TL: added fEval feature to LagrangeApproximation

docs/installation.md

@@ -24,7 +24,7 @@ dependencies:
   ...
   - pip
   - pip:
-    qmat
+    - qmat
 ```
 
 ## Install from source

qmat/lagrange.py

@@ -104,6 +104,8 @@ class LagrangeApproximation(object):
         - 'MAX' : scaling based on the maximum weight value.
 
         The default is 'MAX'.
+    fValues : list, tuple or np.1darray
+        Function values to be used when evaluating the LagrangeApproximation as a function
 
     Attributes
     ----------
@@ -118,7 +120,7 @@ class LagrangeApproximation(object):
            "Barycentric Lagrange interpolation." SIAM review, 46(3), 501-517.
     """
 
-    def __init__(self, points, weightComputation='AUTO', scaleRef='MAX'):
+    def __init__(self, points, weightComputation='AUTO', scaleRef='MAX', fValues=None):
         points = np.asarray(points).ravel()
         assert np.unique(points).size == points.size, "distinct interpolation points are required"
 
@@ -178,6 +180,23 @@ def chebfun(diffs):
         self.weights = weights
         self.weightComputation = weightComputation
 
+        # Store function values if provided
+        if fValues is not None:
+            fValues = np.asarray(fValues)
+            if fValues.shape != points.shape:
+                raise ValueError(f'fValues {fValues.shape} has not the correct shape: {points.shape}')
+        self.fValues = fValues
+
+
+    def __call__(self, t, fValues=None):
+        if fValues is None: fValues=self.fValues
+        assert fValues is not None, "cannot evaluate polynomial without fValues"
+        t = np.asarray(t)
+        fValues = np.asarray(fValues)
+        values = self.getInterpolationMatrix(t.ravel()).dot(fValues)
+        values.shape = t.shape
+        return values
+
 
     @property
     def n(self):

tests/test_2_lagrange.py

@@ -54,9 +54,28 @@ def testInterpolation(nNodes, weightComputation):
     P = approx.getInterpolationMatrix(times)
 
     polyCoeffs = np.random.rand(nNodes)
-    polyNodes = np.polyval(polyCoeffs, nodes)
-    polyTimes = np.polyval(polyCoeffs, times)
-    assert np.allclose(polyTimes, P @ polyNodes)
+    polyValues = np.polyval(polyCoeffs, nodes)
+    refEvals = np.polyval(polyCoeffs, times)
+    assert np.allclose(refEvals, P @ polyValues)
+
+
+@pytest.mark.parametrize("weightComputation", ["AUTO", "FAST", "STABLE", "CHEBFUN"])
+@pytest.mark.parametrize("nNodes", nNodeTests)
+def testEvaluation(nNodes, weightComputation):
+    nodes = np.sort(np.random.rand(nNodes))
+    times = np.random.rand(nNodes*2)
+    polyCoeffs = np.random.rand(nNodes)
+    polyValues = np.polyval(polyCoeffs, nodes)
+
+    approx = LagrangeApproximation(nodes, weightComputation=weightComputation, fValues=polyValues)
+    P = approx.getInterpolationMatrix(times)
+    refEvals = P @ polyValues
+
+    polyEvals = approx(t=times, fValues=polyValues)
+    assert np.allclose(polyEvals, refEvals)
+
+    polyEvals = approx(t=times)
+    assert np.allclose(polyEvals, refEvals)
 
 
 @pytest.mark.parametrize("numQuad", ["LEGENDRE_NUMPY", "LEGENDRE_SCIPY", "FEJER"])