run benchmarks only be file prefix

benchmarks/profiling/mem_huge_memory.py

+import datetime as dt
+
+import numpy as np
+
+import finam as fm
+
+if __name__ == "__main__":
+    start_time = dt.datetime(2000, 1, 1)
+    end_time = dt.datetime(2002, 1, 1)
+
+    size = (1024, 1024)
+
+    info1 = fm.Info(time=None, grid=fm.UniformGrid(size), units="m")
+    data = fm.data.to_xarray(fm.data.full(0.0, info1), info1)
+
+
+    def gen_data(t):
+        return np.copy(data)
+
+
+    source = fm.modules.CallbackGenerator(
+        callbacks={"Out": (gen_data, info1.copy())},
+        start=start_time,
+        step=dt.timedelta(days=1),
+    )
+    sink = fm.modules.DebugConsumer(
+        inputs={
+            "In": info1.copy(),
+        },
+        start=start_time,
+        step=dt.timedelta(days=365),
+    )
+
+    composition = fm.Composition([source, sink])
+    composition.initialize()
+
+    source["Out"] >> sink["In"]
+
+    composition.run(end_time=end_time)

benchmarks/run_profiling.sh

@@ -6,11 +6,14 @@ mkdir -p prof
 for filename in benchmarks/profiling/*.py; do
   fn=$(basename -- "$filename")
   fn="${fn%.*}"
-  echo "$fn"
-  python benchmarks/profiling/"$fn".py prof/"$fn".pstats
-  gprof2dot --colour-nodes-by-selftime -f pstats prof/"$fn".pstats > prof/"$fn".dot
-  dot -Tsvg -o prof/"$fn".svg prof/"$fn".dot
-  dot -Tpng -o prof/"$fn".png prof/"$fn".dot
+  if [[ "$fn" == profile_* ]] ;
+  then
+    echo "$fn"
+    python benchmarks/profiling/"$fn".py prof/"$fn".pstats
+    gprof2dot --colour-nodes-by-selftime -f pstats prof/"$fn".pstats > prof/"$fn".dot
+    dot -Tsvg -o prof/"$fn".svg prof/"$fn".dot
+    dot -Tpng -o prof/"$fn".png prof/"$fn".dot
+  fi
 done
 
 python benchmarks/pstats_to_csv.py