post-processing code for atomistic modeling (at extreme conditions)
pip install extrempy
to install the extrempy package directly from the repository, clone it from GitHub and use pip to install it:
git clone https://github.com/mingzhong15/extrempy.git
cd extrempy
pip install .
from extrempy.dataset import SetSys
fig, ax1 = plt.subplots(figsize=(3,2),dpi=200)
ss = SetSys( SET_DIR, is_printf=False )
ss._read_thermo()
ax.plot( ss.pres, ss.temp, 'o', ms=3, mew=0.2, color='#1f77b4',alpha=0.6, mfc='none')we can use sys._plot_model_devi to visuallize model deviation for different iterations
from extrempy.dpsample import SampleSys
for case_idx in case_list:
fig, ax = plt.subplots(figsize=(3,1),dpi=200)
for iter_idx in [0]:
print("Iter.%.3d Case.%.3d"%(iter_idx, case_idx))
for sys_idx in [0]:
sys._plot_model_devi(ax, iter_idx = iter_idx, sys_idx = sys_idx, case_idx = case_idx)we can use sys._plot_all_sampling to visuallize data sampling in (p,T) space
sys = SampleSys(DIR, printf=False)
fig, ax = plt.subplots(figsize=(5,3),dpi=200)
color_list = ['coral','crimson','firebrick']
sys._plot_all_sampling(ax, color = color_list)we can collect the sampled data from each iterations (containing fparam.npy, aparam.npy)
sys = SampleSys(DIR)
sys._collect_data(OUT_DIR, exe_path='/personal/raw_to_set.sh')
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