From c8fb7715c43c7a6b6e6ac61a9222295abf63f444 Mon Sep 17 00:00:00 2001
From: Dario Coscia <dariocoscia@Dario-Coscia.local>
Date: Sat, 29 Apr 2023 12:25:50 +0200
Subject: [PATCH] is_inside method

---
 pina/ellipsoid.py | 39 ++++++++++++++++++++++++++++++++++++++-
 1 file changed, 38 insertions(+), 1 deletion(-)

diff --git a/pina/ellipsoid.py b/pina/ellipsoid.py
index 58b31a4..bbf5fe1 100644
--- a/pina/ellipsoid.py
+++ b/pina/ellipsoid.py
@@ -31,7 +31,7 @@ class Ellipsoid(Location):
             Vol. 7. No. 8. 2001.
 
         :Example:
-            >>> spatial_domain = Ellipsoid({'x':[1, 0], 'y':[0,1]})
+            >>> spatial_domain = Ellipsoid({'x':[-1, 1], 'y':[-1,1]})
 
         """
         self.fixed_ = {}
@@ -80,6 +80,43 @@ class Ellipsoid(Location):
         """
         return list(self.fixed_.keys()) + list(self.range_.keys())
 
+    def is_inside(self, point, check_border=False):
+        """Check if a point is inside the ellipsoid.
+
+        :param point: Point to be checked
+        :type point: LabelTensor
+        :param check_border: Check if the point is also on the frontier
+            of the ellipsoid, default False.
+        :type check_border: bool
+        :return: Returning True if the point is inside, False otherwise.
+        :rtype: bool
+        """
+
+        if not isinstance(point, LabelTensor):
+            raise ValueError('point expected to be LabelTensor.')
+
+        # get axis ellipse
+        list_dict_vals = list(self._axis.values())
+        tmp = torch.tensor(list_dict_vals, dtype=torch.float)
+        ax_sq = LabelTensor(tmp.reshape(1, -1), list(self._axis.keys()))
+
+        if not all([i in ax_sq.labels for i in point.labels]):
+            raise ValueError('point labels different from constructor'
+                             f' dictionary labels. Got {point.labels},'
+                             f' expected {ax_sq.labels}.')
+
+        # point square
+        point_sq = point.pow(2)
+        point_sq.labels = point.labels
+
+        # calculate ellispoid equation
+        eqn = torch.sum(point_sq.extract(ax_sq.labels) / ax_sq) - 1.
+
+        if check_border:
+            return bool(eqn <= 0)
+
+        return bool(eqn < 0)
+
     def _sample_range(self, n, mode, variables):
         """Rescale the samples to the correct bounds.