Examples

The current version of GWDALI deals only whith waveforms in the frequency space.

Jupyter Notebooks (.ipynb)

  • 1_get_hphx.ipynb

    • Compute polarizations \(h_+,\ h_{\times}\)

  • 2_DrawDetectors.ipynb

    • Vizualization of the GW detectors network choosen by the user

  • 3_get_strains.ipynb

    • Compute detectors output \(h=F_+h_+ + F_{\times}h_{\times}\)

  • 4_get_derivatives.ipynb
    • Compute derivative of the GW signals (differention method choosen by the user: numerical/automatic differentiation)

      • Fisrt derivatives \(\partial_ih\)

      • Second derivatives \(\partial_i\partial_jh\)

      • Thirds derivatives \(\partial_i\partial_j\partial_kh\)

  • 5_get_tensors.ipynb
    • Compute DALI Tensors:

      • Fisher \(F_{ij}\equiv\langle\partial_i h|\partial_j h\rangle\)

      • Doublet

        • \(D_{ijk}\equiv\langle\partial_i h|\partial_{jk} h\rangle\)

        • \(D_{ijkl}\equiv\langle\partial_{ij} h|\partial_{kl} h\rangle\)

      • Triplet

        • \(T_{ijkl}\equiv\langle\partial_i h|\partial_{jkl} h\rangle\)

        • \(T_{ijklm}\equiv\langle\partial_{ij} h|\partial_{klm} h\rangle\)

        • \(T_{ijklmn}\equiv\langle\partial_{ijk} h|\partial_{lmn} h\rangle\)

  • 6_GWDALI_MCMC_Fisher-vs-Singlet.ipynb

    • Sampling parameters for Posteriors estimation via MCMC or Nested Sampling methods (comparing Fisher-Inversion vs Singlet/Fisher_MCMC)

  • 7_GWDALI_MCMC_Exact-vs-Doublet.ipynb

    • Sampling parameters for Posteriors estimation via MCMC or Nested Sampling methods (comparing Exact vs Doublet posteriors)

  • 8_GWDALI_Grid.ipynb

    • Compute GW Posteriors from a N-dimensional Grid (choosen by the user)

Top-Down Python Codes (.py)