floating point error)."""
@jterrace
jterrace / gist:1823320
Created Feb 14, 2012
Automatically log in user after django-registration activation
View gist:1823320
from registration.signals import user_activated
from django.contrib.auth import login, authenticate
def login_on_activation(sender, user, request, **kwargs):
"""Logs in the user after activation"""
user.backend = 'django.contrib.auth.backends.ModelBackend'
login(request, user)
# Registers the function with the django-registration user_activated signal
user_activated.connect(login_on_activation)
@jterrace
jterrace / remove_dup_tris.py
Created Nov 3, 2011
Removing duplicate triangles with numpy
View remove_dup_tris.py
import numpy as np
def remove_duplicates(array_data, return_index=False, return_inverse=False):
"""Removes duplicate rows of a multi-dimensional array. Returns the
array with the duplicates removed. If return_index is True, also
returns the indices of array_data that result in the unique array.
If return_inverse is True, also returns the indices of the unique
array that can be used to reconstruct array_data."""
unique_array_data, index_map, inverse_map = np.unique(
array_data.view([('', array_data.dtype)] * \
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