RocketPy’s User Guide# Getting Started Installation and Requirements Installation Development version Requirements First Simulation Setting Up a Simulation Defining a Environment Defining a Motor Defining a Rocket Running the Simulation Accessing numerical results Plotting the Results Visualizing the Trajectory in Google Earth Manipulating results Exporting Flight Data Saving and Storing Plots Further Analysis Main Classes Usage Positions and Coordinate Systems Motors Rocket Environment Special Case Simulations Compare Flights Class Importing classes Create Environment, Motor and Rocket Creating the Flight objects Start the comparison Plotting results one by one Plotting using the all method Deployable Payload Creating Environment Creating a Motor Simulating the 1st Flight: ascending phase Simulate the 2nd Flight: Rocket Without Payload Simulating the 3rd Flight: Payload Plotting results Air Brakes Example Setting Up The Simulation Setting Up the Air Brakes Simulating a Flight Analyzing the Results Sensor Class usage Create an Environment Create a Motor Create a Rocket Adds Sensors to the Rocket Add Air Brakes to the Rocket Simulate Flight Visualize the results MATLAB® Tutorial RocketPy in MATLAB® Download Files Tutorial Monte Carlo Simulations Stochastic Classes Initialization Parameters Examples Determining Uncertainties Conclusion Monte Carlo class usage Step 1: Standard Simulation Step 2: Stochastic Objects Custom exports using callback functions Monte Carlo Simulations Download data files if using Google Colab Install and Load Necessary Libraries Defining Analysis Parameters Creating a Flight Settings Generator Creating an Export Function Simulating Each Flight Setting Post-processing Monte Carlo Dispersion Results Dispersion Results Parachute Drop from Helicopter Clone data files if using Google Colab Install and Load Necessary Libraries Defining Analysis Parameters Creating a Flight Settings Generator Creating an Export Function Simulating Each Flight Setting Post-processing Monte Carlo Dispersion Results Importing Dispersion Analysis Saved Data Dispersion Results Sensitivity Analysis Sources of Uncertainty Importing Monte Carlo Data Creating and fitting a SensitivityModel Results Interpreting the Results Further Analysis Function 1. Define a Data Source 2. Building your Function 3. Function Features Utilities Utilities module usage
