From 52e5a60b3f2287744ada33150de0c41331c6d933 Mon Sep 17 00:00:00 2001
From: Paul McCarthy <pauldmccarthy@gmail.com>
Date: Thu, 13 Jun 2019 11:12:47 +0930
Subject: [PATCH] RF: Change naninfrange approach to flattening structured
arrays, avoid copying data if possible, and avoid copydeprecation warnings.
---
fsl/utils/naninfrange.py | 24 +++++++++++++++++++++---
1 file changed, 21 insertions(+), 3 deletions(-)
diff --git a/fsl/utils/naninfrange.py b/fsl/utils/naninfrange.py
index bfb497f38..bcdf5b12c 100644
--- a/fsl/utils/naninfrange.py
+++ b/fsl/utils/naninfrange.py
@@ -24,10 +24,28 @@ def naninfrange(data):
"""
# For structured arrays, we assume that
- # all fields are numeric, and we simply
- # take the range across all fields
+ # all fields have the same dtype, and we
+ # simply take the range across all fields
if len(data.dtype) > 0:
- data = np.concatenate([data[n] for n in data.dtype.names])
+
+ # Avoid inducing a data copy if
+ # at all possible. np.ndarray
+ # doesn't preserve the underlying
+ # order, so let's set that. Also,
+ # we're forced to make a copy if
+ # the array is not contiguous,
+ # otherwise ndarray will complain
+ if data.flags['C_CONTIGUOUS']: order = 'C'
+ elif data.flags['F_CONTIGUOUS']: order = 'F'
+ else:
+ data = np.ascontiguousarray(data)
+ order = 'C'
+
+ shape = [len(data.dtype)] + list(data.shape)
+ data = np.ndarray(buffer=data.data,
+ shape=shape,
+ order=order,
+ dtype=data.dtype[0])
if not np.issubdtype(data.dtype, np.floating):
return data.min(), data.max()
--
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