def test_project_2d_random(self):
r = 1
R, x, y = velocity_util.project2d_random(r=r)
assert R <= r
r = np.linspace(0, 10, 100)
R, x, y = velocity_util.project2d_random(r=r)
assert len(R) == 100
def test_project_2d_random(self):
r = 1
R, x, y = velocity_util.project2d_random(r=r)
assert R <= r
num = 100000
r = np.ones(num)
R, x, y = velocity_util.project2d_random(r=r)
assert len(R) == num
def test_draw_light_3d(self):
lightProfile = LightProfile(profile_list=['HERNQUIST'],
min_interpolate=0.0001,
max_interpolate=100.)
kwargs_profile = [{'amp': 1., 'Rs': 0.5}]
r_list = lightProfile.draw_light_3d(kwargs_profile,
n=100000,
new_compute=False)
print(r_list, 'r_list')
# project it
R, x, y = velocity_util.project2d_random(r_list)
# test with draw light 2d profile routine
bins = np.linspace(0.1, 1, 10)
hist, bins_hist = np.histogram(r_list, bins=bins, density=True)
bins_plot = (bins_hist[1:] + bins_hist[:-1]) / 2.
light3d = lightProfile.light_3d(r=bins_plot,
kwargs_list=kwargs_profile)
light3d *= bins_plot**2
light3d /= np.sum(light3d)
hist /= np.sum(hist)
#import matplotlib.pyplot as plt
#plt.plot(bins_plot , light3d/light3d[5], label='3d reference')
#plt.plot(bins_plot, hist / hist[5], label='hist')
#plt.legend()
#plt.show()
print(light3d / hist)
npt.assert_almost_equal(light3d[5] / hist[5], 1, decimal=1)
def draw_light(self, kwargs_light):
"""
:param kwargs_light: keyword argument (list) of the light model
:return: 3d radius (if possible), 2d projected radius, x-projected coordinate, y-projected coordinate
"""
r = self.lightProfile.draw_light_3d(kwargs_light, n=1)[0]
R, x, y = util.project2d_random(r)
return r, R, x, y
def draw_light(kwargs_light):
"""
:param kwargs_light: keyword argument (list) of the light model
:return: 3d radius (if possible), 2d projected radius, x-projected coordinate, y-projected coordinate
"""
if 'a' not in kwargs_light:
kwargs_light['a'] = 0.551 * kwargs_light['r_eff']
a = kwargs_light['a']
r = vel_util.draw_hernquist(a)
R, x, y = vel_util.project2d_random(r)
return r, R, x, y
def test_draw_light_3d_hernquist(self):
lightProfile = LightProfile(profile_list=['HERNQUIST'], min_interpolate=0.0001, max_interpolate=1000.)
kwargs_profile = [{'amp': 1., 'Rs': 0.5}]
r_list = lightProfile.draw_light_3d(kwargs_profile, n=1000000, new_compute=False)
print(r_list, 'r_list')
# project it
# test with draw light 2d profile routine
# compare with 3d analytical solution vs histogram binned
bins = np.linspace(0.0, 10, 20)
hist, bins_hist = np.histogram(r_list, bins=bins, density=True)
bins_plot = (bins_hist[1:] + bins_hist[:-1]) / 2.
light3d = lightProfile.light_3d(r=bins_plot, kwargs_list=kwargs_profile)
light3d *= bins_plot ** 2
light3d /= np.sum(light3d)
hist /= np.sum(hist)
#import matplotlib.pyplot as plt
#plt.plot(bins_plot , light3d/light3d[5], label='3d reference Hernquist')
#plt.plot(bins_plot, hist / hist[5], label='hist')
#plt.legend()
#plt.show()
print(light3d / hist)
#npt.assert_almost_equal(light3d / hist, 1, decimal=1)
# compare with 2d analytical solution vs histogram binned
#bins = np.linspace(0.1, 1, 10)
R, x, y = velocity_util.project2d_random(np.array(r_list))
hist_2d, bins_hist = np.histogram(R, bins=bins, density=True)
hist_2d /= np.sum(hist_2d)
bins_plot = (bins_hist[1:] + bins_hist[:-1]) / 2.
light2d = lightProfile.light_2d(R=bins_plot, kwargs_list=kwargs_profile)
light2d *= bins_plot ** 1
light2d /= np.sum(light2d)
light2d_finite = lightProfile.light_2d_finite(R=bins_plot, kwargs_list=kwargs_profile)
light2d_finite *= bins_plot ** 1
light2d_finite /= np.sum(light2d_finite)
hist /= np.sum(hist)
#import matplotlib.pyplot as plt
#plt.plot(bins_plot, light2d/light2d[5], '--', label='2d reference Hernquist')
#plt.plot(bins_plot, light2d_finite / light2d_finite[5], '-.', label='2d reference Hernquist finite')
#plt.plot(bins_plot, hist_2d / hist_2d[5], label='hist')
#plt.legend()
#plt.show()
print(light2d / hist_2d)
#plt.plot(R, r_list, '.', label='test')
#plt.legend()
#plt.xlim([0, 0.2])
#plt.ylim([0, 0.2])
#plt.show()
npt.assert_almost_equal(light2d / hist_2d, 1, decimal=1)
def draw_light(self, kwargs_light):
"""
:param kwargs_light: keyword argument (list) of the light model
:return: 3d radius (if possible), 2d projected radius, x-projected coordinate, y-projected coordinate
"""
r = self.lightProfile.draw_light_3d(kwargs_light, n=1)[0]
R, x, y = util.project2d_random(r)
# this code is a remnant of the 2d-only rendering
# (can be used when accurate luminosity-weighted integrated velocity dispersion predictions are made)
# R = self.lightProfile.draw_light_2d(kwargs_light, n=1)[0]
# x, y = util.draw_xy(R)
# r = None
return r, R, x, y
def test_draw_light_3d_power_law(self):
lightProfile = LightProfile(profile_list=['POWER_LAW'], min_interpolate=0.0001, max_interpolate=1000.)
kwargs_profile = [{'amp': 1., 'gamma': 2, 'e1': 0, 'e2': 0}]
r_list = lightProfile.draw_light_3d(kwargs_profile, n=1000000, new_compute=False)
print(r_list, 'r_list')
# project it
R, x, y = velocity_util.project2d_random(r_list)
# test with draw light 2d profile routine
# compare with 3d analytical solution vs histogram binned
bins = np.linspace(0.1, 10, 10)
hist, bins_hist = np.histogram(r_list, bins=bins, density=True)
bins_plot = (bins_hist[1:] + bins_hist[:-1]) / 2.
light3d = lightProfile.light_3d(r=bins_plot, kwargs_list=kwargs_profile)
light3d *= bins_plot ** 2
light3d /= np.sum(light3d)
hist /= np.sum(hist)
#import matplotlib.pyplot as plt
#plt.plot(bins_plot , light3d/light3d[5], label='3d reference power-law')
#plt.plot(bins_plot, hist / hist[5], label='hist')
#plt.legend()
#plt.show()
print(light3d / hist)
npt.assert_almost_equal(light3d / hist, 1, decimal=1)
# compare with 2d analytical solution vs histogram binned
#bins = np.linspace(0.1, 1, 10)
hist, bins_hist = np.histogram(R, bins=bins, density=True)
bins_plot = (bins_hist[1:] + bins_hist[:-1]) / 2.
light2d = lightProfile.light_2d_finite(R=bins_plot, kwargs_list=kwargs_profile)
light2d *= bins_plot ** 1
light2d /= np.sum(light2d)
hist /= np.sum(hist)
#import matplotlib.pyplot as plt
#plt.plot(bins_plot , light2d/light2d[5], label='2d reference power-law')
#plt.plot(bins_plot, hist / hist[5], label='hist')
#plt.legend()
#plt.show()
print(light2d / hist)
npt.assert_almost_equal(light2d / hist, 1, decimal=1)