from methods import isnotebook isnotebook_i = isnotebook() if isnotebook_i: from tqdm.notebook import tqdm verbose = True else: from tqdm import tqdm verbose = False # ### Read Data # + df_jobs = get_df_jobs() df_jobs_data = get_df_jobs_data() df_struct_drift_old = get_df_struct_drift() df_struct_drift_old = df_struct_drift_old.set_index("pair_str", drop=False) df_init_slabs = get_df_init_slabs() df_atoms_sorted_ind = get_df_atoms_sorted_ind() # + active="" # # # + # Removing *O calcs that don't have an active site # It messes up the groupby df_jobs_i = df_jobs[df_jobs.active_site != "NaN"]
) # ### Read Data # + df_jobs_anal = get_df_jobs_anal() df_jobs_anal_i = df_jobs_anal df_atoms_sorted_ind = get_df_atoms_sorted_ind() df_active_sites = get_df_active_sites() df_octa_info_prev = get_df_octa_info() # - df_struct_drift = get_df_struct_drift() # + # df_octa_info_prev[df_octa_info_prev.index.duplicated(keep=False)] # + # assert df_octa_info_prev.index.is_unique, "SIDFISDI" # - # ### Filtering down to `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 ]