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: