Merge branch 'master' of github.com:rogeriobonatti/mushr_rhc

mushr_rhc_ros/src/eval_model.py

@@ -0,0 +1,19 @@
+import numpy as np
+import matplotlib.pyplot as plt
+
+# file_path = '/home/azureuser/hackathon_data/e2e_eval/GPTiros_e2e_8gpu_2022-02-17_v2/info.csv'
+file_path = '/home/azureuser/hackathon_data/e2e_eval/model_test/info.csv'
+data = np.genfromtxt(file_path, delimiter=',')
+
+distances = data[:,0]
+times = data[:,1]
+
+# clear data that crashes immediately
+min_time = 3.0
+distances = distances[times>min_time]
+times = times[times>min_time]
+
+plt.hist(distances, bins=30, facecolor='green', alpha=0.75)
+# plt.savefig('/home/azureuser/hackathon_data/e2e_eval/GPTiros_e2e_8gpu_2022-02-17_v2/fig.png')
+plt.savefig('/home/azureuser/hackathon_data/e2e_eval/model_test/fig.png')
+plt.show()

mushr_rhc_ros/src/test2.py

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+# from torchsummary import summary
+import sys
+import os
+import signal
+import threading
+import random
+import numpy as np
+from queue import Queue
+import time
+from collections import OrderedDict
+import matplotlib.pyplot as plt
+
+num_samples = 10000
+results = np.zeros(shape=(num_samples,))
+
+max_seq_length = 100
+for i in range(num_samples):
+    episode_path_length = 0.0
+    iter = 0
+    while iter < max_seq_length:
+        result = np.random.random() < 0.99
+        if result:
+            episode_path_length+= 1.0
+        else:
+            break
+        iter += 1
+    results[i] = episode_path_length
+
+plt.hist(results, bins=30, facecolor='green', alpha=0.75)