def cueDiffTest(numLocations, trialMeans, cue1locTrials, cue2locTrials, choices):
##########################################################
# 3d F-stat plots
##########################################################
# trialMeans = trialMeansDelayOnly
# cue1Fs = [list([]) for _ in range(len(trialMeans[0]))]
# cue2Fs = [list([]) for _ in range(len(trialMeans[0]))]
# cueDiffFs = [list([]) for _ in range(len(trialMeans[0]))]
cue1Fs = []
cue2Fs = []
cueDiffFs = []
# This tracks the "pvalue class" of each point. Basically, how significant
# were the f-statistics to change the markers to match significance
pvalClass = []
# Particularly signficant neurons that may be good to look at
sigNeurons = []
####################################################################
# Compile 3 groups:
# cue1Fs : F-stat of 1xNumlocs anova for each neuron's mean firing rate for each
# trial in which location 'x' appears at cue 1
# cue2Fs : F-stat of 1xNumlocs anova for each neuron's mean firing rate for each
# trial in which location 'x' appears at cue 2
# cueDiffFs : F-stat of 1x2 anova for each neurons' mean firing rate for 2
# groups, split between cue 1 and cue 2
####################################################################
for neuron in range(len(trialMeans[0])):
locs = []
for loc in range(numLocations):
locN = []
for trial in cue1locTrials[loc]:
locN.append(trialMeans[trial][neuron])
locs.append(locN)
ans1 = f_oneway(*locs)
locs = []
for loc in range(numLocations):
locN = []
for trial in cue2locTrials[loc]:
locN.append(trialMeans[trial][neuron])
locs.append(locN)
ans2 = f_oneway(*locs)
choice1 = []
choice2 = []
for trial in choices[0]:
choice1.append(trialMeans[trial][neuron])
for trial in choices[1]:
choice2.append(trialMeans[trial][neuron])
ans3 = f_oneway(choice1, choice2)
# includes pvalue cutoffs
# if ans1.pvalue < pval and ans2.pvalue < pval and ans3.pvalue < pval:
# cue1Fs.append(ans1.statistic)
# cue2Fs.append(ans2.statistic)
# cueDiffFs.append(ans3.statistic)
# without pvalue cutoffs
cue1Fs.append(ans1.statistic)
cue2Fs.append(ans2.statistic)
cueDiffFs.append(ans3.statistic)
# assorted point markets by pval
if ans1.pvalue < 0.001 and ans2.pvalue < 0.001 and ans3.pvalue < 0.001:
pvalClass.append(1)
sigNeurons.append(neuron)
elif ans1.pvalue < 0.01 and ans2.pvalue < 0.01 and ans3.pvalue < 0.01:
pvalClass.append(2)
sigNeurons.append(neuron)
elif ans1.pvalue < 0.05 and ans2.pvalue < 0.05 and ans3.pvalue < 0.05:
pvalClass.append(3)
else:
pvalClass.append(4)
################### z-score the fstats ##################
cue1Fs.remove(max(cue1Fs))
cue1Fs.remove(min(cue1Fs))
# cue1Fs.remove(max(cue1Fs))
# cue1Fs.remove(min(cue1Fs))
cue2Fs.remove(max(cue2Fs))
cue2Fs.remove(min(cue2Fs))
# cue2Fs.remove(max(cue2Fs))
# cue2Fs.remove(min(cue2Fs))
cueDiffFs.remove(max(cueDiffFs))
cueDiffFs.remove(min(cueDiffFs))
# cueDiffFs.remove(max(cueDiffFs))
# cueDiffFs.remove(min(cueDiffFs))
cue1Fs = util.zscore(cue1Fs)
cue2Fs = util.zscore(cue2Fs)
cueDiffFs = util.zscore(cueDiffFs)
plot3dAnova(cue1Fs, cue2Fs, cueDiffFs, 'Cue 1 F-Stat', 'Cue 2 F-Stat',
'Cue Pref F-Stat', pvalClass)
def conjugateTest(numLocations, trialMeans, locTrials, conjTrials):
##########################################################
# 2d F-stat plots
##########################################################
# featFs = [list([]) for _ in range(len(trialMeans[0]))]
# conjFs = [list([]) for _ in range(len(trialMeans[0]))]
featFs = []
conjFs = []
# This tracks the "pvalue class" of each point. Basically, how significant
# were the f-statistics to change the markers to match significance
pvalClass = []
# Particularly signficant neurons that may be good to look at
sigNeurons = []
####################################################################
# Compile 2 groups:
# featFs : F-stat of 1xNumlocs anova for each neuron's mean firing rate for each
# trial in which location 'x' appears at all
# conjFs : F-stat of 1x (NumlocsxNumlocs) anova for each neuron's mean firing
# rate for each trial in which conjunction 'loc1' & 'loc2' appears
####################################################################
for neuron in range(len(trialMeans[0])):
locs = []
for loc in range(numLocations):
means = []
for trial in locTrials[loc]:
means.append(trialMeans[trial][neuron])
locs.append(means)
ans1 = f_oneway(*locs)
conj = []
for trials in conjTrials:
means = []
for trial in trials:
means.append(trialMeans[trial][neuron])
conj.append(means)
ans2 = f_oneway(*conj)
# # includes pvalue cutoffs
# if ans1.pvalue < pval and ans2.pvalue < pval:
# featFs.append(ans1.statistic)
# conjFs.append(ans2.statistic)
# without pvalue cutoffs
featFs.append(ans1.statistic)
conjFs.append(ans2.statistic)
# assorted point markets by pval
if ans1.pvalue < 0.001 and ans2.pvalue < 0.001:
pvalClass.append(1)
sigNeurons.append(neuron)
elif ans1.pvalue < 0.01 and ans2.pvalue < 0.01:
pvalClass.append(2)
sigNeurons.append(neuron)
elif ans1.pvalue < 0.05 and ans2.pvalue < 0.05:
pvalClass.append(3)
else:
pvalClass.append(4)
################### z-score the fstats ##################
# featFs.remove(max(featFs))
# featFs.remove(min(featFs))
# cue1Fs.remove(max(cue1Fs))
# cue1Fs.remove(min(cue1Fs))
# conjFs.remove(max(conjFs))
# conjFs.remove(min(conjFs))
# cue2Fs.remove(max(cue2Fs))
# cue2Fs.remove(min(cue2Fs))
featFs = util.zscore(featFs)
# featFs = [featFs[i] for i in sigNeurons]
conjFs = util.zscore(conjFs)
# conjFs = [conjFs[i] for i in sigNeurons]
plot2dAnova(featFs, conjFs, 'Feature Selectivity', 'Conjunctive Selectivity', pvalClass)
