kwinkunks

kwinkunks

kwinkunks

kwinkunks

kwinkunks

kwinkunks

import numpy as np

def get_data(day, dataset):
    with open(f'../js/day{day:02d}/{dataset}.txt', 'r') as f:
        return np.array(list(map(int, f.read().split(','))))

def part1(data):
    return np.abs(data - np.median(data)).sum()

def part2(data):

kwinkunks

# DTW, Dynamic Thing Warping
# Where Thing might be time, or depth, or some other linear basis.
# Apache 2.0 licence.
import numpy as np

def cost(s1, s2):
    """
    Very basic algorithm, no windowing.
    
    This cost matrix algorithm was adapted from this blog post by Abhishek Mishra:

kwinkunks

def read_zmapplusgrid(inputfile,dtype=np.float64):
    """Read ZmapPlus grids
    Format is explained here http://lists.osgeo.org/pipermail/gdal-dev/2011-June/029173.html"""
    
    # read header, read until second '@', record header lines and content
    infile = open(inputfile,'r')
    comments=[]
    head=[]
    a=0 # count '@'s
    headers=0 # cound header+comment lines

kwinkunks

kwinkunks