from layout__v1 import layout, axis_num_list from plotting.my_plotly import my_plotly_plot, get_xy_axis_info # ############################################################################# from IPython.display import Image # - # ## Read data # + attributes={"classes": [], "id": "", "n": "4"} dataframe_dir = data_dir df_pourbaix, df_ads, df_surf = load_df(from_file=True, root_dir=dataframe_dir, data_dir=dataframe_dir, file_name="df_master.pickle", process_df=True) df_m = df_surf # Filter the jobs that were unsuccessful df_m = df_m[[not i for i in pd.isna(df_m["elec_energy"].tolist())]] df_m = df_m[df_m["job_type"] == "surface_coverage_energy"] cols_to_keep = [ 'facet', 'job_type', 'layers', 'surface_type', 'elec_energy', 'atoms_object',
# # Read/Process DataFrame # + # /mnt/c/Users/raulf/Dropbox/01_acad_folder/01_grad_school/01_norskov/04_comp_clusters/02_DATA/04_IrOx_surfaces_OER data_dir = os.path.join( os.environ["dropbox"], "01_acad_folder/01_grad_school/01_norskov/04_comp_clusters/02_DATA/04_IrOx_surfaces_OER", ) # - df_master = load_df( from_file=False, root_dir="../data", data_dir="../../data", file_name="df_master.pickle", process_df=False, filter_early_revisions=False, ) df_master len(df_master) 2 + 2 def parse_cpu_time(row_i): outcar_list = row_i.outcar search_lines = [i for i in outcar_list if "Total CPU time used" in i] if len(search_lines) == 1:
"cv_ooh": cv_ooh, "Umin": Umin, "Umax": Umax, "print_out": False, "save_dir": save_dir, # "file_name": "_".join(list(key_i)) + ".pdf", "close_plt": close_plt, } # # Read/Process DataFrame # + df_pourbaix, df_ads, df_surf = load_df( from_file=False, root_dir="../data", data_dir="../data", file_name="df_master.pickle", process_df=True, ) # df_m = df_pourbaix # # Elimate structures that aren't converged w.r.t. forces # df_m = df_m[df_m["max_force"] < 0.05] # df_m["name_i"] = df_m["name_i"].str.replace("_", " ") # df_m["name_i"] = df_m["name_i"].str.replace("|", ",") # grouped = df_m.groupby(["facet", "bulk_system"]) # group_dict = {} # for i_ind, (name, group) in enumerate(grouped):
# ***************************************************************************** # Local Imports *************************************************************** # ***************************************************************************** from an_data_processing import load_df # Project Data from proj_data_irox import ( data_dir, ) # - df_pourbaix, df_ads, df_surf = load_df( from_file=False, root_dir=data_dir, data_dir=data_dir + "/190103_new_job_df", file_name="df_master.pickle", process_df=True, ) # + df_oh = df_ads[df_ads["adsorbate"] == "oh"] atoms_list = [] for traj_i in df_oh["atoms_object"].tolist(): atoms_list.append(traj_i[-1]) # - view(atoms_list)
import pandas as pd pd.set_option("display.max_columns", None) pd.set_option('display.max_rows', None) from ase import io from ase.visualize import view import numpy as np # - # # Read/Process DataFrame df_pourbaix, df_ads, df_surf = load_df( from_file=False, root_dir=os.path.join(os.environ["PROJ_irox"], "workflow/data"), data_dir=os.path.join(os.environ["PROJ_irox"], "workflow/data"), file_name="df_master.pickle", process_df=True, ) df_surf from vasp.vasp_methods import parse_incar parse_incar(df_ads.iloc[0].incar) df_ads.iloc[0].incar_parsed # # #