celoyd

import click
import rasterio as rio
import os
import numpy as np
import re
from sys import argv, exit
from math import sin, radians

def get_mtl_path(bundle_path):
  mtls = [

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#!/usr/bin/env python

import rasterio as rio
import numpy as np
import sys

with rio.open(sys.argv[1], 'r') as src:
  meta = src.meta
  cplx = src.read(1)

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#!/usr/bin/env python

import numpy as np
import sys
import rasterio as rio

chip = open(sys.argv[1]).read(4096)
chip = np.fromstring(chip, dtype='uint8')
chip = chip.reshape(64, 64)

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#!/usr/bin/env python
# Very simple rasterio NDVI (or general normalized difference ratio) demo.
# python ndvi.py $landsat_scene/*B{4,5}.TIF ndvi.tif

import rasterio as rio
from sys import argv
import numpy as np

math_type = np.float32 # type for internal calculations

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-- copy eight from '/Users/ch/Desktop/meta/LANDSAT_8.csv' delimiter ',' CSV header;

create type sensor_t as enum ('OLI_TIRS', 'OLI', 'TIRS');
create type daynight_t as enum ('DAY', 'NIGHT');
create type datatype_t as enum ('L1GT', 'L1T', 'PR');
create type nadir_t as enum ('NADIR', 'OFFNADIR');

create table eight (
	sceneID text primary key,
	sensor sensor_t,

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#!/usr/bin/env python

import sys
import os
import numpy

from osgeo import gdal, gdal_array

TAIL_TRIM = 0.01

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#!/usr/bin/env python

# not properly tested

from sys import argv
from PIL import Image
import numpy as np

A, B = (
    np.asarray(Image.open(x)).astype(np.float32)

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# Beta-quality Landsat 8-oriented windowed pansharpener by Charlie Loyd

# python3 panchunk.py $SCENE/*B{4,3,2,8}.TIF ${SCENE}-pansharp.tif

# Then you may want something like this to brighten it up:
# convert -channel B -gamma 0.96 -channel RGB -sigmoidal-contrast 40,14% ${SCENE}-pansharp.tif ${SCENE}-pretty.tif
# But that will strip geo tags.

import asyncio
import rasterio as rio

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# To help with https://xkcd.com/936/

randword () {
	dict=/usr/share/dict/words 
	r=$((($RANDOM * $RANDOM) % `wc -l $dict | awk '{print $1}'`)) 
	head -n $r $dict | tail -n 1
}