Learn by Examples

• 1. Load your first measurement files
• 3. Define calibration sections
• 4. Calculate variance of Stokes and anti-Stokes measurements
• 7. Calibration of single-ended measurement
  • Read the raw data files loaded from your DTS machine
  • Define the reference fiber sections that have a known temperature
  • Estimate the variance of the noise in the Stokes and anti-Stokes measurements
  • Perform calibration and compute the temperature
  • Plot the temperature and uncertainty of the estimated temperature
• 8. Calibration of double-ended measurements
  • Read the raw data files loaded from your DTS machine
  • Align measurements of the forward and backward channels
  • Define the reference fiber sections that have a known temperature
  • Estimate the variance of the noise in the Stokes and anti-Stokes measurements
  • Perform calibration and compute the temperature
  • Uncertainty of the calibrated temperature
    • 1. The variance approximated using linear error propagation
    • 2. Uncertainty approximation using Monte Carlo
• 9. Import a time series
  • Step 1: load the measurement files
  • Step 2: Add the temperature measurements of the external probe to the DataStore.
  • Step 3: Align the time of the external measurements to the Stokes measurement times
• 10. Align double ended measurements
• 11. Merge two single-ended measurements into a double-ended datastore
• 12. Creating a Dataset from numpy arrays
• 13. Fixing calibration parameters
• 14. Calibration over (lossy) splices and connectors
  • Background
  • Demonstration
• 15. Calibration using matching sections
  • Demonstration
• 16. Confidence intervals of average temperatures
  • Calibration procedure
  • Confidence intervals of averages
    • Introduction confidence intervals
    • 1. Averaging over time while the temperature varies over time and along the fiber
    • 2. Averaging over time while assuming the temperature remains constant over time but varies along the fiber
    • 3. Averaging along the fiber while the temperature varies along the cable and over time
    • 4. Averaging along the fiber while assuming the temperature is same along the fiber but varies over time
• 17. Uncertainty of the temperature estimated using single-ended calibration
  • Decomposing the uncertainty
  • Monte Carlo estimate of the uncertainty
    • Monte Carlo estimation of the confidence intervals
• A2. Loading sensornet files
• A3. Loading AP Sensing files
• A4. Loading sensortran files