caffeine-potent

def calculate_taxed_ammount( spent:float, tax_rate:float):
    cost = spent / (1 + tax_rate)
    taxed = spent - cost
    print("Spent: {}\nCost:  {}\nTaxed: {}".format(spent, cost, taxed))

calculate_taxed_ammount( spent = 171.376666667, tax_rate = .10)

caffeine-potent

import numpy as np

def create_n_depth_matrix(n: int) -> np.array:
    """Function used to generate all binary permutations of length n.
    
    Binary permutations of n=3 for example would include:
    [0,0,0],[0,0,1],[0,1,0],[0,1,1],[1,0,0],[1,0,1],[1,1,0],[1,1,1]
    Binary permuataions are  arranged in a 3d matrix of shape (n, a, b)
    such that a*b == (2^n)
    

caffeine-potent

from functools import reduce
flatten = lambda lst: reduce(lambda l, i: l + flatten(i) if isinstance(i, (list, tuple)) else l + [i], lst, [])  

test = [[[[1,0],1],0],1]

print(flatten(test))  

## Credit https://stackoverflow.com/questions/952914/making-a-flat-list-out-of-list-of-lists-in-python

caffeine-potent

caffeine-potent

import datacube
import xarray as xr

def export_xarray_to_netcdf(ds:xr.Dataset, path:str):
    """
    Exports an xarray.Dataset as a single NetCDF file.
    Parameters
    ----------
    ds: xarray.Dataset
        The Dataset to export.

caffeine-potent

from scipy.spatial.distance import euclidean

points = [
    [1,3],
    [2,4],
    [3,3],
    [4,5],
    [3,7],
    [5,1],
    [5,3],