walkerjeffd

# Raspberry Pi Set Up Shell Script
# By Jeff Walker

# I created this to make it easier to re-install/re-configure my pi in case I need to start from scratch.
# Assumes Occidentalis v0.2 is being used.

# Run an update
# sudo apt-get update

# Adafruit WebIDE

walkerjeffd

// log DHT sensor readings to SD card
#include "DHT.h"
#include <SD.h>
#include <Wire.h>
#include "RTClib.h"

RTC_DS1307 RTC;

// how many milliseconds between grabbing data and logging it. 1000 ms is once a second
#define LOG_INTERVAL  60000 // mills between entries (reduce to take more/faster data)

walkerjeffd

import pandas as pd
import csv

colspecs = [(0,11), (12,20), (21,30), (31,35), (36,40), (41,45)]
df = pd.read_fwf('ghcnd-inventory.txt', colspecs=colspecs, header=None, index_col=None)
df.columns = ["ID", "LATITUDE", "LONGITUDE", "ELEMENT", "FIRSTYEAR", "LASTYEAR"]
df.set_index("ID", inplace=True)
df.to_csv('ghcnd-inventory.csv', quoting=csv.QUOTE_NONNUMERIC)

walkerjeffd

is.regular <- function(x) {
  # returns TRUE if vector x of POSIXct datetimes is continuous and regular
  # by checking if there are more than one unique difftime between each row
  x.difftime <- difftime(x[2:length(x)], x[1:(length(x)-1)], units='secs')
  if (length(levels(factor(x.difftime))) > 1) {
    return (FALSE)
  } else {
    return (TRUE)
  }
}

walkerjeffd

zoo.regular <- function(dates, values, by="hour", fill=NA) {
  # convert time series to regular zoo object
  require(zoo)
  z <- zoo(values, dates)
  z <- merge(z, zoo(, seq(floor_date(start(z), 'day'),
                          ceiling_date(end(z), 'day'),
                          by)), 
             fill=fill)
  z <- z[1:(length(z)-1)]
  return(z)

walkerjeffd

assign.events <- function(df, datetime.name="DATETIME", value.name="VALUE", interevent=8, threshold=0.1) {
  # assigns events to data frame such as storm events or discharge events
  require(plyr)
  if (!(datetime.name %in% names(df))) {
    stop(paste0('Could not find datetime column called ', datetime.name))
  }
  if (!(value.name %in% names(df))) {
    stop(paste0('Could not find value column called ', value.name))
  }
  if (!is.regular(df[, datetime.name])) {

walkerjeffd

require(lubridate)
require(plyr)
require(ggplot2)
theme_set(theme_bw())

# create random dataset
df <- data.frame(DATETIME = ymd("2000-01-01") + ddays(runif(100)*365*5))

# compute day/month columns
df <- mutate(df,