# How to use the vector.Vector 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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inasafe / inasafe / safe / storage / test_io.py View on Github
P = numpy.array([[106.7922547, -6.2297884],
[106.7924589, -6.2298087],
[106.7924538, -6.2299127],
[106.7922547, -6.2298899],
[106.7922547, -6.2297884]])

C = calculate_polygon_centroid(P)

# Check against reference centroid from qgis
reference_centroid = [106.79235602697445, -6.229849764722536]
msg = 'Got %s but expected %s' % (str(C), str(reference_centroid))
assert numpy.allclose(C, reference_centroid, rtol=1.0e-8), msg

# Store centroid to file (to e.g. check with qgis)
out_filename = unique_filename(prefix='test_centroid', suffix='.shp')
V = Vector(data=None,
projection=DEFAULT_PROJECTION,
geometry=[C],
name='Test centroid')
V.write_to_file(out_filename)
douban / dpark / examples / kmeans.py View on Github
def parseVector(line):
return Vector(list(map(float, line.strip().split(' '))))
mikaelho / scripter / vector.py View on Github
''' As steps_to, but returns unique points rounded to the nearest integer. '''
previous = Vector(0,0)
for step in self.steps_to(other, step_magnitude):
rounded = round(step)
if rounded != previous:
previous = rounded
yield rounded

if __name__ == '__main__':
v = Vector(x = 1, y = 2)
v2 = Vector(3, 4)
v += v2
assert str(v) == '[4, 6]'
assert v / 2.0 == Vector(2, 3)
assert v * 0.1 == Vector(0.4, 0.6)
assert v.distance_to(v2) == math.sqrt(1+4)

v3 = Vector(Vector(1, 2) - Vector(2, 0)) # -1.0, 2.0
v3.magnitude *= 2
assert v3 == [-2, 4]

v3.radians = math.pi # 180 degrees
v3.magnitude = 2
assert v3 == [-2, 0]
v3.degrees = -90
assert v3 == [0, -2]

assert list(Vector(1, 1).steps_to(Vector(3, 3))) == [[1.7071067811865475, 1.7071067811865475], [2.414213562373095, 2.414213562373095], [3, 3]]
assert list(Vector(1, 1).steps_to(Vector(-1, 1))) == [[0, 1], [-1, 1]]
assert list(Vector(1, 1).rounded_steps_to(Vector(3, 3))) == [[2, 2], [3, 3]]
Minimata / IslandGenerator / main.py View on Github
def warp(p, freq=5.0):
"""
This is the core warp function, gotten from Inigo Quilez ( &lt;3 )

It takes a position (as a vector) on entry.
Returns a float value between -1 and 1.
The more warping, the closer to 0 the value will get, so scale it up before writing it to an image.
"""
updated_value = Vector(0, 0)
for i in range(num_warpings):
off1 = simplex_offsets[2 * i]
off2 = simplex_offsets[2 * i + 1]
updated_value = Vector(fbm(p + updated_value * 4.0 + Vector(off1[0], off1[1]), freq=freq),
fbm(p + updated_value * 4.0 + Vector(off2[0], off2[1]), freq=freq))
return fbm(p + updated_value * 4.0, freq=freq)
omarrayward / Linear-Algebra-Refresher-Udacity / vector.py View on Github
def cross_product(self, other):
[x1, y1, z1] = self.coordinates
[x2, y2, z2] = other.coordinates
x = (y1 * z2) - (y2 * z1)
y = -((x1 * z2) - (x2 * z1))
z = (x1 * y2) - (x2 * y1)
return Vector([x, y, z])
Minimata / IslandGenerator / main.py View on Github
def create_gradient_from_normals(normals):
for normal in normals:
if normal[2] > 0:
gradients.append(Vector(normal[0] / normal[2], normal[1] / normal[2], 0.0))
else:
return gradients
teammcr192 / spherical-k-means / Python / spkmeans.py View on Github
def compute_concept(self, p):
"""
Computes the Concept Vector of given partition p, and returns said
Concept Vector. Parameter p must be a list containing at least one
document Vector.
"""
# computer sum of all vectors in partition p
cv = Vector(self.num_words)
for doc_v in p:
cv += doc_v

# compute the mean vector for partition using the sum vector
cv *= (1/len(p))

# computer the norm of the mean vector
cv.normalize()

return cv
omarrayward / Linear-Algebra-Refresher-Udacity / vector.py View on Github
v = Vector([2.118, 4.827])
w = Vector([0, 0])
is_parallel = v.is_parallel(w)
is_orthogonal = v.is_orthogonal(w)

print('4 parallel: {}, orthogonal: {}'.format(is_parallel, is_orthogonal))

# *****************

v = Vector([3.039, 1.879])
w = Vector([0.825, 2.036])
projected_vector = v.get_projected_vector(w)

print('projected vector is: {}'.format(projected_vector))

v = Vector([-9.88, -3.264, -8.159])
w = Vector([-2.155, -9.353, -9.473])
orthogonal_vector = v.get_orthogonal_vector(w)

print('orthogonal vector is: {}'.format(orthogonal_vector))

v = Vector([3.009, -6.172, 3.692, -2.51])
w = Vector([6.404, -9.144, 2.759, 8.718])
projected_vector = v.get_projected_vector(w)
orthogonal_vector = v.get_orthogonal_vector(w)

print('second projected vector is: {}'.format(projected_vector))

print('second orthogonal vector is: {}'.format(orthogonal_vector))

# *****************
kykamath / streaming_lsh / vector.py View on Github
def getNormalizedVector(self):
modValue = self.mod()
if modValue==0: return Vector(self)
normalizedVector = Vector()
for k, v in self.iteritems(): normalizedVector[k]=v/modValue
return normalizedVector
guaxiao / rasterizer.py / canvas.py View on Github
def draw_scanline(self, va, vb, y):
x1 = int(va.position.x)
x2 = int(vb.position.x)
sign = 1 if x2 > x1 else -1
factor = 0
for x in range(x1, x2 + sign * 1, sign):
if x1 != x2:
factor = (x - x1) / (x2 - x1)
color = interpolate(va.color, vb.color, factor)
self.draw_point(Vector(x, y), color)

## vector

Vector classes and utilities

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