Except for getargspec

Should be collections.abc.Sized as currently implemented.
Also could be collections.abc.Container or even collections.abc.Collection.

The documentation structure needs a major refactor.

See https://github.com/lanl-ansi/bqpjson

Documentation is incomplete and a few NotImplementedErrors floating around

We would like to make the response object use numpy rather than pandas.

dimod.response.infer_vartype expects sample values to be binary {0,1} or {-1,1}, but the real QPU/SAPI response also includes the "third state" for missing qubits.

We can filter those out (in dimod.Response.from_futures) or support them (in infer_vartype).

See also #135

The docstrings for the Sampler/Composite/Structured mixins should make clear that they have been inherited.

https://travis-ci.org/dwavesystems/dimod/jobs/348408848

Error: python 2.7.14 is already installed
To upgrade to 3.6.4_3, run `brew upgrade python`

Add tests for the examples and import the files directly (http://www.sphinx-doc.org/en/stable/markup/code.html#includes)

When testing dimod solvers in qbsolv I ran into an issue where the returned solution was missing variables that should have been present (although may have been zero on all biases/couplings).

It was with either the random or SA ones.

It should at least be ruled out that it cannot be

Even if the documentation is incomplete - create the .rst files for everything so that we can get an idea of the form.

Making it be more than one thing requires the parser of the response to inspect the object before using it. If we want to have the benefits of a numpy array for some of the data_vectors, I think it's worth it to make everything a numpy array

If we don't need it to be a numpy array, might as well make them all lists?

Sphinx documentation

docs/_build/
docs/reference/generated/

A clear documentation of the dimod API is needed.

Also, it would be good for dimod to expose a set of unittests that can be run for any response, sampler or composite that would verify that the API has been set up correctly. Right now there are GenericSamplerTests and GenericResponseTest classes, but they are not imported into dimod nor are they designed with only verifying the API in mind.

dwave_networkx currently implements min_vertex_cover, it would be relatively straightforward to generalize it to min_weighted_vertex_cover as in networkx.

In tests raises a warning:

RuntimeWarning: '<' not supported between instances of 'int' and 'str', sort order is undefined for incomparable objects
  self.assertAlmostEqual(bqm.energy(sample), sample_series.dot(bqm_df.dot(sample_series)))

A composite layer that rescales the Hamiltonian would add a lot of convenience. It would rescale the input Hamiltonian to fit within specified ranges, then reverse the rescaling so the energies match the original Hamiltonian.

I currently use the following function to rescale Hamiltonians, with an additional feature that allows clipping large values so that the Hamiltonian doesn't have to be scaled down as much (the tradeoff being that the Hamiltonian is not a faithful rescaling if clipping < 1).

def rescale(hh, jj, h_range, j_range, clipping=1.):
    """Rescale the Hamiltonian.

    Clipping truncates values at a certain fraction of the provided range to lessen the
    required compression.
    """

    # Unpack the bounds.
    min_h, max_h = map(float, h_range)
    min_j, max_j = map(float, j_range)

    # Find the natural rescaling factor
    scalars = [
        1.,
        max([h / min_h for h in hh]),
        max([h / max_h for h in hh]),
        max([j / min_j for j in jj.values()]),
        max([j / max_j for j in jj.values()]),
        ]

    scalar = max(scalars)
    # Adjust for clipping
    scalar *= clipping

    hh_scaled = [min_h if h/scalar <= min_h else max_h if h/scalar >= max_h else h/scalar for h in hh]
    jj_scaled = {k: min_j if v/scalar <= min_j else max_j if v/scalar >= max_j else v/scalar for k, v in jj.iteritems()}

    return hh_scaled, jj_scaled, scalar

In python 3.6

dimod/dimod/decorators.py:116: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()

Relevant documentation:
https://docs.python.org/3/library/inspect.html#inspect.getfullargspec
https://docs.python.org/3/library/inspect.html#inspect.getargspec

response.to_dataframe

etc

This should be fixed or at least the docstring should state the limitation.

As a followup to #159

download_url is static, should use __version__ instead.