South on I-35, 2 hours
- Ski Jump Campground (unimproved), $14 / night
#!/usr/bin/python3 | |
# Usage: | |
#lh.py on | |
#lh.py off | |
# with no arguments, state will be toggled (off -> on, on -> off) | |
#lh.py | |
from bluepy import btle | |
import sys |
# How to Edit Gameplay Footage | |
## Identify the Narratives | |
There will likely be at least four narratives going on at any one time. | |
1. Channel/life narrative | |
a. Who are you as a person? | |
b. What's this channel about? | |
2. Personal/life narrative (more temporally local than the channel narrative) |
import bcolz | |
import bquery | |
data_path = '/some/place/with/bcolz/data' | |
c_table = bquery.ctable(rootdir=data_path) | |
## Filter | |
# create the criteria |
Assumptions:
three things matter: developer productivity, production performance, infrastructure cost
same code on client and server (developer productivity)
#!/usr/bin/bash | |
# pass in a single argument with the name of the project | |
mkdir git | |
mkdir git/$1 | |
mkdir apps | |
mkdir apps/$1 | |
mkdir deployments |
# First run the python data science script: https://gist.github.com/waylonflinn/506f563573600d944923 | |
# cuda | |
# wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_6.5-14_amd64.deb | |
# dpkg -i cuda-repo-ubuntu1404_6.5-14_amd64.deb | |
# sudo apt-get update | |
# sudo apt-get install -y cuda | |
# pushd installs | |
# wget https://github.com/BVLC/caffe/archive/v0.9999.tar.gz | |
# tar -xvf v0.9999.tar.gz |
#! /usr/bin/env python | |
# this script filters output from ipython notebooks, for use in git repos | |
# http://stackoverflow.com/questions/18734739/using-ipython-notebooks-under-version-control | |
# | |
# put this file in a `bin` directory in your home directory, then run the following commands: | |
# | |
# chmod a+x ~/bin/ipynb_output_filter.py | |
# echo -e "*.ipynb \t filter=dropoutput_ipynb" >> ~/.gitattributes | |
# git config --global core.attributesfile ~/.gitattributes | |
# git config --global filter.dropoutput_ipynb.clean ~/bin/ipynb_output_filter.py |
#!/usr/bin/env bash | |
## create an ubuntu 14.04 hvm instance, then from your home directory: | |
# 1. download this script | |
# wget https://gist.githubusercontent.com/waylonflinn/506f563573600d944923/raw/install-python-data-science.sh | |
# 2. make it executable | |
# chmod a+x install-python-data-science.sh |