## 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',