--- a
+++ b/Analysis/collective_pca.py
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+from sklearn.cross_decomposition import CCA
+import numpy as np
+import pickle
+from scipy.io import loadmat
+from sklearn.decomposition import PCA
+import matplotlib.pyplot as plt
+from scipy.ndimage import gaussian_filter1d
+
+from sklearn.linear_model import LinearRegression, Ridge
+from sklearn.metrics import r2_score
+from scipy.spatial import procrustes
+from sklearn.decomposition import PCA
+
+import sys
+sys.path.insert(0, '../SAC/')
+import kinematics_preprocessing_specs
+
+import config
+
+parser = config.config_parser()
+args, unknown = parser.parse_known_args()
+
+#Load the test data of nusim
+
+with open('../test_data/test_data.pkl', 'rb') as file:
+    test_data = pickle.load(file)
+    
+print(test_data.keys())
+
+#Get the timepoints of each condition per cycle
+with open('../kinematics_data/kinematics.pkl', 'rb') as file:
+    kin_train_test = pickle.load(file)
+    
+kin_train = kin_train_test['train']
+kin_test = kin_train_test['test']
+
+#First update the keys of self.kin_test
+for cond in range(len(kin_test)):
+    kin_test[len(kin_train) + cond] = kin_test.pop(cond)
+    
+kin = kin_train
+kin.update(kin_test)
+
+conds = [kin[cond].shape[-1] for cond in range(len(kin))]
+total_conds = len(conds)
+
+#Select the cycle for each condition (training conditions followed by testing): 0 for 1st cycle and so on
+#The number of elements should be equal to num_train_conditions + num_test_conditions
+cycles = [2, 2, 2, 2, 2, 2]
+
+#Number of fixedsteps in the start of each condition
+#Fix this {todo}: Get the values automatically from ..SAC/kinematics_preprocessing_specs.py
+n_fixedsteps= args.n_fixedsteps
+
+#Load the network activities
+A_agent = []
+
+for idx, cond_activity in test_data['rnn_activity'].items():
+    act_agent = cond_activity
+    act_agent = act_agent[n_fixedsteps + cycles[idx] * conds[idx] : n_fixedsteps + (cycles[idx]+1) * conds[idx]]
+    print(act_agent.shape)
+    A_agent.append(act_agent[:, :])
+
+#Do the collective PCA for all speeds
+nusim_pca = PCA(n_components= 3)
+
+A_agent_c = A_agent
+#concatenate the musim activity for all conditions
+for i_cond in range(len(A_agent_c)):
+    
+    if i_cond == 0:
+        nusim_activity_pca = A_agent_c[i_cond]
+    else:
+        nusim_activity_pca = np.concatenate((nusim_activity_pca, A_agent_c[i_cond]), axis=0)
+
+nusim_activity_pca = nusim_pca.fit_transform(nusim_activity_pca)
+
+#Plot the PCA of the activities
+colors = plt.cm.ocean(np.linspace(0,1,8))
+ax = plt.figure(dpi=100).add_subplot(projection='3d')
+
+prev_cond = 0
+for i_cond in range(len(A_agent_c)):
+    ax.plot(nusim_activity_pca[prev_cond:prev_cond+A_agent_c[i_cond].shape[0],0], 
+            nusim_activity_pca[prev_cond:prev_cond+A_agent_c[i_cond].shape[0], 1], 
+            nusim_activity_pca[prev_cond:prev_cond+A_agent_c[i_cond].shape[0], 2], color= colors[i_cond])
+    
+    prev_cond += A_agent_c[i_cond].shape[0]
+
+    
+# Hide grid lines
+ax.grid(False)
+plt.grid(b=None)
+
+# Hide axes ticks
+ax.set_xticks([])
+ax.set_yticks([])
+ax.set_zticks([])
+plt.axis('off')
+
+plt.show()
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