def musica(cat, carac_1, carac_2, min_1, max_1, min_2, max_2):
m = cat["features"]
m_reps = mp.newMap(4000, maptype='PROBING', loadfactor=0.5)
m_tracks = om.newMap(omaptype='RBT', comparefunction=compareValue)
a = mp.get(m, carac_1)
m_1 = me.getValue(a)
l_1 = om.values(m_1, min_1, max_1)
for lists in lt.iterator(l_1):
for e in lt.iterator(lists):
mp.put(m_reps, e["track_id"], "")
b = mp.get(m, carac_2)
m_2 = me.getValue(b)
l_2 = om.values(m_2, min_2, max_2)
for lists in lt.iterator(l_2):
for e in lt.iterator(lists):
if mp.contains(m_reps, e["track_id"]):
om.put(m_tracks, e["track_id"], e)
n_tracks = om.size(m_tracks)
if n_tracks >= 5:
num = 5
else:
num = n_tracks
random_tracks = random.sample(range(0, n_tracks), num)
keys = lt.newList(datastructure="ARRAY_LIST")
l_tracks = lt.newList(datastructure="ARRAY_LIST")
for n in random_tracks:
key = om.select(m_tracks, n)
lt.addLast(keys, key)
for a in lt.iterator(keys):
rep = om.get(m_tracks, a)
rep_info = me.getValue(rep)
lt.addLast(l_tracks, rep_info)
return (n_tracks, l_tracks)
def newCatalog():
"""Crea un catalogo con las siguientes entradas:
1. halfList: Una lista con las entradas del primer archivo csv.
2. completeList: Una lista de todas las entradas que estan en ambos archivos csv combinadas
3. Una lista de diccionarios con los artistas y el numero de veces que cada uno se repite en el completeList
4. Una lista de diccionarios con las pistas y el numero de veces que cada una se repite en el completeList
5. Un RBT creado para ordenar las entradas del primer archivo csv y poderlas combinar con el segundo archivo."""
catalog = {
"halfList": None,
"completeList": None,
"artistList": None,
"trackList": None,
"trackMap": None
}
catalog["halfList"] = lt.newList("SINGLE_LINKED")
catalog["completeList"] = lt.newList("SINGLE_LINKED")
catalog["artistList"] = lt.newList("SINGLE_LINKED")
catalog["trackList"] = lt.newList("SINGLE_LINKED")
catalog["trackMap"] = om.newMap(omaptype='RBT')
return catalog
def ServiciosPorCompania(analyzer):
ServiciosXCompania = om.newMap(omaptype='RBT', comparefunction=compareIds)
total_servicios = 0
nombres = om.keySet(analyzer['companias'])
# print(nombres)
iter = it.newIterator(nombres)
while it.hasNext(iter):
cada_compania = it.next(iter)
nombre_compania = om.get(analyzer['companias'], cada_compania)
# print(nombre_compania)
if nombre_compania['key'] is not None:
servicios = me.getValue(nombre_compania)['servicios']
# print(servicios)
if servicios is not None:
num_servicios = m.size(servicios)
total_servicios += num_servicios
om.put(ServiciosXCompania, num_servicios, cada_compania)
# print(nombre_compania['key'], num_servicios)
# print(taxis)
# print("TOTAL SERVICIOS: "+ str(total_servicios))
return (ServiciosXCompania)
def newAnalyzer2():
""" Inicializa el analizador
Crea una lista vacia para guardar todos los tracks
Se crean indices (Maps) por los siguientes criterios:
-Fechas
Retorna el analizador inicializado.
""""instrumentalness","liveness","speechiness","danceability","valence"
analyzer = {'tracks': None,
'tempo':None,
'artist_id':None,
'track_id':None,
'cantidad':None,
'vader_avg':None #Para poder usarlos, necesitamos tenerlos como arboles para que se identifiquen (aparezcan en los tracks)
}
analyzer['tracks'] = lt.newList('SINGLE_LINKED', compareIds)
for parte in analyzer:
if parte!='tracks':
analyzer[parte]=om.newMap(omaptype='RBT',
comparefunction=compareTracks)
return analyzer
def Arbolde(catalog, Pistas, criterio):
content = catalog['Content']
characteristic = mp.get(content, criterio)
arbol = me.getValue(characteristic)
if arbol is None:
arbol = om.newMap(omaptype='RBT', comparefunction=compareValue)
keyset = mp.keySet(Pistas)
for num in range(0, lt.size(keyset)):
llave = lt.getElement(keyset, num)
pair = mp.get(Pistas, llave)
if pair != None:
cancion = me.getValue(pair)
val_crit = float(cancion[criterio])
entry = om.get(arbol, val_crit)
if entry is None:
songs = lt.newList()
lt.addLast(songs, cancion)
om.put(arbol, val_crit, songs)
else:
lt.addLast(me.getValue(entry), cancion)
mp.put(content, criterio, arbol)
return arbol
def newAnalyzer():
analyzer = {
'artists': None,
'info': None,
'tracks': None,
'created_at': None,
'instrumentalness': None,
'acousticness': None,
'liveness': None,
'speechiness': None,
'energy': None,
'danceability': None,
'valence': None,
'tempo': None
}
analyzer['info'] = mp.newMap(130000, maptype='PROBING')
analyzer['tracks'] = lt.newList('SINGLE_LINKED')
analyzer['artists'] = lt.newList('SINGLE_LINKED')
for car in analyzer:
if car != 'tracks' and car != 'info' and car != 'artists':
analyzer[car] = om.newMap(omaptype='RBT', comparefunction=compare)
return analyzer
def getEventsByRangeTempoReturn(analyzer, criteria, initial, final):
'''
Retorna el tempo de los eventos organizados en un arbol RBT
dado un criterio y rango en el mismo,
buscándolos en un árbol
Args:
analyzer: Analizador de eventos
criteria: Llave del analyzer a analizar
initial: Inicio del rango
final: Fin del rango
'''
lst = om.values(analyzer[criteria], initial, final)
minimap = {'tempo_map': None}
minimap['tempo_map'] = om.newMap(omaptype='RBT')
for lstevents in lt.iterator(lst):
for soundtrackyourtimeline in lt.iterator(lstevents['events']):
addEventOnOrderedRBTMap(minimap,
float(soundtrackyourtimeline['tempo']),
soundtrackyourtimeline, 'tempo_map')
return minimap
def addTracksByHourTempo(analyzer, track):
hora = hour_int(track)
tempo = float(track['tempo'])
arbol_hora = analyzer['EvByHour'] #arbol principal
entry_hora = om.get(arbol_hora, hora) #llave-valor con hora
if entry_hora is not None:
arbol_tempo = me.getValue(entry_hora)
entry_tempo = om.get(arbol_tempo, tempo)
if entry_tempo is not None:
estructura = me.getValue(entry_tempo)
lt.addLast(estructura['mapa_completo'], track)
mp.put(estructura['mapa_unicos'], track['track_id'], track)
om.put(arbol_tempo, tempo, estructura)
else:
estructura = {
'mapa_completo':
lt.newList(datastructure="SINGLE_LINKED",
cmpfunction=compareTracks),
'mapa_unicos':
mp.newMap(maptype='PROBING', comparefunction=cmpByCarac)
}
lt.addLast(estructura['mapa_completo'], track)
mp.put(estructura['mapa_unicos'], track['track_id'], track)
om.put(arbol_tempo, tempo, estructura)
else:
arbol_tempo = om.newMap(omaptype='RBT', comparefunction=cmpInts)
estructura = {
'mapa_completo':
lt.newList(datastructure="SINGLE_LINKED",
cmpfunction=compareTracks),
'mapa_unicos':
mp.newMap(maptype='PROBING', comparefunction=cmpByCarac)
}
lt.addLast(estructura['mapa_completo'], track)
mp.put(estructura['mapa_unicos'], track['track_id'], track)
om.put(arbol_tempo, tempo, estructura)
om.put(arbol_hora, hora, arbol_tempo)
return None
def newAnalyzer():
""" Inicializa el analizador
Crea una lista vacia para guardar todos los crimenes
Se crean indices (Maps) por los siguientes criterios:
-Fechas
Retorna el analizador inicializado.
"""
analyzer = {
'events': None,
'artists': None,
'tracks': None,
'sentiments': None,
'hashtagsbytrack': None
}
analyzer['events'] = lt.newList('ARRAY_LIST')
analyzer['sentiments'] = lt.newList('ARRAY_LIST')
analyzer['hashtagsbytrack'] = mp.newMap(31000,
maptype='PROBING',
comparefunction=None)
analyzer['artists'] = mp.newMap(11000,
maptype='PROBING',
comparefunction=None)
analyzer['tracks'] = mp.newMap(31000,
maptype='PROBING',
comparefunction=None)
for content in contenttuple:
analyzer[content] = om.newMap(omaptype='RBT',
comparefunction=compareFloat)
return analyzer
def newAnalyzer():
bikes = {
"Stations": None,
"Trips": None,
"grafo": None,
'paths': None,
"table": None
}
bikes["grafo"] = gr.newGraph(datastructure="ADJ_LIST",
directed=True,
size=1000,
comparefunction=compareStations)
bikes["topsalida"] = iminpq.newIndexMinPQ(cmpfunction=cmpimin)
bikes["topllegada"] = iminpq.newIndexMinPQ(cmpfunction=cmpimin)
bikes["topuso"] = iminpq.newIndexMinPQ(cmpfunction=cmpimin)
bikes["stationtree"] = om.newMap(omaptype='',
comparefunction=comparebycoord)
bikes["tablesalida"] = m.newMap(maptype='', comparefunction=compareIds)
bikes["tablellegada"] = m.newMap(maptype='', comparefunction=compareIds)
bikes["mayoressalida"] = m.newMap(maptype='', comparefunction=compareIds)
bikes["mayoresllegada"] = m.newMap(maptype='', comparefunction=compareIds)
bikes["names"] = m.newMap(maptype='', comparefunction=compareIds)
bikes["BikesIDs"] = m.newMap(50000,
maptype='PROBING',
loadfactor=0.5,
comparefunction=compareIds)
bikes["ageVertexAHash"] = m.newMap(maptype='', comparefunction=compareIds)
bikes["ageVertexBHash"] = m.newMap(maptype='', comparefunction=compareIds)
bikes["stationVertexAHash"] = m.newMap(maptype='',
comparefunction=compareIds)
bikes["stationVertexBHash"] = m.newMap(maptype='',
comparefunction=compareIds)
bikes["ageTrips"] = m.newMap(maptype='', comparefunction=compareIds)
bikes["agesA"] = lt.newList('SINGLE_LINKED')
bikes["agesB"] = lt.newList('SINGLE_LINKED')
return bikes
def getCar(analyzer, car: str):
datos = analyzer['eventos']
newTree = om.newMap(omaptype='RBT', comparefunction=cmpCarValue)
for audio in lt.iterator(datos):
valor = audio[car]
entry = om.get(newTree, valor)
if not entry:
lista = lt.newList("ARRAY_LIST")
lt.addLast(lista, audio)
om.put(newTree, valor, lista)
else:
lt.addLast(entry['value'], audio)
return newTree
def getEventsByTimeRange(analyzer, initialValue, finalValue):
"""
Retorna el número de eventos y el map de eventos en un rango determinado
de tiempo en horas, minutos y segundos (%H:%M:%S)
"""
map = om.newMap('RBT')
initialTime = datetime.datetime.strptime(initialValue, '%H:%M:%S').time()
finalTime = datetime.datetime.strptime(finalValue, '%H:%M:%S').time()
lstevents = om.values(analyzer['time'], initialTime, finalTime)
totalevents = 0
for lstevents in lt.iterator(lstevents):
totalevents += lt.size(lstevents['events'])
for event in lt.iterator(lstevents['events']):
key = float(event['tempo'])
existkey = om.contains(map, key)
if existkey:
entry = om.get(map, key)
value = me.getValue(entry)
else:
value = newValue(key)
om.put(map, key, value)
lt.addLast(value['events'], event)
return map, totalevents
def newCatalog():
"""
Inicializa el catalogo crea una lista vacia para guardar todas las musicas
Se crean indices (Maps) por los siguientes criterios:
-Caracterista de contenido
-Genero de la musica
-Fecha de la musica
Retorna el catalogo inicializado.
"""
catalog = {
'videosContext': None,
'caraContenido': None,
'musicalGenero': None,
'fechaMusica': None
}
catalog['videosContext'] = lt.newList('ARRAY_LIST')
catalog['caraContenido'] = mp.newMap(30, maptype='PROBING', loadfactor=0.4)
catalog['musicaGenero'] = mp.newMap(30, maptype='PROBING', loadfactor=0.4)
catalog['fechaMusica'] = om.newMap('RBT')
return catalog
def newAnalyzer():
Taxis = {"Hash": None}
Taxis["Hash"] = m.newMap(maptype='', comparefunction=compareStopIds)
Taxis["lst"] = [
"00:00", "00:15", "00:30", "00:45", "01:00", "01:15", "01:30", "01:45",
"02:00", "02:15", "02:30", "02:45", "03:00", "03:15", "03:30", "03:45",
"04:00", "04:15", "04:30", "04:45", "05:00", "05:15", "05:30", "5:45",
"06:00", "06:15", "06:30", "06:45", "07:00", "07:15", "07:30", "07:45",
"08:00", "08:15", "08:30", "08:45", "09:00", "09:15", "09:30", "09:45",
"10:00", "10:15", "10:30", "10:45", "11:00", "11:15", "11:30", "11:45",
"12:00", "12:15", "12:30", "12:45", "13:00", "13:15", "13:30", "13:45",
"14:00", "14:15", "14:30", "14:45", "15:00", "15:15", "15:30", "15:45",
"16:00", "16:15", "16:30", "16:45", "17:00", "17:15", "17:30", "17:45",
"18:00", "18:15", "18:30", "18:45", "19:00", "19:15", "19:30", "19:45",
"20:00", "20:15", "20:30", "20:45", "21:00", "21:15", "21:30", "21:45",
"22:00", "22:15", "22:30", "22:45", "23:00", "23:15", "23:30", "23:45"
]
Taxis["dateIndex"] = om.newMap(omaptype='BST',
comparefunction=compareDates)
Taxis["Brand"] = m.newMap(maptype='', comparefunction=compareStopIds)
Taxis["ids"] = m.newMap(maptype='', comparefunction=compareStopIds)
Taxis["afiliado"] = imaxpq.newIndexMaxPQ(cmpimin)
Taxis["servicio"] = imaxpq.newIndexMaxPQ(cmpimin)
return Taxis
def top3salida(analyzer):
outree = om.newMap(omaptype="RBT", comparefunction=compareIds)
pqiterator = it.newIterator(vertexNames(analyzer))
while it.hasNext(pqiterator):
vert = int(it.next(pqiterator))
salidas = gr.outdegree(analyzer["graph"], str(vert))
if not om.contains(outree, salidas):
om.put(outree, salidas, str(vert))
else:
A = om.get(outree, salidas)
B = me.getValue(A)
om.put(outree, salidas, str(B)+","+str(vert))
estaciones = lt.newList(datastructure="ARRAY_LIST")
while lt.size(estaciones) < 3:
val = om.get(outree, om.maxKey(outree))
val1 = me.getValue(val)
mayorsalida = val1.split(",")
for i in mayorsalida:
if lt.size(estaciones) < 3:
K = m.get(analyzer["nameIndex"], i)
L = me.getValue(K)
lt.addLast(estaciones, L)
om.deleteMax(outree)
return estaciones
def companyRanking(analyzer, M):
companies = om.keySet(analyzer["companiesRanking"])
taxis = om.valueSet(analyzer["companiesRanking"])
mapa = om.newMap(omaptype="BST", comparefunction=cmpCompanies)
for i in range(lt.size(companies)):
empresa = lt.getElement(companies, i)
numtaxi = lt.getElement(taxis, i)
if om.contains(mapa, numtaxi) == True:
infonum = om.get(mapa, numtaxi)
empresas = infonum["value"]
lt.addLast(empresas, empresa)
om.put(mapa, numtaxi, empresas)
else:
empresas = lt.newList("ARRAY_LIST", None)
lt.addLast(empresas, empresa)
om.put(mapa, numtaxi, empresas)
tam = om.size(mapa)
tam = tam - 1
for i in range(M):
k = tam - i
emp = om.select(mapa, k)
pareja = om.get(mapa, emp)
lista = pareja["value"]
numt = lista["elements"]
info = str(tam - k + 1) + ". " + str(numt[0]) + ": " + str(emp)
print(info)
result = ".."
return result
def caracterizarrep(cat, carac, minimo, maximo):
m = cat["features"]
a = mp.get(m, carac)
m_carac = me.getValue(a)
l_reps = om.values(m_carac, minimo, maximo)
t_artists = om.newMap(omaptype='RBT', comparefunction=compareValue)
num_reps = 0
for lists in lt.iterator(l_reps):
size = lt.size(lists)
num_reps += size
for reps in lt.iterator(lists):
artist = reps["artist_id"]
if om.contains(t_artists, artist):
c = om.get(t_artists, artist)
l_artist = me.getValue(c)
else:
l_artist = lt.newList(datastructure="SINGLE_LINKED")
lt.addLast(l_artist, reps)
om.put(t_artists, artist, l_artist)
artists = om.size(t_artists)
return (num_reps, artists, t_artists)
def newAnalyzer():
""" Inicializa el analizador
"""
analyzer = {
"lista": None,
"mapcompany": None,
"taxids": None,
}
analyzer["lista"] = lt.newList("ARRAY_LIST")
analyzer["mapcompany"] = m.newMap(numelements=37,
maptype="CHAINING",
loadfactor=0.4,
comparefunction=comparecompany)
analyzer["MapaId"] = op.newMap(omaptype='RBT',
comparefunction=compareDates)
analyzer["taxids"] = m.newMap(numelements=37,
maptype="CHAINING",
loadfactor=0.4,
comparefunction=comparecompany)
analyzer['grafo'] = gr.newGraph(datastructure='ADJ_LIST',
directed=True,
size=300,
comparefunction=comparecompany)
return analyzer
def newMapOrdenado():
dicci = {}
dicci['instrumentalness'] = om.newMap(omaptype='BRT',
comparefunction=compareDates)
dicci['acousticness'] = om.newMap(omaptype='BRT',
comparefunction=compareDates)
dicci['liveness'] = om.newMap(omaptype='BRT', comparefunction=compareDates)
dicci['speechiness'] = om.newMap(omaptype='BRT',
comparefunction=compareDates)
dicci['energy'] = om.newMap(omaptype='BRT', comparefunction=compareDates)
dicci['danceability'] = om.newMap(omaptype='BRT',
comparefunction=compareDates)
dicci['valence'] = om.newMap(omaptype='BRT', comparefunction=compareDates)
return dicci
def __init__(self):
self.Route = orderedmap.newMap(comparefunction=cmp.compareDate)
def analyzer_context():
analyzer_context = {
"eventos": None,
"index": None,
"userid": None,
"track_id": None,
"instrumentalness": None,
"liveness": None,
"speechiness": None,
"danceability": None,
"valence": None,
"loudness": None,
"tempo": None,
"acousticness": None,
"energy": None,
"artist_id": None,
"created_at": None,
"time_zone": None
}
analyzer_context["eventos"] = m.newMap(numelements=63400,
prime=109345121,
maptype="CHAINING",
loadfactor=1,
comparefunction=comparer)
analyzer_context["index"] = om.newMap(omaptype="RBT",
comparefunction=compareDates)
analyzer_context["artist_id"] = om.newMap(omaptype="RBT",
comparefunction=compareDates)
analyzer_context["userid"] = om.newMap(omaptype="RBT",
comparefunction=compareDates)
analyzer_context["track_id"] = om.newMap(omaptype="RBT",
comparefunction=compareDates)
analyzer_context["created_at"] = om.newMap(omaptype="RBT",
comparefunction=compareTime)
analyzer_context["instrumentalness"] = om.newMap(
omaptype="RBT", comparefunction=compareLat)
analyzer_context["liveness"] = om.newMap(omaptype="RBT",
comparefunction=compareLiv)
analyzer_context["speechiness"] = om.newMap(omaptype="RBT",
comparefunction=compareLiv)
analyzer_context["danceability"] = om.newMap(omaptype="RBT",
comparefunction=compareLiv)
analyzer_context["valence"] = om.newMap(omaptype="RBT",
comparefunction=compareLiv)
analyzer_context["loudness"] = om.newMap(omaptype="RBT",
comparefunction=compareLiv)
analyzer_context["tempo"] = om.newMap(omaptype="RBT",
comparefunction=compareLiv)
analyzer_context["acousticness"] = om.newMap(omaptype="RBT",
comparefunction=compareLiv)
analyzer_context["energy"] = om.newMap(omaptype="RBT",
comparefunction=compareLiv)
analyzer_context["time_zone"] = om.newMap(omaptype="RBT",
comparefunction=compareZone)
return analyzer_context
def position():
return {
'latitude': orderedmap.newMap('RBT', Comparation.compareIdTree),
'longitude': orderedmap.newMap('RBT', Comparation.compareIdTree),
}
def newCatalogo():
""" Inicializa el analizador
Crea una lista vacia para guardar todos los crimenes
Se crean indices (Maps) por los siguientes criterios:
-Fechas
Retorna el analizador inicializado.
"""
catalogo = {'events': None, 'artistas': None, 'pistas': None}
catalogo['events'] = lt.newList('SINGLE_LINKED', compareIds)
catalogo['eventos_id'] = om.newMap(omaptype='RBT',
comparefunction=compareIds)
catalogo['artistas'] = om.newMap(omaptype='RBT',
comparefunction=compareIds)
catalogo['pistas'] = om.newMap(omaptype='RBT', comparefunction=compareIds)
catalogo['instrumentalness'] = om.newMap(omaptype='RBT',
comparefunction=compareIds)
catalogo['liveness'] = om.newMap(omaptype='RBT',
comparefunction=compareIds)
catalogo['speechiness'] = om.newMap(omaptype='RBT',
comparefunction=compareIds)
catalogo['danceability'] = om.newMap(omaptype='RBT',
comparefunction=compareIds)
catalogo['valence'] = om.newMap(omaptype='RBT', comparefunction=compareIds)
catalogo['loudness'] = om.newMap(omaptype='RBT',
comparefunction=compareIds)
catalogo['tempo'] = om.newMap(omaptype='RBT', comparefunction=compareIds)
catalogo['acousticness'] = om.newMap(omaptype='RBT',
comparefunction=compareIds)
catalogo['energy'] = om.newMap(omaptype='RBT', comparefunction=compareIds)
catalogo['mode'] = om.newMap(omaptype='RBT', comparefunction=compareIds)
catalogo['key'] = om.newMap(omaptype='RBT', comparefunction=compareIds)
catalogo['hashtags'] = om.newMap(omaptype='RBT', comparefunction=None)
catalogo['dates'] = om.newMap(omaptype='RBT', comparefunction=None)
return catalogo
def New_list():
lista = {"Accidentes": None, "Fechas": None, "Horas": None}
lista['Accidentes'] = lt.newList('SINGLE_LINKED', compareAccidentes)
lista['Fechas'] = om.newMap(omaptype='RBT', comparefunction=compararFechas)
lista['Horas'] = om.newMap(omaptype='RBT', comparefunction=compararHoras)
return lista
def newAnalyzer():
analyzer = {
'content_features': None,
'track_hashtag': None,
'Sentiment_values': None,
'instrumentalness': None,
'liveness': None,
'speechiness': None,
'danceability': None,
'valence': None,
'acousticness': None,
'energy': None,
'Nombre_generos': None
}
#Lists
analyzer['content_features'] = lt.newList('ARRAY_LIST')
analyzer['track_hashtag_lst'] = lt.newList('ARRAY_LIST')
#Binary Trees
#analyzer['content_features_req5'] = lt.newList('ARRAY_LIST')
analyzer['track_hashtag'] = lt.newList('ARRAY_LIST')
analyzer['Sentiment_values'] = lt.newList('ARRAY_LIST')
#Binary Trees
analyzer['time_of_event'] = om.newMap(omaptype='BST')
analyzer['instrumentalness'] = om.newMap(omaptype='BST')
analyzer['liveness'] = om.newMap(omaptype='BST')
analyzer['speechiness'] = om.newMap(omaptype='BST')
analyzer['danceability'] = om.newMap(omaptype='BST')
analyzer['valence'] = om.newMap(omaptype='BST')
analyzer['acousticness'] = om.newMap(omaptype='BST')
analyzer['energy'] = om.newMap(omaptype='BST')
analyzer['artistas'] = mp.newMap(numelements=65,
maptype='PROBING',
loadfactor=0.3)
analyzer['unique_tracks_init'] = mp.newMap(numelements=65,
maptype='PROBING',
loadfactor=0.3)
#Binary Trees para generos
analyzer['generos'] = om.newMap(omaptype='BST')
analyzer['Reggae'] = mp.newMap(numelements=65,
maptype='PROBING',
loadfactor=0.3)
analyzer['Down-tempo'] = mp.newMap(numelements=65,
maptype='PROBING',
loadfactor=0.3)
analyzer['Chill-out'] = mp.newMap(numelements=65,
maptype='PROBING',
loadfactor=0.3)
analyzer['Hip-hop'] = mp.newMap(numelements=65,
maptype='PROBING',
loadfactor=0.3)
analyzer['Jazz and Funk'] = mp.newMap(numelements=65,
maptype='PROBING',
loadfactor=0.3)
analyzer['Pop'] = mp.newMap(numelements=65,
maptype='PROBING',
loadfactor=0.3)
analyzer['R&B'] = mp.newMap(numelements=65,
maptype='PROBING',
loadfactor=0.3)
analyzer['Rock'] = mp.newMap(numelements=65,
maptype='PROBING',
loadfactor=0.3)
analyzer['Metal'] = mp.newMap(numelements=65,
maptype='PROBING',
loadfactor=0.3)
#Lista para generos
analyzer['Nombre_generos'] = lt.newList('ARRAY_LIST')
analyzer['generos_tempos'] = mp.newMap(numelements=65,
maptype='PROBING',
loadfactor=0.3)
#arbol binario hash table
analyzer['track_hashtag'] = om.newMap(omaptype='RBT')
analyzer['Sentiment_values'] = om.newMap(omaptype='BST')
analyzer['ferrari'] = mp.newMap(numelements=65,
maptype='PROBING',
loadfactor=0.3)
#Árbol Binario Hashtags
analyzer['track_hashtags'] = om.newMap(omaptype='BST')
#Árbol Binario Sentiment
analyzer['sentiment_values'] = om.newMap(omaptype='BST')
#Árbol Binario Llave Tempo Valor Género
analyzer['tempo_genero'] = om.newMap(omaptype='BST')
#Arbol Binario Completo Req 5
analyzer['hash_generos'] = om.newMap(omaptype='BST')
return analyzer
def tree():
tree = om.newMap(omaptype='BST')
return tree
def newAnalyzer():
""" Inicializa el analizador
Crea una lista vacia para guardar todos los crimenes
Se crean indices (Maps) por los siguientes criterios:
-Fechas
Retorna el analizador inicializado.
"""
analyzer = {
'events': None,
'dateIndex': None,
'sentiments': None,
'content': None,
}
analyzer['sentiments'] = mp.newMap(maptype='PROBING')
analyzer['content'] = mp.newMap(maptype="PROBING")
#Instrumentalness: Crea un tree con los valores
instrumentalnessTree = om.newMap(comparefunction=compareValues)
mp.put(analyzer['content'], 'instrumentalness', instrumentalnessTree)
#Liveness: Crea un tree con los valores
livenessTree = om.newMap(comparefunction=compareValues)
mp.put(analyzer['content'], 'liveness', livenessTree)
#Speechiness: Crea un tree con los valores
speechinessTree = om.newMap(comparefunction=compareValues)
mp.put(analyzer['content'], 'speechiness', speechinessTree)
#Danceability: Crea un tree con los valores
danceabilityTree = om.newMap(comparefunction=compareValues)
mp.put(analyzer['content'], 'danceability', danceabilityTree)
#Valence: Crea un tree con los valores
valenceTree = om.newMap(comparefunction=compareValues)
mp.put(analyzer['content'], 'valence', valenceTree)
#Loudness: Crea un tree con los valores
loudnessTree = om.newMap(comparefunction=compareValues)
mp.put(analyzer['content'], 'loudness', loudnessTree)
#Tempo: Crea un tree con los valores
tempoTree = om.newMap(comparefunction=compareValues)
mp.put(analyzer['content'], 'tempo', tempoTree)
#Acousticness: Crea un tree con los valores
acousticnessTree = om.newMap(comparefunction=compareValues)
mp.put(analyzer['content'], 'acousticness', acousticnessTree)
#Energy: Crea un tree con los valores
energyTree = om.newMap(comparefunction=compareValues)
mp.put(analyzer['content'], 'energy', energyTree)
analyzer['events'] = lt.newList("ARRAY_LIST", cmpfunction=compareIds2)
analyzer['track_id'] = om.newMap(omaptype='RBT')
analyzer['artists'] = om.newMap(omaptype='RBT',
comparefunction=compareIds1)
analyzer['created_at'] = om.newMap(omaptype='RBT',
comparefunction=compareTime)
analyzer['created_at-hashtag'] = om.newMap(omaptype='RBT',
comparefunction=compareTime)
return analyzer
def Req5(analyzer, time1, time2):
arbolHoras = analyzer["created_at"] #Sacamos el arbol organizado por horas
valoresHoras = om.values(
arbolHoras, time1,
time2) #Sacar los valores del arbol en el rango de horas
arbolPorTempo = om.newMap(omaptype="RBT", comparefunction=compareValues
) #Crear un arbol vacio organizado por tempo
for lista in lt.iterator(valoresHoras): #Recorrer la lista de listas
for evento in lt.iterator(lista): #Recorrer cada lista de eventos
tempo = float(evento['tempo']) #Abstraer el tempo del evento
existe = om.get(
arbolPorTempo,
tempo) #Revisar si el tempo ya fue creado en el arbol de tempo
if existe is None: #Si no esta creado
eventList = lt.newList(
'ARRAY_LIST',
cmpfunction=compareValues) # Crear una lista vacia
om.put(
arbolPorTempo, tempo,
eventList) #Agregar la lista al nodo de tempo respectivo
else: #Si está creado
eventList = me.getValue(existe) #Recuperar la lista del nodo
lt.addLast(
eventList, evento
) #Agregar el evento del rango de horas a la lista del respectivo nodo de tempo
mapaPorGenero = mp.newMap(maptype="PROBING") #Se crea un diccionario vacio
mapaPorSize = om.newMap(omaptype="RBT") #Se crea un arbol vacio
for genero in [
"Reggae", "Down-tempo", "Chill-out", "Hip-hop", "Jazz and Funk",
"Pop", "R&B", "Rock", "Metal"
]: #Por cada genero que existe
pistas, minTempo, maxTempo = pistasPorGenero(
arbolPorTempo, genero, None,
None) #Se saca la lista de listas con los eventos por genero
listaEventos = lt.newList(
"ARRAY_LIST", cmpfunction=compareIds2
) #Se crea una lista vacia donde se guardaran los eventos por genero
for lista in lt.iterator(pistas): #Se recorre la lista de listas
for evento in lt.iterator(
lista): #Se recorre cada lista de eventos
lt.addLast(listaEventos,
evento) #Se añade el evento a la lista del genero
sizeLista = lt.size(
listaEventos) #Se saca el tamaño de la lista del genero
mp.put(
mapaPorGenero, genero, listaEventos
) #Se agrega la lista de eventos al mapa con llave el respectivo genero
om.put(
mapaPorSize, sizeLista, genero
) #Se agrega al mapaPorSize el numero de reps del genero como llave y el genero como valor
mayorsize = om.maxKey(mapaPorSize)
entry = om.get(mapaPorSize, mayorsize)
mayorGenero = me.getValue(entry)
entry = mp.get(
mapaPorGenero,
mayorGenero) #Se sacan los eventos del genero que más se repitió
eventosMayorGenero = me.getValue(entry)
uniqueTracks = mp.newMap(
maptype="PROBING"
) #Se crea un mapa vacio para sacar los track_id unicos
for evento in lt.iterator(eventosMayorGenero):
existe = mp.get(uniqueTracks, evento["track_id"])
if existe is None:
lista_hashtags = lt.newList()
mp.put(uniqueTracks, evento["track_id"], lista_hashtags)
else:
lista_hashtags = me.getValue(existe)
for hashtag in lt.iterator(
evento["hashtag"]
): #Se recorre la lista de hashtags del evento
lt.addLast(
lista_hashtags,
hashtag) #Se agrega cada ht a la lista de ht de la pista única
llavesTrackId = mp.keySet(uniqueTracks)
totalUniqueTracks = lt.size(llavesTrackId)
random10Tracks = lt.subList(llavesTrackId,
random.randint(1, totalUniqueTracks - 10), 10)
sample = lt.newList()
for trackId in lt.iterator(random10Tracks):
entry = mp.get(uniqueTracks, trackId)
hashtagsEvento = me.getValue(entry)
vaderAvg = 0
totalHt = 0
for hashtag in lt.iterator(hashtagsEvento):
entry = mp.get(analyzer["sentiments"], hashtag.lower())
if entry is not None:
vader = me.getValue(entry)
if vader is not None:
vaderAvg += vader
totalHt += 1
if totalHt > 0:
vaderAvg /= totalHt
lt.addLast(sample, (trackId, totalHt, round(vaderAvg, 1)))
return om.size(arbolPorTempo), mapaPorSize, totalUniqueTracks, sample
def newCatalog():
""" Inicializa el catálogo
Retorna el catálogo inicializado.
"""
catalog = {
'RepsPor_instrumentalness': None,
}
#-----------------------------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------------------------
#Carga:
catalog['Artistas_Unicos'] = mp.newMap(maptype='PROBING')
catalog['Pistas_Unicas'] = mp.newMap(maptype='PROBING')
#-----------------------------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------------------------
#Req1:
#por cada caracteristica se hace un mapa ordenado que tiene los valores de la caracteristica como llaves y a la lista de
# las reproducciones con el valor correspondiente a la caracteristica
catalog['RepsPor_instrumentalness'] = om.newMap(
omaptype='RBT', comparefunction=MAPcompareDecimals)
catalog['RepsPor_liveness'] = om.newMap(omaptype='RBT',
comparefunction=MAPcompareDecimals)
catalog['RepsPor_speechiness'] = om.newMap(
omaptype='RBT', comparefunction=MAPcompareDecimals)
catalog['RepsPor_energy'] = om.newMap(omaptype='RBT',
comparefunction=MAPcompareDecimals)
catalog['RepsPor_acousticness'] = om.newMap(
omaptype='RBT', comparefunction=MAPcompareDecimals)
catalog['RepsPor_danceability'] = om.newMap(
omaptype='RBT', comparefunction=MAPcompareDecimals)
catalog['RepsPor_valence'] = om.newMap(omaptype='RBT',
comparefunction=MAPcompareDecimals)
#-----------------------------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------------------------
#Req 4
#Generos: Aqui se guardaran las reproducciones partidas por generos:
# es decir: esto es un mapa/hashTable donde una llave es un genero(nombre del genero) y su valor es una tupla:
# 1: una tupla de los limites del rango 2. la lista de reproducciones que segun su tempo/bpm corresponden a ese genero
#OJO una MISMA EXACTA REPRODUCCION puede estar en la lista de distintos generos porque puede pertenecer a mas de un genero a la vez
generos = mp.newMap(loadfactor=4.0)
mp.put(generos, 'Reggae',
((60, 90), lt.newList(datastructure='ARRAY_LIST')))
mp.put(generos, 'Down-Tempo',
((70, 100), lt.newList(datastructure='ARRAY_LIST')))
mp.put(generos, 'Chill-Out',
((90, 120), lt.newList(datastructure='ARRAY_LIST')))
mp.put(generos, 'Hip-Hop',
((85, 115), lt.newList(datastructure='ARRAY_LIST')))
mp.put(generos, 'Jazz and Funk',
((120, 125), lt.newList(datastructure='ARRAY_LIST')))
mp.put(generos, 'Pop',
((100, 130), lt.newList(datastructure='ARRAY_LIST')))
mp.put(generos, 'R&B', ((60, 80), lt.newList(datastructure='ARRAY_LIST')))
mp.put(generos, 'Rock',
((110, 140), lt.newList(datastructure='ARRAY_LIST')))
mp.put(generos, 'Metal',
((100, 160), lt.newList(datastructure='ARRAY_LIST')))
catalog['Generos'] = generos
#reproducciones totales:
catalog['Reproducciones_totales'] = mp.newMap(loadfactor=4.0)
#-----------------------------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------------------------
# REQ 5:
catalog['RepsPor_hora'] = om.newMap(omaptype='RBT',
comparefunction=MAPCompararHoras)
catalog['Hashtags'] = mp.newMap(maptype='PROBING')
return catalog
def newCatalog():
""" Inicializa el analizador
Crea una lista vacia para guardar todos los crimenes
Se crean indices (Maps) por los siguientes criterios:
-Fechas
Retorna el analizador inicializado.
"""
catalog = {
'eventos': None,
'energy': None,
'instrumentalness': None,
'danceability': None,
'tempo': None,
'acousticness': None,
'hashtags': None,
'time': None
}
catalog['eventos'] = lt.newList(datastructure='ARRAY_LIST')
catalog['energy'] = om.newMap(omaptype="RBT")
catalog['instrumentalness'] = om.newMap(omaptype="RBT")
catalog['danceability'] = om.newMap(omaptype="RBT")
catalog['tempo'] = om.newMap(omaptype="RBT")
catalog['acousticness'] = om.newMap(omaptype="RBT")
catalog['liveness'] = om.newMap(omaptype="RBT")
catalog['speechiness'] = om.newMap(omaptype="RBT")
catalog['valence'] = om.newMap(omaptype="RBT")
catalog['time'] = om.newMap(omaptype="RBT")
catalog["energy"] = om.newMap(omaptype="RBT")
catalog["hashtags"] = mp.newMap(maptype="PROBING", loadfactor=0.5)
catalog['hashtagsportrack'] = mp.newMap(maptype="PROBING", loadfactor=0.5)
catalog['tiempo'] = om.newMap(omaptype="RBT")
return catalog
