Merge branch 'main' into 'main'

heading to indicate that only the bug fixes and security fixes are in the bug fix

release.

-->

## [Unreleased]

## [1.0.0-rc2]

### Changed

- Redis queue population pushed to a background thread for non-blocking data processing.

### Fixed

- clip_center property in the roi object was checking for existence using getattr which fails on ndarrays that expect to use all() or any() for boolean checks. Fixed to use only tuples for clip_center.

## [1.0.0-rc1]

### Added

- [`pool_pre_ping`](https://docs.sqlalchemy.org/en/14/core/pooling.html#disconnect-handling-pessimistic) to the sqlalchemy engine connection to handle instances where hundreds of connections are simultaneously connecting to the database.

                    'config':self.config}

            pipeline.rpush(self.processing_queue, json.dumps(msg, cls=JsonEncoder))

            if job_counter % 1000 == 0:

                # Redis can only accept 512MB of messages at a time. This ensures that the pipe

                # stays under the size limit.

                pipeline.execute()

                pipeline = self.redis_queue.pipeline()

        pipeline.execute()

        return job_counter + 1

            log.warning('Use of filters and query_string are mutually exclusive.')

        with self.session_scope() as session:

            t1 = time()

            # Support either an SQL query string, or a simple dict based query

            if query_string:

                log.info(f'Executing query {query_string} to generate objects for queuing.')

                res = session.execute(query_string).fetchall()

            else:

                log.info(f'Executing query to select {query_obj} with filters {filters}')

                query = session.query(query_obj)

                # Now apply any filters that might be passed in.

                res = query.order_by(query_obj.id).all()

            # Expunge so that the connection can be rapidly returned to the pool

            session.expunge_all()

            t2 = time()

        log.debug(f'Query time: {t2-t1}')

        if len(res) == 0:

            raise ValueError('Query returned zero results.')

            msg['config'] = self.config

            pipeline.rpush(self.processing_queue,

                                   json.dumps(msg, cls=JsonEncoder))

            if job_counter % 1000 == 0:

                # Redis can only accept 512MB of messages at a time. This ensures that the pipe

                # stays under the size limit.

                pipeline.execute()

                pipeline = self.redis_queue.pipeline()

        pipeline.execute()

        return job_counter + 1

        # TODO: reapply uses the queue name and reapplies on that queue.

        # Submit the jobs

        rconf = self.config['redis']

        rhost = rconf['host']

        rport = rconf['port']

        try:

            processing_queue = getattr(self, redis_queue)

        except AttributeError:

            log.exception(f'Unable to find attribute {redis_queue} on this object. Valid queue names are: "processing_queue" and "working_queue".')

        if not reapply:

            # Determine which obj will be called

            if isinstance(on, str):

            # Dispatch to either the database object message generator or the autocnet object message generator

            if isinstance(onobj, DeclarativeMeta):

                if just_stage:

                    # Populate the queue in a background thread.

                    th = threading.Thread(target=self._push_row_messages,

                                          args=(self, on, function, walltime, filters, query_string, args, kwargs))

                    th.start()

                else:

                    job_counter = self._push_row_messages(onobj, on, function, walltime, filters, query_string, args, kwargs)

            elif isinstance(onobj, (list, np.ndarray)):

                job_counter = self._push_iterable_message(onobj, function, walltime, args, kwargs)

                job_counter = self._push_obj_messages(onobj, function, walltime, args, kwargs)

            else:

                raise TypeError('The type of the `on` argument is not understood. Must be a database model, iterable, Node or Edge.')

        # Submit the jobs

        rconf = self.config['redis']

        rhost = rconf['host']

        rport = rconf['port']

        try:

            processing_queue = getattr(self, redis_queue)

        except AttributeError:

            log.exception(f'Unable to find attribute {redis_queue} on this object. Valid queue names are: "processing_queue" and "working_queue".')

        env = self.config['env']

        condaenv = env['conda']

        isisroot = env['ISISROOT']

                                        mode=mode,

                                        order=3)

            self._clip_center = (np.array(pixel_locked.shape)[::-1]) / 2.0

            self._clip_center = tuple(np.array(pixel_locked.shape)[::-1]) / 2.0)

            self._clipped_array = img_as_float32(pixel_locked)

        else:

with open('README.md', 'r') as f:

    long_description = f.read()

__version__ = '1.0.0-rc1'

__version__ = '1.0.0-rc2'

def setup_package():

    setup(