From cc702436c38f7e957d473bc7ca359c064318f384 Mon Sep 17 00:00:00 2001
From: Paul McCarthy <pauldmccarthy@gmail.com>
Date: Tue, 27 Feb 2018 11:55:11 +0000
Subject: [PATCH] deprecated imagewrapper.naninfrange
---
fsl/data/imagewrapper.py | 57 +++++++++-------------------------------
1 file changed, 13 insertions(+), 44 deletions(-)
diff --git a/fsl/data/imagewrapper.py b/fsl/data/imagewrapper.py
index d2aae5540..edb4df4e4 100644
--- a/fsl/data/imagewrapper.py
+++ b/fsl/data/imagewrapper.py
@@ -41,11 +41,14 @@ import logging
import collections
import itertools as it
+import deprecation
+
import numpy as np
import nibabel as nib
-import fsl.utils.notifier as notifier
-import fsl.utils.idle as idle
+import fsl.utils.notifier as notifier
+import fsl.utils.naninfrange as nir
+import fsl.utils.idle as idle
log = logging.getLogger(__name__)
@@ -137,7 +140,6 @@ class ImageWrapper(notifier.Notifier):
.. autosummary::
:nosignatures:
- naninfrange
isValidFancySliceObj
canonicalSliceObj
sliceObjToSliceTuple
@@ -469,7 +471,7 @@ class ImageWrapper(notifier.Notifier):
oldvlo, oldvhi = self.__volRanges[vol, :]
voldata = data[..., vi]
- newvlo, newvhi = naninfrange(voldata)
+ newvlo, newvhi = nir.naninfrange(voldata)
if np.isnan(newvlo) or \
(not np.isnan(oldvlo) and oldvlo < newvlo):
@@ -487,7 +489,7 @@ class ImageWrapper(notifier.Notifier):
# Calculate the new known data
# range over the entire image
# (i.e. over all volumes).
- newmin, newmax = naninfrange(self.__volRanges)
+ newmin, newmax = nir.naninfrange(self.__volRanges)
oldmin, oldmax = self.__range
self.__range = (newmin, newmax)
@@ -713,47 +715,14 @@ class ImageWrapper(notifier.Notifier):
self.__updateDataRangeOnWrite(slices, values)
+@deprecation.deprecated(deprecated_in='1.7.0',
+ removed_in='2.0.0',
+ details='Moved to fsl.utils.naninfrange')
def naninfrange(data):
- """Returns the minimum and maximum values in the given ``numpy`` array,
- ignoring ``nan`` and ``inf`` values.
-
- The ``numpy.nanmin``/``numpy.nanmax`` functions do not handle
- positive/negative infinity, so if such values are in the data, we need to
- use an alternate approach to calculating the minimum/maximum.
- """
+ """Deprecated - moved to :mod:`fsl.utils.naninfrange`. """
- if not np.issubdtype(data.dtype, np.floating):
- return data.min(), data.max()
-
- # But np.nanmin/nanmax are substantially
- # faster than the alternate, so we try it
- # first.
- dmin = np.nanmin(data)
- dmax = np.nanmax(data)
-
- # If there are no nans/infs in the data,
- # we can just use nanmin/nanmax
- if np.isfinite(dmin) and np.isfinite(dmax):
- return dmin, dmax
-
- # The entire array contains nans
- if np.isnan(dmin):
- return dmin, dmin
-
- # Otherwise we need to calculate min/max
- # only on finite values. This is the slow
- # option.
-
- # Find all finite values
- finite = np.isfinite(data)
-
- # Try to calculate min/max on those values.
- # An error will be raised if there are no
- # finite values in the array
- try:
- return data[finite].min(), data[finite].max()
- except Exception:
- return np.nan, np.nan
+ from fsl.utils.naninfrange import naninfrange
+ return naninfrange(data)
def isValidFancySliceObj(sliceobj, shape):
--
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