jmcarp

#!/bin/bash

set -e -x

TODAY=$(date +%Y.%m.%d)
YESTERDAY=$(date -d "1 day ago" +%Y.%m.%d)

TTL=${TTL:-600}
JQ_PATH=${JQ_PATH:-jq}
INTERVAL=${INTERVAL:-5m}

jmcarp

/* global console */

var urijs = require('urijs');
var Promise = require('bluebird');
var request = Promise.promisifyAll(require('request'));
var mongodb = Promise.promisifyAll(require('mongodb'));

var baseUrl = 'https://api.travis-ci.org';
var client = mongodb.MongoClient.connect('mongodb://localhost:27017/dashboard');

jmcarp

"""
Scrapy includes an `ItemLoader` class and associated helpers to abstract
data extraction from `Reponse` objects. But this API is verbose and easily
result in more boilerplate, not less. The following is a quick sketch of
a possible interface for using marshmallow, with a few custom fields, to
pull data from Scrapy responses.
"""

class PersonSchema(Schema):
    name = fields.XPath('//title/text()', fields.Str)

jmcarp

with cycles as (
    select
        agg.*,
        link.election_year
    from ofec_sched_a_aggregate_state agg
    join ofec_name_linkage_mv link on
        agg.cmte_id = link.committee_id and
        agg.cycle <= link.election_year and
        agg.cycle > link.election_year -
            case link.committee_type

jmcarp

shortName: OpenFEC
fullName: OpenFEC
stage: alpha
testable: yes
team:
  - githubUser: arowla
    hubUser: arowla
  - githubUser: lindsayyoung
    hubUser: lindsayyoung
services:

jmcarp

{
    "$schema": "http://json-schema.org/schema#",
    "title": ".about.yml",
    "type": "object",
    "properties": {
        "shortName": {
            "type": "string",
            "description": ""
        },
        "fullName": {

jmcarp

drop table if exists ofec_sched_a_aggregate_zip;
create table ofec_sched_a_aggregate_zip as
select
    cmte_id,
    rpt_yr + rpt_yr % 2 as cycle,
    contbr_zip as zip,
    sum(contb_receipt_amt) as total
from sched_a
    where rpt_yr >= 2011
    group by cmte_id, cycle, zip

jmcarp

import time
import random

from webservices import rest
from webservices.common import models


rest.app.app_context().push()

jmcarp

"""Approximate query count based on ANALYZE output for PostgreSQL and SQLAlchemy.

Count logic borrowed from https://wiki.postgresql.org/wiki/Count_estimate
ANALYZE borrowed from https://bitbucket.org/zzzeek/sqlalchemy/wiki/UsageRecipes/Explain
"""

import re

from sqlalchemy.ext.compiler import compiles
from sqlalchemy.sql.expression import Executable, ClauseElement, _literal_as_text

jmcarp

import six
import inflection
import marshmallow as ma


class Model(object):
    
    def __init__(self, **kwargs):
        self._schema = self.Schema()
        self.load(**kwargs)