# ### Writing data to pickle
# Pickling data ###########################################
directory = os.path.join(os.environ["PROJ_irox_oer"],
"dft_workflow/job_analysis/collect_collate_dft_data",
"out_data")
if not os.path.exists(directory): os.makedirs(directory)
with open(os.path.join(directory, "df_ads.pickle"), "wb") as fle:
pickle.dump(df_ads, fle)
# #########################################################
# +
from methods import get_df_ads
df_ads_tmp = get_df_ads()
df_ads_tmp.iloc[0:3]
# -
# #########################################################
print(20 * "# # ")
print("All done!")
print("Run time:", np.round((time.time() - ti) / 60, 3), "min")
print("collect_collate_dft.ipynb")
print(20 * "# # ")
# #########################################################
# + active=""
#
#
#
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()
df_job_ids = get_df_job_ids()
def get_ORR_PLT():
"""
"""
#| - get_ORR_PLT
# #########################################################
df_ads = get_df_ads()
df_ads = df_ads[~df_ads.g_oh.isna()]
df_m = df_ads
# #########################################################
df_dft = get_df_dft()
# #########################################################
df_job_ids = get_df_job_ids()
# df_m.g_ooh = 1.16 * df_m.g_oh + 2.8
df_m["g_ooh"] = df_m.g_oh + 2.8
smart_format_dict = [
[{
"stoich": "AB2"
}, {
"color2": "black"
}],
[{
"stoich": "AB3"
}, {
"color2": "grey"
}],
]
ORR_PLT = ORR_Free_E_Plot(free_energy_df=None,
state_title="ads",
free_e_title="ads_g",
smart_format=smart_format_dict,
color_list=None,
rxn_type="OER")
df_m = df_m.set_index([
"compenv",
"slab_id",
], drop=False)
paths_dict = dict()
for name_i, row_i in df_m.iterrows():
#| - Loop through data and add to ORR_PLT
# #####################################################
g_o_i = row_i.g_o
g_oh_i = row_i.g_oh
g_ooh_i = row_i.g_ooh
slab_id_i = row_i.slab_id
active_site_i = row_i.active_site
job_id_o_i = row_i.job_id_o
job_id_oh_i = row_i.job_id_oh
# #####################################################
# #####################################################
df_job_ids_i = df_job_ids[df_job_ids.slab_id == slab_id_i]
bulk_ids = df_job_ids_i.bulk_id.unique()
mess_i = "SIJFIDSIFJIDSJIf"
assert len(bulk_ids) == 1, mess_i
bulk_id_i = bulk_ids[0]
# #########################################################
row_dft_i = df_dft.loc[bulk_id_i]
# #########################################################
stoich_i = row_dft_i.stoich
# #########################################################
data_dict_list = [
{
"ads_g": g_o_i,
"ads": "o",
},
{
"ads_g": g_oh_i,
"ads": "oh",
},
{
"ads_g": g_ooh_i,
"ads": "ooh",
},
{
"ads_g": 0.,
"ads": "bulk",
},
]
df_i = pd.DataFrame(data_dict_list)
df_i["stoich"] = stoich_i
prop_name_list = [
"stoich",
]
# #########################################################
# name_i = "IDSJFISDf"
name_i = slab_id_i + "__" + str(int(active_site_i))
ORR_PLT.add_series(
df_i,
plot_mode="all",
overpotential_type="OER",
property_key_list=prop_name_list,
add_overpot=False,
name_i=name_i,
)
#__|
return (ORR_PLT)