| require 'digest/md5' | |
| def gfm(text) | |
| # Extract pre blocks | |
| extractions = {} | |
| text.gsub!(%r{<pre>.*?</pre>}m) do |match| | |
| md5 = Digest::MD5.hexdigest(match) | |
| extractions[md5] = match | |
| "{gfm-extraction-#{md5}}" | |
| end |
In this document I am using Sass's SCSS syntax. You can choose to use the indented syntax in sass, if you prefer it, it has no functional differences from the SCSS syntax.
For Less, I'm using the JavaScript version because this is what they suggest on the website. The ruby version may be different.
The goal of this example is to show how an existing C codebase for numerical computing (here c_code.c) can be wrapped in Cython to be exposed in Python.
The meat of the example is that the data is allocated in C, but exposed in Python without a copy using the PyArray_SimpleNewFromData numpy
| {%- macro form_field_label(field) -%} | |
| <label for="{{ field.id }}">{{ field.label.text }} | |
| {%- if field.flags.required -%} | |
| <abbr title="Diese Feld muss angegeben werden">*</abbr> | |
| {%- endif %}</label> | |
| {% endmacro %} | |
| {%- macro form_field_description(field) -%} | |
| {% if field.description %} | |
| <span class="descr">{{ field.description }}</span> |
| """Example of adaptive Lasso to produce event sparser solutions | |
| Adaptive lasso consists in computing many Lasso with feature | |
| reweighting. It's also known as iterated L1. | |
| """ | |
| # Authors: Alexandre Gramfort <[email protected]> | |
| # | |
| # License: BSD (3-clause) | |
| import numpy as np |
| .leaflet-div-icon { | |
| background: transparent; | |
| border: none; | |
| } | |
| .leaflet-marker-icon .number{ | |
| position: relative; | |
| top: -37px; | |
| font-size: 12px; | |
| width: 25px; |
| """ | |
| Non-Negative Garotte implementation with the scikit-learn | |
| """ | |
| # Author: Alexandre Gramfort <[email protected]> | |
| # Jaques Grobler (__main__ script) <[email protected]> | |
| # | |
| # License: BSD Style. | |
| import numpy as np |
| # http://flask.pocoo.org/mailinglist/archive/2012/4/10/serving-static-file-from-a-separate-domain-in-production/ | |
| from flask import Flask, url_for | |
| # Uncomment to set server name. | |
| # SERVER_NAME = 'mianos.com' | |
| app = Flask(__name__, static_folder=None) | |
| app.config.from_object(__name__) | |
| app.add_url_rule('/<path:filename>', endpoint='static', |
| // Given a query string "?to=email&why=because&first=John&Last=smith" | |
| // getUrlVar("to") will return "email" | |
| // getUrlVar("last") will return "smith" | |
| // Slightly more concise and improved version based on http://www.jquery4u.com/snippets/url-parameters-jquery/ | |
| function getUrlVar(key){ | |
| var result = new RegExp(key + "=([^&]*)", "i").exec(window.location.search); | |
| return result && unescape(result[1]) || ""; | |
| } |
| Latency Comparison Numbers (~2012) | |
| ---------------------------------- | |
| L1 cache reference 0.5 ns | |
| Branch mispredict 5 ns | |
| L2 cache reference 7 ns 14x L1 cache | |
| Mutex lock/unlock 25 ns | |
| Main memory reference 100 ns 20x L2 cache, 200x L1 cache | |
| Compress 1K bytes with Zippy 3,000 ns 3 us | |
| Send 1K bytes over 1 Gbps network 10,000 ns 10 us | |
| Read 4K randomly from SSD* 150,000 ns 150 us ~1GB/sec SSD |
