CMCDragonkai

from math import gcd
from functools import reduce

def lcm(numbers):
    return reduce(lambda x, y: int((x * y) / gcd(x, y)), numbers, 1)

print(lcm([3,4,2,5,5])

CMCDragonkai

import math
import skimage.transform
import numpy as np

# you use this just before passing any image to a CNN
# which usually expects square images
# however your input images can be of variable size
# you don't want to just squash the images to a square
# because you will lose valuable aspect ratio information
# you want to resize while preserving the aspect ratio

CMCDragonkai

Using socat to convert unix pipes to TCP connections

Imagine you have:

program-a | program-b

But now program-a and program-b are on different computers.

CMCDragonkai

# this is cool trick to invert array indices
# basically the first becomes the last
# and the last becomes the first

arr = [1,2,3,4]
arr_is = list(range(0, len(arr)))

# iterate forwards
for i in arr_is:
  print(arr[i])

CMCDragonkai

CLI Merging of Text Files

The opposite of diff turns out to be sdiff which is distributed in the same package.

Use it like this:

sdiff --output=./merged file1 file2

CMCDragonkai

// sometimes you need cooperative multitasking
// you don't want to rely on the JS event loop mechanism
// because either you're not doing any IO
// or you want to more tightly control the exact allocation
// of task computation time

// below we show 2 ways of achieving cooperative multitasking
// the first just uses normal function closures
// each time a coroutine step is done
// we return the result plus a closure that can take the next number

CMCDragonkai

# sometimes you're streaming through values in a for loop
# and you need to maintain a running max or min
# it's not elegant to get the head separately from the loop
# to solve this, we need a sentinel value to fold the max/min
# operation over the stream
# also known as the "identity element" of the max/min monoid

# in real world use cases, this could be a CSV stream
stream = (i for i in [1, 2, 3])

CMCDragonkai

# sometimes you want to get the distance matrix
# and once you have that distance matrix
# you want to be able to know what pairs contributed to that distance
# instead of converting it to the squareform
# which has redundant data
# we can do a little calculation

# note that there's a parallel version of pdist https://stackoverflow.com/a/29639465/582917
# however it doesn't return the condensed matrix

CMCDragonkai

import tempfile
import numpy as np

# the backing type is important
# numpy arrays must have a fixed size element
# so if you want to do out-of-core analytics, you need to know your element size
# for example here, we bytelength of 10
# the 'S' here means ASCII bytes, thus you can put bytestrings into it
# you can also use `('U', 10)` for utf-8 strings, this would mean 10 unicode codepoints
backing_type = np.dtype(('S', 10))

CMCDragonkai

Transferring Ledger Nano S to Electrum

Electrum supports importing of the BIP39 24 word seed.

I recommend trying this to make sure you have written your seed down correctly.

You just need to start a new wallet, select a standard wallet and state that you already have a seed.

In the seed entry, select options and enable BIP 39.