mikebirdgeneau

from skyfield.api import load
from scipy import optimize
from datetime import datetime
from dateutil import tz

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

def earthPosition(t1):

mikebirdgeneau

library(ggplot2)
library(data.table)
library(gridExtra)
library(zoo)

babyTiming<-function(ExpectedDate="2016/02/29",DateofInterest="2016/02/2015",VacationDuration=14){
  DateofInterest<-as.Date(DateofInterest)
  ExpectedDate<-as.Date(ExpectedDate)
  currentDate<-Sys.Date()  
  

mikebirdgeneau

library(ggplot2)
library(data.table)
library(lubridate)
library(stringr)

options(stringsAsFactors = FALSE)

# Data Source: Statistics Canada (Table 358-163)
#(http://www5.statcan.gc.ca/cansim/a26?lang=eng&retrLang=eng&id=3580163&&pattern=&stByVal=1&p1=1&p2=31&tabMode=dataTable&csid=)

mikebirdgeneau

library(ggplot2)
library(data.table)
library(lubridate)

# Source: https://stats.oecd.org/index.aspx?queryid=350
dt.gdp<-data.table(read.csv("~/Downloads/QNA_10102015180610407.csv"))

dt.gdp[,Year:=substr(TIME,1,4),]
dt.gdp[,Qtr:=substr(TIME,7,7),]
dt.gdp[,Month:=as.numeric(Qtr)*3-1,]

mikebirdgeneau

library(ggplot2)
library(data.table)

# Metric Table
dt.forceCarb<-data.table(expand.grid(Pressure_bar=seq(0,2,by=0.1),Temperature_C=seq(0,25,by=1)))
dt.forceCarb[,CO2_gl := (Pressure_bar+1.013)*(2.71828182845904^(-10.73797+(2617.25/(Temperature_C+273.15))))*10,]
dt.forceCarb[,CO2_vol:=CO2_gl/1.96,]
dt.forceCarb[,Carbonation:="Over-Carbonated",]
dt.forceCarb[CO2_vol<4,Carbonation:="Highly Carbonated (Lambics / Wheat Beers)",]

mikebirdgeneau

library(ggplot2)
#library(png)
#test<-readPNG("~/Desktop/StormTrooper-loading.png")
#test<-round(test*9,0)

#for(i in 1:48){
#  message(paste0(paste0(test[i,1:48,1],collapse=","),","))
#}

test<-matrix(data = c(

mikebirdgeneau

library(ggplot2)

r2d2.pixels<-matrix(data = c(
  1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,
  1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,
  1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,
  1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,
  1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,
  1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,
  1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,

mikebirdgeneau

library(ggplot2)

f1=jitter(sample(c(2,3),1));f2=jitter(sample(c(2,3),1));f3=jitter(sample(c(2,3),1));f4=jitter(sample(c(2,3),1))
d1=runif(1,0,1e-02);d2=runif(1,0,1e-02);d3=runif(1,0,1e-02);d4=runif(1,0,1e-02)
p1=runif(1,0,pi);p2=runif(1,0,pi);p3=runif(1,0,pi);p4=runif(1,0,pi)
xt = function(t) exp(-d1*t)*sin(t*f1+p1)+exp(-d2*t)*sin(t*f2+p2)
yt = function(t) exp(-d3*t)*sin(t*f3+p3)+exp(-d4*t)*sin(t*f4+p4)
t=seq(1, 100, by=.001)
dat=data.frame(t=t, x=xt(t), y=yt(t))
ggplot(dat,aes(x=x,y=y))+geom_point(cex=0.2)+theme_bw()+theme(line = element_blank(),

mikebirdgeneau

sunPosition <- function(year, month, day, hour=12, min=0, sec=0,
                        lat=46.5, long=6.5) {
  
  twopi <- 2 * pi
  deg2rad <- pi / 180
  
  # Get day of the year, e.g. Feb 1 = 32, Mar 1 = 61 on leap years
  month.days <- c(0,31,28,31,30,31,30,31,31,30,31,30)
  day <- day + cumsum(month.days)[month]
  leapdays <- year %% 4 == 0 & (year %% 400 == 0 | year %% 100 != 0) & 

mikebirdgeneau

# AB River Data

# Packages
require(data.table)
require(ggplot2)
require(reshape)
require(gridExtra)

# Load data (to maintain history, after AEnv removes older data...)
if(file.exists("river_data.Rda")){load("river_data.Rda")}