Sam Gibbes sgibbes

## compare_hadoop_zonalstats_for_tsc_v2.ipynb

      
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                sgibbes
                / compare_hadoop_zonalstats_for_tsc_v2.ipynb
            
            
              Created
              September 12, 2018 16:30
            
          
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## shape_to_tsv
import zipfile
import arcpy
import os
import glob
import subprocess
import shutil
arcpy.env.overwriteOutput = "TRUE"

def unzipfiles(myzipfile):
    print "unzipping file"

## geostore_download.geojson

      
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                sgibbes
                / geostore_download.geojson
            
            
              Created
              August 21, 2018 18:32
            
          
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## fails.geojson

      
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                sgibbes
                / fails.geojson
            
            
              Created
              August 21, 2018 13:00
            
              
                Failed GLAD analysis geostores
              
          
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## clip_landcover_tiles.py
import gdal
import subprocess


def clip_using_input_coords(original):

    file_to_clip = 'sea_land_cover_classified.tif'

    # get coords of file to mock
    src = gdal.Open(original)

## Querying_Country_Pages_Statistics_from_GFW_API.ipynb

      
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                sgibbes
                / Querying_Country_Pages_Statistics_from_GFW_API.ipynb
            
            
              Last active
              April 17, 2018 16:17
            
              
                This is an example of how to return annual forest loss statistics within Indonesia by querying the GFW API. I'll show all possible boundaries to query by, and an example of looking at forest loss within Indonesia Primary Forest
              
          
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## fractal_tile.py
from utilities import util, tile_geom_util
import json
import sys
from shapely.geometry import shape

# big aoi
aoi = {"type":"FeatureCollection","features":[{"type":"Feature","properties":{},"geometry":{"type":"Polygon","coordinates":[[[25.576171875,7.841615185204699],[15.864257812499998,-1.098565496040652],[18.45703125,-9.44906182688142],[31.6845703125,-9.535748998133615],[35.859375,-0.4394488164139641],[25.576171875,7.841615185204699]]]}}]}
aoi = {"type":"FeatureCollection","features":[{"type":"Feature","properties":{},"geometry":{"type":"Polygon","coordinates":[[[14.501953124999998,5.703447982149503],[2.4609375,1.142502403706165],[17.841796875,-2.3723687086440504],[21.708984375,-12.297068292853805],[27.773437499999996,-0.4394488164139641],[38.056640625,-1.4939713066293112],[32.16796875,6.489983332670651],[35.947265625,13.068776734357694],[24.78515625,12.64033830684679],[20.126953125,18.562947442888312],[14.501953124999998,5.703447982149503]]]}}]}


## list_overlapping_tsvs.py
from boto.s3.connection import S3Connection
import boto3
import os

# connect to the s3 bucket
conn = S3Connection(host="s3.amazonaws.com")
bucket = conn.get_bucket('gfw2-data')
s3 = boto3.resource('s3')

# loop through file names in the bucket

## combine.sh
#!/bin/bash
echo "here"
DATA=`find . -name '*.shp'`
ogr2ogr -a_srs EPSG:4326 merge.shp
for i in $DATA
do
SHP=${i:2:100}
FINAL="${SHP/.shp/}"
echo $FINAL
echo $i
	import zipfile
	import arcpy
	import os
	import glob
	import subprocess
	import shutil
	arcpy.env.overwriteOutput = "TRUE"

	def unzipfiles(myzipfile):
	print "unzipping file"
	import gdal
	import subprocess


	def clip_using_input_coords(original):

	file_to_clip = 'sea_land_cover_classified.tif'

	# get coords of file to mock
	src = gdal.Open(original)
	from utilities import util, tile_geom_util
	import json
	import sys
	from shapely.geometry import shape

	# big aoi
	aoi = {"type":"FeatureCollection","features":[{"type":"Feature","properties":{},"geometry":{"type":"Polygon","coordinates":[[[25.576171875,7.841615185204699],[15.864257812499998,-1.098565496040652],[18.45703125,-9.44906182688142],[31.6845703125,-9.535748998133615],[35.859375,-0.4394488164139641],[25.576171875,7.841615185204699]]]}}]}
	aoi = {"type":"FeatureCollection","features":[{"type":"Feature","properties":{},"geometry":{"type":"Polygon","coordinates":[[[14.501953124999998,5.703447982149503],[2.4609375,1.142502403706165],[17.841796875,-2.3723687086440504],[21.708984375,-12.297068292853805],[27.773437499999996,-0.4394488164139641],[38.056640625,-1.4939713066293112],[32.16796875,6.489983332670651],[35.947265625,13.068776734357694],[24.78515625,12.64033830684679],[20.126953125,18.562947442888312],[14.501953124999998,5.703447982149503]]]}}]}
	from boto.s3.connection import S3Connection
	import boto3
	import os

	# connect to the s3 bucket
	conn = S3Connection(host="s3.amazonaws.com")
	bucket = conn.get_bucket('gfw2-data')
	s3 = boto3.resource('s3')

	# loop through file names in the bucket
	#!/bin/bash
	echo "here"
	DATA=`find . -name '*.shp'`
	ogr2ogr -a_srs EPSG:4326 merge.shp
	for i in $DATA
	do
	SHP=${i:2:100}
	FINAL="${SHP/.shp/}"
	echo $FINAL
	echo $i