From 59abad733c71e8847e966e1cfa8c012083d1962e Mon Sep 17 00:00:00 2001
From: Chad Hanna <crh184@psu.edu>
Date: Thu, 1 Jun 2017 03:36:50 -0400
Subject: [PATCH] treebank: back to basics on the finite differencing spacing
---
gstlal-ugly/python/metric.py | 2 +-
gstlal-ugly/python/tree.py | 10 ++++++----
2 files changed, 7 insertions(+), 5 deletions(-)
diff --git a/gstlal-ugly/python/metric.py b/gstlal-ugly/python/metric.py
index 10c251d6c6..ee65d276e9 100644
--- a/gstlal-ugly/python/metric.py
+++ b/gstlal-ugly/python/metric.py
@@ -157,7 +157,7 @@ class Metric(object):
self.delta_t = {}
self.t_factor = {}
self.neg_t_factor = {}
- delta_t = 1e-5
+ delta_t = numpy.finfo(numpy.float32).eps * 2
t_factor = numpy.exp(-2j * numpy.pi * (numpy.arange(self.working_length) * self.df - self.fhigh) * delta_t)
neg_t_factor = numpy.exp(-2j * numpy.pi * (numpy.arange(self.working_length) * self.df - self.fhigh) * (-delta_t))
for t in numpy.array([1.,2.,4.,8.,16.,32.,64.,128.,256.,512.,1024]):
diff --git a/gstlal-ugly/python/tree.py b/gstlal-ugly/python/tree.py
index 5a793feeea..4ae3122fd8 100644
--- a/gstlal-ugly/python/tree.py
+++ b/gstlal-ugly/python/tree.py
@@ -109,9 +109,11 @@ class HyperCube(object):
self.deltas = numpy.array([c[1] - c[0] for c in boundaries])
self.metric = metric
# FIXME don't assume m1 m2 and the spin coords are the coordinates we have here.
- deltas = 5e-7 * numpy.ones(len(self.center))
- #deltas[0:2] *= self.center[0:2]**.5
- deltas[2:] = 1.3e-4
+ deltas = numpy.finfo(numpy.float32).eps * 2 * numpy.ones(len(self.center))
+ #deltas = 5e-7 * numpy.ones(len(self.center))
+ deltas[0:2] *= self.center[0:2]
+ #deltas[2:] = 1.3e-4
+ #deltas[2:] = 1.0e-5
if self.metric is not None and metric_tensor is None:
try:
@@ -236,7 +238,7 @@ class Node(object):
size = self.cube.num_tmps_per_side(mismatch)
splitdim = numpy.argmax(size)
aspect_ratios = size / min(size)
- aspect_factor = max(1., numpy.product(aspect_ratios[aspect_ratios>1.5]) / 1.5**len(aspect_ratios[aspect_ratios>1.5]))
+ aspect_factor = max(1., numpy.product(aspect_ratios[aspect_ratios>2.0]) / 2.0**len(aspect_ratios[aspect_ratios>2.0]))
if numpy.isnan(aspect_factor):
aspect_factor = 1.0
aspect_ratio = max(aspect_ratios)
--
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