def _get_components_est(self, comp, instr, data, beta, pixok):
print(beta, beta.shape)
"""
This function estimate components from MixingMatrix of fgbuster with estimated parameters
"""
nside = 256
A = fgbuster.mixingmatrix.MixingMatrix(*comp)
A_ev = A.evaluator(self.config['frequency'])
invN = np.diag(hp.nside2resol(nside, arcmin=True) / (instr.depth_p))**2
newmaps = np.zeros((len(comp), 3, 12 * nside**2))
if beta.shape[-1] >= 12:
ind = np.where(pixok != 0)[0]
maps_separe = np.zeros((len(comp), 2, 12 * nside**2))
invN = np.diag(
hp.nside2resol(nside, arcmin=True) / (instr.depth_p))**2
for i in range(len(ind)):
A_maxL = A_ev(np.array(beta)[:, ind[i]])
maps_separe[:, :, ind[i]] = fgbuster.algebra.Wd(A_maxL,
data[:, :,
ind[i]].T,
invN=invN).T
else:
A_maxL = A_ev(beta)
maps_separe = fgbuster.algebra.Wd(A_maxL, data.T, invN=invN).T
newmaps[:, 1:, :] = maps_separe.copy()
return newmaps
def __init__(self,
nside=128,
lonCol='fieldRA',
latCol='fieldDec',
verbose=True,
badval=hp.UNSEEN,
useCache=True,
leafsize=100,
radius=1.75,
useCamera=False,
rotSkyPosColName='rotSkyPos',
mjdColName='expMJD',
chipNames='all'):
"""Instantiate and set up healpix slicer object."""
super(HealpixSlicer, self).__init__(verbose=verbose,
lonCol=lonCol,
latCol=latCol,
badval=badval,
radius=radius,
leafsize=leafsize,
useCamera=useCamera,
rotSkyPosColName=rotSkyPosColName,
mjdColName=mjdColName,
chipNames=chipNames)
# Valid values of nside are powers of 2.
# nside=64 gives about 1 deg resolution
# nside=256 gives about 13' resolution (~1 CCD)
# nside=1024 gives about 3' resolution
# Check validity of nside:
if not (hp.isnsideok(nside)):
raise ValueError('Valid values of nside are powers of 2.')
self.nside = int(nside)
self.pixArea = hp.nside2pixarea(self.nside)
self.nslice = hp.nside2npix(self.nside)
self.spatialExtent = [0, self.nslice - 1]
self.shape = self.nslice
if self.verbose:
print('Healpix slicer using NSIDE=%d, ' % (self.nside) + \
'approximate resolution %f arcminutes' % (hp.nside2resol(self.nside, arcmin=True)))
# Set variables so slicer can be re-constructed
self.slicer_init = {
'nside': nside,
'lonCol': lonCol,
'latCol': latCol,
'radius': radius
}
if useCache:
# useCache set the size of the cache for the memoize function in sliceMetric.
binRes = hp.nside2resol(nside) # Pixel size in radians
# Set the cache size to be ~2x the circumference
self.cacheSize = int(np.round(4. * np.pi / binRes))
# Set up slicePoint metadata.
self.slicePoints['nside'] = nside
self.slicePoints['sid'] = np.arange(self.nslice)
self.slicePoints['ra'], self.slicePoints['dec'] = self._pix2radec(
self.slicePoints['sid'])
# Set the default plotting functions.
self.plotFuncs = [
HealpixSkyMap, HealpixHistogram, HealpixPowerSpectrum
]
def get_noise(self, sensitiviy_mode=2, one_over_f_mode=2):
# TODO: put SAT and LAT caracteristics in class atributes
if self.instrument == 'SAT':
print('SAT white noise')
# noise_covariance = np.eye(12)
# inv_noise = np.eye(12)
white_noise = np.repeat(
V3.so_V3_SA_noise(sensitiviy_mode, one_over_f_mode, 1, 0.1,
self.nside * 3)[2], 2, 0)
# noise_covariance = np.diag(
# (white_noise / hp.nside2resol(self.nside, arcmin=True))**2)
# inv_noise = np.diag((hp.nside2resol(
# self.nside, arcmin=True)/white_noise)**2)
#
# noise_N_ell = np.repeat(
# V3.so_V3_SA_noise(sensitiviy_mode, one_over_f_mode, 1, 0.1,
# self.nside*3, beam_corrected=True)[1],
# 2, 0)
# ells = np.shape(noise_N_ell)[-1]
# noise_cov_ell = [np.diag(noise_N_ell[:, k]) for k in range(ells)]
# inv_noise_cov_ell = [np.diag(1/noise_N_ell[:, k])
# for k in range(ells)]
elif self.instrument == 'LAT':
white_noise = np.repeat(V3.so_V3_LA_noise(0, 0.4, 5000)[2], 2, 0)
noise_covariance = np.diag(
(white_noise / hp.nside2resol(self.nside, arcmin=True))**2)
inv_noise = np.diag(
1 / (white_noise / hp.nside2resol(self.nside, arcmin=True))**2)
elif self.instrument == 'LiteBIRD':
sensitivity = np.array([
37.5, 24.0, 19.9, 16.2, 13.5, 11.7, 9.2, 7.6, 5.9, 6.5, 5.8,
7.7, 13.2, 19.5, 37.5
])
elif self.instrument == 'Planck':
print('Planck white noise')
# noise_covariance = np.eye(12)
# inv_noise = np.eye(12)
white_noise = np.repeat(get_instrument('planck_P')['sens_P'], 2, 0)
noise_covariance = np.diag(
(white_noise / hp.nside2resol(self.nside, arcmin=True))**2)
inv_noise = np.diag(
(hp.nside2resol(self.nside, arcmin=True) / white_noise)**2)
# noise_N_ell = np.repeat(
# V3.so_V3_SA_noise(sensitiviy_mode, one_over_f_mode, 1, 0.1,
# self.nside*3, beam_corrected=True)[1],
# 2, 0)
# ells = np.shape(noise_N_ell)[-1]
# noise_cov_ell = [np.diag(noise_N_ell[:, k]) for k in range(ells)]
# inv_noise_cov_ell = [np.diag(1/noise_N_ell[:, k])
# for k in range(ells)]
self.noise_covariance = noise_covariance
self.inv_noise = inv_noise
def ToHealpix(self, nside, RA, Dec=None, T=None, replace=True):
'''
replace:
=True : if there are several values in 1 pixel, use the lagest value to fill this pixel (for diffuse emission)
=False : if there are several values in 1 pixel, fill this pixel with the sum of all values (for point sources)
RA, Dec:
in rad
RA or lon : 0 ~ 2pi
Dec or lat : -pi/2 ~ pi/2
if Dec=None and T=None :
RA, Dec, T = RA[0], RA[1], RA[2] = RA (ROW)
'''
import healpy as hp
import numpy as np
from jizhipy.Array import Asarray
from jizhipy.Basic import Raise
replace = bool(replace)
hp.nside2resol(nside)
if (Dec is None and T is None): RA, Dec, T = RA
RA, Dec, T = 1 * Asarray(RA).flatten(), 1 * Asarray(
Dec).flatten(), 1 * Asarray(T).flatten()
size = max(RA.size, Dec.size, T.size)
if ((RA.size not in [size, 1]) or (Dec.size not in [size, 1])
or (T.size not in [size, 1])):
Raise(
Exception, 'jizhipy.CoordTrans.ToHealpix(): RA.size=' +
str(RA.size) + ', Dec.size=' + str(Dec.size) + ', T.size=' +
str(T.size) + ' not in [' + str(size) + ',1]')
if (RA.size == 1): RA = np.zeros(size) + RA[0]
if (Dec.size == 1): Dec = np.zeros(size) + Dec[0]
if (T.size == 1): T = np.zeros(size) + T[0]
#----------------------------------------
if (replace):
T = np.sort(T + 1j * np.arange(size))
n = T.imag.astype(int)
T = T.real
RA, Dec = RA[n], Dec[n]
#----------------------------------------
hpmap = np.zeros(12 * nside**2)
pix = hp.ang2pix(nside, np.pi / 2 - Dec, RA)
if (replace):
hpmap[pix] = T
return hpmap
#----------------------------------------
for i in range(pix.size):
hpmap[pix[i]] += T[i]
return hpmap
def modify_input(m1, inst):
"""
Modify the input observations to match the desired output noise level.
Parameters
----------
* m1: object, contain the observations
* inst: object, contain the input parameters from the ini file
Outputs
----------
* m1: oject, modified input map
* sigma_t_theo: float, output level of noise [uk.sqrt(s)]
* sigma_p_theo: float, output level of noise [uk.arcmin]
"""
sigma_t_theo, sigma_p_theo = theoretical_noise_level_time_domain(
m1=m1,
pixel_size=hp.nside2resol(m1.mapinfo.nside),
net_per_array=inst.net_per_array,
cut=0.0,
calendar_time_out=inst.calendar_time,
efficiency_out=inst.efficiency,
freq_out=inst.sampling_freq,
tube_factor=inst.tube_factor,
verbose=inst.verbose)
prepare_map(m1, sigma_t_theo)
return m1, sigma_t_theo, sigma_p_theo
def __init__(self, srad=.8, midpoint=True, Nside=128, sigfactor=2):
self.sradius=srad/180*np.pi ##search radius around map pixel
self.midpoint = midpoint
self.Nside=Nside
self.q=Quasars(self.Nside)
self.d=Data(self.q, self.Nside)
if(midpoint):
self.outputname = "_%s_mid_%s" % (str(Nside), str(sigfactor))
else:
self.outputname = "_%s_%s_%s" % (str(Nside), str(srad), str(sigfactor))
self.pixid=self.q.getCover(Nside)
self.pixtheta,self.pixphi=hp.pix2ang(Nside,self.pixid)
self.Np=len(self.pixid) ## number of pixels
self.Nd=self.d.len()
self.setpix = set(self.pixid)
self.sigma=np.sqrt(4*np.pi/(12*self.Nside**2))
self.sigweight = sigfactor*self.sigma**2
self.resolution = hp.nside2resol(self.Nside)
self.setHpix()
print ("# of pixels in a map",self.Np)
print("# of pairs ", self.Nd)
self.run()
def test_attributes(self):
"""Test basic, default attributes are set on creation
"""
skymap = self.TEST_SKY_MAP
assert skymap.info is None
assert (not skymap.nest)
assert skymap.nside == self.NSIDE
assert skymap.npix == self.NPIX
assert_array_equal(skymap.pindex, numpy.arange(self.NPIX))
assert (not skymap.partial)
assert isinstance(skymap.explicit, dict)
assert len(skymap.explicit) == self.NPIX
assert skymap.resolution.value == healpy.nside2resol(
self.NSIDE, arcmin=True)
assert skymap.resolution.unit == units.arcmin
assert skymap.pixrad.value == healpy.max_pixrad(
self.NSIDE) * units.rad.to("arcmin")
assert skymap.pixrad.unit == units.arcmin
assert skymap.pixarea.value == healpy.nside2pixarea(
self.NSIDE, degrees=True)
assert skymap.pixarea.unit == units.deg ** 2
assert skymap.area.value == skymap.size * skymap.pixarea.value
assert skymap.area.unit == units.deg ** 2
assert isinstance(skymap.directions, numpy.ndarray)
assert_array_equal(skymap.directions.shape, (skymap.npix, 3))
def healpy_rejection_sampler(m, nsamples, jitter=False, ret_idx=False):
"""
Sampler to sample positions from given skymaps that are treated as PDFs.
Pixel indices are drawn according to the pixel weight defined by the map.
If 'jitter' is True, positions are sampled with a gaussian with width of
the pixel resolution, to escape from the healpix grid.
Paramters
---------
m : array-like
Valid healpy map.
nsamples : int
How many sampled to draw from the map.
jitter : bool, optional
If True, sample "ungridded" positions as explained above.
(default: False)
ret_idx bool, optional
If True, return the sampled map indices, too. (default: False)
Returns
-------
theta, phi : array-like, shape (nsamples)
Sampled positions in healpy coordinates.
idx : array-like, shape (nsamples), optional
Sampled map indices. If jitter is True, indices may not be the same as
obtained by ``hp.ang2pix`` using the sampled positions.
Only returned if `ret_idx` is True.
"""
if hp.maptype(m) != 0:
raise ValueError(
"Given map is no healpy map (-1) or a series of maps (>0) : " +
"{}.".format(hp.maptype(m)))
# Make sure the map is in pdf form, which is all positive and area = 1
m = norm_healpy_map(m)
# Get weights per pixel from normal space map and normalize
weights = m / np.sum(m)
# Draw N pixels from the weighted indices
NSIDE = hp.get_nside(m)
NPIX = hp.nside2npix(NSIDE)
idx = np.random.choice(np.arange(NPIX), size=nsamples, p=weights)
# Get angles from pix indices
theta, phi = hp.pix2ang(NSIDE, idx)
# Sample new angles from a gaussian with width of half the pixel resolution.
# Otherwise we get angles on a grid, exactly one position from one pixel.
if jitter:
res = hp.nside2resol(NSIDE) / 2.
theta = np.random.normal(theta, res)
phi = np.random.normal(phi, res)
theta, phi = wrap_theta_phi_range(theta, phi)
if ret_idx:
return theta, phi, idx
else:
return theta, phi
def __init__ (self, quas, dat, srad):
print("Quasar: ", quas)
print("Data: ", dat)
print("Search radius: ", srad)
Nside=128
self.sradius=srad ##search radius around map pixel
self.Nside=Nside
self.q=Quasars(self.Nside, quas)
self.d=Data(self.q, self.Nside, dat)
self.pixid=self.q.getCover(Nside)
self.pixtheta,self.pixphi=hp.pix2ang(Nside,self.pixid)
self.Np=len(self.pixid) ## number of pixels
self.Nd=self.d.len()
self.setpix = set(self.pixid)
self.sigma=np.sqrt(4*np.pi/(12*self.Nside**2))
self.sigweight = 2*self.sigma**2
self.resolution = hp.nside2resol(self.Nside)
self.setHpix()
print ("# of pixels in a map",self.Np)
print("# of pairs ", self.Nd)
self.run()
def define_survey_mask(random_catalogue_file):
"""Process a random catalogue file into a `healpy` sky map.
Parameters
----------
random_catalogue_file : *str or* :class:`pathlib.Path`
Random catalogue source file.
Returns
-------
sky_map : float :class:`numpy.ndarray`
Pixelated sky map for a veto mask.
"""
# Read catalogue RA and DEC columns.
with fits.open(random_catalogue_file) as random_catalogue_source:
# pylint: disable=no-member
random_catalogue = random_catalogue_source[1].data
dec, ra = random_catalogue['DEC'], random_catalogue['RA']
logger.info("Loaded random catalogue file %s.", random_catalogue_file)
# Transform coordinates and determine pixel map resolution.
hit_coords = sky_to_spherical(np.column_stack((dec, ra)))
resolution = hp.nside2resol(params.nside, arcmin=True) / 60
logger.info("Sky map resolution is %.2f degrees.", resolution)
# Pixelate catalogue map.
sky_map = np.zeros(hp.nside2npix(params.nside))
hit_pixels = hp.ang2pix(params.nside, hit_coords[:, 0], hit_coords[:, 1])
sky_map[hit_pixels] = 1
logger.info("Sky map pixelation of input catalogue finished.")
return sky_map
def make_healpix_map_for_energy_band(energy_band, order):
log.info(f'Making HEALPix map for energy band: {energy_band} and order: {order}')
# Select events in energy band
table = Table.read('input_data/fermi_hgps_events_selected.fits.gz', hdu=1)
energy = table['ENERGY'].quantity.to('GeV').value
mask = (energy_band['min'] <= energy) & (energy < energy_band['max'])
table = table[mask]
log.info(f'Number of events: {len(table)}')
# Bin the events into a HEALPix counts map
nside = hp.order2nside(order + shift_order)
ipix = hp.ang2pix(
nside=nside, nest=True, lonlat=True,
theta=table['L'], phi=table['B'],
)
npix = hp.nside2npix(nside)
log.debug(f'Number of pixels: {npix}')
resolution = np.rad2deg(hp.nside2resol(nside))
log.debug(f'Pixel resolution: {resolution} deg')
image = np.bincount(ipix, minlength=npix)
image = image.astype('float32')
# TODO: smoothing the HEALPix map with default setting is very slow.
# Maybe chunk the data into local WCS maps and then stitch back together?
# For now: no smoothing
# image = hp.smoothing(image, sigma=np.deg2rad(0.1))
path = DATA_DIR / 'maps' / 'Fermi10GeV_healpix_maps' / 'energy_{min}_{max}.fits.gz'.format_map(energy_band)
path.parent.mkdir(exist_ok=True, parents=True)
log.info(f'Writing {path}')
hp.write_map(str(path), image, coord='G', nest=True)
def calc_loc_area(self, cls=[.5,.9], **kwargs):
"""calculate sky localization region area (unit in sq. deg) for different confidence level region of trigger map
Parameters
----------
cls : `list`
confidence levels for contours,
e.g. cls=[.5, .9] would construct 50% and 90% CL contours
returns
----------
arealist : `dictionary`
dictionary of area corresponding input C.L.
Examples
--------
>>> from kobe import triggers
>>> a = triggers()
>>> a.url('https://gracedb.ligo.org/api/superevents/S190510g/files/bayestar.fits.gz')
>>> a.calc_loc_area()
{0.5: 575.5456172035578, 0.9: 3463.7648737864856}
>>> a.calc_loc_area(cls=[.1,.5,.99])
{0.1: 36.351906008208665, 0.5: 575.5456172035578, 0.99: 11508.39446313552}
"""
kwargs = self.setkeys(kwargs)
_ilist = self.calc_loc_contours(cls=cls, **kwargs)
if _ilist is None: return
nside = hp.get_nside(self.hpmap)
areapix = (hp.nside2resol(nside,arcmin=True)/60.)**2
_alist = {}
for _cl in cls:
_area = areapix*len(_ilist[_cl])
_alist[_cl] = _area
return _alist
def hp_project_script(hp_hdu, coord, shape_out, frame, alpha, delta):
#-------------------------------------------------
# Cut a small piece from the map, taking LMC and SMC for example
# examples of coordinates
# coord_LMC = SkyCoord("05:23:34.60", "-69:45:22.0", unit=(u.hourangle, u.deg))
# coord_SMC = SkyCoord("00h52m38s", "-72:48:01", unit=(u.hourangle, u.deg))
# coord_Perseus = SkyCoord(54, 31.5, frame = 'icrs', unit = 'deg')
# coord_CHA_II = SkyCoord(195, -77, frame = 'icrs', unit = 'deg')
pixsize = hp.nside2resol(hp_hdu.header['NSIDE'], arcmin=True) / 60 / 4
hdu = hp_project(
hp_hdu,
coord,
pixsize=pixsize,
shape_out=shape_out,
projection=("EQUATORIAL", "TAN"),
)
plt.title("healpix {0} at ({1}, {2})".format(frame, alpha, delta))
cut_data = hdu.data
cut_w = WCS(hdu.header)
fig = plt.subplot(111, projection=cut_w)
ax = plt.imshow(cut_data)
cbar = plt.colorbar(ax)
cbar.set_label(hp_hdu.header['UNIT'], rotation=270, labelpad=15)
plt.savefig("healpix_{0}_{1}_{2}.png".format(frame, alpha, delta))
hdu.writeto("healpix_{0}_{1}_{2}.fits".format(frame, alpha, delta),
overwrite=True)
return
def __init__(self, nside):
self.nside = nside
self.npix = hp.nside2npix(self.nside)
res = hp.nside2resol(nside, arcmin=True)
self.res_arcmin = res
logger.info("New Sphere, nside={}. npix={}, res={} arcmin".format(
self.nside, self.npix, self.res_arcmin))
self.pixel_indices = np.arange(self.npix)
theta, phi = hp.pix2ang(nside, self.pixel_indices)
# For limited fields of view
# healpy.query_polygon
self.pixels = np.zeros(self.npix) # + hp.UNSEEN
self.pixel_areas = 1.0 / np.sqrt(self.npix)
el_r, az_r = hp2elaz(theta, phi)
self.el_r = el_r
self.az_r = az_r
self.l, self.m, self.n = elaz2lmn(self.el_r, self.az_r)
self.n_minus_1 = self.n - 1
def _determine_steps(self, max_stepsize=None):
"""
"""
if max_stepsize is None:
max_stepsize = self.max_stepsize
return np.ceil(hp.nside2resol(self.nside) / _d2r / max_stepsize) + 1
def nside2resol(testcase):
cs = []
for norder in range(16):
nside = 1 << norder
args = (nside, )
cs.append(dict(args=args, expected=healpy.nside2resol(*args)))
testcase['nside2resol'] = cs
def make_healpix_image(plt, img, title, num_bins, source_json=None):
"""
Writes out an image as a healpy image
"""
nside = hp.pixelfunc.get_min_valid_nside(num_bins*num_bins*3/4)
npix = hp.nside2npix(nside)
pixels = np.arange(npix)
m = np.zeros(npix) + hp.UNSEEN
window_d = np.degrees(hp.nside2resol(nside))
for i in pixels:
theta, phi = hp.pix2ang(nside, i)
if (theta < np.pi / 2):
el = np.degrees(np.pi/2 - theta)
az = np.degrees(phi)
s = elaz.ElAz(el, az)
x_min, x_max, y_min, y_max, area = s.get_px_window(num_bins, window_deg=window_d)
s_px = img[y_min:y_max, x_min:x_max]
m[i] = np.sum(s_px)/area
hp.orthview(m, rot=(0, 90, 180), title=title, xsize=3000, cbar=False, half_sky=True)
hp.graticule()
if source_json is not None:
src_list = elaz.from_json(source_json, el_limit=20.0, jy_limit=1e4)
output_list = []
for s in src_list:
l, m = s.get_lm()
output_list.append(plt.Circle([-l, m], 0.03, color=(0.9, 0.2, 0.3), fill=False))
ax = plt.gca()
for circle in output_list:
ax.add_artist(circle)
def _getCountLocation(cat=None, ra=None, dec=None, nside=512, nest=False):
ipix = _es_hp.RaDec2Healpix(cat=cat, ra=ra, dec=dec, nside=nside, nest=nest)
bc = _np.bincount(ipix)
pixels = _np.nonzero(bc)[0]
bc = bc[bc>0] / _hp.nside2resol(nside, arcmin=True)**2 # in arcmin^-2
lon, lat = _es_hp.Healpix2RaDec(pixels, nside=nside, nest=nest)
return bc, lat, lon
def __init__(self, config):
# record for posterity
self.maskfile = config.maskfile
maskinfo, hdr = fitsio.read(config.maskfile,
ext=1,
header=True,
upper=True)
# maskinfo converted to a catalog (array of Entrys)
maskinfo = Catalog(maskinfo)
nside_mask = hdr['NSIDE']
nest = hdr['NEST']
hpix_ring = maskinfo.hpix if nest != 1 else hp.nest2ring(
nside_mask, maskinfo.hpix)
# if we have a sub-region of the sky, cut down the mask to save memory
if config.hpix > 0:
border = np.radians(config.border) + hp.nside2resol(nside_mask)
theta, phi = hp.pix2ang(config.nside, config.hpix)
radius = np.sqrt(2) * (hp.nside2resol(config.nside) / 2. + border)
pixint = hp.query_disc(nside_mask,
hp.ang2vec(theta, phi),
radius,
inclusive=False)
suba, subb = esutil.numpy_util.match(pixint, hpix_ring)
hpix_ring = hpix_ring[subb]
muse = subb
else:
muse = np.arange(hpix_ring.size, dtype='i4')
offset, ntot = np.min(hpix_ring) - 1, np.max(hpix_ring) - np.min(
hpix_ring) + 3
self.nside = nside_mask
self.offset = offset
self.npix = ntot
#ntot = np.max(hpix_ring) - np.min(hpix_ring) + 3
self.fracgood = np.zeros(ntot, dtype='f4')
# check if we have a fracgood in the input maskinfo
try:
self.fracgood_float = 1
self.fracgood[hpix_ring - offset] = maskinfo[muse].fracgood
except AttributeError:
self.fracgood_float = 0
self.fracgood[hpix_ring - offset] = 1
super(HPMask, self).__init__(config)
def run_paircount(self, maxang=10):
bw = 3.*hp.nside2resol(self.nside)*180./3.1416 # 3x resol.
bins = np.arange(bw, maxang, bw)
#delta_i, rani = hp.ud_grade(delta, res), hp.ud_grade(weight, res)
theta, phi, deltam, fpixm = maps2pcinput(self.delta, self.weight, self.mask)
w = paircount(theta, phi, deltam, deltam, fpixm, np.deg2rad(bins))
binc = 0.5*(bins[1:]+bins[:-1])
return [binc, w[0]/w[1]]
def best_order_for(pixel_scale):
d = pixel_scale
best_o = 1. / 2. * math.log(math.pi / (3. * d * d)) / math.log(2)
o = int(best_o + 1)
logging.info('pixel scale (arcsec): {:.4f} -> {:.4f}'.format(
rad2arcsec(pixel_scale),
rad2arcsec(healpy.nside2resol(healpy.order2nside(o)))))
return o
def pix2tiles(nside, pixels, tiles=None, radius=None):
'''
Returns subset of tiles that overlap the list of pixels
Args:
nside: integer healpix nside, 2**k with 1 <= k <= 30
pixels: array of integer pixels using nested numbering scheme
Optional:
tiles:
Table-like with RA,DEC columns; or
None to use all DESI tiles from desimodel.io.load_tiles()
radius: tile radius in degrees;
if None use desimodel.focalplane.get_tile_radius_deg()
Returns:
table of tiles that cover these pixels
TODO: add support for tiles as integers or list/array of integer TILEIDs
'''
import healpy as hp
import desimodel.footprint
if tiles is None:
import desimodel.io
tiles = desimodel.io.load_tiles()
if radius is None:
import desimodel.focalplane
radius = desimodel.focalplane.get_tile_radius_deg()
#- Trim tiles to ones that *might* overlap these pixels
theta, phi = hp.pix2ang(nside, pixels, nest=True)
ra, dec = np.degrees(phi), 90 - np.degrees(theta)
pixsize = np.degrees(hp.nside2resol(nside))
ii = desimodel.footprint.find_tiles_over_point(tiles,
ra,
dec,
radius=radius + pixsize)
if np.isscalar(pixels):
tiles = tiles[ii]
else:
ii = np.unique(np.concatenate(ii))
tiles = tiles[ii]
#- Now check in detail
theta, phi = np.radians(90 - tiles['DEC']), np.radians(tiles['RA'])
vec = hp.ang2vec(theta, phi)
ii = list()
for i in range(len(tiles)):
tilepix = hp.query_disc(nside,
vec[i],
radius=np.radians(radius),
inclusive=True,
nest=True)
if np.any(np.in1d(pixels, tilepix)):
ii.append(i)
return tiles[ii]
def calculate_nside_resolution():
NSIDE = [2**i for i in range(11)]
print('given nside | number of pixels | resolution (pixel size in degree) | Maximum angular distance (degree) | pixel area (in square degrees)')
for nside in NSIDE:
npix = hp.nside2npix(nside)
resol = np.rad2deg(hp.nside2resol(nside))
maxrad = np.rad2deg(hp.max_pixrad(nside))
pixarea = hp.nside2pixarea(nside, degrees=True)
print('{0:^11} | {1:^16} | {2:^33.4f} | {3:^33.4f} | {4:^30.6f}'.format(nside, npix, resol, maxrad, pixarea))
def randomPositions(input, nside_pix, n=1):
"""
Generate n random positions within a full HEALPix mask of booleans, or a set of (lon, lat) coordinates.
nside_pix is meant to be at coarser resolution than the input mask or catalog object positions
so that gaps from star holes, bleed trails, cosmic rays, etc. are filled in.
Return the longitude and latitude of the random positions and the total area (deg^2).
Probably there is a faster algorithm, but limited much more by the simulation and fitting time
than by the time it takes to generate random positions within the mask.
"""
input = numpy.array(input)
if len(input.shape) == 1:
subpix = numpy.nonzero(
input
)[0] # All the valid pixels in the mask at the NSIDE for the input mask
lon, lat = pix2ang(healpy.npix2nside(len(input)), subpix)
elif len(input.shape) == 2:
lon, lat = input[0], input[1] # All catalog object positions
else:
logger.warning(
'Unexpected input dimensions for skymap.randomPositions')
pix = surveyPixel(lon, lat, nside_pix)
# Area with which the random points are thrown
area = len(pix) * healpy.nside2pixarea(nside_pix, degrees=True)
lon = []
lat = []
for ii in range(0, n):
# Choose an unmasked pixel at random, which is OK because HEALPix is an equal area scheme
pix_ii = pix[numpy.random.randint(0, len(pix))]
lon_ii, lat_ii = ugali.utils.projector.pixToAng(nside_pix, pix_ii)
projector = ugali.utils.projector.Projector(lon_ii, lat_ii)
inside = False
while not inside:
# Apply random offset
arcminToDegree = 1 / 60.
resolution = arcminToDegree * healpy.nside2resol(nside_pix,
arcmin=True)
x = 2. * (numpy.random.rand() -
0.5) * resolution # Using factor 2 to be conservative
y = 2. * (numpy.random.rand() - 0.5) * resolution
lon_candidate, lat_candidate = projector.imageToSphere(x, y)
# Make sure that the random position does indeed fall within the randomly selected pixel
if ugali.utils.projector.angToPix(nside_pix, lon_candidate,
lat_candidate) == pix_ii:
inside = True
lon.append(lon_candidate)
lat.append(lat_candidate)
return numpy.array(lon), numpy.array(lat), area
def from_resolution(cls,
resolution=None,
nside=None,
theta=0.0,
phi=0.0,
radius=0.0):
# Theta is co-latitude measured southward from the north pole
# Phi is [0..2pi]
if nside is None: # Calculate nside to the appropriate resolution
nside = 1
while True:
res = hp.nside2resol(nside, arcmin=True)
logger.info("nside={} res={} arcmin".format(nside, res))
if res < resolution:
break
nside = nside * 2
ret = cls(nside)
# The coordinates of the unit vector defining the center
x0 = hp.ang2vec(theta, phi)
# https://healpy.readthedocs.io/en/latest/generated/healpy.query_polygon.html
ret.pixel_indices = hp.query_disc(nside=nside,
vec=x0,
radius=radius,
inclusive=False,
nest=False)
#self.pixel_indices = my_query_disk(nside, x0, radius)
ret.npix = ret.pixel_indices.shape[0]
ret.pixel_areas = 1.0 / np.sqrt(ret.npix)
logger.info("New SubSphere, nside={}. npix={}".format(
ret.nside, ret.npix))
theta, phi = hp.pix2ang(nside, ret.pixel_indices)
ret.fov = np.degrees(radius * 2)
ret.pixels = np.zeros(ret.npix) # + hp.UNSEEN
el_r, az_r = hp2elaz(theta, phi)
ret.el_r = el_r
ret.az_r = az_r
ret.l, ret.m, ret.n = elaz2lmn(ret.el_r, ret.az_r)
ret.n_minus_1 = ret.n - 1
if False:
import matplotlib.pyplot as plt
#plt.plot(ret.l, ret.m, 'x')
plt.plot(el_r, az_r, 'x')
plt.show()
return ret
def run(self):
#bw = 3.*hp.nside2resol(self.nside)*180./3.1416 # 3x resol.
#bins = np.arange(bw, 10, bw)
bw = hp.nside2resol(self.nside)
bins = np.arange(bw, np.deg2rad(10), bw)[::-1]
#ti = time()
_,xi = XI(self.theta, self.phi, self.theta, self.phi, self.delta, self.delta2, self.weight, self.weight, bins, 0)
#print('took {}s for the auto correlation '.format(time()-ti))
self.bins = bins
self.output = dict(t=self.bins, w=xi, dmean1=self.mean1, dmean2=self.mean2)
def skypatch(phi, theta, radius, skyres=2.):
import healpy
baseres = (skyres / 4.) * np.pi/180. # require this resolution from healpix grid
nside = 2**int(min(10, np.ceil(np.log2(healpy.nside2resol(1) / baseres)))) # don't go past 2**10 (12.6 million pixels)
npix = healpy.nside2npix(nside)
p = healpy.ang2pix(nside, theta, phi)
n = np.zeros(npix)
n[p] = 1. # put all probability at (p, t)
m = healpy.smoothing(n, sigma=radius) # smooth out Gaussian
return m
def resolution(self):
"""Angular resolution (arcminutes) of pixels in this `SkyMap`
This returns an instance of `~astropy.units.Quantity` with explicit
units, which can then be converted by the user as-needed.
"""
return healpy.nside2resol(
self.nside,
arcmin=True,
) * units.Unit("arcmin")
def get_header_info(fits_map_filename):
h = fitsio.read_header(fits_map_filename, ext=1)
NSIDE = h["NSIDE"]
print("NSIDE: %i" % NSIDE)
print("approx. resolution: %3.1f deg" %
(hp.nside2resol(NSIDE, arcmin=True) / 60))
print("number of pixels: %i" % hp.nside2npix(NSIDE))
print("Process: %s" % h["PROCESS"])
print("Units: %s" % h["TUNIT1"])
return NSIDE
def _getCountLocation(cat=None, ra=None, dec=None, nside=512, nest=False):
ipix = _es_hp.RaDec2Healpix(cat=cat,
ra=ra,
dec=dec,
nside=nside,
nest=nest)
bc = _np.bincount(ipix)
pixels = _np.nonzero(bc)[0]
bc = bc[bc > 0] / _hp.nside2resol(nside, arcmin=True)**2 # in arcmin^-2
lon, lat = _es_hp.Healpix2RaDec(pixels, nside=nside, nest=nest)
return bc, lat, lon
def XIwindow(ouname, randoms, mask):
nside = hp.get_nside(randoms)
bw = hp.nside2resol(nside)
bins = np.arange(bw, np.pi, bw)[::-1]
cbins = np.cos(bins)
theta, phi = hp.pix2ang(nside, np.argwhere(mask).flatten())
delta = np.ones(mask.sum())
weight = randoms[mask]
counts = paircount(theta, phi, theta, phi, delta, delta, weight, weight,
cbins, 1)
return bins, counts
def __init__(self, nside, infile=None, frequency=None, **kwargs):
super().__init__(**kwargs)
self.type_ = "healpix"
self.nside = nside
self.pixelsize_ = (hp.nside2resol(self.nside, arcmin=True) *
AUC.arcmin2arcsec)
self.data = np.zeros(shape=hp.nside2npix(self.nside))
if infile is not None:
self.load(infile, frequency)
def get_lmax(nside, stop_threshold):
pixscale = hp.nside2resol(nside=nside)
sigmabeam = (pixscale) / pl.sqrt(8 * np.log(2))
ell = np.arange(3 * nside - 1)
arr = np.array([
(2 * l + 1) / 4.0 / np.pi * np.exp(-sigmabeam * l * (l + 1))
for l in ell
])
lmax = np.argmin(np.fabs(arr - stop_threshold))
return lmax, sigmabeam
def get_nearby_healpix_list(self, obs, sideHP=32768, nestedHP=True):
''' See original cnd.py for more details '''
# If arcsecRadius < hp.nside2resol(32768, arcmin = True)*60 [ which is ~ 6.4 arc-sec ], ...
# ... then only need to search adjacent HP
listHP = list(hp.get_all_neighbours(sideHP, obs.Healpix,
nest=nestedHP))
listHP.append(obs.Healpix)
# If arcsecRadius > hp.nside2resol(32768, arcmin = True)*60, then need to search neighbors of neighbours ...
arcsecRadius = self.get_arcsecRadius(obs)
if arcsecRadius > hp.nside2resol(sideHP, arcmin=True) * 60:
for i in range(
int(arcsecRadius //
(hp.nside2resol(sideHP, arcmin=True) * 60))):
tmpHP = [h for h in listHP]
for h in tmpHP:
listHP.extend(
list(hp.get_all_neighbours(sideHP, h, nest=nestedHP)))
listHP = list(set(listHP))
return listHP
def pixel_solid_angle(nside, arcmin=False):
"""
Gives the solid angle subtended by a pixel on a Healpix map with a given nside.
Default units ; steradian
if arcmin is True, units = arcmin^2
"""
pix_res = hp.nside2resol(nside, arcmin=True)
if arcmin:
pix_solid_angle = pix_res**2
else:
pix_solid_angle = OMEGA_ARCMIN_SQ*pix_res**2
return pix_solid_angle
def snapshot_fn(self, ra, dec, radius, value=1.0, smooth=False):
radius = max(radius, 2 * np.rad2deg(hp.nside2resol(self.nside)))
res = np.zeros_like(self.coverage)
dists = ang_sep(ra, dec, self.ra, self.dec)
idx = dists < radius
res[idx] = value
if smooth:
res = hp.smoothing(res, fwhm=np.deg2rad(self.r0), verbose=False)
return res
def __init__(self, nside=128, lonCol ='fieldRA',
latCol='fieldDec', latLonDeg=True, verbose=True, badval=hp.UNSEEN,
useCache=True, leafsize=100, radius=1.75,
useCamera=False, rotSkyPosColName='rotSkyPos',
mjdColName='observationStartMJD', chipNames='all'):
"""Instantiate and set up healpix slicer object."""
super(HealpixSlicer, self).__init__(verbose=verbose,
lonCol=lonCol, latCol=latCol,
badval=badval, radius=radius, leafsize=leafsize,
useCamera=useCamera, rotSkyPosColName=rotSkyPosColName,
mjdColName=mjdColName, chipNames=chipNames, latLonDeg=latLonDeg)
# Valid values of nside are powers of 2.
# nside=64 gives about 1 deg resolution
# nside=256 gives about 13' resolution (~1 CCD)
# nside=1024 gives about 3' resolution
# Check validity of nside:
if not(hp.isnsideok(nside)):
raise ValueError('Valid values of nside are powers of 2.')
self.nside = int(nside)
self.pixArea = hp.nside2pixarea(self.nside)
self.nslice = hp.nside2npix(self.nside)
self.spatialExtent = [0, self.nslice-1]
self.shape = self.nslice
if self.verbose:
print('Healpix slicer using NSIDE=%d, ' % (self.nside) + \
'approximate resolution %f arcminutes' % (hp.nside2resol(self.nside, arcmin=True)))
# Set variables so slicer can be re-constructed
self.slicer_init = {'nside': nside, 'lonCol': lonCol, 'latCol': latCol,
'radius': radius}
if useCache:
# useCache set the size of the cache for the memoize function in sliceMetric.
binRes = hp.nside2resol(nside) # Pixel size in radians
# Set the cache size to be ~2x the circumference
self.cacheSize = int(np.round(4.*np.pi/binRes))
# Set up slicePoint metadata.
self.slicePoints['nside'] = nside
self.slicePoints['sid'] = np.arange(self.nslice)
self.slicePoints['ra'], self.slicePoints['dec'] = self._pix2radec(self.slicePoints['sid'])
# Set the default plotting functions.
self.plotFuncs = [HealpixSkyMap, HealpixHistogram, HealpixPowerSpectrum]
def skyhist(posteriorsamples='posterior_samples.dat', skyres=2.):
import healpy
if type(posteriorsamples) is str:
posteriorsamples = loadposteriorsamples(posteriorsamples)
baseres = (skyres / 4.) * np.pi/180. # require this resolution from healpix grid
nside = 2**int(min(10, np.ceil(np.log2(healpy.nside2resol(1) / baseres)))) # don't go past 2**10 (12.6 million pixels)
npix = healpy.nside2npix(nside)
p = healpy.ang2pix(nside, posteriorsamples[:,2], posteriorsamples[:,1]) # convert (theta, phi) to healpix numbers
# n = np.bincount(p, minlength=npix) # bin the samples
n = np.zeros(npix)
n[:max(p)+1] = np.bincount(p) # support old version of numpy that does not have minlength argument
m = healpy.smoothing(n, sigma=skyres*np.pi/180.) # smoothed map
return m
def randomPositions(input, nside_pix, n=1):
"""
Generate n random positions within a full HEALPix mask of booleans, or a set of (lon, lat) coordinates.
nside_pix is meant to be at coarser resolution than the input mask or catalog object positions
so that gaps from star holes, bleed trails, cosmic rays, etc. are filled in.
Return the longitude and latitude of the random positions and the total area (deg^2).
Probably there is a faster algorithm, but limited much more by the simulation and fitting time
than by the time it takes to generate random positions within the mask.
"""
input = numpy.array(input)
if len(input.shape) == 1:
subpix = numpy.nonzero(input)[0] # All the valid pixels in the mask at the NSIDE for the input mask
lon, lat = pix2ang(healpy.npix2nside(len(input)), subpix)
elif len(input.shape) == 2:
lon, lat = input[0], input[1] # All catalog object positions
else:
logger.warning('Unexpected input dimensions for skymap.randomPositions')
pix = surveyPixel(lon, lat, nside_pix)
# Area with which the random points are thrown
area = len(pix) * healpy.nside2pixarea(nside_pix, degrees=True)
lon = []
lat = []
for ii in range(0, n):
# Choose an unmasked pixel at random, which is OK because HEALPix is an equal area scheme
pix_ii = pix[numpy.random.randint(0, len(pix))]
lon_ii, lat_ii = ugali.utils.projector.pixToAng(nside_pix, pix_ii)
projector = ugali.utils.projector.Projector(lon_ii, lat_ii)
inside = False
while not inside:
# Apply random offset
arcminToDegree = 1 / 60.
resolution = arcminToDegree * healpy.nside2resol(nside_pix, arcmin=True)
x = 2. * (numpy.random.rand() - 0.5) * resolution # Using factor 2 to be conservative
y = 2. * (numpy.random.rand() - 0.5) * resolution
lon_candidate, lat_candidate = projector.imageToSphere(x, y)
# Make sure that the random position does indeed fall within the randomly selected pixel
if ugali.utils.projector.angToPix(nside_pix, lon_candidate, lat_candidate) == pix_ii:
inside = True
lon.append(lon_candidate)
lat.append(lat_candidate)
return numpy.array(lon), numpy.array(lat), area
def process_healpix(filename, out_nside):
print 'Process {0} on {1}'.format(MP.current_process().name, filename)
pixval = HP.read_map(filename, verbose=False)
nside = HP.npix2nside(pixval.size)
out_pixres = HP.nside2resol(out_nside)
print 'Before: ', pixval.min(), pixval.max(), NP.mean(pixval), NP.std(pixval)
if nside > out_nside:
if nside/out_nside > 4:
pixval = HP.ud_grade(pixval, 4*out_nside)
nside = 4*out_nside
pix_smoothed = HP.smoothing(pixval, fwhm=out_pixres, regression=False, verbose=False)
pix_resampled = HP.ud_grade(pix_smoothed, out_nside)
print 'After: ', pixval.min(), pixval.max(), NP.mean(pix_resampled), NP.std(pix_resampled)
return pix_resampled
def __init__(self, nside=128, spatialkey1 ='fieldRA' , spatialkey2='fieldDec', verbose=True,
useCache=True, radius=1.75, leafsize=100, plotFuncs='all',
useCamera=False, rotSkyPosColName='rotSkyPos', mjdColName='expMJD'):
"""Instantiate and set up healpix slicer object."""
super(HealpixSlicer, self).__init__(verbose=verbose,
spatialkey1=spatialkey1, spatialkey2=spatialkey2,
badval=hp.UNSEEN, radius=radius, leafsize=leafsize,
plotFuncs=plotFuncs,
useCamera=useCamera, rotSkyPosColName=rotSkyPosColName,
mjdColName=mjdColName)
# Valid values of nside are powers of 2.
# nside=64 gives about 1 deg resolution
# nside=256 gives about 13' resolution (~1 CCD)
# nside=1024 gives about 3' resolution
# Check validity of nside:
if not(hp.isnsideok(nside)):
raise ValueError('Valid values of nside are powers of 2.')
self.nside = int(nside)
self.pixArea = hp.nside2pixarea(self.nside)
self.nslice = hp.nside2npix(self.nside)
if self.verbose:
print 'Healpix slicer using NSIDE=%d, '%(self.nside) + \
'approximate resolution %f arcminutes'%(hp.nside2resol(self.nside,arcmin=True))
# Set variables so slicer can be re-constructed
self.slicer_init = {'nside':nside, 'spatialkey1':spatialkey1, 'spatialkey2':spatialkey2,
'radius':radius}
if useCache:
# useCache set the size of the cache for the memoize function in sliceMetric.
binRes = hp.nside2resol(nside) # Pixel size in radians
# Set the cache size to be ~2x the circumference
self.cacheSize = int(np.round(4.*np.pi/binRes))
# Set up slicePoint metadata.
self.slicePoints['nside'] = nside
self.slicePoints['sid'] = np.arange(self.nslice)
self.slicePoints['ra'], self.slicePoints['dec'] = self._pix2radec(self.slicePoints['sid'])
def __init__(self, zpfile, fill_periphery=True, balrogprint=None):
'''
Input: name of SLR map FITS file
'''
self.balrogprint = balrogprint
if self.balrogprint is not None:
import balrog
self.balrog = balrog
self.zpfile = zpfile
zpshifts,hdr=fitsio.read(zpfile,ext=1,header=True)
self.nside = hdr['NSIDE']
self.zpshifts = zpshifts
self.fill_periphery = fill_periphery
# translate mags and indices...
self.nmag = self.zpshifts['ZP_SHIFT'][0,:].size
self.bands = np.zeros(self.nmag,dtype='S1')
for i in xrange(self.nmag):
magname = hdr['MAG%d' % (i)]
parts=magname.split('_')
# force to be upper case...
self.bands[i] = parts[len(parts)-1].upper()
self._Print("Using zpshift file: %s" % (self.zpfile))
self._Print("Bands: %s" % (str(self.bands)))
self._Print("NSIDE = %d (~%.1f arcmin)" % (self.nside, hp.nside2resol(self.nside, True)))
self._Print("Area = %.2f deg^2" % (hp.nside2pixarea(self.nside, degrees=True) * self.zpshifts.size))
# now we want to build the healpix map...
self.shiftmap = np.zeros((self.nmag,hp.nside2npix(self.nside)),dtype=np.float32) + hp.UNSEEN
for i in xrange(self.nmag):
# all 99s are un-fit
gd,=np.where(self.zpshifts['ZP_SHIFT'][:,i] < 90.0)
self.shiftmap[i,self.zpshifts['HPIX'][gd]] = self.zpshifts['ZP_SHIFT'][gd,i]
if (self.fill_periphery):
self._Print("Filling in periphery pixels...")
self._fill_periphery()
self._Print("Peripheral area:")
for i in xrange(self.nmag):
self._Print(" %s: %.2f deg^2" % (self.bands[i], self.peripheral_area[i]))
else:
self.peripheral_area = None
def gen_ref_EAs_grid(nside=8, psi_step=360):
npix = hp.nside2npix(nside)
resol = np.rad2deg(hp.nside2resol(nside))
theta, phi = hp.pix2ang(nside, range(npix))
theta, phi = np.rad2deg(theta), np.rad2deg(phi)
psi = np.arange(0, 360, psi_step)
# indexing = 'xy'
grid_theta, grid_psi = np.meshgrid(theta, psi)
grid_phi, _ = np.meshgrid(phi, psi)
EAs_tuple = (grid_phi.flatten(), grid_theta.flatten(), grid_psi.flatten())
EAs_grid = np.vstack(EAs_tuple).T
print('number of points on shpere: {0}. \n'
'resolution: {1:.2f} degree, step of inplane-rotation: {2} degree\n'
'total grid size: {3}'.format(npix, resol, psi_step, npix * 360 / psi_step))
return EAs_grid
def getCountAtLocations(ra, dec, nside=512, return_vertices=False):
"""Get number density of objects from RA/Dec in HealPix cells.
Requires: healpy
Args:
ra: list of rectascensions
dec: list of declinations
nside: HealPix nside
return_vertices: whether to also return the boundaries of HealPix cells
Returns:
bc, ra_, dec_, [vertices]
bc: count of objects [per arcmin^2] in a HealPix cell if count > 0
ra_: rectascension of the cell center (same format as ra/dec)
dec_: declinations of the cell center (same format as ra/dec)
vertices: (N,4,2), RA/Dec coordinates of 4 boundary points of cell
"""
import healpy as hp
# get healpix pixels
ipix = hp.ang2pix(nside, (90-dec)/180*np.pi, ra/180*np.pi, nest=False)
# count how often each pixel is hit
bc = np.bincount(ipix)
pixels = np.nonzero(bc)[0]
bc = bc[bc>0] / hp.nside2resol(nside, arcmin=True)**2 # in arcmin^-2
# get position of each pixel in RA/Dec
theta, phi = hp.pix2ang(nside, pixels, nest=False)
ra_ = phi*180/np.pi
dec_ = 90 - theta*180/np.pi
# get the vertices that confine each pixel
# convert to RA/Dec (thanks to Eric Huff)
if return_vertices:
vertices = np.zeros((pixels.size, 4, 2))
for i in xrange(pixels.size):
corners = hp.vec2ang(np.transpose(hp.boundaries(nside,pixels[i])))
corners = np.array(corners) * 180. / np.pi
diff = corners[1] - corners[1][0]
diff[diff > 180] -= 360
diff[diff < -180] += 360
corners[1] = corners[1][0] + diff
vertices[i,:,0] = corners[1]
vertices[i,:,1] = 90.0 - corners[0]
return bc, ra_, dec_, vertices
else:
return bc, ra_, dec_
def footprint(self,nside):
"""
Download the survey footprint for HEALpix pixels.
"""
import healpy
import ugali.utils.projector
if nside > 2**9: raise Exception("Overflow error: nside must be <=2**9")
pix = np.arange(healpy.nside2npix(nside),dtype='int')
footprint = np.zeros(healpy.nside2npix(nside),dtype='bool')
ra,dec = ugali.utils.projector.pixToAng(nside,pix)
table_name = 'Pix%i'%nside
self.upload(np.array([pix,ra,dec]).T, ['pix','ra','dec'], name=table_name)
radius = healpy.nside2resol(nside_superpix,arcmin=True)
query="""
SELECT t.pix, dbo.fInFootprintEq(t.ra, t.dec, %g)
FROM %s AS t
"""%(radius, table_name)
def plot_skymap(output, skypost, pixresol=np.pi/180.0, nest=True,inj=None, fast=True):
nside = 1
while hp.nside2resol(nside) > pixresol:
nside *= 2
pix_post = skypost.as_healpix(nside, nest=nest, fast=fast)
fig = pp.figure(frameon=False)
ax = pp.subplot(111, projection='astro mollweide')
ax.cla()
ax.grid()
plot.healpix_heatmap(pix_post, nest=nest, vmin=0.0, vmax=np.max(pix_post), cmap=pp.get_cmap('cylon'))
if inj is not None:
# If using an injection file, also plot an X at the true position
pp.plot(inj['ra'], inj['dec'], 'kx', ms=30, mew=1)
pp.savefig(output)
def project_map(i):
print('projecting {:.3f} MHz'.format(F[i]))
hpx_array = hp.read_map(
'/data6/piyanat/projects/hera1p/maps/healpix_maps_native_dg/nside512/'
'healpix_map_dg_nside512_{:.3f}MHz.fits'.format(F[i]), verbose=False
)
proj_map_dx = hp.nside2resol(512*2) * 180 / np.pi
proj_map_nx = 1024
proj_map, hdr = healpix2sine(hpx_array, 45.0, 89.99, proj_map_nx, proj_map_dx,
return_fits_header=True)
hdr['crval1'] = 0
hdr['crval2'] = 0
hdr.pop('lonpole')
hdr.pop('latpole')
hdu = fits.PrimaryHDU(data=proj_map, header=hdr)
hdu.writeto(
'/data6/piyanat/projects/hera1p/maps/healpix512_lightcone_lon45d_lat90d_fov60d'
'/healpix512_lightcone_lon45d_lat90d_fov60d_{:.3f}MHz.fits'.format(F[i])
)
def getCountAtLocations(ra, dec, nside=512, per_area=True, return_vertices=False):
"""Get number density of objects from RA/Dec in HealPix cells.
Requires: healpy
Args:
ra: list of rectascensions
dec: list of declinations
nside: HealPix nside
per_area: return counts in units of 1/arcmin^2
return_vertices: whether to also return the boundaries of HealPix cells
Returns:
bc, ra_, dec_, [vertices]
bc: count of objects in a HealPix cell if count > 0
ra_: rectascension of the cell center (same format as ra/dec)
dec_: declinations of the cell center (same format as ra/dec)
vertices: (N,4,2), RA/Dec coordinates of 4 boundary points of cell
"""
import healpy as hp
# get healpix pixels
ipix = hp.ang2pix(nside, (90-dec)/180*np.pi, ra/180*np.pi, nest=False)
# count how often each pixel is hit
bc = np.bincount(ipix)
pixels = np.nonzero(bc)[0]
bc = bc[bc>0]
if per_area:
bc = bc.astype('f8')
bc /= hp.nside2resol(nside, arcmin=True)**2 # in arcmin^-2
# get position of each pixel in RA/Dec
theta, phi = hp.pix2ang(nside, pixels, nest=False)
ra_ = phi*180/np.pi
dec_ = 90 - theta*180/np.pi
# get the vertices that confine each pixel
# convert to RA/Dec (thanks to Eric Huff)
if return_vertices:
vertices = getHealpixVertices(pixels, nside)
return bc, ra_, dec_, vertices
else:
return bc, ra_, dec_
def ring_list(nside=0):
"""
ring_list() - description
---------------------------------------------------------------------------------
generating a list of HEALPIX rings in galactic coordinates using the ring
resolution as input. The AGILE galactic reference system is used.
- nside = 2^res
- npix = 12 nside^2
---------------------------------------------------------------------------------
copyright : (C) 2016 V. Fioretti (INAF/IASF Bologna)
----------------------------------------------
Parameters:
- nside = ring resolution
---------------------------------------------------------------------------------
Output:
- the l and b list of ring centers in galactic coordinates
---------------------------------------------------------------------------------
Caveats:
None
---------------------------------------------------------------------------------
"""
npix_healpix = hp.nside2npix(nside)
res_healpix = (hp.nside2resol(nside, arcmin=True))/60. # deg.
theta_ring_healpix = np.zeros(npix_healpix)
phi_ring_healpix = np.zeros(npix_healpix)
l_ring_healpix = np.zeros(npix_healpix)
b_ring_healpix = np.zeros(npix_healpix)
for jp in xrange(npix_healpix):
theta_ring_healpix[jp], phi_ring_healpix[jp] = hp.pix2ang(nside, jp, nest=True)
b_ring_healpix[jp] = (180./np.pi)*(theta_ring_healpix[jp] - np.pi/2.)
if (phi_ring_healpix[jp] <= np.pi):
l_ring_healpix[jp] = (180./np.pi)*(np.pi - phi_ring_healpix[jp])
if (phi_ring_healpix[jp] > np.pi):
l_ring_healpix[jp] = (180./np.pi)*(np.pi*3. - phi_ring_healpix[jp])
return l_ring_healpix, b_ring_healpix
def plot_skymap(output, skypost, pixresol=np.pi/180.0, nest=True):
from matplotlib import pyplot as plt
import healpy as hp
try:
from lalinference.cmap import cylon as cmap
except ImportError:
cmap = plt.cmap('gray')
cmap.set_under(color='w')
nside = 1
while hp.nside2resol(nside) > pixresol:
nside *= 2
thetas, phis = hp.pix2ang(nside, np.arange(hp.nside2npix(nside), dtype=np.int), nest=nest)
pixels = np.column_stack((phis, np.pi/2.0 - thetas))
pix_post = skypost.posterior(pixels)
plt.clf()
hp.mollview(pix_post, cmap=cmap, title="", nest=nest)
plt.savefig(output)
def plot_patches(src_num, info):
# Define constants #
deg2rad = np.pi/180.
# Define the width of area #
beam = 14.4 # Beam = 3.5'
dbeam = beam/60./2. # Beam = 3.5' -> dbeam = beam/60/2 in degree
offset = dbeam # degree
# HI continuum map and resolution #
cont = hp.read_map(os.getenv("HOME")+'/hdata/hi/lambda_chipass_healpix_r10.fits', field = 0, h=False)
nside = hp.get_nside(cont)
res = hp.nside2resol(nside, arcmin=False)
dd = res/deg2rad/5.0
# OK - Go #
tb = {}
for i in range(0,src_num):
if(i ==2): continue
if(i ==3): continue
if(i ==6): continue
if(i ==11): continue
## Find the values of Continuum temperature #
tb[i] = []
## Longitude and latitude ##
l = info['l'][i]
b = info['b'][i]
# if(i != 14): continue
# Plot cartview a/o mollview #
ll = l
if (l>180):
ll = ll-360.
f = 10.
# if (i == sr):
proj = hp.cartview(cont, title=info['src'][i]+'('+str(info['l'][i])+','+str(info['b'][i])+')', coord='G', unit='',
norm=None, xsize=1920, lonra=[ll-0.5,ll+0.5], latra=[b-0.5,b+0.5],
return_projected_map=True)
# hp.cartview(cont, title=info['src'][i]+'('+str(info['l'][i])+','+str(info['b'][i])+')', coord='G', unit='',
# norm='hist', xsize=800, lonra=[ll-offset-f*offset,ll+offset+f*offset], latra=[b-offset-f*offset,b+offset+f*offset],
# return_projected_map=True)
# hp.orthview(cont, title=info['src'][i]+'('+str(info['l'][i])+','+str(info['b'][i])+')', coord='G', unit='',
# norm='hist', xsize=800, return_projected_map=True)
# hp.mollview(cont, title=info['src'][i]+'('+str(info['l'][i])+','+str(info['b'][i])+')',
# coord='G', unit='', rot=[0,0,0], norm='hist')
# hp.mollzoom(cont, title=info['src'][i]+'('+str(info['l'][i])+','+str(info['b'][i])+')',
# coord='G', unit='', rot=[0,0,0], norm=None, min=4599., max=4600.)
print proj
print proj.shape
# Cal. #
theta = (90.0-b)*deg2rad
phi = l*deg2rad
pix = hp.ang2pix(nside, theta, phi, nest=False)
if (cont[pix] > -1.0e30) : # Some pixels not defined
tb[i].append(cont[pix])
for x in pl.frange(l-offset, l+offset, dd):
for y in pl.frange(b-offset, b+offset, dd):
cosb = np.cos(b*deg2rad)
cosy = np.cos(y*deg2rad)
if ( (((x-l)**2 + (y-b)**2) <= offset**2) ):
theta = (90.0 - y)*deg2rad
phi = x*deg2rad
pix = hp.ang2pix(nside, theta, phi, nest=False)
# hp.projtext(x, y, '.'+str(pix)+str(cont[pix]), lonlat=True, coord='G')
# hp.projtext(x, y, '.'+str(cont[pix]), lonlat=True, coord='G')
hp.projtext(x, y, '.', lonlat=True, coord='G')
plt.show()
## Read Tbg408 from Haslam, Read infor of 23 OH src ##
bg408 = read_tbg408_healpy()
info = read_23oh_src('../../oh/result/23src_with_oh.txt')
# Define constants #
deg2rad = np.pi/180.
# Define the width of area #
beam = 14.4 # Beam = 3.5'
dbeam = beam/60./2. # Beam = 3.5' -> dbeam = beam/60/2 in degree
offset = dbeam # degree
# HI continuum map and resolution #
cont = hp.read_map(os.getenv("HOME")+'/hdata/hi/lambda_chipass_healpix_r10.fits', field = 0, h=False)
nside = hp.get_nside(cont)
res = hp.nside2resol(nside, arcmin=False)
dd = res/deg2rad/5.0
num_of_src = len(info['src'])
plot_patches(num_of_src, info)
sys.exit()
# Product Name
# CHIPASS 1.4 GHz Continuum Map
# Coord. System
# Galactic
# Projection Type
# HEALPix, nested, res 10 (Nside=1024)
# Resolution
# 14.4 arcmin
def get_gas_column_density(map_file, src_num, info):
## Classes ##
msks = masks()
## Infor
src = info['src']
hi = info['nhi']
hier = info['nhi_er']
# Define constants #
deg2rad = np.pi/180.
fukui_cf = 2.10e26
fk_fact_err = 0.0 #unknown
# Define the width of area #
beam = 5. # Beam = 5'
dbeam = beam/120.0 # Beam = 5' -> dbeam = beam/60/2 in degree
offset = dbeam # degree
## Mask, to find Planck Conversion Factor (Dust opacity and Its error) ##
msk = hp.read_map(os.getenv("HOME")+'/hdata/dust/planck_mask.fits', field = 0, h=False)
# tau353 map, err_tau353 map and resolution #
tau_map = hp.read_map(map_file, field = 0)
err_map = hp.read_map(map_file, field = 1)
nside = hp.get_nside(tau_map)
res = hp.nside2resol(nside, arcmin=False)
dd = res/deg2rad/10.0
# OK - Go #
tau353 = []
nh = []
nher = []
nhi = []
tau = {}
t_err = {}
rat_fk = 0.
rat_pl = 0.
for i in range(0,src_num):
# Find the values of Tau353 and Err_tau353 in small area #
tau[i] = []
t_err[i] = []
l = info['l'][i]
b = info['b'][i]
# # Plot cartview a/o mollview #
# ll = l
# if (l>180):
# ll = ll-360.
# hp.cartview(tau_map, title=info['src'][i]+'('+str(info['l'][i])+','+str(info['b'][i])+') - '+map_file, coord='G', unit='',
# norm='hist', xsize=800, lonra=[ll-offset-0.1*offset,ll+offset+0.1*offset], latra=[b-offset-0.1*offset,b+offset+0.1*offset],
# return_projected_map=True)
# hp.cartview(err_map, title='Err', coord='G', unit='',
# norm='hist', xsize=800, lonra=[ll-offset-0.1*offset,ll+offset+0.1*offset], latra=[b-offset-0.1*offset,b+offset+0.1*offset],
# return_projected_map=True)
# # End Plot cartview a/o mollview ##
## find Planck Conversion Factor (Dust opacity and Its error) ##
planck_cf,pl_fact_err = msks.get_dust_opacity(msk,l,b) # 0.84 #8.40e-27, 0.84e-26
# Cal. #
theta = (90.0-b)*deg2rad
phi = l*deg2rad
pix = hp.ang2pix(nside, theta, phi, nest=False)
if ( (err_map[pix] >= 6.9e-11) and (err_map[pix] <= 0.00081) and (tau_map[pix] > -1.0e30) ): # Checked Invalid error & Some pixels not defined
tau[i].append(tau_map[pix])
t_err[i].append(err_map[pix])
for x in pl.frange(l-offset, l+offset, dd):
for y in pl.frange(b-offset, b+offset, dd):
cosb = np.cos(b*deg2rad)
cosy = np.cos(y*deg2rad)
if ( ((x-l)**2 + (y-b)**2) <= offset**2 ):
# hp.projplot(x, y, 'kx', lonlat=True, coord='G')
theta = (90.0 - y)*deg2rad
phi = x*deg2rad
pix = hp.ang2pix(nside, theta, phi, nest=False)
if ( (err_map[pix] >= 6.9e-11) and (err_map[pix] <= 0.00081) and (tau_map[pix] > -1.0e30) ): # Checked Invalid error & Some pixels not defined
tau[i].append(tau_map[pix])
t_err[i].append(err_map[pix])
# plt.show()
# continue
# Make tau353 and err_tau353 correspondent #
# print info['src'][i], len(tau[i]), len(t_err[i])
temp_tau = list(set(tau[i]))
t_err[i] = list(set(t_err[i]))
cnt_tau = len(temp_tau)
npix = len(t_err[i])
if (cnt_tau != npix) :
tmp_tau = []
for k in range(npix):
tmp_tau.append(tau[i][k])
tau[i] = tmp_tau
else:
tau[i] = list(set(tau[i]))
# Calculate mean values of tau353 #
tau353.append(sum(tau[i])/float(npix))
# Calculate the N(HI) from Fukui factor #
nhi_i = fukui_cf*tau353[i]
nhi.append(nhi_i)
# Calculate the NH from Planck factor #
nh_i = tau353[i]/planck_cf
nh.append(nh_i)
# Uncertainties of mean values of tau353 #
sd1_tau353 = 0.
sd2_tau353 = 0.
for j in range(npix):
sd1_tau353 = sd1_tau353 + (tau[i][j]-tau353[i])**2
sd2_tau353 = sd2_tau353 + (t_err[i][j])**2
sd1_tau353 = (sd1_tau353/npix)**0.5 # Just to test, nothing to do with this
sd2_tau353 = (sd2_tau353**0.5)/npix # Uncertainty of a Sum
# Uncertainties of mean values of N(HI) and N(H) #
fukui_sd = (sd2_tau353*fukui_cf)
planck_sd = (sd2_tau353/tau353[i])**2 + (pl_fact_err/planck_cf)**2
planck_sd = (nh_i*planck_sd**0.5)
nhi[i] = nhi[i]*1.0e-20
fukui_sd = fukui_sd*1.0e-20
nh[i] = nh[i]*1.0e-20
planck_sd = planck_sd*1.0e-20
nher.append(planck_sd)
nhi_hi = info['nhi'][i] ## From Carl
rat1 = nhi[i]/nhi_hi ## For Fukui
rat2 = nh[i]/nhi_hi ## for Planck
rat_fk = rat_fk + rat1 ## to cal. Mean of ratio
rat_pl = rat_pl + rat2 ## to cal. Mean of ratio
rat1 = round(rat1, 2)
rat2 = round(rat2, 2)
wnm = info['wnm'][i]
cnm = info['cnm'][i]
print("{} {}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}"
.format(i, info['src'][i], round((nhi[i]), 4), round((fukui_sd), 4), round((nh[i]), 4), round((planck_sd), 4), \
nhi_hi, 0.0, info['nhi_er'][i], wnm, cnm, rat1, rat2, 0, info['thin'][i] ) )
## Plot N(H) vs N(HI)
# plt.xscale("log", nonposx='clip')
# plt.plot(hi,nh, 'rd', label='data', ms=10)
plt.errorbar(hi,nh,xerr=hier, yerr=nher, color='r', marker='o', ls='None', markersize=8, markeredgecolor='b', markeredgewidth=1, label='data')
plt.plot([0,160],[0,160], 'k--', label='$N_{H} = N_{HI}$')
plt.title('$N_{H}$ vs $N_{HI}$ along 94 lines-of-sight', fontsize=30)
plt.ylabel('$N_{H}[10^{20}$ cm$^{-2}]$', fontsize=35)
plt.xlabel('$N_{HI} [10^{20}$ cm$^{-2}]$', fontsize=35)
plt.xlim(-10.0, 165.0)
# plt.ylim(0, 3)
plt.grid(True)
plt.tick_params(axis='x', labelsize=18)
plt.tick_params(axis='y', labelsize=18)
plt.text(100., 30., r'$N_{H} = \frac{\tau_{353}}{\sigma_{353}}$', color='k', fontsize=17)
plt.text(100., 10., r'$\tau_{353}$ from Planck data R1.2', color='k', fontsize=17)
plt.legend(loc='upper left', fontsize=18)
# plt.savefig("test.png",bbox_inches='tight')
# for i in range(len(src)):
# # if (oh[i] > 0) :
# plt.annotate('('+str(src[i])+')', xy=(hi[i], nh[i]), xycoords='data',
# xytext=(-50.,30.), textcoords='offset points',
# arrowprops=dict(arrowstyle="->"),fontsize=18,
# )
plt.show()
def plot_patches(map_file, info):
src = info['src']
# Define constants #
deg2rad = np.pi/180.
# Define the width of area #
beam = 4.3 # Beam = 4.3'
dbeam = beam/120.0 # Beam = 30' -> dbeam = beam/60/2 in degree
offset = dbeam # degree
# TTYPE1 = 'TAU353 ' / Optical depth at 353GHz
# TTYPE2 = 'ERR_TAU ' / Error on optical depth
# TTYPE3 = 'EBV ' / E(B-V) color excess
# TTYPE4 = 'RADIANCE' / Integrated emission
# TTYPE5 = 'TEMP ' / Dust equilibrium temperature
# TTYPE6 = 'ERR_TEMP' / Error on T
# TTYPE7 = 'BETA ' / Dust emission spectral index
# TTYPE8 = 'ERR_BETA' / error on Beta
ir_map = hp.read_map(map_file, field = 0)
nside = hp.get_nside(ir_map)
res = hp.nside2resol(nside, arcmin=False)
dd = res/deg2rad/2.0
# OK - Go #
ebv = []
nh = []
nhi = []
for i in range(0,len(src)):
# if( i != 15):
# continue
l = info['l'][i]
b = info['b'][i]
# Plot cartview a/o mollview #
ll = l
if (l>180):
ll = ll-360.
# offset = 1.
hp.cartview(ir_map, title=info['src'][i], coord='G', unit='',
norm='hist', xsize=800, lonra=[ll-offset-0.1*offset,ll+offset+0.1*offset], latra=[b-offset-0.1*offset,b+offset+0.1*offset],
return_projected_map=True)
# hp.mollview(tau_map, title=info['src'][i]+'('+str(info['l'][i])+','+str(info['b'][i])+') - '+map_file,
# coord='G', unit='', rot=[l,b,0], norm='hist', min=1e-7,max=1e-3, xsize=800)
# Cal. #
hp.projplot(ll, b, 'bo', lonlat=True, coord='G')
hp.projtext(ll, b, ' (' + str(round(ll,2)) + ',' + str(round(b,2)) + ')', lonlat=True, coord='G', fontsize=18, weight='bold')
theta = (90.0-b)*deg2rad
phi = l*deg2rad
pix = hp.ang2pix(nside, theta, phi, nest=False)
for x in pl.frange(l-offset, l+offset, dd):
for y in pl.frange(b-offset, b+offset, dd):
cosb = np.cos(b*deg2rad)
cosy = np.cos(y*deg2rad)
if ( (((x-l)**2 + (y-b)**2) <= offset**2) ):
# hp.projtext(x, y, '.', lonlat=True, coord='G')
hp.projplot(x, y, 'kx', lonlat=True, coord='G')
mpl.rcParams.update({'font.size':30})
plt.show()
filterName = args.filtername
colName = filterName+'mag'
# Set up healpy map and ra, dec centers
nside = 64
# Set the min to 15 since we saturate there. CatSim max is 28
bins = np.arange(15., 28.2, .2)
starDensity = np.zeros((hp.nside2npix(nside), np.size(bins)-1), dtype=float)
overMaxMask = np.zeros(hp.nside2npix(nside), dtype=bool)
lat, ra = hp.pix2ang(nside, np.arange(0, hp.nside2npix(nside)))
dec = np.pi/2.-lat
# Square root of pixel area.
hpsizeDeg = hp.nside2resol(nside, arcmin=True)/60.
# Limit things to a 10 arcmin radius
hpsizeDeg = np.min([10./60., hpsizeDeg])
# Options include galaxyBase, cepheidstars, wdstars, rrlystars, msstars, bhbstars, allstars, and more...
dbobj = CatalogDBObject.from_objid(args.stars)
indxMin = 0
restoreFile = glob.glob('starDensity_%s_%s_nside_%i.npz' % (filterName, starNames, nside))
if len(restoreFile) > 0:
data = np.load(restoreFile[0])
starDensity = data['starDensity'].copy()
indxMin = data['icheck'].copy()
overMaxMask = data['overMaxMask'].copy()
import multiprocessing
import numpy as np
import xarray as xr
import healpy as hp
from astropy.wcs import WCS
from opstats.utils import MWA_FREQ_EOR_ALL_80KHZ
from opstats.modeling import healpix2sine
freq_list = MWA_FREQ_EOR_ALL_80KHZ # MHz
input_dir = '/data6/piyanat/projects/fg1p/foreground'
sine_map_resol = hp.nside2resol(1024, arcmin=True) / 60 # deg
def generate_gsm2016_map(i):
freq = freq_list[i]
map_data = hp.read_map(
'{:s}/gsm2016_{:.3f}MHz.fits'.format(input_dir, freq)
)
# Downgrade input map to NSIDE=512 from NSIDE=1024.
# Output map resolution is equivalent to NSIDE=1024 to make sure
# we oversample healpix pixels.
map_data_nside512 = hp.ud_grade(map_data, 512)
shared_array[i] = healpix2sine(
map_data_nside512, 0, -30, 256, sine_map_resol, hpx_coord='G'
)
shared_array_base = multiprocessing.Array('d', freq_list.size * 256 * 256)
config = yaml.load(open(args.config))
patches = Patches(config)
frequencies = get_frequencies(config) # [MHz]
nfreq = len(frequencies)
pcenter = config['region']['center'] # [deg]
rmin = config['region']['rmin'] # [deg]
rmax = config['region']['rmax'] # [deg]
min_ = config['threshold']['min'] # [K]
max_ = config['threshold']['max'] # [K]
pixelsize = config['input']['pixelsize'] # [arcsec]
clobber = config['output']['clobber']
nside = config['nside']
logger.info('Nside: %d' % nside)
resol = hp.nside2resol(nside, arcmin=True) # [arcmin]
logger.info('HEALPix resolution: %.2f [arcmin]' % resol)
imgsize = int(round(resol * 60 / pixelsize))
logger.info('Image patch size: %d' % imgsize)
plon, plat = get_healpix_coords(nside) # [deg]
npatch = len(plon)
for i, freq in enumerate(frequencies):
logger.info('[%d/%d] %.2f[MHz] ...' % (i+1, nfreq, freq))
outfile = config['output']['filename'].format(freq=freq)
os.makedirs(os.path.dirname(outfile), exist_ok=True)
if os.path.exists(outfile) and (not clobber):
raise FileExistsError(outfile)
results = []
jj = 0
def plot_patches(map_file, src_num, info):
# Define constants #
deg2rad = np.pi/180.
fukui_cf = 2.10 #2.10e26
planck_cf = 0.84 #8.40e-27, 0.84e-26
pl_fact_err = 0.3 #3.0e-27
fk_fact_err = 0.0 #unknown
# Define the width of area #
beam = 5.0 # Beam = map_resolution = 5'
dbeam = beam/120.0 # Beam = 3.5' -> dbeam = beam/60/2 in degree
offset = dbeam # degree
# tau353 map, err_tau353 map and resolution #
tau_map = hp.read_map(map_file, field = 0)
err_map = hp.read_map(map_file, field = 1)
nside = hp.get_nside(tau_map)
res = hp.nside2resol(nside, arcmin=False)
dd = res/deg2rad/5.0
# OK - Go #
tau353 = []
nh = []
nhi = []
tau = {}
t_err = {}
rat_fk = 0.
rat_pl = 0.
for i in range(0,src_num):
# Find the values of Tau353 and Err_tau353 in small area #
tau[i] = []
t_err[i] = []
l = info['l'][i]
b = info['b'][i]
# Plot cartview a/o mollview #
ll = l
if (l>180):
ll = ll-360.
hp.cartview(tau_map, title=info['src'][i]+'('+str(info['l'][i])+','+str(info['b'][i])+') - '+map_file, coord='G', unit='',
norm='hist', xsize=800, lonra=[ll-offset-0.1*offset,ll+offset+0.1*offset], latra=[b-offset-0.1*offset,b+offset+0.1*offset],
return_projected_map=True)
# hp.mollview(tau_map, title=info['src'][i]+'('+str(info['l'][i])+','+str(info['b'][i])+') - '+map_file,
# coord='G', unit='', rot=[l,b,0], norm='hist', min=1e-7,max=1e-3, xsize=800)
# Cal. #
theta = (90.0-b)*deg2rad
phi = l*deg2rad
pix = hp.ang2pix(nside, theta, phi, nest=False)
if (tau_map[pix] > -1.0e30) : # Some pixels not defined
tau[i].append(tau_map[pix])
if (err_map[pix] >= 6.9e-11 and err_map[pix] <= 0.00081): # Checked Invalid error
t_err[i].append(err_map[pix])
for x in pl.frange(l-offset, l+offset, dd):
for y in pl.frange(b-offset, b+offset, dd):
cosb = np.cos(b*deg2rad)
cosy = np.cos(y*deg2rad)
if ( (((x-l)**2 + (y-b)**2) <= offset**2) ):
# hp.projtext(x, y, '.', lonlat=True, coord='G')
hp.projplot(x, y, 'kx', lonlat=True, coord='G')
plt.show()
maxID = np.max(maxID)
# Crop off some of the early junky observations
minMJD = 56900
minID,mjd = sb.allSkyDB(0,'select min(ID) from dates where mjd > %i;' % minMJD, dtypes='int')
altLimit = 10. # Degrees
sunAltLimit = np.radians(-20.)
maxID = 3000
step = 5
for dateID in np.arange(minID.max(),minID.max()+maxID+1, step):
sqlQ = 'select stars.ra, stars.dec, stars.ID, obs.starMag_inst, obs.starMag_err,obs.sky, obs.filter from obs, stars, dates where obs.starID = stars.ID and obs.dateID = dates.ID and obs.filter = "%s" and obs.dateID = %i and obs.starMag_err != 0 and dates.sunAlt < %f and obs.starMag_inst > %f;' % (filt,dateID,sunAltLimit, starBrightLimit)
# Note that RA,Dec are in degrees
data,mjd = sb.allSkyDB(dateID, sqlQ=sqlQ, dtypes=dtypes)
if data.size > nStarLimit:
alt,az,pa = _altAzPaFromRaDec(np.radians(data['ra']), np.radians(data['dec']),
telescope.lon, telescope.lat, mjd)
for i,nside in enumerate(nsides):
tempMap = healbin(az,alt, az*0+1, nside=nside, reduceFunc=np.size)[hpIndices[i]]
ratio = np.size(np.where(tempMap > 0)[0])/float(np.size(tempMap))
mapFractions[i].append(ratio)
print 'nside, resolution (deg), fraction of healpixes populated'
for mf,nside in zip(mapFractions,nsides):
resol = hp.nside2resol(nside, arcmin=True)/60.
print ' %i, %f, %f' % (nside, resol,np.median(mf))
