Python Trajectory.f_store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_store [as 别名]
    def test_storage_service_errors(self):

        traj = Trajectory(filename=make_temp_file('testnoservice.hdf5'))

        traj_name = traj.v_name

        # you cannot store stuff before the trajectory was stored once:
        with self.assertRaises(ValueError):
            traj.v_storage_service.store('FAKESERVICE', self, trajectory_name = traj.v_name)


        traj.f_store()

        with self.assertRaises(ValueError):
            traj.v_storage_service.store('FAKESERVICE', self, trajectory_name = 'test')

        with self.assertRaises(pex.NoSuchServiceError):
            traj.v_storage_service.store('FAKESERVICE', self, trajectory_name = traj.v_name)

        with self.assertRaises(ValueError):
            traj.f_load(name = 'test', index=1)

        with self.assertRaises(RuntimeError):
            traj.v_storage_service.store('LIST', [('LEAF',None,None,None,None)], trajectory_name = traj.v_name)

        with self.assertRaises(ValueError):
            traj.f_load(index=9999)

        with self.assertRaises(ValueError):
            traj.f_load(name='Non-Existising-Traj')

# 需要导入模块: from pypet.trajectory import Trajectory [as 别名]
# 或者: from pypet.trajectory.Trajectory import f_store [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_store [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_store [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_store [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_store [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_store [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_store [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_store [as 别名]
    def test_store_load_with_hdf5_no_data(self):
        traj_name = 'test_%s' % self.__class__.__name__
        filename = make_temp_dir(traj_name + 'nodata.hdf5')
        traj = Trajectory(name=traj_name, dynamic_imports=self.dynamic_imports,
                          filename = filename, overwrite_file=True)

        for param in self.param.values():
            param._data = None
            traj.f_add_parameter(param)

        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_store [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_store [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_store [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_store [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_store [as 别名]
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()

# We free the data:
traj.huge_matrices.f_empty()

# Check if the data was deleted
if traj.huge_matrices.f_is_empty():
    print('As promised: Nothing there!')
else:
    print('What a disappointing peace of crap this software is!')

# Lucky, it worked.
# Ok we could it add some more stuff to the result object if we want to:
traj.huge_matrices.f_set(monty='Always look on the bright side of life!')

# Next we can store our new string called monty to disk. Since our trajectory was already

# 需要导入模块: from pypet.trajectory import Trajectory [as 别名]
# 或者: from pypet.trajectory.Trajectory import f_store [as 别名]
def main():
    
    pool = Pool(2)
    lock = Manager().Lock()
    logging.basicConfig(level=logging.DEBUG)
    log_to_stderr()
    traj = Trajectory(name='MyTrajectory',filename='../experiments/test.hdf5')
    config['multiproc'] = True
    #traj.load_trajectory(trajectoryname='MyTrajectory_2013_05_23_14h29m26s')
    
    traj.f_add_parameter('test.testparam', param_type=ParameterSet, **{'Fuechse':1,'Sapiens':1,'Comment':'ladida'})

    traj.last.foo = 'bar'
    
    traj.f_add_parameter('Network.Cm')
    
    traj.Parameters.Network.Cm.value= 1.0
    traj.Parameters.Network.Cm.unit = 'pF'
    
    print traj.Parameters.Network.Cm()
    
    
    traj.last.herbert = 4.5
    
    traj.Parameters.test.testparam.Konstantin = 'Konstantin'
    

    traj.f_add_parameter(full_parameter_name='honky', **{'mirta':np.array([[1,2,7],[3,2,17]])})

    traj.f_add_parameter('flonky',**{'val' : 10})
    
    
    param1 = traj.Parameters.test.testparam
    param2 = traj.Parameters.honky
    param3 = traj.last
    
    print param1()
    
    print param3('val')
    
    exp2_list = range(3)
    exp1_list = range(3)
    exp3_list = range(3)
    explore_dict = { param1.gfn('Sapiens') : exp1_list,
                     param2.get_fullname('mirta'):exp2_list,
                     param3.gfn('val') : exp3_list}
    
    cmb_list=[(param3.gfn('val'),param1.gfn('Sapiens'))]
    
    traj.f_add_derived_parameter(full_parameter_name='foo', **{'bar' : -1}).moo = 'zip'
    
    lilma = spsp.lil_matrix((10000,1000))
    lilma[0,100] = 555
    lilma[9999,999] = 11
    traj.f_add_parameter('sparse', mat=lilma, param_type = SparseParameter)
    
    traj.f_explore(ut.cartesian_product,explore_dict,cmb_list)
    
    traj._prepare_experiment()
    traj.f_store()
    pathname = config['logfolder']
    it = ((traj._make_single_run(n),lock, pathname) for n in xrange(len(traj)))
    moo = pool.imap(do_stuff, it)
    

    pool.close()
    pool.join()