def cos_angle(y_reco, y_true):
zep, zet, azp, azt = y_reco[:, 1], y_true[:, 1], y_reco[:, 2], y_true[:, 2]
# cosalpha=abs(sin(zep))*cos(azp)*sin(zet)*cos(azt)+abs(sin(zep))*sin(azp)*sin(zet)*sin(azt)+cos(zep)*cos(zet)
cosalpha = abs(sin(zep)) * abs(sin(zet)) * cos(azp - azt) + cos(zep) * cos(
zet) #check for double absolutes
cosalpha -= tf.math.sign(cosalpha) * eps
return cosalpha
def PDF_gen_Si_func(costk, costl , phi, Fl, S3, S4, S5, AFB, S7, S8, S9):
##sint2k: square of sin(thetak); sin2tk: sin(2thetak); sintk: sin(thetak);
cost2k = costk * costk
sint2k = 1 - cost2k
sintk = sqrt(sint2k)
sin2tk = 2 * sintk * costk
cost2l = costl * costl
sint2l = 1 - cost2l
sintl = sqrt(sint2l)
sin2tl = 2 * sintl * costl
cos2tl = 2 * cost2l - 1
sinphi = sin(phi)
cosphi = cos(phi)
cos2phi = cos(2*phi)
sin2phi = sin(2*phi)
dcrate = (0.75 * (1 - Fl) * sint2k + Fl * cost2k
+ 0.25 * (1 - Fl) * sint2k * cos2tl - Fl * cost2k * cos2tl
+ S3 * sint2k * sint2l * cos2phi + S4 * sin2tk * sin2tl * cosphi
+ S5 * sin2tk * sintl * cosphi + 4/3 * AFB * sint2k * costl
+ S7 * sin2tk * sintl * sinphi + S8 * sin2tk * sin2tl * sinphi
+ S9 * sint2k * sint2l * sin2phi)
return 9 / (32 * 3.14159265) * dcrate
def alpha_from_angle(y_reco, y_true):
zep, zet, azp, azt = y_reco[:, 1], y_true[:, 1], y_reco[:, 2], y_true[:, 2]
cosalpha = abs(sin(zep)) * cos(azp) * sin(zet) * cos(azt) + abs(
sin(zep)) * sin(azp) * sin(zet) * sin(azt) + cos(zep) * cos(zet)
cosalpha -= tf.math.sign(cosalpha) * eps
alpha = acos(cosalpha)
return alpha
def PDF_gen_Pi_func(costk, costl, phi, Fl, P1, P2, P3, P4p, P5p, P6p, P8p):
##sint2k: square of sin(thetak); sin2tk: sin(2thetak); sintk: sin(thetak);
cost2k = costk * costk
sint2k = 1 - cost2k
sintk = sqrt(sint2k)
sin2tk = 2 * sintk * costk
cost2l = costl * costl
sint2l = 1 - cost2l
sintl = sqrt(sint2l)
sin2tl = 2 * sintl * costl
cos2tl = 2 * cost2l - 1
sinphi = sin(phi)
cosphi = cos(phi)
cos2phi = cos(2 * phi)
sin2phi = sin(2 * phi)
Ft = 1 - Fl
dcrate = (0.75 * Ft * sint2k + Fl * cost2k + 0.25 * Ft * sint2k * cos2tl -
Fl * cost2k * cos2tl +
0.5 * P1 * Ft * sint2k * sint2l * cos2phi +
sqrt(Fl * Ft) * 0.5 * P4p * sin2tk * sin2tl * cosphi +
sqrt(Fl * Ft) * P5p * sin2tk * sintl * cosphi -
sqrt(Fl * Ft) * P6p * sin2tk * sintl * sinphi +
0.5 * sqrt(Fl * Ft) * P8p * sin2tk * sin2tl * sinphi +
2 * P2 * Ft * sint2k * costl -
P3 * Ft * sint2k * sint2l * sin2phi)
return 9 / (32 * 3.14159265) * dcrate
def sqr_vonMises23D_angle(y_reco, y_true, re=False):
#energy
loss_energy = reduce_mean(
tf.math.squared_difference(y_reco[:, 0], y_true[:, 0])) #mae again
polar_k = abs(y_reco[:, 3]) + eps
zenth_k = abs(y_reco[:, 4]) + eps
cos_azi = cos(subtract(y_true[:, 2], y_reco[:, 2]))
cos_zenth = cos(subtract(y_true[:, 1], y_reco[:, 1]))
lnI0_azi = polar_k + tf.math.log(
1 + tf.math.exp(-2 * polar_k)
) - 0.25 * tf.math.log(1 + 0.25 * tf.square(polar_k)) + tf.math.log(
1 + 0.24273 * tf.square(polar_k)) - tf.math.log(1 + 0.43023 *
tf.square(polar_k))
lnI0_zenth = zenth_k + tf.math.log(
1 + tf.math.exp(-2 * zenth_k)
) - 0.25 * tf.math.log(1 + 0.25 * tf.square(zenth_k)) + tf.math.log(
1 + 0.24273 * tf.square(zenth_k)) - tf.math.log(1 + 0.43023 *
tf.square(zenth_k))
llh_azi = polar_k * cos_azi - lnI0_azi
llh_zenith = zenth_k * cos_zenth - lnI0_zenth
loss_azi = reduce_mean(-llh_azi)
loss_zenith = reduce_mean(-llh_zenith)
kappa = tf.math.abs(y_reco[:, 5]) + eps
cos_alpha = cos_angle(y_reco, y_true)
# tf.debugging.assert_less_equal(tf.math.abs(cos_alpha), 1, message='cos_alpha problem', summarize=None, name=None)
tf.debugging.assert_all_finite(tf.math.abs(cos_alpha),
message='cos_alpha problem infinite/nan',
name=None)
nlogC = -tf.math.log(kappa) + kappa + tf.math.log(1 -
tf.math.exp(-2 * kappa))
tf.debugging.assert_all_finite(nlogC, 'log kappa problem', name=None)
loss_angle = tf.reduce_mean(-kappa * cos_alpha + nlogC)
if not re:
return loss_azi + loss_zenith + loss_energy + loss_angle
if re:
return float(loss_azi + loss_zenith + loss_energy + loss_angle), [
float(loss_energy),
float(loss_zenith),
float(loss_azi),
float(loss_angle)
]
def loss_funcxpos2(y_reco, y_true, re=False):
from tensorflow.math import sin, cos, acos, abs, reduce_mean, subtract, square
# Energy loss
loss_energy = reduce_mean(abs(subtract(y_reco[:,0], y_true[:,0]))) #this works well but could maybe be improved
zeni = [cos(y_true[:,1]) - y_reco[:,1] ,
sin(y_true[:,1]) - y_reco[:,2]]
azi = [cos(y_true[:,2]) - y_reco[:,3] ,
sin(y_true[:,2]) - y_reco[:,4]]
loss_angle = reduce_mean(square(azi[0]))+reduce_mean(square(azi[1]))+reduce_mean(square(zeni[0]))+reduce_mean(square(zeni[1]))
if not re:
return loss_energy+loss_angle
else:
return float(loss_energy+loss_angle), [float(loss_energy), float(loss_angle)]
def integralPi_costk_costl(x, limits, norm_range, params, model):
phi = x.unstack_x()
Fl = params['Fl']
P1 = params['P1']
P3 = params['P3']
return 9. / (32 * 3.14159265) * (
4.0 / 9.0 + (1 - Fl) / 18.0 * P1 * cos(2 * phi) * 2 * 2 +
(Fl - 1) / 9.0 * P3 * sin(2 * phi) * 2 * 2) * (2 * 2)
def integralPi_costl(x, limits, norm_range, params, model):
costk, phi = x.unstack_x()
Fl = params['Fl']
P1 = params['P1']
P3 = params['P3']
P5p = params['P5p']
P6p = params['P6p']
K00 = assoc_legendre_tf(0, 0, costk)
K20 = assoc_legendre_tf(2, 0, costk)
K21 = assoc_legendre_tf(2, 1, costk)
K22 = assoc_legendre_tf(2, 2, costk)
return 9. / (32 * 3.14159265) * (
(4.0 / 9.0) * K00 + (4.0 * Fl / 3.0 - 4.0 / 9.0) * K20 +
(1 - Fl) / 18.0 * P1 * K22 * cos(2 * phi) * 2 +
(Fl - 1) / 9.0 * P3 * K22 * sin(2 * phi) * 2 +
(2.0 / 3.0) * sqrt(Fl - Fl * Fl) * P5p * K21 * cos(phi) * pi / 4 +
(-2.0 / 3.0) * sqrt(Fl - Fl * Fl) * P6p * K21 * sin(phi) * pi / 4) * (
2)
def abs_vM2D_KDE_weak(y_reco, y_true, kdet, re=False):
#energy
loss_energy = reduce_mean(abs(subtract(y_reco[:, 0],
y_true[:, 0]))) #mae again
polar_k = abs(y_reco[:, 3]) + eps
zenth_k = abs(y_reco[:, 4]) + eps
cos_azi = cos(subtract(y_true[:, 2], y_reco[:, 2]))
cos_zenth = cos(subtract(y_true[:, 1], y_reco[:, 1]))
lnI0_azi = polar_k + tf.math.log(
1 + tf.math.exp(-2 * polar_k)
) - 0.25 * tf.math.log(1 + 0.25 * tf.square(polar_k)) + tf.math.log(
1 + 0.24273 * tf.square(polar_k)) - tf.math.log(1 + 0.43023 *
tf.square(polar_k))
lnI0_zenth = zenth_k + tf.math.log(
1 + tf.math.exp(-2 * zenth_k)
) - 0.25 * tf.math.log(1 + 0.25 * tf.square(zenth_k)) + tf.math.log(
1 + 0.24273 * tf.square(zenth_k)) - tf.math.log(1 + 0.43023 *
tf.square(zenth_k))
llh_azi = polar_k * cos_azi - lnI0_azi
llh_zenith = zenth_k * cos_zenth - lnI0_zenth
loss_azi = reduce_mean(-llh_azi)
loss_zenith = reduce_mean(-llh_zenith)
kder = kde(tf.cast(y_reco[:, 1], tf.float32))
kdeloss = tf.reduce_mean(
tf.math.abs(kdet.log_prob(xkde) - kder.log_prob(xkde))) / 10
if not re:
return loss_azi + loss_zenith + loss_energy + tf.cast(
kdeloss, tf.float32)
if re:
return float(loss_azi + loss_zenith + loss_energy + kdeloss), [
float(loss_energy),
float(loss_zenith),
float(loss_azi),
float(kdeloss)
]
def angle_loss_fn_batch(y_true, y_pred):
x_p = math.sin(y_pred[:, 0]) * math.cos(y_pred[:, 1])
y_p = math.sin(y_pred[:, 0]) * math.sin(y_pred[:, 1])
z_p = math.cos(y_pred[:, 0])
x_t = math.sin(y_true[:, 0]) * math.cos(y_true[:, 1])
y_t = math.sin(y_true[:, 0]) * math.sin(y_true[:, 1])
z_t = math.cos(y_true[:, 0])
norm_p = math.sqrt(x_p * x_p + y_p * y_p + z_p * z_p)
norm_t = math.sqrt(x_t * x_t + y_t * y_t + z_t * z_t)
dot_pt = x_p * x_t + y_p * y_t + z_p * z_t
angle_value = dot_pt / (norm_p * norm_t)
angle_value = tf.clip_by_value(angle_value, -0.99999, 0.99999)
loss_val = (math.acos(angle_value))
return loss_val
def _convert_to_pxyz(self, x):
from numpy import cos, sin, sinh, exp, clip, stack
pt = exp(clip(x[:, 0], -7., 7.)) - 0.1
pt *= self._pt_scale
eta = x[:, 1]
phi = x[:, 2]
px = pt * cos(phi)
py = pt * sin(phi)
pz = pt * sinh(clip(eta, -5, 5))
return stack([px, py, pz], axis=1)
def _convert_to_pxyz(self, x):
from tensorflow.math import cos, sin, sinh, exp
from tensorflow import clip_by_value, stack
pt = exp(clip_by_value(x[:, 0], -7., 7.)) - 0.1
pt *= self._pt_scale
eta = x[:, 1]
phi = x[:, 2]
px = pt * cos(phi)
py = pt * sin(phi)
pz = pt * sinh(clip_by_value(eta, -5, 5))
return stack([px, py, pz], axis=1)
def angle_loss_fn(self, y_true, y_pred):
x_p = math.sin(y_pred[:, 0]) * math.cos(y_pred[:, 1])
y_p = math.sin(y_pred[:, 0]) * math.sin(y_pred[:, 1])
z_p = math.cos(y_pred[:, 0])
x_t = math.sin(y_true[:, 0]) * math.cos(y_true[:, 1])
y_t = math.sin(y_true[:, 0]) * math.sin(y_true[:, 1])
z_t = math.cos(y_true[:, 0])
norm_p = math.sqrt(x_p * x_p + y_p * y_p + z_p * z_p)
norm_t = math.sqrt(x_t * x_t + y_t * y_t + z_t * z_t)
dot_pt = x_p * x_t + y_p * y_t + z_p * z_t
angle_value = dot_pt / (norm_p * norm_t)
angle_value = tf.clip_by_value(angle_value, -0.99999, 0.99999)
loss_val = (math.acos(angle_value))
# tf.debugging.check_numerics(
# loss_val, "Vse propalo", name=None
# )
# print(loss_val.shape)
return loss_val
def sqr_vonMises2D_angle(y_reco, y_true, re=False):
#energy
loss_energy = reduce_mean(
tf.math.squared_difference(y_reco[:, 0], y_true[:, 0])) #mae again
polar_k = abs(y_reco[:, 3]) + eps
zenth_k = abs(y_reco[:, 4]) + eps
cos_azi = cos(subtract(y_true[:, 2], y_reco[:, 2]))
cos_zenth = cos(subtract(y_true[:, 1], y_reco[:, 1]))
lnI0_azi = polar_k + tf.math.log(
1 + tf.math.exp(-2 * polar_k)
) - 0.25 * tf.math.log(1 + 0.25 * tf.square(polar_k)) + tf.math.log(
1 + 0.24273 * tf.square(polar_k)) - tf.math.log(1 + 0.43023 *
tf.square(polar_k))
lnI0_zenth = zenth_k + tf.math.log(
1 + tf.math.exp(-2 * zenth_k)
) - 0.25 * tf.math.log(1 + 0.25 * tf.square(zenth_k)) + tf.math.log(
1 + 0.24273 * tf.square(zenth_k)) - tf.math.log(1 + 0.43023 *
tf.square(zenth_k))
llh_azi = polar_k * cos_azi - lnI0_azi
llh_zenith = zenth_k * cos_zenth - lnI0_zenth
loss_azi = reduce_mean(-llh_azi)
loss_zenith = reduce_mean(-llh_zenith)
if not re:
return loss_azi + loss_zenith + loss_energy
if re:
return float(loss_azi + loss_zenith + loss_energy), [
float(loss_energy),
float(loss_zenith),
float(loss_azi)
]
def integralPi_costk(x, limits, norm_range, params, model):
costl, phi = x.unstack_x()
Fl = params['Fl']
P2 = params['P2']
P1 = params['P1']
P3 = params['P3']
L00 = assoc_legendre_tf(0, 0, costl)
L10 = assoc_legendre_tf(1, 0, costl)
L20 = assoc_legendre_tf(2, 0, costl)
L22 = assoc_legendre_tf(2, 2, costl)
return 9. / (32 * 3.14159265) * (
(4.0 / 9.0) * L00 + (2.0 / 9.0 - 2.0 * Fl / 3.0) * L20 +
(4.0 / 3.0) * P2 * (1 - Fl) * L10 +
(1 - Fl) / 18.0 * P1 * L22 * cos(2 * phi) * 2 +
(Fl - 1) / 9.0 * P3 * L22 * sin(2 * phi) * 2) * (2)
def rotate_tensor_by_angle_xyz(input_data, graph, angle_x=0, angle_y=0, angle_z=0):
""" Rotate the point cloud along up direction with certain angle.
Rotate in the order of x, y and then z.
Input:
input_data: Nx3 tensor, original batch of point clouds
angle_x: tf constant
angle_y: tf constant
angle_z: tf constant
Return:
Nx3 tensor, rotated batch of point clouds
"""
with graph.as_default():
# print('angle_x', angle_x)
if angle_x == 0:
angle_x = tflt(0)
if angle_y == 0:
angle_y = tflt(0)
if angle_z == 0:
angle_z = tflt(0)
input_data = tf.cast(input_data, tf.float32)
rotation_matrix1 = tf.stack([1, 0, 0,
0, tm.cos(angle_x), -tm.sin(angle_x),
0, tm.sin(angle_x), tm.cos(angle_x)])
rotation_matrix1 = tf.reshape(rotation_matrix1, (3,3))
rotation_matrix1 = tf.cast(rotation_matrix1, tf.float32)
shape_pc = tf.matmul(input_data, rotation_matrix1)
rotation_matrix2 = tf.stack([tm.cos(angle_y), 0, tm.sin(angle_y),
0, 1, 0,
-tm.sin(angle_y), 0, tm.cos(angle_y)])
rotation_matrix2 = tf.reshape(rotation_matrix2, (3,3))
rotation_matrix2 = tf.cast(rotation_matrix2, tf.float32)
shape_pc = tf.matmul(shape_pc, rotation_matrix2)
rotation_matrix3 = tf.stack([tm.cos(angle_z), -tm.sin(angle_z), 0,
tm.sin(angle_z), tm.cos(angle_z), 0,
0, 0, 1])
rotation_matrix3 = tf.reshape(rotation_matrix3, (3,3))
rotation_matrix3 = tf.cast(rotation_matrix3, tf.float32)
shape_pc = tf.matmul(shape_pc, rotation_matrix3)
return shape_pc
def integralSi_costk(x, limits, norm_range, params, model):
costl, phi = x.unstack_x()
Fl = params['Fl']
S3 = params['S3']
S9 = params['S9']
AFB = params['AFB']
L00 = assoc_legendre_tf(0,0,costl)
L10 = assoc_legendre_tf(1,0,costl)
L20 = assoc_legendre_tf(2,0,costl)
L22 = assoc_legendre_tf(2,2,costl)
return 9./(32 * 3.14159265) * ( (4.0/9.0)*L00 + (2.0/9.0-2.0*Fl/3.0)*L20 + (4.0/3.0)*(3.0/2.0)*AFB*L10 + 1/18.0*2*S3*L22*cos(2*phi)*2 + 1/9.0*S9*L22*sin(2*phi)*2) * (2)
def integralSi_costl(x, limits, norm_range, params, model):
costk, phi = x.unstack_x()
Fl = params['Fl']
S3 = params['S3']
S9 = params['S9']
S5 = params['S5']
S7 = params['S7']
K00 = assoc_legendre_tf(0,0,costk)
K20 = assoc_legendre_tf(2,0,costk)
K21 = assoc_legendre_tf(2,1,costk)
K22 = assoc_legendre_tf(2,2,costk)
return 9./(32 * 3.14159265) * ( (4.0/9.0)*K00 + (4.0*Fl/3.0-4.0/9.0)*K20 + 1/18.0*2*S3*K22*cos(2*phi)*2 + 1/9.0*S9*K22*sin(2*phi)*2 + (2.0/3.0)*S5*K21*cos(phi)*pi/4 + (-2.0/3.0)*S7*K21*sin(phi)*pi/4 ) * (2)
def integralSi_costk_costl(x, limits, norm_range , params , model):
phi = x.unstack_x()
S3 = params['S3']
S9 = params['S9']
return 9./(32 * 3.14159265) * ( 4.0/9.0 + 1/18.0*2*S3*cos(2*phi)*2*2 + 1/9.0*S9*sin(2*phi)*2*2 ) * (2 * 2)
