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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 CSA applied to the travelling salesman problem (TSP)
This example was adapted from https
Step1:  Let's create a set of cities to use for TSP.
Step3:  Now's lets define the function to calculate distances between cities
and create a distance matrix.
Step6:  Next we have to define the target_function, i.e. the cost function to be minimized, and the probe_function, which will randomly update the current state at each annealing process.
Step7:  Okay let's give it a run!
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Python Code:
from __future__ import print_function
import math
import random
from pycsa import CoupledAnnealer
try:
    xrange
except NameError:
    xrange = range
Explanation: CSA applied to the travelling salesman problem (TSP)
This example was adapted from https://github.com/perrygeo/simanneal/blob/master/examples/salesman.py, which applies (single) SA to the TSP.
End of explanation
cities = {
    'New York City': (40.72, 74.00),
    'Los Angeles': (34.05, 118.25),
    'Chicago': (41.88, 87.63),
    'Houston': (29.77, 95.38),
    'Phoenix': (33.45, 112.07),
    'Philadelphia': (39.95, 75.17),
    'San Antonio': (29.53, 98.47),
    'Dallas': (32.78, 96.80),
    'San Diego': (32.78, 117.15),
    'San Jose': (37.30, 121.87),
    'Detroit': (42.33, 83.05),
    'San Francisco': (37.78, 122.42),
    'Jacksonville': (30.32, 81.70),
    'Indianapolis': (39.78, 86.15),
    'Austin': (30.27, 97.77),
    'Columbus': (39.98, 82.98),
    'Fort Worth': (32.75, 97.33),
    'Charlotte': (35.23, 80.85),
    'Memphis': (35.12, 89.97),
    'Baltimore': (39.28, 76.62)
}
Explanation: Let's create a set of cities to use for TSP.
End of explanation
def distance(a, b):
    
    Helper function to calculate the distance between two 
    latitude-longitude coordinates.
    
    R = 3963  # radius of Earth (miles)
    lat1, lon1 = math.radians(a[0]), math.radians(a[1])
    lat2, lon2 = math.radians(b[0]), math.radians(b[1])
    return math.acos(math.sin(lat1) * math.sin(lat2) +
                     math.cos(lat1) * math.cos(lat2) * 
                     math.cos(lon1 - lon2)) * R
# Create the distance matrix between the cities.
distance_matrix = {}
for ka, va in cities.items():
    distance_matrix[ka] = {}
    for kb, vb in cities.items():
        if kb == ka:
            distance_matrix[ka][kb] = 0.0
        else:
            distance_matrix[ka][kb] = distance(va, vb)
Explanation: Now's lets define the function to calculate distances between cities
and create a distance matrix.
End of explanation
def probe(positions, tgen):
    
    Swap two cities in the route.
    
    Note that `tgen` (the generation temperature) is ignored here.
    In general, you can use `tgen` to adjust the variance of
    the probing jumps as the algorithm progress.
    
    a = random.randint(0, len(positions) - 1)
    b = random.randint(0, len(positions) - 1)
    positions[a], positions[b] = positions[b], positions[a]
    return positions
def target(positions):
    
    Calculates the length of the route.
    
    e = 0
    for i in xrange(len(positions)):
        e += distance_matrix[positions[i-1]][positions[i]]
    return e
Explanation: Next we have to define the target_function, i.e. the cost function to be minimized, and the probe_function, which will randomly update the current state at each annealing process.
End of explanation
n_annealers = 10  # the number of coupled annealers
init_state = list(cities.keys())
random.shuffle(init_state)
# Initialize the CSA process.
annealer = CoupledAnnealer(
    target, 
    probe, 
    initial_state=[init_state] * n_annealers,
    steps=100,  # You probably want to set this a lot higher, like 10,000
    processes=1,   # Only use more than 1 process if the target function is costly to compute
    n_annealers=n_annealers,
    tacc_initial=1000.0,
    verbose=1)
# Beging the annealing.
annealer.anneal()
# Get the best result from all `n_annealers`.
energy, state = annealer.get_best()
# Slide the list of cities until NYC is first.
while state[0] != 'New York City':
    state = state[1:] + state[:1]
print()
print("%i mile route:" % energy)
for city in state:
    print("\t", city)
Explanation: Okay let's give it a run!
End of explanation
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