def __init__(self, filename, col_altitude=0, col_thickness=2):
"""
small class that calculates the height of the shower maximum
given a parametrisation of the atmosphere
and certain parameters of the shower itself
Parameters:
-----------
filename : string
path to text file that contains a table of the
atmosphere parameters
col_altitude : int
column in the text file that contains the altitude/height
col_thickness : int
column in the text file that contains the thickness
"""
altitude = []
thickness = []
atm_file = open(filename, "r")
for line in atm_file:
if line.startswith("#"):
continue
altitude.append(float(line.split()[col_altitude]))
thickness.append(float(line.split()[col_thickness]))
self.atmosphere = Histogram(axisNames=["altitude"])
self.atmosphere.hist = thickness * u.g * u.cm ** -2
self.atmosphere.bin_lower_edges = [np.array(altitude) * u.km]
def test_outliers():
"""
Check that out-of-range values work as expected
"""
H = Histogram(nbins=[5, 10], ranges=[[-2.5, 2.5], [-1, 1]])
H.fill(np.array([[1, 1], ]))
val1 = H.get_value((100, 100), outlier_value=-10000)
val2 = H.get_value((-100, 0), outlier_value=None)
assert val1 == -10000
assert val2 == 0
def test_histogram_resample_inplace():
hist = Histogram(nbins=[5, 11], ranges=[[-2.5, 2.5], [-1, 1]])
hist.fill(np.array([[0, 0], [0, 0.5]]))
for testpoint in [(0, 0), (0, 1), (1, 0), (3, 3)]:
val0 = hist.get_value(testpoint)
hist.resample_inplace((10, 22))
hist.resample_inplace((5, 11))
val2 = hist.get_value(testpoint)
# at least check the resampling is undoable
assert np.isclose(val0[0], val2[0])
def test_histogram_resample_inplace():
hist = Histogram(nbins=[5, 11], ranges=[[-2.5, 2.5], [-1, 1]])
hist.fill(np.array([[0, 0],
[0,0.5]]))
for testpoint in [(0,0), (0,1), (1,0), (3,3)]:
val0 = hist.get_value(testpoint)
hist.resample_inplace((10, 22))
val1 = hist.get_value(testpoint)
hist.resample_inplace((5, 11))
val2 = hist.get_value(testpoint)
# at least check the resampling is undoable
assert np.isclose(val0[0], val2[0])
def test_histogram_str():
hist = Histogram(nbins=[5, 10],
ranges=[[-2.5, 2.5], [-1, 1]],
name="testhisto")
expected = ("Histogram(name='testhisto', axes=['axis0', 'axis1'], "
"nbins=[ 5 10], ranges=[[-2.5 2.5]\n [-1. 1. ]])")
assert str(hist) == expected
def test_histogram_range_fill_and_read():
"""
Check that the correct bin is read and written for multiple
binnings and fill positions
"""
num = 100
for nxbins in np.arange(1, 50, 1):
for xx in np.arange(-2.0, 2.0, 0.1):
pp = (xx + 0.01829384, 0.1)
coords = np.ones((num, 2)) * np.array(pp)
hist = Histogram(nbins=[nxbins, 10], ranges=[[-2.5, 2.5], [-1, 1]])
hist.fill(coords)
val = hist.get_value(pp)[0]
assert val == num
del hist
def test_histogram_fill_and_read():
hist = Histogram(nbins=[5, 10], ranges=[[-2.5, 2.5], [-1, 1]])
pa = (0.1, 0.1)
pb = (-0.55, 0.55)
a = np.ones((100, 2)) * pa # point at 0.1,0.1
b = np.ones((10, 2)) * pb # 10 points at -0.5,0.5
hist.fill(a)
hist.fill(b)
va = hist.get_value(pa)[0]
vb = hist.get_value(pb)[0]
assert va == 100
assert vb == 10
def test_histogram_range_fill_and_read():
"""
Check that the correct bin is read and written for multiple
binnings and fill positions
"""
num = 100
for nxbins in np.arange(1, 50, 1):
for xx in np.arange(-2.0, 2.0, 0.1):
pp = (xx + 0.01829384, 0.1)
coords = np.ones((num, 2)) * np.array(pp)
hist = Histogram(nbins=[nxbins, 10],
ranges=[[-2.5, 2.5], [-1, 1]])
hist.fill(coords)
val = hist.get_value(pp)[0]
assert val == num
del hist
def test_histogram_fill_and_read():
hist = Histogram(nbins=[5, 10], ranges=[[-2.5, 2.5], [-1, 1]])
pa = (0.1, 0.1)
pb = (-0.55, 0.55)
a = np.ones((100, 2)) * pa # point at 0.1,0.1
b = np.ones((10, 2)) * pb # 10 points at -0.5,0.5
hist.fill(a)
hist.fill(b)
va = hist.get_value(pa)[0]
vb = hist.get_value(pb)[0]
assert va == 100
assert vb == 10
def test_histogram_fits(histogram_file):
"""
Write to fits,read back, and check
"""
hist = Histogram(nbins=[5, 11], ranges=[[-2.5, 2.5], [-1, 1]])
hist.fill(np.array([[0, 0], [0, 0.5]]))
hist.to_fits().writeto(histogram_file, overwrite=True)
newhist = Histogram.from_fits(histogram_file)
# check that the values are the same
compare_histograms(hist, newhist)
def test_outliers():
"""
Check that out-of-range values work as expected
"""
H = Histogram(nbins=[5, 10], ranges=[[-2.5, 2.5], [-1, 1]])
H.fill(np.array([
[1, 1],
]))
val1 = H.get_value((100, 100), outlier_value=-10000)
val2 = H.get_value((-100, 0), outlier_value=None)
assert val1 == -10000
assert val2 == 0
def test_histogram_fits(histogram_file):
"""
Write to fits,read back, and check
"""
hist = Histogram(nbins=[5, 11], ranges=[[-2.5, 2.5], [-1, 1]])
hist.fill(np.array([[0, 0],
[0, 0.5]]))
hist.to_fits().writeto(histogram_file, overwrite=True)
newhist = Histogram.from_fits(histogram_file)
# check that the values are the same
compare_histograms(hist, newhist)
def plot_muon_efficiency(outputpath):
"""
Plot the muon efficiencies
"""
fig, ax = plt.subplots(1, 1, figsize=(10, 10))
figip, axip = plt.subplots(1, 1, figsize=(10, 10))
figrw, axrw = plt.subplots(1, 1, figsize=(10, 10))
nbins = 16
t = Table.read(str(outputpath) + '_muontable.fits')
print('Reading muon efficiency from table', outputpath, t['MuonEff'])
if len(t['MuonEff']) < 1:
print("No muon events to plot")
return
else:
print("Found", len(t['MuonEff']), "muon events")
(mu, sigma) = norm.fit(t['MuonEff'])
print('Gaussian fit with mu=', mu, 'sigma=', sigma)
conteff = ax.hist(t['MuonEff'], nbins)
ax.set_xlim(0.2 * min(t['MuonEff']), 1.2 * max(t['MuonEff']))
xtest = np.linspace(min(t['MuonEff']), max(t['MuonEff']), nbins)
yg = mlab.normpdf(xtest, mu, sigma)
print('mu', mu, 'sigma', sigma, 'yg', yg)
ax.plot(xtest, yg, 'r', linewidth=2)
ax.set_ylim(0., 1.2 * max(conteff[0]))
ax.set_xlabel('Muon Efficiency')
plt.draw()
yimp = np.linspace(min(t['ImpactP']), max(t['ImpactP']), nbins)
contimp = axip.hist(t['ImpactP'], nbins)
axip.set_xlim(0.2 * min(t['ImpactP']), 1.2 * max(t['ImpactP']))
axip.set_ylim(0., 1.2 * max(contimp[0]))
axip.set_xlabel('Impact Parameter (m)')
plt.draw()
heffimp = Histogram(nbins=[16, 16],
ranges=[
(min(t['MuonEff']), max(t['MuonEff'])),
(min(t['ImpactP']), max(t['ImpactP']))
]) # ,axisNames=["MuonEfficiency","ImpactParameter"])
heffimp.fill([t['MuonEff'], t['ImpactP']])
heffimp.draw_2d()
yrw = np.linspace(min(t['RingWidth']), max(t['RingWidth']), nbins)
contrw = axrw.hist(t['RingWidth'], nbins)
axrw.set_xlim(0.2 * min(t['RingWidth']), 1.2 * max(t['RingWidth']))
axrw.set_ylim(0., 1.2 * max(contrw[0]))
axrw.set_xlabel('Ring Width ($^\circ$)')
plt.draw()
if outputpath is not None:
print("saving figure at", outputpath)
fig.savefig(str(outputpath) + '_MuonEff.png')
figip.savefig(str(outputpath) + '_ImpactParameter.png')
figrw.savefig(str(outputpath) + '_RingWidth.png')
else:
print("Not saving figure, no outputpath")
plt.show()
def plot_muon_efficiency(outputpath):
"""
Plot the muon efficiencies
"""
fig, ax = plt.subplots(1, 1, figsize=(10, 10))
figip, axip = plt.subplots(1, 1, figsize=(10, 10))
figrw, axrw = plt.subplots(1, 1, figsize=(10, 10))
nbins = 16
t = Table.read(str(outputpath) + '_muontable.fits')
print('Reading muon efficiency from table', outputpath, t['MuonEff'])
if len(t['MuonEff']) < 1:
print("No muon events to plot")
return
else:
print("Found", len(t['MuonEff']), "muon events")
(mu, sigma) = norm.fit(t['MuonEff'])
print('Gaussian fit with mu=', mu, 'sigma=', sigma)
#ax = figeff.add_subplot(1,3,1)
conteff = ax.hist(t['MuonEff'], nbins)
ax.set_xlim(0.2 * min(t['MuonEff']), 1.2 * max(t['MuonEff']))
xtest = np.linspace(min(t['MuonEff']), max(t['MuonEff']), nbins)
yg = mlab.normpdf(xtest, mu, sigma)
print('mu', mu, 'sigma', sigma, 'yg', yg)
ax.plot(xtest, yg, 'r', linewidth=2)
ax.set_ylim(0., 1.2 * max(conteff[0]))
ax.set_xlabel('Muon Efficiency')
# plt.figure(fig.number)
plt.draw()
#axip = figeff.add_subplot(1,3,1)
yimp = np.linspace(min(t['ImpactP']), max(t['ImpactP']), nbins)
contimp = axip.hist(t['ImpactP'], nbins)
axip.set_xlim(0.2 * min(t['ImpactP']), 1.2 * max(t['ImpactP']))
axip.set_ylim(0., 1.2 * max(contimp[0]))
axip.set_xlabel('Impact Parameter (m)')
# plt.figure(figip.number)
plt.draw()
heffimp = Histogram(nbins=[16, 16],
ranges=[(min(t['MuonEff']), max(t['MuonEff'])),
(min(t['ImpactP']), max(t['ImpactP']))]) # ,axisNames=["MuonEfficiency","ImpactParameter"])
# embed()
# heffimp.bin_lower_edges([xtest,yimp])
heffimp.fill([t['MuonEff'], t['ImpactP']])
heffimp.draw_2d()
#axrw = figeff.add_subplot(1,3,1)
yrw = np.linspace(min(t['RingWidth']), max(t['RingWidth']), nbins)
contrw = axrw.hist(t['RingWidth'], nbins)
axrw.set_xlim(0.2 * min(t['RingWidth']), 1.2 * max(t['RingWidth']))
axrw.set_ylim(0., 1.2 * max(contrw[0]))
axrw.set_xlabel('Ring Width ($^\circ$)')
# plt.figure(figrw.number)
plt.draw()
# plt.show()
if outputpath is not None:
print("saving figure at", outputpath)
fig.savefig(str(outputpath) + '_MuonEff.png')
figip.savefig(str(outputpath) + '_ImpactParameter.png')
figrw.savefig(str(outputpath) + '_RingWidth.png')
else:
print("Not saving figure, no outputpath")
plt.show()
def plot_muon_efficiency(outputpath):
"""
Plot the muon efficiencies
"""
fig, ax = plt.subplots(1, 1, figsize=(10, 10))
figip, axip = plt.subplots(1, 1, figsize=(10, 10))
figrw, axrw = plt.subplots(1, 1, figsize=(10, 10))
nbins = 16
t = Table.read(str(outputpath) + '_muontable.fits')
logger.info('Reading muon efficiency from table "%s"', outputpath)
if len(t['MuonEff']) < 1:
logger.warning("No muon events to plot")
return
else:
logger.info("Found %d muon events", len(t['MuonEff']))
(mu, sigma) = norm.fit(t['MuonEff'])
logger.debug('Gaussian fit with mu=%f, sigma=%f', mu, sigma)
conteff = ax.hist(t['MuonEff'], nbins)
ax.set_xlim(0.2 * min(t['MuonEff']), 1.2 * max(t['MuonEff']))
xtest = np.linspace(min(t['MuonEff']), max(t['MuonEff']), nbins)
yg = mlab.normpdf(xtest, mu, sigma)
logger.debug('mu=%f sigma=%f yg=%f', mu, sigma, yg)
ax.plot(xtest, yg, 'r', linewidth=2)
ax.set_ylim(0., 1.2 * max(conteff[0]))
ax.set_xlabel('Muon Efficiency')
plt.draw()
contimp = axip.hist(t['ImpactP'], nbins)
axip.set_xlim(0.2 * min(t['ImpactP']), 1.2 * max(t['ImpactP']))
axip.set_ylim(0., 1.2 * max(contimp[0]))
axip.set_xlabel('Impact Parameter (m)')
plt.draw()
heffimp = Histogram(nbins=[16, 16],
ranges=[(min(t['MuonEff']), max(t['MuonEff'])),
(min(t['ImpactP']), max(t[
'ImpactP']))]) #
# ,axisNames=["MuonEfficiency","ImpactParameter"])
heffimp.fill([t['MuonEff'], t['ImpactP']])
heffimp.draw_2d()
contrw = axrw.hist(t['RingWidth'], nbins)
axrw.set_xlim(0.2 * min(t['RingWidth']), 1.2 * max(t['RingWidth']))
axrw.set_ylim(0., 1.2 * max(contrw[0]))
axrw.set_xlabel('Ring Width ($^\circ$)')
plt.draw()
if outputpath is not None:
logger.info("saving figure to '%s'", outputpath)
fig.savefig(str(outputpath) + '_MuonEff.png')
figip.savefig(str(outputpath) + '_ImpactParameter.png')
figrw.savefig(str(outputpath) + '_RingWidth.png')
else:
logger.info("Not saving figure, no outputpath")
plt.show()
class ShowerMaxEstimator:
def __init__(self, filename, col_altitude=0, col_thickness=2):
"""
small class that calculates the height of the shower maximum
given a parametrisation of the atmosphere
and certain parameters of the shower itself
Parameters:
-----------
filename : string
path to text file that contains a table of the
atmosphere parameters
col_altitude : int
column in the text file that contains the altitude/height
col_thickness : int
column in the text file that contains the thickness
"""
altitude = []
thickness = []
atm_file = open(filename, "r")
for line in atm_file:
if line.startswith("#"):
continue
altitude.append(float(line.split()[col_altitude]))
thickness.append(float(line.split()[col_thickness]))
self.atmosphere = Histogram(axisNames=["altitude"])
self.atmosphere.hist = thickness * u.g * u.cm ** -2
self.atmosphere.bin_lower_edges = [np.array(altitude) * u.km]
def interpolate(self, arg, outlierValue=0., order=3):
axis = self.atmosphere._binLowerEdges[0]
bin_u = np.digitize(arg.to(axis.unit), axis)
bin_l = bin_u - 1
unit = arg.unit
argv = arg.value
bin_u_edge = axis[bin_u].to(unit).value
bin_l_edge = axis[bin_l].to(unit).value
coordinate = (argv - bin_u_edge) / (bin_u_edge - bin_l_edge) \
* (bin_u - bin_l) + bin_u
return ndimage.map_coordinates(self.atmosphere.hist,
[[coordinate]],
order=order,
cval=outlierValue)[0]\
* self.atmosphere.hist.unit
def find_shower_max_height(self, energy, h_first_int, gamma_alt):
"""
estimates the height of the shower maximum in the atmosphere
according to equation (3) in [arXiv:0907.2610v3]
Parameters:
-----------
energy : astropy.Quantity
energy of the parent gamma photon
h_first_int : astropy.Quantity
hight of the first interaction
gamma_alt : astropy.Quantity or float
altitude / pi-minus-zenith (in radians in case of float)
of the parent gamma photon
Returns:
--------
shower_max_height : astropy.Quantity
height of the shower maximum
"""
# offset of the shower-maximum in radiation lengths
c = 0.97 * log(energy / (83 * u.MeV)) - 1.32
# radiation length in dry air at 1 atm = 36,62 g / cm**2 [PDG]
c *= 36.62 * u.g * u.cm ** -2
# showers with a more horizontal direction spend more path
# length in each atm. layer the "effective transverse
# thickness" they have to pass is reduced
c *= np.sin(gamma_alt)
# find the thickness at the height of the first interaction
t_first_int = self.interpolate(h_first_int)
# total thickness at shower maximum = thickness at first
# interaction + thickness traversed to shower maximum
t_shower_max = t_first_int + c
# now find the height with the wanted thickness
for ii, thick1 in enumerate(self.atmosphere.hist):
if t_shower_max > thick1:
height1 = self.atmosphere.bin_lower_edges[0][ii]
height2 = self.atmosphere.bin_lower_edges[0][ii - 1]
val = [height2.to(
self.atmosphere._binLowerEdges[0].unit).value]
thick2 = self.atmosphere.get_value(val)[0]
return (height2 - height1) / (thick2 - thick1) \
* (t_shower_max - thick1) + height1
