Python Trajectory.f_add_result方法代码示例

# 需要导入模块: from pypet.trajectory import Trajectory [as 别名]
# 或者: from pypet.trajectory.Trajectory import f_add_result [as 别名]
    def test_auto_load(self):


        traj = Trajectory(name='Test', filename=make_temp_file('autoload.hdf5'))

        traj.v_auto_load = True

        traj.f_add_result('I.am.$.a.mean.resu', 42, comment='Test')

        traj.f_add_derived_parameter('ffa', 42)

        traj.f_store()

        ffa=traj.f_get('ffa')
        ffa.f_unlock()
        ffa.f_empty()

        self.assertTrue(ffa.f_is_empty())

        traj.f_remove_child('results', recursive=True)

        # check auto load
        val = traj.res.I.am.crun.a.mean.resu

        self.assertTrue(val==42)

        val = traj.ffa

        self.assertTrue(val==42)

        with self.assertRaises(Exception):
            traj.kdsfdsf

# 需要导入模块: from pypet.trajectory import Trajectory [as 别名]
# 或者: from pypet.trajectory.Trajectory import f_add_result [as 别名]
    def test_store_items_and_groups(self):

        traj = Trajectory(name='testtraj', filename=make_temp_file('teststoreitems.hdf5'))

        traj.f_store()

        traj.f_add_parameter('group1.test',42, comment= 'TooLong' * pypetconstants.HDF5_STRCOL_MAX_COMMENT_LENGTH)

        traj.f_add_result('testres', 42)

        traj.group1.f_set_annotations(Test=44)

        traj.f_store_items(['test','testres','group1'])


        traj2 = Trajectory(name=traj.v_name, add_time=False,
                           filename=make_temp_file('teststoreitems.hdf5'))

        traj2.f_load(load_results=2,load_parameters=2)

        traj.f_add_result('Im.stored.along.a.path', 43)
        traj.Im.stored.along.v_annotations['wtf'] =4444
        traj.res.f_store_child('Im.stored.along.a.path')


        traj2.res.f_load_child('Im.stored.along.a.path', load_data=2)

        self.compare_trajectories(traj,traj2)

# 需要导入模块: from pypet.trajectory import Trajectory [as 别名]
# 或者: from pypet.trajectory.Trajectory import f_add_result [as 别名]
    def test_store_and_load_large_dictionary(self):
        traj = Trajectory(name='Test', filename=make_temp_file('large_dict.hdf5'))

        large_dict = {}

        for irun in range(1025):
            large_dict['item_%d' % irun] = irun

        large_dict2 = {}

        for irun in range(33):
            large_dict2['item_%d' % irun] = irun

        traj.f_add_result('large_dict', large_dict, comment='Huge_dict!')
        traj.f_add_result('large_dict2', large_dict2, comment='Not so large dict!')

        traj.f_store()

        traj_name = traj.v_name

        traj2 = Trajectory(filename=make_temp_file('large_dict.hdf5'))

        traj2.f_load(name=traj_name, load_all=2)

        self.compare_trajectories(traj, traj2)

# 需要导入模块: from pypet.trajectory import Trajectory [as 别名]
# 或者: from pypet.trajectory.Trajectory import f_add_result [as 别名]
    def test_store_load_with_hdf5(self):
        traj_name = 'test_%s' % self.__class__.__name__
        filename = make_temp_dir(traj_name + '.hdf5')
        traj = Trajectory(name=traj_name, dynamic_imports=self.dynamic_imports,
                          filename = filename, overwrite_file=True)

        for res in self.results.values():
            traj.f_add_result(res)

        traj.f_store()

        new_traj = Trajectory(name=traj_name, dynamic_imports=self.dynamic_imports,
                              filename = filename)

        new_traj.f_load(load_data=2)
        self.compare_trajectories(traj, new_traj)

# 需要导入模块: from pypet.trajectory import Trajectory [as 别名]
# 或者: from pypet.trajectory.Trajectory import f_add_result [as 别名]
    def test_partial_loading(self):
        traj = Trajectory(name='TestPartial', filename=make_temp_file('testpartially.hdf5'))

        res = traj.f_add_result('mytest.test', a='b', c='d')

        traj.f_store()

        traj.f_remove_child('results', recursive=True)

        traj.f_update_skeleton()

        traj.f_load_item(traj.test, load_only=['a', 'x'])

        self.assertTrue('a' in traj.test)
        self.assertTrue('c' not in traj.test)

        traj.f_remove_child('results', recursive=True)

        traj.f_update_skeleton()

        load_except= ['c', 'd']
        traj.f_load_item(traj.test, load_except=load_except)

        self.assertTrue(len(load_except)==2)

        self.assertTrue('a' in traj.test)
        self.assertTrue('c' not in traj.test)

        with self.assertRaises(ValueError):
            traj.f_load_item(traj.test, load_except=['x'], load_only=['y'])

# 需要导入模块: from pypet.trajectory import Trajectory [as 别名]
# 或者: from pypet.trajectory.Trajectory import f_add_result [as 别名]
    def test_overwrite_stuff(self):
        traj = Trajectory(name='Test', filename=make_temp_file('testowrite.hdf5'))

        res = traj.f_add_result('mytest.test', a='b', c='d')

        traj.f_store()

        res['a'] = 333
        res['c'] = 123445

        traj.f_store_item(res, overwrite='a')

        # Should emit a warning
        traj.f_store_item(res, overwrite=['a', 'b'])

        traj.f_load(load_results=3)

        res = traj.test

        self.assertTrue(res['a']==333)
        self.assertTrue(res['c']=='d')

        res['c'] = 123445

        traj.f_store_item(res, overwrite=True)
        res.f_empty()

        traj.f_load(load_results=3)

        self.assertTrue(traj.test['c']==123445)

# 需要导入模块: from pypet.trajectory import Trajectory [as 别名]
# 或者: from pypet.trajectory.Trajectory import f_add_result [as 别名]
    def test_partially_delete_stuff(self):
        traj = Trajectory(name='TestDelete',
                          filename=make_temp_file('testpartiallydel.hdf5'))

        res = traj.f_add_result('mytest.test', a='b', c='d')

        traj.f_store()

        self.assertTrue('a' in res)
        traj.f_delete_item(res, delete_only=['a'], remove_from_item=True)

        self.assertTrue('c' in res)
        self.assertTrue('a' not in res)

        res['a'] = 'offf'

        self.assertTrue('a' in res)

        traj.f_load(load_results=3)

        self.assertTrue('a' not in res)
        self.assertTrue('c' in res)

        traj.f_delete_item(res, remove_from_trajectory=True, remove_empty_groups=True)

        self.assertTrue('results' not in traj)
        self.assertTrue(res not in traj)

# 需要导入模块: from pypet.trajectory import Trajectory [as 别名]
# 或者: from pypet.trajectory.Trajectory import f_add_result [as 别名]
    def test_migrations(self):

        traj = Trajectory(name='Test', filename=make_temp_file('migrate.hdf5'))

        traj.f_add_result('I.am.a.mean.resu', 42, comment='Test')
        traj.f_add_derived_parameter('ffa', 42)

        traj.f_store()

        new_file = make_temp_file('migrate2.hdf5')
        traj.f_migrate(new_filename=new_file)

        traj.f_store()

        new_traj = Trajectory()

        new_traj.f_migrate(new_name=traj.v_name, new_filename=new_file, in_store=True)

        new_traj.v_auto_load=True

        self.assertTrue(new_traj.results.I.am.a.mean.resu == 42)

# 需要导入模块: from pypet.trajectory import Trajectory [as 别名]
# 或者: from pypet.trajectory.Trajectory import f_add_result [as 别名]
    def test_wildcard_search(self):

        traj = Trajectory(name='Test', filename=make_temp_file('wilcard.hdf5'))

        traj.f_add_parameter('expl', 2)
        traj.f_explore({'expl':[1,2,3,4]})

        traj.f_add_result('wc2test.$.hhh', 333)
        traj.f_add_leaf('results.wctest.run_00000000.jjj', 42)
        traj.f_add_result('results.wctest.run_00000001.jjj', 43)

        traj.v_as_run = 1

        self.assertTrue(traj.results.wctest['$'].jjj==43)
        self.assertTrue(traj.results.wc2test.crun.hhh==333)

        traj.f_store()

        traj.f_remove_child('results', recursive=True)

        traj.v_auto_load = True

        self.assertTrue(traj.results.wctest['$'].jjj==43)
        self.assertTrue(traj.results.wc2test.crun.hhh==333)

        traj.f_remove_child('results', recursive=True)

        traj.v_auto_load = True

        self.assertTrue(traj.results.wctest[-2].jjj==43)
        self.assertTrue(traj.results.wc2test[-2].hhh==333)

        traj.f_remove_child('results', recursive=True)

        traj.v_auto_load = True

        self.assertTrue(traj.results.wctest[1].jjj==43)
        self.assertTrue(traj.results.wc2test[-1].hhh==333)

# 需要导入模块: from pypet.trajectory import Trajectory [as 别名]
# 或者: from pypet.trajectory.Trajectory import f_add_result [as 别名]
    def test_file_translation(self):
        filename = make_temp_dir('to_new_tree.hdf5')
        mytraj = Trajectory('SCRATCH', filename=filename)

        mytraj.f_add_parameter('Test.Group.Test', 42)

        mytraj.f_add_derived_parameter('trajectory.saaaa',33)

        mytraj.f_add_derived_parameter('trajectory.intraj.dpar1',33)

        mytraj.f_add_derived_parameter('run_00000008.inrun.dpar2',33)
        mytraj.f_add_derived_parameter('run_00000001.inrun.dpar3',35)


        mytraj.f_add_result('trajectory.intraj.res1',33)

        mytraj.f_add_result('run_00000008.inrun.res1',33)

        mytraj.f_store()

        mytraj.f_migrate(new_name=mytraj.v_name + 'PETER', in_store=True)

        mytraj.f_store()

        fu=FileUpdater(filename=filename, backup=True)

        fu.update_file()

        mytraj = Trajectory(name=mytraj.v_name, add_time=False, filename=filename)
        mytraj.f_load(load_parameters=2, load_derived_parameters=2, load_results=2)

        for node in mytraj.f_iter_nodes():
            self.assertTrue(node.v_name != 'trajectory')

            if 'run_' in node.v_full_name:
                self.assertTrue('.runs.' in node.v_full_name)

        remove_data()

# 需要导入模块: from pypet.trajectory import Trajectory [as 别名]
# 或者: from pypet.trajectory.Trajectory import f_add_result [as 别名]
    def test_version_mismatch(self):
        traj = Trajectory(name='Test', filename=make_temp_file('testversionmismatch.hdf5'))

        traj.f_add_parameter('group1.test',42)

        traj.f_add_result('testres', 42)

        traj.group1.f_set_annotations(Test=44)

        traj._version='0.1a.1'

        traj.f_store()



        traj2 = Trajectory(name=traj.v_name, add_time=False,
                           filename=make_temp_file('testversionmismatch.hdf5'))

        with self.assertRaises(pex.VersionMismatchError):
            traj2.f_load(load_results=2,load_parameters=2)

        traj2.f_load(load_results=2,load_parameters=2, force=True)

        self.compare_trajectories(traj,traj2)

# 需要导入模块: from pypet.trajectory import Trajectory [as 别名]
# 或者: from pypet.trajectory.Trajectory import f_add_result [as 别名]
# We can create a trajectory and hand it a filename directly and it will create an
# HDF5StorageService for us:
filename = os.path.join('hdf5', 'example_09.hdf5')
traj = Trajectory(name='example_09_huge_data',
                  filename=filename)

# Now we directly add a huge result. Note that we could do the exact same procedure during
# a single run, there is no syntactical difference.
# However, the sub branch now is different, the result will be found under `traj.results.trajectory`
# instead of `traj.results.run_XXXXXXXX` (where XXXXXXX is the current run index, e.g. 00000007).
# We will add two large matrices a 100 by 100 by 100 one and 1000 by 1000 one, both containing
# random numbers. They are called `mat1` and `mat2` and are handled by the same result object
# called `huge_matrices`:
traj.f_add_result('huge_matrices',
                  mat1 = np.random.rand(100,100,100),
                  mat2 = np.random.rand(1000,1000))

# Note that the result will not support fast access since it contains more than a single
# data item. Even if there was only `mat1`, because the name is `mat1` instead of `huge_matrices`
# (as the result object itself is called), fast access does not work either.
# Yet, we can access data via natural naming using the names `mat1` and `mat2` e.g.:
val_mat1 = traj.huge_matrices.mat1[10,10,10]
val_mat2 = traj.huge_matrices.mat2[42,13]
print('mat1 contains %f at position [10,10,10]' % val_mat1)
print('mat2 contains %f at position [42,13]' % val_mat2)

# Ok that was enough analysis of the data and should be sufficient for a phd thesis (in economics).
# Let's store our trajectory and subsequently free the space for something completely different.
traj.f_store()

# 需要导入模块: from pypet.trajectory import Trajectory [as 别名]
# 或者: from pypet.trajectory.Trajectory import f_add_result [as 别名]
# But if we are more specific we have again a unique finding
characters = traj.starwars.characters

# Now let's see what fast access is:
print 'The name of the actor playing Luke is %s.' % traj.luke_skywalker

# And now what happens if you forbid it
traj.v_fast_access=False
print 'The object found for luke_skywalker is `%s`.' % str(traj.luke_skywalker)

#Let's store the trajectory:
traj.f_store()

# That was easy, let's assume we already completed a simulation and now we add a veeeery large
# result that we want to store to disk immediately and than empty it
traj.f_add_result('starwars.gross_income_of_film', amount=10.1 ** 11, currency='$$$',
                  comment='George Lucas is rich, dude!')
# This is a large number, we better store it and than free the memory:
traj.f_store_item('gross_income_of_film')
traj.gross_income_of_film.f_empty()


# Now lets reload the trajectory
del traj
traj = Trajectory(filename='experiments/example_02/HDF5/example_02.hdf5')
# We want to load the last trajectory in the file, therefore index = -1
# We want to load the parameters, therefore load_parameters=2
# We only want to load the skeleton of the results, so load_results=1
traj.f_load(index=-1,load_parameters=2,load_results=1)

# Let's check if our result is really empty
if traj.gross_income_of_film.f_is_empty():

# 需要导入模块: from pypet.trajectory import Trajectory [as 别名]
# 或者: from pypet.trajectory.Trajectory import f_add_result [as 别名]
class TrajectoryMergeTest(unittest.TestCase):

    def setUp(self):
        name = 'Moop'

        self.traj = Trajectory(name,[ImAParameterInDisguise])

        comment = 'This is a comment'
        self.traj.v_comment=comment

        self.assertTrue(comment == self.traj.v_comment)

        self.traj.f_add_parameter('IntParam',3)
        sparsemat = spsp.csr_matrix((1000,1000))
        sparsemat[1,2] = 17.777

        #self.traj.f_add_parameter('SparseParam', sparsemat, param_type=PickleParameter)

        self.traj.f_add_parameter('FloatParam')

        self.traj.f_add_derived_parameter(Parameter('FortyTwo', 42))
        self.traj.f_add_parameter('Trials',0)

        self.traj.f_add_result(Result,'Im.A.Simple.Result',44444)

        self.traj.FloatParam=4.0
        self.traj.v_storage_service = LazyStorageService()


        self.traj.f_explore({'FloatParam':[1.0,1.1,1.2,1.3],'Trials':[0,1,2,3]})


        self.assertTrue(len(self.traj) == 4)


        name2 = 'aaaaah'
        self.traj2 = Trajectory(name2,[ImAParameterInDisguise])

        comment = 'This is a comment'
        self.traj2.v_comment=comment

        self.assertTrue(comment == self.traj2.v_comment)

        self.traj2.f_add_parameter('IntParam',3)
        sparsemat = spsp.csr_matrix((1000,1000))
        sparsemat[1,2] = 17.777

        #self.traj2.f_add_parameter('SparseParam', sparsemat, param_type=PickleParameter)
        self.traj2.f_add_parameter('Trials',0)

        self.traj2.f_add_parameter('FloatParam')

        self.traj2.f_add_derived_parameter(Parameter('FortyTwo', 42))

        self.traj2.f_add_result(Result,'Im.A.Simple.Result',44444)

        self.traj2.FloatParam=4.0

        self.traj2.f_explore({'FloatParam':[42.0,43.0,1.2,1.3],'Trials':[0,1,2,3]})
        self.traj2.v_storage_service = LazyStorageService()

        self.assertTrue(len(self.traj2) == 4)



    def test_merge_parameters_without_remove(self):
        # remove_duplicates = True should be discarded by the trial parameter
        self.traj._merge_parameters(self.traj2, trial_parameter_name='Trials',remove_duplicates=True)

    def test_merge_parameters_with_remove(self):
        self.traj._merge_parameters(self.traj2,remove_duplicates=True)

    def test_merge_without_remove(self):
        self.traj.f_merge(self.traj2, remove_duplicates=True,trial_parameter='Trials')

    def test_merge_with_remove(self):
        self.traj.f_merge(self.traj2, remove_duplicates=True)

# 需要导入模块: from pypet.trajectory import Trajectory [as 别名]
# 或者: from pypet.trajectory.Trajectory import f_add_result [as 别名]
class TrajectoryTest(unittest.TestCase):


    def setUp(self):
        name = 'Moop'

        self.traj = Trajectory(name,dynamically_imported_classes=[ImAParameterInDisguise,
                                                'pypet.tests.test_helpers.ImAResultInDisguise'])

        self.assertTrue(self.traj.f_is_empty())

        comment = 'This is a comment'
        self.traj.v_comment=comment

        self.assertTrue(comment == self.traj.v_comment)

        self.traj.f_add_parameter('IntParam',3)
        sparsemat = spsp.csr_matrix((1000,1000))
        sparsemat[1,2] = 17.777

        self.traj.f_add_parameter(PickleParameter,'SparseParam', sparsemat)

        self.traj.f_add_parameter('FloatParam')

        self.traj.f_add_derived_parameter(Parameter('FortyTwo', 42))

        self.traj.f_add_result(Result,'Im.A.Simple.Result',44444)

        self.traj.FloatParam=4.0


        self.explore_dict = {'FloatParam':[1.0,1.1,1.2,1.3]}

        self.traj.f_explore(self.explore_dict)

        self.assertTrue(len(self.traj) == 4)

        self.traj.f_add_parameter_group('peter.paul')

        self.traj.f_add_parameter('peter.markus.yve',6)

        self.traj.f_add_result('Test',42)

        self.traj.peter.f_add_parameter('paul.peter')

        self.traj.f_add_config('make.impossible.promises',1)

        with self.assertRaises(AttributeError):
            self.traj.markus.peter

        with self.assertRaises(ValueError):
            self.traj.f_add_parameter('Peter.  h ._hurz')

    def test_shrink(self):

        self.assertTrue(len(self.traj)>1)

        self.traj.f_shrink()

        self.assertTrue(len(self.traj)==1)

        self.assertTrue(len(self.traj.f_get_explored_parameters())==0)

    def test_get_all(self):
        all_nodes = self.traj.f_get_all('peter')

        self.assertTrue(len(all_nodes)==2)

        all_nodes = self.traj.f_get_all('peter.yve')

        self.assertTrue(len(all_nodes)==1)

        all_nodes = self.traj.f_get_all('paul.yve')

        self.assertTrue(len(all_nodes)==0)

        self.traj.f_add_parameter('paul.paul')

        all_nodes = self.traj.peter.f_get_all('paul')

        self.assertTrue(len(all_nodes)==1)

        self.traj.f_add_result('results.runs.run_00000000.x.y',10)

        self.traj.f_add_result('results.runs.run_00000001.x.y',10)

        self.traj.f_add_derived_parameter('hfhfhf.x.y')

        self.traj.f_add_result('x.y.y')

        self.traj.f_as_run(1)

        all_nodes=self.traj.f_get_all('x.y')

        self.assertTrue(len(all_nodes)==4)

    def test_backwards_search(self):

        x=self.traj.peter.f_get('paul.peter', backwards_search=False)

#.........这里部分代码省略.........