PYTHON/COMPUTATIONAL STOICHIOMETRY Make an organized output of the species and stoichiometric data for the following reaction series . When running the code i ususally run into a list index out of range error at the highlighted Probnlem area in the code. Please help resolve, Thank you. r0 :  C(s) -> C(g)r1 :  2 C(g) + O2 -> 2 COr2 :  CO + H2O <-> CO2 + H2r3 :  CO + 0.5 O2 -> CO2r4 :  CO + 3 H2 -> CH4 + H2Or5 :  CH4 + H2O -> CO + 3 H2r6 :  C(g) + CO2 -> 2 COr7 :  C(g) + H2O -> CO + H2r8 :  CH4 <=> C(g) + 2 H2r9 :  CO2 + H2 <=> CO + H2O A  Sample of the code for the same problem with a different set of reactions has been provided, : (Please use to guide your solution) reactions = [   "C(s) -> C(g)",   "2 C(g) + O2 -> 2 CO",   "CO + 0.5 O2 -> CO2",   "C(g) + H2O -> CO + H2",   "CH4 <=> C(g) + 2 H2"]# Create the species listspecies_tmp = list()for r in reactions:   left  = r.split('<=>')[0].strip() # reactants side   right = r.split('<=>')[1].strip() # products side #Problem Area      left_terms  = left.split('+')   # reactant species w/ stoichiometric coeff.   right_terms = right.split('+')  # product  species w/ stoichiometric coeff.      terms = [t.strip() for t in left_terms] + [t.strip() for t in right_terms] # concatenate list comprehensions      for i in terms:       tmp = i.split(' ') # split stoichiometric coefficient from species name       assert len(tmp)==1 or len(tmp)==2,' terms = %r, i = %r, tmp = %r '%(terms, i, tmp)       if len(tmp) == 2:           species_tmp.append(tmp[1].strip()) # species name if there is a stoichiometric coeff.       else:           species_tmp.append(i.strip()) # species name if there is no stoichiometric coeff.species_filtered = set(species_tmp) # filter species as a setspecies = list(species_filtered)  # convert species set to list print('\nspecies =\n',species)print('# of species =',len(species))# Create the stoichiometric matriximport numpy as np# Initialize the stoichiometric matrix as zeros_mtrx = np.zeros((len(reactions), len(species)))for (i_row, r) in enumerate(reactions):   left  = r.split('<=>')[0].strip()   right = r.split('<=>')[1].strip()      left_terms = left.split('+')   left_terms = [t.strip() for t in left_terms] # in-place clean up      right_terms = right.split('+')   right_terms = [t.strip() for t in right_terms] # in-place clean up      for t in left_terms: # reactants       tmp = t.split(' ') # split stoichiometric coeff and species name       if len(tmp) == 2: # stoich coeff and species name           coeff = float(tmp[0].strip())           species_member = tmp[1].strip()           j_col = species.index(species_member) # find id of species in the species list           assert s_mtrx[i_row,j_col] == 0.0, \                  'duplicates not allowed r%r: %r %r r'%\                  (i_row,r,species_member,s_mtrx[i_row,j_col])                      s_mtrx[i_row,j_col] = -1.0 * coeff       else: # only species name           species_member = tmp[0].strip()           j_col = species.index(species_member)                      assert s_mtrx[i_row,j_col] == 0.0, \                  'duplicates not allowed r%r: %r %r r'%\                  (i_row,r,species_member,s_mtrx[i_row,j_col])                      s_mtrx[i_row,j_col] = -1.0   for t in right_terms: # products       tmp = t.split(' ')       if len(tmp) == 2:           coeff = float(tmp[0].strip())           species_member = tmp[1].strip()           j_col = species.index(species_member)                      assert s_mtrx[i_row,j_col] == 0.0, \                  'duplicates not allowed r%r: %r %r r'%\                  (i_row,r,species_member,s_mtrx[i_row,j_col])                      s_mtrx[i_row,j_col] = 1.0 * coeff       else:           species_member = tmp[0].strip()           j_col = species.index(species_member)                      assert s_mtrx[i_row,j_col] == 0.0, \                  'duplicates not allowed r%r: %r %r r'%\                  (i_row,r,species_member,s_mtrx[i_row,j_col])           s_mtrx[i_row,j_col] = 1.0print('m x n =',s_mtrx.shape)print('s_mtrx =\n',s_mtrx)print('')print('mole balance vector =\n', s_mtrx.sum(1))