Adds runner for all algorithms

runners/all

+#!/usr/bin/env bash
+
+set -euo pipefail
+
+shed=$1
+
+epoch=$((7*24*60*60))
+ticks_per_packet=$((60*120))
+time_to_live=$((3*$epoch))
+buffer_size=$((3*7*24*60*60))
+duty=300
+
+run() {
+  echo 'start:' $@ >&2
+  python main.py \
+    --seed "$3" \
+    --router "$1" \
+    --community "$2" \
+    --community-args \
+      "epoch=${epoch}" \
+    --trace "csv" \
+    --trace-args \
+      "path=shed/data/pp_${shed}_reduced.csv" \
+    --node-args \
+      "tick_time=${duty}" \
+      "buffer_size=${buffer_size}" \
+    --packet-args \
+      "ticks_per_packet=${ticks_per_packet}" \
+      "time_to_live=${time_to_live}" \
+      "start_delay=${epoch}"
+  echo 'done:' $@ >&2
+}
+
+nproc=$(nproc)
+
+mkdir -p logs/$shed
+
+for seed in {1..10}; do
+
+  for router in direct epidemic; do
+    [[ "$(jobs | wc -l)" -ge "$nproc" ]] && wait -n
+    run "$router" "none" "$seed" > "logs/${shed}/${router}_${seed}.log" &
+  done
+
+  for router in bubble hcbf; do
+    for community in kclique louvain; do
+      [[ "$(jobs | wc -l)" -ge "$nproc" ]] && wait -n
+      run "$router" "$community" "$seed" > "logs/${shed}/${router}_${community}_${seed}.log" &
+    done
+  done
+done
+
+wait
+
+for f in direct epidemic {bubble,hcbf}_{kclique,louvain}; do
+  ./util/shed_summary "logs/$shed/$f"*.log > "logs/$shed/${f}_summary.yml"
+done
+
+# send slack notification if the script exists
+status=$?
+notify=$HOME/dev/slack-notifier/send_notification_via_slack.py
+[[ -e "$notify" ]] && $notify "\`$shed done: exit($status)\`"
+

util/shed_summarize

+#!/usr/bin/env python
+
+from collections import defaultdict
+import re
+import sys
+
+import scipy.stats as stats
+import yaml
+
+if __name__ == '__main__':
+    data = defaultdict(list)
+    pattern = re.compile(r'^\w+:')
+    for f in sys.argv[1:]:
+        with open(f) as f:
+            y = ''
+            for line in f:
+                if pattern.match(line):
+                    y += line
+            d = yaml.load(y)
+            for k, v in d.items():
+                data[k].append(v)
+
+    o = {}
+    for k, v in data.items():
+        d = stats.describe(v)
+        o[k] = {
+            'mean': float(d.mean),
+            'stddev': float(d.variance**(1/2)),
+        }
+    print(yaml.dump(o))