celoyd

# Quickie processing for LC81770782013190LGN00
# Cf. http://mapbox.com/blog/processing-landsat-8/

convert -combine *_B{4,3,2}.TIF rgb.tif
convert -resize 200% rgb.tif rgb-big.tif
convert -compose Colorize *_B8.TIF rgb-big.tif -composite pan.tif
convert -channel B -gamma 0.95 -channel RGB -sigmoidal-contrast 60x13% pan.tif ready.tif

convert -crop 3000x2000+8000+6000 ready.tif crater.jpeg
convert -crop 3000x2000+2000+6000 ready.tif cliff.jpeg

celoyd

#!/usr/bin/env python

'''
This code is adapted from the consensus best practice python Twitter feed 
consumer that's been floating around for a while now on StackOverflow etc. 
My only addition to speak of is TenminWriter. (A "tenmin" is ten minutes 
worth of stream. It's just a convenient chunk; you might want to make it 
shorter or longer.)

THERE ARE BUGS HERE. THIS IS BARELY TESTED. DO NOT TRUST IT.

celoyd

char@mako: identify -list format                                    0 ~/Desktop
   Format  Module    Mode  Description
-------------------------------------------------------------------------------
      3FR  DNG       r--   Hasselblad CFV/H3D39II
        A* RAW       rw+   Raw alpha samples
      AAI* AAI       rw+   AAI Dune image
       AI  PDF       rw-   Adobe Illustrator CS2
      ART* ART       rw-   PFS: 1st Publisher Clip Art
      ARW  DNG       r--   Sony Alpha Raw Image Format
      AVI  MPEG      r--   Microsoft Audio/Visual Interleaved

celoyd

#!/usr/bin/env python

import psycopg2 as pg
import Image
from sys import argv

conn = pg.connect(database='tweets', user='char', host='localhost')
cur = conn.cursor()

pxper = 16

celoyd

if tiff_h is None:
    include_tiff_h = os.path.join(os.path.split(lib)[0], '..', 'include', 'tiff.h')
    if not os.path.isfile(include_tiff_h):
        # fix me for windows:
        include_tiff_h = os.path.join('/usr','include','tiff.h')
    if not os.path.isfile(include_tiff_h):
        raise ValueError('Failed to find TIFF header file (may be need to run: sudo apt-get install libtiff4-dev)')
    # Read TIFFTAG_* constants for the header file:
    f = open (include_tiff_h, 'r')
    l = []

celoyd

#!/usr/bin/env python

from sys import argv, exit
import Image
import time
from numpy import *

avgtype = float32

avg = array([])

celoyd

#!/usr/bin/env python

# Simple script to scatterplot one band's values over another's into a third image.
# Usagi Yojimbo: corr.py band_1.tif band_2.tif 2_v_3.tif
# This is for quick testing; don't use it in place of proper statistics.
# It may overflow, will fail if the inputs are not identically formatted, etc.
# You may have to scale up the output (perhaps by increasing gamma).

import Image
from sys import argv

celoyd

I've rendered out two map layers with pre-typhoon Landsat 8 imagery. This is very low resolution (15 m) but may help fill in major highways and rivers for areas that still lack better coverage. They include clouds because I found that splicing them together can introduce seam artifacts that could be mistaken for roads.

The layer templates are:

• https://d.tiles.mapbox.com/v3/char.Leyte1/{z}/{x}/{y}.png – Leyte area (browsable view)

• https://d.tiles.mapbox.com/v3/char.Leyte2/{z}/{x}/{y}.png (browsable view)

• https://d.tiles.mapbox.com/v3/char.Yolanda3/{z}/{x}/{y}.png (Panay/Masbate area; relatively clear Masbate – browsable view)

celoyd

#!/usr/bin/env python

# ndvi.py red.tif nir.tif output-ndvi.tif
# Calculate NDVI (see Wikipedia). Assumes atmospheric correction.
# (Although I use it without all the time for quick experiments.)

import numpy as np
from sys import argv
from osgeo import gdal, gdalconst

celoyd

traceroute to www.e-kyzylorda.gov.kz (92.46.237.194), 64 hops max, 52 byte packets
 1  192.168.1.1 (192.168.1.1)  3.980 ms  0.825 ms  0.732 ms
 2  192.168.0.1 (192.168.0.1)  6.564 ms  3.823 ms  2.980 ms
 3  ptld-dsl-gw48.ptld.qwest.net (207.225.84.48)  21.866 ms  22.800 ms  21.859 ms
 4  ptld-agw1.inet.qwest.net (207.225.86.121)  28.812 ms  21.379 ms  21.606 ms
 5  sea-brdr-02.inet.qwest.net (67.14.41.18)  24.620 ms  25.222 ms  27.285 ms
 6  ae14.edge2.seattle1.level3.net (4.68.62.189)  24.411 ms  27.195 ms  28.180 ms
 7  ae-32-52.ebr2.seattle1.level3.net (4.69.147.182)  377.227 ms  213.377 ms  579.056 ms
 8  ae-2-2.ebr2.denver1.level3.net (4.69.132.54)  417.806 ms  218.987 ms  375.214 ms
 9  ae-3-3.ebr1.chicago2.level3.net (4.69.132.62)  273.500 ms  248.338 ms  626.253 ms