arthur-e

def convex_hull_graham(points):
    '''
    Returns points on convex hull in CCW order according to Graham's scan algorithm. 
    By Tom Switzer <thomas.switzer@gmail.com>.
    '''
    TURN_LEFT, TURN_RIGHT, TURN_NONE = (1, -1, 0)

    def cmp(a, b):
        return (a > b) - (a < b)

arthur-e

import math

def tweensegs(longitude1, latitude1, longitude2, latitude2, num_of_segments):
    ptlon1 = longitude1
    ptlat1 = latitude1
    ptlon2 = longitude2
    ptlat2 = latitude2

    numberofsegments = num_of_segments
    onelessthansegments = numberofsegments - 1

arthur-e

Installation

# Requires Node.js and NPM
sudo npm install -g topojson

# Install the geojson-cli
sudo npm install -g geojson

Tasks

arthur-e

Post-Installation Fixes and Setup

# Install compilers and linking tools; other tools
sudo apt-get install g++ swig curl build-essential python-all-dev

# Install package manager
sudo apt-get install synaptic

# Install Unity customization tool

sudo apt-get install unity-tweak-tool

arthur-e

USERNAME=heyyouguys

# NOTE: Could not determine which jpeg library should be installed; it's probably installed by default
sudo apt-get install libtiff5 libtiff5-dev libgeotiff2 libgeotiff-dev libxml2 libxml2-dev

# http://www.hdfgroup.org/release4/obtainsrc.html
sudo mkdir /usr/local/hdf4 && sudo chown $USERNAME /usr/local/hdf4 && cd /usr/local/hdf4
wget http://www.hdfgroup.org/ftp/HDF/HDF_Current/src/hdf-4.2.11.tar.gz
tar -xzvf hdf-4.2.11.tar.gz
cd hdf-4.2.11

arthur-e

Overview

In general, it seems there are roughly five (5) ways to get "file data" (e.g. a GeoTIFF) out of a PostGIS geoprocessing workflow:

• Export just the raster field as an ASCII grid
• Connect to the database using a desktop client (e.g. QGIS) [1]
• Use a procedural language (like PLPGSQL or PLPYthon) [2]
• Use the COPY declaration to get a hex dump out and convert it to a binary file
• Fill a 2D NumPy array with a byte array and serialize it to a binary file using GDAL or psycopg2 [3, 4]
• Use ST_AsTiff() or the more general ST_AsGDALRaster() to get a byte array, which can be written to a binary file

arthur-e

import csv
from django.core.management.base import BaseCommand, CommandError
from django.core.exceptions import ValidationError, ObjectDoesNotExist
from django.db.models.fields import FieldDoesNotExist
from scour_server.nbi.models import Bridge

class Command(BaseCommand):
    args = '<path>'
    help = ''

arthur-e

'''
Reports the min and max July temperatures for each file
that matches the given filename pattern.
'''

import csv
import os
import sys
import glob

arthur-e

# Clear out any old ppa's that might conflict
sudo rm /etc/apt/sources.list.d/*mapnik*
sudo rm /etc/apt/sources.list.d/*developmentseed*
sudo rm /etc/apt/sources.list.d/*chris-lea*

# Add new ppa's
echo 'yes' | sudo apt-add-repository ppa:chris-lea/node.js
echo 'yes' | sudo apt-add-repository ppa:mapnik/nightly-trunk

# Update

arthur-e

'''
A module for machine learning on Landsat data; implemented and tested,
specifically, for learning water areas on an image. Performance so far:

Gaussian naive Bayes (where validation data chosen by the hydro mask):
    Mean precision: Not water=0.9997, Water=0.3090
    Mean recall: Not water=0.9787, Water=0.9763

Gaussian naive Bayes (where validation data inspected in Google Earth):
    Mean precision: Not water=0.9675, Water=1.0000