Add time interpolation to mpc data

pyomo/contrib/mpc/data/interpolation.py

@@ -0,0 +1,79 @@
+#  ___________________________________________________________________________
+#
+#  Pyomo: Python Optimization Modeling Objects
+#  Copyright (c) 2008-2025
+#  National Technology and Engineering Solutions of Sandia, LLC
+#  Under the terms of Contract DE-NA0003525 with National Technology and
+#  Engineering Solutions of Sandia, LLC, the U.S. Government retains certain
+#  rights in this software.
+#  This software is distributed under the 3-clause BSD License.
+#  ___________________________________________________________________________
+
+from bisect import bisect_right
+
+
+def _get_time_index_vec(time_set, time_data):
+    """Get the position index of time_data above and below the times in
+    time_set. This can be used to find positions of points to interpolate
+    between.
+
+    Parameters
+    ----------
+    time_set: iterable
+        Time points to locate
+    time_data: iterable
+        Sorted time points to locate time_set in
+
+    Returns
+    -------
+    numpy.array
+        Position index of the first time in time_data greater than the
+        corresponding points time_set. If a time is less than all the times
+        in time_data return 1. If a time is greater than all times time_data
+        set return the last index of time_data.
+    """
+    pos = [None] * len(time_set)
+    for i, t in enumerate(time_set):
+        pos[i] = bisect_right(time_data, t)
+        if pos[i] == 0:
+            pos[i] = 1
+        elif pos[i] == len(time_data):
+            pos[i] = len(time_data) - 1
+    return pos
+
+
+def _get_interp_expr_vec(time_set, time_data, data, indexes=None):
+    """Return an array of floats interpolated at the time points in time_set 
+    from data defined at time_data.
+
+    Parameters
+    ----------
+    time_set: iterable
+        Time points to locate
+    time_data: iterable
+        Sorted time points to locate time_set in
+    data: iterable
+        Data corresponding to times in time_data, must have the same
+        length as time data.
+    indexes: numpy.array
+        Numpy array of position indexes of the time points to interpolate in the 
+        time data.  The format is the same as returned by ``_get_time_index_vec()``.
+        If this is None, ``_get_time_index_vec()`` is called.  The reason to pass 
+        this is to avoid multiple position searches when interpolating multiple 
+        outputs with the same time points.
+
+    Returns
+    -------
+    list
+        If data are Pyomo components, this will return Pyomo expressions.
+        If data are floats, this will return floats.
+    """
+    if indexes is None:
+        indexes = _get_time_index_vec(time_set, time_data)
+    expr = [None] * len(time_set)
+    for i, (h, t) in enumerate(zip(indexes, time_set)):
+        l = h - 1
+        expr[i] = data[l] + (data[h] - data[l]) / (time_data[h] - time_data[l]) * (
+            t - time_data[l]
+        )
+    return expr

pyomo/contrib/mpc/data/series_data.py

@@ -15,7 +15,10 @@
 from pyomo.contrib.mpc.data.get_cuid import get_indexed_cuid
 from pyomo.contrib.mpc.data.dynamic_data_base import _is_iterable, _DynamicDataBase
 from pyomo.contrib.mpc.data.scalar_data import ScalarData
-
+from pyomo.contrib.mpc.data.interpolation import (
+    _get_time_index_vec,
+    _get_interp_expr_vec,
+)
 
 TimeSeriesTuple = namedtuple("TimeSeriesTuple", ["data", "time"])
 
@@ -152,6 +155,50 @@ def get_data_at_time(self, time=None, tolerance=0.0):
             indices = indices[0]
         return self.get_data_at_time_indices(indices)
 
+    def get_interpolated_data(self, time=None, tolerance=0.0):
+        """
+        Returns the data associated with the provided time point or points by
+        linear interpolation.
+
+        Parameters
+        ----------
+        time: Float or iterable
+            The time point or points corresponding to returned data.
+        tolerance: float
+            Tolerance used when checking if time points are inside the data
+            range.
+
+        Returns
+        -------
+        TimeSeriesData or ~scalar_data.ScalarData
+            TimeSeriesData containing only the specified time points
+            or dict mapping CUIDs to values at the specified scalar time
+            point.
+
+        """
+        if time is None:
+            # If time is not specified, assume we want the entire time
+            # set. Skip all the overhead, don't create a new object, and
+            # return self.
+            return self
+        is_iterable = _is_iterable(time)
+        if not is_iterable:
+            time = [time]
+        for t in time:
+            if t > self._time[-1] + tolerance or t < self._time[0] - tolerance:
+                raise RuntimeError("Requesting interpolation outside data range.")
+        idxs = _get_time_index_vec(time, self._time)
+        data = {}
+        for cuid in self._data:
+            v = _get_interp_expr_vec(time, self._time, self._data[cuid], idxs)
+            data[cuid] = v
+        if is_iterable:
+            return TimeSeriesData(data, list(time))
+        else:
+            for cuid in self._data:
+                data[cuid] = data[cuid][0]
+            return ScalarData(data)
+
     def to_serializable(self):
         """
         Convert to json-serializable object.

pyomo/contrib/mpc/data/tests/test_series_data.py

@@ -126,6 +126,63 @@ def test_get_data_at_time_with_tolerance(self):
         with self.assertRaisesRegex(RuntimeError, msg):
             new_data = data.get_data_at_time(-0.01, tolerance=1e-3)
 
+    def test_get_data_interpolate(self):
+        m = self._make_model()
+        data_dict = {m.var[:, "A"]: [1, 2, 3], m.var[:, "B"]: [2, 4, 6]}
+        data = TimeSeriesData(data_dict, m.time)
+        new_data = data.get_interpolated_data(0.05)
+        self.assertEqual(ScalarData({m.var[:, "A"]: 1.5, m.var[:, "B"]: 3}), new_data)
+
+        t1 = 0.05
+        new_data = data.get_interpolated_data([t1])
+        self.assertEqual(
+            TimeSeriesData({m.var[:, "A"]: [1.5], m.var[:, "B"]: [3]}, [t1]), new_data
+        )
+
+        new_t = [0.05, 0.15]
+        new_data = data.get_interpolated_data(new_t)
+        self.assertEqual(
+            TimeSeriesData({m.var[:, "A"]: [1.5, 2.5], m.var[:, "B"]: [3, 5]}, new_t),
+            new_data,
+        )
+
+    def test_get_data_interpolate_range_check(self):
+        m = self._make_model()
+        data_dict = {m.var[:, "A"]: [1, 2, 3], m.var[:, "B"]: [2, 4, 6]}
+        data = TimeSeriesData(data_dict, m.time)
+        msg = "Requesting interpolation outside data range."
+        with self.assertRaisesRegex(RuntimeError, msg):
+            new_data = data.get_interpolated_data(0.2 + 1e-6)
+        with self.assertRaisesRegex(RuntimeError, msg):
+            new_data = data.get_interpolated_data(0.0 - 1e-6)
+        new_data = data.get_interpolated_data(0.2 + 1e-6, tolerance=1e-5)
+        self.assertAlmostEqual(new_data.get_data_from_key(m.var[:, "A"]), 3, 4)
+        self.assertAlmostEqual(new_data.get_data_from_key(m.var[:, "B"]), 6, 4)
+
+        t1 = 0.2 + 1e-6
+        with self.assertRaisesRegex(RuntimeError, msg):
+            new_data = data.get_interpolated_data([t1])
+        new_data = data.get_interpolated_data([t1], tolerance=1e-5)
+        self.assertAlmostEqual(new_data.get_data_from_key(m.var[:, "A"])[0], 3, 4)
+        self.assertAlmostEqual(new_data.get_data_from_key(m.var[:, "B"])[0], 6, 4)
+
+        new_t = [0.0 - 1e-6, 0.2 + 1e-6]
+        with self.assertRaisesRegex(RuntimeError, msg):
+            new_data = data.get_interpolated_data(new_t)
+        new_data = data.get_interpolated_data(new_t, tolerance=1e-5)
+        self.assertAlmostEqual(new_data.get_data_from_key(m.var[:, "A"])[0], 1, 4)
+        self.assertAlmostEqual(new_data.get_data_from_key(m.var[:, "B"])[0], 2, 4)
+        self.assertAlmostEqual(new_data.get_data_from_key(m.var[:, "A"])[1], 3, 4)
+        self.assertAlmostEqual(new_data.get_data_from_key(m.var[:, "B"])[1], 6, 4)
+
+        # check that the exact endpoints don't raise an exception with 0 tol
+        new_t = [0.0, 0.2]
+        new_data = data.get_interpolated_data(new_t, tolerance=0.0)
+        self.assertAlmostEqual(new_data.get_data_from_key(m.var[:, "A"])[0], 1, 4)
+        self.assertAlmostEqual(new_data.get_data_from_key(m.var[:, "B"])[0], 2, 4)
+        self.assertAlmostEqual(new_data.get_data_from_key(m.var[:, "A"])[1], 3, 4)
+        self.assertAlmostEqual(new_data.get_data_from_key(m.var[:, "B"])[1], 6, 4)
+
     def test_to_serializable(self):
         m = self._make_model()
         data_dict = {m.var[:, "A"]: [1, 2, 3], m.var[:, "B"]: [2, 4, 6]}