arnavs

# secrets
API_KEY = "something"
API_PASS = "something else"
API_SECRET = "also something else"

# boilerplate
import requests
import json, hmac, hashlib, time, requests, base64
from requests.auth import AuthBase

arnavs

using ModelingToolkit, Test, SparseArrays
@variables a b c

# Auxiliary Functions and Constants
get_sparsity_pattern(h::Array{Expression}) = sparse(Int64.(map(~, h .=== ModelingToolkit.Constant(0))))
get_sparsity_pattern(h::SparseMatrixCSC{Expression,Int64}) = sparse(Int64.(map(~, h .=== ModelingToolkit.Constant(0))))
get_sparsity_pattern(h::SparseVector{Expression,Int64}) = sparse(Int64.(map(~, h .=== ModelingToolkit.Constant(0))))

input = [1, 2, 3]

arnavs

# data source is: https://pomber.github.io/covid19/timeseries.json
# data is updated thrice daily from Johns Hopkins numbers

using Pkg
pkg"activate .."
using JSON, Statistics, Plots, Dates, LsqFit, TimeSeries
data = JSON.parsefile("../data/timeseries_03_31_20.json");

# Aggregate statistics
function unpack_country(country)

arnavs

git checkout --orphan docs
git rm -rf .
git commit --allow-empty -a -m "Initial Commit"
git push origin docs

arnavs

# A script to take one list of words (topic-related), and one list of words (philosophy-related), and get searches for all the related words.

# Dependencies.
using Requests
using JSON
apikey = "919db630ecc84118a1acc2914dc7bd55"

# Words.
topicWords = ["health care", "single payer", "public option"]
philWords = ["genealogy", "phenomenology", "Foucault"]

arnavs

using Requests
using JSON

# Import API key.
include("apikey.jl")

response = get("https://wordsapiv1.p.mashape.com/words/?partOfSpeech=noun&random=true"; headers=Dict("X-Mashape-Key" => "$key"))
word = JSON.parse(readstring(response))["word"]
startup = rand(["Uber", "AirBnB", "Tinder", "Oracle"])