RF: Drop (mostly) redundant ArrayProxy.get_scaled() method

Author: effigies

nibabel/arrayproxy.py

@@ -378,34 +378,27 @@ def get_unscaled(self):
         """
         return self._get_unscaled(slicer=())
 
-    def get_scaled(self, dtype=None):
-        """ Read data from file and apply scaling
+    def __array__(self, dtype=None):
+        """ Read data from file and apply scaling, casting to ``dtype``
 
-        The dtype of the returned array is the narrowest dtype that can
-        represent the data without overflow, and is at least as wide as
-        the dtype parameter.
+        If ``dtype`` is unspecified, the dtype of the returned array is the
+        narrowest dtype that can represent the data without overflow.
+        Generally, it is the wider of the dtypes of the slopes or intercepts.
 
-        If dtype is unspecified, it is the wider of the dtypes of the slope
-        or intercept. This will generally be determined by the parameter
-        size in the image header, and so should be consistent for a given
-        image format, but may vary across formats. Notably, these factors
-        are single-precision (32-bit) floats for NIfTI-1 and double-precision
-        (64-bit) floats for NIfTI-2.
+        The types of the scale factors will generally be determined by the
+        parameter size in the image header, and so should be consistent for a
+        given image format, but may vary across formats.
 
         Parameters
         ----------
-        dtype : numpy dtype specifier
-            A numpy dtype specifier specifying the narrowest acceptable
-            dtype.
+        dtype : numpy dtype specifier, optional
+            A numpy dtype specifier specifying the type of the returned array.
 
         Returns
         -------
         array
-            Scaled of image data of data type `dtype`.
+            Scaled image data with type `dtype`.
         """
-        return self._get_scaled(dtype=dtype, slicer=())
-
-    def __array__(self, dtype=None):
         arr = self._get_scaled(dtype=dtype, slicer=())
         if dtype is not None:
             arr = arr.astype(dtype, copy=False)

nibabel/dataobj_images.py

@@ -10,7 +10,6 @@
 
 import numpy as np
 
-from .arrayproxy import is_proxy
 from .filebasedimages import FileBasedImage
 from .keywordonly import kw_only_meth
 from .deprecated import deprecate_with_version
@@ -351,14 +350,10 @@ def get_fdata(self, caching='fill', dtype=np.float64):
         if self._fdata_cache is not None:
             if self._fdata_cache.dtype.type == dtype.type:
                 return self._fdata_cache
-        dataobj = self._dataobj
-        # Attempt to confine data array to dtype during scaling
-        # On overflow, may still upcast
-        if is_proxy(dataobj):
-            dataobj = dataobj.get_scaled(dtype=dtype)
         # Always return requested data type
-        # For array proxies, will only copy on overflow
-        data = np.asanyarray(dataobj, dtype=dtype)
+        # For array proxies, will attempt to confine data array to dtype
+        # during scaling
+        data = np.asanyarray(self._dataobj, dtype=dtype)
         if caching == 'fill':
             self._fdata_cache = data
         return data

nibabel/ecat.py

@@ -696,6 +696,16 @@ def __array__(self, dtype=None):
 
         If you want to read only some slices, use the slicing syntax
         (``__getitem__``) below, or ``subheader.data_from_fileobj(frame)``
+
+        Parameters
+        ----------
+        dtype : numpy dtype specifier, optional
+            A numpy dtype specifier specifying the type of the returned array.
+
+        Returns
+        -------
+        array
+            Scaled image data with type `dtype`.
         '''
         # dtype=None is interpreted as float64
         data = np.empty(self.shape, dtype=dtype)
@@ -705,32 +715,6 @@ def __array__(self, dtype=None):
                 frame_mapping[i][0])
         return data
 
-    def get_scaled(self, dtype=None):
-        """ Read data from file and apply scaling
-
-        The dtype of the returned array is the narrowest dtype that can
-        represent the data without overflow, and is at least as wide as
-        the dtype parameter.
-
-        If dtype is unspecified, it is automatically determined.
-
-        Parameters
-        ----------
-        dtype : numpy dtype specifier
-            A numpy dtype specifier specifying the narrowest acceptable
-            dtype.
-
-        Returns
-        -------
-        array
-            Scaled of image data of data type `dtype`.
-        """
-        data = self.__array__()
-        if dtype is None:
-            return data
-        final_type = np.promote_types(data.dtype, dtype)
-        return data.astype(final_type, copy=False)
-
     def __getitem__(self, sliceobj):
         """ Return slice `sliceobj` from ECAT data, optimizing if possible
         """

nibabel/minc1.py

@@ -261,45 +261,29 @@ def ndim(self):
     def is_proxy(self):
         return True
 
-    def _get_scaled(self, dtype, slicer):
-        data = self.minc_file.get_scaled_data(slicer)
-        if dtype is None:
-            return data
-        final_type = np.promote_types(data.dtype, dtype)
-        return data.astype(final_type, copy=False)
-
-    def get_scaled(self, dtype=None):
-        """ Read data from file and apply scaling
-
-        The dtype of the returned array is the narrowest dtype that can
-        represent the data without overflow, and is at least as wide as
-        the dtype parameter.
+    def __array__(self, dtype=None):
+        """ Read data from file and apply scaling, casting to ``dtype``
 
-        If dtype is unspecified, it is automatically determined.
+        If ``dtype`` is unspecified, the dtype is automatically determined.
 
         Parameters
         ----------
-        dtype : numpy dtype specifier
-            A numpy dtype specifier specifying the narrowest acceptable
-            dtype.
+        dtype : numpy dtype specifier, optional
+            A numpy dtype specifier specifying the type of the returned array.
 
         Returns
         -------
         array
-            Scaled of image data of data type `dtype`.
+            Scaled image data with type `dtype`.
         """
-        return self._get_scaled(dtype=dtype, slicer=())
-
-    def __array__(self, dtype=None):
-        ''' Read of data from file '''
-        arr = self._get_scaled(dtype=dtype, slicer=())
+        arr = self.minc_file.get_scaled_data(sliceobj=())
         if dtype is not None:
             arr = arr.astype(dtype, copy=False)
         return arr
 
     def __getitem__(self, sliceobj):
         """ Read slice `sliceobj` of data from file """
-        return self._get_scaled(dtype=None, slicer=sliceobj)
+        return self.minc_file.get_scaled_data(sliceobj)
 
 
 class MincHeader(SpatialHeader):

nibabel/parrec.py

@@ -676,30 +676,23 @@ def get_unscaled(self):
         """
         return self._get_unscaled(slicer=())
 
-    def get_scaled(self, dtype=None):
-        """ Read data from file and apply scaling
-
-        The dtype of the returned array is the narrowest dtype that can
-        represent the data without overflow, and is at least as wide as
-        the dtype parameter.
+    def __array__(self, dtype=None):
+        """ Read data from file and apply scaling, casting to ``dtype``
 
-        If dtype is unspecified, it is the wider of the dtypes of the slopes
-        or intercepts
+        If ``dtype`` is unspecified, the dtype of the returned array is the
+        narrowest dtype that can represent the data without overflow.
+        Generally, it is the wider of the dtypes of the slopes or intercepts.
 
         Parameters
         ----------
-        dtype : numpy dtype specifier
-            A numpy dtype specifier specifying the narrowest acceptable
-            dtype.
+        dtype : numpy dtype specifier, optional
+            A numpy dtype specifier specifying the type of the returned array.
 
         Returns
         -------
         array
-            Scaled of image data of data type `dtype`.
+            Scaled image data with type `dtype`.
         """
-        return self._get_scaled(dtype=dtype, slicer=())
-
-    def __array__(self, dtype=None):
         arr = self._get_scaled(dtype=dtype, slicer=())
         if dtype is not None:
             arr = arr.astype(dtype, copy=False)

nibabel/tests/test_proxy_api.py

@@ -142,7 +142,10 @@ def validate_array_interface_with_dtype(self, pmaker, params):
         for dtype in np.sctypes['float'] + np.sctypes['int'] + np.sctypes['uint']:
             # Directly coerce with a dtype
             direct = dtype(prox)
-            assert_almost_equal(direct, orig.astype(dtype))
+            # Half-precision is imprecise. Obviously. It's a bad idea, but don't break
+            # the test over it.
+            rtol = 1e-03 if dtype == np.float16 else 1e-05
+            assert_allclose(direct, orig.astype(dtype), rtol=rtol, atol=1e-08)
             assert_dt_equal(direct.dtype, np.dtype(dtype))
             assert_equal(direct.shape, params['shape'])
             # All three methods should produce equivalent results
@@ -153,25 +156,6 @@ def validate_array_interface_with_dtype(self, pmaker, params):
                 # Shape matches expected shape
                 assert_equal(out.shape, params['shape'])
 
-    def validate_get_scaled(self, pmaker, params):
-        # Check proxy returns expected array from asarray
-        prox, fio, hdr = pmaker()
-        out = prox.get_scaled()
-        assert_array_equal(out, params['arr_out'])
-        assert_dt_equal(out.dtype, params['dtype_out'])
-        # Shape matches expected shape
-        assert_equal(out.shape, params['shape'])
-
-        for dtype in np.sctypes['float'] + np.sctypes['int'] + np.sctypes['uint']:
-            out = prox.get_scaled(dtype=dtype)
-            # Half-precision is imprecise. Obviously. It's a bad idea, but don't break
-            # the test over it.
-            rtol = 1e-03 if dtype == np.float16 else 1e-05
-            assert_allclose(out, params['arr_out'].astype(out.dtype), rtol=rtol, atol=1e-08)
-            assert_greater_equal(out.dtype, np.dtype(dtype))
-            # Shape matches expected shape
-            assert_equal(out.shape, params['shape'])
-
     def validate_header_isolated(self, pmaker, params):
         # Confirm altering input header has no effect
         # Depends on header providing 'get_data_dtype', 'set_data_dtype',