)
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)
