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# channel priority strict (pull from defaults, then look to conda forge, then
# look to twh)
# defaults
# conda-forge
# twh
# these are all the requested packages (conda names)
anaconda
alpaca_kernel_2
anaconda
asciitree

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import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as pat
import sklearn.cluster as clu

# set the seed if you want the same random numbers on eac execution
#np.random.seed(5)

# generate data for random overlapping rectangles
num_rectangles = 50

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# This is a simple point mass "jumping" simulation. Given a point mass of a
# realistic human mass that starts some height above the ground with a force
# that acts vertically between the ground and mass that represents the force
# that legs can generate, we simulate from standstill to maximum height in the
# air and calculate the work done by all forces on the mass as well as the
# potential and kinetic energy at all times in the simulation. There is also a
# Coulomb friction term that can represent some dissapative resistance to the
# motion.
#
#   O   |g

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moorepants

function [x, P] = kalman_filter(A, B, C, Q, R, x, u, y, P)
% kalman_filter - Returns the next state estimate and its covariance matrix
% using the Kalman Filter predict & update. Note that this implementation is
% not numerically stable, use production code for non-textbook problems.
%
% Syntax: [x, P] = kalman_filter(A, B, C, Q, R, x, u, y, P)
%
% Inputs:
%   A - State matrix, size 5 x 5
%   B - Input matrix, size 5 x 1

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Data Management Plan

This assignment will utilize two types of data:

1. Existing static and motion data collected from bicycle and bicyclists during prior research. For example, time series from rate gyros or motion capture systems, geometry of vehicles, and anthropomorphic data of the riders.
2. Bicycle and rider motion data generated from simulations.

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