def test_convolve2d_king(self):
gfn = lambda r, s: np.power(2*np.pi*s**2,-1)*np.exp(-r**2/(2*s**2))
kfn = lambda r, s, g: np.power(2*np.pi*s**2,-1)*(1.-1./g)* \
np.power(1+0.5/g*(r/s)**2,-g)
kfn0 = lambda x, y, mux, muy, s, g: kfn(np.sqrt((x-mux)**2+(y-muy)**2),s,g)
xaxis = Axis.create(-3,3,501)
yaxis = Axis.create(-3,3,501)
x, y = np.meshgrid(xaxis.center,yaxis.center)
xbin, ybin = np.meshgrid(xaxis.width,yaxis.width)
r = np.sqrt(x**2+y**2)
# Scalar Input
mux = 0.5
muy = -0.2
mur = (mux**2+muy**2)**0.5
gsig = 0.1
ksig = 0.2
kgam = 4.0
fval0 = np.sum(kfn0(x,y,mux,muy,ksig,kgam)*gfn(r,gsig)*xbin*ybin)
fval1 = convolve2d_king(lambda t: gfn(t,gsig),mur,ksig,kgam,3.0,
nstep=10000)
# fval2 = convolve2d_gauss(lambda t: kfn(t,ksig,kgam),mur,gsig,3.0,
# nstep=1000)
# print fval0, fval1, fval2, fval1/fval0
assert_almost_equal(fval0,fval1,4)
def test_convolve2d_king(self):
gfn = lambda r, s: np.power(2 * np.pi * s**2, -1) * np.exp(-r**2 /
(2 * s**2))
kfn = lambda r, s, g: np.power(2*np.pi*s**2,-1)*(1.-1./g)* \
np.power(1+0.5/g*(r/s)**2,-g)
kfn0 = lambda x, y, mux, muy, s, g: kfn(
np.sqrt((x - mux)**2 + (y - muy)**2), s, g)
xaxis = Axis.create(-3, 3, 501)
yaxis = Axis.create(-3, 3, 501)
x, y = np.meshgrid(xaxis.center, yaxis.center)
xbin, ybin = np.meshgrid(xaxis.width, yaxis.width)
r = np.sqrt(x**2 + y**2)
# Scalar Input
mux = 0.5
muy = -0.2
mur = (mux**2 + muy**2)**0.5
gsig = 0.1
ksig = 0.2
kgam = 4.0
fval0 = np.sum(
kfn0(x, y, mux, muy, ksig, kgam) * gfn(r, gsig) * xbin * ybin)
fval1 = convolve2d_king(lambda t: gfn(t, gsig),
mur,
ksig,
kgam,
3.0,
nstep=10000)
# fval2 = convolve2d_gauss(lambda t: kfn(t,ksig,kgam),mur,gsig,3.0,
# nstep=1000)
# print fval0, fval1, fval2, fval1/fval0
assert_almost_equal(fval0, fval1, 4)
def createFromFITS(fitsfile,ihdu=0):
hdulist = fits.open(fitsfile)
header = hdulist[ihdu].header
wcs = WCS(header,naxis=[1,2],relax=True)
hdunames = [t.name for t in hdulist]
if 'ENERGIES' in hdunames and hdulist[1].name == 'ENERGIES':
v = bintable_to_array(hdulist[1].data)
v = np.log10(v)
energy_axis = Axis.createFromArray(v)
axes = copy.deepcopy(FITSAxis.create_axes(header))
axes[2]._crval = energy_axis.edges[0]
axes[2]._delta = energy_axis.width[0]
axes[2]._crpix = 0.0
else:
axes = copy.deepcopy(FITSAxis.create_axes(header))
return SkyCube(wcs,axes,
copy.deepcopy(hdulist[ihdu].data.astype(float).T))
def test_convolve2d_gauss(self):
gfn0 = lambda x, y, mux, muy, s: np.power(2*np.pi*s**2,-1)* \
np.exp(-((x-mux)**2+(y-muy)**2)/(2*s**2))
gfn1 = lambda r, s: np.power(2*np.pi*s**2,-1)*np.exp(-r**2/(2*s**2))
xaxis = Axis.create(-3,3,501)
yaxis = Axis.create(-3,3,501)
x, y = np.meshgrid(xaxis.center,yaxis.center)
xbin, ybin = np.meshgrid(xaxis.width,yaxis.width)
# Scalar Input
sigma0 = 0.1
sigma1 = 0.2
mux = 0.5
muy = -0.2
mur = (mux**2+muy**2)**0.5
fval0 = np.sum(gfn0(x,y,mux,muy,sigma1)*gfn0(x,y,0,0,sigma0)*xbin*ybin)
fval1 = convolve2d_gauss(lambda t: gfn1(t,sigma0),mur,sigma1,3.0,
nstep=1000)
assert_almost_equal(fval0,fval1,4)
# Vector Input for Gaussian Width
sigma0 = 0.1
sigma1 = np.array([0.1,0.15,0.2])
mux = 0.5
muy = -0.2
mur = (mux**2+muy**2)**0.5
fval0 = []
for i in range(len(sigma1)):
fval0.append(np.sum(gfn0(x,y,mux,muy,sigma1[i])*
gfn0(x,y,0,0,sigma0)*xbin*ybin))
fval1 = convolve2d_gauss(lambda t: gfn1(t,sigma0),mur,sigma1,3.0,
nstep=1000)
assert_almost_equal(np.ravel(fval0),np.ravel(fval1),4)
# Vector Input
sigma0 = 0.1
sigma1 = 0.2
mux = np.array([0.3,0.4,0.5])
muy = np.array([-0.2,-0.2,0.2])
mur = (mux**2+muy**2)**0.5
fval0 = []
for i in range(len(mux)):
fval0.append(np.sum(gfn0(x,y,mux[i],muy[i],sigma1)*
gfn0(x,y,0,0,sigma0)*
xbin*ybin))
fval1 = convolve2d_gauss(lambda t: gfn1(t,sigma0),mur,sigma1,3.0,
nstep=1000)
assert_almost_equal(fval0,fval1,4)
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)
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)
parser.add_argument('--labels', default=None)
parser.add_argument('--colors', default=None)
parser.add_argument('--ylim_ratio', default='0.6/1.4')
parser.add_argument('--prefix', default='')
args = parser.parse_args()
labels = args.labels.split(',')
if args.colors: colors = args.colors.split(',')
else: colors = ['b','g','r','m','c']
ylim_ratio = [float(t) for t in args.ylim_ratio.split('/')]
hist_set = {'title' : 'test',
'ts' : Histogram(Axis.create(0.,16.,160)),
'fluxul' : Histogram(Axis.create(-12,-8,100)) }
pwl_hists = []
dm_hists = []
hists = {}
results = []
limits = {}
for f in args.files:
# results = AnalysisResults(f)
def test_convolve2d_gauss(self):
gfn0 = lambda x, y, mux, muy, s: np.power(2*np.pi*s**2,-1)* \
np.exp(-((x-mux)**2+(y-muy)**2)/(2*s**2))
gfn1 = lambda r, s: np.power(2 * np.pi * s**2, -1) * np.exp(-r**2 /
(2 * s**2))
xaxis = Axis.create(-3, 3, 501)
yaxis = Axis.create(-3, 3, 501)
x, y = np.meshgrid(xaxis.center, yaxis.center)
xbin, ybin = np.meshgrid(xaxis.width, yaxis.width)
# Scalar Input
sigma0 = 0.1
sigma1 = 0.2
mux = 0.5
muy = -0.2
mur = (mux**2 + muy**2)**0.5
fval0 = np.sum(
gfn0(x, y, mux, muy, sigma1) * gfn0(x, y, 0, 0, sigma0) * xbin *
ybin)
fval1 = convolve2d_gauss(lambda t: gfn1(t, sigma0),
mur,
sigma1,
3.0,
nstep=1000)
assert_almost_equal(fval0, fval1, 4)
# Vector Input for Gaussian Width
sigma0 = 0.1
sigma1 = np.array([0.1, 0.15, 0.2])
mux = 0.5
muy = -0.2
mur = (mux**2 + muy**2)**0.5
fval0 = []
for i in range(len(sigma1)):
fval0.append(
np.sum(
gfn0(x, y, mux, muy, sigma1[i]) *
gfn0(x, y, 0, 0, sigma0) * xbin * ybin))
fval1 = convolve2d_gauss(lambda t: gfn1(t, sigma0),
mur,
sigma1,
3.0,
nstep=1000)
assert_almost_equal(np.ravel(fval0), np.ravel(fval1), 4)
# Vector Input
sigma0 = 0.1
sigma1 = 0.2
mux = np.array([0.3, 0.4, 0.5])
muy = np.array([-0.2, -0.2, 0.2])
mur = (mux**2 + muy**2)**0.5
fval0 = []
for i in range(len(mux)):
fval0.append(
np.sum(
gfn0(x, y, mux[i], muy[i], sigma1) *
gfn0(x, y, 0, 0, sigma0) * xbin * ybin))
fval1 = convolve2d_gauss(lambda t: gfn1(t, sigma0),
mur,
sigma1,
3.0,
nstep=1000)
assert_almost_equal(fval0, fval1, 4)
parser.add_argument('--colors', default=None)
parser.add_argument('--ylim_ratio', default='0.6/1.4')
parser.add_argument('--prefix', default='')
args = parser.parse_args()
labels = args.labels.split(',')
if args.colors: colors = args.colors.split(',')
else: colors = ['b', 'g', 'r', 'm', 'c']
ylim_ratio = [float(t) for t in args.ylim_ratio.split('/')]
hist_set = {
'title': 'test',
'ts': Histogram(Axis.create(0., 16., 160)),
'fluxul': Histogram(Axis.create(-12, -8, 100))
}
pwl_hists = []
dm_hists = []
hists = {}
results = []
limits = {}
for f in args.files:
# results = AnalysisResults(f)
