def get_psf(psf_file, show=False):
"""Get the PSF from the fits file created by gtpsf
"""
hdu_list = pf.open(psf_file)
_th = np.radians(hdu_list['THETA'].data.field('Theta'))
_en = hdu_list['PSF'].data.field('ENERGY')
_psf = hdu_list['PSF'].data.field('PSF')
fmt = dict(yname='Theta', yunits='rad', xname='Energy',
xunits='MeV', zname='PSF')
psf_th_e = xInterpolatedBivariateSplineLinear(_en, _th, _psf, **fmt)
hdu_list.close()
if show == True:
for e in _en:
psf_th = psf_th_e.vslice(e)
fmt = dict(xname='cos(th)', xunits='', yname='psf[cos(th)]',\
yunits='')
_psfxsin = xInterpolatedUnivariateSplineLinear(psf_th.x, \
psf_th.y*np.sin(psf_th.x), **fmt)
plt.plot(np.degrees(psf_th.x), psf_th.y, label='%.2f'%e)
print 'INT(E=%.2f) ='%e, 2*np.pi*_psfxsin.integral(0, \
np.amax(psf_th.x[:-2]))
plt.yscale('log')
plt.legend()
plt.show()
return psf_th_e
def main():
"""Test module
"""
plt.figure()
leg, lab = ref_igrb_band()
igrb, lab2 = ref_igrb_noFGsub()
plt.xscale('log')
plt.yscale('log')
plt.legend([leg, igrb], [lab, lab2])
plt.show()
def main():
"""
"""
cl_f = 'output/output_pol/mask_check_cl.txt'
cov_f = 'output/output_pol/mask_check_cov.fits'
l, cl = pol_cl_parse(cl_f)
clerr = pol_cov_parse(cov_f)
#plt.errorbar(l, cl, fmt='o', markersize=3, elinewidth=1, yerr=clerr)
plt.plot(l, cl, 'o', markersize=3)
plt.xscale('log')
plt.yscale('log')
#plt.ylim(-1e-15, 1e-15)
plt.show()
def main():
"""
crbkg_PSF1_file = os.path.join(GRATOOLS_CONFIG,
'ascii/p8r2_bkg_flux_t8.txt')
crbkg_PSF1 = get_crbkg(crbkg_PSF1_file)
crbkg_PSF1.plot(show=False)
x = crbkg_PSF1.x
xx = x**2
print x
y1 = crbkg_PSF1.y
crbkg_PSF2_file = os.path.join(GRATOOLS_CONFIG,
'ascii/p8r2_bkg_flux_t16.txt')
crbkg_PSF2 = get_crbkg(crbkg_PSF2_file)
crbkg_PSF2.plot(show=False)
y2 = crbkg_PSF2(x)
crbkg_PSF3_file = os.path.join(GRATOOLS_CONFIG,
'ascii/p8r2_bkg_flux_t32.txt')
crbkg_PSF3 = get_crbkg(crbkg_PSF3_file)
crbkg_PSF3.plot(show=False)
y3 = crbkg_PSF2(x)
y = (y1/x+y2/x+y3/x)*x
print y
plt.yscale('log')
plt.xscale('log')
plt.show()
fore_files_list = [os.path.join(GRATOOLS_CONFIG, \
'fits/gll_iem_v06_hp512_146647.fits'),
os.path.join(GRATOOLS_CONFIG, \
'fits/gll_iem_v06_hp512_200561.fits'),
os.path.join(GRATOOLS_CONFIG, \
'fits/gll_iem_v06_hp512_274296.fits'),
os.path.join(GRATOOLS_CONFIG, \
'fits/gll_iem_v06_hp512_375138.fits'),
os.path.join(GRATOOLS_CONFIG, \
'fits/gll_iem_v06_hp512_513056.fits'),
]
e_min, e_max = 524807.00, 575439.00
get_foreground_integral_flux_map(fore_files_list, e_min, e_max)"""
from scipy.optimize import curve_fit
ClFORE_FILE = os.path.join(GRATOOLS_OUT, 'fore_polspicecls.txt')
min1, emax1, clsfore = clfore_parse(ClFORE_FILE)
l = np.arange(len(clsfore[0]))
clsfore_spline = xInterpolatedUnivariateSplineLinear(l, clsfore[0])
clsfore_spline.plot(show=False)
plt.xscale('log')
plt.yscale('log')
plt.show()
def main():
"""Test module
"""
leg, lab = ref_cp_band()
plt.legend()
plt.xscale('log')
plt.yscale('log')
plt.show()
plt.figure()
leg, lab = ref_igrb_band()
igrb, lab2 = ref_igrb_noFGsub()
plt.xscale('log')
plt.yscale('log')
plt.legend([leg, igrb], [lab, lab2])
plt.show()
def main():
"""Simple test unit
"""
from GRATools.utils.gWindowFunc import get_psf_ref
psf_ref_file = os.path.join(GRATOOLS_CONFIG, 'ascii/PSF_UCV_PSF1.txt')
psf_ref = get_psf_ref(psf_ref_file)
energy = np.array([
743.73, 1340.69, 2208.67, 3692.56, 6416.93, 11151.34, 18370.95,
30713.33, 53373.66, 92752.78, 152802.88, 255462.38, 443942.75
])
psf_en = psf_ref(energy)
psf_min, psf_max = psf_ref.y[5], psf_ref.y[-1]
norm_min, norm_max = 1, 0.3
norm = norm_min + psf_en*((norm_max - norm_min)/(psf_max - psf_min)) -\
psf_min*((norm_max - norm_min)/(psf_max - psf_min))
plt.title('Normalization Factor')
plt.plot(energy, norm, 'ro--', ms=5, alpha=0.75)
#plt.plot((1e-12, 1e-5), (2, 2), '-', color='silver', linewidth=1.0)
plt.xlabel('Energy [MeV]')
plt.ylabel('Normalization factor')
plt.yscale('log')
plt.xscale('log')
plt.ylim(0.09, 2)
plt.show()
nside = 512
SRC_CATALOG_FILE = os.path.join(FT_DATA_FOLDER, 'catalogs/gll_psc_v16.fit')
bad_pix = mask_src_weighted(SRC_CATALOG_FILE, 10000, nside)
bad_pix += mask_gp(30, nside)
npix = hp.nside2npix(nside)
mask = np.ones(npix)
for bpix in np.unique(bad_pix):
mask[bpix] = 0
fsky = 1 - (len(np.unique(bad_pix)) / float(npix))
title = 'f$_{sky}$ = %.3f' % fsky
hp.mollview(mask, title=title, coord='G')
plt.show()
FLUX_LABELS = ['UCV (t56) w/o Foreground sub', 'UCV (t56) w/ Foreground sub']
#OUT_LABEL = 'Flux_t56_srcmask2_Rm-Rp'
#OUT_LABEL = 'Flux_types_srcmask2'
#OUT_LABEL = 'Flux_t56_srcmask2-1p5-weighted'
#OUT_LABEL = 'Flux_t56_maskweight_north-south'
#OUT_LABEL = 'Flux_t56_maskweight_east-west'
OUT_LABEL = 'Flux_t56_maskweight'
plt.figure(figsize=(10, 7), dpi=80)
from GRATools.utils.gDrawRef import ref_igrb_band
from GRATools.utils.gDrawRef import ref_igrb_noFGsub
leg, lab = ref_igrb_band()
igrb, lab_igrb = ref_igrb_noFGsub()
flux = []
for f in FLUX_FILES:
from GRATools.utils.gFTools import get_cl_param
_emin, _emax, _emean, _f, _ferr, _cn, fsky = get_cl_param(f)
spec = plt.errorbar(_emean, _f*_emean, fmt='o', markersize=3, \
elinewidth=1, xerr=(_emax-_emin)/2, yerr=_ferr*_emean)
label = os.path.basename(f).replace('_parameters.txt', '')
flux.append(spec)
plt.xscale("log")
plt.yscale("log")
plt.xlabel('Energy [MeV]')
plt.ylabel('E$^{2}$ $\cdot$ Flux [MeV cm$^{-2}$ s$^{-1}$ sr$^{-1}$]')
plt.title(' Energy Spectrum')
plt.legend([igrb, leg]+flux, [lab_igrb, lab]+FLUX_LABELS, loc=3)
#overlay_tag()
save_current_figure(OUT_LABEL+'_ESpec.png')
(emaxs[i] - emeans[i]) / 1000],
yerr=cperrs_tocompare[i] * (emeans[i] / 1000)**4 /
((emaxs[i] - emins[i]) / 1000)**2))
plt.xlabel('E [GeV]')
plt.ylabel('E$^{4}$/$\Delta$E$^{2}$ $\cdot$ C$_{P}$')
plt.xscale('log')
#plt.yscale('log', nonposy='clip')
plt.legend([leg_ref] + leg, [lab_ref] + lab, loc=2)
save_current_figure(OUT_LABEL + '.png')
plt.figure(figsize=(10, 7), dpi=80)
lab = []
leg = []
for i, f in enumerate(Cl_FILES):
lab.append(os.path.basename(f).replace('_13bins_cross.txt', ''))
leg.append(
plt.errorbar(emeans[i] / 1000,
abs(0 - cps_tocompare[i]),
fmt='o',
markersize=3,
elinewidth=1,
xerr=[(emeans[i] - emins[i]) / 1000,
(emaxs[i] - emeans[i]) / 1000],
yerr=cperrs_tocompare[i]))
plt.xlabel('E [GeV]')
plt.ylabel('|$0$-C$_{P}$|')
plt.xscale('log')
plt.yscale('log', nonposy='clip')
plt.legend(leg, lab, loc=2)
save_current_figure(OUT_LABEL + '_residuals.png')
FLUX_LABELS = ['UCV (t56) w/o Foreground sub', 'UCV (t56) w/ Foreground sub']
#OUT_LABEL = 'Flux_t56_srcmask2_Rm-Rp'
#OUT_LABEL = 'Flux_types_srcmask2'
#OUT_LABEL = 'Flux_t56_srcmask2-1p5-weighted'
#OUT_LABEL = 'Flux_t56_maskweight_north-south'
#OUT_LABEL = 'Flux_t56_maskweight_east-west'
OUT_LABEL = 'Flux_t56_maskweight'
plt.figure(figsize=(10, 7), dpi=80)
from GRATools.utils.gDrawRef import ref_igrb_band
from GRATools.utils.gDrawRef import ref_igrb_noFGsub
leg, lab = ref_igrb_band()
igrb, lab_igrb = ref_igrb_noFGsub()
flux = []
for f in FLUX_FILES:
from GRATools.utils.gFTools import get_cl_param
_emin, _emax, _emean, _f, _ferr, _cn, fsky = get_cl_param(f)
spec = plt.errorbar(_emean, _f*_emean, fmt='o', markersize=3, \
elinewidth=1, xerr=(_emax-_emin)/2, yerr=_ferr*_emean)
label = os.path.basename(f).replace('_parameters.txt', '')
flux.append(spec)
plt.xscale("log")
plt.yscale("log")
plt.xlabel('Energy [MeV]')
plt.ylabel('E$^{2}$ $\cdot$ Flux [MeV cm$^{-2}$ s$^{-1}$ sr$^{-1}$]')
plt.title(' Energy Spectrum')
plt.legend([igrb, leg] + flux, [lab_igrb, lab] + FLUX_LABELS, loc=3)
#overlay_tag()
save_current_figure(OUT_LABEL + '_ESpec.png')
leg.append(plt.errorbar(emeans[i]/1000,
cps_tocompare[i]*(emeans[i]/1000)**4/((emaxs[i]-emins[i])/1000)**2,
fmt='o', markersize=3, elinewidth=1,
xerr=[(emeans[i]-emins[i])/1000, (emaxs[i]-emeans[i])/1000],
yerr=cperrs_tocompare[i]*(emeans[i]/1000)**4/((emaxs[i]-emins[i])/1000)**2))
plt.xlabel('E [GeV]')
plt.ylabel('E$^{4}$/$\Delta$E$^{2}$ $\cdot$ C$_{P}$')
plt.xscale('log')
#plt.yscale('log', nonposy='clip')
plt.legend([leg_ref]+leg, [lab_ref]+lab, loc=2)
save_current_figure(OUT_LABEL+'.png')
plt.figure(figsize=(10, 7), dpi=80)
lab = []
leg = []
for i, f in enumerate(Cl_FILES):
lab.append(os.path.basename(f).replace('_13bins_cross.txt', ''))
leg.append(plt.errorbar(emeans[i]/1000,
abs(0-cps_tocompare[i]),
fmt='o', markersize=3, elinewidth=1,
xerr=[(emeans[i]-emins[i])/1000, (emaxs[i]-emeans[i])/1000],
yerr=cperrs_tocompare[i]))
plt.xlabel('E [GeV]')
plt.ylabel('|$0$-C$_{P}$|')
plt.xscale('log')
plt.yscale('log', nonposy='clip')
plt.legend(leg, lab, loc=2)
save_current_figure(OUT_LABEL+'_residuals.png')