celoyd

Homebrew build logs for osgeo/osgeo4mac/gdal2 on macOS 10.12.6
Build date: 2017-08-25 16:05:49

celoyd

Homebrew build logs for osgeo/osgeo4mac/gdal2 on macOS 10.12.6
Build date: 2017-08-25 15:56:21

celoyd

Homebrew build logs for osgeo/osgeo4mac/gdal2 on macOS 10.12.6
Build date: 2017-08-25 15:44:45

celoyd

Getting GSOD

0. Introduction

0₀: What’s in this guide?

This is a discursive recipe for turning the Global Summary of the Day weather dataset into a useful PostgreSQL database on a Unix system. It's aimed at data nerds with no prior knowledge of GSOD. For simplicity, it only describes one way of doing things, but the canny reader will see many possible variations (for example, using staged temporary files rather than a pipeline to do data conversion) and substitutions (mysql for postgres, curl for wget, perl for python, …).

Depending on how you do things, you’ll need something like 50 free gigabytes of disk space to work in, and the final database will use about 20 gigabytes.

celoyd

# python pca_multiband.py input.jpeg output.tif
# n-band image -> PCA -> n-band TIFF image
# with lots of hackety assumptions
# (e.g., output is same type as input)

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

celoyd

# python pca_multiband.py input.jpeg output.tif
# n-band image -> PCA -> n-band TIFF image
# with lots of hackety assumptions
# (e.g., output is same type as input)

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

celoyd

Hi.

On Oct 7, 2016, at 11:31 AM, No on HH Research Team info@nooaklandgrocerytax.com wrote:

If you’re the research team, why are you doing advocacy? What’s the communications team doing right now?

Dear Neighbor,

🤔 Are we neighbors? I would say my neighbors are people close enough that I would walk to visit them. This feels like false intimacy. Why not tell me where you are?

celoyd

#!/usr/bin/env python

import numpy as np
import cv2
from sys import argv

# Gaussian radius (should really be an argument):
r = 21

src, dst = argv[1:]

celoyd

#!/usr/bin/env python

import rasterio as rio
from sys import argv, exit
from skimage import io
import numpy as np
from math import pi, radians

def px_to_rad(n):
  return n / 200.0 # 1 px = 5 mrad

celoyd

#!/usr/bin/env python

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

# Reads an image with a bunch of bands (i.e., layers on the z axis)
# and returns an image with one band, each value in which is the mean
# along the z axis at that x,y.