def plot_polarization_angle(self, show=True, degree=False, **kwargs):
"""Plot the polarization angle as a function of energy.
"""
if self.fit_results == []:
self.fit()
_x = self.emean
_dx = [self.emean - self.emin, self.emax - self.emean]
if degree:
_y = [numpy.degrees(r.phase) for r in self.fit_results]
_dy = [numpy.degrees(r.phase_error) for r in self.fit_results]
else:
_y = [(r.phase) for r in self.fit_results]
_dy = [(r.phase_error) for r in self.fit_results]
# If there's more than one energy binning we also fit the entire
# energy interval, but we don't want the corresponding data point to
# appear in the plot, se we brutally get rid of it.
if len(self.fit_results) > 1:
_x = _x[:-1]
_dx = [_x - self.emin[:-1], self.emax[:-1] - _x]
_y = _y[:-1]
_dy = _dy[:-1]
plt.errorbar(_x, _y, _dy, _dx, fmt='o', **kwargs)
plt.xlabel('Energy [keV]')
if degree:
plt.ylabel('Polarization angle [$^\circ$]')
else:
plt.ylabel('Polarization angle [rad]')
if show:
plt.show()
def view():
_mcube = xBinnedModulationCube(MCUBE_FILE_PATH)
_mcube.fit()
_fit_results = _mcube.fit_results[0]
plt.figure('Polarization degree')
_mcube.plot_polarization_degree(show=False, color='blue')
pol_degree_spline.plot(color='lightgray',label='Spin %s'%spindegree, show=False)
plt.figtext(0.2, 0.85,'XIPE %s ks'%(SIM_DURATION/1000.),size=18)
#plt.errorbar(_energy_mean, _pol_deg, yerr=_pol_deg_err, color='blue',marker='o')
plt.legend()
plt.figure('Polarization angle')
_mcube.plot_polarization_angle(show=False, color='blue', degree=False)
pol_angle_spline.plot(color='lightgray',label='Spin %s'%spindegree, show=False)
plt.figtext(0.2, 0.85,'XIPE %s ks'%(SIM_DURATION/1000.),size=18)
#plt.errorbar(_energy_mean,_pol_angle, yerr= _pol_angle_err,color='blue',marker='o')
plt.xlim([1,10])
plt.legend()
plt.figure('MDP %s'%base_name)
mdp = _mcube.mdp99[:-1]
emean = _mcube.emean[:-1]
emin = _mcube.emin[:-1]
emax = _mcube.emax[:-1]
width = (emax-emin)/2.
plt.errorbar(emean,mdp,xerr=width, label='MDP99',marker='o',linestyle='--')
plt.figtext(0.2, 0.85,'XIPE %s ks'%(SIM_DURATION/1000.),size=18)
plt.xlim([1,10])
plt.ylabel('MPD 99\%')
plt.xlabel('Energy (keV)')
#plt.legend()
plt.show()
def makeMDPComparisonPlot(imaging_file_path):
non_imaging_file_path = imaging_file_path.replace('_imaging.txt','_non_imaging.txt')
print "Using %s for non imaging file path"%non_imaging_file_path
_phase_ave, _phase_err, mdp_imaging = numpy.loadtxt(imaging_file_path, unpack=True)
_phase_ave, _phase_err, mdp_nonimaging = numpy.loadtxt(non_imaging_file_path, unpack=True)
scale_factor = 10.
print "Improvement with imaging"
for i, phase in enumerate(_phase_ave):
imaging = 100*mdp_imaging[i]*(1/numpy.sqrt(scale_factor))
non_imaging = 100*mdp_nonimaging[i]*(1/numpy.sqrt(scale_factor))
print "%s\t Imaging (non):%s (%s) %s"%(phase,imaging,non_imaging,non_imaging/imaging)
sim_label_imaging = 'XIPE %s ks\n Imaging 15"' % (SIM_DURATION*scale_factor/1000.)
sim_label_nonimaging = 'Non imaging'
lc_label = 'Light curve'
plt.errorbar(_phase_ave, 100*mdp_imaging*(1/numpy.sqrt(scale_factor)),xerr=_phase_err, label=sim_label_imaging,fmt='o',markersize=6)
plt.errorbar(_phase_ave, 100*mdp_nonimaging*(1/numpy.sqrt(scale_factor)),xerr=_phase_err, label=sim_label_nonimaging,fmt='v',markersize=6)
pl_normalization_spline.plot(scale=10., show=False,
color='darkgray',label=lc_label)
#on_phase = 0.25, 0.45
#off_phase = 0.45,0.9
plt.axvspan(0.25, 0.45, color='r', alpha=0.45, lw=0)
plt.axvspan(0.45, 0.9, color='gray', alpha=0.25, lw=0)
plt.ylabel('MDP 99\%')
plt.legend()
plt.savefig('crab_complex_mdp_imaging_vs_nonimaging_%i_shaded.png'%(SIM_DURATION*scale_factor/1000.))
plt.show()
def plotmdp():
spin00_pol_degree_spline = buildspline(0.5)
spin00_mcube = xBinnedModulationCube(fetch_mcubepath(0.5))
spin998_pol_degree_spline = buildspline(0.998)
spin998_mcube = xBinnedModulationCube(fetch_mcubepath(0.998))
spin00_mcube.fit()
spin998_mcube.fit()
spin00_fit_results = spin00_mcube.fit_results[0]
spin998_fit_results = spin998_mcube.fit_results[0]
plt.figure('MDP')
spin00_mdp = spin00_mcube.mdp99[:-1]
spin998_mdp = spin998_mcube.mdp99[:-1]
emean = spin00_mcube.emean[:-1]
emin = spin00_mcube.emin[:-1]
emax = spin00_mcube.emax[:-1]
width = (emax-emin)/2.
plt.errorbar(emean,spin00_mdp,xerr=width, label='Spin 0.5',marker='o',linestyle='--')
plt.errorbar(emean,spin998_mdp,xerr=width, label='Spin 0.998',marker='o',linestyle='--')
plt.figtext(0.2, 0.85,'XIPE %s ks'%((SIM_DURATION*NUM_RUNS)/1000.),size=18)
plt.xlim([1,10])
plt.ylabel('MPD 99\%')
plt.xlabel('Energy (keV)')
plt.legend()
plt.show()
def plot(self, show=True):
"""Overloaded plot method.
"""
fig = plt.figure('Phasogram')
plt.errorbar(self.phase, self.counts, yerr=self.error, fmt='o')
plt.xlabel('Phase')
plt.ylabel('Counts/bin')
if show:
plt.show()
def plot(self, show=True):
"""Overloaded plot method.
"""
fig = plt.figure('Phasogram')
plt.errorbar(self.phase, self.counts, yerr=self.error, fmt='o')
plt.xlabel('Phase')
plt.ylabel('Counts/bin')
if show:
plt.show()
def plot(self, show=True):
"""Overloaded plot method.
"""
plt.errorbar(self.time, self.counts/self.timedel,
yerr=self.error/self.timedel, fmt='o')
plt.xlabel('Time [s]')
plt.ylabel('Rate [Hz]')
if show:
plt.show()
def plot(self, show=True):
"""Overloaded plot method.
"""
plt.errorbar(self.channel, self.rate, yerr=self.error, fmt='o')
plt.xlabel('PHA')
plt.ylabel('Rate [Hz]')
plt.yscale('log')
if show:
plt.show()
def plot(save_plots=False):
"""
"""
sim_label = 'XIPE %s ks' % (SIM_DURATION/1000.)
mod_label = 'Input model'
_phase, _phase_err, _pol_deg, _pol_deg_err, _pol_angle,\
_pol_angle_err = numpy.loadtxt(ANALYSIS_FILE_PATH, unpack=True)
_colors = ['blue']*len(_pol_deg)
plt.figure('Polarization degree')
_good_fit = _pol_deg > 2*_pol_deg_err
_bad_fit = numpy.logical_not(_good_fit)
plt.errorbar(_phase[_good_fit], _pol_deg[_good_fit],
xerr=_phase_err[_good_fit], yerr=_pol_deg_err[_good_fit],
fmt='o', label=sim_label, color='blue')
plt.errorbar(_phase[_bad_fit], _pol_deg[_bad_fit],
xerr=_phase_err[_bad_fit], yerr=_pol_deg_err[_bad_fit],
fmt='o', color='gray')
pol_degree_spline.plot(show=False, label=mod_label, color='green')
plt.axis([0., 1., 0., 0.1])
plt.legend(bbox_to_anchor=(0.37, 0.95))
plt.figtext(0.6, 0.8, '%.2f--%.2f keV' %\
(E_BINNING[0], E_BINNING[-1]), size=16)
if save_plots:
plt.savefig('gk_per_polarization_degree.png')
plt.figure('Polarization angle')
plt.errorbar(_phase[_good_fit], _pol_angle[_good_fit],
xerr=_phase_err[_good_fit], yerr=_pol_angle_err[_good_fit],
fmt='o', label=sim_label, color='blue')
plt.errorbar(_phase[_bad_fit], _pol_angle[_bad_fit],
xerr=_phase_err[_bad_fit], yerr=_pol_angle_err[_bad_fit],
fmt='o', color='gray')
pol_angle_spline.plot(show=False, label=mod_label, color='green',
scale=numpy.radians(1.))
plt.axis([0., 1., -0.1, 1.5])
plt.xlabel('Rotational phase')
plt.ylabel('Polarization angle [rad]')
plt.legend(bbox_to_anchor=(0.37, 0.95))
plt.figtext(0.6, 0.8, '%.2f--%.2f keV' %\
(E_BINNING[0], E_BINNING[-1]), size=16)
if save_plots:
plt.savefig('gk_per_polarization_angle.png')
_ebinning = zip(E_BINNING[:-1], E_BINNING[1:])
if len(_ebinning) > 1:
_ebinning.append((E_BINNING[0], E_BINNING[-1]))
for i, (_emin, _emax) in enumerate(_ebinning):
plt.figure('Phasogram %d' % i)
phasogram = xBinnedPhasogram(_phasg_file_path(i))
_scale = phasogram.counts.sum()/phasogram_spline.norm()/\
len(phasogram.counts)
phasogram_spline.plot(show=False, label=mod_label, scale=_scale,
color='green')
phasogram.plot(show=False, color='blue', label=sim_label )
plt.legend(bbox_to_anchor=(0.37, 0.95))
plt.figtext(0.65, 0.8, '%.2f--%.2f keV' % (_emin, _emax), size=16)
if save_plots:
plt.savefig('gk_per_phasogram_%d.png' % i)
plt.show()
def plot(self, show=True):
"""Overloaded plot method.
"""
fig = plt.figure('Count spectrum')
plt.errorbar(self.channel, self.rate, yerr=self.error, fmt='o')
plt.xlabel('PHA')
plt.ylabel('Rate [Hz]')
plt.yscale('log')
if show:
plt.show()
def plot(self, show=True, **kwargs):
"""Overloaded plot method.
"""
if not kwargs.has_key('fmt'):
kwargs['fmt'] = 'o'
plt.errorbar(self.phase, self.counts, yerr=self.error,
xerr=0.5*self.phase_delta, **kwargs)
plt.xlabel('Phase')
plt.ylabel('Counts/bin')
if show:
plt.show()
def plot(self, show=True):
"""Overloaded plot method.
"""
fig = plt.figure('Light curve')
plt.errorbar(self.time,
self.counts / self.timedel,
yerr=self.error / self.timedel,
fmt='o')
plt.xlabel('Time [s]')
plt.ylabel('Rate [Hz]')
if show:
plt.show()
def plot_polarization_degree(self, show=True, **kwargs):
"""Plot the polarization degree as a function of energy.
"""
if self.fit_results == []:
self.fit()
_x = self.emean
_dx = [self.emean - self.emin, self.emax - self.emean]
_y = [r.polarization_degree for r in self.fit_results]
_dy = [r.polarization_degree_error for r in self.fit_results]
plt.errorbar(_x, _y, _dy, _dx, fmt='o', **kwargs)
plt.xlabel('Energy [keV]')
plt.ylabel('Polarization degree')
if show:
plt.show()
def plot_polarization_degree(self, show=True, **kwargs):
"""Plot the polarization degree as a function of energy.
"""
if self.fit_results == []:
self.fit()
_x = self.emean
_dx = [self.emean - self.emin, self.emax - self.emean]
_y = [r.polarization_degree for r in self.fit_results]
_dy = [r.polarization_degree_error for r in self.fit_results]
plt.errorbar(_x, _y, _dy, _dx, fmt='o', **kwargs)
plt.xlabel('Energy [keV]')
plt.ylabel('Polarization degree')
if show:
plt.show()
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 plot_polarization_angle(self, show=True, degree=True, **kwargs):
"""Plot the polarization angle as a function of energy.
"""
if self.fit_results == []:
self.fit()
_x = self.emean
_dx = [self.emean - self.emin, self.emax - self.emean]
if degree:
_y = [numpy.degrees(r.phase) for r in self.fit_results]
_dy = [numpy.degrees(r.phase_error) for r in self.fit_results]
else:
_y = [(r.phase) for r in self.fit_results]
_dy = [(r.phase_error) for r in self.fit_results]
plt.errorbar(_x, _y, _dy, _dx, fmt='o', **kwargs)
plt.xlabel('Energy [keV]')
plt.ylabel('Polarization angle [$^\circ$]')
if show:
plt.show()
def plot_polarization_angle(self, show=True, degree=True, **kwargs):
"""Plot the polarization angle as a function of energy.
"""
if self.fit_results == []:
self.fit()
_x = self.emean
_dx = [self.emean - self.emin, self.emax - self.emean]
if degree:
_y = [numpy.degrees(r.phase) for r in self.fit_results]
_dy = [numpy.degrees(r.phase_error) for r in self.fit_results]
else:
_y = [(r.phase) for r in self.fit_results]
_dy = [(r.phase_error) for r in self.fit_results]
plt.errorbar(_x, _y, _dy, _dx, fmt='o', **kwargs)
plt.xlabel('Energy [keV]')
plt.ylabel('Polarization angle [$^\circ$]')
if show:
plt.show()
def test_cdf(self):
"""Test the one-dimensional azimuthal response underlying pdf.
"""
phi = numpy.linspace(0., 2*numpy.pi, 100)
for visibility in numpy.linspace(1, 0, 5):
cdf = self.generator.cdf(phi, visibility)
plt.plot(phi, cdf, label='$\\xi = %.2f$' % visibility)
spline = xInterpolatedUnivariateSplineLinear(phi, cdf)
self.assertTrue(abs(spline(0.)) < 1e-5, 'cdf(0) = %.3e' % spline(0))
self.assertTrue(abs(spline(2*numpy.pi) - 1) < 1e-5,
'cdf(2pi) = %.3e' % spline(2*numpy.pi))
plt.axis([0., 2*numpy.pi, None, None])
plt.xlabel('$\\phi$ [rad]')
plt.ylabel('cdf($\\phi$)')
plt.legend(bbox_to_anchor=(0.4, 0.92))
overlay_tag()
save_current_figure('test_azimuthal_resp_cdf.png',
show=self.interactive)
def plot(self,
num_points=1000,
overlay=False,
logx=False,
logy=False,
scale=1.,
offset=0.,
show=True,
**kwargs):
"""Plot the spline.
Args
----
num_points : int, optional
The number of sampling points to be used to draw the spline.
overlay : bool, optional
If True, the original arrays passed to the spline are overlaid.
show : bool, optional
If True, `plt.show()` is called at the end, interrupting the flow.
"""
from ximpol.utils.matplotlib_ import pyplot as plt
if not logx:
_x = numpy.linspace(self.xmin(), self.xmax(), num_points)
else:
_x = numpy.logspace(numpy.log10(self.xmin()),
numpy.log10(self.xmax()), num_points)
_y = scale * self(_x) + offset
if overlay:
plt.plot(_x, _y, '-', self.x, self.y, 'o', **kwargs)
else:
plt.plot(_x, _y, '-', **kwargs)
if self.xname is not None:
plt.xlabel(self.xlabel())
if self.yname is not None:
plt.ylabel(self.ylabel())
if logx:
plt.gca().set_xscale('log')
if logy:
plt.gca().set_yscale('log')
if show:
plt.show()
def plot(self,
num_pointsx=100,
num_pointsy=100,
num_contours=75,
logz=False,
show=True):
"""Plot the spline.
Args
----
num_pointsx : int
The number of x sampling points to be used to draw the spline.
num_pointsy : int
The number of y sampling points to be used to draw the spline.
num_contours : int
The number of contours for the color plot.
show : bool, optional
If True, `plt.show()` is called at the end, interrupting the flow.
"""
from ximpol.utils.matplotlib_ import pyplot as plt
_x = numpy.linspace(self.xmin(), self.xmax(), num_pointsx)
_y = numpy.linspace(self.ymin(), self.ymax(), num_pointsy)
_x, _y = numpy.meshgrid(_x, _y)
_z = self(_x, _y, grid=False)
if logz:
from matplotlib.colors import LogNorm
_levels = numpy.logspace(-4, numpy.log10(_z.max()), num_contours)
contour = plt.contourf(_x, _y, _z, levels=_levels, norm=LogNorm())
else:
contour = plt.contourf(_x, _y, _z, num_contours)
bar = plt.colorbar()
if self.xname is not None:
plt.xlabel(self.xlabel())
if self.yname is not None:
plt.ylabel(self.ylabel())
if self.zname is not None:
bar.set_label(self.zlabel())
if show:
plt.show()
def test_cdf(self):
"""Test the one-dimensional azimuthal response underlying pdf.
"""
phi = numpy.linspace(0., 2 * numpy.pi, 100)
for visibility in numpy.linspace(1, 0, 5):
cdf = self.generator.cdf(phi, visibility)
plt.plot(phi, cdf, label='$\\xi = %.2f$' % visibility)
spline = xInterpolatedUnivariateSplineLinear(phi, cdf)
self.assertTrue(
abs(spline(0.)) < 1e-5, 'cdf(0) = %.3e' % spline(0))
self.assertTrue(
abs(spline(2 * numpy.pi) - 1) < 1e-5,
'cdf(2pi) = %.3e' % spline(2 * numpy.pi))
plt.axis([0., 2 * numpy.pi, None, None])
plt.xlabel('$\\phi$ [rad]')
plt.ylabel('cdf($\\phi$)')
plt.legend(bbox_to_anchor=(0.4, 0.92))
overlay_tag()
save_current_figure('test_azimuthal_resp_cdf.png',
show=self.interactive)
def plot_bin(self, i, show=True, fit=True):
"""Plot the azimuthal distribution for the i-th energy slice.
"""
_emin = self.emin[i]
_emax = self.emax[i]
_emean = self.emean[i]
label = '%.2f-%.2f $<$%.2f$>$ keV' % (_emin, _emax, _emean)
plt.errorbar(self.phi_x, self.phi_y[i], yerr=numpy.sqrt(self.phi_y[i]),
fmt='o')
if fit:
fit_results = self.fit_bin(i)
fit_results.plot(label=label)
plt.axis([0., 2*numpy.pi, 0.0, 1.2*self.phi_y[i].max()])
plt.xlabel('Azimuthal angle [rad]')
plt.ylabel('Counts/bin')
plt.text(0.02, 0.92, label, transform=plt.gca().transAxes,
fontsize=15)
if show:
plt.show()
def plot_pol_map_from_ascii():
output_file = get_output_file()
logger.info('Opening file %s for plotting...' % output_file)
emin,emax, degree, degree_error, angle, angle_error, counts = numpy.loadtxt(output_file, delimiter=',', unpack=True)
_ra = numpy.linspace(ra_min, ra_max, num_points)
_dec = numpy.linspace(dec_min, dec_max, num_points)
_ra, _dec = numpy.meshgrid(_ra, _dec)
fig = plt.figure()
for i in range(len(E_BINNING) - 1):
sigma_array = degree[emin==E_BINNING[i]]/degree_error[emin==E_BINNING[i]]
pol_array = degree[emin==E_BINNING[i]].reshape((num_points,num_points))
sigma_array = sigma_array.reshape((num_points,num_points))
ax1 = fig.add_subplot()
label = 'XIPE %.1d ks'%(DURATION/1000.)
plt.contourf(_ra,_dec,pol_array)
cbar = plt.colorbar()
cbar.ax.set_ylabel('Polarization degree')
plt.text(0.02, 0.92, label, transform=plt.gca().transAxes,
fontsize=20,color='w')
plt.xlabel('RA')
plt.ylabel('DEC')
# fig.gca().add_artist(psf)
plt.show()
ax2 = fig.add_subplot()
plt.contourf(_ra,_dec,sigma_array)
cbar2 = plt.colorbar()
cbar2.ax.set_ylabel('Sigma')
plt.text(0.02, 0.92, label, transform=plt.gca().transAxes,
fontsize=20,color='w')
plt.xlabel('RA')
plt.ylabel('DEC')
plt.show()
def test_pdf(self):
"""Test the one-dimensional azimuthal response underlying pdf.
"""
self.generator.plot(show=self.interactive)
overlay_tag(color='white')
save_current_figure('test_azimuthal_resp_generator.png',
show=self.interactive)
phi = numpy.linspace(0., 2*numpy.pi, 100)
for visibility in numpy.linspace(1, 0, 5):
pdf = self.generator.pdf(phi, visibility)
plt.plot(phi, pdf, label='$\\xi = %.2f$' % visibility)
spline = xInterpolatedUnivariateSplineLinear(phi, pdf)
norm = spline.norm()
self.assertTrue(abs(norm - 1.) < 1e-5,
'Normalization is %.3e' % norm)
plt.axis([0., 2*numpy.pi, None, None])
plt.xlabel('$\\phi$ [rad]')
plt.ylabel('pdf($\\phi$) [1/rad]')
plt.legend(bbox_to_anchor=(0.88, 0.92))
overlay_tag()
save_current_figure('test_azimuthal_resp_pdf.png',
show=self.interactive)
def test_pdf(self):
"""Test the one-dimensional azimuthal response underlying pdf.
"""
self.generator.plot(show=self.interactive)
overlay_tag(color='white')
save_current_figure('test_azimuthal_resp_generator.png',
show=self.interactive)
phi = numpy.linspace(0., 2 * numpy.pi, 100)
for visibility in numpy.linspace(1, 0, 5):
pdf = self.generator.pdf(phi, visibility)
plt.plot(phi, pdf, label='$\\xi = %.2f$' % visibility)
spline = xInterpolatedUnivariateSplineLinear(phi, pdf)
norm = spline.norm()
self.assertTrue(
abs(norm - 1.) < 1e-5, 'Normalization is %.3e' % norm)
plt.axis([0., 2 * numpy.pi, None, None])
plt.xlabel('$\\phi$ [rad]')
plt.ylabel('pdf($\\phi$) [1/rad]')
plt.legend(bbox_to_anchor=(0.88, 0.92))
overlay_tag()
save_current_figure('test_azimuthal_resp_pdf.png',
show=self.interactive)
def plot_grb_mdp_vs_obstime(grb_name, _t_obs, t_repoint=21600, \
color='black', show=True):
"""Plot all the MDP (changing the repointing elapsed time defined in *arg)
for a given GRB.
"""
mdp_list = []
for obs in _t_obs:
mdp = get_grb_mdp(grb_name,repointing=t_repoint,obs_time=obs)
if mdp is not None:
mdp_list.append(mdp)
else:
mdp_list.append(0.)
_mdp = numpy.array(mdp_list)*100
plt.plot(_t_obs,_mdp, marker='.',linestyle='-', lw=0.5, color=color,\
label=grb_name)
plt.xlabel('$\Delta t_{obs}$ [s]')
plt.ylabel('2.-10. keV MDP (%)')
plt.title('MDP vs $\Delta t_{obs}$, $ t_{repoint} =$ %i s'\
%(t_repoint))
if show:
plt.show()
return _mdp, _t_obs
def plot_grb_mdp_vs_repoint(grb_name, _t_repoint, t_obs=50000, \
color='black', show=True):
"""Plot all the MDP (changing the repointing elapsed time defined in *arg)
for a given GRB.
"""
mdp_list = []
for repoint in _t_repoint:
mdp = process_grb(grb_name,tstart=repoint,duration=t_obs)[-1]
if mdp is not None:
mdp_list.append(mdp)
else:
mdp_list.append(0.)
_mdp = numpy.array(mdp_list)*100
plt.plot(_t_repoint, _mdp, marker='.',linestyle='-', lw=0.5, color=color,\
label=grb_name)
plt.xlabel('$t_{repoint}$ [s]')
plt.ylabel('2.-10. keV MDP (%)')
plt.title('MDP vs $t_{repoint}$, $\Delta t_{obs} =$ %i s'\
%(t_obs))
if show:
plt.show()
return _mdp, _t_repoint
def plot(self, num_pointsx=100, num_pointsy=100, num_contours=75,
logz=False, show=True):
"""Plot the spline.
Args
----
num_pointsx : int
The number of x sampling points to be used to draw the spline.
num_pointsy : int
The number of y sampling points to be used to draw the spline.
num_contours : int
The number of contours for the color plot.
show : bool, optional
If True, `plt.show()` is called at the end, interrupting the flow.
"""
from ximpol.utils.matplotlib_ import pyplot as plt
_x = numpy.linspace(self.xmin(), self.xmax(), num_pointsx)
_y = numpy.linspace(self.ymin(), self.ymax(), num_pointsy)
_x, _y = numpy.meshgrid(_x, _y)
_z = self(_x, _y, grid=False)
if logz:
from matplotlib.colors import LogNorm
_levels = numpy.logspace(-4, numpy.log10(_z.max()), num_contours)
contour = plt.contourf(_x, _y, _z, levels=_levels, norm = LogNorm())
else:
contour = plt.contourf(_x, _y, _z, num_contours)
bar = plt.colorbar()
if self.xname is not None:
plt.xlabel(self.xlabel())
if self.yname is not None:
plt.ylabel(self.ylabel())
if self.zname is not None:
bar.set_label(self.zlabel())
if show:
plt.show()
def plot_bin(self, i, show=True, fit=True):
"""Plot the azimuthal distribution for the i-th energy slice.
"""
_emin = self.emin[i]
_emax = self.emax[i]
_emean = self.emean[i]
label = '%.2f-%.2f $<$%.2f$>$ keV' % (_emin, _emax, _emean)
plt.errorbar(self.phi_x,
self.phi_y[i],
yerr=numpy.sqrt(self.phi_y[i]),
fmt='o')
if fit:
fit_results = self.fit_bin(i)
fit_results.plot(label=label)
view_range = 1.5 * (self.phi_y[i].max() - self.phi_y[i].min())
view_ymin = self.phi_y[i].min() - view_range
view_ymax = self.phi_y[i].max() + view_range
plt.axis([0., 2 * numpy.pi, view_ymin, view_ymax])
plt.xlabel('Azimuthal angle [rad]')
plt.ylabel('Counts/bin')
plt.text(0.02, 0.92, label, transform=plt.gca().transAxes, fontsize=15)
if show:
plt.show()
def makeMDP_fE_ComparisonPlot(file_path):
scale_factor = 10.
(_phase_ave, _phase_err, _energy_mean, pulsar_e_err, pulsar_e_err, mdp) =\
numpy.loadtxt(file_path, unpack=True)
print "Phase ave:",_phase_ave
print
print "Energy mean", _energy_mean
#phase_values = [0.025, 0.15, 0.35, 0.675, 0.95]
phase_values = [0.35,0.675]
on, on_phase_color = (0.35,'r')
off, off_phase_color = (0.675,'gray')
#for phase in phase_values:
plt.errorbar(_energy_mean[_phase_ave==on], 100*mdp[_phase_ave==on]*(1/numpy.sqrt(scale_factor)),xerr=pulsar_e_err[_phase_ave==on], label='On Phase',fmt='o',markersize=6,ls='--',color=on_phase_color)
plt.errorbar(_energy_mean[_phase_ave==off], 100*mdp[_phase_ave==off]*(1/numpy.sqrt(scale_factor)),xerr=pulsar_e_err[_phase_ave==off], label='Off Phase',fmt='o',markersize=6,ls='--',color=off_phase_color)
plt.legend()
plt.ylabel('MPD 99\%')
plt.xlabel('Energy (keV)')
plt.savefig('crab_complex_mdp_imaging_fE_%i.png'%(SIM_DURATION*scale_factor/1000.))
plt.show()
def plot(self, num_points=1000, overlay=False, logx=False, logy=False,
scale=1., offset=0., show=True, **kwargs):
"""Plot the spline.
Args
----
num_points : int, optional
The number of sampling points to be used to draw the spline.
overlay : bool, optional
If True, the original arrays passed to the spline are overlaid.
show : bool, optional
If True, `plt.show()` is called at the end, interrupting the flow.
"""
from ximpol.utils.matplotlib_ import pyplot as plt
if not logx:
_x = numpy.linspace(self.xmin(), self.xmax(), num_points)
else:
_x = numpy.logspace(numpy.log10(self.xmin()),
numpy.log10(self.xmax()), num_points)
_y = scale*self(_x) + offset
if overlay:
plt.plot(_x, _y, '-', self.x, self.y, 'o', **kwargs)
else:
plt.plot(_x, _y, '-', **kwargs)
if self.xname is not None:
plt.xlabel(self.xlabel())
if self.yname is not None:
plt.ylabel(self.ylabel())
if logx:
plt.gca().set_xscale('log')
if logy:
plt.gca().set_yscale('log')
if show:
plt.show()
def view():
#_energy_mean,_emin, _emax, _pol_deg, _pol_deg_err, _pol_angle, \
# _pol_angle_err = \
# # numpy.loadtxt(ANALYSIS_FILE_PATH, unpack=True)
_mcube = xBinnedModulationCube(MCUBE_FILE_PATH)
_mcube.fit()
_fit_results = _mcube.fit_results[0]
plt.figure('Polarization degree')
_mcube.plot_polarization_degree(show=False, color='blue')
pol_degree_spline.plot(color='lightgray',label='Model %s corona'%model_type, show=False)
plt.figtext(0.2, 0.85,'XIPE %s ks'%(SIM_DURATION/1000.),size=18)
#plt.errorbar(_energy_mean, _pol_deg, yerr=_pol_deg_err, color='blue',marker='o')
plt.legend()
plt.figure('Polarization angle')
_mcube.plot_polarization_angle(show=False, color='blue', degree=False)
pol_angle_spline.plot(color='lightgray',label='Model %s corona'%model_type, show=False)
plt.figtext(0.2, 0.85,'XIPE %s ks'%(SIM_DURATION/1000.),size=18)
#plt.errorbar(_energy_mean,_pol_angle, yerr= _pol_angle_err,color='blue',marker='o')
plt.legend()
plt.figure('MDP %s'%base_name)
mdp = _mcube.mdp99
emean = _mcube.emean
emin = _mcube.emin
emax = _mcube.emax
width = (emax-emin)/2.
plt.errorbar(emean,mdp,xerr=width, label='MDP99',marker='o',linestyle='--')
plt.figtext(0.2, 0.85,'XIPE %s ks'%(SIM_DURATION/1000.),size=18)
plt.xlim([1,10])
plt.ylabel('MPD 99 %')
plt.xlabel('Energy (keV)')
#plt.legend()
plt.show()
for i in range(0, len(mod_cube.emax)):
cnts = mod_cube.counts[i]
logger.info('%.2f--%.2f keV: %d counts in %d s, mu %.3f, MDP %.2f%%' %\
(mod_cube.emin[i], mod_cube.emax[i], mod_cube.counts[i], TIME,
mod_cube.effective_mu[i], 100*mod_cube.mdp99[i]))
logger.info('Done.')
pol_deg = numpy.mean(pol_degree_array, axis=0)
pol_deg_err = numpy.mean(pol_degree_error_array, axis=0)
pol_ang = numpy.mean(pol_angle_array, axis=0)
pol_ang_err = numpy.mean(pol_angle_error_array, axis=0)
fig = plt.figure('Polarization degree')
plt.xlabel('Energy [keV]')
plt.ylabel('Polarization degree')
bad = pol_deg < 3*pol_deg_err
good = numpy.invert(bad)
bad[len(E_BINNING)-1] = False
good[len(E_BINNING)-1] = False
_dx = numpy.array([mod_cube.emean - mod_cube.emin, mod_cube.emax - mod_cube.emean])
if bad.sum() > 0:
plt.errorbar(mod_cube.emean[bad], pol_deg[bad], pol_deg_err[bad],
_dx.T[bad].T, fmt='o', label='Data', color='gray')
if good.sum() > 0:
plt.errorbar(mod_cube.emean[good], pol_deg[good], pol_deg_err[good],
_dx.T[good].T, fmt='o', label='Data', color='blue')
pol_degree.plot(show=False, label='Model', linestyle='dashed', color='green')
plt.legend(bbox_to_anchor=(0.30, 0.95))
plt.axis([1, 10, 0, 0.1])
#plt.savefig('/home/nicco/%s_0_0_10x%dMs_polarization_degree.png' %\
plt.figure("Average polarization degree", (11, 8))
_x = numpy.logspace(-13, -7, 100)
for obs_time in [1.0e3, 10.0e3, 100.0e3, 1.0e6]:
_y = 100.0 * mdp_ref * numpy.sqrt(OBS_TIME_REF / obs_time * FLUX_REF / _x)
plt.plot(_x, _y, color=GRID_COLOR, ls="dashed", lw=0.6)
_i = 51
if obs_time is 1.0e3:
_x_text = _x[_i]
_y_text = _y[_i]
plt.text(_x_text, _y_text, "$T_{obs} =$ %d ks" % (obs_time / 1000.0), color=GRID_COLOR, rotation=-43.0, size=14)
_x_text /= 10
plt.xscale("log")
plt.yscale("log")
plt.xlabel("Integral energy flux %.0f-%.0f keV [erg cm$^{-2}$ s$^{-1}$]" % (E_MIN, E_MAX))
plt.ylabel("MDP 99% CL [%]")
for j, blazar in enumerate(blazar_list):
_x_max = blazar["flux_max"]
_x_min = blazar["flux_min"]
_y_max = blazar["p_opt_max"]
_y_min = blazar["p_opt_min"]
plt.plot([_x_min, _x_max, _x_max], [_y_max, _y_max, _y_min], color=_color[:, j], lw=1.5)
_x_text = numpy.sqrt((_x_max) * (_x_min))
if j in mirror_list:
_y_text = 0.88 * _y_max
else:
_y_text = 1.02 * _y_max
plt.text(_x_text, _y_text, blazar["name"], color=_color[:, j], horizontalalignment="center", size="large")
plt.axis([1e-13, 1e-8, 0.5, 50])
plt.savefig("blazar_mdp_average.png")
numpy.random.seed(17)
_disp = numpy.random.uniform(0.9, 1.4, len(obs_plan_list))
plt.figure('Average polarization degree', (14, 10))
_x = numpy.logspace(-13, -7, 100)
for obs_time in [1.e3, 10.e3, 100.e3, 1.e6]:
_y = 100.* mdp_ref * numpy.sqrt(OBS_TIME_REF/obs_time * FLUX_REF/_x)
plt.plot(_x, _y, color=GRID_COLOR, ls='dashed', lw=0.5)
_i = 53
plt.text(_x[_i], _y[_i], '$T_{obs} =$ %d ks' % (obs_time/1000.),
color=GRID_COLOR, rotation=-45.)
plt.xscale('log')
plt.yscale('log')
plt.xlabel('Integral energy flux %.0f-%.0f keV [erg cm$^{-2}$ s$^{-1}$]' %\
(E_MIN, E_MAX))
plt.ylabel('MDP 99% CL [%]')
for j, source in enumerate(obs_plan_list):
_x = source['flux']
_y = source['mdp']*_disp[j]
plt.plot(_x, _y, 'o', color=_color[:,j])
_text = source['name']
if source['notes'] is not '':
_text += '\n' + source['notes']
if j in mirror_list:
_y *= 0.8
else:
_y *= 1.05
plt.text(_x, _y, _text, color=_color[:,j],
horizontalalignment='center', size='large')
plt.axis([1e-12, 1e-7, 0.4, 30])
def calcMDP(plot=False):
mdp_file = open(MDP_OUTPUT_FILE,'w')
mdp_file.write('#Simulation time %s sec \n'%SIM_DURATION)
mdp_file.write('#Phase Ave delta phase Mean energy delta energy mdp99\n')
nebula_mcube_file = xBinnedModulationCube(NEBULA_MCUBE_FILE_PATH)
nebula_counts = nebula_mcube_file.counts
nebula_mdp = nebula_mcube_file.mdp99
txt = "Pulsar phase\t Emin - Emax\t Pulsar counts\t Nebula counts\t MDP\n"
MDP99_PULSAR = []
phase = []
phase_err = []
for i, (_min, _max) in enumerate(PHASE_BINS):
#zip(PHASE_BINS[:-1],PHASE_BINS[1:])):
pulse_diff = numpy.fabs(_max -_min)
_phase_ave = 0.5*(_min + _max)
phase.append(_phase_ave)
_phase_err = 0.5*(_max - _min)
phase_err.append(_phase_err)
pulsar_phase_mcube_file = xBinnedModulationCube(_mcube_file_path(i))
pulsar_emean = pulsar_phase_mcube_file.emean
for j, _energy_mean in enumerate(pulsar_emean):
pulsar_emin = pulsar_phase_mcube_file.emin[j]
pulsar_emax = pulsar_phase_mcube_file.emax[j]
pulsar_e_err = 0.5*(pulsar_emax-pulsar_emin)
pulsar_phase_counts = pulsar_phase_mcube_file.counts[j]
pulsar_mdp = pulsar_phase_mcube_file.mdp99[j]
#scale the nebula counts for the time used for the pulsar phase
scaled_nebula_counts = pulse_diff*nebula_counts[j]
count_sqrt = numpy.sqrt(pulsar_phase_counts + scaled_nebula_counts)
eff_mu_pulsar = pulsar_phase_mcube_file.effective_mu[j]
mdp = 4.292/eff_mu_pulsar*count_sqrt/pulsar_phase_counts
MDP99_PULSAR.append(100*mdp)
_data = (_phase_ave, _phase_err, _energy_mean, pulsar_e_err, pulsar_e_err, mdp)
_fmt = ('%.4e ' * len(_data)).strip()
_fmt = '%s\n' % _fmt
_line = _fmt % _data
mdp_file.write(_line)
#txt += "%s\t %s - %s\t %s\t %s\t %.3f\n"%(PHASE_BINS[i], pulsar_emin[0], pulsar_emax[0], pulsar_phase_counts[0], scaled_nebula_counts[0], 100*mdp)
MDP99_PULSAR = numpy.array(MDP99_PULSAR)
PHASE = numpy.array(phase)
mdp_file.close()
if plot:
scale_factor = 10
sim_label = 'XIPE %s ks' % (SIM_DURATION*scale_factor/1000.)
lc_label = 'Light curve'
plt.errorbar(PHASE, MDP99_PULSAR*(1/numpy.sqrt(10)),xerr=phase_err, label=sim_label,fmt='o')
pl_normalization_spline.plot(scale=10., show=False,
color='lightgray',label=lc_label)
plt.ylabel('MDP 99\%')
plt.legend()
plt.savefig('crab_complex_mdp_nonimaging_%i.png'%(SIM_DURATION*scale_factor/1000.))
#plt.show()
print txt
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()
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()