plots are not pdf with font type 42

all_functions.py

@@ -11,6 +11,9 @@
 from datetime import datetime
 from copy import deepcopy
 from mujoco_py.generated import const
+import matplotlib
+matplotlib.rcParams['pdf.fonttype'] = 42
+matplotlib.rcParams['ps.fonttype'] = 42
 ################################################
 #Functions for main tests
 def learn_to_move_2_fcn(MuJoCo_model_name, model, cum_kinematics, cum_activations, reward_thresh=6, energy_cost_weight=0, refinement = False, Mj_render = False):

experiment_1_show_errorresults.py

@@ -30,8 +30,8 @@
 errors_all = np.load(file_address)
 errors_all_mean=errors_all.mean(1)
 errors_all_std = errors_all.std(1)
-print("errors_mean: ",errors_all_mean)
-print("errors_std: ",errors_all_std)
+print("Errors_mean: ",errors_all_mean)
+print("Errors_std: ",errors_all_std)
 p0_0 = axes[0].boxplot(
 	errors_all.mean(0).reshape(1,-1,1,N).squeeze(),
 	positions=positions_cyclical,
@@ -69,9 +69,9 @@
 	notch=True,
 	patch_artist=True,
 	showfliers=False)
-axes[0].set_title(r'average across both joints',fontsize=10)
+axes[0].set_title(r'Average across both joints',fontsize=10)
 axes[0].set_ylim(y_lim_p0)
-axes[0].set_xlabel('stiffness (N/m)')
+axes[0].set_xlabel('Stiffness (N/m)')
 axes[0].set_xticklabels(stiffness_values, rotation=45, fontsize=8)
 axes[0].set_ylabel('RMSE')
 
@@ -81,10 +81,10 @@
 	notch=True,
 	patch_artist=True,
 	showfliers=False)
-axes[1].set_title('proximal joint ($q_0$)', fontsize=10)
+axes[1].set_title('Proximal joint ($q_0$)', fontsize=10)
 axes[1].set_ylim(y_lim)
 axes[1].set_yticklabels([])
-axes[1].set_xlabel('stiffness (N/m)')
+axes[1].set_xlabel('Stiffness (N/m)')
 axes[1].set_xticklabels(stiffness_values, rotation=45, fontsize=8)
 
 p1_2 = axes[2].boxplot(
@@ -93,10 +93,10 @@
 	notch=True,
 	patch_artist=True,
 	showfliers=False)
-axes[2].set_title('distal joint ($q_1$)', fontsize=10)
+axes[2].set_title('Distal joint ($q_1$)', fontsize=10)
 axes[2].set_ylim(y_lim)
 axes[2].set_yticklabels([])
-axes[2].set_xlabel('stiffness (N/m)')
+axes[2].set_xlabel('Stiffness (N/m)')
 axes[2].set_xticklabels(stiffness_values, rotation=45, fontsize=8)
 
 # changing the box colors

experiment_1_show_learningrates.py

@@ -38,17 +38,23 @@
 learning_errors_per_stiffness = np.zeros([stiffness_versions, epoch_numbers])
 for stiffness_ver in range(stiffness_versions):
 	learning_errors_per_stiffness[stiffness_ver,:] = learning_errors[:, :, stiffness_ver, :].mean(0).squeeze()
-	axes.plot(np.arange(1,epoch_numbers_to_show+1), learning_errors_per_stiffness[stiffness_ver,:epoch_numbers_to_show],color=colorsys.hsv_to_rgb((8-stiffness_ver)/14,1,.75), alpha=.55, linewidth=2.2)
+	axes.plot(np.arange(1,epoch_numbers_to_show+1), learning_errors_per_stiffness[stiffness_ver,:epoch_numbers_to_show],
+		color=colorsys.hsv_to_rgb((8.5-stiffness_ver)/14,1,.75), alpha=.65, linewidth=3.0)
 #(50, 1, 9, 20) 
 plt.sca(axes)
-plt.legend(['S: 0', 'S: 500', 'S: 1K', 'S: 2K', 'S: 4K', 'S: 7K', 'S: 10K', 'S: 15K', 'S: 20K'])
-plt.xticks(np.arange(2,epoch_numbers_to_show+1,2), np.arange(2,epoch_numbers_to_show+1,2))
+#plt.ylim([0.035,0.1])
+plt.legend(['S: 0', 'S: 500', 'S: 1K', 'S: 2K', 'S: 4K', 'S: 7K', 'S: 10K', 'S: 15K', 'S: 20K'],fontsize='small')
+plt.xticks(np.arange(1,epoch_numbers_to_show+1,1), np.arange(1,epoch_numbers_to_show+1,1),
+	rotation=45, fontsize=8)
 
-plt.xlabel("epoch #")
+plt.xlabel("Epoch #", fontsize=9)
 #axes.set_xlabel("epoch #")
-plt.ylabel("epoch MSE")
-plt.title("learning curves for different stiffness values (S)")
-fig.savefig('./results/{}/learningcurves.png'.format(experiment_ID))
+plt.ylabel("Epoch MSE", fontsize=9)
+plt.yticks(np.arange(0.035,0.096,0.005),rotation=45, fontsize=8)
+plt.title("Learning curves for different stiffness values (S)", fontsize=10)
+plt.grid()
+fig.savefig('./results/{}/exp1_learningcurves.pdf'.format(experiment_ID))
+fig.savefig('./results/figures/exp1_learningcurves.pdf'.format(experiment_ID))
 plt.show()
 
 

experiment_2_run_2way.py

@@ -11,7 +11,7 @@
 simplefilter(action='ignore', category=FutureWarning)
 
 experiment_ID = "experiment_2_2way"
-mc_run_number = 2
+mc_run_number = 50
 babbling_time = 3
 number_of_refinements = 5
 errors_all_cyc_A_A = np.zeros([2, number_of_refinements+1, mc_run_number])
@@ -173,8 +173,8 @@
 
 	np.random.seed(random_seed) # change the seed for different initial conditions
 	tf.random.set_random_seed(random_seed)
-	model_B_refined_B_p2p = copy_model_fcn(original_model=model_B_babble)
-	[model_B_refined_B_p2p, errors, cum_kinematics_B_refined, cum_activations_B_refined] =\
+	model_B_refined_A_p2p = copy_model_fcn(original_model=model_B_babble)
+	[model_B_refined_A_p2p, errors, cum_kinematics_A_refined, cum_activations_A_refined] =\
 		p2p_run_fcn(
 			MuJoCo_model_name=MuJoCo_model_name_A,
 			model=model_B_refined_A_cyc,

experiment_2_show_learningrates.py

@@ -6,6 +6,10 @@
 from all_functions import *
 import pickle
 from warnings import simplefilter
+import matplotlib
+matplotlib.rcParams['pdf.fonttype'] = 42
+matplotlib.rcParams['ps.fonttype'] = 42
+matplotlib.rcParams['mathtext.fontset'] = 'cm'
 
 def exp2_learning_curves_cal_fcn(errors_all):
 	average_curve_mean = errors_all.mean(0).mean(1)
@@ -18,10 +22,6 @@ def exp2_learning_curves_cal_fcn(errors_all):
 
 simplefilter(action='ignore', category=FutureWarning)
 
-import matplotlib
-matplotlib.rcParams['pdf.fonttype'] = 42
-matplotlib.rcParams['ps.fonttype'] = 42
-
 experiment_ID = "experiment_2"
 number_of_refinements = 5
 errors_all_cyc_A_A = np.load("./results/{}/errors_all_cyc_A_A.npy".format(experiment_ID))
@@ -99,6 +99,7 @@ def exp2_learning_curves_cal_fcn(errors_all):
 		axes[subplot_iter].set_ylim(y_lim)
 		axes[subplot_iter].legend(['A_A','A_B','B_B'], fontsize=6)
 fig.subplots_adjust(top=.9, bottom=.2, left=.06, right=.95)
-fig.savefig('./results/{}/learningcurves.png'.format(experiment_ID))
+fig.savefig('./results/{}/exp2_learningcurves.pdf'.format(experiment_ID))
+fig.savefig('./results/figures/exp2_learningcurves.pdf'.format(experiment_ID))
 plt.show()
 #import pdb; pdb.set_trace()

experiment_3_pool_showresults.py

@@ -10,7 +10,11 @@
 from warnings import simplefilter
 
 import matplotlib
+matplotlib.rcParams['pdf.fonttype'] = 42
+matplotlib.rcParams['ps.fonttype'] = 42
+matplotlib.rcParams['mathtext.fontset'] = 'cm'
 
+plt.rcParams['svg.fonttype'] = 'none'
 def calculate_mean_std(data, method='mean'):
 	stiffness_versions = data.shape[0]
 	output = np.zeros(stiffness_versions)
@@ -23,8 +27,7 @@ def calculate_mean_std(data, method='mean'):
 			raise NameError('invalid method: please use mean or std')
 	return output
 
-matplotlib.rcParams['pdf.fonttype'] = 42
-matplotlib.rcParams['ps.fonttype'] = 42
+
 
 experiment_ID = "experiment_3_pool_G"
 
@@ -96,8 +99,8 @@ def calculate_mean_std(data, method='mean'):
 #axes[1].set_xticklabels(stiffness_values, rotation=45, fontsize=8)
 stiffness_values = ["0", "500", "1K", "2K", "4K", "7K", "10K", "15K", "20K"]
 #titles = []
-xlabels = ['stiffness']*3
-ylabels = ['success rate (%)', 'reward', 'energy']
+xlabels = ['Stiffness']*3
+ylabels = ['Success rate (%)', 'Reward', 'Energy']
 
 for ii in range(ncols):
 	plt.sca(axes[ii])
@@ -106,7 +109,8 @@ def calculate_mean_std(data, method='mean'):
 	plt.ylabel(ylabels[ii], fontsize=9)
 	plt.yticks(rotation=45, fontsize=8)
 fig.subplots_adjust(top=.9, bottom=.2, left=.06, right=.95, wspace=.30)
-fig.savefig('./results/{}/learn2walk_results.png'.format(experiment_ID))
+fig.savefig('./results/{}/exp3_learn2walk_results.pdf'.format(experiment_ID))
+fig.savefig('./results/figures/exp3_learn2walk_results.pdf'.format(experiment_ID))
 # p0 = axes[1].boxplot(
 # 	[errors_all_cyc_A_A.mean(0)[0], errors_all_cyc_A_B.mean(0)[0], errors_all_cyc_B_B.mean(0)[0]],
 # 	notch=True,

experiment_4_pool_show_withMC_singlefigure.py

@@ -0,0 +1,132 @@
+import json
+import numpy as np
+from scipy import signal, stats
+from matplotlib import pyplot as plt
+import colorsys
+import pickle
+from warnings import simplefilter
+import os
+
+import matplotlib
+matplotlib.rcParams['pdf.fonttype'] = 42
+matplotlib.rcParams['ps.fonttype'] = 42
+matplotlib.rcParams['mathtext.fontset'] = 'cm'
+
+extended_view = False
+stiffness_versions = 9
+if extended_view:
+	select_stiffness = range(stiffness_versions)
+else:
+	select_stiffness = np.array([0, 2, 4, 6, 8])
+RL_method = "PPO1"
+total_MC_runs = 50 # starts from 1
+experiment_ID = "experiment_4_pool_with_MC_C"
+total_timesteps = 500000
+episode_timesteps = 1000
+total_episodes = int(total_timesteps/episode_timesteps)
+
+episode_rewards_all = np.zeros([total_MC_runs, stiffness_versions, total_episodes])
+
+for stiffness_value in range(stiffness_versions):
+	stiffness_value_str = "stiffness_{}".format(stiffness_value)
+	for mc_cntr in range(total_MC_runs):
+		log_dir = "./logs/{}/MC_{}/{}/{}/".format(experiment_ID, mc_cntr, RL_method, stiffness_value_str)
+		jsonFile = open(log_dir+"monitor/openaigym.episode_batch.{}.Monitor_info.stats.json".format(0))
+		jsonString = jsonFile.read()
+		jsonData = json.loads(jsonString)
+		print("stiffness_value: ", stiffness_value, "mc_cntr: ", mc_cntr)
+		episode_rewards_all[mc_cntr, stiffness_value, :] = np.array(jsonData['episode_rewards'])
+reward_to_displacement_coeficient  = .01
+episode_displacement_all = episode_rewards_all*reward_to_displacement_coeficient
+episode_displacement_average = episode_displacement_all.mean(0)
+episode_displacement_std = episode_displacement_all.std(0)
+final_displacement = np.zeros([total_MC_runs, stiffness_versions])
+pass_displacement_point = np.zeros([total_MC_runs ,stiffness_versions])
+displacement_point = 9
+
+#fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(4.5, 3))
+plt.figure(figsize=(9,3))
+plt.subplot(121)
+stiffness_values_full = ["0", "500", "1K", "2K", "4K", "7K", "10K", "15K", "20K"]
+stiffness_values_legend_full = ["S: 0", "S: 500", "S: 1K", "S: 2K", "S: 4K", "S: 7K", "S: 10K", "S: 15K", "S: 20K"]
+
+if extended_view:
+	stiffness_values = stiffness_values_full
+	stiffness_values_legend = stiffness_values_legend_full
+else:
+	stiffness_values = ["0", "1K", "4K", "10K", "20K"]
+	stiffness_values_legend = ["S: 0", "S: 1K", "S: 4K", "S: 10K", "S: 20K"]
+## Figure 1
+for stiffness_value in select_stiffness:
+	x0=range(total_episodes)
+	y0=episode_displacement_average[stiffness_value,:]
+	std0 = episode_displacement_std[stiffness_value,:]
+	plt.plot(x0, y0, color=colorsys.hsv_to_rgb((8.5-stiffness_value)/14,1,.75), alpha=.75)
+	plt.fill_between(x0, y0-std0/2, y0+std0/2,
+		color=colorsys.hsv_to_rgb((8.5-stiffness_value)/14,1,.75), alpha=0.20)
+plt.legend(stiffness_values_legend, fontsize='x-small',loc='lower right')
+plt.xlabel('Episode #', fontsize=8)
+plt.ylabel('Displacement (m)', fontsize=8)
+plt.xticks(np.arange(0,501,50),rotation=45, fontsize=8)
+plt.yticks(rotation=45, fontsize=8)
+plt.title('a) Learning curves: reward vs. episode plots', fontsize=8)
+plt.grid()
+
+
+for stiffness_value in range(stiffness_versions):	
+	final_displacement[:,stiffness_value] = episode_displacement_all[:,stiffness_value,-1]
+	for mc_cntr in range(total_MC_runs):
+		pass_displacement_point[mc_cntr, stiffness_value] = np.min(np.where(episode_displacement_all[mc_cntr, stiffness_value,:]>=displacement_point))
+
+#fig.subplots_adjust(top=.98, bottom=.15, left=.13, right=.95, wspace=.33)
+#fig.savefig('./results/{}/PPO_results_1.png'.format(experiment_ID))
+## Figure 2
+plt.subplot(143)
+x1=range(stiffness_versions)
+y1 = pass_displacement_point.mean(0)
+std1 = pass_displacement_point.std(0)
+plt.plot(x1, y1, '-')
+plt.fill_between(x1, y1-std1/2, y1+std1/2, alpha=0.25, edgecolor='C9', facecolor='C9')
+for stiffness_value in range(stiffness_versions):
+	plt.plot(x1[stiffness_value], y1[stiffness_value], 'o',alpha=.9, color=colorsys.hsv_to_rgb((8.5-stiffness_value)/14,1,.75))
+plt.xlabel('Stiffness (N/M)', fontsize=8)
+plt.ylabel('Episode #', fontsize=8)
+plt.xticks(range(stiffness_versions), stiffness_values_full, rotation=45, fontsize=8)
+plt.yticks(rotation=45, fontsize=8)
+plt.title('b) Passing threshold episode', fontsize=8)
+plt.grid()
+## Figure 3
+plt.subplot(144)
+x2=range(stiffness_versions)
+y2 = final_displacement.mean(0)
+std2 = final_displacement.std(0)
+plt.plot(x2, y2, '-')
+plt.fill_between(x2, y2-std2/2, y2+std2/2, alpha=0.25, edgecolor='C9', facecolor='C9')
+for stiffness_value in range(stiffness_versions):
+	plt.plot(x2[stiffness_value], y2[stiffness_value], 'o',alpha=.9, color=colorsys.hsv_to_rgb((8.5-stiffness_value)/14,1,.75))
+plt.xlabel('Stiffness (N/M)', fontsize=8)
+plt.ylabel('Displacement (m)', fontsize=8)
+plt.xticks(range(stiffness_versions), stiffness_values_full,  rotation=45, fontsize=8)
+plt.yticks(rotation=45, fontsize=8)
+plt.title('c) Average final rewards', fontsize=8)
+plt.grid()
+# for ii in range(ncols):
+# 	plt.sca(axes[ii])
+# 	plt.xlabel(xlabels[ii], fontsize=9)
+# 	plt.xticks(x1, stiffness_values_full, rotation=45, fontsize=8)
+# # 	plt.ylabel(ylabels[ii], fontsize=9)
+# # 	plt.yticks(rotation=45, fontsize=8)
+# fig.subplots_adjust(top=.95, bottom=.17, left=.11, right=.95, wspace=.33)
+# fig.savefig('./results/{}/PPO_results_2.png'.format(experiment_ID))
+# p0 = axes[1].boxplot(
+# 	[errors_all_cyc_A_A.mean(0)[0], errors_all_cyc_A_B.mean(0)[0], errors_all_cyc_B_B.mean(0)[0]],
+# 	notch=True,
+# 	patch_artist=True)
+os.makedirs("./results/{}".format(experiment_ID), exist_ok=True)
+plt.subplots_adjust(left=0.06, bottom=0.16, right=0.95, top=0.92, wspace=0.32)
+plt.savefig("./results/{}/exp4_PPO_results_combined.pdf".format(experiment_ID))
+plt.savefig("./results/figures/exp4_PPO_results_combined.pdf".format(experiment_ID))
+plt.show() 
+
+
+#import pdb; pdb.set_trace()