Nah forget complex

Author: sichinaga

pydmd/havok.py

@@ -300,7 +300,7 @@ def hankel(self, X):
             )
 
         Hm = m - ((self._delays - 1) * self._lag)
-        H = np.empty((n * self._delays, Hm), dtype="complex")
+        H = np.empty((n * self._delays, Hm))
         for i in range(self._delays):
             H[i * n : (i + 1) * n] = X[:, i * self._lag : i * self._lag + Hm]
 
@@ -321,7 +321,7 @@ def dehankel(self, H):
         Hn, Hm = H.shape
         n = int(Hn / self._delays)
         m = int(Hm + ((self._delays - 1) * self._lag))
-        X = np.empty((n, m), dtype="complex")
+        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

pydmd/utils.py

@@ -233,7 +233,7 @@ def differentiate(X, dt):
         raise ValueError("Please ensure that input data is a 1D or 2D array.")
     if X.ndim == 1:
         X = X[None]
-    X_prime = np.empty(X.shape, dtype="complex")
+    X_prime = np.empty(X.shape)
     X_prime[:, 1:-1] = (X[:, 2:] - X[:, :-2]) / (2 * dt)
     X_prime[:, 0] = (-3 * X[:, 0] + 4 * X[:, 1] - X[:, 2]) / (2 * dt)
     X_prime[:, -1] = (3 * X[:, -1] - 4 * X[:, -2] + X[:, -3]) / (2 * dt)