# -
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_slab = get_df_slab()
# #########################################################
df_jobs_paths = get_df_jobs_paths()
# #########################################################
df_jobs_data = get_df_jobs_data()
# #########################################################
df_jobs_anal = get_df_jobs_anal()
df_jobs_anal_completed = df_jobs_anal[df_jobs_anal.job_completely_done == True]
# #########################################################
get_df_jobs,
get_df_jobs_data,
get_df_slab,
get_df_slab_ids,
get_df_jobs_data_clusters,
get_df_jobs_anal,
get_df_slabs_oh,
get_df_init_slabs,
get_df_magmoms,
)
# # Read Data
df_dft = get_df_dft()
df_job_ids = get_df_job_ids()
df_jobs = get_df_jobs(exclude_wsl_paths=True)
df_jobs_data = get_df_jobs_data(exclude_wsl_paths=True)
df_jobs_data_clusters = get_df_jobs_data_clusters()
df_slab = get_df_slab()
df_slab_ids = get_df_slab_ids()
df_jobs_anal = get_df_jobs_anal()
df_jobs_paths = get_df_jobs_paths()
df_slabs_oh = get_df_slabs_oh()
df_init_slabs = get_df_init_slabs()
df_magmoms = get_df_magmoms()
# # Writing finished *O slabs to file
# +
df_jobs_anal_i = df_jobs_anal[df_jobs_anal.job_completely_done == True]
def process_group_magmom_comp(
name=None,
group=None,
write_atoms_objects=False,
verbose=False,
):
"""
"""
#| - process_group_magmom_comp
# #####################################################
group_w_o = group
# #####################################################
out_dict = dict()
out_dict["df_magmoms_comp"] = None
out_dict["good_triplet_comb"] = None
out_dict["job_ids"] = None
# out_dict[""] =
job_ids_list = list(set(group.job_id_max.tolist()))
#| - Reading data
# #########################################################
df_jobs = get_df_jobs()
# #########################################################
df_atoms_sorted_ind = get_df_atoms_sorted_ind()
df_atoms_sorted_ind = df_atoms_sorted_ind.set_index("job_id")
# #########################################################
df_job_ids = get_df_job_ids()
df_job_ids = df_job_ids.set_index("job_id")
from methods import read_magmom_comp_data
assert name != None, "Must pass name to read previous data"
magmom_comp_data_prev = read_magmom_comp_data(name=name)
if magmom_comp_data_prev is not None:
pair_wise_magmom_comp_data_prev = \
magmom_comp_data_prev["pair_wise_magmom_comp_data"]
#__|
if write_atoms_objects:
#| - Write atoms objects
df_i = pd.concat([
df_job_ids, df_atoms_sorted_ind.loc[group_w_o.job_id_max.tolist()]
],
axis=1,
join="inner")
# #########################################################
df_index_i = group_w_o.index.to_frame()
compenv_i = df_index_i.compenv.unique()[0]
slab_id_i = df_index_i.slab_id.unique()[0]
active_sites = [
i for i in df_index_i.active_site.unique() if i != "NaN"
]
active_site_i = active_sites[0]
folder_name = compenv_i + "__" + slab_id_i + "__" + str(
int(active_site_i))
# #########################################################
for job_id_i, row_i in df_i.iterrows():
tmp = 42
job_id = row_i.name
atoms = row_i.atoms_sorted_good
ads = row_i.ads
file_name = ads + "_" + job_id + ".traj"
print("Is this saving to the right place d9sf")
root_file_path = os.path.join("__temp__", folder_name)
print(os.getcwd(), root_file_path)
if not os.path.exists(root_file_path):
os.makedirs(root_file_path)
file_path = os.path.join(root_file_path, file_name)
atoms.write(file_path)
#__|
# #####################################################
#| - Getting good triplet combinations
all_triplet_comb = list(
itertools.combinations(group_w_o.job_id_max.tolist(), 3))
good_triplet_comb = []
for tri_i in all_triplet_comb:
df_jobs_i = df_jobs.loc[list(tri_i)]
# Triplet must not contain duplicate ads
# Must strictly be a *O, *OH, and *bare triplet
ads_freq_dict = CountFrequency(df_jobs_i.ads.tolist())
tmp_list = list(ads_freq_dict.values())
any_repeat_ads = [True if i > 1 else False for i in tmp_list]
if not any(any_repeat_ads):
good_triplet_comb.append(tri_i)
#__|
# #####################################################
#| - MAIN LOOP
if verbose:
print("Number of viable triplet combinations:", len(good_triplet_comb))
data_dict_list = []
pair_wise_magmom_comp_data = dict()
for tri_i in good_triplet_comb:
#| - Process triplets
data_dict_i = dict()
if verbose:
print("tri_i:", tri_i)
all_pairs = list(itertools.combinations(tri_i, 2))
df_jobs_i = df_jobs.loc[list(tri_i)]
sum_norm_abs_magmom_diff = 0.
for pair_i in all_pairs:
# if pair_i in list(pair_wise_magmom_comp_data_prev.keys()):
if (magmom_comp_data_prev is not None) and \
(pair_i in list(pair_wise_magmom_comp_data_prev.keys())):
magmom_data_out = pair_wise_magmom_comp_data_prev[pair_i]
else:
# print("Need to run manually")
# print("pair_i:", pair_i)
#| - Process pairs
row_jobs_0 = df_jobs.loc[pair_i[0]]
row_jobs_1 = df_jobs.loc[pair_i[1]]
ads_0 = row_jobs_0.ads
ads_1 = row_jobs_1.ads
# #############################################
if set([ads_0, ads_1]) == set(["o", "oh"]):
job_id_0 = df_jobs_i[df_jobs_i.ads == "o"].iloc[0].job_id
job_id_1 = df_jobs_i[df_jobs_i.ads == "oh"].iloc[0].job_id
elif set([ads_0, ads_1]) == set(["o", "bare"]):
job_id_0 = df_jobs_i[df_jobs_i.ads ==
"bare"].iloc[0].job_id
job_id_1 = df_jobs_i[df_jobs_i.ads == "o"].iloc[0].job_id
elif set([ads_0, ads_1]) == set(["oh", "bare"]):
job_id_0 = df_jobs_i[df_jobs_i.ads ==
"bare"].iloc[0].job_id
job_id_1 = df_jobs_i[df_jobs_i.ads == "oh"].iloc[0].job_id
else:
print("Woops something went wrong here")
# #############################################
row_atoms_i = df_atoms_sorted_ind.loc[job_id_0]
# #############################################
atoms_0 = row_atoms_i.atoms_sorted_good
magmoms_sorted_good_0 = row_atoms_i.magmoms_sorted_good
was_sorted_0 = row_atoms_i.was_sorted
# #############################################
# #############################################
row_atoms_i = df_atoms_sorted_ind.loc[job_id_1]
# #############################################
atoms_1 = row_atoms_i.atoms_sorted_good
magmoms_sorted_good_1 = row_atoms_i.magmoms_sorted_good
was_sorted_1 = row_atoms_i.was_sorted
# #############################################
# #############################################
magmom_data_out = get_magmom_diff_data(
ads_atoms=atoms_1,
slab_atoms=atoms_0,
ads_magmoms=magmoms_sorted_good_1,
slab_magmoms=magmoms_sorted_good_0,
)
#__|
pair_wise_magmom_comp_data[pair_i] = magmom_data_out
tot_abs_magmom_diff = magmom_data_out["tot_abs_magmom_diff"]
norm_abs_magmom_diff = magmom_data_out["norm_abs_magmom_diff"]
if verbose:
print(" ",
"pair_i: ",
pair_i,
": ",
np.round(norm_abs_magmom_diff, 3),
sep="")
sum_norm_abs_magmom_diff += norm_abs_magmom_diff
# #################################################
data_dict_i["job_ids_tri"] = set(tri_i)
data_dict_i["sum_norm_abs_magmom_diff"] = sum_norm_abs_magmom_diff
# #################################################
data_dict_list.append(data_dict_i)
# #################################################
#__|
#__|
# #####################################################
df_magmoms_i = pd.DataFrame(data_dict_list)
# #####################################################
out_dict["df_magmoms_comp"] = df_magmoms_i
out_dict["good_triplet_comb"] = good_triplet_comb
out_dict["pair_wise_magmom_comp_data"] = pair_wise_magmom_comp_data
out_dict["job_ids"] = job_ids_list
# #####################################################
return (out_dict)
# +
verbose = False
job_out_size_limit = 5 # MB
# +
compenv = os.environ.get("COMPENV", None)
proj_dir = os.environ.get("PROJ_irox_oer", None)
# -
# # Read Data
# +
# #########################################################
df_jobs = get_df_jobs(exclude_wsl_paths=False)
if compenv != "wsl":
df_i = df_jobs[df_jobs.compenv == compenv]
else:
df_i = df_jobs
# #########################################################
df_jobs_paths = get_df_jobs_paths()
df_jobs_paths_i = df_jobs_paths[df_jobs_paths.compenv == compenv]
# #########################################################
df_jobs_anal = get_df_jobs_anal()
if verbose:
print(60 * "-")
read_magmom_comp_data,
get_df_coord,
get_df_slabs_to_run,
get_df_features,
get_df_jobs_on_clus__all,
get_df_octa_info,
)
from methods import get_df_bader_feat
from methods import get_df_struct_drift
from misc_modules.pandas_methods import reorder_df_columns
# + jupyter={"source_hidden": true}
df_dft = get_df_dft()
df_job_ids = get_df_job_ids()
df_jobs = get_df_jobs(exclude_wsl_paths=True)
df_jobs_data = get_df_jobs_data(exclude_wsl_paths=True)
df_jobs_data_clusters = get_df_jobs_data_clusters()
df_slab = get_df_slab()
df_slab_ids = get_df_slab_ids()
df_jobs_anal = get_df_jobs_anal()
df_jobs_paths = get_df_jobs_paths()
df_slabs_oh = get_df_slabs_oh()
df_init_slabs = get_df_init_slabs()
df_magmoms = get_df_magmoms()
df_ads = get_df_ads()
df_atoms_sorted_ind = get_df_atoms_sorted_ind()
df_rerun_from_oh = get_df_rerun_from_oh()
magmom_data_dict = read_magmom_comp_data()
df_slab_simil = get_df_slab_simil()
df_active_sites = get_df_active_sites()
