ryan-hill

path = 'xyz/'
mask_grid = path + 'nlcd2006.tif'
arcpy.env.mask = mask_grid
PCH = path + 'PCH.tif'
PCH = Con(IsNull(PCH),0,PCH)
PCH.save(path + 'PCH2.tif')

ryan-hill

  #predData is dataframe of predictor variable where 1st column is a unique site ID
vect = names(predData)[2:length(predData)]
combos = cbind(combn(vect, 2)[1,], combn(vect, 2)[2,])
  #Useful for comparing pairwise combo models
combos = data.frame(combos); combos$AUC <- 0.0
names(combos) <- c('Pred1','Pred2','AUC')

ryan-hill

import pysal as ps
import pandas as pd
'''
Arguments
---------
dbfile  : DBF file - Input to be imported
upper   : Condition - If true, make column heads upper case
'''
def dbf2DF(dbfile, upper=True): #Reads in DBF files and returns Pandas DF
    db = ps.open(dbfile) #Pysal to open DBF

ryan-hill

# Create data frame 1
x = c("ID1","ID2","ID3","ID4","ID5")
y = c("C1","C2","C3","C4","C5")
d1 = data.frame("SiteID" = x, "Value" = y)
d1
# Create lookup table
x = c("ID2","ID5")
y = c("C5","C2")
lookup = data.frame("SiteID" = x, "Value" = y)
lookup

ryan-hill

import numpy as np
import rasterio

"""
2014-02-13
Bryan Luman
Use it however you like at your own risk

Problem:
I have a huge DEM converted from LiDAR LAS points. I'd like to make it slightly

ryan-hill

import numpy as np
import pandas as pd
    #Read in .csv file
nlcd = pd.read_csv('NLCD2011_FINAL.csv')
    #Select desired columns - selects 1st column and then 23rd column to end
nlcd = nlcd.iloc[:, np.r_[:1, 23:len(nlcd.columns)]]  
    #Strip out 'Ws' string from column names that contain it
newnames = [w.replace('Ws', '') for w in nlcd.columns] 
    #rename columns
nlcd.columns = newnames

ryan-hill

#Code loops through StreamCat files on drive and combines into long table
#Also removes ancillary columns
combine_streamcat = function(x, wd){ 
  hydro.rgns <- c("01","02","03S","03N","03W","04","05","06","07","08","09","10L","10U","11","12","13","14","15","16","17","18")
  for(i in 1:length(hydro.rgns)){
    print(hydro.rgns[i])    
    if(i == 1){
      outDF = read.csv(paste0(wd, x, '_Region', hydro.rgns[i], '.csv'))
    }else{
      tmpDF = read.csv(paste0(wd, x, '_Region', hydro.rgns[i], '.csv'))

ryan-hill

from StreamCat_functions import dbf2DF

pre = 'D:/NHDPlusV21/NHDPlusGL/NHDPlus04'
fline = dbf2DF('D%s/NHDSnapshot/Hydrography/NHDFlowline.dbf' % pre)
flow = dbf2DF('%s/NHDPlusAttributes/PlusFlow.dbf' pre)[['TOCOMID','FROMCOMID']]

def recurs(val, ups):
    print val
    ups = ups + flow.ix[flow.TOCOMID == val].FROMCOMID.tolist()
    if 0 in ups:

ryan-hill

#Read in gages data and convert to spatial points data frame
#Give it the **pts** CRS and reproject to **pts2** CRS
#Select out Portland and use `gDistance` from `rgeos` package with portand as x and gages as y in the function.
#Sum across TRUE/FALSE values in query. R will count TRUE == 1 and FALSE == 0.

library(sp); library(rgeos)

gages <- read.csv('./data/StreamGages.csv')
gages <- SpatialPointsDataFrame(gages[c('LON_SITE','LAT_SITE')], gages)
gages@proj4string <- pts@proj4string

ryan-hill

#Read in gages data and convert to spatial points data frame
#Give it the **pts** CRS and reproject to **pts2** CRS
#Select out Portland and use `gBuffer` from `rgeos` package with width = 50,000 meters.
#Use `over` function from `sp` package to identify overlapping points with 50 km buffer. 

library(sp); library(rgeos)

gages <- read.csv('./data/StreamGages.csv')
gages <- SpatialPointsDataFrame(gages[c('LON_SITE','LAT_SITE')], gages)
gages@proj4string <- pts@proj4string