diff --git a/cp/ex12/__init__.py b/cp/ex12/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..c9bf24e54d475806d511254e677f9f48d430f544
--- /dev/null
+++ b/cp/ex12/__init__.py
@@ -0,0 +1 @@
+"""DO NOT EDIT/ADD TO THIS FILE.""" 
diff --git a/cp/ex12/cpr_check.py b/cp/ex12/cpr_check.py
new file mode 100644
index 0000000000000000000000000000000000000000..13f049c4fd997b4d6603dbbccbadcbb603677339
--- /dev/null
+++ b/cp/ex12/cpr_check.py
@@ -0,0 +1,9 @@
+"""Exercise 12.1.: CPR-check."""
+
+def cpr_check(cpr : str) -> bool:
+    """Check if CPR number is valid based on check digit.
+    
+    :param cpr: CPR number as str.
+    :return: Boolean whether CPR is valid or not.
+    """
+    # TODO: Code has been removed from here. 
diff --git a/cp/ex12/polygon.py b/cp/ex12/polygon.py
new file mode 100644
index 0000000000000000000000000000000000000000..b710fe3464be9c5a55ff1df9512bf7243b3c5a2b
--- /dev/null
+++ b/cp/ex12/polygon.py
@@ -0,0 +1,10 @@
+"""Exercises 12.2-12.4.: Polygon."""
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+class Polygon:
+    """A class to represent a polygon.""" 
+
+    # TODO: Code has been removed from here. 
+        
diff --git a/cp/project6/__init__.py b/cp/project6/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..c9bf24e54d475806d511254e677f9f48d430f544
--- /dev/null
+++ b/cp/project6/__init__.py
@@ -0,0 +1 @@
+"""DO NOT EDIT/ADD TO THIS FILE.""" 
diff --git a/cp/project6/project6_grade.py b/cp/project6/project6_grade.py
new file mode 100644
index 0000000000000000000000000000000000000000..ee125ac7337be72a7bf71907dd81b30336fadb72
--- /dev/null
+++ b/cp/project6/project6_grade.py
@@ -0,0 +1,4 @@
+# cp/project6/project6_tests.py
+''' WARNING: Modifying, decompiling or otherwise tampering with this script, it's data or the resulting .token file will be investigated as a cheating attempt. '''
+import bz2, base64
+exec(bz2.decompress(base64.b64decode('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')))
\ No newline at end of file
diff --git a/cp/project6/project6_tests.py b/cp/project6/project6_tests.py
new file mode 100644
index 0000000000000000000000000000000000000000..a1c2869e257813a213c433619219b2520effe1a0
--- /dev/null
+++ b/cp/project6/project6_tests.py
@@ -0,0 +1,95 @@
+from unitgrade import UTestCase, Report
+import numpy as np
+
+class Week11Outliers(UTestCase):
+    def assertArrayEqual(self, a, b):
+        self.assertIsNotNone(a)
+        self.assertIsNone(np.testing.assert_array_equal(a, b))
+
+    def test_outliers1(self):
+        from cp.ex11.outliers import outliers
+        from cp.ex11.bacterial_growth import load_data
+        self.assertArrayEqual(outliers(load_data()), np.array([50, 96, 104]))
+
+    def test_outliers2(self):
+        from cp.ex11.outliers import outliers
+        np.random.seed(43)
+        data = (np.random.rand(160, 20)*2)**2
+        self.assertArrayEqual(outliers(data), np.array([19, 39, 68, 92, 101, 154]))
+
+    def test_outliers3(self):
+        from cp.ex11.outliers import outliers
+        np.random.seed(48)
+        data = (np.random.rand(300, 12)*2)**2
+        self.assertArrayEqual(outliers(data), np.array([ 25,  98, 128, 145, 151, 196, 201, 230, 239, 273, 277, 287]))
+
+class Week11BacterialArea(UTestCase):
+    def test_bacterial_area(self):
+        from cp.ex11.bacterial_area import bacterial_area
+        path = 'cp/ex11/files/bacteria.npy'
+        self.assertAlmostEqual(bacterial_area(path), 0.3757223140495868)
+
+class Week12PolygonGetPerimeter(UTestCase):
+    def test_polygon_perimeter(self):
+        from cp.ex12.polygon import Polygon
+        P = Polygon([(0, 3), (3, 1), (1, 0), (0, 0)])
+        self.assertAlmostEqual(P.get_perimeter(), 9.8416192529637, places=7)
+
+        P = Polygon([(0, 2), (1, 1), (2, 1), (0, 0)])
+        self.assertAlmostEqual(P.get_perimeter(), 6.650281539872885, places=7)
+
+        P = Polygon([(4, -1), (1, 4), (1, 0), (0, 0)])
+        self.assertAlmostEqual(P.get_perimeter(), 14.95405752046296, places=7)
+
+        P = Polygon([(0, 0), (-3, 3), (-1, -3)])
+        self.assertAlmostEqual(P.get_perimeter(), 13.729473667624424, places=7)
+
+        P = Polygon([(0, -1), (1, 1), (-1, 0), (0, 0)])       
+        self.assertAlmostEqual(P.get_perimeter(), 6.47213595499958, places=7)
+
+        P = Polygon([(0, 10), (1, 9), (2, 8), (3, 7), (4, 6), (5, 5), (6, 3), (7, 2), (8, -3), (7, 2), (6, 3), (5, 4), (4, 5), (3, 6), (2, 7), (0, 10)])
+        self.assertAlmostEqual(P.get_perimeter(), 31.596007466253397, places=7)
+ 
+class Week12PolygonSmooth(UTestCase):
+    def assertArrayAlmostEqual(self, a, b):
+        self.assertIsNotNone(a)
+        self.assertIsNone(np.testing.assert_almost_equal(a, b))
+
+    def test_polygon_smooth(self):
+        from cp.ex12.polygon import Polygon
+        P = Polygon([(1, 2), (2, 3), (3, 4), (4, 5), (3, 2), (2, 2.5)])
+        P.smooth_polygon(alpha=0.2)
+        self.assertArrayAlmostEqual(P.x, np.array([1.2, 2., 3., 3.8, 3., 2. ]))
+        self.assertArrayAlmostEqual(P.y, np.array([2.15, 3., 4., 4.6, 2.35, 2.4 ]))
+
+        P.smooth_polygon(alpha=0.2)
+        self.assertArrayAlmostEqual(P.x, np.array([1.36, 2.02, 2.98, 3.64, 2.98, 2.02]))
+        self.assertArrayAlmostEqual(P.y, np.array([2.26, 3.015, 3.96 , 4.315, 2.58 , 2.37 ]))
+
+        P.smooth_polygon(alpha=0.5)
+        self.assertArrayAlmostEqual(P.x, np.array([1.69 , 2.095, 2.905, 3.31 , 2.905, 2.095]))
+        self.assertArrayAlmostEqual(P.y, np.array([2.47625, 3.0625 , 3.8125 , 3.7925 , 2.96125, 2.395  ]))
+
+        P.smooth_polygon(alpha=0.5)
+        self.assertArrayAlmostEqual(P.x, np.array([1.8925 , 2.19625, 2.80375, 3.1075 , 2.80375, 2.19625]))
+        self.assertArrayAlmostEqual(P.y, np.array([2.6025   , 3.1034375, 3.62     , 3.5896875, 3.0275   , 2.556875 ]))
+
+questions = [
+            (Week11Outliers, 20),
+            (Week11BacterialArea, 20),
+            (Week12PolygonGetPerimeter,20),
+            (Week12PolygonSmooth,20)
+            ]
+class Project6(Report):
+    title = "Project 6"
+    remote_url = "https://cp.pages.compute.dtu.dk/02002public/_static/evaluation/"
+
+    abbreviate_questions = True
+    questions = questions
+    import cp
+    pack_imports = [cp]
+
+
+if __name__ == "__main__":
+    from unitgrade import evaluate_report_student
+    evaluate_report_student(Project6())
diff --git a/cp/project6/unitgrade_data/Week11BacterialArea.pkl b/cp/project6/unitgrade_data/Week11BacterialArea.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..27657b17299d48b89c7a35980cbb2300e403d4d6
Binary files /dev/null and b/cp/project6/unitgrade_data/Week11BacterialArea.pkl differ
diff --git a/cp/project6/unitgrade_data/Week11Outliers.pkl b/cp/project6/unitgrade_data/Week11Outliers.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..7ff2427dce1ed7ddfbb1de5e85f1e9f9ccc9ea11
Binary files /dev/null and b/cp/project6/unitgrade_data/Week11Outliers.pkl differ
diff --git a/cp/project6/unitgrade_data/Week12PolygonGetPerimeter.pkl b/cp/project6/unitgrade_data/Week12PolygonGetPerimeter.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..2b20574e88d8d2323ece4f7b6755f75e2e2b0dc7
Binary files /dev/null and b/cp/project6/unitgrade_data/Week12PolygonGetPerimeter.pkl differ
diff --git a/cp/project6/unitgrade_data/Week12PolygonSmooth.pkl b/cp/project6/unitgrade_data/Week12PolygonSmooth.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..93f13358eb64a43090b1db54f7edefe97378fb5b
Binary files /dev/null and b/cp/project6/unitgrade_data/Week12PolygonSmooth.pkl differ
diff --git a/cp/tests/tests_week12.py b/cp/tests/tests_week12.py
new file mode 100644
index 0000000000000000000000000000000000000000..d4fa6ae506568886b67452e7461db6a0d0e0bb4a
--- /dev/null
+++ b/cp/tests/tests_week12.py
@@ -0,0 +1,131 @@
+from unitgrade import Report, UTestCase
+import unittest
+import cp
+import inspect
+import numpy as np
+
+class Week12CprCheck(UTestCase):
+    def test_cpr_check(self):
+        from cp.ex12.cpr_check import cpr_check
+        self.assertEqual(cpr_check('1111111111'), False)
+        self.assertEqual(cpr_check('1111111118'), True)
+        # The following CPR numbers are randomly generated, don't worry ;)
+        self.assertEqual(cpr_check('0577561362'), True)
+        self.assertEqual(cpr_check('9497763417'), True)
+        self.assertEqual(cpr_check('3422336957'), True)
+        self.assertEqual(cpr_check('0409275504'), False)
+        self.assertEqual(cpr_check('1466340617'), False)
+        self.assertEqual(cpr_check('4213233632'), True)
+
+class Week12PolygonInit(UTestCase):
+    def assertArrayAlmostEqual(self, a, b):
+        self.assertIsNotNone(a)
+        self.assertIsNone(np.testing.assert_almost_equal(a, b))
+
+    def test_polygon_init(self):
+        from cp.ex12.polygon import Polygon
+
+        P = Polygon([(0, 12), (12, 1), (1, 0), (0, 0)])
+        self.assertArrayAlmostEqual(P.x, np.array([0, 12, 1, 0]))
+        self.assertArrayAlmostEqual(P.y, np.array([12, 1, 0, 0]))
+
+        P = Polygon([(0, 3), (3, 1), (1, 0), (0, 0)])
+        self.assertArrayAlmostEqual(P.x, np.array([0, 3, 1, 0]))
+        self.assertArrayAlmostEqual(P.y, np.array([3, 1, 0, 0]))
+
+        P = Polygon([(4, -1), (1, 4), (1, 0), (0, 0)])
+        self.assertArrayAlmostEqual(P.x, np.array([4, 1, 1, 0]))
+        self.assertArrayAlmostEqual(P.y, np.array([-1, 4, 0, 0]))
+
+        P= Polygon([(0, 10), (1, 9), (2, 8), (3, 7), (4, 6), (5, 5), (6, 3), (7, 2), (8, -3), (7, 2), (6, 3), (5, 4), (4, 5), (3, 6), (2, 7), (0, 10)])
+        self.assertArrayAlmostEqual(P.x, np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 7, 6, 5, 4, 3, 2, 0]))
+        self.assertArrayAlmostEqual(P.y, np.array([10, 9, 8, 7, 6, 5, 3, 2, -3, 2, 3, 4, 5, 6, 7, 10]))
+
+class Week12PolygonGetArea(UTestCase):
+    def test_polygon_area(self):
+        from cp.ex12.polygon import Polygon
+        P = Polygon([(0, 12), (12, 1), (1, 0), (0, 0)])
+        self.assertEqual(P.get_area(),72.5)
+
+        P = Polygon([(0, 3), (1, 1), (3, 1), (0, 0)])
+        self.assertEqual(P.get_area(),2.5)
+
+        P= Polygon([(4, -1), (1, 4), (1, 0), (0, 0)])
+        self.assertEqual(P.get_area(),6.5)
+
+        P= Polygon([(0, 0), (-3, 3), (-1, -3)])
+        self.assertEqual(P.get_area(),6.0)
+
+        P= Polygon([(0, -1), (1, 1), (-1, 0), (0, 0)])
+        self.assertEqual(P.get_area(),1.0)
+
+        P= Polygon([(0, 10), (1, 9), (2, 8), (3, 7), (4, 6), (5, 5), (6, 3), (7, 2), (8, -3), (7, 2), (6, 3), (5, 4), (4, 5), (3, 6), (2, 7), (0, 10)])
+        self.assertEqual(P.get_area(),4.5)
+
+class Week12PolygonGetPerimeter(UTestCase):
+    def test_polygon_perimeter(self):
+        from cp.ex12.polygon import Polygon
+        P = Polygon([(0, 3), (3, 1), (1, 0), (0, 0)])
+        self.assertAlmostEqual(P.get_perimeter(), 9.8416192529637, places=7)
+
+        P = Polygon([(0, 2), (1, 1), (2, 1), (0, 0)])
+        self.assertAlmostEqual(P.get_perimeter(), 6.650281539872885, places=7)
+
+        P = Polygon([(4, -1), (1, 4), (1, 0), (0, 0)])
+        self.assertAlmostEqual(P.get_perimeter(), 14.95405752046296, places=7)
+
+        P = Polygon([(0, 0), (-3, 3), (-1, -3)])
+        self.assertAlmostEqual(P.get_perimeter(), 13.729473667624424, places=7)
+
+        P = Polygon([(0, -1), (1, 1), (-1, 0), (0, 0)])       
+        self.assertAlmostEqual(P.get_perimeter(), 6.47213595499958, places=7)
+
+        P = Polygon([(0, 10), (1, 9), (2, 8), (3, 7), (4, 6), (5, 5), (6, 3), (7, 2), (8, -3), (7, 2), (6, 3), (5, 4), (4, 5), (3, 6), (2, 7), (0, 10)])
+        self.assertAlmostEqual(P.get_perimeter(), 31.596007466253397, places=7)
+
+ 
+class Week12PolygonSmooth(UTestCase):
+    def assertArrayAlmostEqual(self, a, b):
+        self.assertIsNotNone(a)
+        self.assertIsNone(np.testing.assert_almost_equal(a, b))
+
+    def test_polygon_smooth(self):
+        from cp.ex12.polygon import Polygon
+        P = Polygon([(1, 2), (2, 3), (3, 4), (4, 5), (3, 2), (2, 2.5)])
+        P.smooth_polygon(alpha=0.2)
+        self.assertArrayAlmostEqual(P.x, np.array([1.2, 2., 3., 3.8, 3., 2. ]))
+        self.assertArrayAlmostEqual(P.y, np.array([2.15, 3., 4., 4.6, 2.35, 2.4 ]))
+
+        P.smooth_polygon(alpha=0.2)
+        self.assertArrayAlmostEqual(P.x, np.array([1.36, 2.02, 2.98, 3.64, 2.98, 2.02]))
+        self.assertArrayAlmostEqual(P.y, np.array([2.26, 3.015, 3.96 , 4.315, 2.58 , 2.37 ]))
+
+        P.smooth_polygon(alpha=0.5)
+        self.assertArrayAlmostEqual(P.x, np.array([1.69 , 2.095, 2.905, 3.31 , 2.905, 2.095]))
+        self.assertArrayAlmostEqual(P.y, np.array([2.47625, 3.0625 , 3.8125 , 3.7925 , 2.96125, 2.395  ]))
+
+        P.smooth_polygon(alpha=0.5)
+        self.assertArrayAlmostEqual(P.x, np.array([1.8925 , 2.19625, 2.80375, 3.1075 , 2.80375, 2.19625]))
+        self.assertArrayAlmostEqual(P.y, np.array([2.6025   , 3.1034375, 3.62     , 3.5896875, 3.0275   , 2.556875 ]))
+
+      
+
+questions = [
+        (Week12CprCheck, 10),
+        (Week12PolygonInit,10),
+        (Week12PolygonGetArea,20),
+        (Week12PolygonGetPerimeter,20),
+        (Week12PolygonSmooth,20)
+        ]
+
+class Week12Tests(Report):
+    title = "Tests for week 12"
+    #version = 1.0
+    #url = "https://gitlab.compute.dtu.dk/cp/02002students/-/blob/master/cp/tests"
+    pack_imports = [cp]
+    individual_imports = []
+    questions = questions
+
+if __name__ == '__main__':
+    from unitgrade import evaluate_report_student
+    evaluate_report_student(Week12Tests())
diff --git a/cp/tests/unitgrade_data/Week12CprCheck.pkl b/cp/tests/unitgrade_data/Week12CprCheck.pkl
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diff --git a/cp/tests/unitgrade_data/Week12PolygonSmooth.pkl b/cp/tests/unitgrade_data/Week12PolygonSmooth.pkl
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