BedrettoMuons

Custom MIDAS + WaveCatcher integration for Bedretto Muons DAQ, including:

• Native C/C++ MIDAS frontend (midas_frontend/) linked to WaveCatcher Linux library.
• Guided mhttpd custom page (custom/wc_control.html) for run control and live monitoring.
• Python DAQ/bridge tooling (python_daq/) for compatibility workflows and transfer utilities.
• Operator scripts (scripts/) for stack startup and CLI-driven runs.

Repository layout

• midas_frontend/: native frontend, test harness, build rules.
• custom/: custom mhttpd control page.
• scripts/: practical run/start scripts used on the DAQ host.
• python_daq/: Python DAQ service/client/bridge components.
• docs/: compatibility report and technical notes.

Native MIDAS + WaveCatcher quick start

1. Build frontend:
   • cd midas_frontend && make
2. Ensure MIDAS and WaveCatcher libs are visible in environment (MIDASSYS, LD_LIBRARY_PATH, etc.).
3. Start MIDAS services and frontend (or use scripts/wc_start_midas_stack.sh).
4. Open mhttpd and use WaveCatcher Control custom page to configure/start/stop runs.

Web control capabilities (current)

• Trigger mode selection (normal/software/coincidence).
• Per-run channel selection (CSV + primary channel fallback).
• Single-threshold apply workflow for iterative channel tuning.
• Auto-stop mode selection (duration XOR target decoded event count).
• Live status panel, last-run summary, transfer command helper, and live waveform preview with multi-channel overlay legend.

ROOT conversion workflow (offline, recommended)

1. Load ROOT environment:
   • source scripts/wc_setup_root_env.sh
2. Convert a MIDAS run file:
   • scripts/wc_convert_mid_to_root.sh --input /home/morenoma/online_wc/run02429.mid.lz4
   • Optional post-run hook from CLI runner:
     • WC_CONVERT_ROOT_ON_COMPLETE=1 scripts/wc_run_midas_cli.sh ...
3. Output:
   • ROOT file with TTree wc_events containing:
     • MIDAS metadata (midas_serial, midas_timestamp, midas_event_id)
     • WaveCatcher header (wc_event_id, wc_tdc, wc_header_nchannels)
     • Per-channel features from WCFE (channel_id, trig_count, time_count, baseline, peak, charge)
     • Waveform payload from WCWF (wf_channel, wf_offset, wf_n_samples, wf_samples)
   • Histograms: h_peak_mV, h_charge_arb

This keeps acquisition reliable in .mid.lz4 while producing ROOT-native analysis files deterministically.

Python DAQ branch

A dedicated branch python-daq is used to emphasize Python-based DAQ/bridge workflow.
Core files are under python_daq/ and can be used independently of native frontend development.

Notes

• Runtime data/log artifacts are intentionally excluded via .gitignore.
• This repository is structured for outsiders to reproduce setup and understand system components without local legacy clutter.