BF: remove intercept only iuint-iuint scaling

Short version: remove conditional branch trying to preserve precision on
writing arrays by keeping working type as an integer type.

The advantages to removal are:

* fixing a bug for explicit scaling in `array_to_file`
* making the code simpler to think about

The disadvantages are:

* minor loss in precision for int64 / int64 output types.
* float output type rather than integer output type when reading images
 where this scaling trick could be used.

Long version:

``int`` means signed integer type
``uint`` means unsigned integer type
``iuint`` refers to set of all signed and unsigned integer types

When writing data to file, in `volumeutils.array_to_file`.

If:

* input type is iuint
* output type is iuint
* slope is 1
* integer is not 0 and is an integer

Then:

* Working type calculated by `volumeutils._inter_type`
* intercept cast to working type

Here is the relevant code from 1.3.0::

    # (u)int to (u)int with inter alone - select precision
   if (slope == 1 and inter != 0 and
       in_dtype.kind in 'iu' and out_dtype.kind in 'iu' and
       inter == np.round(inter)): # (u)int to (u)int offset only scaling
       # Does range of in type minus inter fit in out type? If so, use that as
       # working type.  Otherwise use biggest float for max integer precision
       inter = inter.astype(_inter_type(in_dtype, -inter, out_dtype))

The problem arises from the sign flip in the line above. Let's call the
internal intercept value in `_inter_type` : `minter` (minus inter in this
case).  Define:

* `in_min = np.iinfo(in_dtype).min`
* `in_max = np.iinfo(in_dtype).max`

Looking at `_inter_type`, we will get a returned uint type if all of `in_min + 
minter`, `in_max + minter`, `in_min`, `in_max`, `minter` are > 0. If
`_inter_type` returns uint as working type and `inter` is negative, then the 
last line above will cast a negative number into a positive one and cause a 
mess.

The problem then arises only when:

* input type is uint (in_min = 0)
* `inter` is negative
* output_type is larger than input type (`in_max + minter` > `in_max`)

See appended script for demonstration.

This is clearly a problem for `array_to_file` - but is it a problem for the 
image reading / writing prior to the recent pull request allowing arbitrary 
scaling?

Then the question is: is there any calculated scaling that results in:

* uint input type
* negative intercept
* larger iuint output type?

But - if the input type is uint and smaller then the output type, then it can 
and will cast to the output type, so calculating scaling between these types 
will never generate a negative intercept.  See the assert in appended script
for demonstration.

The problem doesn't arise for ``volumeutils.apply_read_scaling``, even though 
we are using `_inter_type`.  This is because we pass `inter` to `_inter_type` 
rather than `-inter`.  Because we are checking `minter` in `_inter_type`, and 
this will be negative, we can't get a uint return type for a negative
intercept.

<script>
""" Demonstrating the scaling problem
""" from __future__ import print_function

from itertools import product

import numpy as np

import nibabel.volumeutils as vu import nibabel.arraywriters as na

ints = np.sctypes['int'] uints = np.sctypes['uint'] iuints = ints + uints

def do_inter_type(in_type, inter, out_type):
   inter = np.atleast_1d(inter).astype(np.float)
   return vu._inter_type(in_type, -inter, out_type)

def has_problem(in_type, inter, out_type):
   scaling_type = do_inter_type(in_type, inter, out_type)
   return inter < 0 and np.dtype(scaling_type).kind == 'u'

print("Combinations generating uint scaling problem") print('in type',
'out_type', 'inter', 'castable') for in_type in iuints:
   i_min, i_max = np.iinfo(in_type).min, np.iinfo(in_type).max
   for out_type in iuints:
       problem = False
       for inter in (i_min-1, i_min, -1, 1, i_max):
           if not has_problem(in_type, inter, out_type):
               continue
           problem = True
           print(in_type, out_type, inter, np.can_cast(in_type, out_type))
       if not problem:
           continue
       # Show we can't get a negative intercept scaling between these types
       in_values = (i_min, 0, i_max)
       for values in product(in_values, in_values):
           arr = np.array(values, dtype=in_type)
           writer = na.SlopeInterArrayWriter(arr, out_type)
           assert (writer.slope, writer.inter) == (1, 0)
</script>