from astropy.table import Table, join
# In[2]:
import seaborn as sns
# This notebook uses all the raw data from the XID+PACS catalogue, maps, PSF and relevant MOCs to create XID+ prior object and relevant tiling scheme
# ## Read in MOCs
# The selection functions required are the main MOC associated with the masterlist. As the prior for XID+ is based on IRAC detected sources.
# In[3]:
#Sel_func=pymoc.MOC()
#Sel_func.read('../../dmu4/dmu4_sm_GAMA-09/data/holes_GAMA-09_ukidss_k_O16_20180417.fits')
Final = pymoc.MOC()
Final.read('./data/testMoc.fits')
# ## Read in CIGALE predictions catalogue
# In[4]:
cigale = Table.read(
'../../dmu28/dmu28_GAMA-09/data/GAMA9_Ldust_prediction_results.fits')
# In[5]:
cigale['id'].name = 'help_id'
# In[6]:
'_20190328.fits')
centre = np.long((MIPS_psf[1].header['NAXIS1'] - 1) / 2)
radius = 5
# ## Read in MOCs & Files
# The selection functions required are the main MOC associated with the masterlist. Here we use the DataFusion-Spitzer MOC.
print('read in MOCs')
filename = np.sort(
glob.glob(f"../../dmu17/dmu17_HELP-SEIP-maps/{field}/data/*help.fits",
recursive=True))
moc_file = np.sort(
glob.glob(f"../../dmu17/dmu17_HELP-SEIP-maps/{field}/data/*moc.fits",
recursive=True))
Sel_func = pymoc.MOC()
Sel_func.read('../../dmu4/dmu4_sm_' + field +
'/data/holes_Herschel-Stripe-82_irac_i1_O16_20180423.fits')
######################################################################
with open('./data/large_tiles.csv', 'w') as l_tiles_file:
tiles_writer = csv.writer(l_tiles_file, delimiter=',')
tiles_writer.writerow(['job_array'])
l_tiles_file.close()
with open('./data/tiles.csv', 'w') as tiles_file:
tiles_writer = csv.writer(tiles_file, delimiter=',')
tiles_writer.writerow(['job_array'])
tiles_file.close()
centre = np.long((MIPS_psf[1].header['NAXIS1'] - 1) / 2)
radius = 5
######################################################################
# ## Read in MOCs & Files
# The selection functions required are the main MOC associated with the masterlist. Here we use the DataFusion-Spitzer MOC.
print('read in MOCs')
filename = np.sort(
glob.glob(f"../../dmu17/dmu17_HELP_SEIP_maps/{field}/data/*help.fits",
recursive=True))
moc_file = np.sort(
glob.glob(f"../../dmu17/dmu17_HELP_SEIP_maps/{field}/data/*moc.fits",
recursive=True))
dmu0_MOC = pymoc.MOC()
dmu0_MOC.read('../../dmu0/dmu0_NEP-Spitzer/data/NEP-Spitzer-APJ_MOC.fits')
holes = pymoc.MOC()
holes.read('../../dmu4/dmu4_sm_AKARI-NEP/data/holes_AKARI-NEP_O16_MOC.fits')
Sel_func = dmu0_MOC.intersection(holes)
######################################################################
with open('./data/large_tiles.csv', 'w') as l_tiles_file:
tiles_writer = csv.writer(l_tiles_file, delimiter=',')
tiles_writer.writerow(['job_array'])
l_tiles_file.close()
with open('./data/tiles.csv', 'w') as tiles_file:
import xidplus
import numpy as np
#get_ipython().run_line_magic('matplotlib', 'inline')
from astropy.table import Table, join
from astropy.io import fits
from astropy import wcs
import seaborn as sns
# This notebook uses all the raw data from the XID+PACS catalogue, maps, PSF and relevant MOCs to create XID+ prior object and relevant tiling scheme
# ## Read in MOCs
# The selection functions required are the main MOC associated with the masterlist. As the prior for XID+ is based on IRAC detected sources.
# In[ ]:
Sel_func = pymoc.MOC()
Sel_func.read(
'../../dmu4/dmu4_sm_NGP/data/holes_NGP_ukidss_k_O16_20190204_MOC.fits')
# ## Read in CIGALE predictions catalogue
# In[14]:
cigale = Table.read(
'../../dmu28/dmu28_NGP/data/NGP_results_Ldust_prediction.fits')
# In[5]:
cigale['id'].name = 'help_id'
# In[15]:
ax.set_xlim(0, 10)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
fig.savefig('pointing-offset.pdf', bbox_inches='tight', pad_inches=0.01)
#all sky noise map.
import pymoc
import healpy
averagerms = Table.read(allskynoise)
# Get a dictionary with HPX tilenumber as key.
hpxdict = dict(zip(averagerms['Tilenum'], averagerms['RMS']))
# Turn it into a MOC
mymoc = pymoc.MOC(order=6, cells=hpxdict.keys())
# Turn it into a np array of the right setup for healpy.
order = mymoc.order
skymap = np.zeros(12 * 4**order)
for cell in mymoc.flattened(order):
skymap[cell] = hpxdict[cell]
maskedmap = healpy.ma(skymap, badval=0)
fig = plt.figure(figsize=(7, 5))
cmap = matplotlib.cm.gist_heat
cmap.set_bad('0.7')
cmap.set_under('white')
healpy.visufunc.mollview(maskedmap,
unit=r'mJy arcsec$^{-2}$',
