isnotebook_i = isnotebook()
if isnotebook_i:
from tqdm.notebook import tqdm
verbose = True
else:
from tqdm import tqdm
verbose = False
# # Script Inputs
target_cols = ["g_o", "g_oh", ]
# # Read Data
# +
df_eff_ox = get_df_eff_ox()
df_ads = get_df_ads()
df_ads = df_ads.set_index(["compenv", "slab_id", "active_site", ], drop=False)
df_features = get_df_features()
df_features.index = df_features.index.droplevel(level=5)
df_jobs = get_df_jobs()
df_slab = get_df_slab()
df_jobs_data = get_df_jobs_data()
df_jobs_data["rerun_from_oh"] = df_jobs_data["rerun_from_oh"].fillna(value=False)
df_dft = get_df_dft()
directory = os.path.join(root_path_i, "out_data")
if not os.path.exists(directory): os.makedirs(directory)
path_i = os.path.join(root_path_i, "out_data/df_eff_ox.pickle")
with open(path_i, "wb") as fle:
pickle.dump(df_eff_ox, fle)
# #########################################################
# #########################################################
with open(path_i, "rb") as fle:
df_eff_ox = pickle.load(fle)
# #########################################################
# +
from methods import get_df_eff_ox
df_eff_ox_tmp = get_df_eff_ox()
df_eff_ox_tmp.head()
# -
# #########################################################
print(20 * "# # ")
print("All done!")
print("Run time:", np.round((time.time() - ti) / 60, 3), "min")
print("oxid_state.ipynb")
print(20 * "# # ")
# #########################################################
# + active=""
#
#
#