def setUp(self):
if not hasattr(self, 'data'):
self.data = {}
self.data['integer'] = 42
self.data['float'] = 42.424242
self.data['string'] = 'TestString! 66'
self.data['long'] = compat.long_type(444444444444444444)
self.data['numpy_array'] = np.array([[3232.3, 323232323232.32323232],
[4., 4.]])
self.data['tuple'] = (444, 444, 443)
self.data['list'] = ['3', '4', '666']
self.data['dict'] = {'a': 'b', 'c': 42, 'd': (1, 2, 3)}
self.data['object_table'] = ObjectTable(
data={
'characters': ['Luke', 'Han', 'Spock'],
'Random_Values': [42, 43, 44],
'Arrays':
[np.array([1, 2]),
np.array([3, 4]),
np.array([5, 5])]
})
self.data['pandas_frame'] = pd.DataFrame(
data={
'characters': ['Luke', 'Han', 'Spock'],
'Random_Values': [42, 43, 44],
'Doubles': [1.2, 3.4, 5.6]
})
self.data['nested_data.hui.buh.integer'] = 42
myframe = pd.DataFrame(
data={
'TC1': [1, 2, 3],
'TC2': ['Waaa', np.nan, ''],
'TC3': [1.2, 42.2, np.nan]
})
myseries = myframe['TC1']
mypanel = pd.Panel({
'Item1': pd.DataFrame(np.random.randn(4, 3)),
'Item2': pd.DataFrame(np.random.randn(4, 2))
})
self.data['series'] = myseries
self.data['panel'] = mypanel
# self.data['p4d'] = pd.Panel4D(np.random.randn(2, 2, 5, 4),
# labels=['Label1','Label2'],
# items=['Item1', 'Item2'],
# major_axis=pd.date_range('1/1/2000', periods=5),
# minor_axis=['A', 'B', 'C', 'D'])
self.make_constructor()
self.make_results()
def setUp(self):
if not hasattr(self,'data'):
self.data={}
self.data['integer'] = 42
self.data['float'] = 42.424242
self.data['string'] = 'TestString! 66'
self.data['long'] = compat.long_type(444444444444444444)
self.data['numpy_array'] = np.array([[3232.3,323232323232.32323232],[4.,4.]])
self.data['tuple'] = (444,444,443)
self.data['list'] = ['3','4','666']
self.data['dict'] = {'a':'b','c':42, 'd': (1,2,3)}
self.data['object_table'] = ObjectTable(data={'characters':['Luke', 'Han', 'Spock'],
'Random_Values' :[42,43,44],
'Arrays': [np.array([1,2]),np.array([3, 4]), np.array([5,5])]})
self.data['pandas_frame'] = pd.DataFrame(data={'characters':['Luke', 'Han', 'Spock'],
'Random_Values' :[42,43,44],
'Doubles': [1.2,3.4,5.6]})
self.data['nested_data.hui.buh.integer'] = 42
myframe = pd.DataFrame(data ={'TC1':[1,2,3],'TC2':['Waaa',np.nan,''],'TC3':[1.2,42.2,np.nan]})
myseries = myframe['TC1']
mypanel = pd.Panel({'Item1' : pd.DataFrame(np.random.randn(4, 3)),
'Item2' : pd.DataFrame(np.random.randn(4, 2))})
self.data['series'] = myseries
self.data['panel'] = mypanel
# self.data['p4d'] = pd.Panel4D(np.random.randn(2, 2, 5, 4),
# labels=['Label1','Label2'],
# items=['Item1', 'Item2'],
# major_axis=pd.date_range('1/1/2000', periods=5),
# minor_axis=['A', 'B', 'C', 'D'])
self.make_constructor()
self.make_results()
def create_param_dict(param_dict):
'''Fills a dictionary with some parameters that can be put into a trajectory.
'''
param_dict['Normal'] = {}
param_dict['Numpy'] = {}
param_dict['Sparse'] ={}
param_dict['Numpy_2D'] = {}
param_dict['Numpy_3D'] = {}
param_dict['Tuples'] ={}
param_dict['Pickle']={}
normal_dict = param_dict['Normal']
normal_dict['string'] = 'Im a test string!'
normal_dict['int'] = 42
normal_dict['long'] = compat.long_type(42)
normal_dict['double'] = 42.42
normal_dict['bool'] =True
normal_dict['trial'] = 0
numpy_dict=param_dict['Numpy']
numpy_dict['string'] = np.array(['Uno', 'Dos', 'Tres'])
numpy_dict['int'] = np.array([1,2,3,4])
numpy_dict['double'] = np.array([1.0,2.0,3.0,4.0])
numpy_dict['bool'] = np.array([True,False, True])
param_dict['Numpy_2D']['double'] = np.matrix([[1.0,2.0],[3.0,4.0]])
param_dict['Numpy_3D']['double'] = np.array([[[1.0,2.0],[3.0,4.0]],[[3.0,-3.0],[42.0,41.0]]])
spsparse_csc = spsp.csc_matrix((2222,22))
spsparse_csc[1,2] = 44.6
spsparse_csc[1,9] = 44.5
spsparse_csr = spsp.csr_matrix((2222,22))
spsparse_csr[1,3] = 44.7
spsparse_csr[17,17] = 44.755555
spsparse_bsr = spsp.bsr_matrix(np.matrix([[1, 1, 0, 0, 2, 2],
[1, 1, 0, 0, 2, 2],
[0, 0, 0, 0, 3, 3],
[0, 0, 0, 0, 3, 3],
[4, 4, 5, 5, 6, 6],
[4, 4, 5, 5, 6, 6]]))
spsparse_dia = spsp.dia_matrix(np.matrix([[1, 0, 3, 0],
[1, 2, 0, 4],
[0, 2, 3, 0],
[0, 0, 3, 4]]))
param_dict['Sparse']['bsr_mat'] = spsparse_bsr
param_dict['Sparse']['csc_mat'] = spsparse_csc
param_dict['Sparse']['csr_mat'] = spsparse_csr
param_dict['Sparse']['dia_mat'] = spsparse_dia
param_dict['Tuples']['int'] = (1,2,3)
param_dict['Tuples']['float'] = (44.4,42.1,3.)
param_dict['Tuples']['str'] = ('1','2wei','dr3i')
param_dict['Pickle']['list']= ['b','h', 53, (),0]
param_dict['Pickle']['list']= ['b','h', 42, (),1]
param_dict['Pickle']['list']= ['b',[444,43], 44, (),2]
def simple_calculations(traj, arg1, simple_kwarg):
print('>>>>>Starting Simple Calculations')
my_dict = {}
my_dict2={}
param_dict=traj.parameters.f_to_dict(fast_access=True,short_names=False)
for key in sorted(param_dict.keys())[0:10]:
val = param_dict[key]
if 'trial' in key:
continue
newkey = key.replace('.','_')
my_dict[newkey] = str(val)
my_dict2[newkey] = [str(val)+' juhu!']
my_dict['__FLOAT'] = 44.0
my_dict['__INT'] = 66
my_dict['__NPINT'] = np.int_(55)
my_dict['__INTaRRAy'] = np.array([1,2,3])
my_dict['__FLOATaRRAy'] = np.array([1.0,2.0,41.0])
my_dict['__FLOATaRRAy_nested'] = np.array([np.array([1.0,2.0,41.0]),np.array([1.0,2.0,41.0])])
my_dict['__STRaRRAy'] = np.array(['sds','aea','sf'])
my_dict['__LONG'] = compat.long_type(42)
my_dict['__UNICODE'] = u'sdfdsf'
my_dict['__BYTES'] = b'zweiundvierzig'
my_dict['__NUMPY_UNICODE'] = np.array([u'$%&ddss'])
my_dict['__NUMPY_BYTES'] = np.array([b'zweiundvierzig'])
keys = sorted(to_dict_wo_config(traj).keys())
for idx,key in enumerate(keys[0:10]):
keys[idx] = key.replace('.', '_')
traj.f_add_result_group('List', comment='Im a result group')
traj.f_add_result_group('Iwiiremainempty.yo', comment='Empty group!')
traj.Iwiiremainempty.f_store_child('yo')
traj.f_add_result('List.Of.Keys', dict1=my_dict, dict2=my_dict2, comment='Test')
traj.List.f_store_child('Of', recursive=True)
traj.f_add_result('DictsNFrame', keys=keys, comment='A dict!')
traj.f_add_result('ResMatrix',np.array([1.2,2.3]), comment='ResMatrix')
#traj.f_add_derived_parameter('All.To.String', str(traj.f_to_dict(fast_access=True,short_names=False)))
myframe = pd.DataFrame(data ={'TC1':[1,2,3],'TC2':['Waaa',np.nan,''],'TC3':[1.2,42.2,np.nan]})
myseries = myframe['TC1']
mypanel = pd.Panel({'Item1' : pd.DataFrame(np.ones((4, 3))),'Item2' : pd.DataFrame(np.ones((4, 2)))})
# p4d = pd.Panel4D(np.random.randn(2, 2, 5, 4),
# labels=['Label1','Label2'],
# items=['Item1', 'Item2'],
# major_axis=pd.date_range('1/1/2000', periods=5),
# minor_axis=['A', 'B', 'C', 'D'])
traj.f_add_result('myseries', myseries, comment='dd')
traj.f_store_item('myseries')
traj.f_add_result('mypanel', mypanel, comment='dd')
#traj.f_add_result('mypanel4d', p4d, comment='dd')
traj.f_get('DictsNFrame').f_set(myframe)
traj.f_add_result('IStore.SimpleThings',1.0,3,np.float32(5.0), 'Iamstring',(1,2,3),[4,5,6],zwei=2).v_comment='test'
traj.f_add_derived_parameter('super.mega',33, comment='It is huuuuge!')
traj.super.f_set_annotations(AgainATestAnnotations='I am a string!111elf')
traj.f_add_result(PickleResult,'pickling.result.proto1', my_dict, protocol=1, comment='p1')
traj.f_add_result(PickleResult,'pickling.result.proto2', my_dict, protocol=2, comment='p2')
traj.f_add_result(PickleResult,'pickling.result.proto0', my_dict, protocol=0, comment='p0')
traj.f_add_result(SparseResult, 'sparse.csc',traj.csc_mat,42).v_comment='sdsa'
traj.f_add_result(SparseResult, 'sparse.bsr',traj.bsr_mat,52).v_comment='sdsa'
traj.f_add_result(SparseResult, 'sparse.csr',traj.csr_mat,62).v_comment='sdsa'
traj.f_add_result(SparseResult, 'sparse.dia',traj.dia_mat,72).v_comment='sdsa'
traj.sparse.v_comment = 'I contain sparse data!'
myobjtab = ObjectTable(data={'strings':['a','abc','qwertt'], 'ints':[1,2,3]})
traj.f_add_result('object.table', myobjtab, comment='k').v_annotations.f_set(test=42)
traj.object.f_set_annotations(test2=42.42)
traj.f_add_result('$.here', 77, comment='huhu')
traj.f_add_result('or.not.$', dollah=77, comment='duh!')
traj.f_add_result('or.not.rrr.$.j', 77, comment='duh!')
#traj.f_add_result('PickleTerror', result_type=PickleResult, test=traj.SimpleThings)
print('<<<<<<Finished Simple Calculations')
return 42
def simple_calculations(traj, arg1, simple_kwarg):
if traj.v_idx == 0:
# to shuffle runs
time.sleep(0.1)
rootlogger = get_root_logger()
if not 'runs' in traj.res:
traj.res.f_add_result_group('runs')
rootlogger.info('>>>>>Starting Simple Calculations')
my_dict = {}
my_dict2={}
param_dict=traj.parameters.f_to_dict(fast_access=True,short_names=False)
for key in sorted(param_dict.keys())[0:5]:
val = param_dict[key]
if 'trial' in key:
continue
newkey = key.replace('.','_')
my_dict[newkey] = str(val)
my_dict2[newkey] = [str(val)+' juhu!']
my_dict['__FLOAT'] = 44.0
my_dict['__INT'] = 66
my_dict['__NPINT'] = np.int_(55)
my_dict['__INTaRRAy'] = np.array([1,2,3])
my_dict['__FLOATaRRAy'] = np.array([1.0,2.0,41.0])
my_dict['__FLOATaRRAy_nested'] = np.array([np.array([1.0,2.0,41.0]),np.array([1.0,2.0,41.0])])
my_dict['__STRaRRAy'] = np.array(['sds','aea','sf'])
my_dict['__LONG'] = compat.long_type(4266)
my_dict['__UNICODE'] = u'sdfdsf'
my_dict['__BYTES'] = b'zweiundvierzig'
my_dict['__NUMPY_UNICODE'] = np.array([u'$%&ddss'])
my_dict['__NUMPY_BYTES'] = np.array([b'zweiundvierzig'])
keys = sorted(to_dict_wo_config(traj).keys())
for idx,key in enumerate(keys[0:5]):
keys[idx] = key.replace('.', '_')
listy=traj.f_add_result_group('List', comment='Im a result group')
traj.f_add_result_group('Iwiiremainempty.yo', comment='Empty group!')
traj.Iwiiremainempty.f_store_child('yo')
traj.Iwiiremainempty.f_add_link('kkk',listy )
listy.f_add_link('hhh', traj.Iwiiremainempty)
if not traj.Iwiiremainempty.kkk.v_full_name == traj.List.v_full_name:
raise RuntimeError()
if not traj.Iwiiremainempty.kkk.v_full_name == traj.List.hhh.kkk.v_full_name:
raise RuntimeError()
traj.f_add_result('runs.' + traj.v_crun + '.ggg', 5555, comment='ladida')
traj.res.runs.f_add_result(traj.v_crun + '.ggjg', 5555, comment='didili')
traj.res.runs.f_add_result('hhg', 5555, comment='jjjj')
traj.res.f_add_result(name='lll', comment='duh', data=444)
x = traj.res.f_add_result(name='nested', comment='duh')
x['nested0.nested1.nested2.nested3'] = 44
traj.res.f_add_result(name='test.$set.$', comment='duh', data=444)
try:
traj.f_add_config('teeeeest', 12)
raise RuntimeError()
except TypeError:
pass
# if not traj.f_contains('results.runs.' + traj.v_crun + '.ggjg', shortcuts=False):
# raise RuntimeError()
# if not traj.f_contains('results.runs.' + traj.v_crun + '.ggg', shortcuts=False):
# raise RuntimeError()
if not traj.f_contains('results.runs.' + traj.v_crun + '.hhg', shortcuts=False):
raise RuntimeError()
traj.f_add_result('List.Of.Keys', dict1=my_dict, dict2=my_dict2, comment='Test')
traj.List.f_store_child('Of', recursive=True)
traj.f_add_result('DictsNFrame', keys=keys, comment='A dict!')
traj.f_add_result('ResMatrix',np.array([1.2,2.3]), comment='ResMatrix')
traj.f_add_result('empty.stuff', (), [], {}, np.array([]), comment='empty stuff')
#traj.f_add_derived_parameter('All.To.String', str(traj.f_to_dict(fast_access=True,short_names=False)))
myframe = pd.DataFrame(data ={'TC1':[1,2,3],'TC2':['Waaa','',''],'TC3':[1.2,42.2,77]})
myseries = myframe['TC1']
mypanel = pd.Panel({'Item1' : pd.DataFrame(np.ones((4, 3))),'Item2' : pd.DataFrame(np.ones((4, 2)))})
# p4d = pd.Panel4D(np.random.randn(2, 2, 5, 4),
# labels=['Label1','Label2'],
# items=['Item1', 'Item2'],
# major_axis=pd.date_range('1/1/2000', periods=5),
# minor_axis=['A', 'B', 'C', 'D'])
traj.f_add_result('myseries', myseries, comment='dd')
traj.f_store_item('myseries')
traj.f_add_result('mypanel', mypanel, comment='dd')
#traj.f_add_result('mypanel4d', p4d, comment='dd')
traj.f_get('DictsNFrame').f_set(myframe)
traj.f_add_result('IStore.SimpleThings',1.0,3,np.float32(5.0),
'Iamstring',
(1,2,3), [4,5,6],
zwei=2).v_comment='test'
traj.f_add_derived_parameter('super.mega',33, comment='It is huuuuge!')
traj.super.f_set_annotations(AgainATestAnnotations='I am a string!111elf')
traj.f_add_result(PickleResult,'pickling.result.proto1', my_dict2, protocol=1, comment='p1')
traj.f_add_result(PickleResult,'pickling.result.proto2', my_dict2, protocol=2, comment='p2')
traj.f_add_result(PickleResult,'pickling.result.proto0', my_dict2, protocol=0, comment='p0')
traj.f_add_result(SparseResult, 'sparse.csc', traj.csc_mat, 42).v_comment='sdsa'
traj.f_add_result(SparseResult, 'sparse.bsr', traj.bsr_mat, 52).v_comment='sdsa'
traj.f_add_result(SparseResult, 'sparse.csr', traj.csr_mat, 62).v_comment='sdsa'
traj.f_add_result(SparseResult, 'sparse.dia', traj.dia_mat, 72).v_comment='sdsa'
traj.sparse.v_comment = 'I contain sparse data!'
myobjtab = ObjectTable(data={'strings':['a','abc','qwertt'], 'ints':[1,2,3]})
traj.f_add_result('object.table', myobjtab, comment='k').v_annotations.f_set(test=42)
traj.object.f_set_annotations(test2=42.42)
traj.f_add_result('$.here', 77, comment='huhu')
traj.f_add_result('tor.hot.$', dollah=77, comment='duh!')
traj.f_add_result('tor.hot.rrr.$.j', 77, comment='duh!')
traj.f_add_result('tor.hot.rrr.crun.jjj', 777, comment='duh**2!')
#traj.f_add_result('PickleTerror', result_type=PickleResult, test=traj.SimpleThings)
rootlogger.info('<<<<<<Finished Simple Calculations')
# let's see if the traj can also always be returned
if isinstance(traj.v_storage_service, LockWrapper):
traj.v_storage_service.pickle_lock = False
return 42, traj
def create_param_dict(param_dict):
'''Fills a dictionary with some parameters that can be put into a trajectory.
'''
param_dict['Normal'] = {}
param_dict['Numpy'] = {}
param_dict['Sparse'] = {}
param_dict['Numpy_2D'] = {}
param_dict['Numpy_3D'] = {}
param_dict['Tuples'] = {}
param_dict['Lists'] = {}
param_dict['Pickle'] = {}
normal_dict = param_dict['Normal']
normal_dict['string'] = 'Im a test string!'
normal_dict['int'] = 42
normal_dict['long'] = compat.long_type(42)
normal_dict['double'] = 42.42
normal_dict['bool'] = True
normal_dict['trial'] = 0
numpy_dict = param_dict['Numpy']
numpy_dict['string'] = np.array(['Uno', 'Dos', 'Tres'])
numpy_dict['int'] = np.array([1, 2, 3, 4])
numpy_dict['double'] = np.array([1.0, 2.0, 3.0, 4.0])
numpy_dict['bool'] = np.array([True, False, True])
param_dict['Numpy_2D']['double'] = np.matrix([[1.0, 2.0], [3.0, 4.0]])
param_dict['Numpy_3D']['double'] = np.array([[[1.0, 2.0], [3.0, 4.0]],
[[3.0, -3.0], [42.0, 41.0]]])
spsparse_csc = spsp.lil_matrix((222, 22))
spsparse_csc[1, 2] = 44.6
spsparse_csc[1, 9] = 44.5
spsparse_csc = spsparse_csc.tocsc()
spsparse_csr = spsp.lil_matrix((222, 22))
spsparse_csr[1, 3] = 44.7
spsparse_csr[17, 17] = 44.755555
spsparse_csr = spsparse_csr.tocsr()
spsparse_bsr = spsp.bsr_matrix(
np.matrix([[1, 1, 0, 0, 2, 2], [1, 1, 0, 0, 2, 2], [0, 0, 0, 0, 3, 3],
[0, 0, 0, 0, 3, 3], [4, 4, 5, 5, 6, 6], [4, 4, 5, 5, 6,
6]]))
spsparse_dia = spsp.dia_matrix(
np.matrix([[1, 0, 3, 0], [1, 2, 0, 4], [0, 2, 3, 0], [0, 0, 3, 4]]))
param_dict['Sparse']['bsr_mat'] = spsparse_bsr
param_dict['Sparse']['csc_mat'] = spsparse_csc
param_dict['Sparse']['csr_mat'] = spsparse_csr
param_dict['Sparse']['dia_mat'] = spsparse_dia
param_dict['Tuples']['empty'] = ()
param_dict['Tuples']['int'] = (1, 2, 3)
param_dict['Tuples']['float'] = (44.4, 42.1, 3.)
param_dict['Tuples']['str'] = ('1', '2wei', 'dr3i')
param_dict['Lists']['lempty'] = []
param_dict['Lists']['lint'] = [1, 2, 3]
param_dict['Lists']['lfloat'] = [44.4, 42.1, 3.]
param_dict['Lists']['lstr'] = ['1', '2wei', 'dr3i']
param_dict['Pickle']['list'] = ['b', 'h', 53, (), 0]
param_dict['Pickle']['list'] = ['b', 'h', 42, (), 1]
param_dict['Pickle']['list'] = ['b', [444, 43], 44, (), 2]
def simple_calculations(traj, arg1, simple_kwarg):
if traj.v_idx == 0:
# to shuffle runs
time.sleep(0.1)
rootlogger = get_root_logger()
if not 'runs' in traj.res:
traj.res.f_add_result_group('runs')
rootlogger.info('>>>>>Starting Simple Calculations')
my_dict = {}
my_dict2 = {}
param_dict = traj.parameters.f_to_dict(fast_access=True, short_names=False)
for key in sorted(param_dict.keys())[0:5]:
val = param_dict[key]
if 'trial' in key:
continue
newkey = key.replace('.', '_')
my_dict[newkey] = str(val)
my_dict2[newkey] = [str(val) + ' juhu!']
my_dict['__FLOAT'] = 44.0
my_dict['__INT'] = 66
my_dict['__NPINT'] = np.int_(55)
my_dict['__INTaRRAy'] = np.array([1, 2, 3])
my_dict['__FLOATaRRAy'] = np.array([1.0, 2.0, 41.0])
my_dict['__FLOATaRRAy_nested'] = np.array(
[np.array([1.0, 2.0, 41.0]),
np.array([1.0, 2.0, 41.0])])
my_dict['__STRaRRAy'] = np.array(['sds', 'aea', 'sf'])
my_dict['__LONG'] = compat.long_type(4266)
my_dict['__UNICODE'] = u'sdfdsf'
my_dict['__BYTES'] = b'zweiundvierzig'
my_dict['__NUMPY_UNICODE'] = np.array([u'$%&ddss'])
my_dict['__NUMPY_BYTES'] = np.array([b'zweiundvierzig'])
keys = sorted(to_dict_wo_config(traj).keys())
for idx, key in enumerate(keys[0:5]):
keys[idx] = key.replace('.', '_')
listy = traj.f_add_result_group('List', comment='Im a result group')
traj.f_add_result_group('Iwiiremainempty.yo', comment='Empty group!')
traj.Iwiiremainempty.f_store_child('yo')
traj.Iwiiremainempty.f_add_link('kkk', listy)
listy.f_add_link('hhh', traj.Iwiiremainempty)
if not traj.Iwiiremainempty.kkk.v_full_name == traj.List.v_full_name:
raise RuntimeError()
if not traj.Iwiiremainempty.kkk.v_full_name == traj.List.hhh.kkk.v_full_name:
raise RuntimeError()
traj.f_add_result('runs.' + traj.v_crun + '.ggg', 5555, comment='ladida')
traj.res.runs.f_add_result(traj.v_crun + '.ggjg', 5555, comment='didili')
traj.res.runs.f_add_result('hhg', 5555, comment='jjjj')
traj.res.f_add_result(name='lll', comment='duh', data=444)
x = traj.res.f_add_result(name='nested', comment='duh')
x['nested0.nested1.nested2.nested3'] = 44
traj.res.f_add_result(name='test.$set.$', comment='duh', data=444)
try:
traj.f_add_config('teeeeest', 12)
raise RuntimeError()
except TypeError:
pass
# if not traj.f_contains('results.runs.' + traj.v_crun + '.ggjg', shortcuts=False):
# raise RuntimeError()
# if not traj.f_contains('results.runs.' + traj.v_crun + '.ggg', shortcuts=False):
# raise RuntimeError()
if not traj.f_contains('results.runs.' + traj.v_crun + '.hhg',
shortcuts=False):
raise RuntimeError()
traj.f_add_result('List.Of.Keys',
dict1=my_dict,
dict2=my_dict2,
comment='Test')
traj.List.f_store_child('Of', recursive=True)
traj.f_add_result('DictsNFrame', keys=keys, comment='A dict!')
traj.f_add_result('ResMatrix', np.array([1.2, 2.3]), comment='ResMatrix')
traj.f_add_result('empty.stuff', (), [], {},
np.array([]),
comment='empty stuff')
#traj.f_add_derived_parameter('All.To.String', str(traj.f_to_dict(fast_access=True,short_names=False)))
myframe = pd.DataFrame(data={
'TC1': [1, 2, 3],
'TC2': ['Waaa', '', ''],
'TC3': [1.2, 42.2, 77]
})
myseries = myframe['TC1']
mypanel = pd.Panel({
'Item1': pd.DataFrame(np.ones((4, 3))),
'Item2': pd.DataFrame(np.ones((4, 2)))
})
# p4d = pd.Panel4D(np.random.randn(2, 2, 5, 4),
# labels=['Label1','Label2'],
# items=['Item1', 'Item2'],
# major_axis=pd.date_range('1/1/2000', periods=5),
# minor_axis=['A', 'B', 'C', 'D'])
traj.f_add_result('myseries', myseries, comment='dd')
traj.f_store_item('myseries')
traj.f_add_result('mypanel', mypanel, comment='dd')
#traj.f_add_result('mypanel4d', p4d, comment='dd')
traj.f_get('DictsNFrame').f_set(myframe)
traj.f_add_result('IStore.SimpleThings',
1.0,
3,
np.float32(5.0),
'Iamstring', (1, 2, 3), [4, 5, 6],
zwei=2).v_comment = 'test'
traj.f_add_derived_parameter('super.mega', 33, comment='It is huuuuge!')
traj.super.f_set_annotations(AgainATestAnnotations='I am a string!111elf')
traj.f_add_result(PickleResult,
'pickling.result.proto1',
my_dict2,
protocol=1,
comment='p1')
traj.f_add_result(PickleResult,
'pickling.result.proto2',
my_dict2,
protocol=2,
comment='p2')
traj.f_add_result(PickleResult,
'pickling.result.proto0',
my_dict2,
protocol=0,
comment='p0')
traj.f_add_result(SparseResult, 'sparse.csc', traj.csc_mat,
42).v_comment = 'sdsa'
traj.f_add_result(SparseResult, 'sparse.bsr', traj.bsr_mat,
52).v_comment = 'sdsa'
traj.f_add_result(SparseResult, 'sparse.csr', traj.csr_mat,
62).v_comment = 'sdsa'
traj.f_add_result(SparseResult, 'sparse.dia', traj.dia_mat,
72).v_comment = 'sdsa'
traj.sparse.v_comment = 'I contain sparse data!'
myobjtab = ObjectTable(data={
'strings': ['a', 'abc', 'qwertt'],
'ints': [1, 2, 3]
})
traj.f_add_result('object.table', myobjtab,
comment='k').v_annotations.f_set(test=42)
traj.object.f_set_annotations(test2=42.42)
traj.f_add_result('$.here', 77, comment='huhu')
traj.f_add_result('tor.hot.$', dollah=77, comment='duh!')
traj.f_add_result('tor.hot.rrr.$.j', 77, comment='duh!')
traj.f_add_result('tor.hot.rrr.crun.jjj', 777, comment='duh**2!')
#traj.f_add_result('PickleTerror', result_type=PickleResult, test=traj.SimpleThings)
rootlogger.info('<<<<<<Finished Simple Calculations')
# let's see if the traj can also always be returned
if isinstance(traj.v_storage_service, LockWrapper):
traj.v_storage_service.pickle_lock = False
return 42, traj
