From 27a55d79d6c8c47bc4ebe305d1de5647ff08be1b Mon Sep 17 00:00:00 2001
From: Patrick Godwin <patrick.godwin@ligo.org>
Date: Tue, 23 Jul 2019 07:52:14 -0700
Subject: [PATCH] utils.py: when saving features, only store time-based columns
as double-precision, rest as single-precision
---
gstlal-burst/python/fxtools/utils.py | 12 ++++++++++--
1 file changed, 10 insertions(+), 2 deletions(-)
diff --git a/gstlal-burst/python/fxtools/utils.py b/gstlal-burst/python/fxtools/utils.py
index 64d219164c..606acecf87 100644
--- a/gstlal-burst/python/fxtools/utils.py
+++ b/gstlal-burst/python/fxtools/utils.py
@@ -103,6 +103,14 @@ def create_new_dataset(path, base, data, name = 'data', group = None, tmp = Fals
return fname
+def feature_dtype(columns):
+ """
+ given a set of columns, returns back numpy dtypes associated with those
+ columns. All time-based columns are double-precision, others are stored
+ in single-precision.
+ """
+ return [(column, numpy.float64) if 'time' in column else (column, numpy.float32) for column in columns]
+
#----------------------------------
### gps time utilities
@@ -273,7 +281,7 @@ class HDF5TimeseriesFeatureData(FeatureData):
self.sample_rate = kwargs['sample_rate']
self.waveform = kwargs['waveform']
self.metadata = dict(**kwargs)
- self.dtype = [(column, 'float') for column in self.columns]
+ self.dtype = feature_dtype(self.columns)
self.feature_data = {key: numpy.empty((self.cadence * self.sample_rate,), dtype = self.dtype) for key in keys}
self.last_save_time = 0
self.clear()
@@ -322,7 +330,7 @@ class HDF5ETGFeatureData(FeatureData):
self.cadence = kwargs['cadence']
self.waveform = kwargs['waveform']
self.metadata = dict(**kwargs)
- self.dtype = [(column, 'float') for column in self.columns]
+ self.dtype = feature_dtype(self.columns)
self.feature_data = {key: [] for key in keys}
self.clear()
--
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