225 lines
6.2 KiB
Plaintext
225 lines
6.2 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "924a2a50",
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"metadata": {},
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"source": [
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"## Question 3 (10 marks)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "2b3d1ba2",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "478bad7a",
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"metadata": {},
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"outputs": [],
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"source": [
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"points = np.load('data/points.npy').astype(np.uint8)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "e07b0285",
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"metadata": {},
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"outputs": [],
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"source": [
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"def compute_rotation_matrix(points, theta):\n",
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" \"\"\"\n",
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" Write a function compute_rotation_matrix(points, theta) to compute the rotation matrix in\n",
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" homogeneous coordinate system to rotate a shape depicted with 2-dimensional (x,y) coordinates\n",
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" points with an angle 𝜃 (theta in the definition) in the anticlockwise direction about the centre of the shape.\n",
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"\n",
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" Parameters:\n",
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" points: a 2-dimensional numpy array of data type uint8 with shape 𝑘 × 2. Each row\n",
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" of points is a Cartesian coordinate (x, y).\n",
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" \n",
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" theta: a floating-point number denoting the angle of rotation in degree.\n",
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" \n",
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" Returns:\n",
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" The expected output is a 2-dimensional numpy array of data type float64 with shape 3 × 3.\n",
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" \"\"\"\n",
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"\n",
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" # Convert theta from degrees to radians\n",
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" theta_rad = np.radians(theta)\n",
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"\n",
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" # Calculate the centre of the shape\n",
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" centre = np.mean(points, axis=0)\n",
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"\n",
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" # Define the translation matrices to move the centre of the shape to the origin and back\n",
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" translation_to_origin = np.array([[1, 0, -centre[0]],\n",
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" [0, 1, -centre[1]],\n",
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" [0, 0, 1]], dtype=np.float64)\n",
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"\n",
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" translation_back = np.array([[1, 0, centre[0]],\n",
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" [0, 1, centre[1]],\n",
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" [0, 0, 1]], dtype=np.float64)\n",
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"\n",
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" # Define the rotation matrix about the origin\n",
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" rotation = np.array([[np.cos(theta_rad), -np.sin(theta_rad), 0],\n",
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" [np.sin(theta_rad), np.cos(theta_rad), 0],\n",
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" [0, 0, 1]], dtype=np.float64)\n",
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"\n",
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" # Combine the translation and rotation into a single transformation matrix\n",
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" rotation_matrix = np.dot(np.dot(translation_back, rotation), translation_to_origin)\n",
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" \n",
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" return rotation_matrix"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "16ef4247",
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"metadata": {},
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"outputs": [],
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"source": [
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"rotation_matrices = []\n",
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"\n",
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"for t in range(0, 365, 5):\n",
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" rotation_matrices.append( compute_rotation_matrix(points, t) )"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a130c201",
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"metadata": {},
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"source": [
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"## Save Output"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "23967b5d",
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"metadata": {},
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"outputs": [],
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"source": [
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"np.save('data/question_3_rotation_matrices.npy', rotation_matrices)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a808d8a4",
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"metadata": {},
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"source": [
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"## Put students' implementations here"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "b0836f8d",
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"metadata": {},
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"outputs": [],
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"source": [
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"def compute_rotation_matrix(points, theta):\n",
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" # Convert points to float64\n",
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" points = points.astype(np.float64)\n",
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" # Calculate centre\n",
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" centre = np.mean(points, axis=0)\n",
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" # Compute rotation matrix\n",
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" rotation_matrix = np.array([[np.cos(np.radians(theta)), -np.sin(np.radians(theta)), 0],\n",
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" [np.sin(np.radians(theta)), np.cos(np.radians(theta)), 0],\n",
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" [0, 0, 1]])\n",
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" # Translation matrix to origin\n",
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" translation_to_origin = np.array([[1, 0, -centre[0]],\n",
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" [0, 1, -centre[1]],\n",
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" [0, 0, 1]])\n",
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" # Translation matrix to original position\n",
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" translation_to_centre = np.array([[1, 0, centre[0]],\n",
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" [0, 1, centre[1]],\n",
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" [0, 0, 1]])\n",
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" # Combine transformations with data type float64 \n",
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" combined_matrix = np.dot(np.dot(translation_to_centre, rotation_matrix), translation_to_origin).astype(np.float64)\n",
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" return combined_matrix\n",
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"\n",
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" return 0"
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]
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},
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{
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"cell_type": "markdown",
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"id": "73b68192",
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"metadata": {},
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"source": [
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"## Test (Should output ALL PASS)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1c0a88a6",
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"metadata": {},
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"source": [
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"Restart and Run ALL for each submission"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "132d734b",
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"metadata": {
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"scrolled": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"73 PASS\n",
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"ALL PASS\n"
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]
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}
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],
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"source": [
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"n_pass = 0\n",
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"for t in range(0, 365, 5):\n",
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" if np.allclose(compute_rotation_matrix(points, t), rotation_matrices[int(t / 5)]):\n",
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" n_pass = n_pass + 1\n",
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"\n",
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"print(n_pass, \"PASS\")\n",
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"assert n_pass == len(rotation_matrices)\n",
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"print(\"ALL PASS\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "0fac308a",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "what",
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"language": "python",
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"name": "what"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.16"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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