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2fed1bf8 · Victor Clarizio · b832fe93 · 2fed1bf8
Commit 2fed1bf8 authored 2 years ago by Victor Clarizio
--- a/Códigos/Análisis_Calibración_08-03.py
+++ b/Códigos/Análisis_Calibración_08-03.py
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Nov  7 12:45:11 2022
+
+@author: victor
+"""
+
+import matplotlib
+import numpy as np
+import matplotlib.pyplot as plt
+import math
+import csv, operator
+import scipy.stats as st
+from numpy import random
+import pandas as pd
+from datetime import datetime
+from pandas import *
+
+matplotlib.pyplot.savefig
+
+#df = df.rename(columns={'oldName1': 'newName1', 'oldName2': 'newName2'})
+
+
+##Este código Python servirá para analizar los datos correspondientes al WCD Characatito. 
+##Existen diferentes terminaciones de archivos que corresponden a datos de análisis distintos.
+##Pero todos los archivos comparten el mismo nombre, salvo la terminación.
+
+##Luego de realizar el primer procesamiento de datos (Lo a L1) con ./raw en la terminal, 
+##procedemos a estudiar la data adquirida aquí. Las terminaciones de archivos que nos interesan
+##son: .rte (pulsos detectados por unidad de tiempo fijada por el usuario), .flx (pulsos detec-
+##tados por segundo)
+
+##Los histogramas de carga de los archivos .cal representan el número de conteos registrados
+##en cada canal ACD. Se tienen dos picos característicos: El de menor carga se genera por la 
+#componente electromagnética (electrones, positrones y gammas) y el de mayor por la 
+#componente muónica. Este último es utilizado para la calibración del instrumento (VEM). El VEM 
+#(por su siglas en inglés, Vertical Muon Equivalent) es la pérdida de energía que genera 
+#un muón vertical al pasar por el WCD.
+
+
+##PARA MODIFICAR EL NÚMERO DE BINES, OPACIDAD DE LOS HISTOGRAMAS
+
+Bines = 50
+Opac = 0.08
+Rwidth = 0.80
+
+
+##--------ANALISIS DATOS "l1_v5_07_29_00h00.cal"--------------------------------------
+
+
+dfc0000=pd.read_csv('l1_v5_characatito_nogps_2022_07_29_00h00.cal', sep=' ',skiprows=6)
+dfc0000.columns=['ch1','ch2','ch3','pk1', 'pk2','pk3','']
+
+
+dfc0000['ch1'].hist(bins = Bines, color = 'blue', alpha = Opac, rwidth = Rwidth) 
+plt.yscale('log')
+plt.title("Histogramas de Carga - WCD Characatito (29/07 al 30/07)")
+plt.xlabel("Carga [ADC.bin]")
+plt.xticks(np.arange(0, 6000, 500))
+plt.axvline(x = 1300, color = 'red', linestyle = '-.')
+plt.axvline(x = 1800, color = 'red', linestyle = '-.')
+plt.text(2000, 120, '$\leftarrow$ Zona característica del VEM', color = 'red')   
+#plt.ylabel("Logaritmo de Conteos registrados")
+
+
+
+##-------------ANÁLISIS DATOS "l1_v5_07_29_01h00.cal"---------------------------------
+
+
+dfc0100=pd.read_csv('l1_v5_characatito_nogps_2022_07_29_01h00.cal', sep=' ',skiprows=6)
+dfc0100.columns=['ch1','ch2','ch3','pk1', 'pk2','pk3','']
+
+
+dfc0100['ch1'].hist(bins = Bines, color = 'red', alpha = Opac, rwidth = Rwidth) 
+plt.yscale('log')
+
+##-------------ANÁLISIS DATOS "l1_v5_07_29_02h00.cal"---------------------------------
+
+
+dfc0200=pd.read_csv('l1_v5_characatito_nogps_2022_07_29_02h00.cal', sep=' ',skiprows=6)
+dfc0200.columns=['ch1','ch2','ch3','pk1', 'pk2','pk3','']
+
+
+dfc0200['ch1'].hist(bins = Bines, color = 'blue', alpha = Opac, rwidth = Rwidth) 
+plt.yscale('log')
+
+##-------------ANÁLISIS DATOS "l1_v5_07_29_03h00.cal"---------------------------------
+
+
+dfc0300=pd.read_csv('l1_v5_characatito_nogps_2022_07_29_03h00.cal', sep=' ',skiprows=6)
+dfc0300.columns=['ch1','ch2','ch3','pk1', 'pk2','pk3','']
+
+
+dfc0300['ch1'].hist(bins = Bines, color = 'red', alpha = Opac, rwidth = Rwidth) 
+plt.yscale('log')
+
+##-------------ANÁLISIS DATOS "l1_v5_07_29_04h00.cal"---------------------------------
+
+
+dfc0400=pd.read_csv('l1_v5_characatito_nogps_2022_07_29_04h00.cal', sep=' ',skiprows=6)
+dfc0400.columns=['ch1','ch2','ch3','pk1', 'pk2','pk3','']
+
+
+dfc0400['ch1'].hist(bins = Bines, color = 'blue', alpha = Opac, rwidth = Rwidth) 
+plt.yscale('log')
+
+##-------------ANÁLISIS DATOS "l1_v5_07_29_05h00.cal"---------------------------------
+
+
+dfc0500=pd.read_csv('l1_v5_characatito_nogps_2022_07_29_05h00.cal', sep=' ',skiprows=6)
+dfc0500.columns=['ch1','ch2','ch3','pk1', 'pk2','pk3','']
+
+
+dfc0500['ch1'].hist(bins = Bines, color = 'red', alpha = Opac, rwidth = Rwidth) 
+plt.yscale('log')
+
+##-------------ANÁLISIS DATOS "l1_v5_07_29_06h00.cal"---------------------------------
+
+
+dfc0600=pd.read_csv('l1_v5_characatito_nogps_2022_07_29_06h00.cal', sep=' ',skiprows=6)
+dfc0600.columns=['ch1','ch2','ch3','pk1', 'pk2','pk3','']
+
+
+dfc0600['ch1'].hist(bins = Bines, color = 'blue', alpha = Opac, rwidth = Rwidth) 
+plt.yscale('log')
+
+##-------------ANÁLISIS DATOS "l1_v5_07_29_07h00.cal"---------------------------------
+
+
+dfc0700=pd.read_csv('l1_v5_characatito_nogps_2022_07_29_07h00.cal', sep=' ',skiprows=6)
+dfc0700.columns=['ch1','ch2','ch3','pk1', 'pk2','pk3','']
+
+
+dfc0700['ch1'].hist(bins = Bines, color = 'red', alpha = Opac, rwidth = Rwidth) 
+plt.yscale('log')
+
+##-------------ANÁLISIS DATOS "l1_v5_07_29_08h00.cal"---------------------------------
+
+
+dfc0800=pd.read_csv('l1_v5_characatito_nogps_2022_07_29_08h00.cal', sep=' ',skiprows=6)
+dfc0800.columns=['ch1','ch2','ch3','pk1', 'pk2','pk3','']
+
+
+dfc0800['ch1'].hist(bins = Bines, color = 'blue', alpha = Opac, rwidth = Rwidth) 
+plt.yscale('log')
+
+##-------------ANÁLISIS DATOS "l1_v5_07_29_09h00.cal"---------------------------------
+
+
+dfc0900=pd.read_csv('l1_v5_characatito_nogps_2022_07_29_09h00.cal', sep=' ',skiprows=6)
+dfc0900.columns=['ch1','ch2','ch3','pk1', 'pk2','pk3','']
+
+
+dfc0900['ch1'].hist(bins = Bines, color = 'red', alpha = Opac, rwidth = Rwidth) 
+plt.yscale('log')
+
+##-------------ANÁLISIS DATOS "l1_v5_07_29_10h00.cal"---------------------------------
+
+
+dfc1000=pd.read_csv('l1_v5_characatito_nogps_2022_07_29_10h00.cal', sep=' ',skiprows=6)
+dfc1000.columns=['ch1','ch2','ch3','pk1', 'pk2','pk3','']
+
+
+dfc1000['ch1'].hist(bins = Bines, color = 'blue', alpha = Opac, rwidth = Rwidth) 
+plt.yscale('log')
+
+##-------------ANÁLISIS DATOS "l1_v5_07_29_11h00.cal"---------------------------------
+
+
+dfc1100=pd.read_csv('l1_v5_characatito_nogps_2022_07_29_11h00.cal', sep=' ',skiprows=6)
+dfc1100.columns=['ch1','ch2','ch3','pk1', 'pk2','pk3','']
+
+
+dfc1100['ch1'].hist(bins = Bines, color = 'red', alpha = Opac, rwidth = Rwidth) 
+plt.yscale('log')
+
+##-------------ANÁLISIS DATOS "l1_v5_07_29_12h00.cal"---------------------------------
+
+
+dfc1200=pd.read_csv('l1_v5_characatito_nogps_2022_07_29_12h00.cal', sep=' ',skiprows=6)
+dfc1200.columns=['ch1','ch2','ch3','pk1', 'pk2','pk3','']
+
+
+dfc1200['ch1'].hist(bins = Bines, color = 'blue', alpha = Opac, rwidth = Rwidth) 
+plt.yscale('log')
+
+
+##--------ANALISIS DATOS "l1_v5_07_29_13h00.cal"--------------------------------------
+
+
+dfc1300=pd.read_csv('l1_v5_characatito_nogps_2022_07_29_13h00.cal', sep=' ',skiprows=6)
+dfc1300.columns=['ch1','ch2','ch3','pk1', 'pk2','pk3','']
+
+
+dfc1300['ch1'].hist(bins = Bines, color = 'red', alpha = Opac, rwidth = Rwidth)  
+plt.yscale('log')
+
+##-------------ANÁLISIS DATOS "l1_v5_07_29_14h00.cal"---------------------------------
+
+
+dfc1400=pd.read_csv('l1_v5_characatito_nogps_2022_07_29_14h00.cal', sep=' ',skiprows=6)
+dfc1400.columns=['ch1','ch2','ch3','pk1', 'pk2','pk3','']
+
+
+dfc1400['ch1'].hist(bins = Bines, color = 'blue', alpha = Opac, rwidth = Rwidth)  
+plt.yscale('log')
+
+##-------------ANÁLISIS DATOS "l1_v5_07_29_15h00.cal"---------------------------------
+
+
+dfc1500=pd.read_csv('l1_v5_characatito_nogps_2022_07_29_15h00.cal', sep=' ',skiprows=6)
+dfc1500.columns=['ch1','ch2','ch3','pk1', 'pk2','pk3','']
+
+
+dfc1500['ch1'].hist(bins = Bines, color = 'red', alpha = Opac, rwidth = Rwidth) 
+plt.yscale('log')
+
+##-------------ANÁLISIS DATOS "l1_v5_07_29_16h00.cal"---------------------------------
+
+
+dfc1600=pd.read_csv('l1_v5_characatito_nogps_2022_07_29_16h00.cal', sep=' ',skiprows=6)
+dfc1600.columns=['ch1','ch2','ch3','pk1', 'pk2','pk3','']
+
+
+dfc1600['ch1'].hist(bins = Bines, color = 'blue', alpha = Opac, rwidth = Rwidth)  
+plt.yscale('log')
+
+##-------------ANÁLISIS DATOS "l1_v5_07_29_17h00.cal"---------------------------------
+
+
+dfc1700=pd.read_csv('l1_v5_characatito_nogps_2022_07_29_17h00.cal', sep=' ',skiprows=6)
+dfc1700.columns=['ch1','ch2','ch3','pk1', 'pk2','pk3','']
+
+
+dfc1700['ch1'].hist(bins = Bines, color = 'red', alpha = Opac, rwidth = Rwidth)  
+plt.yscale('log')
+
+##-------------ANÁLISIS DATOS "l1_v5_07_29_18h00.cal"---------------------------------
+
+
+dfc1800=pd.read_csv('l1_v5_characatito_nogps_2022_07_29_18h00.cal', sep=' ',skiprows=6)
+dfc1800.columns=['ch1','ch2','ch3','pk1', 'pk2','pk3','']
+
+
+dfc1800['ch1'].hist(bins = Bines, color = 'blue', alpha = Opac, rwidth = Rwidth)  
+plt.yscale('log')
+
+##-------------ANÁLISIS DATOS "l1_v5_07_29_19h00.cal"---------------------------------
+
+
+dfc1900=pd.read_csv('l1_v5_characatito_nogps_2022_07_29_19h00.cal', sep=' ',skiprows=6)
+dfc1900.columns=['ch1','ch2','ch3','pk1', 'pk2','pk3','']
+
+
+dfc1900['ch1'].hist(bins = Bines, color = 'red', alpha = Opac, rwidth = Rwidth)  
+plt.yscale('log')
+
+##-------------ANÁLISIS DATOS "l1_v5_07_29_20h00.cal"---------------------------------
+
+
+dfc2000=pd.read_csv('l1_v5_characatito_nogps_2022_07_29_20h00.cal', sep=' ',skiprows=6)
+dfc2000.columns=['ch1','ch2','ch3','pk1', 'pk2','pk3','']
+
+
+dfc2000['ch1'].hist(bins = Bines, color = 'blue', alpha = Opac, rwidth = Rwidth) 
+plt.yscale('log')
+
+##-------------ANÁLISIS DATOS "l1_v5_07_29_21h00.cal"---------------------------------
+
+
+dfc2100=pd.read_csv('l1_v5_characatito_nogps_2022_07_29_21h00.cal', sep=' ',skiprows=6)
+dfc2100.columns=['ch1','ch2','ch3','pk1', 'pk2','pk3','']
+
+
+dfc2100['ch1'].hist(bins = Bines, color = 'red', alpha = Opac, rwidth = Rwidth) 
+plt.yscale('log')
+
+##-------------ANÁLISIS DATOS "l1_v5_07_29_22h00.cal"---------------------------------
+
+
+dfc2200=pd.read_csv('l1_v5_characatito_nogps_2022_07_29_22h00.cal', sep=' ',skiprows=6)
+dfc2200.columns=['ch1','ch2','ch3','pk1', 'pk2','pk3','']
+
+
+dfc2200['ch1'].hist(bins = Bines, color = 'blue', alpha = Opac, rwidth = Rwidth) 
+plt.yscale('log')
+
+##-------------ANÁLISIS DATOS "l1_v5_07_29_23h00.cal"---------------------------------
+
+
+dfc2300=pd.read_csv('l1_v5_characatito_nogps_2022_07_29_23h00.cal', sep=' ',skiprows=6)
+dfc2300.columns=['ch1','ch2','ch3','pk1', 'pk2','pk3','']
+
+
+dfc2300['ch1'].hist(bins = Bines, color = 'red', alpha = Opac, rwidth = Rwidth) 
+plt.yscale('log')
+
+##-------------ANÁLISIS DATOS "l1_v5_07_30_00h00.cal"---------------------------------
+
+
+dfc00000=pd.read_csv('l1_v5_characatito_nogps_2022_07_30_00h00.cal', sep=' ',skiprows=6)
+dfc00000.columns=['ch1','ch2','ch3','pk1', 'pk2','pk3','']
+
+
+dfc00000['ch1'].hist(bins = Bines, color = 'blue', alpha = Opac, rwidth = Rwidth)  
+plt.yscale('log')
+
+##-------------ANÁLISIS DATOS "l1_v5_07_30_*.cal"---------------------------------
+
+
+dfc00000=pd.read_csv('l1_v5_characatito_nogps_2022_07_30_01h00.cal', sep=' ',skiprows=6)
+dfc00000.columns=['ch1','ch2','ch3','pk1', 'pk2','pk3','']
+
+
+dfc00000['ch1'].hist(bins = Bines, color = 'blue', alpha = Opac, rwidth = Rwidth)  
+plt.yscale('log')
+
+dfc00000=pd.read_csv('l1_v5_characatito_nogps_2022_07_30_02h00.cal', sep=' ',skiprows=6)
+dfc00000.columns=['ch1','ch2','ch3','pk1', 'pk2','pk3','']
+
+
+dfc00000['ch1'].hist(bins = Bines, color = 'blue', alpha = Opac, rwidth = Rwidth)  
+plt.yscale('log')
+
+dfc00000=pd.read_csv('l1_v5_characatito_nogps_2022_07_30_03h00.cal', sep=' ',skiprows=6)
+dfc00000.columns=['ch1','ch2','ch3','pk1', 'pk2','pk3','']
+
+
+dfc00000['ch1'].hist(bins = Bines, color = 'blue', alpha = Opac, rwidth = Rwidth)  
+plt.yscale('log')
+
+dfc00000=pd.read_csv('l1_v5_characatito_nogps_2022_07_30_04h00.cal', sep=' ',skiprows=6)
+dfc00000.columns=['ch1','ch2','ch3','pk1', 'pk2','pk3','']
+
+
+dfc00000['ch1'].hist(bins = Bines, color = 'blue', alpha = Opac, rwidth = Rwidth)  
+plt.yscale('log')
+
+dfc00000=pd.read_csv('l1_v5_characatito_nogps_2022_07_30_05h00.cal', sep=' ',skiprows=6)
+dfc00000.columns=['ch1','ch2','ch3','pk1', 'pk2','pk3','']
+
+
+dfc00000['ch1'].hist(bins = Bines, color = 'blue', alpha = Opac, rwidth = Rwidth)  
+plt.yscale('log')
+
+dfc00000=pd.read_csv('l1_v5_characatito_nogps_2022_07_30_06h00.cal', sep=' ',skiprows=6)
+dfc00000.columns=['ch1','ch2','ch3','pk1', 'pk2','pk3','']
+
+
+dfc00000['ch1'].hist(bins = Bines, color = 'blue', alpha = Opac, rwidth = Rwidth)  
+plt.yscale('log')
+
+dfc00000=pd.read_csv('l1_v5_characatito_nogps_2022_07_30_07h00.cal', sep=' ',skiprows=6)
+dfc00000.columns=['ch1','ch2','ch3','pk1', 'pk2','pk3','']
+
+
+dfc00000['ch1'].hist(bins = Bines, color = 'blue', alpha = Opac, rwidth = Rwidth)  
+plt.yscale('log')
+
+dfc00000=pd.read_csv('l1_v5_characatito_nogps_2022_07_30_08h00.cal', sep=' ',skiprows=6)
+dfc00000.columns=['ch1','ch2','ch3','pk1', 'pk2','pk3','']
+
+
+dfc00000['ch1'].hist(bins = Bines, color = 'blue', alpha = Opac, rwidth = Rwidth)  
+plt.yscale('log')
+
+plt.savefig("superposicion.png", dpi=300)
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