# aryHlfMaxCnfLw = np.percentile(aryEmpHlfMax, 0.5, axis=1)
# aryHlfMaxCnfUp = np.percentile(aryEmpHlfMax, 99.5, axis=1)
# Mean and standar error:
aryHlfMaxMne = np.mean(aryEmpHlfMax, axis=1)
aryError = np.std(aryEmpHlfMax, axis=1)
funcPltAcrDpth(
aryHlfMaxMne, # aryData[Condition, Depth]
aryError, # aryError[Con., Depth]
varNumDpt, # Number of depth levels (on the x-axis)
varNumIn, # Number of conditions (separate lines)
varDpi, # Resolution of the output figure
0.0, # Minimum of Y axis
2.0, # Maximum of Y axis
False, # Boolean: whether to convert y axis to %
dicPthDpth.keys(), # Labels for conditions (separate lines)
strXlabel, # Label on x axis
strYlabel, # Label on y axis
'Response at 50% contrast', # Figure title
True, # Boolean: whether to plot a legend
(strPthOt + '_' + strFunc + '_half_max_response' + strFleTyp),
varSizeX=2000.0,
varSizeY=1400.0,
# aryCnfLw=aryHlfMaxCnfLw,
# aryCnfUp=aryHlfMaxCnfUp,
lstVrt=list(vecPeakHlfMaxMed))
# ----------------------------------------------------------------------------
print('-Done.')
# SEM across subjects:
lstHlfMaxRespSem[idxIn] = np.divide(np.std(lstHlfMaxResp[idxIn], axis=0),
np.sqrt(varNumSubs))
lstHlfMaxRespSem[idxIn] = np.array(lstHlfMaxRespSem[idxIn], ndmin=2)
# Stack the vectors for the two ROIs (V1 & V2):
aryHlfMaxRespMne = np.vstack(lstHlfMaxRespMne[:])
aryHlfMaxRespSem = np.vstack(lstHlfMaxRespSem[:])
funcPltAcrDpth(aryHlfMaxRespMne, # aryData[Condition, Depth]
aryHlfMaxRespSem, # aryError[Con., Depth]
varNumDpth, # Number of depth levels (on the x-axis)
varNumIn, # Number of conditions (separate lines)
varDpi, # Resolution of the output figure
0.0, # Minimum of Y axis
1.4, # Maximum of Y axis
False, # Boolean: whether to convert y axis to %
dicPthDpth.keys(), # Labels for conditions (separate lines)
strXlabel, # Label on x axis
strYlabel, # Label on y axis
'Response at half maximum contrast', # Figure title
True, # Boolean: whether to plot a legend
(strPthOt + '_half_max_response.png'))
# -----------------------------------------------------------------------------
# *** Plot contrast at half maximum response across depth
# Label for axes:
strXlabel = 'Cortical depth level (equivolume)'
strYlabel = 'Percent luminance contrast'
def funcLinRegAcrSubs(arySubDpthMns,
vecLinRegMdl,
varNumSubs,
varNumDpth,
strTitle,
strXlabel,
strLinRegYlabel,
varLinRegYmin,
varLinRegYmax,
varLinRegP,
varDpi,
strPltOtPre,
strPltOtSuf):
"""
Calculate & plot across-subjects simple linear regression.
A simple linear regression is model is calculated to test the dependence of
the parameter estimates (i.e. signal change) to stimulus condition (i.e.
luminance contrast), separately for each depth level.
"""
# Repeat linear regression model for each subject:
vecLinRegX = np.tile(vecLinRegMdl, varNumSubs)
# Vectors for regression results (one regression per depth level):
# Slope of the regression line:
vecSlpe = np.zeros(varNumDpth)
# Intercept of the regression line:
vecItrcpt = np.zeros(varNumDpth)
# Correlation coefficient:
vecCor = np.zeros(varNumDpth)
# p-value:
vecP = np.zeros(varNumDpth)
# Standard error of the estimate:
vecStdErr = np.zeros(varNumDpth)
# Loop through depth levels to calculate linear regression independently
# at each depth level:
for idxDpth in range(0, varNumDpth):
# Get a flat array with the parameter estiamtes for each condition for
# each subject:
vecLinRegY = arySubDpthMns[:, :, idxDpth].flatten()
# Fit the regression model:
vecSlpe[idxDpth], vecItrcpt[idxDpth], vecCor[idxDpth], vecP[idxDpth], \
vecStdErr[idxDpth] = stats.linregress(vecLinRegX, vecLinRegY)
# File name for figure:
strPltOt = strPltOtPre + 'acrsSubsLinReg' + strPltOtSuf
# Create plot:
funcPltAcrDpth(vecSlpe[None, :], # Data to be plotted
vecStdErr[None, :], # Error shading
varNumDpth, # Number of depth levels
1, # Number of conditions (separate lines)
varDpi, # Resolution of the output figure
varLinRegYmin, # Minimum of Y axis
varLinRegYmax, # Maximum of Y axis
False, # Whether to convert y axis to %
[''], # Labels for conditions
strXlabel, # Label on x axis
strLinRegYlabel, # Label on y axis
strTitle, # Figure title
False, # Boolean: whether to plot a legend
strPltOt) # Output path for the figure
# ----------------------------------------------------------------------------
# *** Plot results
# Path for figure:
strPath = '/home/john/PhD/Tex/Tootell_1988/semiTootell1988_smth_0p2.svg'
# Plot semisaturation contrast:
funcPltAcrDpth(vecSemi,
aryError,
varNumLayers,
1,
80.0,
0.0,
0.5,
True, [''],
'Cortical depth',
'Percent luminance contrast',
'Semisaturation contrast',
False,
strPath,
vecX=vecLayPos,
varNumLblY=6,
varXmin=0.0,
varXmax=1.0)
# Path for figure:
strPath = '/home/john/PhD/Tex/Tootell_1988/hlfmaxTootell1988_smth_0p2.svg'
# Plot response at 50% contrast:
funcPltAcrDpth(vecHlfMax,
aryError,
# Label for axes:
strXlabel = 'Cortical depth level (equivolume)'
strYlabel = 'fMRI signal change [a.u.]'
#funcPltAcrDpth(aryEmpHlfMax[:, 0, :], # aryData[Condition, Depth]
funcPltAcrDpth(
aryHlfMaxMne, # aryData[Condition, Depth]
0, # aryError[Con., Depth]
varNumDpt, # Number of depth levels (on the x-axis)
varNumIn, # Number of conditions (separate lines)
varDpi, # Resolution of the output figure
0.0, # Minimum of Y axis
2.0, # Maximum of Y axis
False, # Boolean: whether to convert y axis to %
dicPthDpth.keys(), # Labels for conditions (separate lines)
strXlabel, # Label on x axis
strYlabel, # Label on y axis
'Response at 50% contrast', # Figure title
True, # Boolean: whether to plot a legend
(strPthOt + '_' + strFunc + '_half_max_response' + strFleTyp),
varSizeX=2000.0,
varSizeY=1400.0,
aryCnfLw=aryHlfMaxCnfLw,
aryCnfUp=aryHlfMaxCnfUp,
lstVrt=list(vecPeakHlfMaxMed))
# ----------------------------------------------------------------------------
# *** Plot semisaturation contrast
# Label for axes:
strXlabel = 'Cortical depth level (equivolume)'
aryPrctUp = np.array(aryPrct[1, :], ndmin=2)
# ----------------------------------------------------------------------------
# *** Plot result
# Median across subjects:
aryDpthMne = np.median(aryDpth, axis=0, keepdims=True)
# Create plot:
funcPltAcrDpth(aryDpthMne,
None,
varNumDpth,
1,
80.0,
varYmin,
varYmax,
False, [''],
strXlabel,
strYlabel,
strRoi.upper(),
False,
strPltOt,
varSizeX=1800.0,
varSizeY=1600.0,
varNumLblY=5,
varPadY=(0.1, 0.1),
aryCnfLw=aryPrctLw,
aryCnfUp=aryPrctUp)
# ----------------------------------------------------------------------------
# Label for axes:
strXlabel = 'Cortical depth level (equivolume)'
strYlabel = 'fMRI signal change [a.u.]'
# Stack the vectors for the two ROIs (V1 & V2):
aryHlfMaxResp = np.vstack(lstHlfMaxResp[:])
funcPltAcrDpth(
aryHlfMaxResp, # aryData[Condition, Depth]
np.zeros(np.shape(aryHlfMaxResp)), # aryError[Con., Depth]
varNumDpth, # Number of depth levels (on the x-axis)
varNumIn, # Number of conditions (separate lines)
varDpi, # Resolution of the output figure
0.0, # Minimum of Y axis
2.0, # Maximum of Y axis
False, # Boolean: whether to convert y axis to %
dicPthDpth.keys(), # Labels for conditions (separate lines)
strXlabel, # Label on x axis
strYlabel, # Label on y axis
'Response at half maximum contrast', # Figure title
True, # Boolean: whether to plot a legend
(strPthOt + '_half_max_response.png'),
varSizeX=2000.0,
varSizeY=1400.0)
# ----------------------------------------------------------------------------
# *** Plot semisaturation contrast
# Label for axes:
strXlabel = 'Cortical depth level (equivolume)'
strYlabel = 'Percent luminance contrast'