def main():
"""
"""
import os
from ximpol import XIMPOL_IRF
from ximpol.utils.matplotlib_ import pyplot as plt
file_path = os.path.join(XIMPOL_IRF,'fits','xipe_baseline.psf')
psf = xPointSpreadFunction(file_path)
print(psf.rvs(10))
ra, dec = 1., 1.
print(psf.smear_single(ra, dec, 10))
rmax = 50
plt.hist(psf.rvs(100000), rmax, (0, rmax), rwidth=1, histtype='step',
lw=2, normed=True)
_r = numpy.linspace(0, rmax, rmax)
_psf = psf(_r)/psf.norm()
plt.plot(_r, _psf)
plt.show()
def test_rvs(self, num_events=100000):
"""
"""
mc_energy = 10.
_ppf = self.edisp.matrix.vppf.vslice(mc_energy)
_e = numpy.full(num_events, mc_energy)
_slice = self.edisp.matrix.slice(mc_energy)
_ch = self.edisp.matrix.rvs(_e)
n, bins, patches = plt.hist(_ch, bins=numpy.linspace(0, 255, 256),
histtype='step')
bin_width = (bins[1] - bins[0])
scale = n.sum()*bin_width/_slice.norm()
plt.plot(_slice.x, scale*_slice.y)
def test_rvs(self):
"""Test the random number generation.
"""
visibility = numpy.full(1000000, 0.5)
phi = self.generator.rvs_phi(visibility, 0.25*numpy.pi)
hist = plt.hist(phi, bins=numpy.linspace(0, 2*numpy.pi, 100),
histtype='step', label='Random angles')
fit_results = self.generator.fit_histogram(hist)
fit_results.plot()
plt.xlabel('$\\phi$ [rad]')
plt.axis([0, 2*numpy.pi, 0, None])
overlay_tag()
save_current_figure('test_azimuthal_resp_rvs.png',
show=self.interactive)
def main():
"""
"""
import os
from ximpol import XIMPOL_IRF
from ximpol.utils.matplotlib_ import pyplot as plt
file_path = os.path.join(XIMPOL_IRF, 'fits', 'xipe_baseline.psf')
psf = xPointSpreadFunction(file_path)
print(psf.rvs(10))
ra, dec = 1., 1.
print(psf.smear_single(ra, dec, 10))
rmax = 50
plt.hist(psf.rvs(100000),
rmax, (0, rmax),
rwidth=1,
histtype='step',
lw=2,
normed=True)
_r = numpy.linspace(0, rmax, rmax)
_psf = psf(_r) / psf.norm()
plt.plot(_r, _psf)
plt.show()
def test_rvs(self, num_events=100000):
"""
"""
mc_energy = 10.
_ppf = self.edisp.matrix.vppf.vslice(mc_energy)
_e = numpy.full(num_events, mc_energy)
_slice = self.edisp.matrix.slice(mc_energy)
_ch = self.edisp.matrix.rvs(_e)
n, bins, patches = plt.hist(_ch,
bins=numpy.linspace(0, 255, 256),
histtype='step')
bin_width = (bins[1] - bins[0])
scale = n.sum() * bin_width / _slice.norm()
plt.plot(_slice.x, scale * _slice.y)
def test_constant(self, num_events=1000000, polarization_degree=1.,
polarization_angle=numpy.radians(20.)):
"""Test the modulation factor as a random number generator when
both the polarization angle and degrees are energy- and
time-independent.
"""
poldegree = numpy.full(num_events, polarization_degree)
polangle = numpy.full(num_events, polarization_angle)
self.modf.generator.plot(show=False)
save_current_figure('test_modulation_constant_generator.png',
show=self.interactive)
emin = self.modf.xmin()
emax = self.modf.xmax()
energy = numpy.random.uniform(emin, emax, num_events)
phi = self.modf.rvs_phi(energy, poldegree, polangle)
ebinning = numpy.linspace(emin, emax, 10)
phi_binning = numpy.linspace(0, 2*numpy.pi, 100)
fit_results = []
for i, (_emin, _emax) in enumerate(zip(ebinning[:-1], ebinning[1:])):
_emean = 0.5*(_emin + _emax)
_mask = (energy > _emin)*(energy < _emax)
_phi = phi[_mask]
_hist = plt.hist(_phi, bins=phi_binning, histtype='step')
_fr = xAzimuthalResponseGenerator.fit_histogram(_hist)
_fr.emean = _emean
fit_results.append(_fr)
_fr.plot(label='Energy: %.2f--%.2f keV' % (_emin, _emax))
plt.axis([0., 2*numpy.pi, 0., 1.2*_hist[0].max()])
overlay_tag()
save_current_figure('test_modulation_constant_fit_slice%d.png' % i,
show=self.interactive)
_x = [_fr.emean for _fr in fit_results]
_y = [_fr.phase for _fr in fit_results]
_dy = [_fr.phase_error for _fr in fit_results]
plt.errorbar(_x, _y, yerr=_dy, fmt='o')
plt.plot(_x, numpy.array([polarization_angle]*len(_x)))
plt.xlabel('Energy [keV]')
plt.ylabel('Modulation angle [$^\circ$]')
save_current_figure('test_modulation_constant_angle.png',
show=self.interactive)
_y = [_fr.visibility for _fr in fit_results]
_dy = [_fr.visibility_error for _fr in fit_results]
plt.errorbar(_x, _y, yerr=_dy, fmt='o')
plt.axis([emin, emax, 0, 1])
self.modf.plot(show=False)
plt.xlabel('Energy [keV]')
plt.ylabel('Modulation visibility')
save_current_figure('test_modulation_constant_visibility.png',
show=self.interactive)
def test_constant(self, num_events=1000000, polarization_degree=1.,
polarization_angle=numpy.radians(20.)):
"""Test the modulation factor as a random number generator when
both the polarization angle and degrees are energy- and
time-independent.
"""
poldegree = numpy.full(num_events, polarization_degree)
polangle = numpy.full(num_events, polarization_angle)
self.modf.generator.plot(show=False)
save_current_figure('test_modulation_constant_generator.png',
show=self.interactive)
emin = self.modf.xmin()
emax = self.modf.xmax()
energy = numpy.random.uniform(emin, emax, num_events)
phi = self.modf.rvs_phi(energy, poldegree, polangle)
ebinning = numpy.linspace(emin, emax, 10)
phi_binning = numpy.linspace(0, 2*numpy.pi, 100)
fit_results = []
for i, (_emin, _emax) in enumerate(zip(ebinning[:-1], ebinning[1:])):
_emean = 0.5*(_emin + _emax)
_mask = (energy > _emin)*(energy < _emax)
_phi = phi[_mask]
_hist = plt.hist(_phi, bins=phi_binning, histtype='step')
_fr = xAzimuthalResponseGenerator.fit_histogram(_hist)
_fr.emean = _emean
fit_results.append(_fr)
_fr.plot(label='Energy: %.2f--%.2f keV' % (_emin, _emax))
plt.axis([0., 2*numpy.pi, 0., 1.2*_hist[0].max()])
overlay_tag()
save_current_figure('test_modulation_constant_fit_slice%d.png' % i,
show=self.interactive)
_x = [_fr.emean for _fr in fit_results]
_y = [_fr.phase for _fr in fit_results]
_dy = [_fr.phase_error for _fr in fit_results]
plt.errorbar(_x, _y, yerr=_dy, fmt='o')
plt.plot(_x, numpy.array([polarization_angle]*len(_x)))
plt.xlabel('Energy [keV]')
plt.ylabel('Modulation angle [$^\circ$]')
save_current_figure('test_modulation_constant_angle.png',
show=self.interactive)
_y = [_fr.visibility for _fr in fit_results]
_dy = [_fr.visibility_error for _fr in fit_results]
plt.errorbar(_x, _y, yerr=_dy, fmt='o')
plt.axis([emin, emax, 0, 1])
self.modf.plot(show=False)
plt.xlabel('Energy [keV]')
plt.ylabel('Modulation visibility')
save_current_figure('test_modulation_constant_visibility.png',
show=self.interactive)
def test_rvs(self):
"""Test the random number generation.
"""
visibility = numpy.full(1000000, 0.5)
phi = self.generator.rvs_phi(visibility, 0.25 * numpy.pi)
hist = plt.hist(phi,
bins=numpy.linspace(0, 2 * numpy.pi, 100),
histtype='step',
label='Random angles')
fit_results = self.generator.fit_histogram(hist)
fit_results.plot()
plt.xlabel('$\\phi$ [rad]')
plt.axis([0, 2 * numpy.pi, 0, None])
overlay_tag()
save_current_figure('test_azimuthal_resp_rvs.png',
show=self.interactive)
def test_simplest(self, size=100000, phase=0.):
"""
"""
modf = 1.
energy = numpy.random.uniform(self.emin, self.emax, size)
visibility = modf*self.polarization_degree(energy)
phi = self.generator.rvs_phi(visibility, phase)
binning = numpy.linspace(0, 2*numpy.pi, 100)
hist = plt.hist(phi, bins=binning)
fit_results = self.generator.fit_histogram(hist)
fit_results.set_polarization(modf)
fit_degree = fit_results.polarization_degree
mean_energy = numpy.mean(energy)
exp_degree = self.polarization_degree(mean_energy)
delta = abs(fit_degree - exp_degree)/exp_degree
msg = 'delta = %.3f' % delta
self.assertTrue(delta < 0.05, msg)
def test_uniform(self, size=100000, phase=0.):
"""
"""
energy = numpy.random.uniform(self.emin, self.emax, size)
visibility = self.modf(energy)*self.polarization_degree(energy)
phi = self.generator.rvs_phi(visibility, phase)
binning = numpy.linspace(0, 2*numpy.pi, 100)
hist = plt.hist(phi, bins=binning)
fit_results = self.generator.fit_histogram(hist)
mean_energy = numpy.mean(energy)
mu_effective = self.modf.weighted_average(energy)
fit_results.set_polarization(mu_effective)
fit_degree = fit_results.polarization_degree
exp_degree = (self.polarization_degree(energy)*self.modf(energy))\
.sum()/self.modf(energy).sum()
delta = abs(fit_degree - exp_degree)/exp_degree
msg = 'delta = %.3f' % delta
self.assertTrue(delta < 0.05, msg)
def test_power_law(self, size=1000000, phase=0., index=2.):
"""
"""
_x = numpy.linspace(self.emin, self.emax, 100)
_y = _x**(-index)
generator = xUnivariateGenerator(_x, _y)
energy = generator.rvs(size)
visibility = self.modf(energy)*self.polarization_degree(energy)
phi = self.generator.rvs_phi(visibility, phase)
binning = numpy.linspace(0, 2*numpy.pi, 100)
hist = plt.hist(phi, bins=binning)
fit_results = self.generator.fit_histogram(hist)
mean_energy = numpy.mean(energy)
mu_effective = self.modf.weighted_average(energy)
fit_results.set_polarization(mu_effective)
fit_degree = fit_results.polarization_degree
exp_degree = (self.polarization_degree(energy)*self.modf(energy))\
.sum()/self.modf(energy).sum()
delta = abs(fit_degree - exp_degree)/exp_degree
msg = 'delta = %.3f' % delta
self.assertTrue(delta < 0.05, msg)
def test_power_law_rvs(self, num_events=1000000):
"""Test the generation of event energies from a count power-law
spectrum convoluted with the effective area.
This turned out to be more tricky than we anticipated. Since the
convolution of the source spectrum with the effective area falls
pretty quickly at high energy, there's typically very few events above
a few keV and, as a consequence, the slope of the corresponding ppf is
fairly steep close to one.
.. image:: ../figures/test_power_law_rvs_vppf.png
This implies that the ppf in each slice must be properly sampled
close to 1 (initial tests showed, e.g., that a uniform grid with
100 points between 0 and 1 was not enough to throw meaningful random
numbers for a typical power-law source spectrum). This is particularly
true for soft spectral indices---which is why we picked `Gamma = 3.`
for this test.
.. image:: ../figures/test_power_law_rvs_counts.png
"""
tmin = 0.
tmax = 100.
tref = 0.5*(tmin + tmax)
C = 1.
Gamma = 3.
def powerlaw(E, t):
"""Function defining a time-dependent energy spectrum.
"""
return C*numpy.power(E, -Gamma)
_t = numpy.linspace(tmin, tmax, 2)
count_spectrum = xCountSpectrum(powerlaw, self.aeff, _t)
count_spectrum.vppf.vslice(tref).plot(show=False, overlay=True)
overlay_tag()
save_current_figure('test_power_law_rvs_vppf.png',
show=self.interactive)
ref_slice = count_spectrum.slice(tref)
_time = numpy.full(num_events, tref)
_energy = count_spectrum.rvs(_time)
_binning = numpy.linspace(self.aeff.xmin(), self.aeff.xmax(), 100)
obs, bins, patches = plt.hist(_energy, bins=_binning,
histtype='step', label='Random energies')
plt.yscale('log')
# We want to overlay the reference count-spectrum slice, normalized
# to the total number of events simulated.
bin_width = (bins[1] - bins[0])
scale = num_events*bin_width/ref_slice.norm()
_x = 0.5*(bins[:-1] + bins[1:])
_y = scale*ref_slice(_x)
plt.plot(_x, _y, label='Underlying pdf')
# And, for the chisquare, we do correctly integrate the slice in each
# energy bin, rather than evaluating it at the bin center.
exp = []
scale = num_events/ref_slice.norm()
for _emin, _emax in zip(bins[:-1], bins[1:]):
exp.append(scale*ref_slice.integral(_emin, _emax))
exp = numpy.array(exp)
_mask = exp > 0.
exp = exp[_mask]
obs = obs[_mask]
chi2 = ((exp - obs)**2/exp).sum()
ndof = len(obs)
chi2_min = ndof - 3*numpy.sqrt(2*ndof)
chi2_max = ndof + 3*numpy.sqrt(2*ndof)
self.assertTrue(chi2 > chi2_min, 'chisquare too low (%.2f/%d)' %\
(chi2, ndof))
self.assertTrue(chi2 < chi2_max, 'chisquare too high (%.2f/%d)' %\
(chi2, ndof))
plt.text(0.5, 0.1, '$\chi^2$/ndof = %.2f/%d' % (chi2, ndof),
transform=plt.gca().transAxes)
plt.legend(bbox_to_anchor=(0.85, 0.75))
overlay_tag()
save_current_figure('test_power_law_rvs_counts.png',
show=self.interactive)
def main():
"""Produce some plots
"""
# If all_mdp = True, produces:
# 1) the plot of the MDP for all the Swift GRBs
# and a given repointing time
# 2) the plot of the correlation between MDP for all the Swift
# GRBs and a given repointing time and the integral prompt
# (first 10 min) flux
all_mdp = True
if all_mdp == True:
grb_list = get_all_swift_grb_names()
t_rep = 21600
t_obs = 100000
promt_time = 600
mdp_list1,mdp_list2, flux_list, t0_list = [], [], [], []
c, good_grb = [], []
for grb in grb_list:
mdp = get_grb_mdp(grb,repointing=t_rep,obs_time=t_obs)
flux, t0 = get_integral_flux(grb,delta_t=promt_time)
if mdp is not None and flux is not None:
mdp_list1.append(mdp*100)
if t0 < 350:
if mdp*100 <= 15:
c.append('red')
else:
c.append('blue')
mdp_list2.append(mdp*100)
flux_list.append(flux)
t0_list.append(t0)
else:
continue
_mdp1 = numpy.array(mdp_list1)
_mdp2 = numpy.array(mdp_list2)
_flux = numpy.array(flux_list)
_t0 = numpy.array(t0_list)
# 1)------------------------------------------------------
histo = plt.figure(figsize=(10, 6), dpi=80)
bins = numpy.linspace(0, 100, 100)
plt.title('%i GRBs, $\Delta t_{obs}=%i s,$ $t_{repoint}=%i s$'\
%(len(_mdp1),t_obs,t_rep))
plt.hist(_mdp1, bins, alpha=0.5)
plt.xlabel('2.-10. keV MDP (%)')
plt.ylabel('Number of GRBs')
overlay_tag()
save_current_figure('all_grbs_MDP_histo', clear=False)
plt.show()
# 1.1)----------------------------------------------------
histo = plt.figure(figsize=(10, 6), dpi=80)
bins = numpy.linspace(0, 100, 100)
plt.title('%i GRBs, $\Delta t_{obs}=%i s,$ $t_{repoint}=%i s$'\
%(len(_mdp1),t_obs,t_rep))
(n, bins, patches) = plt.hist(_mdp1, bins, histtype='step', cumulative=True)
plt.xlabel('2.-10. keV MDP (%)')
plt.ylabel('Cumulative number of GRBs')
for i in range(0,len(bins)):
print 'MDP %.2f%%: %i GRBs'%(i,n[i])
overlay_tag()
save_current_figure('all_grbs_MDP_cumulative', clear=False)
plt.show()
# 2)------------------------------------------------------
plt.figure(figsize=(10, 6), dpi=80)
ax = plt.gca()
plt.scatter(_mdp2, _flux, s=30, marker='.', color=c)
plt.xlabel('2.-10. keV MDP (%)')
plt.ylabel('[keV$^{-1}$ cm$^{-2}$]')
plt.title('$\Delta t_{obs}=%i s,$ $t_{repoint}=%i s$'%(t_obs,t_rep))
plt.xlim(1, 100)
ax.set_yscale('log')
ax.set_xscale('log')
overlay_tag()
save_current_figure('grb_MDP_prompt',clear=False)
plt.show()
# If mdp_vs_time = True Produces:
# 1) the plot of the MDP for a given GRB
# as a function of the repointing time
# 2) the plot of the MDP for a given GRB
# as a function of the observation duration
mdp_vs_time = False
color_list = []
if mdp_vs_time == True:
grb_list = ['GRB 060729', 'GRB 080411', 'GRB 091127', 'GRB 111209A',\
'GRB 120711A', 'GRB 130427A', 'GRB 130505A', 'GRB 130907A',\
'GRB 150403A']
#1)------------------------------------------------------
plt.figure(figsize=(10, 6), dpi=80)
ax = plt.gca()
for grb in grb_list:
c = [random.uniform(0,1),random.uniform(0,1),random.uniform(0,1)]
color_list.append(c)
repointing_time = numpy.logspace(2,4.8,30)
plot_grb_mdp_vs_repoint(grb,repointing_time,show=False,color=c)
ax.legend(loc='upper left', shadow=False, fontsize='small')
plt.plot([21600, 21600], [0, 30], 'k--', lw=1, color='green')
plt.plot([43200, 43200], [0, 30], 'k--', lw=1,color='green')
ax.set_yscale('log')
ax.set_xscale('log')
overlay_tag()
save_current_figure('grb_MDP_vs_repoint',clear=False)
plt.show()
#2)------------------------------------------------------
plt.figure(figsize=(10, 6), dpi=80)
ax = plt.gca()
for i,grb in enumerate(grb_list):
obs_time = numpy.logspace(3,5,30)
plot_grb_mdp_vs_obstime(grb,obs_time,show=False,color=color_list[i])
ax.legend(loc='upper right', shadow=False, fontsize='small')
plt.plot([50000, 50000], [0, 50], 'k--', lw=1, color='green')
ax.set_yscale('log')
ax.set_xscale('log')
overlay_tag(x=0.5)
save_current_figure('grb_MDP_vs_obstime',clear=False)
plt.show()
def main():
"""Produce some plots
"""
# If process_grb_mdp = True, produces a fits file with all the
# main infos on each grb
if process_grb_mdp == True:
data = process_grb_list(duration=50000.)
build_grb_fits_file(data,OUTFILE)
# 1) the plot of the MDP for all the Swift GRBs
# and a given repointing time
# 2) the cumulative of the previous histogram
# 3) the plot of the correlation between MDP for all the Swift
# GRBs and a given repointing time and the integral prompt
# (first 10 min) flux
# 1)------------------------------------------------------
plt.figure(figsize=(10, 6), dpi=80)
bins = numpy.linspace(0, 100, 100)
hdulist = fits.open(OUTFILE)
grbdata = hdulist[1].data
_mdp = grbdata['MDP 99%']
t_obs = '50000'
t_rep = '21600'
plt.title('%i GRBs, $\Delta t_{obs}=%s s,$ $t_{repoint}=%s s$'\
%(len(_mdp),t_obs,t_rep))
plt.hist(_mdp*100, bins, alpha=0.5)
plt.xlabel('2.-10. keV MDP (%)')
plt.ylabel('Number of GRBs')
overlay_tag()
save_current_figure('all_grbs_MDP_histo', clear=False)
# 2)----------------------------------------------------
plt.figure(figsize=(10, 6), dpi=80)
plt.title('%i GRBs, $\Delta t_{obs}=%s s,$ $t_{repoint}=%s s$'\
%(len(_mdp),t_obs,t_rep))
(n, bins, patches) = plt.hist(_mdp*100, bins, histtype='step', \
cumulative=True)
plt.xlabel('2.-10. keV MDP (%)')
plt.ylabel('Cumulative number of GRBs')
for i in range(0,30):
print 'MDP %.2f%%: %i GRBs'%(i,n[i])
overlay_tag()
save_current_figure('all_grbs_MDP_cumulative', clear=False)
# 3)------------------------------------------------------
plt.figure(figsize=(10, 6), dpi=80)
ax = plt.gca()
_prompt_tstart = grbdata['PROMPT_START']
_flux = grbdata['PROMPT_FLUX']
_good_indexes = numpy.where(_prompt_tstart>350)
_flux = numpy.delete(_flux,_good_indexes)
_mdp = numpy.delete(_mdp,_good_indexes)
plt.scatter(_mdp*100, _flux, s=30, marker='.', color='blue')
plt.xlabel('2.-10. keV MDP (%)')
plt.ylabel('[erg $\cdot$ cm$^{-2}$]')
plt.title('%i GRBs, $\Delta t_{obs}=%s s,$ $t_{repoint}=%s s$'%(len(_flux),\
t_obs,t_rep))
plt.xlim(1, 100)
plt.ylim(1e-9,1e-4)
plt.plot([20, 20], [1e-9,1e-4], 'k--', lw=1, color='green')
ax.set_yscale('log')
ax.set_xscale('log')
overlay_tag()
save_current_figure('grb_MDP_prompt',clear=False)
plt.show()
# If mdp_vs_time = True Produces:
# 1) the plot of the MDP for a given GRB
# as a function of the repointing time
# 2) the plot of the MDP for a given GRB
# as a function of the observation duration
color_list = ['red','salmon','goldenrod','darkgreen','limegreen',\
'royalblue','mediumpurple','darkviolet','deeppink']\
#'yellow','darkcyan']
if mdp_vs_time == True:
grb_list = ['GRB 060729', 'GRB 080411', 'GRB 091127', 'GRB 111209A',\
'GRB 120711A', 'GRB 130427A', 'GRB 130505A', 'GRB 130907A',\
'GRB 150403A']
#1)------------------------------------------------------
plt.figure(figsize=(10, 6), dpi=80)
ax = plt.gca()
for i,grb in enumerate(grb_list):
repointing_time = numpy.logspace(2,4.8,20)
plot_grb_mdp_vs_repoint(grb,repointing_time,show=False,\
color=color_list[i])
ax.legend(loc='upper left', shadow=False, fontsize='small')
#plt.ylim(0,100)
plt.plot([21600, 21600], [0, 100], 'k--', lw=1, color='green')
plt.plot([43200, 43200], [0, 100], 'k--', lw=1,color='green')
ax.set_yscale('log')
ax.set_xscale('log')
overlay_tag()
save_current_figure('grb_MDP_vs_repoint',clear=False)
#2)------------------------------------------------------
plt.figure(figsize=(10, 6), dpi=80)
ax = plt.gca()
for i,grb in enumerate(grb_list):
obs_time = numpy.logspace(3,5,30)
plot_grb_mdp_vs_obstime(grb,obs_time,show=False,color=color_list[i])
ax.legend(loc='upper right', shadow=False, fontsize='small')
ax.set_yscale('log')
ax.set_xscale('log')
overlay_tag(x=0.5)
save_current_figure('grb_MDP_vs_obstime',clear=False)
plt.show()
