timothyrenner

from hypothesis import given, settings  # <- NEW CODE
from hypothesis.strategies  import lists, integers
 
@given(
    list1=lists(integers(min_value=1)),
    list2=lists(integers(min_value=1)),
    depth=integers(min_value=1)
)
@settings(deadline=300)  # <- NEW CODE
def test_average_agreement_properties(list1, list2, depth):

timothyrenner

def average_agreement(list1, list2, max_depth):
 
    ### NEW CODE ###
    # Empty lists evaluate to false.
    if (not list1) or (not list2):
        return 0.0
 
    agreements = []
     
    for depth in range(1, max_depth+1):

timothyrenner

from hypothesis import given
from hypothesis.strategies  import lists, integers
 
@given(
    list1=lists(integers(min_value=1)),
    list2=lists(integers(min_value=1)),
    depth=integers(min_value=1)
)
def test_average_agreement_properties(list1, list2, depth):
     

timothyrenner

def average_agreement(list1, list2, max_depth):
     
    agreements = []
     
    for depth in range(1, max_depth+1):
        set1 = set(list1[:depth])
        set2 = set(list2[:depth])
         
        intersection = set1 & set2
         

timothyrenner

import aiohttp
import asyncio
import json

from toolz import get_in, assoc, concat

url = "http://localhost:9200"
index = "stocks"
doc_type = "stock"

timothyrenner

import pandas as pd
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import cartopy.io.shapereader as shpreader

from shapely.ops import cascaded_union

non_conus_states = {'VI', 'AK', 'HI', 'PR', 'GU', 'MP', 'AS'}

timothyrenner

package io.github.timothyrenner.kstreamex.tablejoin;

import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.ProducerRecord;

import org.apache.kafka.common.serialization.Serdes;
import org.apache.kafka.streams.KafkaStreams;
import org.apache.kafka.streams.KeyValue;
import org.apache.kafka.streams.StreamsConfig;
import org.apache.kafka.streams.kstream.KStream;

timothyrenner

(ns gorilla-to-markdown.core
    (:require [clojure.string :as str]
              [cheshire.core :as json]))

(defn- process-output-json
  "Builds the HTML from the output JSON.
   Assumes the JSON has already been parsed, and that j is a map."
  [j]
  (case (:type j)
        "html" (:content j)

timothyrenner

from datetime import datetime

import string

from nltk.stem.lancaster import LancasterStemmer
from nltk.corpus import stopwords

#Gets the tweet time.
def get_time(tweet):
    return datetime.strptime(tweet['created_at'], "%a %b %d %H:%M:%S +0000 %Y")

timothyrenner

using DataFrames

keys = rand(1:100000, 500000);
values = randn(length(keys));

df = DataFrame();
df[:KEY] = keys;
df[:VALUE] = values;

@time by(df, :KEY, x -> sum(x[:VALUE]));