--- a
+++ b/Analysis/find_fp.py
@@ -0,0 +1,116 @@
+'''
+examples/torch/run_FlipFlop.py
+Written for Python 3.8.17 and Pytorch 2.0.1
+@ Matt Golub, June 2023
+Please direct correspondence to mgolub@cs.washington.edu
+'''
+
+import pdb
+import sys
+import numpy as np
+import torch
+import pickle
+
+
+PATH_TO_FIXED_POINT_FINDER = './fixed-point-finder/'
+PATH_TO_HELPER = './fixed-point-finder/examples/helper/'
+PATH_TO_TORCH = './fixed-point-finder/examples/torch/'
+PATH_TO_SAC = '../SAC/'
+sys.path.insert(0, PATH_TO_FIXED_POINT_FINDER)
+sys.path.insert(0, PATH_TO_HELPER)
+sys.path.insert(0, PATH_TO_TORCH)
+sys.path.insert(0, PATH_TO_SAC)
+
+from FlipFlop import FlipFlop
+from FixedPointFinderTorch import FixedPointFinderTorch as FixedPointFinder
+from FlipFlopData import FlipFlopData
+from plot_utils import plot_fps
+
+
+def find_fixed_points(model, rnn_trajectories, rnn_input):
+	''' Find, analyze, and visualize the fixed points of the trained RNN.
+
+	Args:
+		model: 
+
+			Trained RNN model, as returned by uSim training.
+
+		valid_predictions: dict.
+
+			Model trajectories for training and testing conditions.
+
+	Returns:
+		None.
+	'''
+
+	NOISE_SCALE = 0.5 # Standard deviation of noise added to initial states
+	# N_INITS = 1024*10
+	N_INITS = rnn_trajectories.shape[0] * rnn_trajectories.shape[1] # The number of initial states to provide
+
+	n_hidden_units = rnn_trajectories.shape[2]
+
+	'''Fixed point finder hyperparameters. See FixedPointFinder.py for detailed
+	descriptions of available hyperparameters.'''
+	fpf_hps = {
+		'max_iters': 10000,
+		'lr_init': 1.,
+		'outlier_distance_scale': 10.0,
+		'verbose': True, 
+		'super_verbose': True}
+
+	# Setup the fixed point finder
+	fpf = FixedPointFinder(model, **fpf_hps)
+
+	'''Draw random, noise corrupted samples of those state trajectories
+	to use as initial states for the fixed point optimizations.'''
+	initial_states = fpf.sample_states(rnn_trajectories,
+		n_inits=N_INITS,
+		noise_scale=NOISE_SCALE)
+
+	# Study the system in the absence of input pulses (e.g., all inputs are 0)
+	inputs = np.zeros([1, n_hidden_units])
+	# inputs = rnn_input.reshape(-1, n_hidden_units)
+
+	# Run the fixed point finder
+	unique_fps, all_fps = fpf.find_fixed_points(initial_states, inputs)
+
+	# Visualize identified fixed points with overlaid RNN state trajectories
+	# All visualized in the 3D PCA space fit the the example RNN states.
+	fig = plot_fps(unique_fps, rnn_trajectories,
+		plot_batch_idx=list(range(6)),
+		plot_start_time=0)
+
+def main():
+
+	# Step 1: Load the uSim RNN model, RNN trajectories and RNN input
+	PATH_TO_MODEL = '../checkpoint/actor_rnn_best_fpf.pth' 
+	actor_rnn = torch.load(PATH_TO_MODEL)
+	model = actor_rnn
+
+	#Load the test data
+	with open('../test_data/test_data.pkl', 'rb') as file:
+		test_data = pickle.load(file)
+
+	#Get the uSim RNN hidden trajectories and inputs
+	rnn_trajectories = []
+	for cond in range(len(test_data['rnn_activity'])):
+		rnn_trajectories.append(test_data['rnn_activity'][cond])
+
+	rnn_trajectories = np.array(rnn_trajectories)  #[n_conds, n_timepoints, n_hidden_units]
+
+	rnn_input = []
+	for cond in range(len(test_data['rnn_input_fp'])):
+		rnn_input.append(test_data['rnn_input_fp'][cond])
+
+	rnn_input = np.array(rnn_input) #[n_conds, n_timepoints, n_hidden_units]
+
+	# STEP 2: Find, analyze, and visualize the fixed points of the trained RNN
+	find_fixed_points(model, rnn_trajectories, rnn_input)
+
+	print('Entering debug mode to allow interaction with objects and figures.')
+	print('You should see a figure with:')
+
+	pdb.set_trace()
+
+if __name__ == '__main__':
+	main()
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