shippy

image: python:3.8

pipelines:
  # Continuous Integration pipeline
  pull-requests:
    '**':  # run on any branch
      - step:
          name: Set up and build
          caches:
            - pip

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# A proof of concept for implementing re-usable nested rules in Dragonfly.
#
# See http://simon.podhajsky.net/blog/2017/executing-nested-rules-with-dragonfly/
# for the accompanying article.
try:
    import pkg_resources
    pkg_resources.require("dragonfly >= 0.6.5beta1.dev-r99")
except ImportError:
    pass

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#!/usr/bin/env bash
# A script to replicate VundleVim/Vundle.vim/issues/807

## Setup
cd /tmp
rm -rf bugtest
mkdir bugtest && cd bugtest && git init
git submodule add -b vundle_bug https://github.com/shippy/dotvim.git dotvim
git submodule update --init --recursive
hook_path=".git/modules/dotvim/hooks/post-checkout"

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require 'pry'
require 'octokit'
require 'json'

# Part 0: Extract bare repo and push it to GH:
# Follow https://help.github.com/enterprise/2.2/admin/articles/moving-a-repository-from-github-com-to-github-enterprise/

# Part 1: Extract issues & everything else from the source repo
## Setup
Octokit.configure do |c|

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## Dependencies (get them with `install.packages(c("dplyr", "ggplot2", "ggthemes"))`)
library(dplyr)
library(ggplot2)
library(ggthemes)
options(repr.plot.width = 8, repr.plot.height = 5) # Default figure size

## Load + process data
x <- read.csv("gelman_cup_graphic_reporting_challenge_data.csv")

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library(XML)

reg_gdp_url <- "http://en.wikipedia.org/wiki/Regions_of_the_Czech_Republic"
reg_gdp <- readHTMLTable(reg_gdp_url, encoding = "UTF-8")

reg_gdp[[2]][1:14,2] -> regions
as.numeric(gsub(",", "", as.character(reg_gdp[[2]][1:14,8]))) -> GDP

nuts <- c(1100, 2100, 3100, 3200, 4100, 4200, 5100, 5200, 5300, 6200, 8100, 7100, 7200, 6100)

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# Crawls results scraped by scrape.sh; saves resulting nodes and edges into pickles.

import pickle
import urlparse
from objects import *
import pdb

nodes = []
edges = []

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# The output of the script is meant to be piped to an output csv file.
# Usage:
#   python foursquare-parse.py > foursquare.csv

import xml.etree.ElementTree as ET
from dateutil import parser
from datetime import *

tree = ET.parse('foursquare.kml') # or whatever the kml file is called
root = tree.getroot()