Week11

cp/ex11/__init__.py

+"""DO NOT EDIT/ADD TO THIS FILE.""" 

cp/ex11/bacterial_area.py

+"""Exercise 11.10: Bacterial area."""
+import numpy as np
+
+def bacterial_area(npy_path : str) -> float:
+    """Calculate the percentage of the image where pixel intensities are greater than 100.
+
+    Parameters:
+    :param npy_path: Path to the image data file in NumPy format.
+    :return: The percentage of area in an image where there are bacteria.
+    """
+    # TODO: 1 lines missing. 

cp/ex11/bacterial_growth.py

+"""Exercise 11.7-11.8: Bacterial growth."""
+
+import numpy as np 
+def load_data() -> np.ndarray:
+    """Load data from 160 files into one array. Files are located in cp/ex11/files/experiments.
+    
+    :return: The data from the files in one array.
+    """
+    # TODO: Code has been removed from here. 
+def threshold_exceeded(data : np.ndarray, threshold: float) -> np.ndarray:
+    """Return the index at which the threshold is exceeded for each experiment.
+    
+    :param data: The data to search.
+    :param threshold: The threshold to compare against.
+    :return: The index at which the threshold is exceeded for each row.
+    """
+    # TODO: Code has been removed from here. 
+def get_mean(data : np.ndarray) -> np.ndarray:
+    """Calculate the mean of the data.
+    
+    :param data: The data to calculate the mean of.
+    :return: The mean of the data for each time-point.
+    """
+    # TODO: Code has been removed from here. 
+def get_std(data : np.ndarray) -> np.ndarray:
+    """Calculate the standard deviation of the data.
+    
+    :param data: The data to calculate the standard deviation of.
+    :return: The standard deviation of the data for each time-point.
+    """
+    # TODO: Code has been removed from here. 

cp/ex11/extract_subimage.py

+"""Exercise 11.6: Indexing Images."""
+import numpy as np
+
+def extract_subimage(image: np.ndarray, start_point: tuple, size: tuple) -> np.ndarray:
+    """Extract a subimage from an image.
+
+    :param image: Image to extract from.
+    :param start_point: Coordinates of the top left corner of the subimage.
+    :param size: Size of the subimage.
+
+    :return: Subimage.
+    """
+    # TODO: Code has been removed from here. 

cp/ex11/list_exercises_numpy.py

+"""Exercise 11.5: Revisit lists."""
+import numpy as np
+def avg(data: np.ndarray) -> float:
+    """Return the average of an array.
+    
+    :param data: The array to average.
+    :return: The average of the array.
+    """
+    # TODO: 1 lines missing. 
+def conditional_max(data: np.ndarray, threshold: float) -> float:
+    """Return the maximum value in the array that is smaller than the threshold.
+
+    :param data: The array to search.
+    :param threshold: The threshold to compare against.
+    :return: The maximum value in the array that is smaller than the threshold.
+    """
+    # TODO: 1 lines missing. 
+def vector_add(a: np.ndarray, b: np.ndarray) -> np.ndarray:
+    """Add two vectors together.
+
+    :param a: The first vector.
+    :param b: The second vector.
+    :return: The sum of the two vectors.
+    """
+    # TODO: 1 lines missing. 
+def count_multiples(data: np.ndarray, divisor: int) -> int:
+    """Count the number of multiples of a number in an array.
+
+    :param data: The array to search.
+    :param divisor: The number to check for multiples of.
+    :return: The number of multiples of the divisor in the array.
+    """
+    # TODO: 1 lines missing. 
+def return_multiples(data, divisor):
+    """Return the multiples of a number in an array.
+
+    :param data: The array to search.
+    :param divisor: The number to check for multiples of.
+    :return: The multiples of the divisor in the array.
+    """
+    # TODO: 1 lines missing. 

cp/ex11/outliers.py

+"""Exercise 11.9: Outliers."""
+import numpy as np
+
+from cp.ex11.bacterial_growth import get_mean, get_std
+def outliers(data : np.ndarray) -> np.ndarray:
+    """Return the outliers of a dataset.
+    
+    :param data: The data to search.
+    :return: The outliers of the data.
+    """
+    # TODO: Code has been removed from here. 

cp/ex11/threshold_image.py

+"""Exercise 11.7: Threshold Image."""
+
+import numpy as np
+
+def threshold_image(image: np.ndarray, threshold: int) -> np.ndarray:
+    """Threshold an image.
+    
+    :param image: An image in a 2D numpy array.
+    :param threshold: A threshold value.
+
+    :return: A copy of the image with all values above the threshold are set to 1.
+    """
+    # TODO: Code has been removed from here. 

cp/tests/tests_week11.py

+from unitgrade import Report
+import cp
+from unitgrade import UTestCase
+import numpy as np
+
+import os
+os.chdir(os.path.join(os.path.dirname(cp.__file__), '..'))
+
+class Week11Lists(UTestCase):
+    def test_avg(self):
+        from cp.ex11.list_exercises_numpy import avg
+        result = avg(np.array([1,2,3,4,5]))
+        self.assertEqual(result, 3.)
+        result = avg(np.array([2,3,7,8,1,12,5,2,9]))
+        self.assertAlmostEqual(result, 5.444444444444445)
+    def test_conditional_max(self):
+        from cp.ex11.list_exercises_numpy import conditional_max
+        result = conditional_max(np.array([1.,2.,3.,4.,5.]), 3.)
+        self.assertEqual(result, 2)
+        result = conditional_max(np.array([2.,3.,7.,8.,1.,12.,5.,2.,9.]), 5.)
+        self.assertEqual(result, 3)
+    def test_vector_add(self):
+        from cp.ex11.list_exercises_numpy import vector_add
+        result = vector_add(np.array([1,2,3,4,5]), np.array([3,4,5,6,7]))
+        self.assertEqual(result.tolist(), [4,6,8,10,12])
+        result = vector_add(np.array([2,3,7,8,1,12,5,2,9]), np.array([8,4,3,4,5,6,7, 17,2]))
+        self.assertEqual(result.tolist(), [10,7,10,12,6,18,12,19,11])
+    def test_count_multiples(self):
+        from cp.ex11.list_exercises_numpy import count_multiples
+        result = count_multiples(np.array([1,2,3,4,5]), 3)
+        self.assertEqual(result, 1)
+        result = count_multiples(np.array([2,15,7,8,1,10,5,2,9]), 5)
+        self.assertEqual(result, 3)
+        result = count_multiples(np.array([2,15,8,1,10,5,2,9]), 7)
+        self.assertEqual(result, 0)
+    def test_return_multiples(self):
+        from cp.ex11.list_exercises_numpy import return_multiples
+        result = return_multiples(np.array([1,2,3,4,5]), 3)
+        self.assertEqual(result.tolist(), [3])
+        result = return_multiples(np.array([2,15,7,8,1,10,5,2,9]), 5)
+        self.assertEqual(result.tolist(), [15,10,5])
+        result = return_multiples(np.array([2,15,8,1,10,5,2,9]), 7)
+        self.assertEqual(result.tolist(), [])
+
+class Week11Subimage(UTestCase):
+    def test_extract_subimage(self):
+        from cp.ex11.extract_subimage import extract_subimage
+        image = np.array([[1,2,3,4,5],[6,7,8,9,10],[11,12,13,14,15]])
+        result = extract_subimage(image, (0,0), (2,2))
+        self.assertEqual(result.tolist(), [[1,2],[6,7]])
+        result = extract_subimage(image, (1,2), (2,3))
+        self.assertEqual(result.tolist(), [[8,9,10],[13,14,15]])
+        result = extract_subimage(image, (0,2), (3,2))
+        self.assertEqual(result.tolist(), [[3,4],[8,9],[13,14]])
+
+class Week11Threshold(UTestCase):
+    def test_threshold_image(self):
+        from cp.ex11.threshold_image import threshold_image
+        image = np.array([[1,2,3,4,5],[6,7,8,9,10],[11,12,13,14,15]])
+        result = threshold_image(image, 3)
+        self.assertEqual(result.tolist(), [[0,0,0,1,1],[1,1,1,1,1],[1,1,1,1,1]])
+        result = threshold_image(image, 9)
+        self.assertEqual(result.tolist(), [[0,0,0,0,0],[0,0,0,0,1],[1,1,1,1,1]])
+        result = threshold_image(image, 15)
+        self.assertEqual(result.tolist(), [[0,0,0,0,0],[0,0,0,0,0],[0,0,0,0,0]])
+
+class Week11BacterialGrowth(UTestCase): 
+    def test_load_data(self):
+        from cp.ex11.bacterial_growth import load_data
+        result = load_data()
+        self.assertEqual(result.shape, (160,12))
+        self.assertAlmostEqual(result[0,0], 1.44)
+        self.assertAlmostEqual(result[159,11], 16.67)
+    def test_threshold_exceeded(self):
+        from cp.ex11.bacterial_growth import load_data, threshold_exceeded
+        data = load_data()
+        result = threshold_exceeded(data, 8.5)
+        self.assertEqual(result.size, 160)
+        result = threshold_exceeded(data, 9.)
+        self.assertAlmostEqual(np.mean(result), 9.49375)
+
+
+class Week11AverageGrowthCurve(UTestCase):
+    def assertArrayAlmostEqual(self, a, b):
+        self.assertIsNotNone(a)
+        self.assertIsNone(np.testing.assert_almost_equal(a, b))
+
+    def test_get_std(self):
+        from cp.ex11.bacterial_growth import get_std
+        data = np.ones((4,4))
+        data[::2] = 2
+        self.assertArrayAlmostEqual(get_std(data), np.array([0.5, 0.5, 0.5, 0.5]))
+        data = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])**2
+        self.assertArrayAlmostEqual(get_std(data), np.array([20.04993766, 24.85960579, 29.69848481]))
+        data = np.sqrt(np.arange(42)).reshape(6, 7).T
+        self.assertArrayAlmostEqual(get_std(data), np.array([0.7781667, 0.3194126, 0.2433594, 0.2044697, 0.1797871, 0.1623305]))
+    
+    def test_get_mean(self):
+        from cp.ex11.bacterial_growth import get_mean
+        data = np.ones((4,4))
+        data[::2] = 2
+        self.assertArrayAlmostEqual(get_mean(data), np.array([1.5, 1.5, 1.5, 1.5]))
+        data = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])**2
+        self.assertArrayAlmostEqual(get_mean(data), np.array([22., 31., 42.]))
+        data = np.sqrt(np.arange(42)).reshape(6, 7).T
+        self.assertArrayAlmostEqual(get_mean(data), np.array([1.5474032, 3.1461048, 4.1159174, 4.8947106, 5.5648609, 6.1622763]))
+
+
+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)
+
+questions = [
+                (Week11Lists, 25),
+                (Week11Subimage, 10),
+                (Week11Threshold, 10),
+                (Week11BacterialGrowth, 20),
+                (Week11AverageGrowthCurve, 20),
+                (Week11Outliers, 20),
+                (Week11BacterialArea, 20)
+            ]
+
+
+class Week11Tests(Report): #30 total.
+    title = "Tests for week 11"
+    #version = 0.1
+    #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(Week11Tests())