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Code Issues 27 Pull Requests 3 Releases Wiki Activity

Remove notebooks for the time being; they are still in history

This commit is contained in:
Enrico Lumetti 2020-11-09 16:09:12 +01:00
parent c4ae8836e6
commit 8f7e073c4c
121 changed files with 0 additions and 5055 deletions
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87
notebooks/annotation_analysis.ipynb
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18
notebooks/bezier.py
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from __future__ import print_function
from numpy import *
# evaluates cubic bezier at t, return point
def q(ctrlPoly, t):
return (1.0-t)**3 * ctrlPoly[0] + 3*(1.0-t)**2 * t * ctrlPoly[1] + 3*(1.0-t)* t**2 * ctrlPoly[2] + t**3 * ctrlPoly[3]
# evaluates cubic bezier first derivative at t, return point
def qprime(ctrlPoly, t):
return 3*(1.0-t)**2 * (ctrlPoly[1]-ctrlPoly[0]) + 6*(1.0-t) * t * (ctrlPoly[2]-ctrlPoly[1]) + 3*t**2 * (ctrlPoly[3]-ctrlPoly[2])
# evaluates cubic bezier second derivative at t, return point
def qprimeprime(ctrlPoly, t):
return 6*(1.0-t) * (ctrlPoly[2]-2*ctrlPoly[1]+ctrlPoly[0]) + 6*(t) * (ctrlPoly[3]-2*ctrlPoly[2]+ctrlPoly[1])

18
notebooks/common.py
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import numpy as np
import csv
import matplotlib.pyplot as plt
def import_csv_data(filename):
stylus_x = []
stylus_y = []
stylus_pressure = []
time = []
with open(filename) as csv_fp:
reader = csv.reader(csv_fp)
next(reader) # skip header
for row in reader:
for i, v in enumerate([stylus_x, stylus_y]): #, stylus_pressure, time]):
v.append(float(row[i]))
stylus_pos = np.array([stylus_x, stylus_y])
time = np.array(time)
return time, stylus_pos

189
notebooks/entries_per_sec.txt
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@ -1,189 +0,0 @@
x,y,pressure,time_usec
514.3207270994316,401.6605143721634,0.39755799755799753,1820009777
512.9237158321135,401.31195158850227,0.3968253968253968,1820019753
510.8281989311366,401.19576399394856,0.39145299145299145,1820024803
508.07093985090376,401.7767019667171,0.38632478632478634,1820034829
505.2769173162678,403.5195158850227,0.38217338217338215,1820039772
502.3358409640195,406.77276853252647,0.3807081807081807,1820049803
499.1741838853525,411.4202723146747,0.3814407814407814,1820054803
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492.2258910031657,424.8980332829047,0.3860805860805861,1820069831
488.73336283487083,433.2635400907716,0.38925518925518926,1820079769
485.3143615753821,442.7909228441755,0.39267399267399267,1820084763
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549.5768798720087,640.3098335854766,0.49963369963369964,1820630779
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538.9890050039146,688.8762481089259,0.5013431013431013,1820645791
533.511250297852,714.0889561270802,0.5015873015873016,1820650814
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510.20322020628385,816.3340393343419,0.5101343101343101,1820680791
504.1372502297716,841.5467473524963,0.5199023199023199,1820690838
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491.7847295503285,890.2293494704992,0.5416361416361416,1820705771
485.49817884739764,913.3506807866869,0.550915750915751,1820710776
478.954283963645,935.891074130106,0.5560439560439561,1820720770
472.37362562548935,957.8505295007565,0.5611721611721612,1820725805
465.572386560915,979.6937972768533,0.5663003663003663,1820735817
458.7343840419376,1000.9561270801815,0.5711843711843712,1820740823
451.89638152296016,1021.753706505295,0.5755799755799755,1820750770
444.87456173196716,1042.0865355521937,0.578021978021978,1820755825
437.8527419409742,1062.4193645990922,0.5782661782661783,1820764789
430.6838683323689,1082.4036308623297,0.578021978021978,1820770789
423.44146781495726,1101.9231467473526,0.5775335775335775,1820779798
416.1623038431426,1120.5131618759456,0.5772893772893772,1820784791
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401.8980835347381,1152.9295007564297,0.5748473748473748,1820799822
394.9497906525513,1165.9425113464447,0.5706959706959707,1820809842
388.2220784967832,1176.7479576399394,0.566056166056166,1820814790
381.53112979541817,1184.881089258699,0.5619047619047619,1820824782
375.0975252748749,1190.6904689863843,0.5582417582417583,1820829794
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362.8920584130442,1195.57034795764,0.5531135531135531,1820844822
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326.7168192803894,855.0245083207262,0.452014652014652,1820993818
331.01814344555265,833.7621785173978,0.44542124542124545,1821003821
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364.3993600435715,748.8290468986385,0.4026862026862027,1821053818
368.88450148075026,748.1319213313162,0.40244200244200246,1821063833
373.1858256459135,750.571860816944,0.40293040293040294,1821068820
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391.3469721210471,798.6735249621785,0.4117216117216117,1821107822
394.36157538210165,812.8484114977307,0.4139194139194139,1821112770
397.1555979167376,828.0689863842663,0.4163614163614164,1821122830
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408.9566667801341,921.0190620272315,0.4297924297924298,1821157815
411.1624740443204,942.6299546142209,0.4336996336996337,1821167820
413.55209858052217,964.8217851739788,0.4388278388278388,1821172883
416.3461211151581,987.0136157337367,0.4442002442002442,1821182823
419.5077781938251,1009.089258698941,0.4490842490842491,1821187768
423.11059672532934,1030.5839636913768,0.452014652014652,1821197778
427.15457670967083,1051.38154311649,0.4537240537240537,1821202816
431.6397181468496,1071.8305597579424,0.45494505494505494,1821212836
436.56602103686555,1091.5824508320727,0.45665445665445664,1821217783
441.8231950165095,1110.6372163388805,0.46007326007326005,1821227764
447.30094972257206,1128.8786686838123,0.463980463980464,1821232793
452.99928515505326,1145.7258698940998,0.46813186813186813,1821242799
458.9182013139531,1161.1788199697428,0.47228327228327227,1821247790
465.16798856248084,1175.1213313161877,0.4764346764346764,1821256774
471.895700718249,1187.4372163388805,0.4805860805860806,1821261784
478.954283963645,1198.1264750378216,0.48644688644688644,1821271815
486.4172652074752,1207.072919818457,0.4925518925518926,1821276765
494.06406372332094,1213.6956127080182,0.5006105006105006,1821286777
501.89467951118223,1217.7621785173978,0.5081807081807082,1821291791
509.87234911665587,1218.575491679274,0.513064713064713,1821301825
517.5191476325016,1216.0193645990923,0.5172161172161173,1821321774
523.9159886986417,1212.0689863842663,0.5162393162393163,1821322824
529.1731626782857,1207.1891074130106,0.5133089133089133,1821323806
535.5332402900228,1198.358850226929,0.49963369963369964,1821331828
543.5109098954965,1185.694402420575,0.4759462759462759,1821336836
553.7679136739627,1166.639636913767,0.42466422466422465,1821346788
565.0910576301188,1142.472617246596,0.33601953601953605,1821351772
574.9804268645539,1115.981845688351,0.3045177045177045,1821361778
(1821361778-1820009777)*10**-6 = 1.352001 (seconds)
entries = 183-2+1 = 182
entries/sec = 182/1.352001 = 134.61528504786608

217
notebooks/fast_data.csv
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@ -1,217 +0,0 @@
x,y,pressure,time_usec
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327.04769037001734,459.1733736762481,0.3916971916971917,13341433267
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1 x y pressure time_usec
2 327.70943254927323 457.5467473524962 0.36800976800976803 13341423266
3 327.04769037001734 459.1733736762481 0.3916971916971917 13341433267
4 326.09184055553663 461.7295007564297 0.4129426129426129 13341438262
5 324.9521734690404 465.6798789712557 0.4358974358974359 13341448260
6 323.8860332913504 470.2111951588502 0.463003663003663 13341453253
7 322.9669469312728 475.6720121028744 0.4954822954822955 13341463263
8 322.08462402559826 482.0623298033283 0.525030525030525 13341468263
9 321.23906457432685 489.2659606656581 0.5470085470085471 13341478267
10 320.43026857745855 497.28290468986387 0.5641025641025641 13341483253
11 319.8420533070089 504.95128593040846 0.5711843711843712 13341493262
12 319.43765530857473 511.80635400907715 0.5758241758241758 13341498256
13 319.547945671784 515.7567322239032 0.5782661782661783 13341507264
14 320.31997821424926 516.4538577912255 0.5804639804639805 13341512275
15 322.04786057119514 512.9682299546142 0.5846153846153846 13341522420
16 324.7683561970249 505.2998487140696 0.5914529914529915 13341527280
17 328.3711747285291 494.4944024205749 0.6034188034188034 13341537276
18 332.67249889369236 481.48139183055974 0.6173382173382174 13341542263
19 336.97382305885554 468.4683812405446 0.6322344322344322 13341552267
20 341.01780304319703 457.08199697428137 0.6456654456654457 13341557287
21 343.8485890322361 450.69167927382756 0.6598290598290598 13341567268
22 344.95149266432924 450.9240544629349 0.6722832722832723 13341572419
23 343.811825577833 460.9161875945537 0.6825396825396826 13341582312
24 304.2543486400926 675.0499243570348 0.6991452991452991 13341617304
25 301.57061646866595 689.9219364599093 0.7137973137973138 13341627298
26 298.2619055723866 708.1633888048411 0.7299145299145299 13341632308
27 295.10024849371956 725.7077155824509 0.7555555555555555 13341642309
28 294.3282159512544 730.9361573373676 0.7892551892551892 13341647270
29 295.87228103618475 723.9649016641453 0.8178266178266178 13341657427
30 299.8794975661232 705.026323751891 0.8459096459096459 13341662307
31 306.0925213602478 677.0251134644478 0.8656898656898657 13341672414
32 313.9966640569153 642.8659606656581 0.8776556776556776 13341677324
33 322.56254893283864 608.8229954614221 0.8815628815628815 13341686308
34 330.9078530823433 578.8465960665658 0.8815628815628815 13341691312
35 337.56203832930527 558.3975794251135 0.8761904761904762 13341701310
36 341.9368894032747 549.2187594553707 0.8691086691086691 13341706456
37 343.51771794260816 552.8205748865355 0.8522588522588522 13341716309
38 342.1574701296933 569.3192133131619 0.8305250305250306 13341721310
39 338.2237805085611 597.4366111951589 0.802930402930403 13341731267
40 332.0842836232427 634.8490166414523 0.7763125763125763 13341736288
41 324.69482928821867 676.7927382753404 0.7587301587301587 13341746310
42 316.717159682745 720.1307110438729 0.7467643467643468 13341751319
43 309.4747591653334 758.2402420574887 0.7567765567765568 13341761311
44 303.5926064608367 789.6108925869894 0.7721611721611722 13341766301
45 299.62215338530143 809.943721633888 0.7892551892551892 13341776418
46 297.8942710283555 818.4254160363087 0.8136752136752137 13341781299
47 298.4824862988052 814.8236006051437 0.8344322344322345 13341791355
48 301.42356265105354 798.3249621785174 0.852014652014652 13341796307
49 306.8277904483099 770.6723146747353 0.8695970695970696 13341806489
50 314.5113524185587 733.2599092284418 0.8808302808302808 13341811286
51 323.9963236545597 690.7352496217852 0.8847374847374847 13341821318
52 334.65772543146 647.0487140695915 0.884981684981685 13341826271
53 344.84120230111995 608.5906202723147 0.8757020757020757 13341836311
54 352.92916226980293 581.518910741301 0.8605616605616606 13341850311
55 358.6274977022841 565.1364599092284 0.8398046398046398 13341851308
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notebooks/fitCurves.py
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@ -1,162 +0,0 @@
""" Python implementation of
Algorithm for Automatically Fitting Digitized Curves
by Philip J. Schneider
"Graphics Gems", Academic Press, 1990
"""
from __future__ import print_function
from numpy import *
import bezier
# Fit one (ore more) Bezier curves to a set of points
def fitCurve(points, maxError):
leftTangent = normalize(points[1] - points[0])
rightTangent = normalize(points[-2] - points[-1])
return fitCubic(points, leftTangent, rightTangent, maxError)
def fitCubic(points, leftTangent, rightTangent, error):
# Use heuristic if region only has two points in it
if (len(points) == 2):
dist = linalg.norm(points[0] - points[1]) / 3.0
bezCurve = [points[0], points[0] + leftTangent * dist, points[1] + rightTangent * dist, points[1]]
return [bezCurve]
# Parameterize points, and attempt to fit curve
u = chordLengthParameterize(points)
bezCurve = generateBezier(points, u, leftTangent, rightTangent)
# Find max deviation of points to fitted curve
maxError, splitPoint = computeMaxError(points, bezCurve, u)
if maxError < error:
return [bezCurve]
# If error not too large, try some reparameterization and iteration
if maxError < error**2:
for i in range(20):
uPrime = reparameterize(bezCurve, points, u)
bezCurve = generateBezier(points, uPrime, leftTangent, rightTangent)
maxError, splitPoint = computeMaxError(points, bezCurve, uPrime)
if maxError < error:
return [bezCurve]
u = uPrime
# Fitting failed -- split at max error point and fit recursively
beziers = []
centerTangent = normalize(points[splitPoint-1] - points[splitPoint+1])
beziers += fitCubic(points[:splitPoint+1], leftTangent, centerTangent, error)
beziers += fitCubic(points[splitPoint:], -centerTangent, rightTangent, error)
return beziers
def generateBezier(points, parameters, leftTangent, rightTangent):
bezCurve = [points[0], None, None, points[-1]]
# compute the A's
A = zeros((len(parameters), 2, 2))
for i, u in enumerate(parameters):
A[i][0] = leftTangent * 3*(1-u)**2 * u
A[i][1] = rightTangent * 3*(1-u) * u**2
# Create the C and X matrices
C = zeros((2, 2))
X = zeros(2)
for i, (point, u) in enumerate(zip(points, parameters)):
C[0][0] += dot(A[i][0], A[i][0])
C[0][1] += dot(A[i][0], A[i][1])
C[1][0] += dot(A[i][0], A[i][1])
C[1][1] += dot(A[i][1], A[i][1])
tmp = point - bezier.q([points[0], points[0], points[-1], points[-1]], u)
X[0] += dot(A[i][0], tmp)
X[1] += dot(A[i][1], tmp)
# Compute the determinants of C and X
det_C0_C1 = C[0][0] * C[1][1] - C[1][0] * C[0][1]
det_C0_X = C[0][0] * X[1] - C[1][0] * X[0]
det_X_C1 = X[0] * C[1][1] - X[1] * C[0][1]
# Finally, derive alpha values
alpha_l = 0.0 if det_C0_C1 == 0 else det_X_C1 / det_C0_C1
alpha_r = 0.0 if det_C0_C1 == 0 else det_C0_X / det_C0_C1
# If alpha negative, use the Wu/Barsky heuristic (see text) */
# (if alpha is 0, you get coincident control points that lead to
# divide by zero in any subsequent NewtonRaphsonRootFind() call. */
segLength = linalg.norm(points[0] - points[-1])
epsilon = 1.0e-6 * segLength
if alpha_l < epsilon or alpha_r < epsilon:
# fall back on standard (probably inaccurate) formula, and subdivide further if needed.
bezCurve[1] = bezCurve[0] + leftTangent * (segLength / 3.0)
bezCurve[2] = bezCurve[3] + rightTangent * (segLength / 3.0)
else:
# First and last control points of the Bezier curve are
# positioned exactly at the first and last data points
# Control points 1 and 2 are positioned an alpha distance out
# on the tangent vectors, left and right, respectively
bezCurve[1] = bezCurve[0] + leftTangent * alpha_l
bezCurve[2] = bezCurve[3] + rightTangent * alpha_r
return bezCurve
def reparameterize(bezier, points, parameters):
return [newtonRaphsonRootFind(bezier, point, u) for point, u in zip(points, parameters)]
def newtonRaphsonRootFind(bez, point, u):
"""
Newton's root finding algorithm calculates f(x)=0 by reiterating
x_n+1 = x_n - f(x_n)/f'(x_n)
We are trying to find curve parameter u for some point p that minimizes
the distance from that point to the curve. Distance point to curve is d=q(u)-p.
At minimum distance the point is perpendicular to the curve.
We are solving
f = q(u)-p * q'(u) = 0
with
f' = q'(u) * q'(u) + q(u)-p * q''(u)
gives
u_n+1 = u_n - |q(u_n)-p * q'(u_n)| / |q'(u_n)**2 + q(u_n)-p * q''(u_n)|
"""
d = bezier.q(bez, u)-point
numerator = (d * bezier.qprime(bez, u)).sum()
denominator = (bezier.qprime(bez, u)**2 + d * bezier.qprimeprime(bez, u)).sum()
if denominator == 0.0:
return u
else:
return u - numerator/denominator
def chordLengthParameterize(points):
u = [0.0]
for i in range(1, len(points)):
u.append(u[i-1] + linalg.norm(points[i] - points[i-1]))
for i, _ in enumerate(u):
u[i] = u[i] / u[-1]
return u
def computeMaxError(points, bez, parameters):
maxDist = 0.0
splitPoint = len(points)/2
for i, (point, u) in enumerate(zip(points, parameters)):
dist = linalg.norm(bezier.q(bez, u)-point)**2
if dist > maxDist:
maxDist = dist
splitPoint = i
return maxDist, splitPoint
def normalize(v):
return v / linalg.norm(v)

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notebooks/frequency_analysis.ipynb
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10
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20
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34
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16
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21
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15
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21 60.6454 279.847
22 60.5002 279.508
23 60.1856 279.096
24 59.8952 278.758
25 59.6048 278.395
26 59.266 278.104
27 59.0483 277.935
28 58.8063 277.765
29 58.7095 277.741
30 58.7095 277.765
31 58.8305 277.911
32 59.0725 278.104
33 59.4112 278.516
34 59.8468 278.927
35 60.3792 279.459

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