'''
For Halloween in your neighborhood, you get a list with adresses randomly 
distributed over a map. It is up to you to find the optimal route to pass 
by every house and walk the shortest distance overall. 

Exercise A:
Make a function named `dist` which calculates the distance between the 
current location and a future location. 

Hint: you might want to use `np.sqrt`.
'''

current_location = [0,0]
future_location = [3,4] 

def dist (current, future):
# Your code here

'''
Exercise B:
Now instead of getting one future location, you get an array with locations. 
Make a function `best_location` to find the best future location, which is 
closest to the current location.

Hint: you might want to use `np.where`, and feel free to reuse (part of) your 
above `dist` function.
'''

future_locations= np.array( [ [10,0],[10,5] , [10,-5],[-8,0], [-8,4],[-8,-4],[-2,1],[-5,2.5],[3,1.5],[7,3.5]])
# print(np.shape(future_locations))

def best_location(current, future):
# Your code here


'''
Exercise C:
By using the previous `best_location` function, write code to automatically 
find the most optimal route along all `future_locations`. Do this by repeatedly 
finding the best next step. 

An array, called `route`, should be generated, which is the optimally 
rearranged version of `future_locations`, starting and ending at current 
location [0,0]. Whether you are correct can be directly seen after plotting 
the route below. 

Hint: you might want to use `numpy.delete(arr, obj, axis=None)`
* arr: Input array
* obj: Row or column number to delete
* axis: Axis to delete
'''

def find_best_location(current,future):
    route = np.zeros((len(future)+2, 2))
    # Your code here
    return route

'''
With this code you can visualize your route:
'''

route = find_best_location(current_location, future_locations)
print(route)

import matplotlib.pyplot as plt

plt.plot(route[:,0],route[:,1],'*-')