def test_nearest_neighbor_connections():
"""
The answer to this did not match that in PASSaGE 2. I have found an error in the old PASSaGE 2 code leading
to the discrepancy. At this point I believe this code is correct, but do not have a formal test yet.
For reference, the error in the PASSaGE code was the inadvertent resetting of positive connections to
negative when looking at nearest neighbors for later points in the list, thus eliminating some potential
asymmetric connections
"""
coords = create_test_coords()
distances = pyssage.distances.spherical_distance_matrix(coords[:, 0], coords[:, 1])
connections = pyssage.connections.nearest_neighbor_connections(distances, 1)
pyssage.graph.draw_connections(connections, coords[:, 0], coords[:, 1],
title="Nearest Neighbor (k=1) Symmetric Test",
figoutput=pyssage.graph.FigOutput(figshow=True))
connections = pyssage.connections.nearest_neighbor_connections(distances, 2)
pyssage.graph.draw_connections(connections, coords[:, 0], coords[:, 1],
title="Nearest Neighbor (k=2) Symmetric Test",
figoutput=pyssage.graph.FigOutput(figshow=True))
connections = pyssage.connections.nearest_neighbor_connections(distances, 2, symmetric=False)
pyssage.graph.draw_connections(connections, coords[:, 0], coords[:, 1],
title="Nearest Neighbor (k=2) Asymmetric Test",
figoutput=pyssage.graph.FigOutput(figshow=True))
def test_bearing_correlogram_mantel():
# The Passage 2 Mantel correlogram code appears to be buggy, which effects the Mantel bearing correlogram as well
# There was also potentially a logic error in using "reverse binary weights" with non-binary weighting which
# also would have led to problematic results, even if the first error hadn't overridden everything
data, _ = create_test_scattered()
coords = create_test_coords()
distances = pyssage.distances.euclidean_distance_matrix(
coords[:, 0], coords[:, 1])
angles = pyssage.distances.euclidean_angle_matrix(coords[:, 0], coords[:,
1])
data_distances = pyssage.distances.data_distance_matrix(
data, pyssage.distances.data_distance_euclidean)
dist_classes = pyssage.distances.create_distance_classes(
distances, "determine pair count", 15)
dc_con = pyssage.connections.distance_classes_to_connections(
dist_classes, distances)
output, output_text, _ = pyssage.correlogram.bearing_correlogram(
data_distances,
dc_con,
angles,
metric=pyssage.correlogram.mantel_correl)
pyssage.graph.draw_bearing_correlogram(
numpy.array(output),
"Mantel Bearing Correlogram",
figoutput=pyssage.graph.FigOutput(figshow=True))
for line in output_text:
print(line)
def test_bearing_correlogram_mantel_perm():
data, _ = create_test_scattered()
coords = create_test_coords()
distances = pyssage.distances.euclidean_distance_matrix(
coords[:, 0], coords[:, 1])
angles = pyssage.distances.euclidean_angle_matrix(coords[:, 0], coords[:,
1])
data_distances = pyssage.distances.data_distance_matrix(
data, pyssage.distances.data_distance_euclidean)
dist_classes = pyssage.distances.create_distance_classes(
distances, "determine pair count", 15)
dc_con = pyssage.connections.distance_classes_to_connections(
dist_classes, distances)
output, output_text, _ = pyssage.correlogram.bearing_correlogram(
data_distances,
dc_con,
angles,
metric=pyssage.correlogram.mantel_correl,
permutations=100)
pyssage.graph.draw_bearing_correlogram(
numpy.array(output),
"Mantel Bearing Correlogram",
figoutput=pyssage.graph.FigOutput(figshow=True))
for line in output_text:
print(line)
def test_mantel_correl():
# The Passage 2 Mantel correlogram code appears to be buggy
# Some manual tests do seem to indicate this is working correctly
data, _ = create_test_scattered()
coords = create_test_coords()
distances = pyssage.distances.euclidean_distance_matrix(
coords[:, 0], coords[:, 1])
dist_classes = pyssage.distances.create_distance_classes(
distances, "determine pair count", 15)
dc_con = pyssage.connections.distance_classes_to_connections(
dist_classes, distances)
data_distances = pyssage.distances.data_distance_matrix(
data, pyssage.distances.data_distance_euclidean)
output, output_text, _ = pyssage.correlogram.correlogram(
data_distances,
dc_con,
pyssage.correlogram.mantel_correl,
variance=None)
pyssage.graph.draw_correlogram(
numpy.array(output),
"Mantel r",
"Correlogram",
figoutput=pyssage.graph.FigOutput(figshow=True))
for line in output_text:
print(line)
def test_mantel():
"""
correct answer calculated from PASSaGE 2
Matrix 1: Distance Matrix 1
Matrix 2: Angle Matrix 1
Matrices are 355 x 355
Observed Z = 2731350.67512
Correlation = 0.29830
t = 19.83088
Left-tailed p = 1.00000
Right-tailed p = 0.00000
Two-tailed p = 0.00000
although not asserted (as not part of function output), manual inspection of Z and t are also identical
"""
coords = create_test_coords()
distances = pyssage.distances.euclidean_distance_matrix(
coords[:, 0], coords[:, 1])
angles = pyssage.distances.euclidean_angle_matrix(coords[:, 0], coords[:,
1])
r, p_value, output_text, _, _, _, _, _ = pyssage.mantel.mantel(
distances, angles, [])
for line in output_text:
print(line)
assert round(r, 5) == 0.29830
assert round(p_value, 5) == 0.00000 # two-tailed p by default
def test_mantel_with_partial_multi():
"""
Mantel Test
Matrix 1: Data Distance Matrix 1
Matrix 2: Data Distance Matrix 2
Matrices held constant: Distance Matrix 1, Angle Matrix 1
Matrices are 355 x 355
Observed Z = 44597376.68626
Correlation = 0.89973
t = 55.06521
Left-tailed p = 1.00000
Right-tailed p = 0.00000
Two-tailed p = 0.00000
"""
data, _ = create_test_scattered()
coords = create_test_coords()
distances = pyssage.distances.euclidean_distance_matrix(
coords[:, 0], coords[:, 1])
angles = pyssage.distances.euclidean_angle_matrix(coords[:, 0], coords[:,
1])
data_distances1 = pyssage.distances.data_distance_matrix(
data, pyssage.distances.data_distance_euclidean)
data_distances2 = pyssage.distances.data_distance_matrix(
data, pyssage.distances.data_distance_manhattan)
r, p_value, output_text, _, _, _, _, _ = pyssage.mantel.mantel(
data_distances1, data_distances2, [distances, angles])
for line in output_text:
print(line)
assert round(r, 5) == 0.89973
def test_mantel_with_partial_single():
"""
correct answer calculated from PASSaGE 2
Mantel Test
Matrix 1: Data Distance Matrix 2
Matrix 2: Distance Matrix 1
Matrices held constant: Angle Matrix 1
Matrices are 355 x 355
Observed Z = 2512061.39465
Correlation = 0.26699
t = 15.67369
Left-tailed p = 1.00000
Right-tailed p = 0.00000
Two-tailed p = 0.00000
"""
data, _ = create_test_scattered()
coords = create_test_coords()
distances = pyssage.distances.euclidean_distance_matrix(
coords[:, 0], coords[:, 1])
angles = pyssage.distances.euclidean_angle_matrix(coords[:, 0], coords[:,
1])
data_distances = pyssage.distances.data_distance_matrix(
data, pyssage.distances.data_distance_euclidean)
r, p_value, output_text, _, _, _, _, _ = pyssage.mantel.mantel(
data_distances, distances, [angles])
for line in output_text:
print(line)
assert round(r, 5) == 0.26699
def test_morans_i_perm():
data, _ = create_test_scattered()
coords = create_test_coords()
distances = pyssage.distances.euclidean_distance_matrix(
coords[:, 0], coords[:, 1])
dist_classes = pyssage.distances.create_distance_classes(
distances, "determine pair count", 15)
dc_con = pyssage.connections.distance_classes_to_connections(
dist_classes, distances)
output, output_text, perm_values = pyssage.correlogram.correlogram(
data[:, 0], dc_con, pyssage.correlogram.morans_i, permutations=1000)
pyssage.graph.draw_correlogram(
numpy.array(output),
"Moran's I",
"Correlogram",
figoutput=pyssage.graph.FigOutput(figshow=True))
for line in output_text:
print(line)
test_values = perm_values[3]
pyssage.graph.draw_histogram(test_values,
nbins=50,
obs_value=test_values[0],
title="4th distance class")
test_values = perm_values[4]
pyssage.graph.draw_histogram(test_values,
nbins=50,
obs_value=test_values[0],
title="5th distance class")
def test_sph_angle_matrix():
"""
completely rewritten the way this is done, for a variety of reasons it is difficult to impossible to match
against PASSaGE 2 output
"""
coords = create_test_coords()
pyssage.distances.spherical_angle_matrix(coords[:, 0], coords[:, 1])
def test_windrose_correlogram_mantel_perm():
data, _ = create_test_scattered()
coords = create_test_coords()
distances = pyssage.distances.euclidean_distance_matrix(
coords[:, 0], coords[:, 1])
angles = pyssage.distances.euclidean_angle_matrix(coords[:, 0], coords[:,
1])
data_distances = pyssage.distances.data_distance_matrix(
data, pyssage.distances.data_distance_euclidean)
(output, output_text, all_output,
_) = pyssage.correlogram.windrose_correlogram(
data_distances,
distances,
angles,
radius_c=3,
radius_d=0,
radius_e=0,
metric=pyssage.correlogram.mantel_correl,
permutations=100)
pyssage.graph.draw_windrose_correlogram(
numpy.array(all_output),
title="Mantel Windrose Correlogram Pair Counts",
show_counts=True,
figoutput=pyssage.graph.FigOutput(figshow=True))
pyssage.graph.draw_windrose_correlogram(
numpy.array(all_output),
title="Mantel Windrose Correlogram",
figoutput=pyssage.graph.FigOutput(figshow=True))
for line in output_text:
print(line)
def test_least_diagonal_network():
coords = create_test_coords()
distances = pyssage.distances.spherical_distance_matrix(coords[:, 0], coords[:, 1])
connections = pyssage.connections.least_diagonal_network(coords[:, 0], coords[:, 1], distances)
pyssage.graph.draw_connections(connections, coords[:, 0], coords[:, 1], title="Least Diagonal Network Test",
figoutput=pyssage.graph.FigOutput(figshow=True))
"""
def test_euc_dist_matrix():
# answer calculated from PASSaGE 2 and exported to 5 decimals
answer = load_answer("answers/euc_distmat_answer.txt")
coords = create_test_coords()
output = pyssage.distances.euclidean_distance_matrix(coords[:, 0], coords[:, 1])
for i in range(len(answer)):
for j in range(len(answer)):
assert round(output[i, j], 5) == answer[i, j]
def test_gabriel_network():
# answer calculated from PASSaGE 2 and exported as a binary connection matrix
answer = load_answer("answers/gabriel_answer.txt")
coords = create_test_coords()
distances = pyssage.distances.spherical_distance_matrix(coords[:, 0], coords[:, 1])
connections = pyssage.connections.gabriel_network(distances)
pyssage.graph.draw_connections(connections, coords[:, 0], coords[:, 1], title="Gabriel Graph/Network Test",
figoutput=pyssage.graph.FigOutput(figshow=True))
for i in range(len(answer)):
for j in range(len(answer)):
assert connections[i, j] == answer[i, j]
def test_sph_dist_matrix():
"""
The original code matched the PASSaGE output, but I decided to go away from predetermined constants for
conversion of angels to radians, meaning the numbers shift a bit due to greater floating-point accuracy. I am
also be using a better estimate of the radius of the Earth in the calculations
I have cross-checked the output in a small way using online calculators
this change might cascade to other tests that relied upon these distances
"""
coords = create_test_coords()
pyssage.distances.spherical_distance_matrix(coords[:, 0], coords[:, 1])
def test_minimum_spanning_tree():
# answer calculated from PASSaGE 2 and exported as a binary connection matrix
answer = load_answer("answers/minspan_answer.txt")
coords = create_test_coords()
distances = pyssage.distances.spherical_distance_matrix(coords[:, 0], coords[:, 1])
connections = pyssage.connections.minimum_spanning_tree(distances)
pyssage.graph.draw_connections(connections, coords[:, 0], coords[:, 1], title="Minimum-Spanning Tree Test",
figoutput=pyssage.graph.FigOutput(figshow=True))
for i in range(len(answer)):
for j in range(len(answer)):
assert connections[i, j] == answer[i, j]
def test_connect_distance_range():
# answer calculated from PASSaGE 2 and exported as a binary connection matrix
answer = load_answer("answers/distance_based_connect_7-12_answer.txt")
coords = create_test_coords()
distances = pyssage.distances.euclidean_distance_matrix(coords[:, 0], coords[:, 1])
connections = pyssage.connections.connect_distance_range(distances, mindist=7, maxdist=12)
pyssage.graph.draw_connections(connections, coords[:, 0], coords[:, 1],
title="Distance-based Connections (7-12) Test",
figoutput=pyssage.graph.FigOutput(figshow=True))
for i in range(len(answer)):
for j in range(len(answer)):
assert connections[i, j] == answer[i, j]
def test_delaunay_tessellation():
# answer calculated from PASSaGE 2 and exported as a binary connection matrix
answer = load_answer("answers/delaunay_answer.txt")
coords = create_test_coords()
tessellation, connections = pyssage.connections.delaunay_tessellation(coords[:, 0], coords[:, 1])
pyssage.graph.draw_tessellation(tessellation, coords[:, 0], coords[:, 1], title="Tessellation Test",
figoutput=pyssage.graph.FigOutput(figshow=True))
pyssage.graph.draw_connections(connections, coords[:, 0], coords[:, 1], title="Delaunay Connections Test",
figoutput=pyssage.graph.FigOutput(figshow=True))
for i in range(len(answer)):
for j in range(len(answer)):
assert connections[i, j] == answer[i, j]
def test_mantel_with_permutation():
"""
cannot formally test permutation results
"""
coords = create_test_coords()
distances = pyssage.distances.euclidean_distance_matrix(
coords[:, 0], coords[:, 1])
angles = pyssage.distances.euclidean_angle_matrix(coords[:, 0], coords[:,
1])
r, p_value, output_text, _, _, _, perm_values, _ = pyssage.mantel.mantel(
distances, angles, [], permutations=100)
for line in output_text:
print(line)
def test_bearing_analysis_rand():
# permutation test prevents formal test answer; function used to look for stability
data, _ = create_test_scattered()
data_distances = pyssage.distances.data_distance_matrix(
data, pyssage.distances.data_distance_euclidean)
coords = create_test_coords()
distances = pyssage.distances.euclidean_distance_matrix(
coords[:, 0], coords[:, 1])
angles = pyssage.distances.euclidean_angle_matrix(coords[:, 0], coords[:,
1])
output, output_text = pyssage.anisotropy.bearing_analysis(
data_distances, distances, angles, 36, 100)
pyssage.graph.draw_bearing(numpy.array(output),
figoutput=pyssage.graph.FigOutput(figshow=True))
for line in output_text:
print(line)
def test_gearys_c_perm():
data, _ = create_test_scattered()
coords = create_test_coords()
distances = pyssage.distances.euclidean_distance_matrix(
coords[:, 0], coords[:, 1])
dist_classes = pyssage.distances.create_distance_classes(
distances, "determine pair count", 15)
dc_con = pyssage.connections.distance_classes_to_connections(
dist_classes, distances)
output, output_text, _ = pyssage.correlogram.correlogram(
data[:, 0], dc_con, pyssage.correlogram.gearys_c, permutations=100)
pyssage.graph.draw_correlogram(
numpy.array(output),
"Geary's c",
"Correlogram",
figoutput=pyssage.graph.FigOutput(figshow=True))
for line in output_text:
print(line)
def test_bearing_analysis():
# answer calculated from PASSaGE 2 and exported to 5 decimals, but only matches perfectly to 2 decimals
answer = [[0.00000, 0.11071, 0.00000], [5.00000, 0.09024, 0.00000],
[10.00000, 0.06913, 0.00001], [15.00000, 0.04791, 0.00205],
[20.00000, 0.02750, 0.08670], [25.00000, 0.00930, 0.58459],
[30.00000, -0.00492, 0.78827], [35.00000, -0.01323, 0.50467],
[40.00000, -0.01393, 0.51275], [45.00000, -0.00591, 0.79260],
[50.00000, 0.01125, 0.62907], [55.00000, 0.03736, 0.11396],
[60.00000, 0.07162, 0.00229], [65.00000, 0.11251, 0.00000],
[70.00000, 0.15754, 0.00000], [75.00000, 0.20305, 0.00000],
[80.00000, 0.24466, 0.00000], [85.00000, 0.27850, 0.00000],
[90.00000, 0.30241, 0.00000], [95.00000, 0.31635, 0.00000],
[100.00000, 0.32181, 0.00000], [105.00000, 0.32082, 0.00000],
[110.00000, 0.31529, 0.00000], [115.00000, 0.30672, 0.00000],
[120.00000, 0.29615, 0.00000], [125.00000, 0.28427, 0.00000],
[130.00000, 0.27152, 0.00000], [135.00000, 0.25813, 0.00000],
[140.00000, 0.24422, 0.00000], [145.00000, 0.22984, 0.00000],
[150.00000, 0.21495, 0.00000], [155.00000, 0.19950, 0.00000],
[160.00000, 0.18341, 0.00000], [165.00000, 0.16657, 0.00000],
[170.00000, 0.14889, 0.00000], [175.00000, 0.13028, 0.00000]]
data, _ = create_test_scattered()
data_distances = pyssage.distances.data_distance_matrix(
data, pyssage.distances.data_distance_euclidean)
coords = create_test_coords()
distances = pyssage.distances.euclidean_distance_matrix(
coords[:, 0], coords[:, 1])
angles = pyssage.distances.euclidean_angle_matrix(coords[:, 0], coords[:,
1])
output, output_text = pyssage.anisotropy.bearing_analysis(
data_distances, distances, angles, 36)
pyssage.graph.draw_bearing(numpy.array(output),
figoutput=pyssage.graph.FigOutput(figshow=True))
pyssage.graph.draw_bearing(numpy.array(output),
draw_polar=True,
figoutput=pyssage.graph.FigOutput(figshow=True))
for line in output_text:
print(line)
for i, row in enumerate(answer):
for j, ans in enumerate(row):
assert round(output[i][j], 2) == round(ans, 2)
def test_mantel_with_partial_single_permuted():
"""
cannot formally test permutation results
"""
data, _ = create_test_scattered()
coords = create_test_coords()
distances = pyssage.distances.euclidean_distance_matrix(
coords[:, 0], coords[:, 1])
angles = pyssage.distances.euclidean_angle_matrix(coords[:, 0], coords[:,
1])
data_distances = pyssage.distances.data_distance_matrix(
data, pyssage.distances.data_distance_euclidean)
r, p_value, output_text, _, _, _, _, _ = pyssage.mantel.mantel(
data_distances, distances, [angles], permutations=100)
for line in output_text:
print(line)
def test_angular_correlation_analysis():
# answer calculated from PASSaGE 2
data, _ = create_test_scattered()
coords = create_test_coords()
col = 22 # prostate cancer column
(r_max, theta_max, f, p_value, output,
output_text) = pyssage.anisotropy.angular_correlation_analysis(
coords[:, 0], coords[:, 1], data[:, col])
for line in output_text:
print(line)
pyssage.graph.draw_angular_correlation(
numpy.array(output), figoutput=pyssage.graph.FigOutput(figshow=True))
pyssage.graph.draw_angular_correlation(
numpy.array(output),
draw_polar=False,
figoutput=pyssage.graph.FigOutput(figshow=True))
assert round(r_max, 5) == 0.44382
def test_windrose_correlogram():
data, _ = create_test_scattered()
coords = create_test_coords()
distances = pyssage.distances.euclidean_distance_matrix(
coords[:, 0], coords[:, 1])
angles = pyssage.distances.euclidean_angle_matrix(coords[:, 0], coords[:,
1])
output, output_text, all_output, _ = pyssage.correlogram.windrose_correlogram(
data[:, 22], distances, angles, radius_c=3, radius_d=0, radius_e=0)
pyssage.graph.draw_windrose_correlogram(
numpy.array(all_output),
title="Moran's I Windrose Correlogram Pair Counts",
show_counts=True,
figoutput=pyssage.graph.FigOutput(figshow=True))
pyssage.graph.draw_windrose_correlogram(
numpy.array(all_output),
title="Moran's I Windrose Correlogram",
figoutput=pyssage.graph.FigOutput(figshow=True))
for line in output_text:
print(line)
def test_shortest_path_distances():
"""
testing with euclidean distances as the spherical estimation procedure is now a bit different
"""
# answer calculated from PASSaGE 2 and exported to 5 decimals
answer = load_answer("answers/shortest_path_minspan_answer.txt")
# test a fully connected network
coords = create_test_coords()
distances = pyssage.distances.euclidean_distance_matrix(coords[:, 0], coords[:, 1])
connections = pyssage.connections.minimum_spanning_tree(distances)
geodists, trace = pyssage.distances.shortest_path_distances(distances, connections)
for i in range(len(answer)):
for j in range(len(answer)):
assert round(geodists[i, j], 5) == answer[i, j]
pyssage.graph.draw_shortest_path(connections, coords[:, 0], coords[:, 1], trace, 0, 300,
figoutput=pyssage.graph.FigOutput(figshow=True))
# test a partially connected network
connections = pyssage.connections.nearest_neighbor_connections(distances, 1)
pyssage.distances.shortest_path_distances(distances, connections)
def test_create_distance_classes():
coords = create_test_coords()
distances = pyssage.distances.spherical_distance_matrix(coords[:, 0], coords[:, 1])
dc = pyssage.distances.create_distance_classes(distances, "determine class width", 10)
pyssage.graph.draw_distance_class_distribution(distances, dc, title="Ten Equal Width Distance Classes",
figoutput=pyssage.graph.FigOutput(figshow=True))
dc = pyssage.distances.create_distance_classes(distances, "determine pair count", 10)
pyssage.graph.draw_distance_class_distribution(distances, dc, title="Ten Equal Count Distance Classes",
figoutput=pyssage.graph.FigOutput(figshow=True))
dc = pyssage.distances.create_distance_classes(distances, "set class width", 200)
pyssage.graph.draw_distance_class_distribution(distances, dc, title="Distance Class Width Set to 200",
figoutput=pyssage.graph.FigOutput(figshow=True))
dc = pyssage.distances.create_distance_classes(distances, "set pair count", 5000)
pyssage.graph.draw_distance_class_distribution(distances, dc, title="Distance Class Pair Count Set to 5000",
figoutput=pyssage.graph.FigOutput(figshow=True))
dc = pyssage.distances.create_distance_classes(distances, "determine class width", 10, set_max_dist=2000)
pyssage.graph.draw_distance_class_distribution(distances, dc,
title="Ten Equal Width Distance Classes (max dist 2000)",
figoutput=pyssage.graph.FigOutput(figshow=True))
dc = pyssage.distances.create_distance_classes(distances, "determine class width", 10, set_max_dist="0.5")
pyssage.graph.draw_distance_class_distribution(distances, dc,
title="Ten Equal Width Distance Classes (max dist 50%)",
figoutput=pyssage.graph.FigOutput(figshow=True))
def test_gearys_c():
# answer calculated from PASSaGE 2 and exported to 5 decimals
# tests both random and normal variance assumption
answer = [
(0.00000, 2.44706, 4187, 1.00000, 0.33204, 0.03535, 18.89357, 0.00000),
(2.44706, 3.79348, 4190, 1.00000, 0.82819, 0.03635, 4.72694, 0.00000),
(3.79348, 4.89556, 4189, 1.00000, 1.01299, 0.03812, 0.34080, 0.73325),
(4.89556, 5.89960, 4189, 1.00000, 1.08074, 0.03382, 2.38726, 0.01697),
(5.89960, 6.87800, 4189, 1.00000, 1.17545, 0.03478, 5.04462, 0.00000),
(6.87800, 7.83495, 4188, 1.00000, 1.24647, 0.03477, 7.08797, 0.00000),
(7.83495, 8.80877, 4190, 1.00000, 1.25130, 0.03493, 7.19388, 0.00000),
(8.80877, 9.86668, 4189, 1.00000, 1.32815, 0.03148, 10.42263, 0.00000),
(9.86668, 10.96444, 4189, 1.00000, 1.31421, 0.03133, 10.02750,
0.00000),
(10.96444, 12.14837, 4189, 1.00000, 1.21946, 0.03256, 6.73952,
0.00000),
(12.14837, 13.46790, 4189, 1.00000, 1.12783, 0.03041, 4.20371,
0.00003),
(13.46790, 14.96656, 4189, 1.00000, 1.12163, 0.03385, 3.59348,
0.00033),
(14.96656, 16.90406, 4189, 1.00000, 1.00765, 0.03881, 0.19715,
0.84371),
(16.90406, 20.06888, 4189, 1.00000, 0.68403, 0.05482, 5.76401, 0.00000)
]
data, _ = create_test_scattered()
coords = create_test_coords()
distances = pyssage.distances.euclidean_distance_matrix(
coords[:, 0], coords[:, 1])
dist_classes = pyssage.distances.create_distance_classes(
distances, "determine pair count", 15)
dc_con = pyssage.connections.distance_classes_to_connections(
dist_classes, distances)
output, output_text, _ = pyssage.correlogram.correlogram(
data[:, 0], dc_con, pyssage.correlogram.gearys_c)
pyssage.graph.draw_correlogram(
numpy.array(output),
"Geary's c",
"Correlogram",
figoutput=pyssage.graph.FigOutput(figshow=True))
for line in output_text:
print(line)
for i, row in enumerate(answer):
for j, ans in enumerate(row):
assert round(output[i][j], 5) == ans
answer = [(0, 2.44706, 4187, 1, 0.33204, 0.03477, 19.20937, 0),
(2.44706, 3.79348, 4190, 1, 0.82819, 0.03574, 4.80691, 0),
(3.79348, 4.89556, 4189, 1, 1.01299, 0.03748, 0.34668, 0.72884),
(4.89556, 5.8996, 4189, 1, 1.08074, 0.03327, 2.42634, 0.01525),
(5.8996, 6.878, 4189, 1, 1.17545, 0.03421, 5.12833, 0),
(6.878, 7.83495, 4188, 1, 1.24647, 0.03421, 7.20556, 0),
(7.83495, 8.80877, 4190, 1, 1.2513, 0.03436, 7.3135, 0),
(8.80877, 9.86668, 4189, 1, 1.32815, 0.031, 10.58677, 0),
(9.86668, 10.96444, 4189, 1, 1.31421, 0.03085, 10.18496, 0),
(10.96444, 12.14837, 4189, 1, 1.21946, 0.03205, 6.8477, 0),
(12.14837, 13.4679, 4189, 1, 1.12783, 0.02995, 4.2685, 0.00002),
(13.4679, 14.96656, 4189, 1, 1.12163, 0.0333, 3.65234, 0.00026),
(14.96656, 16.90406, 4189, 1, 1.00765, 0.03815, 0.20057,
0.84104),
(16.90406, 20.06888, 4189, 1, 0.68403, 0.0538, 5.87292, 0)]
output, output_text, _ = pyssage.correlogram.correlogram(
data[:, 0], dc_con, pyssage.correlogram.gearys_c, variance="normal")
for line in output_text:
print(line)
for i, row in enumerate(answer):
for j, ans in enumerate(row):
assert round(output[i][j], 5) == ans
def test_morans_i():
# answers calculated from PASSaGE 2 and exported to 5 decimals
# tests both random and normal variance assumption
answer = [(0.00000, 2.44706, 4187, -0.00282, 0.65306, 0.01474, 44.49577,
0.00000),
(2.44706, 3.79348, 4190, -0.00282, 0.36807, 0.01472, 25.19434,
0.00000),
(3.79348, 4.89556, 4189, -0.00282, 0.22621, 0.01470, 15.58188,
0.00000),
(4.89556, 5.89960, 4189, -0.00282, 0.12944, 0.01476, 8.96358,
0.00000),
(5.89960, 6.87800, 4189, -0.00282, -0.01105, 0.01474, 0.55805,
0.57681),
(6.87800, 7.83495, 4188, -0.00282, -0.11995, 0.01475, 7.94297,
0.00000),
(7.83495, 8.80877, 4190, -0.00282, -0.20705, 0.01474, 13.85548,
0.00000),
(8.80877, 9.86668, 4189, -0.00282, -0.27155, 0.01478, 18.17685,
0.00000),
(9.86668, 10.96444, 4189, -0.00282, -0.27587, 0.01479, 18.46680,
0.00000),
(10.96444, 12.14837, 4189, -0.00282, -0.24051, 0.01477, 16.09118,
0.00000),
(12.14837, 13.46790, 4189, -0.00282, -0.19934, 0.01480, 13.28110,
0.00000),
(13.46790, 14.96656, 4189, -0.00282, -0.20647, 0.01476, 13.80134,
0.00000),
(14.96656, 16.90406, 4189, -0.00282, -0.14810, 0.01469, 9.88984,
0.00000),
(16.90406, 20.06888, 4189, -0.00282, 0.00519, 0.01441, 0.55641,
0.57793)]
data, _ = create_test_scattered()
coords = create_test_coords()
distances = pyssage.distances.euclidean_distance_matrix(
coords[:, 0], coords[:, 1])
dist_classes = pyssage.distances.create_distance_classes(
distances, "determine pair count", 15)
dc_con = pyssage.connections.distance_classes_to_connections(
dist_classes, distances)
output, output_text, _ = pyssage.correlogram.correlogram(
data[:, 0], dc_con, pyssage.correlogram.morans_i)
pyssage.graph.draw_correlogram(
numpy.array(output),
"Moran's I",
"Correlogram",
figoutput=pyssage.graph.FigOutput(figshow=True))
for line in output_text:
print(line)
for i, row in enumerate(answer):
for j, ans in enumerate(row):
assert round(output[i][j], 5) == ans
answer = [
(0, 2.44706, 4187, -0.00282, 0.65306, 0.01501, 43.69575, 0),
(2.44706, 3.79348, 4190, -0.00282, 0.36807, 0.01499, 24.74022, 0),
(3.79348, 4.89556, 4189, -0.00282, 0.22621, 0.01497, 15.3002, 0),
(4.89556, 5.8996, 4189, -0.00282, 0.12944, 0.01503, 8.80275, 0),
(5.8996, 6.878, 4189, -0.00282, -0.01105, 0.01501, 0.54802, 0.58368),
(6.878, 7.83495, 4188, -0.00282, -0.11995, 0.01502, 7.80026, 0),
(7.83495, 8.80877, 4190, -0.00282, -0.20705, 0.01501, 13.60635, 0),
(8.80877, 9.86668, 4189, -0.00282, -0.27155, 0.01505, 17.85189, 0),
(9.86668, 10.96444, 4189, -0.00282, -0.27587, 0.01505, 18.13673, 0),
(10.96444, 12.14837, 4189, -0.00282, -0.24051, 0.01504, 15.80303, 0),
(12.14837, 13.4679, 4189, -0.00282, -0.19934, 0.01507, 13.04404, 0),
(13.4679, 14.96656, 4189, -0.00282, -0.20647, 0.01503, 13.55369, 0),
(14.96656, 16.90406, 4189, -0.00282, -0.1481, 0.01496, 9.71083, 0),
(16.90406, 20.06888, 4189, -0.00282, 0.00519, 0.01468, 0.54596,
0.58509)
]
output, output_text, _ = pyssage.correlogram.correlogram(
data[:, 0], dc_con, pyssage.correlogram.morans_i, variance="normal")
for line in output_text:
print(line)
for i, row in enumerate(answer):
for j, ans in enumerate(row):
assert round(output[i][j], 5) == ans
def test_bearing_correlogram():
# answer calculated from PASSaGE 2 and exported to 5 decimals, but only matches perfectly to 2 decimals
answer = [
(0, 2.44706, 0, 4187, -0.00282, 0.48074, 0.01802, 26.83519, 0),
(2.44706, 3.79348, 0, 4190, -0.00282, 0.38042, 0.01796, 21.3409, 0),
(3.79348, 4.89556, 0, 4189, -0.00282, 0.31827, 0.01796, 17.87411, 0),
(4.89556, 5.8996, 0, 4189, -0.00282, 0.2432, 0.01777, 13.84554, 0),
(5.8996, 6.878, 0, 4189, -0.00282, 0.1433, 0.01747, 8.36592, 0),
(6.878, 7.83495, 0, 4188, -0.00282, 0.08647, 0.01738, 5.13659, 0),
(7.83495, 8.80877, 0, 4190, -0.00282, 0.03599, 0.01738, 2.23299,
0.02555),
(8.80877, 9.86668, 0, 4189, -0.00282, -0.06002, 0.01699, 3.36671,
0.00076),
(9.86668, 10.96444, 0, 4189, -0.00282, -0.12693, 0.01679, 7.39314, 0),
(10.96444, 12.14837, 0, 4189, -0.00282, -0.14255, 0.0167, 8.36824, 0),
(12.14837, 13.4679, 0, 4189, -0.00282, -0.19782, 0.01641, 11.88437, 0),
(13.4679, 14.96656, 0, 4189, -0.00282, -0.17445, 0.01598, 10.73789, 0),
(14.96656, 16.90406, 0, 4189, -0.00282, -0.13395, 0.01576, 8.32041, 0),
(16.90406, 20.06888, 0, 4189, -0.00282, -0.09655, 0.01513, 6.19295, 0),
(20.06888, 30.77475, 0, 4190, -0.00282, -0.08202, 0.01338, 5.91971, 0),
(0, 2.44706, 10, 4187, -0.00282, 0.47307, 0.01807, 26.33563, 0),
(2.44706, 3.79348, 10, 4190, -0.00282, 0.38604, 0.01814, 21.43293, 0),
(3.79348, 4.89556, 10, 4189, -0.00282, 0.33896, 0.01822, 18.75573, 0),
(4.89556, 5.8996, 10, 4189, -0.00282, 0.26948, 0.01813, 15.02122, 0),
(5.8996, 6.878, 10, 4189, -0.00282, 0.17156, 0.01786, 9.76293, 0),
(6.878, 7.83495, 10, 4188, -0.00282, 0.11858, 0.01791, 6.77797, 0),
(7.83495, 8.80877, 10, 4190, -0.00282, 0.07241, 0.018, 4.17932,
0.00003),
(8.80877, 9.86668, 10, 4189, -0.00282, -0.01655, 0.01761, 0.77927,
0.43582),
(9.86668, 10.96444, 10, 4189, -0.00282, -0.08255, 0.01739, 4.58481, 0),
(10.96444, 12.14837, 10, 4189, -0.00282, -0.09822, 0.01722, 5.53833,
0),
(12.14837, 13.4679, 10, 4189, -0.00282, -0.1476, 0.01698, 8.52601, 0),
(13.4679, 14.96656, 10, 4189, -0.00282, -0.13423, 0.01651, 7.95905, 0),
(14.96656, 16.90406, 10, 4189, -0.00282, -0.09395, 0.01631, 5.58802,
0),
(16.90406, 20.06888, 10, 4189, -0.00282, -0.08283, 0.01564, 5.11522,
0),
(20.06888, 30.77475, 10, 4190, -0.00282, -0.10641, 0.01371, 7.55398,
0), (0, 2.44706, 20, 4187, -0.00282, 0.46412, 0.01816, 25.71993, 0),
(2.44706, 3.79348, 20, 4190, -0.00282, 0.39231, 0.01836, 21.51603, 0),
(3.79348, 4.89556, 20, 4189, -0.00282, 0.35983, 0.01849, 19.60887, 0),
(4.89556, 5.8996, 20, 4189, -0.00282, 0.29332, 0.01853, 15.98136, 0),
(5.8996, 6.878, 20, 4189, -0.00282, 0.19639, 0.01835, 10.85861, 0),
(6.878, 7.83495, 20, 4188, -0.00282, 0.14687, 0.01856, 8.06648, 0),
(7.83495, 8.80877, 20, 4190, -0.00282, 0.10385, 0.01877, 5.68364, 0),
(8.80877, 9.86668, 20, 4189, -0.00282, 0.02409, 0.01848, 1.45688,
0.14515),
(9.86668, 10.96444, 20, 4189, -0.00282, -0.03928, 0.01828, 1.99433,
0.04612),
(10.96444, 12.14837, 20, 4189, -0.00282, -0.0573, 0.018, 3.02626,
0.00248),
(12.14837, 13.4679, 20, 4189, -0.00282, -0.09885, 0.01788, 5.37144, 0),
(13.4679, 14.96656, 20, 4189, -0.00282, -0.09371, 0.01743, 5.2138, 0),
(14.96656, 16.90406, 20, 4189, -0.00282, -0.0545, 0.01727, 2.99216,
0.00277),
(16.90406, 20.06888, 20, 4189, -0.00282, -0.06657, 0.01654, 3.8536,
0.00012),
(20.06888, 30.77475, 20, 4190, -0.00282, -0.13482, 0.01438, 9.17691,
0), (0, 2.44706, 30, 4187, -0.00282, 0.45484, 0.01827, 25.05425, 0),
(2.44706, 3.79348, 30, 4190, -0.00282, 0.39849, 0.01859, 21.58777, 0),
(3.79348, 4.89556, 30, 4189, -0.00282, 0.37807, 0.01875, 20.31387, 0),
(4.89556, 5.8996, 30, 4189, -0.00282, 0.31096, 0.01894, 16.57148, 0),
(5.8996, 6.878, 30, 4189, -0.00282, 0.21354, 0.01888, 11.45773, 0),
(6.878, 7.83495, 30, 4188, -0.00282, 0.16569, 0.01927, 8.746, 0),
(7.83495, 8.80877, 30, 4190, -0.00282, 0.12295, 0.0196, 6.41713, 0),
(8.80877, 9.86668, 30, 4189, -0.00282, 0.05402, 0.01955, 2.9079,
0.00364),
(9.86668, 10.96444, 30, 4189, -0.00282, -0.00478, 0.01947, 0.10036,
0.92006),
(10.96444, 12.14837, 30, 4189, -0.00282, -0.02791, 0.01907, 1.31546,
0.18836),
(12.14837, 13.4679, 30, 4189, -0.00282, -0.06017, 0.01919, 2.98788,
0.00281),
(13.4679, 14.96656, 30, 4189, -0.00282, -0.05904, 0.01897, 2.96275,
0.00305),
(14.96656, 16.90406, 30, 4189, -0.00282, -0.02338, 0.01895, 1.08516,
0.27785),
(16.90406, 20.06888, 30, 4189, -0.00282, -0.04755, 0.01817, 2.46153,
0.01383),
(20.06888, 30.77475, 30, 4190, -0.00282, -0.16708, 0.01586, 10.35664,
0), (0, 2.44706, 40, 4187, -0.00282, 0.4463, 0.01839, 24.41848, 0),
(2.44706, 3.79348, 40, 4190, -0.00282, 0.40373, 0.01878, 21.64978, 0),
(3.79348, 4.89556, 40, 4189, -0.00282, 0.39061, 0.01896, 20.74913, 0),
(4.89556, 5.8996, 40, 4189, -0.00282, 0.31829, 0.01929, 16.64843, 0),
(5.8996, 6.878, 40, 4189, -0.00282, 0.21793, 0.01941, 11.37286, 0),
(6.878, 7.83495, 40, 4188, -0.00282, 0.1678, 0.01994, 8.55854, 0),
(7.83495, 8.80877, 40, 4190, -0.00282, 0.12035, 0.02034, 6.05681, 0),
(8.80877, 9.86668, 40, 4189, -0.00282, 0.06057, 0.02065, 3.07035,
0.00214),
(9.86668, 10.96444, 40, 4189, -0.00282, 0.00666, 0.02083, 0.45545,
0.64879),
(10.96444, 12.14837, 40, 4189, -0.00282, -0.02482, 0.02038, 1.07925,
0.28047),
(12.14837, 13.4679, 40, 4189, -0.00282, -0.05071, 0.0209, 2.29079,
0.02198),
(13.4679, 14.96656, 40, 4189, -0.00282, -0.04765, 0.02132, 2.1021,
0.03554),
(14.96656, 16.90406, 40, 4189, -0.00282, -0.02418, 0.02168, 0.98505,
0.3246),
(16.90406, 20.06888, 40, 4189, -0.00282, -0.02923, 0.0211, 1.2517,
0.21068),
(20.06888, 30.77475, 40, 4190, -0.00282, -0.18898, 0.01958, 9.50984,
0), (0, 2.44706, 50, 4187, -0.00282, 0.43961, 0.01851, 23.8969, 0),
(2.44706, 3.79348, 50, 4190, -0.00282, 0.40718, 0.01889, 21.70227, 0),
(3.79348, 4.89556, 50, 4189, -0.00282, 0.39485, 0.0191, 20.82434, 0),
(4.89556, 5.8996, 50, 4189, -0.00282, 0.31222, 0.01953, 16.13272, 0),
(5.8996, 6.878, 50, 4189, -0.00282, 0.20547, 0.01983, 10.50219, 0),
(6.878, 7.83495, 50, 4188, -0.00282, 0.14745, 0.02041, 7.36247, 0),
(7.83495, 8.80877, 50, 4190, -0.00282, 0.08949, 0.02077, 4.44492,
0.00001),
(8.80877, 9.86668, 50, 4189, -0.00282, 0.03063, 0.02143, 1.56096,
0.11853),
(9.86668, 10.96444, 50, 4189, -0.00282, -0.0225, 0.02194, 0.8967,
0.36988),
(10.96444, 12.14837, 50, 4189, -0.00282, -0.06619, 0.02162, 2.93126,
0.00338),
(12.14837, 13.4679, 50, 4189, -0.00282, -0.09994, 0.02261, 4.29525,
0.00002),
(13.4679, 14.96656, 50, 4189, -0.00282, -0.09427, 0.0241, 3.79459,
0.00015),
(14.96656, 16.90406, 50, 4189, -0.00282, -0.107, 0.02516, 4.1415,
0.00003),
(16.90406, 20.06888, 50, 4189, -0.00282, -0.03092, 0.02561, 1.09705,
0.27262),
(20.06888, 30.77475, 50, 4190, -0.00282, -0.0715, 0.02915, 2.3554,
0.0185),
(0, 2.44706, 60, 4187, -0.00282, 0.43572, 0.01861, 23.56472, 0),
(2.44706, 3.79348, 60, 4190, -0.00282, 0.40824, 0.01891, 21.73918, 0),
(3.79348, 4.89556, 60, 4189, -0.00282, 0.38962, 0.01913, 20.51508, 0),
(4.89556, 5.8996, 60, 4189, -0.00282, 0.29256, 0.01961, 15.06519, 0),
(5.8996, 6.878, 60, 4189, -0.00282, 0.17599, 0.02005, 8.92038, 0),
(6.878, 7.83495, 60, 4188, -0.00282, 0.10569, 0.02055, 5.28017, 0),
(7.83495, 8.80877, 60, 4190, -0.00282, 0.03413, 0.02075, 1.78084,
0.07494),
(8.80877, 9.86668, 60, 4189, -0.00282, -0.03686, 0.02156, 1.5782,
0.11452),
(9.86668, 10.96444, 60, 4189, -0.00282, -0.09679, 0.02226, 4.2206,
0.00002),
(10.96444, 12.14837, 60, 4189, -0.00282, -0.15626, 0.02224, 6.90031,
0),
(12.14837, 13.4679, 60, 4189, -0.00282, -0.22083, 0.0234, 9.3154, 0),
(13.4679, 14.96656, 60, 4189, -0.00282, -0.22276, 0.02556, 8.6043, 0),
(14.96656, 16.90406, 60, 4189, -0.00282, -0.29684, 0.02687, 10.94201,
0),
(16.90406, 20.06888, 60, 4189, -0.00282, -0.09189, 0.0285, 3.12579,
0.00177),
(20.06888, 30.77475, 60, 4190, -0.00282, 0.49247, 0.03251, 15.23424,
0), (0, 2.44706, 70, 4187, -0.00282, 0.43529, 0.01866, 23.47349, 0),
(2.44706, 3.79348, 70, 4190, -0.00282, 0.40672, 0.01883, 21.74789, 0),
(3.79348, 4.89556, 70, 4189, -0.00282, 0.37575, 0.01905, 19.87629, 0),
(4.89556, 5.8996, 70, 4189, -0.00282, 0.26283, 0.0195, 13.62277, 0),
(5.8996, 6.878, 70, 4189, -0.00282, 0.13511, 0.01998, 6.90465, 0),
(6.878, 7.83495, 70, 4188, -0.00282, 0.05265, 0.0203, 2.73215,
0.00629),
(7.83495, 8.80877, 70, 4190, -0.00282, -0.03029, 0.02031, 1.35272,
0.17615),
(8.80877, 9.86668, 70, 4189, -0.00282, -0.12205, 0.02101, 5.67541, 0),
(9.86668, 10.96444, 70, 4189, -0.00282, -0.19225, 0.02161, 8.76695, 0),
(10.96444, 12.14837, 70, 4189, -0.00282, -0.26658, 0.02185, 12.07043,
0),
(12.14837, 13.4679, 70, 4189, -0.00282, -0.36851, 0.02269, 16.1172, 0),
(13.4679, 14.96656, 70, 4189, -0.00282, -0.37466, 0.02421, 15.35972,
0),
(14.96656, 16.90406, 70, 4189, -0.00282, -0.47438, 0.02472, 19.07718,
0),
(16.90406, 20.06888, 70, 4189, -0.00282, -0.17217, 0.0254, 6.66709, 0),
(20.06888, 30.77475, 70, 4190, -0.00282, 0.47201, 0.02011, 23.6116, 0),
(0, 2.44706, 80, 4187, -0.00282, 0.43845, 0.01867, 23.63945, 0),
(2.44706, 3.79348, 80, 4190, -0.00282, 0.40288, 0.01868, 21.71519, 0),
(3.79348, 4.89556, 80, 4189, -0.00282, 0.35586, 0.01885, 19.02364, 0),
(4.89556, 5.8996, 80, 4189, -0.00282, 0.22916, 0.01923, 12.06622, 0),
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(6.878, 7.83495, 80, 4188, -0.00282, 0.00239, 0.01975, 0.26419,
0.79164),
(7.83495, 8.80877, 80, 4190, -0.00282, -0.08623, 0.0196, 4.25445,
0.00002),
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0),
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0),
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0),
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(14.96656, 16.90406, 80, 4189, -0.00282, -0.53191, 0.02127, 24.87785,
0),
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(20.06888, 30.77475, 80, 4190, -0.00282, 0.29056, 0.01611, 18.21245,
0), (0, 2.44706, 90, 4187, -0.00282, 0.4448, 0.01862, 24.03875, 0),
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(6.878, 7.83495, 90, 4188, -0.00282, -0.03508, 0.01905, 1.69383,
0.0903),
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(9.86668, 10.96444, 90, 4189, -0.00282, -0.32249, 0.01914, 16.69749,
0),
(10.96444, 12.14837, 90, 4189, -0.00282, -0.40141, 0.0194, 20.54101,
0),
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(13.4679, 14.96656, 90, 4189, -0.00282, -0.47944, 0.01919, 24.83787,
0),
(14.96656, 16.90406, 90, 4189, -0.00282, -0.51009, 0.01877, 27.02716,
0),
(16.90406, 20.06888, 90, 4189, -0.00282, -0.19556, 0.01825, 10.56136,
0),
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0), (0, 2.44706, 100, 4187, -0.00282, 0.45342, 0.01854, 24.61181, 0),
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(4.89556, 5.8996, 100, 4189, -0.00282, 0.17316, 0.0184, 9.56412, 0),
(5.8996, 6.878, 100, 4189, -0.00282, 0.03208, 0.01853, 1.88349,
0.05963),
(6.878, 7.83495, 100, 4188, -0.00282, -0.05668, 0.01834, 2.937,
0.00331),
(7.83495, 8.80877, 100, 4190, -0.00282, -0.14262, 0.01811, 7.71925, 0),
(8.80877, 9.86668, 100, 4189, -0.00282, -0.26826, 0.01823, 14.55902,
0),
(9.86668, 10.96444, 100, 4189, -0.00282, -0.34194, 0.01812, 18.71055,
0),
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0),
(12.14837, 13.4679, 100, 4189, -0.00282, -0.51825, 0.01812, 28.4373,
0),
(13.4679, 14.96656, 100, 4189, -0.00282, -0.4604, 0.01772, 25.82695,
0),
(14.96656, 16.90406, 100, 4189, -0.00282, -0.46328, 0.01728, 26.64575,
0),
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0),
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0), (0, 2.44706, 110, 4187, -0.00282, 0.4631, 0.01843, 25.27599, 0),
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0.19959),
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0.00057),
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0),
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0),
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0),
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0),
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0),
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0),
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0),
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0), (0, 2.44706, 120, 4187, -0.00282, 0.47254, 0.01832, 25.94233, 0),
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0.20415),
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0.00096),
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0),
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0),
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0),
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0),
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0.00014),
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(3.79348, 4.89556, 130, 4189, -0.00282, 0.27296, 0.01756, 15.70279, 0),
(4.89556, 5.8996, 130, 4189, -0.00282, 0.15147, 0.01738, 8.87842, 0),
(5.8996, 6.878, 130, 4189, -0.00282, 0.02755, 0.01721, 1.76513,
0.07754),
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0.00877),
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(8.80877, 9.86668, 130, 4189, -0.00282, -0.23759, 0.01667, 14.07958,
0),
(9.86668, 10.96444, 130, 4189, -0.00282, -0.30381, 0.01648, 18.26765,
0),
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0),
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0),
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0),
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0.0577),
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0.0076),
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0.13012),
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0),
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0),
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0),
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0),
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0),
(14.96656, 16.90406, 140, 4189, -0.00282, -0.28529, 0.01537, 18.37384,
0),
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0),
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0.82848),
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0.0001),
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0.93221),
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0.00008),
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0),
(9.86668, 10.96444, 150, 4189, -0.00282, -0.24389, 0.01618, 14.89982,
0),
(10.96444, 12.14837, 150, 4189, -0.00282, -0.26987, 0.01615, 16.53313,
0),
(12.14837, 13.4679, 150, 4189, -0.00282, -0.33754, 0.01588, 21.07267,
0),
(13.4679, 14.96656, 150, 4189, -0.00282, -0.28516, 0.01558, 18.12305,
0),
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0),
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(20.06888, 30.77475, 150, 4190, -0.00282, -0.02038, 0.01311, 1.33937,
0.18045),
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(6.878, 7.83495, 160, 4188, -0.00282, 0.02346, 0.01676, 1.56884,
0.11668),
(7.83495, 8.80877, 160, 4190, -0.00282, -0.03609, 0.01665, 1.99755,
0.04577),
(8.80877, 9.86668, 160, 4189, -0.00282, -0.14202, 0.01639, 8.49262, 0),
(9.86668, 10.96444, 160, 4189, -0.00282, -0.20826, 0.01622, 12.66257,
0),
(10.96444, 12.14837, 160, 4189, -0.00282, -0.22926, 0.01619, 13.98361,
0),
(12.14837, 13.4679, 160, 4189, -0.00282, -0.29302, 0.01591, 18.24289,
0),
(13.4679, 14.96656, 160, 4189, -0.00282, -0.24954, 0.01558, 15.83508,
0),
(14.96656, 16.90406, 160, 4189, -0.00282, -0.21024, 0.01532, 13.53841,
0),
(16.90406, 20.06888, 160, 4189, -0.00282, -0.11919, 0.01472, 7.90316,
0),
(20.06888, 30.77475, 160, 4190, -0.00282, -0.04016, 0.01313, 2.84379,
0.00446),
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(2.44706, 3.79348, 170, 4190, -0.00282, 0.376, 0.01783, 21.24901, 0),
(3.79348, 4.89556, 170, 4189, -0.00282, 0.29991, 0.01774, 17.0631, 0),
(4.89556, 5.8996, 170, 4189, -0.00282, 0.21735, 0.01748, 12.59266, 0),
(5.8996, 6.878, 170, 4189, -0.00282, 0.11457, 0.01718, 6.83448, 0),
(6.878, 7.83495, 170, 4188, -0.00282, 0.0541, 0.017, 3.34871, 0.00081),
(7.83495, 8.80877, 170, 4190, -0.00282, -0.00097, 0.01693, 0.10976,
0.9126),
(8.80877, 9.86668, 170, 4189, -0.00282, -0.10241, 0.01659, 6.00114, 0),
(9.86668, 10.96444, 170, 4189, -0.00282, -0.16919, 0.01642, 10.13488,
0),
(10.96444, 12.14837, 170, 4189, -0.00282, -0.18668, 0.01637, 11.23348,
0),
(12.14837, 13.4679, 170, 4189, -0.00282, -0.24651, 0.01607, 15.1636,
0),
(13.4679, 14.96656, 170, 4189, -0.00282, -0.21284, 0.0157, 13.37633,
0),
(14.96656, 16.90406, 170, 4189, -0.00282, -0.17267, 0.01546, 10.98582,
0),
(16.90406, 20.06888, 170, 4189, -0.00282, -0.10845, 0.01486, 7.11028,
0),
(20.06888, 30.77475, 170, 4190, -0.00282, -0.06035, 0.01321, 4.35532,
0.00001)
]
data, _ = create_test_scattered()
coords = create_test_coords()
distances = pyssage.distances.euclidean_distance_matrix(
coords[:, 0], coords[:, 1])
angles = pyssage.distances.euclidean_angle_matrix(coords[:, 0], coords[:,
1])
dist_classes = pyssage.distances.create_distance_classes(
distances, "determine pair count", 15)
dc_con = pyssage.connections.distance_classes_to_connections(
dist_classes, distances)
output, output_text, _ = pyssage.correlogram.bearing_correlogram(
data[:, 22], dc_con, angles)
# pyssage.graph.draw_bearing_correlogram_old(numpy.array(output), "Moran's I Bearing Correlogram")
pyssage.graph.draw_bearing_correlogram(
numpy.array(output),
"Moran's I Bearing Correlogram",
figoutput=pyssage.graph.FigOutput(figshow=True))
for line in output_text:
print(line)
for i, row in enumerate(answer):
for j, ans in enumerate(row):
assert round(output[i][j], 2) == round(ans, 2)
