def myopic_exp0(db_url, criterion):
df = jbprep.sql2pandas(db_url, 'myopic_exp0', criterion)
df['experiment'] = 'myopic_exp0'
# order properly
SORTORDER = ['condition','counterbalance','workerid', 'round']
df = df.sort(SORTORDER)
# fields saved as lists, but actually only containing scalars
LO1NUMBER2SCALARFIELDS = {'samX', 'samY'}
df = jbprep.lo1number2scalar(df, LO1NUMBER2SCALARFIELDS)
# make fields with lists of numbers numpy arrays
LONUMBERFIELDS = {'d2locsX', 'd2locsY', 'pxObs', 'pyObs'}
df = jbprep.lonumbers2nparray(df, LONUMBERFIELDS)
# rename columns
OLDNEWCOLNAMES = {'xObs': 'obsX',
'yObs': 'obsY',
'pxObs': 'pobsX',
'pyObs': 'pobsY',
'LENSCALE': 'lenscale',
'NOISEVAR2': 'noisevar2',
'RNGSEED': 'rngseed',
'SIGVAR': 'sigvar'}
df.rename(columns=OLDNEWCOLNAMES, inplace=True)
# convert any improperly converted fields
FIELDTYPES = {'lenscale': 'float64',
'noisevar2': 'float64',
'rngseed': 'int64',
'sigvar': 'float64',
'condition': 'int64',
'counterbalance': 'int64',
'd2locsX': 'O',
'd2locsY': 'O',
'pobsX': 'O',
'pobsY': 'O',
'obsX': 'O',
'obsY': 'O',
'drillX': 'float64',
'drillY': 'float64',
'psamX': 'float64',
'psamY': 'float64',
'samX': 'float64',
'samY': 'float64',
'expScore': 'float64',
'nObs': 'int64',
'round': 'int64',
'roundGross': 'float64',
'roundNet': 'float64',
'workerid': 'O'}
df = jbprep.enforceFieldTypes(df, FIELDTYPES)
# convert condition to a factor
df.condition = df.condition.astype(str)
# something got goofed and psiTurk save pxObs and pyObs but not xObs and yObs.
# Have to hack a fix here instead
SCREENW = 1028
SCREENH = 784
GROUNDLINEY = SCREENH - SCREENH*0.9
YMIN = -3
YMAX = 3
df['obsX'] = df['pobsX'].apply(lambda row: jbprep.pix2mathX(row, SCREENW))
df['obsY'] = df['pobsY'].apply(lambda row:
jbprep.pix2mathY(row, SCREENH, GROUNDLINEY, YMIN, YMAX))
# remove extra info
# (1 drops along cols, 0 along rows)
DROPCOLS = ['uniqueid', 'hitid', 'assignmentid', 'pdrillX', 'pdrillY',
'pobsX', 'pobsY', 'psamX', 'psamY']
df = df.drop(DROPCOLS, 1)
return df
def chooseSam_noNorm(db_url, criterion):
################################
# PREPROCESSING DATA
################################
print 'extracting from db...'
df = jbprep.sql2pandas(db_url, 'chooseSam_noNorm', criterion)
df['experiment'] = 'chooseSam_noNorm'
print 'prepping df...'
# order properly
SORTORDER = ['condition','counterbalance','workerid', 'round']
df = df.sort(SORTORDER)
# need to change to new format, where each trial is a row
# (currently, each click is a row)
# all fields not here will be collapsed into one value for the trial
LONUMBERFIELDS = ['drillPX', 'drillPY', 'drillX', 'drillY', 'expScore',
'sampleInRound']
OLDNEWSAMSECNAMEPAIRS = {'drillPX': 'psamX',
'drillPY': 'psamY',
'drillX': 'samX',
'drillY': 'samY',
'expScore': 'samExpScore'}
def trialToOneRow(dfs, lonumberfields, oldnewsamsecnamepairs):
collapsedFields = {field: [dfs[field].values]
for field in lonumberfields}
collapsedTrial = DataFrame(collapsedFields)
collapsedTrial.rename(columns=oldnewsamsecnamepairs, inplace=True)
return collapsedTrial
minidfs = []
for workerid in unique(df.workerid):
dfw = df[df.workerid==workerid]
for trial in unique(dfw.round):
dft = dfw[dfw.round==trial]
dfs = dft[dft.roundSection=='sampling']
dfd = dft[dft.roundSection=='drilling']
dfsCollapsed = trialToOneRow(dfs, LONUMBERFIELDS, OLDNEWSAMSECNAMEPAIRS)
dftCollapsed = concat([dfd.reset_index(drop=True), dfsCollapsed], axis=1)
minidfs.append(dftCollapsed)
df = concat(minidfs).reset_index(drop=True)
# make fields with lists of numbers numpy arrays
# rename columns
OLDNEWCOLNAMES = {'drillPX': 'pdrillX',
'drillPY': 'pdrillY'}
df.rename(columns=OLDNEWCOLNAMES, inplace=True)
# convert condition to a factor
df.condition = df.condition.astype(str)
df.counterbalance = df.counterbalance.astype(str)
# convert any improperly converted fields
FIELDTYPES = {'gpsd': 'float64',
'roundRandSeed': 'int64',
'condition': 'str',
'counterbalance': 'str',
'drillX': 'float64',
'drillY': 'float64',
'samX': 'O',
'samY': 'O',
'expScore': 'float64',
'samExpScore': 'O',
'round': 'int64',
# 'roundGross': 'float64',
# 'roundNet': 'float64',
'workerid': 'O'}
df = jbprep.enforceFieldTypes(df, FIELDTYPES)
# remove extra info
# (1 drops along cols, 0 along rows)
DROPCOLS = ['uniqueid', 'hitid', 'assignmentid', 'pdrillX', 'pdrillY',
'pobsX', 'pobsY']
df = df.drop(DROPCOLS, 1)
return df
