Sarc-Graph/run_code.py at main · elejeune11/Sarc-Graph · GitHub

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import file_pre_processing as fpp
import segmentation as seg
import tracking as track
import spatial_graph as sg
import time_series as ts
import analysis_tools as at
import functional_metrics as fm
import numpy as np
##########################################################################################
folder_name_list = ['synthetic_data_S1']

##########################################################################################
# saving functional metrics -->
num_movies = len(folder_name_list)
OOP_selected_all = np.zeros((num_movies))
avg_contract_all = np.zeros((num_movies))
avg_aligned_contract_all  = np.zeros((num_movies))
s_til_all = np.zeros((num_movies))
s_avg_all = np.zeros((num_movies))


kk = 0
##########################################################################################
for folder_name in folder_name_list:
	include_eps = False
	##########################################################################################
	##########################################################################################
	##########################################################################################
	# Convert the movie into a folder of .npy arrays, one for each frame
	##########################################################################################
	fpp.file_pre_processing(folder_name,'avi')
	#fpp.file_pre_processing_tif2(folder_name)
	print(folder_name,"file pre processing complete")
	fpp.make_movie_from_npy(folder_name)
	print(folder_name, "movie from file pre processing complete")
	# ##########################################################################################
	# # Run segmentation
	# ##########################################################################################
	gaussian_filter_size = 1
	seg.segmentation_all(folder_name, gaussian_filter_size)
	print(folder_name,"segmentation complete")
	# ##########################################################################################
	# # Run tracking
	# ##########################################################################################
	tp_depth = 4
	track.run_all_tracking(folder_name,tp_depth)
	print(folder_name,"tracking complete")
	##########################################################################################
	# Create spatial graph
	##########################################################################################
	sg.create_spatial_graph(folder_name)
	print(folder_name,"spatial graph complete")
	##########################################################################################
	# Process timeseries
	##########################################################################################
	keep_thresh = 0.75
	ts.timeseries_all(folder_name, keep_thresh)
	print(folder_name,"timeseries complete")
	##########################################################################################
	# Additional analysis
	##########################################################################################
	# --> visualize segmentation
	gaussian_filter_size = 1
	frame = 0
	at.visualize_segmentation(folder_name, gaussian_filter_size, frame, include_eps)
	print(folder_name,"visualize segmentation complete")

	# # --> visualize contract anim movie
	at.visualize_contract_anim_movie(folder_name,True,True,0.75,include_eps) # 3 peaks identified
	print(folder_name,"visualize contract anim movie complete")

	# --> perform timeseries clustering
	compute_dist_DTW = True; compute_dist_euclidean = False
	at.cluster_timeseries_plot_dendrogram(folder_name,compute_dist_DTW,compute_dist_euclidean)
	print(folder_name,"cluster timeseries complete")

	# # --> plot normalized tracked timeseries
	at.plot_normalized_tracked_timeseries(folder_name,include_eps)
	print(folder_name,"plot tracked timeseries complete")

	# --> plot untracked absolute timeseries
	at.plot_untracked_absolute_timeseries(folder_name,include_eps)
	print(folder_name,"plot absolute timeseries complete")

	# --> compute timeseries individual parameters
	at.compute_timeseries_individual_parameters(folder_name,include_eps)
	print(folder_name,"compute timeseries parameters complete")

	# --> compare tracked and untracked samples
	at.compare_tracked_untracked(folder_name,include_eps)
	print(folder_name,"compare tracked/untracked complete")

	# --> perform preliminary spatial/temporal analysis
	compute_network_distances = True
	at.preliminary_spatial_temporal_correlation_info(folder_name,compute_network_distances,include_eps)
	print(folder_name,"preliminary spatial/temporal analysis complete")

	# --> create and plot F
	at.compute_F_whole_movie(folder_name,include_eps)
	print(folder_name,"compute F complete")

	# --> plot J with some additional analysis
	at.analyze_J_full_movie(folder_name,include_eps)
	print(folder_name,"plot J with parameters")

	# --> visualize F
	at.visualize_F_full_movie(folder_name)
	print(folder_name,"visualize F")

	# --> reset F
	at.adjust_F_if_movie_starts_not_contracted(folder_name)

	# --> redo visualization
	at.visualize_F_full_movie(folder_name)
	print(folder_name,"visualize F full movie complete")

	# --> compute function metrics
	OOP_selected, avg_contract, avg_aligned_contract, s_til, s_avg = fm.compute_metrics(folder_name)
	OOP_selected_all[kk] = OOP_selected
	avg_contract_all[kk] = avg_contract
	avg_aligned_contract_all[kk]  = avg_aligned_contract
	s_til_all[kk] = s_til
	s_avg_all[kk] = s_avg
	print(folder_name,"compute metrics complete")
	print(folder_name,"OOP:", OOP_selected)
	print(folder_name,"Ciso:", avg_contract)
	print(folder_name,"Caligned:", avg_aligned_contract)
	print(folder_name,"s_til:", avg_aligned_contract)
	print(folder_name,"s_avg:", avg_aligned_contract)

	# --> make summary movie of Favg
	include_eps = False
	fm.visualize_lambda_as_functional_metric(folder_name, include_eps)

	kk += 1