) plt_dist.plot_similarities( neuron_distance_csv, data_DIR, algorithms_ordered, metric="neuron_distance_perc", CASE_BY_CASE_PLOT=0, value_label="Similarity (0~1) based on Neuron Different Structure Percentage (D3)", ) plt_dist.plot_neuron_distance(neuron_distance_csv, data_DIR, algorithms_ordered, CASE_BY_CASE_PLOT=0) ######################################## for all generated reconstructions ############################ # plot runnign time output_time_csv = data_DIR + "/running_time_algorithm.csv" rp.summerize_running_time(time_csv, algorithm_plugin_match_csv, lookup_image_id_table_file, output_time_csv) df_gold = pd.read_csv(GOLD_CSV) df_silver_time = pd.read_csv(output_time_csv) df_share_time = pd.merge(df_silver_time, df_gold, on="image_file_name") df_share_time.to_csv(data_DIR + "/running_time_algorithm_gold.csv", index=False) plt_dist.plot_running_time(data_DIR + "/running_time_algorithm_gold.csv", data_DIR, algorithms_ordered) plt_dist.plot_running_time_validation( data_DIR + "/running_time_algorithm_gold.csv", neuron_distance_csv, data_DIR, algorithms_ordered )
data_DIR, algorithms_ordered, metric='neuron_distance_perc', CASE_BY_CASE_PLOT=0, value_label='Similarity (0~1) on Neuron Different Structure Percentage (D3)' ) plt_dist.plot_neuron_distance(neuron_distance_csv, data_DIR, algorithms_ordered, CASE_BY_CASE_PLOT=0) ######################################## for all generated reconstructions ############################ #plot runnign time df_silver_gt = pd.read_csv(SILVER_CSV) output_time_csv = data_DIR + "/running_time_algorithm.csv" rp.summerize_running_time(time_csv, algorithm_plugin_match_csv, lookup_image_id_table_file, output_time_csv) df_rc_time = pd.read_csv(output_time_csv) df_share_time = pd.merge(df_rc_time, df_silver_gt, on="image_file_name") df_share_time.to_csv(data_DIR + "/running_time_algorithm_silver_gt.csv", index=False) plt_dist.plot_running_time(data_DIR + "/running_time_algorithm_silver_gt.csv", data_DIR, algorithms_ordered) plt_dist.plot_running_time_validation( data_DIR + "/running_time_algorithm_silver_gt.csv", neuron_distance_csv, data_DIR, algorithms_ordered)
df_nd = pd.read_csv(neuron_distance_csv) algorithms = np.unique(df_nd.algorithm) print algorithms dfg = df_nd.groupby('algorithm') sample_size_per_algorithm = np.zeros(algorithms.size) for i in range( algorithms.size): print algorithms[i] sample_size_per_algorithm[i] = (dfg.get_group(algorithms[i]).shape[0]) order = sample_size_per_algorithm.argsort() algorithms_ordered = algorithms[order[::-1]] time_csv="/data/mat/xiaoxiaol/data/reconstructions_2015_1214/auto_recons/running_time.csv" output_time_csv= "/data/mat/xiaoxiaol/data/reconstructions_2015_1214/running_time_algorithm.csv" algorithm_plugin_match_csv ="/data/mat/xiaoxiaol/data/reconstructions_2015_1214/ported_neuron_tracing_spreadsheet.csv" rp.summerize_running_time(time_csv, algorithm_plugin_match_csv,output_time_csv) df_gold = pd.read_csv(GOLD_CSV) df_silver = pd.read_csv(SILVER_CSV) #print df_silver.columns #print df_gold.columns df_share = pd.merge(df_silver,df_gold,on="image_file_name") plt_dist.plot_running_time(output_time_csv, data_DIR,algorithms_ordered)