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Lehre
TP2-BSMPhysics-ForStudents
Commits
8c5abfe0
Commit
8c5abfe0
authored
1 year ago
by
Thorsten Chwalek
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exercise03/Sample.py
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# Assigns an ID to each sample along with different access functions
# Author in C++: Matthias Schroeder
# matthias.schroeder@AT@desy.de
# November 2013
import
glob
import
numpy
as
np
# manipulations and operations on arrays
import
pandas
as
pd
# easy access to data structures
import
matplotlib.pyplot
as
plt
# plotting
from
matplotlib.lines
import
Line2D
import
time
from
scipy.stats
import
poisson
from
scipy.stats
import
norm
import
ast
# useful for converting strings to lists
class
Sample
:
#path to data needs to be set manually
path
=
"
/Unknown Path/
"
# check if ID is correct
def
checkId
(
id
):
if
(
id
>
6
or
id
<
0
):
print
(
"
\n\n
ERROR invalid sample id
"
,
id
)
# input id output full file name
def
fileNameFullSample
(
id
):
Sample
.
checkId
(
id
)
if
id
==
0
:
name
=
"
4VectorData.csv
"
if
id
==
7
:
name
=
"
MuHadUnnested.csv
"
if
id
==
1
:
name
=
"
4VectorWJets.csv
"
if
id
==
2
:
name
=
"
4VectorTTJets.csv
"
if
id
==
3
:
name
=
"
4VectorZJets.csv
"
if
id
==
4
:
name
=
"
4VectorQCD.csv
"
if
id
==
5
:
name
=
"
4VectorLM6.csv
"
if
id
==
6
:
name
=
"
4VectorLM9.csv
"
name
=
Sample
.
path
+
name
#for i in range(len(name)):
# name[i] = Sample.path+name[i]
return
name
# input id output name of data (label) for legends in plot
def
label
(
id
):
Sample
.
checkId
(
id
)
label
=
""
if
(
id
==
0
):
label
+=
"
Data
"
if
(
id
==
1
):
label
+=
"
W(l#nu)+Jets
"
if
(
id
==
2
):
label
+=
"
t#bar{t}+Jets
"
if
(
id
==
3
):
label
+=
"
Z(#nu#bar{#nu})+Jets
"
if
(
id
==
4
):
label
+=
"
QCD
"
if
(
id
==
5
):
label
+=
"
LM6
"
if
(
id
==
6
):
label
+=
"
LM9
"
return
label
# sample name without spaces for files
def
toString
(
id
):
Sample
.
checkId
(
id
)
string
=
""
if
(
id
==
0
):
string
+=
"
Data
"
if
(
id
==
1
):
string
+=
"
WJets
"
if
(
id
==
2
):
string
+=
"
TTJets
"
if
(
id
==
3
):
string
+=
"
ZJets
"
if
(
id
==
4
):
string
+=
"
QCD
"
if
(
id
==
5
):
string
+=
"
LM6
"
if
(
id
==
6
):
string
+=
"
LM9
"
return
string
# assigns color to each sample for plotting
def
color
(
id
):
Sample
.
checkId
(
id
)
color
=
0
;
if
(
id
==
0
):
color
=
'
k
'
if
(
id
==
1
):
color
=
'
g
'
if
(
id
==
2
):
color
=
'
r
'
if
(
id
==
3
):
color
=
'
y
'
if
(
id
==
4
):
color
=
'
m
'
if
(
id
==
5
):
color
=
'
b
'
if
(
id
==
6
):
color
=
'
c
'
return
color
# returns cross section:
def
CrossSection
(
ID
):
if
ID
==
1
:
return
(
30.08
)
if
ID
==
2
:
return
(
127.06
)
if
ID
==
3
:
return
(
6.26
)
if
ID
==
4
:
return
(
8630.0
)
if
ID
==
5
:
return
(
0.502
)
if
ID
==
6
:
return
(
9.287
)
else
:
return
(
'
Wrong ID: cross section not found
'
)
# total Number of MC events prior to selection
def
TotalEvents
(
ID
):
if
ID
==
1
:
return
(
6619654.
)
if
ID
==
2
:
return
(
1925324.
)
if
ID
==
3
:
return
(
51637.
)
if
ID
==
4
:
return
(
44443155.
)
if
ID
==
5
:
return
(
51282.
)
if
ID
==
6
:
return
(
51282.
)
else
:
return
(
'
Wrong ID: Total Number of MC Events not found
'
)
# takes a dataframe or a list of dataframes and returns the maximum number of jets in the dataframe
def
MaxNJets
(
df
):
if
isinstance
(
df
,
list
):
out
=
[(
len
(
x
.
columns
)
-
2
)
//
4
for
x
in
df
]
elif
isinstance
(
df
,
pd
.
DataFrame
):
out
=
(
len
(
df
.
columns
)
-
2
)
//
4
else
:
print
(
'
MaxNJets Error: Input is not a dataframe or a list of dataframes
'
)
return
(
out
)
# returns list of MaxNJets for the dataframes in input
# takes a dataframe or a list of dataframes and returns list of the jets as strings
def
JetNames
(
df
):
if
isinstance
(
df
,
list
):
out
=
[]
for
element
in
df
:
Jets_in_df
=
[
'
Jet {}
'
.
format
(
i
)
for
i
in
range
(
Sample
.
MaxNJets
(
element
))]
out
.
append
(
Jets_in_df
)
elif
isinstance
(
df
,
pd
.
DataFrame
):
out
=
[
'
Jet {}
'
.
format
(
i
)
for
i
in
range
(
Sample
.
MaxNJets
(
df
))]
else
:
print
(
'
Jets Error: Input is not a dataframe or a list of dataframes
'
)
return
(
out
)
# takes a dataframe and a Jetnumber and returns needed jet as tuble
def
Get_Jet
(
df
,
Jet_Number
):
return
(
df
[
Sample
.
JetNames
(
df
)[
Jet_Number
]])
# subtract arrays while ignoring NaN values
def
nansubtract
(
x1
,
x2
):
return
(
np
.
nansum
(
np
.
array
([
x1
,
-
1
*
x2
]),
axis
=
0
))
def
Bin_Values
(
Data
,
Bins
,
Weights
):
binValues
,
binEdges
=
np
.
histogram
(
Data
,
bins
=
Bins
,
weights
=
Weights
)
bincenters
=
0.5
*
(
binEdges
[
1
:]
+
binEdges
[:
-
1
])
out
=
[
'
{:.10f}
'
.
format
(
a
)
for
a
in
binValues
]
return
(
out
)
# Compare data with background and signal distributions
def
Compare
(
Background
,
Signal
,
Data
,
Bins
,
Labels
=
None
,
Colors
=
None
):
# Function Variables:
#Bkg: List of background dataframes
#Sgl: List of signal dataframes
#Data: Data Dataframe NOT a List
#Bins: list of bins for MaxNJets, Ht, and MHt in this order
#labels: list of labels in following order: [Data,Background,Signal]
#color: list of colors in following order: [Data,Background,Signal]
np
.
set_printoptions
(
formatter
=
{
'
float_kind
'
:
'
{:f}
'
.
format
})
fig
,
axs
=
plt
.
subplots
(
1
,
3
,
figsize
=
(
21.6
,
4.8
))
# Plotting Background
Bkg_Weights
=
[
Bkg
[
'
Weights
'
]
for
Bkg
in
Background
]
Bkg_Njets
=
[
Bkg
[
'
NJets
'
]
for
Bkg
in
Background
]
Bkg_Ht
=
[
Bkg
[
'
Ht
'
]
for
Bkg
in
Background
]
Bkg_MHt
=
[
Bkg
[
'
MHt
'
]
for
Bkg
in
Background
]
Bkg_labels
=
[
Labels
[
i
+
1
]
for
i
in
range
(
len
(
Background
))]
# +1 to aviod counting data as background
Bkg_colors
=
[
Colors
[
i
+
1
]
for
i
in
range
(
len
(
Background
))]
axs
[
0
].
hist
(
Bkg_Njets
,
bins
=
Bins
[
0
]
,
color
=
Bkg_colors
,
label
=
Bkg_labels
,
stacked
=
True
,
weights
=
Bkg_Weights
)
axs
[
1
].
hist
(
Bkg_Ht
,
bins
=
Bins
[
1
]
,
color
=
Bkg_colors
,
label
=
Bkg_labels
,
stacked
=
True
,
weights
=
Bkg_Weights
)
axs
[
2
].
hist
(
Bkg_MHt
,
bins
=
Bins
[
2
]
,
color
=
Bkg_colors
,
label
=
Bkg_labels
,
stacked
=
True
,
weights
=
Bkg_Weights
)
# Plotting Signals
for
signal_index
,
signal_dataframe
in
enumerate
(
Signal
):
Signal_Weights
=
signal_dataframe
[
'
Weights
'
]
Signal_Njets
=
signal_dataframe
[
'
NJets
'
]
Signal_Ht
=
signal_dataframe
[
'
Ht
'
]
Signal_MHt
=
signal_dataframe
[
'
MHt
'
]
Signal_labels
=
Labels
[
len
(
Background
)
+
1
+
signal_index
]
# note that Labels = [Data_label, Background_labels, Signal_labels]
Signal_colors
=
Colors
[
len
(
Background
)
+
1
+
signal_index
]
# note that Labels = [Data_label, Background_labels, Signal_labels]
axs
[
0
].
hist
(
Signal_Njets
,
bins
=
Bins
[
0
],
color
=
Signal_colors
,
label
=
Signal_labels
,
stacked
=
False
,
weights
=
Signal_Weights
,
histtype
=
u
'
step
'
)
axs
[
1
].
hist
(
Signal_Ht
,
bins
=
Bins
[
1
],
color
=
Signal_colors
,
label
=
Signal_labels
,
stacked
=
False
,
weights
=
Signal_Weights
,
histtype
=
u
'
step
'
)
axs
[
2
].
hist
(
Signal_MHt
,
bins
=
Bins
[
2
],
color
=
Signal_colors
,
label
=
Signal_labels
,
stacked
=
False
,
weights
=
Signal_Weights
,
histtype
=
u
'
step
'
)
# Plotting Data
for
Variable_index
,
Variable
in
enumerate
([
'
NJets
'
,
'
Ht
'
,
'
MHt
'
]):
binValues
,
binEdges
=
np
.
histogram
(
Data
[
Variable
],
bins
=
Bins
[
Variable_index
],
weights
=
Data
[
'
Weights
'
])
bincenters
=
0.5
*
(
binEdges
[
1
:]
+
binEdges
[:
-
1
])
menStd
=
np
.
sqrt
(
binValues
)
axs
[
Variable_index
].
errorbar
(
bincenters
,
binValues
,
color
=
Colors
[
0
],
yerr
=
np
.
sqrt
(
binValues
)
,
label
=
Labels
[
0
]
,
fmt
=
'
o
'
)
#----Adjust--Axis---
for
N_axs
in
[
0
,
1
,
2
]:
axs
[
N_axs
].
set_yscale
(
"
log
"
)
axs
[
N_axs
].
set_ylabel
(
'
Events
'
,
fontsize
=
16
)
axs
[
N_axs
].
legend
()
axs
[
0
].
set_xlabel
(
'
N(jets)
'
,
fontsize
=
16
)
axs
[
1
].
set_xlabel
(
'
$H_{t}$ [GeV]
'
,
fontsize
=
16
)
axs
[
2
].
set_xlabel
(
"
$ MH_{t}[GeV] $
"
,
fontsize
=
16
)
plt
.
show
()
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