<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
 Easy Ab initio calculation with ASE-Siesta-Pyscf
No installation necessary, just download a ready to go container for any system, or run it into the cloud
Are we really on the Amazon cloud??
Step1:  I do not have on my laptop an
Step2:  We can then run the DFT calculation using Siesta
Step3:  The TDDFT calculations with PySCF-NAO
<ASSISTANT_TASK:>
Python Code:
cat /proc/cpuinfo
Explanation: Easy Ab initio calculation with ASE-Siesta-Pyscf
No installation necessary, just download a ready to go container for any system, or run it into the cloud
Are we really on the Amazon cloud??
End of explanation
# import libraries and set up the molecule geometry
from ase.units import Ry, eV, Ha
from ase.calculators.siesta import Siesta
from ase import Atoms
import numpy as np
import matplotlib.pyplot as plt
H2O = Atoms('H2O', positions = [[-0.757,  0.586,  0.000],
                                [0.757,  0.586,  0.000],
                                [0.0, 0.0, 0.0]],
            cell=[20, 20, 20])
# visualization of the particle
from ase.visualize import view
view(H2O, viewer='x3d')
Explanation: I do not have on my laptop an:  Intel(R) Xeon(R) CPU E5-2676 v3 @ 2.40GHz
We first import the necessary libraries and define the system using ASE
End of explanation
# enter siesta input and run siesta
siesta = Siesta(
    mesh_cutoff=150 * Ry,
    basis_set='DZP',
    pseudo_qualifier='lda',
    energy_shift=(10 * 10**-3) * eV,
    fdf_arguments={
        'SCFMustConverge': False,
        'COOP.Write': True,
        'WriteDenchar': True,
        'PAO.BasisType': 'split',
        'DM.Tolerance': 1e-4,
        'DM.MixingWeight': 0.1,
        'MaxSCFIterations': 300,
        'DM.NumberPulay': 4,
        'XML.Write': True})
H2O.set_calculator(siesta)
e = H2O.get_potential_energy()
Explanation: We can then run the DFT calculation using Siesta
End of explanation
# compute polarizability using pyscf-nao
siesta.pyscf_tddft(label="siesta", jcutoff=7, iter_broadening=0.15/Ha,
        xc_code='LDA,PZ', tol_loc=1e-6, tol_biloc=1e-7, freq = np.arange(0.0, 15.0, 0.05))
# plot polarizability with matplotlib
%matplotlib inline
fig = plt.figure(1)
ax1 = fig.add_subplot(121)
ax2 = fig.add_subplot(122)
ax1.plot(siesta.results["freq range"], siesta.results["polarizability nonin"][:, 0, 0].imag)
ax2.plot(siesta.results["freq range"], siesta.results["polarizability inter"][:, 0, 0].imag)
ax1.set_xlabel(r"$\omega$ (eV)")
ax2.set_xlabel(r"$\omega$ (eV)")
ax1.set_ylabel(r"Im($P_{xx}$) (au)")
ax2.set_ylabel(r"Im($P_{xx}$) (au)")
ax1.set_title(r"Non interacting")
ax2.set_title(r"Interacting")
fig.tight_layout()
Explanation: The TDDFT calculations with PySCF-NAO
End of explanation
<END_TASK>