Rename this. Also break down the apply method.

ch-13/2.2-Moving with encoders/banana_shaped.py

@@ -1,51 +0,0 @@
-import numpy as np
-from matplotlib import pyplot as plt, patches
-from robot import arena
-import random
-
-fig, ax = plt.subplots()
-
-# for line in arena.boundary_lines:
-#     plt.plot([line[0][0], line[1][0]], [line[0][1], line[1][1]], color="black")
-
-poses = np.random.normal([250, 300], [2, 2], size=(200, 2))
-ax.scatter(poses[:, 0], poses[:, 1])
-
-# create a line for a motion vector
-motion_angle = 30
-motion_scale = 300
-motion_line = np.array([[250, 300], 
-  [motion_scale * np.cos(np.radians(motion_angle)), motion_scale * np.sin(np.radians(motion_angle))]])
-
-
-
-triangular_proportion = np.sqrt(6) / 2
-def get_triangular_sample(mean, standard_deviation):
-    base = triangular_proportion * (random.uniform(-standard_deviation, standard_deviation) + random.uniform(-standard_deviation, standard_deviation))
-    return mean + base
-
-
-motion_rotation = np.array([get_triangular_sample(motion_angle, 15) for _ in range(poses.shape[0])])
-motion_translation = np.array([get_triangular_sample(motion_scale, 10) for _ in range(poses.shape[0])])
-
-new_poses = np.zeros_like(poses)
-new_poses[:, 0] = poses[:, 0] + motion_translation * np.cos(np.radians(motion_rotation))
-new_poses[:, 1] = poses[:, 1] + motion_translation * np.sin(np.radians(motion_rotation))
-# new_poses[:, 0] = poses[:, 0] + motion_scale * np.cos(np.radians(motion_angle))
-# new_poses[:, 1] = poses[:, 1] + motion_scale * np.sin(np.radians(motion_angle))
-
-ax.scatter(new_poses[:, 0], new_poses[:, 1])
-
-# plot the vector arrow
-ax.arrow(*motion_line[0], *motion_line[1], color="red", width=5)
-
-# plot the angles on top
-# line from original cluster middle, going east.
-ax.plot([250, 250+100], [300, 300], color="black")
-
-angle = patches.Wedge((250, 300), 100, 0, motion_angle, width=20, color=(0.3, 0.3, 0.3, 0.3))
-ax.add_patch(angle)
-ax.text(370, 330, f"{motion_angle}°", horizontalalignment="center", verticalalignment="center", 
-  fontsize="x-large")
-
-plt.show()

ch-13/2.2-moving-with-encoders/analyse_output.ipynb

@@ -1201,7 +1201,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.15"
+   "version": "3.9.15 (main, Oct 11 2022, 22:27:25) \n[Clang 14.0.0 (clang-1400.0.29.102)]"
   },
   "orig_nbformat": 4,
   "vscode": {

ch-13/2.2-moving-with-encoders/robot/code.py

@@ -104,6 +104,14 @@ class Simulation:
         rot2 = rot1
         return rot1, arc_length, rot2
 
+    def apply_motion_to_poses(self, rot1, trans, rot2):
+        self.poses[:,2] += rot1
+        rot1_radians = np.radians(self.poses[:,2])
+        self.poses[:,0] += trans * np.cos(rot1_radians)
+        self.poses[:,1] += trans * np.sin(rot1_radians)
+        self.poses[:,2] += rot2
+        self.poses[:,2] = np.array([float(theta % 360) for theta in self.poses[:,2]])
+
     def motion_model(self):
         """Apply the motion model"""
         new_encoder_left = robot.left_encoder.read()
@@ -114,12 +122,8 @@ class Simulation:
             new_encoder_right - self.last_encoder_right)
         self.last_encoder_left = new_encoder_left
         self.last_encoder_right = new_encoder_right
-        self.poses[:,2] += rot1
-        rot1_radians = np.radians(self.poses[:,2])
-        self.poses[:,0] += trans * np.cos(rot1_radians)
-        self.poses[:,1] += trans * np.sin(rot1_radians)
-        self.poses[:,2] += rot2
-        self.poses[:,2] = np.array([float(theta % 360) for theta in self.poses[:,2]])
+
+        self.apply_motion_to_poses(rot1, trans, rot2)
         print(
             json.dumps(
                 [self.poses.tolist(), rot1, trans, rot2]