def plotByUser(alias=["Todos"],
completado=True,
expList=['UmbralAngulosPiloto', 'UmbralParalelismoPiloto']):
from scripts.db import pandasUtilPiloto
from IPython.display import display
db = pandasUtilPiloto()
if alias == ["Todos"]:
users = db['alias'].unique()
else:
users = alias
for user in users:
userDb = db[db['alias'] == user]
userAlias = userDb.iloc[0]['alias']
if completado:
userDb = userDb[userDb['convergenciaFinalizada'] == True]
exps = userDb['expName'].unique()
for exp in exps:
if exp in expList:
plotConvergenciaVersionPiloto(userDb, exp)
def sessionStats():
from scripts.db import pandasUtilPiloto
from IPython.display import display
from scripts.general import fechaLocal
dbPiloto = pandasUtilPiloto()
sessionInstances = dbPiloto['sessionInstance'].unique()
for sessionInstance in sessionInstances:
dbPilotoBySession = dbPiloto[dbPiloto['sessionInstance'] == sessionInstance]
display ('El usuario ' + dbPilotoBySession.iloc[0]['alias'] + ' inicio sesion el ' + str(fechaLocal(sessionInstance)) + ' y juego ' + str(len(dbPilotoBySession['levelInstance'])) + ' niveles.')
def sessionStats():
from scripts.db import pandasUtilPiloto
from IPython.display import display
from scripts.general import fechaLocal
dbPiloto = pandasUtilPiloto()
sessionInstances = dbPiloto['sessionInstance'].unique()
for sessionInstance in sessionInstances:
dbPilotoBySession = dbPiloto[dbPiloto['sessionInstance'] ==
sessionInstance]
display('El usuario ' + dbPilotoBySession.iloc[0]['alias'] +
' inicio sesion el ' + str(fechaLocal(sessionInstance)) +
' y juego ' + str(len(dbPilotoBySession['levelInstance'])) +
' niveles.')
def plotByUser(alias=["Todos"], completado=True, expList=["UmbralAngulosPiloto", "UmbralParalelismoPiloto"]):
from scripts.db import pandasUtilPiloto
from IPython.display import display
db = pandasUtilPiloto()
if alias == ["Todos"]:
users = db["alias"].unique()
else:
users = alias
for user in users:
userDb = db[db["alias"] == user]
userAlias = userDb.iloc[0]["alias"]
if completado:
userDb = userDb[userDb["convergenciaFinalizada"] == True]
exps = userDb["expName"].unique()
for exp in exps:
if exp in expList:
plotConvergenciaVersionPiloto(userDb, exp)
def statByUser(alias=["Todos"],
completado=True,
expList=['UmbralAngulosPiloto', 'UmbralParalelismoPiloto']):
from scripts.db import pandasUtilPiloto
from IPython.display import display
from scripts.plots import plotConvergenciaVersionPiloto
import pandas as pd
db = pandasUtilPiloto()
if alias == ["Todos"]:
users = db['alias'].unique()
else:
users = alias
stats = pd.DataFrame()
for user in users:
userDb = db[db['alias'] == user]
if completado:
userDb = userDb[userDb['convergenciaFinalizada'] == True]
exps = userDb['expName'].unique()
for exp in exps:
if exp in expList:
expDb = userDb[userDb['expName'] == exp]
for index, row in expDb.iterrows():
#plotConvergenciaVersionPiloto(db.ix[[index]], exp, fromStadistics=True)
if exp == 'UmbralAngulosPiloto':
M = 3
if exp == 'UmbralParalelismoPiloto':
M = 2
aciertos = [
elemento['acertado'] for elemento in row['historial']
]
valores = [
elemento['estimulo']['desviacion']
for elemento in row['historial']
]
senales = [
elemento['estimulo']['nivelSenal']
for elemento in row['historial']
]
mean = sum(valores[-6:]) / 6
if mean < 0:
mean = -mean
# Creamos la entrada para el DataFrame
elemento = {}
elemento['userId'] = row['userId']
elemento['userAlias'] = row['alias']
elemento['expName'] = row['expName']
elemento['identificador'] = row['identificador']
elemento['levelInstance'] = row['levelInstance']
elemento['referencia'] = row['referencia']
elemento['serieSenal'] = [senales]
elemento['serieValores'] = [valores]
elemento['serieAciertos'] = [aciertos]
elemento['meanValue'] = mean
elemento['pValueCorrectas'], elemento[
'pValueFisher'] = condicion(aciertos, M)
if elemento['pValueCorrectas'] < 0.05:
elemento['deteccionCorrecta'] = True
else:
elemento['deteccionCorrecta'] = False
if elemento['pValueCorrectas'] > 0.9:
elemento['deteccionInCorrecta'] = True
else:
elemento['deteccionInCorrecta'] = False
if elemento['pValueFisher'] < 0.1:
elemento['evolucionDetectada'] = True
else:
elemento['evolucionDetectada'] = False
elemento['contar'] = 1
if elemento['expName'] == 'UmbralParalelismoPiloto':
idCategoria = 'Paralelismo '
if elemento['expName'] == 'UmbralAngulosPiloto':
idCategoria = 'Angulo '
idCategoria = idCategoria + str(
elemento['referencia']) + ' grados'
elemento['idCategoria'] = idCategoria
stats = pd.concat(
[stats, pd.DataFrame.from_dict(elemento)])
return stats
def statByUser (alias=["Todos"], completado=True, expList=['UmbralAngulosPiloto', 'UmbralParalelismoPiloto']):
from scripts.db import pandasUtilPiloto
from IPython.display import display
from scripts.plots import plotConvergenciaVersionPiloto
import pandas as pd
db = pandasUtilPiloto()
if alias == ["Todos"]:
users = db['alias'].unique()
else:
users = alias
stats = pd.DataFrame()
for user in users:
userDb = db[db['alias']==user]
if completado:
userDb = userDb[userDb['convergenciaFinalizada']==True]
exps = userDb['expName'].unique()
for exp in exps:
if exp in expList:
expDb = userDb[userDb['expName']==exp]
for index, row in expDb.iterrows():
#plotConvergenciaVersionPiloto(db.ix[[index]], exp, fromStadistics=True)
if exp == 'UmbralAngulosPiloto':
M=3
if exp == 'UmbralParalelismoPiloto':
M=2
aciertos = [elemento['acertado'] for elemento in row['historial']]
valores = [elemento['estimulo']['desviacion'] for elemento in row['historial']]
senales = [elemento['estimulo']['nivelSenal'] for elemento in row['historial']]
mean = sum (valores[-6:]) / 6
if mean < 0:
mean = - mean
# Creamos la entrada para el DataFrame
elemento = {}
elemento['userId'] = row['userId']
elemento['userAlias'] = row['alias']
elemento['expName'] = row['expName']
elemento['identificador'] = row['identificador']
elemento['levelInstance'] = row['levelInstance']
elemento['referencia'] = row['referencia']
elemento['serieSenal'] = [senales]
elemento['serieValores'] = [valores]
elemento['serieAciertos'] = [aciertos]
elemento['meanValue'] = mean
elemento['pValueCorrectas'], elemento['pValueFisher'] = condicion (aciertos,M)
if elemento['pValueCorrectas'] < 0.05:
elemento['deteccionCorrecta'] = True
else:
elemento['deteccionCorrecta'] = False
if elemento['pValueCorrectas'] > 0.9:
elemento['deteccionInCorrecta'] = True
else:
elemento['deteccionInCorrecta'] = False
if elemento['pValueFisher'] < 0.1:
elemento['evolucionDetectada'] = True
else:
elemento['evolucionDetectada'] = False
elemento['contar'] = 1
if elemento['expName'] == 'UmbralParalelismoPiloto':
idCategoria = 'Paralelismo '
if elemento['expName'] == 'UmbralAngulosPiloto':
idCategoria = 'Angulo '
idCategoria = idCategoria + str(elemento['referencia']) + ' grados'
elemento['idCategoria'] = idCategoria
stats = pd.concat([stats, pd.DataFrame.from_dict(elemento)])
return stats
