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_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_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_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_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_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_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_data_distance_cosine():
# answer calculated from PASSaGE 2 and exported to 5 decimals
answer = load_answer("answers/data_cosine_dists_answer.txt")
data, _ = create_test_scattered()
output = pyssage.distances.data_distance_matrix(data, pyssage.distances.data_distance_cosine)
for i in range(len(answer)):
for j in range(len(answer)):
assert round(output[i, j], 5) == answer[i, j]
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_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_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_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_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_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),
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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)
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
