def generate_realizations(self, p=99, marks=None):
neighbor_perms = []
for i in range(p):
neighbor_perms.append(
analytics.average_nearest_neighbor_distance(
self.generate_random_points(100)))
return neighbor_perms
def test_crit_tations(self):
observed_avg = analytics.average_nearest_neighbor_distance(self.points)
permutations = utils.permutations(99)
lower, upper = utils.crit_tations(permutations)
self.assertTrue(lower > 0.03)
self.assertTrue(upper < 0.07)
self.assertTrue(observed_avg < lower or observed_avg > upper)
def generate_realizations(self, p = 99, marks = None):
neighbor_perms = []
for i in range(p):
neighbor_perms.append(
analytics.average_nearest_neighbor_distance(
self.generate_random_points(100)))
return neighbor_perms
def test_point_pattern(self):
"""
This test checks that the code can compute an observed mean
nearest neighbor distance and then use Monte Carlo simulation to
generate some number of permutations. A permutation is the mean
nearest neighbor distance computed using a random realization of
the point process.
"""
random.seed(3673673) # Reset the random number generator using system time
# I do not know where you have moved avarege_nearest_neighbor_distance, so update the point_pattern module
observed_avg = analytics.average_nearest_neighbor_distance(self.points)
self.assertAlmostEqual(0.037507819095864134, observed_avg, 3)
# Again, update the point_pattern module name for where you have placed the point_pattern module
# Also update the create_random function name for whatever you named the function to generate
# random points
rand_points = utils.create_n_rand_pts(100)
self.assertEqual(100, len(rand_points))
# As above, update the module and function name.
permutations = analytics.p_perms(99)
self.assertEqual(len(permutations), 99)
self.assertNotEqual(permutations[0], permutations[1])
# As above, update the module and function name.
lower, upper = utils.critical_pts(permutations)
self.assertTrue(lower > 0.03)
self.assertTrue(upper < 0.07)
self.assertTrue(observed_avg < lower or observed_avg > upper)
# As above, update the module and function name.
significant = analytics.monte_carlo_critical_bound_check(lower, upper, 0)
self.assertTrue(significant)
self.assertTrue(True)
def test_crit_tations(self):
observed_avg = analytics.average_nearest_neighbor_distance(self.points)
permutations = utils.permutations(99)
lower, upper = utils.crit_tations(permutations)
self.assertTrue(lower > 0.03)
self.assertTrue(upper < 0.07)
self.assertTrue(observed_avg < lower or observed_avg > upper)
def test_marks(self):
random.seed(942323) # Reset the random number generator using system time
# I do not know where you have moved avarege_nearest_neighbor_distance, so update the point_pattern module
observed_avg = analytics.average_nearest_neighbor_distance(self.points)
self.assertAlmostEqual(0.037507819095864134, observed_avg, 3)
# Again, update the point_pattern module name for where you have placed the point_pattern module
# Also update the create_random function name for whatever you named the function to generate
# random points
rand_points = utils.create_marked_rand_pts(100,self.marks)
self.assertEqual(100, len(rand_points))
# As above, update the module and function name.
permutations = analytics.p_perms_marks(99,self.marks)
self.assertEqual(len(permutations), 99)
#print(permutations)
self.assertNotEqual(permutations[0], permutations[1])
# As above, update the module and function name.
lower, upper = utils.critical_pts(permutations)
self.assertTrue(lower > 0.03)
self.assertTrue(upper < 0.07)
self.assertTrue(observed_avg < lower or observed_avg > upper)
# As above, update the module and function name.
significant = analytics.monte_carlo_critical_bound_check(lower, upper, 0)
self.assertTrue(significant)
self.assertTrue(True)
def permutations(number_of_permutations, number_of_points):
result = []
for i in range(number_of_permutations):
points = create_random(number_of_points)
observed_avg = analytics.average_nearest_neighbor_distance(points)
result.append(observed_avg)
return result
def permutations(p=99, n=100, marks = None):
"""
Calculate p number of average_nearest_neighbor_distances from n number
of randomly generated points. Return list of size p with distance values.
Parameter(s): integer p, integer n
Return(s): list perm
"""
perm = []
for x in range(p):
points = create_random_marked_points(n, marks)
avg_nnd = average_nearest_neighbor_distance(points)
perm.append(avg_nnd)
return perm
def permutations(p=99, n=100, marks = None):
"""
Calculate p number of average_nearest_neighbor_distances from n number
of randomly generated points. Return list of size p with distance values.
Parameter(s): integer p, integer n
Return(s): list perm
"""
perm = []
for x in range(p):
points = create_random_marked_points(n, marks)
avg_nnd = average_nearest_neighbor_distance(points)
perm.append(avg_nnd)
return perm
def permutation(p=99, n=100):
"""
Return the mean nearest neighbor distance of p permutations.
Parameters
----------
p : integer
n : integer
Returns
-------
permutations : list
the mean nearest neighbor distance list.
"""
permutation_list=[]
for i in range(p):
permutation_list.append(average_nearest_neighbor_distance(generate_random_points(n)))
return permutation_list
def nearestNeighborTweets(self):
""" In this function, I will normalize it to the unit mile
by multiplying the result by the distance in miles traveling
about 1 unit longitudinally at roughly the same latitude.
first coord = (33.471798, -112.445462)
second coord = (33.451355, -111.442852)
Distance (Miles / Spherical Earth) = 57.68 miles
"""
root = Tk()
root.withdraw()
if self.sentiment == None:
mb.showerror("Error", 'Please Open or Visualize tweets first.')
else:
if self.sentiment == 'All':
mark = None
else:
mark = self.sentiment
nnd = NORMALIZED_PHX_DISTANCE * average_nearest_neighbor_distance(self.tweetObjArr, mark)
mb.showinfo("Info", 'The average nearest neighbor of\n{0} tweets is {1}'.format(self.sentiment, nnd))
def permutation_mark(p=99, n=100 ,marks=None,mark=None):
"""
Return the mean nearest neighbor distance of p permutations.
Parameters
----------
p : integer
n : integer
marks : list
Returns
-------
permutations : list
the mean nearest neighbor distance list.
"""
permutation_list=[]
for i in range(p):
permutation_list.append(average_nearest_neighbor_distance(create_random_marked_points(n,marks),mark))
return permutation_list
def permutation(p=99, n=100):
"""
Return the mean nearest neighbor distance of p permutations.
Parameters
----------
p : integer
n : integer
Returns
-------
permutations : list
the mean nearest neighbor distance list.
"""
permutation_list = []
for i in range(p):
permutation_list.append(
average_nearest_neighbor_distance(generate_random_points(n)))
return permutation_list
def permutation_mark(p=99, n=100, marks=None, mark=None):
"""
Return the mean nearest neighbor distance of p permutations.
Parameters
----------
p : integer
n : integer
marks : list
Returns
-------
permutations : list
the mean nearest neighbor distance list.
"""
permutation_list = []
for i in range(p):
permutation_list.append(
average_nearest_neighbor_distance(
create_random_marked_points(n, marks), mark))
return permutation_list
def permutations(p=99, n=100):
solution = []
for tations in range(p):
solution.append(analytics.average_nearest_neighbor_distance(generate_random_points(n)))
return solution
def test_average_nearest_neighbor_distance(self):
mean_d = analytics.average_nearest_neighbor_distance(self.points)
self.assertAlmostEqual(mean_d, 7.629178, 5)
def average_nearest_neighbor(self, mark=None):
return analytics.average_nearest_neighbor_distance(self.points, mark)
def average_nearest_neighbor_distance(self, marks=None):
return analytics.average_nearest_neighbor_distance(self.points,marks)
def test_check_yer_sig(self):
permutations = utils.permutations(99)
lower, upper = utils.crit_tations(permutations)
significant = utils.check_yer_sig(lower, upper, analytics.average_nearest_neighbor_distance(self.points))
self.assertTrue(significant)
def permutations(p=99, n=100):
solution = []
for tations in range(p):
solution.append(analytics.average_nearest_neighbor_distance(generate_random_points(n)))
return solution