Compare commits
No commits in common. "3f520cdf098a0aea5ee1ce52d9eab2091e2e9461" and "380f656535856db99ed0c17a99a5ef4b67cc42e4" have entirely different histories.
3f520cdf09
...
380f656535
3 changed files with 1 additions and 432 deletions
|
|
@ -1,204 +0,0 @@
|
|||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
import numpy as np
|
||||
import argparse
|
||||
import sys
|
||||
import pandas as pd
|
||||
from pathlib import Path
|
||||
|
||||
from DORNconfiguration import NMAHEPAM_channels, SETH_channels, AHEPAM_channels
|
||||
from event_filters import prepare_trigger_indices, check_trigger
|
||||
|
||||
# ---------------------------
|
||||
# Argumente
|
||||
# ---------------------------
|
||||
parser = argparse.ArgumentParser(description="Parse ED-data for DORN files with triggers")
|
||||
parser.add_argument("file", type=str)
|
||||
parser.add_argument("-map", type=str, default="ALL",
|
||||
choices=["ALL", "NMAHEPAM", "SETH", "AHEPAM"])
|
||||
parser.add_argument("-eventhist", action="store_true")
|
||||
parser.add_argument("-Bhist", action="store_true")
|
||||
parser.add_argument("-trigger", nargs="+", default=None)
|
||||
parser.add_argument("-time", type=int, default=200)
|
||||
parser.add_argument("-nameadd", type=str, default="")
|
||||
args = parser.parse_args()
|
||||
|
||||
file = args.file
|
||||
if not Path(file).is_file():
|
||||
print("No valid file:", file)
|
||||
sys.exit()
|
||||
|
||||
filename = Path(file).stem
|
||||
Path("hists").mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# ---------------------------
|
||||
# Parameter
|
||||
# ---------------------------
|
||||
minX_event, maxX_event, resX_event = -100, 5000, 0.838214 * 4
|
||||
minX_B, maxX_B, resX_B = -5, 300, 0.838214 / 2
|
||||
|
||||
# ---------------------------
|
||||
# Mapping (OPTIMIERT)
|
||||
# ---------------------------
|
||||
def Evaluate_mapping(mapping_name):
|
||||
if mapping_name == "ALL":
|
||||
columns = [f"0-{i}" for i in range(24)] + [f"1-{i}" for i in range(24)]
|
||||
resolver_dict = {i: i for i in range(len(columns))}
|
||||
return columns, lambda ch, sl: ch + sl*24, None, resolver_dict
|
||||
|
||||
if mapping_name == "NMAHEPAM":
|
||||
channels = NMAHEPAM_channels()
|
||||
elif mapping_name == "SETH":
|
||||
channels = SETH_channels()
|
||||
elif mapping_name == "AHEPAM":
|
||||
channels = AHEPAM_channels()
|
||||
else:
|
||||
sys.exit("Unknown mapping")
|
||||
|
||||
valid = [ch for ch in channels if ch["name"]]
|
||||
columns = [ch["name"] for ch in valid]
|
||||
|
||||
# schneller Resolver
|
||||
hwc_to_idx = {ch["hwc"]: i for i, ch in enumerate(valid)}
|
||||
|
||||
def resolver(ch, sl):
|
||||
return hwc_to_idx.get(ch + sl*24)
|
||||
|
||||
return columns, resolver, valid, hwc_to_idx
|
||||
|
||||
# ---------------------------
|
||||
# Histogram
|
||||
# ---------------------------
|
||||
def create_event_histogram(columns, resolver, mapping_channels, time_threshold=200,
|
||||
triggers=None, B_only=False):
|
||||
|
||||
# index map statt .index()
|
||||
col_index = {name: i for i, name in enumerate(columns)}
|
||||
|
||||
if B_only:
|
||||
B_channels = ["B1","B2","B3","B4","B5","B6"]
|
||||
B_index = {b: i for i, b in enumerate(B_channels)}
|
||||
|
||||
bins = int((maxX_B - minX_B) / resX_B)
|
||||
hist = np.zeros((bins+1, len(B_channels)+2))
|
||||
hist[:,0] = np.linspace(minX_B, maxX_B, bins+1)
|
||||
else:
|
||||
bins = int((maxX_event - minX_event) / resX_event)
|
||||
hist = np.zeros((bins+1, len(columns)+1))
|
||||
hist[:,0] = np.linspace(minX_event, maxX_event, bins+1)
|
||||
|
||||
u_dict = {ch["name"]: ch["u"] for ch in (mapping_channels or [])}
|
||||
thr_dict = {ch["name"]: ch["thr"] for ch in (mapping_channels or [])}
|
||||
|
||||
trigchans = None
|
||||
if triggers:
|
||||
from event_filters import NMAHEPAM_triggers
|
||||
trigchans = prepare_trigger_indices(triggers, columns, NMAHEPAM_triggers)
|
||||
|
||||
current_event = []
|
||||
event_start = None
|
||||
|
||||
with open(file, "r", encoding="utf-8", errors="ignore") as f:
|
||||
for line in f:
|
||||
if not line.startswith("ED"):
|
||||
continue
|
||||
|
||||
parts = line.split()
|
||||
try:
|
||||
time = int(float(parts[1]))
|
||||
sli = int(parts[2])
|
||||
cha = int(parts[3])
|
||||
raw = float(parts[-1]) / 0x20000
|
||||
except:
|
||||
continue
|
||||
|
||||
idx = resolver(cha, sli)
|
||||
if idx is None:
|
||||
continue
|
||||
|
||||
name = columns[idx]
|
||||
val = raw * u_dict.get(name, 1.0)
|
||||
|
||||
if event_start is None:
|
||||
event_start = time
|
||||
|
||||
if abs(time - event_start) > time_threshold:
|
||||
process_event(current_event, hist, B_only, trigchans,
|
||||
columns, thr_dict,
|
||||
col_index, B_index if B_only else None)
|
||||
|
||||
current_event = []
|
||||
event_start = time
|
||||
|
||||
current_event.append((name, val))
|
||||
|
||||
if current_event:
|
||||
process_event(current_event, hist, B_only, trigchans,
|
||||
columns, thr_dict,
|
||||
col_index, B_index if B_only else None)
|
||||
|
||||
return hist
|
||||
|
||||
# ---------------------------
|
||||
# Event Processing (neu)
|
||||
# ---------------------------
|
||||
def process_event(event, hist, B_only, trigchans, columns, thr_dict,
|
||||
col_index, B_index):
|
||||
|
||||
event_dict = dict(event)
|
||||
|
||||
if (trigchans is not None) and not check_trigger(event_dict, columns, trigchans, thr_dict):
|
||||
return
|
||||
|
||||
if B_only:
|
||||
# B-Channels + Sum
|
||||
sum_val = 0
|
||||
for b, i in B_index.items():
|
||||
val = event_dict.get(b, 0)
|
||||
sum_val += val
|
||||
if minX_B <= val <= maxX_B:
|
||||
x = int((val - minX_B) / resX_B)
|
||||
hist[x, i+1] += 1
|
||||
|
||||
if minX_B <= sum_val <= maxX_B:
|
||||
x = int((sum_val - minX_B) / resX_B)
|
||||
hist[x, -1] += 1
|
||||
|
||||
else:
|
||||
for name, val in event:
|
||||
if minX_event <= val <= maxX_event:
|
||||
x = int((val - minX_event) / resX_event)
|
||||
hist[x, col_index[name]+1] += 1
|
||||
|
||||
# ---------------------------
|
||||
# Save
|
||||
# ---------------------------
|
||||
def save_hist(hist, columns, suffix):
|
||||
add = f"_{args.nameadd}" if args.nameadd else ""
|
||||
df = pd.DataFrame(hist, columns=["value"] + columns)
|
||||
fname = f"hists/{filename}{add}.{suffix}"
|
||||
df.to_csv(fname, sep=" ", index=False)
|
||||
print(fname, "created")
|
||||
|
||||
# ---------------------------
|
||||
# MAIN
|
||||
# ---------------------------
|
||||
def main():
|
||||
columns, resolver, channels, _ = Evaluate_mapping(args.map)
|
||||
|
||||
if args.eventhist:
|
||||
hist = create_event_histogram(columns, resolver, channels,
|
||||
time_threshold=args.time,
|
||||
triggers=args.trigger)
|
||||
save_hist(hist, columns, "eventhist")
|
||||
|
||||
if args.Bhist:
|
||||
hist = create_event_histogram(columns, resolver, channels,
|
||||
time_threshold=args.time,
|
||||
triggers=args.trigger,
|
||||
B_only=True)
|
||||
save_hist(hist, ["B1","B2","B3","B4","B5","B6","SUM"], "Bhist")
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
|
@ -1,227 +0,0 @@
|
|||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Tue Aug 26 13:47:24 2025
|
||||
|
||||
@author: ava
|
||||
Parse ED-data for DORN files, create histograms and optional calculations
|
||||
Trigger-enabled event histograms added
|
||||
Filename suffix via -nameadd
|
||||
"""
|
||||
|
||||
import numpy as np
|
||||
import argparse
|
||||
import sys
|
||||
import pandas as pd
|
||||
from pathlib import Path
|
||||
|
||||
from DORNconfiguration import NMAHEPAM_channels, SETH_channels, AHEPAM_channels
|
||||
from event_filters import prepare_trigger_indices, check_trigger
|
||||
|
||||
# ---------------------------
|
||||
# Argumente
|
||||
# ---------------------------
|
||||
parser = argparse.ArgumentParser(description="Parse ED-data for DORN files with triggers")
|
||||
parser.add_argument("file", type=str)
|
||||
parser.add_argument("-map", type=str, default="ALL",
|
||||
choices=["ALL", "NMAHEPAM", "SETH", "AHEPAM"])
|
||||
parser.add_argument("-eventhist", action="store_true")
|
||||
parser.add_argument("-Bhist", action="store_true", help="Histogram for B-channels + sum")
|
||||
parser.add_argument("-trigger", nargs="+", default=None,
|
||||
help="Trigger selection: e.g. B1, allB, allV, V1")
|
||||
parser.add_argument("-time", type=int, default=200, help="Event time window threshold")
|
||||
parser.add_argument("-nameadd", type=str, default="", help="Optional suffix for output filename")
|
||||
args = parser.parse_args()
|
||||
|
||||
file = args.file
|
||||
if not Path(file).is_file():
|
||||
print("No valid file:", file)
|
||||
sys.exit()
|
||||
|
||||
filename = Path(file).stem
|
||||
Path("hists").mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# ---------------------------
|
||||
# Eventhist default
|
||||
# ---------------------------
|
||||
minX_event = -100
|
||||
maxX_event = 5000
|
||||
resX_event = 0.838214 * 3
|
||||
|
||||
# ---------------------------
|
||||
# B-Histogramm Parameter
|
||||
# ---------------------------
|
||||
minX_B = -5
|
||||
maxX_B = 300
|
||||
resX_B = 0.838214 / 2
|
||||
|
||||
# ---------------------------
|
||||
# Helper
|
||||
# ---------------------------
|
||||
def apply_calibration(raw_value, name, u_dict):
|
||||
return raw_value * u_dict.get(name, 1.0)
|
||||
|
||||
# ---------------------------
|
||||
# Mapping
|
||||
# ---------------------------
|
||||
def Evaluate_mapping(mapping_name):
|
||||
if mapping_name == "ALL":
|
||||
columns = [f"0-{i}" for i in range(24)] + [f"1-{i}" for i in range(24)]
|
||||
return columns, lambda ch, plane: ch + plane * 24, None, None
|
||||
elif mapping_name == "NMAHEPAM":
|
||||
channels = NMAHEPAM_channels()
|
||||
elif mapping_name == "SETH":
|
||||
channels = SETH_channels()
|
||||
elif mapping_name == "AHEPAM":
|
||||
channels = AHEPAM_channels()
|
||||
else:
|
||||
print("Unknown mapping")
|
||||
sys.exit()
|
||||
|
||||
valid_channels = [ch for ch in channels if ch["name"]]
|
||||
columns = [ch["name"] for ch in valid_channels]
|
||||
|
||||
def resolver(ch, sl):
|
||||
hwc_index = ch + sl * 24
|
||||
for idx, ch_dict in enumerate(valid_channels):
|
||||
if ch_dict["hwc"] == hwc_index:
|
||||
return idx
|
||||
return None
|
||||
|
||||
return columns, resolver, valid_channels, None
|
||||
|
||||
# ---------------------------
|
||||
# Generic Event Histogram
|
||||
# ---------------------------
|
||||
def create_event_histogram(columns, resolver, mapping_channels, time_threshold=200, triggers=None,
|
||||
minX=minX_event, maxX=maxX_event, resX=resX_event, B_only=False):
|
||||
|
||||
if B_only:
|
||||
channels_to_use = ["B1","B2","B3","B4","B5","B6"]
|
||||
n_cols = len(channels_to_use) + 1
|
||||
bins = int((maxX_B - minX_B) / resX_B)
|
||||
hist = np.zeros((bins+1, n_cols + 1))
|
||||
hist[:,0] = np.linspace(minX_B, maxX_B, bins+1)
|
||||
else:
|
||||
bins = int((maxX - minX) / resX)
|
||||
hist = np.zeros((bins+1, len(columns)+1))
|
||||
hist[:,0] = np.linspace(minX, maxX, bins+1)
|
||||
channels_to_use = columns
|
||||
|
||||
u_dict = {ch["name"]: ch["u"] for ch in (mapping_channels or [])}
|
||||
thr_dict = {ch["name"]: ch["thr"] for ch in (mapping_channels or [])}
|
||||
|
||||
# Trigger vorbereiten (IMMER mit allen Kanälen!)
|
||||
trigchans = None
|
||||
if triggers:
|
||||
from event_filters import NMAHEPAM_triggers
|
||||
trigchans = prepare_trigger_indices(triggers, columns, NMAHEPAM_triggers)
|
||||
|
||||
current_event = []
|
||||
event_start = None
|
||||
|
||||
with open(file, "r", encoding="utf-8", errors="ignore") as f:
|
||||
for line in f:
|
||||
if not line.startswith("ED"):
|
||||
continue
|
||||
|
||||
parts = line.split()
|
||||
try:
|
||||
time = int(float(parts[1]))
|
||||
sli = int(parts[2])
|
||||
cha = int(parts[3])
|
||||
raw = float(parts[-1]) / 0x20000
|
||||
except:
|
||||
continue
|
||||
|
||||
# 🔧 FIX: IMMER alle Kanäle auflösen
|
||||
idx = resolver(cha, sli)
|
||||
if idx is None:
|
||||
continue
|
||||
|
||||
name = columns[idx]
|
||||
val = apply_calibration(raw, name, u_dict)
|
||||
|
||||
if event_start is None:
|
||||
event_start = time
|
||||
|
||||
if abs(time - event_start) > time_threshold:
|
||||
event_dict = {n:v for n,v in current_event}
|
||||
|
||||
if (trigchans is None) or check_trigger(event_dict, columns, trigchans, thr_dict):
|
||||
if B_only:
|
||||
fill_B_event(hist, event_dict, channels_to_use, minX_B, maxX_B, resX_B)
|
||||
else:
|
||||
fill_event(hist, current_event, columns, minX, maxX, resX)
|
||||
|
||||
current_event = []
|
||||
event_start = time
|
||||
|
||||
current_event.append((name, val))
|
||||
|
||||
# letztes Event
|
||||
if current_event:
|
||||
event_dict = {n:v for n,v in current_event}
|
||||
|
||||
if (trigchans is None) or check_trigger(event_dict, columns, trigchans, thr_dict):
|
||||
if B_only:
|
||||
fill_B_event(hist, event_dict, channels_to_use, minX_B, maxX_B, resX_B)
|
||||
else:
|
||||
fill_event(hist, current_event, columns, minX, maxX, resX)
|
||||
|
||||
return hist
|
||||
|
||||
# ---------------------------
|
||||
# Fill functions
|
||||
# ---------------------------
|
||||
def fill_event(hist, event, columns, minX, maxX, resX):
|
||||
for name, val in event:
|
||||
if val is None:
|
||||
continue
|
||||
if minX <= val <= maxX:
|
||||
x = int((val - minX) / resX)
|
||||
hist[x, columns.index(name)+1] += 1
|
||||
|
||||
def fill_B_event(hist, event_dict, B_channels, minX, maxX, resX):
|
||||
for i, b in enumerate(B_channels):
|
||||
val = event_dict.get(b, 0)
|
||||
if minX <= val <= maxX:
|
||||
x = int((val - minX) / resX)
|
||||
hist[x, i+1] += 1
|
||||
|
||||
sum_val = sum(event_dict.get(b, 0) for b in B_channels)
|
||||
if minX <= sum_val <= maxX:
|
||||
x = int((sum_val - minX) / resX)
|
||||
hist[x, -1] += 1
|
||||
|
||||
# ---------------------------
|
||||
# Save
|
||||
# ---------------------------
|
||||
def save_hist(hist, columns, suffix):
|
||||
add = f"_{args.nameadd}" if args.nameadd else ""
|
||||
df = pd.DataFrame(hist, columns=["value"] + columns)
|
||||
fname = f"hists/{filename}{add}.{suffix}"
|
||||
df.to_csv(fname, sep=" ", index=False)
|
||||
print(fname, "created")
|
||||
|
||||
# ---------------------------
|
||||
# MAIN
|
||||
# ---------------------------
|
||||
def main():
|
||||
columns, resolver, channels, _ = Evaluate_mapping(args.map)
|
||||
|
||||
if args.eventhist:
|
||||
hist = create_event_histogram(columns, resolver, channels,
|
||||
time_threshold=args.time,
|
||||
triggers=args.trigger)
|
||||
save_hist(hist, columns, "eventhist")
|
||||
|
||||
if args.Bhist:
|
||||
hist = create_event_histogram(columns, resolver, channels,
|
||||
time_threshold=args.time,
|
||||
triggers=args.trigger,
|
||||
B_only=True)
|
||||
save_hist(hist, ["B1","B2","B3","B4","B5","B6","SUM"], "Bhist")
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
|
@ -79,7 +79,7 @@ def prepare_trigger_indices(trig_args, columns, triggers_dict=None):
|
|||
# prod ist Tupel von AND-Gruppen → kombinieren für ein großes AND
|
||||
combined = [i for group in prod for i in group]
|
||||
combined_groups.append(combined)
|
||||
#print(combined_groups)
|
||||
print(combined_groups)
|
||||
return combined_groups
|
||||
|
||||
def check_trigger(event_dict, columns, trigchans, thr_dict):
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue