black-tea

# a method to quickly loop through all the folders/subfolders in a path and create a list of all csv files
# used regex to include only files with "output" in the name
ls.file <- list.files(path = "Z:/GIS/DataLibrary/test", pattern = "^.*output.*.csv$", full.names = TRUE, recursive = TRUE)

lapply(ls.file, function(i) {
  ExtractDates(i)
})

black-tea

# this takes a csv with a beginning & end address, pulls google walking directions between those points,
# and then saves the file in the ESRI Shapefile format, with each path as a feature
import googlemaps
import csv
import arcpy
from shapely.geometry import LineString, mapping, shape
from fiona import collection
from datetime import datetime
from osgeo import ogr

black-tea

library(data.table)

### Inputs ###
meta.file <- "/Volumes/Seagate Backup Plus Drive/Volumes/arterial/arterial-config.csv"

### Load Data ###
meta.df <- fread(meta.file, header=T,sep="|",stringsAsFactors=F)

### Clean XY Data ###
x <- gsub('.+\\((-[0-9]+\\.[0-9]+),([0-9]+\\.[0-9]+).+','\\1',meta.df$START_LAT_LONG)

black-tea

# This script is intended to follow the ArcPy_AssignNodeIDstoSegment.py
# Once the segments have the Node IDs, this script will assign collisions
# to each segment and intersection.
import arcpy
from arcpy import env

##### Input #####
env.workspace = "Z:/GIS/DataLibrary/Transportation/BOE_Centerline_Intersections150930/collisiontoInt_test.gdb"
env.overwriteOutput = True  #so we can rerun this script and overwrite our output feature class

black-tea

library(data.table)
library(fasttime)
start.time <- proc.time()

#parameters that will apply to the data
chunkSize <- 1000000
colnames <- c('Date.Time','XD.Segment','Speed','Score')

#import csv with list of XD.Segments to filter through
clipped.csv <- "Z:/GIS/DataLibrary/Inrix/LA County_15.2_Shapefiles/ClipforVZTechnicalAnalysis/ClippedInrixSegIDList.csv"

black-tea

import arcpy
from arcpy import env

##### Input #####
env.workspace = "Z:/GIS/DataLibrary/Transportation/BOE_Centerline_Intersections150930/collisiontoInt_test.gdb"
env.overwriteOutput = True  #so we can rerun this script and overwrite our output feature class

#input fc
col_fc = "Collisions_2009to2013"
input_centerline_fc = "Street_Centerline_Raw"

black-tea

library(data.table)

### Inputs ###
meta.file <- "Z:/GIS/DataLibrary/Riits/TrafficVolumes/data dump/EXPORT - 2015/config.dsv"

### Load Data ###
meta.df <- fread(meta.file, header=T,sep="|",stringsAsFactors=F)

### Clean XY Data ###
x <- gsub('.+\\((-[0-9]+\\.[0-9]+) {1}([0-9]+\\.[0-9]+).+','\\1',meta.df$START_LAT_LONG)

black-tea

#### This script will take the Riits Sensor Shp, and copy the assetIDs of all intersecting points to the attribute table of the Riits layer

import arcpy
from arcpy import env

env.workspace = "Z:/GIS/DataLibrary/Transportation/BOE_Centerline_Intersections150930/collisiontoInt_test.gdb"
env.overwriteOutput = True  #so we can rerun this script and overwrite our output feature class

#input
Riits_sensor_shp = "Z:/GIS/DataLibrary/Riits/TrafficVolumes/data dump/EXPORT - 2015/RiitsSensorsproj.shp"

black-tea

library(data.table)
library(fasttime)

#input
week1.file = "Z:/GIS/DataLibrary/Riits/TrafficVolumes/data dump/EXPORT - 2015/october.dsv"

#parameters that will apply to the data
chunkSize <- 500000

#date ranges

black-tea

import arcpy
from arcpy import env

##### Input #####
shp = "C:/Users/dotcid034/Desktop/Export_Output (1)/Export_Output.shp" 

col_fields = ["Cross_St","AllStreets","Doyourname"]
cur = arcpy.da.UpdateCursor(shp,col_fields)
for row in cur:
	val = None