Currently no MOOCs (Massive Open Online Courses) or e-learning systems support a peer instruction method, which has shown some advantages over traditional lecture-based learning techniques. In this project I will implement a tool that helps instructors and their students experiment with this teaching method online. The project will mainly leverage `WebRTC`_ technology.
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#coding: utf-8 | |
from cStringIO import StringIO | |
from email.mime.multipart import MIMEMultipart | |
from email.mime.text import MIMEText | |
from email.header import Header | |
from email import Charset | |
from email.generator import Generator | |
import smtplib | |
# Example address data |
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# Usage: | |
# source iterm2.zsh | |
# iTerm2 tab color commands | |
# https://iterm2.com/documentation-escape-codes.html | |
if [[ -n "$ITERM_SESSION_ID" ]]; then | |
tab-color() { | |
echo -ne "\033]6;1;bg;red;brightness;$1\a" | |
echo -ne "\033]6;1;bg;green;brightness;$2\a" |
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## credit: http://fabian-affolter.ch/blog/the-lineinfile-module-of-ansible/ | |
--- | |
- hosts: alpine_install | |
user: root | |
tasks: | |
# - name: create a complete empty file | |
# command: /usr/bin/touch /test/test.conf | |
- name: create a new file with lineinfile |
(by @andrestaltz)
If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.
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[program:_appname_] | |
command = /home/_username_/.pyenv/versions/venvname/bin/gunicorn -w 1 -b 127.0.0.1:8000 --pythonpath=. --reload wsgi:application | |
directory=/home/_username_/sites/_appname_/ | |
environment=PATH="/home/_username_/.pyenv/versions/venvname/bin:/home/_username_/.pyenv/shims:/home/_username_/.pyenv/bin:",DJANGO_SETTINGS_MODULE="_appname_.settings",HOME="/home/_username_" | |
user=_username_ | |
autostart=true | |
autorestart=true | |
redirect_stderr=true | |
stopsignal=QUIT | |
stderr_logfile=/home/_username_/log/_appname_-stderr.log |
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# Profile with cProfile python module and save to output | |
python -m cProfile -o output.cprof script.py | |
# Transform into tree format for QCacheGrind | |
pyprof2calltree -i output.cprof -o callgrind.cprof | |
# Open QCacheGrind and load callgrind.cprof file |