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]
# #########################################################
df_init_slabs = get_df_init_slabs()
# -
# --- # # Manually modify and resave `df_slab` # --- # # Import Modules # + import os print(os.getcwd()) import sys from methods import get_df_slab # + df_slab = get_df_slab() df_slab_0 = get_df_slab(mode="almost-final") # + # assert False # + df_i_0 = df_slab_0[df_slab_0.phase == 2] df_i = df_slab[df_slab.phase == 2] # + # assert False # +
'kuwurupu_88',
'fodopilu_17',
'dawakuvo_55',
'wihuwone_95',
'taparogo_67',
'lufinanu_76',
'helisogi_82',
'gerokede_95',
'vugupopo_89',
'viholaba_21',
'runopeno_56',
'sosewedu_22',
]
# +
df_slab_0 = get_df_slab(mode="almost-final")
df_slab_1 = get_df_slab(mode="final")
# df_slab_1 = df_slab
df_slab_0.loc[slab_ids]
for slab_id_i in slab_ids:
row_1 = df_slab_1.loc[slab_id_i]
row_0 = df_slab_0.loc[slab_id_i]
slab_final_1 = row_1.slab_final
slab_final_0 = row_0.slab_final
cell = slab_final_0.cell.array
x_mag_0 = np.linalg.norm(cell[0])
def create_linear_model_plot(
df=None,
feature_columns=None,
ads=None,
feature_ads=None,
format_dict=None,
layout=None,
verbose=True,
save_plot_to_file=False,
):
"""
"""
#| - create_linear_model_plot
# #####################################################
df_i = df
features_cols_to_include = feature_columns
# #####################################################
#| - Dropping feature columns
if features_cols_to_include is None or features_cols_to_include == "all":
features_cols_to_include = df_i["features_stan"][feature_ads].columns
cols_to_drop = []
for col_i in df_i["features_stan"][feature_ads].columns:
if col_i not in features_cols_to_include:
cols_to_drop.append(col_i)
df_tmp = copy.deepcopy(df_i)
for col_i in cols_to_drop:
df_i = df_i.drop(columns=[("features_stan", feature_ads, col_i)])
# feature_cols = list(df_i.features_stan.columns)
feature_cols = list(df_i["features_stan"][feature_ads].columns)
# print(feature_cols)
plot_title = " | ".join(feature_cols)
plot_title = "Features: " + plot_title
#__|
#| - Creating linear model
X = df_i["features_stan"][feature_ads].to_numpy()
X = X.reshape(-1, len(df_i["features_stan"][feature_ads].columns))
y = df_i.targets[df_i.targets.columns[0]]
model = LinearRegression()
model.fit(X, y)
y_predict = model.predict(X)
if verbose:
print(20 * "-")
print("model.score(X, y):", model.score(X, y))
print("")
# print(feature_cols)
# print(model.coef_)
for i, j in zip(list(df_i["features_stan"][ads].columns), model.coef_):
print(i, ": ", j, sep="")
print(20 * "-")
#__|
#| - Plotting
data = []
from methods import get_df_slab
df_slab = get_df_slab()
#| - DEPRECATED | Getting colors ready
# df_slab_tmp = df_slab[["slab_id", "bulk_id"]]
#
# bulk_id_slab_id_lists = np.reshape(
# df_slab_tmp.to_numpy(),
# (
# 2,
# df_slab_tmp.shape[0],
# )
# )
#
# slab_bulk_mapp_dict = dict(zip(
# list(bulk_id_slab_id_lists[0]),
# list(bulk_id_slab_id_lists[1]),
# ))
#
#
# slab_bulk_id_map_dict = dict()
# for i in df_slab_tmp.to_numpy():
# slab_bulk_id_map_dict[i[0]] = i[1]
#
# # print("list(bulk_id_slab_id_lists[0]):", list(bulk_id_slab_id_lists[0]))
# # print("")
# # print("list(bulk_id_slab_id_lists[1]):", list(bulk_id_slab_id_lists[1]))
# # print("")
# # print("slab_bulk_mapp_dict:", slab_bulk_mapp_dict)
#
# import random
# get_colors = lambda n: list(map(lambda i: "#" + "%06x" % random.randint(0, 0xFFFFFF),range(n)))
#
# slab_id_unique_list = df_i.index.to_frame()["slab_id"].unique().tolist()
#
# bulk_id_list = []
# for slab_id_i in slab_id_unique_list:
# # bulk_id_i = slab_bulk_mapp_dict[slab_id_i]
# bulk_id_i = slab_bulk_id_map_dict[slab_id_i]
# bulk_id_list.append(bulk_id_i)
#
# color_map_dict = dict(zip(
# bulk_id_list,
# get_colors(len(slab_id_unique_list)),
# ))
#
# # Formatting processing
# color_list = []
# for name_i, row_i in df_i.iterrows():
# # #################################################
# slab_id_i = name_i[1]
# # #################################################
# phase_i = row_i["data"]["phase"][""]
# stoich_i = row_i["data"]["stoich"][""]
# sum_norm_abs_magmom_diff_i = row_i["data"]["sum_norm_abs_magmom_diff"][""]
# norm_sum_norm_abs_magmom_diff_i = row_i["data"]["norm_sum_norm_abs_magmom_diff"][""]
# # #################################################
#
# # #################################################
# row_slab_i = df_slab.loc[slab_id_i]
# # #################################################
# bulk_id_i = row_slab_i.bulk_id
# # #################################################
#
# bulk_color_i = color_map_dict[bulk_id_i]
#
# if stoich_i == "AB2":
# color_list.append("#46cf44")
# elif stoich_i == "AB3":
# color_list.append("#42e3e3")
#
# # color_list.append(norm_sum_norm_abs_magmom_diff_i)
# # color_list.append(bulk_color_i)
#__|
#| - Creating parity line
# x_parity = y_parity = np.linspace(0., 8., num=100, )
x_parity = y_parity = np.linspace(
-2.,
8.,
num=100,
)
trace_i = go.Scatter(x=x_parity,
y=y_parity,
line=go.scatter.Line(color="black", width=2.),
mode="lines")
data.append(trace_i)
#__|
#| - Main Data Trace
color_list_i = df_i["format"]["color"][format_dict["color"]]
trace_i = go.Scatter(
y=y,
x=y_predict,
mode="markers",
marker=go.scatter.Marker(
size=12,
color=color_list_i,
colorscale='Viridis',
colorbar=dict(thickness=20),
opacity=0.8,
),
# text=df_i.name_str,
text=df_i.data.name_str,
textposition="bottom center",
)
data.append(trace_i)
#__|
#| - Layout
# y_axis_target_col = df_i.target_cols.columns[0]
y_axis_target_col = df_i.targets.columns[0]
y_axis_target_col = y_axis_target_col[0]
# print("y_axis_target_col:", y_axis_target_col)
# print("y_axis_target_col:", y_axis_target_col)
if y_axis_target_col == "g_o":
# print("11111")
layout.xaxis.title.text = "Predicted ΔG<sub>*O</sub>"
layout.yaxis.title.text = "Simulated ΔG<sub>*O</sub>"
elif y_axis_target_col == "g_oh":
# print("22222")
layout.xaxis.title.text = "Predicted ΔG<sub>*OH</sub>"
layout.yaxis.title.text = "Simulated ΔG<sub>*OH</sub>"
else:
print("Woops isdfsdf8osdfio")
layout.xaxis.title.font.size = 25
layout.yaxis.title.font.size = 25
layout.yaxis.tickfont.size = 20
layout.xaxis.tickfont.size = 20
layout.xaxis.range = [2.5, 5.5]
layout.showlegend = False
dd = 0.2
layout.xaxis.range = [
np.min(y_predict) - dd,
np.max(y_predict) + dd,
]
layout.yaxis.range = [
np.min(y) - dd,
np.max(y) + dd,
]
# layout.title = "TEMP isdjfijsd"
layout.title = plot_title
#__|
fig = go.Figure(data=data, layout=layout)
if save_plot_to_file:
my_plotly_plot(figure=fig,
save_dir=os.path.join(
os.environ["PROJ_irox_oer"],
"workflow/oer_vs_features",
),
plot_name="parity_plot",
write_html=True)
#__|
# #####################################################
out_dict = dict()
# #####################################################
out_dict["fig"] = fig
# #####################################################
return (out_dict)
# #########################################################
from methods import get_df_bulk_manual_class
df_bulk_manual_class = get_df_bulk_manual_class()
# #########################################################
from methods import get_bulk_selection_data
bulk_selection_data = get_bulk_selection_data()
bulk_ids__octa_unique = bulk_selection_data["bulk_ids__octa_unique"]
# #########################################################
from methods import get_df_slab_ids, get_slab_id
df_slab_ids = get_df_slab_ids()
# #########################################################
from methods import get_df_slab
df_slab_old = get_df_slab(mode="almost-final")
# #########################################################
from local_methods import df_dft_for_slab_creation
df_dft_i = df_dft_for_slab_creation(
df_dft=df_dft,
bulk_ids__octa_unique=bulk_ids__octa_unique,
bulks_to_not_run=bulks_to_not_run,
df_bulk_manual_class=df_bulk_manual_class,
frac_of_layered_to_include=frac_of_layered_to_include,
verbose=False,
)
# +
# assert False
# -
