Python OrderedCollection.remake方法代码示例

# 需要导入模块: from gnome.utilities.orderedcollection import OrderedCollection [as 别名]
# 或者: from gnome.utilities.orderedcollection.OrderedCollection import remake [as 别名]
def test_remake():
    'delete automatically remakes internal lists without None'
    oc = OrderedCollection(['p', 'q', 'ab', 'adsf', 'ss'])
    del oc[0]
    del oc[2]
    oc.remake()
    for ix, elem in enumerate(oc._elems):
        assert elem is not None
        assert oc._d_index[s_id(elem)] == ix

# 需要导入模块: from gnome.utilities.orderedcollection import OrderedCollection [as 别名]
# 或者: from gnome.utilities.orderedcollection.OrderedCollection import remake [as 别名]
def test_remake():
    "remakes internal lists without None enteries"
    oc = OrderedCollection(["p", "q", "ab", "adsf", "ss"])
    del oc[0]
    del oc[3]
    assert oc._elems[0] is None
    assert oc._elems[3] is None
    oc.remake()
    for ix, elem in enumerate(oc._elems):
        assert elem is not None
        assert oc._index[s_id(elem)] == ix

# 需要导入模块: from gnome.utilities.orderedcollection import OrderedCollection [as 别名]
# 或者: from gnome.utilities.orderedcollection.OrderedCollection import remake [as 别名]
def test_remake_emptyoc():
    "empty OC"
    oc = OrderedCollection(dtype=int)
    oc.remake()

# 需要导入模块: from gnome.utilities.orderedcollection import OrderedCollection [as 别名]
# 或者: from gnome.utilities.orderedcollection.OrderedCollection import remake [as 别名]

#.........这里部分代码省略.........
        Make current_time optional since SpillContainer doesn't require it
        especially for 0th step; however, the model needs to set it because
        it will write_output() after each step. The data_arrays along with
        the current_time_stamp must be set in order to write_output()

        :param model_start_time: model_start_time to initialize
            current_time_stamp. This is the time_stamp associated with 0-th
            step so initial conditions for data arrays
        :param array_types: a dict of additional array_types to append to
            standard array_types attribute. The data_arrays are initialized and
            appended based on the values of array_types attribute

        .. note:: The SpillContainer cycles through each of the keys in
        array_types and checks to see if there is an associated initializer
        in each Spill. If a corresponding initializer is found, it gets the
        array_types from initializer and appends them to its own list. For
        most initializers like 
        """
        # Question - should we purge any new arrays that were added in previous
        # call to prepare_for_model_run()?
        # No! If user made modifications to _array_types before running model,
        # let's keep those. A rewind will reset data_arrays.
        self._array_types.update(array_types)

        # for each array_types, use the key to get the associated initializer
        for key in array_types:
            for spill in self.spills:
                if spill.is_initializer(key):
                    self._array_types.update(
                        spill.get_initializer(key).array_types)
        self.initialize_data_arrays()

        # remake() spills ordered collection
        self.spills.remake()

    def initialize_data_arrays(self):
        """
        initialize_data_arrays() is called without input data during rewind
        and prepare_for_model_run to define all data arrays.
        At this time the arrays are empty.
        """
        for name, atype in self._array_types.iteritems():
            # Initialize data_arrays with 0 elements
            self._data_arrays[name] = atype.initialize_null()

    def _append_data_arrays(self, num_released):
        """
        initialize data arrays once spill has spawned particles
        Data arrays are set to their initial_values

        :param num_released: number of particles released
        :type num_released: int

        """
        for name, atype in self._array_types.iteritems():
            # initialize all arrays even if 0 length
            self._data_arrays[name] = np.r_[self._data_arrays[name],
                                            atype.initialize(num_released)]

    def release_elements(self, time_step, model_time):
        """
        Called at the end of a time step

        This calls release_elements on all of the contained spills, and adds
        the elements to the data arrays
        """