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