def thresholds(save_pdf=False):
conf = ss_timing_analysis.conf.get_conf()
# load the fit parameters, excluding the bad subjects
# this will be subj x onsets x oris x (a,b) x (est, 2.5, 97.5)
(fit, _, _) = ss_timing_analysis.group_fit.load_fit_data(exclude=True)
# restrict to just the alpha estimates
data = fit[..., 0, 0]
embed = veusz.embed.Embedded("veusz")
figutils.set_veusz_style(embed)
page = embed.Root.Add("page")
page.width.val = "13.5cm"
page.height.val = "6cm"
# separate columns for simultaneous and leading
grid = page.Add("grid")
grid.rows.val = 1
grid.columns.val = 2
grid.leftMargin.val = grid.rightMargin.val = "0cm"
grid.topMargin.val = grid.bottomMargin.val = "0cm"
onset_order = [1, 0] # sim, leading
ori_order = [0, 1] # orth, para
for i_onset in onset_order:
graph = grid.Add("graph", autoadd=False)
graph.bottomMargin.val = "1cm"
graph.topMargin.val = "0.6cm"
x_axis = graph.Add("axis")
y_axis = graph.Add("axis")
for i_ori in ori_order:
curr_data = data[:, i_onset, i_ori]
boxplot = graph.Add("boxplot")
dataset_str = "data_{onset:d}_{ori:d}".format(onset=i_onset,
ori=i_ori)
embed.SetData(dataset_str, curr_data)
boxplot.values.val = dataset_str
boxplot.posn.val = i_ori
boxplot.labels.val = conf.surr_ori_labels[i_ori]
boxplot.fillfraction.val = 0.3
boxplot.markerSize.val = "2pt"
for i_subj in xrange(conf.n_subj):
subj_data_str = "subj_data_{subj:d}_{onset:d}".format(
subj=i_subj, onset=i_onset)
embed.SetData(subj_data_str, data[i_subj, i_onset, :])
xy = graph.Add("xy")
xy.xData.val = [0, 1]
xy.yData.val = subj_data_str
xy.MarkerFill.hide.val = True
xy.MarkerLine.hide.val = True
xy.PlotLine.transparency.val = 80
x_axis.mode.val = "labels"
x_axis.MajorTicks.manualTicks.val = [0, 1]
x_axis.MinorTicks.hide.val = True
x_axis.label.val = "Relative orientation"
y_axis.log.val = True
y_axis.TickLabels.format.val = "%.3g"
y_axis.MajorTicks.manualTicks.val = [0.001, 0.01, 0.1, 0.5, 1]
y_axis.min.val = 0.005
y_axis.max.val = 1.0
y_axis.label.val = "Threshold contrast"
cond_label = graph.Add("label")
cond_label.label.val = conf.surr_onset_labels[i_onset]
cond_label.yPos.val = 1.02
cond_label.xPos.val = 0.5
cond_label.alignHorz.val = "centre"
cond_label.Text.size.val = "8pt"
if save_pdf:
_save(embed, conf, "ss_timing_thresholds")
embed.EnableToolbar(True)
embed.WaitForClose()
def eg_subject(subj_id="p1022", save_pdf=False):
conf = ss_timing_analysis.conf.get_conf()
# find the index for this subject
# we'll load the data without excluding participants, so this needs to be
# in the all subjects list
i_subj = conf.all_subj_ids.index(subj_id)
# subj x onsets x oris x bins x (prop, n)
data = ss_timing_analysis.group_data.bin_group_data()
# restrict to this subject
data = data[i_subj, ...]
# fit is: subj x onsets x oris x (a, b) x (est, 2.5, 97.5)
# fit_fine is: subj x onsets x oris x X x 2 (2.5, 97.5)
(fit, fit_fine, _) = ss_timing_analysis.group_fit.load_fit_data()
# again, restrict to this subject
fit = fit[i_subj, ...]
fit_fine = fit_fine[i_subj, ...]
embed = veusz.embed.Embedded("veusz")
figutils.set_veusz_style(embed)
embed.SetData("bin_centres", conf.bin_centres)
embed.SetData("fine_x", conf.fine_x)
page = embed.Root.Add("page")
page.width.val = "8cm"
page.height.val = "20cm"
grid = page.Add("grid")
grid.rows.val = 4
grid.columns.val = 1
grid.leftMargin.val = grid.rightMargin.val = "0cm"
grid.topMargin.val = grid.bottomMargin.val = "0cm"
onset_order = [1, 0] # sim, leading
ori_order = [0, 1] # orth, par
for i_onset in onset_order:
for i_ori in ori_order:
graph = grid.Add("graph", autoadd=False)
graph.leftMargin.val = "1.2cm"
graph.rightMargin.val = "0.5cm"
graph.topMargin.val = "0.7cm"
graph.bottomMargin.val = "0.85cm"
x_axis = graph.Add("axis")
y_axis = graph.Add("axis")
cond_label = graph.Add("label")
cond_label.label.val = ", ".join(
[conf.surr_ori_labels[i_ori], conf.surr_onset_labels[i_onset]])
cond_label.yPos.val = 1.02
cond_label.xPos.val = 0.5
cond_label.alignHorz.val = "centre"
cond_label.Text.size.val = "8pt"
# CROSSHAIRS
pse_y = graph.Add("xy")
pse_y.xData.val = [0.001, fit[i_onset, i_ori, 0, 0]]
pse_y.yData.val = [
conf.psych_func(fit[i_onset, i_ori, 0, 0],
fit[i_onset, i_ori, 0, 0], fit[i_onset, i_ori,
0, 1])
] * 2
pse_x = graph.Add("xy")
pse_x.xData.val = [fit[i_onset, i_ori, 0, 0]] * 2
pse_x.yData.val = [
-0.05,
conf.psych_func(fit[i_onset, i_ori, 0, 0],
fit[i_onset, i_ori, 0, 0], fit[i_onset, i_ori,
0, 1])
]
for pse_ax in (pse_y, pse_x):
pse_ax.MarkerFill.hide.val = True
pse_ax.MarkerLine.hide.val = True
pse_ax.PlotLine.style.val = "dashed"
# POINTS
points = graph.Add("xy")
prop_name = "resp_prop_{t:d}_{o:d}".format(t=i_onset, o=i_ori)
embed.SetData(prop_name, data[i_onset, i_ori, :, 0])
k_name = "resp_k_{t:d}_{o:d}".format(t=i_onset, o=i_ori)
point_scale = np.sqrt(
(data[i_onset, i_ori, :, 1] * 1) / np.pi) * 2 * 0.35
embed.SetData(k_name, point_scale)
points.xData.val = "bin_centres"
points.yData.val = prop_name
points.scalePoints.val = k_name
points.MarkerLine.hide.val = True
points.MarkerFill.transparency.val = 65
points.PlotLine.hide.val = True
points.MarkerFill.color.val = "blue"
# FIT
fit_plot = graph.Add("xy")
fit_name = "fit_{t:d}_{o:d}".format(t=i_onset, o=i_ori)
fit_y = conf.psych_func(conf.fine_x,
alpha=fit[i_onset, i_ori, 0, 0],
beta=fit[i_onset, i_ori, 1, 0])
embed.SetData(
fit_name,
fit_y,
poserr=np.abs(fit_fine[i_onset, i_ori, :, 1] - fit_y),
negerr=np.abs(fit_fine[i_onset, i_ori, :, 0] - fit_y))
fit_plot.xData.val = "fine_x"
fit_plot.yData.val = fit_name
fit_plot.MarkerFill.hide.val = True
fit_plot.MarkerLine.hide.val = True
fit_plot.errorStyle.val = "fillvert"
fit_plot.ErrorBarLine.hide.val = True
x_axis.log.val = True
x_axis.label.val = "Target contrast"
x_axis.TickLabels.format.val = "%.3g"
x_axis.MajorTicks.manualTicks.val = [0.001, 0.01, 0.1, 0.5, 1]
y_axis.min.val = -0.1
y_axis.max.val = 1.1
y_axis.label.val = "Accuracy (prop. correct)"
y_axis.MinorTicks.hide.val = True
y_axis.MajorTicks.manualTicks.val = [0, 0.25, 0.5, 0.69, 1]
y_axis.TickLabels.format.val = "%.02f"
if save_pdf:
_save(embed, conf, "ss_timing_eg_subject")
embed.EnableToolbar(True)
embed.WaitForClose()
def context_by_booth(save_pdf=False):
conf = ss_timing_analysis.conf.get_conf()
dem = ss_timing_analysis.dem.demographics()
booths = np.array(
[dem[subj_id]["testing_booth"] for subj_id in conf.subj_ids])
potential_booths = np.unique(booths)
n_booths = len(potential_booths)
assert n_booths == 2
# load the fit parameters, excluding the bad subjects
# this will be subj x onsets x oris x (a,b) x (est, 2.5, 97.5)
(fit, _, _) = ss_timing_analysis.group_fit.load_fit_data(exclude=True)
# restrict to just the alpha estimates
data = fit[..., 0, 0]
# and look at the context effect for simultaneous
data = data[:, 1, 1] - data[:, 1, 0]
embed = veusz.embed.Embedded("veusz")
figutils.set_veusz_style(embed)
page = embed.Root.Add("page")
page.width.val = "12cm"
page.height.val = "8cm"
graph = page.Add("graph", autoadd=False)
graph.bottomMargin.val = "1cm"
graph.topMargin.val = "0.6cm"
x_axis = graph.Add("axis")
y_axis = graph.Add("axis")
for i_booth in xrange(n_booths):
curr_booth = data[booths == potential_booths[i_booth]]
boxplot = graph.Add("boxplot")
dataset_str = "data_{b:d}".format(b=i_booth)
embed.SetData(dataset_str, curr_booth)
boxplot.values.val = dataset_str
boxplot.posn.val = i_booth
boxplot.labels.val = "{b:d} (n={n:d})".format(b=i_booth + 1,
n=len(curr_booth))
boxplot.fillfraction.val = 0.3
boxplot.markerSize.val = "2pt"
x_axis.mode.val = "labels"
x_axis.MajorTicks.manualTicks.val = [0, 1]
x_axis.MinorTicks.hide.val = True
x_axis.label.val = "Testing booth"
y_axis.TickLabels.format.val = "%.3g"
y_axis.min.val = 0.0
y_axis.max.val = 0.5
y_axis.label.val = "Context effect for simultaneous (par - orth)"
if save_pdf:
_save(embed, conf, "ss_timing_context_by_booth")
embed.EnableToolbar(True)
embed.WaitForClose()
def scatter(cond, form, save_pdf=False):
if cond not in ["sim", "sim_orth", "lead"]:
raise ValueError()
if form not in ["linear", "rank"]:
raise ValueError()
conf = ss_timing_analysis.conf.get_conf()
# load the fit parameters, excluding the bad subjects
# this will be subj x onsets x oris x (a,b) x (est, 2.5, 97.5)
(fit, _, _) = ss_timing_analysis.group_fit.load_fit_data(exclude=True)
# restrict to just the alpha estimates
data = fit[..., 0, 0]
if cond == "sim":
data = data[:, 1, 1] - data[:, 1, 0]
elif cond == "sim_orth":
data = data[:, 1, 0]
elif cond == "lead":
#data = (data[:, 1, 1] - data[:, 1, 0]) - (data[:, 0, 1] - data[:, 0, 0])
data = data[:, 0, 1] - data[:, 0, 0]
if form == "rank":
data = scipy.stats.rankdata(data)
embed = veusz.embed.Embedded("veusz")
figutils.set_veusz_style(embed)
embed.SetData("data", data)
page = embed.Root.Add("page")
page.width.val = "13.5cm"
page.height.val = "10cm"
grid = page.Add("grid")
grid.rows.val = 2
grid.columns.val = 2
grid.leftMargin.val = grid.rightMargin.val = "2cm"
grid.topMargin.val = grid.bottomMargin.val = "0cm"
ss_nice = [
"Unusual experiences", "Cognitive disorganisation",
"Introvertive anhedonia", "Impulsive nonconformity"
]
for (i_sub, subscale) in enumerate(
("un_ex", "cog_dis", "int_anh", "imp_non")):
curr_ss = ss_timing_analysis.dem.get_olife_subscale(subscale,
exclude=True)
if form == "rank":
curr_ss = scipy.stats.rankdata(curr_ss)
assert len(curr_ss) == len(data)
graph = grid.Add("graph", autoadd=False)
graph.bottomMargin.val = "1cm"
graph.topMargin.val = "0.6cm"
graph.leftMargin.val = "1cm"
graph.aspect.val = 1
label = graph.Add("label")
label.label.val = ss_nice[i_sub]
label.yPos.val = 1.025
label.xPos.val = 0.5
label.alignHorz.val = "centre"
x_axis = graph.Add("axis")
y_axis = graph.Add("axis")
xy = graph.Add("xy")
xy.xData.val = curr_ss
xy.yData.val = data
xy.PlotLine.hide.val = True
xy.MarkerFill.transparency.val = 60
xy.MarkerLine.hide.val = True
xy.markerSize.val = "3pt"
if form == "rank":
x_axis.label.val = "O-LIFE subscale (rank; 1 = lowest)"
else:
x_axis.label.val = "O-LIFE subscale score"
if cond == "sim":
if form == "rank":
y_axis.label.val = "Context effect (rank; 1 = lowest)"
else:
y_axis.label.val = "Context effect (contrast units)"
elif cond == "sim_orth":
if form == "rank":
y_axis.label.val = "Orthogonal threshold (rank; 1 = lowest)"
else:
y_axis.label.val = "Orthogonal threshold (contrast units)"
elif cond == "lead":
if form == "rank":
y_axis.label.val = "Leading context effect (rank; 1 = lowest)"
else:
y_axis.label.val = "Leading context effect (contrast units)"
if form == "linear":
if cond == "sim":
y_max = 0.5
elif cond == "sim_orth":
y_max = 0.03
y_axis.TickLabels.format.val = "%.3g"
elif cond == "lead":
y_max = 0.085
y_axis.max.val = y_max
y_axis.min.val = 0.0
x_axis.min.val = -2
else:
x_axis.min.val = -10
x_axis.max.val = 105
y_axis.min.val = -10
y_axis.max.val = 105
x_axis.MajorTicks.manualTicks.val = [1] + range(20, 83, 20) + [93]
x_axis.MinorTicks.hide.val = True
y_axis.MajorTicks.manualTicks.val = [1] + range(20, 83, 20) + [93]
y_axis.MinorTicks.hide.val = True
if save_pdf:
_save(embed, conf, "ss_timing_{c:s}_{f:s}_scatter".format(c=cond,
f=form))
embed.EnableToolbar(True)
embed.WaitForClose()
def split(save_pdf=False):
conf = ss_timing_analysis.conf.get_conf()
split_locs = [50, 40, 30, 20, 10]
n_splits = len(split_locs)
data = ss_timing_analysis.split.split_ana()
embed = veusz.embed.Embedded("veusz")
figutils.set_veusz_style(embed)
page = embed.Root.Add("page")
page.width.val = "10cm"
page.height.val = "10cm"
graph = page.Add("graph", autoadd=False)
graph.bottomMargin.val = "2.5cm"
graph.topMargin.val = "0.2cm"
graph.leftMargin.val = "1.5cm"
graph.rightMargin.val = "0cm"
x_axis = graph.Add("axis")
y_axis = graph.Add("axis")
subscale_colours = ["#e41a1c", "#377eb8", "#4daf4a", "#984ea3"]
split_lbl = [
"< {s1:d}%, >= {s2:d}%".format(s1=sloc, s2=100 - sloc)
for sloc in split_locs
]
embed.SetDataText("split_lbl", split_lbl)
for (i_sub, sub) in enumerate(conf.subscales):
embed.SetData(sub, data[:, i_sub, 0], symerr=data[:, i_sub, 1])
xy = graph.Add("xy")
x = np.arange(n_splits) + np.linspace(-0.15, 0.15, 4)[i_sub]
xy.xData.val = x
xy.yData.val = sub
if i_sub == 0:
xy.labels.val = "split_lbl"
xy.MarkerLine.hide.val = False
#xy.MarkerLine.transparency.val = 20
xy.MarkerFill.hide.val = True
#xy.PlotLine.hide.val = True
xy.markerSize.val = "2pt"
xy.MarkerLine.color.val = subscale_colours[i_sub]
xy.PlotLine.color.val = subscale_colours[i_sub]
xy.ErrorBarLine.color.val = subscale_colours[i_sub]
#xy.marker.val = ana_markers[i_ana]
xy.key.val = "".join(sub.title().split("_"))
xy.Label.hide.val = True
key = graph.Add("key")
key.horzPosn.val = "manual"
key.vertPosn.val = "manual"
key.horzManual.val = 0.051125165504591
key.vertManual.val = 0.79086156527087
x_axis.mode.val = "labels"
x_axis.min.val = -0.5
x_axis.max.val = 4.5
x_axis.MinorTicks.hide.val = True
x_axis.MajorTicks.manualTicks.val = range(n_splits)
x_axis.TickLabels.rotate.val = "45"
x_axis.label.val = "Group split"
y_axis.label.val = "Context effect difference (high - low)"
if save_pdf:
_save(embed, conf, "ss_timing_split")
embed.EnableToolbar(True)
embed.WaitForClose()
def subjects(save_pdf=False):
conf = ss_timing_analysis.conf.get_conf()
# subj x onsets x oris x bins x (prop, n)
data = ss_timing_analysis.group_data.bin_group_data()
# fit is: subj x onsets x oris x (a, b) x (est, 2.5, 97.5)
# fit_fine is: subj x onsets x oris x X x 2 (2.5, 97.5)
(fit, fit_fine, _) = ss_timing_analysis.group_fit.load_fit_data()
embed = veusz.embed.Embedded("veusz")
figutils.set_veusz_style(embed)
embed.SetData("bin_centres", conf.bin_centres)
embed.SetData("fine_x", conf.fine_x)
for (i_subj, subj_id) in enumerate(conf.all_subj_ids):
page = embed.Root.Add("page")
page.width.val = "21cm"
page.height.val = "29.7cm"
label = page.Add("label")
label.label.val = subj_id
label.yPos.val = 0.77
if subj_id in conf.exclude_ids:
label.label.val += " (excluded)"
label.Text.color.val = "red"
grid = page.Add("grid")
grid.rows.val = 2
grid.columns.val = 2
grid.leftMargin.val = grid.rightMargin.val = "3cm"
grid.topMargin.val = "8cm"
grid.bottomMargin.val = "8cm"
for i_onset in xrange(conf.n_surr_onsets):
for i_ori in xrange(conf.n_surr_oris):
graph = grid.Add("graph", autoadd=False)
x_axis = graph.Add("axis")
y_axis = graph.Add("axis")
cond_label = graph.Add("label")
cond_label.label.val = ", ".join([
conf.surr_ori_labels[i_ori],
conf.surr_onset_labels[i_onset]
])
cond_label.yPos.val = 1.02
cond_label.xPos.val = 0.5
cond_label.alignHorz.val = "centre"
cond_label.Text.size.val = "8pt"
# CROSSHAIRS
pse_y = graph.Add("xy")
pse_y.xData.val = [0.001, fit[i_subj, i_onset, i_ori, 0, 0]]
pse_y.yData.val = [
conf.psych_func(fit[i_subj, i_onset, i_ori, 0,
0], fit[i_subj, i_onset, i_ori, 0, 0],
fit[i_subj, i_onset, i_ori, 0, 1])
] * 2
pse_x = graph.Add("xy")
pse_x.xData.val = [fit[i_subj, i_onset, i_ori, 0, 0]] * 2
pse_x.yData.val = [
-0.05,
conf.psych_func(fit[i_subj, i_onset, i_ori, 0,
0], fit[i_subj, i_onset, i_ori, 0, 0],
fit[i_subj, i_onset, i_ori, 0, 1])
]
for pse_ax in (pse_y, pse_x):
pse_ax.MarkerFill.hide.val = True
pse_ax.MarkerLine.hide.val = True
pse_ax.PlotLine.style.val = "dashed"
# POINTS
points = graph.Add("xy")
prop_name = "resp_prop_{s:d}_{t:d}_{o:d}".format(s=i_subj,
t=i_onset,
o=i_ori)
embed.SetData(prop_name, data[i_subj, i_onset, i_ori, :, 0])
k_name = "resp_k_{s:d}_{t:d}_{o:d}".format(s=i_subj,
t=i_onset,
o=i_ori)
point_scale = np.sqrt(
data[i_subj, i_onset, i_ori, :, 1] / np.pi) * 2 * 0.35
embed.SetData(k_name, point_scale)
points.xData.val = "bin_centres"
points.yData.val = prop_name
points.scalePoints.val = k_name
points.MarkerLine.hide.val = True
points.MarkerFill.transparency.val = 50
points.PlotLine.hide.val = True
points.MarkerFill.color.val = "blue"
# FIT
fit_plot = graph.Add("xy")
fit_name = "fit_{s:d}_{t:d}_{o:d}".format(s=i_subj,
t=i_onset,
o=i_ori)
fit_y = conf.psych_func(conf.fine_x,
alpha=fit[i_subj, i_onset, i_ori, 0,
0],
beta=fit[i_subj, i_onset, i_ori, 1, 0])
embed.SetData(
fit_name,
fit_y,
poserr=np.abs(fit_fine[i_subj, i_onset, i_ori, :, 1] -
fit_y),
negerr=np.abs(fit_fine[i_subj, i_onset, i_ori, :, 0] -
fit_y))
fit_plot.xData.val = "fine_x"
fit_plot.yData.val = fit_name
fit_plot.MarkerFill.hide.val = True
fit_plot.MarkerLine.hide.val = True
fit_plot.errorStyle.val = "fillvert"
fit_plot.ErrorBarLine.hide.val = True
x_axis.log.val = True
x_axis.label.val = "Contrast"
x_axis.TickLabels.format.val = "%.3g"
x_axis.MajorTicks.manualTicks.val = [0.001, 0.01, 0.1, 0.5, 1]
y_axis.min.val = -0.1
y_axis.max.val = 1.1
y_axis.label.val = "Accuracy (prop. correct)"
y_axis.MinorTicks.hide.val = True
y_axis.MajorTicks.manualTicks.val = [0, 0.25, 0.5, 0.69, 1]
y_axis.TickLabels.format.val = "%.02f"
if save_pdf:
_save(embed, conf, "ss_timing_subjects", page=range(conf.n_all_subj))
embed.EnableToolbar(True)
embed.WaitForClose()
def figure(study):
conf = ss_timing_analysis.conf.get_conf()
if study == "yoon":
study_pkg = ss_timing_analysis.ext_yoon
elif study == "s-p":
study_pkg = ss_timing_analysis.ext_s_p
# two-item list (controls, patients)
# each item is an array, n subj x 3 (ns, os, ps)
data = study_pkg.load_data()
embed = veusz.embed.Embedded("veusz")
figutils.set_veusz_style(embed)
# x to evaluate for the regressions
fine_x = np.linspace(0, 100, 101)
if study == "s-p":
fine_x /= 100.0
nice_surrs = {"O": "Orthogonal", "P": "Parallel"}
nice_grps = {"C": "Control", "P": "Patient"}
limits = {
"yoon": {
"C": {
"x": 40.0,
"y": {"O": 100.0, "P": 100.0},
"r": {"O": [0.5, 2.25], "P": [1.4, 4.0]}
},
"P": {
"x": 40.0,
"y": {"O": 100.0, "P": 100.0},
"r": {"O": [0.5, 4.0], "P": [1.25, 3.75]}
}
},
"s-p": {
"C": {
"x": 0.04,
"y": {"O": 0.03, "P": 0.6},
"r": {"O": [-1.2, 1.1], "P": [0.5, 4.25]}
},
"P": {
"x": 0.1,
"y": {"O": 0.06, "P": 0.5},
"r": {"O": [-2.2, 0.5], "P": [0, 3.05]}
}
}
}
study_limits = limits[study]
page = embed.Root.Add("page")
page.width.val = "12.5cm"
page.height.val = "20cm"
grp_grid = page.Add("grid")
grp_grid.rows.val = 2
grp_grid.columns.val = 1
grp_grid.leftMargin.val = grp_grid.rightMargin.val = "0cm"
grp_grid.topMargin.val = grp_grid.bottomMargin.val = "0cm"
for (i_grp, (grp_data, grp_name)) in enumerate(zip(data, ("C", "P"))):
grid = grp_grid.Add("grid")
# OS, PS
grid.rows.val = 2
# NS x AS, (AS/NS) x NS
grid.columns.val = 2
grid.leftMargin.val = grid.rightMargin.val = grid.bottomMargin.val = "0cm"
grid.topMargin.val = "1cm"
label = page.Add("label")
label.label.val = nice_grps[grp_name]
label.Text.bold.val = True
label.Text.size.val = "10pt"
label.alignHorz.val = "centre"
label.alignVert.val = "centre"
label.yPos.val = 1 - (0.5 * i_grp + 0.025)
# denominator is the no-surround condition
denom = grp_data[:, 0]
# loop over the orthogonal and parallel conditions
for (i_num, num_name) in enumerate(("O", "P"), 1):
numerator = grp_data[:, i_num]
# first, NS x AS graph
graph = grid.Add("graph", autoadd=False)
x_axis = graph.Add("axis")
y_axis = graph.Add("axis")
xy = graph.Add("xy")
xy.xData.val = denom
xy.yData.val = numerator
_format_points(xy)
coef = regress(x=denom, y=numerator)
fit_y = regress_ci(coef=coef, x=fine_x)
fit_y_str = "_".join([grp_name, num_name])
embed.SetData(
fit_y_str,
fit_y[0, :],
poserr=abs(fit_y[2, :] - fit_y[0, :]),
negerr=abs(fit_y[0, :] - fit_y[1, :])
)
fit_xy = graph.Add("xy")
fit_xy.xData.val = fine_x
fit_xy.yData.val = fit_y_str
fit_xy.MarkerFill.hide.val = fit_xy.MarkerLine.hide.val = True
fit_xy.errorStyle.val = "linevert"
fit_xy.ErrorBarLine.style.val = "dashed"
x_axis.label.val = "No-surround threshold"
y_axis.label.val = "{s:s} threshold".format(
s=nice_surrs[num_name]
)
x_axis.min.val = 0.0
x_axis.max.val = study_limits[grp_name]["x"]
y_axis.min.val = 0.0
y_axis.max.val = study_limits[grp_name]["y"][num_name]
x_axis.outerticks.val = y_axis.outerticks.val = True
# now, the ratio
graph = grid.Add("graph", autoadd=False)
x_axis = graph.Add("axis")
y_axis = graph.Add("axis")
x_axis.outerticks.val = y_axis.outerticks.val = True
ratio = numerator / denom
# take the log ratio if the study is Serrano-Pedraza et al.
if study == "s-p":
ratio = np.log(ratio)
xy = graph.Add("xy")
xy.xData.val = denom
xy.yData.val = ratio
_format_points(xy)
# calculate the correlation
(r, p) = scipy.stats.spearmanr(denom, ratio)
c_str = "\italic{{r}}_{{s}} = {r:.2f}\\\\\italic{{p}} = {p:.3f}".format(r=r, p=p)
c_label = graph.Add("label")
c_label.label.val = c_str
c_label.xPos.val = 0.75
c_label.yPos.val = 0.8
c_label.Text.size.val = "6pt"
x_axis.label.val = "No-surround threshold"
y_axis.label.val = "{s:s} suppression index".format(
s=nice_surrs[num_name]
)
x_axis.min.val = 0.0
x_axis.max.val = study_limits[grp_name]["x"]
(y_axis.min.val, y_axis.max.val) = study_limits[grp_name]["r"][num_name]
stem = os.path.join(
conf.figures_path,
"_".join(("ss_timing", study))
)
#embed.Zoom(0.5)
embed.Save(stem + ".vsz")
embed.Export(stem + ".pdf")
embed.WaitForClose()