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# Filename: Find_nth_Degree_Neighbors.r
# Author: Anthony Louis D'Agostino 
# Date Created: 01/18/2017
# Last Edited: 
# Purpose: Tutorial example of identifying n-th degree neighbors using North Carolina county boundaries 
# Notes: Borrows from Edzer Pebesma's sf Vignette #5 (https://cran.r-project.org/web/packages/sf/vignettes/sf5.html)

# -- preamble; installs necessary packages 

remove(list = ls(all.names = TRUE))

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/*
Filename: ZIP5_County_Lookup.do
Author: Anthony D'Agostino (ald at satanford dot edu) 
Date Created: 02/09/2018
Last Edited: 03/27/2018
Purpose: Generate a merged zip5-county crosswalk that leverages the strengths of both the US Census and HUD ZCTA crosswalks.  

*/ 

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# -- DistrictNameMatching.py 
# Author: Anthony Louis D'Agostino (ald2187 [at] columbia.edu)
# Purpose: Given CSV lists of district-state name pairs, identifies the best match given the fuzzywuzzy library
# Notes: Default number of matches currently set to 3, though can be modified as input argument.

import os
import numpy as np
from fuzzywuzzy import fuzz
from fuzzywuzzy import process
import pandas as pd

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# Filename: convert_to_GrADS.R  
# Author: Anthony Louis D'Agostino (ald at stanford dot edu)
# Date Created: September 16, 2015
# Last Edited: October 07, 2017
# Purpose: Modifies the NCC-provided CTL file and generates unique versions for each year of data, each type of temperature
#   max, min, mean. 

# Notes: While perhaps an overkill, this is generalizable to a setting where grids are file-specific.  

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# Filename: IMD_convertRawData.py
# Author: Anthony Louis D'Agostino (ald at stanford dot edu)
# Date Created: 06/01/2017 
# Last Edited: 10/07/2017
# Data: from NCC ZIP file   
# Purpose: Reads in GRaDS data files, concatenates them, and then exports netCDF versions  
# Notes: To be run after "convert_to_GrADS.R"


import os 

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returnBESTtemp <- function(tempFile) {
  
  # tempFile: complete path to a netcdf BEST temperature file of daily records [need not be geographic subset]
  
  ave <- brick(tempFile, var = "climatology")
  anomalies <- brick(tempFile, var = "temperature")
  
  # -- deal with absence of leap year in anomaly series
  ave.dates <- as_date(as.numeric(unlist(lapply(strsplit(names(ave), "X"), function(x) x[[2]]))), origin = "1949-12-31")

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