def test_tree_handler(): # pylint: disable=too-many-statements
"""
Test the TreeHandler class functionalities.
"""
# define the working directory
test_dir = Path(__file__).resolve().parent
# initialize TreeHandler test
test_data, references = init_tree_handler_test_workspace(test_dir)
# instantiate tree handler objects
data_hdlr = TreeHandler(test_data[0], 'treeMLDplus')
prompt_hdlr = TreeHandler(test_data[1], 'treeMLDplus')
data_pq_hdlr = TreeHandler(test_data[2])
prompt_pq_hdlr = TreeHandler(test_data[3])
mult_hdlr = TreeHandler(test_data[:2], 'treeMLDplus')
mult_pq_hdlr = TreeHandler(test_data[2:])
# open refernces objects
reference_data_slice_df = pd.read_pickle(references[0])
reference_prompt_slice_df = pd.read_pickle(references[1])
with open(references[2], 'rb') as handle:
reference_dict = pickle.load(handle)
terminate_tree_handler_test_workspace(test_dir)
# test that data is the same in root and parquet
assert data_hdlr.get_data_frame().equals(data_pq_hdlr.get_data_frame()), \
'data Dataframe from parquet file differs from the root file one!'
assert prompt_hdlr.get_data_frame().equals(prompt_pq_hdlr.get_data_frame()), \
'prompt Dataframe from parquet file differs from the root file one!'
# test loading from multiple files
merged_df = pd.concat(
[data_hdlr.get_data_frame(),
prompt_hdlr.get_data_frame()],
ignore_index=True)
assert mult_hdlr.get_data_frame().equals(
merged_df), 'loading of multiple root files not working!'
merged_pq_df = pd.concat(
[data_pq_hdlr.get_data_frame(),
prompt_pq_hdlr.get_data_frame()],
ignore_index=True)
assert mult_pq_hdlr.get_data_frame().equals(
merged_pq_df), 'loading of multiple parquet files not working!'
# define the info dict that will be compared with the reference
info_dict = {}
# get the number of candidates in the original data sample
info_dict['n_data'] = data_hdlr.get_n_cand()
info_dict['n_prompt'] = prompt_hdlr.get_n_cand()
# get the original variable list
info_dict['data_var_list'] = prompt_hdlr.get_var_names()
info_dict['prompt_var_list'] = prompt_hdlr.get_var_names()
# shuffle dataframes
new_hndl = data_hdlr.shuffle_data_frame(size=10,
random_state=5,
inplace=False)
copied_hndl = copy.deepcopy(data_hdlr)
copied_hndl.shuffle_data_frame(size=10, random_state=5, inplace=True)
assert copied_hndl.get_data_frame().equals(new_hndl.get_data_frame()), \
'Inplaced dataframe differs from the not inplaced one after shuffling'
# apply preselections
preselections_data = '(pt_cand > 1.30 and pt_cand < 42.00) and (inv_mass > 1.6690 and inv_mass < 2.0690)'
preselections_prompt = '(pt_cand > 1.00 and pt_cand < 25.60) and (inv_mass > 1.8320 and inv_mass < 1.8940)'
new_hndl = data_hdlr.apply_preselections(preselections_data, inplace=False)
data_hdlr.apply_preselections(preselections_data)
assert data_hdlr.get_data_frame().equals(new_hndl.get_data_frame()), \
'Inplaced dataframe differs from the not inplaced one after the preselections'
prompt_hdlr.apply_preselections(preselections_prompt)
# get the number of selected data
info_dict['n_data_preselected'] = data_hdlr.get_n_cand()
info_dict['n_prompt_preselected'] = prompt_hdlr.get_n_cand()
# get the preselections
info_dict['data_preselections'] = data_hdlr.get_preselections()
info_dict['prompt_preselections'] = prompt_hdlr.get_preselections()
# apply dummy eval() on the underlying data frame
d_len_z_def = 'd_len_z = sqrt(d_len**2 - d_len_xy**2)'
new_hndl = data_hdlr.eval_data_frame(d_len_z_def, inplace=False)
data_hdlr.eval_data_frame(d_len_z_def)
assert data_hdlr.get_data_frame().equals(new_hndl.get_data_frame()), \
'Inplaced dataframe differs from the not inplaced one after eval'
prompt_hdlr.eval_data_frame(d_len_z_def)
# get the new variable list
info_dict['data_new_var_list'] = prompt_hdlr.get_var_names()
info_dict['prompt_new_var_list'] = prompt_hdlr.get_var_names()
# get a random subset of the original data
data_hdlr = data_hdlr.get_subset(size=3000, rndm_state=SEED)
prompt_hdlr = prompt_hdlr.get_subset(size=55, rndm_state=SEED)
# slice both data and prompt data frame respect to the pT
bins = [[0, 2], [2, 10], [10, 25]]
data_hdlr.slice_data_frame('pt_cand', bins)
prompt_hdlr.slice_data_frame('pt_cand', bins)
# store projection variable and binning
info_dict['data_proj_variable'] = data_hdlr.get_projection_variable()
info_dict['prompt_proj_variable'] = prompt_hdlr.get_projection_variable()
info_dict['data_binning'] = data_hdlr.get_projection_binning()
info_dict['prompt_binning'] = prompt_hdlr.get_projection_binning()
# get info from a single data slice
data_slice_df = data_hdlr.get_slice(2)
prompt_slice_df = prompt_hdlr.get_slice(2)
info_dict['n_data_slice'] = len(data_slice_df)
info_dict['n_prompt_slice'] = len(prompt_slice_df)
# test info_dict reproduction
assert info_dict == reference_dict, 'dictionary with the data info differs from the reference!'
# test sliced data frames reproduction
assert data_slice_df.equals(
reference_data_slice_df
), 'data sliced DataFrame differs from the reference!'
assert prompt_slice_df.equals(
reference_prompt_slice_df
), 'prompt sliced DataFrame differs from the reference!'
INFO_DICT['data_var_list'] = PROMPT_HDLR.get_var_names()
INFO_DICT['prompt_var_list'] = PROMPT_HDLR.get_var_names()
# apply preselections
PRESEL_DATA = '(pt_cand > 1.30 and pt_cand < 42.00) and (inv_mass > 1.6690 and inv_mass < 2.0690)'
PRESEL_PROMPT = '(pt_cand > 1.00 and pt_cand < 25.60) and (inv_mass > 1.8320 and inv_mass < 1.8940)'
DATA_HDLR.apply_preselections(PRESEL_DATA)
PROMPT_HDLR.apply_preselections(PRESEL_PROMPT)
# store number of selcted data
INFO_DICT['n_data_preselected'] = DATA_HDLR.get_n_cand()
INFO_DICT['n_prompt_preselected'] = PROMPT_HDLR.get_n_cand()
# store preselections
INFO_DICT['data_preselections'] = DATA_HDLR.get_preselections()
INFO_DICT['prompt_preselections'] = PROMPT_HDLR.get_preselections()
# apply dummy eval() on the underlying data frame
DATA_HDLR.eval_data_frame('d_len_z = sqrt(d_len**2 - d_len_xy**2)')
PROMPT_HDLR.eval_data_frame('d_len_z = sqrt(d_len**2 - d_len_xy**2)')
# store new variable list
INFO_DICT['data_new_var_list'] = PROMPT_HDLR.get_var_names()
INFO_DICT['prompt_new_var_list'] = PROMPT_HDLR.get_var_names()
# get a random subset of the original data
DATA_HDLR = DATA_HDLR.get_subset(size=3000, rndm_state=42)
PROMPT_HDLR = PROMPT_HDLR.get_subset(size=55, rndm_state=42)
# slice both data and prompt data frame respect to the pT