def saliency():
"""
Plots avg LPs simulation
"""
fig = plt.figure(figsize = (1.5,4))
dm = DataMatrix(np.load(".cache/dm_sim_select_driftcorr.npy"))
# PLOT SIMULATED SACCADES:
for stimType in dm.unique("stim_type"):
if stimType == "object":
col = green[1]
elif stimType == "non-object":
col = red[1]
lSacc = []
for sacc in [1,2]:
stimDm = dm.select("stim_type == '%s'" % stimType)
m = stimDm["xNorm%s" % sacc].mean()
lSacc.append(m)
plt.plot([1,2], lSacc, color = col, marker = 'o', markerfacecolor = 'white',
markeredgecolor = col, markeredgewidth = 1)
# HACK:
# PLOT REAL LPs:
for stimType in dm.unique("stim_type"):
if stimType == "object":
col = blue[1]
elif stimType == "non-object":
col = orange[1]
lSacc = []
for sacc in [1,2]:
stimDm = dm.select("stim_type == '%s'" % stimType)
m = stimDm["xNorm%s" % sacc].mean()
lSacc.append(m)
plt.plot([1,2], lSacc, color = col, marker = 'o', markerfacecolor = 'white',
markeredgecolor = col, markeredgewidth = 1, label = stimType)
plt.axhline(0, linestyle = "--", color = gray[3])
plt.ylim(-.3, .07)
plt.xlim(0.8, 2.2)
plt.legend(frameon = False)
plt.savefig("./plots/simulation.png")
plt.savefig("./plots/simulation.svg")
from exparser.DataMatrix import DataMatrix
import numpy as np
f = ".cache/004C_lat_driftcorr_False.npy"
dm = DataMatrix(np.load(f))
for pp in dm.unique("file"):
ppDm = dm.select("file == '%s'" % pp, verbose = False)
_ppDm = ppDm.select("xNorm1 == -1000", verbose = False)
print pp, float(len(_ppDm))/float(len(ppDm))
def getDM(exp, driftCorr = True, excludeErrors = True, saccTooFast = None, saccTooSlow = None,\
rtTooFast = None, rtTooSlow = None, rtVar = 'rtFromStim', \
exclFailedChecks = True, \
fixCheck1TooLong = 1000, fixCheck2TooLong = 1000, cutoffHeavy = 0, \
onlyControl = True, excludeFillers = True):
"""
Applies several exclusion criteria to obtain a filtered dm.
Arguments:
dm --- data matrix
exp --- {"004A", "004B"}, string indicating whether first or second
experiment is analysed
Keyword arguments:
excludeErrors --- Boolean indicating whether error trials should be
excluded. Default = False.
saccTooFast --- Cutoff for exclusion of too-fast saccades. Set to None
to deactivate this filter. Default = 80.
saccTooSlow --- Cutoff for exclusion of too-slow saccades. Set to None
to deactivate this filter.
rtTooFast --- Cutoff for exclusion of too-fast RTs. Set to None to
deactivate this filter.
rtTooSlow --- Cutoff for exclusion of too-slow RTs. Set to None to
deactivate this filter.
rtVar --- rt variable to use for excluding too fast and/or
too slow response times.
exclFailedChecks --- Boolean indicating whether or not to excude trials on
which one or more fix checks failed. Default = False
fixCheckTooLong1 --- Cutoff for excluding too long fixation checks. Set
to None to deactivate this filter. Default = 700,
based on looking at plt.hist(dm['durCheck1'])
fixCheckTooLong2 --- Cutoff for excluding too long fixation check on
object. Set to None to deactivate. Default = 450,
based on looking at plt.hist(dm['durCheck2'])
drifCorr ---
cutoffHeavy --- minimum deviation necessary to indicate one side of the object
as heavier. In px. Default = 8.
onlyControl --- indicates whether only trials in which manipulation was not
manipulated, should be included. Default = True.
excludeFillers --- Default = True.
"""
if driftCorr:
fName = "selected_dm_%s_WITH_drift_corr.csv" % exp
else:
fName = "selected_dm_%s_NO_drift_corr.csv" % exp
if exp == "004C":
data = "./dm_004C_simulation.csv"
a = np.genfromtxt(data, dtype=None, delimiter=",")
dm = DataMatrix(a)
# Add some important column headers:
dm = dm.addField("file", dtype = str)
dm = dm.addField("accuracy")
dm = dm.addField("contrast_side", dtype = str)
dm["file"] = "simulation"
dm["accuracy"] = 1
dm["contrast_side"] = "test"
dm["contrast_side"][np.where(dm["mask_side"] == "right")] = "_left"
dm["contrast_side"][np.where(dm["mask_side"] == "control")] = "control"
dm["contrast_side"][np.where(dm["mask_side"] == "left")] = "right"
else:
dm = ascParser.parseAsc(exp = exp, driftCorr = driftCorr)
# Start by applying some general selections (such that
# the reported exclusion percentages will not differ depending
# on when you execute those):
# Exclude practice trials:
dm = dm.select("rep != 'practice'")
# For the second experiment, exclude Dash and Sebastiaan:
if exp == "004B":
# Exclude Sebastiaan:
dm = dm.select('file != "00401.asc"')
# Exclude Dash because he's left handed:
dm = dm.select('file != "00402.asc"')
if onlyControl:
dm = dm.select("mask_side == 'control'")
if excludeFillers:
dm = dm.select("symm == 'asymm'")
if excludeErrors:
dm = dm.select('accuracy == 1')
# 'Real' selections:
# During parsing, tirals on which the variable 'response' did not contain
# an int were given the value -1. Otherwise the dm will make all the
# 'response' values strings. Therefore, start by excluding those eventual
# -1's:
dm = dm.select("response != -1")
# Remove all trials on which saccLat1 doesn't have a value:
dm = dm.select("saccLat1 != ''")
# Negative initial saccade latencies should not happen:
dm = dm.select('saccLat1 > 0.')
# Add column header indicating whether drift correction was used:
dm = dm.addField("driftCorr", dtype = str)
dm["driftCorr"] = driftCorr
# Add a column header indicating which experiment is analysed:
dm = dm.addField("exp", dtype = str)
dm["exp"] = exp
# Add variable indicating whether CoG is to the left or to the
# right:
# For objects with handle right, and without mask applied:
# - negative xCoG means: heavier on the left
# - pos means: heavier on the right
dm = dm.addField('heavySide', dtype = str)
dm["heavySide"] = 'control'
dm["heavySide"][np.where(dm["xCoG"] >= cutoffHeavy)] = 'right__'
dm["heavySide"][np.where(dm["xCoG"] <= -cutoffHeavy)] = 'left__'
# Some stuff we can't do for the simulation, because those columns don't
# exist:
if exp != "004C":
# Add variable indicating experimental half (first or second):
dm = dm.addField("half", dtype = str)
dm["half"] = "first"
dm["half"][np.where(dm["block_count"] >= 3)] = "second"
if saccTooFast != None:
dm = dm.select('saccLat1 > %s'%saccTooFast)
if saccTooSlow != None:
dm = dm.select('saccLat1 < %s'%saccTooSlow)
if rtTooFast != None:
dm = dm.select('%s > %s'%(rtVar,rtTooFast))
if rtTooSlow != None:
dm = dm.select('%s < %s'%(rtVar,rtTooSlow))
# Filter on screen coordinates:
# Y:
if exp != "004C":
dm = dm.select("endYRaw1 < %s" % constants.screenH)
dm = dm.select("endYRaw1 > 0")
# X:
dm = dm.select("endXRaw1 < %s" % constants.screenW)
dm = dm.select("endXRaw1 > 0")
# Re-code initial y coordinates such that we can use one cutoff
# for both upwards and downwards saccade to see whether they were in
# the right direction (i.e. > center coordinate):
dm = dm.addField("sacc_dir")
dm["sacc_dir"] = dm["endYRaw1"]
dm["sacc_dir"][dm.where("visual_field == 'upper'")] = \
dm["endYRaw1"][dm.where("visual_field == 'upper'")]+\
constants.yCen
# Select saccades that were in the right vertical direction:
dm.select("sacc_dir > %s" % str(constants.yCen))
# Exclude trials on which a fixation check failed or took too long:
if exp == "004A":
if exclFailedChecks:
# Note why those will probably never lead to exclusions anymore:
# in those cases durCheck1 or durCheck2 would have the value -1000 (because they
# couldnt be calculated) and therefore are already excluded above.
dm = dm.select('checkFixDotFailed == "False"')
dm = dm.select('checkObjectFailed == "False"')
if fixCheck1TooLong != None:
dm = dm.select('durCheck1 < %s' % fixCheck1TooLong)
if fixCheck2TooLong != None:
dm = dm.select('durCheck2 < %s' % fixCheck2TooLong)
if exp != "004C":
# Negative new RT's should not happen:
dm = dm.select('rtFromStim > 0')
dm.save(fName)
return dm
os.makedirs(expPath)
figPath = os.path.join(expPath, "%s_%s.png" % (trialDm["file"][0], \
str(trialDm["trialId"][0])))
plt.savefig(figPath)
#plt.show()
plt.clf()
pylab.close()
#raw_input()
if __name__ == "__main__":
f = ".cache/dm_sim_select_driftcorr.npy"
dm = DataMatrix(np.load(f))
dm = dm.select("gap == 'zero'")
for i in dm.range():
#if dm["stim_name"][i] != "hammer":
# continue
#if dm["stim_type"][i] != "object":
# continue
#if dm["mask_side"][i] != "control":
# continue
# Save 1 in 40 trials:
#if i % 100 != 99:
# continue
trialDm = dm[i]
debugPlot(trialDm)
mDev = gapDm['xNorm%s' % sacc].mean()
d[stimType, sacc]= mDev
return d
if __name__ == "__main__":
dm = dmSim(cacheId = "dm_sim_raw")
dm = addCommonFactors.addCommonFactors(dm, \
cacheId = "dm_sim_common_factors")
dm = addCoord.addCoord(dm, cacheId = "dm_sim_coord")
#dm = addLat.addLat(dm, cacheId = "dm_sim_lat_driftcorr")
dm = selectDm.selectDm(dm, cacheId = "dm_sim_select_driftcorr")
f = ".cache/dm_sim_select_driftcorr.npy"
dm = DataMatrix(np.load(f))
fig = plt.figure()
nPlot = 0
for stimType in dm.unique("stim_type"):
stimDm = dm.select("stim_type == '%s'" % stimType)
for sacc in (1,2):
nPlot +=1
plt.subplot(2,2,nPlot)
dv = "xNorm%s" % sacc
plt.title("%s sacc %s" % (stimType, sacc))
plt.hist(stimDm[dv], bins = 5)
plt.savefig("Distribution_simulated_saccades.png")
def getDM(driftCorr, excludeErrors = False, saccTooFast = 80, saccTooSlow = None,\
rtTooFast = None, rtTooSlow = None, rtVar = 'rtFromStim'):
"""
Applies several exclusion criteria to obtain a filtered dm.
Arguments:
dm --- data matrix
Keyword arguments:
excludeErrors --- Boolean indicating whether error trials should be
excluded. Default = False.
saccTooFast --- Cutoff for exclusion of too-fast saccades. Set to None
to deactivate this filter. Default = 80.
saccTooSlow --- Cutoff for exclusion of too-slow saccades. Set to None
to deactivate this filter.
rtTooFast --- Cutoff for exclusion of too-fast RTs. Set to None to
deactivate this filter.
rtTooSlow --- Cutoff for exclusion of too-slow RTs. Set to None to
deactivate this filter.
rtVar --- rt variable to use for excluding too fast and/or
too slow response times.
drifCorr ---
"""
# Declare constants:
#src = '/home/lotje/python-libs/studies/_004'
if driftCorr:
fName = "selected_dm_WITH_drift_corr.csv"
else:
fName = "selected_dm_NO_drift_corr.csv"
if '--shortcut' in sys.argv:
a = np.genfromtxt(fName, dtype=None, delimiter=",")
dm = DataMatrix(a)
#dm = CsvReader(fName, delimiter=',').dataMatrix()
return dm
dm = ascParser004B.parseAsc(driftCorr = driftCorr)
# Exclude Sebastiaan:
dm = dm.select('file != "00401.asc"')
# Exclude Dash because he's left handed:
dm = dm.select('file != "00402.asc"')
# Add column header indicating whether drift correction was used:
dm = dm.addField("driftCorr", dtype = str)
dm["driftCorr"] = driftCorr
# Make a new variable (instead of 'mask_side') that indicates the
# highest contrast side:
dm = dm.addField('contrast_side', dtype = str)
dm['contrast_side'][np.where(dm['mask_side'] == "right")] = "_left"
dm['contrast_side'][np.where(dm['mask_side'] == "left")] = "right"
dm['contrast_side'][np.where(dm['mask_side'] == "control")] = "control"
# Add variable indicating whether CoG is to the left or to the
# right:
# For objects with handle right, and without mask applied:
# - negative xCoG means: heavier on the left
# - pos means: heavier on the right
dm = dm.addField('heavySide', dtype = str)
dm["heavySide"] = 'left'
dm["heavySide"][np.where(dm["xCoG"] >= 0)] = 'right'
# Add variable indicating experimental half (first or second):
dm = dm.addField("half", dtype = str)
dm["half"] = "first"
dm["half"][np.where(dm["block_count"] >= 3)] = "second"
# Exclude practice trials:
dm = dm.select("rep != 'practice'")
# During parsing, tirals on which the variable 'response' did not contain
# an int were given the value -1. Otherwise the dm will make all the
# 'response' values strings. Therefore, start by excluding those eventual
# -1's:
dm = dm.select("response != -1")
# Saccade latencies of 0 (or -1) should not happen:
# If no saccade is detected during a trial, 'saccLat' is changed from -1000
# to -1 during parsing:
dm = dm.select('saccLat1 > 0')
# Negative new RT's should not happen:
dm = dm.select('rtFromStim > 0')
# Add values indicating whether the eyes landed towards the handle (positive
# values) or towards the other side of the object (negative values):
# Also, make sure all values are eventually converted to visual degrees:
# If first landing position is empty, skip the trial:
dm = dm.select("endX1 != ''")
# Without any correction:
dm = dm.addField("pxTowardsHandle")
dm = dm.addField("degrTowardsHandle")
indicesRight = np.where(dm["handle_side"] == 'right')
indicesLeft = np.where(dm["handle_side"] == 'left')
dm["pxTowardsHandle"][indicesRight] = (dm["endX1"][indicesRight])
dm["pxTowardsHandle"][indicesLeft] = dm["endX1"][indicesRight]*-1
dm["degrTowardsHandle"] = dm["pxTowardsHandle"]/studies._004.constants.ratioPxDegr
## With CoG correction based on the original bitmap (without mask):
#dm = dm.addField("pxTowardsHandleCorr")
#dm = dm.addField("degrTowardsHandleCorr")
#indicesRight = np.where(dm["handle_side"] == 'right')
#indicesLeft = np.where(dm["handle_side"] == 'left')
#dm["pxTowardsHandleCorr"][indicesRight] = (dm["endXCorr"][indicesRight])
#dm["pxTowardsHandleCorr"][indicesLeft] = dm["endXCorr"][indicesRight]*-1
#dm["degrTowardsHandleCorr"] = dm["pxTowardsHandleCorr"]/constants.ratioPxDegr
## Wit CoG correction with taking mask into account::
#dm = dm.addField("pxTowardsHandleCorrMask")
#dm = dm.addField("degrTowardsHandleCorrMask")
#indicesRight = np.where(dm["handle_side"] == 'right')
#indicesLeft = np.where(dm["handle_side"] == 'left')
#dm["pxTowardsHandleCorrMask"][indicesRight] = \
#(dm["endXCorrMask"][indicesRight])
#dm["pxTowardsHandleCorrMask"][indicesLeft] = \
#dm["endXCorrMask"][indicesRight]*-1
#dm["degrTowardsHandleCorrMask"] = \
#dm["pxTowardsHandleCorrMask"]/constants.ratioPxDegr
# Convert landing positions to visual degrees:
dm = dm.addField("endXDegr")
#dm = dm.addField("endXCorrDegr")
#dm = dm.addField("endXCorrMaskDegr")
dm["endXDegr"] = dm["endX1"]/studies._004.constants.ratioPxDegr
#dm["endXCorrDegr"] = dm["endXCorr"]/constants.ratioPxDegr
#dm["endXCorrMaskDegr"] = dm["endXCorrMask"]/constants.ratioPxDegr
# Convert starting position to visual degrees:
dm = dm.addField("startX1Degr")
dm["startX1Degr"] = dm["startX1"]/studies._004.constants.ratioPxDegr
#dm = dm.addField("rtFromLanding")
#dm['rtFromLanding'] = dm['rtFromStim']-dm['saccLandingTime1']
if saccTooFast != None:
dm = dm.select('saccLat1 > %s'%saccTooFast)
if saccTooSlow != None:
dm = dm.select('saccLat1 < %s'%saccTooSlow)
if rtTooFast != None:
dm = dm.select('%s > %s'%(rtVar,rtTooFast))
if rtTooSlow != None:
dm = dm.select('%s < %s'%(rtVar,rtTooSlow))
if excludeErrors:
dm = dm.select('accuracy == 1')
dm.save(fName)
return dm