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 henshin(thetas, kplanets):
for i in range(kplanets):
Pk = thetas[:, i * 5]
Ask = thetas[:, i * 5 + 1]
Ack = thetas[:, i * 5 + 2]
Sk = thetas[:, i * 5 + 3]
Ck = thetas[:, i * 5 + 4]
Ak = Ask**2 + Ack**2
Phasek = sp.arccos(Ack / (Ak**0.5))
ecck = Sk**2 + Ck**2
wk = sp.arccos(Ck / (ecck**0.5))
for j in range(len(Sk)):
if Sk[j] < 0:
wk[j] = 2 * sp.pi - sp.arccos(Ck[j] / (ecck[j]**0.5))
if Ask[j] < 0:
Phasek[j] = 2 * sp.pi - sp.arccos(Ack[j] / (Ak[j]**0.5))
thetas[:, i * 5] = sp.exp(Pk)
thetas[:, i * 5 + 1] = Ak
thetas[:, i * 5 + 2] = Phasek
thetas[:, i * 5 + 3] = ecck
thetas[:, i * 5 + 4] = wk
return thetas
def nano_henshin_hou(thetas, kplanets, tags, fixed_values, anticoor):
t2 = sp.ones_like(thetas)
for i in range(kplanets):
if tags[i][0]:
t2[i * 5] = sp.exp(thetas[i * 5])
print('changed period! (devs note)')
if tags[i][1]:
Ask = thetas[i * 5 + 1]
Ack = thetas[i * 5 + 2]
Ak = Ask**2 + Ack**2
Phasek = sp.where(Ask >= 0, sp.arccos(Ack / (Ak**0.5)),
2 * sp.pi - sp.arccos(Ack / (Ak**0.5)))
t2[i * 5 + 1] = Ak
t2[i * 5 + 2] = Phasek
print('changed amplitude! (devs note)')
if tags[i][2]:
Sk = thetas[i * 5 + 3]
Ck = thetas[i * 5 + 4]
ecck = Sk**2 + Ck**2
wk = sp.where(Sk >= 0, sp.arccos(Ck / (ecck**0.5)),
2 * sp.pi - sp.arccos(Ck / (ecck**0.5)))
t2[i * 5 + 3] = ecck
t2[i * 5 + 4] = wk
print('changed eccentricity! (devs note)')
for i in range(len(thetas))[5 * kplanets:]:
t2[i] = thetas[i]
return thetas
def overlap(x, xdown, y, ydown, r, alpha):
overlap_fraction = np.zeros(np.size(x))
for i in range(0, np.size(x)):
#define dx as the upstream x coordinate - the downstream x coordinate then rotate according to wind direction
dx = xdown - x[i]
#define dy as the upstream y coordinate - the downstream y coordinate then rotate according to wind direction
dy = ydown - y[i]
R = r + dx * alpha #The radius of the wake depending how far it is from the turbine
A = r**2 * np.pi #The area of the turbine
if dx > 0:
if np.abs(dy) <= R - r:
overlap_fraction[
i] = 1 #if the turbine is completely in the wake, overlap is 1, or 100%
elif np.abs(dy) >= R + r:
overlap_fraction[
i] = 0 #if none of it touches the wake, the overlap is 0
else:
#if part is in and part is out of the wake, the overlap fraction is defied by the overlap area/rotor area
overlap_area = r**2. * sp.arccos(
(dy**2. + r**2. - R**2.) /
(2.0 * dy * r)) + R**2. * sp.arccos(
(dy**2. + R**2. - r**2.) /
(2.0 * dy * R)) - 0.5 * sp.sqrt(
(-dy + r + R) * (dy + r - R) * (dy - r + R) *
(dy + r + R))
overlap_fraction[i] = overlap_area / A
else:
overlap_fraction[
i] = 0 #turbines cannot be affected by any wakes that start downstream from them
# print overlap_fraction
return overlap_fraction #retrun the n x n matrix of how each turbine is affected by all of the others
def wahre_Anom(planet, t=0, t0=0):
"""Wahre Anomalie wird aus exzentrischer Anomalie berechnet,
Fallunterscheidung bei E => 180° und E <= 180°."""
E = ex_Anom(planet, t)
eps = planet["numEx"]
if (E >= 0 and E <= sc.pi):
return sc.arccos((sc.cos(E) - eps) / (1 - eps * sc.cos(E)))
else:
return 2 * sc.pi - sc.arccos((sc.cos(E) - eps) / (1 - eps * sc.cos(E)))
def s2d_angular(s):
if s.shape == (1, 1):
# DPX: to prevent the stupid scipy to collapse the array into scalar
d = np.array([[np.float64(scipy.arccos(s) / np.pi)]])
elif s.shape == (1, ):
d = np.array([np.float64(scipy.arccos(s) / np.pi)])
else:
# costly in time (half of the time)
d = np.float64(scipy.arccos(s) / np.pi)
return d
def _ang2vert_eq(nside, theta, phi, z):
a = 4. / 3. / nside
b = 8. / 3. / sc.pi
deltaZ = a
deltaPhi = a / b
out = []
out.append([sc.arccos(z + deltaZ / 2), phi])
out.append([theta, phi - deltaPhi / 2.])
out.append([sc.arccos(z - deltaZ / 2), phi])
out.append([theta, phi + deltaPhi / 2.])
return sc.array(out)
def _ang2vert_eq(nside,theta,phi,z): a= 4./3./nside b= 8./3./sc.pi deltaZ= a deltaPhi= a/b out= [] out.append([sc.arccos(z+deltaZ/2),phi]) out.append([theta,phi-deltaPhi/2.]) out.append([sc.arccos(z-deltaZ/2),phi]) out.append([theta,phi+deltaPhi/2.]) return sc.array(out)
def _xsys2ang(xs,ys): if sc.fabs(ys) <= sc.pi/4.: return [sc.arccos(8./3./sc.pi*ys),xs] else: xt= (xs % (sc.pi/2.)) fabsys= sc.fabs(ys) theta= sc.arccos((1.-1./3.*(2.-4.*fabsys/sc.pi)**2.)*ys/fabsys) if fabsys == sc.pi/2.: phi= xs-fabsys+sc.pi/4. else: phi= xs-(fabsys-sc.pi/4.)/(fabsys-sc.pi/2.)*(xt-sc.pi/4.) return [theta % (sc.pi+0.0000000001),phi % (2.*sc.pi)] #Hack
def rext_calc(df, lat=float):
"""
Function to calculate extraterrestrial radiation output in J/m2/day
Ref:http://www.fao.org/docrep/x0490e/x0490e07.htm
:param df: dataframe with datetime index
:param lat: latitude (negative for Southern hemisphere)
:return: Rext (J/m2)
"""
# set solar constant [MJ m^-2 min^-1]
s = 0.0820
#convert latitude [degrees] to radians
latrad = lat * math.pi / 180.0
#have to add in function for calculating single value here
# extract date, month, year from index
date = pd.DatetimeIndex(df.index).day
month = pd.DatetimeIndex(df.index).month
year = pd.DatetimeIndex(df.index).year
doy = met.date2doy(dd=date, mm=month,
yyyy=year) # create day of year(1-366) acc to date
l = sp.size(doy)
if l < 2:
dt = 0.409 * math.sin(2 * math.pi / 365 * doy - 1.39)
ws = sp.arccos(-math.tan(latrad) * math.tan(dt))
j = 2 * math.pi / 365.25 * doy
dr = 1.0 + 0.03344 * math.cos(j - 0.048869)
rext = s * 86400 / math.pi * dr * (
ws * math.sin(latrad) * math.sin(dt) +
math.sin(ws) * math.cos(latrad) * math.cos(dt))
#Create dummy output arrays sp refers to scipy
else:
rext = sp.zeros(l)
dt = sp.zeros(l)
ws = sp.zeros(l)
j = sp.zeros(l)
dr = sp.zeros(l)
#calculate Rext
for i in range(0, l):
#Calculate solar decimation dt(d in FAO) [rad]
dt[i] = 0.409 * math.sin(2 * math.pi / 365 * doy[i] - 1.39)
#calculate sunset hour angle [rad]
ws[i] = sp.arccos(-math.tan(latrad) * math.tan(dt[i]))
# calculate day angle j [radians]
j[i] = 2 * math.pi / 365.25 * doy[i]
# calculate relative distance to sun
dr[i] = 1.0 + 0.03344 * math.cos(j[i] - 0.048869)
#calculate Rext dt = d(FAO) and latrad = j(FAO)
rext[i] = (s * 86400.0 / math.pi) * dr[i] * (
ws[i] * math.sin(latrad) * math.sin(dt[i]) +
math.sin(ws[i]) * math.cos(latrad) * math.cos(dt[i]))
rext = sp.array(rext) * 1000000
return rext
def array_angles(array_list, acute = False):
"""
given the two arrays of directions, return there angles
can be set to return the acute angles
"""
from scipy import arccos, pi
# use dot product to calculate the angles
if acute:
return [arccos(abs(np.dot(array_list[0][i], array_list[1][i])) / (norm(array_list[0][i]) \
* norm(array_list[1][i]))) / pi * 180 for i in range(len(array_list[0]))]
else:
return [arccos(np.dot(array_list[0][i], array_list[1][i]) / (norm(array_list[0][i]) \
* norm(array_list[1][i]))) / pi * 180 for i in range(len(array_list[0]))]
def W_Anom(planet,t=0,t0=0):
"""Wahre Anomalie"""
E = ex_Anom(planet,t,t0)
eps = Planet[planet]["numEx"] #Zur besseren Lesbarkeit
if (E >= 0 and E <= sc.pi):
return sc.arccos((sc.cos(E) - eps) / (1 - eps*sc.cos(E)))
elif (E >= sc.pi and E <= 2*sc.pi):
return 2*sc.pi - sc.arccos((sc.cos(E) - eps) / (1 - eps*sc.cos(E)))
else:
return 0 #Außerhalb der Intervalle nicht definiert
def _xsys2ang(xs, ys):
if sc.fabs(ys) <= sc.pi / 4.:
return [sc.arccos(8. / 3. / sc.pi * ys), xs]
else:
xt = (xs % (sc.pi / 2.))
fabsys = sc.fabs(ys)
theta = sc.arccos(
(1. - 1. / 3. * (2. - 4. * fabsys / sc.pi)**2.) * ys / fabsys)
if fabsys == sc.pi / 2.:
phi = xs - fabsys + sc.pi / 4.
else:
phi = xs - (fabsys - sc.pi / 4.) / (fabsys - sc.pi / 2.) * (
xt - sc.pi / 4.)
return [theta % (sc.pi + 0.0000000001), phi % (2. * sc.pi)] #Hack
def XyzToPol(coordxy):
""" converti les coordonnees carthesiennes d'un point (x,y,z) en coordonnees polaires (r,azi,incli)"""
x, y, z = coordxy[0], coordxy[1], coordxy[2]
r = scipy.sqrt(x * x + y * y + z * z)
if r == 0:
incli = 0
else:
incli = scipy.arcsin(z / r)
if (x == 0 and y == 0):
azi = 0
elif (y >= 0):
azi = scipy.arccos(x / scipy.sqrt(x * x + y * y))
else:
azi = -scipy.arccos(x / scipy.sqrt(x * x + y * y))
return scipy.array([r, azi, incli])
def rext_calc(df, lat=float):
"""
Function to calculate extraterrestrial radiation output in J/m2/day
Ref:http://www.fao.org/docrep/x0490e/x0490e07.htm
:param df: dataframe with datetime index
:param lat: latitude (negative for Southern hemisphere)
:return: Rext (J/m2)
"""
# set solar constant [MJ m^-2 min^-1]
s = 0.08166
#convert latitude [degrees] to radians
latrad = lat*math.pi / 180.0
#have to add in function for calculating single value here
# extract date, month, year from index
date = pd.DatetimeIndex(df.index).day
month = pd.DatetimeIndex(df.index).month
year = pd.DatetimeIndex(df.index).year
doy = met.date2doy(dd=date, mm=month, yyyy=year) # create day of year(1-366) acc to date
l = sp.size(doy)
if l < 2:
dt = 0.409 * math.sin(2 * math.pi / 365 * doy - 1.39)
ws = sp.arccos(-math.tan(latrad) * math.tan(dt))
j = 2 * math.pi / 365.25 * doy
dr = 1.0 + 0.03344 * math.cos(j - 0.048869)
rext = s * 1440 / math.pi * dr * (ws * math.sin(latrad) * math.sin(dt) + math.sin(ws) * math.cos(latrad) * math.cos(dt))
#Create dummy output arrays sp refers to scipy
else:
rext = sp.zeros(l)
dt = sp.zeros(l)
ws = sp.zeros(l)
j = sp.zeros(l)
dr = sp.zeros(l)
#calculate Rext
for i in range(0, l):
#Calculate solar decimation dt(d in FAO) [rad]
dt[i] = 0.409 * math.sin(2 * math.pi / 365 * doy[i] - 1.39)
#calculate sunset hour angle [rad]
ws[i] = sp.arccos(-math.tan(latrad) * math.tan(dt[i]))
# calculate day angle j [radians]
j[i] = 2 * math.pi / 365.25 * doy[i]
# calculate relative distance to sun
dr[i] = 1.0 + 0.03344 * math.cos(j[i] - 0.048869)
#calculate Rext dt = d(FAO) and latrad = j(FAO)
rext[i] = (s * 1440.0 / math.pi) * dr[i] * (ws[i] * math.sin(latrad) * math.sin(dt[i]) + math.sin(ws[i])* math.cos(latrad) * math.cos(dt[i]))
rext = sp.array(rext) * 1000000
return rext
def overlap(x, xdown, y, ydown, z, zdown, r, rdown, alpha):
overlap_fraction = np.zeros(np.size(x))
for i in range(0, np.size(x)):
#define dx as the upstream x coordinate - the downstream x coordinate then rotate according to wind direction
dx = xdown - x[i]
#define dy as the upstream y coordinate - the downstream y coordinate then rotate according to wind direction
dy = abs(ydown - y[i])
dz = abs(zdown - z[i])
d = sp.sqrt(dy**2.+dz**2.)
R = r[i]+dx*alpha #The radius of the wake depending how far it is from the turbine
A = rdown**2*pi #The area of the turbine
if dx > 0:
#if d <= R-rdown:
# overlap_fraction[i] = 1 #if the turbine is completely in the wake, overlap is 1, or 100%
if d == 0:
print "Area of turbine: ", A
print "Area of wake: ", pi*R**2
if A <= pi*R**2:
overlap_fraction[i] = 1.
else:
overlap_fraction[i] = pi*R**2/A
elif d >= R+rdown:
overlap_fraction[i] = 0 #if none of it touches the wake, the overlap is 0
else:
#if part is in and part is out of the wake, the overlap fraction is defied by the overlap area/rotor area
overlap_area = rdown**2.*sp.arccos((d**2.+rdown**2.-R**2.)/(2.0*d*rdown))+R**2.*sp.arccos((d**2.+R**2.-rdown**2.)/(2.0*d*R))-0.5*sp.sqrt((-d+rdown+R)*(d+rdown-R)*(d-rdown+R)*(d+rdown+R))
overlap_fraction[i] = overlap_area/A
else:
overlap_fraction[i] = 0 #turbines cannot be affected by any wakes that start downstream from them
# print overlap_fraction
print overlap_fraction
return overlap_fraction #retrun the n x n matrix of how each turbine is affected by all of the others
def __new__(self, nbinaries=1e6):
arr = sp.ones(nbinaries, dtype=[(name, 'f8') for name in ['period', 'mass_ratio', 'eccentricity', 'phase', 'theta', 'inclination']])
arr['eccentricity'] = 0.
arr['phase'] = sp.rand(nbinaries)
arr['theta'] = sp.rand(nbinaries) * 2 * sp.pi
arr['inclination'] = sp.arccos(sp.rand(nbinaries) * 2. - 1.)
return arr.view(OrbitalParameters)
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 vec2polar(vec):
'''transform a vector to polar axis.'''
r = np.linalg.norm(vec, axis=-1, keepdims=True)
theta = np.arccos(vec[..., 2:3] / r)
phi = np.arctan2(vec[..., 1:2], vec[..., :1])
res = np.concatenate([r, theta, phi], axis=-1)
return res
def resolve_tri(A,B,a,b,up=True):
AB=A-B
c=l.norm(AB)
aa=s.arctan2(AB[1],AB[0])
bb=s.arccos((b**2+c**2-a**2)/(2*b*c))
if up: return B+b*s.array((s.cos(aa+bb),s.sin(aa+bb)))
else: return B+b*s.array((s.cos(aa-bb),s.sin(aa-bb)))
def create_map(parameters,thetas,phis,vrs):
pix = hp.ang2pix(parameters.nside,thetas,phis)
number_of_pixels = hp.nside2npix(parameters.nside)
vrmap = sp.ones(number_of_pixels)*parameters.badval
# pdb.set_trace()
vrs = sp.array(vrs)
vrs_mean_of_repeated_pixels = copy.copy(vrs)
for p in set(pix):
vrs_mean_of_repeated_pixels[pix == p] = sp.mean(vrs[pix == p])
vrmap[pix] = vrs_mean_of_repeated_pixels
# pdb.set_trace()
theta_max = sp.arccos(1-2*parameters.skyfraction)
pix_all = sp.array(range(number_of_pixels))
pix_unseen = pix_all[hp.pix2ang(parameters.nside,pix_all)[0]>theta_max]
vrmap[pix_unseen] = parameters.unseen
empty_pixels = number_of_pixels-len(set(pix))
print "The number of empty pixels inside the survey is", empty_pixels
print "The corresponds to a fraction of", empty_pixels/number_of_pixels
print "The number of pixels outside survey is", len(pix_unseen)
# pdb.set_trace()
return vrmap
def get_bl(self, ra=None, dec=None):
"""
http://scienceworld.wolfram.com/astronomy/GalacticCoordinates.html
"""
if ra == None: ra = self.get_column("RA")
if dec == None: dec = self.get_column("DEC")
if type(ra) == float:
ral = scipy.zeros(1)
ral[0] = ra
ra = ral
if type(dec) == float:
decl = scipy.zeros(1)
decl[0] = dec
dec = decl
c62 = math.cos(62.6 * D2R)
s62 = math.sin(62.6 * D2R)
b = scipy.sin(dec * D2R) * c62
b -= scipy.cos(dec * D2R) * scipy.sin((ra - 282.25) * D2R) * s62
b = scipy.arcsin(b) * R2D
cosb = scipy.cos(b * D2R)
#l minus 33 degrees
lm33 = (scipy.cos(dec * D2R) / cosb) * scipy.cos((ra - 282.25))
l = scipy.arccos(lm33) * R2D + 33.0
return b, l
def besselFourierKernel(m, zero, rho):
""" Function kernel for the bessel Fourier method inversion
Uses the mathematical formulation laid out in L. Wang and R. Granetz,
Review of Scientific Instruments, 62, p.842, 1991. This generates
the necessary weighting for a given chord in bessel/fourier space
for a given tangency radius rho. There should be little reason to
use this function unless modified and generating a new inversion
scheme utilizing this kernel.
Args:
m: geometry Object with reference origin
zero: geometry Object with reference origin
rho: normalized tangency radius
Returns:
numpy array: Vector points from pt1 to pt2.
"""
# I SHOULD TRY AND VECTORIZE THIS AS MUCH AS POSSIBLE
jprime = (scipy.special.jn(m + 1, zero) - scipy.special.jn(m - 1, zero))
return jprime * scipy.integrate.quad(
_beam.bessel_fourier_kernel, 0, scipy.arccos(rho),
args=(m, zero, rho))[0]
def correct_z_orientation(accelerations, angular_velocities, stationary_times):
""" Use gravity vector direction to align reference frame to correct z-axis
Assumes the car is stationary for the first 10000 times and the gravity haven't been removed
:param accelerations: 3xn numpy array angular velocities
:param angular_velocities: 3xn numpy array angular velocities
:param stationary_times: list of tuples (start,end)
:return: numpy arrays: rotated accelerations, rotated angular velocities
"""
# get value of g in all stationary times
g = np.mean(np.concatenate([
accelerations[:, stationary_time[0]:stationary_time[1]]
for stationary_time in stationary_times
],
axis=1),
axis=1)
def align_from_g_vector(accelerations, angular_velocities, g):
g_norm = norm(g)
u = cross(g, (0, 0, 1))
# rotation axis
u_unit = u / norm(u)
# rotate angle
theta = arccos(dot(g, (0, 0, 1)) / g_norm)
print("rotating vectors of " + str(np.rad2deg(theta)) +
" degrees align to z")
rotator = np.exp(quaternion(*(theta * u_unit)) / 2)
rotated_accelerations = np.array([
(rotator * quaternion(*acceleration_vector) *
~rotator).components[1:]
for acceleration_vector in accelerations.T
])
rotated_angular_velocities = np.array([
(rotator * quaternion(*angular_velocity) * ~rotator).components[1:]
for angular_velocity in angular_velocities.T
])
return rotated_accelerations.T, rotated_angular_velocities.T
accelerations, angular_velocities = align_from_g_vector(
accelerations, angular_velocities, g)
# for the remaining stationary times
for stationary_time in stationary_times[1:]:
# calculate bad align angle
g = accelerations[:,
stationary_time[0]:stationary_time[1]].mean(axis=1)
bad_align_angle = arccos(dot(g, (0, 0, 1)) / norm(g))
# if the bad align angle is greater than 2 degrees
if bad_align_angle > np.deg2rad(10):
# print a warning
import warnings
message = " \n Found additional bad z axis of {} degrees alignment at time {} , " \
"realigning from now \n".format(np.rad2deg(bad_align_angle), stationary_time[0])
warnings.warn(message)
# re-align
accelerations, angular_velocities = align_from_g_vector(
accelerations, angular_velocities, g)
return accelerations, angular_velocities
def add_geometric_edge_properties(G):
""" Adds angle to cortical surface (in degrees), cortical depth, volume and
cross section to each edge in the graph.
INPUT: G: Vascular graph in iGraph format.
OUTPUT: None - the vascular graph G is modified in place.
"""
depth = []
angle = []
crossSection = []
volume = []
ez = array([0,0,1])
for edge in G.es:
a = G.vs[edge.source]['r']
b = G.vs[edge.target]['r']
v = a-b
depth.append((a[2]+b[2])/2.0)
theta=arccos(dot(v,ez)/norm(v))/2/pi*360
if theta > 90:
theta = 180-theta
angle.append(theta)
crossSection.append(np.pi * edge['diameter']**2. / 4.)
volume.append(crossSection[-1] * edge['length'])
G.es['depth'] = depth
G.es['angle'] = angle
G.es['volume'] = volume
G.es['crossSection'] = crossSection
def Rz_to_uv(R,z,delta=1.):
"""
NAME:
Rz_to_uv
PURPOSE:
calculate prolate confocal u and v coordinates from R,z, and delta
INPUT:
R - radius
z - height
delta= focus
OUTPUT:
(u,v)
HISTORY:
2012-11-27 - Written - Bovy (IAS)
"""
coshu, cosv= Rz_to_coshucosv(R,z,delta)
u= sc.arccosh(coshu)
v= sc.arccos(cosv)
return (u,v)
def pd_output(p, m, alpha):
N = 500
PD = np.zeros(N)
if p == 1:
theta_end = def2sca(theta_def(90, p, m), p)
theta = np.linspace(1e-10, theta_end - 1e-10, N)
theta_range = theta_end
else:
ths_end = arccos(np.sqrt((m**2 - 1) / (p**2 - 1))) * 180 / pi # 彩虹角位置
theta_end = def2sca(theta_def(ths_end, p, m), p)
theta = np.linspace(theta_end + 1e-10, 180, N)
theta_range = 180 - theta_end
print(theta_end)
dtheta = theta_range / (N - 1)
i = 0
for ths in theta:
# result = find_theta_i(ths, p, m, error=1e-12)
pd = phase_diff(ths, alpha, 0, p, m)
PD[i] = pd
i += 1
# 输出: 散射角,相位差, 散射角, 相位差频率
return (theta, PD, theta[1:], abs(diff(PD)) / (2 * pi * dtheta))
def dotties():
data = fi.read_data("../sgrnorth_paper/planefit_results.txt", ",")
planes = []
for i in range(6):
planes.append(data[i, :])
#for plane in planes: print plane
dots = sc.zeros((6, 6))
for i in range(6):
for j in range(6):
dots[i, j] = (sc.arccos(planes[i][0] * planes[j][0] +
planes[i][1] * planes[j][1] +
planes[i][2] * planes[j][2])) * deg
print dots
print
errors = sc.zeros((6, 6))
for i in range(6):
for j in range(6):
dotty = planes[i][0] * planes[j][0] + planes[i][1] * planes[j][
1] + planes[i][2] * planes[j][2]
factor = -1.0 / sc.sqrt(1 - dotty * dotty)
print factor
errors[i, j] = factor * (
planes[j][0] * planes[i][4] + planes[i][0] * planes[j][4] +
planes[j][1] * planes[i][5] + planes[i][1] * planes[j][5] +
planes[j][2] * planes[i][6] +
planes[i][2] * planes[j][6]) * deg
#if i==3 and j==5: print dotty
print errors
Sgr = [14.655645800014774, 2.2704309216189817, -5.8873772610912614]
print "# - dist to Sgr Core:"
for plane in planes:
print (Sgr[0]*plane[0] + Sgr[1]*plane[1] + Sgr[2]*plane[2] + plane[3]), \
(Sgr[0]*plane[4] + Sgr[1]*plane[5] + Sgr[2]*plane[6] + plane[7])
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 besselFourierKernel(m, zero, rho):
""" Function kernel for the bessel Fourier method inversion
Uses the mathematical formulation laid out in L. Wang and R. Granetz,
Review of Scientific Instruments, 62, p.842, 1991. This generates
the necessary weighting for a given chord in bessel/fourier space
for a given tangency radius rho. There should be little reason to
use this function unless modified and generating a new inversion
scheme utilizing this kernel.
Args:
m: geometry Object with reference origin
zero: geometry Object with reference origin
rho: normalized tangency radius
Returns:
numpy array: Vector points from pt1 to pt2.
"""
# I SHOULD TRY AND VECTORIZE THIS AS MUCH AS POSSIBLE
jprime = (scipy.special.jn(m+1, zero) - scipy.special.jn(m-1, zero))
return jprime*scipy.integrate.quad(_beam.bessel_fourier_kernel,
0,
scipy.arccos(rho),
args = (m, zero, rho))[0]
def elaz2radec_lst(el, az, lst, lat = 38.43312) :
"""DO NOT USE THIS ROUTINE FOR ANTHING THAT NEEDS TO BE RIGHT. IT DOES NOT
CORRECT FOR PRECESSION.
Calculates the Ra and Dec from elavation, aximuth, LST and Latitude.
This function is vectorized with numpy so should be fast. Standart numpy
broadcasting should also work.
All angles in degrees, lst in seconds. Latitude defaults to GBT.
"""
# Convert everything to radians.
el = sp.radians(el)
az = sp.radians(az)
lst = sp.array(lst, dtype = float)*2*sp.pi/86400
lat = sp.radians(lat)
# Calculate dec.
dec = sp.arcsin(sp.sin(el)*sp.sin(lat) +
sp.cos(el)*sp.cos(lat)*sp.cos(az))
# Calculate the hour angle
ha = sp.arccos((sp.sin(el) - sp.sin(lat)*sp.sin(dec)) /
(sp.cos(lat)*sp.cos(dec)))
ra = sp.degrees(lst - ha) % 360
return ra, sp.degrees(dec)
def get_bl(self,ra=None,dec=None):
"""
http://scienceworld.wolfram.com/astronomy/GalacticCoordinates.html
"""
if ra==None: ra=self.get_column("RA")
if dec==None: dec=self.get_column("DEC")
if type(ra)==float:
ral=scipy.zeros(1)
ral[0]=ra
ra=ral
if type(dec)==float:
decl=scipy.zeros(1)
decl[0]=dec
dec=decl
c62=math.cos(62.6*D2R)
s62=math.sin(62.6*D2R)
b=scipy.sin(dec*D2R)*c62
b-=scipy.cos(dec*D2R)*scipy.sin((ra-282.25)*D2R)*s62
b=scipy.arcsin(b)*R2D
cosb=scipy.cos(b*D2R)
#l minus 33 degrees
lm33=(scipy.cos(dec*D2R)/cosb)*scipy.cos((ra-282.25))
l=scipy.arccos(lm33)*R2D+33.0
return b,l
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 young(phase,
sigma_sg,
sigma_sl,
surface_tension='pore.surface_tension',
**kwargs):
r'''
Calculate contact angle using Young's equation
Notes
-----
Young's equation is: sigma_lg*Cos(theta)=sigma_sg - sigma_sl
where
sigma_lg is the liquid-gas surface tension [N/m]
sigma_sg is the solid-gas surface tension [N/m]
sigma_sl is the solid-liquid interfacial tension [J/m^2]
theta is the Young contact angle [rad]
'''
if surface_tension.split('.')[0] == 'pore':
sigma = phase[surface_tension]
sigma = phase.interpolate_data(data=sigma)
else:
sigma = phase[surface_tension]
theta = sp.arccos((sigma_sg - sigma_sl) / phase[surface_tension])
theta = sp.rad2deg(theta)
return theta
def geo_dist(P,Q):
# Make sure these are valid distributions
assert all(np.isreal(P))
assert all(P >= 0)
assert any(P > 0)
assert all(np.isreal(Q))
assert all(Q >= 0)
assert any(Q > 0)
# Enforce proper normalization
P_prob = P/sp.sum(P)
Q_prob = Q/sp.sum(Q)
# Return geodistance. arccos can behave badly if argument
# is too close to one, so prepare for this
try:
dist = 2*sp.arccos(sp.sum(sp.sqrt(P_prob*Q_prob)))
assert np.isreal(dist)
except:
if sp.sum(sp.sqrt(P_prob*Q_prob)) > 1 - TINY_FLOAT32:
dist = 0
else:
raise
return dist
def elaz2radec_lst(el, az, lst, lat=38.43312):
"""DO NOT USE THIS ROUTINE FOR ANTHING THAT NEEDS TO BE RIGHT. IT DOES NOT
CORRECT FOR PRECESSION.
Calculates the Ra and Dec from elavation, aximuth, LST and Latitude.
This function is vectorized with numpy so should be fast. Standart numpy
broadcasting should also work.
All angles in degrees, lst in seconds. Latitude defaults to GBT.
"""
# Convert everything to radians.
el = sp.radians(el)
az = sp.radians(az)
lst = sp.array(lst, dtype=float) * 2 * sp.pi / 86400
lat = sp.radians(lat)
# Calculate dec.
dec = sp.arcsin(
sp.sin(el) * sp.sin(lat) + sp.cos(el) * sp.cos(lat) * sp.cos(az))
# Calculate the hour angle
ha = sp.arccos(
(sp.sin(el) - sp.sin(lat) * sp.sin(dec)) / (sp.cos(lat) * sp.cos(dec)))
ra = sp.degrees(lst - ha) % 360
return ra, sp.degrees(dec)
def young(phase,
sigma_sg,
sigma_sl,
surface_tension='pore.surface_tension',
**kwargs):
r'''
Calculate contact angle using Young's equation
Notes
-----
Young's equation is: sigma_lg*Cos(theta)=sigma_sg - sigma_sl
where
sigma_lg is the liquid-gas surface tension [N/m]
sigma_sg is the solid-gas surface tension [N/m]
sigma_sl is the solid-liquid interfacial tension [J/m^2]
theta is the Young contact angle [rad]
'''
if surface_tension.split('.')[0] == 'pore':
sigma = phase[surface_tension]
sigma = phase.interpolate_data(data=sigma)
else:
sigma = phase[surface_tension]
theta = sp.arccos((sigma_sg - sigma_sl)/phase[surface_tension])
theta = sp.rad2deg(theta)
return theta
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 pix2sky(header,x,y): hdr_info = parse_header(header) x0 = x-hdr_info[1][0]+1. # Plus 1 python->image y0 = y-hdr_info[1][1]+1. x0 = x0.astype(scipy.float64) y0 = y0.astype(scipy.float64) x = hdr_info[2][0,0]*x0 + hdr_info[2][0,1]*y0 y = hdr_info[2][1,0]*x0 + hdr_info[2][1,1]*y0 if hdr_info[3]=="DEC": a = x.copy() x = y.copy() y = a.copy() ra0 = hdr_info[0][1] dec0 = hdr_info[0][0]/raddeg else: ra0 = hdr_info[0][0] dec0 = hdr_info[0][1]/raddeg if hdr_info[5]=="TAN": r_theta = scipy.sqrt(x*x+y*y)/raddeg theta = arctan(1./r_theta) phi = arctan2(x,-1.*y) elif hdr_info[5]=="SIN": r_theta = scipy.sqrt(x*x+y*y)/raddeg theta = arccos(r_theta) phi = artan2(x,-1.*y) ra = ra0 + raddeg*arctan2(-1.*cos(theta)*sin(phi-pi), sin(theta)*cos(dec0)-cos(theta)*sin(dec0)*cos(phi-pi)) dec = raddeg*arcsin(sin(theta)*sin(dec0)+cos(theta)*cos(dec0)*cos(phi-pi)) return ra,dec
def dotties():
data = fi.read_data("../sgrnorth_paper/planefit_results.txt", ",")
planes = []
for i in range(6):
planes.append(data[i,:])
#for plane in planes: print plane
dots = sc.zeros((6,6))
for i in range(6):
for j in range(6):
dots[i,j] = (sc.arccos(planes[i][0]*planes[j][0] + planes[i][1]*planes[j][1]
+ planes[i][2]*planes[j][2]))*deg
print dots
print
errors = sc.zeros((6,6))
for i in range(6):
for j in range(6):
dotty = planes[i][0]*planes[j][0]+planes[i][1]*planes[j][1]+planes[i][2]*planes[j][2]
factor = -1.0 / sc.sqrt(1 - dotty*dotty)
print factor
errors[i,j] = factor*(planes[j][0]*planes[i][4] + planes[i][0]*planes[j][4] +
planes[j][1]*planes[i][5] + planes[i][1]*planes[j][5] +
planes[j][2]*planes[i][6] + planes[i][2]*planes[j][6])*deg
#if i==3 and j==5: print dotty
print errors
Sgr = [14.655645800014774, 2.2704309216189817, -5.8873772610912614]
print "# - dist to Sgr Core:"
for plane in planes:
print (Sgr[0]*plane[0] + Sgr[1]*plane[1] + Sgr[2]*plane[2] + plane[3]), \
(Sgr[0]*plane[4] + Sgr[1]*plane[5] + Sgr[2]*plane[6] + plane[7])
def add_geometric_edge_properties(G):
""" Adds angle to cortical surface (in degrees), cortical depth, volume and
cross section to each edge in the graph.
INPUT: G: Vascular graph in iGraph format.
OUTPUT: None - the vascular graph G is modified in place.
"""
depth = []
angle = []
crossSection = []
volume = []
ez = array([0, 0, 1])
for edge in G.es:
a = G.vs[edge.source]['r']
b = G.vs[edge.target]['r']
v = a - b
depth.append((a[2] + b[2]) / 2.0)
theta = arccos(dot(v, ez) / norm(v)) / 2 / pi * 360
if theta > 90:
theta = 180 - theta
angle.append(theta)
crossSection.append(np.pi * edge['diameter']**2. / 4.)
volume.append(crossSection[-1] * edge['length'])
G.es['depth'] = depth
G.es['angle'] = angle
G.es['volume'] = volume
G.es['crossSection'] = crossSection
def pix2sky(header,x,y): hdr_info = parse_header(header) x0 = x-hdr_info[1][0]+1. # Plus 1 python->image y0 = y-hdr_info[1][1]+1. x0 = x0.astype(scipy.float64) y0 = y0.astype(scipy.float64) x = hdr_info[2][0,0]*x0 + hdr_info[2][0,1]*y0 y = hdr_info[2][1,0]*x0 + hdr_info[2][1,1]*y0 if hdr_info[3]=="DEC": a = x.copy() x = y.copy() y = a.copy() ra0 = hdr_info[0][1] dec0 = hdr_info[0][0]/raddeg else: ra0 = hdr_info[0][0] dec0 = hdr_info[0][1]/raddeg if hdr_info[5]=="TAN": r_theta = scipy.sqrt(x*x+y*y)/raddeg theta = arctan(1./r_theta) phi = arctan2(x,-1.*y) elif hdr_info[5]=="SIN": r_theta = scipy.sqrt(x*x+y*y)/raddeg theta = arccos(r_theta) phi = artan2(x,-1.*y) ra = ra0 + raddeg*arctan2(-1.*cos(theta)*sin(phi-pi), sin(theta)*cos(dec0)-cos(theta)*sin(dec0)*cos(phi-pi)) dec = raddeg*arcsin(sin(theta)*sin(dec0)+cos(theta)*cos(dec0)*cos(phi-pi)) return ra,dec
def compute_amono_analytic(x1, x2, leaf_temperature, vpd, gammax, rd, psi, model='misson',
g0=0.019, rbt=2. / 3., ca=400., m0=5.278, psi0=-0.1,
d0_leuning=30., steepness_tuzet=1.85):
"""Computes the gross CO2 assimilation rate analytically.
Args:
x1 (float): Vcmax or J/4.
x2 (float): KmC(1+O/KmO) or 2 gamma_asterisk
leaf_temperature (float): [°C] leaf temperature
vpd (float): [kPa] leaf-to-air vapor pressure deficit
gammax (float): [ubar] CO2 compensation point
rd (float): [umol m-2 s-1] mitochondrial respiration rate in the light
psi (float): [MPa] bulk water potential of the leaf
model (str): stomatal conductance reduction model, one of 'misson','tuzet', 'linear' or 'vpd'
g0 (float): [umol m-2 s-1] residual stomatal conductance to CO2
rbt (float): [m2 s ubar umol-1] combined turbulance and boundary layer resistance to CO2
ca (float): [ubar] CO2 partial pressure of the air
m0 (float): [umol mmol-1] maximum slope An/gs (absence of water deficit), see :func:`fvpd_3`
psi0 (float): [MPa] critical thershold for water potential, see :func:`fvpd_3`
d0_leuning (float): [kPa-1] shape parameter, see :func:`fvpd_3`
steepness_tuzet (float): [MPa-1] shape parameter, see :func:`fvpd_3`
Returns:
(float): [umolCO2 m-2 s-1] gross CO2 assimilation rate
References:
Evers et al. 2010.
Simulation of wheat growth and development based on organ-level photosynthesis and assimilate allocation.
Journal of Experimental Botany 61, 2203 – 2216.
Notes:
A is called 'mono' since it is calculated for either of RuBisco- or e transport-limited phases.
The net A (An) is calculated using the function 'compute_an_analytic'
"""
f_vpd = fvpd_3(model, vpd, psi, psi_crit=psi0, m0=m0, steepness_tuzet=steepness_tuzet, d0_leuning=d0_leuning)
cube_a = g0 * (x2 + gammax) + (g0 / mesophyll_conductance(leaf_temperature) + f_vpd) * (x1 - rd)
cube_b = utils.cmol2cpa(leaf_temperature, ca) * (x1 - rd) - gammax * x1 - rd * x2
cube_c = utils.cmol2cpa(leaf_temperature, ca) + x2 + (1. / mesophyll_conductance(leaf_temperature) + rbt) * (
x1 - rd)
cube_d = x2 + gammax + (x1 - rd) / mesophyll_conductance(leaf_temperature)
cube_e = 1. / mesophyll_conductance(leaf_temperature) + (g0 / mesophyll_conductance(leaf_temperature) + f_vpd) * (
1. / mesophyll_conductance(leaf_temperature) + rbt)
cube_p = -(cube_d + (x1 - rd) / mesophyll_conductance(leaf_temperature) + cube_a * (
1. / mesophyll_conductance(leaf_temperature) + rbt) + (
g0 / mesophyll_conductance(leaf_temperature) + f_vpd) * cube_c) / cube_e
cube_q = (cube_d * (x1 - rd) + cube_a * cube_c + (
g0 / mesophyll_conductance(leaf_temperature) + f_vpd) * cube_b) / cube_e
cube_r = -(cube_a * cube_b / cube_e)
cube_Q = (cube_p ** 2. - 3. * cube_q) / 9.
cube_R = (2. * cube_p ** 3. - 9. * cube_p * cube_q + 27. * cube_r) / 54.
cube_xi = arccos(max(-1., min(1., cube_R / sqrt(cube_Q ** 3.))))
a_mono = -2. * sqrt(cube_Q) * cos(cube_xi / 3.) - cube_p / 3.
return a_mono
def __xor__(self, data):
try:
x = sp.array([d.xcart for d in data])
y = sp.array([d.ycart for d in data])
z = sp.array([d.zcart for d in data])
ra = sp.array([d.ra for d in data])
dec = sp.array([d.dec for d in data])
cos = x * self.xcart + y * self.ycart + z * self.zcart
w = cos >= 1.
if w.sum() > 1:
print('WARNING: {} pairs have cosinus>=1.'.format(w.sum()))
cos[w] = 1.
w = cos <= -1.
if w.sum() > 1:
print('WARNING: {} pairs have cosinus<=-1.'.format(w.sum()))
cos[w] = -1.
angl = sp.arccos(cos)
w = (sp.absolute(ra - self.ra) < constants.small_angle_cut_off) & (
sp.absolute(dec - self.dec) < constants.small_angle_cut_off)
if w.sum() != 0:
angl[w] = sp.sqrt((dec[w] - self.dec)**2 +
(self.cosdec * (ra[w] - self.ra))**2)
except:
x = data.xcart
y = data.ycart
z = data.zcart
ra = data.ra
dec = data.dec
cos = x * self.xcart + y * self.ycart + z * self.zcart
if cos >= 1.:
print('WARNING: 1 pair has cosinus>=1.')
cos = 1.
elif cos <= -1.:
print('WARNING: 1 pair has cosinus<=-1.')
cos = -1.
angl = sp.arccos(cos)
if (sp.absolute(ra - self.ra) < constants.small_angle_cut_off) & (
sp.absolute(dec - self.dec) <
constants.small_angle_cut_off):
angl = sp.sqrt((dec - self.dec)**2 + (self.cosdec *
(ra - self.ra))**2)
return angl
def aoa_diff_rad(aoa_a, aoa_b):
"""
Calculate the difference between two angles of arrival, in radians.
"""
# Prepend radius 1 and convert to cartesian.
cart_a = sph2cart((1, ) + tuple(aoa_a))
cart_b = sph2cart((1, ) + tuple(aoa_b))
# Property of dot product between vectors.
return sp.arccos(sp.dot(cart_a, cart_b))
def random_3D_direction():
phi = sp.random.uniform(0, sp.pi * 2)
cos_theta = sp.random.uniform(-1, 1)
theta = sp.arccos(cos_theta)
sin_theta = sp.sin(theta)
x = sin_theta * sp.cos(phi)
y = sin_theta * sp.sin(phi)
z = cos_theta
return sp.array([x, y, z])
def aoa_diff_rad(aoa_a,aoa_b):
"""
Calculate the difference between two angles of arrival, in radians.
"""
# Prepend radius 1 and convert to cartesian.
cart_a = sph2cart( (1,) + tuple(aoa_a) )
cart_b = sph2cart( (1,) + tuple(aoa_b) )
# Property of dot product between vectors.
return sp.arccos(sp.dot(cart_a,cart_b))
def guess_initial_parameters(mean, phi0, omega):
mean_max = mean.max()
mean_min = mean.min()
offset = (mean_max + mean_min)/2.
amplitude = (mean_max - mean_min)/2.
if phi0 is None or omega is None:
y = mean-offset
y2 = savgol_filter(y, 11, 1, mode="nearest")
if phi0 is None:
cos_phi0 = clip(y2[0]/amplitude, -1., 1.)
if y2[1] > y2[0]:
phi0 = -arccos(cos_phi0)
else:
phi0 = arccos(cos_phi0)
if omega is None:
zero_crossings = scipy.where(scipy.diff(scipy.sign(y2)))[0]
omega = pi/scipy.average(scipy.diff(zero_crossings))
return offset, amplitude, phi0, omega
def errorAngle(actual,estimate):
error = actual*estimate.conjugate()
error.normalise()
# Cast error to float so that we can deal with fixed point numbers
# throws an error otherwise
eAngle = degrees(2*arccos(float(error.w)))
eAngle = abs(eAngle - 360) if eAngle > 180 else eAngle
return eAngle
def asAxisAndAngle(self):
axis = array([nan,nan,nan])
angle = 2*arccos(self.w)
if angle != 0:
s = sqrt(1 - self.w**2)
axis[0] = self.x / s
axis[1] = self.y / s
axis[2] = self.z / s
return axis, angle
def tosph(self):
x, y, z = self.coord
rho = self.norm()
if sp.absolute(x) < 1e-8:
if y >= 0:
theta = sp.pi/2
else:
theta = 3*sp.pi/2
else:
theta = sp.arctan(y/x)
phi = sp.arccos(z/rho)
return rho, theta, phi
def As_The_Cockey_Flies(lat0, lon0, lat, lon):
# Algorithm from GA website
# Uses spherical geometry (rather than a projection)
# to calculate epicentral distance
lat = lat[:, newaxis]
lon = lon[:, newaxis]
L1 = lat0 * (pi / 180)
L2 = lat * (pi / 180)
DG = (lon - lon0) * (pi / 180)
D = 1.852 * 60 * 180 / pi * \
arccos(sin(L1) * sin(L2) + cos(L1) * cos(L2) * cos(DG))
return D
def cosine_distance(x, y):
assert (x.dtype == np.float64 and y.dtype == np.float64) or (
x.dtype == np.float32 and y.dtype == np.float32)
x2 = np.sqrt(np.sum(x ** 2, axis=1))
y2 = np.sqrt(np.sum(y ** 2, axis=1))
ix = x2 == 0.
iy = y2 == 0.
d = np.dot(x, y.T) / (np.outer(x2, y2))
# DPX: to prevent the stupid scipy to collapse the array into scalar
if d.shape == (1, 1):
d = np.array([[np.float64(scipy.arccos(d[0,0]) / np.pi)]])
else:
# costly in time (half of the time), so check if really useful for dtw
d = np.float64(scipy.arccos(d) / np.pi)
d[ix, :] = 1.
d[:, iy] = 1.
for i in np.where(ix)[0]:
d[i, iy] = 0.
assert np.all(d >= 0)
return d
def set_orientation(self, value):
if isinstance(value, (list, N.ndarray)):
value = N.asarray(value)
# find quaternion for rotation
n = N.cross([0, 0, 1], unit_vector(value[:3]))
phi = N.arccos(N.dot([0, 0, 1], unit_vector(value[:3])))
self._orientation = quaternion_about_axis(phi, n)
elif value is True:
# random orientation
self._orientation = random_quaternion()
else:
# other stuff goes no orientation
self._orientation = False
def angle(Vec1, Vec2):
"""Returns angle between two vectors.
Args:
Vec1: Vec Object
Vec2: Vec Object
Returns:
angle in radians :math:`[0,\pi]` seperating the two vectors
on the plane defined by the two.
"""
return scipy.arccos((Vec1 * Vec2)/(Vec1.s * Vec2.s))
def matrixToEuler(m,order='Aerospace',inDegrees=True):
if order == 'Aerospace' or order == 'ZYX':
sp = -m[2,0]
if sp < (1-EPS):
if sp > (-1+EPS):
p = arcsin(sp)
r = arctan2(m[2,1],m[2,2])
y = arctan2(m[1,0],m[0,0])
else:
p = -pi/2.
r = 0
y = pi-arctan2(-m[0,1],m[0,2])
else:
p = pi/2.
y = arctan2(-m[0,1],m[0,2])
r = 0
if inDegrees:
return degrees((y,p,r))
else:
return (y,p,r)
elif order == 'BVH' or order == 'ZXY':
sx = m[2,1]
if sx < (1-EPS):
if sx > (-1+EPS):
x = arcsin(sx)
z = arctan2(-m[0,1],m[1,1])
y = arctan2(-m[2,0],m[2,2])
else:
x = -pi/2
y = 0
z = -arctan2(m[0,2],m[0,0])
else:
x = pi/2
y = 0
z = arctan2(m[0,2],m[0,0])
if inDegrees:
return degrees((z,x,y))
else:
return (z,x,y)
elif order == "ZXZ":
x = arccos(m[2,2])
z2 = arctan2(m[2,0],m[2,1])
z1 = arctan2(m[0,2],-m[1,2])
if inDegrees:
return degrees((z1,x,z2))
else:
return (z1,x,z2)
def __contact_greensfunction(self, ind_contact, node_i, node_j, E):
"""calculates the value of the transverse mode's square at the point
of the contact_node, essentially sin(pi)sin(pi) multiplied with
appropriate amplitude and constats """
from scipy import arccos, exp, sin, pi, sqrt
length = (ind_contact.shape[1]-1.0)
amplitude = 1/sqrt(length)
mode_energy = self.p.hbar**2 * pi**2/(2*self.mass * (length*self.a)**2)
#Amplitude = 1
ka = arccos(1-(E+mode_energy-self.band_bottom)/(2*self.t0))
Phase = exp(1j*ka)
xi_i = amplitude * sin(pi * node_i/length)
xi_j = amplitude * sin(pi * node_j/length)
greensfunction =xi_j * xi_i * Phase
return greensfunction
def SelectParticlesInCone(self,x,y,z,OpeningAngle):
Theta = scipy.arccos((self.x*x+self.y*y+self.z*z) / scipy.sqrt(x**2+y**2+z**2) / scipy.sqrt(self.x**2+self.y**2+self.z**2))
GoodIds = scipy.where(Theta<OpeningAngle)
self.x = self.x[GoodIds]
self.y = self.y[GoodIds]
self.z = self.z[GoodIds]
self.vx = self.vx[GoodIds]
self.vy = self.vy[GoodIds]
self.vz = self.vz[GoodIds]
self.m = self.m[GoodIds]
self.ID = self.ID[GoodIds]
if self.V != None:
self.V = self.V[GoodIds]
self.NPartTotal = len(self.x)
self.NPart = [0,len(self.x),0,0,0,0]
def cs_dist(
trains, smoothing_filter, sampling_rate,
filter_area_fraction=sigproc.default_kernel_area_fraction):
""" Calculates the Cauchy-Schwarz distance between two spike trains given
a smoothing filter.
Let :math:`v_a(t)` and :math:`v_b(t)` with :math:`t \\in \\mathcal{T}` be
the spike trains convolved with some smoothing filter and :math:`V(a, b)
= \\int_{\\mathcal{T}} v_a(t) v_b(t) dt`. Then, the Cauchy-Schwarz distance
of the spike trains is defined as :math:`d_{CS}(a, b) = \\arccos \\frac{V(a,
b)^2}{V(a, a) V(b, b)}`.
The Cauchy-Schwarz distance is closely related to the Schreiber et al.
similarity measure :math:`S_S` by :math:`d_{CS} = \\arccos S_S^2`
This function numerically convolves the spike trains with the smoothing
filter which can be quite slow and inaccurate. If the analytical result of
the autocorrelation of the smoothing filter is known, one can use
:func:`schreiber_similarity` for a more efficient and precise calculation.
Further information can be found in *Paiva, A. R. C., Park, I., & Principe,
J. (2010). Inner products for representation and learning in the spike
train domain. Statistical Signal Processing for Neuroscience and
Neurotechnology, Academic Press, New York.*
:param sequence trains: Sequence of :class:`neo.core.SpikeTrain` objects of
which the distance will be calculated pairwise.
:param smoothing_filter: Smoothing filter to be convolved with the spike
trains.
:type smoothing_filter: :class:`.signal_processing.Kernel`
:param sampling_rate: The sampling rate which will be used to bin
the spike trains as inverse time scalar.
:type sampling_rate: Quantity scalar
:param float filter_area_fraction: A value between 0 and 1 which controls
the interval over which the smoothing filter will be discretized. At
least the given fraction of the complete smoothing filter area will be
covered. Higher values can lead to more accurate results (besides the
sampling rate).
:returns: Matrix containing the Cauchy-Schwarz distance of all pairs of
spike trains
:rtype: 2-D array
"""
inner = st_inner(
trains, trains, smoothing_filter, sampling_rate, filter_area_fraction)
return sp.arccos(
inner ** 2 / sp.diag(inner) / sp.atleast_2d(sp.diag(inner)).T)