celoyd

# PRE-ALPHA!

import asyncio
import rasterio as rio
import numpy as np
from rasterio.warp import reproject, RESAMPLING

'''
./pan.py print/LC81100362014076LGN00/*B{4,3,2,8}.TIF out.tif

celoyd

# dirty l8 scaling in python
# Charlie Loyd, fall 2013
# note use of arbitrary 160000 scale factor in L48

from math import sin
from sys import argv, exit
from osgeo import gdal, gdalconst
import os.path
from numpy import *

celoyd

#!/usr/bin/env python

# ./sscan.py input.mov rownumber output.png

# This is meant to be hyper-simple and makes
# some assumptions like: you want a row (not
# a column), the video is RGB (not gray), etc.

# Bug: frame_count is sometimes fractional.
# int() and the "if not okay" are workarounds.

celoyd

### Keybase proof

I hereby claim:

  * I am celoyd on github.
  * I am vruba (https://keybase.io/vruba) on keybase.
  * I have a public key whose fingerprint is ACAD 5D75 5F30 8F1B 1504  B132 EB31 5C66 7D17 6C9D

To claim this, I am signing this object:

celoyd

celoyd

24.42129435, 54.51402311, ar, الحفلة 👌
سوف نضيع شرف الكثير الليلة 😂👊
21.45165137, 39.96484549, ar, صبرك وتوفيقك يارب 💜?
24.3936663, 56.7277276, ar, '

قال رسول الله ﷺ :
 { إن من أفضل أيامكم يوم الجمعة
 فأكثروا عليّ من الصلاة فيه } 🌷
'
29.33803968, 48.06777154, ht, Chenni ga3da ashof msr7yat 6ar6angy

celoyd

This is my default career advice for people starting out in geo/GIS, especially remote sensing, adapted from a response to a letter in 2013.

I'm currently about to start a Geography degree at the University of [Redacted] at [Redacted] with a focus in GIS, and I've been finding that I have an interest in working with imagery. Obviously I should take Remote Sensing and other similar classes, but I'm the type of person who likes to self learn as well. So my question is this: What recommendations would you give to a student who is interested in working with imagery? Are there any self study paths that you could recommend?

I learned on my own and on the job, and there are a lot of important topics in GIS that I don’t know anything about, so I can’t give comprehensive advice. I haven’t arrived anywhere; I’m just ten minutes ahead in the convoy we’re both in. Take these recommendations critically.

Find interesting people. You’ll learn a lot more from a great professor (or mentor, or friend, or conference) o

celoyd

What zoom level is equivalent to a given altitude? In other words, if I’m looking at a map at ZL n, how high off the ground would I be to make out the same features?

This is from a question from Eric Fischer; my algebra and arithmetic are extremely rusty, so please check everything! Major assumptions:

1. We’re at the equator.

2. We’re looking straight down (which you can’t realistically do out a plane window, for example).

3. We have 20/20 vision (which is not as good as median corrected vision).

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

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