diff --git a/workflow/src/legenddataflow/scripts/tier/raw_blind.py b/workflow/src/legenddataflow/scripts/tier/raw_blind.py
index 57df92db..72c6ab35 100644
--- a/workflow/src/legenddataflow/scripts/tier/raw_blind.py
+++ b/workflow/src/legenddataflow/scripts/tier/raw_blind.py
@@ -11,7 +11,6 @@
"""
import argparse
-from pathlib import Path
import numexpr as ne
import numpy as np
@@ -111,12 +110,6 @@ def build_tier_raw_blind() -> None:
# gets events that should not be blinded
tokeep = allind[np.logical_not(np.isin(allind, toblind))]
- # make some temp file to write the output to before renaming it
- rng = np.random.default_rng()
- rand_num = f"{rng.integers(0,99999):05d}"
- temp_output = f"{args.output}.{rand_num}"
- Path(temp_output).parent.mkdir(parents=True, exist_ok=True)
-
for channel in all_channels:
try:
chnum = int(channel[2::])
@@ -126,7 +119,7 @@ def build_tier_raw_blind() -> None:
store.write_object(
chobj,
channel,
- lh5_file=temp_output,
+ lh5_file=args.output,
wo_mode="w",
**hdf_settings,
)
@@ -139,7 +132,7 @@ def build_tier_raw_blind() -> None:
chobj,
group=channel,
name="raw",
- lh5_file=temp_output,
+ lh5_file=args.output,
wo_mode="w",
**hdf_settings,
)
@@ -157,11 +150,7 @@ def build_tier_raw_blind() -> None:
blinded_chobj,
group=channel,
name="raw",
- lh5_file=temp_output,
+ lh5_file=args.output,
wo_mode="w",
**hdf_settings,
)
-
- # rename the temp file
- Path(args.output).parent.mkdir(parents=True, exist_ok=True)
- Path(temp_output).rename(args.output)
diff --git a/workflow/src/legenddataflow/scripts/tier/raw_fcio.py b/workflow/src/legenddataflow/scripts/tier/raw_fcio.py
index c52f441a..67942b93 100644
--- a/workflow/src/legenddataflow/scripts/tier/raw_fcio.py
+++ b/workflow/src/legenddataflow/scripts/tier/raw_fcio.py
@@ -1,8 +1,6 @@
import argparse
-from copy import deepcopy
from pathlib import Path
-import numpy as np
from daq2lh5 import build_raw
from dbetto import TextDB
from dbetto.catalog import Props
@@ -36,24 +34,11 @@ def build_tier_raw_fcio() -> None:
channel_dict = channel_dict.out_spec
all_config = Props.read_from(channel_dict.gen_config)
- chmap = (
- TextDB(args.chan_maps, lazy=True).channelmaps.on(args.timestamp).group("system")
- )
-
if "geds_config" in channel_dict:
raise NotImplementedError()
if "spms_config" in channel_dict:
spm_config = Props.read_from(channel_dict.spms_config)
- spm_channels = chmap.spms.map("daq.rawid")
-
- for rawid, chinfo in spm_channels.items():
- cfg_block = deepcopy(spm_config["FCEventDecoder"]["__output_table_name__"])
- cfg_block["key_list"] = [chinfo.daq.fc_channel]
- spm_config["FCEventDecoder"][f"ch{rawid:07d}/raw"] = cfg_block
-
- spm_config["FCEventDecoder"].pop("__output_table_name__")
-
Props.add_to(all_config, spm_config)
if "auxs_config" in channel_dict:
@@ -62,11 +47,4 @@ def build_tier_raw_fcio() -> None:
if "muon_config" in channel_dict:
raise NotImplementedError()
- rng = np.random.default_rng()
- rand_num = f"{rng.integers(0,99999):05d}"
- temp_output = f"{args.output}.{rand_num}"
-
- build_raw(args.input, out_spec=all_config, filekey=temp_output, **settings)
-
- # rename the temp file
- Path(temp_output).rename(args.output)
+ build_raw(args.input, out_spec=all_config, filekey=args.output, **settings)
diff --git a/workflow/src/legenddataflow/scripts/tier/raw_orca.py b/workflow/src/legenddataflow/scripts/tier/raw_orca.py
index ca6a9f3b..9ee5a0f1 100644
--- a/workflow/src/legenddataflow/scripts/tier/raw_orca.py
+++ b/workflow/src/legenddataflow/scripts/tier/raw_orca.py
@@ -2,7 +2,6 @@
import logging
from pathlib import Path
-import numpy as np
from daq2lh5 import build_raw
from dbetto import TextDB
from dbetto.catalog import Props
@@ -100,11 +99,4 @@ def build_tier_raw_orca() -> None:
)
Props.add_to(all_config, muon_config)
- rng = np.random.default_rng()
- rand_num = f"{rng.integers(0,99999):05d}"
- temp_output = f"{args.output}.{rand_num}"
-
- build_raw(args.input, out_spec=all_config, filekey=temp_output, **settings)
-
- # rename the temp file
- Path(temp_output).rename(args.output)
+ build_raw(args.input, out_spec=all_config, filekey=args.output, **settings)
diff --git a/workflow/src/legenddataflow/scripts/tier/tcm.py b/workflow/src/legenddataflow/scripts/tier/tcm.py
index 6f53b1fe..8152c0bd 100644
--- a/workflow/src/legenddataflow/scripts/tier/tcm.py
+++ b/workflow/src/legenddataflow/scripts/tier/tcm.py
@@ -1,8 +1,6 @@
import argparse
-from pathlib import Path
import lgdo.lh5 as lh5
-import numpy as np
from daq2lh5.orca import orca_flashcam
from dbetto import TextDB
from dbetto.catalog import Props
@@ -28,10 +26,6 @@ def build_tier_tcm() -> None:
settings = Props.read_from(config_dict["inputs"]["config"])
- rng = np.random.default_rng()
- temp_output = f"{args.output}.{rng.integers(0, 99999):05d}"
- Path(args.output).parent.mkdir(parents=True, exist_ok=True)
-
# get the list of channels by fcid
ch_list = lh5.ls(args.input, "/ch*")
fcid_channels = {}
@@ -46,10 +40,8 @@ def build_tier_tcm() -> None:
for fcid, fcid_dict in fcid_channels.items():
build_tcm(
[(args.input, fcid_dict)],
- out_file=temp_output,
+ out_file=args.output,
out_name=f"hardware_tcm_{fcid}",
wo_mode="o",
**settings,
)
-
- Path(temp_output).rename(args.output)