#!/usr/bin/env python
# coding: utf-8

# # A simple analysis of the ANTARES neutrino data
# 
# This example directly uses pyvo. For the full KM3NeT course see
# https://edu.km3net.de/lesson/a-simple-analysis-in-python/

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import pyvo as vo
import matplotlib.pyplot as plt


# The data is directly available from the KM3NeT VO server, which is searchable with ADQL (SQL-like query language)

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service = vo.dal.TAPService("http://vo.km3net.de/__system__/tap/run/tap")
resultset = service.search("SELECT * FROM ant20_01.main")


# ## distribution of number of hits

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nhits_array = []
i=0
while i < len(resultset): 
    for row in resultset:
        nhits = resultset['nhit', i ]
        i=i+1
        nhits_array.append(nhits)

plt.hist(nhits_array, bins=60 , range=[0,300] , label = "all_events")
plt.show()


# ## choose the best reconstructed events (angular_error<=0.2)

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nhits_array_gr = []
i=0
while i < len(resultset): 
    for row in resultset:
        beta = resultset['beta', i ]
        nhits = resultset['nhit', i ]
        i=i+1
        if beta<=0.2 :
            nhits_array_gr.append(nhits)


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plt.hist(nhits_array_gr, bins=60 , range=[0,300]  , label = "angular_error<=0.2")
plt.legend()
plt.show()


# ## Create a RA vs declination scatter plot

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ra_array = []
decl_array = []
i=0
while i < len(resultset): 
    for row in resultset:
        ra = resultset['ra', i ]
        decl = resultset['decl', i ]
        i=i+1
        ra_array.append(ra)
        decl_array.append(decl)

plt.scatter( ra_array , decl_array , s=5 )
plt.show()


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