def test_shift(self):
sed1 = Sed(
x=numpy.arange(1000, 10001, 500),
y=numpy.arange(1000, 10001, 500),
yerr=numpy.arange(1000, 10001, 500) * 0.01,
)
sed2 = Spectrum(
x=numpy.arange(3000, 9501, 10), y=numpy.arange(3000, 9501, 10), yerr=numpy.arange(3000, 9501, 10) * 0.01
)
sed3 = Sed(
x=numpy.arange(30000, 40001, 500),
y=numpy.arange(30000, 40001, 500),
yerr=numpy.arange(30000, 40001, 500) * 0.01,
)
aggsed = AggregateSed([sed1, sed2, sed3], z=1.0)
shifted_aggsed = aggsed.shift(0.0)
control_x0 = sed1.x / 2.0
control_x1 = sed2.x / 2.0
numpy.testing.assert_array_almost_equal(shifted_aggsed[0].x, control_x0)
numpy.testing.assert_array_almost_equal(shifted_aggsed[1].x, control_x1)
def test_normalize_at_point_seds(self):
sed1 = Sed(
x=numpy.arange(1000, 10001, 500),
y=numpy.arange(1000, 10001, 500),
yerr=numpy.arange(1000, 10001, 500) * 0.01,
)
sed2 = Sed(
x=numpy.arange(10500, 20501, 500),
y=numpy.arange(10500, 20501, 500),
yerr=numpy.arange(10500, 20501, 500) * 0.01,
)
sed3 = Sed(
x=numpy.arange(30000, 40001, 500),
y=numpy.arange(30000, 40001, 500),
yerr=numpy.arange(30000, 40001, 500) * 0.01,
)
aggsed = AggregateSed([sed1, sed2, sed3])
norm_aggsed = aggsed.normalize_at_point(10350, 1)
control_norm_constant = 9.523809523809524e-05 # 1./10500.
self.assertAlmostEqual(norm_aggsed.norm_constant, control_norm_constant)
# The AggregateSed should
# retain all Segments after the normalization, even though
# none of the Segments do not contain the point (10350).
self.assertEqual(len(norm_aggsed), len(aggsed))
norm_aggsed = aggsed.normalize_at_point(10251, 1)
self.assertAlmostEqual(norm_aggsed.norm_constant, 1.0 / 10500)
def test_normalize_by_int(self):
sed1 = Sed(
x=numpy.arange(1000, 10001, 500),
y=numpy.arange(1000, 10001, 500),
yerr=numpy.arange(1000, 10001, 500) * 0.01,
)
sed2 = Spectrum(
x=numpy.arange(3000, 9501, 10), y=numpy.arange(3000, 9501, 10), yerr=numpy.arange(3000, 9501, 10) * 0.01
)
sed3 = Sed(
x=numpy.arange(30000, 40001, 500),
y=numpy.arange(30000, 40001, 500),
yerr=numpy.arange(30000, 40001, 500) * 0.01,
)
aggsed = AggregateSed([sed1, sed2, sed3])
norm_aggsed = aggsed.normalize_by_int()
x = numpy.hstack([sed1.x, sed2.x, sed3.x])
y = numpy.hstack([sed1.y, sed2.y, sed3.y])
points = []
for i, point in enumerate(x):
points.append((x[i], y[i]))
points = zip(*sorted(points))
tot_flux = numpy.trapz(points[1], points[0])
control_norm_constant = 1.0 / tot_flux # 1.0/779625000.0
self.assertAlmostEqual(norm_aggsed.norm_constant, control_norm_constant)
# The AggregateSed should retain all Segments after the normalization.
self.assertEqual(len(norm_aggsed), len(aggsed))
def test_aggsed_write_file_exists(self):
sed = Sed(x=self.x,y=self.y,yerr=self.yerr)
spectrum = Spectrum(x=self.x, y=self.y, yerr=self.yerr)
aggsed = AggregateSed([sed, spectrum])
filename = test_directory+'aggsed_file_exists.dat'
aggsed.write(filename)
self.assertEqual(os.path.exists(filename), True)
def test_aggsed_no_counts2(self):
sed = Sed(x=self.x,y=self.y,yerr=self.yerr)
spectrum = Spectrum(x=self.x, y=self.y, yerr=self.yerr)
aggsed = AggregateSed([sed, spectrum])
filename = test_directory+'aggsed_counts2.dat'
aggsed.write(filename)
data = astropy.io.ascii.read(filename)
self.assertEqual(data.colnames, ['x','y','y_err'])
def test_aggsed_counts1(self):
sed = Sed(x=self.x,y=self.y,yerr=self.yerr)
sed.counts = numpy.ones(numpy.array(sed._toarray()[0]).size, dtype=numpy.int_)
spectrum = Spectrum(x=self.x, y=self.y, yerr=self.yerr)
aggsed = AggregateSed([sed, spectrum])
filename = test_directory+'aggsed_counts1.dat'
aggsed.write(filename)
self.assert_(os.path.exists(filename))
data = astropy.io.ascii.read(filename)
self.assertEqual(all(counts == numpy.nan for counts in data['counts']), False)
def test_sorting(self):
sed1 = Sed(x=[1, 5, 10, 57, 60], y=[1, 1, 1, 1, 1], yerr=[0.1, 0.1, 0.1, 0.1, 0.1])
sed2 = Spectrum(x=[0.5, 2, 15, 50, 60], y=[2, 2, 2, 2, 2], yerr=[0.2, 0.2, 0.2, 0.2, 0.2])
sed3 = Sed(x=[3, 6, 9, 20], y=[3, 3, 3, 3], yerr=[0.3, 0.3, 0.3, 0.3])
aggsed = AggregateSed([sed1, sed2, sed3])
control_x = numpy.array([0.5, 1, 2, 3, 5, 6, 9, 10, 15, 20, 50, 57, 60, 60])
control_y = numpy.array([2, 1, 2, 3, 1, 3, 3, 1, 2, 3, 2, 1, 1, 2])
x, y, yerr = aggsed._sorts()
numpy.testing.assert_array_equal(x, control_x)
numpy.testing.assert_array_equal(y, control_y)
def test_normalize_at_point_spectra(self):
sed1 = Spectrum(x=numpy.arange(1000, 10001), y=numpy.arange(1000, 10001), yerr=numpy.arange(1000, 10001) * 0.01)
sed2 = Spectrum(
x=numpy.arange(10500, 20501), y=numpy.arange(10500, 20501), yerr=numpy.arange(10500, 20501) * 0.01
)
sed3 = Spectrum(
x=numpy.arange(30000, 40001), y=numpy.arange(30000, 40001), yerr=numpy.arange(30000, 40001) * 0.01
)
aggsed = AggregateSed([sed1, sed2, sed3])
norm_aggsed = aggsed.normalize_at_point(20000, 1, dx=50)
control_norm_constant = 1.0 / 20000.0
self.assertAlmostEqual(norm_aggsed.norm_constant, control_norm_constant)
# The AggregateSed should
# retain all Segments after the normalization, even though
# none of the Segments do not contain the point (10350).
self.assertEqual(len(norm_aggsed), len(aggsed))
def test_add_segment(self):
sed1 = Sed(x=[1, 5, 10, 57, 60], y=[1, 1, 1, 1, 1], yerr=[0.1, 0.1, 0.1, 0.1, 0.1])
sed2 = Spectrum(x=[0.5, 2, 15, 50, 60], y=[2, 2, 2, 2, 2], yerr=[0.2, 0.2, 0.2, 0.2, 0.2])
sed3 = Sed(x=[3, 6, 9, 20], y=[3, 3, 3, 3], yerr=[0.3, 0.3, 0.3, 0.3])
aggsed = AggregateSed([sed1, sed2])
aggsed.add_segment(sed3)
self.assertEqual(len(aggsed), 3)
numpy.testing.assert_array_equal(aggsed[2].x, [3, 6, 9, 20])
# to see if the class methods are working properly
# with the added segment
norm_aggsed = aggsed.normalize_by_int()
integral = numpy.trapz(
numpy.array([2, 1, 2, 3, 1, 3, 3, 1, 2, 3, 2, 1, 1, 2]),
numpy.array([0.5, 1, 2, 3, 5, 6, 9, 10, 15, 20, 50, 57, 60, 60]),
)
control_norm_constant = 1.0 / integral
self.assertAlmostEqual(norm_aggsed.norm_constant, control_norm_constant)
def test_normalize_at_point_segments(self):
spec1 = Spectrum(
x=numpy.arange(1000, 10001), y=numpy.arange(1000, 10001), yerr=numpy.arange(1000, 10001) * 0.01
)
spec2 = Spectrum(
x=numpy.arange(10500, 20501), y=numpy.arange(10500, 20501), yerr=numpy.arange(10500, 20501) * 0.01
)
spec3 = Spectrum(
x=numpy.arange(30000, 40001), y=numpy.arange(30000, 40001), yerr=numpy.arange(30000, 40001) * 0.01
)
sed1 = Sed(x=numpy.arange(3000, 11001, 500), y=numpy.arange(300, 1101, 50), yerr=numpy.arange(300, 1101, 50))
sed2 = Sed(
x=numpy.arange(25000, 30001, 500), y=numpy.arange(2500, 3001, 50), yerr=numpy.arange(2500, 3001, 50) * 0.01
)
aggsed = AggregateSed([spec1, spec2, spec3, sed1, sed2])
norm_aggsed = aggsed.normalize_at_point(4505.0, 1000.0, dx=50)
avg = numpy.average(numpy.append(numpy.arange(4456, 4555), [450]))
control_norm_constant = 1000.0 / avg
self.assertAlmostEqual(norm_aggsed.norm_constant, control_norm_constant)
os.system('rm '+test_directory+'stacked_spectra_maskcc10.dat')
files = os.listdir(test_directory)
specs = []
counter = 0
while counter < 14:
spec = load_sed(test_directory+files[counter], sed_type="spectrum")
specs.append(spec)
counter += 1
#specs = [load_sed(test_directory+f, sed_type="spectrum") for f in files]
for spec in specs:
spec.z = np.random.random_sample()
aggsed = AggregateSed(specs)
norm_aggsed = aggsed.normalize_at_point(3500.0, 1.0)
stack_spectra = stack(norm_aggsed, 10.0, 'avg', fill='fill')
stack_spectra.write(test_directory+'stacked_spectra_maskcc10.dat')
stack_spectra = load_sed(test_directory+'stacked_spectra_maskcc10.dat')
end = time.clock()
plt.plot(stack_.x, stack_.y)
plt.show()
print ''
