arose13

<?xml version="1.0" encoding="utf-8"?>
<resources>

<!-- google's material design colours from 
http://www.google.com/design/spec/style/color.html#color-ui-color-palette -->

    <!--reds-->
    <color name="md_red_50">#fde0dc</color>
    <color name="md_red_100">#f9bdbb</color>
    <color name="md_red_200">#f69988</color>

arose13

// Put this where plugins are at
apply plugin: 'maven'

// Put this at the bottom of the build.gradle file
task generatePom << {
    pom {
        project {
            inceptionYear = '2015'
        }
    }.writeTo("pom.xml")

arose13

// Variables
def javaVersionLang = 1.8

// Buildscript
buildscript {
    repositories {
        mavenCentral()
    }
    dependencies {
        classpath("org.springframework.boot:spring-boot-gradle-plugin:1.2.6.RELEASE")

arose13

# Number of Combinations for pool size (n) and choosing (k) number per set.
# Author arose13
import math

def number_of_combinations(n, k):
    numer = n
    denom = k

    for i in range(1, k):
        numer = numer * (n - i)

arose13

import pandas as pd
from collections import Counter
from itertools import combinations_with_replacement


population = [
    'pp',
    'pp',
    'qq',
    'pq',

arose13

import numpy as np


def swarm(func, bounds, ieqcons=[], f_ieqcons=None, args=(), kwargs={},
          swarmsize=100, omega=0.5, phip=0.5, phig=0.5, maxiter=100,
          minstep=1e-12, minfunc=1e-12, debug=False):
    """
    Perform a particle swarm optimization (PSO)

    Parameters

arose13

// Find all unique characters in the string
String uniqueLettersInTestString = "";
for (int i = 0; i < testString.length(); i++) {
  if (!uniqueLettersInTestString.contains(String.valueOf(testString.charAt(i)))) {
    uniqueLettersInTestString = uniqueLettersInTestString + String.valueOf(testString.charAt(i));
  }
}
System.out.println("Unique Letters are: " + uniqueLettersInTestString);

arose13

import glob
import serial


def serial_ports():
    ports = glob.glob('/dev/tty[A-Za-z]*')

    working_ports = []
    for port in ports:
        try:

arose13

import warnings
import numpy as np
import pandas as pd
import scipy.stats as st
from tqdm import tqdm
from scipy.stats import iqr


def solve_n_bins(x):
    """

arose13

def inverse_normal_cdf(p, mean, std):
    """
    This is the inverse to a normal distribution's CDF.

    While much slower this means you do not need Scipy as a project requirement.
    :param p: list of p = (0, 1)
    :param mean:
    :param std:
    :return:
    """