mrkrause

using Plot

function calculate(a::Number, b::Number, c::Number)
    # Placeholder for a more complicated model, obviously
    t = 0:0.01:5
    return @. -5*max(t-a, 0) * sin(2π*b*t + c)
end

function myplot(a::Number, b::Number, c::Number; kwargs...)
    results = calculate(a, b, c)

mrkrause

using Statistics
using Plots

function generate_candidates(;n=1)
    return 100 .+ (15 .* randn(n, 1))
end

N_HIRES = 100
N_REPS = 1000

mrkrause

species🤡island🤡culmen_length_mm🤡culmen_depth_mm🤡flipper_length_mm🤡body_mass_g🤡sex
Adelie🤡Torgersen🤡39.1🤡18.7🤡181.0🤡3750.0🤡MALE
Adelie🤡Torgersen🤡39.5🤡17.4🤡186.0🤡3800.0🤡FEMALE
Adelie🤡Torgersen🤡40.3🤡18.0🤡195.0🤡3250.0🤡FEMALE
Adelie🤡Torgersen🤡🤡🤡🤡🤡
Adelie🤡Torgersen🤡36.7🤡19.3🤡193.0🤡3450.0🤡FEMALE
Adelie🤡Torgersen🤡39.3🤡20.6🤡190.0🤡3650.0🤡MALE
Adelie🤡Torgersen🤡38.9🤡17.8🤡181.0🤡3625.0🤡FEMALE
Adelie🤡Torgersen🤡39.2🤡19.6🤡195.0🤡4675.0🤡MALE
Adelie🤡Torgersen🤡34.1🤡18.1🤡193.0🤡3475.0🤡

mrkrause

using Plots
using StatsBase
using Printf
import StatsBase.mode

"""
    Find the location of a (unimodal) histogram's mode
"""
function mode(histo::Histogram)
    modeloc = findmax(histo.weights)

mrkrause

function ppc0 = ppc(phases, varargin)
%% PPC Compute pairwise phase consistency (PPC0 of Vinck et al, 2010)
% INPUT:
%  - phases: Vector of phases (radians, single-precision)
% OUTPUT:
%  - ppc0: pairwise phase consistency of the phases in `phases`.
% OPTIONS:
%  - UseParallel: When to use the parallel pool: 'always', 'never', or 'auto'
%      If auto, the parallel pool is used whenever there are at least
%      `ParallelThreshold` items in vector and a pool is available or

mrkrause

import requests
from bs4 import BeautifulSoup
from collections import namedtuple
from datetime import datetime
from typing import List, Sequence

# Tiny little class for holding publication history
ArticleHistory = namedtuple('ArticleHistory', ['received', 'accepted', 'published', 'doi', 'title', 'journal'])
ArticleHistory.__new__.__defaults__ = (None,)*6

mrkrause

NREP = 30000;
N = 1000;
K = 5; 

p_random = nan(NREP, 1);
p_cluster = nan(NREP, 1);

parfor rep=1:NREP
    data = randn(N, 1);
    random_group = mod(randperm(N), K);