diff --git a/scripts/compare_spike_trains.py b/scripts/compare_spike_trains
old mode 100644
new mode 100755
similarity index 100%
rename from scripts/compare_spike_trains.py
rename to scripts/compare_spike_trains
diff --git a/scripts/create_tarball.sh b/scripts/create_tarball
similarity index 100%
rename from scripts/create_tarball.sh
rename to scripts/create_tarball
diff --git a/scripts/profstats b/scripts/profstats
deleted file mode 120000
index 8170d8312648ad82df61e58c3d3de18f02e0f3fb..0000000000000000000000000000000000000000
--- a/scripts/profstats
+++ /dev/null
@@ -1 +0,0 @@
-profstats.py
\ No newline at end of file
diff --git a/scripts/profstats b/scripts/profstats
new file mode 100755
index 0000000000000000000000000000000000000000..86611e33a3b00e873b2698946e98ce4e19461789
--- /dev/null
+++ b/scripts/profstats
@@ -0,0 +1,99 @@
+#!/usr/bin/env python3
+#coding: utf-8
+
+import json
+import argparse
+import re
+import numpy as np
+from itertools import chain
+
+def parse_clargs():
+ P = argparse.ArgumentParser(description='Aggregate and analyse MPI profile output.')
+ P.add_argument('inputs', metavar='FILE', nargs='+',
+ help='MPI profile output in JSON format')
+ P.add_argument('-r', '--raw', action='store_true',
+ help='emit raw times in csv table')
+
+ return P.parse_args()
+
+def parse_profile_json(source):
+ j = json.load(source)
+ rank = j['rank']
+ if rank is None:
+ raise ValueError('missing rank information in profile')
+
+ tx = dict()
+
+ def collect_times(j, prefix):
+ t = j['time']
+ n = j['name']
+
+ if t is None or n is None:
+ return
+
+ prefix = prefix + n
+ tx[prefix] = t
+
+ try:
+ children = j['regions']
+ # special case for top level
+ if prefix == 'total':
+ prefix = ''
+ else:
+ prefix = prefix + '/'
+
+ for j in children:
+ collect_times(j, prefix)
+ except KeyError:
+ pass
+
+ collect_times(j['regions'], '')
+ return rank, tx
+
+def csv_escape(x):
+ s = re.sub('"','""',str(x))
+ if re.search('["\t\n,]',s):
+ s = '"'+s+'"'
+ return s
+
+def emit_csv(cols, rows, stdout):
+ stdout.write(",".join([csv_escape(c) for c in cols]))
+ stdout.write("\n")
+ for r in rows:
+ stdout.write(",".join([csv_escape(r[c]) if c in r else '' for c in cols]))
+ stdout.write("\n")
+
+def main(raw, inputs, stdout):
+ rank_times = dict()
+ for filename in inputs:
+ with open(filename) as f:
+ rank, times = parse_profile_json(f)
+ rank_times[rank] = times
+
+ if raw:
+ rows = [rank_times[rank] for rank in sorted(rank_times.keys())]
+ cols = sorted({col for tbl in rows for col in tbl.keys()})
+ emit_csv(cols, rows, stdout)
+ else:
+ rank_entry = [rank_times[rank] for rank in sorted(rank_times.keys())]
+ bins = sorted({col for tbl in rank_entry for col in tbl.keys()})
+
+ rows = []
+ for b in bins:
+ qs = np.percentile([entry[b] for entry in rank_times.values() if b in entry],
+ [0., 0.25, 0.5, 0.75, 1.])
+ rows.append({
+ 'region': b,
+ 'min': qs[0],
+ 'q25': qs[1],
+ 'median': qs[2],
+ 'q75': qs[3],
+ 'max': qs[4]
+ })
+
+ emit_csv(['region','min','q25','median','q75','max'], rows, stdout)
+
+if __name__ == "__main__":
+ import sys
+ args = parse_clargs()
+ main(args.raw, args.inputs, sys.stdout)
diff --git a/scripts/profstats.py b/scripts/profstats.py
deleted file mode 100755
index 86611e33a3b00e873b2698946e98ce4e19461789..0000000000000000000000000000000000000000
--- a/scripts/profstats.py
+++ /dev/null
@@ -1,99 +0,0 @@
-#!/usr/bin/env python3
-#coding: utf-8
-
-import json
-import argparse
-import re
-import numpy as np
-from itertools import chain
-
-def parse_clargs():
- P = argparse.ArgumentParser(description='Aggregate and analyse MPI profile output.')
- P.add_argument('inputs', metavar='FILE', nargs='+',
- help='MPI profile output in JSON format')
- P.add_argument('-r', '--raw', action='store_true',
- help='emit raw times in csv table')
-
- return P.parse_args()
-
-def parse_profile_json(source):
- j = json.load(source)
- rank = j['rank']
- if rank is None:
- raise ValueError('missing rank information in profile')
-
- tx = dict()
-
- def collect_times(j, prefix):
- t = j['time']
- n = j['name']
-
- if t is None or n is None:
- return
-
- prefix = prefix + n
- tx[prefix] = t
-
- try:
- children = j['regions']
- # special case for top level
- if prefix == 'total':
- prefix = ''
- else:
- prefix = prefix + '/'
-
- for j in children:
- collect_times(j, prefix)
- except KeyError:
- pass
-
- collect_times(j['regions'], '')
- return rank, tx
-
-def csv_escape(x):
- s = re.sub('"','""',str(x))
- if re.search('["\t\n,]',s):
- s = '"'+s+'"'
- return s
-
-def emit_csv(cols, rows, stdout):
- stdout.write(",".join([csv_escape(c) for c in cols]))
- stdout.write("\n")
- for r in rows:
- stdout.write(",".join([csv_escape(r[c]) if c in r else '' for c in cols]))
- stdout.write("\n")
-
-def main(raw, inputs, stdout):
- rank_times = dict()
- for filename in inputs:
- with open(filename) as f:
- rank, times = parse_profile_json(f)
- rank_times[rank] = times
-
- if raw:
- rows = [rank_times[rank] for rank in sorted(rank_times.keys())]
- cols = sorted({col for tbl in rows for col in tbl.keys()})
- emit_csv(cols, rows, stdout)
- else:
- rank_entry = [rank_times[rank] for rank in sorted(rank_times.keys())]
- bins = sorted({col for tbl in rank_entry for col in tbl.keys()})
-
- rows = []
- for b in bins:
- qs = np.percentile([entry[b] for entry in rank_times.values() if b in entry],
- [0., 0.25, 0.5, 0.75, 1.])
- rows.append({
- 'region': b,
- 'min': qs[0],
- 'q25': qs[1],
- 'median': qs[2],
- 'q75': qs[3],
- 'max': qs[4]
- })
-
- emit_csv(['region','min','q25','median','q75','max'], rows, stdout)
-
-if __name__ == "__main__":
- import sys
- args = parse_clargs()
- main(args.raw, args.inputs, sys.stdout)