Exemplo n.º 1
0
    def fillLivetime(self, ltc, src_type):

        self._tau = Histogram(self._cth_axis)

        if src_type == 'iso':
            self._tau += self._cth_axis.width
        elif src_type == 'isodec':
            sinlat = np.linspace(-1, 1, 48)

            m = ltc._ltmap[:, i]

            self._tau[i] = 0
            for s in sinlat:
                lat = np.arcsin(s)
                th = np.pi / 2. - lat
                ipix = healpy.ang2pix(64, th, 0, nest=True)
                self._tau[i] += m[ipix]
        else:

            if not isinstance(src_type, list):
                src_type = [src_type]

            for s in src_type:
                cat = Catalog.get()
                src = cat.get_source_by_name(s)
                lonlat = (src['RAJ2000'], src['DEJ2000'])

                self._tau += ltc.get_src_lthist(lonlat[0],
                                                lonlat[1],
                                                cth_axis=self._cth_axis)
Exemplo n.º 2
0
    def loadsrclist(self, fname):
        self.srcs = []
        self.src_names = []
        self.src_redshifts = []
        self.src_ra_deg = []
        self.src_dec_deg = []
        self.src_radec = []

        #        if not fname is None:
        #            src_names = np.genfromtxt(fname,unpack=True,dtype=None)
        #        else:
        #            src_names = np.array(srcs.split(','))

        if isinstance(fname, list):
            src_names = np.array(fname)

        if src_names.ndim == 0: src_names = src_names.reshape(1)

        cat = Catalog.get()

        for name in src_names:
            src = cat.get_source_by_name(name)
            name = src['Source_Name']
            ra = src['RAJ2000']
            dec = src['DEJ2000']
            #            self._photon_data._srcs.append(src)
            self.src_names.append(name)
            self.src_ra_deg.append(ra)
            self.src_dec_deg.append(dec)
            self.src_radec.append((np.radians(ra), np.radians(dec)))
Exemplo n.º 3
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    def fillLivetime(self,ltc,src_type):
        
        self._tau = Histogram(self._cth_axis)
            
        if src_type == 'iso':
            self._tau += self._cth_axis.width
        elif src_type == 'isodec':
            sinlat = np.linspace(-1,1,48)

            m = ltc._ltmap[:,i]

            self._tau[i] = 0
            for s in sinlat:                    
                lat = np.arcsin(s)
                th = np.pi/2. - lat                    
                ipix = healpy.ang2pix(64,th,0,nest=True)
                self._tau[i] += m[ipix]                                       
        else:

            if not isinstance(src_type,list):
                src_type = [src_type]

            for s in src_type:                
                cat = Catalog.get()
                src = cat.get_source_by_name(s)
                lonlat = (src['RAJ2000'], src['DEJ2000'])

                self._tau += ltc.get_src_lthist(lonlat[0],
                                                lonlat[1],
                                                cth_axis=self._cth_axis)
Exemplo n.º 4
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    def loadsrclist(self,fname):
        self.srcs = []
        self.src_names = []
        self.src_redshifts = []
        self.src_ra_deg = []
        self.src_dec_deg = []
        self.src_radec = []

#        if not fname is None:
#            src_names = np.genfromtxt(fname,unpack=True,dtype=None)
#        else:
#            src_names = np.array(srcs.split(','))

        if isinstance(fname,list):
            src_names = np.array(fname)
            
        if src_names.ndim == 0: src_names = src_names.reshape(1)

        cat = Catalog.get()
        
        for name in src_names:
            src = cat.get_source_by_name(name)
            name = src['Source_Name']
            ra = src['RAJ2000']
            dec = src['DEJ2000']
#            self._photon_data._srcs.append(src)
            self.src_names.append(name)
            self.src_ra_deg.append(ra)
            self.src_dec_deg.append(dec)
            self.src_radec.append((np.radians(ra),np.radians(dec)))
Exemplo n.º 5
0
    def update_image(self):

        self.scf()

        bin_range = [self.slice,self.slice+self.nbin]

        self._cm = []

        for im in self._im:
            if isinstance(im,SkyCube):
                self._cm.append(im.marginalize(2,bin_range=bin_range))
            else:
                self._cm.append(im)

        for i in range(len(self._cm)):
            if self.smooth: self._cm[i] = self._cm[i].smooth(0.1)

        if len(self._cm) == 0: return

        cm = self._cm
        
        cat = Catalog.get('3fgl')
        
        if len(self._axim) == 0:

            axim = cm[0].plot(**self._style[0])

            cat.plot(cm[0],src_color='w')
            
            self._axim.append(axim)
            self._ax = plt.gca()

            
            
            
            
            plt.colorbar(axim,orientation='horizontal',shrink=0.7,pad=0.15,
                         fraction=0.05)

            return

        self._axim[0].set_data(cm[0].counts.T)
        self._axim[0].autoscale()

        if self._style[0]['logz']:
            self._axim[0].set_norm(LogNorm())
        else:
            self._axim[0].set_norm(Normalize())

        self.canvas.draw()
Exemplo n.º 6
0
    def __init__(self,
                 irf,
                 nbin=600,
                 ebins_per_decade=16,
                 src_type='iso',
                 spectrum='powerlaw',
                 spectrum_pars=[2.0],
                 build_model=True,
                 ltfile=None,
                 edisp_table=None):

        PSFModel.__init__(self, model=spectrum, sp_param=spectrum_pars)

        self._src_type = src_type
        self._nbin_dtheta = nbin
        self._irf = irf
        self._edisp_table = edisp_table

        self._lonlat = (0, 0)
        if src_type != 'iso' and src_type != 'isodec':
            cat = Catalog.get()
            src = cat.get_source_by_name(src_type)
            self._lonlat = (src['RAJ2000'], src['DEJ2000'])

        loge_step = 1. / float(ebins_per_decade)
        emin = 1.0 + loge_step / 2.
        emax = 6.0 - loge_step / 2.
        nbin = int((emax - emin) / loge_step) + 1
        self._loge_axis = Axis.create(emin, emax, nbin)

        self._energy = np.power(10, self._loge_axis.center)
        self._exps = np.zeros(self._loge_axis.nbins)

        self._psf = np.zeros((self._loge_axis.nbins, self._nbin_dtheta))

        self._dtheta = np.array([
            self._dtheta_max_deg * (float(i) / float(self._nbin_dtheta))**2
            for i in range(self._nbin_dtheta)
        ])

        self._dtheta_axis = Axis(self._dtheta)
        self._ctheta_axis = Axis.create(0.2, 1.0, 40)
        self._tau = np.zeros(self._ctheta_axis.nbins)

        self.loadLTCube(ltfile)
        self.fillLivetime()

        if build_model: self.buildModel()
Exemplo n.º 7
0
    def __init__(self,irf,nbin=600,
                 ebins_per_decade=16,
                 src_type='iso',
                 spectrum='powerlaw',
                 spectrum_pars=[2.0],
                 build_model=True,
                 ltfile=None,
                 edisp_table=None):

        PSFModel.__init__(self,model=spectrum,sp_param=spectrum_pars)

        self._src_type = src_type
        self._nbin_dtheta = nbin
        self._irf = irf
        self._edisp_table = edisp_table

        self._lonlat = (0, 0)
        if src_type != 'iso' and src_type != 'isodec':
            cat = Catalog.get()
            src = cat.get_source_by_name(src_type)
            self._lonlat = (src['RAJ2000'], src['DEJ2000'])
        
        loge_step = 1./float(ebins_per_decade)
        emin = 1.0+loge_step/2.
        emax = 6.0-loge_step/2.
        nbin = int((emax-emin)/loge_step)+1
        self._loge_axis = Axis.create(emin,emax,nbin)
        
        self._energy = np.power(10,self._loge_axis.center)
        self._exps = np.zeros(self._loge_axis.nbins)
        
        self._psf = np.zeros((self._loge_axis.nbins,self._nbin_dtheta))

        self._dtheta = np.array([self._dtheta_max_deg*
                                 (float(i)/float(self._nbin_dtheta))**2 
                                 for i in range(self._nbin_dtheta)])


        self._dtheta_axis = Axis(self._dtheta)
        self._ctheta_axis = Axis.create(0.2,1.0,40)
        self._tau = np.zeros(self._ctheta_axis.nbins)
        
        self.loadLTCube(ltfile)
        self.fillLivetime()

        if build_model: self.buildModel()
Exemplo n.º 8
0
    def loadsrclist(self,fname,srcs):
        self.srcs = []
        self.src_names = []
        self.src_redshifts = []
        self.src_ra_deg = []
        self.src_dec_deg = []
        self.src_radec = []

        if not fname is None:
            src_names = np.genfromtxt(fname,unpack=True,dtype=None)
        else:
            src_names = np.array(srcs.split(','))
            
        if src_names.ndim == 0: src_names = src_names.reshape(1)

        cat = Catalog.get()
        
        for name in src_names:

            src = cat.get_source_by_name(name)

            name = src['Source_Name']
            ra = src['RAJ2000']
            dec = src['DEJ2000']

            print 'Loading ', name
            
            self._photon_data._srcs.append(src)

            sd = skymaps.SkyDir(ra,dec)
            self.srcs.append(sd)
            self.src_names.append(name)

            self.src_ra_deg.append(ra)
            self.src_dec_deg.append(dec)
            self.src_radec.append((np.radians(ra),np.radians(dec)))
            
            self.src_redshifts.append(0.)
Exemplo n.º 9
0
    # Integrate over 3rd (energy) dimension
    im = im.marginalize(2)

plt.figure()

im.plot(cmap='ds9_b')

plt.figure()

# Smooth by 0.2 deg
im.smooth(0.2).plot(cmap='ds9_b')

# Draw an arbitrary contour in Galactic Coordinates
phi = np.linspace(0, 2 * np.pi, 10000)
r = np.sqrt(2 * np.cos(2 * phi))
x = im.lon + r * np.cos(phi)
y = im.lat + r * np.sin(phi)
im.ax()['gal'].plot(x, y, color='w')

cat = Catalog.get('2fgl')
cat.plot(im, ax=plt.gca(), label_threshold=5, src_color='w')

# Make 1D projection on LON axis
plt.figure()
pim = im.project(0, offset_coord=True)
pim.plot()
plt.gca().grid(True)

plt.show()
Exemplo n.º 10
0
    def make_image_plot(self,subplot,h,fig,fig2,title,rpsf68,rpsf95,
                        smooth=False,
                        resid_type=None,mc_resid=None,**kwargs):

        plt.figure(fig.get_label())

        cb_label='Counts'

        if resid_type == 'significance':
            kwargs['vmin'] = -5
            kwargs['vmax'] = 5
            kwargs['levels'] = [-5.0,-3.0,3.0,5.0]
            cb_label = 'Significance [$\sigma$]'
        elif resid_type == 'fractional':
            kwargs['vmin'] = -1.0
            kwargs['vmax'] = 1.0
            kwargs['levels'] = [-1.0,-0.5,0.5,1.0]
            cb_label = 'Fractional Residual'

        if smooth:
            kwargs['beam_size'] = [self._rsmooth,self._rsmooth,0.0,4]
            
        axim = h.plot(subplot=subplot,cmap='ds9_b',**kwargs)
        h.plot_circle(rpsf68,color='w',lw=1.5)
        h.plot_circle(rpsf95,color='w',linestyle='--',lw=1.5)
        h.plot_marker(marker='+',color='w',linestyle='--')
        ax = h.ax()
        ax.set_title(title)
        cb = plt.colorbar(axim,orientation='horizontal',
                          shrink=0.9,pad=0.15,
                          fraction=0.05)

        cb.set_label(cb_label)

        cat = Catalog.get('3fgl')
        cat.plot(h,ax=ax,src_color='w',label_threshold=5.0)

        if resid_type is None: return
        
        plt.figure(fig2.get_label())        
        ax2 = fig2.add_subplot(subplot)

        z = h.counts[10:-10,10:-10]

        if resid_type == 'significance':
            zproj_axis = Axis.create(-6,6,120)
        elif resid_type == 'fractional':
            zproj_axis = Axis.create(-1.0,1.0,120)
        else:
            zproj_axis = Axis.create(-10,10,120)


        hz = Histogram(zproj_axis)
        hz.fill(np.ravel(z))

        nbin = np.prod(z.shape)
        
        hz_mc = Histogram(zproj_axis)    
        
        if mc_resid:
            for mch in mc_resid:
                z = mch.counts[10:-10,10:-10]
                hz_mc.fill(np.ravel(z))

            hz_mc /= float(len(mc_resid))

            
        fn = lambda t : 1./np.sqrt(2*np.pi)*np.exp(-t**2/2.)
        
        hz.plot(label='Data',linestyle='None')

        if resid_type == 'significance':
            plt.plot(hz.axis().center,
                     fn(hz.axis().center)*hz.axis().width*nbin,
                     color='k',label='Gaussian ($\sigma = 1$)')
        
        hz_mc.plot(label='MC',hist_style='line')
        plt.gca().grid(True)
        plt.gca().set_yscale('log')
        plt.gca().set_ylim(0.5)

        ax2.legend(loc='upper right',prop= {'size' : 10 })

        data_stats = 'Mean = %.2f\nRMS = %.2f'%(hz.mean(),hz.stddev())
        mc_stats = 'MC Mean = %.2f\nMC RMS = %.2f'%(hz_mc.mean(),
                                                    hz_mc.stddev())
        
        ax2.set_xlabel(cb_label)
        ax2.set_title(title)
        ax2.text(0.05,0.95,
                 '%s\n%s'%(data_stats,mc_stats),
                 verticalalignment='top',
                 transform=ax2.transAxes,fontsize=10)
Exemplo n.º 11
0
    def make_image_plot(self,
                        subplot,
                        h,
                        fig,
                        fig2,
                        title,
                        rpsf68,
                        rpsf95,
                        smooth=False,
                        resid_type=None,
                        mc_resid=None,
                        **kwargs):

        plt.figure(fig.get_label())

        cb_label = 'Counts'

        if resid_type == 'significance':
            kwargs['vmin'] = -5
            kwargs['vmax'] = 5
            kwargs['levels'] = [-5.0, -3.0, 3.0, 5.0]
            cb_label = 'Significance [$\sigma$]'
        elif resid_type == 'fractional':
            kwargs['vmin'] = -1.0
            kwargs['vmax'] = 1.0
            kwargs['levels'] = [-1.0, -0.5, 0.5, 1.0]
            cb_label = 'Fractional Residual'

        if smooth:
            kwargs['beam_size'] = [self._rsmooth, self._rsmooth, 0.0, 4]

        axim = h.plot(subplot=subplot, cmap='ds9_b', **kwargs)
        h.plot_circle(rpsf68, color='w', lw=1.5)
        h.plot_circle(rpsf95, color='w', linestyle='--', lw=1.5)
        h.plot_marker(marker='x', color='w', linestyle='--')
        ax = h.ax()
        ax.set_title(title)
        cb = plt.colorbar(axim,
                          orientation='horizontal',
                          shrink=0.9,
                          pad=0.15,
                          fraction=0.05)

        if kwargs.get('zscale', None) is not None:
            import matplotlib.ticker
            cb.locator = matplotlib.ticker.MaxNLocator(nbins=5)
            cb.update_ticks()

        cb.set_label(cb_label)

        cat = Catalog.get('3fgl')
        cat.plot(h,
                 ax=ax,
                 src_color='w',
                 label_threshold=self._srclabels_thresh)

        if resid_type is None: return

        plt.figure(fig2.get_label())
        ax2 = fig2.add_subplot(subplot)

        z = h.counts[10:-10, 10:-10]

        if resid_type == 'significance':
            zproj_axis = Axis.create(-6, 6, 120)
        elif resid_type == 'fractional':
            zproj_axis = Axis.create(-1.0, 1.0, 120)
        else:
            zproj_axis = Axis.create(-10, 10, 120)

        hz = Histogram(zproj_axis)
        hz.fill(np.ravel(z))

        nbin = np.prod(z.shape)

        hz_mc = Histogram(zproj_axis)

        if mc_resid:
            for mch in mc_resid:
                z = mch.counts[10:-10, 10:-10]
                hz_mc.fill(np.ravel(z))

            hz_mc /= float(len(mc_resid))

        fn = lambda t: 1. / np.sqrt(2 * np.pi) * np.exp(-t**2 / 2.)

        hz.plot(label='Data', linestyle='None')

        if resid_type == 'significance':
            plt.plot(hz.axis().center,
                     fn(hz.axis().center) * hz.axis().width * nbin,
                     color='k',
                     label='Gaussian ($\sigma = 1$)')

        hz_mc.plot(label='MC', hist_style='line')
        plt.gca().grid(True)
        plt.gca().set_yscale('log')
        plt.gca().set_ylim(0.5)

        ax2.legend(loc='upper right', prop={'size': 10})

        data_stats = 'Mean = %.2f\nRMS = %.2f' % (hz.mean(), hz.stddev())
        mc_stats = 'MC Mean = %.2f\nMC RMS = %.2f' % (hz_mc.mean(),
                                                      hz_mc.stddev())

        ax2.set_xlabel(cb_label)
        ax2.set_title(title)
        ax2.text(0.05,
                 0.95,
                 '%s\n%s' % (data_stats, mc_stats),
                 verticalalignment='top',
                 transform=ax2.transAxes,
                 fontsize=10)
Exemplo n.º 12
0
    # Integrate over 3rd (energy) dimension
    im = im.marginalize(2)


plt.figure()

im.plot(cmap='ds9_b')

plt.figure()

# Smooth by 0.2 deg
im.smooth(0.2).plot(cmap='ds9_b')

# Draw an arbitrary contour in Galactic Coordinates
phi = np.linspace(0,2*np.pi,10000)
r = np.sqrt(2*np.cos(2*phi))
x = im.lon + r*np.cos(phi)
y = im.lat + r*np.sin(phi)
im.ax()['gal'].plot(x,y,color='w')

cat = Catalog.get('2fgl')
cat.plot(im,ax=plt.gca(),label_threshold=5,src_color='w')

# Make 1D projection on LON axis
plt.figure()
pim = im.project(0,offset_coord=True)
pim.plot()
plt.gca().grid(True)

plt.show()