def cross(X, Y, axis=1):
''' Cross product
axis=1 Numpy default
axis=0 MATLAB, Octave, SciLab default
'''
if axis == 0:
X = X.T
Y = Y.T
if (X.n in (2, 3)) and (Y.n in (2, 3)):
if X.m == Y.m:
Z = []
for k in range(min(X.m, Y.m)):
z = X[k, 0] * Y[k, 1] - X[k, 1] * Y[k, 0]
if (X.n == 3) and (Y.n == 3):
Z.append([X[k, 1] * Y[k, 2] - X[k, 2] * Y[k, 1],
X[k, 2] * Y[k, 0] - X[k, 0] * Y[k, 2], z])
else:
Z.append([z])
if axis == 0:
return umatrix.matrix(Z).T
else:
return umatrix.matrix(Z)
else:
raise ValueError('shape mismatch')
else:
raise ValueError('incompatible dimensions for cross product'
' (must be 2 or 3)')
def __init__(self, w_or_q, x=None, y=None, z=None):
"""
Initializes a Quaternion object
:param w_or_q: A scalar representing the real part of the quaternion, another Quaternion object or a
four-element array containing the quaternion values
:param x: The first imaginary part if w_or_q is a scalar
:param y: The second imaginary part if w_or_q is a scalar
:param z: The third imaginary part if w_or_q is a scalar
"""
self._q = umatrix.matrix([1, 0, 0, 0], cstride=1, rstride=4, dtype=float)
if x is not None and y is not None and z is not None:
w = w_or_q
q = umatrix.matrix([w, x, y, z], cstride=1, rstride=4, dtype=float)
elif isinstance(w_or_q, Quaternion):
q = umatrix.matrix(w_or_q.q, cstride=1, rstride=4, dtype=float)
elif isinstance(w_or_q, umatrix.matrix):
q = w_or_q
else:
q = umatrix.matrix(w_or_q, cstride=1, rstride=4, dtype=float)
if len(q) != 4:
raise ValueError("Expecting a 4-element array or w x y z as parameters")
self._set_q(q)
def equality():
result = {}
x10 = umatrix.matrix([[0.03, 1.2, 2.45], [4.5, 5.45, 6.98],
[8, 9.0001, 10.2], [12.123, 13.45, 14.0]])
x11 = umatrix.matrix([[0.03, 1.2, 2.45], [4.5, 5.45, 6.98],
[8, 9.0001, 10.2], [12.123, 13.45, 14.0]])
x12 = umatrix.matrix([[0.03, 1.2, 2.451], [4.5, 5.45, 6.98],
[9, 9.0002, 10.2], [12.123, 13.45 + eps, 14.0]])
x13 = umatrix.matrix([[0.03, 1.2, 2.451], [4.5, 5.45, 6.98],
[9, 9.0003, 10.2], [12.123, 13.450001, 14.0]])
result['x == y and x.__eq__(y)'] = (x10 == x11) and (x10.__eq__(x11))
result['umatrix.matrix_isclose(x, y) True'] = matrix_compare(x10, x11)
result['umatrix.matrix_isclose(x, y) False'] = matrix_compare(x10,
x12) == False
result['umatrix.matrix_isclose(x, y, tol) False tol'] = matrix_compare(
x12, x13, tol=eps / 2) == False
result['umatrix.matrix_isclose(x, y, tol) True tol'] = matrix_compare(
x12, x13, tol=0.001)
try:
result['umatrix.matrix_equal(x, y)'] = umatrix.matrix_equal(
x10, x12) == False
except Exception as e:
result['umatrix.matrix_equal(x, y)'] = (False, e)
result['umatrix.matrix_equiv(x, y) same shape'] = umatrix.matrix_equiv(
x10, x11)
result['umatrix.matrix_equiv(x, y.reshape) shape'] = umatrix.matrix_equiv(
x10, x11.reshape((3, 4)))
return result
def cross(X, Y, axis=1):
""" Cross product
axis=1 Numpy default
axis=0 MATLAB, Octave, SciLab default
"""
if axis == 0:
X = X.T
Y = Y.T
if (X.n in (2, 3)) and (Y.n in (2, 3)):
if X.m == Y.m:
Z = []
for k in range(min(X.m, Y.m)):
z = X[k, 0] * Y[k, 1] - X[k, 1] * Y[k, 0]
if (X.n == 3) and (Y.n == 3):
Z.append([
X[k, 1] * Y[k, 2] - X[k, 2] * Y[k, 1],
X[k, 2] * Y[k, 0] - X[k, 0] * Y[k, 2],
z,
])
else:
Z.append([z])
if axis == 0:
return umatrix.matrix(Z).T
else:
return umatrix.matrix(Z)
else:
raise ValueError("shape mismatch")
else:
raise ValueError("incompatible dimensions for cross product"
" (must be 2 or 3)")
def __init__(self, lbda):
self.std_pos = 0.00448355705097 * 10
self.std_vel = 0.0014239412603
self.lbda = lbda
moy = pi / 180.0 * umatrix.matrix([[-5.0, 4.0]]).T
cho = umatrix.matrix([[self.std_pos, 0.0], [0.0, self.std_vel]])
ACapteur.__init__(self, [u"pos_mes", "vel_mes"], moy, cho)
def list_ops():
result = {}
X = umatrix.matrix([[0, 1, 2], [4, 5, 6], [8, 9, 10], [12, 13, 14]])
Ylist = [1, 2, 3]
Zlist = [1, 2, 3, 4]
Ymatrix = umatrix.matrix([[1, 2, 3]])
Zmatrix = umatrix.matrix([[1, 2, 3, 4]])
try:
result['matrix + list row defualt'] = matrix_compare(
X + Ylist,
umatrix.matrix([[1, 3, 5], [5, 7, 9], [9, 11, 13], [13, 15, 17]]))
except Exception as e:
result['matrix + list row default'] = (False, e)
try:
result['matrix + list row default broadcast error'] = matrix_compare(
X + Zlist,
umatrix.matrix([[1, 3, 5], [5, 7, 9], [9, 11, 13], [13, 15, 17]]))
except Exception as e:
result['matrix + list row default broadcast error'] = True
try:
result['matrix col + list'] = matrix_compare(
X[:, 1] + Zlist,
umatrix.matrix([[2, 7, 12, 17]]).T)
except Exception as e:
result['matrix col + list'] = (False, e)
try:
result['matrix col + list broadcast error'] = matrix_compare(
X[:, 1] + Ylist,
umatrix.matrix([[2, 7, 12, 17]]).T)
except Exception as e:
result['matrix col + list broadcast error'] = True
try:
result['matrix + row matrix'] = matrix_compare(
X + Ymatrix,
umatrix.matrix([[1, 3, 5], [5, 7, 9], [9, 11, 13], [13, 15, 17]]))
except Exception as e:
result['matrix + row matrix'] = (False, e)
try:
result['matrix + col matrix'] = matrix_compare(
X + Zmatrix.T,
umatrix.matrix([[1, 2, 3], [6, 7, 8], [11, 12, 13], [16, 17, 18]]))
except Exception as e:
result['matrix + col matrix'] = (False, e)
try:
result['matrix + col broadcast error'] = matrix_compare(
X + Ymatrix.T,
umatrix.matrix([[1, 3, 5], [5, 7, 9], [9, 11, 13], [13, 15, 17]]))
except Exception as e:
result['matrix + col broadcast error'] = True
return result
def iteration():
result = {}
x10 = umatrix.matrix([[0,1,2],[4,5,6],[8,9,10],[12,13,14]])
Z = [i for i in x10]
result['iteration over matrix'] = Z == [umatrix.matrix([[0, 1, 2]]), umatrix.matrix([[4, 5, 6]]), umatrix.matrix([[8 , 9 , 10]]), umatrix.matrix([[12, 13, 14]])]
Z = [i for i in x10[1,:]]
result['iteration over row slice'] = Z == [4, 5, 6]
Z = [i for i in x10[:,1]]
result['iteration over col slice'] = Z == [1, 5, 9, 13]
Z = [i for i in x10[1:3,1:2]]
result['iteration over submatrix'] = Z == [5, 9]
return result
def iteration():
result = {}
x10 = umatrix.matrix([[0,1,2],[4,5,6],[8,9,10],[12,13,14]])
Z = [i for i in x10]
result['iteration over matrix'] = Z == [umatrix.matrix([[0, 1, 2]]), umatrix.matrix([[4, 5, 6]]), umatrix.matrix([[8 , 9 , 10]]), umatrix.matrix([[12, 13, 14]])]
Z = [i for i in x10[1,:]]
result['iteration over row slice'] = Z == [4, 5, 6]
Z = [i for i in x10[:,1]]
result['iteration over col slice'] = Z == [1, 5, 9, 13]
Z = [i for i in x10[1:3,1:2]]
result['iteration over submatrix'] = Z == [5, 9]
return result
def main():
# Cadence de calcul
fs = 100.0
# Init système
lbda = 5.0
sys = SystemeTest(lbda)
sys.set_state(umatrix.matrix([[-pi / 4]]).T, 0.0)
# Init Kalman
A = umatrix.matrix([[exp(-lbda / fs), 0.0, 0.0], [0.0, 1.0, 0.0],
[0.0, 0.0, 1.0]])
B = umatrix.matrix([[1.0 - exp(-lbda / fs), 0.0, 0.0]]).T
C = umatrix.matrix([[1.0, 1.0, 0.0], [-lbda, 0.0, 1.0]])
D = umatrix.matrix([[0.0, lbda]]).T
Q = 0 * ulinalg.eye(3)
std_pos = 0.00448355705097
std_vel = 0.0014239412603
R = umatrix.matrix([[std_pos**2, 0.0], [0.0, std_vel**2]])
kal = FiltreKalman(["pos_est", "biais_pos_est", "biais_vel_est"])
kal.set_matrices(A, B, C, D, Q, R, 1.0 / fs)
kal.set_state(
umatrix.matrix([[0.0, 0.0, 0.0]]).T,
ulinalg.eye(3) / 10.0, 0.0)
c = Capteur(lbda)
ctrl = Controller(1.0 / fs, sys)
sim = Simulation(1.0 / fs, ctrl, sys, c, kal)
sim.simule(20.0)
log = sim.get_loggeur()
def update_imu(self, gyroscope, accelerometer):
"""
Perform one update step with data from a IMU sensor array
:param gyroscope: A three-element array containing the gyroscope data in radians per second.
:param accelerometer: A three-element array containing the accelerometer data. Can be any unit since a normalized value is used.
"""
q = self.quaternion
# Normalise accelerometer measurement
if ulinalg.norm(accelerometer) is 0:
print("accelerometer is zero")
return
accelerometer = accelerometer / ulinalg.norm(accelerometer)
# Gradient descent algorithm corrective step
f = umatrix.matrix([
2 * (q[1] * q[3] - q[0] * q[2]) - accelerometer[0, 0], 2 *
(q[0] * q[1] + q[2] * q[3]) - accelerometer[0, 1], 2 *
(0.5 - q[1]**2 - q[2]**2) - accelerometer[0, 2]
],
cstride=1,
rstride=1,
dtype=float)
j = umatrix.matrix([
-2 * q[2], 2 * q[3], -2 * q[0], 2 * q[1], 2 * q[1], 2 * q[0],
2 * q[3], 2 * q[2], 0, -4 * q[1], -4 * q[2], 0
],
cstride=1,
rstride=4,
dtype=float)
step = ulinalg.dot(j.transpose(), f)
step = step / ulinalg.norm(step) # normalise step magnitude
# Compute rate of change of quaternion
qdot = (q * Quaternion(0, gyroscope[0, 0], gyroscope[0, 1],
gyroscope[0, 2])) * 0.5 - Quaternion(
step.T * self.beta)
# Integrate to yield quaternion
q = q + qdot * (self.samplePeriod)
self.quaternion = q * (1 / ulinalg.norm(q._q)) # normalise quaternion
def dot(X, Y):
""" Dot product """
if X.size(2) == Y.size(1):
Z = []
for k in range(X.size(1)):
for j in range(Y.size(2)):
Z.append(sum([X[k, i] * Y[i, j] for i in range(Y.size(1))]))
return umatrix.matrix(Z, cstride=1, rstride=Y.size(2))
else:
raise ValueError("shapes not aligned")
def dot(X, Y):
''' Dot product '''
if X.size(2) == Y.size(1):
Z = []
for k in range(X.size(1)):
for j in range(Y.size(2)):
Z.append(sum([X[k, i] * Y[i, j] for i in range(Y.size(1))]))
return umatrix.matrix(Z, cstride=1, rstride=Y.size(2))
else:
raise ValueError('shapes not aligned')
def slicing():
result = {}
x10 = umatrix.matrix([[0,1,2],[4,5,6],[8,9,10],[12,13,14]])
result['extract single element'] = x10[1,2] == 6
result['extract a row'] = matrix_compare(x10[1,:], umatrix.matrix([[4, 5, 6]]))
result['extract a col'] = matrix_compare(x10[:,1], umatrix.matrix([1, 5, 9, 13], cstride=1, rstride=1))
result['extract rows'] = matrix_compare(x10[1:4,:], umatrix.matrix([[4, 5, 6],[8, 9, 10],[12, 13, 14]]))
result['extract columns'] = matrix_compare(x10[:,1:3], umatrix.matrix([[1, 2],[5, 6],[9, 10],[13, 14]]))
result['extract sub'] = matrix_compare(x10[1:3,1:3], umatrix.matrix([[5, 6],[9, 10]]))
result['extract row from transpose'] = matrix_compare(x10.T[0], umatrix.matrix([[0, 4, 8, 12]]))
result['extract col from transpose'] = matrix_compare(x10.T[:,1], umatrix.matrix([4, 5, 6], cstride=1, rstride=1))
result['extract sub from transpose'] = matrix_compare(x10.T[1:3,1:3], umatrix.matrix([[5, 9],[6, 10]]))
return result
def slicing():
result = {}
x10 = umatrix.matrix([[0,1,2],[4,5,6],[8,9,10],[12,13,14]])
result['extract single element'] = x10[1,2] == 6
result['extract a row'] = matrix_compare(x10[1,:], umatrix.matrix([[4, 5, 6]]))
result['extract a col'] = matrix_compare(x10[:,1], umatrix.matrix([1, 5, 9, 13], cstride=1, rstride=1))
result['extract rows'] = matrix_compare(x10[1:4,:], umatrix.matrix([[4, 5, 6],[8, 9, 10],[12, 13, 14]]))
result['extract columns'] = matrix_compare(x10[:,1:3], umatrix.matrix([[1, 2],[5, 6],[9, 10],[13, 14]]))
result['extract sub'] = matrix_compare(x10[1:3,1:3], umatrix.matrix([[5, 6],[9, 10]]))
result['extract row from transpose'] = matrix_compare(x10.T[0], umatrix.matrix([[0, 4, 8, 12]]))
result['extract col from transpose'] = matrix_compare(x10.T[:,1], umatrix.matrix([4, 5, 6], cstride=1, rstride=1))
result['extract sub from transpose'] = matrix_compare(x10.T[1:3,1:3], umatrix.matrix([[5, 9],[6, 10]]))
return result
def element_wise():
result = {}
X = umatrix.matrix([[0,1,2],[4,5,6],[8,9,10]])
try:
result['matrix - matrix'] = matrix_compare(X-X, umatrix.matrix([[0,0,0],[0,0,0],[0,0,0]]))
except Exception as e:
result['matrix + list row default'] = (False, e)
try:
result['matrix - matrix view'] = matrix_compare(X-X.T, umatrix.matrix([[0,-3,-6],[3,0,-3],[6,3,0]]))
except Exception as e:
result['matrix + list row default'] = (False, e)
try:
result['matrix view - matrix'] = matrix_compare(X.T-X, umatrix.matrix([[0,-3,-6],[3,0,-3],[6,3,0]]).T)
except Exception as e:
result['matrix + list row default'] = (False, e)
return result
def element_wise():
result = {}
X = umatrix.matrix([[0,1,2],[4,5,6],[8,9,10]])
try:
result['matrix - matrix'] = matrix_compare(X-X, umatrix.matrix([[0,0,0],[0,0,0],[0,0,0]]))
except Exception as e:
result['matrix + list row default'] = (False, e)
try:
result['matrix - matrix view'] = matrix_compare(X-X.T, umatrix.matrix([[0,-3,-6],[3,0,-3],[6,3,0]]))
except Exception as e:
result['matrix + list row default'] = (False, e)
try:
result['matrix view - matrix'] = matrix_compare(X.T-X, umatrix.matrix([[0,-3,-6],[3,0,-3],[6,3,0]]).T)
except Exception as e:
result['matrix + list row default'] = (False, e)
return result
def equality():
result = {}
x10 = umatrix.matrix([[0.03,1.2,2.45],[4.5,5.45,6.98],[8,9.0001,10.2],[12.123,13.45,14.0]])
x11 = umatrix.matrix([[0.03,1.2,2.45],[4.5,5.45,6.98],[8,9.0001,10.2],[12.123,13.45,14.0]])
x12 = umatrix.matrix([[0.03,1.2,2.451],[4.5,5.45,6.98],[9,9.0002,10.2],[12.123,13.45+eps,14.0]])
x13 = umatrix.matrix([[0.03,1.2,2.451],[4.5,5.45,6.98],[9,9.0003,10.2],[12.123,13.450001,14.0]])
result['x == y and x.__eq__(y)'] = (x10 == x11) and (x10.__eq__(x11))
result['umatrix.matrix_isclose(x, y) True'] = matrix_compare(x10, x11)
result['umatrix.matrix_isclose(x, y) False'] = matrix_compare(x10, x12) == False
result['umatrix.matrix_isclose(x, y, tol) False tol'] = matrix_compare(x12, x13, tol=eps/2) == False
result['umatrix.matrix_isclose(x, y, tol) True tol'] = matrix_compare(x12, x13, tol=0.001)
try:
result['umatrix.matrix_equal(x, y)'] = umatrix.matrix_equal(x10, x12) == False
except Exception as e:
result['umatrix.matrix_equal(x, y)'] = (False, e)
result['umatrix.matrix_equiv(x, y) same shape'] = umatrix.matrix_equiv(x10, x11)
result['umatrix.matrix_equiv(x, y.reshape) shape'] = umatrix.matrix_equiv(x10, x11.reshape((3,4)))
return result
def det_inv_test():
result = {}
# first test
g = umatrix.matrix([[0., 0., 0., 0., 0., 1.],
[0.03125, 0.0625, 0.125, 0.25, 0.5, 1.],
[0., 0., 0., 0., 1., 0.],
[0.3125, 0.5, 0.75, 1., 1., 0.],
[0., 0., 0., 2., 0., 0.], [2.5, 3., 3., 2., 0., 0.]])
g_inv = umatrix.matrix([[-192., 192., -48., -48., -4., 4.],
[240., -240., 64., 56., 6., -4.],
[-80., 80., -24., -16., -3., 1.],
[0., 0., 0., 0., 0.5,
0.], [0., 0., 1., 0., 0., 0.],
[1., 0., 0., 0., 0., 0.]])
(det, inv) = ulinalg.det_inv(g)
result['determinant 1'] = (abs(det - 0.0078125) < 0.000001)
result['inverse 1'] = umatrix.matrix_equal(inv, g_inv, 0.000001)
# second test
x = umatrix.matrix([[3., 2., 0., 1.], [4., 0., 1., 2.], [3., 0., 2., 1.],
[9., 2., 3., 1.]])
(det, inv) = ulinalg.det_inv(x)
det_res = det == 24.0
f = umatrix.matrix([[-0.25, 0.25, -0.5, 0.25],
[
0.66666666666666667, -0.49999999999999999,
0.50000000000000001, -0.16666666666666667
],
[
0.16666666666666667, -0.49999999999999999,
1.00000000000000002, -0.16666666666666667
],
[
0.41666666666666667, 0.25, 0.50000000000000001,
-0.41666666666666667
]])
result['inverse 2'] = matrix_compare(inv, f, tol=eps *
2) #, tol=0.000000000000001)
f[3, 3] = -0.416668
result['determinant 2'] = det_res
result['matrix_equal True'] = umatrix.matrix_equal(inv, f, tol=0.0001)
result['matrix_equal False'] = umatrix.matrix_equal(inv, f) == False
x1 = umatrix.matrix([[0.71, -0.71, 0.7], [0.71, 0.71, 0.5], [0, 0, 1]])
z = ulinalg.pinv(x1)
result['psuedo inverse'] = matrix_compare(
z,
umatrix.matrix([
[0.7042253521126759, 0.704225352112676, -0.8450704225352109],
[-0.704225352112676, 0.704225352112676, 0.1408450704225352],
[1.110223024625157e-16, 5.551115123125783e-17, 0.9999999999999998]
]),
tol=2 * eps)
return result
def list_ops():
result = {}
X = umatrix.matrix([[0,1,2],[4,5,6],[8,9,10],[12,13,14]])
Ylist = [1,2,3]
Zlist = [1,2,3,4]
Ymatrix = umatrix.matrix([[1,2,3]])
Zmatrix = umatrix.matrix([[1,2,3,4]])
try:
result['matrix + list row defualt'] = matrix_compare(X+Ylist, umatrix.matrix([[1,3,5],[5,7,9],[9,11,13],[13,15,17]]))
except Exception as e:
result['matrix + list row default'] = (False, e)
try:
result['matrix + list row default broadcast error'] = matrix_compare(X+Zlist, umatrix.matrix([[1,3,5],[5,7,9],[9,11,13],[13,15,17]]))
except Exception as e:
result['matrix + list row default broadcast error'] = True
try:
result['matrix col + list'] = matrix_compare(X[:,1]+Zlist, umatrix.matrix([[2,7,12,17]]).T)
except Exception as e:
result['matrix col + list'] = (False, e)
try:
result['matrix col + list broadcast error'] = matrix_compare(X[:,1]+Ylist, umatrix.matrix([[2,7,12,17]]).T)
except Exception as e:
result['matrix col + list broadcast error'] = True
try:
result['matrix + row matrix'] = matrix_compare(X+Ymatrix, umatrix.matrix([[1,3,5],[5,7,9],[9,11,13],[13,15,17]]))
except Exception as e:
result['matrix + row matrix'] = (False, e)
try:
result['matrix + col matrix'] = matrix_compare(X+Zmatrix.T, umatrix.matrix([[1,2,3],[6,7,8],[11,12,13],[16,17,18]]))
except Exception as e:
result['matrix + col matrix'] = (False, e)
try:
result['matrix + col broadcast error'] = matrix_compare(X+Ymatrix.T, umatrix.matrix([[1,3,5],[5,7,9],[9,11,13],[13,15,17]]))
except Exception as e:
result['matrix + col broadcast error'] = True
return result
def mesure(self, x, u, t):
z = self.comportement(x, u, t)
n = len(self._name_of_mes)
n2 = int((n + 1) / 2) * 2
# Vecteur de nombre aleatoires uniformes entre 0 et 1
vu = [urandom.getrandbits(8 * 4) / float(2 ** 32)] * n2
for i in range(n2 / 2):
vu[2 * i + 0], vu[2 * i + 1] = (
sqrt(-log(vu[2 * i + 0])) * cos(2 * pi * vu[2 * i + 1]),
sqrt(-log(vu[2 * i + 0])) * sin(2 * pi * vu[2 * i + 1]),
)
bn = umatrix.matrix([vu[:n]]).T
bg = self._cho * bn + self._moy
self._mes = z + bg
def construct():
result = {}
x11 = umatrix.matrix([[0, 1, 2], [4, 5, 6], [8, 9, 10], [12, 13, 14]])
x10 = umatrix.matrix([[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11],
[12, 13, 14, 15]])
result['square test 1'] = x10.is_square
result['square test 2'] = not x11.is_square
result['transpose column vector'] = matrix_compare(
umatrix.matrix([1, 2, 3, 4], cstride=1, rstride=1).T,
umatrix.matrix([[1, 2, 3, 4]]))
result['transpose test square'] = matrix_compare(
x10.transpose(),
umatrix.matrix([[0, 4, 8, 12], [1, 5, 9, 13], [2, 6, 10, 14],
[3, 7, 11, 15]]))
result['transpose property'] = matrix_compare(
x10.T,
umatrix.matrix([[0, 4, 8, 12], [1, 5, 9, 13], [2, 6, 10, 14],
[3, 7, 11, 15]]))
# check for shape change view
x12 = x11
result['shape'] = x11.shape == (4, 3)
x11.shape = (3, 4)
result['shape change'] = matrix_compare(
x11, umatrix.matrix([[0, 1, 2, 4], [5, 6, 8, 9], [10, 12, 13, 14]
])) and (x11 == x12)
x11 = umatrix.matrix([[0, 1, 2], [4, 5, 6], [8, 9, 10], [12, 13, 14]])
# check for shape change copy
x12 = x11.reshape((3, 4))
result['shape copy'] = matrix_compare(
x12, umatrix.matrix([[0, 1, 2, 4], [5, 6, 8, 9], [10, 12, 13, 14]
])) and (x12.shape != x11.shape)
return result
def construct():
result = {}
x11 = umatrix.matrix([[0,1,2],[4,5,6],[8,9,10],[12,13,14]])
x10 = umatrix.matrix([[0,1,2,3],[4,5,6,7],[8,9,10,11],[12,13,14,15]])
result['square test 1'] = x10.is_square
result['square test 2'] = not x11.is_square
result['transpose column vector'] = matrix_compare(umatrix.matrix([1,2,3,4], cstride=1, rstride=1).T, umatrix.matrix([[1,2,3,4]]))
result['transpose test square'] = matrix_compare(x10.transpose(), umatrix.matrix([[0, 4, 8, 12],[1, 5, 9, 13],[2, 6, 10, 14],[3, 7, 11, 15]]))
result['transpose property'] = matrix_compare(x10.T, umatrix.matrix([[0, 4, 8, 12],[1, 5, 9, 13],[2, 6, 10, 14],[3, 7, 11, 15]]))
# check for shape change view
x12 = x11
result['shape'] = x11.shape == (4, 3)
x11.shape=(3, 4)
result['shape change'] = matrix_compare(x11, umatrix.matrix([[0,1,2,4],[5,6,8,9],[10,12,13,14]])) and (x11 == x12)
x11 = umatrix.matrix([[0,1,2],[4,5,6],[8,9,10],[12,13,14]])
# check for shape change copy
x12 = x11.reshape((3,4))
result['shape copy'] = matrix_compare(x12, umatrix.matrix([[0,1,2,4],[5,6,8,9],[10,12,13,14]])) and (x12.shape != x11.shape)
return result
def det_inv_test():
result = {}
# first test
g = umatrix.matrix([[ 0. , 0. , 0. , 0. , 0. , 1. ],
[ 0.03125, 0.0625 , 0.125 , 0.25 , 0.5 , 1. ],
[ 0. , 0. , 0. , 0. , 1. , 0. ],
[ 0.3125 , 0.5 , 0.75 , 1. , 1. , 0. ],
[ 0. , 0. , 0. , 2. , 0. , 0. ],
[ 2.5 , 3. , 3. , 2. , 0. , 0. ]])
g_inv = umatrix.matrix([[-192. , 192. , -48. , -48. , -4. , 4. ],
[ 240. , -240. , 64. , 56. , 6. , -4. ],
[ -80. , 80. , -24. , -16. , -3. , 1. ],
[ 0. , 0. , 0. , 0. , 0.5, 0. ],
[ 0. , 0. , 1. , 0. , 0. , 0. ],
[ 1. , 0. , 0. , 0. , 0. , 0. ]])
(det,inv) = ulinalg.det_inv(g)
result['determinant 1'] = (abs(det - 0.0078125) < 0.000001)
result['inverse 1'] = umatrix.matrix_equal(inv, g_inv, 0.000001)
# second test
x = umatrix.matrix([[3.,2.,0.,1.],[4.,0.,1.,2.],[3.,0.,2.,1.],[9.,2.,3.,1.]])
(det,inv) = ulinalg.det_inv(x)
det_res = det == 24.0
f = umatrix.matrix([[-0.25 , 0.25 , -0.5 , 0.25 ],
[0.66666666666666667 , -0.49999999999999999, 0.50000000000000001 , -0.16666666666666667],
[0.16666666666666667 , -0.49999999999999999, 1.00000000000000002 , -0.16666666666666667],
[0.41666666666666667 , 0.25 , 0.50000000000000001 , -0.41666666666666667]])
result['inverse 2'] = matrix_compare(inv, f, tol=eps*2)#, tol=0.000000000000001)
f[3,3] = -0.416668
result['determinant 2'] = det_res
result['matrix_equal True'] = umatrix.matrix_equal(inv, f, tol=0.0001)
result['matrix_equal False'] = umatrix.matrix_equal(inv, f) == False
x1 = umatrix.matrix([[0.71,-0.71,0.7],[0.71,0.71,0.5],[0,0,1]])
z = ulinalg.pinv(x1)
result['psuedo inverse'] = matrix_compare(z, umatrix.matrix([[0.7042253521126759 , 0.704225352112676 , -0.8450704225352109 ],
[-0.704225352112676 , 0.704225352112676 , 0.1408450704225352 ],
[1.110223024625157e-16, 5.551115123125783e-17, 0.9999999999999998 ]]), tol=2*eps)
return result
def comportement(self, x, t):
return umatrix.matrix([[pi / 180.0 * 20]])
def comportement(self, x, u, t):
th = x[0]
dth = self.A * th + self.K * u[0, 0]
return umatrix.matrix([[dth]])
y = int(round(rd * math.sin(math.radians(azm_i[index] - 90)), 0))
xm = xc + x
ym = yc + y
graphics.fill_circle(xm, ym, 2, 1)
# show current location if visible
if visible_i[index] == 'Yes':
rd = int(round(rad * math.cos(math.radians(alc_i[index])), 0))
x = int(round(rd * math.cos(math.radians(azc_i[index] - 90)), 0))
y = int(round(rd * math.sin(math.radians(azc_i[index] - 90)), 0))
xcu = xc + x
ycu = yc + y
graphics.circle(xcu, ycu, 2, 1)
# find path of transit
A = matrix.matrix([[xr**2, xr, 1], [xm**2, xm, 1], [xs**2, xs, 1]])
B = matrix.matrix([[yr], [ym], [ys]])
d_i = linalg.det_inv(A)
invA = d_i[1]
X = linalg.dot(invA, B)
for i in range(xr - xs):
x = xs + i
oled.pixel(x, yc - int((X[0] * x**2)) + int(
(X[1] * x)) + int(X[2]), 1)
oled.show()
utime.sleep(10)
# collect garbage just in case that is causing the crashes
gc.collect()
def comportement(self, x, u, t):
th = x[0, 0]
dth = -self.lbda * (th - u[0, 0])
return umatrix.matrix([[th, dth]]).T
def skyChart(name):
# display stuff
oled.text(name, 0, 0)
oled.text("Visible:", 0, 10)
oled.text(visible_i[names.index(name)], 80, 10)
oled.show()
# sky chart
xc = 94
yc = 31
rad = 29
graphics.circle(xc, yc, rad, 1)
# show planet rise azimuth
x = int(
round(rad * math.cos(math.radians(azr_i[names.index(name)] - 90)), 0))
y = int(
round(rad * math.sin(math.radians(azr_i[names.index(name)] - 90)), 0))
xr = xc + x
yr = yc + y
graphics.fill_circle(xr, yr, 2, 1)
# show planet set azimuth
x = int(
round(rad * math.cos(math.radians(azs_i[names.index(name)] - 90)), 0))
y = int(
round(rad * math.sin(math.radians(azs_i[names.index(name)] - 90)), 0))
xs = xc + x
ys = yc + y
graphics.fill_circle(xs, ys, 2, 1)
# show location at maximum altitude
rd = int(round(rad * math.cos(math.radians(alm_i[names.index(name)])), 0))
x = int(
round(rd * math.cos(math.radians(azm_i[names.index(name)] - 90)), 0))
y = int(
round(rd * math.sin(math.radians(azm_i[names.index(name)] - 90)), 0))
xm = xc + x
ym = yc + y
graphics.fill_circle(xm, ym, 2, 1)
# show current location if visible
if visible_i[index] == 'Yes':
rd = int(
round(rad * math.cos(math.radians(alc_i[names.index(name)])), 0))
x = int(
round(rd * math.cos(math.radians(azc_i[names.index(name)] - 90)),
0))
y = int(
round(rd * math.sin(math.radians(azc_i[names.index(name)] - 90)),
0))
xcu = xc + x
ycu = yc + y
graphics.circle(xcu, ycu, 2, 1)
# find path of transit
A = matrix.matrix([[xr**2, xr, 1], [xm**2, xm, 1], [xs**2, xs, 1]])
B = matrix.matrix([[yr], [ym], [ys]])
d_i = linalg.det_inv(A)
invA = d_i[1]
X = linalg.dot(invA, B)
for i in range(xr - xs):
x = xs + i
oled.pixel(x, yc - int((X[0] * x**2)) + int((X[1] * x)) + int(X[2]), 1)
oled.show()
# collect garbage just in case that is causing the crashes
gc.collect()
def zeros(m, n, dtype=umatrix.ddtype):
return umatrix.matrix([[0 for i in range(n)] for j in range(m)],
dtype=dtype)
import umatrix, ulinalg
import data as d
import show
A = umatrix.matrix(d.A)
B = umatrix.matrix(d.B).transpose()
print('=== Multiply Integer matrix ...')
X = show.time(ulinalg.dot)(A, B)
print(X)
C = umatrix.matrix(d.C)
D = umatrix.matrix(d.D).transpose()
print('=== Multiply Float matrix ...')
X = show.time(ulinalg.dot)(C, D)
print(X)
def scaler():
result = {}
x10 = umatrix.matrix([[0,1,2,3],[4,5,6,7],[8,9,10,11],[12,13,14,15]])
x11 = umatrix.matrix([[0,1,2],[4,5,6],[8,9,10],[12,13,14]])
try:
result['scaler * matrix'] = matrix_compare(2*x10, umatrix.matrix([[0, 2, 4, 6],[8, 10, 12, 14],[16, 18, 20, 22],[24, 26, 28, 30]]))
except TypeError:
result['scaler * matrix'] = (False, 'TypeError')
try:
result['scaler + matrix'] = matrix_compare(2.1+x10, umatrix.matrix([[2.1 , 3.1 , 4.1 , 5.1 ],[6.1 , 7.1 , 8.1 , 9.1 ],[10.1, 11.1, 12.1, 13.1],[14.1, 15.1, 16.1, 17.1]]))
except TypeError:
result['scaler + matrix'] = (False, 'TypeError')
try:
result['scaler - matrix'] = matrix_compare(1-x10, umatrix.matrix([[1, 0, -1 , -2],[-3, -4, -5, -6],[-7, -8, -9, -10],[-11, -12, -13, -14]]))
except TypeError:
result['scaler - matrix'] = (False, 'TypeError')
result['matrix + scaler'] = matrix_compare(x10+2.1, umatrix.matrix([[2.1 , 3.1 , 4.1 , 5.1 ],[6.1 , 7.1 , 8.1 , 9.1 ],[10.1, 11.1, 12.1, 13.1],[14.1, 15.1, 16.1, 17.1]]))
result['matrix - scaler'] = matrix_compare(x10-1.4, umatrix.matrix([[-1.4, -0.3999999999999999, 0.6000000000000001 , 1.6],[2.6, 3.6, 4.6, 5.6],[6.6, 7.6, 8.6, 9.6],[10.6, 11.6, 12.6, 13.6]]), tol=eps)
result['matrix * scaler'] = matrix_compare(x10*3, umatrix.matrix([[0, 3, 6, 9],[12, 15, 18, 21],[24, 27, 30, 33],[36, 39, 42, 45]]))
result['matrix / scaler'] = matrix_compare(x10/3, umatrix.matrix([[0.0, 0.3333333333333333, 0.6666666666666667, 1.0],
[1.3333333333333333 , 1.6666666666666667 , 2.0, 2.3333333333333333 ],
[2.66666666666666667 , 3.0, 3.3333333333333333 , 3.6666666666666667 ],
[4.0, 4.3333333333333333 , 4.6666666666666667 , 5.0]]), tol=2*eps)
result['matrix // scaler'] = matrix_compare(x10//3, umatrix.matrix([[0, 0, 0, 1],[1, 1, 2, 2],[2, 3, 3, 3],[4, 4, 4, 5]]))
result['negate matrix'] = matrix_compare(-x10, umatrix.matrix([[0, -1, -2, -3],[-4, -5, -6, -7],[-8, -9, -10, -11],[-12, -13, -14, -15]]))
try:
result['matrix ** scaler'] = matrix_compare(x10**2, umatrix.matrix([[ 0, 1, 4, 9],[16 , 25, 36, 49],[64, 81, 100, 121],[144, 169, 196, 225]]))
except:
result['matrix ** scaler'] = (False, 'NotImplemented')
return result
def assignment():
result = {}
x11 = umatrix.matrix([[0,1,2],[4,5,6],[8,9,10],[12,13,14]])
x11[1,1] = 20
result['matrix element <- value'] = matrix_compare(x11, umatrix.matrix([[0,1,2],[4,20,6],[8,9,10],[12,13,14]]))
x11 = umatrix.matrix([[0,1,2],[4,5,6],[8,9,10],[12,13,14]])
x11[1,1] = [21, 23]
result['matrix element <- list'] = matrix_compare(x11, umatrix.matrix([[0,1,2],[4,21,6],[8,9,10],[12,13,14]]))
x11 = umatrix.matrix([[0,1,2],[4,5,6],[8,9,10],[12,13,14]])
x11[1,:] = 21
result['matrix row <- value'] = matrix_compare(x11, umatrix.matrix([[0,1,2],[21,21,21],[8,9,10],[12,13,14]]))
x11 = umatrix.matrix([[0,1,2],[4,5,6],[8,9,10],[12,13,14]])
x11[1,:] = [20, 21, 22]
result['matrix row <- list'] = matrix_compare(x11, umatrix.matrix([[0,1,2],[20,21,22],[8,9,10],[12,13,14]]))
x11 = umatrix.matrix([[0,1,2],[4,5,6],[8,9,10],[12,13,14]])
x11[:,1] = 20
result['matrix col <- value'] = matrix_compare(x11, umatrix.matrix([[0,20,2],[4,20,6],[8,20,10],[12,20,14]]))
x11 = umatrix.matrix([[0,1,2],[4,5,6],[8,9,10],[12,13,14]])
x11[:,1] = [20, 21, 22, 23]
result['matrix col <- list'] = matrix_compare(x11, umatrix.matrix([[0,20,2],[4,21,6],[8,22,10],[12,23,14]]))
x10 = umatrix.matrix([[0,1,2,3],[4,5,6,7],[8,9,10,11],[12,13,14,15]])
x10[1:3,1:3] = 20
result['submatrix <- value'] = matrix_compare(x10, umatrix.matrix([[0,1,2,3],[4,20,20,7],[8,20,20,11],[12,13,14,15]]))
x10 = umatrix.matrix([[0,1,2,3],[4,5,6,7],[8,9,10,11],[12,13,14,15]])
x10[1:3,1:3] = [20,21,22,23]
result['submatrix <- list'] = matrix_compare(x10, umatrix.matrix([[0,1,2,3],[4,20,21,7],[8,22,23,11],[12,13,14,15]]))
x10 = umatrix.matrix([[0,1,2,3],[4,5,6,7],[8,9,10,11],[12,13,14,15]])
x10[1:3,1:3] = umatrix.matrix([[20,21],[22,23]])
result['submatrix <- matrix'] = matrix_compare(x10, umatrix.matrix([[0,1,2,3],[4,20,21,7],[8,22,23,11],[12,13,14,15]]))
x11 = umatrix.matrix([[0,1,2],[4,5,6],[8,9,10],[12,13,14]])
try:
x11[1] = [18,19]
result['matrix non-splice <- matrix/vector'] = not matrix_compare(x11, umatrix.matrix([[0,1,2],[18,19,6],[8,9,10],[12,13,14]]))
except NotImplementedError:
result['matrix non-splice <- matrix/vector'] = True
return result
def __init__(self, _name_of_cmd, dt):
self._name_of_cmd = _name_of_cmd
self._dt = dt
self._u = umatrix.matrix([[0.0] * len(self._name_of_cmd)])
def products():
result = {}
x11 = umatrix.matrix([[0,1,2],[4,5,6],[8,9,10],[12,13,14]])
x10 = umatrix.matrix([[0,1,2,3],[4,5,6,7],[8,9,10,11],[12,13,14,15]])
x1 = umatrix.matrix([[0.71,-0.71,0.7],[0.71,0.71,0.5],[0,0,1]])
y_row = umatrix.matrix([[1,0,1]])
y_col = umatrix.matrix([1, 0, 1], cstride=1, rstride=1)
result['matrix * scaler'] = matrix_compare(x10*2, umatrix.matrix([[0, 2, 4, 6],[8, 10, 12, 14],[16, 18, 20, 22],[24, 26, 28, 30]]))
result['matrix * matrix elementwise'] = matrix_compare(x11*x11, umatrix.matrix([[0, 1, 4],[16, 25, 36],[64, 81, 100],[144, 169, 196]]))
result['row dot matrix 1x3 . 3x3'] = matrix_compare(ulinalg.dot(y_row,x1), umatrix.matrix([[ 0.71, -0.71, 1.7 ]]))
try:
result['matrix dot col 3x3 . 3x1'] = matrix_compare(ulinalg.dot(x1,y_col), umatrix.matrix([1.41, 1.21, 1.0], cstride=1, rstride=1), tol=eps)
except ValueError:
result['matrix dot col 3x3 . 3x1'] = False
x = umatrix.matrix([[ 3., -2., -2.]])
y = umatrix.matrix([[-1., 0., 5.]])
x1 = umatrix.matrix([[ 3., -2.]])
y1 = umatrix.matrix([[-1., 0.]])
result['cross product (x,y)'] = matrix_compare(ulinalg.cross(x,y), umatrix.matrix([[-10.0 , -13.0 , -2.0]]))
result['cross product (y,x)'] = matrix_compare(ulinalg.cross(y,x), umatrix.matrix([[10.0 , 13.0 , 2.0]]))
result['cross product 2 (x,y)'] = matrix_compare(ulinalg.cross(x1,y1), umatrix.matrix([[-2.0]]))
x = umatrix.matrix([[ 3., -2., -2.],[-1., 0., 5.]])
y = umatrix.matrix([[-1., 0., 5.]])
try:
result['cross product shape mismatch (x,y)'] = matrix_compare(ulinalg.cross(x,y), umatrix.matrix([[-10.0 , -13.0 , -2.0]]))
except ValueError:
result['cross product shape mismatch (x,y)'] = True
return result
FREQUENCY = 50 # Hz
ROTATIONAL_RANGE_100 = math.pi
PIN0 = Pin.exp_board.G24
PIN1 = Pin.exp_board.G14
servo0 = Servo(PIN0, 0, FREQUENCY, ROTATIONAL_RANGE_100, PULSE_MIN, PULSE_MAX)
servo1 = Servo(PIN1, 1, FREQUENCY, ROTATIONAL_RANGE_100, PULSE_MIN, PULSE_MAX)
acc = []
gyro = []
while True:
#start = utime.ticks_ms()
accelerometer = sensor.accel
gyrometer = sensor.gyro
acc = umatrix.matrix([accelerometer.x, accelerometer.y, accelerometer.z],
cstride=1,
rstride=3,
dtype=float)
gyro = umatrix.matrix([gyrometer.x, gyrometer.y, gyrometer.z],
cstride=1,
rstride=3,
dtype=float)
#print("acceleration [X:%0.2f, Y:%0.2f, Z:%0.2f] gyro [X:%0.2f, Y:%0.2f, Z:%0.2f] \n"
# %(accelerometer.x, accelerometer.y, accelerometer.z, gyrometer.x * math.pi/180, gyrometer.y * math.pi/180, gyrometer.z * math.pi/180))
#print("temperature [T:%0.2f]\n" %(sensor.temperature))
ahrs.update_imu(gyro * math.pi / 180.0, acc)
(roll, pitch, yaw) = ahrs.quaternion.to_euler()
def assignment():
result = {}
x11 = umatrix.matrix([[0,1,2],[4,5,6],[8,9,10],[12,13,14]])
x11[1,1] = 20
result['matrix element <- value'] = matrix_compare(x11, umatrix.matrix([[0,1,2],[4,20,6],[8,9,10],[12,13,14]]))
x11 = umatrix.matrix([[0,1,2],[4,5,6],[8,9,10],[12,13,14]])
x11[1,1] = [21, 23]
result['matrix element <- list'] = matrix_compare(x11, umatrix.matrix([[0,1,2],[4,21,6],[8,9,10],[12,13,14]]))
x11 = umatrix.matrix([[0,1,2],[4,5,6],[8,9,10],[12,13,14]])
x11[1,:] = 21
result['matrix row <- value'] = matrix_compare(x11, umatrix.matrix([[0,1,2],[21,21,21],[8,9,10],[12,13,14]]))
x11 = umatrix.matrix([[0,1,2],[4,5,6],[8,9,10],[12,13,14]])
x11[1,:] = [20, 21, 22]
result['matrix row <- list'] = matrix_compare(x11, umatrix.matrix([[0,1,2],[20,21,22],[8,9,10],[12,13,14]]))
x11 = umatrix.matrix([[0,1,2],[4,5,6],[8,9,10],[12,13,14]])
x11[:,1] = 20
result['matrix col <- value'] = matrix_compare(x11, umatrix.matrix([[0,20,2],[4,20,6],[8,20,10],[12,20,14]]))
x11 = umatrix.matrix([[0,1,2],[4,5,6],[8,9,10],[12,13,14]])
x11[:,1] = [20, 21, 22, 23]
result['matrix col <- list'] = matrix_compare(x11, umatrix.matrix([[0,20,2],[4,21,6],[8,22,10],[12,23,14]]))
x10 = umatrix.matrix([[0,1,2,3],[4,5,6,7],[8,9,10,11],[12,13,14,15]])
x10[1:3,1:3] = 20
result['submatrix <- value'] = matrix_compare(x10, umatrix.matrix([[0,1,2,3],[4,20,20,7],[8,20,20,11],[12,13,14,15]]))
x10 = umatrix.matrix([[0,1,2,3],[4,5,6,7],[8,9,10,11],[12,13,14,15]])
x10[1:3,1:3] = [20,21,22,23]
result['submatrix <- list'] = matrix_compare(x10, umatrix.matrix([[0,1,2,3],[4,20,21,7],[8,22,23,11],[12,13,14,15]]))
x10 = umatrix.matrix([[0,1,2,3],[4,5,6,7],[8,9,10,11],[12,13,14,15]])
x10[1:3,1:3] = umatrix.matrix([[20,21],[22,23]])
result['submatrix <- matrix'] = matrix_compare(x10, umatrix.matrix([[0,1,2,3],[4,20,21,7],[8,22,23,11],[12,13,14,15]]))
x11 = umatrix.matrix([[0,1,2],[4,5,6],[8,9,10],[12,13,14]])
try:
x11[1] = [18,19]
result['matrix non-splice <- matrix/vector'] = not matrix_compare(x11, umatrix.matrix([[0,1,2],[18,19,6],[8,9,10],[12,13,14]]))
except NotImplementedError:
result['matrix non-splice <- matrix/vector'] = True
return result
import umatrix A = umatrix.matrix([[1, 2], [0, 3]]) B = umatrix.matrix([[1, 2, 3], [5, 6, 7]]) print(A) print(B) print(A * B)
import umatrix
import data as d
import show
A = umatrix.matrix(*d.A)
B = umatrix.matrix(*d.B).transpose
print('=== Multiply Integer matrix ...')
X = show.time(lambda: A * B)()
print(X)
C = umatrix.matrix(*d.C)
D = umatrix.matrix(*d.D).transpose
print('=== Multiply Float matrix ...')
X = show.time(lambda: C * D)()
print(X)
def products():
result = {}
x11 = umatrix.matrix([[0,1,2],[4,5,6],[8,9,10],[12,13,14]])
x10 = umatrix.matrix([[0,1,2,3],[4,5,6,7],[8,9,10,11],[12,13,14,15]])
x1 = umatrix.matrix([[0.71,-0.71,0.7],[0.71,0.71,0.5],[0,0,1]])
y_row = umatrix.matrix([[1,0,1]])
y_col = umatrix.matrix([1, 0, 1], cstride=1, rstride=1)
result['matrix * scaler'] = matrix_compare(x10*2, umatrix.matrix([[0, 2, 4, 6],[8, 10, 12, 14],[16, 18, 20, 22],[24, 26, 28, 30]]))
result['matrix * matrix elementwise'] = matrix_compare(x11*x11, umatrix.matrix([[0, 1, 4],[16, 25, 36],[64, 81, 100],[144, 169, 196]]))
result['row dot matrix 1x3 . 3x3'] = matrix_compare(ulinalg.dot(y_row,x1), umatrix.matrix([[ 0.71, -0.71, 1.7 ]]))
try:
result['matrix dot col 3x3 . 3x1'] = matrix_compare(ulinalg.dot(x1,y_col), umatrix.matrix([1.41, 1.21, 1.0], cstride=1, rstride=1))
except ValueError:
result['matrix dot col 3x3 . 3x1'] = False
x = umatrix.matrix([[ 3., -2., -2.]])
y = umatrix.matrix([[-1., 0., 5.]])
x1 = umatrix.matrix([[ 3., -2.]])
y1 = umatrix.matrix([[-1., 0.]])
result['cross product (x,y)'] = matrix_compare(ulinalg.cross(x,y), umatrix.matrix([[-10.0 , -13.0 , -2.0]]))
result['cross product (y,x)'] = matrix_compare(ulinalg.cross(y,x), umatrix.matrix([[10.0 , 13.0 , 2.0]]))
result['cross product 2 (x,y)'] = matrix_compare(ulinalg.cross(x1,y1), umatrix.matrix([[-2.0]]))
x = umatrix.matrix([[ 3., -2., -2.],[-1., 0., 5.]])
y = umatrix.matrix([[-1., 0., 5.]])
try:
result['cross product shape mismatch (x,y)'] = matrix_compare(ulinalg.cross(x,y), umatrix.matrix([[-10.0 , -13.0 , -2.0]]))
except ValueError:
result['cross product shape mismatch (x,y)'] = True
return result
def zeros(m, n, dtype=umatrix.ddtype):
return umatrix.matrix([[0 for i in range(n)] for j in range(m)], dtype=dtype)
14,
] # G
set_B = [3, 7, 11, 15] #B
R_val = 0
G_val = 0
B_val = 0
num_total_packets = 960
packet_count = 0
Q = umatrix.matrix([[16, 11, 10, 16, 24, 40, 51, 61],
[12, 12, 14, 19, 26, 58, 60, 55],
[14, 13, 16, 24, 40, 57, 69, 56],
[14, 17, 22, 29, 51, 87, 80, 62],
[18, 22, 37, 56, 68, 109, 103, 77],
[24, 35, 55, 64, 81, 104, 113, 92],
[49, 64, 78, 87, 103, 121, 120, 101],
[72, 92, 95, 98, 112, 100, 103,
99]]) # Quantization matrix for quality = 50
for i in range(num_total_packets):
packet_count = packet_count + 1
with open('byte_array_to_send' + str(packet_count) + '_of_960' + '.txt',
'rb') as f:
byte_array_five_tiles = f.read()
print(i, byte_array_five_tiles) #matches
for j in range(5): # nuber of tiles per packet
count = 0
B = ulinalg.zeros(8, 8)
G = ulinalg.zeros(8, 8)
def scaler():
result = {}
x10 = umatrix.matrix([[0,1,2,3],[4,5,6,7],[8,9,10,11],[12,13,14,15]])
x11 = umatrix.matrix([[0,1,2],[4,5,6],[8,9,10],[12,13,14]])
try:
result['scaler * matrix'] = matrix_compare(2*x10, umatrix.matrix([[0, 2, 4, 6],[8, 10, 12, 14],[16, 18, 20, 22],[24, 26, 28, 30]]))
except TypeError:
result['scaler * matrix'] = (False, 'TypeError')
try:
result['scaler + matrix'] = matrix_compare(2.1+x10, umatrix.matrix([[2.1 , 3.1 , 4.1 , 5.1 ],[6.1 , 7.1 , 8.1 , 9.1 ],[10.1, 11.1, 12.1, 13.1],[14.1, 15.1, 16.1, 17.1]]))
except TypeError:
result['scaler + matrix'] = (False, 'TypeError')
try:
result['scaler - matrix'] = matrix_compare(1-x10, umatrix.matrix([[1, 0, -1 , -2],[-3, -4, -5, -6],[-7, -8, -9, -10],[-11, -12, -13, -14]]))
except TypeError:
result['scaler - matrix'] = (False, 'TypeError')
result['matrix + scaler'] = matrix_compare(x10+2.1, umatrix.matrix([[2.1 , 3.1 , 4.1 , 5.1 ],[6.1 , 7.1 , 8.1 , 9.1 ],[10.1, 11.1, 12.1, 13.1],[14.1, 15.1, 16.1, 17.1]]))
result['matrix - scaler'] = matrix_compare(x10-1.4, umatrix.matrix([[-1.4, -0.3999999999999999, 0.6000000000000001 , 1.6],[2.6, 3.6, 4.6, 5.6],[6.6, 7.6, 8.6, 9.6],[10.6, 11.6, 12.6, 13.6]]), tol=eps)
result['matrix * scaler'] = matrix_compare(x10*3, umatrix.matrix([[0, 3, 6, 9],[12, 15, 18, 21],[24, 27, 30, 33],[36, 39, 42, 45]]))
result['matrix / scaler'] = matrix_compare(x10/3, umatrix.matrix([[0.0, 0.3333333333333333, 0.6666666666666667, 1.0],
[1.3333333333333333 , 1.6666666666666667 , 2.0, 2.3333333333333333 ],
[2.66666666666666667 , 3.0, 3.3333333333333333 , 3.6666666666666667 ],
[4.0, 4.3333333333333333 , 4.6666666666666667 , 5.0]]), tol=2*eps)
result['matrix // scaler'] = matrix_compare(x10//3, umatrix.matrix([[0, 0, 0, 1],[1, 1, 2, 2],[2, 3, 3, 3],[4, 4, 4, 5]]))
result['negate matrix'] = matrix_compare(-x10, umatrix.matrix([[0, -1, -2, -3],[-4, -5, -6, -7],[-8, -9, -10, -11],[-12, -13, -14, -15]]))
try:
result['matrix ** scaler'] = matrix_compare(x10**2, umatrix.matrix([[ 0, 1, 4, 9],[16 , 25, 36, 49],[64, 81, 100, 121],[144, 169, 196, 225]]))
except:
result['matrix ** scaler'] = (False, 'NotImplemented')
return result
def getEcartsTypes(self, num_var=None):
if num_var is None:
return umatrix.matrix([[sqrt(self._P[i, i]) for i in range(self._n)]])
else:
return sqrt(self._P[num_var, num_var])
