# How to use the vector.cross function in vector

## To help you get started, weβve selected a few vector examples, based on popular ways it is used in public projects.

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pypilot / pypilot / pypilot / calibration_fit.py View on Github
g = list(numpy.array(numpy.matrix(x[3:]).transpose()))
fac = 1 # weight deviation
def dip(y, z):
n = min(max(vector.dot(y, z)/vector.norm(y), -1), 1)
return n
r1 = lmap(lambda y, z : fac*beta[2]*(beta[3]-dip(y, z)), m, g)
return r0 + r1
new_sphere2d_fit = FitLeastSq([0, 0, initial[3], 0], f_new_sphere2, zpoints, 2)
if not new_sphere2d_fit or new_sphere2d_fit[2] &lt; 0 or abs(new_sphere2d_fit[3]) &gt;= 1:
debug('FitLeastSq sphere2 failed!!!! ', len(points), new_sphere2d_fit)
return False
new_sphere2d_fit = lmap(lambda x, a, b: x + new_sphere2d_fit[0]*a + new_sphere2d_fit[1]*b, initial[:3], u, v) + [new_sphere2d_fit[2], math.degrees(math.asin(new_sphere2d_fit[3]))]
new_sphere2d_fit = [new_sphere2d_fit, ComputeDeviation(points, new_sphere2d_fit), 2]

if plane_max_dev &lt; 1.2:
ang = math.degrees(math.asin(vector.norm(vector.cross(plane_fit[1], norm))))

debug('plane fit found, 2D fit only', ang, plane_fit, plane_dev, plane_max_dev)
if ang &gt; 30:
debug('angle of plane not aligned to normal: no 2d fit')
new_sphere2d_fit = False

return [new_sphere1d_fit, new_sphere2d_fit, False]

# ok to use best guess for 3d fit
initial = guess
'''
def f_sphere3(beta, x):
bias = beta[:3]
b = numpy.matrix(lmap(lambda a, b : a - b, x[:3], bias))
m = list(numpy.array(b.transpose()))
r0 = lmap(lambda y : beta[3] - vector.norm(y), m)
pypilot / pypilot / pypilot / calibration_fit.py View on Github
return r0 + r1
new_sphere1d_fit = FitLeastSq([0, initial[3], 0], f_new_sphere1, zpoints, 2)
if not new_sphere1d_fit or new_sphere1d_fit[1] &lt; 0 or abs(new_sphere1d_fit[2]) &gt; 1:
debug('FitLeastSq new_sphere1 failed!!!! ', len(points), new_sphere1d_fit)
new_sphere1d_fit = current
else:
new_sphere1d_fit = lmap(lambda x, a: x + new_sphere1d_fit[0]*a, initial[:3], norm) + [new_sphere1d_fit[1], math.degrees(math.asin(new_sphere1d_fit[2]))]
new_sphere1d_fit = [new_sphere1d_fit, ComputeDeviation(points, new_sphere1d_fit), 1]
#print('new sphere1 fit', new_sphere1d_fit)

if line_max_dev &lt; 2:
debug('line fit found, insufficient data', line_dev, line_max_dev)
return False

# 2d sphere fit across normal vector
u = vector.cross(norm, [norm[1]-norm[2], norm[2]-norm[0], norm[0]-norm[1]])
v = vector.cross(norm, u)
u = vector.normalize(u)
v = vector.normalize(v)

# initial is the closest to guess on the uv plane containing current
initial = vector.add(guess[:3], vector.project(vector.sub(current[:3], guess[:3]), norm))
initial.append(current[3])
#debug('initial 2d fit', initial)

'''
def f_sphere2(beta, x):
bias = lmap(lambda x, a, b: x + beta[0]*a + beta[1]*b, initial[:3], u, v)
b = numpy.matrix(lmap(lambda a, b : a - b, x[:3], bias))
m = list(numpy.array(b.transpose()))
r0 = lmap(lambda y : beta[2] - vector.norm(y), m)
return r0
pypilot / pypilot / pypilot / calibration_fit.py View on Github
new_sphere1d_fit = FitLeastSq([0, initial[3], 0], f_new_sphere1, zpoints, 2)
if not new_sphere1d_fit or new_sphere1d_fit[1] &lt; 0 or abs(new_sphere1d_fit[2]) &gt; 1:
debug('FitLeastSq new_sphere1 failed!!!! ', len(points), new_sphere1d_fit)
new_sphere1d_fit = current
else:
new_sphere1d_fit = lmap(lambda x, a: x + new_sphere1d_fit[0]*a, initial[:3], norm) + [new_sphere1d_fit[1], math.degrees(math.asin(new_sphere1d_fit[2]))]
new_sphere1d_fit = [new_sphere1d_fit, ComputeDeviation(points, new_sphere1d_fit), 1]
#print('new sphere1 fit', new_sphere1d_fit)

if line_max_dev &lt; 2:
debug('line fit found, insufficient data', line_dev, line_max_dev)
return False

# 2d sphere fit across normal vector
u = vector.cross(norm, [norm[1]-norm[2], norm[2]-norm[0], norm[0]-norm[1]])
v = vector.cross(norm, u)
u = vector.normalize(u)
v = vector.normalize(v)

# initial is the closest to guess on the uv plane containing current
initial = vector.add(guess[:3], vector.project(vector.sub(current[:3], guess[:3]), norm))
initial.append(current[3])
#debug('initial 2d fit', initial)

'''
def f_sphere2(beta, x):
bias = lmap(lambda x, a, b: x + beta[0]*a + beta[1]*b, initial[:3], u, v)
b = numpy.matrix(lmap(lambda a, b : a - b, x[:3], bias))
m = list(numpy.array(b.transpose()))
r0 = lmap(lambda y : beta[2] - vector.norm(y), m)
return r0
sphere2d_fit = FitLeastSq([0, 0, initial[3]], f_sphere2, zpoints)

## vector

Vector classes and utilities

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