#!/usr/bin/env python
# coding: utf-8

# ## Distinction of solid and liquid

# In[1]:


import os
import pyiron
from ase.io import read
import pyscal.core as pc
import matplotlib.pyplot as plt
import numpy as np


# In[2]:


pr = pyiron.Project('ADIS')


# In[3]:


pr.job_table()


# We will start with a small configuration in which a small solid cluster is embedded in a liquid.

# In[4]:


job = pr.load("Nucleus")
struct = job.get_structure()


# One good idea is to get the q6 values.

# Pyiron can calculate the q6 values and cluster them. Let's see how that works.

# In[5]:


from pyiron.atomistics.structure.pyscal import get_steinhardt_parameter_job


# In[6]:


q46, cluster = get_steinhardt_parameter_job(job, clustering=True)


# We will switch a bit onto pyscal now. All the features of pyscal are not yet available on pyiron.

# In[7]:


sys = pc.System()
sys.read_inputfile(struct, format="ase")
sys.find_neighbors(method="cutoff", cutoff=0)
sys.calculate_q(6, averaged=True)


# from the previous example, we saw that the solid structures had average q6 roughly above 0.3

# In[8]:


atoms = sys.atoms
for count, atom in enumerate(atoms):
    if atom.get_q(6, averaged=True) > 0.3:
        atom.solid = 1
    else:
        atom.solid = 0