Python API Reference

• QMatrix
  • QMatrix
• Log10Likelihood
  • Log10Likelihood
• MissedEventsG
  • MissedEventsG
• IdealG
  • IdealG
• ExactSurvivor
  • ExactSurvivor
• ApproxSurvivor
  • ApproxSurvivor
• DeterminantEq
  • DeterminantEq
• Searching for Roots
  • find_roots()
  • Bracketing all Roots
  • Finding intervals for each root
  • Root optimization

Extras

Internal Numpy Type

Many of the python bindings accept both scalars and values that can be converted to numpy arrays. The latter are first converted to a specific numpy type, depending on how this package was compiled.

HJCFIT.internal_dtype

Numpy type used internally

If the package is configured and compiled with -HJCFIT_LONG_DOUBLE (see wiki), then long doubles are used through out. The exact size of long doubles is platform dependent and is typically 80-bit on Linux and OSX and 64-bit on Windows. See Wikipedia for more information. Otherwise, the package defaults to garden variety 64-bit doubles.

Linear Algebra

Numpy only provides a subset of its utilities for arrays consisting of long doubles. As a result, this package exposes some of Eigen’s capabilities, as needed. Their interface is generally reminiscent of the numpy utility they mirror.

HJCFIT.likelihood.eig(_matrix)

Computes eigenvalues of a matrix

HJCFIT.likelihood.inv(_matrix)

Computes inverse of a non-singular matrix

HJCFIT.likelihood.svd(_matrix)

HJCFIT.likelihood.det(_matrix)

Computes determinant of a matrix

HJCFIT.likelihood.expm(_matrix)

Computes singular value decomposition of a matrix