.. _sphx_glr_dimer_lattice_plot_dos_transition.py:


===========================================
Evolution of DOS as function of temperature
===========================================

Using a real frequency solver in the IPT scheme the Density of states
is tracked through the first orders transition.




.. image:: /dimer_lattice/images/sphx_glr_plot_dos_transition_001.png
    :align: center


.. rst-class:: sphx-glr-script-out

 Out::

    D:  0.3333333333333333 tp/U:  0.1 Beta 250
    D:  0.3333333333333333 tp/U:  0.12 Beta 250
    D:  0.37037037037037035 tp/U:  0.12 Beta 250
    0.999198986174
    0.999197678296
    0.999197999918




|


.. code-block:: python

    # Created Tue Jun 14 15:44:38 2016
    # Author: Óscar Nájera

    from __future__ import division, absolute_import, print_function

    import numpy as np
    import scipy.signal as signal
    from scipy.integrate import trapz
    import matplotlib.pyplot as plt
    from joblib import Parallel, delayed

    import dmft.common as gf
    import dmft.ipt_real as ipt
    from dmft.utils import optical_conductivity

    from slaveparticles.quantum.operators import fermi_dist
    import slaveparticles.quantum.dos as dos

    plt.matplotlib.rcParams.update({'axes.labelsize': 22,
                                    'xtick.labelsize': 14, 'ytick.labelsize': 14,
                                    'axes.titlesize': 22})


    def loop_bandwidth(simval, beta, seed='mott gap'):
        """Solves IPT dimer and return Im Sigma_AA, Re Simga_AB

        returns list len(betarange) x 2 Sigma arrays
    """

        s = []
        g = []
        w = np.linspace(-4, 4, 2**12)
        dw = w[1] - w[0]
        gss = gf.semi_circle_hiltrans(w + 5e-3j - 1)
        gsa = gf.semi_circle_hiltrans(w + 5e-3j + 1)
        nfp = dos.fermi_dist(w, beta)
        for D, tp in simval:
            print('D: ', D, 'tp/U: ', tp, 'Beta', beta)
            (gss, gsa), (ss, sa) = ipt.dimer_dmft(
                1, tp, nfp, w, dw, gss, gsa, conv=1e-4, t=(D / 2))
            g.append((gss, gsa))
            s.append((ss, sa))

        return np.array(g), np.array(s), w, nfp


    def plot_spectralfunc(gwi, simval, rf, yshift=False):
        shift = 0
        for (gss, gsa), (D, tp), r in zip(gwi, simval, rf):
            Awloc = -.5 * (gss + gsa).imag / np.pi
            print(trapz(Awloc, w))
            plt.plot(w, r * Awloc, label=r'D/U={:.2},tp={}'.format(D, tp))
            #plt.plot(w,  Awloc * nfp, 'k:')

        plt.xlabel(r'$\omega$')
        plt.ylabel(r'$A(\omega)$')
        plt.legend(loc=0)
        plt.xlim([-2, 0])

    plt.figure()
    simvals = [(1 / 3., .1), (1 / 3., .12), (1 / 2.7, .12)]
    giw, swi, w, nfp = loop_bandwidth(simvals, 250)
    renorm = [1. / .88, 1 / .85, 1 / .82]
    plot_spectralfunc(giw, simvals, renorm)

**Total running time of the script:** ( 0 minutes  0.526 seconds)



.. container:: sphx-glr-footer


  .. container:: sphx-glr-download

     :download:`Download Python source code: plot_dos_transition.py <plot_dos_transition.py>`



  .. container:: sphx-glr-download

     :download:`Download Jupyter notebook: plot_dos_transition.ipynb <plot_dos_transition.ipynb>`

.. rst-class:: sphx-glr-signature

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