Troubleshooting Common Issues in Lunar Occultation Workbench

# pseudocode for file in incoming_files:     convert_with_ffmpeg(file, out_format='ser')     timestamps = extract_timestamps(file)  # OCR or log parsing     save_frame_timestamp_index(file, timestamps) 

4) Photometry automation

Use photutils or LOW’s photometry engine (if scriptable) to extract a light curve:

• Define aperture sizes and background annuli based on camera plate scale or measure from the first few frames.
• Optionally auto-detect optimal aperture using SNR maximization or curve-of-growth routines.
• Save raw counts and background estimates per frame.

Important: log aperture parameters and centroid method so results are reproducible.

5) Event detection and timing

Automate event detection with a combination of:

• Edge-detection (sudden flux step) algorithms
• Matched filtering with expected occultation profile (consider limb diffraction if timing sub-millisecond)
• Bayesian or MCMC fitting to quantify mid-time and uncertainties

Simple approach:

• Compute normalized flux and its derivative.
• Identify candidate drops exceeding threshold (e.g., 5σ over local noise).
• Fit step function + linear trend to refine time and uncertainty.

Example fitting model (conceptual):

• Flux(t) = A * step(t – t0) + B * t + C + noise Solve for t0 and σ_t0.

6) QA, human-in-the-loop checks

Flag marginal detections for human review:

• Low SNR
• Multiple candidate events
• Saturation or lost frames Create an automated report with plots (light curve, residuals, thumbnail frames) to speed review.

7) Batch processing & parallelization

For large sets, use task queues (Celery, RQ) or GNU parallel to run independent processing tasks concurrently. Ensure each task writes atomic outputs and updates the database to avoid duplicate work.

8) Reporting & exports

Automate generation of science-ready outputs:

• Standard timing reports (UTC mid-time, uncertainty, observer metadata)
• Plots for archives and publication
• Machine-readable files (CSV, JSON, FITS tables)
• Submission-ready formats for organizations (e.g., IOTA) if applicable

Include metadata and provenance (script versions, parameter values).

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Example pipeline (small observatory) — step-by-step

1. Camera writes recording to /data/raw/.
2. File-watcher moves file to /data/processing/, writes metadata JSON.
3. Conversion script transforms to SER and extracts per-frame timestamps.
4. Photometry script runs, creates light curve and candidate list.
5. Timing fitter runs and produces time, uncertainty, fit diagnostics.
6. QA script checks SNR — passes results to auto-report; failures generate an email to observer with a link to preview.
7. Final results are archived to /data/finished/ and appended to the central SQLite database.

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Example code snippets

Below are concise patterns — not full programs — to illustrate key steps.

1. FFmpeg conversion (shell):

   ffmpeg -i input.avi -c:v copy output.ser 

2. Python file-watcher (watchdog skeleton): “`python from watchdog.observers import Observer from watchdog.events import FileSystemEventHandler

class NewFileHandler(FileSystemEventHandler):

def on_created(self, event):     if event.is_directory: return     process_new_file(event.src_path) 

observer = Observer() observer.schedule(NewFileHandler(), path=‘/data/raw’, recursive=False) observer.start()

3) Photometry with photutils (conceptual): ```python from photutils import CircularAperture, aperture_photometry positions = [(x_centroid, y_centroid)] apertures = CircularAperture(positions, r=5) phot_table = aperture_photometry(data_frame, apertures) 

1. Simple step fit (scipy least-squares sketch): “`python import numpy as np from scipy.optimize import least_squares

def model(params, t):

t0, A, B, C = params return A * (t > t0).astype(float) + B*t + C 

def residuals(params, t, y):

return y - model(params, t) 

res = least_squares(residuals, x0=[t_guess, A0, B0, C0], args=(t, y)) t0_best = res.x[0] “`

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Common pitfalls and how to avoid them

• Time synchronization errors: verify GPS/IRIG-B/PC clock logs; cross-check with known stellar occultations or comparison stars.
• OCR failures for burned-in timestamps: preprocess contrast and binarize; fall back to external logs.
• Saturation and non-linearity: detect saturated frames and exclude or correct them.
• Overfitting noise: prefer simple step models for low SNR; avoid overly flexible models that bias t0.
• Versioning chaos: record script, library, and LOW versions in output metadata.

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Scaling to networks and collaborative workflows

When multiple observers contribute:

• Standardize formats (required fields in metadata).
• Use a central repository or API for result submission.
• Implement authentication and provenance (who processed what, with which scripts).
• Provide a shared QA dashboard for reviewers to triage flagged events.

Consider cloud storage (S3) with lifecycle rules for archiving and a simple web UI for browsing processed results.

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Validation & verification

• Run automated pipelines on past well-characterized events and compare derived times to published values.
• Inject synthetic events (simulate occultations with known t0) into raw data to test recovery and uncertainty estimates.
• Maintain unit tests for critical code (timestamp parsing, photometry, fitting).

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Final notes

Automation isn’t a one-size-fits-all; tailor the pipeline complexity to your observing program. Start small: automate conversion and photometry first, then add fitting, QA, and reporting. Keep detailed metadata and version control to ensure reproducibility. With a robust automated LOW pipeline, you’ll process more data with higher consistency — turning nights of recordings into reliable scientific results.

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