isnotebook_i = isnotebook()
if isnotebook_i:
from tqdm.notebook import tqdm
verbose = True
else:
from tqdm import tqdm
verbose = False
# ### Read data objects with methods
# +
df_jobs_data = get_df_jobs_data(exclude_wsl_paths=True)
df_jobs_anal = get_df_jobs_anal()
df_active_sites = get_df_active_sites()
df_atoms_sorted_ind = get_df_atoms_sorted_ind()
# -
# ### Filtering down to only `oer_adsorbate` jobs
# +
df_ind = df_jobs_anal.index.to_frame()
df_jobs_anal = df_jobs_anal.loc[
df_ind[df_ind.job_type == "oer_adsorbate"].index
]
# +
df_jobs_anal_i = df_jobs_anal[df_jobs_anal.job_completely_done == True]
mean_O_metal_coord,
get_all_active_sites,
get_unique_active_sites,
get_unique_active_sites_temp,
)
# -
# # Read Data
# +
# #########################################################
df_slab = get_df_slab()
df_slab = df_slab.set_index("slab_id")
# #########################################################
df_active_sites_prev = get_df_active_sites()
if df_active_sites_prev is None:
df_active_sites_prev = pd.DataFrame()
# -
# # Create Directories
directory = "out_data"
assert False, "Fix os.makedirs"
if not os.path.exists(directory):
os.makedirs(directory)
slab_ids_to_proc = []
for slab_id_i, row_i in df_slab.iterrows():
if slab_id_i not in df_active_sites_prev.index:
