def test_plot_influence(self):
infl = self.res.get_influence()
fig = influence_plot(self.res)
assert_equal(isinstance(fig, plt.Figure), True)
# test that we have the correct criterion for sizes #3103
try:
sizes = fig.axes[0].get_children()[0]._sizes
ex = sm.add_constant(infl.cooks_distance[0])
ssr = sm.OLS(sizes, ex).fit().ssr
assert_array_less(ssr, 1e-12)
except AttributeError:
import warnings
warnings.warn('test not compatible with matplotlib version')
plt.close(fig)
fig = influence_plot(self.res, criterion='DFFITS')
assert_equal(isinstance(fig, plt.Figure), True)
try:
sizes = fig.axes[0].get_children()[0]._sizes
ex = sm.add_constant(np.abs(infl.dffits[0]))
ssr = sm.OLS(sizes, ex).fit().ssr
assert_array_less(ssr, 1e-12)
except AttributeError:
pass
plt.close(fig)
assert_raises(ValueError,
influence_plot,
self.res,
criterion='unknown')
def test_growth(cosmo=None):
if cosmo is None:
cosmo = cosmo_wmap_5()
print "Comparing growth factor with calculations from http://icosmo.org/"
# load external distance calculations
# z D
growth_file = os.path.dirname(os.path.abspath(__file__))
growth_file = os.path.join(growth_file, 'icosmo_testdata', 'growth.txt')
ic_growth = numpy.loadtxt(growth_file)
z = ic_growth[:,0]
cp_growth = cp.fgrowth(z, cosmo['omega_M_0'])
label = r"$D(z)$"
pylab.figure()
pylab.plot(z, ic_growth[:,1],
label=label + ' IC', ls=':')
pylab.plot(z, cp_growth, label=label + ' perturbation.py', ls='-')
pylab.legend(loc='best')
pylab.figure()
diff = (ic_growth[:,1] - cp_growth) / ic_growth[:,1]
maxdiff = numpy.max(numpy.abs(diff))
print "Maximum fraction difference in %s is %e." % (label,
maxdiff)
pylab.plot(z,
diff,
label=label, ls='-')
pylab.legend(loc='best')
ntest.assert_array_less(numpy.abs(diff), 5e-3)
def test_plot_influence(self, close_figures):
infl = self.res.get_influence()
fig = influence_plot(self.res)
assert_equal(isinstance(fig, plt.Figure), True)
# test that we have the correct criterion for sizes #3103
try:
sizes = fig.axes[0].get_children()[0]._sizes
ex = sm.add_constant(infl.cooks_distance[0])
ssr = sm.OLS(sizes, ex).fit().ssr
assert_array_less(ssr, 1e-12)
except AttributeError:
import warnings
warnings.warn('test not compatible with matplotlib version')
fig = influence_plot(self.res, criterion='DFFITS')
assert_equal(isinstance(fig, plt.Figure), True)
try:
sizes = fig.axes[0].get_children()[0]._sizes
ex = sm.add_constant(np.abs(infl.dffits[0]))
ssr = sm.OLS(sizes, ex).fit().ssr
assert_array_less(ssr, 1e-12)
except AttributeError:
pass
assert_raises(ValueError, influence_plot, self.res, criterion='unknown')
def test_age():
"""Test integrated age against analytical age."""
z = numpy.arange(0, 10.0, 0.05)
cosmo = {}
cosmo['omega_M_0'] = numpy.array([[0.99],[0.01],[0.3]])
cosmo['omega_lambda_0'] = 1. - cosmo['omega_M_0']
cosmo['h'] = 0.7
cd.set_omega_k_0(cosmo)
linestyle = ['-', ':', '--']
gyr = 1e9 * cc.yr_s
tl = cd.lookback_time(z, **cosmo)
age = cd.age(z, **cosmo)
age_ana = cd.age_flat(z, **cosmo)
pylab.figure(figsize=(6,6))
for i in range(len(linestyle)):
pylab.plot(z, (tl/gyr)[i], ls=linestyle[i], color='0.5')
pylab.plot(z, (age/gyr)[i], ls=linestyle[i], color='r')
pylab.plot(z, (age_ana/gyr)[i], ls=linestyle[i], color='k')
pylab.xlabel("redshift z")
pylab.ylabel(r"age $t_L/$Gyr")
pylab.figure(figsize=(6,6))
for i in range(len(linestyle)):
pylab.plot(z, ((age - age_ana)/age_ana)[i], ls=linestyle[i],
color='k')
# Make sure errors are small:
ntest.assert_array_less((numpy.abs((age - age_ana)/age_ana)[i]),
3e-13)
pylab.xlabel("redshift z")
pylab.ylabel(r"age: (integral - analytical)/analytical")
def test_attenuation(fiber_layout):
"""Should fail if the output power not less than initial pulse."""
outputs = fiber_layout(gssn_1_ch, True, False, False, False, False, False)
in_temporal_power = outputs[0]
out_temporal_power = outputs[4]
for i in range(len(out_temporal_power)):
assert_array_less(out_temporal_power[i], in_temporal_power)
def test_GBL_tau_inst():
"""Test match between analytical and numerical tau with instant
reionization.
Also makes a plot reproducing figure 1 of arXiv:astro-ph/9812125v3.
"""
dz = 0.05
z = numpy.arange(0., 80. + 1.5 * dz, dz)
# Fully ionized H and He
x_ionH = 1.0
x_ionHe = 2.0
cosmo = {}
cosmo['omega_M_0'] = numpy.array([[0.3], [0.6], [1.0]])
cosmo['omega_lambda_0'] = 1. - cosmo['omega_M_0']
cosmo['h'] = 0.65
cosmo['omega_b_0'] = 0.02 / cosmo['h']**2.
cosmo['Y_He'] = 0.24
cd.set_omega_k_0(cosmo)
tau_inst = cr.optical_depth_instant(z,
x_ionH=x_ionH,
x_ionHe=x_ionHe,
**cosmo)
tau_int = cr.integrate_optical_depth(x_ionH, x_ionHe, z, **cosmo)
linestyle = ['-', ':', '--']
pylab.figure()
pylab.subplot(2, 1, 1)
pylab.title("Compare to GB&L fig. 1 (astro-ph/9812125v3.)")
for i in range(len(linestyle)):
pylab.plot(z, tau_inst[i], ls=linestyle[i], color='b')
pylab.plot(z, tau_int[i], ls=linestyle[i], color='r')
pylab.xlim(0, 80)
pylab.ylim(0, 1)
pylab.xlabel(r"$\mathrm{z_{ion}}$")
pylab.ylabel(r"$\tau$")
pylab.subplot(2, 1, 2)
for i in range(len(linestyle)):
pylab.plot(z,
1.e4 * (tau_int[i] - tau_inst[i]) / tau_inst[i],
ls=linestyle[i],
color='k')
diff = (tau_int[i] - tau_inst[i]) / tau_inst[i]
diff[numpy.isnan(diff)] = 0.0
print("max fractional error in num. int. = %.3g" %
numpy.max(numpy.abs(diff)))
ntest.assert_array_less(numpy.abs(diff),
numpy.zeros(diff.shape) + 2.e-4)
pylab.xlim(0, 40)
pylab.xlabel(r"$\mathrm{z_{ion}}$")
pylab.ylabel(r"$\mathrm{10^4 \times (num.\tau - ana.\tau)/ana.\tau}$")
def test_GBL_tau_inst():
"""Test match between analytical and numerical tau with instant
reionization.
Also makes a plot reproducing figure 1 of arXiv:astro-ph/9812125v3.
"""
dz = 0.05
z = numpy.arange(0., 80. + 1.5*dz, dz)
# Fully ionized H and He
x_ionH = 1.0
x_ionHe = 2.0
cosmo = {}
cosmo['omega_M_0'] = numpy.array([[0.3],[0.6],[1.0]])
cosmo['omega_lambda_0'] = 1. - cosmo['omega_M_0']
cosmo['h'] = 0.65
cosmo['omega_b_0'] = 0.02 / cosmo['h']**2.
cosmo['Y_He'] = 0.24
cd.set_omega_k_0(cosmo)
tau_inst = cr.optical_depth_instant(z, x_ionH=x_ionH, x_ionHe=x_ionHe,
**cosmo)
tau_int = cr.integrate_optical_depth(x_ionH, x_ionHe, z, **cosmo)
linestyle = ['-', ':', '--']
pylab.figure()
pylab.subplot(2,1,1)
pylab.title("Compare to GB&L fig. 1 (astro-ph/9812125v3.)")
for i in range(len(linestyle)):
pylab.plot(z, tau_inst[i], ls=linestyle[i], color='b')
pylab.plot(z, tau_int[i], ls=linestyle[i], color='r')
pylab.xlim(0,80)
pylab.ylim(0,1)
pylab.xlabel(r"$\mathrm{z_{ion}}$")
pylab.ylabel(r"$\tau$")
pylab.subplot(2,1,2)
for i in range(len(linestyle)):
pylab.plot(z,
1.e4 * (tau_int[i] - tau_inst[i])/tau_inst[i],
ls=linestyle[i], color='k')
diff = (tau_int[i] - tau_inst[i]) / tau_inst[i]
diff[numpy.isnan(diff)] = 0.0
print ("max fractional error in num. int. = %.3g" %
numpy.max(numpy.abs(diff))
)
ntest.assert_array_less(numpy.abs(diff),
numpy.zeros(diff.shape) + 2.e-4)
pylab.xlim(0,40)
pylab.xlabel(r"$\mathrm{z_{ion}}$")
pylab.ylabel(r"$\mathrm{10^4 \times (num.\tau - ana.\tau)/ana.\tau}$")
def test_kwta_presentation():
"""Tests one kwta presentation to half of the units, followed by
another presentation to the second half."""
s = InputSample(8, 8, [[1] * 8] * 4 + [[0] * 8] * 4)
kwta_presentation(Tns.p2, Tns.p1, s, 2)
assert get_current_time() == 2
rates = get_rate_encoder(Tns.p1).get_rates()
assert_array_less(rates[4:8], rates[0:4])
s = InputSample(8, 8, [[0] * 8] * 4 + [[1] * 8] * 4)
kwta_presentation(Tns.p2, Tns.p1, s, 2)
assert get_current_time() == 4
rates = get_rate_encoder(Tns.p1).get_rates()
assert_array_less(rates[0:4], rates[4:8])
def test_kwta_presentation():
"""Tests one kwta presentation to half of the units, followed by
another presentation to the second half."""
s = InputSample(8, 8, [[1] * 8] * 4 + [[0] * 8] * 4)
kwta_presentation(Tns.p2, Tns.p1, s, 2)
assert get_current_time() == 2
rates = get_rate_encoder(Tns.p1).get_rates()
assert_array_less(rates[4:8], rates[0:4])
s = InputSample(8, 8, [[0] * 8] * 4 + [[1] * 8] * 4)
kwta_presentation(Tns.p2, Tns.p1, s, 2)
assert get_current_time() == 4
rates = get_rate_encoder(Tns.p1).get_rates()
assert_array_less(rates[0:4], rates[4:8])
def test_conversion(lifetime_function):
"""Tests conversion of mass into lifetime and back. Note that this
test is not passed right now because of the numerical
discontinuties in the lifetime functions where their different
segments meet up."""
print lifetime_function.__name__
import numpy.testing.utils as ntest
mass = numpy.arange(0., 200., 0.1)
time_from_mass = lifetime_function(mass)
mass_from_time_func = \
mass_from_main_sequence_life_function(lifetime_function)
mass_from_time_from_mass = mass_from_time_func(time_from_mass)
frac_diff = (mass_from_time_from_mass-mass)/mass
pylab.subplot(221)
pylab.plot(mass, mass_from_time_from_mass)
pylab.subplot(222)
pylab.plot(mass, time_from_mass, label=lifetime_function.__name__)
pylab.yscale('log')
pylab.legend(loc='best')
pylab.subplot(223)
pylab.plot(mass, frac_diff)
goodmask = numpy.ones(len(time_from_mass), dtype=numpy.bool)
if hasattr(lifetime_function, 'minlife'):
goodmask[time_from_mass <= lifetime_function.minlife] = False
if hasattr(lifetime_function, 'maxlife'):
goodmask[time_from_mass >= lifetime_function.maxlife] = False
goodmask[numpy.logical_not(numpy.isfinite(time_from_mass))] = False
print "Max frac. diff: ",
print numpy.nanmax(numpy.abs(frac_diff[goodmask]))
print "SKIPPING TEST THAT FAILS!!!!"
if (False):
threshold = 1e-9
ntest.assert_array_less(numpy.abs(frac_diff[goodmask]),
threshold * numpy.ones(len(frac_diff[goodmask])),
err_msg='Error in mass recovery exceeds %s' %
threshold)
print 'Mass recovery discrepency is below %s' % threshold
def test_age():
"""Test integrated age against analytical age."""
z = numpy.arange(0, 10.0, 0.05)
cosmo = {}
cosmo['omega_M_0'] = numpy.array([[0.99], [0.01], [0.3]])
cosmo['omega_lambda_0'] = 1. - cosmo['omega_M_0']
cosmo['h'] = 0.7
cd.set_omega_k_0(cosmo)
linestyle = ['-', ':', '--']
gyr = 1e9 * cc.yr_s
tl = cd.lookback_time(z, **cosmo)
age = cd.age(z, **cosmo)
age_ana = cd.age_flat(z, **cosmo)
pylab.figure(figsize=(6, 6))
for i in range(len(linestyle)):
pylab.plot(z, (tl / gyr)[i], ls=linestyle[i], color='0.5')
pylab.plot(z, (age / gyr)[i], ls=linestyle[i], color='r')
pylab.plot(z, (age_ana / gyr)[i], ls=linestyle[i], color='k')
pylab.xlabel("redshift z")
pylab.ylabel(r"age $t_L/$Gyr")
pylab.figure(figsize=(6, 6))
for i in range(len(linestyle)):
pylab.plot(z, ((age - age_ana) / age_ana)[i],
ls=linestyle[i],
color='k')
# Make sure errors are small:
ntest.assert_array_less((numpy.abs((age - age_ana) / age_ana)[i]),
3e-13)
pylab.xlabel("redshift z")
pylab.ylabel(r"age: (integral - analytical)/analytical")
def test_interplay_dispersion_and_spm():
"""Should fail if (i) fwhm of temporal output powers for small
N square number is greater than fwhm of initial pulse or (ii)
fwhm of temporal output powers for big N square number is
greater than initial fwhm in case of positive GVD and smaller
than initial fwhm in case of negative GVD.
Notes
-----
Test case::
gssn ___ fiber
"""
# Environment creation
domain = Domain()
lt = Layout(domain)
dtime = domain.dtime
fwhms_pos_gvd = []
fwhms_neg_gvd = []
time_pos_gvd = []
time_neg_gvd = []
nlse_method = "ssfm_symmetric"
# Make sure to have a positive GVD to pass the test
gssn = Gaussian(channels=1, peak_power=[1.0], center_lambda=[1030.])
flag = True
for i in range(1, 5):
if (i == 4):
flag = False
fiber = Fiber(length=1.0,
nlse_method=nlse_method,
alpha=[0.46],
gamma=2.0,
nl_approx=flag,
ATT=False,
DISP=True,
SPM=True,
SS=False,
RS=False,
steps=1000,
save=True,
approx_type=i,
beta=[1e5, 1e3, 20.0])
lt.add_link(gssn[0], fiber[0])
lt.run(gssn)
lt.reset()
time_pos_gvd.append(fiber[1][0].time)
fwhms_pos_gvd.append(fwhm(temporal_power(fiber[1][0].channels), dtime))
flag = True
for i in range(1, 5):
if (i == 4):
flag = False
fiber = Fiber(length=1.0,
nlse_method=nlse_method,
alpha=[0.46],
gamma=2.0,
nl_approx=flag,
ATT=False,
DISP=True,
SPM=True,
SS=False,
RS=False,
steps=1000,
save=True,
approx_type=i,
beta=[1e5, 1e3, -20.0])
lt.add_link(gssn[0], fiber[0])
lt.run(gssn)
lt.reset()
time_neg_gvd.append(fiber[1][0].time)
fwhms_neg_gvd.append(fwhm(temporal_power(fiber[1][0].channels), dtime))
fwhm_temporal_gssn = fwhm(temporal_power(gssn[0][0].channels), dtime)
time_gssn = gssn[0][0].time
# Testing
for i in range(len(fwhms_neg_gvd)):
assert_array_less(time_gssn, time_pos_gvd[i])
assert_array_less(time_gssn, time_neg_gvd[i])
assert fwhm_temporal_gssn[0] < fwhms_pos_gvd[i][0]
assert fwhms_neg_gvd[i][0] < fwhm_temporal_gssn[0]
def assert_cosine_similarity(x, y, atol=1e-6):
x_normalized = normalize_vector_to_unit_length(x)
y_normalized = normalize_vector_to_unit_length(y)
distance = 1 - np.abs(vector_H_vector(x_normalized, y_normalized))**2
assert_array_less(distance, atol)
