<jupyter_start><jupyter_text>2mo_benzene example with new cutting strategy (2 fragments) R label only<jupyter_code>import os

from rmgpy import settings
from rmgpy.species import Species
from rmgpy.data.rmg import RMGDatabase

import afm.react
import afm.fragment 
# load kinetics database
db_path = settings['database.directory']
database = RMGDatabase()

# forbidden structure loading
database.loadForbiddenStructures(os.path.join(db_path, 'forbiddenStructures.py'))
# kinetics family loading
database.loadKinetics(os.path.join(db_path, 'kinetics'),
                      kineticsFamilies='default',
                      reactionLibraries=[]
                      )

#def test_react_fragments1(self):
frag1 = afm.fragment.Fragment(label='frag1').from_SMILES_like_string('c1ccccc1CC(C)CR')
## 2 fragments reactions
frag2 = afm.fragment.Fragment(label='frag2').from_SMILES_like_string('CCCCCR')
fragment_tuple = (frag1, frag2)
reactions = afm.react.react_fragments(database.kinetics, 
                                      fragment_tuple,
                                      only_families=[],
                                      prod_resonance=False)
2**15 
# means 2^15 = 32768
rxn_index = range(0,len(reactions))
for index in rxn_index:
    display(reactions[index])
len(reactions)
frag1 = afm.fragment.Fragment(label='frag1').from_SMILES_like_string('CCCCCR')
spec1 = Species(molecule=[frag1])
spec_tuple = (spec1,)

reactions = database.kinetics.generate_reactions_from_families(spec_tuple)
len(reactions)
rxn_index = range(0,len(reactions))
for index in rxn_index:
    display(reactions[index])
reactions[0]<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.10321797207043108, 'fraction_numerical': 0.018822100789313904, 'mean_word_length': 3.919402985074627, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 3, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '505982', 'license': 'MIT', 'n_tokens_mistral': 548, 'n_tokens_neox': 513, 'n_words': 93, 'path': 'examples/2mobenzene/2mo_2_frag_1_slded.ipynb', 'repo_name': 'lily90502/AutoFragmentModeling', 'size': '1647'}