A long due update of the version number

v1.3.0:

• arch can now be "precise" in addition to "cpu" or "gpu". "precise" is a CPU implementation that does not use an optimized summation algorithm to speed up the calculation of the sum of the squared data. Thus, "precise" is less fast than "cpu" but does not lose in accuracy when large amplitudes are encountered in the data (which can sometimes happen with "cpu").
• The sum of the squared templates is computed only once at the beginning.
• The station and component axes of the input arrays can be merged into a single axis of traces.
  v1.3.1:
• only in Python for now: new argument "check_zeros" to control how thoroughly the wrapper checks for zeros after running FMF.

v1.2: Reducing the block size for the sum_cc kernel improves performance

See pull request #26 for details.

v1.1: Merge pull request #19 from beridel/ebeauce-version-number

Implements a cumulative Neumaier sum in the CPU implementation of FMF, both for python and Matlab. This summation technique (https://en.wikipedia.org/wiki/Kahan_summation_algorithm) compensates for numerical errors that can accumulate when summing large floating point numbers. This should also speed up the CPU implementation by a few seconds, depending on hardware and matched-filter search parameters.