""" ============== Using Bicycles ============== """ import pprint from pathlib import Path import numpy as np import bicycleparameters as bp from bicycleparameters import Bicycle, tables # %% # Loading Bicycle Data # ==================== # # To load the data from one of the bicycles in the data folder, instantiate a # :py:class:`~bicycleparameters.main.Bicycle` object using the bicycle's name: bicycle = Bicycle('Stratos', pathToData='../data') # %% # This will create an instance of the Bicycle class in the variable bicycle # based off of input data from the ``./bicycles/Stratos/`` directory. The # program first looks to see if there are any parameter sets in # ``./bicycles/Stratos/Parameters/``. If so, it loads the data, if not it looks # for ``./bicycles/Stratos/RawData/StratosMeasurments.txt`` so that it can # generate the parameter set. The raw measurement file may or may not contain # the oscillation period data for the bicycle moment of inertia calculations. # If it doesn't then the program will look for the series of ``.mat`` files # need to calculate the periods. If no data is there, then you get an error. # # There are other loading options:: # # bicycle = Bicycle('Stratos', pathToData='..', forceRawCalc=True, # forcePeriodCalc=True) # # The ``pathToData`` option allows you specify a directory other than the # current directory as your data directory. The ``forceRawCalc`` forces the # program to load ``./bicycles/Stratos/RawData/StratosMeasurments.txt`` and # recalculate the parameters regardless if there are any parameter files # available in ``./bicycles/Stratos/Parameters/``. The ``forcePeriodCalc`` # option forces the period calculation from the ``.mat`` files regardless if # they already exist in the raw measurement file. # # Exploring Bicycle Parameter Data # ================================ # # The bicycle has a name: bicycle.bicycleName # %% # and a directory where its data is sourced: bicycle.directory # %% # The benchmark bicycle parameters from [Meijaard2007]_ are the fundamental # parameter set that is used behind the scenes for calculations. To access them # type: b_par = bicycle.parameters['Benchmark'] pprint.pprint(b_par) # %% b_par['xB'] # %% # The program automatically calculates the uncertainties in the parameters # based on the raw measurements or the uncertainties provided in the parameter # files. If you'd like to work with the pure values you can remove them from # the entire dictionary: b_par_pure = bp.io.remove_uncertainties(b_par) b_par_pure['xB'] # %% # or any single uncertainity quantity's nominal value can be extracted with: b_par['xB'].nominal_value # %% # That goes the same for all values with uncertainties. Check out the # `uncertainties `_ package details # for more ways to manipulate the quantities. # # If the bicycle was calculated from raw data measurements you can access them # by: pprint.pprint(bicycle.parameters['Measured']) # %% # All parameter sets are stored in the parameter dictionary of the bicycle # instance, which is mutable. To modify a parameter type: bicycle.parameters['Benchmark']['mB'] = 50.0 bicycle.parameters['Benchmark']['mB'] # %% # You can regenerate the parameter sets from the raw data stored in the # bicycle's directory by calling and resetting the parameters: par, extra = bicycle.calculate_from_measured() bicycle.parameters['Benchmark'] = par bicycle.parameters['Benchmark']['mB'] # %% # Basic Linear Model Analysis # =========================== # # The :py:class:`~bicycleparameters.main.Bicycle` object has some basic bicycle # analysis tools based on the linear Carvallo-Whipple bicycle model which has # been linearized about the upright configuration. For example, the canonical # coefficient matrices for the equations of motion can be computed: M, C1, K0, K2 = bicycle.canonical() M # %% C1 # %% K0 # %% K2 # %% # as well as the state and input matrices for state space form at a particular # speed, here 1.34 m/s: A, B = bicycle.state_space(1.34) A # %% B # %% # You can calculate the eigenvalues and eigenvectors at any speed, e.g. 4.28 # m/s, by calling: w, v = bicycle.eig(4.28) # %% # eigenvalues: w # %% # eigenvectors: v # %% # The ``eig`` function also accepts a one dimensional array of speeds and # returns eigenvalues for all speeds. Note that uncertainty propagation into # the eigenvalue calculations is not supported yet. # # The moment of inertia of the steer assembly (handlebar, fork and/or front # wheel) can be computed either about the center of mass or a point on the # steer axis, both with reference to a frame aligned with the steer axis: bicycle.steer_assembly_moment_of_inertia(aboutSteerAxis=True) # %% # Plots # ===== # You can plot the geometry of the bicycle and include the mass centers of the # various bodies, the inertia ellipsoids and the torsional pendulum axes from # the raw measurement data: _ = bicycle.plot_bicycle_geometry() # %% # A Bode plot for any input output pair can be generated with: _ = bicycle.plot_bode(3.0, 1, 2) # %% # For visualization of the linear analysis you can plot the root loci of the # real and imaginary parts of the eigenvalues as a function of speed: speeds = np.linspace(0., 10., num=100) _ = bicycle.plot_eigenvalues_vs_speed(speeds) # %% # You can also compare the eigenvalues of two or more bicycles: yellowrev = Bicycle('Yellowrev', pathToData='../data') _ = bp.plot_eigenvalues([bicycle, yellowrev], speeds) # %% # Tables # ====== # You can generate reStructuredText tables of the bicycle parameters with the # ``Table`` class: tab = tables.Table('Benchmark', False, (bicycle, yellowrev)) rst = tab.create_rst_table() print(rst) # %% # Which renders in Sphinx like: # # +----------+------------------+------------------+ # | | Stratos | Yellowrev | # +==========+=========+========+=========+========+ # | Variable | v | sigma | v | sigma | # +----------+---------+--------+---------+--------+ # | IBxx | 0.373 | 0.002 | 0.2254 | 0.0009 | # +----------+---------+--------+---------+--------+ # | IBxz | -0.0383 | 0.0004 | 0.0179 | 0.0001 | # +----------+---------+--------+---------+--------+ # | IByy | 0.717 | 0.003 | 0.388 | 0.005 | # +----------+---------+--------+---------+--------+ # | IBzz | 0.455 | 0.002 | 0.2147 | 0.0009 | # +----------+---------+--------+---------+--------+ # | IFxx | 0.0916 | 0.0004 | 0.0852 | 0.0003 | # +----------+---------+--------+---------+--------+ # | IFyy | 0.157 | 0.001 | 0.147 | 0.002 | # +----------+---------+--------+---------+--------+ # | IHxx | 0.1768 | 0.0008 | 0.1475 | 0.0006 | # +----------+---------+--------+---------+--------+ # | IHxz | -0.0273 | 0.0006 | -0.0172 | 0.0005 | # +----------+---------+--------+---------+--------+ # | IHyy | 0.144 | 0.002 | 0.120 | 0.002 | # +----------+---------+--------+---------+--------+ # | IHzz | 0.0446 | 0.0003 | 0.0294 | 0.0004 | # +----------+---------+--------+---------+--------+ # | IRxx | 0.0939 | 0.0004 | 0.0877 | 0.0004 | # +----------+---------+--------+---------+--------+ # | IRyy | 0.154 | 0.001 | 0.149 | 0.001 | # +----------+---------+--------+---------+--------+ # | c | 0.056 | 0.002 | 0.180 | 0.002 | # +----------+---------+--------+---------+--------+ # | g | 9.81 | 0.01 | 9.81 | 0.01 | # +----------+---------+--------+---------+--------+ # | lam | 0.295 | 0.003 | 0.339 | 0.003 | # +----------+---------+--------+---------+--------+ # | mB | 7.22 | 0.02 | 3.31 | 0.02 | # +----------+---------+--------+---------+--------+ # | mF | 3.33 | 0.02 | 1.90 | 0.02 | # +----------+---------+--------+---------+--------+ # | mH | 3.04 | 0.02 | 2.45 | 0.02 | # +----------+---------+--------+---------+--------+ # | mR | 3.96 | 0.02 | 2.57 | 0.02 | # +----------+---------+--------+---------+--------+ # | rF | 0.3400 | 0.0001 | 0.3419 | 0.0001 | # +----------+---------+--------+---------+--------+ # | rR | 0.3385 | 0.0001 | 0.3414 | 0.0001 | # +----------+---------+--------+---------+--------+ # | w | 1.037 | 0.002 | 0.985 | 0.002 | # +----------+---------+--------+---------+--------+ # | xB | 0.326 | 0.003 | 0.412 | 0.004 | # +----------+---------+--------+---------+--------+ # | xH | 0.911 | 0.004 | 0.919 | 0.005 | # +----------+---------+--------+---------+--------+ # | zB | -0.483 | 0.003 | -0.618 | 0.004 | # +----------+---------+--------+---------+--------+ # | zH | -0.730 | 0.002 | -0.816 | 0.002 | # +----------+---------+--------+---------+--------+ # # Adding a Rigid Rider # ==================== # The program also allows one to add the inertial affects of a rigid rider to # the Whipple bicycle model. # # Rider Data # ---------- # You can provide rider data in one of two ways, much in the same way as the # bicycle. If you have the inertial parameters of a rider, e.g. Jason, simply # add a file into the ``./riders/Jason/Parameters/`` directory. Or if you have # raw measurements of the rider add the two files to # ``./riders/Jason/RawData/``. The `yeadon documentation`_ explains how to # collect the data for a rider. # # .. _yeadon documentation: http://packages.python.org/yeadon # # Adding a Rider # -------------- # To add a rider key in: bicycle.add_rider('Jason') # %% # The program first looks for a parameter for for Jason sitting on the Stratos # and if it can't find one, it looks for the raw data for Jason and computes # the inertial parameters. You can force calculation from raw data with:: # # bicycle.add_rider('Jason', reCalc=True) # # Exploring the rider # ------------------- # The bicycle has a few new attributes now that it has a rider: bicycle.hasRider # %% bicycle.riderName # %% # inertial parmeters of the rider pprint.pprint(bicycle.riderPar) # %% # this is a yeadon.human object representing the Jason bicycle.human # %% try: bicycle.human.print_properties() except: pass # %% # The bicycle parameters now reflect that a rigid rider has been added to the # bicycle frame: bicycle.parameters['Benchmark']['mB'] # %% # At this point, the uncertainties don't necessarily offer much information for # any of the parameters that are functions of the rider, because we do not have # a good idea of the uncertainty in the human inertia calculations in the # Yeadon method. # # Analysis # -------- # The same linear analysis can be performed now that a rider has been added, # albeit the reported values and graphs will reflect the fact that the bicycle # frame has the added inertial effects of the rider. # # Plots # ----- # The bicycle geometry plot now reflects that there is a rider on the bicycle # and displays a simplified depiction: _ = bicycle.plot_bicycle_geometry() # %% # The Bode plot reflects the changes: _ = bicycle.plot_bode(3.0, 1, 2) # %% # The eigenvalue plot reflects the changes: _ = bicycle.plot_eigenvalues_vs_speed(speeds)