def run(self):
subject = self.pipeline.subject
task = self.pipeline.task
self.create_dir_in_workspace("reports")
session_summary_array = self.get_passed_object("session_summary_array")
serial_positions = np.arange(1, 13)
for session_summary in session_summary_array:
panel_plot = PanelPlot(xfigsize=15, yfigsize=7.5, i_max=1, j_max=2, title="", wspace=0.3, hspace=0.3)
pd1 = PlotData(
x=serial_positions,
y=session_summary.prob_recall,
xlim=(0, 12),
ylim=(0.0, 1.0),
xlabel="Serial position\n(a)",
ylabel="Probability of recall",
)
pd2 = PlotData(
x=serial_positions,
y=session_summary.prob_first_recall,
xlim=(0, 12),
ylim=(0.0, 1.0),
xlabel="Serial position\n(b)",
ylabel="Probability of first recall",
)
panel_plot.add_plot_data(0, 0, plot_data=pd1)
panel_plot.add_plot_data(0, 1, plot_data=pd2)
plot = panel_plot.generate_plot()
plot_out_fname = self.get_path_to_resource_in_workspace(
"reports/" + task + "-" + subject + "-prob_recall_plot_" + session_summary.name + ".pdf"
)
plot.savefig(plot_out_fname, dpi=300, bboxinches="tight")
if task == "RAM_CatFR1":
panel_plot = PanelPlot(xfigsize=6.0, yfigsize=6.0, i_max=1, j_max=1, title="", wspace=0.3, hspace=0.3)
pd = BarPlotData(
x=[0, 1],
y=[session_summary.irt_within_cat, session_summary.irt_between_cat],
ylabel="IRT (msec)",
x_tick_labels=["Within Cat", "Between Cat"],
barcolors=["grey", "grey"],
barwidth=0.5,
)
panel_plot.add_plot_data(0, 0, plot_data=pd)
plot = panel_plot.generate_plot()
plot_out_fname = self.get_path_to_resource_in_workspace(
"reports/" + task + "-" + subject + "-irt_plot_" + session_summary.name + ".pdf"
)
plot.savefig(plot_out_fname, dpi=300, bboxinches="tight")
panel_plot = PanelPlot(xfigsize=15, yfigsize=7.5, i_max=1, j_max=2, title="", wspace=0.3, hspace=0.3)
pd1 = PlotData(
x=session_summary.fpr,
y=session_summary.tpr,
xlim=[0.0, 1.0],
ylim=[0.0, 1.0],
xlabel="False Alarm Rate\n(a)",
ylabel="Hit Rate",
levelline=((0.0, 1.0), (0.0, 1.0)),
)
ylim = np.max(np.abs(session_summary.pc_diff_from_mean)) + 5.0
if ylim > 100.0:
ylim = 100.0
pd2 = BarPlotData(
x=(0, 1, 2),
y=session_summary.pc_diff_from_mean,
ylim=[-ylim, ylim],
xlabel="Tercile of Classifier Estimate\n(b)",
ylabel="Recall Change From Mean (%)",
x_tick_labels=["Low", "Middle", "High"],
xhline_pos=0.0,
barcolors=["grey", "grey", "grey"],
barwidth=0.5,
)
panel_plot.add_plot_data(0, 0, plot_data=pd1)
panel_plot.add_plot_data(0, 1, plot_data=pd2)
plot = panel_plot.generate_plot()
plot_out_fname = self.get_path_to_resource_in_workspace(
"reports/" + task + "-" + subject + "-roc_and_terc_plot_" + session_summary.name + ".pdf"
)
plot.savefig(plot_out_fname, dpi=300, bboxinches="tight")
cumulative_summary = self.get_passed_object("cumulative_summary")
panel_plot = PanelPlot(xfigsize=15, yfigsize=7.5, i_max=1, j_max=2, title="", wspace=0.3, hspace=0.3)
pd1 = PlotData(
x=serial_positions,
y=cumulative_summary.prob_recall,
xlim=(0, 12),
ylim=(0.0, 1.0),
xlabel="Serial position\n(a)",
ylabel="Probability of recall",
)
pd2 = PlotData(
x=serial_positions,
y=cumulative_summary.prob_first_recall,
xlim=(0, 12),
ylim=(0.0, 1.0),
xlabel="Serial position\n(b)",
ylabel="Probability of first recall",
)
panel_plot.add_plot_data(0, 0, plot_data=pd1)
panel_plot.add_plot_data(0, 1, plot_data=pd2)
plot = panel_plot.generate_plot()
plot_out_fname = self.get_path_to_resource_in_workspace(
"reports/" + task + "-" + subject + "-prob_recall_plot_combined.pdf"
)
plot.savefig(plot_out_fname, dpi=300, bboxinches="tight")
if task == "RAM_CatFR1":
panel_plot = PanelPlot(xfigsize=6.0, yfigsize=6.0, i_max=1, j_max=1, title="", wspace=0.3, hspace=0.3)
pd = BarPlotData(
x=[0, 1],
y=[cumulative_summary.irt_within_cat, cumulative_summary.irt_between_cat],
ylabel="IRT (msec)",
x_tick_labels=["Within Cat", "Between Cat"],
barcolors=["grey", "grey"],
barwidth=0.5,
)
panel_plot.add_plot_data(0, 0, plot_data=pd)
plot = panel_plot.generate_plot()
plot_out_fname = self.get_path_to_resource_in_workspace(
"reports/" + task + "-" + subject + "-irt_plot_combined.pdf"
)
plot.savefig(plot_out_fname, dpi=300, bboxinches="tight")
panel_plot = PanelPlot(xfigsize=15, yfigsize=7.5, i_max=1, j_max=2, title="", wspace=0.3, hspace=0.3)
pd1 = PlotData(
x=cumulative_summary.fpr,
y=cumulative_summary.tpr,
xlim=[0.0, 1.0],
ylim=[0.0, 1.0],
xlabel="False Alarm Rate\n(a)",
ylabel="Hit Rate",
levelline=((0.001, 0.999), (0.001, 0.999)),
)
ylim = np.max(np.abs(cumulative_summary.pc_diff_from_mean)) + 5.0
if ylim > 100.0:
ylim = 100.0
pd2 = BarPlotData(
x=(0, 1, 2),
y=cumulative_summary.pc_diff_from_mean,
ylim=[-ylim, ylim],
xlabel="Tercile of Classifier Estimate\n(b)",
ylabel="Recall Change From Mean (%)",
x_tick_labels=["Low", "Middle", "High"],
xhline_pos=0.0,
barcolors=["grey", "grey", "grey"],
barwidth=0.5,
)
panel_plot.add_plot_data(0, 0, plot_data=pd1)
panel_plot.add_plot_data(0, 1, plot_data=pd2)
plot = panel_plot.generate_plot()
plot_out_fname = self.get_path_to_resource_in_workspace(
"reports/" + task + "-" + subject + "-roc_and_terc_plot_combined.pdf"
)
plot.savefig(plot_out_fname, dpi=300, bboxinches="tight")
def run(self):
subject = self.pipeline.subject
task = self.pipeline.task
self.create_dir_in_workspace('reports')
session_summary_array = self.get_passed_object('session_summary_array')
serial_positions = np.arange(1,13)
for session_summary in session_summary_array:
panel_plot = PanelPlot(xfigsize=15, yfigsize=7.5, i_max=1, j_max=2, title='', wspace=0.3, hspace=0.3)
pd1 = PlotData(x=serial_positions, y=session_summary.prob_recall, xlim=(0,12), ylim=(0.0, 1.0), xlabel='Serial position\n(a)', ylabel='Probability of recall')
pd2 = PlotData(x=serial_positions, y=session_summary.prob_first_recall, xlim=(0,12), ylim=(0.0, 1.0), xlabel='Serial position\n(b)', ylabel='Probability of first recall')
panel_plot.add_plot_data(0, 0, plot_data=pd1)
panel_plot.add_plot_data(0, 1, plot_data=pd2)
plot = panel_plot.generate_plot()
plot_out_fname = self.get_path_to_resource_in_workspace('reports/' + task + '-' + subject + '-prob_recall_plot_' + session_summary.name + '.pdf')
plot.savefig(plot_out_fname, dpi=300, bboxinches='tight')
if task == 'RAM_CatFR1':
panel_plot = PanelPlot(xfigsize=6.0, yfigsize=6.0, i_max=1, j_max=1, title='', wspace=0.3, hspace=0.3)
pd = BarPlotData(x=[0,1], y=[session_summary.irt_within_cat, session_summary.irt_between_cat], ylabel='IRT (msec)', x_tick_labels=['Within Cat', 'Between Cat'], barcolors=['grey','grey'], barwidth=0.5)
panel_plot.add_plot_data(0, 0, plot_data=pd)
plot = panel_plot.generate_plot()
plot_out_fname = self.get_path_to_resource_in_workspace('reports/' + task + '-' + subject + '-irt_plot_' + session_summary.name + '.pdf')
plot.savefig(plot_out_fname, dpi=300, bboxinches='tight')
panel_plot = PanelPlot(xfigsize=10.0, yfigsize=10.0, i_max=1, j_max=1, title='', xlabel='List', ylabel='# of items')
pdc = PlotDataCollection()
n_lists = len(session_summary.n_stims_per_list)
print 'Number of lists', n_lists
bpd_1 = BarPlotData(x=np.arange(n_lists), y=session_summary.n_stims_per_list, title='', alpha=0.1)
pd_1 = PlotData(x=np.where(session_summary.is_stim_list)[0], y=session_summary.n_recalls_per_list[session_summary.is_stim_list],
title='', linestyle='', color='red', marker='o')
pd_2 = PlotData(x=np.where(~session_summary.is_stim_list)[0], y=session_summary.n_recalls_per_list[~session_summary.is_stim_list],
title='', linestyle='', color='blue', marker='o')
pdc.add_plot_data(pd_1)
pdc.add_plot_data(pd_2)
pdc.add_plot_data(bpd_1)
panel_plot.add_plot_data_collection(0, 0, plot_data_collection=pdc)
plot = panel_plot.generate_plot()
plot_out_fname = self.get_path_to_resource_in_workspace('reports/' + task + '-' + subject + '-stim_and_recall_plot_' + session_summary.name + '.pdf')
plot.savefig(plot_out_fname, dpi=300, bboxinches='tight')
cumulative_summary = self.get_passed_object('cumulative_summary')
panel_plot = PanelPlot(xfigsize=15, yfigsize=7.5, i_max=1, j_max=2, title='', wspace=0.3, hspace=0.3)
pd1 = PlotData(x=serial_positions, y=cumulative_summary.prob_recall, xlim=(0, 12), ylim=(0.0, 1.0), xlabel='Serial position\n(a)', ylabel='Probability of recall')
pd2 = PlotData(x=serial_positions, y=cumulative_summary.prob_first_recall, xlim=(0, 12), ylim=(0.0, 1.0), xlabel='Serial position\n(b)', ylabel='Probability of first recall')
panel_plot.add_plot_data(0, 0, plot_data=pd1)
panel_plot.add_plot_data(0, 1, plot_data=pd2)
plot = panel_plot.generate_plot()
plot_out_fname = self.get_path_to_resource_in_workspace('reports/' + task + '-' + subject + '-prob_recall_plot_combined.pdf')
plot.savefig(plot_out_fname, dpi=300, bboxinches='tight')
if task == 'RAM_CatFR1':
panel_plot = PanelPlot(xfigsize=6.0, yfigsize=6.0, i_max=1, j_max=1, title='', wspace=0.3, hspace=0.3)
pd = BarPlotData(x=[0,1], y=[cumulative_summary.irt_within_cat, cumulative_summary.irt_between_cat], ylabel='IRT (msec)', x_tick_labels=['Within Cat', 'Between Cat'], barcolors=['grey','grey'], barwidth=0.5)
panel_plot.add_plot_data(0, 0, plot_data=pd)
plot = panel_plot.generate_plot()
plot_out_fname = self.get_path_to_resource_in_workspace('reports/' + task + '-' + subject + '-irt_plot_combined.pdf')
plot.savefig(plot_out_fname, dpi=300, bboxinches='tight')
def run(self):
experiment = self.pipeline.experiment
self.create_dir_in_workspace('reports')
session_summary_array = self.get_passed_object('session_summary_array')
from PlotUtils import PanelPlot
for session_summary in session_summary_array:
panel_plot = PanelPlot(i_max=3, j_max=2, title='',xtitle='', ytitle='$\Delta$ Post-Pre Classifier Output', wspace=0.3,hspace=0.3)
for plot_panel_index, pd in session_summary.plot_data_dict.iteritems():
plot_letter = chr(ord('a') + 2 * plot_panel_index[0] + plot_panel_index[1])
pd.xlabel = '(' + plot_letter + ')\n'
if plot_panel_index[0]==2 and plot_panel_index[1]==0:
pd.xlabel += 'Pulse Frequency (Hz)'
if plot_panel_index[0]==2 and plot_panel_index[1]==1:
pd.xlabel += 'Duration (ms)' if experiment=='PS1' else 'Amplitude (mA)' if experiment=='PS2' else 'Burst Frequency (Hz)'
if plot_panel_index[1]==0:
pd.ylabel = '$\Delta$ Post-Pre Classifier Output'
pd.xhline_pos=0.0
panel_plot.add_plot_data(plot_panel_index[0], plot_panel_index[1],plot_data=pd)
# panel_plot.add_plot_data(plot_panel_index[0], plot_panel_index[1], x=pd.x, y=pd.y, yerr=pd.yerr,
# x_tick_labels=pd.x_tick_labels, title='(' + plot_letter + ')', ylim=pd.ylim)
plot = panel_plot.generate_plot()
# plot.subplots_adjust(wspace=0.3, hspace=0.3)
# plt.savefig(join(plotsDir, quantity_name+'.png'), dpi=300,bboxinches='tight')
plot_out_fname = self.get_path_to_resource_in_workspace('reports/' + self.pipeline.experiment + '-' + self.pipeline.subject + '-report_plot_' + session_summary.name + '.pdf')
plot.savefig(plot_out_fname, dpi=300, bboxinches='tight')
cumulative_plot_data_dict = self.get_passed_object('cumulative_plot_data_dict')
panel_plot = PanelPlot(i_max=3, j_max=2, title='', y_axis_title='$\Delta$ Post-Pre Classifier Output')
for plot_panel_index, pd in cumulative_plot_data_dict.iteritems():
# print 'plot_panel_index=', plot_panel_index
# print 'pd.x=', pd.x
# print 'pd.y=', pd.y
plot_letter = chr(ord('a') + 2 * plot_panel_index[0] + plot_panel_index[1])
pd.xlabel = '(' + plot_letter + ')\n'
if plot_panel_index[0]==2 and plot_panel_index[1]==0:
pd.xlabel += 'Pulse Frequency (Hz)'
if plot_panel_index[0]==2 and plot_panel_index[1]==1:
pd.xlabel += 'Duration (ms)' if experiment=='PS1' else 'Amplitude (mA)' if experiment=='PS2' else 'Burst Frequency (Hz)'
if plot_panel_index[1]==0:
pd.ylabel = '$\Delta$ Post-Pre Classifier Output'
pd.xhline_pos=0.0
panel_plot.add_plot_data(plot_panel_index[0], plot_panel_index[1], plot_data=pd)
# panel_plot.add_plot_data(plot_panel_index[0], plot_panel_index[1], x=pd.x, y=pd.y, yerr=pd.yerr,
# x_tick_labels=pd.x_tick_labels, title='(' + plot_letter + ')', ylim=pd.ylim)
plot = panel_plot.generate_plot()
plot.subplots_adjust(wspace=0.3, hspace=0.3)
# plt.savefig(join(plotsDir, quantity_name+'.png'), dpi=300,bboxinches='tight')
plot_out_fname = self.get_path_to_resource_in_workspace('reports/' + self.pipeline.experiment + '-' + self.pipeline.subject + '-report_plot_Cumulative.pdf')
plot.savefig(plot_out_fname, dpi=300, bboxinches='tight')
def run(self):
#experiment = self.pipeline.experiment
self.create_dir_in_workspace('reports')
xval_output = self.get_passed_object('xval_output')
fr1_summary = xval_output[-1]
panel_plot = PanelPlot(xfigsize=15, yfigsize=7.5, i_max=1, j_max=2, title='', wspace=0.3, hspace=0.3)
pd1 = PlotData(x=fr1_summary.fpr, y=fr1_summary.tpr, xlim=[0.0,1.0], ylim=[0.0,1.0], xlabel='False Alarm Rate\n(a)', ylabel='Hit Rate', levelline=((0.0,1.0),(0.0,1.0)))
pc_diff_from_mean = (fr1_summary.low_pc_diff_from_mean, fr1_summary.mid_pc_diff_from_mean, fr1_summary.high_pc_diff_from_mean)
ylim = np.max(np.abs(pc_diff_from_mean)) + 5.0
if ylim > 100.0:
ylim = 100.0
# pd2 = BarPlotData(x=(0,1,2), y=pc_diff_from_mean, ylim=[-ylim,ylim], xlabel='Tercile of Classifier Estimate\n(b)', ylabel='Recall Change From Mean (%)', x_tick_labels=['Low', 'Middle', 'High'], xhline_pos=0.0, barcolors=['grey','grey', 'grey'], barwidth=0.5)
pd2 = BarPlotData(x=(0,1,2), y=pc_diff_from_mean, ylim=[-ylim,ylim], xlabel='Tercile of Classifier Estimate\n(b)', ylabel='Recall Change From Mean (%)', x_tick_labels=['Low', 'Middle', 'High'], xhline_pos=0.0, barcolors=['grey','grey', 'grey'], barwidth=0.5)
panel_plot.add_plot_data(0, 0, plot_data=pd1)
panel_plot.add_plot_data(0, 1, plot_data=pd2)
plot = panel_plot.generate_plot()
plot_out_fname = self.get_path_to_resource_in_workspace('reports/' + self.pipeline.subject + '-roc_and_terc_plot_combined.pdf')
plot.savefig(plot_out_fname, dpi=300, bboxinches='tight')
session_summary_array = self.get_passed_object('session_summary_array')
param1_name = self.get_passed_object('param1_name')
param1_unit = self.get_passed_object('param1_unit')
param1_title = '%s (%s)' % (param1_name,param1_unit)
for session_summary in session_summary_array:
# panel_plot = PanelPlot(i_max=1, j_max=1, title='', xtitle=param1_title, xtitle_fontsize=24, ytitle='$\Delta$ Post-Pre Classifier Output',ytitle_fontsize=24, wspace=0.3, hspace=0.3)
panel_plot = PanelPlot(i_max=1, j_max=1, title='', ytitle='$\Delta$ Post-Pre Classifier Output',ytitle_fontsize=24, wspace=0.3, hspace=0.3)
pdc = PlotDataCollection(legend_on=True)
pdc.xlabel = param1_title
pdc.xlabel_fontsize = 24
for v,p in session_summary.plots.iteritems():
p.xhline_pos=0.0
pdc.add_plot_data(p)
panel_plot.add_plot_data_collection(0, 0, plot_data_collection=pdc)
plot = panel_plot.generate_plot()
plot_out_fname = self.get_path_to_resource_in_workspace('reports/' + self.pipeline.experiment + '-' + self.pipeline.subject + '-report_plot_' + session_summary.name + '.pdf')
plot.savefig(plot_out_fname, dpi=300, bboxinches='tight')
cumulative_plots = self.get_passed_object('cumulative_plots')
# panel_plot = PanelPlot(i_max=1, j_max=1, title='', xtitle=param1_title, y_axis_title='$\Delta$ Post-Pre Classifier Output')
panel_plot = PanelPlot(i_max=1, j_max=1, title='',ytitle='$\Delta$ Post-Pre Classifier Output', ytitle_fontsize=24)
pdc = PlotDataCollection(legend_on=True)
pdc.xlabel = param1_title
pdc.xlabel_fontsize = 24
for v,p in cumulative_plots.iteritems():
p.xhline_pos=0.0
pdc.add_plot_data(p)
panel_plot.add_plot_data_collection(0, 0, plot_data_collection=pdc)
plot = panel_plot.generate_plot()
plot_out_fname = self.get_path_to_resource_in_workspace('reports/' + self.pipeline.experiment + '-' + self.pipeline.subject + '-report_plot_Cumulative.pdf')
plot.savefig(plot_out_fname, dpi=300, bboxinches='tight')
def run(self):
subject = self.pipeline.subject
task = self.pipeline.task
self.create_dir_in_workspace("reports")
session_summary_array = self.get_passed_object("session_summary_array")
serial_positions = np.arange(1, 13)
for session_summary in session_summary_array:
panel_plot = PanelPlot(
xfigsize=15, yfigsize=7.5, i_max=1, j_max=2, title="", wspace=0.3, hspace=0.3, labelsize=20
)
pd1 = PlotData(
x=serial_positions,
y=session_summary.prob_recall,
xlim=(0, 12),
ylim=(0.0, 1.0),
xlabel="Serial position\n(a)",
ylabel="Probability of recall",
)
pd1.xlabel_fontsize = 20
pd1.ylabel_fontsize = 20
pd2 = PlotData(
x=serial_positions,
y=session_summary.prob_first_recall,
xlim=(0, 12),
ylim=(0.0, 1.0),
xlabel="Serial position\n(b)",
ylabel="Probability of first recall",
)
pd2.xlabel_fontsize = 20
pd2.ylabel_fontsize = 20
# panel_plot.add_plot_data(0, 0, plot_data=pd1)
# panel_plot.add_plot_data(0, 1, plot_data=pd2)
#
# plot = panel_plot.generate_plot()
panel_plot.add_plot_data(0, 0, plot_data=pd1)
panel_plot.add_plot_data(0, 1, plot_data=pd2)
plot = panel_plot.generate_plot()
plot_out_fname = self.get_path_to_resource_in_workspace(
"reports/" + task + "-" + subject + "-prob_recall_plot_" + session_summary.name + ".pdf"
)
plot.savefig(plot_out_fname, dpi=300, bboxinches="tight")
if task == "RAM_CatFR1":
panel_plot = PanelPlot(xfigsize=6.0, yfigsize=6.0, i_max=1, j_max=1, title="", wspace=0.3, hspace=0.3)
pd = BarPlotData(
x=[0, 1],
y=[session_summary.irt_within_cat, session_summary.irt_between_cat],
ylabel="IRT (msec)",
x_tick_labels=["Within Cat", "Between Cat"],
barcolors=["grey", "grey"],
barwidth=0.5,
)
panel_plot.add_plot_data(0, 0, plot_data=pd)
plot = panel_plot.generate_plot()
plot_out_fname = self.get_path_to_resource_in_workspace(
"reports/" + task + "-" + subject + "-irt_plot_" + session_summary.name + ".pdf"
)
plot.savefig(plot_out_fname, dpi=300, bboxinches="tight")
panel_plot = PanelPlot(
xfigsize=10.0,
yfigsize=10.0,
i_max=1,
j_max=1,
title="",
xlabel="List",
ylabel="# of items",
labelsize=20,
)
pdc = PlotDataCollection()
# ----------------- FORMATTING
pdc.xlabel = "List number"
pdc.xlabel_fontsize = 20
pdc.ylabel = "# recalled words"
pdc.ylabel_fontsize = 20
n_lists = len(session_summary.n_stims_per_list)
print "Number of lists", n_lists
bpd_1 = BarPlotData(x=np.arange(n_lists), y=session_summary.n_stims_per_list, title="", alpha=0.1)
pd_1 = PlotData(
x=np.where(session_summary.is_stim_list)[0],
y=session_summary.n_recalls_per_list[session_summary.is_stim_list],
title="",
linestyle="",
color="red",
marker="o",
markersize=20,
)
pd_2 = PlotData(
x=np.where(~session_summary.is_stim_list)[0],
y=session_summary.n_recalls_per_list[~session_summary.is_stim_list],
title="",
linestyle="",
color="blue",
marker="o",
markersize=20,
)
print "np.where(session_summary.is_stim_list)[0]=", np.where(session_summary.is_stim_list)[0]
print "np.where(~session_summary.is_stim_list)[0]=", np.where(~session_summary.is_stim_list)[0]
pdc.add_plot_data(pd_1)
pdc.add_plot_data(pd_2)
pdc.add_plot_data(bpd_1)
panel_plot.add_plot_data_collection(0, 0, plot_data_collection=pdc)
plot = panel_plot.generate_plot()
plot_out_fname = self.get_path_to_resource_in_workspace(
"reports/" + task + "-" + subject + "-stim_and_recall_plot_" + session_summary.name + ".pdf"
)
plot.savefig(plot_out_fname, dpi=300, bboxinches="tight")
cumulative_summary = self.get_passed_object("cumulative_summary")
panel_plot = PanelPlot(xfigsize=15, yfigsize=7.5, i_max=1, j_max=2, title="", wspace=0.3, hspace=0.3)
pd1 = PlotData(
x=serial_positions,
y=cumulative_summary.prob_recall,
xlim=(0, 12),
ylim=(0.0, 1.0),
xlabel="Serial position\n(a)",
ylabel="Probability of recall",
)
pd2 = PlotData(
x=serial_positions,
y=cumulative_summary.prob_first_recall,
xlim=(0, 12),
ylim=(0.0, 1.0),
xlabel="Serial position\n(b)",
ylabel="Probability of first recall",
)
panel_plot.add_plot_data(0, 0, plot_data=pd1)
panel_plot.add_plot_data(0, 1, plot_data=pd2)
plot = panel_plot.generate_plot()
plot_out_fname = self.get_path_to_resource_in_workspace(
"reports/" + task + "-" + subject + "-prob_recall_plot_combined.pdf"
)
plot.savefig(plot_out_fname, dpi=300, bboxinches="tight")
if task == "RAM_CatFR1":
panel_plot = PanelPlot(xfigsize=6.0, yfigsize=6.0, i_max=1, j_max=1, title="", wspace=0.3, hspace=0.3)
pd = BarPlotData(
x=[0, 1],
y=[cumulative_summary.irt_within_cat, cumulative_summary.irt_between_cat],
ylabel="IRT (msec)",
x_tick_labels=["Within Cat", "Between Cat"],
barcolors=["grey", "grey"],
barwidth=0.5,
)
panel_plot.add_plot_data(0, 0, plot_data=pd)
plot = panel_plot.generate_plot()
plot_out_fname = self.get_path_to_resource_in_workspace(
"reports/" + task + "-" + subject + "-irt_plot_combined.pdf"
)
plot.savefig(plot_out_fname, dpi=300, bboxinches="tight")
def run(self):
subject = self.pipeline.subject
task = self.pipeline.task
self.create_dir_in_workspace('reports')
session_summary_array = self.get_passed_object('session_summary_array')
serial_positions = np.arange(1, 13)
for session_summary in session_summary_array:
panel_plot = PanelPlot(xfigsize=15,
yfigsize=7.5,
i_max=1,
j_max=2,
title='',
wspace=0.3,
hspace=0.3)
pd1 = PlotData(x=serial_positions,
y=session_summary.prob_recall,
xlim=(0, 12),
ylim=(0.0, 1.0),
xlabel='Serial position\n(a)',
ylabel='Probability of recall')
pd2 = PlotData(x=serial_positions,
y=session_summary.prob_first_recall,
xlim=(0, 12),
ylim=(0.0, 1.0),
xlabel='Serial position\n(b)',
ylabel='Probability of first recall')
panel_plot.add_plot_data(0, 0, plot_data=pd1)
panel_plot.add_plot_data(0, 1, plot_data=pd2)
plot = panel_plot.generate_plot()
plot_out_fname = self.get_path_to_resource_in_workspace(
'reports/' + task + '-' + subject + '-prob_recall_plot_' +
session_summary.name + '.pdf')
plot.savefig(plot_out_fname, dpi=300, bboxinches='tight')
if task == 'RAM_CatFR1':
panel_plot = PanelPlot(xfigsize=6.0,
yfigsize=6.0,
i_max=1,
j_max=1,
title='',
xtitle='',
wspace=0.3,
hspace=0.3)
pd = BarPlotData(x=[0, 1],
y=[
session_summary.irt_within_cat,
session_summary.irt_between_cat
],
ylabel='IRT (msec)',
xlabel='',
x_tick_labels=['Within Cat', 'Between Cat'],
barcolors=['grey', 'grey'],
barwidth=0.5)
panel_plot.add_plot_data(0, 0, plot_data=pd)
plot = panel_plot.generate_plot()
plot_out_fname = self.get_path_to_resource_in_workspace(
'reports/' + task + '-' + subject + '-irt_plot_' +
session_summary.name + '.pdf')
plot.savefig(plot_out_fname, dpi=300, bboxinches='tight')
panel_plot = PanelPlot(xfigsize=15,
yfigsize=7.5,
i_max=1,
j_max=2,
title='',
wspace=0.3,
hspace=0.3)
pd1 = PlotData(x=session_summary.fpr,
y=session_summary.tpr,
xlim=[0.0, 1.0],
ylim=[0.0, 1.0],
xlabel='False Alarm Rate\n(a)',
ylabel='Hit Rate',
levelline=((0.0, 1.0), (0.0, 1.0)))
ylim = np.max(np.abs(session_summary.pc_diff_from_mean)) + 5.0
if ylim > 100.0:
ylim = 100.0
pd2 = BarPlotData(x=(0, 1, 2),
y=session_summary.pc_diff_from_mean,
ylim=[-ylim, ylim],
xlabel='Tercile of Classifier Estimate\n(b)',
ylabel='Recall Change From Mean (%)',
x_tick_labels=['Low', 'Middle', 'High'],
xhline_pos=0.0,
barcolors=['grey', 'grey', 'grey'],
barwidth=0.5)
panel_plot.add_plot_data(0, 0, plot_data=pd1)
panel_plot.add_plot_data(0, 1, plot_data=pd2)
plot = panel_plot.generate_plot()
plot_out_fname = self.get_path_to_resource_in_workspace(
'reports/' + task + '-' + subject + '-roc_and_terc_plot_' +
session_summary.name + '.pdf')
plot.savefig(plot_out_fname, dpi=300, bboxinches='tight')
cumulative_summary = self.get_passed_object('cumulative_summary')
panel_plot = PanelPlot(xfigsize=15,
yfigsize=7.5,
i_max=1,
j_max=2,
title='',
wspace=0.3,
hspace=0.3)
pd1 = PlotData(x=serial_positions,
y=cumulative_summary.prob_recall,
xlim=(0, 12),
ylim=(0.0, 1.0),
xlabel='Serial position\n(a)',
ylabel='Probability of recall')
pd2 = PlotData(x=serial_positions,
y=cumulative_summary.prob_first_recall,
xlim=(0, 12),
ylim=(0.0, 1.0),
xlabel='Serial position\n(b)',
ylabel='Probability of first recall')
panel_plot.add_plot_data(0, 0, plot_data=pd1)
panel_plot.add_plot_data(0, 1, plot_data=pd2)
plot = panel_plot.generate_plot()
plot_out_fname = self.get_path_to_resource_in_workspace(
'reports/' + task + '-' + subject +
'-prob_recall_plot_combined.pdf')
plot.savefig(plot_out_fname, dpi=300, bboxinches='tight')
if task == 'RAM_CatFR1':
panel_plot = PanelPlot(xfigsize=6.0,
yfigsize=6.0,
i_max=1,
j_max=1,
title='',
xtitle='',
wspace=0.3,
hspace=0.3)
pd = BarPlotData(x=[0, 1],
y=[
cumulative_summary.irt_within_cat,
cumulative_summary.irt_between_cat
],
ylabel='IRT (msec)',
xlabel='',
x_tick_labels=['Within Cat', 'Between Cat'],
barcolors=['grey', 'grey'],
barwidth=0.5)
panel_plot.add_plot_data(0, 0, plot_data=pd)
plot = panel_plot.generate_plot()
plot_out_fname = self.get_path_to_resource_in_workspace(
'reports/' + task + '-' + subject + '-irt_plot_combined.pdf')
plot.savefig(plot_out_fname, dpi=300, bboxinches='tight')
panel_plot = PanelPlot(xfigsize=15,
yfigsize=7.5,
i_max=1,
j_max=2,
title='',
wspace=0.3,
hspace=0.3)
pd1 = PlotData(x=cumulative_summary.fpr,
y=cumulative_summary.tpr,
xlim=[0.0, 1.0],
ylim=[0.0, 1.0],
xlabel='False Alarm Rate\n(a)',
ylabel='Hit Rate',
levelline=((0.001, 0.999), (0.001, 0.999)))
ylim = np.max(np.abs(cumulative_summary.pc_diff_from_mean)) + 5.0
if ylim > 100.0:
ylim = 100.0
pd2 = BarPlotData(x=(0, 1, 2),
y=cumulative_summary.pc_diff_from_mean,
ylim=[-ylim, ylim],
xlabel='Tercile of Classifier Estimate\n(b)',
ylabel='Recall Change From Mean (%)',
x_tick_labels=['Low', 'Middle', 'High'],
xhline_pos=0.0,
barcolors=['grey', 'grey', 'grey'],
barwidth=0.5)
panel_plot.add_plot_data(0, 0, plot_data=pd1)
panel_plot.add_plot_data(0, 1, plot_data=pd2)
plot = panel_plot.generate_plot()
plot_out_fname = self.get_path_to_resource_in_workspace(
'reports/' + task + '-' + subject +
'-roc_and_terc_plot_combined.pdf')
plot.savefig(plot_out_fname, dpi=300, bboxinches='tight')
def run(self):
self.create_dir_in_workspace('reports')
frequency_plot_data = self.get_passed_object('frequency_plot')
centralized_frequency_plot_data = self.get_passed_object('centralized_frequency_plot')
frequency_region_plot_data = self.get_passed_object('frequency_region_plot')
frequency_frequency_plot_data = self.get_passed_object('frequency_frequency_plot')
low_freq_duration_plot_data = self.get_passed_object('low_freq_duration_plot')
high_freq_duration_plot_data = self.get_passed_object('high_freq_duration_plot')
low_freq_amplitude_plot_data = self.get_passed_object('low_freq_amplitude_plot')
high_freq_amplitude_plot_data = self.get_passed_object('high_freq_amplitude_plot')
# panel_plot = PanelPlot(xfigsize=11, yfigsize=11, i_max=1, j_max=1, title='', ytitle=self.params.output_title, ytitle_fontsize=16, wspace=0.3, hspace=0.3)
#
# panel_plot.add_plot_data_collection(0, 0, plot_data_collection=pdc)
#
# plot = panel_plot.generate_plot()
#
# plot_out_fname = self.get_path_to_resource_in_workspace('reports/ps_frequency_aggregate_plots.pdf')
#
# plot.savefig(plot_out_fname, dpi=300, bboxinches='tight')
pdc = PlotDataCollection(legend_on=True)
pdc.xlabel = 'Pulse Frequency (Hz)'
pdc.xlabel_fontsize = 16
for v,p in frequency_plot_data.iteritems():
p.xhline_pos=0.0
pdc.add_plot_data(p)
# panel_plot = PanelPlot(xfigsize=15, yfigsize=7.5, i_max=1, j_max=3, title='', ytitle=self.params.output_title, wspace=0.3, hspace=0.3)
panel_plot = PanelPlot(xfigsize=18.5, yfigsize=7.5, i_max=1, j_max=3, title='', ytitle=self.params.output_title, labelsize=20, ytitle_fontsize = 20)
min_y_list =[]
max_y_list =[]
r =pdc.get_yrange()
min_y_list.append(r[0])
max_y_list.append(r[1])
r =frequency_region_plot_data.get_yrange()
min_y_list.append(r[0])
max_y_list.append(r[1])
r =frequency_frequency_plot_data.get_yrange()
min_y_list.append(r[0])
max_y_list.append(r[1])
y_min = np.min(min_y_list)
y_max = np.max(max_y_list)
r = y_max - y_min
y_min -= 0.05*r
y_max += 0.15*r
pdc.ylim=[y_min,y_max]
frequency_region_plot_data.ylim=[y_min,y_max]
frequency_frequency_plot_data.ylim=[y_min,y_max]
pdc.xhline_pos = 0.0
frequency_region_plot_data.xhline_pos = 0.0
frequency_frequency_plot_data.xhline_pos = 0.0
# label fontsize
pdc.xlabel_fontsize = 20
pdc.ylabel_fontsize = 20
frequency_region_plot_data.xlabel_fontsize = 20
frequency_region_plot_data.ylabel_fontsize = 20
frequency_frequency_plot_data.xlabel_fontsize = 20
frequency_frequency_plot_data.ylabel_fontsize = 20
print '[y_min,y_max]=',[y_min,y_max]
panel_plot.add_plot_data_collection(0, 0, plot_data_collection=pdc)
panel_plot.add_plot_data(0, 1, plot_data=frequency_region_plot_data)
panel_plot.add_plot_data(0, 2, plot_data=frequency_frequency_plot_data)
plot = panel_plot.generate_plot()
plot_out_fname = self.get_path_to_resource_in_workspace('reports/ps_frequency_projection_plots.pdf')
plot.savefig(plot_out_fname, dpi=300, bboxinches='tight')
panel_plot = PanelPlot(i_max=1, j_max=1, title='', ytitle=self.params.output_title, ytitle_fontsize=24, wspace=0.3, hspace=0.3)
pdc = PlotDataCollection(legend_on=True)
pdc.xlabel = 'Duration (ms)'
pdc.xlabel_fontsize = 24
for v,p in low_freq_duration_plot_data.iteritems():
p.xhline_pos=0.0
pdc.add_plot_data(p)
panel_plot.add_plot_data_collection(0, 0, plot_data_collection=pdc)
plot = panel_plot.generate_plot()
plot_out_fname = self.get_path_to_resource_in_workspace('reports/ps_duration_low_aggregate_plots.pdf')
plot.savefig(plot_out_fname, dpi=300, bboxinches='tight')
panel_plot = PanelPlot(i_max=1, j_max=1, title='', ytitle=self.params.output_title, ytitle_fontsize=24, wspace=0.3, hspace=0.3)
pdc = PlotDataCollection(legend_on=True)
pdc.xlabel = 'Duration (ms)'
pdc.xlabel_fontsize = 24
for v,p in high_freq_duration_plot_data.iteritems():
p.xhline_pos=0.0
pdc.add_plot_data(p)
panel_plot.add_plot_data_collection(0, 0, plot_data_collection=pdc)
plot = panel_plot.generate_plot()
plot_out_fname = self.get_path_to_resource_in_workspace('reports/ps_duration_high_aggregate_plots.pdf')
plot.savefig(plot_out_fname, dpi=300, bboxinches='tight')
panel_plot = PanelPlot(i_max=1, j_max=1, title='', ytitle=self.params.output_title, ytitle_fontsize=24, wspace=0.3, hspace=0.3)
pdc = PlotDataCollection(legend_on=True)
pdc.xlabel = 'Amplitude (mA)'
pdc.xlabel_fontsize = 24
for v,p in low_freq_amplitude_plot_data.iteritems():
p.xhline_pos=0.0
pdc.add_plot_data(p)
panel_plot.add_plot_data_collection(0, 0, plot_data_collection=pdc)
plot = panel_plot.generate_plot()
plot_out_fname = self.get_path_to_resource_in_workspace('reports/ps_amplitude_low_aggregate_plots.pdf')
plot.savefig(plot_out_fname, dpi=300, bboxinches='tight')
panel_plot = PanelPlot(i_max=1, j_max=1, title='', ytitle=self.params.output_title, ytitle_fontsize=24, wspace=0.3, hspace=0.3)
pdc = PlotDataCollection(legend_on=True)
pdc.xlabel = 'Amplitude (mA)'
pdc.xlabel_fontsize = 24
for v,p in high_freq_amplitude_plot_data.iteritems():
p.xhline_pos=0.0
pdc.add_plot_data(p)
panel_plot.add_plot_data_collection(0, 0, plot_data_collection=pdc)
plot = panel_plot.generate_plot()
plot_out_fname = self.get_path_to_resource_in_workspace('reports/ps_amplitude_high_aggregate_plots.pdf')
plot.savefig(plot_out_fname, dpi=300, bboxinches='tight')
def run(self):
#experiment = self.pipeline.experiment
self.create_dir_in_workspace('reports')
xval_output = self.get_passed_object('xval_output')
fr1_summary = xval_output[-1]
panel_plot = PanelPlot(xfigsize=15,
yfigsize=7.5,
i_max=1,
j_max=2,
title='',
wspace=0.3,
hspace=0.3)
pd1 = PlotData(x=fr1_summary.fpr,
y=fr1_summary.tpr,
xlim=[0.0, 1.0],
ylim=[0.0, 1.0],
xlabel='False Alarm Rate\n(a)',
ylabel='Hit Rate',
levelline=((0.0, 1.0), (0.0, 1.0)))
pc_diff_from_mean = (fr1_summary.low_pc_diff_from_mean,
fr1_summary.mid_pc_diff_from_mean,
fr1_summary.high_pc_diff_from_mean)
ylim = np.max(np.abs(pc_diff_from_mean)) + 5.0
if ylim > 100.0:
ylim = 100.0
# pd2 = BarPlotData(x=(0,1,2), y=pc_diff_from_mean, ylim=[-ylim,ylim], xlabel='Tercile of Classifier Estimate\n(b)', ylabel='Recall Change From Mean (%)', x_tick_labels=['Low', 'Middle', 'High'], xhline_pos=0.0, barcolors=['grey','grey', 'grey'], barwidth=0.5)
pd2 = BarPlotData(x=(0, 1, 2),
y=pc_diff_from_mean,
ylim=[-ylim, ylim],
xlabel='Tercile of Classifier Estimate\n(b)',
ylabel='Recall Change From Mean (%)',
x_tick_labels=['Low', 'Middle', 'High'],
xhline_pos=0.0,
barcolors=['grey', 'grey', 'grey'],
barwidth=0.5)
panel_plot.add_plot_data(0, 0, plot_data=pd1)
panel_plot.add_plot_data(0, 1, plot_data=pd2)
plot = panel_plot.generate_plot()
plot_out_fname = self.get_path_to_resource_in_workspace(
'reports/' + self.pipeline.subject +
'-roc_and_terc_plot_combined.pdf')
plot.savefig(plot_out_fname, dpi=300, bboxinches='tight')
session_summary_array = self.get_passed_object('session_summary_array')
param1_name = self.get_passed_object('param1_name')
param1_unit = self.get_passed_object('param1_unit')
param1_title = '%s (%s)' % (param1_name, param1_unit)
for session_summary in session_summary_array:
# panel_plot = PanelPlot(i_max=1, j_max=1, title='', xtitle=param1_title, xtitle_fontsize=24, ytitle='$\Delta$ Post-Pre Classifier Output',ytitle_fontsize=24, wspace=0.3, hspace=0.3)
panel_plot = PanelPlot(
i_max=1,
j_max=1,
title='',
ytitle='$\Delta$ Post-Pre Classifier Output',
ytitle_fontsize=24,
wspace=0.3,
hspace=0.3)
pdc = PlotDataCollection(legend_on=True)
pdc.xlabel = param1_title
pdc.xlabel_fontsize = 24
for v, p in session_summary.plots.iteritems():
p.xhline_pos = 0.0
pdc.add_plot_data(p)
panel_plot.add_plot_data_collection(0, 0, plot_data_collection=pdc)
plot = panel_plot.generate_plot()
plot_out_fname = self.get_path_to_resource_in_workspace(
'reports/' + self.pipeline.experiment + '-' +
self.pipeline.subject + '-report_plot_' +
session_summary.name + '.pdf')
plot.savefig(plot_out_fname, dpi=300, bboxinches='tight')
cumulative_plots = self.get_passed_object('cumulative_plots')
# panel_plot = PanelPlot(i_max=1, j_max=1, title='', xtitle=param1_title, y_axis_title='$\Delta$ Post-Pre Classifier Output')
panel_plot = PanelPlot(i_max=1,
j_max=1,
title='',
ytitle='$\Delta$ Post-Pre Classifier Output',
ytitle_fontsize=24)
pdc = PlotDataCollection(legend_on=True)
pdc.xlabel = param1_title
pdc.xlabel_fontsize = 24
for v, p in cumulative_plots.iteritems():
p.xhline_pos = 0.0
pdc.add_plot_data(p)
panel_plot.add_plot_data_collection(0, 0, plot_data_collection=pdc)
plot = panel_plot.generate_plot()
plot_out_fname = self.get_path_to_resource_in_workspace(
'reports/' + self.pipeline.experiment + '-' +
self.pipeline.subject + '-report_plot_Cumulative.pdf')
plot.savefig(plot_out_fname, dpi=300, bboxinches='tight')
def run(self):
xval_results = self.get_passed_object('xval_results')
auc_results = xval_results.auc_results
threshold_values = np.unique(auc_results.t_thresh)
x = threshold_values
y = np.empty_like(x)
yerr = np.empty_like(x)
y_features = np.empty_like(x)
y_features_err = np.empty_like(x)
for i,t_thresh in enumerate(threshold_values):
auc_results_t_thresh = auc_results[auc_results.t_thresh == t_thresh]
auc_mean = np.mean(auc_results_t_thresh.auc)
auc_median = np.median(auc_results_t_thresh.auc)
auc_min = np.min(auc_results_t_thresh.auc)
auc_max = np.max(auc_results_t_thresh.auc)
y[i] = auc_median
yerr[i] = auc_max-auc_median
num_features_mean = np.mean(auc_results_t_thresh.num_features)
num_features_median = np.median(auc_results_t_thresh.num_features)
num_features_min = np.min(auc_results_t_thresh.num_features)
num_features_max = np.max(auc_results_t_thresh.num_features)
y_features[i] = num_features_median
y_features_err[i] = num_features_max - num_features_median
pd = PlotData(x=x, y=y, yerr=yerr, xhline_pos=0.5, ylim=(0.0, 1.0),ylabel='AUC', xlabel='t-threshold')
pd_features = PlotData(x=x, y=y_features,yerr=y_features_err,ylabel='Number of features', xlabel='t-threshold')
pd1 = PlotData(x=x, y=y, yerr=yerr, xhline_pos=0.5, ylim=(0.0, 1.0),ylabel='AUC', xlabel='t-threshold_1')
pd_features1 = PlotData(x=x, y=y_features,yerr=y_features_err,ylabel='Number of features', xlabel='t-threshold_1')
# pd_dict={(0,0):pd, (0,1):pd_features,(1,0):pd1, (1,1):pd_features1}
pd_dict={(0,0):pd, (0,1):pd_features}
panel_plot = PanelPlot(i_max=2, j_max=2, title='', ytitle='AUC',wspace=0.3,hspace=0.3)
for plot_panel_index, pd in pd_dict.iteritems():
print 'plot_panel_index=', plot_panel_index
print 'pd.x=', pd.x
print 'pd.y=', pd.y
# plot_letter = chr(ord('a') + 2 * plot_panel_index[0] + plot_panel_index[1])
panel_plot.add_plot_data(plot_panel_index[0], plot_panel_index[1], plot_data=pd)
# ,
# x_tick_labels=pd.x_tick_labels, title='(' + plot_letter + ')', ylim=pd.ylim)
plot = panel_plot.generate_plot()
# plot.subplots_adjust(wspace=0.3, hspace=0.3)
# # plt.savefig(join(plotsDir, quantity_name+'.png'), dpi=300,bboxinches='tight')
#
# plot_out_fname = self.get_path_to_resource_in_workspace('reports/' + self.pipeline.experiment + '-' + self.pipeline.subject + '-report_plot_' + session_summary.name + '.pdf')
#
# plot.savefig(plot_out_fname, dpi=300, bboxinches='tight')
#
# cumulative_plot_data_dict = self.get_passed_object('cumulative_plot_data_dict')
#
# panel_plot = PanelPlot(i_max=3, j_max=2, title='', y_axis_title='$\Delta$ Post-Pre Classifier Output')
#
# for plot_panel_index, pd in cumulative_plot_data_dict.iteritems():
# # print 'plot_panel_index=', plot_panel_index
# # print 'pd.x=', pd.x
# # print 'pd.y=', pd.y
# plot_letter = chr(ord('a') + 2 * plot_panel_index[0] + plot_panel_index[1])
# panel_plot.add_plot_data(plot_panel_index[0], plot_panel_index[1], x=pd.x, y=pd.y, yerr=pd.yerr,
# x_tick_labels=pd.x_tick_labels, title='(' + plot_letter + ')', ylim=pd.ylim)
#
# plot = panel_plot.generate_plot()
# plot.subplots_adjust(wspace=0.3, hspace=0.3)
# # plt.savefig(join(plotsDir, quantity_name+'.png'), dpi=300,bboxinches='tight')
#
# plot_out_fname = self.get_path_to_resource_in_workspace('reports/' + self.pipeline.experiment + '-' + self.pipeline.subject + '-report_plot_Cumulative.pdf')
plot_out_fname = join(self.pipeline.output_dir, 'plot_'+self.pipeline.subject+'.pdf')
plot.savefig(plot_out_fname, dpi=300, bboxinches='tight')
def run(self):
subject = self.pipeline.subject
task = self.pipeline.task
self.create_dir_in_workspace('reports')
session_summary_array = self.get_passed_object('session_summary_array')
serial_positions = np.arange(1,13)
for session_summary in session_summary_array:
panel_plot = PanelPlot(xfigsize=15, yfigsize=7.5, i_max=1, j_max=2, title='', wspace=0.3, hspace=0.3)
pd1 = PlotData(x=serial_positions, y=session_summary.prob_recall, xlim=(0,12), ylim=(0.0, 1.0), xlabel='Serial position\n(a)', ylabel='Probability of recall')
pd2 = PlotData(x=serial_positions, y=session_summary.prob_first_recall, xlim=(0,12), ylim=(0.0, 1.0), xlabel='Serial position\n(b)', ylabel='Probability of first recall')
panel_plot.add_plot_data(0, 0, plot_data=pd1)
panel_plot.add_plot_data(0, 1, plot_data=pd2)
plot = panel_plot.generate_plot()
plot_out_fname = self.get_path_to_resource_in_workspace('reports/' + task + '-' + subject + '-prob_recall_plot_' + session_summary.name + '.pdf')
plot.savefig(plot_out_fname, dpi=300, bboxinches='tight')
if task == 'RAM_CatFR1':
panel_plot = PanelPlot(xfigsize=6.0, yfigsize=6.0, i_max=1, j_max=1, title='', xtitle='',wspace=0.3, hspace=0.3)
pd = BarPlotData(x=[0,1], y=[session_summary.irt_within_cat, session_summary.irt_between_cat], ylabel='IRT (msec)',xlabel='', x_tick_labels=['Within Cat', 'Between Cat'], barcolors=['grey','grey'], barwidth=0.5)
panel_plot.add_plot_data(0, 0, plot_data=pd)
plot = panel_plot.generate_plot()
plot_out_fname = self.get_path_to_resource_in_workspace('reports/' + task + '-' + subject + '-irt_plot_' + session_summary.name + '.pdf')
plot.savefig(plot_out_fname, dpi=300, bboxinches='tight')
panel_plot = PanelPlot(xfigsize=15, yfigsize=7.5, i_max=1, j_max=2, title='', wspace=0.3, hspace=0.3)
pd1 = PlotData(x=session_summary.fpr, y=session_summary.tpr, xlim=[0.0,1.0], ylim=[0.0,1.0], xlabel='False Alarm Rate\n(a)', ylabel='Hit Rate', levelline=((0.0,1.0),(0.0,1.0)))
ylim = np.max(np.abs(session_summary.pc_diff_from_mean)) + 5.0
if ylim > 100.0:
ylim = 100.0
pd2 = BarPlotData(x=(0,1,2), y=session_summary.pc_diff_from_mean, ylim=[-ylim,ylim], xlabel='Tercile of Classifier Estimate\n(b)', ylabel='Recall Change From Mean (%)', x_tick_labels=['Low', 'Middle', 'High'], xhline_pos=0.0, barcolors=['grey','grey', 'grey'], barwidth=0.5)
panel_plot.add_plot_data(0, 0, plot_data=pd1)
panel_plot.add_plot_data(0, 1, plot_data=pd2)
plot = panel_plot.generate_plot()
plot_out_fname = self.get_path_to_resource_in_workspace('reports/' + task + '-' + subject + '-roc_and_terc_plot_' + session_summary.name + '.pdf')
plot.savefig(plot_out_fname, dpi=300, bboxinches='tight')
cumulative_summary = self.get_passed_object('cumulative_summary')
panel_plot = PanelPlot(xfigsize=15, yfigsize=7.5, i_max=1, j_max=2, title='', wspace=0.3, hspace=0.3)
pd1 = PlotData(x=serial_positions, y=cumulative_summary.prob_recall, xlim=(0, 12), ylim=(0.0, 1.0), xlabel='Serial position\n(a)', ylabel='Probability of recall')
pd2 = PlotData(x=serial_positions, y=cumulative_summary.prob_first_recall, xlim=(0, 12), ylim=(0.0, 1.0), xlabel='Serial position\n(b)', ylabel='Probability of first recall')
panel_plot.add_plot_data(0, 0, plot_data=pd1)
panel_plot.add_plot_data(0, 1, plot_data=pd2)
plot = panel_plot.generate_plot()
plot_out_fname = self.get_path_to_resource_in_workspace('reports/' + task + '-' + subject + '-prob_recall_plot_combined.pdf')
plot.savefig(plot_out_fname, dpi=300, bboxinches='tight')
if task == 'RAM_CatFR1':
panel_plot = PanelPlot(xfigsize=6.0, yfigsize=6.0, i_max=1, j_max=1, title='',xtitle='', wspace=0.3, hspace=0.3)
pd = BarPlotData(x=[0,1], y=[cumulative_summary.irt_within_cat, cumulative_summary.irt_between_cat], ylabel='IRT (msec)', xlabel='',x_tick_labels=['Within Cat', 'Between Cat'], barcolors=['grey','grey'], barwidth=0.5)
panel_plot.add_plot_data(0, 0, plot_data=pd)
plot = panel_plot.generate_plot()
plot_out_fname = self.get_path_to_resource_in_workspace('reports/' + task + '-' + subject + '-irt_plot_combined.pdf')
plot.savefig(plot_out_fname, dpi=300, bboxinches='tight')
panel_plot = PanelPlot(xfigsize=15, yfigsize=7.5, i_max=1, j_max=2, title='', wspace=0.3, hspace=0.3)
pd1 = PlotData(x=cumulative_summary.fpr, y=cumulative_summary.tpr, xlim=[0.0,1.0], ylim=[0.0,1.0], xlabel='False Alarm Rate\n(a)', ylabel='Hit Rate', levelline=((0.001,0.999),(0.001,0.999)))
ylim = np.max(np.abs(cumulative_summary.pc_diff_from_mean)) + 5.0
if ylim > 100.0:
ylim = 100.0
pd2 = BarPlotData(x=(0,1,2), y=cumulative_summary.pc_diff_from_mean, ylim=[-ylim,ylim], xlabel='Tercile of Classifier Estimate\n(b)', ylabel='Recall Change From Mean (%)', x_tick_labels=['Low', 'Middle', 'High'], xhline_pos=0.0, barcolors=['grey','grey', 'grey'],barwidth=0.5)
panel_plot.add_plot_data(0, 0, plot_data=pd1)
panel_plot.add_plot_data(0, 1, plot_data=pd2)
plot = panel_plot.generate_plot()
plot_out_fname = self.get_path_to_resource_in_workspace('reports/' + task + '-' + subject + '-roc_and_terc_plot_combined.pdf')
plot.savefig(plot_out_fname, dpi=300, bboxinches='tight')
def run(self):
xval_results = self.get_passed_object('xval_results')
auc_results = xval_results.auc_results
threshold_values = np.unique(auc_results.t_thresh)
x = threshold_values
y = np.empty_like(x)
yerr = np.empty_like(x)
y_features = np.empty_like(x)
y_features_err = np.empty_like(x)
for i, t_thresh in enumerate(threshold_values):
auc_results_t_thresh = auc_results[auc_results.t_thresh ==
t_thresh]
auc_mean = np.mean(auc_results_t_thresh.auc)
auc_median = np.median(auc_results_t_thresh.auc)
auc_min = np.min(auc_results_t_thresh.auc)
auc_max = np.max(auc_results_t_thresh.auc)
y[i] = auc_median
yerr[i] = auc_max - auc_median
num_features_mean = np.mean(auc_results_t_thresh.num_features)
num_features_median = np.median(auc_results_t_thresh.num_features)
num_features_min = np.min(auc_results_t_thresh.num_features)
num_features_max = np.max(auc_results_t_thresh.num_features)
y_features[i] = num_features_median
y_features_err[i] = num_features_max - num_features_median
pd = PlotData(x=x,
y=y,
yerr=yerr,
xhline_pos=0.5,
ylim=(0.0, 1.0),
ylabel='AUC',
xlabel='t-threshold')
pd_features = PlotData(x=x,
y=y_features,
yerr=y_features_err,
ylabel='Number of features',
xlabel='t-threshold')
pd1 = PlotData(x=x,
y=y,
yerr=yerr,
xhline_pos=0.5,
ylim=(0.0, 1.0),
ylabel='AUC',
xlabel='t-threshold_1')
pd_features1 = PlotData(x=x,
y=y_features,
yerr=y_features_err,
ylabel='Number of features',
xlabel='t-threshold_1')
# pd_dict={(0,0):pd, (0,1):pd_features,(1,0):pd1, (1,1):pd_features1}
pd_dict = {(0, 0): pd, (0, 1): pd_features}
panel_plot = PanelPlot(i_max=2,
j_max=2,
title='',
ytitle='AUC',
wspace=0.3,
hspace=0.3)
for plot_panel_index, pd in pd_dict.iteritems():
print 'plot_panel_index=', plot_panel_index
print 'pd.x=', pd.x
print 'pd.y=', pd.y
# plot_letter = chr(ord('a') + 2 * plot_panel_index[0] + plot_panel_index[1])
panel_plot.add_plot_data(plot_panel_index[0],
plot_panel_index[1],
plot_data=pd)
# ,
# x_tick_labels=pd.x_tick_labels, title='(' + plot_letter + ')', ylim=pd.ylim)
plot = panel_plot.generate_plot()
# plot.subplots_adjust(wspace=0.3, hspace=0.3)
# # plt.savefig(join(plotsDir, quantity_name+'.png'), dpi=300,bboxinches='tight')
#
# plot_out_fname = self.get_path_to_resource_in_workspace('reports/' + self.pipeline.experiment + '-' + self.pipeline.subject + '-report_plot_' + session_summary.name + '.pdf')
#
# plot.savefig(plot_out_fname, dpi=300, bboxinches='tight')
#
# cumulative_plot_data_dict = self.get_passed_object('cumulative_plot_data_dict')
#
# panel_plot = PanelPlot(i_max=3, j_max=2, title='', y_axis_title='$\Delta$ Post-Pre Classifier Output')
#
# for plot_panel_index, pd in cumulative_plot_data_dict.iteritems():
# # print 'plot_panel_index=', plot_panel_index
# # print 'pd.x=', pd.x
# # print 'pd.y=', pd.y
# plot_letter = chr(ord('a') + 2 * plot_panel_index[0] + plot_panel_index[1])
# panel_plot.add_plot_data(plot_panel_index[0], plot_panel_index[1], x=pd.x, y=pd.y, yerr=pd.yerr,
# x_tick_labels=pd.x_tick_labels, title='(' + plot_letter + ')', ylim=pd.ylim)
#
# plot = panel_plot.generate_plot()
# plot.subplots_adjust(wspace=0.3, hspace=0.3)
# # plt.savefig(join(plotsDir, quantity_name+'.png'), dpi=300,bboxinches='tight')
#
# plot_out_fname = self.get_path_to_resource_in_workspace('reports/' + self.pipeline.experiment + '-' + self.pipeline.subject + '-report_plot_Cumulative.pdf')
plot_out_fname = join(self.pipeline.output_dir,
'plot_' + self.pipeline.subject + '.pdf')
plot.savefig(plot_out_fname, dpi=300, bboxinches='tight')
def run(self):
subject = self.pipeline.subject
task = self.pipeline.task
self.create_dir_in_workspace('reports')
session_summary_array = self.get_passed_object('session_summary_array')
serial_positions = np.arange(1,13)
for session_summary in session_summary_array:
panel_plot = PanelPlot(xfigsize=15, yfigsize=7.5, i_max=1, j_max=2, title='', wspace=0.3, hspace=0.3, labelsize=20)
pd1 = PlotData(x=serial_positions, y=session_summary.prob_recall, xlim=(0,12), ylim=(0.0, 1.0), xlabel='Serial position\n(a)', ylabel='Probability of recall')
pd1.xlabel_fontsize = 20
pd1.ylabel_fontsize = 20
pd2 = PlotData(x=serial_positions, y=session_summary.prob_first_recall, xlim=(0,12), ylim=(0.0, 1.0), xlabel='Serial position\n(b)', ylabel='Probability of first recall')
pd2.xlabel_fontsize = 20
pd2.ylabel_fontsize = 20
# panel_plot.add_plot_data(0, 0, plot_data=pd1)
# panel_plot.add_plot_data(0, 1, plot_data=pd2)
#
# plot = panel_plot.generate_plot()
panel_plot.add_plot_data(0, 0, plot_data=pd1)
panel_plot.add_plot_data(0, 1, plot_data=pd2)
plot = panel_plot.generate_plot()
plot_out_fname = self.get_path_to_resource_in_workspace('reports/' + task + '-' + subject + '-prob_recall_plot_' + session_summary.name + '.pdf')
plot.savefig(plot_out_fname, dpi=300, bboxinches='tight')
if task == 'RAM_CatFR1':
panel_plot = PanelPlot(xfigsize=6.0, yfigsize=6.0, i_max=1, j_max=1, title='', wspace=0.3, hspace=0.3)
pd = BarPlotData(x=[0,1], y=[session_summary.irt_within_cat, session_summary.irt_between_cat], ylabel='IRT (msec)', x_tick_labels=['Within Cat', 'Between Cat'], barcolors=['grey','grey'], barwidth=0.5)
panel_plot.add_plot_data(0, 0, plot_data=pd)
plot = panel_plot.generate_plot()
plot_out_fname = self.get_path_to_resource_in_workspace('reports/' + task + '-' + subject + '-irt_plot_' + session_summary.name + '.pdf')
plot.savefig(plot_out_fname, dpi=300, bboxinches='tight')
panel_plot = PanelPlot(xfigsize=10.0, yfigsize=10.0, i_max=1, j_max=1, title='', xlabel='List', ylabel='# of items', labelsize=20)
pdc = PlotDataCollection()
#----------------- FORMATTING
pdc.xlabel = 'List number'
pdc.xlabel_fontsize = 20
pdc.ylabel ='# recalled words'
pdc.ylabel_fontsize = 20
n_lists = len(session_summary.n_stims_per_list)
print 'Number of lists', n_lists
bpd_1 = BarPlotData(x=np.arange(n_lists), y=session_summary.n_stims_per_list, title='', alpha=0.1)
pd_1 = PlotData(x=np.where(session_summary.is_stim_list)[0], y=session_summary.n_recalls_per_list[session_summary.is_stim_list],
title='', linestyle='', color='red', marker='o',markersize=20)
pd_2 = PlotData(x=np.where(~session_summary.is_stim_list)[0], y=session_summary.n_recalls_per_list[~session_summary.is_stim_list],
title='', linestyle='', color='blue', marker='o',markersize=20)
print 'np.where(session_summary.is_stim_list)[0]=',np.where(session_summary.is_stim_list)[0]
print 'np.where(~session_summary.is_stim_list)[0]=',np.where(~session_summary.is_stim_list)[0]
pdc.add_plot_data(pd_1)
pdc.add_plot_data(pd_2)
pdc.add_plot_data(bpd_1)
panel_plot.add_plot_data_collection(0, 0, plot_data_collection=pdc)
plot = panel_plot.generate_plot()
plot_out_fname = self.get_path_to_resource_in_workspace('reports/' + task + '-' + subject + '-stim_and_recall_plot_' + session_summary.name + '.pdf')
plot.savefig(plot_out_fname, dpi=300, bboxinches='tight')
cumulative_summary = self.get_passed_object('cumulative_summary')
panel_plot = PanelPlot(xfigsize=15, yfigsize=7.5, i_max=1, j_max=2, title='', wspace=0.3, hspace=0.3)
pd1 = PlotData(x=serial_positions, y=cumulative_summary.prob_recall, xlim=(0, 12), ylim=(0.0, 1.0), xlabel='Serial position\n(a)', ylabel='Probability of recall')
pd2 = PlotData(x=serial_positions, y=cumulative_summary.prob_first_recall, xlim=(0, 12), ylim=(0.0, 1.0), xlabel='Serial position\n(b)', ylabel='Probability of first recall')
panel_plot.add_plot_data(0, 0, plot_data=pd1)
panel_plot.add_plot_data(0, 1, plot_data=pd2)
plot = panel_plot.generate_plot()
plot_out_fname = self.get_path_to_resource_in_workspace('reports/' + task + '-' + subject + '-prob_recall_plot_combined.pdf')
plot.savefig(plot_out_fname, dpi=300, bboxinches='tight')
if task == 'RAM_CatFR1':
panel_plot = PanelPlot(xfigsize=6.0, yfigsize=6.0, i_max=1, j_max=1, title='', wspace=0.3, hspace=0.3)
pd = BarPlotData(x=[0,1], y=[cumulative_summary.irt_within_cat, cumulative_summary.irt_between_cat], ylabel='IRT (msec)', x_tick_labels=['Within Cat', 'Between Cat'], barcolors=['grey','grey'], barwidth=0.5)
panel_plot.add_plot_data(0, 0, plot_data=pd)
plot = panel_plot.generate_plot()
plot_out_fname = self.get_path_to_resource_in_workspace('reports/' + task + '-' + subject + '-irt_plot_combined.pdf')
plot.savefig(plot_out_fname, dpi=300, bboxinches='tight')
def run(self):
self.create_dir_in_workspace('reports')
frequency_plot_data = self.get_passed_object('frequency_plot')
centralized_frequency_plot_data = self.get_passed_object('centralized_frequency_plot')
frequency_region_plot_data = self.get_passed_object('frequency_region_plot')
frequency_frequency_plot_data = self.get_passed_object('frequency_frequency_plot')
panel_plot = PanelPlot(xfigsize=11, yfigsize=11, i_max=1, j_max=1, title='', ytitle=self.params.output_title, ytitle_fontsize=16, wspace=0.3, hspace=0.3)
pdc = PlotDataCollection(legend_on=True)
pdc.xlabel = 'Burst Frequency (Hz)'
pdc.xlabel_fontsize = 16
for v,p in frequency_plot_data.iteritems():
p.xhline_pos=0.0
pdc.add_plot_data(p)
panel_plot.add_plot_data_collection(0, 0, plot_data_collection=pdc)
plot = panel_plot.generate_plot()
plot_out_fname = self.get_path_to_resource_in_workspace('reports/ps3_frequency_aggregate_plots.pdf')
plot.savefig(plot_out_fname, dpi=300, bboxinches='tight')
panel_plot = PanelPlot(xfigsize=15, yfigsize=7.5, i_max=1, j_max=2, title='', ytitle=self.params.output_title, wspace=0.3, hspace=0.3)
panel_plot.add_plot_data(0, 0, plot_data=frequency_region_plot_data)
panel_plot.add_plot_data(0, 1, plot_data=frequency_frequency_plot_data)
plot = panel_plot.generate_plot()
plot_out_fname = self.get_path_to_resource_in_workspace('reports/ps3_frequency_projection_plots.pdf')
plot.savefig(plot_out_fname, dpi=300, bboxinches='tight')
