diff options
| -rw-r--r-- | .gitignore | 2 | ||||
| -rw-r--r-- | README.md | 5 | ||||
| -rw-r--r-- | data/robot6_trial2_edited.csv | 40 | ||||
| -rw-r--r-- | regression.py | 33 | ||||
| -rw-r--r-- | requirements.txt | 5 | ||||
| -rw-r--r-- | run.py | 24 |
6 files changed, 69 insertions, 40 deletions
diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..743e88b --- /dev/null +++ b/.gitignore @@ -0,0 +1,2 @@ +__pycache__/ +env/ diff --git a/README.md b/README.md new file mode 100644 index 0000000..5a2b0c0 --- /dev/null +++ b/README.md @@ -0,0 +1,5 @@ +Data units are ms and ft. Data derived from Kinovea. + +Create a venv and then `pip install -r requirements.txt` and then +`python run.py` after data is entered in the data folder corresponding +to `data/robot[robot number]_trial[trial number].csv` diff --git a/data/robot6_trial2_edited.csv b/data/robot6_trial2_edited.csv deleted file mode 100644 index 7935553..0000000 --- a/data/robot6_trial2_edited.csv +++ /dev/null @@ -1,40 +0,0 @@ -,Time (ms),Trajectory 1 -0,0,-0.979554891586304 -1,4,-0.835712134838104 -2,8,-0.699505805969238 -3,12,-0.548177599906921 -4,16,-0.321406573057175 -5,20,-0.174405112862587 -6,25,-0.0335861444473267 -7,29,0.0799026042222977 -8,33,0.256950944662094 -9,37,0.400715202093124 -10,41,0.544477641582489 -11,45,0.680721402168274 -12,49,0.824513614177704 -13,54,0.968290090560913 -14,58,1.10454130172729 -15,61,1.24079287052155 -16,65,1.39210069179535 -17,70,1.52830684185028 -18,74,1.66456472873688 -19,78,1.81589031219482 -20,82,1.95213425159454 -21,86,2.09591507911682 -22,90,2.23969602584839 -23,94,2.37587356567383 -24,99,2.50452637672424 -25,103,2.64827156066895 -26,106,2.78445959091187 -27,111,2.95850706100464 -28,115,3.0796012878418 -29,119,3.2309718132019 -30,123,3.35961222648621 -31,127,3.49582982063293 -32,131,3.63205075263977 -33,135,3.77579402923584 -34,139,3.91195726394653 -35,144,4.04812145233154 -36,148,4.18429899215698 -37,151,4.32052373886108 -38,156,4.44540596008301 diff --git a/regression.py b/regression.py new file mode 100644 index 0000000..b6ac7db --- /dev/null +++ b/regression.py @@ -0,0 +1,33 @@ +import numpy as np + +# data = [(0,0), (1,1), (2, 4), (3, 9), (4, 16) ] ; t = 3 +def velvar(data, t): + n = len(data) # number of entries + X = np.array([(datum[0]**2, datum[0], 1) for datum in data]) + Xt = np.transpose(X) # X^T + Y = [datum[1] for datum in data] + M = np.matmul( np.linalg.inv(np.matmul(Xt, X)), Xt ) # solution = My + est = np.matmul(M, Y); + P = np.matmul(X, M) # \hat y = Py + R = np.subtract(np.identity(n), P) # residual = Ry + bias = sum([sum([entry**2 for entry in row]) for row in R]) + Ry = np.matmul(R, Y); + S2 = np.dot(Ry, Ry); # S^2 estimator + variance = S2/bias + v = 2*M[0]*t + M[1] # V = v\cdot y + velocity = 2*est[0]*t+est[1] + variancevel = np.dot(v,v)*variance + return (np.abs(velocity), variancevel) + +def combine_estimates(velvars): + avg = np.average(list(a[0] for a in velvars)) + var = (np.sum(a[1] + a[0]**2 for a in velvars) - avg**2)/len(velvars) + return((avg, var)) + +if __name__ == '__main__': + tot = (velvar([(0,0), (1,1), (2, 4), (3, 9), (4, 17)], 3), + velvar([(0, 0), (1,2), (2, 8), (3, 18), (4, 32)], 3)) + n = 5 + k = 2 + print(sum([datum[1]**2+n*datum[0]**2 for datum in tot]) + - n*k*sum([datum[0]/k for datum in tot])**2) diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..c0358f4 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,5 @@ +numpy==1.21.3 +pandas==1.3.4 +python-dateutil==2.8.2 +pytz==2021.3 +six==1.16.0 @@ -0,0 +1,24 @@ +#!/usr/bin/python3 +import os +import re +import pandas as pd +import numpy as np +from regression import velvar, combine_estimates + +robot_velvars = {} +for entry in os.scandir('data'): + if not (entry.is_file() and entry.name.endswith('.csv')): + continue + match = re.match(r'^robot(\d+)_trial(\d+).csv$', entry.name) + if match == None: + continue + robot = int(match.group(1)) + if not robot in robot_velvars: + robot_velvars[robot] = [] + trial = int(match.group(2)) + data = pd.read_csv(entry.path).values + robot_velvars[robot].append(velvar(data, data[0][0])) + +for robot in robot_velvars: + est = [x*1000/3.28 for x in combine_estimates(robot_velvars[robot])] + print(f"robot {robot}'s kick velocity is {est[0]} ± {np.sqrt(est[1])*2} m/s (p > .95)") |