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@property 
	def serialize(self):
		'''return as a json object so we can use it in RESTful API'''
		return {'id': self.id, 
		        'date': self.date.strftime("%Y-%m-%d"), 
		        'duration': self.duration, 
		        'event_type': self.event_type, 
		        'event_name': self.event_name }

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# API endpoints
@webapp.route('/api/all')
def api_all():
  events = Events.query.all()
  return jsonify(json_list = [event.serialize() for event in events])

@webapp.route('/api/<event_type>')
def api_by_event_type(event_type):
  events = Events.query.filter_by(event_type = event_type).all()
  return jsonify(json_list = [event.serialize() for event in events])

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function makeGraph(eventType) {
  url = "http://127.0.0.1:5000/api/".concat(eventType)
  d3.json(url, function(error, data) {
    // d3 code to create bar/line/pie charts
  })
}

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@webapp.route('/calendar')
def plot_d3_calendar():
  return render_template("calendar.html", calendarMap = calendarMap)

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{% extends "base.html" %}

{% block title %} Calendar View {% endblock %}

{% block content %}

{% with calendarMap=calendarMap %}
	{% include "buttons.html" %}
{% endwith %}

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Object.keys(window.calendarMap).forEach(function(key){
	document.getElementById(key)
		.addEventListener(‘click’, function(){ 
			makeCalendar(key); 
		});
});

// ... a bunch of D3 code to set up my calendar visualization ... //

function makeCalendar(eventType) {

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source: {
  type: hive
  query:"""
    SELECT
        id_listing as listing
      , dim_city as city
      , dim_country as country
      , dim_is_active as is_active
      , CONCAT(ds, ' 23:59:59.999') as ts
    FROM

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transforms = []

transforms.append(
    ('select_binary', ColumnSelector(features=binary))
)

transforms.append(
    ('numeric', ExtendedPipeline([
        ('select', ColumnSelector(features=numeric)),
        ('impute', Imputer(missing_values='NaN', strategy='mean', axis=0)),

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def fit(X_train, y_train):
    import multiprocessing
    from ml_helpers.sklearn_extensions import DenseMatrixConverter
    from ml_helpers.data import split_records
    from xgboost import XGBRegressor

    global model
    
    model = {}
    n_subset = N_EXAMPLES

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"""
A DAG definition file in Airflow, written in Python.
"""
from datetime import datetime, timedelta
from airflow.models import DAG  # Import the DAG class
from airflow.operators.bash_operator import BashOperator
from airflow.operators.sensors import TimeDeltaSensor

default_args = {
    'owner': 'you',