def plot_doc():
"""
"""
img = xFITSImage(os.path.join(XIMPOL_DATA, 'crab_complex_cmap.fits'))
fig = img.plot(show=False)
xFITSImage.add_label(fig, 'XIPE %d ks' %DURATION/1000.)
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 plot(self, show=True):
"""Plot the energy dispersion.
"""
from ximpol.utils.matplotlib_ import pyplot as plt
from ximpol.utils.matplotlib_ import context_two_by_two
emin = self.matrix.xmin()
emax = self.matrix.xmax()
def _plot_vslice(energy, position):
"""Convenience function to plot a generic vertical slice of the
energy dispersion.
"""
ax = plt.subplot(2, 2, position)
vslice = self.matrix.vslice(energy)
vslice.plot(overlay=False, show=False)
plt.text(0.1, 0.9, '$E = %.2f\\ \\rm{keV}$' % energy,
transform=ax.transAxes)
ppf = vslice.build_ppf()
eres = 0.5*(ppf(0.8413) - ppf(0.1586))/ppf(0.5)
plt.text(0.1, 0.85, '$\sigma_E/E = %.3f$' % eres,
transform=ax.transAxes)
with context_two_by_two():
plt.figure(1)
ax = plt.subplot(2, 2, 1)
self.matrix.plot(show=False)
ax = plt.subplot(2, 2, 2)
self.ebounds.plot(overlay=False, show=False)
_plot_vslice(emin + 0.333*(emax - emin), 3)
_plot_vslice(emin + 0.666*(emax - emin), 4)
if show:
plt.show()
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 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 test_plots(self):
"""
"""
model = xpeInterstellarAbsorptionModel()
plt.figure()
model.xsection.plot(logx=True, logy=True, show=False)
save_current_figure('gabs_xsection.png', clear=False)
plt.figure()
model.xsection_ecube().plot(logx=True, show=False)
save_current_figure('gabs_xsection_ecube.png', clear=False)
plt.figure()
ra, dec = 10.684, 41.269
column_density = mapped_column_density_HI(ra, dec, 'LAB')
trans = model.transmission_factor(column_density)
trans.plot(logx=True, show=False, label='$n_H = $%.3e' % column_density)
plt.legend(loc='upper left')
save_current_figure('gabs_trans_lab.png', clear=False)
plt.figure()
for column_density in [1.e20, 1.e21, 1.e22, 1.e23]:
trans = model.transmission_factor(column_density)
trans.plot(logx=True, show=False, label='$n_H = $%.1e' %\
column_density)
plt.legend(loc='upper left')
save_current_figure('gabs_trans_samples.png', clear=False)
if sys.flags.interactive:
plt.show()
def main():
"""
"""
plt.figure(figsize=(10, 6), dpi=80)
plt.title('Swift XRT light curves of GRBs up to 130427A')
#get_all_swift_grb_names = ['GRB 130427A','GRB 041223']
num_grb = 0
for i,grb_name in enumerate(get_all_swift_grb_names()):
flux_outfile = download_swift_grb_lc_file(grb_name)
if type(flux_outfile) is str:
integral_flux_spline = parse_light_curve(flux_outfile)
if integral_flux_spline != 0:
if grb_name == 'GRB 130427A':
integral_flux_spline.plot(num_points=1000,logx=True,\
logy=True,show=False,\
color="red",linewidth=1.0)
num_grb += 1
break
plt.title('Swift XRT light curves of GRBs up to now')
else:
c = random.uniform(0.4,0.8)
integral_flux_spline.plot(num_points=1000,logx=True,\
logy=True,show=False,\
color='%f'%c,linewidth=1.0)
num_grb += 1
print num_grb
plt.show()
def plot_swift_lc(grb_list,show=True):
"""Plots Swift GRB light curves.
"""
plt.figure(figsize=(10, 8), dpi=80)
plt.title('Swift XRT light curves')
num_grb = 0
for grb_name in grb_list:
flux_outfile = download_swift_grb_lc_file(grb_name, min_obs_time=21600)
if flux_outfile is not None:
integral_flux_spline = parse_light_curve(flux_outfile)
if integral_flux_spline is not None:
if grb_name == 'GRB 130427A':
integral_flux_spline.plot(num_points=1000,logx=True,\
logy=True,show=False,\
color="red",linewidth=1.0)
num_grb += 1
else:
c = random.uniform(0.4,0.8)
integral_flux_spline.plot(num_points=1000,logx=True,\
logy=True,show=False,\
color='%f'%c,linewidth=1.0)
num_grb += 1
else:
continue
logger.info('%i GRBs included in the plot.'%num_grb)
if show:
plt.show()
def plot_doc():
"""
"""
img = xFITSImage(os.path.join(XIMPOL_DATA, 'tycho_cmap.fits'))
fig = img.plot(show=False)
xFITSImage.add_label(fig, 'XIPE 300 ks')
plt.show()
def display():
"""Display the source model.
"""
from ximpol.utils.matplotlib_ import pyplot as plt
print(ROI_MODEL)
fig = plt.figure('Energy spectrum')
total_spectral_model.plot(logy=True, show=False, label='Total')
nonthermal_spectral_model.plot(logy=True, show=False, label='Non-thermal')
thermal_spectral_model.plot(logy=True, show=False, label='Thermal')
plt.legend(bbox_to_anchor=(0.95, 0.95))
fig = thermal_component.image.plot(show=False)
fig.add_label(0.1,
0.92,
'1.5-3 keV',
relative=True,
size='xx-large',
color='white',
horizontalalignment='left')
fig = nonthermal_component.image.plot(show=False)
fig.add_label(0.1,
0.92,
'4-6 keV',
relative=True,
size='xx-large',
color='white',
horizontalalignment='left')
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 plot_doc():
"""
"""
img = xFITSImage(os.path.join(XIMPOL_DATA, 'tycho_cmap.fits'))
fig = img.plot(show=False)
xFITSImage.add_label(fig, 'XIPE 300 ks')
plt.show()
def plot(self, show=True, zlabel='Counts/pixel', subplot=(1, 1, 1)):
"""Plot the image.
This is using aplpy to render the image.
Warning
-------
We have to figure out the subplot issue, here. I put in a horrible
hack to recover the previous behavior when there's only one
subplot.
"""
import aplpy
with context_no_grids():
if subplot == (1, 1, 1):
fig = aplpy.FITSFigure(self.hdu_list[0], figure=plt.figure())
else:
fig = aplpy.FITSFigure(self.hdu_list[0],
figure=plt.figure(
0,
figsize=(10 * subplot[1],
10 * subplot[0])),
subplot=subplot)
fig.add_grid()
fig.show_colorscale(cmap='afmhot', vmin=self.vmin, vmax=self.vmax)
fig.add_colorbar()
fig.colorbar.set_axis_label_text(zlabel)
if show:
plt.show()
return fig
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(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(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 display():
"""Display the source model.
"""
from ximpol.utils.matplotlib_ import pyplot as plt
from ximpol.srcmodel.img import xFITSImage
print(ROI_MODEL)
fig = plt.figure('Energy spectrum')
spectral_model_spline.plot(logy=True, show=False, label='Total')
plt.show()
def display():
"""Display the source model.
"""
from ximpol.utils.matplotlib_ import pyplot as plt
from ximpol.srcmodel.img import xFITSImage
print(ROI_MODEL)
fig = plt.figure('Energy spectrum')
spectral_model_spline.plot(logy=True, show=False, label='Total')
plt.show()
def plot_ymap(self, overlay=True, show=False):
"""Plot the y polarization map.
"""
if self.y_img is None:
self.y_img = xFITSImage(self.ymap_file_path, build_cdf=False)
fig = self.y_img.plot(show=False, zlabel="Polarization degree (y)")
if overlay:
self.overlay_arrows(fig)
if show:
plt.show()
return fig
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(save=False):
"""Plot the stuff in the analysis file.
"""
for j in range(len(E_BINNING)):
if (j==len(E_BINNING)-1):
analysis_file = ANALYSIS_FILE_PATH
emin=E_BINNING[0]
emax=E_BINNING[-1]
else:
analysis_file = '%s_%d' % (ANALYSIS_FILE_PATH,j)
emin=E_BINNING[j]
emax=E_BINNING[j+1]
sim_label = 'XIPE %s ks' % (SIM_DURATION/1000.)
mod_label = 'Input model'
lc_label = 'Light curve'
_phase, _phase_err, _pol_deg, _pol_deg_err, _pol_angle,\
_pol_angle_err, _index, _index_err, _norm,\
_norm_err = numpy.loadtxt(ANALYSIS_FILE_PATH, unpack=True)
plt.figure('Polarization degree (%g-%g keV)' % (emin,emax))
plt.title('Her X-1 (%g-%g keV)' % (emin,emax))
pl_normalization_spline.plot(scale=20, show=False, color='lightgray',
label=lc_label)
plt.errorbar(_phase, _pol_deg, xerr=_phase_err, yerr=_pol_deg_err, fmt='o',
label=sim_label)
pol_degree_spline.plot(show=False, label=mod_label)
# pol_degree_spline_noqed.plot(show=False, label='No QED')
plt.axis([0., 1., 0., 1.1])
plt.legend(bbox_to_anchor=(0.4, 0.5))
if save:
save_current_figure('herx1_polarization_degree_%d' % j, OUTPUT_FOLDER, False)
plt.figure('Polarization angle (%g-%g keV)' % (emin,emax))
plt.title('Her X-1 (%g-%g keV)' % (emin,emax))
pl_normalization_spline.plot(scale=60, offset=0, show=False,
color='lightgray', label=lc_label)
plt.errorbar(_phase, _pol_angle, xerr=_phase_err, yerr=_pol_angle_err,
fmt='o', label=sim_label)
pol_angle_spline.plot(show=False, label=mod_label)
plt.axis([0., 1., 0, 3.15])
plt.legend(bbox_to_anchor=(0.4, 0.3))
if save:
save_current_figure('herx1_polarization_angle_%d' %j, OUTPUT_FOLDER, False)
plt.figure('Flux (%g-%g keV)' % (emin,emax))
plt.title('Her X-1 (%g-%g keV)' % (emin,emax))
plt.errorbar(_phase,
0.21*_norm/(2-_index)*(10.**(2.0-_index)-2.**(2.-_index)), xerr=_phase_err,
yerr=0.21*_norm_err/(2-_index)*(10.**(2-_index)-2.**(2.-_index)), fmt='o',
label=sim_label)
pl_normalization_spline.plot(scale=1,show=False, label=mod_label)
plt.axis([0., 1., None, None])
plt.legend(bbox_to_anchor=(0.75, 0.95))
if save:
save_current_figure('herx1_flux_%d' % j, OUTPUT_FOLDER, False)
plt.show()
def plot(save=False):
"""Plot the stuff in the analysis file.
"""
sim_label = 'XIPE %s ks' % (SIM_DURATION/1000.)
sim_label = 'OBS: %s ks' % (SIM_DURATION/1000.)
mod_label = 'Input model'
lc_label = 'Light curve'
_phase, _phase_err, _pol_deg, _pol_deg_err, _pol_angle,\
_pol_angle_err, _index, _index_err, _norm,\
_norm_err = numpy.loadtxt(ANALYSIS_FILE_PATH, unpack=True)
plt.figure('Polarization degree')
pl_normalization_spline.plot(scale=0.12, show=False, color='lightgray',
label=lc_label)
_pol_deg_err_p=numpy.where((_pol_deg+_pol_deg_err)<1.0,_pol_deg_err,1.0-_pol_deg)
_pol_deg_err_m=numpy.where((_pol_deg-_pol_deg_err)>0.0,_pol_deg_err,_pol_deg)
#if (_pol_deg+_pol_deg_err)>1.0: _pol_deg_err=1.0-yerr_p
plt.errorbar(_phase, _pol_deg, xerr=_phase_err, yerr=[_pol_deg_err_m,_pol_deg_err_p], fmt='o',
label=sim_label)
pol_degree_spline.plot(show=False, label=mod_label)
plt.axis([0., 1., 0., 0.5])
plt.legend(bbox_to_anchor=(0.45, 0.95))
if save:
save_current_figure('crab_polarization_degree', OUTPUT_FOLDER, False)
plt.figure('Polarization angle')
pl_normalization_spline.plot(scale=0.4, offset=1.25, show=False,
color='lightgray', label=lc_label)
plt.errorbar(_phase, _pol_angle, xerr=_phase_err, yerr=_pol_angle_err,
fmt='o', label=sim_label)
pol_angle_spline.plot(show=False, label=mod_label)
plt.axis([0., 1., 1.25, 3.])
plt.legend(bbox_to_anchor=(0.45, 0.95))
if save:
save_current_figure('crab_polarization_angle', OUTPUT_FOLDER, False)
plt.figure('PL normalization')
plt.errorbar(_phase, _norm, xerr=_phase_err, yerr=_norm_err, fmt='o',
label=sim_label)
pl_normalization_spline.plot(show=False, label=mod_label)
plt.axis([0., 1., None, None])
plt.legend(bbox_to_anchor=(0.45, 0.95))
if save:
save_current_figure('crab_pl_norm', OUTPUT_FOLDER, False)
plt.figure('PL index')
pl_normalization_spline.plot(scale=0.18, offset=1.3, show=False,
color='lightgray', label=lc_label)
plt.errorbar(_phase, _index, xerr=_phase_err, yerr=_index_err, fmt='o',
label=sim_label)
pl_index_spline.plot(show=False, label=mod_label)
plt.axis([0., 1., 1.3, 2.1])
plt.legend(bbox_to_anchor=(0.45, 0.95))
if save:
save_current_figure('crab_pl_index', OUTPUT_FOLDER, False)
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 display():
"""Display the source model.
"""
print(ROI_MODEL)
from ximpol.utils.matplotlib_ import pyplot as plt
fig = plt.figure('Energy spectrum')
spectral_model.plot(show=False, logy=True)
fig = plt.figure('Polarization degree')
pol_degree.plot(show=False)
fig = plt.figure('Polarization angle')
pol_angle.plot(show=False)
plt.show()
def display():
"""Display the source model.
"""
print(ROI_MODEL)
from ximpol.utils.matplotlib_ import pyplot as plt
fig = plt.figure('Energy spectrum')
spectral_model.plot(show=False, logy=True)
fig = plt.figure('Polarization degree')
pol_degree.plot(show=False)
fig = plt.figure('Polarization angle')
pol_angle.plot(show=False)
plt.show()
def main(interactive=False):
"""Test the script plotting the light curve og GRB 130427A
"""
#If you want all the GRBs use the following line:
grb_list = get_all_swift_grb_names()
#grb_list = ['GRB 130427A','GRB 050124']
plot_swift_lc(grb_list,show=False)
overlay_tag()
save_current_figure('Swift_XRT_light_curves',
clear=False)
if interactive:
plt.show()
def plot_doc():
"""
"""
img = xFITSImage(os.path.join(XIMPOL_DATA, 'casa_cmap.fits'))
fig = img.plot(show=False)
#Option to draw the psf circle on the count map
RAD_PSF = 11/60.
fig.show_circles(350.769, 58.873, RAD_PSF/60., lw=2, color='white')
fig.add_label(0.73,0.90, 'PSF', relative=True, size='x-large',
color='white', horizontalalignment='left')
xFITSImage.add_label(fig, 'XIPE 250 ks')
plt.show()
def view(self, show=True):
"""Overloaded plot method (with default log scale on the y-axis).
"""
from ximpol.utils.matplotlib_ import pyplot as plt
plt.figure("PSF")
xInterpolatedUnivariateSpline.plot(self, logy=True, show=False)
plt.figure("Solid-angle convolution")
self.generator.plot(logy=True, show=False)
plt.figure("EEF")
self.eef.plot(show=False)
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 view(self, off_axis_angle = 10., show=True):
"""Plot the effective area.
"""
from ximpol.utils.matplotlib_ import pyplot as plt
plt.figure('Effective area')
xInterpolatedUnivariateSplineLinear.plot(self, show=False,
label='On axis')
plt.plot(self.x, self.eval_(self.x, off_axis_angle),
label='%s arcmin off-axis' % off_axis_angle)
plt.legend(bbox_to_anchor=(0.85, 0.75))
plt.figure('Vignetting')
self.vignetting.plot(show=False)
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(self, off_axis_angle=10., show=True):
"""Plot the effective area.
"""
from ximpol.utils.matplotlib_ import pyplot as plt
plt.figure('Effective area')
xInterpolatedUnivariateSplineLinear.plot(self,
show=False,
label='On axis')
plt.plot(self.x,
self.eval_(self.x, off_axis_angle),
label='%s arcmin off-axis' % off_axis_angle)
plt.legend(bbox_to_anchor=(0.85, 0.75))
plt.figure('Vignetting')
self.vignetting.plot(show=False)
if show:
plt.show()
def display():
"""Display the source model.
"""
from ximpol.utils.matplotlib_ import pyplot as plt
print(ROI_MODEL)
fig = plt.figure('Energy spectrum')
total_spectral_model.plot(logy=True, show=False, label='Total')
nonthermal_spectral_model.plot(logy=True, show=False, label='Non-thermal')
thermal_spectral_model.plot(logy=True, show=False, label='Thermal')
plt.legend(bbox_to_anchor=(0.95, 0.95))
fig = thermal_component.image.plot(show=False)
fig.add_label(0.1, 0.92, '1.5-3 keV', relative=True, size='xx-large',
color='white', horizontalalignment='left')
fig = nonthermal_component.image.plot(show=False)
fig.add_label(0.1, 0.92, '4-6 keV', relative=True, size='xx-large',
color='white', horizontalalignment='left')
plt.show()
def plot(self, show=False, stat=True, text_size=15, **options):
"""Plot the fit results.
"""
from ximpol.utils.matplotlib_ import pyplot as plt
_x = numpy.linspace(0., 2*numpy.pi, 100)
_y = xAzimuthalResponseGenerator.fit_function(_x, *self.popt)
plt.plot(_x, _y, **options)
if stat:
posh = 0.02
posv = 0.25
delv = 0.07
for text in self.latex().split(','):
text = text.strip()
plt.text(posh, posv, text, transform=plt.gca().transAxes,
fontsize=text_size)
posv -= delv
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$]')
if show:
plt.show()
def plot(self, show=False, stat=True, text_size=15, **options):
"""Plot the fit results.
"""
from ximpol.utils.matplotlib_ import pyplot as plt
_x = numpy.linspace(0., 2*numpy.pi, 100)
_y = xAzimuthalResponseGenerator.fit_function(_x, *self.popt)
plt.plot(_x, _y, **options)
if stat:
posh = 0.02
posv = 0.25
delv = 0.07
for text in self.latex().split(','):
text = text.strip()
plt.text(posh, posv, text, transform=plt.gca().transAxes,
fontsize=text_size)
posv -= delv
if show:
plt.show()
def plot_polarization_angle(self, show=True, degree=True, **kwargs):
"""Plot the polarization angle as a function of energy.
"""
if self.fit_results == []:
self.fit()
_x = self.emean
_dx = [self.emean - self.emin, self.emax - self.emean]
if degree:
_y = [numpy.degrees(r.phase) for r in self.fit_results]
_dy = [numpy.degrees(r.phase_error) for r in self.fit_results]
else:
_y = [(r.phase) for r in self.fit_results]
_dy = [(r.phase_error) for r in self.fit_results]
plt.errorbar(_x, _y, _dy, _dx, fmt='o', **kwargs)
plt.xlabel('Energy [keV]')
plt.ylabel('Polarization angle [$^\circ$]')
if show:
plt.show()
def 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 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 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)
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 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', show=False)
#plt.errorbar(_energy_mean, _pol_deg, yerr=_pol_deg_err, color='blue',marker='o')
plt.figure('Polarization angle')
_mcube.plot_polarization_angle(show=False, color='blue', degree=False)
pol_angle_spline.plot(color='lightgray', label='Model', show=False)
#plt.errorbar(_energy_mean,_pol_angle, yerr= _pol_angle_err,color='blue',marker='o')
plt.legend()
plt.show()
def display():
"""Display the source model.
"""
from ximpol.utils.matplotlib_ import pyplot as plt
from ximpol.srcmodel.img import xFITSImage
print(ROI_MODEL)
fig = plt.figure('Energy spectrum')
total_spectral_model.plot(logy=True, show=False, label='Total')
nonthermal_spectral_model.plot(logy=True, show=False, label='Non-thermal')
thermal_spectral_model.plot(logy=True, show=False, label='Thermal')
plt.legend(bbox_to_anchor=(0.95, 0.95))
fig = thermal_component.image.plot(show=False)
xFITSImage.add_label(fig, 'Chandra 1.5-3.0 keV')
fig = nonthermal_component.image.plot(show=False)
xFITSImage.add_label(fig, 'Chandra 4.0-6.0 keV')
img = xFITSImage(he_img_file_path)
fig = img.plot(show=False)
polarization_map.build_grid_sample(ROI_MODEL.ra, ROI_MODEL.dec)
polarization_map.overlay_arrows(fig)
plt.show()
def plot(self, show=True, fit=True, analyze=True, xsubplot=0):
"""Plot the azimuthal distributions for all the energy bins.
"""
if analyze:
fit = True
for i, _emean in enumerate(self.emean):
if xsubplot == 0:
plt.figure()
else:
plt.subplot(len(self.emean), xsubplot + 1,
(xsubplot + 1) * (i + 1))
self.plot_bin(i, False, False)
if fit:
_res = self.fit_bin(i)
_res.plot(color=xpColor(i))
if analyze:
fig = plt.figure('Polarization degree vs. energy')
self.plot_polarization_degree(show=False)
fig = plt.figure('Polarization angle vs. energy')
self.plot_polarization_angle(show=False)
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 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 plot(self, show=True):
"""Plot the energy dispersion.
"""
from ximpol.utils.matplotlib_ import pyplot as plt
from ximpol.utils.matplotlib_ import context_two_by_two
emin = self.matrix.xmin()
emax = self.matrix.xmax()
def _plot_vslice(energy, position):
"""Convenience function to plot a generic vertical slice of the
energy dispersion.
"""
ax = plt.subplot(2, 2, position)
vslice = self.matrix.vslice(energy)
vslice.plot(overlay=False, show=False)
plt.text(0.1,
0.9,
'$E = %.2f\\ \\rm{keV}$' % energy,
transform=ax.transAxes)
ppf = vslice.build_ppf()
eres = 0.5 * (ppf(0.8413) - ppf(0.1586)) / ppf(0.5)
plt.text(0.1,
0.85,
'$\sigma_E/E = %.3f$' % eres,
transform=ax.transAxes)
with context_two_by_two():
plt.figure(1)
ax = plt.subplot(2, 2, 1)
self.matrix.plot(show=False)
ax = plt.subplot(2, 2, 2)
self.ebounds.plot(overlay=False, show=False)
_plot_vslice(emin + 0.333 * (emax - emin), 3)
_plot_vslice(emin + 0.666 * (emax - emin), 4)
if show:
plt.show()
def plot(save=False):
"""Plot the stuff in the analysis file.
"""
def draw_time_grid(color='blue'):
"""
"""
times = [3600., 3600. * 24., 3600. * 24. * 7.]
labels = ['1 hour', '1 day', '1 week']
for t, l in zip(times, labels):
plt.axvline(t, linestyle='dashed', color=color)
ymin, ymax = plt.gca().get_ylim()
y = ymin + 1.05 * (ymax - ymin)
plt.text(t, y, l, ha='center', color=color)
sim_label = 'XIPE'
mod_label = 'Input model'
lc_label = 'Light curve'
_time, _time_errp, _time_errm, _pol_deg, _pol_deg_err, _pol_angle,\
_pol_angle_err, _index, _index_err, _norm,\
_norm_err = numpy.loadtxt(ANALYSIS_FILE_PATH, unpack=True)
logger.info(_time)
logger.info((_time_errp + _time_errm) / 3600.)
_pol_angle = numpy.degrees(_pol_angle)
_pol_angle_err = numpy.degrees(_pol_angle_err)
plt.figure('Polarization degree')
pol_degree_spline.plot(show=False, label=mod_label, logx=True)
plt.errorbar(_time,
_pol_deg,
xerr=[_time_errm, _time_errp],
yerr=_pol_deg_err,
fmt='o',
label=sim_label)
plt.axis([100., 1e6, 0., 0.6])
plt.legend(bbox_to_anchor=(0.4, 0.95))
draw_time_grid()
if save:
save_current_figure('grb130427_swift_polarization_degree',
OUTPUT_FOLDER, False)
plt.figure('Polarization angle')
_x = pol_degree_spline.x
_y = numpy.full(len(_x), polarization_angle(_x, None, None, None))
_y = numpy.degrees(_y)
fmt = dict(xname='Time',
xunits='s',
yname='Polarization angle',
yunits=r'$^\circ$')
_s = xInterpolatedUnivariateSpline(_x, _y, **fmt)
_s.plot(logx=True, show=False, label=mod_label)
plt.errorbar(_time,
_pol_angle,
xerr=[_time_errm, _time_errp],
yerr=_pol_angle_err,
fmt='o',
label=sim_label)
plt.axis([100., 1e6, None, None])
plt.legend(bbox_to_anchor=(0.4, 0.95))
draw_time_grid()
if save:
save_current_figure('grb130427_swift_polarization_angle',
OUTPUT_FOLDER, False)
plt.figure('PL index')
_y = numpy.full(len(_x), PL_INDEX)
fmt = dict(xname='Time', xunits='s', yname='PL index')
_s = xInterpolatedUnivariateSpline(_x, _y, **fmt)
_s.plot(logx=True, show=False, label=mod_label)
plt.errorbar(_time,
_index,
xerr=[_time_errm, _time_errp],
yerr=_index_err,
fmt='o',
label=sim_label)
plt.axis([100., 1e6, None, None])
plt.legend(bbox_to_anchor=(0.4, 0.95))
draw_time_grid()
if save:
save_current_figure('grb130427_swift_pl_index', OUTPUT_FOLDER, False)
#plt.figure('PL normalization')
#plt.errorbar(_time, _norm, xerr=[_time_errm, _time_errp], yerr=_norm_err,
# fmt='o', label=sim_label)
#pl_normalization_spline.plot(show=False, label=mod_label)
#plt.axis([100., 1e6, None, None])
#plt.legend(bbox_to_anchor=(0.4, 0.95))
#draw_time_grid()
#if save:
# save_current_figure('grb130427_swift_pl_norm', OUTPUT_FOLDER, False)
plt.figure('Light curve')
lc = xBinnedLightCurve(_lc_file_path())
lc.plot(show=False)
# This should be implemented in the binned LC class.
plt.xscale('log')
plt.yscale('log')
plt.axis([100., 1e6, None, None])
draw_time_grid()
if save:
save_current_figure('grb130427_swift_lc', OUTPUT_FOLDER, False)
plt.show()
#put together the flux for the source starting from the txt file
_energy, _flux = numpy.loadtxt(FLUX_FILE_PATH, unpack=True)
_mask = (_energy >= MIN_ENERGY) * (_energy <= MAX_ENERGY)
_energy = _energy[_mask]
_flux = _flux[_mask]
fmt = dict(xname='Energy',
xunits='keV',
yname='Flux',
yunits='cm$^{-2}$ s$^{-1}$ keV$^{-1}$')
_energy_spectrum = xInterpolatedUnivariateSpline(_energy, _flux, **fmt)
#"""
ROI_MODEL = xROIModel(CYGX1_RA, CYGX1_DEC)
src = xPointSource('Cyg-X1', ROI_MODEL.ra, ROI_MODEL.dec, energy_spectrum,
polarization_degree, polarization_angle)
ROI_MODEL.add_source(src)
if __name__ == '__main__':
print(ROI_MODEL)
from ximpol.utils.matplotlib_ import pyplot as plt
fig = plt.figure('Spectrum')
_energy_spectrum.plot(show=False)
fig = plt.figure('Polarization angle')
pol_angle_spline.plot(show=False)
fig = plt.figure('Polarization degree')
pol_degree_spline.plot(show=False)
plt.show()
def plot(save=False):
logger.info('Plotting stuff...')
pipeline.xpbin(evt_file_path, algorithm='CMAP', outfile=map_file_path)
regions = pyregion.open(reg_file_path)
full_map = xBinnedMap(map_file_path)
fig_all = full_map.plot(show=False)
for i, region in enumerate(regions):
ra, dec, rad = region.coord_list
#fig_all.show_circles(ra, dec, rad, lw=1)
fig = full_map.plot(show=False, subplot=(1, 2, 1))
plt.subplots_adjust(hspace=0.001)
fig.show_circles(ra, dec, rad, lw=1)
mcube_file_path = get_mcube_file_path(i)
mcube = xBinnedModulationCube(mcube_file_path)
mcube.plot(show=False, analyze=False, xsubplot=1)
scale_x = rad/numpy.cos(numpy.deg2rad(dec)) # This is to take into account the effect of the projection.
scale_y = rad
for j,fit in enumerate(mcube.fit_results):
angle = fit.phase
angle_error = fit.phase_error
degree = fit.polarization_degree
#nangles=20
#for t in range(nangles):
# dx = scale_x*numpy.cos(numpy.pi*2.0*t/float(nangles))#angle)
# dy = scale_y*numpy.sin(numpy.pi*2.0*t/float(nangles))#angle)
# fig.show_arrows(ra, dec, dx, dy, color='w', alpha=1, width=1,head_width=0, head_length=0)
# pass
dx = scale_x*numpy.cos(angle)
dy = scale_y*numpy.sin(angle)
dx1 = scale_x*degree*numpy.cos(angle+angle_error)
dy1 = scale_y*degree*numpy.sin(angle+angle_error)
dx2 = scale_x*degree*numpy.cos(angle-angle_error)
dy2 = scale_y*degree*numpy.sin(angle-angle_error)
fig.show_arrows(ra, dec, dx, dy, color=xpColor(j), alpha=1, linestyle='dashed', width=0.2,head_width=0, head_length=0)
fig.show_arrows(ra, dec, -dx, -dy, color=xpColor(j), alpha=1, linestyle='dashed', width=0.2,head_width=0, head_length=0)
fig.show_arrows(ra, dec, dx1, dy1, color=xpColor(j), alpha=1, width=1,head_width=0, head_length=0)
fig.show_arrows(ra, dec, -dx1, -dy1, color=xpColor(j), alpha=1, width=1,head_width=0, head_length=0)
fig.show_arrows(ra, dec, dx2, dy2, color=xpColor(j), alpha=1, width=1,head_width=0, head_length=0)
fig.show_arrows(ra, dec, -dx2, -dy2, color=xpColor(j), alpha=1, width=1,head_width=0, head_length=0)
fig_all.show_arrows(ra, dec, dx, dy, color=xpColor(j), alpha=1, linestyle='dashed', width=0.2,head_width=0, head_length=0)
fig_all.show_arrows(ra, dec, -dx, -dy, color=xpColor(j), alpha=1, linestyle='dashed', width=0.2,head_width=0, head_length=0)
fig_all.show_arrows(ra, dec, dx1, dy1, color=xpColor(j), alpha=1, width=1,head_width=0, head_length=0)
fig_all.show_arrows(ra, dec, -dx1, -dy1, color=xpColor(j), alpha=1, width=1,head_width=0, head_length=0)
fig_all.show_arrows(ra, dec, dx2, dy2, color=xpColor(j), alpha=1, width=1,head_width=0, head_length=0)
fig_all.show_arrows(ra, dec, -dx2, -dy2, color=xpColor(j), alpha=1, width=1,head_width=0, head_length=0)
if save:
fig.save(mcube_file_path.replace('.fits', '.png'))
plt.clf()
else:
plt.show()
pass
fig_all.save(os.path.join(XIMPOL_DATA, 'tycho_reg_all.png'))