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Deep Learning Examples at VISPA
airshower-deeplearning
Commits
e3d794ec
Commit
e3d794ec
authored
Sep 08, 2017
by
DavidWalz
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add scripts for fitting arrival direction and energy
parent
543b050a
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fitting/fit-energy.py
fitting/fit-energy.py
+90
-0
fitting/fit-planewave.py
fitting/fit-planewave.py
+56
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fitting/fit-energy.py
0 → 100644
View file @
e3d794ec
import
numpy
as
np
import
matplotlib.pyplot
as
plt
# load data
with
np
.
load
(
'../RawData/showers-A1-0.npz'
)
as
data
:
vd
=
data
[
'detector'
]
vc
=
data
[
'showercore'
]
va
=
data
[
'showeraxis'
]
logE
=
data
[
'logE'
]
signal
=
data
[
'signal'
].
sum
(
axis
=-
1
)
# total signal
nb_events
=
len
(
vc
)
nb_stations
=
len
(
vd
)
# for each event, calculate distances of detectors to shower core, plane and axis
xd
=
np
.
repeat
(
np
.
expand_dims
(
vd
,
0
),
nb_events
,
axis
=
0
)
xc
=
np
.
repeat
(
np
.
expand_dims
(
vc
,
1
),
nb_stations
,
axis
=
1
)
xa
=
np
.
repeat
(
np
.
expand_dims
(
va
,
1
),
nb_stations
,
axis
=
1
)
d1
=
np
.
linalg
.
norm
(
xd
-
xc
,
axis
=-
1
)
# distance to shower core
d2
=
np
.
sum
((
xd
-
xc
)
*
xa
,
axis
=-
1
)
# distance to shower plane
d3
=
np
.
linalg
.
norm
(
xd
-
xc
-
xa
*
d2
[...,
np
.
newaxis
],
axis
=-
1
)
# distance to shower axis
# fit lateral distribution to each event, S(r) = S1000 * (r/1000)**a
logS1000
=
np
.
zeros
(
nb_events
)
for
i
in
range
(
nb_events
):
m
=
~
np
.
isnan
(
signal
[
i
])
# mask for triggered stations
m
*=
d3
[
i
]
>
200
# cut stations close to the shower core
x
=
np
.
log10
(
d3
[
i
][
m
]
/
1000
)
# log10(r/1000m) distance to shower axis
y
=
np
.
log10
(
signal
[
i
][
m
]
-
100
)
# log10(signal - noise level)
p
=
np
.
polyfit
(
x
,
y
,
1
)
# fit linear model
logS1000
[
i
]
=
p
[
1
]
# plt.figure()
# plt.scatter(x + 3, y)
# x = np.linspace(-1, 1)
# y = np.polyval(p, x)
# plt.plot(x + 3, y, 'r')
# plt.xlabel('$\log_{10}(r)$')
# plt.ylabel('$\log_{10}(S)$')
# plt.grid()
# fit zenith angle dependency: logS1000 = logS38 + p * (cos(zenith)**2 - cos(38)**2)
x
,
y
,
z
=
va
.
T
zenith
=
np
.
pi
/
2
-
np
.
arctan2
(
z
,
(
x
*
x
+
y
*
y
)
**
.
5
)
cz
=
np
.
cos
(
zenith
)
**
2
-
np
.
cos
(
np
.
deg2rad
(
38
))
**
2
pcal1
=
np
.
polyfit
(
cz
,
logS1000
,
1
)
cic
=
np
.
polyval
(
pcal1
,
cz
)
logS38
=
logS1000
/
cic
# remove the zenith angle dependency
plt
.
figure
()
plt
.
scatter
(
cz
,
logS1000
)
plt
.
plot
(
cz
,
cic
,
'r'
)
plt
.
xlabel
(
r
'$\cos(\theta)^2 - \cos(38)^2$'
)
plt
.
ylabel
(
r
'$\log_{10}(S_{1000})$'
)
plt
.
savefig
(
'energy-calibration1.png'
)
# fit energy dependency: logE = p0 + p1 * logS
pcal2
=
np
.
polyfit
(
logS38
,
logE
,
1
)
logE_rec
=
np
.
polyval
(
pcal2
,
logS38
)
plt
.
figure
()
plt
.
scatter
(
logS38
,
logE
)
x
=
np
.
linspace
(
min
(
logS38
),
max
(
logS38
))
y
=
np
.
polyval
(
pcal2
,
x
)
plt
.
plot
(
x
,
y
,
'r'
)
plt
.
xlabel
(
'$\log_{10}(S_{38})$'
)
plt
.
ylabel
(
'$\log_{10}(E / \mathrm{eV})$'
)
plt
.
grid
()
plt
.
savefig
(
'energy-calibration2.png'
)
# evaluate resolution
r
=
10
**
(
logE_rec
-
logE
)
-
1
# (E_rec - E_true) / E_true
plt
.
figure
()
plt
.
hist
(
r
,
bins
=
np
.
linspace
(
-
0.6
,
0.6
,
20
))
plt
.
xlabel
(
'$(E_\mathrm{rec} - E_\mathrm{true}) / E_\mathrm{true}$'
)
plt
.
ylabel
(
'\#'
)
plt
.
grid
()
plt
.
text
(
0.95
,
0.95
,
'mean = %.3f
\n
std = %.3f'
%
(
np
.
mean
(
r
),
np
.
std
(
r
)),
transform
=
plt
.
gca
().
transAxes
,
ha
=
'right'
,
va
=
'top'
,
bbox
=
dict
(
boxstyle
=
'round'
,
facecolor
=
'white'
))
plt
.
savefig
(
'energy-resolution.png'
)
fitting/fit-planewave.py
0 → 100644
View file @
e3d794ec
import
numpy
as
np
import
matplotlib.pyplot
as
plt
# load data
with
np
.
load
(
'../RawData/showers-A1-0.npz'
)
as
data
:
vd
=
data
[
'detector'
]
va
=
data
[
'showeraxis'
]
time
=
data
[
'time'
]
nb_events
=
len
(
va
)
# reconstruct shower directions with plane wave fit
# cf. ftp://ftp.mi.ingv.it/download/augliera/PER-MARA/article_del_pezzo.pdf
va_rec
=
np
.
zeros
((
nb_events
,
3
))
for
i
in
range
(
nb_events
):
m
=
~
np
.
isnan
(
time
[
i
])
# mask for triggered stations
t
=
time
[
i
][
m
]
x
,
y
,
z
=
vd
[
m
].
T
# distances between all pairs of stations
idx
=
np
.
triu_indices
(
len
(
t
),
k
=
1
)
dt
=
np
.
subtract
.
outer
(
t
,
t
)[
idx
]
dx
=
np
.
subtract
.
outer
(
x
,
x
)[
idx
]
dy
=
np
.
subtract
.
outer
(
y
,
y
)[
idx
]
# all stations on a plane --> neglect z-position
dv
=
np
.
stack
((
dx
,
dy
),
axis
=-
1
)
a1
=
np
.
linalg
.
inv
(
np
.
dot
(
dv
.
T
,
dv
))
# # for using z-position as well
# dz = np.subtract.outer(z, z)[idx]
# dv = np.stack((dx, dy, dz), axis=-1)
# a1 = np.linalg.pinv(np.dot(dv.T, dv))
a2
=
np
.
einsum
(
'ij,i->j'
,
dv
,
dt
)
a3
=
np
.
einsum
(
'ij,j->i'
,
a1
,
a2
)
# determine z-component using known speed of propagation = c
px
,
py
=
-
a3
*
3E8
pz
=
(
1
-
px
**
2
-
py
**
2
)
**
.
5
va_rec
[
i
]
=
[
px
,
py
,
pz
]
# plot angular separation between true and reconstructed angle
ang
=
np
.
arccos
(
np
.
clip
(
np
.
sum
(
va
*
va_rec
,
axis
=
1
),
-
1
,
1
))
*
180
/
np
.
pi
r68
=
np
.
percentile
(
ang
,
68
)
plt
.
figure
()
plt
.
hist
(
ang
,
bins
=
np
.
linspace
(
0
,
5
,
41
))
plt
.
axvline
(
r68
,
c
=
'r'
)
plt
.
xlabel
(
'Angular distance [deg]'
)
plt
.
ylabel
(
'\#'
)
plt
.
grid
()
plt
.
savefig
(
'fit-planewave.png'
,
bbox_inches
=
'tight'
)
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