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from GriddedGeoBox import GriddedGeoBox
from io import read_subset
import rasterio
fname = '/g/data/v10/eoancillarydata/Land_Sea_Rasters/WORLDzone56.tif'
ds = rasterio.open(fname)
gb = GriddedGeoBox.from_dataset(ds)
gb.origin
ds.get_transform()
q = read_subset(fname, gb.convert_coordinates((100,100)), gb.convert_coordinates((200,100)), gb.convert_coordinates((200,200)), gb.convert_coordinates((100,200)))
q[1]

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#!/usr/bin/env python

import numpy
from scipy import ndimage
from image_processing.segmentation.segmentation import SegmentVisitor

"""
This example looks at segmentation from the results of a binary threshold.
The pixel quality code in the ga-neo-landsat-processor repo is a production
example of this methodolgy.

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#!/usr/bin/env python

import os
from os.path import join as pjoin, dirname, basename
import cPickle as pickle
import argparse

import numpy
import luigi
import pandas

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A demo of how to calculate the NDVI and compute statistics across the z-axis (time) in a tiled/chunking workflow.

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#!/usr/bin/env python

import numpy
import luigi
import luigi.contrib.mpi as mpi
import cPickle as pickle
from os.path import exists
import time
from datetime import datetime

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[work]
output_directory = /g/data/v10/testing_ground/jps547/sue-fyfe/results
logs_directory = %(output_directory)s/logs
satellites = LS5,LS7,LS8
min_date = 1985_01_01
max_date = 2015_12_31
vector_filename = /g/data/v10/testing_ground/jps547/sue-fyfe/LH_Nominated_EC_Saltmarsh.shp

[internals]
envelope = 0

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A MATLAB translation of a function that reads OTPS GRID binary files.

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A baresoils percentiles mosaicing workflow. Just to be used as a computational baseline.

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KEA Image Format

A h5py interface to the KEA image format

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Cloud analysis

A draft analysis into clouds across Australia through time. Metrics include percent coverage, minimum, maximum, and average cloud size. Would be ideal if access to the thermal data was made available, then metrics on cloud heights could be calculated as well.

Spectral metrics can come later.