def main(): # Reads from the data file and runs estimate for each row # Then plots the trajectory data_array = util.read_csv(config.DATASET_ABSOLUTE_PATH) row = data_array[0] time, encoder, angular_velocity, steering_angle = np.ravel(row) resulting_pos_heading = [] pose_estimator = PoseEstimator((0, 0, 0), time, encoder, angular_velocity, steering_angle) i = 1 while i < len(data_array): row = data_array[i] time, encoder, angular_velocity, steering_angle = np.ravel(row) x, y, heading = pose_estimator.estimate( time=time, steering_angle=steering_angle, encoder_ticks=encoder, angular_velocity=angular_velocity) resulting_pos_heading.append([x, y, heading]) i = i + 1 visualizer.plot_points( np.asarray(resulting_pos_heading)[:, 0], np.asarray(resulting_pos_heading)[:, 1]) visualizer.show()
def test_turn_location_position(self): # Human assisted test # Ensure that the car returns to the same location if we drive around in circles. import visualizer pose_estimator = PoseEstimator((0, 0, 0), 0, 0, 0, 0) d = 4 steering_angle_radians = np.pi / d outer_turn_radius_meters = pose_estimator.calc_outer_turn_radius( steering_angle_radians) front_wheel_turn_circumference = 2 * np.pi * config.FRONT_WHEEL_RADIUS_METERS turn_circle_circumference = 2 * np.pi * outer_turn_radius_meters ticks_required = config.ENCODER_RESOLUTION_FRONT_WHEEL * turn_circle_circumference / front_wheel_turn_circumference result_loc = [] ticks = 0 for i in range(2 * d): result_loc.append( pose_estimator.estimate(time=i, steering_angle=steering_angle_radians, encoder_ticks=ticks, angular_velocity=0)) ticks = ticks + ticks_required / (2 * d) for loc in result_loc: visualizer.draw_car(loc[0], loc[1], loc[2]) visualizer.show()
def _btnDescribeClicked(self): """ Callback for button btnDescribe Click Event """ self.txtOutput.delete(0.0,tk.END) result=self.parent.btnDescribeClicked(self.varsVar.get()) self.txtOutput.insert(tk.END,result) if result is not None: visualizer.show(result)
def _btnDisplayClicked(self): """ Callback For button btnDisplay Click event """ self.txtOutput.delete(0.0,tk.END) result=self.parent.btnDisplayClicked(self.varsVar.get()) self.txtOutput.insert(tk.END,str(result)) if result is not None: visualizer.show(result)
import random def get_random_array(length): n = list(range(length)) random.shuffle(n) return n array_size = 50 algorithms = [s.bubble_sort, s.heap_sort, s.selection_sort, s.insertion_sort, s.quick_sort, s.insertion_sort, s.merge_sort] if len(sys.argv) < 2: print('Usage:', 'python', sys.argv[0], 'function_name', '\n') print('Choose from:', '\n') for algorithm in algorithms: print(algorithm.__name__) print('\n') sys.exit(0) algorithms = list(filter(lambda a: a.__name__ == sys.argv[1], algorithms)) if (len(algorithms) == 0): print('Method does not exist.') else: algorithm = algorithms[0] arr = s.Array(get_random_array(array_size)) algorithm(arr) vs.show()
import visualizer import sys from PyQt4 import QtGui import ui_mainwindow if __name__ == '__main__': app = QtGui.QApplication(sys.argv) ui = ui_mainwindow.Ui_MainWindow() visualizer = visualizer.GPIOVisualizer(ui) # # visualizer.createGPIO(ui.gridLayoutWidget, # ui.gridLayout) # # visualizer.setButton(ui.pushButton, ui.label) # visualizer.setTextEdit(ui.textEdit) visualizer.show() sys.exit(app.exec_())
import torch import torch.optim as optim from model import NeuralNet from train import train, test, test_dataset from visualizer import show device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model = NeuralNet().to(device) optimizer = optim.SGD(model.parameters(), lr=0.01) if __name__ == '__main__': for epoch in range(1, 10 + 1): train(epoch, model, optimizer, device) test(model, device) show(model, test_dataset, device)