PEP8/PyFlakes cleanup of nibabel/*/ excluding tests (tests pass)

Author: effigies

nibabel/benchmarks/bench_array_to_file.py

@@ -6,8 +6,8 @@
     nib.bench()
 
 If you have doctests enabled by default in nose (with a noserc file or
-environment variable), and you have a numpy version <= 1.6.1, this will also run
-the doctests, let's hope they pass.
+environment variable), and you have a numpy version <= 1.6.1, this will also
+run the doctests, let's hope they pass.
 
 Run this benchmark with:
 
@@ -26,6 +26,7 @@
 
 from numpy.testing import measure
 
+
 def bench_array_to_file():
     rng = np.random.RandomState(20111001)
     repeat = 10

nibabel/benchmarks/bench_fileslice.py

@@ -4,8 +4,8 @@
     nib.bench()
 
 If you have doctests enabled by default in nose (with a noserc file or
-environment variable), and you have a numpy version <= 1.6.1, this will also run
-the doctests, let's hope they pass.
+environment variable), and you have a numpy version <= 1.6.1, this will also
+run the doctests, let's hope they pass.
 
 Run this benchmark with:
 
@@ -55,6 +55,7 @@ def run_slices(file_like, repeat=3, offset=0, order='F'):
             for j, slicer in enumerate(_slices_for_len(L)):
                 sliceobj = [slice(None)] * n_dim
                 sliceobj[i] = slicer
+
                 def f():
                     fileslice(fobj,
                               tuple(sliceobj),
@@ -63,10 +64,11 @@ def f():
                               offset,
                               order)
                 times_arr[i, j] = timeit(f, number=repeat)
+
         def g():
             fobj.seek(offset)
             data = fobj.read()
-            _ = np.ndarray(SHAPE, arr.dtype, buffer=data, order=order)
+            np.ndarray(SHAPE, arr.dtype, buffer=data, order=order)
         base_time = timeit(g, number=repeat)
     return times_arr, base_time
 
@@ -77,33 +79,34 @@ def bench_fileslice(bytes=True,
                     bz2=False):
     sys.stdout.flush()
     repeat = 2
+
     def my_table(title, times, base):
         print()
         print(rst_table(times, ROW_NAMES, COL_NAMES, title,
-                              val_fmt='{0[0]:3.2f} ({0[1]:3.2f})'))
+                        val_fmt='{0[0]:3.2f} ({0[1]:3.2f})'))
         print('Base time: {0:3.2f}'.format(base))
     if bytes:
         fobj = BytesIO()
         times, base = run_slices(fobj, repeat)
         my_table('Bytes slice - raw (ratio)',
-                np.dstack((times, times / base)),
-                base)
+                 np.dstack((times, times / base)),
+                 base)
     if file_:
         with InTemporaryDirectory():
             file_times, file_base = run_slices('data.bin', repeat)
         my_table('File slice - raw (ratio)',
-                np.dstack((file_times, file_times / file_base)),
-                file_base)
+                 np.dstack((file_times, file_times / file_base)),
+                 file_base)
     if gz:
         with InTemporaryDirectory():
             gz_times, gz_base = run_slices('data.gz', repeat)
         my_table('gz slice - raw (ratio)',
-                np.dstack((gz_times, gz_times / gz_base)),
-                gz_base)
+                 np.dstack((gz_times, gz_times / gz_base)),
+                 gz_base)
     if bz2:
         with InTemporaryDirectory():
             bz2_times, bz2_base = run_slices('data.bz2', repeat)
         my_table('bz2 slice - raw (ratio)',
-                np.dstack((bz2_times, bz2_times / bz2_base)),
-                bz2_base)
+                 np.dstack((bz2_times, bz2_times / bz2_base)),
+                 bz2_base)
     sys.stdout.flush()

nibabel/benchmarks/bench_finite_range.py

@@ -6,8 +6,8 @@
     nib.bench()
 
 If you have doctests enabled by default in nose (with a noserc file or
-environment variable), and you have a numpy version <= 1.6.1, this will also run
-the doctests, let's hope they pass.
+environment variable), and you have a numpy version <= 1.6.1, this will also
+run the doctests, let's hope they pass.
 
 Run this benchmark with:
 
@@ -26,6 +26,7 @@
 
 from numpy.testing import measure
 
+
 def bench_finite_range():
     rng = np.random.RandomState(20111001)
     repeat = 10

nibabel/benchmarks/bench_load_save.py

@@ -6,8 +6,8 @@
     nib.bench()
 
 If you have doctests enabled by default in nose (with a noserc file or
-environment variable), and you have a numpy version <= 1.6.1, this will also run
-the doctests, let's hope they pass.
+environment variable), and you have a numpy version <= 1.6.1, this will also
+run the doctests, let's hope they pass.
 
 Run this benchmark with:
 
@@ -27,6 +27,7 @@
 
 from numpy.testing import measure
 
+
 def bench_load_save():
     rng = np.random.RandomState(20111001)
     repeat = 10

nibabel/benchmarks/butils.py

@@ -4,6 +4,7 @@
 
 from .. import get_info
 
+
 def print_git_title(title):
     """ Prints title string with git hash if possible, and underline
     """

nibabel/externals/__init__.py

@@ -1,5 +1,5 @@
 # init for externals package
 try:
     from collections import OrderedDict
-except ImportError: # < Python 2.7
+except ImportError:  # < Python 2.7
     from .ordereddict import OrderedDict

nibabel/freesurfer/mghformat.py

@@ -105,7 +105,7 @@ def __init__(self,
         hdr = np.ndarray(shape=(),
                          dtype=self.template_dtype,
                          buffer=binaryblock)
-        #if goodRASFlag, discard delta, Mdc and c_ras stuff
+        # if goodRASFlag, discard delta, Mdc and c_ras stuff
         if int(hdr['goodRASFlag']) < 0:
             hdr = self._set_affine_default(hdr)
         self._header_data = hdr.copy()
@@ -179,15 +179,14 @@ def from_fileobj(klass, fileobj, check=True):
         # dimensions from the header, skip over and then read the footer
         # information
         hdr_str = fileobj.read(klass._hdrdtype.itemsize)
-        hdr_str_to_np = np.ndarray(shape=(),
-                         dtype=klass._hdrdtype,
-                         buffer=hdr_str)
+        hdr_str_to_np = np.ndarray(shape=(), dtype=klass._hdrdtype,
+                                   buffer=hdr_str)
         if not np.all(hdr_str_to_np['dims']):
             raise MGHError('Dimensions of the data should be non-zero')
         tp = int(hdr_str_to_np['type'])
-        fileobj.seek(DATA_OFFSET + \
-                int(klass._data_type_codes.bytespervox[tp]) * \
-                np.prod(hdr_str_to_np['dims']))
+        fileobj.seek(DATA_OFFSET +
+                     int(klass._data_type_codes.bytespervox[tp]) *
+                     np.prod(hdr_str_to_np['dims']))
         ftr_str = fileobj.read(klass._ftrdtype.itemsize)
         return klass(hdr_str + ftr_str, check)
 
@@ -344,7 +343,7 @@ def set_data_shape(self, shape):
     def get_data_bytespervox(self):
         ''' Get the number of bytes per voxel of the data
         '''
-        return int(self._data_type_codes.bytespervox[ \
+        return int(self._data_type_codes.bytespervox[
             int(self._header_data['type'])])
 
     def get_data_size(self):
@@ -483,10 +482,10 @@ def from_file_map(klass, file_map, mmap=True):
         mmap : {True, False, 'c', 'r'}, optional, keyword only
             `mmap` controls the use of numpy memory mapping for reading image
             array data.  If False, do not try numpy ``memmap`` for data array.
-            If one of {'c', 'r'}, try numpy memmap with ``mode=mmap``.  A `mmap`
-            value of True gives the same behavior as ``mmap='c'``.  If image
-            data file cannot be memory-mapped, ignore `mmap` value and read
-            array from file.
+            If one of {'c', 'r'}, try numpy memmap with ``mode=mmap``.  A
+            `mmap` value of True gives the same behavior as ``mmap='c'``.  If
+            image data file cannot be memory-mapped, ignore `mmap` value and
+            read array from file.
         '''
         if not mmap in (True, False, 'c', 'r'):
             raise ValueError("mmap should be one of {True, False, 'c', 'r'}")
@@ -513,10 +512,10 @@ def from_filename(klass, filename, mmap=True):
         mmap : {True, False, 'c', 'r'}, optional, keyword only
             `mmap` controls the use of numpy memory mapping for reading image
             array data.  If False, do not try numpy ``memmap`` for data array.
-            If one of {'c', 'r'}, try numpy memmap with ``mode=mmap``.  A `mmap`
-            value of True gives the same behavior as ``mmap='c'``.  If image
-            data file cannot be memory-mapped, ignore `mmap` value and read
-            array from file.
+            If one of {'c', 'r'}, try numpy memmap with ``mode=mmap``.  A
+            `mmap` value of True gives the same behavior as ``mmap='c'``.  If
+            image data file cannot be memory-mapped, ignore `mmap` value and
+            read array from file.
 
         Returns
         -------

nibabel/gifti/__init__.py

@@ -20,4 +20,3 @@
 from .giftiio import read, write
 from .gifti import (GiftiMetaData, GiftiNVPairs, GiftiLabelTable, GiftiLabel,
                     GiftiCoordSystem, data_tag, GiftiDataArray, GiftiImage)
-