start working on bopdmd fit_econ function

Author: dsj976

pydmd/bopdmd.py

@@ -1461,6 +1461,55 @@ def fit(self, X, t):
 
         return self
 
+    def fit_econ(self, U, s, V, t):
+        """
+        Compute the Optimized Dynamic Mode Decomposition using the
+        SVD results of the snapshot matrix.
+        Use this method if you have pre-computed the SVD and the
+        original snapshot matrix is not avaialble or too large to
+        store in memory.
+
+        :param U: the left singular vectors.
+        :type U: numpy.ndarray
+        :param s: the singular values.
+        :type s: numpy.ndarray
+        :param V: the right singular vectors.
+        :type V: numpy.ndarray
+        :param t: the input time vector.
+        :type t: numpy.ndarray
+        """
+        self._reset()
+        self._time = np.array(t).squeeze()
+
+        # Check that input time vector is one-dimensional.
+        if self._time.ndim > 1:
+            msg = "Input time vector t must be one-dimensional."
+            raise ValueError(msg)
+
+        # Check that U is a 2D numpy.ndarray.
+        if not isinstance(U, np.ndarray) or U.ndim != 2:
+            msg = "U must be a 2D numpy.ndarray."
+            raise ValueError(msg)
+
+        # Check that s is a 1D numpy.ndarray.
+        if not isinstance(s, np.ndarray) or s.ndim != 1 or len(s) != U.shape[1]:
+            msg = "s must be a 1D numpy.ndarray with length equal to the number of columns in U."
+            raise ValueError(msg)
+
+        # Check that V is a 2D numpy.ndarray.
+        if (
+            not isinstance(V, np.ndarray)
+            or V.ndim != 2
+            or V.shape[0] != U.shape[1]
+            or V.shape[1] != len(self._time)
+        ):
+            msg = """
+            V must be a 2D numpy.ndarray with the same number of rows
+            as U has columns and the same number of columns as the length
+            of the time vector t.
+            """
+            raise ValueError(msg)
+
     def forecast(self, t):
         """
         Predict the output X given the input time t using the fitted DMD model.