## Open thw data base with all data states_df = db.open_analysis_states_database() ##Select one specific row of the data base specified by mouse, session, trial, is_rest, decoding_v, cropping_v, etc. ## If one experimental parameters is not specifies, it chooses al previos one with same id ## If one analysis version is not specified it selects the latest one selected_row = db.select('decoding', 56165, 1, 1) ##Select one row from selected_rows row = selected_row.iloc[0] ##This gives an array with the experimental details and analysis version. index = row.name #%% ##run decoding on data specified by index (one row in the data base) main_decoding(index, row) #%% ## Define parameters from cropping the movie. ## paramenters is given to the funcion or can be retreived from the data base (I think) parameters_cropping = { 'crop_spatial': True, 'cropping_points_spatial': [80, 450, 210, 680], 'crop_temporal': False, 'cropping_points_temporal': [] } ## Call main cropping funcion main_cropping(index, row, parameters_cropping)
#%% Paths analysis_states_database_path = 'references/analysis/analysis_states_database.xlsx' backup_path = 'references/analysis/backup/' #parameters_path = 'references/analysis/parameters_database.xlsx' ## Open thw data base with all data states_df = db.open_analysis_states_database() #%% DECODING # Select all the data corresponding to a particular mouse. Ex: 56165. selected_rows = db.select(states_df,'decoding',56165) mouse_row = selected_rows.iloc[0] mouse_row = main_decoding(mouse_row) states_df = db.append_to_or_merge_with_states_df(states_df, mouse_row) db.save_analysis_states_database(states_df, analysis_states_database_path, backup_path) #%% CROPPING # Select the rows for cropping selected_rows = db.select(states_df,'cropping',56165) mouse_row = selected_rows.iloc[0] plot_movie_frame(mouse_row) #%% parameters_cropping = cropping_interval() #check whether it is better to do it like this or to use the functions get # and set parameters from the data_base_manipulation file mouse_row = main_cropping(mouse_row, parameters_cropping) plot_movie_frame_cropped(mouse_row) # verify that the cropping is the desired one # Now cropping parameters had been selected. Next step is selection version analysis.
r_values_min = 0.85 # threshold on space consistency (if you lower more components # will be accepted, potentially with worst quality) parameters_component_evaluation = { 'min_SNR': min_SNR, 'rval_thr': r_values_min, 'use_cnn': False } #%% for i in range(0, len(selected_rows)): # Get the row from the selected rows as a series using simple indexing row = selected_rows.iloc[i] index = row.name # Get the index from the row row = main_decoding(row) print('Decoding for mouse' + str(index[0]) + 'session' + str(index[1]) + 'trial' + str(index[2])) states_df = db.append_to_or_merge_with_states_df(states_df, row) db.save_analysis_states_database(states_df, analysis_states_database_path, backup_path) row = main_cropping(row, parameters_cropping) #upload_to_server_cropped_movie(index,row) print('Cropping for mouse' + str(index[0]) + 'session' + str(index[1]) + 'trial' + str(index[2])) n_processes = psutil.cpu_count() cm.cluster.stop_server() # Start a new cluster c, dview, n_processes = cm.cluster.setup_cluster( backend='local',