def test_region(self):
pos = SkyCoord(81, 21, unit='deg', frame='icrs')
radius = Angle(1, 'deg')
circ = CircleSkyRegion(pos, radius)
idx = circ.contains(self.events.radec)
table = self.events.table[idx]
assert_allclose(table[4]['RA'], 81, rtol=1)
assert_allclose(table[2]['DEC'], 21, rtol=1)
assert len(table) == 5
def test_region(self):
pos = SkyCoord(81, 21, unit='deg', frame='icrs')
radius = Angle(1, 'deg')
circ = CircleSkyRegion(pos, radius)
idx = circ.contains(self.events.radec)
table = self.events.table[idx]
assert_allclose(table[4]['RA'], 81, rtol=1)
assert_allclose(table[2]['DEC'], 21, rtol=1)
assert len(table) == 5
def test_EventList_region():
filename = gammapy_extra.filename("test_datasets/unbundled/hess/run_0023037_hard_eventlist.fits.gz")
event_list = EventList.read(filename, hdu="EVENTS")
pos = SkyCoord(81, 21, unit="deg", frame="icrs")
radius = Angle(1, "deg")
circ = CircleSkyRegion(pos, radius)
idx = circ.contains(event_list.radec)
filtered_list = event_list[idx]
assert_allclose(filtered_list[4]["RA"], 81, rtol=1)
assert_allclose(filtered_list[2]["DEC"], 21, rtol=1)
assert len(filtered_list) == 5
def test_EventList_region():
filename = gammapy_extra.filename(
'test_datasets/unbundled/hess/run_0023037_hard_eventlist.fits.gz')
event_list = EventList.read(filename, hdu='EVENTS')
pos = SkyCoord(81, 21, unit='deg', frame='icrs')
radius = Angle(1, 'deg')
circ = CircleSkyRegion(pos, radius)
idx = circ.contains(event_list.radec)
filtered_list = event_list[idx]
assert_allclose(filtered_list[4]['RA'], 81, rtol=1)
assert_allclose(filtered_list[2]['DEC'], 21, rtol=1)
assert len(filtered_list) == 5
# We first create a circular region centered on a given SkyCoord with a given radius.
# In[30]:
from regions import CircleSkyRegion
#center = SkyCoord.from_name("M31")
center = SkyCoord(ra='0h42m44.31s', dec='41d16m09.4s')
circle_region = CircleSkyRegion(center=center, radius=Angle(50, 'deg'))
# We now use the contains method to search objects in this circular region in the sky.
# In[31]:
in_region = circle_region.contains(coord)
# In[32]:
fig = plt.figure(figsize=(14, 10))
ax = fig.add_subplot(111, projection="aitoff")
ax.scatter(coord[in_region].l.radian, coord[in_region].b.radian)
# ### Tables and pandas
#
# [pandas](http://pandas.pydata.org/) is one of the most-used packages in the scientific Python stack. Numpy provides the `ndarray` object and functions that operate on `ndarray` objects. Pandas provides the `Dataframe` and `Series` objects, which roughly correspond to the Astropy `Table` and `Column` objects. While both `pandas.Dataframe` and `astropy.table.Table` can often be used to work with tabular data, each has features that the other doesn't. When Astropy was started, it was decided to not base it on `pandas.Dataframe`, but to introduce `Table`, mainly because `pandas.Dataframe` doesn't support multi-dimensional columns, but FITS does and astronomers use sometimes.
#
# But `pandas.Dataframe` has a ton of features that `Table` doesn't, and is highly optimised, so if you find something to be hard with `Table`, you can convert it to a `Dataframe` and do your work there. As explained in the [interfacing with the pandas package](http://docs.astropy.org/en/stable/table/pandas.html) page in the Astropy docs, it is easy to go back and forth between Table and Dataframe:
#
# table = Table.from_pandas(dataframe)
# dataframe = table.to_pandas()
def num_rings(t, index, tdetect, rings):
"""
Find the total number of HETDEX sources from tdetect inside a set of scaled ellipsoidal rings.
Input
t - the RC3 catalog table
index - the index corresponding to the specific galaxy
tdetect - the HETDEX ctalog
rings = np.array([0.5, 1.0, 1.5, 2.0, 2.5, 3.5])
"""
# Read in specific galaxy parameters from the table.
c1 = SkyCoord(t["Coords"][index], frame="icrs")
pgc = t["PGC"][index]
name = t["Name1"][index]
major = t["SemiMajorAxis"][index] * u.arcmin
minor = t["SemiMinorAxis"][index] * u.arcmin
pa = t["PositionAngle"][index] * u.deg
source_scale = rings[-1]
rlimit = major * source_scale
nrings = len(rings)
temprings = np.zeros(nrings)
# Now, create a list of sources near the galaxy from the detect table.
catalog = SkyCoord(tdetect["ra"], tdetect["dec"], unit=(u.deg, u.deg))
d2d = c1.separation(catalog) # Find all of separations
catalogmsk = d2d < rlimit # Create Boolean mask of objects inside of this
near_sources = catalog[catalogmsk]
# Create fake WCS, centered on the galaxy
mywcs = create_dummy_wcs(c1)
# Loop over all but the last ring, and find the number of sources within each scaled ellipse
for i in range(0, nrings - 1):
scale = rings[i]
ellipse = create_ellreg(t, index, d25scale=scale)
nellipse = 0
for skycoord in near_sources:
if ellipse.contains(skycoord, mywcs):
nellipse += 1
temprings[i] = nellipse
# Finally, for the last ring, find the number of sources within the large outer circle
# Create the outerRing circle, which shows the radius that we searched to.
ntotal = 0
outerRing = CircleSkyRegion(center=c1, radius=rlimit)
for skycoord in near_sources:
if outerRing.contains(skycoord, mywcs):
ntotal += 1
temprings[-1] = ntotal
outrings = temprings
return outrings