Python multiprocessing.Array方法代碼示例

# 需要導入模塊: import multiprocessing [as 別名]
# 或者: from multiprocessing import Array [as 別名]
def test_chain_sampling_multidim_model(self):
        """Test that sampling from DREAM history for multi-dimensional model when the history is known matches with expected possible samples."""
        self.params, self.like = multidmodel()
        model = Model(likelihood=self.like, sampled_parameters=self.params)
        dream = Dream(model=model)
        history_arr = mp.Array('d', [0]*2*dream.total_var_dimension)
        n = mp.Value('i', 0)
        pydream.Dream_shared_vars.history = history_arr
        pydream.Dream_shared_vars.count = n
        chains_added_to_history = []
        for i in range(2):
            start = i*dream.total_var_dimension
            end = start+dream.total_var_dimension
            chain = dream.draw_from_prior(model.sampled_parameters)
            pydream.Dream_shared_vars.history[start:end] = chain
            chains_added_to_history.append(chain)       
        sampled_chains = dream.sample_from_history(nseedchains=2, DEpairs=1, ndimensions=dream.total_var_dimension)
        sampled_chains = np.array(sampled_chains)
        chains_added_to_history = np.array(chains_added_to_history)
        self.assertIs(np.array_equal(chains_added_to_history[chains_added_to_history[:,0].argsort()], sampled_chains[sampled_chains[:,0].argsort()]), True) 

# 需要導入模塊: import multiprocessing [as 別名]
# 或者: from multiprocessing import Array [as 別名]
def test_history_recording_simple_model(self):
        """Test that history in memory matches with that recorded for test one-dimensional model."""
        self.param, self.like = onedmodel()
        model = Model(self.like, self.param)
        step = Dream(model=model, model_name='test_history_recording')
        history_arr = mp.Array('d', [0]*4*step.total_var_dimension)
        n = mp.Value('i', 0)
        nchains = mp.Value('i', 3)
        pydream.Dream_shared_vars.history = history_arr
        pydream.Dream_shared_vars.count = n
        pydream.Dream_shared_vars.nchains = nchains
        test_history = np.array([[1], [3], [5], [7]])
        for chainpoint in test_history:
            for point in chainpoint:
                step.record_history(nseedchains=0, ndimensions=step.total_var_dimension, q_new=point, len_history=len(history_arr))
        history_arr_np = np.frombuffer(pydream.Dream_shared_vars.history.get_obj())
        history_arr_np_reshaped = history_arr_np.reshape(np.shape(test_history))
        self.assertIs(np.array_equal(history_arr_np_reshaped, test_history), True)
        remove('test_history_recording_DREAM_chain_history.npy')
        remove('test_history_recording_DREAM_chain_adapted_crossoverprob.npy')
        remove('test_history_recording_DREAM_chain_adapted_gammalevelprob.npy') 

# 需要導入模塊: import multiprocessing [as 別名]
# 或者: from multiprocessing import Array [as 別名]
def test_history_recording_multidim_model(self):
        """Test that history in memory matches with that recorded for test multi-dimensional model."""
        self.param, self.like = multidmodel()
        model = Model(self.like, self.param)
        dream = Dream(model=model, model_name='test_history_recording')
        history_arr = mp.Array('d', [0]*4*dream.total_var_dimension*3)
        n = mp.Value('i', 0)
        nchains = mp.Value('i', 3)
        pydream.Dream_shared_vars.history = history_arr
        pydream.Dream_shared_vars.count = n
        pydream.Dream_shared_vars.nchains = nchains
        test_history = np.array([[[1, 2, 3, 4], [3, 4, 5, 6], [5, 6, 7, 8]], [[7, 8, 9, 10], [9, 12, 18, 20], [11, 14, 18, 8]], [[13, 14, 18, 4], [15, 17, 11, 8], [17, 28, 50, 4]], [[19, 21, 1, 18], [21, 19, 19, 11], [23, 4, 3, 2]]])
        for chainpoint in test_history:
            for point in chainpoint:
                dream.record_history(nseedchains=0, ndimensions=dream.total_var_dimension, q_new=point, len_history=len(history_arr))
        history_arr_np = np.frombuffer(pydream.Dream_shared_vars.history.get_obj())
        history_arr_np_reshaped = history_arr_np.reshape(np.shape(test_history))
        self.assertIs(np.array_equal(history_arr_np_reshaped, test_history), True)
        remove('test_history_recording_DREAM_chain_history.npy')
        remove('test_history_recording_DREAM_chain_adapted_crossoverprob.npy')
        remove('test_history_recording_DREAM_chain_adapted_gammalevelprob.npy') 

# 需要導入模塊: import multiprocessing [as 別名]
# 或者: from multiprocessing import Array [as 別名]
def __init__(self, action_dim, observation_dim, **kwargs):
        # shared variable that all processes will see
        self.crash_flag = Value('i', 0)
        self.reset_call_flag = Value('i', 0)

        # Communicator Parameters
        communicator_setups = {'generic1': {'Communicator': MockCommunicator,
                                            'kwargs': {}},
                               'generic2': {'Communicator': MockCommunicator,
                                            'kwargs': {}}
                              }

        self._uniform_array_ = np.frombuffer(Array('d', 3).get_obj(), dtype=np.float64)

        super().__init__(communicator_setups=communicator_setups,
                         action_dim=action_dim,
                         observation_dim=observation_dim,
                         **kwargs) 

# 需要導入模塊: import multiprocessing [as 別名]
# 或者: from multiprocessing import Array [as 別名]
def test_random_state_array(self):
        rand_obj = np.random.RandomState(1)
        rand_state = rand_obj.get_state()
        original_uniform_values = rand_obj.uniform(-1, 1, 100)
        original_normal_values = rand_obj.randn(100)

        rand_state_array_type, rand_state_array_size, rand_state_array = get_random_state_array(rand_state)
        shared_rand_array = np.frombuffer(Array('b', rand_state_array_size).get_obj(), dtype=rand_state_array_type)
        np.copyto(shared_rand_array, np.frombuffer(rand_state_array, dtype=rand_state_array_type))

        new_rand_obj = np.random.RandomState()
        new_rand_obj.set_state(get_random_state_from_array(shared_rand_array))
        new_uniform_values = new_rand_obj.uniform(-1, 1, 100)
        new_normal_values = new_rand_obj.randn(100)

        assert np.all(original_uniform_values == new_uniform_values)
        assert np.all(original_normal_values == new_normal_values) 

# 需要導入模塊: import multiprocessing [as 別名]
# 或者: from multiprocessing import Array [as 別名]
def array(shape, dtype=_np.float64, autolock=False):
    """Factory method for shared memory arrays supporting all numpy dtypes."""
    assert _NP_AVAILABLE, "To use the shared array object, numpy must be available!"
    if not isinstance(dtype, _np.dtype):
        dtype = _np.dtype(dtype)
    # Not bothering to translate the numpy dtypes to ctype types directly,
    # because they're only partially supported. Instead, create a byte ctypes
    # array of the right size and use a view of the appropriate datatype.
    shared_arr = _multiprocessing.Array(
        "b", int(_np.prod(shape) * dtype.alignment), lock=autolock
    )
    with _warnings.catch_warnings():
        # For more information on why this is necessary, see
        # https://www.reddit.com/r/Python/comments/j3qjb/parformatlabpool_replacement
        _warnings.simplefilter("ignore", RuntimeWarning)
        data = _np.ctypeslib.as_array(shared_arr).view(dtype).reshape(shape)
    return data 

# 需要導入模塊: import multiprocessing [as 別名]
# 或者: from multiprocessing import Array [as 別名]
def test_array(self, raw=False):
        seq = [680, 626, 934, 821, 150, 233, 548, 982, 714, 831]
        if raw:
            arr = self.RawArray('i', seq)
        else:
            arr = self.Array('i', seq)

        self.assertEqual(len(arr), len(seq))
        self.assertEqual(arr[3], seq[3])
        self.assertEqual(list(arr[2:7]), list(seq[2:7]))

        arr[4:8] = seq[4:8] = array.array('i', [1, 2, 3, 4])

        self.assertEqual(list(arr[:]), seq)

        self.f(seq)

        p = self.Process(target=self.f, args=(arr,))
        p.daemon = True
        p.start()
        p.join()

        self.assertEqual(list(arr[:]), seq) 

# 需要導入模塊: import multiprocessing [as 別名]
# 或者: from multiprocessing import Array [as 別名]
def test_sharedctypes(self, lock=False):
        x = Value('i', 7, lock=lock)
        y = Value(c_double, 1.0/3.0, lock=lock)
        foo = Value(_Foo, 3, 2, lock=lock)
        arr = self.Array('d', list(range(10)), lock=lock)
        string = self.Array('c', 20, lock=lock)
        string.value = latin('hello')

        p = self.Process(target=self._double, args=(x, y, foo, arr, string))
        p.daemon = True
        p.start()
        p.join()

        self.assertEqual(x.value, 14)
        self.assertAlmostEqual(y.value, 2.0/3.0)
        self.assertEqual(foo.x, 6)
        self.assertAlmostEqual(foo.y, 4.0)
        for i in range(10):
            self.assertAlmostEqual(arr[i], i*2)
        self.assertEqual(string.value, latin('hellohello')) 

# 需要導入模塊: import multiprocessing [as 別名]
# 或者: from multiprocessing import Array [as 別名]
def __init__(self, make_env, observation_space):
        self.observation_space = observation_space
        if isinstance(observation_space, gym.spaces.Box):
            num_elems = len(np.array(observation_space.low).flatten())
            zeros = [0] * num_elems
            self._obs_buf = Array('b', zeros)
        else:
            self._obs_buf = None
        self._pipe, other_end = Pipe()
        self._proc = Process(target=self._worker,
                             args=(other_end,
                                   self._obs_buf,
                                   cloudpickle.dumps(make_env)),
                             daemon=True)
        self._proc.start()
        self._running_cmd = None
        other_end.close()
        self._pipe.send(('action_space', None))
        self.action_space = self._get_response() 

# 需要導入模塊: import multiprocessing [as 別名]
# 或者: from multiprocessing import Array [as 別名]
def CompleteAndWriteOGMatrices(self, ogs, ogMatrices):
        """
        ogMatrices - each matrix is a list of mp.Array  (so that each represents an nSeq x nSeq matrix
        """
        for iog, (og, m) in enumerate(zip(ogs, ogMatrices)):
            # dendroblast scores
            n = len(m)
            max_og = -9e99
            # Careful not to over-write a value and then attempt to try to use the old value
            for i in range(n):
                for j in range(i):
                    m[i][j] = -np.log(m[i][j] + m[j][i])  
                    m[j][i] = m[i][j]  
                    max_og = max(max_og, m[i][j])
            self.WritePhylipMatrix(m, [g.ToString() for g in og], files.FileHandler.GetOGsDistMatFN(iog), max_og)
        return ogMatrices 

# 需要導入模塊: import multiprocessing [as 別名]
# 或者: from multiprocessing import Array [as 別名]
def WritePhylipMatrix(m, names, outFN, max_og):
        """
        m - list of mp.Array  (so that each represents an nSeq x nSeq matrix
        """
        max_og = 1.1*max_og
        sliver = 1e-6
        with open(outFN, 'w') as outfile:
            n = len(m)
            outfile.write("%d\n" % n)
            for i in range(n):
                outfile.write(names[i] + " ")
                # values could be -inf, these are the most distantly related so replace with max_og
                V = [0. + (0. if i==j else m[i][j] if m[i][j] > -9e99 else max_og) for j in range(n)] # "0. +": hack to avoid printing out "-0"
                V = [sliver if 0 < v < sliver  else v for v in V]  # make sure scientific notation is not used (not accepted by fastme)
                values = " ".join(["%.6f" % v for v in V])   
                outfile.write(values + "\n") 

# 需要導入模塊: import multiprocessing [as 別名]
# 或者: from multiprocessing import Array [as 別名]
def SpeciesTreeDistances(self, ogs, ogMatrices, method = 0):
        """
        ogMatrices - each matrix is a list of mp.Array  (so that each represents an nSeq x nSeq matrix
        """
        spPairs = list(itertools.combinations(self.ogSet.seqsInfo.speciesToUse, 2))
        D = [[] for _ in spPairs]
        if method == 0:
            """ closest distance for each species pair in each orthogroup"""
            for og, m in zip(ogs, ogMatrices):
                spDict = defaultdict(list)
                for i, g in enumerate(og):
                    spDict[g.iSp].append(i)
                for (sp1, sp2), d_list in zip(spPairs, D):
                    distances = [m[i][j] for i in spDict[sp1] for j in spDict[sp2]]
                    if len(distances) > 0: d_list.append(min(distances))
#                    d_list.append(min(distances) if len(distances) > 0 else None)
        return D, spPairs 

# 需要導入模塊: import multiprocessing [as 別名]
# 或者: from multiprocessing import Array [as 別名]
def test_DistanceMatrixEvalues(self):
        if qBinary:
            self.skipTest("Skipping unit test. Test can be run on sourcecode version of OrthoFinder.") 
        import orthologues
        m = np.zeros((2,2))
        m = [mp.Array('d', [0, 1e-9, 0.1, 1]), mp.Array('d', [1e-9, 0, 1, 1]), mp.Array('d', [0.1, 1, 0, 1]), mp.Array('d', [1, 1, 1, 0])]
#        m[0,1] = 
#        m[1,0] = 0.1
        names = ["a", "b", "c", "d"]
        outFN = baseDir + "Input/Distances.phy"
        max_og = 1.
        orthologues.DendroBLASTTrees.WritePhylipMatrix(m, names, outFN, max_og)
        # read values and check they are written in the corect format
        with open(outFN, 'rb') as infile:
            infile.next()
            line = infile.next().rstrip().split()
            self.assertEqual('0.000000', line[1]) # expected format for writing 0
            self.assertEqual('0.000001', line[2]) # min non-zero value. Should be writen in decimal rather than scientific format
            line = infile.next().rstrip().split()
            self.assertEqual('0.000001', line[1]) 
        os.remove(outFN) 

# 需要導入模塊: import multiprocessing [as 別名]
# 或者: from multiprocessing import Array [as 別名]
def test_server_multiproc(mocker, set_timeout, restore_signal, start_method):

    mpctx = mp.get_context(start_method)
    mocker.patch('aiotools.server.mp', mpctx)

    started = mpctx.Value('i', 0)
    terminated = mpctx.Value('i', 0)
    proc_idxs = mpctx.Array('i', 3)

    set_timeout(0.2, interrupt)
    aiotools.start_server(myserver_multiproc, num_workers=3,
                          args=(started, terminated, proc_idxs))

    assert started.value == 3
    assert terminated.value == 3
    assert list(proc_idxs) == [0, 1, 2]
    assert len(mp.active_children()) == 0 

# 需要導入模塊: import multiprocessing [as 別名]
# 或者: from multiprocessing import Array [as 別名]
def test_server_multiproc_custom_stop_signals(
        mocker, set_timeout, restore_signal, start_method):

    mpctx = mp.get_context(start_method)
    mocker.patch('aiotools.server.mp', mpctx)

    started = mpctx.Value('i', 0)
    terminated = mpctx.Value('i', 0)
    received_signals = mpctx.Array('i', 2)
    proc_idxs = mpctx.Array('i', 2)

    set_timeout(0.2, interrupt_usr1)
    aiotools.start_server(myserver_multiproc_custom_stop_signals,
                          num_workers=2,
                          stop_signals={signal.SIGUSR1},
                          args=(started, terminated, received_signals, proc_idxs))

    assert started.value == 2
    assert terminated.value == 2
    assert list(received_signals) == [signal.SIGUSR1, signal.SIGUSR1]
    assert list(proc_idxs) == [0, 1]
    assert len(mpctx.active_children()) == 0