Also check for invalid bounds when low= and high= are arraylike rather than scalar (closes #8226)

In some places, just remove aparently unnecessary filters.

After this, all cases of ignore filters should be removed from
the tests, making testing (even multiple runs) normally fully
predictable.

Allow many distributions to have a scale of 0.

np.random.shuffle will allocate a buffer based on the size of the first
element of an array of strings. If the first element is smaller than
another in the array this buffer will overflow, causing a segfault
when garbage is collected.

Additionally if the array contains objects then one would be left
in the buffer and have it's refcount erroniously decrimented on
function exit, causing that object to be deallocated too early.

To fix this we change the buffer to be an array of int8 of the
the size of the array's dtype, which sidesteps both issues.

Fixes #7710

At least the gamma generator doesn't support it.

The warning turned up when the numpy/randome/tests were run using

$ python runtests.py -t numpy/random/tests/

It doesn't show when all the tests are run.

(in which case a stream of 0's is usually returned (or 1's)).

See #5818.

The 'pandas' library expects Python integers to be
returned, so this commit changes the API so that
the default is 'np.int' which converts to native
Python integer types when a singleton is being
generated with this function.

Closes gh-7284.

Closes gh-7203.

Fixes bug in np.random methods that would return scalars
when passed one-element array inputs. This is because
one-element ndarrays can be cast to integers / floats, which
is what functions like PyFloat_AsDouble do before converting
to the intended data type.

This commit changes the check used to determine whether the
inputs are purely scalar by converting all inputs to arrays
and checking if the resulting shape is an empty tuple (scalar)
or not (array).

Closes gh-4263.