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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

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# 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:

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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):

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from scipy.signal import remez, minimum_phase
import matplotlib.pyplot as plt

# Based on this example from the scipy docs:
# https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.minimum_phase.html
freq = [0, 0.2, 0.3, 1.0]
desired = [1, 0]
h_linear = remez(151, freq, desired, Hz=2.0)
h_min_hil = minimum_phase(h_linear, method='hilbert')