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# -*- coding: utf-8 -*-
"""Extract descriptive statistics for the time series
This script is used to extract descriptive statistics about the number of
annotations from the summary files.
Author: Gertjan van den Burg
Copyright (c) 2020 - The Alan Turing Institute
License: See the LICENSE file.
"""
import argparse
import json
import os
import statistics
N_DATASETS = 42
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument(
"-s",
"--summary-dir",
help="Directory with summary files",
required=True,
)
parser.add_argument(
"-t",
"--type",
help="Type of statistic to compute",
choices=["min", "max", "mean", "std"],
required=True,
)
return parser.parse_args()
def load_unique_annotations(summary_dir):
files = os.listdir(summary_dir)
assert len(files) == N_DATASETS
n_uniq_anno = []
for f in sorted(files):
path = os.path.join(summary_dir, f)
with open(path, "r") as fp:
data = json.load(fp)
all_anno = set()
for annotations in data["annotations"].values():
for cp in annotations:
all_anno.add(cp)
n_uniq_anno.append(len(all_anno))
return n_uniq_anno
def main():
args = parse_args()
if args.type == "max":
func = max
elif args.type == "mean":
func = statistics.mean
elif args.type == "std":
func = statistics.stdev
elif args.type == "min":
func = min
else:
raise ValueError("Unknown type")
n_uniq_anno = load_unique_annotations(args.summary_dir)
if args.type in ["min", "max"]:
print("%i%%" % func(n_uniq_anno))
else:
print("%.1f%%" % func(n_uniq_anno))
if __name__ == "__main__":
main()
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