def plotFitOfHighOrderData():
plt.title("Polynomial function regression")
plt.grid()
size = 5000
testSize = 100
order = 10
covs = [0.03,3.41,-1.8,3,0.091,5,30.0,3.12,-0.02,1.2]
# Generate input features from uniform distribution
np.random.seed(20) # Set the seed so we can get reproducible results
x_poly = np.array(sorted(uniform(-1, 1, testSize)))
# Evaluate the real function for training example inputs
y_poly = real_function(4, 6, 0, x_poly,covs)
x_samples = np.array(sorted(uniform(-1, 1, size)))
sigma = 20
y_samples = real_function(4, 6, sigma, x_samples,covs )
plt.plot(x_poly, y_poly, c='black', label='poly data')
plt.scatter(x_samples, y_samples, s=1, c='green', label='sample')
train(x_samples, y_samples, x_poly, y_poly, order-1, plotFlag= True)
plt.legend()
plt.show()
def testProperties01(self):
"""The magnitudue of the product is the product of the magnitudes"""
q1 = Quaternion(tuple(RA.uniform(self.min,
self.max, (1, 4)).tolist()[0]))
q2 = Quaternion(tuple(RA.uniform(self.min,
self.max, (1, 4)).tolist()[0]))
self.assertEqual((q1*q2).magnitude(), q1.magnitude()*q2.magnitude())
def IMRPhenomC_param_generator(flow, tmplt_class, bank, **kwargs):
"""
Generate random parameters for the IMRPhenomC waveform model.
Specify the min and max mass of the bigger component, then the min
and max mass of the total mass. This function includes
restrictions on q and chi based on IMRPhenomC's range of
believability, namely q <=20 and |chi| <= 0.9.
@param flow: low frequency cutoff
@param tmplt_class: Template generation class for this waveform
@param bank: sbank bank object
@param kwargs: constraints on waveform parameters. See urand_tau0tau3_generator for more usage help. If no spin limits are specified, the IMRPhenomC limits will be used.
"""
# get spin limits. IMRPhenomC has special bounds on chi, so we
# will silently truncate
s1min, s1max = kwargs.pop('spin1', (-0.9, 0.9))
s2min, s2max = kwargs.pop('spin2', (s1min, s1max))
s1min, s1max = (max(-0.9, s1min), min(0.9, s1max))
s2min, s2max = (max(-0.9, s2min), min(0.9, s2max))
for mass1, mass2 in urand_tau0tau3_generator(flow, **kwargs):
q = max(mass1/mass2, mass2/mass1)
if q <= 20:
spin1 = uniform(s1min, s1max)
spin2 = uniform(s2min, s2max)
else:
raise ValueError("mass ratio out of range")
yield tmplt_class(mass1, mass2, spin1, spin2, bank=bank)
def derive_qois(df_original):
df = df_original.copy()
ns = df.shape[0]
rstar_d = normal(rstar.value, rstare.value, size=ns) * Rsun
period = df.p.values if 'p' in df.columns else df.pr.values
df['period'] = period
df['k_true'] = sqrt(df.k2_true)
df['k_app'] = sqrt(df.k2_app)
df['cnt'] = 1. - df.k2_app / df.k2_true
df['a_st'] = as_from_rhop(df.rho.values, period)
df['a_au'] = df.a_st * rstar_d.to(AU)
df['inc'] = degrees(i_from_ba(df.b.values, df.a_st.values))
df['t14'] = d_from_pkaiews(period, df.k_true.values, df.a_st.values,
radians(df.inc.values), 0.0, 0.0, 1)
df['t14_h'] = 24 * df.t14
df['r_app'] = df.k_app.values * rstar_d.to(Rjup)
df['r_true'] = df.k_true.values * rstar_d.to(Rjup)
df['r_app_point'] = df.k_app.values * rstar.to(Rjup)
df['r_true_point'] = df.k_true.values * rstar.to(Rjup)
df['r_app_rsun'] = df.k_app.values * rstar_d.to(Rsun)
df['r_true_rsun'] = df.k_true.values * rstar_d.to(Rsun)
df['teff_p'] = Teq(normal(*star_teff, size=ns), df.a_st,
uniform(0.25, 0.50, ns), uniform(0, 0.4, ns))
return df
def double_spin_precessing_param_generator(flow, tmplt_class, bank, **kwargs):
"""
Currently a stub to test precessing template generation.
"""
spin1_bounds = kwargs.pop('spin1', (0., 0.9))
spin2_bounds = kwargs.pop('spin2', (0., 0.9))
for mass1, mass2 in urand_tau0tau3_generator(flow, **kwargs):
# Choose the rest from hardcoded limits
spin1mag = uniform(*spin1_bounds)
spin2mag = uniform(*spin2_bounds)
spin1ang1 = uniform(0, numpy.pi)
spin1ang2 = uniform(0, 2*numpy.pi)
spin2ang1 = uniform(0, numpy.pi)
spin2ang2 = uniform(0, 2*numpy.pi)
spin1z = spin1mag * numpy.cos(spin1ang1)
spin1x = spin1mag * numpy.sin(spin1ang1) * numpy.cos(spin1ang2)
spin1y = spin1mag * numpy.sin(spin1ang1) * numpy.sin(spin1ang2)
spin2z = spin2mag * numpy.cos(spin2ang1)
spin2x = spin2mag * numpy.sin(spin2ang1) * numpy.cos(spin2ang2)
spin2y = spin2mag * numpy.sin(spin2ang1) * numpy.sin(spin2ang2)
# Check orientation angles use correct limits
theta = uniform(0, numpy.pi)
phi = uniform(0, 2*numpy.pi)
psi = uniform(0, 2*numpy.pi)
iota = uniform(0, numpy.pi)
yield tmplt_class(mass1, mass2, spin1x, spin1y, spin1z, spin2x, spin2y,
spin2z, theta, phi, iota, psi, bank=bank)
def aligned_spin_param_generator(flow, **kwargs):
"""
Specify the min and max mass of the bigger component, the min and
max mass of the total mass and the min and max values for the
z-axis spin angular momentum.
"""
if 'ns_bh_boundary_mass' in kwargs and 'bh_spin' in kwargs \
and 'ns_spin' in kwargs:
# get args
bh_spin_bounds = kwargs.pop('bh_spin')
ns_spin_bounds = kwargs.pop('ns_spin')
ns_bh_boundary = kwargs.pop('ns_bh_boundary_mass')
# the rest will be checked in the call to urand_tau0tau3_generator
for mass1, mass2 in urand_tau0tau3_generator(flow, **kwargs):
spin1 = uniform(*(
bh_spin_bounds if mass1 > ns_bh_boundary else ns_spin_bounds))
spin2 = uniform(*(
bh_spin_bounds if mass2 > ns_bh_boundary else ns_spin_bounds))
yield mass1, mass2, spin1, spin2
else:
# get args
spin1_bounds = kwargs.pop('spin1', (-1., 1.))
spin2_bounds = kwargs.pop('spin2', (-1., 1.))
# the rest will be checked in the call to urand_tau0tau3_generator
for mass1, mass2 in urand_tau0tau3_generator(flow, **kwargs):
spin1 = uniform(*spin1_bounds)
spin2 = uniform(*spin2_bounds)
yield mass1, mass2, spin1, spin2
def aligned_spin_param_generator(flow, **kwargs):
"""
Specify the min and max mass of the bigger component, the min and
max mass of the total mass and the min and max values for the
z-axis spin angular momentum.
"""
if 'ns_bh_boundary_mass' in kwargs and 'bh_spin' in kwargs \
and 'ns_spin' in kwargs:
# get args
bh_spin_bounds = kwargs.pop('bh_spin')
ns_spin_bounds = kwargs.pop('ns_spin')
ns_bh_boundary = kwargs.pop('ns_bh_boundary_mass')
# the rest will be checked in the call to urand_tau0tau3_generator
for mass1, mass2 in urand_tau0tau3_generator(flow, **kwargs):
spin1 = uniform(*(bh_spin_bounds if mass1 > ns_bh_boundary else ns_spin_bounds))
spin2 = uniform(*(bh_spin_bounds if mass2 > ns_bh_boundary else ns_spin_bounds))
yield mass1, mass2, spin1, spin2
else:
# get args
spin1_bounds = kwargs.pop('spin1', (-1., 1.))
spin2_bounds = kwargs.pop('spin2', (-1., 1.))
# the rest will be checked in the call to urand_tau0tau3_generator
for mass1, mass2 in urand_tau0tau3_generator(flow, **kwargs):
spin1 = uniform(*spin1_bounds)
spin2 = uniform(*spin2_bounds)
yield mass1, mass2, spin1, spin2
def IMRPhenomC_param_generator(flow, tmplt_class, bank, **kwargs):
"""
Generate random parameters for the IMRPhenomC waveform model.
Specify the min and max mass of the bigger component, then the min
and max mass of the total mass. This function includes
restrictions on q and chi based on IMRPhenomC's range of
believability, namely q <=20 and |chi| <= 0.9.
@param flow: low frequency cutoff
@param tmplt_class: Template generation class for this waveform
@param bank: sbank bank object
@param kwargs: constraints on waveform parameters. See urand_tau0tau3_generator for more usage help. If no spin limits are specified, the IMRPhenomC limits will be used.
"""
# get spin limits. IMRPhenomC has special bounds on chi, so we
# will silently truncate
s1min, s1max = kwargs.pop('spin1', (-0.9, 0.9))
s2min, s2max = kwargs.pop('spin2', (s1min, s1max))
s1min, s1max = (max(-0.9, s1min), min(0.9, s1max))
s2min, s2max = (max(-0.9, s2min), min(0.9, s2max))
for mass1, mass2 in urand_tau0tau3_generator(flow, **kwargs):
q = max(mass1 / mass2, mass2 / mass1)
if q <= 20:
spin1 = uniform(s1min, s1max)
spin2 = uniform(s2min, s2max)
else:
raise ValueError("mass ratio out of range")
yield tmplt_class(mass1, mass2, spin1, spin2, bank=bank)
def testFitOfHighOrderData():
testSize = 5000
order = 10
covsSet = [[1.1,0,0,0,0,-1.89,0,0,9.1,0],[0.03,3.41,-1.8,3,0.091,5,30.0,3.12,-0.02,1.2],[30,341,-1132,322,91,5231,30765,388,1344,87]]
sizes = [10,100,1000]
sigmas = [0.01,10,500]
np.random.seed(20) # Set the seed so we can get reproducible results
x_poly = np.array(sorted(uniform(-1, 1, testSize)))
modelDict = {0:'LS', 1:'RLS' ,2:'LASSO' ,3:'RR' ,4:'BR'}
with open('./outcome.txt', 'w') as f:
tempWrite = []
for covs in covsSet:
for size in sizes:
for sigma in sigmas:
tempWrite.append('\r\n==============paras=============\r\n')
tempWrite.append(' covs:'+str(covs))
tempWrite.append(' size:'+str(size))
tempWrite.append(' sigma:'+str(sigma))
y_poly = real_function(4, 6, 0, x_poly,covs)
x_samples = np.array(sorted(uniform(-1, 1, size)))
y_samples = real_function(4, 6, sigma, x_samples,covs )
out = train(x_samples, y_samples, x_poly, y_poly, order-1, plotFlag= False)
out = [elem for elem in out if elem != None]
maxELe = np.max(out)
minELe = np.min(out)
tempWrite.append('\r\n WORST:'+str(modelDict.get(out.index(maxELe)))+' ,MSE:'+str(maxELe))
tempWrite.append('\r\n BEST:'+str(modelDict.get(out.index(minELe)))+' ,MSE:'+str(minELe))
f.writelines(tempWrite)
print 'complete!'
def create_uniform_particles(x_range, y_range, hdg_range, N):
particles = np.empty((N, 3))
particles[:, 1] = uniform(x_range[0], x_range[1], size=N)
particles[:, 2] = uniform(y_range[0], y_range[1], size=N)
particles[:, 0] = uniform(hdg_range[0], hdg_range[1], size=N)
particles[:, 0] %= 2 * np.pi
return particles
def testProperties01(self):
"""The magnitudue of the product is the product of the magnitudes"""
q1 = Quaternion(
tuple(RA.uniform(self.min, self.max, (1, 4)).tolist()[0]))
q2 = Quaternion(
tuple(RA.uniform(self.min, self.max, (1, 4)).tolist()[0]))
self.assertEqual((q1 * q2).magnitude(),
q1.magnitude() * q2.magnitude())
def test_computeRMSD_Random(self):
"""3. for two random sets of points, rmsd(x,y) == rmsd(y,x)"""
min = -10000.
max = 10000.
num_points = 20
dimension = 3
point_list_1 = RandomArray.uniform(min, max, (num_points, dimension))
point_list_2 = RandomArray.uniform(min, max, (num_points, dimension))
self.assertEqual(
rmsd.RMSDCalculator(point_list_1).computeRMSD(point_list_2),
rmsd.RMSDCalculator(point_list_2).computeRMSD(point_list_1))
def test_computeRMSD_Random(self):
"""3. for two random sets of points, rmsd(x,y) == rmsd(y,x)"""
min = -10000.
max = 10000.
num_points = 20
dimension = 3
point_list_1 = RandomArray.uniform(min, max, (num_points, dimension))
point_list_2 = RandomArray.uniform(min, max, (num_points, dimension))
self.assertEqual(
rmsd.RMSDCalculator(point_list_1).computeRMSD(point_list_2),
rmsd.RMSDCalculator(point_list_2).computeRMSD(point_list_1))
def testProperties00(self):
"""The product of the conjugate is the conjucate of the product"""
q1 = Quaternion(tuple(RA.uniform(self.min,
self.max, (1, 4)).tolist()[0]))
q2 = Quaternion(tuple(RA.uniform(self.min,
self.max, (1, 4)).tolist()[0]))
## pc = q1.conjugate()*q2.conjugate()
## qp = q1*q2
## cp = qp.conjugate()
## self.assertEqual( pc, cp)
# the commented code fails with the same error as this line...
self.assertEqual( q1.conjugate()*q2.conjugate(), (q2*q1).conjugate())
def testProperties00(self):
"""The product of the conjugate is the conjucate of the product"""
q1 = Quaternion(
tuple(RA.uniform(self.min, self.max, (1, 4)).tolist()[0]))
q2 = Quaternion(
tuple(RA.uniform(self.min, self.max, (1, 4)).tolist()[0]))
## pc = q1.conjugate()*q2.conjugate()
## qp = q1*q2
## cp = qp.conjugate()
## self.assertEqual( pc, cp)
# the commented code fails with the same error as this line...
self.assertEqual(q1.conjugate() * q2.conjugate(),
(q2 * q1).conjugate())
def aligned_spin_param_generator(flow, tmplt_class, bank, **kwargs):
"""
Specify the min and max mass of the bigger component, the min and
max mass of the total mass and the min and max values for the
z-axis spin angular momentum.
"""
dur_min, dur_max = kwargs.pop('duration', (None, None))
if 'ns_bh_boundary_mass' in kwargs and 'bh_spin' in kwargs \
and 'ns_spin' in kwargs:
# get args
bh_spin_bounds = kwargs.pop('bh_spin')
ns_spin_bounds = kwargs.pop('ns_spin')
ns_bh_boundary = kwargs.pop('ns_bh_boundary_mass')
# the rest will be checked in the call to urand_tau0tau3_generator
for mass1, mass2 in urand_tau0tau3_generator(flow, **kwargs):
spin1 = uniform(*(bh_spin_bounds if mass1 > ns_bh_boundary else ns_spin_bounds))
spin2 = uniform(*(bh_spin_bounds if mass2 > ns_bh_boundary else ns_spin_bounds))
t = tmplt_class(mass1, mass2, spin1, spin2, bank=bank)
if (dur_min is not None and t._dur < dur_min) \
or (dur_max is not None and t._dur > dur_max):
continue
yield t
else:
# get args
spin1_bounds = kwargs.pop('spin1', (-1., 1.))
spin2_bounds = kwargs.pop('spin2', (-1., 1.))
# the rest will be checked in the call to urand_tau0tau3_generator
for mass1, mass2 in urand_tau0tau3_generator(flow, **kwargs):
mtot = mass1 + mass2
chis_min = (mass1*spin1_bounds[0] + mass2*spin2_bounds[0])/mtot
chis_max = (mass1*spin1_bounds[1] + mass2*spin2_bounds[1])/mtot
chis = uniform(chis_min, chis_max)
s2min = max(spin2_bounds[0], (mtot*chis - mass1*spin1_bounds[1])/mass2)
s2max = min(spin2_bounds[1], (mtot*chis - mass1*spin1_bounds[0])/mass2)
spin2 = uniform(s2min, s2max)
spin1 = (chis*mtot - mass2*spin2)/mass1
t = tmplt_class(mass1, mass2, spin1, spin2, bank=bank)
if (dur_min is not None and t._dur < dur_min) \
or (dur_max is not None and t._dur > dur_max):
continue
yield t
def aligned_spin_param_generator(flow, **kwargs):
"""
Specify the min and max mass of the bigger component, the min and
max mass of the total mass and the min and max values for the
z-axis spin angular momentum.
"""
# get args
spin1_bounds = kwargs.pop('spin1', (-1., 1.))
spin2_bounds = kwargs.pop('spin2', (-1., 1.))
# the rest will be checked in the call to urand_tau0tau3_generator
for mass1, mass2 in urand_tau0tau3_generator(flow, **kwargs):
spin1 = uniform(*spin1_bounds)
spin2 = uniform(*spin2_bounds)
yield mass1, mass2, spin1, spin2
def nonspin_hom_param_generator(flow, tmplt_class, bank, **constraints):
"""
Wrapper for urand_tau0tau3_generator() to remove spin options
for EOBNRv2 waveforms.
"""
constraints.pop('spin1', None)
constraints.pop('spin2', None)
for mass1, mass2 in urand_tau0tau3_generator(flow, **constraints):
theta = uniform(0, numpy.pi)
phi = uniform(0, 2*numpy.pi)
psi = uniform(0, 2*numpy.pi)
iota = uniform(0, numpy.pi)
orb_phase = uniform(0, 2*numpy.pi)
yield tmplt_class(mass1, mass2, 0, 0, 0, 0, 0, 0,
theta, phi, iota, psi, orb_phase, bank)
def aligned_spin_param_generator(flow, tmplt_class, bank, **kwargs):
"""
Specify the min and max mass of the bigger component, the min and
max mass of the total mass and the min and max values for the
z-axis spin angular momentum.
"""
dur_min, dur_max = kwargs.pop('duration', (None, None))
# define a helper function to apply the appropriate spin bounds
if 'ns_bh_boundary_mass' in kwargs and 'bh_spin' in kwargs \
and 'ns_spin' in kwargs:
bh_spin_bounds = kwargs.pop('bh_spin')
ns_spin_bounds = kwargs.pop('ns_spin')
ns_bh_boundary = kwargs.pop('ns_bh_boundary_mass')
def spin_bounds(mass1, mass2):
return (bh_spin_bounds if mass1 > ns_bh_boundary else ns_spin_bounds), \
(bh_spin_bounds if mass2 > ns_bh_boundary else ns_spin_bounds)
else:
spin1b = kwargs.pop('spin1', (-1., 1.))
spin2b = kwargs.pop('spin2', (-1., 1.))
def spin_bounds(mass1, mass2):
return spin1b, spin2b
# the rest will be checked in the call to urand_tau0tau3_generator
for mass1, mass2 in urand_tau0tau3_generator(flow, **kwargs):
spin1_bounds, spin2_bounds = spin_bounds(mass1, mass2)
mtot = mass1 + mass2
chis_min = (mass1 * spin1_bounds[0] + mass2 * spin2_bounds[0]) / mtot
chis_max = (mass1 * spin1_bounds[1] + mass2 * spin2_bounds[1]) / mtot
chis = uniform(chis_min, chis_max)
s2min = max(spin2_bounds[0],
(mtot * chis - mass1 * spin1_bounds[1]) / mass2)
s2max = min(spin2_bounds[1],
(mtot * chis - mass1 * spin1_bounds[0]) / mass2)
spin2 = uniform(s2min, s2max)
spin1 = (chis * mtot - mass2 * spin2) / mass1
t = tmplt_class(mass1, mass2, spin1, spin2, bank=bank)
if (dur_min is not None and t.dur < dur_min) \
or (dur_max is not None and t.dur > dur_max):
continue
yield t
return [pasta, nome, serie]
def series_lineares_abruptas_revista(self, tamanho_conceitos, qtd_series, grafico):
'''
método para criar as series lineares feitas no artigo ICTAI
:param: tamanho_conceitos: tamanho das observacoes que cada conceito vai ter
:param: qtd_series: quantidade de series que serão criadas
:param: grafico: variavel booleana para plotar apos a criacao da serie
'''
observacoes_iniciais = array([uniform(0, 0.5) for i in range(4)])
variancia = 0.02
self.modelo_AR(parametros=[0.14876092573738822, 0.05087244788237593, 0.4330193805067835, 0.3667339588762431], variancia=variancia, tamanho_do_conceito=tamanho_conceitos, observacoes_iniciais=observacoes_iniciais)
self.modelo_AR(parametros=[-0.318229212036593, 0.4133521130815502, 1.14841972367221, -0.24486472090297637], variancia=variancia, tamanho_do_conceito=tamanho_conceitos)
self.modelo_AR(parametros=[0.00301790735789223, -0.3277435418893056, 0.14635639512590287, 1.171740105825536], variancia=variancia, tamanho_do_conceito=tamanho_conceitos)
self.modelo_AR(parametros=[-0.4429405258368569, 0.4466229373805038, 1.351792157828681, -0.3561327432116702], variancia=variancia, tamanho_do_conceito=tamanho_conceitos)
self.modelo_AR(parametros=[-0.026851477518947557, 0.22016898814054223, -0.03814933593273199, 0.8447046999175475], variancia=variancia, tamanho_do_conceito=tamanho_conceitos)
self.modelo_AR(parametros=[-0.4785914515620302, 0.8558602481837317, 0.024539136949191378, 0.5980008075169353], variancia=variancia, tamanho_do_conceito=tamanho_conceitos)
self.modelo_AR(parametros=[-0.026851477518947557, 0.22016898814054223, -0.03814933593273199, 0.8447046999175475], variancia=variancia, tamanho_do_conceito=tamanho_conceitos)
self.modelo_AR(parametros=[-0.4429405258368569, 0.4466229373805038, 1.351792157828681, -0.3561327432116702], variancia=variancia, tamanho_do_conceito=tamanho_conceitos)
self.modelo_AR(parametros=[0.00301790735789223, -0.3277435418893056, 0.14635639512590287, 1.171740105825536], variancia=variancia, tamanho_do_conceito=tamanho_conceitos)
self.modelo_AR(parametros=[-0.318229212036593, 0.4133521130815502, 1.14841972367221, -0.24486472090297637], variancia=variancia, tamanho_do_conceito=tamanho_conceitos)
serie = self.Obter_Serie()
nome = "lin-abt"
pasta = "lineares/abruptas/"
if(grafico == True):
self.Plotar()
return [pasta, nome, serie]
def series_hibridas_ictai(self, tamanho_conceitos, qtd_series, grafico):
'''
método para criar as series hibridas feitas no artigo ICTAI
:param: tamanho_conceitos: tamanho das observacoes que cada conceito vai ter
:param: qtd_series: quantidade de series que serão criadas
:param: grafico: variavel booleana para plotar apos a criacao da serie
'''
variancia = 0.1
observacoes_iniciais = array([uniform(-2, 2) for i in range(3)])
self.modelo_AR(parametros=[0.003, -0.005, 1.0], variancia=variancia, tamanho_do_conceito=tamanho_conceitos, observacoes_iniciais= observacoes_iniciais)
#self.modelo_AR(parametros=[0.003, -0.005, 1.0], variancia=variancia, tamanho_do_conceito=tamanho_conceitos)
self.modelo_sazonal(tendencia=1, beta_vetor=self.observacoes, variancia=variancia, tamanho_do_conceito=tamanho_conceitos)
self.modelo_nlinear2(parametros=[0.059, 0.086, 0.62, 0.21], variancia=variancia, tamanho_do_conceito=tamanho_conceitos)
self.modelo_AR(parametros=[0.018, 0.95, 0.032], variancia=variancia, tamanho_do_conceito=tamanho_conceitos)
self.modelo_sazonal(tendencia=2, beta_vetor=self.observacoes, variancia=variancia, tamanho_do_conceito=tamanho_conceitos)
self.modelo_nlinear1(parametros=[0.55, 0.024, 0.41, 0.009], variancia=variancia, tamanho_do_conceito=tamanho_conceitos)
self.modelo_AR(parametros=[0.018, 0.95, 0.032], variancia=variancia, tamanho_do_conceito=tamanho_conceitos)
self.modelo_sazonal(tendencia=2, beta_vetor=self.observacoes, variancia=variancia, tamanho_do_conceito=tamanho_conceitos)
self.modelo_nlinear2(parametros=[0.059, 0.086, 0.62, 0.21], variancia=variancia, tamanho_do_conceito=tamanho_conceitos)
self.modelo_nlinear1(parametros=[0.55, 0.024, 0.41, 0.009], variancia=variancia, tamanho_do_conceito=tamanho_conceitos)
serie = self.Obter_Serie()
pasta = "hibridas/"
nome = "hib-"
if(grafico == True):
self.Plotar()
def setUp(self):
"""Called for every test."""
self.decimals = 4 # for Numeric.around; 7 is SciPy default.
self.idmtx = Transformation().getMatrix(transpose=1)
self.known_points = [ [1., 0., 2.],
[1., 0., 1.],
[1., 1., 1.],
[0., 0., 1.],
[0., 0., 0.],
[0., 1., 0.],
[0., 1., -1.],
[1., 1., -1.],
[1., 2., -1.],
[1., 1., -2.]]
npts = len(self.known_points)
dim = 3
self.max = 9999999.
self.min = -self.max
self.random_points = RandomArray.uniform(self.min,
self.max, (npts,dim)).tolist()
# create a simple torsion system for both point lists
torTree = TestTorTree()
torTree.append(TestTorsion(4, 3, [0,1,2]))
torTree.append(TestTorsion(3, 1, [0,2]))
torTree.append(TestTorsion(6, 7, [8,9]))
self.torTree = torTree
del df
def series_lineares_ictai(self, tamanho_conceitos, qtd_series, grafico):
'''
método para criar as series lineares feitas no artigo ICTAI
:param: tamanho_conceitos: tamanho das observacoes que cada conceito vai ter
:param: qtd_series: quantidade de series que serão criadas
:param: grafico: variavel booleana para plotar apos a criacao da serie
'''
observacoes_iniciais = array([uniform(-2, 2) for i in range(3)])
variancia = 0.1
self.modelo_AR(parametros=[0.42, 0.28, 0.005], variancia=variancia, tamanho_do_conceito=tamanho_conceitos, observacoes_iniciais=observacoes_iniciais)
#self.modelo_AR(parametros=[0.42, 0.28, 0.005], variancia=variancia, tamanho_do_conceito=tamanho_conceitos)
self.modelo_AR(parametros=[0.003, -0.005, 1.0], variancia=variancia, tamanho_do_conceito=tamanho_conceitos)
self.modelo_AR(parametros=[0.018, 0.95, 0.032], variancia=variancia, tamanho_do_conceito=tamanho_conceitos)
self.modelo_AR(parametros=[0.11, 0.32, -0.028, 0.038, 0.48], variancia=variancia, tamanho_do_conceito=tamanho_conceitos)
self.modelo_AR(parametros=[0.032, 0.41, -0.24, -0.22, 1.0], variancia=variancia, tamanho_do_conceito=tamanho_conceitos)
self.modelo_AR(parametros=[0.14, -0.29, 0.0025, 1.0], variancia=variancia, tamanho_do_conceito=tamanho_conceitos)
self.modelo_AR(parametros=[0.032, 0.41, -0.24, -0.22, 1.0], variancia=variancia, tamanho_do_conceito=tamanho_conceitos)
self.modelo_AR(parametros=[0.018, 0.95, 0.032], variancia=variancia, tamanho_do_conceito=tamanho_conceitos)
self.modelo_AR(parametros=[0.003, -0.005, 1.0], variancia=variancia, tamanho_do_conceito=tamanho_conceitos)
self.modelo_AR(parametros=[0.42, 0.28, 0.005], variancia=variancia, tamanho_do_conceito=tamanho_conceitos)
serie = self.Obter_Serie()
nome = "lin-"
pasta = "lineares/"
if(grafico == True):
self.Plotar()
def setUp(self):
"""Called for every test."""
self.decimals = 2 # for rounding; 7 is SciPy default.
self.known_points = [ [1., 0., 2.],
[1., 0., 1.],
[1., 1., 1.],
[0., 0., 1.],
[0., 0., 0.],
[0., 1., 0.],
[0., 1., -1.],
[1., 1., -1.],
[1., 2., -1.],
[1., 1., -2.]]
# create a simple torsion system for known_points
torTree = TestTorTree()
torTree.append(TestTorsion(4, 3, [0,1,2]))
torTree.append(TestTorsion(3, 1, [0,2]))
torTree.append(TestTorsion(6, 7, [8,9]))
self.torTree = torTree
npts = 5
dim = 3
self.max = 9999.
self.min = -self.max
self.random_points = RandomArray.uniform(self.min,
self.max, (npts,dim)).tolist()
def test_applyTranslation03(self):
"""applyTranslation03 -- random pts x (random there and back)"""
state = StateToCoords(self.random_points, tolist=1)
trn = RandomArray.uniform(self.min, self.max, (3,))
state.applyTranslation(trn)
trn = -1*trn
self.assertArrayEqual(self.random_points, state.applyTranslation(trn))
def test_applyQuaternion04(self):
"""applyQuaternion04 -- random pts 360 about random-axis"""
state = StateToCoords(self.random_points, tolist=1)
q = RandomArray.uniform(self.min, self.max, (4,))
q[3] = 360.0
result = state.applyQuaternion(q)
self.assertArrayEqual(self.random_points, result)
return [pasta, nome, serie]
def series_nlineares_graduais_revista(self, tamanho_conceitos, qtd_series, grafico):
'''
método para criar as series lineares feitas no artigo ICTAI
:param: tamanho_conceitos: tamanho das observacoes que cada conceito vai ter
:param: qtd_series: quantidade de series que serão criadas
:param: grafico: variavel booleana para plotar apos a criacao da serie
'''
observacoes_iniciais = array([uniform(0, 0.5) for i in range(4)])
variancia = 0.02
self.modelo_nlinear1(parametros=[0.0203825939140348, 0.14856960377126693, 0.12154840218302701, 0.6913077037309644], variancia=variancia, tamanho_do_conceito=tamanho_conceitos, observacoes_iniciais=observacoes_iniciais)
self.modelo_nlinear1(parametros=[0.21432208811179806, 0.1747177586312132, 0.25627781880181116, 0.34924372007037097], variancia=variancia, tamanho_do_conceito=tamanho_conceitos)
self.modelo_nlinear1(parametros=[0.6747722679575656, 0.0400499490190765, 0.12859434021708172, 0.1411115580708043], variancia=variancia, tamanho_do_conceito=tamanho_conceitos)
self.modelo_nlinear1(parametros=[0.2592127990366997, 0.18679044833178132, 0.2510160243812225, 0.29144511960870556], variancia=variancia, tamanho_do_conceito=tamanho_conceitos)
self.modelo_nlinear1(parametros=[0.33266433992324546, -0.11265182345778371, 0.05425610414373307, 0.715124757201], variancia=variancia, tamanho_do_conceito=tamanho_conceitos)
self.modelo_nlinear1(parametros=[0.1779748207049134, -0.09139762327444532, 0.3628849251594744, 0.5451838112044337], variancia=variancia, tamanho_do_conceito=tamanho_conceitos)
self.modelo_nlinear1(parametros=[0.33266433992324546, -0.11265182345778371, 0.05425610414373307, 0.715124757201], variancia=variancia, tamanho_do_conceito=tamanho_conceitos)
self.modelo_nlinear1(parametros=[0.2592127990366997, 0.18679044833178132, 0.2510160243812225, 0.29144511960870556], variancia=variancia, tamanho_do_conceito=tamanho_conceitos)
self.modelo_nlinear1(parametros=[0.6747722679575656, 0.0400499490190765, 0.12859434021708172, 0.1411115580708043], variancia=variancia, tamanho_do_conceito=tamanho_conceitos)
self.modelo_nlinear1(parametros=[0.21432208811179806, 0.1747177586312132, 0.25627781880181116, 0.34924372007037097], variancia=variancia, tamanho_do_conceito=tamanho_conceitos)
serie = self.Obter_Serie()
nome = "nlin-grad"
pasta = "nlineares/graduais/"
if(grafico == True):
self.Plotar()
return [pasta, nome, serie]
def series_nlineares_abruptas_revista(self, tamanho_conceitos, qtd_series, grafico):
'''
método para criar as series lineares feitas no artigo ICTAI
:param: tamanho_conceitos: tamanho das observacoes que cada conceito vai ter
:param: qtd_series: quantidade de series que serão criadas
:param: grafico: variavel booleana para plotar apos a criacao da serie
'''
observacoes_iniciais = array([uniform(0, 0.5) for i in range(4)])
variancia = 0.02
self.modelo_nlinear1(parametros=[-0.06658679980732536, 0.23421353635081468, 0.15495114023325046, 0.6768101219541569], variancia=variancia, tamanho_do_conceito=tamanho_conceitos, observacoes_iniciais=observacoes_iniciais)
self.modelo_nlinear1(parametros=[-0.506870130353138, 0.2589111765633722, 1.3970013340136547, -0.14964763809967313], variancia=variancia, tamanho_do_conceito=tamanho_conceitos)
self.modelo_nlinear1(parametros=[-0.4387715888915295, 0.3747437070432394, 1.3330941335780706, -0.26908562619916504], variancia=variancia, tamanho_do_conceito=tamanho_conceitos)
self.modelo_nlinear1(parametros=[0.06975366774909564, -0.05196107339800573, 0.6352865482608727, 0.3344985733604905], variancia=variancia, tamanho_do_conceito=tamanho_conceitos)
self.modelo_nlinear1(parametros=[-0.4429405258368569, 0.4466229373805038, 1.351792157828681, -0.3561327432116702], variancia=variancia, tamanho_do_conceito=tamanho_conceitos)
self.modelo_nlinear1(parametros=[-0.2763541765783329, 0.3343598857377247, 0.4102952504128611, 0.5315753100371876], variancia=variancia, tamanho_do_conceito=tamanho_conceitos)
self.modelo_nlinear1(parametros=[-0.4429405258368569, 0.4466229373805038, 1.351792157828681, -0.3561327432116702], variancia=variancia, tamanho_do_conceito=tamanho_conceitos)
self.modelo_nlinear1(parametros=[0.06975366774909564, -0.05196107339800573, 0.6352865482608727, 0.3344985733604905], variancia=variancia, tamanho_do_conceito=tamanho_conceitos)
self.modelo_nlinear1(parametros=[-0.506870130353138, 0.2589111765633722, 1.3970013340136547, -0.14964763809967313], variancia=variancia, tamanho_do_conceito=tamanho_conceitos)
self.modelo_nlinear1(parametros=[-0.06658679980732536, 0.23421353635081468, 0.15495114023325046, 0.6768101219541569], variancia=variancia, tamanho_do_conceito=tamanho_conceitos)
serie = self.Obter_Serie()
nome = "nlin-abt"
pasta = "nlineares/abruptas/"
if(grafico == True):
self.Plotar()
return [pasta, nome, serie]
def series_lineares_graduais_revista(self, tamanho_conceitos, qtd_series, grafico):
'''
método para criar as series lineares feitas no artigo ICTAI
:param: tamanho_conceitos: tamanho das observacoes que cada conceito vai ter
:param: qtd_series: quantidade de series que serão criadas
:param: grafico: variavel booleana para plotar apos a criacao da serie
'''
observacoes_iniciais = array([uniform(0, 0.5) for i in range(4)])
variancia = 0.02
self.modelo_AR(parametros=[0.006607488803146307, -0.2529881594354167, 0.8552562304577513, 0.3905674250981309], variancia=variancia, tamanho_do_conceito=tamanho_conceitos, observacoes_iniciais=observacoes_iniciais)
self.modelo_AR(parametros=[-0.4429405258368569, 0.4466229373805038, 1.351792157828681, -0.3561327432116702], variancia=variancia, tamanho_do_conceito=tamanho_conceitos)
self.modelo_AR(parametros=[0.00301790735789223, -0.3277435418893056, 0.14635639512590287, 1.171740105825536], variancia=variancia, tamanho_do_conceito=tamanho_conceitos)
self.modelo_AR(parametros=[0.33266433992324546, -0.11265182345778371, 0.05425610414373307, 0.7151247572018152], variancia=variancia, tamanho_do_conceito=tamanho_conceitos)
self.modelo_AR(parametros=[-0.6335599337412476, 0.334852076313965, 1.3595185287185048, -0.07363691675509275], variancia=variancia, tamanho_do_conceito=tamanho_conceitos)
self.modelo_AR(parametros=[-0.44071503228306824, 0.07407529241129636, 1.2573688275751191, 0.1076310711909298], variancia=variancia, tamanho_do_conceito=tamanho_conceitos)
self.modelo_AR(parametros=[-0.6335599337412476, 0.334852076313965, 1.3595185287185048, -0.07363691675509275], variancia=variancia, tamanho_do_conceito=tamanho_conceitos)
self.modelo_AR(parametros=[0.33266433992324546, -0.11265182345778371, 0.05425610414373307, 0.7151247572018152], variancia=variancia, tamanho_do_conceito=tamanho_conceitos)
self.modelo_AR(parametros=[0.00301790735789223, -0.3277435418893056, 0.14635639512590287, 1.171740105825536], variancia=variancia, tamanho_do_conceito=tamanho_conceitos)
self.modelo_AR(parametros=[-0.4429405258368569, 0.4466229373805038, 1.351792157828681, -0.3561327432116702], variancia=variancia, tamanho_do_conceito=tamanho_conceitos)
serie = self.Obter_Serie()
nome = "lin-grad"
pasta = "lineares/graduais/"
if(grafico == True):
self.Plotar()
return [pasta, nome, serie]
def series_nlineares_ictai(self, tamanho_conceitos, qtd_series, grafico):
'''
método para criar as series não lineares feitas no artigo ICTAI
:param: tamanho_conceitos: tamanho das observacoes que cada conceito vai ter
:param: qtd_series: quantidade de series que serão criadas
:param: grafico: variavel booleana para plotar apos a criacao da serie
'''
observacoes_iniciais = array([uniform(-2, 2) for i in range(4)])
variancia = 0.1
self.modelo_nlinear1(parametros=[0.55, 0.024, 0.41, 0.009], variancia=variancia, tamanho_do_conceito=tamanho_conceitos, observacoes_iniciais=observacoes_iniciais)
#self.modelo_nlinear1(parametros=[0.55, 0.024, 0.41, 0.009], variancia=variancia, tamanho_do_conceito=tamanho_conceitos)
self.modelo_nlinear2(parametros=[0.059, 0.086, 0.62, 0.21], variancia=variancia, tamanho_do_conceito=tamanho_conceitos)
self.modelo_nlinear1(parametros=[0.47, 0.57, 0.14, -0.19], variancia=0.3, tamanho_do_conceito=tamanho_conceitos)
self.modelo_nlinear1(parametros=[0.55, 0.024, 0.41, 0.009], variancia=0.4, tamanho_do_conceito=tamanho_conceitos)
self.modelo_nlinear2(parametros=[0.059, 0.086, 0.62, 0.21], variancia=0.4, tamanho_do_conceito=tamanho_conceitos)
self.modelo_nlinear2(parametros=[0.55, 1.0, 0.0028, -0.58], variancia=variancia, tamanho_do_conceito=tamanho_conceitos)
self.modelo_nlinear2(parametros=[0.059, 0.086, 0.62, 0.21], variancia=0.4, tamanho_do_conceito=tamanho_conceitos)
self.modelo_nlinear1(parametros=[0.55, 0.024, 0.41, 0.009], variancia=0.4, tamanho_do_conceito=tamanho_conceitos)
self.modelo_nlinear1(parametros=[0.47, 0.57, 0.14, -0.19], variancia=0.3, tamanho_do_conceito=tamanho_conceitos)
self.modelo_nlinear2(parametros=[0.059, 0.086, 0.62, 0.21], variancia=variancia, tamanho_do_conceito=tamanho_conceitos)
serie = self.Obter_Serie()
pasta = "nlineares/"
nome = "nlin-"
if(grafico == True):
self.Plotar()
def test_large_collision_domain_network__smoke():
"""In this test we validate that all stations are really in a single
domain and run the model for some time. We actually do not test any
meaningful properties, except connections and that only server receives
data.
"""
num_stations = randint(5, 15)
source_interval = Exponential(uniform(1.0, 10.0))
payload_size = Exponential(randint(10, 100))
sr = simulate(
CollisionDomainNetwork,
stime_limit=500,
params=dict(
num_stations=num_stations,
payload_size=payload_size,
source_interval=source_interval,
mac_header_size=MAC_HEADER,
phy_header_size=PHY_HEADER,
ack_size=ACK_SIZE,
preamble=PREAMBLE,
bitrate=BITRATE,
difs=DIFS,
sifs=SIFS,
slot=SLOT,
cwmin=CWMIN,
cwmax=CWMAX,
connection_radius=CONNECTION_RADIUS,
speed_of_light=SPEED_OF_LIGHT,
queue_capacity=None,
),
loglevel=Logger.Level.WARNING
)
access_point = sr.data.stations[0]
clients = sr.data.stations[1:]
conn_man = sr.data.connection_manager
# Test that connections are established between all stations:
for i in range(num_stations):
radio = sr.data.get_iface(i).radio
peers = set(conn_man.get_peers(radio))
assert len(peers) == num_stations - 1 and radio not in peers
# Test that the number of packets received by any client sink is 0:
for client in clients:
assert client.sink.num_packets_received == 0
# Test that the number of packets generated by the sources - (queue sizes
# + number of packets in transceivers) at the end of simulation is
# almost equal to the number of received packets by the access point sink:
num_packets_sent = [
(cli.source.num_packets_sent -
cli.interfaces[0].queue.size() -
(1 if cli.interfaces[0].transmitter.state else 0))
for cli in clients
]
num_packets_received = access_point.sink.num_packets_received
assert_allclose(sum(num_packets_sent), num_packets_received, rtol=0.05)
def nonspin_hom_param_generator(flow, tmplt_class, bank, **constraints):
"""
Wrapper for urand_tau0tau3_generator() to remove spin options
for EOBNRv2 waveforms.
"""
if constraints.has_key('spin1'):
constraints.pop('spin1')
if constraints.has_key('spin2'):
constraints.pop('spin2')
for mass1, mass2 in urand_tau0tau3_generator(flow, **constraints):
theta = uniform(0, numpy.pi)
phi = uniform(0, 2*numpy.pi)
psi = uniform(0, 2*numpy.pi)
iota = uniform(0, numpy.pi)
orb_phase = uniform(0, 2*numpy.pi)
yield tmplt_class(mass1, mass2, 0, 0, 0, 0, 0, 0,
theta, phi, iota, psi, orb_phase, bank)
def test_applyQuaternion06(self):
"""applyQuaternion06 -- random pts 2*180 about random-axis"""
state = StateToCoords(self.random_points, tolist=1)
q = RandomArray.uniform(self.min, self.max, (4,))
q[3] = 180.0
for n in xrange(2):
result = state.applyQuaternion(q)
self.assertArrayEqual(self.random_points, result)
def single_spin_precessing_param_generator(flow, tmplt_class, bank, **kwargs):
"""
Currently a stub to test precessing template generation.
"""
spin1_bounds = kwargs.pop('spin1', (0., 0.9))
spin2_bounds = kwargs.pop('spin2', (0., 0.9))
for mass1, mass2 in urand_tau0tau3_generator(flow, **kwargs):
# Choose the rest from hardcoded limits
spin1mag = uniform(*spin1_bounds)
spin1ang1 = uniform(0, numpy.pi)
spin1ang2 = uniform(0, 2 * numpy.pi)
spin1z = spin1mag * numpy.cos(spin1ang1)
spin1x = spin1mag * numpy.sin(spin1ang1) * numpy.cos(spin1ang2)
spin1y = spin1mag * numpy.sin(spin1ang1) * numpy.sin(spin1ang2)
# Check orientation angles use correct limits
theta = uniform(0, numpy.pi)
phi = uniform(0, 2 * numpy.pi)
psi = uniform(0, 2 * numpy.pi)
iota = uniform(0, numpy.pi)
yield tmplt_class(mass1,
mass2,
spin1x,
spin1y,
spin1z,
theta,
phi,
iota,
psi,
bank=bank)
def setUp(self):
"""Called for every test."""
npts = 500
dim = 3
self.max = 9999999.
self.min = -self.max
self.random_points = RandomArray.uniform(self.min, self.max,
(npts, dim)).tolist()
def add_poisson_gaussian_noise(image,
alpha=5,
sigma=0.01,
sap=0.0,
quant_bits=8,
dtype=numpy.float32,
clip=True,
fix_seed=True):
if fix_seed:
numpy.random.seed(0)
rnd = normal(size=image.shape)
rnd_bool = uniform(size=image.shape) < sap
noisy = image + numpy.sqrt(alpha * image + sigma**2) * rnd
noisy = noisy * (1 - rnd_bool) + rnd_bool * uniform(size=image.shape)
noisy = numpy.around((2**quant_bits) * noisy) / 2**quant_bits
noisy = numpy.clip(noisy, 0, 1) if clip else noisy
noisy = noisy.astype(dtype)
return noisy
def IMRPhenomB_param_generator(flow, tmplt_class, bank, **kwargs):
"""
Specify the min and max mass of the bigger component, then
the min and max mass of the total mass. This function includes
restrictions on q and chi based on IMRPhenomB's range of believability.
Ref: http://arxiv.org/pdf/0908.2090
@param flow: Lower frequency at which to generate waveform
@param tmplt_class: Template generation class for this waveform
@param bank: sbank bank object
@param kwargs: must specify a component_mass range; mtotal, q, chi, and tau0
ranges are optional. If no chi is specified, the IMRPhenomB limits will be used.
See urand_tau0tau3_generator for more usage help.
"""
# get args
# FIXME: PhenomB ignores spin2 and therefore requires symmetry in
# the spins. In BBH use cases, this is OK, but for NSBH
# applications this is undesired. The weird chi-q bounds make
# implementing this trick
smin, smax = kwargs.pop('spin1', (-1.0, 1.0))
kwargs.pop('spin2')
Warning(
"PhenomB: spin2 limits not implemented. Using spin1 limits for both components."
)
# the rest will be checked in the call to urand_tau0tau3_generator
# IMRPhenomB has special bounds on chi, so we will silently truncate
chi_low_bounds = (max(-0.85, smin), min(0.85, smax))
chi_high_bounds = (max(-0.5, smin), min(0.75, smax))
for mass1, mass2 in urand_tau0tau3_generator(flow, **kwargs):
q = max(mass1 / mass2, mass2 / mass1)
if 4 < q <= 10:
spin1 = uniform(
*chi_high_bounds) #guaranteed to give chi in correct range
spin2 = uniform(*chi_high_bounds)
elif 1 <= q <= 4:
spin1 = uniform(
*chi_low_bounds) #guaranteed to give chi in correct range
spin2 = uniform(*chi_low_bounds)
else:
raise ValueError("mass ratio out of range")
yield tmplt_class(mass1, mass2, spin1, spin2, bank=bank)
def urand_mtotal_generator(mtotal_min, mtotal_max):
"""
This is a generator for random total mass values corresponding to a
uniform distribution of mass pairs in (tau0, tau3) space. See also
urand_eta_generator(), and see LIGO-T1300127 for details.
"""
alpha = mtotal_min*(1-(mtotal_min/mtotal_max)**(7./3.))**(-3./7.)
beta = (mtotal_min/mtotal_max)**(7./3.)/(1-(mtotal_min/mtotal_max)**(7./3.))
n = -3./7.
while 1: # NB: "while 1" is inexplicably much faster than "while True"
yield alpha*(uniform(0, 1)+beta)**n
def urand_mtotal_generator(mtotal_min, mtotal_max):
"""
This is a generator for random total mass values corresponding to a
uniform distribution of mass pairs in (tau0, tau3) space. See also
urand_eta_generator(), and see LIGO-T1300127 for details.
"""
alpha = mtotal_min*(1-(mtotal_min/mtotal_max)**(7./3.))**(-3./7.)
beta = (mtotal_min/mtotal_max)**(7./3.)/(1-(mtotal_min/mtotal_max)**(7./3.))
n = -3./7.
while 1: # NB: "while 1" is inexplicably much faster than "while True"
yield alpha*(uniform(0, 1)+beta)**n
def urand_eta_generator(eta_min, eta_max):
"""
This is a generator for random eta (symmetric mass ratio) values
corresponding to a uniform distribution of mass pairs in (tau0, tau3)
space. See also urand_mtotal_generator(), and see LIGO-T1300127 for
details.
"""
alpha = eta_min / sqrt(1 - (eta_min / eta_max)**2)
beta = (eta_min / eta_max)**2 / (1 - (eta_min / eta_max)**2)
while 1: # NB: "while 1" is inexplicably much faster than "while True"
yield alpha / sqrt(uniform(0, 1) + beta)
def test_infinite_queue_stores_many_enough_packets():
n = 50
packets = [
NetworkPacket(data=AppData(0, uniform(0, 1000), 0, 0))
for _ in range(n)
]
times = list(cumsum(uniform(0, 20, n)))
sim = Mock()
sim.stime = 0
queue = Queue(sim)
for pkt, t in zip(packets, times):
sim.stime = t
queue.push(pkt)
assert queue.size() == n
assert len(queue.size_trace) == n + 1
assert queue.num_dropped == 0
def urand_eta_generator(eta_min, eta_max):
"""
This is a generator for random eta (symmetric mass ratio) values
corresponding to a uniform distribution of mass pairs in (tau0, tau3)
space. See also urand_mtotal_generator(), and see LIGO-T1300127 for
details.
"""
alpha = eta_min/sqrt(1-(eta_min/eta_max)**2)
beta = (eta_min/eta_max)**2/(1-(eta_min/eta_max)**2)
while 1: # NB: "while 1" is inexplicably much faster than "while True"
yield alpha/sqrt(uniform(0, 1)+beta)
def derive_qois(data: DataFrame,
rstar: tuple = None,
teff: tuple = None,
distance_unit: Unit = R_jup):
df = data.copy()
ns = df.shape[0]
df['period'] = period = df.p.values if 'p' in df else df.pr.values
if 'k2_true' in df:
df['k_true'] = sqrt(df.k2_true)
if 'k2_app' in df:
df['k_app'] = sqrt(df.k2_app)
if 'k2_true' in df and 'k2_app' in df:
df['cnt'] = 1. - df.k2_app / df.k2_true
if 'g' in df:
if 'k' in df:
df['b'] = df.g * (1 + df.k)
elif 'k_true' in df:
df['b'] = df.g * (1 + df.k_true)
df['a'] = as_from_rhop(df.rho.values, period)
df['inc'] = i_from_ba(df.b.values, df.a.values)
df['t14'] = d_from_pkaiews(period, df.k_true.values, df.a.values,
df.inc.values, 0.0, 0.0, 1)
df['t14_h'] = 24 * df.t14
if rstar is not None:
from astropy.units import R_sun
rstar_d = (normal(*rstar, size=ns) * R_sun).to(distance_unit).value
df['r_app'] = df.k_app.values * rstar_d
df['r_true'] = df.k_true.values * rstar_d
df['a_au'] = df.a * (rstar_d * distance_unit).to(AU)
if teff is not None:
df['teq_p'] = equilibrium_temperature(normal(*teff, size=ns), df.a,
uniform(0.25, 0.50, ns),
uniform(0, 0.4, ns))
return df
def test_computeRMSD_RandomOffset(self):
"""5. offset point by random value returns offset*sqrt(3)"""
min = -10000.
max = 10000.
num_points = 20
dimension = 3
point_list_1 = RandomArray.uniform(min, max, (num_points, dimension))
delta = point_list_1[0][0]
point_list_2 = point_list_1 + delta
answer = rmsd.RMSDCalculator(point_list_1).computeRMSD(point_list_2)
self.assertEqual(round(answer, self.decimals),
round(abs(delta) * math.sqrt(3.0), self.decimals))
def IMRPhenomB_param_generator(flow, tmplt_class, bank, **kwargs):
"""
Specify the min and max mass of the bigger component, then
the min and max mass of the total mass. This function includes
restrictions on q and chi based on IMRPhenomB's range of believability.
Ref: http://arxiv.org/pdf/0908.2090
@param flow: Lower frequency at which to generate waveform
@param tmplt_class: Template generation class for this waveform
@param bank: sbank bank object
@param kwargs: must specify a component_mass range; mtotal, q, chi, and tau0
ranges are optional. If no chi is specified, the IMRPhenomB limits will be used.
See urand_tau0tau3_generator for more usage help.
"""
# get args
# FIXME: PhenomB ignores spin2 and therefore requires symmetry in
# the spins. In BBH use cases, this is OK, but for NSBH
# applications this is undesired. The weird chi-q bounds make
# implementing this trick
smin, smax = kwargs.pop('spin1', (-1.0, 1.0))
kwargs.pop('spin2')
Warning("PhenomB: spin2 limits not implemented. Using spin1 limits for both components.")
# the rest will be checked in the call to urand_tau0tau3_generator
# IMRPhenomB has special bounds on chi, so we will silently truncate
chi_low_bounds = (max(-0.85, smin), min(0.85, smax))
chi_high_bounds = (max(-0.5, smin), min(0.75, smax))
for mass1, mass2 in urand_tau0tau3_generator(flow, **kwargs):
q = max(mass1/mass2, mass2/mass1)
if 4 < q <= 10:
spin1 = uniform(*chi_high_bounds) #guaranteed to give chi in correct range
spin2 = uniform(*chi_high_bounds)
elif 1 <= q <= 4:
spin1 = uniform(*chi_low_bounds) #guaranteed to give chi in correct range
spin2 = uniform(*chi_low_bounds)
else:
raise ValueError("mass ratio out of range")
yield tmplt_class(mass1, mass2, spin1, spin2, bank=bank)
def test_computeRMSD_RandomOffset(self):
"""5. offset point by random value returns offset*sqrt(3)"""
min = -10000.
max = 10000.
num_points = 20
dimension = 3
point_list_1 = RandomArray.uniform(min, max, (num_points, dimension))
delta = point_list_1[0][0]
point_list_2 = point_list_1 + delta
answer = rmsd.RMSDCalculator(point_list_1).computeRMSD(point_list_2)
self.assertEqual(
round(answer, self.decimals),
round(abs(delta)*math.sqrt(3.0), self.decimals))
def aligned_spin_param_generator(flow, **kwargs):
"""
Specify the min and max mass of the bigger component, the min and
max mass of the total mass and the min and max values for the
z-axis spin angular momentum.
"""
if 'ns_bh_boundary_mass' in kwargs and 'bh_spin' in kwargs \
and 'ns_spin' in kwargs:
# get args
bh_spin_bounds = kwargs.pop('bh_spin')
ns_spin_bounds = kwargs.pop('ns_spin')
ns_bh_boundary = kwargs.pop('ns_bh_boundary_mass')
# the rest will be checked in the call to urand_tau0tau3_generator
for mass1, mass2 in urand_tau0tau3_generator(flow, **kwargs):
spin1 = uniform(*(bh_spin_bounds if mass1 > ns_bh_boundary else ns_spin_bounds))
spin2 = uniform(*(bh_spin_bounds if mass2 > ns_bh_boundary else ns_spin_bounds))
yield mass1, mass2, spin1, spin2
else:
# get args
spin1_bounds = kwargs.pop('spin1', (-1., 1.))
spin2_bounds = kwargs.pop('spin2', (-1., 1.))
# the rest will be checked in the call to urand_tau0tau3_generator
for mass1, mass2 in urand_tau0tau3_generator(flow, **kwargs):
mtot = mass1 + mass2
chis_min = (mass1*spin1_bounds[0] + mass2*spin2_bounds[0])/mtot
chis_max = (mass1*spin1_bounds[1] + mass2*spin2_bounds[1])/mtot
chis = uniform(chis_min, chis_max)
s2min = max(spin2_bounds[0], (mtot*chis - mass1*spin1_bounds[1])/mass2)
s2max = min(spin2_bounds[1], (mtot*chis - mass1*spin1_bounds[0])/mass2)
spin2 = uniform(s2min, s2max)
spin1 = (chis*mtot - mass2*spin2)/mass1
yield mass1, mass2, spin1, spin2
def create_pv_population(self, npop=50):
pvp = zeros((0, len(self.ps)))
npv, i = 0, 0
while npv < npop and i < 10:
pvp_trial = self.ps.sample_from_prior(npop)
pvp_trial[:, 5] = pvp_trial[:, 4]
cref = uniform(0, 0.99, size=npop)
pvp_trial[:, 4] = pvp_trial[:, 5] / (1. - cref)
lnl = self.lnposterior(pvp_trial)
ids = where(isfinite(lnl))
pvp = concatenate([pvp, pvp_trial[ids]])
npv = pvp.shape[0]
i += 1
pvp = pvp[:npop]
return pvp
def Criar_Particula(self):
for i in range(self.numero_particulas):
p = Particulas()
p.dimensao = array([
uniform(posMin, posMax) for i in range(self.numero_dimensoes)
])
p.fitness = self.Funcao(p.dimensao)
p.velocidade = array([0.0 for i in range(self.numero_dimensoes)])
p.best = p.dimensao
p.fit_best = p.fitness
p.c1 = self.c1_fixo
p.c2 = self.c2_fixo
p.inercia = self.inercia_inicial
p.phi = 0
self.particulas.append(p)
self.gbest = self.particulas[0]
def generate_trajectory(
scenario: Scenario,
planning_problem: PlanningProblem,
time_steps: int,
max_tries: int = 1000
) -> Tuple[TrajectoryPrediction, List[VehicleInfo]]:
"""
Generates an as straight as possible linear trajectory.
:param scenario: The scenario the car is driving in.
:param planning_problem: The preplanning problem to solve.
:param time_steps: The number of time steps to simulate.
:param max_tries: The maximum number of tries to find a valid next state. It this number is succeeded some time
during generation the trajectory may not have as many time steps as specified.
:return: A tuple containing the generated prediction for a trajectory and a list containing drawable
representations of all the states of the trajectory.
"""
shape: Shape = Rectangle(DrawConfig.car_length, DrawConfig.car_width,
planning_problem.initial_state.position,
planning_problem.initial_state.orientation)
states: List[MyState] = [MyState(planning_problem.initial_state)]
vehicles: List[VehicleInfo] = [
VehicleInfo(
MyState(planning_problem.initial_state), None,
DrawHelp.convert_to_drawable(planning_problem.initial_state))
]
for i in range(1, time_steps):
last_state_copy: MyState = deepcopy(states[i - 1])
found_valid_next: bool = False
tries: int = 0
while not found_valid_next and tries < max_tries:
next_state: MyState = GenerationHelp.predict_next_state(
scenario, last_state_copy)
next_vehicle: VehicleInfo = VehicleInfo(next_state, None)
if is_valid(next_vehicle, scenario):
states.append(next_state)
vehicles.append(next_vehicle)
found_valid_next = True
else:
tries += 1
last_state_copy.orientation \
= states[i - 1].state.orientation + uniform(-GenerationConfig.max_yaw, GenerationConfig.max_yaw)
if not found_valid_next:
break
return TrajectoryPrediction(
Trajectory(0, list(map(lambda s: s.state, states))),
shape), vehicles
def Fine_Tuning(self, execucao):
x_new = []
p_x_new = 0
global r_cloud
rj = uniform(-1, 1)
w_max = 0.9
w_min = 0.6
for k in range(len(self.gbest.dimensao)):
x_new.append(self.gbest.dimensao[k] + (r_cloud * rj))
p_x_new = self.old_tree.get_node(x_new, self.old_tree.maturidade)
p_g_best = self.old_tree.get_node(self.gbest.dimensao,
self.old_tree.maturidade)
if (p_x_new <= p_g_best):
p_x_new = self.Funcao(x_new, execucao)
if (p_x_new <= self.gbest.fitness):
self.gbest.dimensao = copy.deepcopy(x_new)
self.gbest.fitness = copy.deepcopy(p_x_new)
r_cloud = r_cloud * (w_min + (random.random() * (w_max - w_min)))
def setUp(self):
"""Called for every test."""
self.decimals = 4 # for Numeric.around; 7 is SciPy default.
self.idmtx = Transformation().getMatrix(transpose=1)
self.known_points = [[1., 0., 2.], [1., 0., 1.], [1., 1., 1.],
[0., 0., 1.], [0., 0., 0.], [0., 1., 0.],
[0., 1., -1.], [1., 1., -1.], [1., 2., -1.],
[1., 1., -2.]]
npts = len(self.known_points)
dim = 3
self.max = 9999999.
self.min = -self.max
self.random_points = RandomArray.uniform(self.min, self.max,
(npts, dim)).tolist()
# create a simple torsion system for both point lists
torTree = TestTorTree()
torTree.append(TestTorsion(4, 3, [0, 1, 2]))
torTree.append(TestTorsion(3, 1, [0, 2]))
torTree.append(TestTorsion(6, 7, [8, 9]))
self.torTree = torTree
def uniform(minimum, maximum, shape=[]):
"""uniform(minimum, maximum, shape=[]) returns array of given shape of random reals
in given range"""
if shape == []:
shape = None
return mt.uniform(minimum, maximum, shape)
import numpy # no unlimited dimension, just assign to slice. lats = numpy.arange(-90,91,2.5) lons = numpy.arange(-180,180,2.5) latitudes[:] = lats longitudes[:] = lons print 'latitudes =\n',latitudes[:] print 'longitudes =\n',longitudes[:] # append along two unlimited dimensions by assigning to slice. nlats = len(rootgrp.dimensions['lat']) nlons = len(rootgrp.dimensions['lon']) print 'temp shape before adding data = ',temp.shape from numpy.random.mtrand import uniform # random number generator. temp[0:5,0:10,:,:] = uniform(size=(5,10,nlats,nlons)) print 'temp shape after adding data = ',temp.shape # levels have grown, but no values yet assigned. print 'levels shape after adding pressure data = ',levels.shape # assign values to levels dimension variable. levels[:] = [1000.,850.,700.,500.,300.,250.,200.,150.,100.,50.] # fancy slicing tempdat = temp[::2, [1,3,6], lats>0, lons>0] print 'shape of fancy temp slice = ',tempdat.shape print temp[0, 0, [0,1,2,3], [0,1,2,3]].shape # fill in times. from datetime import datetime, timedelta from netCDF4 import num2date, date2num, date2index dates = [datetime(2001,3,1)+n*timedelta(hours=12) for n in range(temp.shape[0])]
import unittest
import os
import tempfile
import numpy as NP
from numpy import ma
from numpy.testing import assert_array_equal, assert_array_almost_equal
from numpy.random.mtrand import uniform
import netCDF4p as netCDF4
# test automatic conversion of masked arrays, and
# packing/unpacking of short ints.
# create an n1dim by n2dim random ranarr.
FILE_NAME = tempfile.mktemp(".nc")
ndim = 10
ranarr = 100.*uniform(size=(ndim))
ranarr2 = 100.*uniform(size=(ndim))
# used for checking vector missing_values
arr3 = NP.linspace(0,9,ndim)
mask = NP.zeros(ndim,NP.bool); mask[-1]=True; mask[-2]=True
marr3 = NP.ma.array(arr3, mask=mask, dtype=NP.int32)
packeddata = 10.*uniform(size=(ndim))
missing_value = -9999.
missing_value2 = NP.nan
missing_value3 = [8,9]
ranarr[::2] = missing_value
ranarr2[::2] = missing_value2
NP.seterr(invalid='ignore') # silence warnings from ma.masked_values
maskedarr = ma.masked_values(ranarr,missing_value)
maskedarr2 = ma.masked_values(ranarr2,missing_value2)
scale_factor = (packeddata.max()-packeddata.min())/(2.*32766.)
import sys
import unittest
import os
import tempfile
import numpy as np
from numpy.testing import assert_array_equal, assert_array_almost_equal
from numpy.random.mtrand import uniform
import netCDF4
# test primitive data types.
# create an n1dim by n2dim random ranarr.
FILE_NAME = tempfile.NamedTemporaryFile(suffix='.nc', delete=False).name
n1dim = 5
n2dim = 10
ranarr = 100.*uniform(size=(n1dim,n2dim))
zlib=False;complevel=0;shuffle=0;least_significant_digit=None
datatypes = ['f8','f4','i1','i2','i4','i8','u1','u2','u4','u8','S1']
FillValue = 1.0
issue273_data = np.ma.array(['z']*10,dtype='S1',\
mask=[False,False,False,False,False,True,False,False,False,False])
class PrimitiveTypesTestCase(unittest.TestCase):
def setUp(self):
self.file = FILE_NAME
f = netCDF4.Dataset(self.file,'w')
f.createDimension('n1', None)
f.createDimension('n2', n2dim)
for typ in datatypes:
foo = f.createVariable('data_'+typ, typ, ('n1','n2',),zlib=zlib,complevel=complevel,shuffle=shuffle,least_significant_digit=least_significant_digit,fill_value=FillValue)
