def _acquire_data(cls, config, unlock_new_data_callback):
        """
        Ensures required keys (such as stream_id) are available from config, configures the publisher and then calls:
             BaseDataHandler._new_data_constraints (only if config does not contain 'constraints')
             BaseDataHandler._publish_data passing BaseDataHandler._get_data as a parameter
        @param config Dict containing configuration parameters, may include constraints, formatters, etc
        @param unlock_new_data_callback BaseDataHandler callback function to allow conditional unlocking of the BaseDataHandler._semaphore
        """
        stream_id = get_safe(config, 'stream_id')
        if not stream_id:
            raise ConfigurationError('Configuration does not contain required \'stream_id\' key')
        #TODO: Configure the publisher
        publisher=None

        constraints = get_safe(config,'constraints')
        if not constraints:
            gevent.getcurrent().link(unlock_new_data_callback)
            constraints = cls._new_data_constraints(config)
            config['constraints']=constraints

        cls._publish_data(publisher, config, cls._get_data(config))

        # Publish a 'TestFinished' event
        if get_safe(config,'TESTING'):
            log.debug('Publish TestingFinished event')
            pub = EventPublisher('DeviceCommonLifecycleEvent')
            pub.publish_event(origin='BaseDataHandler._acquire_data', description='TestingFinished')

    def _get_data(cls, config):
        parser = get_safe(config, 'parser', None)
        ext_dset_res = get_safe(config, 'external_dataset_res', None)
        if ext_dset_res and parser:
            #CBM: Not in use yet...
#            t_vname = ext_dset_res.dataset_description.parameters['temporal_dimension']
#            x_vname = ext_dset_res.dataset_description.parameters['zonal_dimension']
#            y_vname = ext_dset_res.dataset_description.parameters['meridional_dimension']
#            z_vname = ext_dset_res.dataset_description.parameters['vertical_dimension']
#            var_lst = ext_dset_res.dataset_description.parameters['variables']

            max_rec = get_safe(config, 'max_records', 1)
            dprod_id = get_safe(config, 'data_producer_id', 'unknown data producer')
            tx_yml = get_safe(config, 'taxonomy')
            ttool = TaxyTool.load(tx_yml) #CBM: Assertion inside RDT.__setitem__ requires same instance of TaxyTool

            cnt = cls._calc_iter_cnt(len(parser.sensor_map), max_rec)
            for x in xrange(cnt):
                rdt = RecordDictionaryTool(taxonomy=ttool)

                for name in parser.sensor_map:
                    d = parser.data_map[name][x*max_rec:(x+1)*max_rec]
                    rdt[name]=d

                g = build_granule(data_producer_id=dprod_id, taxonomy=ttool, record_dictionary=rdt)
                yield g
        else:
            log.warn('No parser object found in config')

    def _make_management_call(self, url, method="get", data=None):
        """
        Makes a call to the Rabbit HTTP management API using the passed in HTTP method.
        """
        log.debug("Calling rabbit API management (%s): %s", method, url)

        meth = getattr(requests, method)

        try:
            mgmt_cfg_key = CFG.get_safe("container.messaging.management.server", "rabbit_manage")
            mgmt_cfg = CFG.get_safe("server." + mgmt_cfg_key)
            username = get_safe(mgmt_cfg, "username") or "guest"
            password = get_safe(mgmt_cfg, "password") or "guest"

            with gevent.timeout.Timeout(10):
                r = meth(url, auth=(username, password), data=data)
            r.raise_for_status()

            if not r.content == "":
                content = json.loads(r.content)
            else:
                content = None

        except gevent.timeout.Timeout as ex:
            raise Timeout(str(ex))
        except requests.exceptions.Timeout as ex:
            raise Timeout(str(ex))
        except (requests.exceptions.ConnectionError, socket.error) as ex:
            raise ServiceUnavailable(str(ex))
        except requests.exceptions.RequestException as ex:
            # the generic base exception all requests' exceptions inherit from, raise our
            # general server error too.
            raise ServerError(str(ex))

        return content

    def set_configuration(self, config):
        log.warn("DRIVER: set_configuration")
        """
        expect configuration to have:
        - parser module/class
        - directory, wildcard to find data files
        - optional timestamp of last granule
        - optional poll rate
        - publish info
        """
        log.error("Log level: %s", log.getEffectiveLevel())
        log.debug('using configuration: %s', config)
        self.config = config
        self.max_records = get_safe(config, 'max_records', 100)
        self.stream_config = self.CFG.get('stream_config', {})
        if len(self.stream_config) == 1:
            stream_cfg = self.stream_config.values()[0]
        elif len(self.stream_config) > 1:
            stream_cfg = self.stream_config.values()[0]

        stream_id = stream_cfg['stream_id']
        stream_route = IonObject(OT.StreamRoute, routing_key=stream_cfg['routing_key'], exchange_point=stream_cfg['exchange_point'])
        param_dict = stream_cfg['stream_def_dict']['parameter_dictionary']
        self.publisher = StandaloneStreamPublisher(stream_id=stream_id, stream_route=stream_route)
        self.parameter_dictionary = ParameterDictionary.load(param_dict)
        self.time_field = self.parameter_dictionary.get_temporal_context()
        self.latest_granule_time = get_safe(config, 'last_time', 0)

    def _constraints_for_new_request(cls, config):
        old_list = get_safe(config, 'new_data_check') or []
        # CBM: Fix this when the DotList crap is sorted out
        old_list = list(old_list)  # NOTE that the internal tuples are also DotList objects

        ret = {}
        base_url = get_safe(config, 'ds_params.base_url')
        list_pattern = get_safe(config, 'ds_params.list_pattern')
        date_pattern = get_safe(config, 'ds_params.date_pattern')
        date_extraction_pattern = get_safe(config, 'ds_params.date_extraction_pattern')

        curr_list = list_file_info(base_url, list_pattern)

        # Determine which files are new
        new_list = [x for x in curr_list if x not in old_list]

        if len(new_list) is 0:
            raise NoNewDataWarning()

        # The curr_list is the new new_data_check - used for the next "new data" evaluation
        config['set_new_data_check'] = curr_list

        # The new_list is the set of new files - these will be processed
        ret['new_files'] = new_list
        ret['start_time'] = get_time_from_filename(new_list[0][0], date_extraction_pattern, date_pattern)
        ret['end_time'] = get_time_from_filename(new_list[len(new_list) - 1][0], date_extraction_pattern, date_pattern)
        ret['bounding_box'] = {}
        ret['vars'] = []

        return ret

    def _acquire_data(cls, config, publisher, unlock_new_data_callback):
        """
        Ensures required keys (such as stream_id) are available from config, configures the publisher and then calls:
             BaseDataHandler._new_data_constraints (only if config does not contain 'constraints')
             BaseDataHandler._publish_data passing BaseDataHandler._get_data as a parameter
        @param config Dict containing configuration parameters, may include constraints, formatters, etc
        @param unlock_new_data_callback BaseDataHandler callback function to allow conditional unlocking of the BaseDataHandler._semaphore
        """
        log.debug('start _acquire_data: config={0}'.format(config))

        cls._init_acquisition_cycle(config)

        constraints = get_safe(config,'constraints')
        if not constraints:
            gevent.getcurrent().link(unlock_new_data_callback)
            constraints = cls._new_data_constraints(config)
            if constraints is None:
                raise InstrumentParameterException("Data constraints returned from _new_data_constraints cannot be None")
            config['constraints'] = constraints

        cls._publish_data(publisher, cls._get_data(config))

        # Publish a 'TestFinished' event
        if get_safe(config,'TESTING'):
            log.debug('Publish TestingFinished event')
            pub = EventPublisher('DeviceCommonLifecycleEvent')
            pub.publish_event(origin='BaseDataHandler._acquire_data', description='TestingFinished')

    def _get_data(cls, config):
        new_flst = get_safe(config, 'constraints.new_files', [])
        hdr_cnt = get_safe(config, 'header_count', SlocumParser.DEFAULT_HEADER_SIZE)
        for f in new_flst:
            try:
                parser = SlocumParser(f[0], hdr_cnt)
                #CBM: Not in use yet...
    #            ext_dset_res = get_safe(config, 'external_dataset_res', None)
    #            t_vname = ext_dset_res.dataset_description.parameters['temporal_dimension']
    #            x_vname = ext_dset_res.dataset_description.parameters['zonal_dimension']
    #            y_vname = ext_dset_res.dataset_description.parameters['meridional_dimension']
    #            z_vname = ext_dset_res.dataset_description.parameters['vertical_dimension']
    #            var_lst = ext_dset_res.dataset_description.parameters['variables']

                max_rec = get_safe(config, 'max_records', 1)
                dprod_id = get_safe(config, 'data_producer_id', 'unknown data producer')
                #tx_yml = get_safe(config, 'taxonomy')
                #ttool = TaxyTool.load(tx_yml) #CBM: Assertion inside RDT.__setitem__ requires same instance of TaxyTool
                pdict = ParameterDictionary.load(get_safe(config, 'param_dictionary'))

                cnt = calculate_iteration_count(len(parser.sensor_map), max_rec)
                for x in xrange(cnt):
                    #rdt = RecordDictionaryTool(taxonomy=ttool)
                    rdt = RecordDictionaryTool(param_dictionary=pdict)

                    for name in parser.sensor_map:
                        d = parser.data_map[name][x*max_rec:(x+1)*max_rec]
                        rdt[name]=d

                    #g = build_granule(data_producer_id=dprod_id, taxonomy=ttool, record_dictionary=rdt)
                    g = build_granule(data_producer_id=dprod_id, record_dictionary=rdt, param_dictionary=pdict)
                    yield g
            except SlocumParseException as spe:
                # TODO: Decide what to do here, raise an exception or carry on
                log.error('Error parsing data file: \'{0}\''.format(f))

    def _constraints_for_new_request(cls, config):
        old_list = get_safe(config, 'new_data_check') or []
        # CBM: Fix this when the DotList crap is sorted out
        old_list = list(old_list)  # NOTE that the internal tuples are also DotList objects

        ret = {}
        base_url = get_safe(config, 'ds_params.base_url')
        list_pattern = get_safe(config, 'ds_params.list_pattern')
        date_pattern = get_safe(config, 'ds_params.date_pattern')
        date_extraction_pattern = get_safe(config, 'ds_params.date_extraction_pattern')

        curr_list = list_file_info(base_url, list_pattern)

        #compare the last read files (old_list) with the current directory contents (curr_list)
        #if the file names are the same (curr_file[0] and old_file[0]) check the size of the
        #current file (curr_file[2]) with the file position when the last file was read (old_file[3])
        #if there's more data now that was read last time, add the file to the list
        new_list = []
        for curr_file in curr_list:
            found = False
            for old_file in old_list:
                if curr_file[0] == old_file[0]:      #if filenames are the same, that means the file is still in the directory, and was previously read
                    found = True
                    if curr_file[2] > old_file[3]:   #f2[2] is the current file size, f2[3] is the last read file size
                        new_list.append((curr_file[0], curr_file[1], curr_file[2], old_file[-1]))     #add it in if the current file size is bigger than the last time
            if not found:
                new_list.append(curr_file)

        config['set_new_data_check'] = curr_list

        ret['new_files'] = new_list
        ret['bounding_box'] = {}
        ret['vars'] = []

        return ret

    def execute(self, granule):
        """Processes incoming data!!!!
        """

        rdt = RecordDictionaryTool.load_from_granule(granule)
        #todo: use only flat dicts for now, may change later...
#        rdt0 = rdt['coordinates']
#        rdt1 = rdt['data']

        pressure = get_safe(rdt, 'pres') #psd.get_values('conductivity')

        longitude = get_safe(rdt, 'lon') # psd.get_values('longitude')
        latitude = get_safe(rdt, 'lat')  #psd.get_values('latitude')
        time = get_safe(rdt, 'time') # psd.get_values('time')
        height = get_safe(rdt, 'height') # psd.get_values('time')

        log.warn('Got pressure: %s' % str(pressure))


        # L1
        # 1) The algorithm input is the L0 pressure data product (p_hex) and, in the case of the SBE 37IM, the pressure range (P_rng) from metadata.
        # 2) Convert the hexadecimal string to a decimal string
        # 3) For the SBE 37IM only, convert the pressure range (P_rng) from psia to dbar SBE 37IM
        #    Convert P_rng (input from metadata) from psia to dbar
        # 4) Perform scaling operation
        #    SBE 37IM
        #    L1 pressure data product (in dbar):


        # Use the constructor to put data into a granule
        psc = PointSupplementConstructor(point_definition=self.outgoing_stream_def, stream_id=self.streams['output'])
        ### Assumes the config argument for output streams is known and there is only one 'output'.
        ### the stream id is part of the metadata which much go in each stream granule - this is awkward to do at the
        ### application level like this!

        scaled_pressure = pressure

        for i in xrange(len(pressure)):
            #todo: get pressure range from metadata (if present) and include in calc
            scaled_pressure[i] = ( pressure[i])

        root_rdt = RecordDictionaryTool(taxonomy=self.tx)

        #todo: use only flat dicts for now, may change later...
#        data_rdt = RecordDictionaryTool(taxonomy=self.tx)
#        coord_rdt = RecordDictionaryTool(taxonomy=self.tx)

        root_rdt['pres'] = scaled_pressure
        root_rdt['time'] = time
        root_rdt['lat'] = latitude
        root_rdt['lon'] = longitude
        root_rdt['height'] = height

#        root_rdt['coordinates'] = coord_rdt
#        root_rdt['data'] = data_rdt

        return build_granule(data_producer_id='ctd_L1_pressure', taxonomy=self.tx, record_dictionary=root_rdt)

        return psc.close_stream_granule()

    def get_datastore(self, ds_name, profile=DataStore.DS_PROFILE.BASIC, config=None):
        """
        Factory method to get a datastore instance from given name, profile and config.
        This is the central point to cache these instances, to decide persistent or mock
        and to force clean the store on first use.
        @param ds_name  Logical name of datastore (will be scoped with sysname)
        @param profile  One of known constants determining the use of the store
        @param config  Override config to use
        """
        assert ds_name, "Must provide ds_name"
        if ds_name in self._datastores:
            log.debug("get_datastore(): Found instance of store '%s'" % ds_name)
            return self._datastores[ds_name]

        scoped_name = ("%s_%s" % (get_sys_name(), ds_name)).lower()

        # Imports here to prevent cyclic module dependency
        from pyon.core.bootstrap import CFG

        config = config or CFG

        persistent = not bool(get_safe(config, "system.mockdb"))
        force_clean = bool(get_safe(config, "system.force_clean"))

        log.info(
            "get_datastore(): Create instance of store '%s' {persistent=%s, force_clean=%s, scoped_name=%s}"
            % (ds_name, persistent, force_clean, scoped_name)
        )

        # Persistent (CouchDB) or MockDB?
        if persistent:
            # Use inline import to prevent circular import dependency
            from pyon.datastore.couchdb.couchdb_datastore import CouchDB_DataStore

            new_ds = CouchDB_DataStore(datastore_name=scoped_name, profile=profile)
        else:
            # Use inline import to prevent circular import dependency
            from pyon.datastore.mockdb.mockdb_datastore import MockDB_DataStore

            new_ds = MockDB_DataStore(datastore_name=scoped_name)  # , profile=profile)

        # Clean the store instance
        if force_clean:
            try:
                new_ds.delete_datastore(scoped_name)
            except NotFound as nf:
                pass

        # Create store if not existing
        if not new_ds.datastore_exists(scoped_name):
            new_ds.create_datastore(scoped_name)

        # Set a few standard datastore instance fields
        new_ds.local_name = ds_name
        new_ds.ds_profile = profile

        self._datastores[ds_name] = new_ds

        return new_ds

    def execute(input=None, context=None, config=None, params=None, state=None, fileName = None):

        stream_definition_id = params
        mpl_allowed_numerical_types = ['int32', 'int64', 'uint32', 'uint64', 'float32', 'float64']

        if stream_definition_id == None:
            log.error("Matplotlib transform: Need a output stream definition to process graphs")
            return None

        # parse the incoming data
        rdt = RecordDictionaryTool.load_from_granule(input)

        # build a list of fields/variables that need to be plotted. Use the list provided by the UI
        # since the retrieved granule might have extra fields.
        fields = rdt.fields
        resolution = "640x480"
        if config:
            if 'parameters' in config:
                fields = config['parameters']
            if 'resolution' in config:
                resolution = config['resolution']

        vardict = {}
        vardict['time'] = get_safe(rdt, 'time')
        if vardict['time'] == None:
            print "Matplotlib transform: Did not receive a time field to work with"
            log.error("Matplotlib transform: Did not receive a time field to work with")
            return None

        for field in fields:
            if field == 'time':
                continue

            # only consider fields which are supposed to be numbers.
            if (rdt[field] != None) and (rdt[field].dtype not in mpl_allowed_numerical_types):
                continue

            vardict[field] = get_safe(rdt, field)

            print

        arrLen = len(vardict['time'])
        # init the graph_data structure for storing values
        graph_data = {}
        for varname in vardict.keys():
            graph_data[varname] = []

        # If code reached here, the graph data storage has been initialized. Just add values
        # to the list
        for varname in vardict.keys():  # psd.list_field_names():
            if vardict[varname] == None:
                # create an array of zeros to compensate for missing values
                graph_data[varname].extend([0.0]*arrLen)
            else:
                graph_data[varname].extend(vardict[varname])

        out_granule = VizTransformMatplotlibGraphsAlgorithm.render_graphs(graph_data, stream_definition_id, fileName, resolution=resolution)

        return out_granule

    def execute_acquire_data(self, *args):
        """
        Creates a copy of self._dh_config, creates a publisher, and spawns a greenlet to perform a data acquisition cycle
        If the args[0] is a dict, any entries keyed with one of the 'PATCHABLE_CONFIG_KEYS' are used to patch the config
        Greenlet binds to BaseDataHandler._acquire_data and passes the publisher and config
        Disallows multiple "new data" (unconstrained) requests using BaseDataHandler._semaphore lock
        Called from:
                      InstrumentAgent._handler_observatory_execute_resource
                       |-->  ExternalDataAgent._handler_streaming_execute_resource

        @parameter args First argument can be a config dictionary
        """
        log.debug('Executing acquire_data: args = {0}'.format(args))

        # Make a copy of the config to ensure no cross-pollution
        config = self._dh_config.copy()

        # Patch the config if mods are passed in
        try:
            config_mods = args[0]
            if not isinstance(config_mods, dict):
                raise IndexError()

            log.debug('Configuration modifications provided: {0}'.format(config_mods))
            for k in self._params['PATCHABLE_CONFIG_KEYS']:
                p=get_safe(config_mods, k)
                if not p is None:
                    config[k] = p

        except IndexError:
            log.info('No configuration modifications were provided')

        # Verify that there is a stream_id member in the config
        stream_id = get_safe(config, 'stream_id')
        if not stream_id:
            raise ConfigurationError('Configuration does not contain required \'stream_id\' member')

        isNew = get_safe(config, 'constraints') is None

        if isNew and not self._semaphore.acquire(blocking=False):
            log.warn('Already acquiring new data - action not duplicated')
            return

        ndc = None
        if isNew:
            # Get the NewDataCheck attachment and add it's content to the config
            ext_ds_id = get_safe(config,'external_dataset_res_id')
            if ext_ds_id:
                ndc = self._find_new_data_check_attachment(ext_ds_id)

        config['new_data_check'] = ndc

            # Create a publisher to pass into the greenlet
        publisher = self._stream_registrar.create_publisher(stream_id=stream_id)

        # Spawn a greenlet to do the data acquisition and publishing
        g = spawn(self._acquire_data, config, publisher, self._unlock_new_data_callback, self._update_new_data_check_attachment)
        log.debug('** Spawned {0}'.format(g))
        self._glet_queue.append(g)

    def _get_data(cls, config):
        """
        Retrieves config['constraints']['count'] number of random samples of length config['constraints']['array_len']
        @param config Dict of configuration parameters - must contain ['constraints']['count'] and ['constraints']['count']
        """
        ext_dset_res = get_safe(config, 'external_dataset_res', None)

        # Get the Dataset object from the config (should have been instantiated in _init_acquisition_cycle)
        ds = get_safe(config, 'dataset_object')

        if ext_dset_res and ds:
            t_vname = ext_dset_res.dataset_description.parameters['temporal_dimension']
            x_vname = ext_dset_res.dataset_description.parameters['zonal_dimension']
            y_vname = ext_dset_res.dataset_description.parameters['meridional_dimension']
            z_vname = ext_dset_res.dataset_description.parameters['vertical_dimension']
            var_lst = ext_dset_res.dataset_description.parameters['variables']

            t_slice = get_safe(config, 'constraints.temporal_slice', (slice(0, 1)))
            #TODO: Using 'eval' here is BAD - need to find a less sketchy way to pass constraints
            if isinstance(t_slice, str):
                t_slice = eval(t_slice)

            lon = ds.variables[x_vname][:]
            lat = ds.variables[y_vname][:]
            z = ds.variables[z_vname][:]

            t_arr = ds.variables[t_vname][t_slice]
            data_arrays = {}
            for varn in var_lst:
                data_arrays[varn] = ds.variables[varn][t_slice]

            max_rec = get_safe(config, 'max_records', 1)
            #dprod_id = get_safe(config, 'data_producer_id', 'unknown data producer')

            stream_def = get_safe(config, 'stream_def')

            cnt = calculate_iteration_count(t_arr.size, max_rec)
            for x in xrange(cnt):
                ta = t_arr[x * max_rec:(x + 1) * max_rec]

                # Make a 'master' RecDict
                rdt = RecordDictionaryTool(stream_definition_id=stream_def)

                # Assign coordinate values to the RecDict
                rdt[x_vname] = lon
                rdt[y_vname] = lat
                rdt[z_vname] = z

                # Assign data values to the RecDict
                rdt[t_vname] = ta
                for key, arr in data_arrays.iteritems():
                    d = arr[x * max_rec:(x + 1) * max_rec]
                    rdt[key] = d

                g = rdt.to_granule()
                yield g

            ds.close()

    def get_visualization_image(self, data_product_id='', visualization_parameters=None, callback=''):

        # Error check
        if not data_product_id:
            raise BadRequest("The data_product_id parameter is missing")
        if visualization_parameters == {}:
            visualization_parameters = None

        # Extract the retrieval related parameters. Definitely init all parameters first
        query = None
        if visualization_parameters :
            query = {'parameters':[]}
            # Error check and damage control. Definitely need time
            if 'parameters' in visualization_parameters:
                if not 'time' in visualization_parameters['parameters']:
                    visualization_parameters['parameters'].append('time')
                query['parameters'] = visualization_parameters['parameters']

            if 'stride_time' in visualization_parameters:
                query['stride_time'] = visualization_parameters['stride_time']
            if 'start_time' in visualization_parameters:
                query['start_time'] = visualization_parameters['start_time']
            if 'end_time' in visualization_parameters:
                query['end_time'] = visualization_parameters['end_time']

        # get the dataset_id associated with the data_product. Need it to do the data retrieval
        ds_ids,_ = self.clients.resource_registry.find_objects(data_product_id, PRED.hasDataset, RT.DataSet, True)
        if ds_ids is None or not ds_ids:
            return None

        # Ideally just need the latest granule to figure out the list of images
        #replay_granule = self.clients.data_retriever.retrieve(ds_ids[0],{'start_time':0,'end_time':2})
        retrieved_granule = self.clients.data_retriever.retrieve(ds_ids[0], query=query)

        if retrieved_granule is None:
            return None

        # send the granule through the transform to get the matplotlib graphs
        mpl_pdict_id = self.clients.dataset_management.read_parameter_dictionary_by_name('graph_image_param_dict',id_only=True)
        mpl_stream_def = self.clients.pubsub_management.create_stream_definition('mpl', parameter_dictionary_id=mpl_pdict_id)
        mpl_data_granule = VizTransformMatplotlibGraphsAlgorithm.execute(retrieved_granule, config=visualization_parameters, params=mpl_stream_def)

        if mpl_data_granule == None:
            return None

        mpl_rdt = RecordDictionaryTool.load_from_granule(mpl_data_granule)

        ret_dict = dict()
        ret_dict['content_type'] = (get_safe(mpl_rdt, "content_type"))[0]
        ret_dict['image_name'] = (get_safe(mpl_rdt, "image_name"))[0]
        # reason for encoding as base64 string is otherwise message pack complains about the bit stream
        ret_dict['image_obj'] = base64.encodestring((get_safe(mpl_rdt, "image_obj"))[0])

        if callback == '':
            return ret_dict
        else:
            return callback + "(" + simplejson.dumps(ret_dict) + ")"

    def _get_data(cls, config):
        """
        Iterable function that acquires data from a source iteratively based on constraints provided by config
        Passed into BaseDataHandler._publish_data and iterated to publish samples.
        @param config dict containing configuration parameters, may include constraints, formatters, etc
        @retval an iterable that returns well-formed Granule objects on each iteration
        """
        new_flst = get_safe(config, "constraints.new_files", [])
        parser_mod = get_safe(config, "parser_mod", "")
        parser_cls = get_safe(config, "parser_cls", "")

        module = __import__(parser_mod, fromlist=[parser_cls])
        classobj = getattr(module, parser_cls)

        for f in new_flst:
            try:
                size = os.stat(f[0]).st_size
                try:
                    # find the new data check index in config
                    index = -1
                    for ndc in config["set_new_data_check"]:
                        if ndc[0] == f[0]:
                            index = config["set_new_data_check"].index(ndc)
                            break
                except:
                    log.error("File name not found in attachment")

                parser = classobj(f[0], f[3])

                max_rec = get_safe(config, "max_records", 1)
                stream_def = get_safe(config, "stream_def")
                while True:
                    particles = parser.get_records(max_count=max_rec)
                    if not particles:
                        break

                    rdt = RecordDictionaryTool(stream_definition_id=stream_def)

                    populate_rdt(rdt, particles)

                    g = rdt.to_granule()

                    # TODO: record files already read for future additions...
                    #                    #update new data check with the latest file position
                    if "set_new_data_check" in config and index > -1:
                        # WRONG: should only record this after file finished parsing,
                        # but may not have another yield at that point to trigger update
                        config["set_new_data_check"][index] = (f[0], f[1], f[2], size)

                    yield g

            #                parser.close()

            except Exception as ex:
                # TODO: Decide what to do here, raise an exception or carry on
                log.error("Error parsing data file '{0}': {1}".format(f, ex))

    def _constraints_for_new_request(cls, config):
        """
        Returns a constraints dictionary with
        @param config Dict of configuration parameters - may be used to generate the returned 'constraints' dict
        """
        #TODO: Sort out what the config needs to look like - dataset_in??
        ext_dset_res = get_safe(config, 'external_dataset_res', None)
        log.debug('ExternalDataset Resource: {0}'.format(ext_dset_res))
        if ext_dset_res:
            #TODO: Use the external dataset resource to determine what data is new (i.e. pull 'old' fingerprint from here)
            log.debug('ext_dset_res.dataset_description = {0}'.format(ext_dset_res.dataset_description))
            log.debug('ext_dset_res.update_description = {0}'.format(ext_dset_res.update_description))
#            base_fingerprint = ext_dset_res.update_description
            base_nd_check = get_safe(ext_dset_res.update_description.parameters,'new_data_check')
#            base_nd_check = '\x83\xa7content\xdc\x00\xc9\xceM\xa0\xf3\x00\xceM\xa2D\x80\xceM\xa3\x96\x00\xceM\xa4\xe7\x80\xceM\xa69\x00\xceM\xa7\x8a\x80\xceM\xa8\xdc\x00\xceM\xaa-\x80\xceM\xab\x7f\x00\xceM\xac\xd0\x80\xceM\xae"\x00\xceM\xafs\x80\xceM\xb0\xc5\x00\xceM\xb2\x16\x80\xceM\xb3h\x00\xceM\xb4\xb9\x80\xceM\xb6\x0b\x00\xceM\xb7\\\x80\xceM\xb8\xae\x00\xceM\xb9\xff\x80\xceM\xbbQ\x00\xceM\xbc\xa2\x80\xceM\xbd\xf4\x00\xceM\xbfE\x80\xceM\xc0\x97\x00\xceM\xc1\xe8\x80\xceM\xc3:\x00\xceM\xc4\x8b\x80\xceM\xc5\xdd\x00\xceM\xc7.\x80\xceM\xc8\x80\x00\xceM\xc9\xd1\x80\xceM\xcb#\x00\xceM\xcct\x80\xceM\xcd\xc6\x00\xceM\xcf\x17\x80\xceM\xd0i\x00\xceM\xd1\xba\x80\xceM\xd3\x0c\x00\xceM\xd4]\x80\xceM\xd5\xaf\x00\xceM\xd7\x00\x80\xceM\xd8R\x00\xceM\xd9\xa3\x80\xceM\xda\xf5\x00\xceM\xdcF\x80\xceM\xdd\x98\x00\xceM\xde\xe9\x80\xceM\xe0;\x00\xceM\xe1\x8c\x80\xceM\xe2\xde\x00\xceM\xe4/\x80\xceM\xe5\x81\x00\xceM\xe6\xd2\x80\xceM\xe8$\x00\xceM\xe9u\x80\xceM\xea\xc7\x00\xceM\xec\x18\x80\xceM\xedj\x00\xceM\xee\xbb\x80\xceM\xf0\r\x00\xceM\xf1^\x80\xceM\xf2\xb0\x00\xceM\xf4\x01\x80\xceM\xf5S\x00\xceM\xf6\xa4\x80\xceM\xf7\xf6\x00\xceM\xf9G\x80\xceM\xfa\x99\x00\xceM\xfb\xea\x80\xceM\xfd<\x00\xceM\xfe\x8d\x80\xceM\xff\xdf\x00\xceN\x010\x80\xceN\x02\x82\x00\xceN\x03\xd3\x80\xceN\x05%\x00\xceN\x06v\x80\xceN\x07\xc8\x00\xceN\t\x19\x80\xceN\nk\x00\xceN\x0b\xbc\x80\xceN\r\x0e\x00\xceN\x0e_\x80\xceN\x0f\xb1\x00\xceN\x11\x02\x80\xceN\x12T\x00\xceN\x13\xa5\x80\xceN\x14\xf7\x00\xceN\x16H\x80\xceN\x17\x9a\x00\xceN\x18\xeb\x80\xceN\x1a=\x00\xceN\x1b\x8e\x80\xceN\x1c\xe0\x00\xceN\x1e1\x80\xceN\x1f\x83\x00\xceN \xd4\x80\xceN"&\x00\xceN#w\x80\xceN$\xc9\x00\xceN&\x1a\x80\xceN\'l\x00\xceN(\xbd\x80\xceN*\x0f\x00\xceN+`\x80\xceN,\xb2\x00\xceN.\x03\x80\xceN/U\x00\xceN0\xa6\x80\xceN1\xf8\x00\xceN3I\x80\xceN4\x9b\x00\xceN5\xec\x80\xceN7>\x00\xceN8\x8f\x80\xceN9\xe1\x00\xceN;2\x80\xceN<\x84\x00\xceN=\xd5\x80\xceN?\'\x00\[email protected]\x80\xceNA\xca\x00\xceNC\x1b\x80\xceNDm\x00\xceNE\xbe\x80\xceNG\x10\x00\xceNHa\x80\xceNI\xb3\x00\xceNK\x04\x80\xceNLV\x00\xceNM\xa7\x80\xceNN\xf9\x00\xceNPJ\x80\xceNQ\x9c\x00\xceNR\xed\x80\xceNT?\x00\xceNU\x90\x80\xceNV\xe2\x00\xceNX3\x80\xceNY\x85\x00\xceNZ\xd6\x80\xceN\\(\x00\xceN]y\x80\xceN^\xcb\x00\xceN`\x1c\x80\xceNan\x00\xceNb\xbf\x80\xceNd\x11\x00\xceNeb\x80\xceNf\xb4\x00\xceNh\x05\x80\xceNiW\x00\xceNj\xa8\x80\xceNk\xfa\x00\xceNmK\x80\xceNn\x9d\x00\xceNo\xee\x80\[email protected]\x00\xceNr\x91\x80\xceNs\xe3\x00\xceNu4\x80\xceNv\x86\x00\xceNw\xd7\x80\xceNy)\x00\xceNzz\x80\xceN{\xcc\x00\xceN}\x1d\x80\xceN~o\x00\xceN\x7f\xc0\x80\xceN\x81\x12\x00\xceN\x82c\x80\xceN\x83\xb5\x00\xceN\x85\x06\x80\xceN\x86X\x00\xceN\x87\xa9\x80\xceN\x88\xfb\x00\xceN\x8aL\x80\xceN\x8b\x9e\x00\xceN\x8c\xef\x80\xceN\x8eA\x00\xceN\x8f\x92\x80\xceN\x90\xe4\x00\xceN\x925\x80\xceN\x93\x87\x00\xceN\x94\xd8\x80\xceN\x96*\x00\xceN\x97{\x80\xceN\x98\xcd\x00\xceN\x9a\x1e\x80\xceN\x9bp\x00\xceN\x9c\xc1\x80\xceN\x9e\x13\x00\xceN\x9fd\x80\xceN\xa0\xb6\x00\xceN\xa2\x07\x80\xceN\xa3Y\x00\xceN\xa4\xaa\x80\xceN\xa5\xfc\x00\xceN\xa7M\x80\xceN\xa8\x9f\x00\xa6header\x83\xa2nd\x01\xa5shape\x91\xcc\xc9\xa4type\xa5int32\xad__ion_array__\xc3'

#            log.warn(base_nd_check)

#
#
#
#            log.warn(old_arr)


            t_slice = slice(None)
            if base_nd_check:
                t_new_vname = ext_dset_res.dataset_description.parameters['temporal_dimension']
                t_new_arr = ds.variables[t_new_vname][t_slice]

                new_data = msgpack.packb(t_new_arr, default=encode_ion)
                if new_data != base_nd_check:
                    #new time data has arrived, figure out what's different and build the new slice
                    first_index = -1
                    last_index = -1
                    t_old_arr = msgpack.unpackb(base_nd_check, object_hook=decode_ion)
                    for old_data in t_old_arr:
                        if not old_data in t_new_arr:
                            if first_index == -1:
                                first_index = np.nonzero(t_new_arr == old_data)[0][0]
                                last_index = np.nonzero(t_new_arr == old_data)[0][0]
                            else:
                                last_index = np.nonzero(t_new_arr == old_data)[0][0]

                    t_slice = slice(first_index, last_index)


                #TG: Get new temporal data and encode it
                #TG: Compare the old with the new, if different, decode old and sort out what's different
                #TG: Build appropriate temproral_slice

            return {
                'temporal_slice':t_slice
            }

        return None

    def get_datastore(self, ds_name, profile=DataStore.DS_PROFILE.BASIC, config=None):
        """
        Factory method to get a datastore instance from given name, profile and config.
        This is the central point to cache these instances, to decide persistent or mock
        and to force clean the store on first use.
        @param ds_name  Logical name of datastore (will be scoped with sysname)
        @param profile  One of known constants determining the use of the store
        @param config  Override config to use
        """
        assert ds_name, "Must provide ds_name"
        if ds_name in self._datastores:
            log.debug("get_datastore(): Found instance of store '%s'" % ds_name)
            return self._datastores[ds_name]

        scoped_name = DatastoreManager.get_scoped_name(ds_name)

        # Imports here to prevent cyclic module dependency
        from pyon.core.bootstrap import CFG
        config = config or CFG

        if self.persistent is None:
            self.persistent = not bool(get_safe(config, "system.mockdb"))
        if self.force_clean is None:
            self.force_clean = bool(get_safe(config, "system.force_clean"))

        # Create a datastore instance
        log.info("get_datastore(): Create instance of store '%s' {persistent=%s, scoped_name=%s}" % (
            ds_name, self.persistent, scoped_name))
        new_ds = DatastoreManager.get_datastore_instance(ds_name, self.persistent, profile)

        # Clean the store instance
        # TBD: Do we really want to do it here? or make it more manual?
        if self.force_clean:
            log.info("get_datastore(): Force clean store '%s'" % ds_name)
            try:
                new_ds.delete_datastore(scoped_name)
            except NotFound:
                pass

        # Create store if not existing
        if not new_ds.datastore_exists(scoped_name):
            new_ds.create_datastore(scoped_name)
        else:
            if self.persistent:
                # NOTE: This may be expensive if called more than once per container
                # If views exist and are dropped and recreated
                new_ds._define_views(profile=profile, keepviews=True)

        # Set a few standard datastore instance fields
        new_ds.local_name = ds_name
        new_ds.ds_profile = profile

        self._datastores[ds_name] = new_ds

        return new_ds