mhfs

require 'formula'

# some credit to http://github.com/maddox/magick-installer
# NOTE please be aware that the GraphicsMagick formula derives this formula

def ghostscript_srsly?
  ARGV.include? '--with-ghostscript'
end

def x11?

codeinthehole

from celery import chain

from django.core.management.base import BaseCommand

from . import tasks


class Command(BaseCommand):

    def handle(self, *args, **kwargs):

sap1ens

var MyModel = Backbone.Model.extend({
    someMethod: function(opts) {
        var url = this.url() + '/someMethod',
            // note that these are just $.ajax() options
            options = {
                url: url,
                type: 'POST'
            };

        // add any additional options, e.g. a "success" callback or data

wrburgess

Grab new backup of database

Command: heroku pgbackups:capture --remote production

Response: >>> HEROKU_POSTGRESQL_COLOR_URL (DATABASE_URL) ----backup---> a712

Get url of backup download

Command: heroku pgbackups:url [db_key] --remote production

epicserve

#!/usr/bin/env bash

# This script prints out all of your Redis keys and their size in a human readable format
# Copyright 2013 Brent O'Connor
# License: http://www.apache.org/licenses/LICENSE-2.0

human_size() {
        awk -v sum="$1" ' BEGIN {hum[1024^3]="Gb"; hum[1024^2]="Mb"; hum[1024]="Kb"; for (x=1024^3; x>=1024; x/=1024) { if (sum>=x) { printf "%.2f %s\n",sum/x,hum[x]; break; } } if (sum<1024) print "1kb"; } '
}

tenderlove

require 'fiddle'

module Python
  def __class__= k
    value = _wrap self
    [k.object_id<<1].pack('Q').unpack('C8').each_with_index {|n,i|value[i+8]=n}
  end
  def _wrap klass; Fiddle::Pointer.new Fiddle.dlwrap klass; end
end

mblondel

# (C) Mathieu Blondel, November 2013
# License: BSD 3 clause

import numpy as np


def ranking_precision_score(y_true, y_score, k=10):
    """Precision at rank k

    Parameters

astrojuanlu

from functools import reduce
from itertools import groupby


def reduceByKey(func, iterable):
    """Reduce by key.

    Equivalent to the Spark counterpart
    Inspired by http://stackoverflow.com/q/33648581/554319

charanpald

# Run some recommendation experiments using MovieLens 100K
import pandas
import numpy
import scipy.sparse
import scipy.sparse.linalg
import matplotlib.pyplot as plt
from sklearn.metrics import mean_absolute_error

data_dir = "data/ml-100k/"
data_shape = (943, 1682)

jgillman

# Assumes the database container is named 'db'

DOCKER_DB_NAME="$(docker-compose ps -q db)"
DB_HOSTNAME=db
DB_USER=postgres
LOCAL_DUMP_PATH="path/to/local.dump"

docker-compose up -d db
docker exec -i "${DOCKER_DB_NAME}" pg_restore -C --clean --no-acl --no-owner -U "${DB_USER}" -d "${DB_HOSTNAME}" < "${LOCAL_DUMP_PATH}"
docker-compose stop db