def test_remote_regressor_cm_intercept(monkeypatch):
"""Test remote regressor process with an intercept term"""
from resistics.regression.remote import RemoteRegressor
from mocks import mock_window_selector
import numpy as np
# patch out the writeTF function
monkeypatch.setattr(RemoteRegressor, "writeResult",
mock_remote_regressor_writeResult)
selector = mock_window_selector(intercept=True)
regressor = RemoteRegressor(selector, "test")
regressor.setInput("local", ["Hx", "Hy"])
regressor.setOutput("local", ["Ex", "Ey"])
regressor.setRemote("remote", ["Hx", "Hy"])
regressor.setMethod("cm", intercept=True)
regressor.setSmooth("hann", 1)
assert regressor.inCross == []
assert regressor.outCross == []
assert regressor.remoteCross == ["Hx", "Hy"]
regressor.process()
# expected
expected_evalfreq = np.array([24, 40])
expected_impedances = [
np.array([
[4.5708818 + 0.0j, 3.83094409 + 0.0j, 0.61631404 + 0.0j],
[-0.34507489 + 0.0j, 8.7452132 + 0.0j, -2.45585909 + 0.0j],
]),
np.array([
[3.78599245 + 0.0j, 4.36870089 + 0.0j, 0.04394717 + 0.0j],
[0.71686719 + 0.0j, 8.0305959 + 0.0j, -0.9092429 + 0.0j],
]),
]
expected_variances = [
np.array([
[7.10053809, 4.20189219, 27.17637004],
[15.82405112, 9.36421381, 60.56446191],
]),
np.array([
[3.28799791, 4.62734876, 37.21651907],
[8.76270122, 12.33214734, 99.1841377],
]),
]
np.testing.assert_equal(regressor.evalFreq, expected_evalfreq)
np.testing.assert_almost_equal(regressor.parameters[0],
expected_impedances[0])
np.testing.assert_almost_equal(regressor.parameters[1],
expected_impedances[1])
np.testing.assert_almost_equal(regressor.variances[0],
expected_variances[0])
np.testing.assert_almost_equal(regressor.variances[1],
expected_variances[1])
def test_remote_regressor_ols_intercept(monkeypatch):
"""Test remote regressor process with an intercept term"""
from resistics.regression.remote import RemoteRegressor
from mocks import mock_window_selector
import numpy as np
# patch out the writeTF function
monkeypatch.setattr(RemoteRegressor, "writeResult",
mock_remote_regressor_writeResult)
selector = mock_window_selector(intercept=True)
regressor = RemoteRegressor(selector, "test")
regressor.setInput("local", ["Hx", "Hy"])
regressor.setOutput("local", ["Ex", "Ey"])
regressor.setRemote("remote", ["Hx", "Hy"])
regressor.setMethod("ols", intercept=True)
regressor.setSmooth("hann", 1)
assert regressor.inCross == []
assert regressor.outCross == []
assert regressor.remoteCross == ["Hx", "Hy"]
regressor.process()
# expected
expected_evalfreq = np.array([24, 40])
expected_impedances = [
np.array([
[3.0 + 0.0j, 5.0 + 0.0j, 5.0 + 0.0j],
[2.0 + 0.0j, 7.0 + 0.0j, -9.0 + 0.0j],
]),
np.array([
[3.0 + 0.0j, 5.0 + 0.0j, 5.0 + 0.0j],
[2.0 + 0.0j, 7.0 + 0.0j, -9.0 + 0.0j],
]),
]
expected_variances = [
np.array([
[1.21088688e-23, 7.25740911e-24, 3.34802360e-23],
[3.95687739e-23, 2.37154094e-23, 1.09405091e-22],
]),
np.array([
[2.48945809e-27, 3.32569517e-27, 2.95106525e-26],
[1.00812766e-27, 1.34676912e-27, 1.19505948e-26],
]),
]
np.testing.assert_equal(regressor.evalFreq, expected_evalfreq)
np.testing.assert_almost_equal(regressor.parameters[0],
expected_impedances[0])
np.testing.assert_almost_equal(regressor.parameters[1],
expected_impedances[1])
np.testing.assert_almost_equal(regressor.variances[0],
expected_variances[0])
np.testing.assert_almost_equal(regressor.variances[1],
expected_variances[1])
def test_remote_regressor_mm_intercept(monkeypatch):
"""Test remote regressor process with an intercept term"""
from resistics.regression.remote import RemoteRegressor
from mocks import mock_window_selector
import numpy as np
# patch out the writeTF function
monkeypatch.setattr(RemoteRegressor, "writeResult",
mock_remote_regressor_writeResult)
selector = mock_window_selector(intercept=True)
regressor = RemoteRegressor(selector, "test")
regressor.setInput("local", ["Hx", "Hy"])
regressor.setOutput("local", ["Ex", "Ey"])
regressor.setRemote("remote", ["Hx", "Hy"])
regressor.setMethod("mm", intercept=True)
regressor.setSmooth("hann", 1)
assert regressor.inCross == []
assert regressor.outCross == []
assert regressor.remoteCross == ["Hx", "Hy"]
regressor.process()
# expected
expected_evalfreq = np.array([24, 40])
expected_impedances = [
np.array([
[5.1887448 + 0.0j, 3.23769067 + 0.0j, 1.72606573 + 0.0j],
[-0.50578302 + 0.0j, 9.02951015 + 0.0j, -5.3106386 + 0.0j],
]),
np.array([
[4.16876826 + 0.0j, 3.93359941 + 0.0j, 0.1393923 + 0.0j],
[2.56283624 + 0.0j, 5.80703278 + 0.0j, 0.57298163 + 0.0j],
]),
]
expected_variances = [
np.array([
[0.13001168, 0.07693714, 0.4976025],
[0.2015383, 0.11926451, 0.77136119],
]),
np.array([
[4.28780289, 6.03441972, 48.5332116],
[8.09793129, 11.39658644, 91.65967348],
]),
]
np.testing.assert_equal(regressor.evalFreq, expected_evalfreq)
np.testing.assert_almost_equal(regressor.parameters[0],
expected_impedances[0])
np.testing.assert_almost_equal(regressor.parameters[1],
expected_impedances[1])
np.testing.assert_almost_equal(regressor.variances[0],
expected_variances[0])
np.testing.assert_almost_equal(regressor.variances[1],
expected_variances[1])
def test_local_regressor_cm_noise(monkeypatch):
"""Test local regressor process with some noise added to the localsite"""
from resistics.regression.remote import RemoteRegressor
from mocks import mock_window_selector
import numpy as np
# patch out the writeTF function
monkeypatch.setattr(RemoteRegressor, "writeResult",
mock_remote_regressor_writeResult)
selector = mock_window_selector(localnoise=True)
regressor = RemoteRegressor(selector, "test")
regressor.setInput("local", ["Hx", "Hy"])
regressor.setOutput("local", ["Ex", "Ey"])
regressor.setRemote("remote", ["Hx", "Hy"])
regressor.setMethod("cm", intercept=False)
regressor.setSmooth("hann", 1)
assert regressor.inCross == []
assert regressor.outCross == []
regressor.process()
# expected
expected_evalfreq = np.array([24, 40])
expected_impedances = [
np.array([
[4.32416997 + 0.0j, 3.36294245 + 0.0j],
[1.06908193 + 0.0j, 7.60932995 + 0.0j],
]),
np.array([
[4.10868381 + 0.0j, 3.465675 + 0.0j],
[1.78789942 + 0.0j, 7.24148737 + 0.0j],
]),
]
expected_variances = [
np.array([[7.01571556e-04, 4.47737982e-04],
[4.71858833e-01, 3.01136955e-01]]),
np.array([[0.03930329, 0.05217041], [0.02580595, 0.03425431]]),
]
np.testing.assert_equal(regressor.evalFreq, expected_evalfreq)
np.testing.assert_almost_equal(regressor.parameters[0],
expected_impedances[0])
np.testing.assert_almost_equal(regressor.parameters[1],
expected_impedances[1])
np.testing.assert_almost_equal(regressor.variances[0],
expected_variances[0])
np.testing.assert_almost_equal(regressor.variances[1],
expected_variances[1])
def test_local_regressor_mm_noise(monkeypatch):
"""Test local regressor process with some noise added to the localsite"""
from resistics.regression.remote import RemoteRegressor
from mocks import mock_window_selector
import numpy as np
# patch out the writeTF function
monkeypatch.setattr(RemoteRegressor, "writeResult",
mock_remote_regressor_writeResult)
selector = mock_window_selector(localnoise=True)
regressor = RemoteRegressor(selector, "test")
regressor.setInput("local", ["Hx", "Hy"])
regressor.setOutput("local", ["Ex", "Ey"])
regressor.setRemote("remote", ["Hx", "Hy"])
regressor.setMethod("mm", intercept=False)
regressor.setSmooth("hann", 1)
assert regressor.inCross == []
assert regressor.outCross == []
regressor.process()
# expected
expected_evalfreq = np.array([24, 40])
expected_impedances = [
np.array([
[4.28467732 + 0.0j, 3.39812143 + 0.0j],
[2.33069538 + 0.0j, 6.59175726 + 0.0j],
]),
np.array([
[3.77407493 + 0.0j, 3.86441015 + 0.0j],
[1.21575149 + 0.0j, 7.35576611 + 0.0j],
]),
]
expected_variances = [
np.array([[5.31246449e-04, 3.39037709e-04],
[3.13860843e00, 2.00303760e00]]),
np.array([[0.11543121, 0.15322112], [47.59264394, 63.17353725]]),
]
np.testing.assert_equal(regressor.evalFreq, expected_evalfreq)
np.testing.assert_almost_equal(regressor.parameters[0],
expected_impedances[0])
np.testing.assert_almost_equal(regressor.parameters[1],
expected_impedances[1])
np.testing.assert_almost_equal(regressor.variances[0],
expected_variances[0])
np.testing.assert_almost_equal(regressor.variances[1],
expected_variances[1])
def test_local_regressor_ols_noise(monkeypatch):
"""Test local regressor process with some noise added to the localsite"""
from resistics.regression.remote import RemoteRegressor
from mocks import mock_window_selector
import numpy as np
# patch out the writeTF function
monkeypatch.setattr(RemoteRegressor, "writeResult",
mock_remote_regressor_writeResult)
selector = mock_window_selector(localnoise=True)
regressor = RemoteRegressor(selector, "test")
regressor.setInput("local", ["Hx", "Hy"])
regressor.setOutput("local", ["Ex", "Ey"])
regressor.setRemote("remote", ["Hx", "Hy"])
regressor.setMethod("ols", intercept=False)
regressor.setSmooth("hann", 1)
assert regressor.inCross == []
assert regressor.outCross == []
regressor.process()
# expected
expected_evalfreq = np.array([24, 40])
expected_impedances = [
np.array([
[4.17149691 + 0.0j, 3.48769594 + 0.0j],
[1.48229546 + 0.0j, 7.28114671 + 0.0j],
]),
np.array([
[4.08086708 + 0.0j, 3.51508884 + 0.0j],
[-1.80159334 + 0.0j, 10.70916092 + 0.0j],
]),
]
expected_variances = [
np.array([[0.01284865, 0.00819992], [0.68188147, 0.43517191]]),
np.array([[0.03504211, 0.04651421], [18.06330838, 23.97687941]]),
]
np.testing.assert_equal(regressor.evalFreq, expected_evalfreq)
np.testing.assert_almost_equal(regressor.parameters[0],
expected_impedances[0])
np.testing.assert_almost_equal(regressor.parameters[1],
expected_impedances[1])
np.testing.assert_almost_equal(regressor.variances[0],
expected_variances[0])
np.testing.assert_almost_equal(regressor.variances[1],
expected_variances[1])
def test_remote_regressor_cm_intersite(monkeypatch):
"""Test remote regressor process with a different site for the ouput"""
from resistics.regression.remote import RemoteRegressor
from mocks import mock_window_selector
import numpy as np
# patch out the writeTF function
monkeypatch.setattr(RemoteRegressor, "writeResult",
mock_remote_regressor_writeResult)
selector = mock_window_selector()
regressor = RemoteRegressor(selector, "test")
regressor.setInput("local", ["Hx", "Hy"])
regressor.setOutput("inter", ["Ex", "Ey"])
regressor.setRemote("remote", ["Hx", "Hy"])
regressor.setMethod("cm", intercept=False)
regressor.setSmooth("hann", 1)
assert regressor.inCross == []
assert regressor.outCross == []
regressor.process()
# expected
expected_evalfreq = np.array([24, 40])
expected_impedances = [
np.array([[6.0 + 0.0j, 8.0 + 0.0j], [4.0 + 0.0j, 1.0 + 0.0j]]),
np.array([[6.0 + 0.0j, 8.0 + 0.0j], [4.0 + 0.0j, 1.0 + 0.0j]]),
]
expected_variances = [
np.array([[9.14649740e-29, 5.89343794e-29],
[1.42914022e-30, 9.20849678e-31]]),
np.array([[1.38145310e-28, 1.84606779e-28],
[3.45363276e-29, 4.61516947e-29]]),
]
np.testing.assert_equal(regressor.evalFreq, expected_evalfreq)
np.testing.assert_almost_equal(regressor.parameters[0],
expected_impedances[0])
np.testing.assert_almost_equal(regressor.parameters[1],
expected_impedances[1])
np.testing.assert_almost_equal(regressor.variances[0],
expected_variances[0])
np.testing.assert_almost_equal(regressor.variances[1],
expected_variances[1])
def test_remote_regressor_cm(monkeypatch):
"""Test remote regressor process"""
from resistics.regression.remote import RemoteRegressor
from mocks import mock_window_selector
import numpy as np
# patch out the writeTF function
monkeypatch.setattr(RemoteRegressor, "writeResult",
mock_remote_regressor_writeResult)
selector = mock_window_selector()
regressor = RemoteRegressor(selector, "test")
regressor.setInput("local", ["Hx", "Hy"])
regressor.setOutput("local", ["Ex", "Ey"])
regressor.setRemote("remote", ["Hx", "Hy"])
regressor.setMethod("cm", intercept=False)
regressor.setSmooth("hann", 1)
assert regressor.inCross == []
assert regressor.outCross == []
regressor.process()
# expected
expected_evalfreq = np.array([24, 40])
expected_impedances = [
np.array([[3.0 + 0.0j, 5.0 + 0.0j], [2.0 + 0.0j, 7.0 + 0.0j]]),
np.array([[3.0 + 0.0j, 5.0 + 0.0j], [2.0 + 0.0j, 7.0 + 0.0j]]),
]
expected_variances = [
np.array([[2.78469341e-28, 1.79044909e-28],
[1.64240080e-27, 1.05599956e-27]]),
np.array([[1.47463485e-29, 1.96730235e-29],
[5.89853939e-29, 7.86920938e-29]]),
]
np.testing.assert_equal(regressor.evalFreq, expected_evalfreq)
np.testing.assert_almost_equal(regressor.parameters[0],
expected_impedances[0])
np.testing.assert_almost_equal(regressor.parameters[1],
expected_impedances[1])
np.testing.assert_almost_equal(regressor.variances[0],
expected_variances[0])
np.testing.assert_almost_equal(regressor.variances[1],
expected_variances[1])