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Update test and example curve clustering
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@yudgugger
yudgugger committed May 7, 2024
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19 changes: 10 additions & 9 deletions examples/toolbox/ex_curve_clustering.py
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Original file line number Diff line number Diff line change
Expand Up @@ -24,22 +24,23 @@ def ex_curve_clustering(label_meas, verbose=False, make_plots=False):
Returns
-------
cluster_labels : dict
Cluster indices for each data point for each mode.
cluster_info : dict
Information about each cluster for each mode.
inertia : dict
Inertia (within-cluster sum of squares) for each mode.
avg_curve : dict
Contain all list of average curve for each cluster in each mode.
cluster_labels : list
Cluster indices for each data point
cluster_info : list
Information about each cluster.
inertia : float
For K-Means : Inertia (within-cluster sum of squares).
For GMM : Bayesian Information Criterion.
avg_curve: numpy.ndarray
List of average curve.
"""
example_root_path_in = get_setting("example_root_path_in")
dir_path_in = os.path.join(example_root_path_in, "PZT100n")
user_pars = {"extension": "spm",
"mode": "dfrt",
'method': 'kmeans',
'label meas': label_meas,
'nb clusters': 4}
'nb clusters': 3}
dim_pix = {'x': 7, 'y': 3}
dim_mic = {'x': 7, 'y': 3}

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176 changes: 48 additions & 128 deletions tests/toolbox/test_curve_clustering.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,141 +11,61 @@
# class TestReaderAllMaps(unittest.TestCase):


def test_curve_clustering_piezoresponse():
""" Test ex_curve_clustering for piezoresponse signal """
def test_curve_clustering_deflection():
""" Test ex_curve_clustering for deflection signal """
if os.getenv('GITHUB_ACTIONS') == 'true':
skip("Test skipped for Github source")

out = ex_curve_clustering(['piezoresponse'])
out = ex_curve_clustering(['deflection'])
(cluster_indexs, cluster_info, inertia, avg_curve) = out

# print(np.sum(avg_curve["off"][0]))
# print(np.sum(avg_curve["off"][1]))
# print(np.sum(avg_curve["off"][2]))
# print(np.sum(avg_curve["off"][3]))
# print(np.sum(avg_curve["off"][4]))
#
# print(np.sum(avg_curve["on"][0]))
# print(np.sum(avg_curve["on"][1]))
#
# print(np.sum(avg_curve["coupled"][0]))
# print(np.sum(avg_curve["coupled"][1]))
# print(np.sum(avg_curve["coupled"][2]))
# print(np.sum(avg_curve["coupled"][3]))

indexs_off = [3, 1, 3, 3, 3, 4, 1, 3, 2, 2, 2, 1, 3, 3, 3, 3, 2, 2, 2, 2,
4, 4, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0,
0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 2,
3, 3, 3]
indexs_on = [0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0]
indexs_coupled = [2, 3, 2, 2, 2, 1, 3, 2, 1, 1, 1, 3, 2, 2, 1, 1, 0, 1,
1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 2, 2, 1]

assert cluster_indexs["off"] == indexs_off
assert cluster_indexs["on"] == indexs_on
assert cluster_indexs["coupled"] == indexs_coupled

assert inertia["off"] == approx(0.13523678975557835)
assert inertia["on"] == approx(0.28663984068106296)
assert inertia["coupled"] == approx(0.1620727015123082)

assert np.sum(avg_curve["off"][0]) == approx(-0.5604060286538461)
assert np.sum(avg_curve["off"][1]) == approx(-0.32359891999833335)
assert np.sum(avg_curve["off"][2]) == approx(-0.8637471184000002)
assert np.sum(avg_curve["off"][3]) == approx(-0.15399308193333336)
assert np.sum(avg_curve["off"][4]) == approx(-2.242400310125)

assert np.sum(avg_curve["on"][0]) == approx(-0.4599520117610169)
assert np.sum(avg_curve["on"][1]) == approx(-0.434709211)

assert np.sum(avg_curve["coupled"][0]) == approx(
-0.3895835032539335)
assert np.sum(avg_curve["coupled"][1]) == approx(
-0.7495477722962939)
assert np.sum(avg_curve["coupled"][2]) == approx(
0.2412386496437579)
assert np.sum(avg_curve["coupled"][3]) == approx(-0.39787115575800425)

assert cluster_info["off"][0] == [0.0, 0.07695897085508642, 'B', 26, 'A']
assert cluster_info["off"][1] == [0.07695897085508642, 0.07695897085508642,
'A', 6, 'B']
assert cluster_info["off"][2] == [0.0991076879668917, 0.09910768796689168,
'A', 15, 'C']
assert cluster_info["off"][3] == [0.17859580758813837, 0.11473940080561688,
'C', 12, 'D']
assert cluster_info["off"][4] == [0.1911772239978829, 0.14257219838645985,
'C', 4, 'E']

assert cluster_info["on"][0] == [0.0, 0.25579396755296596, 'B', 59, 'A']
assert cluster_info["on"][1] == [0.25579396755296596, 0.25579396755296596,
'A', 4, 'B']

assert cluster_info["coupled"][0] == \
[0.0, 0.09424982911577919, 'B', 33, 'A']
assert cluster_info["coupled"][1] == \
[0.09424982911577919, 0.09424982911577919, 'A', 18, 'B']
assert cluster_info["coupled"][2] == \
[0.169070781895064, 0.12835058633199692, 'B', 9, 'C']
assert cluster_info["coupled"][3] == \
[0.31060770971176105, 0.31060770971176105, 'A', 3, 'D']


def test_curve_clustering_amp_pha():
""" Test ex_curve_clustering for composed amplitude and phase signal """
# print(np.sum(avg_curve[0]))
# print(np.sum(avg_curve[1]))
# print(np.sum(avg_curve[2]))

target_cluster_indexs = [1, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 1, 1, 0]

assert cluster_indexs == target_cluster_indexs

assert inertia == approx(354140.1914109529)

assert np.sum(avg_curve[0]) == approx(3659427.028688297)
assert np.sum(avg_curve[1]) == approx(3669009.191778517)
assert np.sum(avg_curve[2]) == approx(3629449.0314160236)

assert cluster_info[0] == [0.0, 146.99328263341397, 'B', 12, 'A']
assert cluster_info[1] == [146.99328263341397, 146.99328263341397, 'A', 4,
'B']
assert cluster_info[2] == [147.71545254648586, 147.71545254648586, 'A', 5,
'C']


def test_curve_clustering_height_deflection():
""" Test ex_curve_clustering for composed height and deflection signal """
if os.getenv('GITHUB_ACTIONS') == 'true':
skip("Test skipped for Github source")

out = ex_curve_clustering(['amplitude', 'phase'])
out = ex_curve_clustering(['deflection', 'height'])
(cluster_indexs, cluster_info, inertia, avg_curve) = out

# print(np.sum(avg_curve["off"][0]))
# print(np.sum(avg_curve["off"][1]))
# print(np.sum(avg_curve["off"][2]))
# print(np.sum(avg_curve["off"][3]))
# print(np.sum(avg_curve["off"][4]))
#
# print(np.sum(avg_curve["on"][0]))
# print(np.sum(avg_curve["on"][1]))

indexs_on = [0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0]
indexs_off = [3, 2, 3, 3, 3, 4, 2, 3, 1, 1, 1, 2, 3, 3, 3, 3, 1, 1, 1, 1,
4, 4, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 3,
3, 3, 3]

assert cluster_indexs["off"] == indexs_off
assert cluster_indexs["on"] == indexs_on

assert inertia["off"] == approx(111.13578587730699)
assert inertia["on"] == approx(91.94596697427032)

assert np.sum(avg_curve["off"][0]) == approx(67.44480269778579)
assert np.sum(avg_curve["off"][1]) == approx(85.049189857557)
assert np.sum(avg_curve["off"][2]) == approx(47.21474992974796)
assert np.sum(avg_curve["off"][3]) == approx(98.4523948723894)
assert np.sum(avg_curve["off"][4]) == approx(73.05500191702258)

assert np.sum(avg_curve["on"][0]) == approx(114.13229998089432)
assert np.sum(avg_curve["on"][1]) == approx(87.0701917281026)

assert cluster_info["off"][0] == [0.0, 2.436570085411072, 'B', 26, 'A']
assert cluster_info["off"][1] == \
[2.436570085411072, 2.436570085411072, 'A', 14, 'B']
assert cluster_info["off"][2] == \
[3.1368359442253464, 3.1368359442253464, 'A', 6, 'C']
assert cluster_info["off"][3] == \
[3.8185458951727846, 2.4426456403161194, 'B', 13, 'D']
assert cluster_info["off"][4] == \
[4.861100035372178, 3.620470997547831, 'C', 4, 'E']

assert cluster_info["on"][0] == [0.0, 2.9493831147512637, 'B', 56, 'A']
assert cluster_info["on"][1] == \
[2.9493831147512637, 2.9493831147512637, 'A', 7, 'B']
# print(np.sum(avg_curve[0]))
# print(np.sum(avg_curve[1]))
# print(np.sum(avg_curve[2]))

target_cluster_indexs = [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 2,
2, 2, 2, 2]

assert cluster_indexs == target_cluster_indexs

assert inertia == approx(76.06496724200191)

assert np.sum(avg_curve[0]) == approx(388455.9994825708)
assert np.sum(avg_curve[1]) == approx(390740.1297729185)
assert np.sum(avg_curve[2]) == approx(392387.2263338187)

assert cluster_info[0] == [0.0, 4.517480463827678, 'B', 9, 'A']
assert cluster_info[1] == [4.517480463827678, 3.684231929402842, 'C', 7,
'B']
assert cluster_info[2] == [8.006028411446396, 3.684231929402842, 'B', 5,
'C']

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