def test_loading_as_new(self):
filename = make_temp_dir('asnew.h5')
traj = Trajectory(name='TestPartial', filename=filename, add_time=True)
traj.f_add_parameter('x', 3)
traj.f_add_parameter('y', 2)
traj.f_explore({'x': [12,3,44], 'y':[1,23,4]})
traj.f_store()
traj = load_trajectory(name=traj.v_name, filename=filename)
with self.assertRaises(TypeError):
traj.f_shrink()
traj = load_trajectory(name=traj.v_name, filename=filename, as_new=True,
new_name='TestTraj', add_time=False)
self.assertTrue(traj.v_name == 'TestTraj')
self.assertEqual(len(traj), 3)
self.assertEqual(len(traj._explored_parameters), 2)
traj.f_shrink()
self.assertTrue(len(traj) == 1)
def load_trajectories(filename, brian2=True):
loading, idx = True, 0
trajectories = []
while loading:
if brian2:
tr = load_trajectory(
index=idx,
filename=filename,
dynamic_imports=[Brian2MonitorResult, Brian2Parameter])
else:
tr = load_trajectory(index=idx, filename=filename)
trajectories.append(tr)
idx += 1
try:
if brian2:
tr = load_trajectory(
index=idx,
filename=filename,
dynamic_imports=[Brian2MonitorResult, Brian2Parameter])
else:
tr = load_trajectory(index=idx, filename=filename)
except ValueError:
loading = False
return trajectories
def test_loading_as_new(self):
filename = make_temp_dir('asnew.h5')
traj = Trajectory(name='TestPartial', filename=filename)
traj.f_add_parameter('x', 3)
traj.f_add_parameter('y', 2)
traj.f_explore({'x': [12,3,44], 'y':[1,23,4]})
traj.f_store()
traj = load_trajectory(name=traj.v_name, filename=filename)
with self.assertRaises(TypeError):
traj.f_shrink()
traj = load_trajectory(name=traj.v_name, filename=filename, as_new=True,
new_name='TestTraj', add_time=False)
self.assertTrue(traj.v_name == 'TestTraj')
self.assertTrue(len(traj) == 3)
traj.f_shrink()
self.assertTrue(len(traj) == 1)
def test_table_read(self):
the_reading_table = self.traj.results.shared_data.table
self.assertTrue(the_reading_table is self.shared_table)
the_reading_table.create_shared_data(description=MyTable)
with StorageContextManager(self.traj):
row = the_reading_table.row
for i in range(10):
row['id'] = i
row['name'] = 'mehmet %d' % i
row['surname'] = 'Timur'
row['weight'] = 65.5 + i * 1.5
row.append()
the_reading_table.flush()
for idx, row in enumerate(the_reading_table.iterrows()):
self.assertEqual(row['id'], idx)
self.traj.f_store()
traj2 = load_trajectory(name=self.traj.v_name, filename=self.filename, load_all=2, dynamic_imports=SharedResult)
second_reading_table = traj2.results.shared_data.table
self.assertTrue(np.all(the_reading_table.read() == second_reading_table.read()))
second_reading_table.append([(21, 'aaa', 'bbb', 100)])
self.assertTrue(np.all(the_reading_table.read() == second_reading_table.read()))
traj3 = load_trajectory(name=self.traj.v_name, filename=self.filename, load_all=2, dynamic_imports=SharedResult)
third_reading_table = traj3.results.shared_data.table
self.assertTrue(np.all(the_reading_table.read() == third_reading_table.read()))
def test_all_arrays(self):
filename = make_temp_dir("hdf5arrays.hdf5")
traj = Trajectory(name=make_trajectory_name(self), filename=filename)
trajname = traj.v_name
npearray = np.ones((2, 10, 3), dtype=np.float)
thevlarray = np.array([compat.tobytes("j"), 22.2, compat.tobytes("gutter")])
traj.f_store(only_init=True)
res = traj.f_add_result(SharedResult, "arrays")
res["carray"] = SharedCArray()
res["carray"].create_shared_data(shape=(10, 10), atom=pt.atom.FloatAtom())
res["earray"] = SharedEArray()
res["earray"].create_shared_data(obj=npearray)
res["vlarray"] = SharedVLArray()
res["vlarray"].create_shared_data(obj=thevlarray)
res["array"] = SharedArray()
res["array"].create_shared_data(data=npearray)
traj.f_store()
traj = load_trajectory(name=trajname, filename=filename, load_all=2, dynamic_imports=SharedResult)
toappned = [44, compat.tobytes("k")]
with StorageContextManager(traj) as cm:
a1 = traj.arrays.array
a1[0, 0, 0] = 4.0
a2 = traj.arrays.carray
a2[0, 1] = 4
a4 = traj.arrays.vlarray
a4.append(toappned)
a3 = traj.arrays.earray
a3.append(np.zeros((1, 10, 3)))
# cm.f_flush_storage()
traj = load_trajectory(name=trajname, filename=filename, load_all=2, dynamic_imports=SharedResult)
with StorageContextManager(traj) as cm:
a1 = traj.arrays.array
self.assertTrue(a1[0, 0, 0] == 4.0)
a2 = traj.arrays.carray
self.assertTrue(a2[0, 1] == 4)
a3 = traj.arrays.earray
self.assertTrue(a3.read().shape == (3, 10, 3))
a4 = traj.arrays.vlarray
for idx, x in enumerate(a4):
if idx == 0:
self.assertTrue(np.all(x == np.array(thevlarray)))
elif idx == 1:
self.assertTrue(np.all(x == np.array(toappned)))
else:
raise RuntimeError()
def test_table_append(self):
the_append_table = self.traj.results.shared_data.table
self.assertTrue(the_append_table is self.shared_table)
the_append_table.create_shared_data(description=MyTable)
with StorageContextManager(self.traj):
row = the_append_table.row
for i in range(15):
row['id'] = i * 2
row['name'] = 'name %d' % i
row['surname'] = '%d surname' % i
row['weight'] = (i*0.5 + 50.0)
row.append()
the_append_table.flush()
for idx, row in enumerate(the_append_table.iterrows()):
self.assertEqual(row['id'], idx * 2)
self.assertEqual(row['name'], compat.tobytes('name %d' % idx))
self.assertEqual(row['surname'], compat.tobytes('%d surname' % idx))
self.assertEqual(row['weight'], idx*0.5+50.0)
self.traj.f_store()
traj2 = load_trajectory(name=self.traj.v_name, filename=self.filename, load_all=2, dynamic_imports=SharedResult)
second_append_table = traj2.results.shared_data.table
with StorageContextManager(traj2):
for idx, row in enumerate(second_append_table.iterrows()):
self.assertEqual(row['id'], idx * 2)
self.assertEqual(row['name'], compat.tobytes('name %d' % idx))
self.assertEqual(row['surname'], compat.tobytes('%d surname' % idx))
self.assertEqual(row['weight'], idx*0.5+50.0)
second_append_table.append([(30, 'mehmet', 'timur', 65.5)])
self.assertEqual(second_append_table.read(field='id')[-1], 30)
self.assertEqual(second_append_table.read(field='name')[-1], compat.tobytes('mehmet'))
self.assertEqual(second_append_table.read(field='surname')[-1], compat.tobytes('timur'))
self.assertEqual(second_append_table.read(field='weight')[-1], 65.5)
traj2.f_store()
traj3 = load_trajectory(name=self.traj.v_name, filename=self.filename, load_all=2, dynamic_imports=SharedResult)
third_append_table = traj3.results.shared_data.table
self.assertEqual((third_append_table.read(field='id')[-1]), 30)
self.assertEqual((third_append_table.read(field='name')[-1]), compat.tobytes('mehmet'))
self.assertEqual((third_append_table.read(field='surname')[-1]), compat.tobytes('timur'))
self.assertEqual((third_append_table.read(field='weight')[-1]), 65.5)
third_append_table.append([(33, 'Harrison', 'Ford', 95.5)])
self.assertEqual((third_append_table.read(field='id')[-1]), 33)
self.assertEqual((third_append_table.read(field='name')[-1]), compat.tobytes('Harrison'))
self.assertEqual((third_append_table.read(field='surname')[-1]), compat.tobytes('Ford'))
self.assertEqual((third_append_table.read(field='weight')[-1]), 95.5)
def test_table_getitem(self):
the_getitem_table = self.traj.results.shared_data.table
self.assertTrue(the_getitem_table is self.shared_table)
the_getitem_table.create_shared_data(description=MyTable)
with StorageContextManager(self.traj):
row = the_getitem_table.row
for i in range(10):
row['id'] = i
row['name'] = 'mehmet %d' % i
row['surname'] = 'Timur'
row['weight'] = 65.5 + i * 1.5
row.append()
the_getitem_table.flush()
for idx, row in enumerate(the_getitem_table.iterrows()):
self.assertEqual(row['id'], idx)
self.traj.f_store()
traj2 = load_trajectory(name=self.traj.v_name,
filename=self.filename,
load_all=2,
dynamic_imports=SharedResult)
second_getitem_table = traj2.results.shared_data.table
with StorageContextManager(traj2):
for idx, row in enumerate(second_getitem_table.iterrows()):
self.assertTrue(
np.all(second_getitem_table.read()[idx] ==
second_getitem_table[idx]))
second_getitem_table.append([(30, 'mehmet nevvaf', 'timur', 65.5)])
for idx, row in enumerate(second_getitem_table.iterrows(-1)):
self.assertEqual(row['id'], 30)
self.assertEqual(row['name'], compat.tobytes('mehmet nevvaf'))
self.assertEqual(row['surname'], compat.tobytes('timur'))
self.assertEqual(row['weight'], 65.5)
traj2.f_store()
traj3 = load_trajectory(name=self.traj.v_name,
filename=self.filename,
load_all=2,
dynamic_imports=SharedResult)
third_getitem_table = traj3.results.shared_data.table
with StorageContextManager(traj3):
for idx, row in enumerate(third_getitem_table.iterrows()):
self.assertTrue(
np.all(third_getitem_table.read()[idx] ==
third_getitem_table[idx]))
def test_table_setitem(self):
the_setitem_table = self.traj.results.shared_data.table
self.assertTrue(the_setitem_table is self.shared_table)
the_setitem_table.create_shared_data(description=MyTable)
with StorageContextManager(self.traj):
row = the_setitem_table.row
for i in range(10):
row['id'] = i
row['name'] = 'mehmet %d' % i
row['surname'] = 'Timur'
row['weight'] = 65.5 + i * 1.5
row.append()
the_setitem_table.flush()
for idx, row in enumerate(the_setitem_table.iterrows()):
self.assertEqual(row['id'], idx)
self.traj.f_store()
traj2 = load_trajectory(name=self.traj.v_name,
filename=self.filename,
load_all=2,
dynamic_imports=SharedResult)
second_setitem_table = traj2.results.shared_data.table
second_setitem_table[0] = [(100, 'Mehmet Nevvaf', 'TIMUR', 75.5)]
self.assertEqual(second_setitem_table.read(field='id')[0], 100)
self.assertEqual(
second_setitem_table.read(field='name')[0],
compat.tobytes('Mehmet Nevvaf'))
self.assertEqual(
second_setitem_table.read(field='surname')[0],
compat.tobytes('TIMUR'))
self.assertEqual(second_setitem_table.read(field='weight')[0], 75.5)
traj2.f_store()
traj3 = load_trajectory(name=self.traj.v_name,
filename=self.filename,
load_all=2,
dynamic_imports=SharedResult)
third_setitem_table = traj3.results.shared_data.table
self.assertEqual(third_setitem_table.read(field='id')[0], 100)
self.assertEqual(
third_setitem_table.read(field='name')[0],
compat.tobytes('Mehmet Nevvaf'))
self.assertEqual(
third_setitem_table.read(field='surname')[0],
compat.tobytes('TIMUR'))
self.assertEqual(third_setitem_table.read(field='weight')[0], 75.5)
def test_max_depth_loading_and_storing(self):
filename = make_temp_dir('newassignment.hdf5')
traj = Trajectory(filename=filename, overwrite_file=True)
traj.par = Parameter('d1.d2.d3.d4.d5', 55)
traj.f_store(max_depth=4)
traj = load_trajectory(index=-1, filename=filename)
traj.f_load(load_data=2)
self.assertTrue('d3' in traj)
self.assertFalse('d4' in traj)
traj = load_trajectory(index=-1, filename=filename, max_depth=3)
self.assertTrue('d2' in traj)
self.assertFalse('d3' in traj)
traj.par.f_remove(recursive=True)
traj.dpar = Parameter('d1.d2.d3.d4.d5', 123)
traj.dpar.f_store_child('d1', recursive=True, max_depth=3)
traj.dpar.f_remove_child('d1', recursive=True)
self.assertTrue('d1' not in traj)
traj.dpar.f_load_child('d1', recursive=True)
self.assertTrue('d3' in traj)
self.assertTrue('d4' not in traj)
traj.dpar.f_remove_child('d1', recursive=True)
self.assertTrue('d1' not in traj)
traj.dpar.f_load_child('d1', recursive=True, max_depth=2)
self.assertTrue('d2' in traj)
self.assertTrue('d3' not in traj)
traj.dpar = Parameter('l1.l2.l3.l4.l5', 123)
traj.dpar.f_store(recursive=True, max_depth=0)
self.assertFalse(traj.dpar.l1._stored)
traj.dpar.f_store(recursive=True, max_depth=4)
traj.dpar.f_remove()
self.assertTrue('l1' not in traj)
traj.dpar.f_load(recursive=True)
self.assertTrue('l4' in traj)
self.assertTrue('l5' not in traj)
traj.dpar.f_remove()
self.assertTrue('l1' not in traj)
traj.dpar.f_load(recursive=True, max_depth=3)
self.assertTrue('l3' in traj)
self.assertTrue('l4' not in traj)
def test_max_depth_loading_and_storing(self):
filename = make_temp_dir('newassignment.hdf5')
traj = Trajectory(filename=filename, overwrite_file=True)
traj.par.d1 = Parameter('d1.d2.d3.d4.d5', 55)
traj.f_store(max_depth=4)
traj = load_trajectory(index=-1, filename=filename)
traj.f_load(load_data=2)
self.assertTrue('d3' in traj)
self.assertFalse('d4' in traj)
traj = load_trajectory(index=-1, filename=filename, max_depth=3)
self.assertTrue('d2' in traj)
self.assertFalse('d3' in traj)
traj.par.f_remove(recursive=True)
traj.dpar.d1 = Parameter('d1.d2.d3.d4.d5', 123)
traj.dpar.f_store_child('d1', recursive=True, max_depth=3)
traj.dpar.f_remove_child('d1', recursive=True)
self.assertTrue('d1' not in traj)
traj.dpar.f_load_child('d1', recursive=True)
self.assertTrue('d3' in traj)
self.assertTrue('d4' not in traj)
traj.dpar.f_remove_child('d1', recursive=True)
self.assertTrue('d1' not in traj)
traj.dpar.f_load_child('d1', recursive=True, max_depth=2)
self.assertTrue('d2' in traj)
self.assertTrue('d3' not in traj)
traj.dpar.l1 = Parameter('l1.l2.l3.l4.l5', 123)
traj.dpar.f_store(recursive=True, max_depth=0)
self.assertFalse(traj.dpar.l1._stored)
traj.dpar.f_store(recursive=True, max_depth=4)
traj.dpar.f_remove()
self.assertTrue('l1' not in traj)
traj.dpar.f_load(recursive=True)
self.assertTrue('l4' in traj)
self.assertTrue('l5' not in traj)
traj.dpar.f_remove()
self.assertTrue('l1' not in traj)
traj.dpar.f_load(recursive=True, max_depth=3)
self.assertTrue('l3' in traj)
self.assertTrue('l4' not in traj)
def test_hdf5_store_load_monitorresult(self):
traj_name = make_trajectory_name(self)
file_name = make_temp_dir(os.path.join('brian2', 'tests', 'hdf5', 'test_%s.hdf5' % traj_name))
env = Environment(trajectory=traj_name, filename=file_name, log_config=get_log_config(),
dynamic_imports=[Brian2MonitorResult], add_time=False, storage_service=HDF5StorageService)
traj = env.v_trajectory
traj.v_standard_result = Brian2MonitorResult
traj.f_add_result('brian2.single.millivolts_single_a', 10*mvolt, comment='single value a')
traj.f_add_result('brian2.single.millivolts_single_c', 11*mvolt, comment='single value b')
traj.f_add_result('brian2.array.millivolts_array_a', [11, 12]*mvolt, comment='array')
traj.f_add_result('mV1', 42.0*mV)
# results can hold much more than a single data item:
traj.f_add_result('ampere1', 1*mA, 44, test=300*mV, test2=[1,2,3],
test3=np.array([1,2,3])*mA, comment='Result keeping track of many things')
traj.f_add_result('integer', 16)
traj.f_add_result('kHz05', 0.5*kHz)
traj.f_add_result('nested_array', np.array([[6.,7.,8.],[9.,10.,11.]]) * ms)
traj.f_add_result('b2a', np.array([1., 2.]) * mV)
traj.f_store()
traj2 = load_trajectory(filename=file_name, name=traj_name, dynamic_imports=[Brian2MonitorResult], load_data=2)
#traj._logger.error('traj :'+str(traj))
#traj._logger.error('traj2:'+str(traj2))
self.compare_trajectories(traj, traj2)
def test_table_where(self):
the_where_table = self.traj.results.shared_data.table
self.assertTrue(the_where_table is self.shared_table)
the_where_table.create_shared_data(description=MyTable)
with StorageContextManager(self.traj):
row = the_where_table.row
for i in range(10):
row['id'] = i
row['name'] = 'mehmet %d' % i
row['surname'] = 'Timur'
row['weight'] = 65.5 + i
row.append()
the_where_table.flush()
for idx, row in enumerate(the_where_table.iterrows()):
self.assertEqual(row['id'], idx)
self.traj.f_store()
traj2 = load_trajectory(name=self.traj.v_name, filename=self.filename, load_all=2, dynamic_imports=SharedResult)
second_where_table = traj2.results.shared_data.table
with StorageContextManager(traj2):
result = second_where_table.where('(id == 2)&(name == b"mehmet 2")&(surname ==b"Timur")&(weight == 67.5)')
there = False
for row in result:
there = True
self.assertTrue(there)
def test_array_setitem(self):
the_setitem_array = np.zeros((50, 50))
first_setitem_array = self.traj.results.shared_data.array
first_setitem_array.create_shared_data(obj=the_setitem_array)
first_setitem_array[2, 2] = 10
self.assertEqual(first_setitem_array[2, 2], 10)
self.traj.f_store()
traj2 = load_trajectory(name=self.traj.v_name,
filename=self.filename,
load_all=2,
dynamic_imports=SharedResult)
second_setitem_array = traj2.results.shared_data.array
self.assertEqual(second_setitem_array[2, 2], 10)
second_setitem_array[3, 3] = 17
self.assertEqual(second_setitem_array[3, 3], 17)
def import_traj(folder_path,
file_name,
order_face=None,
traj_name='explore_perf'):
print "importing data..."
traj = pypet.load_trajectory(traj_name,
filename=os.path.join(folder_path,
file_name + '.hdf5'),
force=True)
traj.v_auto_load = True
perc_correct = np.array([])
perc_correct_all = []
stat_diff = []
ok_runs = []
for run in traj.f_iter_runs():
perc_correct = np.append(perc_correct,
np.mean(traj.results[run].test_perf))
perc_correct_all.append(traj.results[run].test_perf)
stat_diff.append(traj.results[run].stat_diff)
ok_runs.append(int(run[4:]))
param_traj = traj.f_get_explored_parameters()
param = {}
for k in param_traj:
if k[11:] != 'name':
xplr_values = np.array(param_traj[k].f_get_range())[ok_runs]
if len(np.unique(xplr_values)) > 1:
param[k[11:]] = xplr_values
return perc_correct, np.array(perc_correct_all), np.array(stat_diff), param
def test_array_iter(self):
the_iterrows_array = np.random.randint(0, 100, (100, 100))
first_iterrows_array = self.traj.results.shared_data.array
first_iterrows_array.create_shared_data(obj=the_iterrows_array)
with StorageContextManager(self.traj):
for idx, row in enumerate(first_iterrows_array):
self.assertTrue(np.all(row == the_iterrows_array[idx, :]))
self.assertTrue(np.all(the_iterrows_array == first_iterrows_array.read()))
for idx, row in enumerate(the_iterrows_array):
self.assertTrue(np.all(row == the_iterrows_array[idx, :]))
self.traj.f_store()
traj2 = load_trajectory(name=self.traj.v_name, filename=self.filename, load_all=2, dynamic_imports=SharedResult)
second_iterrows_array = traj2.results.shared_data.array
with StorageContextManager(traj2):
for idx, row in enumerate(second_iterrows_array):
self.assertTrue(np.all(row == the_iterrows_array[idx, :]))
self.assertTrue(np.all(the_iterrows_array == second_iterrows_array.read()))
for idx, row in enumerate(second_iterrows_array):
self.assertTrue(np.all(row == the_iterrows_array[idx, :]))
def test_hdf5_store_load_parameter(self):
traj_name = make_trajectory_name(self)
file_name = make_temp_dir(os.path.join('brian2', 'tests', 'hdf5', 'test_%s.hdf5' % traj_name))
env = Environment(trajectory=traj_name, filename=file_name, log_config=get_log_config(),
dynamic_imports=[Brian2Parameter], add_time=False, storage_service=HDF5StorageService)
traj = env.v_trajectory
traj.v_standard_parameter = Brian2Parameter
traj.f_add_parameter('brian2.single.millivolts', 10*mvolt, comment='single value')
#traj.f_add_parameter('brian2.array.millivolts', [11, 12]*mvolt, comment='array')
#traj.f_add_parameter('mV1', 42.0*mV)
#traj.f_add_parameter('ampere1', 1*mA)
#traj.f_add_parameter('integer', 16)
#traj.f_add_parameter('kHz05', 0.5*kHz)
#traj.f_add_parameter('nested_array', np.array([[6.,7.,8.],[9.,10.,11.]]) * ms)
#traj.f_add_parameter('b2a', np.array([1., 2.]) * mV)
# We also need to check if explorations work with hdf5 store!
#explore_dict = {'ampere1': [1*mA, 2*mA, 3*mA],
# 'integer': [42,43,44],
# 'b2a': [np.array([1., 2.]) * mV, np.array([1., 4.]) * mV,
# np.array([1., 2.]) * mV]}
#traj.f_explore(explore_dict)
traj.f_store()
traj2 = load_trajectory(filename=file_name, name=traj_name, dynamic_imports=[Brian2Parameter],
load_data=2)
self.compare_trajectories(traj, traj2)
def test_table_iterrows(self):
the_iterrows_table = self.traj.results.shared_data.table
self.assertTrue(the_iterrows_table is self.shared_table)
the_iterrows_table.create_shared_data(description=MyTable)
with StorageContextManager(self.traj):
row = the_iterrows_table.row
for i in range(10):
row['id'] = i
row['name'] = 'mehmet %d' % i
row['surname'] = 'Timur'
row['weight'] = 65.5 + i * 1.5
row.append()
the_iterrows_table.flush()
for idx, row in enumerate(the_iterrows_table.iterrows()):
self.assertEqual(row['id'], idx)
self.traj.f_store()
traj2 = load_trajectory(name=self.traj.v_name,
filename=self.filename,
load_all=2,
dynamic_imports=SharedResult)
second_iterrows_table = traj2.results.shared_data.table
with StorageContextManager(traj2):
for idx, row in enumerate(second_iterrows_table.iterrows()):
self.assertEqual(row['id'], idx)
def test_table_getitem(self):
the_getitem_table = self.traj.results.shared_data.table
self.assertTrue(the_getitem_table is self.shared_table)
the_getitem_table.create_shared_data(description=MyTable)
with StorageContextManager(self.traj):
row = the_getitem_table.row
for i in range(10):
row['id'] = i
row['name'] = 'mehmet %d' % i
row['surname'] = 'Timur'
row['weight'] = 65.5 + i * 1.5
row.append()
the_getitem_table.flush()
for idx, row in enumerate(the_getitem_table.iterrows()):
self.assertEqual(row['id'], idx)
self.traj.f_store()
traj2 = load_trajectory(name=self.traj.v_name, filename=self.filename, load_all=2, dynamic_imports=SharedResult)
second_getitem_table = traj2.results.shared_data.table
with StorageContextManager(traj2):
for idx, row in enumerate(second_getitem_table.iterrows()):
self.assertTrue(np.all(second_getitem_table.read()[idx] == second_getitem_table[idx]))
second_getitem_table.append([(30, 'mehmet nevvaf', 'timur', 65.5)])
for idx, row in enumerate(second_getitem_table.iterrows(-1)):
self.assertEqual(row['id'], 30)
self.assertEqual(row['name'], compat.tobytes('mehmet nevvaf'))
self.assertEqual(row['surname'], compat.tobytes('timur'))
self.assertEqual(row['weight'], 65.5)
traj2.f_store()
traj3 = load_trajectory(name=self.traj.v_name, filename=self.filename, load_all=2, dynamic_imports=SharedResult)
third_getitem_table = traj3.results.shared_data.table
with StorageContextManager(traj3):
for idx, row in enumerate(third_getitem_table.iterrows()):
self.assertTrue(np.all(third_getitem_table.read()[idx] == third_getitem_table[idx]))
def test_storing_and_manipulating(self):
filename = make_temp_dir("hdf5manipulation.hdf5")
traj = Trajectory(name=make_trajectory_name(self), filename=filename)
trajname = traj.v_name
thedata = np.zeros((1000, 1000))
res = traj.f_add_result(SharedResult, "shared")
myarray = SharedArray("array", res, trajectory=traj, add_to_parent=True)
mytable = SharedTable("t1", res, trajectory=traj, add_to_parent=True)
mytable2 = SharedTable("t2", res, trajectory=traj, add_to_parent=True)
mytable3 = SharedTable("t3", res, trajectory=traj, add_to_parent=True)
traj.f_store(only_init=True)
myarray.create_shared_data(data=thedata)
mytable.create_shared_data(first_row={"hi": compat.tobytes("hi"), "huhu": np.ones(3)})
mytable2.create_shared_data(description={"ha": pt.StringCol(2, pos=0), "haha": pt.FloatCol(pos=1)})
mytable3.create_shared_data(description={"ha": pt.StringCol(2, pos=0), "haha": pt.FloatCol(pos=1)})
traj.f_store()
newrow = {"ha": "hu", "haha": 4.0}
with self.assertRaises(TypeError):
row = traj.shared.t2.row
with StorageContextManager(traj) as cm:
row = traj.shared.t2.row
for irun in range(11):
for key, val in newrow.items():
row[key] = val
row.append()
traj.shared.t3.flush()
data = myarray.read()
arr = myarray.get_data_node()
self.assertTrue(np.all(data == thedata))
with StorageContextManager(traj) as cm:
myarray[2, 2] = 10
data = myarray.read()
self.assertTrue(data[2, 2] == 10)
self.assertTrue(data[2, 2] == 10)
self.assertFalse(traj.v_storage_service.is_open)
traj = load_trajectory(name=trajname, filename=filename)
traj.f_load(load_data=2)
traj.shared.t2.traj = traj
traj.shared.t1.traj = traj
traj.shared.array.traj = traj
self.assertTrue(traj.shared.t2.nrows == 11, "%s != 11" % str(traj.shared.t2.nrows))
self.assertTrue(traj.shared.t2[0]["ha"] == compat.tobytes("hu"), traj.shared.t2[0]["ha"])
self.assertTrue(traj.shared.t2[1]["ha"] == compat.tobytes("hu"), traj.shared.t2[1]["ha"])
self.assertTrue("huhu" in traj.shared.t1.colnames)
self.assertTrue(traj.shared.array[2, 2] == 10)
def test_loading_and_storing_empty_containers(self):
filename = make_temp_dir('empty_containers.hdf5')
traj = Trajectory(filename=filename, add_time=True)
# traj.f_add_parameter('empty.dict', {})
# traj.f_add_parameter('empty.list', [])
traj.f_add_parameter(ArrayParameter, 'empty.tuple', ())
traj.f_add_parameter(ArrayParameter, 'empty.array',
np.array([], dtype=float))
spsparse_csc = spsp.csc_matrix((2, 10))
spsparse_csr = spsp.csr_matrix((6660, 660))
spsparse_bsr = spsp.bsr_matrix((3330, 2220))
spsparse_dia = spsp.dia_matrix((1230, 1230))
traj.f_add_parameter(SparseParameter, 'empty.csc', spsparse_csc)
traj.f_add_parameter(SparseParameter, 'empty.csr', spsparse_csr)
traj.f_add_parameter(SparseParameter, 'empty.bsr', spsparse_bsr)
traj.f_add_parameter(SparseParameter, 'empty.dia', spsparse_dia)
traj.f_add_result(SparseResult,
'empty.all',
dict={},
list=[],
series=pd.Series(),
frame=pd.DataFrame(),
panel=pd.Panel(),
**traj.par.f_to_dict(short_names=True,
fast_access=True))
traj.f_store()
newtraj = load_trajectory(index=-1, filename=filename)
newtraj.f_load(load_data=2)
epg = newtraj.par.empty
self.assertTrue(type(epg.tuple) is tuple)
self.assertTrue(len(epg.tuple) == 0)
self.assertTrue(type(epg.array) is np.ndarray)
self.assertTrue(epg.array.size == 0)
self.assertTrue(spsp.isspmatrix_csr(epg.csr))
self.assertTrue(epg.csr.size == 0)
self.assertTrue(spsp.isspmatrix_csc(epg.csc))
self.assertTrue(epg.csc.size == 0)
self.assertTrue(spsp.isspmatrix_bsr(epg.bsr))
self.assertTrue(epg.bsr.size == 0)
self.assertTrue(spsp.isspmatrix_dia(epg.dia))
self.assertTrue(epg.dia.size == 0)
self.compare_trajectories(traj, newtraj)
def test_table_setitem(self):
the_setitem_table = self.traj.results.shared_data.table
self.assertTrue(the_setitem_table is self.shared_table)
the_setitem_table.create_shared_data(description=MyTable)
with StorageContextManager(self.traj):
row = the_setitem_table.row
for i in range(10):
row['id'] = i
row['name'] = 'mehmet %d' % i
row['surname'] = 'Timur'
row['weight'] = 65.5 + i * 1.5
row.append()
the_setitem_table.flush()
for idx, row in enumerate(the_setitem_table.iterrows()):
self.assertEqual(row['id'], idx)
self.traj.f_store()
traj2 = load_trajectory(name=self.traj.v_name, filename=self.filename, load_all=2, dynamic_imports=SharedResult)
second_setitem_table = traj2.results.shared_data.table
second_setitem_table[0] = [(100, 'Mehmet Nevvaf', 'TIMUR', 75.5)]
self.assertEqual(second_setitem_table.read(field='id')[0], 100)
self.assertEqual(second_setitem_table.read(field='name')[0], compat.tobytes('Mehmet Nevvaf'))
self.assertEqual(second_setitem_table.read(field='surname')[0], compat.tobytes('TIMUR'))
self.assertEqual(second_setitem_table.read(field='weight')[0], 75.5)
traj2.f_store()
traj3 = load_trajectory(name=self.traj.v_name, filename=self.filename, load_all=2, dynamic_imports=SharedResult)
third_setitem_table = traj3.results.shared_data.table
self.assertEqual(third_setitem_table.read(field='id')[0], 100)
self.assertEqual(third_setitem_table.read(field='name')[0], compat.tobytes('Mehmet Nevvaf'))
self.assertEqual(third_setitem_table.read(field='surname')[0], compat.tobytes('TIMUR'))
self.assertEqual(third_setitem_table.read(field='weight')[0], 75.5)
def test_compacting(self):
filename = make_temp_dir('hdf5compacting.hdf5')
traj = Trajectory(name=make_trajectory_name(self), filename=filename)
trajname = traj.v_name
traj.v_storage_service.complevel = 7
first_row = {'ha': compat.tobytes('hi'), 'haha': np.zeros((3, 3))}
traj.f_store(only_init=True)
traj.f_add_result('My.Tree.Will.Be.Deleted', 42)
traj.f_add_result('Mine.Too.HomeBoy', 42, comment='Don`t cry for me!')
res = traj.f_add_result(SharedResult, 'myres')
res['myres'] = SharedTable()
res['myres'].create_shared_data(first_row=first_row)
with StorageContextManager(traj):
traj.myres
for irun in range(10000):
row = traj.myres.row
for key in first_row:
row[key] = first_row[key]
row.append()
traj.f_store()
del traj
traj = load_trajectory(name=trajname, filename=filename, load_all=2)
with StorageContextManager(traj) as cm:
tb = traj.myres.get_data_node()
ptcompat.remove_rows(tb, 1000, 10000)
cm.flush_store()
self.assertTrue(traj.myres.nrows == 1001)
traj.f_delete_item(traj.My, recursive=True)
traj.f_delete_item(traj.Mine, recursive=True)
size = os.path.getsize(filename)
get_root_logger().info('Filesize is %s' % str(size))
name_wo_ext, ext = os.path.splitext(filename)
backup_file_name = name_wo_ext + '_backup' + ext
code = compact_hdf5_file(filename, keep_backup=True)
if code != 0:
raise RuntimeError('ptrepack fail')
backup_size = os.path.getsize(backup_file_name)
self.assertTrue(backup_size == size)
new_size = os.path.getsize(filename)
get_root_logger().info('New filesize is %s' % str(new_size))
self.assertTrue(new_size < size,
"%s > %s" % (str(new_size), str(size)))
def test_loading_and_storing_empty_containers(self):
filename = make_temp_dir('empty_containers.hdf5')
traj = Trajectory(filename=filename)
# traj.f_add_parameter('empty.dict', {})
# traj.f_add_parameter('empty.list', [])
traj.f_add_parameter(ArrayParameter, 'empty.tuple', ())
traj.f_add_parameter(ArrayParameter, 'empty.array', np.array([], dtype=float))
spsparse_csc = spsp.csc_matrix((2,10))
spsparse_csr = spsp.csr_matrix((6660,660))
spsparse_bsr = spsp.bsr_matrix((3330,2220))
spsparse_dia = spsp.dia_matrix((1230,1230))
traj.f_add_parameter(SparseParameter, 'empty.csc', spsparse_csc)
traj.f_add_parameter(SparseParameter, 'empty.csr', spsparse_csr)
traj.f_add_parameter(SparseParameter, 'empty.bsr', spsparse_bsr)
traj.f_add_parameter(SparseParameter, 'empty.dia', spsparse_dia)
traj.f_add_result(SparseResult, 'empty.all', dict={}, list=[],
series = pd.Series(),
frame = pd.DataFrame(),
panel = pd.Panel(),
**traj.par.f_to_dict(short_names=True, fast_access=True))
traj.f_store()
newtraj = load_trajectory(index=-1, filename=filename)
newtraj.f_load(load_data=2)
epg = newtraj.par.empty
self.assertTrue(type(epg.tuple) is tuple)
self.assertTrue(len(epg.tuple) == 0)
self.assertTrue(type(epg.array) is np.ndarray)
self.assertTrue(epg.array.size == 0)
self.assertTrue(spsp.isspmatrix_csr(epg.csr))
self.assertTrue(epg.csr.size == 0)
self.assertTrue(spsp.isspmatrix_csc(epg.csc))
self.assertTrue(epg.csc.size == 0)
self.assertTrue(spsp.isspmatrix_bsr(epg.bsr))
self.assertTrue(epg.bsr.size == 0)
self.assertTrue(spsp.isspmatrix_dia(epg.dia))
self.assertTrue(epg.dia.size == 0)
self.compare_trajectories(traj, newtraj)
def test_compacting(self):
filename = make_temp_dir("hdf5compacting.hdf5")
traj = Trajectory(name=make_trajectory_name(self), filename=filename)
trajname = traj.v_name
traj.v_storage_service.complevel = 7
first_row = {"ha": compat.tobytes("hi"), "haha": np.zeros((3, 3))}
traj.f_store(only_init=True)
res1 = traj.f_add_result("My.Tree.Will.Be.Deleted", 42)
res2 = traj.f_add_result("Mine.Too.HomeBoy", 42, comment="Don`t cry for me!")
res = traj.f_add_result(SharedResult, "myres")
res["myres"] = SharedTable()
res["myres"].create_shared_data(first_row=first_row)
with StorageContextManager(traj):
tab = traj.myres
for irun in range(10000):
row = traj.myres.row
for key in first_row:
row[key] = first_row[key]
row.append()
traj.f_store()
del traj
traj = load_trajectory(name=trajname, filename=filename, load_all=2)
with StorageContextManager(traj) as cm:
tb = traj.myres.get_data_node()
ptcompat.remove_rows(tb, 1000, 10000)
cm.f_flush_store()
self.assertTrue(traj.myres.nrows == 1001)
traj.f_delete_item(traj.My, recursive=True)
traj.f_delete_item(traj.Mine, recursive=True)
size = os.path.getsize(filename)
get_root_logger().info("Filesize is %s" % str(size))
name_wo_ext, ext = os.path.splitext(filename)
backup_file_name = name_wo_ext + "_backup" + ext
code = compact_hdf5_file(filename, keep_backup=True)
if code != 0:
raise RuntimeError("ptrepack fail")
backup_size = os.path.getsize(backup_file_name)
self.assertTrue(backup_size == size)
new_size = os.path.getsize(filename)
get_root_logger().info("New filesize is %s" % str(new_size))
self.assertTrue(new_size < size, "%s > %s" % (str(new_size), str(size)))
def test_table_flush(self):
the_flush_table = self.traj.results.shared_data.table
self.assertTrue(the_flush_table is self.shared_table)
the_flush_table.create_shared_data(description=MyTable)
with StorageContextManager(self.traj):
row = the_flush_table.row
for i in range(10):
row['id'] = i
row['name'] = 'mehmet %d' % i
row['surname'] = 'Timur'
row['weight'] = 65.5 + i
row.append()
the_flush_table.flush()
for idx, row in enumerate(the_flush_table.iterrows()):
self.assertEqual(row['id'], idx)
self.assertEqual(row['name'], compat.tobytes('mehmet %d' % idx))
self.assertEqual(row['surname'], compat.tobytes('Timur'))
self.assertEqual(row['weight'], 65.5+idx)
self.traj.f_store()
traj2 = load_trajectory(name=self.traj.v_name, filename=self.filename, load_all=2, dynamic_imports=SharedResult)
second_flush_table = traj2.results.shared_data.table
with StorageContextManager(traj2):
for idx, row in enumerate(second_flush_table.iterrows()):
self.assertEqual(row['id'], idx)
self.assertEqual(row['name'], compat.tobytes('mehmet %d' % idx))
self.assertEqual(row['surname'], compat.tobytes('Timur'))
self.assertEqual(row['weight'], 65.5+idx)
row = second_flush_table.row
for i in range(10, 11):
row['id'] = i
row['name'] = 'mehmet %d' % i
row['surname'] = 'Timur'
row['weight'] = 65.5 + i
row.append()
second_flush_table.flush()
for idx, row in enumerate(second_flush_table.iterrows()):
self.assertEqual(row['id'], idx)
self.assertEqual(row['name'], compat.tobytes('mehmet %d' % idx))
self.assertEqual(row['surname'], compat.tobytes('Timur'))
self.assertEqual(row['weight'], 65.5+idx)
def test_storing_and_loading_groups(self):
filename = make_temp_dir('grpgrp.hdf5')
traj = Trajectory(name='traj', add_time=True, filename=filename)
res = traj.f_add_result('aaa.bbb.ccc.iii', 42, 43, comment=7777 * '6')
traj.ccc.v_annotations['gg'] = 4
res = traj.f_add_result('aaa.ddd.eee.jjj', 42, 43, comment=777 * '6')
traj.ccc.v_annotations['j'] = 'osajdsojds'
traj.f_store(only_init=True)
traj.f_store_item('aaa', recursive=True)
newtraj = load_trajectory(traj.v_name, filename=filename, load_all=2)
self.compare_trajectories(traj, newtraj)
traj.iii.f_set(55)
self.assertFalse(results_equal(traj.iii, newtraj.iii))
traj.aaa.f_store(recursive=True, store_data=3)
newtraj.bbb.f_load(recursive=True, load_data=3)
self.compare_trajectories(traj, newtraj)
traj.ccc.v_annotations['gg'] = 5
traj.f_load(load_data=3)
self.assertTrue(traj.ccc.v_annotations['gg'] == 4)
traj.ccc.v_annotations['gg'] = 5
traj.f_store(store_data=3)
newtraj.f_load(load_data=2)
self.assertTrue(newtraj.ccc.v_annotations['gg'] == 4)
newtraj.f_load(load_data=3)
self.assertTrue(newtraj.ccc.v_annotations['gg'] == 5)
traj.ccc.f_add_link('link', res)
traj.f_store_item(traj.ccc, store_data=3, with_links=False)
newtraj.f_load(load_data=3)
self.assertTrue('link' not in newtraj.ccc)
traj.f_store_item(traj.ccc,
store_data=3,
with_links=True,
recursive=True)
newtraj.f_load_item(newtraj.ccc, with_links=False, recursive=True)
self.assertTrue('link' not in newtraj.ccc)
newtraj.f_load_item(newtraj.ccc, recursive=True)
self.assertTrue('link' in newtraj.ccc)
def test_no_run_information_loading(self):
filename = make_temp_dir('testnoruninfo.hdf5')
traj = Trajectory(name='TestDelete',
filename=filename,
add_time=True)
length = 100000
traj.par.x = Parameter('', 42)
traj.f_explore({'x': range(length)})
traj.f_store()
traj = load_trajectory(index=-1, filename=filename, with_run_information=False)
self.assertEqual(len(traj), length)
self.assertEqual(len(traj._run_information), 1)
def test_no_run_information_loading(self):
filename = make_temp_dir('testnoruninfo.hdf5')
traj = Trajectory(name='TestDelete',
filename=filename)
length = 100000
traj.v_lazy_adding = True
traj.par.x = 42
traj.f_explore({'x': range(length)})
traj.f_store()
traj = load_trajectory(index=-1, filename=filename, with_run_information=False)
self.assertEqual(len(traj), length)
self.assertEqual(len(traj._run_information), 1)
def test_hdf5_store_load_result(self):
traj_name = make_trajectory_name(self)
file_name = make_temp_dir(
os.path.join('brian2', 'tests', 'hdf5',
'test_%s.hdf5' % traj_name))
env = Environment(trajectory=traj_name,
filename=file_name,
log_config=get_log_config(),
dynamic_imports=[Brian2Result],
add_time=False,
storage_service=HDF5StorageService)
traj = env.v_trajectory
traj.v_standard_result = Brian2Result
traj.f_add_result('brian2.single.millivolts_single_a',
10 * mvolt,
comment='single value a')
traj.f_add_result('brian2.single.millivolts_single_c',
11 * mvolt,
comment='single value b')
traj.f_add_result('brian2.array.millivolts_array_a', [11, 12] * mvolt,
comment='array')
traj.f_add_result('mV1', 42.0 * mV)
# results can hold much more than a single data item:
traj.f_add_result('ampere1',
1 * mA,
44,
test=300 * mV,
test2=[1, 2, 3],
test3=np.array([1, 2, 3]) * mA,
comment='Result keeping track of many things')
traj.f_add_result('integer', 16)
traj.f_add_result('kHz05', 0.5 * kHz)
traj.f_add_result('nested_array',
np.array([[6., 7., 8.], [9., 10., 11.]]) * ms)
traj.f_add_result('b2a', np.array([1., 2.]) * mV)
traj.f_add_result('nounit',
Quantity(np.array([[6., 7., 8.], [9., 10., 11.]])))
traj.f_store()
traj2 = load_trajectory(filename=file_name,
name=traj_name,
dynamic_imports=[Brian2Result],
load_data=2)
self.compare_trajectories(traj, traj2)
def test_array_getitem(self):
the_getitem_array = np.array(range(100))
first_getitem_array = self.traj.results.shared_data.array
first_getitem_array.create_shared_data(obj=the_getitem_array)
for k in range(len(the_getitem_array)):
self.assertEqual(the_getitem_array[k], first_getitem_array[k])
self.traj.f_store()
traj2 = load_trajectory(name=self.traj.v_name, filename=self.filename, load_all=2, dynamic_imports=SharedResult)
for j in range(len(the_getitem_array)):
self.assertEqual(the_getitem_array[j], traj2.results.shared_data.array[j])
def test_storing_and_loading_groups(self):
filename = make_temp_dir('grpgrp.hdf5')
traj = Trajectory(name='traj', add_time=True, filename=filename)
res=traj.f_add_result('aaa.bbb.ccc.iii', 42, 43, comment=7777 * '6')
traj.ccc.v_annotations['gg']=4
res=traj.f_add_result('aaa.ddd.eee.jjj', 42, 43, comment=777 * '6')
traj.ccc.v_annotations['j'] = 'osajdsojds'
traj.f_store(only_init=True)
traj.f_store_item('aaa', recursive=True)
newtraj = load_trajectory(traj.v_name, filename=filename, load_all=2)
self.compare_trajectories(traj, newtraj)
traj.iii.f_set(55)
self.assertFalse(results_equal(traj.iii, newtraj.iii))
traj.aaa.f_store(recursive=True, store_data=3)
newtraj.bbb.f_load(recursive=True, load_data=3)
self.compare_trajectories(traj, newtraj)
traj.ccc.v_annotations['gg'] = 5
traj.f_load(load_data=3)
self.assertTrue(traj.ccc.v_annotations['gg'] == 4)
traj.ccc.v_annotations['gg'] = 5
traj.f_store(store_data=3)
newtraj.f_load(load_data=2)
self.assertTrue(newtraj.ccc.v_annotations['gg'] == 4)
newtraj.f_load(load_data=3)
self.assertTrue(newtraj.ccc.v_annotations['gg'] == 5)
traj.ccc.f_add_link('link', res)
traj.f_store_item(traj.ccc, store_data=3, with_links=False)
newtraj.f_load(load_data=3)
self.assertTrue('link' not in newtraj.ccc)
traj.f_store_item(traj.ccc, store_data=3, with_links=True, recursive=True)
newtraj.f_load_item(newtraj.ccc, with_links=False, recursive=True)
self.assertTrue('link' not in newtraj.ccc)
newtraj.f_load_item(newtraj.ccc, recursive=True)
self.assertTrue('link' in newtraj.ccc)
def test_array_read(self):
the_reading_array = np.ones((100, 100)) * 4
first_reading_array = self.traj.results.shared_data.array
self.assertTrue(first_reading_array is self.shared_array)
first_reading_array.create_shared_data(obj=the_reading_array)
self.traj.f_store()
traj2 = load_trajectory(name=self.traj.v_name, filename=self.filename, load_all=2, dynamic_imports=SharedResult)
second_reading_array = traj2.shared_data.array.read()
self.assertTrue(np.all(the_reading_array == second_reading_array),
'%s != %s' % (str(the_reading_array), str(second_reading_array)))
def test_array_len(self):
the_len_array = np.ones((100, 100))
first_len_array = self.traj.results.shared_data.array
self.assertTrue(first_len_array is self.shared_array)
first_len_array.create_shared_data(obj=the_len_array)
self.assertEqual(len(first_len_array), 100)
self.traj.f_store()
traj2 = load_trajectory(name=self.traj.v_name, filename=self.filename, load_all=2, dynamic_imports=SharedResult)
second_len_array = traj2.results.shared_data.array
self.assertEqual(len(second_len_array), 100)
def test_array_getenum(self):
the_getenum_array = np.array(range(100))
first_getenum_array = self.traj.results.shared_data.array
first_getenum_array.create_shared_data(obj=the_getenum_array)
with self.assertRaises(TypeError):
first_getenum_array.get_enum()
self.traj.f_store()
traj2 = load_trajectory(name=self.traj.v_name, filename=self.filename, load_all=2, dynamic_imports=SharedResult)
second_enum_array = traj2.results.shared_data.array
with self.assertRaises(TypeError):
second_enum_array.get_enum()
def test_array_getitem(self):
the_getitem_array = np.array(range(100))
first_getitem_array = self.traj.results.shared_data.array
first_getitem_array.create_shared_data(obj=the_getitem_array)
for k in range(len(the_getitem_array)):
self.assertEqual(the_getitem_array[k], first_getitem_array[k])
self.traj.f_store()
traj2 = load_trajectory(name=self.traj.v_name,
filename=self.filename,
load_all=2,
dynamic_imports=SharedResult)
for j in range(len(the_getitem_array)):
self.assertEqual(the_getitem_array[j],
traj2.results.shared_data.array[j])
def test_array_getenum(self):
the_getenum_array = np.array(range(100))
first_getenum_array = self.traj.results.shared_data.array
first_getenum_array.create_shared_data(obj=the_getenum_array)
with self.assertRaises(TypeError):
first_getenum_array.get_enum()
self.traj.f_store()
traj2 = load_trajectory(name=self.traj.v_name,
filename=self.filename,
load_all=2,
dynamic_imports=SharedResult)
second_enum_array = traj2.results.shared_data.array
with self.assertRaises(TypeError):
second_enum_array.get_enum()
def test_array_read(self):
the_reading_array = np.ones((100, 100)) * 4
first_reading_array = self.traj.results.shared_data.array
self.assertTrue(first_reading_array is self.shared_array)
first_reading_array.create_shared_data(obj=the_reading_array)
self.traj.f_store()
traj2 = load_trajectory(name=self.traj.v_name,
filename=self.filename,
load_all=2,
dynamic_imports=SharedResult)
second_reading_array = traj2.shared_data.array.read()
self.assertTrue(
np.all(the_reading_array == second_reading_array),
'%s != %s' % (str(the_reading_array), str(second_reading_array)))
def test_array_len(self):
the_len_array = np.ones((100, 100))
first_len_array = self.traj.results.shared_data.array
self.assertTrue(first_len_array is self.shared_array)
first_len_array.create_shared_data(obj=the_len_array)
self.assertEqual(len(first_len_array), 100)
self.traj.f_store()
traj2 = load_trajectory(name=self.traj.v_name,
filename=self.filename,
load_all=2,
dynamic_imports=SharedResult)
second_len_array = traj2.results.shared_data.array
self.assertEqual(len(second_len_array), 100)
def test_hdf5_store_load_parameter(self):
traj_name = make_trajectory_name(self)
file_name = make_temp_dir(
os.path.join('brian2', 'tests', 'hdf5',
'test_%s.hdf5' % traj_name))
env = Environment(trajectory=traj_name,
filename=file_name,
log_config=get_log_config(),
dynamic_imports=[Brian2Parameter],
add_time=False,
storage_service=HDF5StorageService)
traj = env.v_trajectory
traj.v_standard_parameter = Brian2Parameter
traj.f_add_parameter('brian2.single.millivolts',
10 * mvolt,
comment='single value')
#traj.f_add_parameter('brian2.array.millivolts', [11, 12]*mvolt, comment='array')
#traj.f_add_parameter('mV1', 42.0*mV)
#traj.f_add_parameter('ampere1', 1*mA)
#traj.f_add_parameter('integer', 16)
#traj.f_add_parameter('kHz05', 0.5*kHz)
#traj.f_add_parameter('nested_array', np.array([[6.,7.,8.],[9.,10.,11.]]) * ms)
#traj.f_add_parameter('b2a', np.array([1., 2.]) * mV)
# We also need to check if explorations work with hdf5 store!
#explore_dict = {'ampere1': [1*mA, 2*mA, 3*mA],
# 'integer': [42,43,44],
# 'b2a': [np.array([1., 2.]) * mV, np.array([1., 4.]) * mV,
# np.array([1., 2.]) * mV]}
#traj.f_explore(explore_dict)
traj.f_store()
traj2 = load_trajectory(filename=file_name,
name=traj_name,
dynamic_imports=[Brian2Parameter],
load_data=2)
self.compare_trajectories(traj, traj2)
def test_array_setitem(self):
the_setitem_array = np.zeros((50, 50))
first_setitem_array = self.traj.results.shared_data.array
first_setitem_array.create_shared_data(obj=the_setitem_array)
first_setitem_array[2, 2] = 10
self.assertEqual(first_setitem_array[2, 2], 10)
self.traj.f_store()
traj2 = load_trajectory(name=self.traj.v_name, filename=self.filename, load_all=2, dynamic_imports=SharedResult)
second_setitem_array = traj2.results.shared_data.array
self.assertEqual(second_setitem_array[2, 2], 10)
second_setitem_array[3, 3] = 17
self.assertEqual(second_setitem_array[3, 3], 17)
def test_array_iterrows(self):
the_iterrows_array = np.random.randint(0, 100, (100, 100))
first_iterrows_array = self.traj.results.shared_data.array
first_iterrows_array.create_shared_data(obj=the_iterrows_array)
with StorageContextManager(self.traj):
for idx, row in enumerate(first_iterrows_array.iterrows()):
self.assertTrue(np.all(row == the_iterrows_array[idx, :]))
self.traj.f_store()
traj2 = load_trajectory(name=self.traj.v_name,
filename=self.filename,
load_all=2,
dynamic_imports=SharedResult)
second_iterrows_array = traj2.results.shared_data.array
with StorageContextManager(traj2):
for idx, row in enumerate(second_iterrows_array.iterrows()):
self.assertTrue(np.all(row == the_iterrows_array[idx, :]))
def import_traj(folder_path, file_name, order_face=None, traj_name='explore_perf'):
print "importing data..."
traj = pypet.load_trajectory(traj_name, filename=os.path.join(folder_path, file_name+'.hdf5'), force=True)
traj.v_auto_load = True
perc_correct = np.array([])
perc_correct_all = []
stat_diff = []
ok_runs = []
for run in traj.f_iter_runs():
perc_correct = np.append(perc_correct, np.mean(traj.results[run].test_perf))
perc_correct_all.append(traj.results[run].test_perf)
stat_diff.append(traj.results[run].stat_diff)
ok_runs.append(int(run[4:]))
param_traj = traj.f_get_explored_parameters()
param = {}
for k in param_traj:
if k[11:] != 'name':
xplr_values = np.array(param_traj[k].f_get_range())[ok_runs]
if len(np.unique(xplr_values)) > 1:
param[k[11:]] = xplr_values
return perc_correct, np.array(perc_correct_all), np.array(stat_diff), param
def test_new_assignment_method(self):
filename = make_temp_dir('newassignment.hdf5')
traj = Trajectory(filename=filename)
traj.v_lazy_adding = True
comment = 'A number'
traj.par.x = 44, comment
self.assertTrue(traj.f_get('x').v_comment == comment)
traj.par.iamgroup = a_new_group
self.assertTrue(isinstance(traj.iamgroup, ParameterGroup))
traj.v_lazy_adding = False
traj.x = 45
self.assertTrue(traj.par.f_get('x').f_get() == 45)
self.assertTrue(isinstance(traj.f_get('x'), Parameter))
traj.f = Parameter('lll', 444, 'lll')
self.assertTrue(traj.f_get('f').v_name == 'f')
traj.v_lazy_adding = True
traj.res.k = 22, 'Hi'
self.assertTrue(isinstance(traj.f_get('k'), Result))
self.assertTrue(traj.f_get('k')[1] == 'Hi')
with self.assertRaises(AttributeError):
traj.res.k = 33, 'adsd'
conf = traj.conf
with self.assertRaises(AttributeError):
conf = traj.conf.jjjj
traj.f_set_properties(fast_access=True)
traj.crun = 43, 'JJJ'
self.assertTrue(traj.run_A[0] == 43)
with self.assertRaises(AttributeError):
traj.f_set_properties(j=7)
with self.assertRaises(AttributeError):
traj.f_set_properties(depth=7)
traj.hui = (('444', 'kkkk',), 'l')
self.assertTrue(traj.f_get('hui')[1] == 'l')
with self.assertRaises(AttributeError):
traj.hui = ('445', 'kkkk',)
traj.f_get('hui').f_set(('445', 'kkkk',))
self.assertTrue(traj.f_get('hui')[1] == 'l')
self.assertTrue(traj.hui[0] == ('445', 'kkkk',))
traj.f_add_link('klkikju', traj.par) # for shizzle
traj.meee = Result('h', 43, hui = 3213, comment='du')
self.assertTrue(traj.meee.h.h == 43)
with self.assertRaises(TypeError):
traj.par.mu = NNGroupNode('jj', comment='mi')
with self.assertRaises(TypeError):
traj.res.mu = NNGroupNode('jj', comment='mi')
with self.assertRaises(TypeError):
traj.conf.mu = NNGroupNode('jj', comment='mi')
with self.assertRaises(TypeError):
traj.dpar.mu = NNGroupNode('jj', comment='mi')
with self.assertRaises(TypeError):
traj.par.mu = ResultGroup('jj', comment='mi')
with self.assertRaises(TypeError):
traj.dpar.mu = ResultGroup('jj', comment='mi')
with self.assertRaises(TypeError):
traj.conf.mu = ResultGroup('jj', comment='mi')
with self.assertRaises(TypeError):
traj.mu = ResultGroup('jj', comment='mi')
with self.assertRaises(TypeError):
traj.par.mu = ConfigGroup('jj', comment='mi')
with self.assertRaises(TypeError):
traj.dpar.mu = ConfigGroup('jj', comment='mi')
with self.assertRaises(TypeError):
traj.res.mu = ConfigGroup('jj', comment='mi')
with self.assertRaises(TypeError):
traj.mu = ConfigGroup('jj', comment='mi')
with self.assertRaises(TypeError):
traj.par.mu = DerivedParameterGroup('jj', comment='mi')
with self.assertRaises(TypeError):
traj.conf.mu = DerivedParameterGroup('jj', comment='mi')
with self.assertRaises(TypeError):
traj.res.mu = DerivedParameterGroup('jj', comment='mi')
with self.assertRaises(TypeError):
traj.mu = DerivedParameterGroup('jj', comment='mi')
with self.assertRaises(TypeError):
traj.dpar.mu = ParameterGroup('jj', comment='mi')
with self.assertRaises(TypeError):
traj.res.mu = ParameterGroup('jj', comment='mi')
with self.assertRaises(TypeError):
traj.conf.mu = ParameterGroup('jj', comment='mi')
with self.assertRaises(TypeError):
traj.mu = ParameterGroup('jj', comment='mi')
traj.par.mu = ParameterGroup('jj', comment='mi')
traj.res.mus = ResultGroup('jj', comment='mi')
traj.mu = NNGroupNode('jj')
cg = ConfigGroup('a.g')
traj.conf.a = cg
self.assertTrue(traj.f_get('conf.a.a.g', shortcuts=False) is cg)
dg = DerivedParameterGroup('ttt')
traj.dpar.ttt = dg
self.assertTrue(traj.f_get('dpar.ttt', shortcuts=False) is dg)
traj.mylink = traj.par
self.assertTrue(traj.mylink is traj.par)
traj.vvv = NNGroupNode('', comment='kkk')
self.assertTrue(traj.vvv.v_full_name == 'vvv')
self.assertTrue(traj.par.mu.v_name == 'mu')
traj.rrr = MyParamGroup('ff')
traj.par.g = MyParamGroup('')
pg = traj.f_add_parameter_group(comment='gg', full_name='me')
self.assertTrue(traj.par.me is pg)
traj.f_store()
traj = load_trajectory(index=-1, filename=filename, dynamic_imports=MyParamGroup)
self.assertTrue(isinstance(traj.rrr, NNGroupNode))
self.assertTrue(isinstance(traj.rrr.ff, MyParamGroup))
self.assertTrue(isinstance(traj.par.g, MyParamGroup))
traj.par = Parameter('hiho', 42, comment='you')
traj.par = Parameter('g1.g2.g3.g4.g5', 43)
self.assertTrue(traj.hiho == 42)
self.assertTrue(isinstance(traj.par.g1, ParameterGroup ))
self.assertTrue(isinstance(traj.par.g3, ParameterGroup ))
self.assertTrue(traj.g3.g5 == 43)
def _batch_run(dir='unnamed',
batch_id='template',
space=None,
save_data_in_hdf5=False,
single_method=single_run,
process_method=null_processing,
post_process_method=None,
final_process_method=None,
multiprocessing=True,
resumable=True,
overwrite=False,
sim_config=None,
params=None,
config=None,
post_kwargs={},
run_kwargs={}):
saved_args = locals()
# print(locals())
traj_name = f'{batch_id}_traj'
parent_dir_path = f'{BatchRunFolder}/{dir}'
dir_path = os.path.join(parent_dir_path, batch_id)
plot_path = os.path.join(dir_path, f'{batch_id}.pdf')
data_path = os.path.join(dir_path, f'{batch_id}.csv')
filename = f'{dir_path}/{batch_id}.hdf5'
build_new = True
if os.path.exists(parent_dir_path) and os.path.exists(
dir_path) and overwrite == False:
build_new = False
try:
print('Trying to resume existing trajectory')
env = Environment(continuable=True)
env.resume(trajectory_name=traj_name, resume_folder=dir_path)
print('Resumed existing trajectory')
build_new = False
except:
try:
print('Trying to load existing trajectory')
traj = load_trajectory(filename=filename,
name=traj_name,
load_all=0)
env = Environment(trajectory=traj)
traj.f_load(index=None, load_parameters=2, load_results=0)
traj.f_expand(space)
print('Loaded existing trajectory')
build_new = False
except:
print(
'Neither of resuming or expanding of existing trajectory worked'
)
if build_new:
if multiprocessing:
multiproc = True
resumable = False
wrap_mode = pypetconstants.WRAP_MODE_QUEUE
else:
multiproc = False
resumable = True
wrap_mode = pypetconstants.WRAP_MODE_LOCK
# try:
print('Trying to create novel environment')
env = Environment(
trajectory=traj_name,
filename=filename,
file_title=batch_id,
comment=f'{batch_id} batch run!',
large_overview_tables=True,
overwrite_file=True,
resumable=False,
resume_folder=dir_path,
multiproc=multiproc,
ncores=4,
use_pool=
True, # Our runs are inexpensive we can get rid of overhead by using a pool
freeze_input=
True, # We can avoid some overhead by freezing the input to the pool
wrap_mode=wrap_mode,
graceful_exit=True)
traj = env.traj
print('Created novel environment')
fly_params, env_params, sim_params = sim_config[
'fly_params'], sim_config['env_params'], sim_config['sim_params']
if all(v is not None for v in [sim_params, env_params, fly_params]):
traj = load_default_configuration(traj,
sim_params=sim_params,
env_params=env_params,
fly_params=fly_params)
elif params is not None:
for p in params:
traj.f_apar(p, 0.0)
if config is not None:
for k, v in config.items():
traj.f_aconf(k, v)
traj.f_aconf('parent_dir_path',
parent_dir_path,
comment='The parent directory')
traj.f_aconf('dir_path',
dir_path,
comment='The directory path for saving data')
traj.f_aconf('plot_path',
plot_path,
comment='The file path for saving plot')
traj.f_aconf('data_path',
data_path,
comment='The file path for saving data')
traj.f_aconf('dataset_path',
f'{dir_path}/{batch_id}',
comment='The directory path for saving datasets')
traj.f_explore(space)
# except:
# raise ValueError(f'Failed to perform batch run {batch_id}')
if post_process_method is not None:
env.add_postprocessing(post_process_method, **post_kwargs)
env.run(single_method,
process_method,
save_data_in_hdf5=save_data_in_hdf5,
save_to=dir_path,
common_folder=batch_id,
**run_kwargs)
env.disable_logging()
print('Batch run complete')
if final_process_method is not None:
return final_process_method(env.traj)
def test_new_assignment_method(self):
filename = make_temp_dir('newassignment.hdf5')
traj = Trajectory(filename=filename, add_time=True)
comment = 'A number'
traj.par.x = Parameter('', 44, comment)
self.assertTrue(traj.f_get('x').v_comment == comment)
traj.x = 45
self.assertTrue(traj.par.f_get('x').f_get() == 45)
self.assertTrue(isinstance(traj.f_get('x'), Parameter))
with self.assertRaises(AttributeError):
traj.f = Parameter('lll', 444, 'lll')
traj.f = Parameter('', 444, 'lll')
self.assertTrue(traj.f_get('f').v_name == 'f')
conf = traj.conf
with self.assertRaises(AttributeError):
conf = traj.conf.jjjj
traj.f_set_properties(fast_access=True)
traj.crun = Result('', k=43, m='JJJ')
self.assertTrue(traj.run_A['k'] == 43)
with self.assertRaises(AttributeError):
traj.f_set_properties(j=7)
with self.assertRaises(AttributeError):
traj.f_set_properties(depth=7)
traj.hui = Result('hui', ('444', 'kkkk',), 'l')
self.assertTrue(traj.f_get('hui')[1] == 'l')
traj.f_get('hui').f_set(('445', 'kkkk',))
self.assertTrue(traj.f_get('hui')[1] == 'l')
self.assertTrue(traj.hui[0] == ('445', 'kkkk',))
traj.f_add_link('klkikju', traj.par) # for shizzle
traj.meee = Result('meee.h', 43, hui = 3213, comment='du')
self.assertTrue(traj.meee.h.h == 43)
with self.assertRaises(TypeError):
traj.par.jj = NNGroupNode('jj', comment='mi')
with self.assertRaises(TypeError):
traj.res.jj = NNGroupNode('jj', comment='mi')
with self.assertRaises(TypeError):
traj.conf.jj = NNGroupNode('jj', comment='mi')
with self.assertRaises(TypeError):
traj.dpar.jj = NNGroupNode('jj', comment='mi')
with self.assertRaises(TypeError):
traj.par.jj = ResultGroup('jj', comment='mi')
with self.assertRaises(TypeError):
traj.dpar.jj = ResultGroup('jj', comment='mi')
with self.assertRaises(TypeError):
traj.conf.jj = ResultGroup('jj', comment='mi')
with self.assertRaises(TypeError):
traj.jj = ResultGroup('jj', comment='mi')
with self.assertRaises(TypeError):
traj.par.jj = ConfigGroup('jj', comment='mi')
with self.assertRaises(TypeError):
traj.dpar.jj = ConfigGroup('jj', comment='mi')
with self.assertRaises(TypeError):
traj.res.jj = ConfigGroup('jj', comment='mi')
with self.assertRaises(TypeError):
traj.jj = ConfigGroup('jj', comment='mi')
with self.assertRaises(TypeError):
traj.par.jj = DerivedParameterGroup('jj', comment='mi')
with self.assertRaises(TypeError):
traj.conf.jj = DerivedParameterGroup('jj', comment='mi')
with self.assertRaises(TypeError):
traj.res.jj = DerivedParameterGroup('jj', comment='mi')
with self.assertRaises(TypeError):
traj.jj = DerivedParameterGroup('jj', comment='mi')
with self.assertRaises(TypeError):
traj.dpar.jj = ParameterGroup('jj', comment='mi')
with self.assertRaises(TypeError):
traj.res.jj = ParameterGroup('jj', comment='mi')
with self.assertRaises(TypeError):
traj.conf.jj = ParameterGroup('jj', comment='mi')
with self.assertRaises(TypeError):
traj.jj = ParameterGroup('jj', comment='mi')
traj.par.jj = ParameterGroup('jj', comment='mi')
traj.res.jj = ResultGroup('jj', comment='mi')
traj.jj = NNGroupNode('jj')
cg = ConfigGroup('a.g')
traj.conf.a = cg
self.assertTrue(traj.f_get('conf.a.g', shortcuts=False) is cg)
dg = DerivedParameterGroup('ttt')
traj.dpar.ttt = dg
self.assertTrue(traj.f_get('dpar.ttt', shortcuts=False) is dg)
traj.mylink = traj.par
self.assertTrue(traj.mylink is traj.par)
traj.vvv = NNGroupNode('', comment='kkk')
self.assertTrue(traj.vvv.v_full_name == 'vvv')
self.assertTrue(traj.par.jj.v_name == 'jj')
traj.ff = MyParamGroup('ff')
traj.par.g = MyParamGroup('')
pg = traj.f_add_parameter_group(comment='gg', full_name='me')
self.assertTrue(traj.par.me is pg)
traj.f_store()
traj = load_trajectory(index=-1, filename=filename, dynamic_imports=MyParamGroup)
self.assertTrue(isinstance(traj.ff, MyParamGroup))
self.assertTrue(isinstance(traj.par.g, MyParamGroup))
traj.par.hiho = Parameter('hiho', 42, comment='you')
traj.par.g1 = Parameter('g1.g2.g3.g4.g5', 43)
self.assertTrue(traj.hiho == 42)
self.assertTrue(isinstance(traj.par.g1, ParameterGroup ))
self.assertTrue(isinstance(traj.par.g3, ParameterGroup ))
self.assertTrue(traj.g3.g5 == 43)
def test_load():
newtraj = load_trajectory(index=-1, filename=filename, load_data=1)
def test_table_append(self):
the_append_table = self.traj.results.shared_data.table
self.assertTrue(the_append_table is self.shared_table)
the_append_table.create_shared_data(description=MyTable)
with StorageContextManager(self.traj):
row = the_append_table.row
for i in range(15):
row['id'] = i * 2
row['name'] = 'name %d' % i
row['surname'] = '%d surname' % i
row['weight'] = (i * 0.5 + 50.0)
row.append()
the_append_table.flush()
for idx, row in enumerate(the_append_table.iterrows()):
self.assertEqual(row['id'], idx * 2)
self.assertEqual(row['name'], compat.tobytes('name %d' % idx))
self.assertEqual(row['surname'],
compat.tobytes('%d surname' % idx))
self.assertEqual(row['weight'], idx * 0.5 + 50.0)
self.traj.f_store()
traj2 = load_trajectory(name=self.traj.v_name,
filename=self.filename,
load_all=2,
dynamic_imports=SharedResult)
second_append_table = traj2.results.shared_data.table
with StorageContextManager(traj2):
for idx, row in enumerate(second_append_table.iterrows()):
self.assertEqual(row['id'], idx * 2)
self.assertEqual(row['name'], compat.tobytes('name %d' % idx))
self.assertEqual(row['surname'],
compat.tobytes('%d surname' % idx))
self.assertEqual(row['weight'], idx * 0.5 + 50.0)
second_append_table.append([(30, 'mehmet', 'timur', 65.5)])
self.assertEqual(second_append_table.read(field='id')[-1], 30)
self.assertEqual(
second_append_table.read(field='name')[-1],
compat.tobytes('mehmet'))
self.assertEqual(
second_append_table.read(field='surname')[-1],
compat.tobytes('timur'))
self.assertEqual(
second_append_table.read(field='weight')[-1], 65.5)
traj2.f_store()
traj3 = load_trajectory(name=self.traj.v_name,
filename=self.filename,
load_all=2,
dynamic_imports=SharedResult)
third_append_table = traj3.results.shared_data.table
self.assertEqual((third_append_table.read(field='id')[-1]), 30)
self.assertEqual((third_append_table.read(field='name')[-1]),
compat.tobytes('mehmet'))
self.assertEqual((third_append_table.read(field='surname')[-1]),
compat.tobytes('timur'))
self.assertEqual((third_append_table.read(field='weight')[-1]), 65.5)
third_append_table.append([(33, 'Harrison', 'Ford', 95.5)])
self.assertEqual((third_append_table.read(field='id')[-1]), 33)
self.assertEqual((third_append_table.read(field='name')[-1]),
compat.tobytes('Harrison'))
self.assertEqual((third_append_table.read(field='surname')[-1]),
compat.tobytes('Ford'))
self.assertEqual((third_append_table.read(field='weight')[-1]), 95.5)
def test_conversions(self):
filename = make_temp_dir('hdf5manipulation.hdf5')
traj = Trajectory(name=make_trajectory_name(self), filename=filename)
trajname = traj.v_name
traj.v_standard_result = SharedResult
traj.f_store(only_init=True)
traj.f_add_result('shared_data')
thedata = np.zeros((1000, 1000))
myarray = SharedArray('array', traj.shared_data, trajectory=traj)
traj.shared_data['array'] = myarray
mytable = SharedTable('t1', traj.shared_data, trajectory=traj)
traj.shared_data['t1'] = mytable
dadict = {
'hi': [1, 2, 3, 4, 5],
'shu': ['bi', 'du', 'da', 'ha', 'hui']
}
dadict2 = {'answer': [42]}
res = traj.f_add_result('shared.dfs')
res['df'] = SharedPandasFrame()
res['df'].create_shared_data(data=pd.DataFrame(dadict),
trajectory=traj)
frame = SharedPandasFrame('df1',
traj.f_get('shared.dfs'),
trajectory=traj,
add_to_parent=True)
frame.create_shared_data(data=pd.DataFrame(dadict2), )
res['df1'] = frame
traj.f_add_result('mylist', [1, 2, 3])
traj.f_add_result('my.mytuple', k=(1, 2, 3), wa=42)
traj.f_add_result('my.myarray', np.zeros((50, 50)))
traj.f_add_result('my.myframe', data=pd.DataFrame(dadict2))
traj.f_add_result('my.mytable', ObjectTable(data=dadict2))
myarray.create_shared_data(data=thedata)
mytable.create_shared_data(first_row={
'hi': compat.tobytes('hi'),
'huhu': np.ones(3)
})
traj.f_store()
data = myarray.read()
myarray.get_data_node()
self.assertTrue(np.all(data == thedata))
with StorageContextManager(traj):
myarray[2, 2] = 10
data = myarray.read()
self.assertTrue(data[2, 2] == 10)
self.assertTrue(data[2, 2] == 10)
self.assertFalse(traj.v_storage_service.is_open)
traj = load_trajectory(name=trajname,
filename=filename,
load_all=2,
dynamic_imports=SharedResult)
make_ordinary_result(traj.shared_data, 'array', trajectory=traj)
array = traj.shared_data.array
self.assertTrue(isinstance(array, np.ndarray))
thedata[2, 2] = 10
self.assertTrue(np.all(array == thedata))
make_ordinary_result(
traj.shared_data,
't1',
trajectory=traj,
)
t1 = traj.shared_data.t1
self.assertTrue(isinstance(t1, ObjectTable))
self.assertTrue(np.all(t1['huhu'][0] == np.ones(3)))
dfs = traj.shared.dfs
make_ordinary_result(traj.shared.dfs, 'df', trajectory=traj)
theframe = dfs.f_get('df')
self.assertTrue(isinstance(dfs, Result))
self.assertTrue(isinstance(theframe, pd.DataFrame))
self.assertTrue(theframe['hi'][0] == 1)
listres = traj.f_get('mylist')
listres = make_shared_result(listres, 0, trajectory=traj)
with StorageContextManager(traj):
self.assertTrue(listres[0][2] == 3)
listres[0][0] = 4
self.assertTrue(listres[0][0] == 4)
listres = make_ordinary_result(listres, 0, trajectory=traj)
traj = load_trajectory(name=trajname,
filename=filename,
load_all=2,
dynamic_imports=SharedResult)
mylist = traj.mylist
self.assertTrue(isinstance(listres, Result))
self.assertTrue(mylist[0] == 4)
self.assertTrue(isinstance(mylist, list))
mytuple = traj.mytuple
with self.assertRaises(AttributeError):
mytuple = make_shared_result(mytuple,
'mylist',
traj,
new_class=SharedArray)
mytuple = make_shared_result(mytuple, 'k', traj, new_class=SharedArray)
self.assertTrue(mytuple.k[1] == 2)
mytuple = make_ordinary_result(mytuple, 'k', trajectory=traj)
self.assertTrue(isinstance(mytuple.k, tuple))
self.assertTrue(mytuple.k[2] == 3)
myframe = traj.myframe
myframe = make_shared_result(myframe, 'data', traj)
theframe = myframe.data.read()
self.assertTrue(theframe['answer'][0] == 42)
myframe = make_ordinary_result(myframe, 'data', trajectory=traj)
traj.f_load_item(myframe)
self.assertTrue(myframe.data['answer'][0] == 42)
mytable = traj.f_get('mytable')
mytable = make_shared_result(mytable, 0, traj)
self.assertTrue(isinstance(mytable[0], SharedTable))
rows = mytable.mytable.read()
self.assertTrue(rows[0][0] == 42)
mytable = make_ordinary_result(mytable, 0, trajectory=traj)
self.assertTrue(isinstance(mytable, Result))
self.assertTrue(mytable[0]['answer'][0] == 42)
def test_all_arrays(self):
filename = make_temp_dir('hdf5arrays.hdf5')
traj = Trajectory(name=make_trajectory_name(self), filename=filename)
trajname = traj.v_name
npearray = np.ones((2, 10, 3), dtype=np.float)
thevlarray = np.array(
[compat.tobytes('j'), 22.2,
compat.tobytes('gutter')])
traj.f_store(only_init=True)
res = traj.f_add_result(SharedResult, 'arrays')
res['carray'] = SharedCArray()
res['carray'].create_shared_data(shape=(10, 10),
atom=pt.atom.FloatAtom())
res['earray'] = SharedEArray()
res['earray'].create_shared_data(obj=npearray)
res['vlarray'] = SharedVLArray()
res['vlarray'].create_shared_data(obj=thevlarray)
res['array'] = SharedArray()
res['array'].create_shared_data(data=npearray)
traj.f_store()
traj = load_trajectory(name=trajname,
filename=filename,
load_all=2,
dynamic_imports=SharedResult)
toappned = [44, compat.tobytes('k')]
with StorageContextManager(traj):
a1 = traj.arrays.array
a1[0, 0, 0] = 4.0
a2 = traj.arrays.carray
a2[0, 1] = 4
a4 = traj.arrays.vlarray
a4.append(toappned)
a3 = traj.arrays.earray
a3.append(np.zeros((1, 10, 3)))
traj = load_trajectory(name=trajname,
filename=filename,
load_all=2,
dynamic_imports=SharedResult)
with StorageContextManager(traj):
a1 = traj.arrays.array
self.assertTrue(a1[0, 0, 0] == 4.0)
a2 = traj.arrays.carray
self.assertTrue(a2[0, 1] == 4)
a3 = traj.arrays.earray
self.assertTrue(a3.read().shape == (3, 10, 3))
a4 = traj.arrays.vlarray
for idx, x in enumerate(a4):
if idx == 0:
self.assertTrue(np.all(x == np.array(thevlarray)))
elif idx == 1:
self.assertTrue(np.all(x == np.array(toappned)))
else:
raise RuntimeError()
def plot_results(folder_path=''):
if folder_path=='':
folder_path = '/Users/raphaelholca/Dropbox/hebbian_net/output/test_pypet_0/'
# folder_path = '/Users/raphaelholca/Mountpoint/hebbianRL/output/proba_two_lin/'
traj_name = 'explore_perf'
traj = pypet.load_trajectory(traj_name, filename=os.path.join(folder_path, 'explore_perf.hdf5'), force=True)
traj.v_auto_load = True
perf_all = []
for run in traj.f_iter_runs():
perf_all.append(traj.results[run].test_perf)
perf_all = np.array(perf_all)
perf = np.mean(perf_all,1)
param_traj = traj.f_get_explored_parameters()
param = {}
for k in param_traj:
if k[11:] != 'name':
xplr_values = np.array(param_traj[k].f_get_range())
param[k[11:]] = xplr_values
arg_best = np.argmax(perf)
best_param = {}
print 'best parameters:'
print '================'
for k in param.keys():
best_param[k] = param[k][arg_best]
print k + ' : ' + str(param[k][arg_best]) + '\t\t' + str(np.round(np.unique(param[k]),3))
print "\nbest performance: " + str(np.round(np.max(perf)*100,2)) + "\n"
keys = param.keys()
for ik in range(len(keys)):
if len(keys)==1: ik=-1
for k in keys[ik+1:]:
others = keys[:]
if len(keys)>1:
others.remove(keys[ik])
others.remove(k)
mask = np.ones_like(param[k], dtype=bool)
if len(param)>2:
for o in others:
mask = np.logical_and(mask, param[o]==best_param[o])
pX = param[keys[ik]][mask]
pY = param[k][mask]
rC = np.hstack(perf)[mask]
if True: #True: non-linear representation of results; False: linear representation
ipX = np.zeros(len(pX))
ipY = np.zeros(len(pY))
for i in range(len(pX)):
ipX[i] = np.argwhere(pX[i]==np.sort(np.unique(pX)))
ipY[i] = np.argwhere(pY[i]==np.sort(np.unique(pY)))
else:
ipX = np.copy(pX)
ipY = np.copy(pY)
fig = plt.figure()
fig.patch.set_facecolor('white')
plt.scatter(ipX, ipY, c=rC, cmap='CMRmap', vmin=np.min(perf)-0.1, vmax=np.max(perf), s=1000, marker='s')
# plt.scatter(param[keys[ik]][arg_best], param[k][arg_best], c='r', s=50, marker='x')
for i in range(len(pX)):
if pX[i]==param[keys[ik]][arg_best] and pY[i]==param[k][arg_best]:
plt.text(ipX[i], ipY[i], str(np.round(rC[i]*100,1)), horizontalalignment='center', verticalalignment='center', weight='bold', bbox=dict(facecolor='red', alpha=0.5))
else:
plt.text(ipX[i], ipY[i], str(np.round(rC[i]*100,1)), horizontalalignment='center', verticalalignment='center')
plt.xticks(ipX, pX)
plt.yticks(ipY, pY)
plt.xlabel(keys[ik], fontsize=25)
plt.ylabel(k, fontsize=25)
plt.tick_params(axis='both', which='major', labelsize=18)
plt.tight_layout()
plt.savefig(os.path.join(folder_path, keys[ik] + '_' + k + '.pdf'))
plt.close(fig)
name_best = ''
for k in sorted(param.keys()):
name_best += '_'
name_best += k
name_best += str(best_param[k]).replace('.', ',')
return name_best
def test_load():
newtraj = load_trajectory(index=-1, filename=filename, load_data=1)
def _batch_run(dir='unnamed',
batch_id='template',
space=None,
save_data_in_hdf5=False,
single_method=single_run,
process_method=null_processing,
post_process_method=None,
final_process_method=None,
multiprocessing=True,
resumable=True,
overwrite=False,
sim_config=None,
params=None,
optimization=None,
post_kwargs={},
run_kwargs={}):
saved_args = locals()
traj_name = f'{batch_id}_traj'
parent_dir_path = f'{paths.BatchRunFolder}/{dir}'
dir_path = os.path.join(parent_dir_path, batch_id)
filename = f'{dir_path}/{batch_id}.hdf5'
build_new = True
if os.path.exists(parent_dir_path) and os.path.exists(
dir_path) and overwrite == False:
build_new = False
try:
# print('Trying to resume existing trajectory')
env = Environment(continuable=True)
env.resume(trajectory_name=traj_name, resume_folder=dir_path)
print('Resumed existing trajectory')
build_new = False
except:
try:
# print('Trying to load existing trajectory')
traj = load_trajectory(filename=filename,
name=traj_name,
load_all=0)
env = Environment(trajectory=traj, multiproc=True, ncores=4)
traj = config_traj(traj, optimization)
traj.f_load(index=None, load_parameters=2, load_results=0)
traj.f_expand(space)
print('Loaded existing trajectory')
build_new = False
except:
print(
'Neither of resuming or expanding of existing trajectory worked'
)
if build_new:
if multiprocessing:
multiproc = True
resumable = False
wrap_mode = pypetconstants.WRAP_MODE_QUEUE
else:
multiproc = False
resumable = True
wrap_mode = pypetconstants.WRAP_MODE_LOCK
# print('Trying to create novel environment')
env = Environment(
trajectory=traj_name,
filename=filename,
file_title=batch_id,
comment=f'{batch_id} batch run!',
large_overview_tables=True,
overwrite_file=True,
resumable=False,
resume_folder=dir_path,
multiproc=multiproc,
ncores=4,
use_pool=
True, # Our runs are inexpensive we can get rid of overhead by using a pool
freeze_input=
True, # We can avoid some overhead by freezing the input to the pool
wrap_mode=wrap_mode,
graceful_exit=True)
print('Created novel environment')
traj = prepare_traj(env.traj, sim_config, params, batch_id,
parent_dir_path, dir_path)
traj = config_traj(traj, optimization)
traj.f_explore(space)
if post_process_method is not None:
env.add_postprocessing(post_process_method, **post_kwargs)
env.run(single_method,
process_method,
save_data_in_hdf5=save_data_in_hdf5,
save_to=dir_path,
**run_kwargs)
env.disable_logging()
print('Batch run complete')
if final_process_method is not None:
results = final_process_method(env.traj)
# print(results)
return results
def test_conversions(self):
filename = make_temp_dir("hdf5manipulation.hdf5")
traj = Trajectory(name=make_trajectory_name(self), filename=filename)
trajname = traj.v_name
traj.v_standard_result = SharedResult
traj.f_store(only_init=True)
traj.f_add_result("shared_data")
thedata = np.zeros((1000, 1000))
myarray = SharedArray("array", traj.shared_data, trajectory=traj)
traj.shared_data["array"] = myarray
mytable = SharedTable("t1", traj.shared_data, trajectory=traj)
traj.shared_data["t1"] = mytable
# mytable2 = SharedTableResult('h.t2', trajectory=traj)
# mytable3 = SharedTableResult('jjj.t3', trajectory=traj)
dadict = {"hi": [1, 2, 3, 4, 5], "shu": ["bi", "du", "da", "ha", "hui"]}
dadict2 = {"answer": [42]}
res = traj.f_add_result("shared.dfs")
res["df"] = SharedPandasFrame()
res["df"].create_shared_data(data=pd.DataFrame(dadict), trajectory=traj)
frame = SharedPandasFrame("df1", traj.f_get("shared.dfs"), trajectory=traj)
frame.create_shared_data(data=pd.DataFrame(dadict2))
res["df1"] = frame
traj.f_add_result("mylist", [1, 2, 3])
traj.f_add_result("my.mytuple", k=(1, 2, 3), wa=42)
traj.f_add_result("my.myarray", np.zeros((50, 50)))
traj.f_add_result("my.myframe", data=pd.DataFrame(dadict2))
traj.f_add_result("my.mytable", ObjectTable(data=dadict2))
myarray.create_shared_data(data=thedata)
mytable.create_shared_data(first_row={"hi": compat.tobytes("hi"), "huhu": np.ones(3)})
traj.f_store()
data = myarray.read()
arr = myarray.get_data_node()
self.assertTrue(np.all(data == thedata))
with StorageContextManager(traj) as cm:
myarray[2, 2] = 10
data = myarray.read()
self.assertTrue(data[2, 2] == 10)
self.assertTrue(data[2, 2] == 10)
self.assertFalse(traj.v_storage_service.is_open)
traj = load_trajectory(name=trajname, filename=filename, load_all=2, dynamic_imports=SharedResult)
make_ordinary_result(traj.shared_data, "array", trajectory=traj)
array = traj.shared_data.array
self.assertTrue(isinstance(array, np.ndarray))
thedata[2, 2] = 10
self.assertTrue(np.all(array == thedata))
make_ordinary_result(traj.shared_data, "t1", trajectory=traj)
t1 = traj.shared_data.t1
self.assertTrue(isinstance(t1, ObjectTable)) #
self.assertTrue(np.all(t1["huhu"][0] == np.ones(3)))
dfs = traj.shared.dfs
make_ordinary_result(traj.shared.dfs, "df", trajectory=traj)
theframe = dfs.f_get("df")
self.assertTrue(isinstance(dfs, Result))
self.assertTrue(isinstance(theframe, pd.DataFrame))
self.assertTrue(theframe["hi"][0] == 1)
listres = traj.f_get("mylist")
listres = make_shared_result(listres, 0, trajectory=traj)
with StorageContextManager(traj) as cm:
self.assertTrue(listres[0][2] == 3)
listres[0][0] = 4
self.assertTrue(listres[0][0] == 4)
listres = make_ordinary_result(listres, 0, trajectory=traj)
traj = load_trajectory(name=trajname, filename=filename, load_all=2, dynamic_imports=SharedResult)
mylist = traj.mylist
self.assertTrue(isinstance(listres, Result))
self.assertTrue(mylist[0] == 4)
self.assertTrue(isinstance(mylist, list))
mytuple = traj.mytuple
with self.assertRaises(AttributeError):
mytuple = make_shared_result(mytuple, "mylist", traj, new_class=SharedArray)
mytuple = make_shared_result(mytuple, "k", traj, new_class=SharedArray)
self.assertTrue(mytuple.k[1] == 2)
mytuple = make_ordinary_result(mytuple, "k", trajectory=traj)
self.assertTrue(isinstance(mytuple.k, tuple))
self.assertTrue(mytuple.k[2] == 3)
myframe = traj.myframe
myframe = make_shared_result(myframe, "data", traj)
theframe = myframe.data.read()
self.assertTrue(theframe["answer"][0] == 42)
myframe = make_ordinary_result(myframe, "data", trajectory=traj)
traj.f_load_item(myframe)
self.assertTrue(myframe.data["answer"][0] == 42)
mytable = traj.f_get("mytable")
mytable = make_shared_result(mytable, 0, traj)
self.assertTrue(isinstance(mytable[0], SharedTable))
rows = mytable.mytable.read()
self.assertTrue(rows[0][0] == 42)
mytable = make_ordinary_result(mytable, 0, trajectory=traj)
self.assertTrue(isinstance(mytable, Result))
self.assertTrue(mytable[0]["answer"][0] == 42)
def test_storing_and_manipulating(self):
filename = make_temp_dir('hdf5manipulation.hdf5')
traj = Trajectory(name=make_trajectory_name(self), filename=filename)
trajname = traj.v_name
thedata = np.zeros((1000, 1000))
res = traj.f_add_result(SharedResult, 'shared')
myarray = SharedArray('array',
res,
trajectory=traj,
add_to_parent=True)
mytable = SharedTable('t1', res, trajectory=traj, add_to_parent=True)
mytable2 = SharedTable('t2', res, trajectory=traj, add_to_parent=True)
mytable3 = SharedTable('t3', res, trajectory=traj, add_to_parent=True)
traj.f_store(only_init=True)
myarray.create_shared_data(data=thedata)
mytable.create_shared_data(first_row={
'hi': compat.tobytes('hi'),
'huhu': np.ones(3)
})
mytable2.create_shared_data(description={
'ha': pt.StringCol(2, pos=0),
'haha': pt.FloatCol(pos=1)
})
mytable3.create_shared_data(description={
'ha': pt.StringCol(2, pos=0),
'haha': pt.FloatCol(pos=1)
})
traj.f_store()
newrow = {'ha': 'hu', 'haha': 4.0}
with self.assertRaises(TypeError):
traj.shared.t2.row
with StorageContextManager(traj) as cm:
row = traj.shared.t2.row
for irun in range(11):
for key, val in newrow.items():
row[key] = val
row.append()
traj.shared.t3.flush()
data = myarray.read()
myarray.get_data_node()
self.assertTrue(np.all(data == thedata))
with StorageContextManager(traj):
myarray[2, 2] = 10
data = myarray.read()
self.assertTrue(data[2, 2] == 10)
self.assertTrue(data[2, 2] == 10)
self.assertFalse(traj.v_storage_service.is_open)
traj = load_trajectory(name=trajname, filename=filename)
traj.f_load(load_data=2)
traj.shared.t2.traj = traj
traj.shared.t1.traj = traj
traj.shared.array.traj = traj
self.assertTrue(traj.shared.t2.nrows == 11,
'%s != 11' % str(traj.shared.t2.nrows))
self.assertTrue(traj.shared.t2[0]['ha'] == compat.tobytes('hu'),
traj.shared.t2[0]['ha'])
self.assertTrue(traj.shared.t2[1]['ha'] == compat.tobytes('hu'),
traj.shared.t2[1]['ha'])
self.assertTrue('huhu' in traj.shared.t1.colnames)
self.assertTrue(traj.shared.array[2, 2] == 10)
def plot_results(folder_path=''):
if folder_path == '':
folder_path = '/Users/raphaelholca/Dropbox/hebbian_net/output/test_pypet_0/'
# folder_path = '/Users/raphaelholca/Mountpoint/hebbianRL/output/proba_two_lin/'
traj_name = 'explore_perf'
traj = pypet.load_trajectory(traj_name,
filename=os.path.join(folder_path,
'explore_perf.hdf5'),
force=True)
traj.v_auto_load = True
perf_all = []
for run in traj.f_iter_runs():
perf_all.append(traj.results[run].test_perf)
perf_all = np.array(perf_all)
perf = np.mean(perf_all, 1)
param_traj = traj.f_get_explored_parameters()
param = {}
for k in param_traj:
if k[11:] != 'name':
xplr_values = np.array(param_traj[k].f_get_range())
param[k[11:]] = xplr_values
arg_best = np.argmax(perf)
best_param = {}
print 'best parameters:'
print '================'
for k in param.keys():
best_param[k] = param[k][arg_best]
print k + ' : ' + str(param[k][arg_best]) + '\t\t' + str(
np.round(np.unique(param[k]), 3))
print "\nbest performance: " + str(np.round(np.max(perf) * 100, 2)) + "\n"
keys = param.keys()
for ik in range(len(keys)):
if len(keys) == 1: ik = -1
for k in keys[ik + 1:]:
others = keys[:]
if len(keys) > 1:
others.remove(keys[ik])
others.remove(k)
mask = np.ones_like(param[k], dtype=bool)
if len(param) > 2:
for o in others:
mask = np.logical_and(mask, param[o] == best_param[o])
pX = param[keys[ik]][mask]
pY = param[k][mask]
rC = np.hstack(perf)[mask]
if True: #True: non-linear representation of results; False: linear representation
ipX = np.zeros(len(pX))
ipY = np.zeros(len(pY))
for i in range(len(pX)):
ipX[i] = np.argwhere(pX[i] == np.sort(np.unique(pX)))
ipY[i] = np.argwhere(pY[i] == np.sort(np.unique(pY)))
else:
ipX = np.copy(pX)
ipY = np.copy(pY)
fig = plt.figure()
fig.patch.set_facecolor('white')
plt.scatter(ipX,
ipY,
c=rC,
cmap='CMRmap',
vmin=np.min(perf) - 0.1,
vmax=np.max(perf),
s=1000,
marker='s')
# plt.scatter(param[keys[ik]][arg_best], param[k][arg_best], c='r', s=50, marker='x')
for i in range(len(pX)):
if pX[i] == param[
keys[ik]][arg_best] and pY[i] == param[k][arg_best]:
plt.text(ipX[i],
ipY[i],
str(np.round(rC[i] * 100, 1)),
horizontalalignment='center',
verticalalignment='center',
weight='bold',
bbox=dict(facecolor='red', alpha=0.5))
else:
plt.text(ipX[i],
ipY[i],
str(np.round(rC[i] * 100, 1)),
horizontalalignment='center',
verticalalignment='center')
plt.xticks(ipX, pX)
plt.yticks(ipY, pY)
plt.xlabel(keys[ik], fontsize=25)
plt.ylabel(k, fontsize=25)
plt.tick_params(axis='both', which='major', labelsize=18)
plt.tight_layout()
plt.savefig(os.path.join(folder_path, keys[ik] + '_' + k + '.pdf'))
plt.close(fig)
name_best = ''
for k in sorted(param.keys()):
name_best += '_'
name_best += k
name_best += str(best_param[k]).replace('.', ',')
return name_best
def test_conversions(self):
filename = make_temp_dir('hdf5manipulation.hdf5')
traj = Trajectory(name=make_trajectory_name(self), filename=filename)
trajname = traj.v_name
traj.v_standard_result = SharedResult
traj.f_store(only_init=True)
traj.f_add_result('shared_data')
thedata = np.zeros((1000, 1000))
myarray = SharedArray('array', traj.shared_data, trajectory=traj)
traj.shared_data['array'] = myarray
mytable = SharedTable('t1', traj.shared_data, trajectory=traj)
traj.shared_data['t1'] = mytable
dadict = {'hi': [1, 2, 3, 4, 5], 'shu': ['bi', 'du', 'da', 'ha', 'hui']}
dadict2 = {'answer': [42]}
res = traj.f_add_result('shared.dfs')
res['df'] = SharedPandasFrame()
res['df'].create_shared_data(data=pd.DataFrame(dadict), trajectory=traj)
frame = SharedPandasFrame('df1', traj.f_get('shared.dfs'), trajectory=traj,
add_to_parent=True)
frame.create_shared_data(data=pd.DataFrame(dadict2),)
res['df1'] = frame
traj.f_add_result('mylist', [1, 2, 3])
traj.f_add_result('my.mytuple', k=(1, 2, 3), wa=42)
traj.f_add_result('my.myarray', np.zeros((50, 50)))
traj.f_add_result('my.myframe', data=pd.DataFrame(dadict2))
traj.f_add_result('my.mytable', ObjectTable(data=dadict2))
myarray.create_shared_data(data=thedata)
mytable.create_shared_data(first_row={'hi': compat.tobytes('hi'), 'huhu': np.ones(3)})
traj.f_store()
data = myarray.read()
myarray.get_data_node()
self.assertTrue(np.all(data == thedata))
with StorageContextManager(traj):
myarray[2, 2] = 10
data = myarray.read()
self.assertTrue(data[2, 2] == 10)
self.assertTrue(data[2, 2] == 10)
self.assertFalse(traj.v_storage_service.is_open)
traj = load_trajectory(name=trajname, filename=filename, load_all=2,
dynamic_imports=SharedResult)
make_ordinary_result(traj.shared_data, 'array', trajectory=traj)
array = traj.shared_data.array
self.assertTrue(isinstance(array, np.ndarray))
thedata[2, 2] = 10
self.assertTrue(np.all(array == thedata))
make_ordinary_result(traj.shared_data, 't1', trajectory=traj,)
t1 = traj.shared_data.t1
self.assertTrue(isinstance(t1, ObjectTable))
self.assertTrue(np.all(t1['huhu'][0] == np.ones(3)))
dfs = traj.shared.dfs
make_ordinary_result(traj.shared.dfs, 'df', trajectory=traj)
theframe = dfs.f_get('df')
self.assertTrue(isinstance(dfs, Result))
self.assertTrue(isinstance(theframe, pd.DataFrame))
self.assertTrue(theframe['hi'][0] == 1)
listres = traj.f_get('mylist')
listres = make_shared_result(listres, 0, trajectory=traj)
with StorageContextManager(traj):
self.assertTrue(listres[0][2] == 3)
listres[0][0] = 4
self.assertTrue(listres[0][0] == 4)
listres = make_ordinary_result(listres, 0, trajectory=traj)
traj = load_trajectory(name=trajname, filename=filename, load_all=2,
dynamic_imports=SharedResult)
mylist = traj.mylist
self.assertTrue(isinstance(listres, Result))
self.assertTrue(mylist[0] == 4)
self.assertTrue(isinstance(mylist, list))
mytuple = traj.mytuple
with self.assertRaises(AttributeError):
mytuple = make_shared_result(mytuple, 'mylist', traj, new_class=SharedArray)
mytuple = make_shared_result(mytuple, 'k', traj, new_class=SharedArray)
self.assertTrue(mytuple.k[1] == 2)
mytuple = make_ordinary_result(mytuple, 'k', trajectory=traj)
self.assertTrue(isinstance(mytuple.k, tuple))
self.assertTrue(mytuple.k[2] == 3)
myframe = traj.myframe
myframe = make_shared_result(myframe, 'data', traj)
theframe = myframe.data.read()
self.assertTrue(theframe['answer'][0] == 42)
myframe = make_ordinary_result(myframe, 'data', trajectory=traj)
traj.f_load_item(myframe)
self.assertTrue(myframe.data['answer'][0] == 42)
mytable = traj.f_get('mytable')
mytable = make_shared_result(mytable, 0, traj)
self.assertTrue(isinstance(mytable[0], SharedTable))
rows = mytable.mytable.read()
self.assertTrue(rows[0][0] == 42)
mytable = make_ordinary_result(mytable, 0, trajectory=traj)
self.assertTrue(isinstance(mytable, Result))
self.assertTrue(mytable[0]['answer'][0] == 42)
