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paper_plots_3.jl
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paper_plots_3.jl
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# for preprint article August 2017
print("Zeroing results!!\n");
include("multi_critic_detailed_recording.jl");
using PyPlot
using LaTeXStrings;
@pyimport seaborn as sns
#sns.set(font_scale=1.5)
# sns.set_context("poster")
#sns.set_context("talk")
#sns.set(font_scale=3)
#exp_results = [];
# Run the full set of simulations
# compare_three_trial_types_with_multiple_subjects()
biased_compare_three_trial_types_with_multiple_subjects()
function plot_figure_2(results_id=1::Int, exp_id=2::Int)
# uses:
# compare_three_trial_types_with_multiple_subjects()
# paper_binary_inputs_parameters_critic_simulations.jl
# shows:
#
plotting_separate_choices_on = true;
latest_experiment_results = exp_results[results_id];
figure(figsize=(10,12))
block_id = linspace(1,no_blocks_in_experiment, no_blocks_in_experiment);
xlim((0-0.1,no_blocks_in_experiment+0.1))
ylim((0-0.02,1+0.02))
xlabel("Block number")
ylabel("Proportion correct")
if(plotting_scatter_plot_on)
for i = 1:no_blocks_in_experiment
for j = 1:no_subjects
# TODO: How do I get separate left-right performance out of roving task?
if(plotting_separate_choices_on)
# adding plotting of sub-task related results
scatter(i+0., latest_experiment_results.subjects_roving_task[j,exp_id].blocks[i].proportion_task_correct[1], marker="o", c="c")
scatter(i+0., latest_experiment_results.subjects_roving_task[j,exp_id].blocks[i].proportion_task_correct[2], marker="o", c="m")
end
# END paste
#scatter(i-0., latest_experiment_results.subjects_roving_task[j,1].blocks[i].proportion_correct, marker="o", edgecolors="face", c="b", alpha=0.7)
if(plotting_task_by_task_on)
scatter(i-0., latest_experiment_results.subjects_roving_task[j,exp_id].blocks[i].proportion_task_correct[1], marker="o", edgecolors="face", c="r", alpha=0.3)
scatter(i-0., latest_experiment_results.subjects_roving_task[j,exp_id].blocks[i].proportion_task_correct[2], marker="o", edgecolors="face", c="g", alpha=0.3)
end
end
end
end
if(plotting_error_bars_on)
errorbar(block_id-0., latest_experiment_results.roving_correct[:,exp_id], latest_experiment_results.roving_range[:,exp_id], ecolor="b", color="b", linewidth=2)
errorbar(block_id-0., latest_experiment_results.roving_correct[:,exp_id], latest_experiment_results.roving_error[:,exp_id], ecolor="k", color="b", linewidth=2)
end
if(plotting_individual_subjects_on)
for j = 1:no_subjects
local_prop_roving_correct = zeros(no_blocks_in_experiment);
local_prop_roving_task_1_correct = zeros(no_blocks_in_experiment);
local_prop_roving_task_2_correct = zeros(no_blocks_in_experiment);
for i = 1:no_blocks_in_experiment
local_prop_roving_correct[i] = latest_experiment_results.subjects_roving_task[j,exp_id].blocks[i].proportion_correct;
local_prop_roving_task_1_correct[i] = latest_experiment_results.subjects_roving_task[j,exp_id].blocks[i].proportion_task_correct[1];
local_prop_roving_task_2_correct[i] = latest_experiment_results.subjects_roving_task[j,exp_id].blocks[i].proportion_task_correct[2];
end
if(plotting_task_by_task_on)
plot(block_id-0., local_prop_roving_task_1_correct, "c")#, alpha=0.1)
plot(block_id-0., local_prop_roving_task_2_correct, "m")#, alpha=0.1)
end
#plot(block_id-0., local_prop_roving_correct, "b", alpha=0.1)
end
end
#plot(block_id-0., latest_experiment_results.roving_correct[:,1], "b", linewidth=3, label="Block mean performance average")
# plot(block_id-0., latest_experiment_results.roving_task_correct[:,1,exp_id], "r", linewidth=3, label="Block mean performance on Bisection Task 1")
plot(block_id-0., latest_experiment_results.roving_task_correct[:,2,exp_id], "g", linewidth=3, label="Block mean performance on Bisection Task 2")
# legend(loc=4)
#savefig("figure_1_2.pdf", transparent="True", bbox_inches="tight", pad_inches=0.1)
savefig("figure_biased_sim_strong_bias.pdf", bbox_inches="tight", pad_inches=0.1)
end
function plot_figure_1(results_id=1::Int)
# uses:
# compare_three_trial_types_with_multiple_subjects()
# paper_binary_inputs_parameters_critic_simulations.jl
# shows:
#
plotting_separate_choices_on = true;
latest_experiment_results = exp_results[results_id];
figure(figsize=(10,12))
block_id = linspace(1,no_blocks_in_experiment, no_blocks_in_experiment);
## Task 2 subplot
xlim((0-0.1,no_blocks_in_experiment+0.1))
ylim((0-0.02,1+0.02))
xlabel("Block number")
ylabel("Proportion correct")
if(plotting_scatter_plot_on)
for i = 1:no_blocks_in_experiment
for j = 1:no_subjects
scatter(i+0., latest_experiment_results.subjects_task[j,2].blocks[i].proportion_correct, marker="o", edgecolors="face", c="g", alpha=0.5)
if(plotting_separate_choices_on)
# adding plotting of sub-task related results
scatter(i+0., latest_experiment_results.subjects_task[j,2].blocks[i].proportion_task_correct[1], marker="o", c="c")
scatter(i+0., latest_experiment_results.subjects_task[j,2].blocks[i].proportion_task_correct[2], marker="o", c="m")
end
end
end
end
if(plotting_error_bars_on)
errorbar(block_id+0., latest_experiment_results.task_correct[:,2], latest_experiment_results.task_range[:,2], ecolor="g", color="g", linewidth=2)
errorbar(block_id+0., latest_experiment_results.task_correct[:,2], latest_experiment_results.task_error[:,2], ecolor="k", color="g", linewidth=2)
end
if(plotting_individual_subjects_on)
for j = 1:no_subjects
local_prop_2_correct = zeros(no_blocks_in_experiment);
# adding plotting of sub-task related results
local_prop_sub_1_correct = zeros(no_blocks_in_experiment);
local_prop_sub_2_correct = zeros(no_blocks_in_experiment);
for i = 1:no_blocks_in_experiment
local_prop_2_correct[i] = latest_experiment_results.subjects_task[j,2].blocks[i].proportion_correct;
local_prop_sub_1_correct[i] = latest_experiment_results.subjects_task[j,2].blocks[i].proportion_task_correct[1];
local_prop_sub_2_correct[i] = latest_experiment_results.subjects_task[j,2].blocks[i].proportion_task_correct[2];
end
plot(block_id, local_prop_2_correct, "g", alpha=0.1)
if(plotting_separate_choices_on)
# adding plotting of sub-task related results
plot(block_id, local_prop_sub_1_correct, "c")
plot(block_id, local_prop_sub_2_correct, "m")
end
end
end
plot(block_id+0., latest_experiment_results.task_correct[:,2], "g", linewidth=2, label="Subjects expect 50:50")
# legend(loc=4)
#savefig("figure_1_1.pdf", transparent="True", bbox_inches="tight", pad_inches=0.1)
savefig("figure_biased_sim_no_bias.pdf", bbox_inches="tight", pad_inches=0.1)
end
function plot_figure_3(results_id=1::Int)
# uses:
# compare_three_trial_types_with_multiple_subjects()
# paper_binary_inputs_parameters_critic_simulations.jl
# shows:
# learning occurs on hard task when it is performed alone
plotting_separate_choices_on = false;
latest_experiment_results = exp_results[results_id];
figure(figsize=(10,12))
block_id = linspace(1,no_blocks_in_experiment, no_blocks_in_experiment);
## Task 2 subplot
xlim((0-0.1,no_blocks_in_experiment+0.1))
ylim((0-0.02,1+0.02))
xlabel("Block number")
ylabel("Proportion correct")
if(plotting_scatter_plot_on)
for i = 1:no_blocks_in_experiment
for j = 1:no_subjects
scatter(i+0., latest_experiment_results.subjects_task[j,2].blocks[i].proportion_correct, marker="o", edgecolors="face", c="g", alpha=0.5)
if(plotting_separate_choices_on)
# adding plotting of sub-task related results
scatter(i+0., latest_experiment_results.subjects_task[j,2].blocks[i].proportion_task_correct[1], marker="o", c="c")
scatter(i+0., latest_experiment_results.subjects_task[j,2].blocks[i].proportion_task_correct[2], marker="o", c="m")
end
end
end
end
if(plotting_error_bars_on)
errorbar(block_id+0., latest_experiment_results.task_correct[:,2], latest_experiment_results.task_range[:,2], ecolor="g", color="g", linewidth=2)
errorbar(block_id+0., latest_experiment_results.task_correct[:,2], latest_experiment_results.task_error[:,2], ecolor="k", color="g", linewidth=2)
end
if(plotting_individual_subjects_on)
for j = 1:no_subjects
local_prop_2_correct = zeros(no_blocks_in_experiment);
# adding plotting of sub-task related results
local_prop_sub_1_correct = zeros(no_blocks_in_experiment);
local_prop_sub_2_correct = zeros(no_blocks_in_experiment);
for i = 1:no_blocks_in_experiment
local_prop_2_correct[i] = latest_experiment_results.subjects_task[j,2].blocks[i].proportion_correct;
local_prop_sub_1_correct[i] = latest_experiment_results.subjects_task[j,2].blocks[i].proportion_task_correct[1];
local_prop_sub_2_correct[i] = latest_experiment_results.subjects_task[j,2].blocks[i].proportion_task_correct[2];
end
plot(block_id, local_prop_2_correct, "g", alpha=0.1)
if(plotting_separate_choices_on)
# adding plotting of sub-task related results
plot(block_id, local_prop_sub_1_correct, "c")
plot(block_id, local_prop_sub_2_correct, "m")
end
end
end
plot(block_id+0., latest_experiment_results.task_correct[:,2], "g", linewidth=2, label="Subjects learning difficult task")
# legend(loc=4)
#savefig("figure_1_1.pdf", transparent="True", bbox_inches="tight", pad_inches=0.1)
savefig("figure_pop_size.pdf", bbox_inches="tight", pad_inches=0.1)
end
plot_figure_1()
plot_figure_2()
# Update number of critics in plot_D_vector.jl,
# make sure you're using all plotting options
# then import it
# include("plot_D_vector.jl")
# Run scripted plotting of flow fields with overlaid trajectories
# include("script_check_S.jl")
# savefig("sim_trace_no_bias.pdf", bbox_inches="tight", pad_inches=0.1)
# change settings in nuber of critics
# change which data is loaded in script_check_S
# savefig("sim_trace_strong_bias.pdf", bbox_inches="tight", pad_inches=0.1)