# Source code for pycbc.inference.geweke

```
# Copyright (C) 2017 Christopher M. Biwer
#
# This program is free software; you can redistribute it and/or modify it
# under the terms of the GNU General Public License as published by the
# Free Software Foundation; either version 3 of the License, or (at your
# option) any later version.
#
# This program is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
# Public License for more details.
#
# You should have received a copy of the GNU General Public License along
# with this program; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
""" Functions for computing the Geweke convergence statistic.
"""
import numpy
[docs]def geweke(x, seg_length, seg_stride, end_idx, ref_start,
ref_end=None, seg_start=0):
""" Calculates Geweke conervergence statistic for a chain of data.
This function will advance along the chain and calculate the
statistic for each step.
Parameters
----------
x : numpy.array
A one-dimensional array of data.
seg_length : int
Number of samples to use for each Geweke calculation.
seg_stride : int
Number of samples to advance before next Geweke calculation.
end_idx : int
Index of last start.
ref_start : int
Index of beginning of end reference segment.
ref_end : int
Index of end of end reference segment. Default is None which
will go to the end of the data array.
seg_start : int
What index to start computing the statistic. Default is 0 which
will go to the beginning of the data array.
Returns
-------
starts : numpy.array
The start index of the first segment in the chain.
ends : numpy.array
The end index of the first segment in the chain.
stats : numpy.array
The Geweke convergence diagnostic statistic for the segment.
"""
# lists to hold statistic and end index
stats = []
ends = []
# get the beginning of all segments
starts = numpy.arange(seg_start, end_idx, seg_stride)
# get second segment of data at the end to compare
x_end = x[ref_start:ref_end]
# loop over all segments
for start in starts:
# find the end of the first segment
x_start_end = int(start + seg_length)
# get first segment
x_start = x[start:x_start_end]
# compute statistic
stats.append((x_start.mean() - x_end.mean()) / numpy.sqrt(
x_start.var() + x_end.var()))
# store end of first segment
ends.append(x_start_end)
return numpy.array(starts), numpy.array(ends), numpy.array(stats)
```