Ressolve hankel dehankel shaping

Author: sichinaga

pydmd/havok.py

@@ -278,16 +278,18 @@ def r(self):
 
     def hankel(self, X):
         """
-        Given a data matrix X as a 2D numpy.ndarray, uses the `_delays`
-        and `_lag` attributes to return the data as a Hankel matrix.
+        Given a data matrix X as a 1D or 2D numpy.ndarray, uses the `delays`
+        and `lag` attributes to return the data as a 2D Hankel matrix.
 
-        :param X: (n, m) array of data.
+        :param X: (m,) or (n, m) array of data.
         :type X: numpy.ndarray
         :return: Hankel matrix of data.
         :rtype: numpy.ndarray
         """
-        if not isinstance(X, np.ndarray) or X.ndim != 2:
-            raise ValueError("Please ensure that input data is a 2D array.")
+        if not isinstance(X, np.ndarray) or X.ndim > 2:
+            raise ValueError("Data must be a 1D or 2D numpy array.")
+        if X.ndim == 1:
+            X = X[None]
         n, m = X.shape
 
         # Check that the input data contains enough observations.
@@ -308,23 +310,25 @@ def hankel(self, X):
 
     def dehankel(self, H):
         """
-        Given a Hankel matrix H as a 2D numpy.ndarray, uses the `_delays`
-        and `_lag` attributes to unravel the data in the Hankel matrix.
+        Given a Hankel matrix H as a 2D numpy.ndarray, uses the `delays`
+        and `lag` attributes to unravel the data in the Hankel matrix.
 
         :param H: Hankel matrix of data.
         :type H: numpy.ndarray
-        :return: de-Hankeled (n, m) array of data.
+        :return: de-Hankeled (m,) or (n, m) array of data.
         :rtype: numpy.ndarray
         """
         if not isinstance(H, np.ndarray) or H.ndim != 2:
-            raise ValueError("Please ensure that input data is a 2D array.")
+            raise ValueError("Data must be a 2D numpy array.")
+
         Hn, Hm = H.shape
         n = int(Hn / self._delays)
         m = int(Hm + ((self._delays - 1) * self._lag))
         X = np.empty((n, m))
         for i in range(self._delays):
             X[:, i * self._lag : i * self._lag + Hm] = H[i * n : (i + 1) * n]
-        return X
+
+        return np.squeeze(X)
 
     def fit(self, X, t):
         """