def binary_ephem(P, T, e, a, i, O_node, o_peri, t):
# Grados a radianes
d2rad = pi/180.
rad2d = 180./pi
i = i*d2rad
O_node = (O_node*d2rad)%(2*pi)
o_peri = (o_peri*d2rad)%(2*pi)
# Anomalia media
M = ((2.0*pi)/P)*(t - T) # radianes
if M >2*pi: M = M - 2*pi
M=M%(2*pi)
# Anomalia excentrica (1ra aproximacion)
E0 = M + e*sin(M) + (e**2/M) * sin(2.0*M)
for itera in range(15):
M0 = E0 - e*sin(E0)
E0 = E0 + (M-M0)/(1-e*cos(E0))
true_anom = 2.0*arctan(sqrt((1+e)/(1-e))*tan(E0/2.0))
#radius = (a*(1-e**2))/(1+e*cos(true_anom))
radius = a*(1-e*cos(E0))
theta = arctan( tan(true_anom + o_peri)*cos(i) ) + O_node
rho = radius * (cos(true_anom + o_peri)/cos(theta - O_node))
# revuelve rho ("), theta (grad), Anomalia excentrica (grad), Anomalia verdadera (grad)
return rho, (theta*rad2d)%360. #, E0*rad2d, M*rad2d, true_anom*rad2d
def __calc_laplacian(self):
numDims = self.polygons.shape[0];
numPoints = self.points.shape[1];
numPolygons = self.polygons.shape[1];
sparseMatrix = csr_matrix((numPoints, numPoints));
for i in range(0, numDims):
i1 = (i + 0)%3;
i2 = (i + 1)%3;
i3 = (i + 2)%3;
distP2P1 = self.points[:, self.polygons[i2, :]] - self.points[:, self.polygons[i1, :]];
distP3P1 = self.points[:, self.polygons[i3, :]] - self.points[:, self.polygons[i1, :]];
distP2P1 = distP2P1 / repmat(sqrt((distP2P1**2).sum(0)), 3, 1);
distP3P1 = distP3P1 / repmat(sqrt((distP3P1**2).sum(0)), 3, 1);
angles = arccos((distP2P1 * distP3P1).sum(0));
iterData1 = csr_matrix((1/tan(angles),
(self.polygons[i2,:],
self.polygons[i3,:])),
shape=(numPoints, numPoints));
iterData2 = csr_matrix((1/tan(angles), (self.polygons[i3,:], self.polygons[i2,:])), shape=(numPoints, numPoints));
sparseMatrix = sparseMatrix + iterData1 + iterData2;
diagonal = sparseMatrix.sum(0);
diagonalSparse = spdiags(diagonal, 0, numPoints, numPoints);
self.__laplacian = diagonalSparse - sparseMatrix;
def azimuth(self, date):
dec = self.declination(date)
ha = self.hour_angle(date)
az = sp.arcsin(sp.cos(dec) * sp.sin(ha) / sp.cos(self.elevation(date)))
if (sp.cos(ha) >= (sp.tan(dec) / sp.tan(self.lat))):
return az
else:
return (sp.pi - az)
def Kaklamanos_Rrup_prime(Rx, widths, dips, depths_to_top):
"""
Calculation for the in-plane rupture distance based on 3 zones
(Fig. 4 Kaklamanos et al. (2011)
~-----------Zone A--------->|<--Zone B-->|<--Zone C--~
____________________________|____________|____________
^ | / / ##### ^
| | / / /
| | / / Ground _/
| | / / Surface
| | / /
| | / /
| | / /
depth | / /
| | / /
| | / /
| | / /
| | / /
| | / /
| | / /
___._____|/____ /
/ dip /
/ # /
/ # /
/^ # /
\ # /
width # / 90 degrees
\ #/
\ /
. /
/
"""
d = dips * pi / 180 # define dips in terms of radians
Ztor = depths_to_top
W = widths
Rrup_p = zeros(Rx.shape)
# Zone A
Rrup_p = where(Rx < Ztor * tan(d),
sqrt(Rx ** 2 + Ztor ** 2),
Rrup_p)
# Zone B
Rrup_p = where((Rx >= Ztor * tan(d)) & (Rx <= Ztor * tan(d) + W * sec(d)),
Rx * sin(d) + Ztor * cos(d),
Rrup_p)
# Zone C
Rrup_p = where(Rx > Ztor * tan(d) + W * sec(d),
sqrt((Rx - W * cos(d)) ** 2 + (Ztor + W * sin(d)) ** 2),
Rrup_p)
return Rrup_p
def det(origin, ang1=120., ang2=150., offset=1e-3, angle=(0., 0., 0.)):
norm = TRIPPy.Vecx((0., 0., 1.))
meri = TRIPPy.Vecx((0., 1., 0.))
area = [
2 * offset * scipy.tan(ang1 * scipy.pi / 360.),
2 * offset * scipy.tan(ang2 * scipy.pi / 360.)
]
return TRIPPy.surface.Rect((0., 0., -offset), origin, area, angle=angle)
def pixelate(self, sagi, meri):
""" convert surface into number of rectangular surfaces"""
ins = old_div(float((sagi - 1)), sagi)
inm = old_div(float((meri - 1)), meri)
stemp = old_div(self.norm.s, sagi)
mtemp = old_div(self.meri.s, meri)
z, theta = scipy.meshgrid(
scipy.linspace(-self.norm.s * ins, self.norm.s * ins, sagi),
scipy.linspace(-self.meri.s * inm, self.meri.s * inm, meri))
vecin = geometry.Vecr(
(self.sagi.s * scipy.ones(theta.shape),
theta + old_div(scipy.pi, 2),
scipy.zeros(theta.shape))) #this produces an artificial
# meri vector, which is in the 'y_hat' direction in the space of the cylinder
# This is a definite patch over the larger problem, where norm is not normal
# to the cylinder surface, but is instead the axis of rotation. This was
# done to match the Vecr input, which works better with norm in the z direction
pt1 = geometry.Point(
geometry.Vecr((self.sagi.s * scipy.ones(theta.shape), theta, z)),
self)
pt1.redefine(self._origin)
vecin = vecin.split()
x_hat = self + pt1 #creates a vector which includes all the centers of the subsurface
out = []
#this for loop makes me cringe super hard
for i in range(meri):
try:
temp = []
for j in range(sagi):
inp = self.rot(vecin[i][j])
temp += [
Rect(geometry.Vecx(x_hat.x()[:, i, j]),
self._origin,
[2 * stemp, 2 * scipy.tan(mtemp) * self.sagi.s],
vec=[inp, self.sagi.copy()],
flag=self.flag)
]
out += [temp]
except IndexError:
inp = self.rot(vecin[i])
out += [
Rect(geometry.Vecx(x_hat.x()[:, i]),
self._origin,
[2 * stemp, 2 * scipy.tan(mtemp) * self.sagi.s],
vec=[inp, self.sagi.copy()],
flag=self.flag)
]
return out
def pixelate(self, sagi, meri):
""" convert surface into number of rectangular surfaces"""
ins = float((sagi - 1))/sagi
inm = float((meri - 1))/meri
stemp = self.norm.s/sagi
mtemp = self.meri.s/meri
z,theta = scipy.meshgrid(scipy.linspace(-self.norm.s*ins,
self.norm.s*ins,
sagi),
scipy.linspace(-self.meri.s*inm,
self.meri.s*inm,
meri))
vecin = geometry.Vecr((self.sagi.s*scipy.ones(theta.shape),
theta+scipy.pi/2,
scipy.zeros(theta.shape))) #this produces an artificial
# meri vector, which is in the 'y_hat' direction in the space of the cylinder
# This is a definite patch over the larger problem, where norm is not normal
# to the cylinder surface, but is instead the axis of rotation. This was
# done to match the Vecr input, which works better with norm in the z direction
pt1 = geometry.Point(geometry.Vecr((self.sagi.s*scipy.ones(theta.shape),
theta,
z)),
self)
pt1.redefine(self._origin)
vecin = vecin.split()
x_hat = self + pt1 #creates a vector which includes all the centers of the subsurface
out = []
#this for loop makes me cringe super hard
for i in xrange(meri):
try:
temp = []
for j in xrange(sagi):
inp = self.rot(vecin[i][j])
temp += [Rect(geometry.Vecx(x_hat.x()[:,i,j]),
self._origin,
[2*stemp,2*scipy.tan(mtemp)*self.sagi.s],
vec=[inp, self.sagi.copy()],
flag=self.flag)]
out += [temp]
except IndexError:
inp = self.rot(vecin[i])
out += [Rect(geometry.Vecx(x_hat.x()[:,i]),
self._origin,
[2*stemp,2*scipy.tan(mtemp)*self.sagi.s],
vec=[inp, self.sagi.copy()],
flag=self.flag)]
return out
def fov_vectors(fov_h, fov_v):
h_diff = scipy.tan(fov_h / 2.0)
v_diff = scipy.tan(fov_v / 2.0)
return [
scipy.array([1, h_diff, v_diff]),
scipy.array([1, h_diff, -v_diff]),
scipy.array([1, -h_diff, v_diff]),
scipy.array([1, -h_diff, -v_diff]),
]
def fov_vectors(fov_h, fov_v):
h_diff = scipy.tan(fov_h / 2.0)
v_diff = scipy.tan(fov_v / 2.0)
return [
scipy.array([1, h_diff, v_diff]),
scipy.array([1, h_diff, -v_diff]),
scipy.array([1, -h_diff, v_diff]),
scipy.array([1, -h_diff, -v_diff])
]
def f(_lamba):
# mi1 = sqrt(self.delta[0] / self.delta[1]) * _lamba
# mi2 = sqrt(self.delta[0] / self.delta[2]) * _lamba
mi1, mi2 = self._calc_mi(_lamba)
result = -tan(_lamba) - (
self.Delta1 * tan(mi1) + self.Delta2 * tan(mi2)) / (
1.0 - (self.Delta2 / self.Delta1) * tan(mi1) * tan(mi2))
return result
def Kaklamanos_Rrup_prime(Rx, widths, dips, depths_to_top):
"""
Calculation for the in-plane rupture distance based on 3 zones
(Fig. 4 Kaklamanos et al. (2011)
~-----------Zone A--------->|<--Zone B-->|<--Zone C--~
____________________________|____________|____________
^ | / / ##### ^
| | / / /
| | / / Ground _/
| | / / Surface
| | / /
| | / /
| | / /
depth | / /
| | / /
| | / /
| | / /
| | / /
| | / /
| | / /
___._____|/____ /
/ dip /
/ # /
/ # /
/^ # /
\ # /
width # / 90 degrees
\ #/
\ /
. /
/
"""
d = dips * pi / 180 # define dips in terms of radians
Ztor = depths_to_top
W = widths
Rrup_p = zeros(Rx.shape)
# Zone A
Rrup_p = where(Rx < Ztor * tan(d), sqrt(Rx**2 + Ztor**2), Rrup_p)
# Zone B
Rrup_p = where((Rx >= Ztor * tan(d)) & (Rx <= Ztor * tan(d) + W * sec(d)),
Rx * sin(d) + Ztor * cos(d), Rrup_p)
# Zone C
Rrup_p = where(Rx > Ztor * tan(d) + W * sec(d),
sqrt((Rx - W * cos(d))**2 + (Ztor + W * sin(d))**2), Rrup_p)
return Rrup_p
def draw_hough_line(array, theta, r, value=1):
"""Replaces all the pixels in ``array`` that fall under a Hough line with parameters ``(theta, r) with ``value``."""
h, w = array.shape
x_bounds = [r/sp.cos(theta) - (h - 1)*sp.tan(theta), r/sp.cos(theta)]
x_min = max(min(x_bounds), 0)
x_max = min(max(x_bounds), w - 1)
xs = sp.array([x_min, x_max])
ys = -xs/sp.tan(theta) + r/sp.sin(theta)
line_indices = skimage.draw.line(int(ys[0]), int(x_min), int(ys[1]), int(x_max))
array[line_indices] = value
def draw_hough_line(array, theta, r, value=1):
"""Replaces all the pixels in ``array`` that fall under a Hough line with parameters ``(theta, r) with ``value``."""
h, w = array.shape
x_bounds = [r / sp.cos(theta) - (h - 1) * sp.tan(theta), r / sp.cos(theta)]
x_min = max(min(x_bounds), 0)
x_max = min(max(x_bounds), w - 1)
xs = sp.array([x_min, x_max])
ys = -xs / sp.tan(theta) + r / sp.sin(theta)
line_indices = skimage.draw.line(int(ys[0]), int(x_min), int(ys[1]),
int(x_max))
array[line_indices] = value
def HybOffDiagFiniteW(GammaL,GammaR,Delta,P,W,iw): ''' off-diagonal part of the finite-bandwidth sc lead hybridization PhiS angle keeps the hybridization real ''' Phi = P*sp.pi PhiS = sp.arctan((GammaL-GammaR)/(GammaL+GammaR+1e-12)*sp.tan(Phi/2.0)) return Delta*sp.exp(1.0j*PhiS)*\ (GammaL*sp.exp(-1.0j*Phi/2.0)+GammaR*sp.exp(1.0j*Phi/2.0))*IntFiniteBW(Delta,W,iw)
def select(self, alpha=None, beta=None):
"""Roullet wheel selection"""
scores = [c.score for c in self.chromossomes]
if beta is not None:
if beta > max(scores):
raise ValueError("Negative score increase beta?")
for i in xrange(len(scores)):
scores[i] -= beta
if alpha is not None:
start = max(scores) - tan(alpha) * len(scores)
if start < 0:
raise ValueError("Negative score decrease alpha?")
for i in xrange(len(scores)):
scores[i] = start + alpha * i
n_scores = scores / sum(scores)
number = random.random()
acc = 0
for idx, value in enumerate(n_scores):
acc += value
if acc > number:
return self.chromossomes[idx]
def f_Refl_Thick_Film():
a = P4Rm()
wl = a.AllDataDict['wavelength']
t = a.AllDataDict['damaged_depth']
N = a.AllDataDict['number_slices']
t_film = a.AllDataDict['film_thick']
G = a.ParamDict['G']
thB_S = a.ParamDict['thB_S']
resol = a.ParamDict['resol']
phi = a.ConstDict['phi']
t_l = a.ParamDict['t_l']
z = a.ParamDict['z']
FH = a.ParamDict['FH']
FmH = a.ParamDict['FmH']
F0 = a.ParamDict['F0']
sp = a.ParamDict['sp']
dwp = a.ParamDict['dwp']
th = a.ParamDict['th']
spline_DW = a.splinenumber[1]
spline_strain = a.splinenumber[0]
param = a.ParamDict['par']
delta_t = t_film - t
strain = f_strain(z, param[:len(sp):], t, spline_strain)
DW = f_DW(z, param[len(sp):len(sp) + len(dwp):], t, spline_DW)
thB = thB_S - strain * tan(thB_S) # angle de Bragg dans chaque lamelle
eta = 0
res = 0
g0 = sin(thB[0] - phi) # gamma 0
gH = -sin(thB[0] + phi) # gamma H
b = g0 / gH
T = pi * G * ((FH[0] * FmH[0])**0.5) * delta_t / (wl * (abs(g0 * gH)**0.5))
eta = (-b * (th - thB[0]) * sin(2 * thB_S) - 0.5 * G * F0[0] *
(1 - b)) / ((abs(b)**0.5) * G * (FH[0] * FmH[0])**0.5)
S1 = (res - eta + (eta * eta - 1)**0.5) * exp(-1j * T *
(eta * eta - 1)**0.5)
S2 = (res - eta - (eta * eta - 1)**0.5) * exp(1j * T *
(eta * eta - 1)**0.5)
res = (eta + ((eta * eta - 1)**0.5) * ((S1 + S2) / (S1 - S2)))
n = 1
while (n <= N):
g0 = sin(thB[n] - phi) # gamma 0
gH = -sin(thB[n] + phi) # gamma H
b = g0 / gH
T = pi * G * (
(FH[n] * FmH[n])**0.5) * t_l * DW[n] / (wl * (abs(g0 * gH)**0.5))
eta = (-b * (th - thB[n]) * sin(2 * thB_S) - 0.5 * G * F0[n] *
(1 - b)) / ((abs(b)**0.5) * G * DW[n] * (FH[n] * FmH[n])**0.5)
S1 = (res - eta + (eta * eta - 1)**0.5) * exp(-1j * T *
(eta * eta - 1)**0.5)
S2 = (res - eta - (eta * eta - 1)**0.5) * exp(1j * T *
(eta * eta - 1)**0.5)
res = (eta + ((eta * eta - 1)**0.5) * ((S1 + S2) / (S1 - S2)))
n += 1
return convolve(abs(res)**2, resol, mode='same')
def RV_model(THETA, time, kplanets):
modelo = 0.0
#sp.seterr(all=='raise')
if kplanets == 0:
return 0.0
for i in range(kplanets):
P, As, Ac, S, C = THETA[5 * i:5 * (i + 1)]
#P, As, Ac, ecc, w = THETA[5*i:5*(i+1)]
A = As**2 + Ac**2
ecc = S**2 + C**2
phase = sp.arccos(Ac / (A**0.5))
w = sp.arccos(C / (ecc**0.5)) # longitude of periastron
### test
if S < 0:
w = 2 * sp.pi - sp.arccos(C / (ecc**0.5))
if As < 0:
phase = 2 * sp.pi - sp.arccos(Ac / (A**0.5))
###
# sp.seterr(all='raise') # DEL
per = sp.exp(P)
freq = 2. * sp.pi / per
M = freq * time + phase # mean anomaly
E = sp.array([MarkleyKESolver().getE(m, ecc)
for m in M]) # eccentric anomaly
f = (sp.arctan(
((1. + ecc)**0.5 / (1. - ecc)**0.5) * sp.tan(E / 2.)) * 2.
) # true anomaly
modelo += A * (sp.cos(f + w) + ecc * sp.cos(w))
return modelo
def pos2Ray(pos, tokamak, angle=None, eps=1e-6):
r"""Take in GENIE pos vectors and convert it into TRIPPy rays
Args:
pos: 4 element tuple or 4x scipy-array
Each pos is assembled into points of (R1,Z1,RT,phi)
tokamak:
Tokamak object in which the pos vectors are defined.
Returns:
Ray: Ray object or typle of ray objects.
"""
r1 = scipy.array(pos[0])
z1 = scipy.array(pos[1])
rt = scipy.array(pos[2])
phi = scipy.array(pos[3])
zt = z1 - scipy.tan(phi)*scipy.sqrt(r1**2 - rt**2)
angle2 = scipy.arccos(rt/r1)
if angle is None:
angle = scipy.zeros(r1.shape)
pt1 = geometry.Point((r1,angle,z1),tokamak)
pt2 = geometry.Point((rt,angle+angle2,zt),tokamak)
output = Ray(pt1,pt2)
output.norm.s[-1] = eps
tokamak.trace(output)
return output
def Jensen_Wake_Model(x):
nTurbs = (len(x) - 2) / 2
xHAWT = x[0:nTurbs]
yHAWT = x[nTurbs:nTurbs * 2]
zTall = x[len(x) - 2]
zShort = x[len(x) - 1]
nTall = nTurbs / 2
zHAWT = np.zeros(nTurbs)
zHAWT[0:nTall] = zTall
zHAWT[nTall:nTurbs] = zShort
theta = 0.1
alpha = sp.tan(theta)
rho = 1.1716
a = 1. / 3.
Cp = 4. * a * (1 - a) ** 2.
# nTurbines = len(xin)/2.
r_0 = np.ones(nTurbs) * 63.2
U_velocity = 8.
U_direction = pi / 3.5
"Make the graphic for the turbines and wakes"
jensen_plot(x, y, r_0, alpha, U_direction_radians)
"Calculate power from each turbine"
return jensen_power(xHAWT, yHAWT, zHAWT, r_0, alpha, a, U_velocity, rho, Cp, U_direction)
def cotan_alpha_beta_angle(triangles, vertices):
# cotan of all angle = (tan(a))^-1
ctn_ab_angles = scipy.tan(edge_alpha_angle(triangles, vertices))
ctn_ab_angles.data **= -1
# matrix = cot(a) + cot(b) = cot(a_ij) + cot(b_ji)
ctn_ab_angles = ctn_ab_angles + ctn_ab_angles.T
return ctn_ab_angles
def __init__(self, yaml):
self._tf_listener = tf.TransformListener()
self._grid = SearchGrid(10, 10, 2.0, 2.0)
camera = yaml.sensors[0].camera
self._fov_h = camera.horizontal_fov
self._fov_v = 2.0 * scipy.arctan(scipy.tan(self._fov_h / 2.0) * (camera.image_height / camera.image_width))
self._fov_vectors = fov_vectors(self._fov_h, self._fov_v)
def f_Refl_Thin_Film_fit(Data):
strain = Data[0]
DW = Data[1]
dat = Data[2]
wl = dat[0]
N = dat[2]
phi = dat[3]
t_l = dat[4]
thB_S = dat[6]
G = dat[7]
F0 = dat[8]
FH = dat[9]
FmH = dat[10]
th = dat[19]
thB = thB_S - strain * tan(thB_S) # angle de Bragg dans chaque lamelle
eta = 0
res = 0
n = 1
while (n <= N):
g0 = sin(thB[n] - phi) # gamma 0
gH = -sin(thB[n] + phi) # gamma H
b = g0 / gH
T = pi * G * ((FH[n]*FmH[n])**0.5) * t_l * DW[n] / (wl * (abs(g0*gH)**0.5))
eta = (-b*(th-thB[n])*sin(2*thB_S) - 0.5*G*F0[n]*(1-b)) / ((abs(b)**0.5) * G * DW[n] * (FH[n]*FmH[n])**0.5)
S1 = (res - eta + (eta*eta-1)**0.5)*exp(-1j*T*(eta*eta-1)**0.5)
S2 = (res - eta - (eta*eta-1)**0.5)*exp(1j*T*(eta*eta-1)**0.5)
res = (eta + ((eta*eta-1)**0.5) * ((S1+S2)/(S1-S2)))
n += 1
return res
def Jensen_Wake_Model(x):
nTurbs = (len(x) - 2) / 2
xHAWT = x[0:nTurbs]
yHAWT = x[nTurbs:nTurbs * 2]
zTall = x[len(x) - 2]
zShort = x[len(x) - 1]
nTall = nTurbs / 2
zHAWT = np.zeros(nTurbs)
zHAWT[0:nTall] = zTall
zHAWT[nTall:nTurbs] = zShort
theta = 0.1
alpha = sp.tan(theta)
rho = 1.1716
a = 1. / 3.
Cp = 4. * a * (1 - a)**2.
# nTurbines = len(xin)/2.
r_0 = np.ones(nTurbs) * 63.2
U_velocity = 8.
U_direction = pi / 3.5
"Make the graphic for the turbines and wakes"
jensen_plot(x, y, r_0, alpha, U_direction_radians)
"Calculate power from each turbine"
return jensen_power(xHAWT, yHAWT, zHAWT, r_0, alpha, a, U_velocity, rho,
Cp, U_direction)
def cotan_alpha_beta_angle(triangles, vertices):
# cotan of all angle = (tan(a))^-1
ctn_ab_angles = scipy.tan(edge_alpha_angle(triangles, vertices))
ctn_ab_angles.data **= -1
# matrix = cot(a) + cot(b) = cot(a_ij) + cot(b_ji)
ctn_ab_angles = ctn_ab_angles + ctn_ab_angles.T
return ctn_ab_angles
def integrand(z):
"""
Determine the integrand for calculating the apparent elevation angle.
:param z: The altitude (km).
:return: The integrand.
"""
# Effective radius of the Earth (km)
re = 6378.137
# Standard values
a = 0.000315
b = 0.1361
# Refractive index and derivative as a function of altitude
n_z = 1. + a * exp(-b * z)
np_z = -(a * b * exp(-b * z))
# Refractive index at the given height
n_h = 1. + a * exp(-b * height)
tan_phi = tan(
arccos(((re + height) * n_h) / ((re + z) * n_z) *
cos(radians(theta_true))))
return np_z / (n_z * tan_phi)
def pos2Ray(pos, tokamak, angle=None, eps=1e-6):
r"""Take in GENIE pos vectors and convert it into TRIPPy rays
Args:
pos: 4 element tuple or 4x scipy-array
Each pos is assembled into points of (R1,Z1,RT,phi)
tokamak:
Tokamak object in which the pos vectors are defined.
Returns:
Ray: Ray object or typle of ray objects.
"""
r1 = scipy.array(pos[0])
z1 = scipy.array(pos[1])
rt = scipy.array(pos[2])
phi = scipy.array(pos[3])
zt = z1 - scipy.tan(phi) * scipy.sqrt(r1**2 - rt**2)
angle2 = scipy.arccos(old_div(rt, r1))
if angle is None:
angle = scipy.zeros(r1.shape)
pt1 = geometry.Point((r1, angle, z1), tokamak)
pt2 = geometry.Point((rt, angle + angle2, zt), tokamak)
output = Ray(pt1, pt2)
output.norm.s[-1] = eps
tokamak.trace(output)
return output
def cotangentWeightsLaplacianMatrix(polydata, points=None, polygons=None):
"""Calculates the laplacian matrix from the cotangent weights of the mesh.
Return:
laplacianMatrix (scipy.sparse.csr.csr_matrix): laplacian matrix.
Args:
polydata (vtkPolyData): vtkPolyData object with information of the mesh
points (numpy.ndarray): information of the coordinates of each vertex
polygons (numpy.ndarray): information on the identifiers of every point in every triangle of the mesh
"""
if points is None:
points = vtkPointsToNumpy(polydata)
if polygons is None:
polygons = vtkCellsToNumpy(polydata)
numPoints = polydata.GetNumberOfPoints()
numPolygons = polydata.GetNumberOfPolys()
numDims = polydata.GetPoints().GetData().GetNumberOfComponents()
laplacianMatrix = csr_matrix((numPoints, numPoints))
for i in range(0, numDims):
i1 = (i + 0) % 3
i2 = (i + 1) % 3
i3 = (i + 2) % 3
vectP2P1 = (points[:, polygons[i2, :]] - points[:, polygons[i1, :]])
vectP3P1 = (points[:, polygons[i3, :]] - points[:, polygons[i1, :]])
vectP2P1 = vectP2P1 / repmat(sqrt((vectP2P1**2).sum(0)), numDims, 1)
vectP3P1 = vectP3P1 / repmat(sqrt((vectP3P1**2).sum(0)), numDims, 1)
angles = arccos((vectP2P1 * vectP3P1).sum(0))
iterData1 = csr_matrix(
(1 / tan(angles), (polygons[i2, :], polygons[i3, :])),
shape=(numPoints, numPoints))
iterData2 = csr_matrix(
(1 / tan(angles), (polygons[i3, :], polygons[i2, :])),
shape=(numPoints, numPoints))
laplacianMatrix = laplacianMatrix + iterData1 + iterData2
return laplacianMatrix
def D_integrand(th):
"""Initial D field is calculated by integrating D_integrand w.r.t.
theta. Assumes the input is between theta0 and theta1.
Note that this function operates on vectorized input."""
from scipy import sin, exp, tan
f = 2.0 * Omega * sin(th)
u_zon = (80.0 / e_n) * exp(1.0 / ((th - theta_0) * (th - theta_1)))
return u_zon * (f + tan(th) * u_zon / R)
def mini_RV_model(params, time):
P, A, phase, ecc, w = params
freq = 2. * sp.pi / P
M = freq * time + phase
E = sp.array([MarkleyKESolver().getE(m, ecc) for m in M])
f = (sp.arctan(((1. + ecc)**0.5 / (1. - ecc)**0.5) * sp.tan(E / 2.)) * 2.)
modelo = A * (sp.cos(f + w) + ecc * sp.cos(w))
return modelo
def __init__(self, yaml):
self._tf_listener = tf.TransformListener()
self._grid = SearchGrid(10, 10, 2.0, 2.0)
camera = yaml.sensors[0].camera
self._fov_h = camera.horizontal_fov
self._fov_v = 2.0 * scipy.arctan(
scipy.tan(self._fov_h / 2.0) *
(camera.image_height / camera.image_width))
self._fov_vectors = fov_vectors(self._fov_h, self._fov_v)
def f_Refl_Thick_Film_and_Substrate_fit(Data):
strain = Data[0]
DW = Data[1]
dat = Data[2]
wl = dat[0]
t = dat[1]
N = dat[2]
phi = dat[3]
t_l = dat[4]
thB_S = dat[6]
G = dat[7]
F0 = dat[8]
FH = dat[9]
FmH = dat[10]
b_Sub = dat[11]
thB_Sub = dat[12]
G_Sub = dat[13]
F0_Sub = dat[14]
FH_Sub = dat[15]
FmH_Sub = dat[16]
t_film = dat[17]
dw_film = dat[18]
th = dat[19]
delta_t = t_film - t
thB = thB_S - strain * tan(thB_S) # angle de Bragg dans chaque lamelle
temp1 = -b_Sub*(th-thB_Sub)*sin(2*thB_Sub) - (0.5*G_Sub*F0_Sub[0]*(1-b_Sub))
temp2 = (abs(b_Sub)**0.5) * G_Sub * (FH_Sub[0]*FmH_Sub[0])**0.5
eta = temp1/temp2
res = (eta - signe(eta.real)*((eta*eta - 1)**0.5))
g0 = sin(thB[0] - phi) # gamma 0
gH = -sin(thB[0] + phi) # gamma H
b = g0 / gH
T = pi * G * ((FH[0]*FmH[0])**0.5) * delta_t * dw_film / (wl * (abs(g0*gH)**0.5))
eta = (-b*(th-thB[0])*sin(2*thB_S) - 0.5*G*F0[0]*(1-b)) / ((abs(b)**0.5) * G * dw_film * (FH[0]*FmH[0])**0.5)
S1 = (res - eta + (eta*eta-1)**0.5)*exp(-1j*T*(eta*eta-1)**0.5)
S2 = (res - eta - (eta*eta-1)**0.5)*exp(1j*T*(eta*eta-1)**0.5)
res = (eta + ((eta*eta-1)**0.5) * ((S1+S2)/(S1-S2)))
n = 1
while (n <= N):
g0 = sin(thB[n] - phi) # gamma 0
gH = -sin(thB[n] + phi) # gamma H
b = g0 / gH
T = pi * G * ((FH[n]*FmH[n])**0.5) * t_l * DW[n] * dw_film / (wl * (abs(g0*gH)**0.5))
eta = (-b*(th-thB[n])*sin(2*thB_S) - 0.5*G*F0[n]*(1-b)) / ((abs(b)**0.5) * G * DW[n] * dw_film * (FH[n]*FmH[n])**0.5 )
S1 = (res - eta + (eta*eta-1)**0.5)*exp(-1j*T*(eta*eta-1)**0.5)
S2 = (res - eta - (eta*eta-1)**0.5)*exp(1j*T*(eta*eta-1)**0.5)
res = (eta + ((eta*eta-1)**0.5) * ((S1+S2)/(S1-S2)))
n += 1
return res
def __LaplacianMatrix(self):
numDims = self.polygonData.shape[0];
numPoints = self.pointData.shape[1];
numPolygons = self.polygonData.shape[1];
boundary = self.boundary;
boundaryConstrain = scipy.zeros((2,numPoints));
sparseMatrix = scipy.sparse.csr_matrix((numPoints, numPoints));
for i in range(0, numDims):
i1 = (i + 0)%3;
i2 = (i + 1)%3;
i3 = (i + 2)%3;
distP2P1 = self.pointData[:, self.polygonData[i2, :]] \
- self.pointData[:, self.polygonData[i1, :]];
distP3P1 = self.pointData[:, self.polygonData[i3, :]] \
- self.pointData[:, self.polygonData[i1, :]];
distP2P1 = distP2P1 / numpy.matlib.repmat(scipy.sqrt((distP2P1**2).sum(0)), 3, 1);
distP3P1 = distP3P1 / numpy.matlib.repmat(scipy.sqrt((distP3P1**2).sum(0)), 3, 1);
angles = scipy.arccos((distP2P1 * distP3P1).sum(0));
iterData1 = scipy.sparse.csr_matrix((1/scipy.tan(angles), \
(self.polygonData[i2,:], \
self.polygonData[i3,:])), \
shape=(numPoints, \
numPoints));
iterData2 = scipy.sparse.csr_matrix((1/scipy.tan(angles), \
(self.polygonData[i3,:], \
self.polygonData[i2,:])), \
shape=(numPoints, \
numPoints));
sparseMatrix = sparseMatrix + iterData1 + iterData2;
diagonal = sparseMatrix.sum(0);
diagonalSparse = scipy.sparse.spdiags(diagonal, 0, \
numPoints, \
numPoints);
self.laplacianMatrix = diagonalSparse - sparseMatrix;
def __calc_laplacian(self):
""" """
print("TO-DO: DOCUMENTATION")
numPoints = self.polydata.GetNumberOfPoints()
numPolygons = self.polydata.GetNumberOfPolys()
numDims = self.polydata.GetPoints().GetData().GetNumberOfComponents()
sparseMatrix = csr_matrix((numPoints, numPoints))
for i in range(0, numDims):
i1 = (i + 0) % 3
i2 = (i + 1) % 3
i3 = (i + 2) % 3
vectP2P1 = (self.points[:, self.polygons[i2, :]] -
self.points[:, self.polygons[i1, :]])
vectP3P1 = (self.points[:, self.polygons[i3, :]] -
self.points[:, self.polygons[i1, :]])
vectP2P1 = vectP2P1 / repmat(sqrt(
(vectP2P1**2).sum(0)), numDims, 1)
vectP3P1 = vectP3P1 / repmat(sqrt(
(vectP3P1**2).sum(0)), numDims, 1)
angles = arccos((vectP2P1 * vectP3P1).sum(0))
iterData1 = csr_matrix(
(1 / tan(angles),
(self.polygons[i2, :], self.polygons[i3, :])),
shape=(numPoints, numPoints))
iterData2 = csr_matrix(
(1 / tan(angles),
(self.polygons[i3, :], self.polygons[i2, :])),
shape=(numPoints, numPoints))
sparseMatrix = sparseMatrix + iterData1 + iterData2
# diagonal = sparseMatrix.sum(0)
# diagonalSparse = spdiags(diagonal, 0, numPoints, numPoints)
# self.__laplacian = diagonalSparse - sparseMatrix
self.__laplacian = sparseMatrix
def __LaplacianMatrix(self):
numDims = self.__polygonData.shape[0]
numPoints = self.__pointData.shape[1]
numPolygons = self.__polygonData.shape[1]
boundary = self.__boundary
boundaryConstrain = scipy.zeros((2, numPoints))
sparseMatrix = scipy.sparse.csr_matrix((numPoints, numPoints))
for i in range(0, numDims):
i1 = (i + 0) % 3
i2 = (i + 1) % 3
i3 = (i + 2) % 3
distP2P1 = self.__pointData[:, self.__polygonData[
i2, :]] - self.__pointData[:, self.__polygonData[i1, :]]
distP3P1 = self.__pointData[:, self.__polygonData[
i3, :]] - self.__pointData[:, self.__polygonData[i1, :]]
distP2P1 = distP2P1 / numpy.matlib.repmat(
scipy.sqrt((distP2P1**2).sum(0)), 3, 1)
distP3P1 = distP3P1 / numpy.matlib.repmat(
scipy.sqrt((distP3P1**2).sum(0)), 3, 1)
angles = scipy.arccos((distP2P1 * distP3P1).sum(0))
iterData1 = scipy.sparse.csr_matrix(
(1 / scipy.tan(angles),
(self.__polygonData[i2, :], self.__polygonData[i3, :])),
shape=(numPoints, numPoints))
iterData2 = scipy.sparse.csr_matrix(
(1 / scipy.tan(angles),
(self.__polygonData[i3, :], self.__polygonData[i2, :])),
shape=(numPoints, numPoints))
sparseMatrix = sparseMatrix + iterData1 + iterData2
diagonal = sparseMatrix.sum(0)
diagonalSparse = scipy.sparse.spdiags(diagonal, 0, numPoints,
numPoints)
self.__laplacianMatrix = diagonalSparse - sparseMatrix
def propagateRay(initCoords,theta,w,h):
'''
From a starting point with a given direction, determine how far a ray travels before encountering a side wall of the resonator.
Returns the distance traveled, the coordinates of the intersection point at the boundary, and a number 0-3 indicating which wall
was struck [right,top,left,bottom]
'''
cornerTheta = getCornerAngles(initCoords,w,h)
# slope of line through dipole point
m = tan(theta)
xo = initCoords[0]
yo = initCoords[1]
horizIntersect = lambda y: 1/m*(y-yo)+xo #returns x
vertIntersect = lambda x: m*(x-xo)+yo #returns y
side = -1
# Top
if theta >= cornerTheta[0] and theta < cornerTheta[1]:
y = h/2.
x = horizIntersect(y)
side = 1
# Left
elif theta >= cornerTheta[1] and theta < cornerTheta[2]:
x = -w/2.
y = vertIntersect(x)
side = 2
# Bottom
elif theta >= cornerTheta[2] and theta < cornerTheta[3]:
y = -h/2.
x = horizIntersect(y)
side = 3
# Right
elif (theta >= cornerTheta[3] and theta <= 2*pi) or (theta < cornerTheta[0] and theta >= 0):
x = w/2.
y = vertIntersect(x)
side = 0
else:
print 'Invalid Theta.'
print theta*180/pi
print cornerTheta*180/pi
exit()
d = sqrt((x-xo)**2 + (y-yo)**2)
finalCoords = newCoords((xo,yo),d,theta)
return d,finalCoords,side
def f_Refl_Thick_Film():
a = P4Rm()
wl = a.AllDataDict['wavelength']
t = a.AllDataDict['damaged_depth']
N = a.AllDataDict['number_slices']
t_film = a.AllDataDict['film_thick']
G = a.ParamDict['G']
thB_S = a.ParamDict['thB_S']
resol = a.ParamDict['resol']
phi = a.ConstDict['phi']
t_l = a.ParamDict['t_l']
z = a.ParamDict['z']
FH = a.ParamDict['FH']
FmH = a.ParamDict['FmH']
F0 = a.ParamDict['F0']
sp = a.ParamDict['sp']
dwp = a.ParamDict['dwp']
th = a.ParamDict['th']
spline_DW = a.splinenumber[1]
spline_strain = a.splinenumber[0]
param = a.ParamDict['par']
delta_t = t_film - t
strain = f_strain(z, param[:len(sp):], t, spline_strain)
DW = f_DW(z, param[len(sp):len(sp)+len(dwp):], t, spline_DW)
thB = thB_S - strain * tan(thB_S) # angle de Bragg dans chaque lamelle
eta = 0
res = 0
g0 = sin(thB[0] - phi) # gamma 0
gH = -sin(thB[0] + phi) # gamma H
b = g0 / gH
T = pi * G * ((FH[0]*FmH[0])**0.5) * delta_t / (wl * (abs(g0*gH)**0.5))
eta = (-b*(th-thB[0])*sin(2*thB_S) - 0.5*G*F0[0]*(1-b)) / ((abs(b)**0.5) * G * (FH[0]*FmH[0])**0.5)
S1 = (res - eta + (eta*eta-1)**0.5)*exp(-1j*T*(eta*eta-1)**0.5)
S2 = (res - eta - (eta*eta-1)**0.5)*exp(1j*T*(eta*eta-1)**0.5)
res = (eta + ((eta*eta-1)**0.5) * ((S1+S2)/(S1-S2)))
n = 1
while (n <= N):
g0 = sin(thB[n] - phi) # gamma 0
gH = -sin(thB[n] + phi) # gamma H
b = g0 / gH
T = pi * G * ((FH[n]*FmH[n])**0.5) * t_l * DW[n] / (wl * (abs(g0*gH)**0.5))
eta = (-b*(th-thB[n])*sin(2*thB_S) - 0.5*G*F0[n]*(1-b)) / ((abs(b)**0.5) * G * DW[n] * (FH[n]*FmH[n])**0.5)
S1 = (res - eta + (eta*eta-1)**0.5)*exp(-1j*T*(eta*eta-1)**0.5)
S2 = (res - eta - (eta*eta-1)**0.5)*exp(1j*T*(eta*eta-1)**0.5)
res = (eta + ((eta*eta-1)**0.5) * ((S1+S2)/(S1-S2)))
n += 1
return convolve(abs(res)**2, resol, mode='same')
def f_Refl_Thick_Film_fit(Data):
strain = Data[0]
DW = Data[1]
dat = Data[2]
wl = dat[0]
t = dat[1]
N = dat[2]
phi = dat[3]
t_l = dat[4]
thB_S = dat[6]
G = dat[7]
F0 = dat[8]
FH = dat[9]
FmH = dat[10]
t_film = dat[17]
th = dat[19]
delta_t = t_film - t
thB = thB_S - strain * tan(thB_S) # angle de Bragg dans chaque lamelle
eta = 0
res = 0
g0 = sin(thB[0] - phi) # gamma 0
gH = -sin(thB[0] + phi) # gamma H
b = g0 / gH
T = pi * G * ((FH[0] * FmH[0])**0.5) * delta_t / (wl * (abs(g0 * gH)**0.5))
eta = (-b * (th - thB[0]) * sin(2 * thB_S) - 0.5 * G * F0[0] *
(1 - b)) / ((abs(b)**0.5) * G * (FH[0] * FmH[0])**0.5)
S1 = (res - eta + (eta * eta - 1)**0.5) * exp(-1j * T *
(eta * eta - 1)**0.5)
S2 = (res - eta - (eta * eta - 1)**0.5) * exp(1j * T *
(eta * eta - 1)**0.5)
res = (eta + ((eta * eta - 1)**0.5) * ((S1 + S2) / (S1 - S2)))
n = 1
while (n <= N):
g0 = sin(thB[n] - phi) # gamma 0
gH = -sin(thB[n] + phi) # gamma H
b = g0 / gH
T = pi * G * (
(FH[n] * FmH[n])**0.5) * t_l * DW[n] / (wl * (abs(g0 * gH)**0.5))
eta = (-b * (th - thB[n]) * sin(2 * thB_S) - 0.5 * G * F0[n] *
(1 - b)) / ((abs(b)**0.5) * G * DW[n] * (FH[n] * FmH[n])**0.5)
S1 = (res - eta + (eta * eta - 1)**0.5) * exp(-1j * T *
(eta * eta - 1)**0.5)
S2 = (res - eta - (eta * eta - 1)**0.5) * exp(1j * T *
(eta * eta - 1)**0.5)
res = (eta + ((eta * eta - 1)**0.5) * ((S1 + S2) / (S1 - S2)))
n += 1
return res
def multivariateCauchy(mu, sigma, onlyDiagonal=True):
""" Generates a sample according to a given multivariate Cauchy distribution. """
if not onlyDiagonal:
u, s, d = svd(sigma)
coeffs = sqrt(s)
else:
coeffs = diag(sigma)
r = rand(len(mu))
res = coeffs * tan(pi * (r - 0.5))
if not onlyDiagonal:
res = dot(d, dot(res, u))
return res + mu
def k_teta_DC(mean_incl, elevations=[9.23,10.81,26.57,31.08,47.41,52.62,69.16,90]):
#computes extinction coefficient from mean inclination
# equations from SIRASCA. Calculations for each elevation angle (p210-211 crop stucture and light microclimate)
# default elevation for turtle 46 directions
#elevations (beam, degre)-> 90 = vertical ; 0=horizontal
#mean_incl (feuille, degre) -> 90 = vertical ; 0=horizontal
nh=len(elevations)
xinc = radians(mean_incl)
lst_xk=[]
for ih in elevations:
hh = radians(ih)
if (hh >=xinc):
xk = cos(xinc)
else:
xmm = -tan(hh)/tan(xinc)
#print xmm
xmm = arccos(xmm)
xk = cos(xinc)*(2*(xmm - tan(xmm))/pi - 1.)
lst_xk.append(xk)
return lst_xk
def multivariateCauchy(mu, sigma, onlyDiagonal=True):
""" Generates a sample according to a given multivariate Cauchy distribution. """
if not onlyDiagonal:
u, s, d = svd(sigma)
coeffs = sqrt(s)
else:
coeffs = diag(sigma)
r = rand(len(mu))
res = coeffs * tan(pi * (r - 0.5))
if not onlyDiagonal:
res = dot(d, dot(res, u))
return res + mu
def show_hough_lines(im, angles, dists):
"""Displays a set of Hough lines on top of an image"""
fig, ax = show_image(im)
for angle, dist in zip(angles, dists):
xs = sp.array([0, im.shape[1]])
ys = -xs / sp.tan(angle) + dist / sp.sin(angle)
ax.plot(xs, ys, 'r')
ax.set_xlim(0, im.shape[1])
ax.set_ylim(im.shape[0], 0)
return fig, ax
def show_hough_lines(im, angles, dists):
"""Displays a set of Hough lines on top of an image"""
fig, ax = show_image(im)
for angle, dist in zip(angles, dists):
xs = sp.array([0, im.shape[1]])
ys = -xs/sp.tan(angle) + dist/sp.sin(angle)
ax.plot(xs, ys, 'r')
ax.set_xlim(0, im.shape[1])
ax.set_ylim(im.shape[0], 0)
return fig, ax
def Jensen_Wake_Model(xHAWT, yHAWT, zHAWT, params):
theta = 0.1
alpha = sp.tan(theta)
rho = 1.1716
a = 1. / 3.
Cp = 4.*a*(1-a)**2.
# nTurbines = len(xin)/2.
r_0, U_velocity = params
"Make the graphic for the turbines and wakes"
# jensen_plot(x, y, r_0, alpha, U_direction_radians)
"Calculate power from each turbine"
return jensen_power(xHAWT, yHAWT, zHAWT, r_0, alpha, a, U_velocity, rho, Cp)
def radar_cross_section(frequency, nose_radius, base_radius, length,
incident_angle):
"""
Calculate the radar cross section of a frustum.
:param frequency: The operating frequency (Hz).
:param nose_radius: The radius of the nose (m).
:param base_radius: The radius of the base (m).
:param length: The length (m).
:param incident_angle: The incident angle (rad).
:return: The radar cross section of the frustum (m^2).
"""
# Wavelength
wavelength = c / frequency
# Wavenumber
k = 2.0 * pi / wavelength
# Calculate the half cone angle
half_cone_angle = arctan((base_radius - nose_radius) / length)
# Calculate the heights
z2 = base_radius * tan(half_cone_angle)
z1 = nose_radius * tan(half_cone_angle)
# Calculate the RCS
if abs(incident_angle - (0.5 * pi + half_cone_angle)) < 1e-12:
# Specular
rcs = 8.0 / 9.0 * pi * (z2 ** 1.5 - z1 ** 1.5) ** 2 * sin(half_cone_angle) / \
(wavelength * cos(half_cone_angle) ** 4)
elif incident_angle < 1e-3:
rcs = wavelength**2 * (k * base_radius)**4 / (4.0 * pi)
elif pi - incident_angle < 1e-3:
rcs = wavelength**2 * (k * nose_radius)**4 / (4.0 * pi)
else:
rcs = wavelength * base_radius / (8.0 * pi * sin(incident_angle)) * (
tan(incident_angle - half_cone_angle))**2
return rcs
def calculateCentroids(self, zern):
"""
Calcualates the locations of the centroids under the given
Zernike coefficients
"""
self.wavefront.setZern(zern)
dx = 10.0 # Microns
dy = 10.0 # Microns
centroids = []
intensities = []
DCx, DCy = self.pupil.getDecenter() # Decenter of Pupil
for c in self.lenslet.coordinates:
# Calculates the partial derivatives
zxp = self.wavefront.calcWaveFront(c[0]+DCx+dx, c[1]+DCy)
zxm = self.wavefront.calcWaveFront(c[0]+DCx-dx, c[1]+DCy)
zyp = self.wavefront.calcWaveFront(c[0]+DCx, c[1]+DCy+dy)
zym = self.wavefront.calcWaveFront(c[0]+DCx, c[1]+DCy-dy)
delx = (zxp - zxm)/(2)
dely = (zyp - zym)/(2)
# Computes the normal vector to the surface
normalx = -delx*dy
normaly = -dely*dx
normalz = dx*dy
#Calculates the shift in microns on the detector
theta_x = scipy.arctan2(normalx, normalz)
theta_y = scipy.arctan2(normaly, normalz)
shift_x = scipy.tan(theta_x)*self.lenslet.fl
shift_y = scipy.tan(theta_y)*self.lenslet.fl
centroids.append([c[0]+shift_x, c[1]+shift_y])
intensities.append(self.pupil.calculateApertureIllumination(
c[0]-self.lenslet.spacing/2.0,
c[1]-self.lenslet.spacing/2.0,
self.lenslet.spacing))
return numpy.array(centroids), numpy.array(intensities)
def Jensen_Wake_Model(xHAWT, yHAWT, zHAWT, params):
theta = 0.1
alpha = sp.tan(theta)
rho = 1.1716
a = 1. / 3.
Cp = 4. * a * (1 - a)**2.
# nTurbines = len(xin)/2.
r_0, U_velocity = params
"Make the graphic for the turbines and wakes"
# jensen_plot(x, y, r_0, alpha, U_direction_radians)
"Calculate power from each turbine"
return jensen_power(xHAWT, yHAWT, zHAWT, r_0, alpha, a, U_velocity, rho,
Cp)
def edge_voronoi_area(triangles, vertices):
vv_l2_sqr_map = edge_sqr_length(triangles, vertices)
alpha = edge_alpha_angle(triangles, vertices)
gamma = edge_gamma_angle(triangles, vertices)
cot_alpha = scipy.tan(alpha)
cot_alpha.data **= -1
inv_sin2_alpha = scipy.sin(alpha)
inv_sin2_alpha.data **= -2
w_angle = scipy.sin(2.0 * gamma).multiply(inv_sin2_alpha) / 2.0
vv_area = vv_l2_sqr_map.multiply(cot_alpha + w_angle) / 8.0
return vv_area
def __LaplacianMatrix(self):
numDims = self.__polygonData.shape[0]
numPoints = self.__pointData.shape[1]
numPolygons = self.__polygonData.shape[1]
sparseMatrix = csr_matrix((numPoints, numPoints))
for i in range(0, numDims):
i1 = (i + 0) % 3
i2 = (i + 1) % 3
i3 = (i + 2) % 3
distP2P1 = self.__pointData[:, self.__polygonData[
i2, :]] - self.__pointData[:, self.__polygonData[i1, :]]
distP3P1 = self.__pointData[:, self.__polygonData[
i3, :]] - self.__pointData[:, self.__polygonData[i1, :]]
distP2P1 = distP2P1 / repmat(sqrt((distP2P1**2).sum(0)), 3, 1)
distP3P1 = distP3P1 / repmat(sqrt((distP3P1**2).sum(0)), 3, 1)
angles = arccos((distP2P1 * distP3P1).sum(0))
iterData1 = csr_matrix(
(1 / tan(angles),
(self.__polygonData[i2, :], self.__polygonData[i3, :])),
shape=(numPoints, numPoints))
iterData2 = csr_matrix(
(1 / tan(angles),
(self.__polygonData[i3, :], self.__polygonData[i2, :])),
shape=(numPoints, numPoints))
sparseMatrix = sparseMatrix + iterData1 + iterData2
diagonal = sparseMatrix.sum(0)
diagonalSparse = spdiags(diagonal, 0, numPoints, numPoints)
self.__laplacianMatrix = diagonalSparse - sparseMatrix
def edge_voronoi_area(triangles, vertices):
from trimeshpy.math.angle import edge_alpha_angle, edge_gamma_angle
vv_l2_sqr_map = edge_sqr_length(triangles, vertices)
alpha = edge_alpha_angle(triangles, vertices)
gamma = edge_gamma_angle(triangles, vertices)
cot_alpha = scipy.tan(alpha)
cot_alpha.data **= -1
inv_sin2_alpha = scipy.sin(alpha)
inv_sin2_alpha.data **= -2
w_angle = scipy.sin(2.0 * gamma).multiply(inv_sin2_alpha) / 2.0
vv_area = vv_l2_sqr_map.multiply(cot_alpha + w_angle) / 8.0
return vv_area
def sky2pix(header,ra,dec): hdr_info = parse_header(header) if scipy.isscalar(ra): ra /= raddeg dec /= raddeg else: ra = ra.astype(scipy.float64)/raddeg dec = dec.astype(scipy.float64)/raddeg if hdr_info[3]=="DEC": ra0 = hdr_info[0][1]/raddeg dec0 = hdr_info[0][0]/raddeg else: ra0 = hdr_info[0][0]/raddeg dec0 = hdr_info[0][1]/raddeg phi = pi + arctan2(-1*cos(dec)*sin(ra-ra0),sin(dec)*cos(dec0)-cos(dec)*sin(dec0)*cos(ra-ra0)) argtheta = sin(dec)*sin(dec0)+cos(dec)*cos(dec0)*cos(ra-ra0) if scipy.isscalar(argtheta): theta = arcsin(argtheta) else: argtheta[argtheta>1.] = 1. theta = arcsin(argtheta) if hdr_info[5]=="TAN": r_theta = raddeg/tan(theta) x = r_theta*sin(phi) y = -1.*r_theta*cos(phi) elif hdr_info[5]=="SIN": r_theta = raddeg*cos(theta) x = r_theta*sin(phi) y = -1.*r_theta*cos(phi) if hdr_info[3]=="DEC": a = x.copy() x = y.copy() y = a.copy() inv = linalg.inv(hdr_info[2]) x0 = inv[0,0]*x + inv[0,1]*y y0 = inv[1,0]*x + inv[1,1]*y x = x0+hdr_info[1][0]-1 y = y0+hdr_info[1][1]-1 return x,y
# -*- coding: utf-8 -*- # Herramientas para computación científica # Lección 1 # SciPy: Introducción import scipy x = 0 print(scipy.sin(x)) print(scipy.cos(x)) print(scipy.tan(x)) print(scipy.exp(2 + x)) print(scipy.log(2 + x)) print(type(scipy.inf)) print(scipy.pi) print(scipy.e)
def model(A, P, w, phase, ecc, jitt, offset, time):
freq = 2. * sp.pi / P
M = freq * time + phase
E = sp.array([ks.getE(m, ecc) for m in M])
f = (sp.arctan(sp.sqrt((1. + ecc) / (1. - ecc)) * sp.tan(E / 2.)) * 2)
return A * (sp.cos(f + w) + ecc * sp.cos(w)) + offset
# -*- coding: utf-8 -*- """ Created on Sat Aug 15 12:42:52 2015 @author: Chris """ import scipy as sc import matplotlib.pyplot as plt x = sc.arange(0, 5*sc.pi, 0.1); y = sc.sin(x) plt.plot(x, y) z = sc.cos(x) plt.plot(x, z) a = sc.tan(x) plt.plot(x,a) plt.axis([0,5*sc.pi,-1,1])
def sun_NR(doy=scipy.array([]),\
lat=float):
'''
Function to calculate the maximum sunshine duration [h] and incoming
radiation [MJ/day] at the top of the atmosphere from day of year and
latitude.
Parameters:
- doy: (array of) day of year.
- lat: latitude in decimal degrees, negative for southern hemisphere.
Returns:
- N: (float, array) maximum sunshine hours [h].
- Rext: (float, array) extraterrestrial radiation [J day-1].
Notes
-----
Only valid for latitudes between 0 and 67 degrees (i.e. tropics
and temperate zone).
References
----------
R.G. Allen, L.S. Pereira, D. Raes and M. Smith (1998). Crop
Evaporation - Guidelines for computing crop water requirements,
FAO - Food and Agriculture Organization of the United Nations.
Irrigation and drainage paper 56, Chapter 3. Rome, Italy.
(http://www.fao.org/docrep/x0490e/x0490e07.htm)
Examples
--------
>>> sun_NR(50,60)
(9.1631820597268163, 9346987.824773483)
>>> days = [100,200,300]
>>> latitude = 52.
>>> sun_NR(days,latitude)
(array([ 13.31552077, 15.87073276, 9.54607624]), array([ 29354803.66244921, 39422316.42084264, 12619144.54566777]))
'''
# Test input array/value
doy,lat = _arraytest(doy,lat)
# Set solar constant [W/m2]
S = 1367.0 # [W/m2]
# Print warning if latitude is above 67 degrees
if abs(lat) > 67.:
print('WARNING: Latitude outside range of application (0-67 degrees).\n)')
# Convert latitude [degrees] to radians
latrad = lat * math.pi / 180.0
# calculate solar declination dt [radians]
dt = 0.409 * scipy.sin(2 * math.pi / 365 * doy - 1.39)
# calculate sunset hour angle [radians]
ws = scipy.arccos(-scipy.tan(latrad) * scipy.tan(dt))
# Calculate sunshine duration N [h]
N = 24 / math.pi * ws
# Calculate day angle j [radians]
j = 2 * math.pi / 365.25 * doy
# Calculate relative distance to sun
dr = 1.0 + 0.03344 * scipy.cos(j - 0.048869)
# Calculate Rext
Rext = S * 86400 / math.pi * dr * (ws * scipy.sin(latrad) * scipy.sin(dt)\
+ scipy.sin(ws) * scipy.cos(latrad) * scipy.cos(dt))
return N, Rext
if __name__== "__main__":
app = QtGui.QApplication(sys.argv)
win_plot = nihms.QtGui.QWidget()
uiplot = nihms.Ui_Form()
uiplot.setupUi(win_plot)
start()
uiplot.lcdNumber.display(heartrate)
uiplot.lcdNumber_2.display(spo2)
n=1000
x=numpy.arange(n)
y=scipy.tan(2*3.14*x)
uiplot.pushButton_2.clicked.connect(lambda: timer.setInterval(5))
uiplot.pushButton.clicked.connect(lambda: timer.setInterval(10000))
uiplot.pushButton_3.clicked.connect(sms())
c=Qwt.QwtPlotCurve()
c.attach(uiplot.plot)
timer = QtCore.QTimer()
timer.start(5.0)
win_plot.connect(timer, QtCore.SIGNAL('timeout()'),plot)
win_plot.show()
def det(origin,ang1=120.,ang2=150.,offset=1e-3,angle=(0.,0.,0.)):
norm = TRIPPy.Vecx((0.,0.,1.))
meri = TRIPPy.Vecx((0.,1.,0.))
area = [2*offset*scipy.tan(ang1*scipy.pi/360.),2*offset*scipy.tan(ang2*scipy.pi/360.)]
return TRIPPy.surface.Rect((0.,0.,-offset), origin, area, angle=angle)
def gz (self):
"""Calcula a função Gz"""
bm = self.dic['km']-self.dic['kb']
return (self.dic['gm'] + bm*0.5*sp.tan(self.pos[3])**2)*sp.sin(self.pos[3])/self.dic['lpp']
"Define Variables"
x = np.array([0, 0, 0, 500, 500, 500, 1000, 1000, 1000]) # x coordinates of the turbines
y = np.array([0, 500, 1000, 0, 500, 1000, 0, 500, 1000]) # y coordinates of the turbines
z = np.array([150, 150, 150, 250, 250, 250, 350, 500, 350]) #hub height of each turbine
# r_0 = np.array([40, 40, 40, 50, 50, 50, 60, 75, 60])
# r_0 = np.ones(len(x))*20
zTall = 150
zShort = 50
xin = np.hstack([x, y, zTall, zShort])
nTurbs = (len(xin) - 2) / 2
"0 degrees is coming from due North. +90 degrees means the wind is coming from due East, -90 from due West"
U_direction = -90.
r_0 = np.ones(nTurbs) * 63.2
U_direction_radians = (U_direction + 90) * pi / 180.
theta = 0.1
alpha = sp.tan(theta)
# print U_direction_radians
# x_r, y_r = rotate(x, y, U_direction_radians)
# Jensen_Wake_Model(overlap, loss, jensen_power, jensen_plot, x, y, r_0, alpha, U_direction_radians)
# xin = np.hstack([x, y])
print Jensen_Wake_Model(xin)
# ax = Axes3D(plt.gcf())
# for i in range(len(x)):
# ax.scatter(x[i], y[i], z[i], c = 'r', s=pi*r_0[i]**2, marker='.')
# fillstyles = ('none')
# #plt.axis(U_direction_radians)
# xtemp = (x[i], x[i])
# ytemp = (y[i], y[i])
# ztemp = (0, z[i]-r_0[i])
import os
import sys
#Z = lambda x: sp.cos(x[0]-0.02) + sp.cos(x[1]+0.01)
Z = lambda x: (x[0]) + (x[1])
InternalAngle = sp.deg2rad(80);
quad_x = sp.array([10.0, 11.0, 11+sp.cos(InternalAngle), 10+sp.cos(InternalAngle) ])
quad_y = sp.array([10.0, 10.0, 10+sp.sin(InternalAngle), 10+sp.sin(InternalAngle) ])
quad_z = Z([quad_x, quad_y])
x_samples = sp.zeros((100,100))
y_samples = sp.zeros((100,100))
for i in range(100):
y_samples[:,i]= sp.linspace(10,10+sp.sin(InternalAngle),100)
x_lim = 10+(y_samples[i,0]-10)/sp.tan(InternalAngle)
x_samples[i,:] = sp.linspace(x_lim,1+x_lim,100)
#The correct values
z_samples = Z([x_samples, y_samples])
#The SciPy values
interp_points_x = sp.copy(x_samples).reshape((100*100))
interp_points_y = sp.copy(y_samples).reshape((100*100))
interp_points = sp.column_stack((interp_points_x,interp_points_y))
scipy_interp = sp.interpolate.griddata(sp.column_stack((quad_x,quad_y)),\
quad_z,interp_points,\
method='linear')
scipy_interp = scipy_interp.reshape((100,100))
scipy_error = (scipy_interp - z_samples)/z_samples
#The SU2 values
SU2_Z = SU2_interp.interp2d(quad_x,quad_y,quad_z)
