add test for codecov

aurora/transfer_function/regression/base.py

@@ -135,12 +135,22 @@ def solve_underdetermined(self):
         """
         20210806
         This method was originally in TRME.m, but it does not depend in
-        general on using RME method so I am putting it in the base class.
+        general on using RME method so putting it in the base class.
 
-        We basically never get here and when we do we dont trust the results
+        We basically never get here and when we do, we don't trust the results
         https://docs.scipy.org/doc/numpy-1.9.2/reference/generated/numpy.linalg.svd.html
         https://www.mathworks.com/help/matlab/ref/double.svd.html
 
+        Note that the svd outputs are different in matlab and numpy
+        https://stackoverflow.com/questions/50930899/svd-command-in-python-v-s-matlab
+        "The SVD of a matrix can be written as
+
+        A = U S V^H
+
+        Where the ^H signifies the conjugate transpose. Matlab's svd command returns U, S and V,
+        while numpy.linalg.svd returns U, the diagonal of S, and V^H.
+        Thus, to get the same S and V as in Matlab you need to reconstruct the S and also get the V:
+
         <ORIGINAL MATLAB>
             <COMMENT>
         Overdetermined problem...use svd to invert, return
@@ -167,7 +177,7 @@ def solve_underdetermined(self):
         """
         U, s, V = np.linalg.svd(self.X, full_matrices=False)
         S_inv = np.diag(1.0 / s)
-        self.b = (V.T @ S_inv @ U.T) * self.Y
+        self.b = (V.T.conj() @ S_inv @ U.T.conj()) * self.Y
         if self.iter_control.return_covariance:
             logger.warning(
                 "Warning covariances are not xarray, may break things downstream"

requirements-dev.txt

@@ -3,6 +3,7 @@ flake8
 nbsphinx
 numpydoc
 pre-commit
+pytest
 sphinx_gallery
 sphinx_rtd_theme
 ipython

tests/transfer_function/regression/test_base.py

@@ -0,0 +1,114 @@
+import unittest
+
+import numpy as np
+import pandas as pd
+from aurora.transfer_function.regression.base import RegressionEstimator
+
+
+def make_mini_dataset(n_rows=None):
+    """
+    TODO: Make this a pytest fixture
+    Parameters
+    ----------
+    n_rows
+
+    Returns
+    -------
+
+    """
+    ex_data = np.array(
+        [
+            4.39080123e-07 - 2.41097397e-06j,
+            -2.33418464e-06 + 2.10752581e-06j,
+            1.38642624e-06 - 1.87333571e-06j,
+        ]
+    )
+    hx_data = np.array(
+        [
+            7.00767250e-07 - 9.18819198e-07j,
+            -1.06648904e-07 + 8.19420154e-07j,
+            -1.02700963e-07 - 3.73904463e-07j,
+        ]
+    )
+
+    hy_data = np.array(
+        [
+            1.94321684e-07 + 3.71934877e-07j,
+            1.15361101e-08 - 6.32581646e-07j,
+            3.86095787e-08 + 4.33155345e-07j,
+        ]
+    )
+    timestamps = pd.date_range(
+        start=pd.Timestamp("1977-03-02T06:00:00"), periods=len(ex_data), freq="S"
+    )
+    frequency = 0.666 * np.ones(len(ex_data))
+
+    df = pd.DataFrame(
+        data={
+            "time": timestamps,
+            "frequency": frequency,
+            "ex": ex_data,
+            "hx": hx_data,
+            "hy": hy_data,
+        }
+    )
+    if n_rows:
+        df = df.iloc[0:n_rows]
+    df = df.set_index(["time", "frequency"])
+    xr_ds = df.to_xarray()
+    return xr_ds
+
+
+class TestRegressionBase(unittest.TestCase):
+    """ """
+
+    @classmethod
+    def setUpClass(self):
+        self.dataset = make_mini_dataset(n_rows=1)
+        self.expected_solution = np.array(
+            [-0.04192569 - 0.36502722j, -3.65284496 - 4.05194938j]
+        )
+
+    def setUp(self):
+        pass
+
+    def test_regression(self):
+        dataset = make_mini_dataset()
+        X = dataset[["hx", "hy"]]
+        X = X.stack(observation=("frequency", "time"))
+        Y = dataset[
+            [
+                "ex",
+            ]
+        ]
+        Y = Y.stack(observation=("frequency", "time"))
+        re = RegressionEstimator(X=X, Y=Y)
+        re.estimate_ols()
+        difference = re.b - np.atleast_2d(self.expected_solution).T
+        assert np.isclose(difference, 0).all()
+        re.estimate()
+        difference = re.b - np.atleast_2d(self.expected_solution).T
+        assert np.isclose(difference, 0).all()
+
+    def test_underdetermined_regression(self):
+        """ """
+        dataset = make_mini_dataset(n_rows=1)
+        X = dataset[["hx", "hy"]]
+        X = X.stack(observation=("frequency", "time"))
+        Y = dataset[
+            [
+                "ex",
+            ]
+        ]
+        Y = Y.stack(observation=("frequency", "time"))
+        re = RegressionEstimator(X=X, Y=Y)
+        re.solve_underdetermined()
+        assert re.b is not None
+
+
+def main():
+    unittest.main()
+
+
+if __name__ == "__main__":
+    main()