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#!/bin/bash

function to_tiles(){
  geojson=$1;
  zoom=$2;
  jq -c .features[] < "$geojson" | mercantile tiles $zoom | sort | uniq
}

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#!/bin/bash

function s3du(){
   bucket=`cut -d/ -f3 <<< $1`
   prefix=`awk -F/ '{for (i=4; i<NF; i++) printf $i"/"; print $NF}' <<< $1`
   aws s3api list-objects --bucket $bucket --prefix=$prefix --output json --query '[sum(Contents[].Size), length(Contents[])]' | jq '. |{ size:.[0],num_objects: .[1]}'
}

s3du $1;

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#!/usr/bin/env node
var parser = require('xml2json');
var fs = require('fs');

function onFile(err, data) {
  if (err) {
    console.log('Uh oh: ' + err);
    return;
  }
  var json = parser.toJson(data);

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perf guide

Profiling tools are critical for trying to understand performance bottlenecks in code.

Without real data about what a program is doing while running, detecting bottlenecks is at best a process of trial and error for both users and developers. While thoughtful testing of various program configurations along with measuring time elapsed for decrete runs can often be enough - why not learn faster and funner ways to rapidly collect real data about what a program is doing?

Actual data about program execution like which functions are being called while a program is active helps point to hot parts of the code where most time may be being spent. While users of applications may not easily understand the output of profiling tools, being equipped to generate profiling output can be extremely useful for sharing with developers, since the time to set up robust test cases for developers is can be greater than the time it takes to understand and optimize slow code paths. Therefore it can be invaluable to get

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Following advice of Psql: could not connect...:

Running the following command resets PostgreSQL's transaction log, which resolves the problem.

 pg_resetxlog -f /usr/local/Cellar/postgresql/9.3.1/data

you should replace /usr/local/Cellar/postgresql/9.3.1/data with your own pg_datadir (eg., pg_ctl -D $DATADIR start)

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#!/bin/bash

# simple function to check http response code before downloading a remote file
# example usage:
# if `validate_url $url >/dev/null`; then dosomething; else echo "does not exist"; fi


function validate_url(){
  if [[ `wget -S --spider $1  2>&1 | grep 'HTTP/1.1 200 OK'` ]]; then echo "true"; fi
}

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Landsat 4-5	Wavelength	Landsat 7	Wavelength	Landsat 8	Wavelength (micrometers)
			Band 1 - Coastal aerosol	0.43 - 0.45
Band 1	0.45-0.52	Band 1	0.45-0.52	Band 2 - Blue	0.45 - 0.51
Band 2	0.52-0.60	Band 2	0.52-0.60	Band 3 - Green	0.53 - 0.59
Band 3	0.63-0.69	Band 3	0.63-0.69	Band 4 - Red	0.64 - 0.67
Band 4	0.76-0.90	Band 4	0.77-0.90	Band 5 - Near Infrared (NIR)	0.85 - 0.88
			Band 9 - Cirrus	1.36 - 1.38
Band 5	1.55-1.75	Band 5	1.55-1.75	Band 6 - SWIR 1	1.57 - 1.65
Band 7	2.08-2.35	Band 7	2.09-2.35	Band 7 - SWIR 2	2.11 - 2.29

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function clean_git_branches(){
  git checkout master;
  BRANCHES=`git branch | grep -Ev '^\s{0,}\*{0,}\s{0,}master$'`
    git branch -D ${BRANCHES[@]}
}

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#!/usr/bin/env python
import os

def CopyToClipboard(text):
    cmd = "echo '%s' | tr -d \"\\\n\" |  pbcopy" % text
    os.system(cmd)

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create table 
closest_point as 
select distinct on (a.featureid) a.featureid as image_id,
 (select b.eventid from fl_events as b order by st_distance(a.wkb_geometry,b.wkb_geometry) limit 1) as event_id,
 a.wkb_geometry from images as a,
 fl_events as b;

update images set eventid = closest_point.event_id from closest_point where images.featureid = closest_point.image_id;