From 9ce90e1a41375844e28eafa96b9936b8cd131a6e Mon Sep 17 00:00:00 2001 From: Sofia-ME Date: Sun, 28 Aug 2022 18:47:31 -0300 Subject: [PATCH 1/9] Dispersion class --- rocketpy/Dispersion.py | 997 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 997 insertions(+) create mode 100644 rocketpy/Dispersion.py diff --git a/rocketpy/Dispersion.py b/rocketpy/Dispersion.py new file mode 100644 index 000000000..2364c39c7 --- /dev/null +++ b/rocketpy/Dispersion.py @@ -0,0 +1,997 @@ +# -*- coding: utf-8 -*- + +__author__ = "" +__copyright__ = "Copyright 20XX, Projeto Jupiter" +__license__ = "MIT" + + +from rocketpy import * +import netCDF4 + + + +from datetime import datetime +from os import _Environ +from time import process_time, perf_counter, time +import glob +import traceback + + +import numpy as np +from numpy.random import normal, uniform, choice, beta, binomial, chisquare, dirichlet, exponential, f, gamma, geometric, gumbel, hypergeometric, laplace, logistic, lognormal, logseries, multinomial, multivariate_normal, negative_binomial, noncentral_chisquare, noncentral_f, pareto, poisson, power, rayleigh, standard_cauchy, standard_exponential, standard_gamma, standard_normal, standard_t, triangular, vonmises, wald, weibull, zipf +from IPython.display import display +from rocketpy.Environment import Environment +import matplotlib.pyplot as plt +from imageio import imread +from matplotlib.patches import Ellipse + + + + +class Dispersion: + + """Monte Carlo analysis to predict probability distributions of the rocket's + landing point, apogee and other relevant information. + + Attributes + ---------- + + Parameters: + Dispersion.filename: string + When running a new simulation, this attribute represents the initial + part of the export filenames (e.g. 'filename.disp_outputs.txt'). + When analyzing the results of a previous simulation, this attribute + shall be the filename containing the outputs of a dispersion calculation. + Dispersion.image: string + Launch site PNG file to be plotted along with the dispersion ellipses. + Attribute needed to run a new simulation. + Dispersion.realLandingPoint: tuple + Rocket's experimental landing point relative to launch point. + Dispersion.N: integer + Number of simulations in an output file. + + Other classes: + Dispersion.environment: Environment + Launch environment. + Attribute needed to run a new simulation, when Dispersion.flight remains unchanged. + Dispersion.motor: Motor + Rocket's motor. + Attribute needed to run a new simulation, when Dispersion.flight remains unchanged. + Dispersion.rocket: Rocket + Rocket with nominal values. + Attribute needed to run a new simulation, when Dispersion.flight remains unchanged. + + """ + + def __init__( + self, + filename, + number_of_simulations=0, + flight=None, + image=None, + dispersionDict={}, + environment=None, + motor=None, + rocket=None, + distributionType='normal', + realLandingPoint=None + ): + + ''' + Parameters + ---------- + + filename: string + When running a new simulation, this parameter represents the initial + part of the export filenames (e.g. 'filename.disp_outputs.txt'). + When analyzing the results of a previous simulation, this parameter + shall be the .txt filename containing the outputs of a dispersion calculation. + + number_of_simulations: integer, needed when running a new simulation + Number of simulations desired, must be greater than zero. + Default is zero. + + flight: Flight + Original rocket's flight with nominal values. + Parameter needed to run a new simulation, when environment, + motor and rocket remain unchanged. + Default is None. + + image: string, needed when running a new simulation + Launch site PNG file to be plotted along with the dispersion ellipses. + + dispersionDict: dictionary, optional + Contains the information of which environment, motor, rocket and flight variables + will vary according to its standard deviation. + Format {'parameter0': (nominal value, standard deviation), 'parameter1': + (nominal value, standard deviation), ...} + (e.g. {'rocketMass':(20, 0.2), + 'burnOut': (3.9, 0.3), 'railLength': (5.2, 0.05)}) + Default is {}. + + environment: Environment + Launch environment. + Parameter needed to run a new simulation, when Dispersion.flight remains unchanged. + Default is None. + + motor: Motor, optional + Rocket's motor. + Parameter needed to run a new simulation, when Dispersion.flight remains unchanged. + Default is None. + + rocket: Rocket, optional + Rocket with nominal values. + Parameter needed to run a new simulation, when Dispersion.flight remains unchanged. + Default is None. + + distributionType: string, optional + Determines which type of distribution will be applied to variable parameters and + its respective standard deviation. + Default is 'normal' + + realLandingPoint: tuple, optional + Rocket's experimental landing point relative to launch point. + Format (horizontal distance, vertical distance) + + Returns + ------- + None + + ''' + + # Save parameters + self.filename = filename + self.image = image + self.realLandingPoint = realLandingPoint + + # Run a new simulation + if number_of_simulations > 0: + + self.flight = flight + self.environment = flight.env if not environment else environment + self.rocket = flight.rocket if not rocket else rocket + self.motor = flight.rocket.motor if not motor else motor + + analysis_parameters = {i: j for i, j in dispersionDict.items()} + + def flight_settings(analysis_parameters, number_of_simulations): + i = 0 + while i < number_of_simulations: + # Generate a flight setting + flight_setting = {} + for parameter_key, parameter_value in analysis_parameters.items(): + if type(parameter_value) is tuple: + if distributionType == "normal" or distributionType == None: + flight_setting[parameter_key] = normal(*parameter_value) + if distributionType == "beta": + flight_setting[parameter_key] = beta(*parameter_value) + if distributionType == "binomial": + flight_setting[parameter_key] = binomial(*parameter_value) + if distributionType == "chisquare": + flight_setting[parameter_key] = chisquare(*parameter_value) + if distributionType == "dirichlet": + flight_setting[parameter_key] = dirichlet(*parameter_value) + if distributionType == "exponential": + flight_setting[parameter_key] = exponential(*parameter_value) + if distributionType == "f": + flight_setting[parameter_key] = f(*parameter_value) + if distributionType == "gamma": + flight_setting[parameter_key] = gamma(*parameter_value) + if distributionType == "geometric": + flight_setting[parameter_key] = geometric(*parameter_value) + if distributionType == "gumbel": + flight_setting[parameter_key] = gumbel(*parameter_value) + if distributionType == "hypergeometric": + flight_setting[parameter_key] = hypergeometric(*parameter_value) + if distributionType == "laplace": + flight_setting[parameter_key] = laplace(*parameter_value) + if distributionType == "logistic": + flight_setting[parameter_key] = logistic(*parameter_value) + if distributionType == "lognormal": + flight_setting[parameter_key] = lognormal(*parameter_value) + if distributionType == "logseries": + flight_setting[parameter_key] = logseries(*parameter_value) + if distributionType == "multinomial": + flight_setting[parameter_key] = multinomial(*parameter_value) + if distributionType == "multivariate_normal": + flight_setting[parameter_key] = multivariate_normal(*parameter_value) + if distributionType == "negative_binomial": + flight_setting[parameter_key] = negative_binomial(*parameter_value) + if distributionType == "noncentral_chisquare": + flight_setting[parameter_key] = noncentral_chisquare(*parameter_value) + if distributionType == "noncentral_f": + flight_setting[parameter_key] = noncentral_f(*parameter_value) + if distributionType == "pareto": + flight_setting[parameter_key] = pareto(*parameter_value) + if distributionType == "poisson": + flight_setting[parameter_key] = poisson(*parameter_value) + if distributionType == "power": + flight_setting[parameter_key] = power(*parameter_value) + if distributionType == "rayleigh": + flight_setting[parameter_key] = rayleigh(*parameter_value) + if distributionType == "standard_cauchy": + flight_setting[parameter_key] = standard_cauchy(*parameter_value) + if distributionType == "standard_exponential": + flight_setting[parameter_key] = standard_exponential(*parameter_value) + if distributionType == "standard_gamma": + flight_setting[parameter_key] = standard_gamma(*parameter_value) + if distributionType == "standard_normal": + flight_setting[parameter_key] = standard_normal(*parameter_value) + if distributionType == "standard_t": + flight_setting[parameter_key] = standard_t(*parameter_value) + if distributionType == "triangular": + flight_setting[parameter_key] = triangular(*parameter_value) + if distributionType == "uniform": + flight_setting[parameter_key] = uniform(*parameter_value) + if distributionType == "vonmises": + flight_setting[parameter_key] = vonmises(*parameter_value) + if distributionType == "wald": + flight_setting[parameter_key] = wald(*parameter_value) + if distributionType == "weibull": + flight_setting[parameter_key] = weibull(*parameter_value) + if distributionType == "zipf": + flight_setting[parameter_key] = zipf(*parameter_value) + else: + flight_setting[parameter_key] = choice(parameter_value) + + # Skip if certain values are negative, which happens due to the normal curve but isnt realistic + if "lag_rec" in analysis_parameters and flight_setting["lag_rec"] < 0: + continue + if "lag_se" in analysis_parameters and flight_setting["lag_se"] < 0: + continue + # Update counter + i += 1 + # Yield a flight setting + yield flight_setting + + def export_flight_data(flight_setting, flight_data, exec_time): + # Generate flight results + flight_result = { + "outOfRailTime": flight_data.outOfRailTime, + "outOfRailVelocity": flight_data.outOfRailVelocity, + "apogeeTime": flight_data.apogeeTime, + "apogeeAltitude": flight_data.apogee - flight_data.env.elevation, + "apogeeX": flight_data.apogeeX, + "apogeeY": flight_data.apogeeY, + "impactTime": flight_data.tFinal, + "impactX": flight_data.xImpact, + "impactY": flight_data.yImpact, + "impactVelocity": flight_data.impactVelocity, + "initialStaticMargin": flight_data.rocket.staticMargin(0), + "outOfRailStaticMargin": flight_data.rocket.staticMargin(flight_data.outOfRailTime), + "finalStaticMargin": flight_data.rocket.staticMargin(flight_data.rocket.motor.burnOutTime), + "numberOfEvents": len(flight_data.parachuteEvents), + "drogueTriggerTime": [], + "drogueInflatedTime": [], + "drogueInflatedVelocity": [], + "executionTime": exec_time, + 'lateralWind': flight_data.lateralSurfaceWind, + 'frontalWind': flight_data.frontalSurfaceWind} + + # Calculate maximum reached velocity + sol = np.array(flight_data.solution) + flight_data.vx = Function(sol[:, [0, 4]], "Time (s)", "Vx (m/s)", "linear", extrapolation="natural") + flight_data.vy = Function(sol[:, [0, 5]], "Time (s)", "Vy (m/s)", "linear", extrapolation="natural") + flight_data.vz = Function(sol[:, [0, 6]], "Time (s)", "Vz (m/s)", "linear", extrapolation="natural") + flight_data.v = (flight_data.vx**2 + flight_data.vy**2 + flight_data.vz**2) ** 0.5 + flight_data.maxVel = np.amax(flight_data.v.source[:, 1]) + flight_result["maxVelocity"] = flight_data.maxVel + + # Take care of parachute results + if len(flight_data.parachuteEvents) > 0: + flight_result["drogueTriggerTime"] = flight_data.parachuteEvents[0][0] + flight_result["drogueInflatedTime"] = ( + flight_data.parachuteEvents[0][0] + flight_data.parachuteEvents[0][1].lag + ) + flight_result["drogueInflatedVelocity"] = flight_data.v( + flight_data.parachuteEvents[0][0] + flight_data.parachuteEvents[0][1].lag + ) + else: + flight_result["drogueTriggerTime"] = 0 + flight_result["drogueInflatedTime"] = 0 + flight_result["drogueInflatedVelocity"] = 0 + + # Write flight setting and results to file + dispersion_input_file.write(str(flight_setting) + "\n") + dispersion_output_file.write(str(flight_result) + "\n") + + def export_flight_error(flight_setting): + dispersion_error_file.write(str(flight_setting) + "\n") + + # Basic analysis info + + + # Create data files for inputs, outputs and error logging + dispersion_error_file = open(str(filename) + ".disp_errors.txt", "w") + dispersion_input_file = open(str(filename) + ".disp_inputs.txt", "w") + dispersion_output_file = open(str(filename) + ".disp_outputs.txt", "w") + + # Initialize counter and timer + i = 0 + + initial_wall_time = time() + initial_cpu_time = process_time() + + # Iterate over flight settings + out = display("Starting", display_id=True) + for setting in flight_settings(analysis_parameters, number_of_simulations): + start_time = process_time() + i += 1 + + # Creates copy of environment + envDispersion = self.environment + + # Apply environment parameters variations on each iteration if possible + envDispersion.railLength = ( + setting["railLength"] + if "railLength" in setting + else envDispersion.rL + ) + envDispersion.gravity = ( + setting["gravity"] if "gravity" in setting + else envDispersion.g + ) + envDispersion.date = ( + setting["date"] if "date" in setting + else envDispersion.date + ) + envDispersion.latitude = ( + setting["latitude"] if "latitude" in setting + else envDispersion.lat + ) + envDispersion.longitude = ( + setting["longitude"] + if "longitude" in setting + else envDispersion.lon + ) + envDispersion.elevation = ( + setting["elevation"] + if "elevation" in setting + else envDispersion.elevation + ) + envDispersion.datum = ( + setting["datum"] if "datum" in setting + else envDispersion.datum + ) + if "ensembleMember" in setting: + envDispersion.selectEnsembleMember(setting["ensembleMember"]) + + + + + + # Creates copy of motor + motorDispersion = self.motor + + # Apply motor parameters variations on each iteration if possible + motorDispersion = SolidMotor( + thrustSource = setting["thrustSource"] if "thrustSource" in setting else motorDispersion.thrustSource, + burnOut = setting["burnOut"] if "burnOut" in setting else motorDispersion.burnOut, + grainNumber = setting["grainNumber"] if "grainNumber" in setting else motorDispersion.grainNumber, + grainDensity = setting["grainDensity"] if "grainDensity" in setting else motorDispersion.grainDensity, + grainOuterRadius = setting["grainOuterRadius"] if "grainOuterRadius" in setting else motorDispersion.grainOuterRadius, + grainInitialInnerRadius = setting["grainInitialInnerRadius"] if "grainInitialInnerRadius" in setting else motorDispersion.grainInitialInnerRadius, + grainInitialHeight = setting["grainInitialHeight"] if "grainInitialHeight" in setting else motorDispersion.grainInitialHeight, + grainSeparation = setting["grainSeparation"] if "grainSeparation" in setting else motorDispersion.grainSeparation, + nozzleRadius = setting["nozzleRadius"] if "nozzleRadius" in setting else motorDispersion.nozzleRadius, + throatRadius = setting["throatRadius"] if "throatRadius" in setting else motorDispersion.throatRadius, + reshapeThrustCurve = setting["reshapeThrustCurve"] if "reshapeThrustCurve" in setting else motorDispersion.reshapeThrustCurve, + interpolationMethod = setting["interpolationMethod"] if "interpolationMethod" in setting else motorDispersion.interpolationMethod, + ) + + + + # Creates copy of rocket + rocketDispersion = self.rocket + + # Apply rocket parameters variations on each iteration if possible + rocketDispersion = Rocket( + motor=motorDispersion, + mass = setting["rocketMass"] if "rocketMass" in setting else self.rocket.mass, + inertiaI = setting["inertiaI"] if "inertiaI" in setting else self.rocket.inertiaI, + inertiaZ = setting["inertiaZ"] if "inertiaZ" in setting else self.rocket.inertiaZ, + radius = setting["radius"] if "radius" in setting else self.rocket.radius, + distanceRocketNozzle = setting["distanceRocketNozzle"] if "distanceRocketNozzle" in setting else self.rocket.distanceRocketNozzle, + distanceRocketPropellant = setting["distanceRocketPropellant"] if "distanceRocketPropellant" in setting else self.rocket.distanceRocketPropellant, + powerOffDrag = setting["powerOffDrag"] if "powerOffDrag" in setting else self.rocket.powerOffDrag, + powerOnDrag = setting["powerOnDrag"] if "powerOnDrag" in setting else self.rocket.powerOnDrag, + ) + + # Add rocket nose, fins and tail + rocketDispersion.addNose( + length=setting["noseLength"] if "noseLength" in setting else self.rocket.noseLength, + kind=setting["noseKind"]if "noseKind" in setting else self.rocket.noseKind, + distanceToCM=setting["noseDistanceToCM"]if "noseDistanceToCM" in setting else self.rocket.noseDistanceToCM, + ) + rocketDispersion.addFins( + n=setting["n"] if "n" in setting else self.rocket.numberOfFins, + rootChord=setting["rootChord"] if "rootChord" in setting else self.rocket.rootChord, + tipChord=setting["tipChord"] if "tipChord" in setting else self.rocket.tipChord, + span=setting["span"] if "span" in setting else self.rocket.span, + distanceToCM=setting["finDistanceToCM"] if "finDistanceToCM" in setting else self.rocket.distanceRocketFins, + radius=setting["radius"] if "radius" in setting else self.rocket.finRadius, + airfoil=setting["airfoil"] if "airfoil" in setting else self.rocket.finAirfoil + ) + rocketDispersion.addTail( + topRadius =setting["topRadius"] if "topRadius" in setting else self.rocket.tailTopRadius, + bottomRadius=setting["bottomRadius"] if "bottomRadius" in setting else self.rocket.tailBottomRadius, + length=setting["length"] if "length" in setting else self.rocket.tailLength, + distanceToCM=setting["distanceToCM"] if "distanceToCM" in setting else self.rocket.tailDistanceToCM + ) + + # Add parachute + rocketDispersion.addParachute( + name=setting["name"] if "name" in setting else self.rocket.parachuteName, + CdS=setting["CdS"] if "CdS" in setting else self.rocket.parachuteCdS, + trigger=setting["trigger"] if "trigger" in setting else self.rocket.parachuteTrigger, + samplingRate=setting["samplingRate"] if "samplingRate" in setting else self.rocket.parachuteSamplingRate, + lag=setting["lag_rec"] if "lag_rec" in setting else self.rocket.lag_rec + setting["lag_se"] if "lag_se" in setting else self.rocket.parachuteLag, + noise=setting["noise"] if "noise" in setting else self.rocket.parachuteNoise, + ) + + rocketDispersion.setRailButtons( + distanceToCM = setting["RBdistanceToCM"] if "RBdistanceToCM" in setting else self.rocket.RBdistanceToCM, + angularPosition = setting["angularPosition"] if "angularPosition" in setting else self.rocket.angularPosition + ) + + # Run trajectory simulation + try: + TestFlight = Flight( + rocket=rocketDispersion, + environment=envDispersion, + inclination=setting["inclination"] if "inclination" in setting else self.flight.inclination, + heading= setting["heading"] if "heading" in setting else self.flight.heading, + #initialSolution=setting["initialSolution"] if "initialSolution" in setting else self.flight.initialSolution, + terminateOnApogee=setting["terminateOnApogee"] if "terminateOnApogee" in setting else self.flight.terminateOnApogee, + maxTime=setting["maxTime"] if "maxTime" in setting else self.flight.maxTime, + maxTimeStep=setting["maxTimeStep"] if "maxTimeStep" in setting else self.flight.maxTimeStep, + minTimeStep=setting["minTimeStep"] if "minTimeStep" in setting else self.flight.minTimeStep, + rtol=setting["rtol"] if "rtol" in setting else self.flight.rtol, + atol=setting["atol"] if "atol" in setting else self.flight.atol, + timeOvershoot=setting["timeOvershoot"] if "timeOvershoot" in setting else self.flight.timeOvershoot, + verbose=False, + ) + + export_flight_data(setting, TestFlight, process_time() - start_time) + except Exception as E: + print(E) + print(traceback.format_exc()) + export_flight_error(setting) + + # Register time + out.update( + f"Curent iteration: {i:06d} | Average Time per Iteration: {(process_time() - initial_cpu_time)/i:2.6f} s | Estimated time left: {int((number_of_simulations - i)*((process_time() - initial_cpu_time)/i))} s" + ) + + # Done + + ## Print and save total time + final_string = f"Completed {i} iterations successfully. Total CPU time: {process_time() - initial_cpu_time} s. Total wall time: {time() - initial_wall_time} s" + out.update(final_string) + dispersion_input_file.write(final_string + "\n") + dispersion_output_file.write(final_string + "\n") + dispersion_error_file.write(final_string + "\n") + + ## Close files + dispersion_input_file.close() + dispersion_output_file.close() + dispersion_error_file.close() + + def importingDispersionResultsFromFile(self,dispersion_output_file): + + # Initialize variable to store all results + dispersion_general_results = [] + + dispersion_results = {"outOfRailTime": [], + "outOfRailVelocity": [], + "apogeeTime": [], + "apogeeAltitude": [], + "apogeeX": [], + "apogeeY": [], + "impactTime": [], + "impactX": [], + "impactY": [], + "impactVelocity": [], + "initialStaticMargin": [], + "outOfRailStaticMargin": [], + "finalStaticMargin": [], + "numberOfEvents": [], + "maxVelocity": [], + "drogueTriggerTime": [], + "drogueInflatedTime": [], + "drogueInflatedVelocity": [], + "executionTime": [], + 'railDepartureAngleOfAttack': [], + 'lateralWind': [], + 'frontalWind': []} + + # Get all dispersion results + # Get file + dispersion_output_file = open(dispersion_output_file, "r+") + + # Read each line of the file and convert to dict + for line in dispersion_output_file: + # Skip comments lines + if line[0] != "{": + continue + # Eval results and store them + flight_result = eval(line) + dispersion_general_results.append(flight_result) + for parameter_key, parameter_value in flight_result.items(): + dispersion_results[parameter_key].append(parameter_value) + + # Close data file + dispersion_output_file.close() + + #Number of flights simulated + self.N = len(dispersion_general_results) + + return dispersion_results + + def plotOutOfRailTime(self, dispersion_results): + print( + f'Out of Rail Time - Mean Value: {np.mean(dispersion_results["outOfRailTime"]):0.3f} s' + ) + print( + f'Out of Rail Time - Standard Deviation: {np.std(dispersion_results["outOfRailTime"]):0.3f} s' + ) + + plt.figure() + plt.hist(dispersion_results["outOfRailTime"], bins=int(self.N**0.5)) + plt.title("Out of Rail Time") + plt.xlabel("Time (s)") + plt.ylabel("Number of Occurences") + plt.show() + + return None + + def plotOutOfRailVelocity(self, dispersion_results): + print( + f'Out of Rail Velocity - Mean Value: {np.mean(dispersion_results["outOfRailVelocity"]):0.3f} m/s' + ) + print( + f'Out of Rail Velocity - Standard Deviation: {np.std(dispersion_results["outOfRailVelocity"]):0.3f} m/s' + ) + + plt.figure() + plt.hist(dispersion_results["outOfRailVelocity"], bins=int(self.N**0.5)) + plt.title("Out of Rail Velocity") + plt.xlabel("Velocity (m/s)") + plt.ylabel("Number of Occurences") + plt.show() + + return None + + def plotApogeeTime(self, dispersion_results): + print( + f'Impact Time - Mean Value: {np.mean(dispersion_results["impactTime"]):0.3f} s' + ) + print( + f'Impact Time - Standard Deviation: {np.std(dispersion_results["impactTime"]):0.3f} s' + ) + + plt.figure() + plt.hist(dispersion_results["impactTime"], bins=int(self.N**0.5)) + plt.title("Impact Time") + plt.xlabel("Time (s)") + plt.ylabel("Number of Occurences") + plt.show() + + return None + + def plotApogeeAltitude(self, dispersion_results): + print( + f'Apogee Altitude - Mean Value: {np.mean(dispersion_results["apogeeAltitude"]):0.3f} m' + ) + print( + f'Apogee Altitude - Standard Deviation: {np.std(dispersion_results["apogeeAltitude"]):0.3f} m' + ) + + plt.figure() + plt.hist(dispersion_results["apogeeAltitude"], bins=int(self.N**0.5)) + plt.title("Apogee Altitude") + plt.xlabel("Altitude (m)") + plt.ylabel("Number of Occurences") + plt.show() + + return None + + def plotApogeeXPosition(self, dispersion_results): + print( + f'Apogee X Position - Mean Value: {np.mean(dispersion_results["apogeeX"]):0.3f} m' + ) + print( + f'Apogee X Position - Standard Deviation: {np.std(dispersion_results["apogeeX"]):0.3f} m' + ) + + plt.figure() + plt.hist(dispersion_results["apogeeX"], bins=int(self.N**0.5)) + plt.title("Apogee X Position") + plt.xlabel("Apogee X Position (m)") + plt.ylabel("Number of Occurences") + plt.show() + + return None + + def plotApogeeYPosition(self, dispersion_results): + print( + f'Apogee Y Position - Mean Value: {np.mean(dispersion_results["apogeeY"]):0.3f} m' + ) + print( + f'Apogee Y Position - Standard Deviation: {np.std(dispersion_results["apogeeY"]):0.3f} m' + ) + + plt.figure() + plt.hist(dispersion_results["apogeeY"], bins=int(self.N**0.5)) + plt.title("Apogee Y Position") + plt.xlabel("Apogee Y Position (m)") + plt.ylabel("Number of Occurences") + plt.show() + + return None + + def plotImpactTime(self, dispersion_results): + print( + f'Impact Time - Mean Value: {np.mean(dispersion_results["impactTime"]):0.3f} s' + ) + print( + f'Impact Time - Standard Deviation: {np.std(dispersion_results["impactTime"]):0.3f} s' + ) + + plt.figure() + plt.hist(dispersion_results["impactTime"], bins=int(self.N**0.5)) + plt.title("Impact Time") + plt.xlabel("Time (s)") + plt.ylabel("Number of Occurences") + plt.show() + + return None + + def plotImpactXPosition(self, dispersion_results): + print( + f'Impact X Position - Mean Value: {np.mean(dispersion_results["impactX"]):0.3f} m' + ) + print( + f'Impact X Position - Standard Deviation: {np.std(dispersion_results["impactX"]):0.3f} m' + ) + + plt.figure() + plt.hist(dispersion_results["impactX"], bins=int(self.N**0.5)) + plt.title("Impact X Position") + plt.xlabel("Impact X Position (m)") + plt.ylabel("Number of Occurences") + plt.show() + + return None + + def plotImpactYPosition(self, dispersion_results): + print( + f'Impact Y Position - Mean Value: {np.mean(dispersion_results["impactY"]):0.3f} m' + ) + print( + f'Impact Y Position - Standard Deviation: {np.std(dispersion_results["impactY"]):0.3f} m' + ) + + plt.figure() + plt.hist(dispersion_results["impactY"], bins=int(self.N**0.5)) + plt.title("Impact Y Position") + plt.xlabel("Impact Y Position (m)") + plt.ylabel("Number of Occurences") + plt.show() + + return None + + def plotImpactVelocity(self, dispersion_results): + print( + f'Impact Velocity - Mean Value: {np.mean(dispersion_results["impactVelocity"]):0.3f} m/s' + ) + print( + f'Impact Velocity - Standard Deviation: {np.std(dispersion_results["impactVelocity"]):0.3f} m/s' + ) + + plt.figure() + plt.hist(dispersion_results["impactVelocity"], bins=int(self.N**0.5)) + plt.title("Impact Velocity") + plt.xlim(-35, 0) + plt.xlabel("Velocity (m/s)") + plt.ylabel("Number of Occurences") + plt.show() + + return None + + def plotStaticMargin(self, dispersion_results): + print( + f'Initial Static Margin - Mean Value: {np.mean(dispersion_results["initialStaticMargin"]):0.3f} c' + ) + print( + f'Initial Static Margin - Standard Deviation: {np.std(dispersion_results["initialStaticMargin"]):0.3f} c' + ) + + print( + f'Out of Rail Static Margin - Mean Value: {np.mean(dispersion_results["outOfRailStaticMargin"]):0.3f} c' + ) + print( + f'Out of Rail Static Margin - Standard Deviation: {np.std(dispersion_results["outOfRailStaticMargin"]):0.3f} c' + ) + + print( + f'Final Static Margin - Mean Value: {np.mean(dispersion_results["finalStaticMargin"]):0.3f} c' + ) + print( + f'Final Static Margin - Standard Deviation: {np.std(dispersion_results["finalStaticMargin"]):0.3f} c' + ) + + plt.figure() + plt.hist( + dispersion_results["initialStaticMargin"], + label="Initial", + bins=int(self.N**0.5), + ) + plt.hist( + dispersion_results["outOfRailStaticMargin"], + label="Out of Rail", + bins=int(self.N**0.5), + ) + plt.hist( + dispersion_results["finalStaticMargin"], label="Final", bins=int(self.N**0.5) + ) + plt.legend() + plt.title("Static Margin") + plt.xlabel("Static Margin (c)") + plt.ylabel("Number of Occurences") + plt.show() + + return None + + def plotMaximumVelocity(self, dispersion_results): + print( + f'Maximum Velocity - Mean Value: {np.mean(dispersion_results["maxVelocity"]):0.3f} m/s' + ) + print( + f'Maximum Velocity - Standard Deviation: {np.std(dispersion_results["maxVelocity"]):0.3f} m/s' + ) + + plt.figure() + plt.hist(dispersion_results["maxVelocity"], bins=int(self.N**0.5)) + plt.title("Maximum Velocity") + plt.xlabel("Velocity (m/s)") + plt.ylabel("Number of Occurences") + plt.show() + + return None + + def plotNumberOfParachuteEvents(self, dispersion_results): + plt.figure() + plt.hist(dispersion_results["numberOfEvents"]) + plt.title("Parachute Events") + plt.xlabel("Number of Parachute Events") + plt.ylabel("Number of Occurences") + plt.show() + + return None + + def plotParachuteTriggerTime(self, dispersion_results): + print( + f'Parachute Trigger Time - Mean Value: {np.mean(dispersion_results["parachuteTriggerTime"]):0.3f} s' + ) + print( + f'Parachute Trigger Time - Standard Deviation: {np.std(dispersion_results["parachuteTriggerTime"]):0.3f} s' + ) + + plt.figure() + plt.hist(dispersion_results["parachuteTriggerTime"], bins=int(self.N**0.5)) + plt.title("Parachute Trigger Time") + plt.xlabel("Time (s)") + plt.ylabel("Number of Occurences") + plt.show() + + return None + + def plotParachuteFullyInflatedTime(self, dispersion_results): + print( + f'Parachute Fully Inflated Time - Mean Value: {np.mean(dispersion_results["parachuteInflatedTime"]):0.3f} s' + ) + print( + f'Parachute Fully Inflated Time - Standard Deviation: {np.std(dispersion_results["parachuteInflatedTime"]):0.3f} s' + ) + + plt.figure() + plt.hist(dispersion_results["parachuteInflatedTime"], bins=int(self.N**0.5)) + plt.title("Parachute Fully Inflated Time") + plt.xlabel("Time (s)") + plt.ylabel("Number of Occurences") + plt.show() + + return None + + def plotParachuteFullyVelocity(self, dispersion_results): + print( + f'Drogue Parachute Fully Inflated Velocity - Mean Value: {np.mean(dispersion_results["parachuteInflatedVelocity"]):0.3f} m/s' + ) + print( + f'Drogue Parachute Fully Inflated Velocity - Standard Deviation: {np.std(dispersion_results["parachuteInflatedVelocity"]):0.3f} m/s' + ) + + plt.figure() + plt.hist(dispersion_results["parachuteInflatedVelocity"], bins=int(self.N**0.5)) + plt.title("Drogue Parachute Fully Inflated Velocity") + plt.xlabel("Velocity m/s)") + plt.ylabel("Number of Occurences") + plt.show() + + return None + + def plotEllipses(self, dispersion_results, image, realLandingPoint): + + # Import background map + img = imread(image) + + # Retrieve dispersion data por apogee and impact XY position + apogeeX = np.array(dispersion_results["apogeeX"]) + apogeeY = np.array(dispersion_results["apogeeY"]) + impactX = np.array(dispersion_results["impactX"]) + impactY = np.array(dispersion_results["impactY"]) + + # Define function to calculate eigen values + def eigsorted(cov): + vals, vecs = np.linalg.eigh(cov) + order = vals.argsort()[::-1] + return vals[order], vecs[:, order] + + # Create plot figure + plt.figure(num=None, figsize=(8, 6), dpi=150, facecolor="w", edgecolor="k") + ax = plt.subplot(111) + + # Calculate error ellipses for impact + impactCov = np.cov(impactX, impactY) + impactVals, impactVecs = eigsorted(impactCov) + impactTheta = np.degrees(np.arctan2(*impactVecs[:, 0][::-1])) + impactW, impactH = 2 * np.sqrt(impactVals) + + # Draw error ellipses for impact + impact_ellipses = [] + for j in [1, 2, 3]: + impactEll = Ellipse( + xy=(np.mean(impactX), np.mean(impactY)), + width=impactW * j, + height=impactH * j, + angle=impactTheta, + color="black", + ) + impactEll.set_facecolor((0, 0, 1, 0.2)) + impact_ellipses.append(impactEll) + ax.add_artist(impactEll) + + # Calculate error ellipses for apogee + apogeeCov = np.cov(apogeeX, apogeeY) + apogeeVals, apogeeVecs = eigsorted(apogeeCov) + apogeeTheta = np.degrees(np.arctan2(*apogeeVecs[:, 0][::-1])) + apogeeW, apogeeH = 2 * np.sqrt(apogeeVals) + + # Draw error ellipses for apogee + for j in [1, 2, 3]: + apogeeEll = Ellipse( + xy=(np.mean(apogeeX), np.mean(apogeeY)), + width=apogeeW * j, + height=apogeeH * j, + angle=apogeeTheta, + color="black", + ) + apogeeEll.set_facecolor((0, 1, 0, 0.2)) + ax.add_artist(apogeeEll) + + # Draw launch point + plt.scatter(0, 0, s=30, marker="*", color="black", label="Launch Point") + # Draw apogee points + plt.scatter( + apogeeX, apogeeY, s=5, marker="^", color="green", label="Simulated Apogee" + ) + # Draw impact points + plt.scatter( + impactX, + impactY, + s=5, + marker="v", + color="blue", + label="Simulated Landing Point", + ) + # Draw real landing point + if realLandingPoint != None: + plt.scatter( + realLandingPoint[0], + realLandingPoint[1], + s=20, + marker="X", + color="red", + label="Measured Landing Point", + ) + + plt.legend() + + # Add title and labels to plot + ax.set_title( + "1$\sigma$, 2$\sigma$ and 3$\sigma$ Dispersion Ellipses: Apogee and Lading Points" + ) + ax.set_ylabel("North (m)") + ax.set_xlabel("East (m)") + + # Add background image to plot + # You can translate the basemap by changing dx and dy (in meters) + dx = 0 + dy = 0 + plt.imshow(img, zorder=0, extent=[-3000 - dx, 3000 - dx, -3000 - dy, 3000 - dy]) + plt.axhline(0, color="black", linewidth=0.5) + plt.axvline(0, color="black", linewidth=0.5) + plt.xlim(-3000, 3000) + plt.ylim(-3000, 3000) + + # Save plot and show result + plt.savefig(str(self.filename) + ".pdf", bbox_inches="tight", pad_inches=0) + plt.savefig(str(self.filename) + ".svg", bbox_inches="tight", pad_inches=0) + plt.show() + + def plotLateralWindSpeed(self, dispersion_results): + print(f'Lateral Surface Wind Speed - Mean Value: {np.mean(dispersion_results["lateralWind"]):0.3f} m/s') + print(f'Lateral Surface Wind Speed - Standard Deviation: {np.std(dispersion_results["lateralWind"]):0.3f} m/s') + + plt.figure() + plt.hist(dispersion_results["lateralWind"], bins=int(self.N**0.5)) + plt.title('Lateral Surface Wind Speed') + plt.xlabel('Velocity (m/s)') + plt.ylabel('Number of Occurences') + plt.show() + + def plotFrontalWindSpeed(self, dispersion_results): + print(f'Frontal Surface Wind Speed - Mean Value: {np.mean(dispersion_results["frontalWind"]):0.3f} m/s') + print(f'Frontal Surface Wind Speed - Standard Deviation: {np.std(dispersion_results["frontalWind"]):0.3f} m/s') + + plt.figure() + plt.hist(dispersion_results["frontalWind"], bins=int(self.N**0.5)) + plt.title('Frontal Surface Wind Speed') + plt.xlabel('Velocity (m/s)') + plt.ylabel('Number of Occurences') + plt.show() + + def info(self): + dispersion_results = self.importingDispersionResultsFromFile(self.filename) + + self.plotEllipses(dispersion_results, self.image, self.realLandingPoint) + + self.plotApogeeAltitude(dispersion_results) + + self.plotOutOfRailVelocity(dispersion_results) + + self.plotStaticMargin(dispersion_results) + + self.plotLateralWindSpeed(dispersion_results) + + self.plotFrontalWindSpeed(dispersion_results) + + self.plotOutOfRailTime(dispersion_results) + + self.plotApogeeTime(dispersion_results) + + self.plotApogeeXPosition(dispersion_results) + + self.plotApogeeYPosition(dispersion_results) + + self.plotImpactTime(dispersion_results) + + self.plotImpactVelocity(dispersion_results) + + self.plotImpactXPosition(dispersion_results) + + self.plotImpactYPosition(dispersion_results) + + self.plotMaximumVelocity(dispersion_results) + + self.plotNumberOfParachuteEvents(dispersion_results) + + self.plotParachuteFullyInflatedTime(dispersion_results) + + self.plotParachuteFullyVelocity(dispersion_results) + + self.plotParachuteTriggerTime(dispersion_results) + + + \ No newline at end of file From b4dd24d98aaf9e484f15534769447a42e81bfd82 Mon Sep 17 00:00:00 2001 From: Sofia-ME Date: Sun, 28 Aug 2022 18:49:10 -0300 Subject: [PATCH 2/9] Dispersion test output files --- teste.disp_errors.txt | 1 + teste.disp_inputs.txt | 11 + teste.disp_outputs.txt | 11 + teste.disp_outputs.txt.svg | 1320 ++++++++++++++++++++++++++++++++++++ 4 files changed, 1343 insertions(+) create mode 100644 teste.disp_errors.txt create mode 100644 teste.disp_inputs.txt create mode 100644 teste.disp_outputs.txt create mode 100644 teste.disp_outputs.txt.svg diff --git a/teste.disp_errors.txt b/teste.disp_errors.txt new file mode 100644 index 000000000..d07f51ef6 --- /dev/null +++ b/teste.disp_errors.txt @@ -0,0 +1 @@ +Completed 10 iterations successfully. Total CPU time: 15.234375 s. Total wall time: 16.870558500289917 s diff --git a/teste.disp_inputs.txt b/teste.disp_inputs.txt new file mode 100644 index 000000000..2bd63732e --- /dev/null +++ b/teste.disp_inputs.txt @@ -0,0 +1,11 @@ +{'rocketMass': 20.008347201798376, 'burnOut': 3.0318961771107666, 'railLength': 5.366767647785012} +{'rocketMass': 19.868491040257794, 'burnOut': 4.208376494843162, 'railLength': 4.6839989670450555} +{'rocketMass': 20.135989957141533, 'burnOut': 3.806715415064825, 'railLength': 5.13998316224309} +{'rocketMass': 19.971391481265243, 'burnOut': 3.476905506103181, 'railLength': 4.750330224670434} +{'rocketMass': 20.073343579331063, 'burnOut': 3.6110998325633266, 'railLength': 4.805494160686089} +{'rocketMass': 20.22562069204278, 'burnOut': 3.8813016560142137, 'railLength': 4.995155834049197} +{'rocketMass': 19.961094608635875, 'burnOut': 3.8520701210022392, 'railLength': 5.093286049385885} +{'rocketMass': 20.211624418125783, 'burnOut': 4.515462131800648, 'railLength': 5.614833482664771} +{'rocketMass': 19.920025649349387, 'burnOut': 4.401484988812121, 'railLength': 4.631576329326545} +{'rocketMass': 20.042685209955962, 'burnOut': 4.644967735603474, 'railLength': 4.840182990387156} +Completed 10 iterations successfully. Total CPU time: 15.234375 s. Total wall time: 16.870558500289917 s diff --git a/teste.disp_outputs.txt b/teste.disp_outputs.txt new file mode 100644 index 000000000..b8d420c64 --- /dev/null +++ b/teste.disp_outputs.txt @@ -0,0 +1,11 @@ +{'outOfRailTime': 0.3944911040820447, 'outOfRailVelocity': 23.343591914713187, 'apogeeTime': 23.422611788985137, 'apogeeAltitude': 2550.847800876637, 'apogeeX': -241.25187886810394, 'apogeeY': 874.0542581881214, 'impactTime': 149.34140181231277, 'impactX': 539.5839528863794, 'impactY': 571.707870066667, 'impactVelocity': -19.511995266459902, 'initialStaticMargin': 2.221085597645965, 'outOfRailStaticMargin': 2.2996812448558366, 'finalStaticMargin': 3.0897188014956276, 'numberOfEvents': 1, 'drogueTriggerTime': 23.42857142857143, 'drogueInflatedTime': 24.92857142857143, 'drogueInflatedVelocity': 39.77277357935216, 'executionTime': 1.484375, 'lateralWind': -3.7535914518728144, 'frontalWind': -6.317898247256396, 'maxVelocity': 234.82629670353523} +{'outOfRailTime': 0.3933473368288729, 'outOfRailVelocity': 23.417304978330783, 'apogeeTime': 23.504827270186265, 'apogeeAltitude': 2573.983545714522, 'apogeeX': -241.9777857681424, 'apogeeY': 878.9438657886889, 'impactTime': 150.9049565617285, 'impactX': 550.3660998292023, 'impactY': 573.0171413207022, 'impactVelocity': -19.443572039075093, 'initialStaticMargin': 2.2157630616888, 'outOfRailStaticMargin': 2.2944993992694833, 'finalStaticMargin': 3.0897188014956276, 'numberOfEvents': 1, 'drogueTriggerTime': 23.514285714285716, 'drogueInflatedTime': 25.014285714285716, 'drogueInflatedVelocity': 39.78276550446817, 'executionTime': 1.421875, 'lateralWind': -3.7535914518728144, 'frontalWind': -6.317898247256396, 'maxVelocity': 236.43889053048326} +{'outOfRailTime': 0.3955257632345688, 'outOfRailVelocity': 23.265328658803195, 'apogeeTime': 23.346725982326763, 'apogeeAltitude': 2529.6737392152395, 'apogeeX': -240.57041897714802, 'apogeeY': 869.5506727076573, 'impactTime': 147.91828516700116, 'impactX': 529.7635399062008, 'impactY': 570.5651977790892, 'impactVelocity': -19.57424036207042, 'initialStaticMargin': 2.2258870529748975, 'outOfRailStaticMargin': 2.304353256852157, 'finalStaticMargin': 3.0897188014956276, 'numberOfEvents': 1, 'drogueTriggerTime': 23.352380952380955, 'drogueInflatedTime': 24.852380952380955, 'drogueInflatedVelocity': 39.772460440155825, 'executionTime': 1.40625, 'lateralWind': -3.7535914518728144, 'frontalWind': -6.317898247256396, 'maxVelocity': 233.36000368365737} +{'outOfRailTime': 0.3940203980287493, 'outOfRailVelocity': 23.34964864524333, 'apogeeTime': 23.444188978637907, 'apogeeAltitude': 2556.905980730848, 'apogeeX': -241.47216230231416, 'apogeeY': 875.3966980120088, 'impactTime': 149.7533665641162, 'impactX': 542.4317120065745, 'impactY': 572.0030551237272, 'impactVelocity': -19.49393849599613, 'initialStaticMargin': 2.219685478089492, 'outOfRailStaticMargin': 2.298277522820546, 'finalStaticMargin': 3.0897188014956276, 'numberOfEvents': 1, 'drogueTriggerTime': 23.44761904761905, 'drogueInflatedTime': 24.94761904761905, 'drogueInflatedVelocity': 39.76609319107769, 'executionTime': 1.265625, 'lateralWind': -3.7535914518728144, 'frontalWind': -6.317898247256396, 'maxVelocity': 235.2488688482768} +{'outOfRailTime': 0.3956649451405656, 'outOfRailVelocity': 23.302967225323197, 'apogeeTime': 23.381190753952595, 'apogeeAltitude': 2539.2548503202906, 'apogeeX': -240.80875151262012, 'apogeeY': 871.4528889635619, 'impactTime': 148.5735672447039, 'impactX': 534.3115181512142, 'impactY': 570.9680948737387, 'impactVelocity': -19.543717126215927, 'initialStaticMargin': 2.223537175446863, 'outOfRailStaticMargin': 2.3022222578405653, 'finalStaticMargin': 3.0897188014956276, 'numberOfEvents': 1, 'drogueTriggerTime': 23.390476190476193, 'drogueInflatedTime': 24.890476190476193, 'drogueInflatedVelocity': 39.778203965403414, 'executionTime': 1.203125, 'lateralWind': -3.7535914518728144, 'frontalWind': -6.317898247256396, 'maxVelocity': 234.03139735995427} +{'outOfRailTime': 0.3960354392624567, 'outOfRailVelocity': 23.20753830401942, 'apogeeTime': 23.293717054938607, 'apogeeAltitude': 2515.0108921396395, 'apogeeX': -240.10849393477596, 'apogeeY': 866.4499873119054, 'impactTime': 146.93649026598447, 'impactX': 523.0543277579958, 'impactY': 569.6911137463339, 'impactVelocity': -19.617815821752888, 'initialStaticMargin': 2.2292270337953406, 'outOfRailStaticMargin': 2.3075511986050548, 'finalStaticMargin': 3.0897188014956276, 'numberOfEvents': 1, 'drogueTriggerTime': 23.295238095238098, 'drogueInflatedTime': 24.795238095238098, 'drogueInflatedVelocity': 39.759782526764205, 'executionTime': 1.375, 'lateralWind': -3.7535914518728144, 'frontalWind': -6.317898247256396, 'maxVelocity': 232.34454573898722} +{'outOfRailTime': 0.39405134456771157, 'outOfRailVelocity': 23.363836360675425, 'apogeeTime': 23.44927479017892, 'apogeeAltitude': 2558.376943642568, 'apogeeX': -241.47628615667128, 'apogeeY': 875.6256898824369, 'impactTime': 149.85401905683776, 'impactX': 543.119123262707, 'impactY': 572.1073434765261, 'impactVelocity': -19.48890318493032, 'initialStaticMargin': 2.2192945621866214, 'outOfRailStaticMargin': 2.2979247234803344, 'finalStaticMargin': 3.0897188014956276, 'numberOfEvents': 1, 'drogueTriggerTime': 23.45714285714286, 'drogueInflatedTime': 24.95714285714286, 'drogueInflatedVelocity': 39.777571032905385, 'executionTime': 1.5625, 'lateralWind': -3.7535914518728144, 'frontalWind': -6.317898247256396, 'maxVelocity': 235.3509967790653} +{'outOfRailTime': 0.39609430016365427, 'outOfRailVelocity': 23.226165158859775, 'apogeeTime': 23.30208203880564, 'apogeeAltitude': 2517.309094224132, 'apogeeX': -240.15968511862917, 'apogeeY': 866.8890582835511, 'impactTime': 147.08975851827537, 'impactX': 524.1155729577022, 'impactY': 569.7891746159438, 'impactVelocity': -19.61104687672425, 'initialStaticMargin': 2.2287071822976774, 'outOfRailStaticMargin': 2.3070865669804856, 'finalStaticMargin': 3.0897188014956276, 'numberOfEvents': 1, 'drogueTriggerTime': 23.304761904761907, 'drogueInflatedTime': 24.804761904761907, 'drogueInflatedVelocity': 39.76165709545755, 'executionTime': 1.828125, 'lateralWind': -3.7535914518728144, 'frontalWind': -6.317898247256396, 'maxVelocity': 232.50254234623392} +{'outOfRailTime': 0.39375416648016054, 'outOfRailVelocity': 23.385862921060706, 'apogeeTime': 23.47429660412743, 'apogeeAltitude': 2565.3728733242815, 'apogeeX': -241.71195410864763, 'apogeeY': 877.1350910041677, 'impactTime': 150.32771190083938, 'impactX': 546.4731029639205, 'impactY': 572.3372399457699, 'impactVelocity': -19.46881174895606, 'initialStaticMargin': 2.217731897597548, 'outOfRailStaticMargin': 2.296412699405396, 'finalStaticMargin': 3.0897188014956276, 'numberOfEvents': 1, 'drogueTriggerTime': 23.476190476190478, 'drogueInflatedTime': 24.976190476190478, 'drogueInflatedVelocity': 39.75793896364731, 'executionTime': 1.96875, 'lateralWind': -3.7535914518728144, 'frontalWind': -6.317898247256396, 'maxVelocity': 235.83954893078507} +{'outOfRailTime': 0.3949237725394638, 'outOfRailVelocity': 23.317176845964415, 'apogeeTime': 23.40122800752664, 'apogeeAltitude': 2544.860372090726, 'apogeeX': -241.04632315684603, 'apogeeY': 872.7589242629534, 'impactTime': 148.9418856568964, 'impactX': 536.7963733214742, 'impactY': 571.4080792073252, 'impactVelocity': -19.52875978509873, 'initialStaticMargin': 2.2223825086626467, 'outOfRailStaticMargin': 2.3009803548281917, 'finalStaticMargin': 3.0897188014956276, 'numberOfEvents': 1, 'drogueTriggerTime': 23.40952380952381, 'drogueInflatedTime': 24.90952380952381, 'drogueInflatedVelocity': 39.77983384543699, 'executionTime': 1.609375, 'lateralWind': -3.7535914518728144, 'frontalWind': -6.317898247256396, 'maxVelocity': 234.41374430423554} +Completed 10 iterations successfully. Total CPU time: 15.234375 s. Total wall time: 16.870558500289917 s diff --git a/teste.disp_outputs.txt.svg b/teste.disp_outputs.txt.svg new file mode 100644 index 000000000..d3bbf0357 --- /dev/null +++ b/teste.disp_outputs.txt.svg @@ -0,0 +1,1320 @@ + + + + + + + + 2022-08-28T18:40:49.936436 + image/svg+xml + + + Matplotlib v3.5.3, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + From 7eb532fea5a4989656f205a4a00e56cbc9a44e89 Mon Sep 17 00:00:00 2001 From: Sofia-ME Date: Sun, 28 Aug 2022 18:51:31 -0300 Subject: [PATCH 3/9] Environment, Flight, Motor and Rocket modifications in order to implement Dispersion class --- rocketpy/Environment.py | 4 ++++ rocketpy/Flight.py | 1 + rocketpy/Motor.py | 10 ++++++++++ rocketpy/Rocket.py | 32 ++++++++++++++++++++++++++++++++ 4 files changed, 47 insertions(+) diff --git a/rocketpy/Environment.py b/rocketpy/Environment.py index 0292b0e1c..9ae378b8b 100644 --- a/rocketpy/Environment.py +++ b/rocketpy/Environment.py @@ -358,6 +358,7 @@ def __init__( self.datum = datum # Save date + self.date = date if date != None: self.setDate(date, timeZone) else: @@ -373,6 +374,8 @@ def __init__( self.setAtmosphericModel("StandardAtmosphere") # Save latitude and longitude + self.lat = latitude + self.lon = longitude if latitude != None and longitude != None: self.setLocation(latitude, longitude) else: @@ -389,6 +392,7 @@ def __init__( self.initialEW = convert[5] # Save elevation + self.elevation = elevation self.setElevation(elevation) # Recalculate Earth Radius diff --git a/rocketpy/Flight.py b/rocketpy/Flight.py index 50ac5d97d..2e21dfb54 100644 --- a/rocketpy/Flight.py +++ b/rocketpy/Flight.py @@ -603,6 +603,7 @@ def __init__( self.initialSolution = initialSolution self.timeOvershoot = timeOvershoot self.terminateOnApogee = terminateOnApogee + self.verbose = verbose # Modifying Rail Length for a better out of rail condition upperRButton = max(self.rocket.railButtons[0]) diff --git a/rocketpy/Motor.py b/rocketpy/Motor.py index be9d62707..7fc605cdb 100644 --- a/rocketpy/Motor.py +++ b/rocketpy/Motor.py @@ -151,6 +151,16 @@ def __init__( ------- None """ + + #Save parameters + self.thrustSource = thrustSource + self.burnOut = burnOut + self.nozzleRadius = nozzleRadius + self.throatRadius = throatRadius + self.reshapeThrustCurve = reshapeThrustCurve + self.interpolationMethod = interpolationMethod + + # Thrust parameters self.interpolate = interpolationMethod self.burnOutTime = burnOut diff --git a/rocketpy/Rocket.py b/rocketpy/Rocket.py index 80d93fc89..1bfb585a9 100644 --- a/rocketpy/Rocket.py +++ b/rocketpy/Rocket.py @@ -368,6 +368,13 @@ def addTail(self, topRadius, bottomRadius, length, distanceToCM): self : Rocket Object of the Rocket class. """ + + # Save parameters for Dispersion + self.tailTopRadius = topRadius + self.tailBottomRadius = bottomRadius + self.tailLength = length + self.tailDistanceToCM = distanceToCM + # Calculate ratio between top and bottom radius r = topRadius / bottomRadius @@ -431,6 +438,13 @@ def addNose(self, length, kind, distanceToCM): self : Rocket Object of the Rocket class. """ + + # Save parameters for Dispersion + self.noseLength = length + self.noseKind = kind + self.noseDistanceToCM = distanceToCM + + # Analyze type if kind == "conical": k = 1 - 1 / 3 @@ -524,6 +538,11 @@ def addFins( Object of the Rocket class. """ + # Save parameters for Dispersion + self.numberOfFins = n + self.finRadius = radius + self.finAirfoil = airfoil + # Retrieve parameters for calculations Cr = rootChord Ct = tipChord @@ -749,6 +768,15 @@ def addParachute( noiseSignal and noisyPressureSignal which are filled in during Flight simulation. """ + + # Save parameters for Dispersion + self.parachuteName = name + self.parachuteCdS = CdS + self.parachuteTrigger = trigger + self.parachuteSamplingRate = samplingRate + self.parachuteLag = lag + self.parachuteNoise = noise + # Create a parachute parachute = Parachute(name, CdS, trigger, samplingRate, lag, noise) @@ -787,11 +815,15 @@ def setRailButtons(self, distanceToCM, angularPosition=45): ------- None """ + + # Order distance to CM if distanceToCM[0] < distanceToCM[1]: distanceToCM.reverse() # Save self.railButtons = self.railButtonPair(distanceToCM, angularPosition) + self.RBdistanceToCM = distanceToCM + self.angularPosition = angularPosition return None From a5811f40fbf0ea662b103b123700203790abd92c Mon Sep 17 00:00:00 2001 From: Sofia-ME Date: Sun, 28 Aug 2022 18:52:34 -0300 Subject: [PATCH 4/9] _init_ modifications in order to implement Dispersion class --- rocketpy/__init__.py | 1 + 1 file changed, 1 insertion(+) diff --git a/rocketpy/__init__.py b/rocketpy/__init__.py index 88f9cba9f..6446fc604 100644 --- a/rocketpy/__init__.py +++ b/rocketpy/__init__.py @@ -27,4 +27,5 @@ from .Function import Function from .Motor import HybridMotor, SolidMotor from .Rocket import Rocket +from .Dispersion import Dispersion from .utilities import * From ec635753ac36c7faf134929266988ed351a18750 Mon Sep 17 00:00:00 2001 From: Sofia-ME Date: Sun, 28 Aug 2022 18:53:26 -0300 Subject: [PATCH 5/9] getting_started with Dispersion class --- getting_started copy.ipynb | 1149 ++++++++++++++++++++++++++++++++++++ 1 file changed, 1149 insertions(+) create mode 100644 getting_started copy.ipynb diff --git a/getting_started copy.ipynb b/getting_started copy.ipynb new file mode 100644 index 000000000..57ebbeb69 --- /dev/null +++ b/getting_started copy.ipynb @@ -0,0 +1,1149 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Getting Started" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we go through a simplified rocket trajectory simulation to get you started. Let's start by importing the rocketpy module." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "from rocketpy import Environment, SolidMotor, Rocket, Flight, Dispersion\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you are using Jupyter Notebooks, it is recommended to run the following line to make matplotlib plots which will be shown later interactive and higher quality." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib widget\n", + "%config InlineBackend.figure_formats = ['svg']\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setting Up a Simulation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Creating an Environment for Spaceport America" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "Env = Environment(\n", + " railLength=5.2, latitude = -23.363611, longitude = -48.011389, elevation=1400)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To get weather data from the GFS forecast, available online, we run the following lines.\n", + "\n", + "First, we set tomorrow's date." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import datetime\n", + "\n", + "tomorrow = datetime.date.today() + datetime.timedelta(days=1)\n", + "\n", + "Env.setDate((2019, 8, 10, 12)) # Hour given in UTC time" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then, we tell Env to use a GFS forecast to get the atmospheric conditions for flight.\n", + "\n", + "Don't mind the warning, it just means that not all variables, such as wind speed or atmospheric temperature, are available at all altitudes given by the forecast." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "Env.setAtmosphericModel(type='Ensemble', file='data\\weather\\LASC2019_TATUI_reanalysis_ensemble.nc', dictionary=\"ECMWF\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see what the weather will look like by calling the info method!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Env.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Creating a Motor\n", + "\n", + "A solid rocket motor is used in this case. To create a motor, the SolidMotor class is used and the required arguments are given.\n", + "\n", + "The SolidMotor class requires the user to have a thrust curve ready. This can come either from a .eng file for a commercial motor, such as below, or a .csv file from a static test measurement.\n", + "\n", + "Besides the thrust curve, other parameters such as grain properties and nozzle dimensions must also be given." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "Pro75M1670 = SolidMotor(\n", + " thrustSource=\"data\\motors\\Cesaroni_M1670.eng\",\n", + " burnOut=3.9,\n", + " grainNumber=5,\n", + " grainSeparation=5 / 1000,\n", + " grainDensity=1815,\n", + " grainOuterRadius=33 / 1000,\n", + " grainInitialInnerRadius=15 / 1000,\n", + " grainInitialHeight=120 / 1000,\n", + " nozzleRadius=33 / 1000,\n", + " throatRadius=11 / 1000,\n", + " interpolationMethod=\"linear\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To see what our thrust curve looks like, along with other import properties, we invoke the info method yet again. You may try the allInfo method if you want more information all at once!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Pro75M1670.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Creating a Rocket" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A rocket is composed of several components. Namely, we must have a motor (good thing we have the Pro75M1670 ready), a couple of aerodynamic surfaces (nose cone, fins and tail) and parachutes (if we are not launching a missile).\n", + "\n", + "Let's start by initializing our rocket, named Calisto, supplying it with the Pro75M1670 engine, entering its inertia properties, some dimensions and also its drag curves." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "Calisto = Rocket(\n", + " motor=Pro75M1670,\n", + " radius=127 / 2000,\n", + " mass=19.197 - 2.956,\n", + " inertiaI=6.60,\n", + " inertiaZ=0.0351,\n", + " distanceRocketNozzle=-1.255,\n", + " distanceRocketPropellant=-0.85704,\n", + " powerOffDrag=\"data/calisto/powerOffDragCurve.csv\",\n", + " powerOnDrag=\"data/calisto/powerOnDragCurve.csv\",\n", + ")\n", + "\n", + "Calisto.setRailButtons([0.2, -0.5])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Adding Aerodynamic Surfaces" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we define the aerodynamic surfaces. They are really straight forward." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "NoseCone = Calisto.addNose(length=0.55829, kind=\"vonKarman\", distanceToCM=0.71971)\n", + "\n", + "FinSet = Calisto.addFins(\n", + " 4, span=0.100, rootChord=0.120, tipChord=0.040, distanceToCM=-1.04956\n", + ")\n", + "\n", + "Tail = Calisto.addTail(\n", + " topRadius=0.0635, bottomRadius=0.0435, length=0.060, distanceToCM=-1.194656\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Adding Parachutes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we have parachutes! Calisto will have two parachutes, Drogue and Main.\n", + "\n", + "Both parachutes are activated by some special algorithm, which is usually really complex and a trade secret. Most algorithms are based on pressure sampling only, while some also use acceleration info.\n", + "\n", + "RocketPy allows you to define a trigger function which will decide when to activate the ejection event for each parachute. This trigger function is supplied with pressure measurement at a predefined sampling rate. This pressure signal is usually noisy, so artificial noise parameters can be given. Call help(Rocket.addParachute) for more details. Furthermore, the trigger function also receives the complete state vector of the rocket, allowing us to use velocity, acceleration or even attitude to decide when the parachute event should be triggered.\n", + "\n", + "Here, we define our trigger functions rather simply using Python. However, you can call the exact code which will fly inside your rocket as well." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "def drogueTrigger(p, y):\n", + " # p = pressure\n", + " # y = [x, y, z, vx, vy, vz, e0, e1, e2, e3, w1, w2, w3]\n", + " # activate drogue when vz < 0 m/s.\n", + " return True if y[5] < 0 else False\n", + "\n", + "\n", + "def mainTrigger(p, y):\n", + " # p = pressure\n", + " # y = [x, y, z, vx, vy, vz, e0, e1, e2, e3, w1, w2, w3]\n", + " # activate main when vz < 0 m/s and z < 800 + 1400 m (+1400 due to surface elevation).\n", + " return True if y[5] < 0 and y[2] < 800 + 1400 else False\n", + "\n", + "\n", + "Main = Calisto.addParachute(\n", + " \"Main\",\n", + " CdS=10.0,\n", + " trigger=mainTrigger,\n", + " samplingRate=105,\n", + " lag=1.5,\n", + " noise=(0, 8.3, 0.5),\n", + ")\n", + "\n", + "Drogue = Calisto.addParachute(\n", + " \"Drogue\",\n", + " CdS=1.0,\n", + " trigger=drogueTrigger,\n", + " samplingRate=105,\n", + " lag=1.5,\n", + " noise=(0, 8.3, 0.5),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Just be careful if you run this last cell multiple times! If you do so, your rocket will end up with lots of parachutes which activate together, which may cause problems during the flight simulation. We advise you to re-run all cells which define our rocket before running this, preventing unwanted old parachutes. Alternatively, you can run the following lines to remove parachutes.\n", + "\n", + "```python\n", + "Calisto.parachutes.remove(Drogue)\n", + "Calisto.parachutes.remove(Main)\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Simulating a Flight\n", + "\n", + "Simulating a flight trajectory is as simple as initializing a Flight class object givin the rocket and environnement set up above as inputs. The launch rail inclination and heading are also given here." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "TestFlight = Flight(rocket=Calisto, environment=Env, inclination=85, heading=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analyzing the Results\n", + "\n", + "RocketPy gives you many plots, thats for sure! They are divided into sections to keep them organized. Alternatively, see the Flight class documentation to see how to get plots for specific variables only, instead of all of them at once." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "TestFlight.allInfo()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Export Flight Trajectory to a .kml file so it can be opened on Google Earth" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "TestFlight.exportKML(\n", + " fileName=\"trajectory.kml\",\n", + " extrude=True,\n", + " altitudeMode=\"relativetoground\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Dispersion analysis\n", + "\n", + "Monte Carlo analysis to predict probability distributions of the rocket's \n", + "landing point, apogee and other relevant information." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Completed 10 iterations successfully. Total CPU time: 15.234375 s. Total wall time: 16.870558500289917 s'" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "D = Dispersion(number_of_simulations=10, flight=TestFlight, filename='teste', dispersionDict={'rocketMass':(20, 0.2), 'burnOut': (3.9,0.3), 'railLength': (5.2, 0.5)},environment=None, motor=None, rocket=None, distributionType='normal')" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": "\n\n\n \n \n \n \n 2022-08-28T18:40:51.908052\n image/svg+xml\n \n \n Matplotlib v3.5.3, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Apogee Altitude - Mean Value: 2545.160 m\n", + "Apogee Altitude - Standard Deviation: 18.825 m\n" + ] + }, + { + "data": { + "image/svg+xml": "\n\n\n \n \n \n \n 2022-08-28T18:40:53.317724\n image/svg+xml\n \n \n Matplotlib v3.5.3, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Out of Rail Velocity - Mean Value: 23.318 m/s\n", + "Out of Rail Velocity - Standard Deviation: 0.065 m/s\n" + ] + }, + { + "data": { + "image/svg+xml": "\n\n\n \n \n \n \n 2022-08-28T18:40:53.675656\n image/svg+xml\n \n \n Matplotlib v3.5.3, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial Static Margin - Mean Value: 2.222 c\n", + "Initial Static Margin - Standard Deviation: 0.004 c\n", + "Out of Rail Static Margin - Mean Value: 2.301 c\n", + "Out of Rail Static Margin - Standard Deviation: 0.004 c\n", + "Final Static Margin - Mean Value: 3.090 c\n", + "Final Static Margin - Standard Deviation: 0.000 c\n" + ] + }, + { + "data": { + "image/svg+xml": "\n\n\n \n \n \n \n 2022-08-28T18:40:54.407950\n image/svg+xml\n \n \n Matplotlib v3.5.3, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Lateral Surface Wind Speed - Mean Value: -3.754 m/s\n", + "Lateral Surface Wind Speed - Standard Deviation: 0.000 m/s\n" + ] + }, + { + "data": { + "image/svg+xml": "\n\n\n \n \n \n \n 2022-08-28T18:40:54.956571\n image/svg+xml\n \n \n Matplotlib v3.5.3, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "text/plain": [ + "
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Maximum Velocity - Mean Value: 234.436 m/s\n", + "Maximum Velocity - Standard Deviation: 1.306 m/s\n" + ] + }, + { + "data": { + "image/svg+xml": "\n\n\n \n \n \n \n 2022-08-28T18:41:00.106960\n image/svg+xml\n \n \n Matplotlib v3.5.3, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "text/plain": [ + "
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Unexpected exception formatting exception. Falling back to standard exception\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Traceback (most recent call last):\n", + " File \"c:\\Users\\anast\\anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3398, in run_code\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + " File \"C:\\Users\\anast\\AppData\\Local\\Temp\\ipykernel_4372\\2400805620.py\", line 2, in \n", + " D.info()\n", + " File \"c:\\Users\\anast\\Documents\\GitHub\\RocketPy-Hackathon-2022\\rocketpy\\Dispersion.py\", line 990, in info\n", + " self.plotParachuteFullyInflatedTime(dispersion_results)\n", + " File \"c:\\Users\\anast\\Documents\\GitHub\\RocketPy-Hackathon-2022\\rocketpy\\Dispersion.py\", line 792, in plotParachuteFullyInflatedTime\n", + " f'Parachute Fully Inflated Time - Mean Value: {np.mean(dispersion_results[\"parachuteInflatedTime\"]):0.3f} s'\n", + "KeyError: 'parachuteInflatedTime'\n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\anast\\anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 1993, in showtraceback\n", + " stb = self.InteractiveTB.structured_traceback(\n", + " File \"c:\\Users\\anast\\anaconda3\\lib\\site-packages\\IPython\\core\\ultratb.py\", line 1118, in structured_traceback\n", + " return FormattedTB.structured_traceback(\n", + " File \"c:\\Users\\anast\\anaconda3\\lib\\site-packages\\IPython\\core\\ultratb.py\", line 1012, in structured_traceback\n", + " return VerboseTB.structured_traceback(\n", + " File \"c:\\Users\\anast\\anaconda3\\lib\\site-packages\\IPython\\core\\ultratb.py\", line 865, in structured_traceback\n", + " formatted_exception = self.format_exception_as_a_whole(etype, evalue, etb, number_of_lines_of_context,\n", + " File \"c:\\Users\\anast\\anaconda3\\lib\\site-packages\\IPython\\core\\ultratb.py\", line 818, in format_exception_as_a_whole\n", + " frames.append(self.format_record(r))\n", + " File \"c:\\Users\\anast\\anaconda3\\lib\\site-packages\\IPython\\core\\ultratb.py\", line 736, in format_record\n", + " result += ''.join(_format_traceback_lines(frame_info.lines, Colors, self.has_colors, lvals))\n", + " File \"c:\\Users\\anast\\anaconda3\\lib\\site-packages\\stack_data\\utils.py\", line 145, in cached_property_wrapper\n", + " value = obj.__dict__[self.func.__name__] = self.func(obj)\n", + " File \"c:\\Users\\anast\\anaconda3\\lib\\site-packages\\stack_data\\core.py\", line 734, in lines\n", + " pieces = self.included_pieces\n", + " File \"c:\\Users\\anast\\anaconda3\\lib\\site-packages\\stack_data\\utils.py\", line 145, in cached_property_wrapper\n", + " value = obj.__dict__[self.func.__name__] = self.func(obj)\n", + " File \"c:\\Users\\anast\\anaconda3\\lib\\site-packages\\stack_data\\core.py\", line 681, in included_pieces\n", + " pos = scope_pieces.index(self.executing_piece)\n", + " File \"c:\\Users\\anast\\anaconda3\\lib\\site-packages\\stack_data\\utils.py\", line 145, in cached_property_wrapper\n", + " value = obj.__dict__[self.func.__name__] = self.func(obj)\n", + " File \"c:\\Users\\anast\\anaconda3\\lib\\site-packages\\stack_data\\core.py\", line 660, in executing_piece\n", + " return only(\n", + " File \"c:\\Users\\anast\\anaconda3\\lib\\site-packages\\executing\\executing.py\", line 164, in only\n", + " raise NotOneValueFound('Expected one value, found 0')\n", + "executing.executing.NotOneValueFound: Expected one value, found 0\n" + ] + } + ], + "source": [ + "D = Dispersion(filename='teste.disp_outputs.txt', image=\"data/valetudo/valetudo_basemap.png\")\n", + "D.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using Simulation for Design\n", + "\n", + "Here, we go through a couple of examples which make use of RocketPy in cool ways to help us design our rocket." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Dynamic Stability Analysis" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Ever wondered how static stability translates into dynamic stability? Different static margins result in different dynamic behavior, which also depends on the rocket's rotational inertial.\n", + "\n", + "Let's make use of RocketPy's helper class called Function to explore how the dynamic stability of Calisto varies if we change the fins span by a certain factor." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Helper class\n", + "from rocketpy import Function\n", + "\n", + "# Prepare Rocket Class\n", + "Calisto = Rocket(\n", + " motor=Pro75M1670,\n", + " radius=127 / 2000,\n", + " mass=19.197 - 2.956,\n", + " inertiaI=6.60,\n", + " inertiaZ=0.0351,\n", + " distanceRocketNozzle=-1.255,\n", + " distanceRocketPropellant=-0.85704,\n", + " powerOffDrag=\"../../data/calisto/powerOffDragCurve.csv\",\n", + " powerOnDrag=\"../../data/calisto/powerOnDragCurve.csv\",\n", + ")\n", + "Calisto.setRailButtons([0.2, -0.5])\n", + "Nose = Calisto.addNose(length=0.55829, kind=\"vonKarman\", distanceToCM=0.71971)\n", + "FinSet = Calisto.addFins(\n", + " 4, span=0.1, rootChord=0.120, tipChord=0.040, distanceToCM=-1.04956\n", + ")\n", + "Tail = Calisto.addTail(\n", + " topRadius=0.0635, bottomRadius=0.0435, length=0.060, distanceToCM=-1.194656\n", + ")\n", + "\n", + "# Prepare Environment Class\n", + "Env = Environment(5.2, 9.8)\n", + "Env.setAtmosphericModel(type=\"CustomAtmosphere\", wind_v=-5)\n", + "\n", + "# Simulate Different Static Margins by Varying Fin Position\n", + "simulation_results = []\n", + "\n", + "for factor in [0.5, 0.7, 0.9, 1.1, 1.3]:\n", + " # Modify rocket fin set by removing previous one and adding new one\n", + " Calisto.aerodynamicSurfaces.remove(FinSet)\n", + " FinSet = Calisto.addFins(\n", + " 4, span=0.1, rootChord=0.120, tipChord=0.040, distanceToCM=-1.04956 * factor\n", + " )\n", + " # Simulate\n", + " print(\n", + " \"Simulating Rocket with Static Margin of {:1.3f}->{:1.3f} c\".format(\n", + " Calisto.staticMargin(0), Calisto.staticMargin(Calisto.motor.burnOutTime)\n", + " )\n", + " )\n", + " TestFlight = Flight(\n", + " rocket=Calisto,\n", + " environment=Env,\n", + " inclination=90,\n", + " heading=0,\n", + " maxTimeStep=0.01,\n", + " maxTime=5,\n", + " terminateOnApogee=True,\n", + " verbose=True,\n", + " )\n", + " # Post process flight data\n", + " TestFlight.postProcess()\n", + " # Store Results\n", + " staticMarginAtIgnition = Calisto.staticMargin(0)\n", + " staticMarginAtOutOfRail = Calisto.staticMargin(TestFlight.outOfRailTime)\n", + " staticMarginAtSteadyState = Calisto.staticMargin(TestFlight.tFinal)\n", + " simulation_results += [\n", + " (\n", + " TestFlight.attitudeAngle,\n", + " \"{:1.2f} c | {:1.2f} c | {:1.2f} c\".format(\n", + " staticMarginAtIgnition,\n", + " staticMarginAtOutOfRail,\n", + " staticMarginAtSteadyState,\n", + " ),\n", + " )\n", + " ]\n", + "\n", + "Function.comparePlots(\n", + " simulation_results,\n", + " lower=0,\n", + " upper=1.5,\n", + " xlabel=\"Time (s)\",\n", + " ylabel=\"Attitude Angle (deg)\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Characteristic Frequency Calculation\n", + "\n", + "Here we analyze the characteristic frequency of oscillation of our rocket just as it leaves the launch rail. Note that when we ran TestFlight.allInfo(), one of the plots already showed us the frequency spectrum of our flight. Here, however, we have more control of what we are plotting." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "Env = Environment(\n", + " railLength=5.2, latitude=32.990254, longitude=-106.974998, elevation=1400\n", + ")\n", + "\n", + "Env.setAtmosphericModel(type=\"CustomAtmosphere\", wind_v=-5)\n", + "\n", + "# Prepare Motor\n", + "Pro75M1670 = SolidMotor(\n", + " thrustSource=\"../../data/motors/Cesaroni_M1670.eng\",\n", + " burnOut=3.9,\n", + " grainNumber=5,\n", + " grainSeparation=5 / 1000,\n", + " grainDensity=1815,\n", + " grainOuterRadius=33 / 1000,\n", + " grainInitialInnerRadius=15 / 1000,\n", + " grainInitialHeight=120 / 1000,\n", + " nozzleRadius=33 / 1000,\n", + " throatRadius=11 / 1000,\n", + " interpolationMethod=\"linear\",\n", + ")\n", + "\n", + "# Prepare Rocket\n", + "Calisto = Rocket(\n", + " motor=Pro75M1670,\n", + " radius=127 / 2000,\n", + " mass=19.197 - 2.956,\n", + " inertiaI=6.60,\n", + " inertiaZ=0.0351,\n", + " distanceRocketNozzle=-1.255,\n", + " distanceRocketPropellant=-0.85704,\n", + " powerOffDrag=\"../../data/calisto/powerOffDragCurve.csv\",\n", + " powerOnDrag=\"../../data/calisto/powerOnDragCurve.csv\",\n", + ")\n", + "\n", + "Calisto.setRailButtons([0.2, -0.5])\n", + "\n", + "Nose = Calisto.addNose(length=0.55829, kind=\"vonKarman\", distanceToCM=0.71971)\n", + "FinSet = Calisto.addFins(\n", + " 4, span=0.1, rootChord=0.120, tipChord=0.040, distanceToCM=-1.04956\n", + ")\n", + "Tail = Calisto.addTail(\n", + " topRadius=0.0635, bottomRadius=0.0435, length=0.060, distanceToCM=-1.194656\n", + ")\n", + "\n", + "# Simulate first 5 seconds of Flight\n", + "TestFlight = Flight(\n", + " rocket=Calisto,\n", + " environment=Env,\n", + " inclination=90,\n", + " heading=0,\n", + " maxTimeStep=0.01,\n", + " maxTime=5,\n", + ")\n", + "TestFlight.postProcess()\n", + "\n", + "# Perform a Fourier Analysis\n", + "Fs = 100.0\n", + "# sampling rate\n", + "Ts = 1.0 / Fs\n", + "# sampling interval\n", + "t = np.arange(1, 400, Ts) # time vector\n", + "ff = 5\n", + "# frequency of the signal\n", + "y = TestFlight.attitudeAngle(t) - np.mean(TestFlight.attitudeAngle(t))\n", + "n = len(y) # length of the signal\n", + "k = np.arange(n)\n", + "T = n / Fs\n", + "frq = k / T # two sides frequency range\n", + "frq = frq[range(n // 2)] # one side frequency range\n", + "Y = np.fft.fft(y) / n # fft computing and normalization\n", + "Y = Y[range(n // 2)]\n", + "fig, ax = plt.subplots(2, 1)\n", + "ax[0].plot(t, y)\n", + "ax[0].set_xlabel(\"Time\")\n", + "ax[0].set_ylabel(\"Signal\")\n", + "ax[0].set_xlim((0, 5))\n", + "ax[1].plot(frq, abs(Y), \"r\") # plotting the spectrum\n", + "ax[1].set_xlabel(\"Freq (Hz)\")\n", + "ax[1].set_ylabel(\"|Y(freq)|\")\n", + "ax[1].set_xlim((0, 5))\n", + "plt.subplots_adjust(hspace=0.5)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Apogee as a Function of Mass\n", + "\n", + "This one is a classic one! We always need to know how much our rocket's apogee will change when our payload gets heavier." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def apogee(mass):\n", + " # Prepare Environment\n", + " Env = Environment(\n", + " railLength=5.2,\n", + " latitude=32.990254,\n", + " longitude=-106.974998,\n", + " elevation=1400,\n", + " date=(2018, 6, 20, 18),\n", + " )\n", + "\n", + " Env.setAtmosphericModel(type=\"CustomAtmosphere\", wind_v=-5)\n", + "\n", + " # Prepare Motor\n", + " Pro75M1670 = SolidMotor(\n", + " thrustSource=\"../../data/motors/Cesaroni_M1670.eng\",\n", + " burnOut=3.9,\n", + " grainNumber=5,\n", + " grainSeparation=5 / 1000,\n", + " grainDensity=1815,\n", + " grainOuterRadius=33 / 1000,\n", + " grainInitialInnerRadius=15 / 1000,\n", + " grainInitialHeight=120 / 1000,\n", + " nozzleRadius=33 / 1000,\n", + " throatRadius=11 / 1000,\n", + " interpolationMethod=\"linear\",\n", + " )\n", + "\n", + " # Prepare Rocket\n", + " Calisto = Rocket(\n", + " motor=Pro75M1670,\n", + " radius=127 / 2000,\n", + " mass=mass,\n", + " inertiaI=6.60,\n", + " inertiaZ=0.0351,\n", + " distanceRocketNozzle=-1.255,\n", + " distanceRocketPropellant=-0.85704,\n", + " powerOffDrag=\"../../data/calisto/powerOffDragCurve.csv\",\n", + " powerOnDrag=\"../../data/calisto/powerOnDragCurve.csv\",\n", + " )\n", + "\n", + " Calisto.setRailButtons([0.2, -0.5])\n", + " Nose = Calisto.addNose(length=0.55829, kind=\"vonKarman\", distanceToCM=0.71971)\n", + " FinSet = Calisto.addFins(\n", + " 4, span=0.1, rootChord=0.120, tipChord=0.040, distanceToCM=-1.04956\n", + " )\n", + " Tail = Calisto.addTail(\n", + " topRadius=0.0635, bottomRadius=0.0435, length=0.060, distanceToCM=-1.194656\n", + " )\n", + "\n", + " # Simulate Flight until Apogee\n", + " TestFlight = Flight(\n", + " rocket=Calisto,\n", + " environment=Env,\n", + " inclination=85,\n", + " heading=0,\n", + " terminateOnApogee=True,\n", + " )\n", + " return TestFlight.apogee\n", + "\n", + "\n", + "apogeebymass = Function(apogee, inputs=\"Mass (kg)\", outputs=\"Estimated Apogee (m)\")\n", + "apogeebymass.plot(8, 20, 20)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Out of Rail Speed as a Function of Mass\n", + "\n", + "To finish off, lets make a really important plot. Out of rail speed is the speed our rocket has when it is leaving the launch rail. This is crucial to make sure it can fly safely after leaving the rail. A common rule of thumb is that our rocket's out of rail speed should be 4 times the wind speed so that it does not stall and become unstable." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def speed(mass):\n", + " # Prepare Environment\n", + " Env = Environment(\n", + " railLength=5.2,\n", + " latitude=32.990254,\n", + " longitude=-106.974998,\n", + " elevation=1400,\n", + " date=(2018, 6, 20, 18),\n", + " )\n", + "\n", + " Env.setAtmosphericModel(type=\"CustomAtmosphere\", wind_v=-5)\n", + "\n", + " # Prepare Motor\n", + " Pro75M1670 = SolidMotor(\n", + " thrustSource=\"../../data/motors/Cesaroni_M1670.eng\",\n", + " burnOut=3.9,\n", + " grainNumber=5,\n", + " grainSeparation=5 / 1000,\n", + " grainDensity=1815,\n", + " grainOuterRadius=33 / 1000,\n", + " grainInitialInnerRadius=15 / 1000,\n", + " grainInitialHeight=120 / 1000,\n", + " nozzleRadius=33 / 1000,\n", + " throatRadius=11 / 1000,\n", + " interpolationMethod=\"linear\",\n", + " )\n", + "\n", + " # Prepare Rocket\n", + " Calisto = Rocket(\n", + " motor=Pro75M1670,\n", + " radius=127 / 2000,\n", + " mass=mass,\n", + " inertiaI=6.60,\n", + " inertiaZ=0.0351,\n", + " distanceRocketNozzle=-1.255,\n", + " distanceRocketPropellant=-0.85704,\n", + " powerOffDrag=\"../../data/calisto/powerOffDragCurve.csv\",\n", + " powerOnDrag=\"../../data/calisto/powerOnDragCurve.csv\",\n", + " )\n", + "\n", + " Calisto.setRailButtons([0.2, -0.5])\n", + " Nose = Calisto.addNose(length=0.55829, kind=\"vonKarman\", distanceToCM=0.71971)\n", + " FinSet = Calisto.addFins(\n", + " 4, span=0.1, rootChord=0.120, tipChord=0.040, distanceToCM=-1.04956\n", + " )\n", + " Tail = Calisto.addTail(\n", + " topRadius=0.0635, bottomRadius=0.0435, length=0.060, distanceToCM=-1.194656\n", + " )\n", + "\n", + " # Simulate Flight until Apogee\n", + " TestFlight = Flight(\n", + " rocket=Calisto,\n", + " environment=Env,\n", + " inclination=85,\n", + " heading=0,\n", + " terminateOnApogee=True,\n", + " )\n", + " return TestFlight.outOfRailVelocity\n", + "\n", + "\n", + "speedbymass = Function(speed, inputs=\"Mass (kg)\", outputs=\"Out of Rail Speed (m/s)\")\n", + "speedbymass.plot(8, 20, 20)" + ] + } + ], + "metadata": { + "hide_input": false, + "kernelspec": { + "display_name": "Python 3.8.8 64-bit ('base': conda)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + }, + "vscode": { + "interpreter": { + "hash": "ea183bb76f01b1c0f19d4faefaf72022d209702e86a95c61a348be375d9bcd4f" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 078aaca2de54f8509efa1afabd7a74ebfbe721b0 Mon Sep 17 00:00:00 2001 From: Sofia-ME Date: Sun, 28 Aug 2022 20:36:10 -0300 Subject: [PATCH 6/9] Fix parachute plots --- rocketpy/Dispersion.py | 34 +++++++++++++++++----------------- 1 file changed, 17 insertions(+), 17 deletions(-) diff --git a/rocketpy/Dispersion.py b/rocketpy/Dispersion.py index 2364c39c7..2ecdcab75 100644 --- a/rocketpy/Dispersion.py +++ b/rocketpy/Dispersion.py @@ -770,50 +770,50 @@ def plotNumberOfParachuteEvents(self, dispersion_results): return None - def plotParachuteTriggerTime(self, dispersion_results): + def plotDrogueTriggerTime(self, dispersion_results): print( - f'Parachute Trigger Time - Mean Value: {np.mean(dispersion_results["parachuteTriggerTime"]):0.3f} s' + f'Drogue Trigger Time - Mean Value: {np.mean(dispersion_results["drogueTriggerTime"]):0.3f} s' ) print( - f'Parachute Trigger Time - Standard Deviation: {np.std(dispersion_results["parachuteTriggerTime"]):0.3f} s' + f'Drogue Trigger Time - Standard Deviation: {np.std(dispersion_results["drogueTriggerTime"]):0.3f} s' ) plt.figure() - plt.hist(dispersion_results["parachuteTriggerTime"], bins=int(self.N**0.5)) - plt.title("Parachute Trigger Time") + plt.hist(dispersion_results["drogueTriggerTime"], bins=int(self.N**0.5)) + plt.title("Drogue Trigger Time") plt.xlabel("Time (s)") plt.ylabel("Number of Occurences") plt.show() return None - def plotParachuteFullyInflatedTime(self, dispersion_results): + def plotDrogueFullyInflatedTime(self, dispersion_results): print( - f'Parachute Fully Inflated Time - Mean Value: {np.mean(dispersion_results["parachuteInflatedTime"]):0.3f} s' + f'Drogue Fully Inflated Time - Mean Value: {np.mean(dispersion_results["drogueInflatedTime"]):0.3f} s' ) print( - f'Parachute Fully Inflated Time - Standard Deviation: {np.std(dispersion_results["parachuteInflatedTime"]):0.3f} s' + f'Drogue Fully Inflated Time - Standard Deviation: {np.std(dispersion_results["drogueInflatedTime"]):0.3f} s' ) plt.figure() - plt.hist(dispersion_results["parachuteInflatedTime"], bins=int(self.N**0.5)) - plt.title("Parachute Fully Inflated Time") + plt.hist(dispersion_results["drogueInflatedTime"], bins=int(self.N**0.5)) + plt.title("Drogue Fully Inflated Time") plt.xlabel("Time (s)") plt.ylabel("Number of Occurences") plt.show() return None - def plotParachuteFullyVelocity(self, dispersion_results): + def plotDrogueFullyVelocity(self, dispersion_results): print( - f'Drogue Parachute Fully Inflated Velocity - Mean Value: {np.mean(dispersion_results["parachuteInflatedVelocity"]):0.3f} m/s' + f'Drogue Parachute Fully Inflated Velocity - Mean Value: {np.mean(dispersion_results["drogueInflatedVelocity"]):0.3f} m/s' ) print( - f'Drogue Parachute Fully Inflated Velocity - Standard Deviation: {np.std(dispersion_results["parachuteInflatedVelocity"]):0.3f} m/s' + f'Drogue Parachute Fully Inflated Velocity - Standard Deviation: {np.std(dispersion_results["drogueInflatedVelocity"]):0.3f} m/s' ) plt.figure() - plt.hist(dispersion_results["parachuteInflatedVelocity"], bins=int(self.N**0.5)) + plt.hist(dispersion_results["drogueInflatedVelocity"], bins=int(self.N**0.5)) plt.title("Drogue Parachute Fully Inflated Velocity") plt.xlabel("Velocity m/s)") plt.ylabel("Number of Occurences") @@ -987,11 +987,11 @@ def info(self): self.plotNumberOfParachuteEvents(dispersion_results) - self.plotParachuteFullyInflatedTime(dispersion_results) + self.plotDrogueFullyInflatedTime(dispersion_results) - self.plotParachuteFullyVelocity(dispersion_results) + self.plotDrogueFullyVelocity(dispersion_results) - self.plotParachuteTriggerTime(dispersion_results) + self.plotDrogueTriggerTime(dispersion_results) \ No newline at end of file From 1fe00a8e64c99051f6f4962971f4562a7a7533d5 Mon Sep 17 00:00:00 2001 From: Sofia-ME Date: Sun, 28 Aug 2022 20:37:18 -0300 Subject: [PATCH 7/9] Test simulation with changes in parachute plots --- getting_started copy.ipynb | 168 ++++++++++++++-------------- teste.disp_errors.txt | 2 +- teste.disp_inputs.txt | 22 ++-- teste.disp_outputs.txt | 22 ++-- teste.disp_outputs.txt.svg | 224 ++++++++++++++++++------------------- 5 files changed, 219 insertions(+), 219 deletions(-) diff --git a/getting_started copy.ipynb b/getting_started copy.ipynb index 57ebbeb69..8a67849bf 100644 --- a/getting_started copy.ipynb +++ b/getting_started copy.ipynb @@ -390,13 +390,13 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'Completed 10 iterations successfully. Total CPU time: 15.234375 s. Total wall time: 16.870558500289917 s'" + "'Completed 10 iterations successfully. Total CPU time: 17.6875 s. Total wall time: 19.050102710723877 s'" ] }, "metadata": {}, @@ -409,12 +409,12 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 22, "metadata": {}, "outputs": [ { "data": { - "image/svg+xml": "\n\n\n \n \n \n \n 2022-08-28T18:40:51.908052\n image/svg+xml\n \n \n Matplotlib v3.5.3, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-08-28T20:31:22.524557\n image/svg+xml\n \n \n Matplotlib v3.5.3, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ "
" ] @@ -426,13 +426,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Apogee Altitude - Mean Value: 2545.160 m\n", - "Apogee Altitude - Standard Deviation: 18.825 m\n" + "Apogee Altitude - Mean Value: 2555.689 m\n", + "Apogee Altitude - Standard Deviation: 18.910 m\n" ] }, { "data": { - "image/svg+xml": "\n\n\n \n \n \n \n 2022-08-28T18:40:53.317724\n image/svg+xml\n \n \n Matplotlib v3.5.3, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-08-28T20:31:23.787835\n image/svg+xml\n \n \n Matplotlib v3.5.3, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ "
" ] @@ -444,13 +444,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Out of Rail Velocity - Mean Value: 23.318 m/s\n", - "Out of Rail Velocity - Standard Deviation: 0.065 m/s\n" + "Out of Rail Velocity - Mean Value: 23.353 m/s\n", + "Out of Rail Velocity - Standard Deviation: 0.062 m/s\n" ] }, { "data": { - "image/svg+xml": "\n\n\n \n \n \n \n 2022-08-28T18:40:53.675656\n image/svg+xml\n \n \n Matplotlib v3.5.3, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-08-28T20:31:24.173669\n image/svg+xml\n \n \n Matplotlib v3.5.3, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ "
" ] @@ -462,9 +462,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "Initial Static Margin - Mean Value: 2.222 c\n", + "Initial Static Margin - Mean Value: 2.220 c\n", "Initial Static Margin - Standard Deviation: 0.004 c\n", - "Out of Rail Static Margin - Mean Value: 2.301 c\n", + "Out of Rail Static Margin - Mean Value: 2.299 c\n", "Out of Rail Static Margin - Standard Deviation: 0.004 c\n", "Final Static Margin - Mean Value: 3.090 c\n", "Final Static Margin - Standard Deviation: 0.000 c\n" @@ -472,7 +472,7 @@ }, { "data": { - "image/svg+xml": "\n\n\n \n \n \n \n 2022-08-28T18:40:54.407950\n image/svg+xml\n \n \n Matplotlib v3.5.3, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-08-28T20:31:24.559068\n image/svg+xml\n \n \n Matplotlib v3.5.3, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ "
" ] @@ -490,7 +490,7 @@ }, { "data": { - "image/svg+xml": "\n\n\n \n \n \n \n 2022-08-28T18:40:54.956571\n image/svg+xml\n \n \n Matplotlib v3.5.3, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-08-28T20:31:24.963129\n image/svg+xml\n \n \n Matplotlib v3.5.3, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ "
" ] @@ -508,7 +508,7 @@ }, { "data": { - "image/svg+xml": "\n\n\n \n \n \n \n 2022-08-28T18:40:55.425483\n image/svg+xml\n \n \n Matplotlib v3.5.3, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-08-28T20:31:25.316787\n image/svg+xml\n \n \n Matplotlib v3.5.3, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ "
" ] @@ -520,13 +520,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Out of Rail Time - Mean Value: 0.395 s\n", + "Out of Rail Time - Mean Value: 0.394 s\n", "Out of Rail Time - Standard Deviation: 0.001 s\n" ] }, { "data": { - "image/svg+xml": "\n\n\n \n \n \n \n 2022-08-28T18:40:55.884726\n image/svg+xml\n \n \n Matplotlib v3.5.3, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-08-28T20:31:25.652678\n image/svg+xml\n \n \n Matplotlib v3.5.3, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ "
" ] @@ -538,13 +538,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Impact Time - Mean Value: 148.964 s\n", - "Impact Time - Standard Deviation: 1.267 s\n" + "Impact Time - Mean Value: 149.673 s\n", + "Impact Time - Standard Deviation: 1.276 s\n" ] }, { "data": { - "image/svg+xml": "\n\n\n \n \n \n \n 2022-08-28T18:40:56.388560\n image/svg+xml\n \n \n Matplotlib v3.5.3, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-08-28T20:31:25.987736\n image/svg+xml\n \n \n Matplotlib v3.5.3, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ "
" ] @@ -556,13 +556,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Apogee X Position - Mean Value: -241.058 m\n", - "Apogee X Position - Standard Deviation: 0.605 m\n" + "Apogee X Position - Mean Value: -241.404 m\n", + "Apogee X Position - Standard Deviation: 0.608 m\n" ] }, { "data": { - "image/svg+xml": "\n\n\n \n \n \n \n 2022-08-28T18:40:56.857275\n image/svg+xml\n \n \n Matplotlib v3.5.3, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-08-28T20:31:26.366790\n image/svg+xml\n \n \n Matplotlib v3.5.3, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ "
" ] @@ -574,13 +574,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Apogee Y Position - Mean Value: 872.826 m\n", - "Apogee Y Position - Standard Deviation: 4.006 m\n" + "Apogee Y Position - Mean Value: 875.081 m\n", + "Apogee Y Position - Standard Deviation: 4.025 m\n" ] }, { "data": { - "image/svg+xml": "\n\n\n \n \n \n \n 2022-08-28T18:40:57.325538\n image/svg+xml\n \n \n Matplotlib v3.5.3, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-08-28T20:31:26.713085\n image/svg+xml\n \n \n Matplotlib v3.5.3, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ "
" ] @@ -592,13 +592,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Impact Time - Mean Value: 148.964 s\n", - "Impact Time - Standard Deviation: 1.267 s\n" + "Impact Time - Mean Value: 149.673 s\n", + "Impact Time - Standard Deviation: 1.276 s\n" ] }, { "data": { - "image/svg+xml": "\n\n\n \n \n \n \n 2022-08-28T18:40:57.875331\n image/svg+xml\n \n \n Matplotlib v3.5.3, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-08-28T20:31:27.078022\n image/svg+xml\n \n \n Matplotlib v3.5.3, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ "
" ] @@ -610,13 +610,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Impact Velocity - Mean Value: -19.528 m/s\n", + "Impact Velocity - Mean Value: -19.497 m/s\n", "Impact Velocity - Standard Deviation: 0.056 m/s\n" ] }, { "data": { - "image/svg+xml": "\n\n\n \n \n \n \n 2022-08-28T18:40:58.387441\n image/svg+xml\n \n \n Matplotlib v3.5.3, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-08-28T20:31:27.427401\n image/svg+xml\n \n \n Matplotlib v3.5.3, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ "
" ] @@ -628,13 +628,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Impact X Position - Mean Value: 537.002 m\n", - "Impact X Position - Standard Deviation: 8.730 m\n" + "Impact X Position - Mean Value: 541.903 m\n", + "Impact X Position - Standard Deviation: 8.830 m\n" ] }, { "data": { - "image/svg+xml": "\n\n\n \n \n \n \n 2022-08-28T18:40:58.919595\n image/svg+xml\n \n \n Matplotlib v3.5.3, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-08-28T20:31:27.737374\n image/svg+xml\n \n \n Matplotlib v3.5.3, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ "
" ] @@ -646,13 +646,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Impact Y Position - Mean Value: 571.359 m\n", - "Impact Y Position - Standard Deviation: 1.041 m\n" + "Impact Y Position - Mean Value: 571.933 m\n", + "Impact Y Position - Standard Deviation: 0.985 m\n" ] }, { "data": { - "image/svg+xml": "\n\n\n \n \n \n \n 2022-08-28T18:40:59.578264\n image/svg+xml\n \n \n Matplotlib v3.5.3, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-08-28T20:31:28.044229\n image/svg+xml\n \n \n Matplotlib v3.5.3, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ "
" ] @@ -664,13 +664,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Maximum Velocity - Mean Value: 234.436 m/s\n", - "Maximum Velocity - Standard Deviation: 1.306 m/s\n" + "Maximum Velocity - Mean Value: 235.166 m/s\n", + "Maximum Velocity - Standard Deviation: 1.315 m/s\n" ] }, { "data": { - "image/svg+xml": "\n\n\n \n \n \n \n 2022-08-28T18:41:00.106960\n image/svg+xml\n \n \n Matplotlib v3.5.3, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-08-28T20:31:28.371504\n image/svg+xml\n \n \n Matplotlib v3.5.3, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ "
" ] @@ -680,7 +680,7 @@ }, { "data": { - "image/svg+xml": "\n\n\n \n \n \n \n 2022-08-28T18:41:00.585162\n image/svg+xml\n \n \n Matplotlib v3.5.3, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-08-28T20:31:28.743000\n image/svg+xml\n \n \n Matplotlib v3.5.3, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ "
" ] @@ -692,55 +692,55 @@ "name": "stdout", "output_type": "stream", "text": [ - "Unexpected exception formatting exception. Falling back to standard exception\n" + "Drogue Fully Inflated Time - Mean Value: 24.945 s\n", + "Drogue Fully Inflated Time - Standard Deviation: 0.067 s\n" ] }, { - "name": "stderr", + "data": { + "image/svg+xml": "\n\n\n \n \n \n \n 2022-08-28T20:31:29.142821\n image/svg+xml\n \n \n Matplotlib v3.5.3, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drogue Parachute Fully Inflated Velocity - Mean Value: 39.770 m/s\n", + "Drogue Parachute Fully Inflated Velocity - Standard Deviation: 0.009 m/s\n" + ] + }, + { + "data": { + "image/svg+xml": "\n\n\n \n \n \n \n 2022-08-28T20:31:29.518859\n image/svg+xml\n \n \n Matplotlib v3.5.3, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", "output_type": "stream", "text": [ - "Traceback (most recent call last):\n", - " File \"c:\\Users\\anast\\anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3398, in run_code\n", - " exec(code_obj, self.user_global_ns, self.user_ns)\n", - " File \"C:\\Users\\anast\\AppData\\Local\\Temp\\ipykernel_4372\\2400805620.py\", line 2, in \n", - " D.info()\n", - " File \"c:\\Users\\anast\\Documents\\GitHub\\RocketPy-Hackathon-2022\\rocketpy\\Dispersion.py\", line 990, in info\n", - " self.plotParachuteFullyInflatedTime(dispersion_results)\n", - " File \"c:\\Users\\anast\\Documents\\GitHub\\RocketPy-Hackathon-2022\\rocketpy\\Dispersion.py\", line 792, in plotParachuteFullyInflatedTime\n", - " f'Parachute Fully Inflated Time - Mean Value: {np.mean(dispersion_results[\"parachuteInflatedTime\"]):0.3f} s'\n", - "KeyError: 'parachuteInflatedTime'\n", - "\n", - "During handling of the above exception, another exception occurred:\n", - "\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\anast\\anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 1993, in showtraceback\n", - " stb = self.InteractiveTB.structured_traceback(\n", - " File \"c:\\Users\\anast\\anaconda3\\lib\\site-packages\\IPython\\core\\ultratb.py\", line 1118, in structured_traceback\n", - " return FormattedTB.structured_traceback(\n", - " File \"c:\\Users\\anast\\anaconda3\\lib\\site-packages\\IPython\\core\\ultratb.py\", line 1012, in structured_traceback\n", - " return VerboseTB.structured_traceback(\n", - " File \"c:\\Users\\anast\\anaconda3\\lib\\site-packages\\IPython\\core\\ultratb.py\", line 865, in structured_traceback\n", - " formatted_exception = self.format_exception_as_a_whole(etype, evalue, etb, number_of_lines_of_context,\n", - " File \"c:\\Users\\anast\\anaconda3\\lib\\site-packages\\IPython\\core\\ultratb.py\", line 818, in format_exception_as_a_whole\n", - " frames.append(self.format_record(r))\n", - " File \"c:\\Users\\anast\\anaconda3\\lib\\site-packages\\IPython\\core\\ultratb.py\", line 736, in format_record\n", - " result += ''.join(_format_traceback_lines(frame_info.lines, Colors, self.has_colors, lvals))\n", - " File \"c:\\Users\\anast\\anaconda3\\lib\\site-packages\\stack_data\\utils.py\", line 145, in cached_property_wrapper\n", - " value = obj.__dict__[self.func.__name__] = self.func(obj)\n", - " File \"c:\\Users\\anast\\anaconda3\\lib\\site-packages\\stack_data\\core.py\", line 734, in lines\n", - " pieces = self.included_pieces\n", - " File \"c:\\Users\\anast\\anaconda3\\lib\\site-packages\\stack_data\\utils.py\", line 145, in cached_property_wrapper\n", - " value = obj.__dict__[self.func.__name__] = self.func(obj)\n", - " File \"c:\\Users\\anast\\anaconda3\\lib\\site-packages\\stack_data\\core.py\", line 681, in included_pieces\n", - " pos = scope_pieces.index(self.executing_piece)\n", - " File \"c:\\Users\\anast\\anaconda3\\lib\\site-packages\\stack_data\\utils.py\", line 145, in cached_property_wrapper\n", - " value = obj.__dict__[self.func.__name__] = self.func(obj)\n", - " File \"c:\\Users\\anast\\anaconda3\\lib\\site-packages\\stack_data\\core.py\", line 660, in executing_piece\n", - " return only(\n", - " File \"c:\\Users\\anast\\anaconda3\\lib\\site-packages\\executing\\executing.py\", line 164, in only\n", - " raise NotOneValueFound('Expected one value, found 0')\n", - "executing.executing.NotOneValueFound: Expected one value, found 0\n" + "Drogue Trigger Time - Mean Value: 23.445 s\n", + "Drogue Trigger Time - Standard Deviation: 0.067 s\n" ] + }, + { + "data": { + "image/svg+xml": "\n\n\n \n \n \n \n 2022-08-28T20:31:30.072940\n image/svg+xml\n \n \n Matplotlib v3.5.3, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ diff --git a/teste.disp_errors.txt b/teste.disp_errors.txt index d07f51ef6..63ae3e1a5 100644 --- a/teste.disp_errors.txt +++ b/teste.disp_errors.txt @@ -1 +1 @@ -Completed 10 iterations successfully. Total CPU time: 15.234375 s. Total wall time: 16.870558500289917 s +Completed 10 iterations successfully. Total CPU time: 17.6875 s. Total wall time: 19.050102710723877 s diff --git a/teste.disp_inputs.txt b/teste.disp_inputs.txt index 2bd63732e..458975e50 100644 --- a/teste.disp_inputs.txt +++ b/teste.disp_inputs.txt @@ -1,11 +1,11 @@ -{'rocketMass': 20.008347201798376, 'burnOut': 3.0318961771107666, 'railLength': 5.366767647785012} -{'rocketMass': 19.868491040257794, 'burnOut': 4.208376494843162, 'railLength': 4.6839989670450555} -{'rocketMass': 20.135989957141533, 'burnOut': 3.806715415064825, 'railLength': 5.13998316224309} -{'rocketMass': 19.971391481265243, 'burnOut': 3.476905506103181, 'railLength': 4.750330224670434} -{'rocketMass': 20.073343579331063, 'burnOut': 3.6110998325633266, 'railLength': 4.805494160686089} -{'rocketMass': 20.22562069204278, 'burnOut': 3.8813016560142137, 'railLength': 4.995155834049197} -{'rocketMass': 19.961094608635875, 'burnOut': 3.8520701210022392, 'railLength': 5.093286049385885} -{'rocketMass': 20.211624418125783, 'burnOut': 4.515462131800648, 'railLength': 5.614833482664771} -{'rocketMass': 19.920025649349387, 'burnOut': 4.401484988812121, 'railLength': 4.631576329326545} -{'rocketMass': 20.042685209955962, 'burnOut': 4.644967735603474, 'railLength': 4.840182990387156} -Completed 10 iterations successfully. Total CPU time: 15.234375 s. Total wall time: 16.870558500289917 s +{'rocketMass': 19.90652137182852, 'burnOut': 4.122751149659427, 'railLength': 5.565123045049819} +{'rocketMass': 20.040442434623223, 'burnOut': 4.040194754660794, 'railLength': 5.4460671407624535} +{'rocketMass': 19.930837701395934, 'burnOut': 3.468467598491397, 'railLength': 5.150566738117259} +{'rocketMass': 19.8506753734651, 'burnOut': 3.0803468079703005, 'railLength': 5.821128682799574} +{'rocketMass': 20.040395390137192, 'burnOut': 3.4398519219680628, 'railLength': 5.0211721343161} +{'rocketMass': 19.92409646924012, 'burnOut': 4.114787103323786, 'railLength': 5.2566996814270395} +{'rocketMass': 19.83694037126801, 'burnOut': 4.363111003241498, 'railLength': 5.428369692561595} +{'rocketMass': 20.170452293227846, 'burnOut': 4.235807754853636, 'railLength': 5.049820100552791} +{'rocketMass': 20.168066054386124, 'burnOut': 4.369091258708742, 'railLength': 5.921815945979148} +{'rocketMass': 19.91857803920068, 'burnOut': 4.342779134206488, 'railLength': 5.080259704139901} +Completed 10 iterations successfully. Total CPU time: 17.6875 s. Total wall time: 19.050102710723877 s diff --git a/teste.disp_outputs.txt b/teste.disp_outputs.txt index b8d420c64..f4c3a0e35 100644 --- a/teste.disp_outputs.txt +++ b/teste.disp_outputs.txt @@ -1,11 +1,11 @@ -{'outOfRailTime': 0.3944911040820447, 'outOfRailVelocity': 23.343591914713187, 'apogeeTime': 23.422611788985137, 'apogeeAltitude': 2550.847800876637, 'apogeeX': -241.25187886810394, 'apogeeY': 874.0542581881214, 'impactTime': 149.34140181231277, 'impactX': 539.5839528863794, 'impactY': 571.707870066667, 'impactVelocity': -19.511995266459902, 'initialStaticMargin': 2.221085597645965, 'outOfRailStaticMargin': 2.2996812448558366, 'finalStaticMargin': 3.0897188014956276, 'numberOfEvents': 1, 'drogueTriggerTime': 23.42857142857143, 'drogueInflatedTime': 24.92857142857143, 'drogueInflatedVelocity': 39.77277357935216, 'executionTime': 1.484375, 'lateralWind': -3.7535914518728144, 'frontalWind': -6.317898247256396, 'maxVelocity': 234.82629670353523} -{'outOfRailTime': 0.3933473368288729, 'outOfRailVelocity': 23.417304978330783, 'apogeeTime': 23.504827270186265, 'apogeeAltitude': 2573.983545714522, 'apogeeX': -241.9777857681424, 'apogeeY': 878.9438657886889, 'impactTime': 150.9049565617285, 'impactX': 550.3660998292023, 'impactY': 573.0171413207022, 'impactVelocity': -19.443572039075093, 'initialStaticMargin': 2.2157630616888, 'outOfRailStaticMargin': 2.2944993992694833, 'finalStaticMargin': 3.0897188014956276, 'numberOfEvents': 1, 'drogueTriggerTime': 23.514285714285716, 'drogueInflatedTime': 25.014285714285716, 'drogueInflatedVelocity': 39.78276550446817, 'executionTime': 1.421875, 'lateralWind': -3.7535914518728144, 'frontalWind': -6.317898247256396, 'maxVelocity': 236.43889053048326} -{'outOfRailTime': 0.3955257632345688, 'outOfRailVelocity': 23.265328658803195, 'apogeeTime': 23.346725982326763, 'apogeeAltitude': 2529.6737392152395, 'apogeeX': -240.57041897714802, 'apogeeY': 869.5506727076573, 'impactTime': 147.91828516700116, 'impactX': 529.7635399062008, 'impactY': 570.5651977790892, 'impactVelocity': -19.57424036207042, 'initialStaticMargin': 2.2258870529748975, 'outOfRailStaticMargin': 2.304353256852157, 'finalStaticMargin': 3.0897188014956276, 'numberOfEvents': 1, 'drogueTriggerTime': 23.352380952380955, 'drogueInflatedTime': 24.852380952380955, 'drogueInflatedVelocity': 39.772460440155825, 'executionTime': 1.40625, 'lateralWind': -3.7535914518728144, 'frontalWind': -6.317898247256396, 'maxVelocity': 233.36000368365737} -{'outOfRailTime': 0.3940203980287493, 'outOfRailVelocity': 23.34964864524333, 'apogeeTime': 23.444188978637907, 'apogeeAltitude': 2556.905980730848, 'apogeeX': -241.47216230231416, 'apogeeY': 875.3966980120088, 'impactTime': 149.7533665641162, 'impactX': 542.4317120065745, 'impactY': 572.0030551237272, 'impactVelocity': -19.49393849599613, 'initialStaticMargin': 2.219685478089492, 'outOfRailStaticMargin': 2.298277522820546, 'finalStaticMargin': 3.0897188014956276, 'numberOfEvents': 1, 'drogueTriggerTime': 23.44761904761905, 'drogueInflatedTime': 24.94761904761905, 'drogueInflatedVelocity': 39.76609319107769, 'executionTime': 1.265625, 'lateralWind': -3.7535914518728144, 'frontalWind': -6.317898247256396, 'maxVelocity': 235.2488688482768} -{'outOfRailTime': 0.3956649451405656, 'outOfRailVelocity': 23.302967225323197, 'apogeeTime': 23.381190753952595, 'apogeeAltitude': 2539.2548503202906, 'apogeeX': -240.80875151262012, 'apogeeY': 871.4528889635619, 'impactTime': 148.5735672447039, 'impactX': 534.3115181512142, 'impactY': 570.9680948737387, 'impactVelocity': -19.543717126215927, 'initialStaticMargin': 2.223537175446863, 'outOfRailStaticMargin': 2.3022222578405653, 'finalStaticMargin': 3.0897188014956276, 'numberOfEvents': 1, 'drogueTriggerTime': 23.390476190476193, 'drogueInflatedTime': 24.890476190476193, 'drogueInflatedVelocity': 39.778203965403414, 'executionTime': 1.203125, 'lateralWind': -3.7535914518728144, 'frontalWind': -6.317898247256396, 'maxVelocity': 234.03139735995427} -{'outOfRailTime': 0.3960354392624567, 'outOfRailVelocity': 23.20753830401942, 'apogeeTime': 23.293717054938607, 'apogeeAltitude': 2515.0108921396395, 'apogeeX': -240.10849393477596, 'apogeeY': 866.4499873119054, 'impactTime': 146.93649026598447, 'impactX': 523.0543277579958, 'impactY': 569.6911137463339, 'impactVelocity': -19.617815821752888, 'initialStaticMargin': 2.2292270337953406, 'outOfRailStaticMargin': 2.3075511986050548, 'finalStaticMargin': 3.0897188014956276, 'numberOfEvents': 1, 'drogueTriggerTime': 23.295238095238098, 'drogueInflatedTime': 24.795238095238098, 'drogueInflatedVelocity': 39.759782526764205, 'executionTime': 1.375, 'lateralWind': -3.7535914518728144, 'frontalWind': -6.317898247256396, 'maxVelocity': 232.34454573898722} -{'outOfRailTime': 0.39405134456771157, 'outOfRailVelocity': 23.363836360675425, 'apogeeTime': 23.44927479017892, 'apogeeAltitude': 2558.376943642568, 'apogeeX': -241.47628615667128, 'apogeeY': 875.6256898824369, 'impactTime': 149.85401905683776, 'impactX': 543.119123262707, 'impactY': 572.1073434765261, 'impactVelocity': -19.48890318493032, 'initialStaticMargin': 2.2192945621866214, 'outOfRailStaticMargin': 2.2979247234803344, 'finalStaticMargin': 3.0897188014956276, 'numberOfEvents': 1, 'drogueTriggerTime': 23.45714285714286, 'drogueInflatedTime': 24.95714285714286, 'drogueInflatedVelocity': 39.777571032905385, 'executionTime': 1.5625, 'lateralWind': -3.7535914518728144, 'frontalWind': -6.317898247256396, 'maxVelocity': 235.3509967790653} -{'outOfRailTime': 0.39609430016365427, 'outOfRailVelocity': 23.226165158859775, 'apogeeTime': 23.30208203880564, 'apogeeAltitude': 2517.309094224132, 'apogeeX': -240.15968511862917, 'apogeeY': 866.8890582835511, 'impactTime': 147.08975851827537, 'impactX': 524.1155729577022, 'impactY': 569.7891746159438, 'impactVelocity': -19.61104687672425, 'initialStaticMargin': 2.2287071822976774, 'outOfRailStaticMargin': 2.3070865669804856, 'finalStaticMargin': 3.0897188014956276, 'numberOfEvents': 1, 'drogueTriggerTime': 23.304761904761907, 'drogueInflatedTime': 24.804761904761907, 'drogueInflatedVelocity': 39.76165709545755, 'executionTime': 1.828125, 'lateralWind': -3.7535914518728144, 'frontalWind': -6.317898247256396, 'maxVelocity': 232.50254234623392} -{'outOfRailTime': 0.39375416648016054, 'outOfRailVelocity': 23.385862921060706, 'apogeeTime': 23.47429660412743, 'apogeeAltitude': 2565.3728733242815, 'apogeeX': -241.71195410864763, 'apogeeY': 877.1350910041677, 'impactTime': 150.32771190083938, 'impactX': 546.4731029639205, 'impactY': 572.3372399457699, 'impactVelocity': -19.46881174895606, 'initialStaticMargin': 2.217731897597548, 'outOfRailStaticMargin': 2.296412699405396, 'finalStaticMargin': 3.0897188014956276, 'numberOfEvents': 1, 'drogueTriggerTime': 23.476190476190478, 'drogueInflatedTime': 24.976190476190478, 'drogueInflatedVelocity': 39.75793896364731, 'executionTime': 1.96875, 'lateralWind': -3.7535914518728144, 'frontalWind': -6.317898247256396, 'maxVelocity': 235.83954893078507} -{'outOfRailTime': 0.3949237725394638, 'outOfRailVelocity': 23.317176845964415, 'apogeeTime': 23.40122800752664, 'apogeeAltitude': 2544.860372090726, 'apogeeX': -241.04632315684603, 'apogeeY': 872.7589242629534, 'impactTime': 148.9418856568964, 'impactX': 536.7963733214742, 'impactY': 571.4080792073252, 'impactVelocity': -19.52875978509873, 'initialStaticMargin': 2.2223825086626467, 'outOfRailStaticMargin': 2.3009803548281917, 'finalStaticMargin': 3.0897188014956276, 'numberOfEvents': 1, 'drogueTriggerTime': 23.40952380952381, 'drogueInflatedTime': 24.90952380952381, 'drogueInflatedVelocity': 39.77983384543699, 'executionTime': 1.609375, 'lateralWind': -3.7535914518728144, 'frontalWind': -6.317898247256396, 'maxVelocity': 234.41374430423554} -Completed 10 iterations successfully. Total CPU time: 15.234375 s. Total wall time: 16.870558500289917 s +{'outOfRailTime': 0.39364752882342674, 'outOfRailVelocity': 23.393901564816144, 'apogeeTime': 23.48209630922026, 'apogeeAltitude': 2567.5769822795232, 'apogeeX': -241.77524247257443, 'apogeeY': 877.5895761489942, 'impactTime': 150.4764074480069, 'impactX': 547.4755585200927, 'impactY': 572.4876365926631, 'impactVelocity': -19.462199163839447, 'initialStaticMargin': 2.2172168363626756, 'outOfRailStaticMargin': 2.2959121643050797, 'finalStaticMargin': 3.0897188014956276, 'numberOfEvents': 1, 'drogueTriggerTime': 23.485714285714288, 'drogueInflatedTime': 24.985714285714288, 'drogueInflatedVelocity': 39.7631366423571, 'executionTime': 1.328125, 'lateralWind': -3.7535914518728144, 'frontalWind': -6.317898247256396, 'maxVelocity': 235.99104212868633} +{'outOfRailTime': 0.3948759737597698, 'outOfRailVelocity': 23.319771149981346, 'apogeeTime': 23.4028291433462, 'apogeeAltitude': 2545.3078375989367, 'apogeeX': -241.06417203223654, 'apogeeY': 872.8585625767016, 'impactTime': 148.97167317343016, 'impactX': 537.0216691616343, 'impactY': 571.393383082964, 'impactVelocity': -19.52766516726175, 'initialStaticMargin': 2.2222979195834713, 'outOfRailStaticMargin': 2.300890920849731, 'finalStaticMargin': 3.0897188014956276, 'numberOfEvents': 1, 'drogueTriggerTime': 23.40952380952381, 'drogueInflatedTime': 24.90952380952381, 'drogueInflatedVelocity': 39.77449604815257, 'executionTime': 1.34375, 'lateralWind': -3.7535914518728144, 'frontalWind': -6.317898247256396, 'maxVelocity': 234.4425381986718} +{'outOfRailTime': 0.39383938438037336, 'outOfRailVelocity': 23.379426763634843, 'apogeeTime': 23.46794207038415, 'apogeeAltitude': 2563.5792893018133, 'apogeeX': -241.66211213218298, 'apogeeY': 876.7672315674648, 'impactTime': 150.20226074219605, 'impactX': 545.5025868758161, 'impactY': 572.4551802719707, 'impactVelocity': -19.47410279394695, 'initialStaticMargin': 2.2181438376033484, 'outOfRailStaticMargin': 2.2968129802515764, 'finalStaticMargin': 3.0897188014956276, 'numberOfEvents': 1, 'drogueTriggerTime': 23.476190476190478, 'drogueInflatedTime': 24.976190476190478, 'drogueInflatedVelocity': 39.78032246823763, 'executionTime': 1.703125, 'lateralWind': -3.7535914518728144, 'frontalWind': -6.317898247256396, 'maxVelocity': 235.71407446451917} +{'outOfRailTime': 0.3930704664495948, 'outOfRailVelocity': 23.418332470677253, 'apogeeTime': 23.515367492889425, 'apogeeAltitude': 2576.967280631924, 'apogeeX': -242.10277007283392, 'apogeeY': 879.6372495529988, 'impactTime': 151.10771800521675, 'impactX': 551.7575545784338, 'impactY': 573.2154731676559, 'impactVelocity': -19.43483438695117, 'initialStaticMargin': 2.215080359624552, 'outOfRailStaticMargin': 2.2938029229466124, 'finalStaticMargin': 3.0897188014956276, 'numberOfEvents': 1, 'drogueTriggerTime': 23.523809523809526, 'drogueInflatedTime': 25.023809523809526, 'drogueInflatedVelocity': 39.78158608378997, 'executionTime': 1.4375, 'lateralWind': -3.7535914518728144, 'frontalWind': -6.317898247256396, 'maxVelocity': 236.64866640993696} +{'outOfRailTime': 0.39500696060662027, 'outOfRailVelocity': 23.325764409221105, 'apogeeTime': 23.402730892947652, 'apogeeAltitude': 2545.268387833093, 'apogeeX': -241.04337919100507, 'apogeeY': 872.8104354829267, 'impactTime': 148.96979322782306, 'impactX': 537.0293697505082, 'impactY': 571.3452280041232, 'impactVelocity': -19.527642212283883, 'initialStaticMargin': 2.2222961450651133, 'outOfRailStaticMargin': 2.3009208054590156, 'finalStaticMargin': 3.0897188014956276, 'numberOfEvents': 1, 'drogueTriggerTime': 23.40952380952381, 'drogueInflatedTime': 24.90952380952381, 'drogueInflatedVelocity': 39.77324414527358, 'executionTime': 2.15625, 'lateralWind': -3.7535914518728144, 'frontalWind': -6.317898247256396, 'maxVelocity': 234.43854100088942} +{'outOfRailTime': 0.393786187885889, 'outOfRailVelocity': 23.383441067614577, 'apogeeTime': 23.47201858836762, 'apogeeAltitude': 2564.719672077175, 'apogeeX': -241.6949708415784, 'apogeeY': 877.0039280151173, 'impactTime': 150.28163849653603, 'impactX': 546.1156054888656, 'impactY': 572.3842677454285, 'impactVelocity': -19.47080301448961, 'initialStaticMargin': 2.2178870418425163, 'outOfRailStaticMargin': 2.2965634374473396, 'finalStaticMargin': 3.0897188014956276, 'numberOfEvents': 1, 'drogueTriggerTime': 23.476190476190478, 'drogueInflatedTime': 24.976190476190478, 'drogueInflatedVelocity': 39.76614816730068, 'executionTime': 2.546875, 'lateralWind': -3.7535914518728144, 'frontalWind': -6.317898247256396, 'maxVelocity': 235.79377306150013} +{'outOfRailTime': 0.3929585800142951, 'outOfRailVelocity': 23.426281445945772, 'apogeeTime': 23.52347772692958, 'apogeeAltitude': 2579.2615570345256, 'apogeeX': -242.17290460742268, 'apogeeY': 880.1177661016592, 'impactTime': 151.26832676009747, 'impactX': 552.9942843408794, 'impactY': 573.080316911245, 'impactVelocity': -19.42810182800753, 'initialStaticMargin': 2.2145533013720162, 'outOfRailStaticMargin': 2.2932898618648787, 'finalStaticMargin': 3.0897188014956276, 'numberOfEvents': 1, 'drogueTriggerTime': 23.523809523809526, 'drogueInflatedTime': 25.023809523809526, 'drogueInflatedVelocity': 39.753529828392345, 'executionTime': 1.953125, 'lateralWind': -3.7535914518728144, 'frontalWind': -6.317898247256396, 'maxVelocity': 236.80796514901837} +{'outOfRailTime': 0.3955845155910141, 'outOfRailVelocity': 23.239490423502964, 'apogeeTime': 23.32661064243672, 'apogeeAltitude': 2524.1052140855963, 'apogeeX': -240.41223767499687, 'apogeeY': 868.4084069471148, 'impactTime': 147.5418539513837, 'impactX': 527.1436049410614, 'impactY': 570.3680499438507, 'impactVelocity': -19.591010733074842, 'initialStaticMargin': 2.2271743136835758, 'outOfRailStaticMargin': 2.305552942215501, 'finalStaticMargin': 3.0897188014956276, 'numberOfEvents': 1, 'drogueTriggerTime': 23.333333333333336, 'drogueInflatedTime': 24.833333333333336, 'drogueInflatedVelocity': 39.778177727954976, 'executionTime': 1.1875, 'lateralWind': -3.7535914518728144, 'frontalWind': -6.317898247256396, 'maxVelocity': 232.9728405593581} +{'outOfRailTime': 0.395702891244475, 'outOfRailVelocity': 23.25256596431927, 'apogeeTime': 23.328153215748333, 'apogeeAltitude': 2524.534808308825, 'apogeeX': -240.40043656304366, 'apogeeY': 868.4449324828357, 'impactTime': 147.5709274420821, 'impactX': 527.396121496818, 'impactY': 570.2834551160805, 'impactVelocity': -19.589849853526715, 'initialStaticMargin': 2.2270853048997874, 'outOfRailStaticMargin': 2.305499311444709, 'finalStaticMargin': 3.0897188014956276, 'numberOfEvents': 1, 'drogueTriggerTime': 23.333333333333336, 'drogueInflatedTime': 24.833333333333336, 'drogueInflatedVelocity': 39.770636265523834, 'executionTime': 1.71875, 'lateralWind': -3.7535914518728144, 'frontalWind': -6.317898247256396, 'maxVelocity': 233.001280524876} +{'outOfRailTime': 0.3937427949045639, 'outOfRailVelocity': 23.386723682784606, 'apogeeTime': 23.47499691340987, 'apogeeAltitude': 2565.5736138519483, 'apogeeX': -241.71498339389854, 'apogeeY': 877.1714488168865, 'impactTime': 150.34230311624643, 'impactX': 546.5900688232151, 'impactY': 572.3173877624449, 'impactVelocity': -19.468105805968417, 'initialStaticMargin': 2.217676713980707, 'outOfRailStaticMargin': 2.2963590866966, 'finalStaticMargin': 3.0897188014956276, 'numberOfEvents': 1, 'drogueTriggerTime': 23.476190476190478, 'drogueInflatedTime': 24.976190476190478, 'drogueInflatedVelocity': 39.75527823501804, 'executionTime': 2.265625, 'lateralWind': -3.7535914518728144, 'frontalWind': -6.317898247256396, 'maxVelocity': 235.85313212739368} +Completed 10 iterations successfully. Total CPU time: 17.6875 s. Total wall time: 19.050102710723877 s diff --git a/teste.disp_outputs.txt.svg b/teste.disp_outputs.txt.svg index d3bbf0357..587db8574 100644 --- a/teste.disp_outputs.txt.svg +++ b/teste.disp_outputs.txt.svg @@ -6,7 +6,7 @@ - 2022-08-28T18:40:49.936436 + 2022-08-28T20:31:20.810526 image/svg+xml @@ -37,91 +37,91 @@ L 54.507813 15.12 z " style="fill: #ffffff"/> - + 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" id="imagea8ebc273cf" transform="scale(1 -1)translate(0 -332.64)" x="54.507813" y="-15.12" width="332.64" height="332.64"/> - +" clip-path="url(#pe0b118f36c)" style="fill: #0000ff; fill-opacity: 0.2; stroke: #000000; stroke-linejoin: miter"/> - +" clip-path="url(#pe0b118f36c)" style="fill: #0000ff; fill-opacity: 0.2; stroke: #000000; stroke-linejoin: miter"/> - +" clip-path="url(#pe0b118f36c)" style="fill: #0000ff; fill-opacity: 0.2; stroke: #000000; stroke-linejoin: miter"/> - +" clip-path="url(#pe0b118f36c)" style="fill: #00ff00; fill-opacity: 0.2; stroke: #000000; stroke-linejoin: miter"/> - +" clip-path="url(#pe0b118f36c)" style="fill: #00ff00; fill-opacity: 0.2; stroke: #000000; stroke-linejoin: miter"/> - +" clip-path="url(#pe0b118f36c)" style="fill: #00ff00; fill-opacity: 0.2; stroke: #000000; stroke-linejoin: miter"/> - - - + + - - - - - - - - - - - - + + + + + + + + + + + - - - - - - - - - - - - + + + + + + + + + + + - - + @@ -268,7 +268,7 @@ z - + @@ -311,7 +311,7 @@ z - + @@ -344,7 +344,7 @@ z - + @@ -357,7 +357,7 @@ z - + @@ -373,7 +373,7 @@ z - + @@ -389,7 +389,7 @@ z - + @@ -579,12 +579,12 @@ z - - + @@ -601,7 +601,7 @@ L -3.5 0 - + @@ -618,7 +618,7 @@ L -3.5 0 - + @@ -635,7 +635,7 @@ L -3.5 0 - + @@ -648,7 +648,7 @@ L -3.5 0 - + @@ -664,7 +664,7 @@ L -3.5 0 - + @@ -680,7 +680,7 @@ L -3.5 0 - + @@ -783,12 +783,12 @@ z +" clip-path="url(#pe0b118f36c)" style="fill: none; stroke: #000000; stroke-width: 0.5; stroke-linecap: square"/> +" clip-path="url(#pe0b118f36c)" style="fill: none; stroke: #000000; stroke-width: 0.5; stroke-linecap: square"/> - + @@ -1219,7 +1219,7 @@ z - + @@ -1278,7 +1278,7 @@ z - + @@ -1313,7 +1313,7 @@ z - + From b3288b2dc860f66f33e86cbc8a855bf88904c84a Mon Sep 17 00:00:00 2001 From: Sofia-ME Date: Sun, 28 Aug 2022 20:45:19 -0300 Subject: [PATCH 8/9] addTail new geomtries --- rocketpy/Rocket.py | 52 ++++++++++++++++++++++++++++++++++------------ 1 file changed, 39 insertions(+), 13 deletions(-) diff --git a/rocketpy/Rocket.py b/rocketpy/Rocket.py index 1bfb585a9..c12d9fa5a 100644 --- a/rocketpy/Rocket.py +++ b/rocketpy/Rocket.py @@ -334,7 +334,7 @@ def evaluateStaticMargin(self): # Return self return self - def addTail(self, topRadius, bottomRadius, length, distanceToCM): + def addTail(self, topRadius, bottomRadius, length, distanceToCM,format,n=0): """Create a new tail or rocket diameter change, storing its parameters as part of the aerodynamicSurfaces list. Its parameters are the axial position along the rocket and its @@ -356,6 +356,12 @@ def addTail(self, topRadius, bottomRadius, length, distanceToCM): cone. Consider the point belonging to the tail which is closest to the unloaded center of mass to calculate distance. + format: string + Tail geometric format, varying between conical,circular-arc, eliptical and parabolic PS (power series). + n: int, float, optional + Tail caracterisc only for parabolical formats, varying from 0 to 1 for Power Series. For other formats, leave n=0. + Daefault is zero + Returns ------- cl : Function @@ -368,26 +374,46 @@ def addTail(self, topRadius, bottomRadius, length, distanceToCM): self : Rocket Object of the Rocket class. """ - - # Save parameters for Dispersion - self.tailTopRadius = topRadius - self.tailBottomRadius = bottomRadius - self.tailLength = length - self.tailDistanceToCM = distanceToCM - # Calculate ratio between top and bottom radius r = topRadius / bottomRadius # Retrieve reference radius rref = self.radius - # Calculate cp position relative to cm - if distanceToCM < 0: - cpz = distanceToCM - (length / 3) * (1 + (1 - r) / (1 - r**2)) - else: - cpz = distanceToCM + (length / 3) * (1 + (1 - r) / (1 - r**2)) + #Separate cp position formula by format + if format == "conical" : + + # Calculate cp position relative to cm + if distanceToCM < 0: + cpz = distanceToCM - (length / 3) * (1 + (1 - r) / (1 - r**2)) + else: + cpz = distanceToCM + (length / 3) * (1 + (1 - r) / (1 - r**2)) + elif format == "circular arc": + if distanceToCM < 0: + cpz= distanceToCM - ((length-(topRadius/6)-(bottomRadius^3/topRadius^2))/(1-(bottomRadius/topRadius)^2)) + else: + cpz= distanceToCM + ((length-(topRadius/6)-(bottomRadius^3/topRadius^2))/(1-(bottomRadius/topRadius)^2)) + elif format == "eliptical": + #calculate the volume + L= length *(topRadius**2/(bottomRadius**2-topRadius**2)**(1/2)) + v= 2/3 *((topRadius**2 *length)-(bottomRadius**2*(L-length))) + + #Calculate cp position relative to cm + if distanceToCM < 0: + cpz= distanceToCM - ((length-(v/np.pi*bottomRadius**2))/(1-(bottomRadius/topRadius)**2)) + else: + cpz= distanceToCM + ((length-(v/np.pi*bottomRadius**2))/(1-(bottomRadius/topRadius)**2)) + elif format == "parabolic PS": + # calculate the volume + L= (topRadius/bottomRadius *length**n)**(1/n) + v = (np.pi/3 )* (topRadius**2 *L) + if distanceToCM < 0: + cpz= distanceToCM - ((length-(v/np.pi*bottomRadius**2))/(1-(bottomRadius/topRadius)**2)) + else: + cpz= distanceToCM + ((length-(v/np.pi*bottomRadius**2))/(1-(bottomRadius/topRadius)**2)) # Calculate clalpha + # clalpha não altera com o formato da cauda para voo subsônico, então assim como das ogivas, a sua fórmula se mantém a mesa clalpha = -2 * (1 - r ** (-2)) * (topRadius / rref) ** 2 cl = Function( lambda alpha, mach: clalpha * alpha, From 260f5c08f2565152fea9897176eac6e7ea00163b Mon Sep 17 00:00:00 2001 From: Sofia-ME Date: Sun, 28 Aug 2022 20:49:22 -0300 Subject: [PATCH 9/9] fixing pull request --- rocketpy/Rocket.py | 45 ++++++--------------------------------------- 1 file changed, 6 insertions(+), 39 deletions(-) diff --git a/rocketpy/Rocket.py b/rocketpy/Rocket.py index c12d9fa5a..b8181489b 100644 --- a/rocketpy/Rocket.py +++ b/rocketpy/Rocket.py @@ -334,7 +334,7 @@ def evaluateStaticMargin(self): # Return self return self - def addTail(self, topRadius, bottomRadius, length, distanceToCM,format,n=0): + def addTail(self, topRadius, bottomRadius, length, distanceToCM): """Create a new tail or rocket diameter change, storing its parameters as part of the aerodynamicSurfaces list. Its parameters are the axial position along the rocket and its @@ -356,12 +356,6 @@ def addTail(self, topRadius, bottomRadius, length, distanceToCM,format,n=0): cone. Consider the point belonging to the tail which is closest to the unloaded center of mass to calculate distance. - format: string - Tail geometric format, varying between conical,circular-arc, eliptical and parabolic PS (power series). - n: int, float, optional - Tail caracterisc only for parabolical formats, varying from 0 to 1 for Power Series. For other formats, leave n=0. - Daefault is zero - Returns ------- cl : Function @@ -380,40 +374,13 @@ def addTail(self, topRadius, bottomRadius, length, distanceToCM,format,n=0): # Retrieve reference radius rref = self.radius - #Separate cp position formula by format - if format == "conical" : - - # Calculate cp position relative to cm - if distanceToCM < 0: - cpz = distanceToCM - (length / 3) * (1 + (1 - r) / (1 - r**2)) - else: - cpz = distanceToCM + (length / 3) * (1 + (1 - r) / (1 - r**2)) - elif format == "circular arc": - if distanceToCM < 0: - cpz= distanceToCM - ((length-(topRadius/6)-(bottomRadius^3/topRadius^2))/(1-(bottomRadius/topRadius)^2)) - else: - cpz= distanceToCM + ((length-(topRadius/6)-(bottomRadius^3/topRadius^2))/(1-(bottomRadius/topRadius)^2)) - elif format == "eliptical": - #calculate the volume - L= length *(topRadius**2/(bottomRadius**2-topRadius**2)**(1/2)) - v= 2/3 *((topRadius**2 *length)-(bottomRadius**2*(L-length))) - - #Calculate cp position relative to cm - if distanceToCM < 0: - cpz= distanceToCM - ((length-(v/np.pi*bottomRadius**2))/(1-(bottomRadius/topRadius)**2)) - else: - cpz= distanceToCM + ((length-(v/np.pi*bottomRadius**2))/(1-(bottomRadius/topRadius)**2)) - elif format == "parabolic PS": - # calculate the volume - L= (topRadius/bottomRadius *length**n)**(1/n) - v = (np.pi/3 )* (topRadius**2 *L) - if distanceToCM < 0: - cpz= distanceToCM - ((length-(v/np.pi*bottomRadius**2))/(1-(bottomRadius/topRadius)**2)) - else: - cpz= distanceToCM + ((length-(v/np.pi*bottomRadius**2))/(1-(bottomRadius/topRadius)**2)) + # Calculate cp position relative to cm + if distanceToCM < 0: + cpz = distanceToCM - (length / 3) * (1 + (1 - r) / (1 - r**2)) + else: + cpz = distanceToCM + (length / 3) * (1 + (1 - r) / (1 - r**2)) # Calculate clalpha - # clalpha não altera com o formato da cauda para voo subsônico, então assim como das ogivas, a sua fórmula se mantém a mesa clalpha = -2 * (1 - r ** (-2)) * (topRadius / rref) ** 2 cl = Function( lambda alpha, mach: clalpha * alpha,