We wrote this code in order to plot a charge spectrum like this (histo from HG (columun) for a specific CH (another column)):
This plot was obtained from a dataset, where in it there is a column named "Ts (ns)":
CH LG HG Ts(ns) ToT(ns)
0 0.0 75.0 80.0 0.0 0.0
1 1.0 73.0 77.0 0.0 0.0
2 2.0 74.0 65.0 0.0 0.0
3 3.0 72.0 73.0 0.0 0.0
4 4.0 74.0 74.0 0.0 0.0
... ... ... ... ... ...
2613307 59.0 75.0 74.0 0.0 0.0
2613308 60.0 76.0 106.0 811.5 10.5
2613309 61.0 75.0 75.0 0.0 0.0
2613310 62.0 76.0 73.0 0.0 0.0
2613311 63.0 74.0 100.0 0.0 0.0
Our goal is to remove, from the plot, ONLY the data that have a "Ts" different from "-". The code is the following:
import pandas as pd
import plotly.express as px
import matplotlib.pyplot as plt
import plotly.express as px
import plotly.graph_objects as go
import re
import numpy as np
import os
from tqdm import tqdm, trange
print('''
_____ _ _ _ _
| __ \ | | | | (_) | |
| |__) | ___ __ _ __| | | | _ ___ | |_ _ __ _ _
| _ / / _ \ / _` | / _` | | | | |/ __|| __| | '_ \ | | | |
| | \ \| __/| (_| || (_| | | |____ | |\__ \| |_ _ | |_) || |_| |
|_| \_\\___| \__,_| \__,_| |______||_||___/ \__|(_)| .__/ \__, |
| | __/ |
|_| |___/
''')
names = ['CH','LG','HG','Ts(ns)','ToT(ns)']
righe_primo_header = 5
righe_header = 5
canali = 64
# input file
infile = 'Run1_list_14_03_2022.txt'
# determinare numero di righe, poi di eventi nel file di input
stream = os.popen('wc -l '+ infile)
nrighe = stream.read()
match = re.match(r"\s*(\S+) (.*)", nrighe, re.I)
items = match.groups()
nrighe = float(items[0])
# numero di blocchi di dati da leggere
ntrigger = (nrighe - righe_primo_header) / (canali + righe_header) - 1
ntrigger = int( ntrigger)
print('\nNel file sono stati trovati ',ntrigger,' eventi.\n')
ncanali_histo = int(np.sqrt(ntrigger))
ncanali_histo = 4096
events = []
file1 = open( infile, 'r')
for line in range(righe_primo_header-1):
line = file1.readline()
line=file1.readline()
pbar = tqdm(total=ntrigger)
for trigger in range(ntrigger):
for lineh in range(righe_header):
line = file1.readline()
#print('saltiamo ',line)
for canale in range(canali):
line = file1.readline()
#print(' elaboriamo ',line)
match = re.match(r"(\S+)\s+(\S+)\s+(\S+)\s+(\S+)\s+(\S+)\s+", line, re.I)
temparray = []
if match:
items = match.groups()
for colonna in range(len(items)):
col = items[colonna]
if col == '-':
valore = 0
else:
valore = float(items[colonna])
temparray.append(valore)
events.append(temparray)
pbar.update(1)
file1.close()
df = pd.DataFrame( events, columns = names)
print('\nIl dataframe totale è il seguente:\n', df)
# istogramma di HG per canale fissato
plot_df = df.loc[ (df['CH'] == 44) | (df['CH'] == 13)| (df['CH'] == 19) | (df['CH'] == 50) ]
print('\nIl dataframe selezionato è il seguente:\n', plot_df)
pd.options.mode.chained_assignment = None # default='warn' permette di sovrascrivere il dataframe.
plot_df['HG'][df['CH']==13] -= (99.2910)
plot_df['HG'][df['CH']==13] /= (33.1946) #effettua una prima calibrazione
plot_df['HG'][df['CH']==13] += (1)
plot_df['HG'][df['CH']==19] -= (99.2910)
plot_df['HG'][df['CH']==19] /= (34.2973) #effettua una prima calibrazione
plot_df['HG'][df['CH']==19] += (1)
plot_df['HG'][df['CH']==44] -= (99.0216)
plot_df['HG'][df['CH']==44] /= (32.8129) #effettua una prima calibrazione
plot_df['HG'][df['CH']==44] += (1)
plot_df['HG'][df['CH']==50] -= (98.1520)
plot_df['HG'][df['CH']==50] /= (34.1027) #effettua una prima calibrazione
plot_df['HG'][df['CH']==50] += (1)
layout = go.Layout(
title="Equalized HG Multiphoton Spectrum",
xaxis=dict(
title="#p.e."
),
yaxis=dict(
title="Counts"
) )
fig3 = go.Figure(layout=layout)
fig3.add_trace(go.Histogram(x=plot_df['HG'][df['CH']==13],name='CH 13'))
fig3.update_traces(xbins=dict(start=0, end=10, size=1/33.1946)) #gain canale 13
fig3.add_trace(go.Histogram(x=plot_df['HG'][df['CH']==19],name='CH 19'))
fig3.update_traces(xbins=dict(start=0, end=10, size=1/34.2973)) #gain canale 19
fig3.add_trace(go.Histogram(x=plot_df['HG'][df['CH']==44],name='CH 44'))
fig3.update_traces(xbins=dict(start=0, end=10, size=1/32.8129)) #gain canale 44
fig3.add_trace(go.Histogram(x=plot_df['HG'][df['CH']==50],name='CH 50'))
fig3.update_traces(xbins=dict(start=0, end=10, size=1/34.1027)) #gain canale 50
fig3.update_xaxes(showline=True, linewidth=1, linecolor='black', gridcolor='black')
fig3.update_yaxes(showline=True, linewidth=1, linecolor='black', gridcolor='black')
fig3.update_layout(barmode='stack') #per il cumulativo
#fig3.update_layout(barmode='overlay') #per i plot singoli
fig3.update_layout({
'plot_bgcolor': 'rgba(0, 0, 0, 0)',
'paper_bgcolor': 'rgba(0, 0, 0, 0)',
})
fig3.show()
"""
evento = 3
plot_df2 = df[(canali * evento):(canali*evento + canali)]
print('Il dataframe per lo scatter plot HG vs Ch relativo all evento ',evento, 'è il seguente: ', plot_df2)
fig2 = px.scatter(plot_df2, x='CH', y='HG', title='HG vs CH event ' + str(evento) )
fig2.show()
"""
Meanwhile the file is uploaded here: https://mega.nz/file/E1oEwLjD#PQcIiRrSDJD9LT4enrN1bQoWLmKpUTzBZLWVylVn_7g
How we can do that? Thanks in advance.
>Solution :
You can delete them basing on boolean conditions. In your case this should work:
df = df[df['Ts(ns)'] != '-'] # or change the '-' with the value you want filter on
You can have a look at this for more as a reference.