def get_gal(c):
gl_all = Longitude(c.galactic.l)
gl_all.wrap_angle = 180 * u.deg
gl_start = np.array(gl_all.rad[np.argmax(gl_all) + 1:])
gl_end = np.array(gl_all.rad[:np.argmax(gl_all)])
gb_start = np.array(c.galactic.b.rad[np.argmax(gl_all) + 1:])
gb_end = np.array(c.galactic.b.rad[:np.argmax(gl_all)])
gl = np.concatenate((gl_start, gl_end)) * (-1)
gb = np.concatenate((gb_start, gb_end))
gl0 = np.full(90, gl[0])
gl1 = np.full(90, gl[len(gl) - 1])
gb0 = np.linspace(-1.57, gb[0], 90)
gb1 = np.linspace(gb[len(gb) - 1], -1.57, 90)
for i in range(len(gl) - 1):
if (gl[i] * gl[i + 1] < 0 and gl[i] > 3):
print gl[i], gl[i + 1]
gl2 = np.full(90, gl[i])
gb2 = np.linspace(gb[i], 1.57, 90)
gl3 = np.full(90, gl[i + 1])
gb3 = np.linspace(1.57, gb[i + 1], 90)
gl = np.concatenate((gl0, gl[:i], gl2, gl3, gl[i + 1:], gl1))
gb = np.concatenate((gb0, gb[:i], gb2, gb3, gb[i + 1:], gb1))
break
print i
return gl, gb
def preprocessing(data, sample_fctr=1, threshold=9500):
global sample_factor
sample_factor = sample_fctr
# 1/ transforms parallax data into distances in parsec
data_metrics = data
data_metrics['parallax'] = data_metrics['parallax'].to(
u.parsec, equivalencies=u.parallax())
# 2/ Adapt data metrics to a numpy array
np_data_metrics = np.transpose(
np.array([
data_metrics['ra'], data_metrics['dec'], data_metrics['parallax'],
data_metrics['pmra'], data_metrics['pmdec']
]))
# 3/ Sample data: if a specific sampling is requested or too many points
n_points = len(np_data_metrics)
if n_points * sample_factor > threshold:
sample_factor = threshold / n_points
if (sample_factor != 1):
np.random.seed(0)
idx = np.random.choice(np_data_metrics.shape[0],
replace=False,
size=int(sample_factor *
np_data_metrics.shape[0]))
np_data_metrics = np_data_metrics[idx, :]
# 4/ Change coordinates from Spherical to Cartesian coordinate system
ra = Longitude(np_data_metrics[:, 0], unit=u.deg)
ra.wrap_angle = 180 * u.deg
dec = Latitude(np_data_metrics[:, 1], unit=u.deg)
dist = Distance(np_data_metrics[:, 2], unit=u.parsec)
sphe_coordinates = SkyCoord(ra,
dec,
distance=dist,
frame='icrs',
representation_type='spherical')
cart_coordinates = sphe_coordinates.cartesian
# 5/ Adapt data to normalize it correctly
data_sphe_adapted = np.transpose(
np.array([
sphe_coordinates.ra, sphe_coordinates.dec,
sphe_coordinates.distance
]))
data_cart_adapted = np.transpose(
np.array([cart_coordinates.x, cart_coordinates.y, cart_coordinates.z]))
data_pm_adapted = np_data_metrics[:, 3:5]
data_all_adapted = np.append(data_cart_adapted, data_pm_adapted, axis=1)
return (data_sphe_adapted, data_cart_adapted, data_all_adapted)
def preprocessing(data, sample_factor=None):
#Preprocessing
data_metrics = data
data_metrics['parallax'] = data_metrics['parallax'].to(
u.parsec, equivalencies=u.parallax())
#Adapt data metrics to a numpy array
np_data_metrics = np.transpose(
np.array([
data_metrics['ra'], data_metrics['dec'], data_metrics['parallax'],
data_metrics['pmra'], data_metrics['pmdec']
]))
#Sample data
if (sample_factor != None):
np.random.seed(0)
idx = np.random.choice(np_data_metrics.shape[0],
size=int(sample_factor *
np_data_metrics.shape[0]),
replace=False)
np_data_metrics = np_data_metrics[idx, :]
#Change coordinates from Spherical to Cartesian coordinate system
ra = Longitude(np_data_metrics[:, 0], unit=u.deg)
ra.wrap_angle = 180 * u.deg
dec = Latitude(np_data_metrics[:, 1], unit=u.deg)
dist = Distance(np_data_metrics[:, 2], unit=u.parsec)
sphe_coordinates = SkyCoord(ra,
dec,
distance=dist,
frame='icrs',
representation_type='spherical')
cart_coordinates = sphe_coordinates.cartesian
#Adapt data to normalize it correctly
data_sphe_adapted = np.transpose(
np.array([
sphe_coordinates.ra, sphe_coordinates.dec,
sphe_coordinates.distance
]))
data_cart_adapted = np.transpose(
np.array([cart_coordinates.x, cart_coordinates.y, cart_coordinates.z]))
data_pm_adapted = np_data_metrics[:, 3:5]
data_all_adapted = np.append(data_cart_adapted, data_pm_adapted, axis=1)
return (data_sphe_adapted, data_cart_adapted, data_all_adapted)
# To plot the celestial equator in galactic coordinates
alpha = np.linspace(-180., 180., 3600.)
delta0 = np.zeros(len(alpha))
cline_0 = SkyCoord(ra=alpha * u.degree, dec=(delta0 - 10) * u.degree)
gl_0, gb_0 = get_gal(cline_0)
cline_1 = SkyCoord(ra=alpha * u.degree, dec=(delta0 + 30) * u.degree)
gl_1, gb_1 = get_gal(cline_1)
sPath_1 = get_path(gl_1, gb_1)
cline_2 = SkyCoord(ra=alpha * u.degree, dec=(delta0 - 50) * u.degree)
gl_2, gb_2 = get_gal(cline_2)
sPath_2 = get_path(gl_2, gb_2)
cpsr_S = SkyCoord(ra=ra_S * u.deg, dec=dec_S * u.deg)
gl_S = Longitude(cpsr_S.galactic.l)
gl_S.wrap_angle = 180 * u.deg
cpsr_N = SkyCoord(ra=ra_N * u.deg, dec=dec_N * u.deg)
gl_N = Longitude(cpsr_N.galactic.l)
gl_N.wrap_angle = 180 * u.deg
cpsr_MWA = SkyCoord(ra=ra_MWA * u.deg, dec=dec_MWA * u.deg)
gl_MWA = Longitude(cpsr_MWA.galactic.l)
gl_MWA.wrap_angle = 180 * u.deg
plt.figure(figsize=(10, 6), dpi=300)
bx = plt.subplot(111, projection='mollweide')
#bx = plt.subplot(111, projection = 'aitoff')
p_S = bx.scatter(-gl_S.rad,
cpsr_S.galactic.b.rad,
1.5,
lw=0,
def displayText(self, value, locale):
a = Longitude(value, u.deg)
a.wrap_angle = 180 * u.deg
value = str(
a.to_string(unit=u.degree, precision=0, sep=("°", "'", "''")))
return super(LongDelegate, self).displayText(value, locale)