def plot(angle=False):
spin05_pol_degree_spline, spin05_pol_angle_spline = buildspline(0.5)
spin05_mcube = xBinnedModulationCube(fetch_mcubepath(0.5))
spin09_pol_degree_spline, spin09_pol_angle_spline = buildspline(0.9)
spin09_mcube = xBinnedModulationCube(fetch_mcubepath(0.9))
spin998_pol_degree_spline, spin998_pol_angle_spline = buildspline(0.998)
spin998_mcube = xBinnedModulationCube(fetch_mcubepath(0.998))
spin05_mcube.fit()
spin09_mcube.fit()
spin998_mcube.fit()
spin05_fit_results = spin05_mcube.fit_results[0]
spin09_fit_results = spin09_mcube.fit_results[0]
spin998_fit_results = spin998_mcube.fit_results[0]
plt.figure('Polarization degree')
spin05_mcube.plot_polarization_degree(show=False, color='blue')
spin05_pol_degree_spline.plot(color='lightblue',label='Spin 0.5', show=False)
spin998_mcube.plot_polarization_degree(show=False, color='red')
spin998_pol_degree_spline.plot(color='lightsalmon',label='Spin 0.998', show=False)
plt.figtext(0.2, 0.85,'XIPE %s ks'%((SIM_DURATION*NUM_RUNS)/1000.),size=18)
plt.ylim([0.00,0.045])
plt.xlim([1,10])
plt.legend()
plt.show()
if angle:
plt.figure('Polarization angle')
spin05_mcube.plot_polarization_angle(show=False, degree=True, color='blue')
#Converting to degrees
spin05_y = numpy.degrees(spin05_pol_angle_spline.y)
energy = spin05_pol_angle_spline.x
plt.plot(energy, spin05_y, color='lightblue',label='Spin 0.5')
spin09_mcube.plot_polarization_angle(show=False, degree=True, color='gray')
#Converting to degrees
spin09_y = numpy.degrees(spin09_pol_angle_spline.y)
energy = spin09_pol_angle_spline.x
plt.plot(energy, spin09_y, color='lightgray',label='Spin 0.9')
spin998_mcube.plot_polarization_angle(show=False, degree=True, color='red')
spin998_y = numpy.degrees(spin998_pol_angle_spline.y)
energy = spin998_pol_angle_spline.x
plt.plot(energy, spin998_y, color='lightsalmon',label='Spin 0.998')
#spin998_pol_angle_spline.plot(color='lightsalmon',label='Spin 0.998', show=False)
plt.figtext(0.2, 0.85,'XIPE %s ks'%((SIM_DURATION*NUM_RUNS)/1000.),size=18)
plt.xlim([1,10])
plt.ylim([40,200])
plt.legend()
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()
