Legacy cleanup for 0.8.0

docs/conf.py

@@ -102,7 +102,9 @@ def iad_add_directive_header(self, sig):
 
 
 # Link to Python standard lib objects
-intersphinx_mapping = {'python': ('https://docs.python.org/3', None),
-    'qbsolv': ('https://docs.ocean.dwavesys.com/projects/qbsolv/en/latest/', None),
-    'oceandocs': ('https://docs.ocean.dwavesys.com/en/latest/', None),
-    'sysdocs_gettingstarted': ('https://docs.dwavesys.com/docs/latest/', None)}
+intersphinx_mapping = {
+    'python': ('https://docs.python.org/3', None),
+    'numpy': ('https://numpy.org/doc/stable/', None),
+    'oceandocs': ('https://docs.ocean.dwavesys.com/en/stable/', None),
+    'sysdocs_gettingstarted': ('https://docs.dwavesys.com/docs/latest/', None),
+}

docs/intro.rst

@@ -24,9 +24,9 @@ Configuration
 It's recommended you set up your D-Wave Cloud Client configuration through the
 :std:doc:`interactive CLI utility <oceandocs:docs_cli>`.
 
-As described in the :std:doc:`Using a D-Wave System <oceandocs:overview/dwavesys>` section
-of Ocean Documentation, for your code to access remote D-Wave compute resources, you must
-configure communication through SAPI; for example, your code needs your API
+As described in the :std:doc:`Configuring Access to D-Wave Solvers <oceandocs:overview/sapi>`
+section of Ocean Documentation, for your code to access remote D-Wave compute resources,
+you must configure communication through SAPI; for example, your code needs your API
 token for authentication. D-Wave Cloud Client provides multiple options for configuring
 the required information:
 
@@ -166,75 +166,3 @@ A typical workflow may include the following steps:
 3. Submit your problem, using your solver, and then process the returned
    :class:`~dwave.cloud.computation.Future`, instantiated by your solver to handle
    remotely executed problem solving.
-
-Terminology
-===========
-
-.. glossary::
-
-    Ising
-         Traditionally used in statistical mechanics. Variables are "spin up"
-         (:math:`\uparrow`) and "spin down" (:math:`\downarrow`), states that
-         correspond to :math:`+1` and :math:`-1` values. Relationships between
-         the spins, represented by couplings, are correlations or anti-correlations.
-         The objective function expressed as an Ising model is as follows:
-
-         .. math::
-
-                  \begin{equation}
-                       \text{E}_{ising}(\pmb{s}) = \sum_{i=1}^N h_i s_i + \sum_{i=1}^N \sum_{j=i+1}^N J_{i,j} s_i s_j
-                  \end{equation}
-
-         where the linear coefficients corresponding to qubit biases
-         are :math:`h_i`, and the quadratic coefficients corresponding to coupling
-         strengths are :math:`J_{i,j}`.
-
-    model
-        A collection of variables with associated linear and
-        quadratic biases.
-
-    QUBO
-         Quadratic unconstrained binary optimization.
-         QUBO problems are traditionally used in computer science. Variables
-         are TRUE and FALSE, states that correspond to 1 and 0 values.
-         A QUBO problem is defined using an upper-diagonal matrix :math:`Q`,
-         which is an :math:`N` x :math:`N` upper-triangular matrix of real weights,
-         and :math:`x`, a vector of binary variables, as minimizing the function
-
-         .. math::
-
-            \begin{equation}
-              f(x) = \sum_{i} {Q_{i,i}}{x_i} + \sum_{i<j} {Q_{i,j}}{x_i}{x_j}
-            \end{equation}
-
-         where the diagonal terms :math:`Q_{i,i}` are the linear coefficients and
-         the nonzero off-diagonal terms are the quadratic coefficients
-         :math:`Q_{i,j}`.
-         This can be expressed more concisely as
-
-         .. math::
-
-            \begin{equation}
-              \min_{{x} \in {\{0,1\}^n}} {x}^{T} {Q}{x}.
-            \end{equation}
-
-         In scalar notation, the objective function expressed as a QUBO
-         is as follows:
-
-         .. math::
-
-            \begin{equation}
-                        \text{E}_{qubo}(a_i, b_{i,j}; q_i) = \sum_{i} a_i q_i + \sum_{i<j} b_{i,j} q_i q_j.
-            \end{equation}
-
-    sampler
-        A process that samples from low energy states of a problem’s objective function.
-        A binary quadratic model (BQM) sampler samples from low energy states in models such
-        as those defined by an Ising equation or a Quadratic Unconstrained Binary Optimization
-        (QUBO) problem and returns an iterable of samples, in order of increasing energy. A dimod
-        sampler provides ‘sample_qubo’ and ‘sample_ising’ methods as well as the generic
-        BQM sampler method.
-
-    solver
-        A resource that runs a problem. Some solvers interface to the QPU; others leverage CPU
-        and GPU resources.

dwave/cloud/cli.py

@@ -31,7 +31,7 @@
     default_text_input, click_info_switch, generate_random_ising_problem,
     datetime_to_timestamp, utcnow, strtrunc, CLIError, set_loglevel,
     get_contrib_packages, user_agent)
-from dwave.cloud.coders import encode_problem_as_bq
+from dwave.cloud.coders import bqm_as_file
 from dwave.cloud.package_info import __title__, __version__
 from dwave.cloud.exceptions import (
     SolverAuthenticationError, InvalidAPIResponseError, UnsupportedSolverError,
@@ -473,8 +473,8 @@ def echo(s, maxlen=100):
               help='Connection profile (section) name')
 @click.option('--problem-id', '-i', default=None,
               help='Problem ID (optional)')
-@click.option('--format', '-f', default='bq-zlib',
-              type=click.Choice(['coo', 'bq-zlib'], case_sensitive=False),
+@click.option('--format', '-f', default='dimodbqm',
+              type=click.Choice(['coo', 'dimodbqm'], case_sensitive=False),
               help='Problem data encoding')
 @click.argument('input_file', metavar='FILE', type=click.File('rb'))
 def upload(config_file, profile, problem_id, format, input_file):
@@ -495,7 +495,7 @@ def upload(config_file, profile, problem_id, format, input_file):
                 "in {!r} format.").format(input_file.name, format))
 
     if format == 'coo':
-        click.echo("Transcoding 'coo' to 'bq-zlib'.")
+        click.echo("Transcoding 'coo' to 'dimodbqm'.")
 
         try:
             import dimod
@@ -506,17 +506,17 @@ def upload(config_file, profile, problem_id, format, input_file):
         # note: `BQM.from_coo` doesn't support files opened in binary (yet);
         # fallback to reopen for now
         with open(input_file.name, 'rt') as fp:
-            bqm = dimod.BinaryQuadraticModel.from_coo(fp)
-            problem = encode_problem_as_bq(bqm, compress=True)['data']
+            bqm = dimod.AdjVectorBQM.from_coo(fp)
+            problem_file = bqm_as_file(bqm)
 
-    elif format == 'bq-zlib':
-        problem = input_file
+    elif format == 'dimodbqm':
+        problem_file = input_file
 
     click.echo("Uploading...")
 
     try:
         future = client.upload_problem_encoded(
-            problem=problem, problem_id=problem_id)
+            problem=problem_file, problem_id=problem_id)
         remote_problem_id = future.result()
     except Exception as e:
         click.echo(e)

dwave/cloud/client.py

@@ -48,7 +48,6 @@
 import requests
 import warnings
 import operator
-import collections
 import queue
 
 import base64
@@ -58,6 +57,7 @@
 
 from itertools import chain
 from functools import partial, wraps
+from collections import abc, namedtuple, OrderedDict
 from concurrent.futures import ThreadPoolExecutor
 
 from dateutil.parser import parse as parse_datetime
@@ -66,7 +66,7 @@
 from dwave.cloud.package_info import __packagename__, __version__
 from dwave.cloud.exceptions import *
 from dwave.cloud.computation import Future
-from dwave.cloud.config import load_config, legacy_load_config, parse_float
+from dwave.cloud.config import load_config, parse_float
 from dwave.cloud.solver import Solver, available_solvers
 from dwave.cloud.concurrency import PriorityThreadPoolExecutor
 from dwave.cloud.upload import ChunkedData
@@ -186,7 +186,7 @@ class Client(object):
     @classmethod
     def from_config(cls, config_file=None, profile=None, client=None,
                     endpoint=None, token=None, solver=None, proxy=None,
-                    headers=None, legacy_config_fallback=False, **kwargs):
+                    headers=None, **kwargs):
         """Client factory method to instantiate a client instance from configuration.
 
         Configuration values can be specified in multiple ways, ranked in the following
@@ -277,10 +277,6 @@ def from_config(cls, config_file=None, profile=None, client=None,
                 Newline-separated additional HTTP headers to include with each
                 API request, or a dictionary of (key, value) pairs.
 
-            legacy_config_fallback (bool, default=False):
-                If True and loading from a standard D-Wave Cloud Client configuration
-                file (``dwave.conf``) fails, tries loading a legacy configuration file (``~/.dwrc``).
-
         Other Parameters:
             Unrecognized keys (str):
                 All unrecognized keys are passed through to the appropriate client class constructor
@@ -321,19 +317,6 @@ def from_config(cls, config_file=None, profile=None, client=None,
             headers=headers)
         logger.debug("Config loaded: %r", config)
 
-        # fallback to legacy `.dwrc` if key variables missing
-        if legacy_config_fallback:
-            warnings.warn(
-                "'legacy_config_fallback' is deprecated, and it will be removed "
-                "in 0.7.0. please convert your legacy .dwrc file to the new "
-                "config format.", DeprecationWarning)
-
-            if not config.get('token'):
-                config = legacy_load_config(
-                    profile=profile, client=client, endpoint=endpoint,
-                    token=token, solver=solver, proxy=proxy, headers=headers)
-                logger.debug("Legacy config loaded: %r", config)
-
         # manual override of other (client-custom) arguments
         config.update(kwargs)
 
@@ -401,7 +384,7 @@ def __init__(self, endpoint=None, token=None, solver=None, proxy=None,
         # parse solver
         if not solver:
             solver_def = {}
-        elif isinstance(solver, collections.Mapping):
+        elif isinstance(solver, abc.Mapping):
             solver_def = solver
         elif isinstance(solver, str):
             # support features dict encoded as JSON in our config INI file
@@ -422,7 +405,7 @@ def __init__(self, endpoint=None, token=None, solver=None, proxy=None,
         # parse headers
         if not headers:
             headers_dict = {}
-        elif isinstance(headers, collections.Mapping):
+        elif isinstance(headers, abc.Mapping):
             headers_dict = headers
         elif isinstance(headers, str):
             try:
@@ -1037,8 +1020,14 @@ def predicate(solver, query, val):
         return solvers
 
     def solvers(self, refresh=False, **filters):
-        """Deprecated in favor of :meth:`.get_solvers`."""
-        warnings.warn("'solvers' is deprecated in favor of 'get_solvers'.", DeprecationWarning)
+        """Deprecated in favor of :meth:`.get_solvers`.
+
+        Scheduled for removal in 0.9.0.
+        """
+        warnings.warn(
+            "'solvers' is deprecated, and it will be removed "
+            "in 0.9.0. please convert your code to use 'get_solvers'",
+            DeprecationWarning)
         return self.get_solvers(refresh=refresh, **filters)
 
     def get_solver(self, name=None, refresh=False, **filters):
@@ -1122,7 +1111,7 @@ def _submit(self, body, future):
         This method is thread safe.
         """
         self._submission_queue.put(self._submit.Message(body, future))
-    _submit.Message = collections.namedtuple('Message', ['body', 'future'])
+    _submit.Message = namedtuple('Message', ['body', 'future'])
 
     def _do_submit_problems(self):
         """Pull problems from the submission queue and submit them.