Matt Gregory grovduck

## aoc202108.py
"""AoC 8, 2021"""

# Standard library imports
import pathlib
import sys
from itertools import permutations


def parse(puzzle_input):
    """Parse input"""

## _readme.md

      
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    Overview

This is an attempt to create a simple script for downloading new GNN data for the NWFP visualization group through Google Earth Engine.  This code will only work for members of the nwfp-vis or nwfp25-users groups as we don't intend to make the GNN neighbor rasters available outside these groups.
Note that our standard GNN download tool will actually NOT use GEE given the complexities of managing exports from GEE to Google Drive and the possible demands put on specific user accounts when multiple downloads are requested.  But this is a way that folks from NWFP-25 working groups can access the data in a somewhat easy API (although this may be a bit too little, too late ...)
Note that this "API" will likely change in the future, although I will try to minimize these changes.  Please let me know if you come across any configurations that are not working and provide the configuration object that you used when you triggered the bug.  I have not tested many of the different combinations.
A

  
## esri_knn.py
class FeatureSpace(object):
    def __init__(self, max_k):
        self.max_k = max_k
        self.knn_classifier = KNeighborsClassifier(max_k)

    def train(self):
        raise NotImplementedError

    def transform(self, raw_scores):
        raise NotImplementedError

## dynamic-earthquakes.html
<!DOCTYPE html>
<html>
<head>
  <meta charset='utf-8' />
  <title></title>
  <meta name='viewport' content='initial-scale=1,maximum-scale=1,user-scalable=no' />
  <script src='https://api.tiles.mapbox.com/mapbox-gl-js/v0.37.0/mapbox-gl.js'></script>
  <link href='https://api.tiles.mapbox.com/mapbox-gl-js/v0.37.0/mapbox-gl.css' rel='stylesheet' />
  <link href='https://fonts.googleapis.com/css?family=Source+Sans+Pro:400,700' rel='stylesheet'>
  <style>

## get_ravioli_windows.py
import sys
import click
import rasterio


def expand(window, size=1):
    r, c = window
    return ((r[0] - size, r[1] + size), (c[0] - size, c[1] + size))


## get_rasterio_window_chunks.py
"""
This script returns window addresses based on user-defined maximum RAM
usage.  It is similar to rasterio.block_windows but allows the user to
potentially do fewer reads on rasterio data.
"""

import rasterio
import numpy as np


## extract_masked_window.py
import numpy.ma as ma


def extract_masked_window(arr, mask, offset=(0, 0)):
    """
    Extract the (possibly) masked window from a numpy array

    Parameters
    ----------
    arr : array-like

## generate_random_points.py
def generate_random_points(r, num_points):
    """
    Generate random points with replacement using a raster as a bound.  Points
    will be the centers of pixels in the raster

    Parameters
    ----------
    r: rasterio.RasterReader
        The raster to use as the bounding area
    num_points: int

## gee-filter.js
// Examples of using filters with collections
// See https://groups.google.com/forum/#!topic/google-earth-engine-developers/J50mCM-LEMk
// for context

// Set up two constant images with 'a', 'b' and 'c' properties
var im1 = ee.Image.constant(0).set({
  a: [1,2,3], b: 4, c: 4
});
var im2 = ee.Image.constant(0).set({
  a: [4,5,6], b: 4, c: 5

## _base.pxd
# Add in _raster_attributes object
cdef class DatasetReader:
    cdef public object _crs_wkt
    cdef public object _transform
    cdef public object _block_shapes
    cdef public object _raster_attributes
	"""AoC 8, 2021"""

	# Standard library imports
	import pathlib
	import sys
	from itertools import permutations


	def parse(puzzle_input):
	"""Parse input"""
	class FeatureSpace(object):
	def __init__(self, max_k):
	self.max_k = max_k
	self.knn_classifier = KNeighborsClassifier(max_k)

	def train(self):
	raise NotImplementedError

	def transform(self, raw_scores):
	raise NotImplementedError
	<!DOCTYPE html>
	<html>
	<head>
	<meta charset='utf-8' />
	<title></title>
	<meta name='viewport' content='initial-scale=1,maximum-scale=1,user-scalable=no' />
	<script src='https://api.tiles.mapbox.com/mapbox-gl-js/v0.37.0/mapbox-gl.js'></script>
	<link href='https://api.tiles.mapbox.com/mapbox-gl-js/v0.37.0/mapbox-gl.css' rel='stylesheet' />
	<link href='https://fonts.googleapis.com/css?family=Source+Sans+Pro:400,700' rel='stylesheet'>
	<style>
	import sys
	import click
	import rasterio


	def expand(window, size=1):
	r, c = window
	return ((r[0] - size, r[1] + size), (c[0] - size, c[1] + size))
	"""
	This script returns window addresses based on user-defined maximum RAM
	usage. It is similar to rasterio.block_windows but allows the user to
	potentially do fewer reads on rasterio data.
	"""

	import rasterio
	import numpy as np
	import numpy.ma as ma


	def extract_masked_window(arr, mask, offset=(0, 0)):
	"""
	Extract the (possibly) masked window from a numpy array

	Parameters
	----------
	arr : array-like
	def generate_random_points(r, num_points):
	"""
	Generate random points with replacement using a raster as a bound. Points
	will be the centers of pixels in the raster

	Parameters
	----------
	r: rasterio.RasterReader
	The raster to use as the bounding area
	num_points: int
	// Examples of using filters with collections
	// See https://groups.google.com/forum/#!topic/google-earth-engine-developers/J50mCM-LEMk
	// for context

	// Set up two constant images with 'a', 'b' and 'c' properties
	var im1 = ee.Image.constant(0).set({
	a: [1,2,3], b: 4, c: 4
	});
	var im2 = ee.Image.constant(0).set({
	a: [4,5,6], b: 4, c: 5
	# Add in _raster_attributes object
	cdef class DatasetReader:
	cdef public object _crs_wkt
	cdef public object _transform
	cdef public object _block_shapes
	cdef public object _raster_attributes