{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Back to the main [Index](../index.ipynb) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "

Phonon linewidths and Eliashberg function in metals

\n", "

Aluminium

\n", "
\n", "

\n", "In this lesson, we discuss how to compute the phonon linewidths and the Eliashberg function in metals.\n", "We start with a very brief discussion of the basic equations and some technical details about the Abinit implementation. Then we show how to build an AbiPy flow to automate the multiple steps required \n", "by these calculations.\n", "

\n", "

\n", "In the second part, we use the AbiPy objects to analyze the results. We start from the simplest case of a single calculation whose most important results are stored in the A2F.nc netcdf file. Then we show how to use the A2fRobot to handle multiple netcdf files and perform convergence studies. \n", "

\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Table of Contents\n", "[[back to top](#top)]\n", "\n", "* [A bit of theory](#A-bit-of-theory-and-some-details-about-the-implementation)\n", "* [Technical details](#Technical-details)\n", "* [Building the Flow](#Building-the-Flow)\n", "* [Electronic properties](#Electronic-properties)\n", "* [Vibrational properties](#Vibrational-properties)\n", "* [Linewidths and Eliashberg function](#Phonon-linewidths-and-Eliashberg-function)\n", "* [Convergence studies with A2FRobot](#Using-the-A2FRobot-for-convergence-studies)\n", "\n", "\n", "## A bit of theory and some details about the implementation\n", "[[back to top](#top)]\n", "$\\newcommand{\\kk}{{\\mathbf k}}$\n", "$\\newcommand{\\qq}{{\\mathbf q}}$\n", "$\\newcommand{\\kpq}{{\\kk+\\qq}}$\n", "$\\newcommand{\\RR}{{\\mathbf R}}$\n", "$\\newcommand{\\rr}{{\\mathbf r}}$\n", "$\\newcommand{\\<}{\\langle}$\n", "$\\renewcommand{\\>}{\\rangle}$\n", "$\\newcommand{\\KS}{{\\text{KS}}}$\n", "$\\newcommand{\\ww}{\\omega}$\n", "$\\newcommand{\\ee}{\\epsilon}$\n", "$\\newcommand{\\dd}{{\\,\\text{d}}}$\n", "\n", "\n", "The phonon linewidths are proportional to the electron phonon coupling, and depend on the phonon wavevector $q$\n", "and the branch index $\\nu$:\n", "\n", "\\begin{equation}\n", " \\gamma_{\\qq\\nu} = 2\\pi \\omega_{\\qq\\nu} \\sum_{mn\\kk} |g_{mn\\nu}(\\kk, \\qq)|^2 \\delta(\\ee_{\\kpq m}) \\delta(\\ee_{\\kk n})\n", "\\end{equation}\n", "\n", "Throughout these notes we shall use Hartree atomic units ($e = \\hbar = m_e = 1$), the Fermi level is set to zero.\n", "\n", "The electron-phonon matrix elements are defined by:\n", "\n", "\\begin{equation} %\\label{eq:eph_matrix_elements}\n", " g_{mn}^{\\nu}(\\kk,\\qq) = \\dfrac{1}{\\sqrt{2\\omega_{\\qq\\nu}}} \\<\\psi_{m \\kpq} | \\Delta_{\\qq\\nu} V^\\KS |\\psi_{n\\kk}\\> \n", "\\end{equation}\n", "\n", "For further details about $\\Delta_{\\qq\\nu} V^\\KS$ and their Fourier interpolation\n", "see the [previous lesson](https://nbviewer.jupyter.org/github/abinit/abitutorials/blob/master/abitutorials/sigeph/lesson_sigeph.ipynb)\n", "on the EPH self-energy and [Phys. Rev. B 78, 045124](http://dx.doi.org/10.1103/PhysRevB.78.045124).\n", "\n", "The Eliashberg function, $\\alpha^2F(\\omega)$, is similar to the density of states of the phonons, $F(\\omega)$, \n", "but is weighted according to the coupling of the phonons to the electrons:\n", "\n", "\\begin{equation}\n", " \\alpha^2F(\\omega) = -\\dfrac{1}{N_F} \\sum_{\\qq\\nu} \\dfrac{\\gamma_{\\qq\\nu}}{\\omega_{\\qq\\nu}} \\delta(\\ww - \\ww_{\\qq \\nu})\n", "\\end{equation}\n", "\n", "\n", "\n", "The first inverse moment of $\\alpha^2F(\\omega)$ gives the total coupling strength, \n", "or mass renormalization factor, $\\lambda$.\n", "\n", "\\begin{equation}\n", " \\lambda = \\int \\dfrac{\\alpha^2F(\\omega)}{\\omega}\\dd\\omega = \\sum_{\\qq\\nu} \\lambda_{\\qq\\nu}\n", "\\end{equation}\n", "\n", "\\begin{equation}\n", " \\lambda_{\\qq\\nu} = \\dfrac{\\gamma_{\\qq\\nu}}{\\pi N_F \\ww_{\\qq\\nu}^2}\n", "\\end{equation}\n", "\n", "From $\\lambda$, using the [McMillan formula](https://doi.org/10.1103/PhysRev.167.331) \n", "as modified by Allen and Dynes in [Phys. Rev. B 12 905](https://dx.doi.org/10.1103/PhysRevB.12.905), \n", "one can estimate the superconducting critical temperature $T_c$ in the **isotropic** case.\n", "\n", "\\begin{equation}\n", " T_c = \\dfrac{\\omega_{log}}{1.2} \\exp \\Biggl [ \n", " \\dfrac{-1.04 (1 + \\lambda)}{\\lambda ( 1 - 0.62 \\mu^*) - \\mu^*} \n", " \\Biggr ]\n", "\\end{equation}\n", "\n", "where the logarithmic moment, $\\omega_{log}$, is defined by:\n", "\n", "\\begin{equation}\n", " \\omega_{log} = \\exp \\Biggl [ \n", " \\dfrac{2}{\\lambda} \\int \\dfrac{\\alpha^2F(\\omega)}{\\omega}\\log(\\omega)\\dd\\omega \\Biggr \n", " ]\n", "\\end{equation}\n", "\n", "The formula contains the $\\mu^*$ parameter which approximates the effect of the Coulomb interaction.\n", "It is common to treat $\\mu^*$ as an *adjustable parameter* to reproduce (explain) the experimental $T_c$ \n", "from the ab-initio computed $\\lambda$.\n", "\n", "It is worth noting that, if we assume constant e-ph matrix elements in the BZ, the phonon linewidths \n", "become proportional to the so-called *nesting* function defined by:\n", "\n", "\\begin{equation}\n", " N(\\qq) = \\sum_{mn\\kk} \\delta(\\ee_{\\kpq m}) \\delta(\\ee_{\\kk n})\n", "\\end{equation}\n", "\n", "Roughly speaking, there are two factors entering into play in the equation for the phonon linewidths: \n", "the behaviour in (k, q) space of the matrix elements and the *geometrical* contribution related \n", "to the shape of the Fermi surface (described by the nesting term).\n", "\n", "### Implementation details:\n", "\n", "The input variables *optdriver = 7* and *eph_task = 1* activate the computation of:\n", "\n", "\\begin{equation}\n", " \\gamma_{\\qq\\nu} = 2\\pi \\omega_{\\qq\\nu} \\sum_{pp'} d_{\\qq p}^* \\tilde\\gamma_{p p'}(\\qq) d_{\\qq p'}^*\n", "\\end{equation}\n", "\n", "for all q-points in the IBZ as determined by the `ddb_ngqpt` input variable.\n", "In the above equation, $p$ is a short-hand notation for atom index and direction, \n", "$\\vec d$ is the phonon displacement and $\\tilde\\gamma_{p p'}(\\qq)$ is given by:\n", "\n", "\\begin{equation}\n", " \\tilde\\gamma_{p p'}(\\qq) = \n", " \\sum_{mn\\kk} g_{mn,p}(\\kk, \\qq)^* g_{mn,p'}(\\kk, \\qq) \\delta(\\ee_{\\kpq m}) \\delta(\\ee_{\\kk n}).\n", "\\end{equation}\n", "\n", "The $\\tilde\\gamma_{p p'}(\\qq)$ matrix has the same symmetries as the dynamical matrix\n", "and the elements in the *full* BZ can be obtained by *rotating* the initial set of q-points in the IBZ.\n", "\n", "Once $\\tilde\\gamma(\\qq)$ is known in the *full* BZ, one can Fourier transform to real space with:\n", "\n", "\\begin{equation}\n", " \\tilde\\gamma_{p p'}(\\RR) = \\sum_\\qq e^{i\\qq\\cdot\\RR} \\tilde\\gamma_{p p'}(\\qq)\n", "\\end{equation}\n", "\n", "and use Fourier interpolation to obtain $\\gamma_{\\qq\\nu}$ everywhere in the BZ at low cost\n", "(a similar approach is used for the dynamical matrix and phonon frequencies).\n", "\n", "\n", "Thanks to the relatively inexpensive Fourier interpolation, one can obtain the phonon linewidths along \n", "an arbitrary q-path and evaluate the Eliashberg function on a q-mesh (specified by *ph_ngqpt*) \n", "that can be made **much denser** than the initial ab-initio DDB sampling (specified by *ddb_ngqpt*) and\n", "even denser than the `eph_ngqpt_fine` q-mesh used to interpolate the DFPT potentials.\n", "\n", "Note that the calculation of phonon linewidths in metals is made difficult \n", "by the slow convergence of the double-delta integral over the Fermi surface. \n", "It is therefore convenient to use a coarse k-mesh to calculate phonons with DFPT on a suitable q-grid\n", "and then use a denser k-mesh to perform the integration over the Fermi surface.\n", "The resolution in q-space can be improved by interpolating the DFPT potentials via the\n", "`eph_ngqpt_fine` input variable as discussed in the [previous EPH lesson](https://nbviewer.jupyter.org/github/abinit/abitutorials/blob/master/abitutorials/sigeph/lesson_sigeph.ipynb).\n", "\n", "\n", "## Suggested references\n", "[[back to top](#top)]\n", "\n", "The general theory of electron-phonon coupling and Eliashberg superconductivity is reviewed \n", "by P.B. Allen and B. Mitrovic\n", "in [Theory of Superconducting Tc](https://www.sciencedirect.com/science/article/pii/S0081194708606657). \n", "The first implementations similar to that in Abinit are those in \n", "[Savrasov](https://doi.org/10.1103/PhysRevB.54.16487) and\n", "[Liu](https://doi.org/10.1103/PhysRevB.53.R7575)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Technical details\n", "\n", "From the previous discussion, it should be clear that a typical calculation of phonon linewidths requires:\n", "\n", "1. An initial set of KS wavefunctions and eigenvalues.\n", "2. The knowledge of the dynamical matrix $D(\\qq)$ from which one \n", " can obtain frequencies and displacements everywhere in the BZ via Fourier interpolation.\n", "3. A set of DFPT potentials given in the IBZ\n", "\n", "Thanks to these three ingredients, the code can compute the EPH matrix elements\n", "and perform the integration over the Fermi surface.\n", "A schematic representation of a typical workflow with Abinit is given in the figure below:\n", "\n", "\n", "\n", "The `mrgddb` and `mrgdv` are Fortran executables whose main goal is to produce the final files\n", "required in the EPH code.\n", "\n", "Note, in particular, how the computation of the WFK file and the DFPT part of the graph are now decoupled.\n", "This is the approach we are going to implement with AbiPy to converge the phonon linewidths in Al:\n", "\n", "1. Compute DFPT phonons on a 4x4x4 q-mesh with a coarse 8x8x8 k-sampling\n", "\n", "2. Generate 3 WFK files on a much denser k-mesh (x16, x24, x32) \n", "\n", "3. Run the EPH code with\n", "\n", " - one of the WFK files generated in point 2.\n", " - interpolated DFPT potentials (from the initial 4x4x4 to a 8x8x8 q-mesh)\n", "\n", "4. Compute the Eliashberg function on the `ph_ngqpt` mesh via Fourier interpolation. \n", "5. Analyze the convergence of the results wrt `nkpt`. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Building the Flow\n", "[[back to top](#top)]\n", "\n", "Before starting, we need to import the python modules used in this notebook:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Use this at the beginning of your script so that your code will be compatible with python3\n", "from __future__ import print_function, division, unicode_literals\n", "\n", "import numpy as np\n", "import warnings\n", "warnings.filterwarnings(\"ignore\") # to get rid of deprecation warnings\n", "\n", "from abipy import abilab\n", "abilab.enable_notebook() # This line tells AbiPy we are running inside a notebook\n", "import abipy.flowtk as flowtk\n", "\n", "# This line configures matplotlib to show figures embedded in the notebook.\n", "# Replace `inline` with `notebook` in classic notebook\n", "%matplotlib inline \n", "\n", "# Option available in jupyterlab. See https://github.com/matplotlib/jupyter-matplotlib\n", "#%matplotlib widget " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and a function from the `lesson_eph_al` module to build our AbiPy flow." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from lesson_eph_al import build_flow" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "Please read the code carefully, in particular the comments.\n", "Don't worry if the meaning of some input variables is not immediately clear\n", "as we will try to clarify the most technical parts in the rest of this notebook.\n", "
" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", " \n", " \n", " \n", "\n", "\n", "

\n", "\n", "
def build_flow(options):\n",
       "    """\n",
       "    Build and return an AbiPy flow to compute phonon linewidths and Eliashberg function in Aluminium:\n",
       "\n",
       "        1. Compute DFPT phonons on a 4x4x4 q-mesh with a coarse 8x8x8 k-sampling\n",
       "\n",
       "        2. Generate 3 WFK files on a much denser k-mesh (x16, x24, x32)\n",
       "\n",
       "        3. Run the EPH code with:\n",
       "\n",
       "          - one of the WFK files generated in point 2.\n",
       "          - interpolated DFPT potentials (from the initial 4x4x4 to a 8x8x8 q-mesh)\n",
       "\n",
       "        4. Analyze the convergence of the results wrt nkpt.\n",
       "\n",
       "    Note that the q-point grid must be a sub-grid of the k-point grid\n",
       "    """\n",
       "    workdir = options.workdir if (options and options.workdir) else "flow_eph_al"\n",
       "\n",
       "    # Create empty flow.\n",
       "    flow = flowtk.Flow(workdir=workdir)\n",
       "\n",
       "    # Init structure. Use NC pseudo\n",
       "    structure = abilab.Structure.fcc(a=7.5, species=["Al"], units="bohr")\n",
       "    pseudos = abidata.pseudos("Al.oncvpsp")\n",
       "\n",
       "    # Input for GS part.\n",
       "    gs_inp = abilab.AbinitInput(structure, pseudos)\n",
       "    gs_inp.set_vars(\n",
       "        istwfk="*1",\n",
       "        ecut=8.0,\n",
       "        nband=4,\n",
       "        occopt=7,      # Include metallic occupation function with a small smearing\n",
       "        tsmear=0.04,\n",
       "        tolvrs=1e-7,\n",
       "    )\n",
       "\n",
       "    # The k-grid is minimalistic to keep the calculation manageable.\n",
       "    gs_inp.set_kmesh(\n",
       "        ngkpt=[8, 8, 8],\n",
       "        shiftk=[0.0, 0.0, 0.0],\n",
       "    )\n",
       "\n",
       "    # Build new input for NSCF calculation along k-path (automatically selected by AbiPy)\n",
       "    # Used to plot the KS band structure.\n",
       "    nscf_kpath_inp = gs_inp.new_with_vars(\n",
       "        nband=4,\n",
       "        tolwfr=1e-16,\n",
       "        iscf=-2,\n",
       "    )\n",
       "    nscf_kpath_inp.set_kpath(ndivsm=10)\n",
       "\n",
       "    # Build NSCF inputs with denser k-meshes\n",
       "    # This step generates the WFK files used to compute the Eliashberg function.\n",
       "    # We have a cubic material so we only need to specify the first number of divisions.\n",
       "    nk_list = [16, 24, 32]\n",
       "\n",
       "    nscf_kmesh_inputs = []\n",
       "    for nk in nk_list:\n",
       "        new_inp = gs_inp.new_with_vars(\n",
       "            tolwfr=1e-16,\n",
       "            iscf=-2,\n",
       "            ngkpt=[nk] * 3,\n",
       "            shiftk=[0.0, 0.0, 0.0],\n",
       "        )\n",
       "        nscf_kmesh_inputs.append(new_inp)\n",
       "\n",
       "    # Register GS + NSCF kpath + NSCF with k-meshes in work0.\n",
       "    work0 = flowtk.BandStructureWork(gs_inp, nscf_kpath_inp, dos_inputs=nscf_kmesh_inputs)\n",
       "    flow.register_work(work0)\n",
       "\n",
       "    # Generate Phonon work with 4x4x4 q-mesh\n",
       "    # Reuse the variables from GS input and let AbiPy handle the generation of the input files\n",
       "    # Note that the q-point grid is a sub-grid of the k-mesh so we do not need WFQ on k+q mesh.\n",
       "    ddb_ngqpt = [4, 4, 4]\n",
       "    ph_work = flowtk.PhononWork.from_scf_task(work0[0], ddb_ngqpt, is_ngqpt=True)\n",
       "    flow.register_work(ph_work)\n",
       "\n",
       "    # Ssction for EPH calculation: compute linewidths with different WKK files.\n",
       "    eph_work = flowtk.Work()\n",
       "    for ik, nk in enumerate(nk_list):\n",
       "        # Each task uses a different WFK file. DDB and DBDB do not change.\n",
       "        eph_deps = {work0[2 + ik]: "WFK", ph_work: ["DDB", "DVDB"]}\n",
       "\n",
       "        # Interpolate DFPT potentials 4x4x4 --> 8x8x8\n",
       "        eph_ngqpt_fine = (8, 8, 8)\n",
       "\n",
       "        # Build input for E-PH run. See also v7/Input/t85.in\n",
       "        # The k-points must be in the WFK file\n",
       "        eph_inp = gs_inp.new_with_vars(\n",
       "            optdriver=7,                    # Enter EPH driver.\n",
       "            eph_task=1,                     # Compute phonon linewidths in metals.\n",
       "            ddb_ngqpt=ddb_ngqpt,            # q-mesh used to produce the DDB file (must be consistent with DDB data)\n",
       "            eph_fsewin="0.8 eV",            # Energy window around Ef (only states in this window are included)\n",
       "            eph_intmeth=2,                  # Tetra method\n",
       "            #eph_intmeth=1,                 # Gaussian\n",
       "            #eph_fsmear=eph_fsmear * abilab.units.eV_to_Ha, # Broadening\n",
       "            eph_ngqpt_fine=eph_ngqpt_fine,  # Interpolate DFPT potentials if != ddb_ngqpt\n",
       "            eph_mustar=0.12,                # mustar parameter\n",
       "            ngkpt=[nk] * 3,\n",
       "            shiftk=[0.0, 0.0, 0.0],\n",
       "        )\n",
       "\n",
       "        # Set q-path to interpolate phonons and phonon linewidths.\n",
       "        eph_inp.set_qpath(10)\n",
       "\n",
       "        # Set q-mesh for phonons DOS and a2F(w)\n",
       "        eph_inp.set_phdos_qmesh(nqsmall=24, method="tetra")\n",
       "        eph_work.register_eph_task(eph_inp, deps=eph_deps)\n",
       "\n",
       "    flow.register_work(eph_work)\n",
       "\n",
       "    # Avoid producing (big) output files that not required by children.\n",
       "    flow.allocate(use_smartio=True)\n",
       "\n",
       "    return flow\n",
       "
\n", "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "abilab.print_source(build_flow)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "OK the function is a little bit long but it is normal as we are computing\n", "in a single workflow the *electronic* properties, the *vibrational* spectrum \n", "and the *phonon linewidths* with different k-point samplings.\n", "\n", "Note that we have already encountered similar flows in the previous AbiPy lessons. \n", "The calculation of electronic band structures is\n", "discussed in \n", "[lesson_base3](https://nbviewer.jupyter.org/github/abinit/abitutorials/blob/master/abitutorials/base3/lesson_base3.ipynb)\n", "while an example of `Flow` for phonon calculations is given in \n", "[lesson_dfpt](https://nbviewer.jupyter.org/github/abinit/abitutorials/blob/master/abitutorials/dfpt/lesson_dfpt.ipynb).\n", "\n", "The novelty is represented by the generation of the `EphTasks` \n", "in which we have to specify several variables related to phonons and the EPH self-energy.\n", "For your convenience, we have grouped the variables used in the `EphTask` in sub-groups:\n", "\n", "\n", "\n", "Hopefully things will become clearer if we build the flow and start to play with it:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Number of works: 3, total number of tasks: 22\n", "Number of tasks with a given class:\n", "\n", "Task Class Number\n", "------------ --------\n", "ScfTask 1\n", "NscfTask 4\n", "PhononTask 14\n", "EphTask 3" ] } ], "source": [ "flow = build_flow(options=None)\n", "flow.show_info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We then print the input of the last `EphTask`. \n", "Please read carefully the documentation of the variables, in particular \n", "those in the `dfpt` and `eph` sections.\n", "\n", "In a nutshell: we read a WKF on a 32x32x32 k-mesh and a DDB on a 4x4x4 q-mesh,\n", "activate the computation of phonon linewidths with `optdriver` and `eph_task`,\n", "interpolate the potentials onto a 8x8x8 q-mesh with `eph_ngqpt_fine` and set other variables \n", "for the computation of the phonon linewidths around the Fermi surface." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] }, { "data": { "text/html": [ "############################################################################################
# SECTION: basic
############################################################################################
ecut 8.0
nband 4
occopt 7
tolvrs 1e-07
ngkpt 32 32 32
kptopt 1
nshiftk 1
shiftk 0.0 0.0 0.0
############################################################################################
# SECTION: dev
############################################################################################
istwfk *1
############################################################################################
# SECTION: eph
############################################################################################
eph_task 1
ddb_ngqpt 4 4 4
eph_fsewin 0.8 eV
eph_intmeth 2
eph_ngqpt_fine 8 8 8
eph_mustar 0.12
ph_ndivsm 10
ph_nqpath 12
ph_qpath
0.0 0.0 0.0
0.5 0.0 0.5
0.5 0.25 0.75
0.375 0.375 0.75
0.0 0.0 0.0
0.5 0.5 0.5
0.625 0.25 0.625
0.5 0.25 0.75
0.5 0.5 0.5
0.375 0.375 0.75
0.625 0.25 0.625
0.5 0.0 0.5
ph_intmeth 2
ph_wstep 0.0001 eV
ph_ngqpt 24 24 24
ph_qshift 0 0 0
ph_nqshift 1
############################################################################################
# SECTION: files
############################################################################################
prtwf -1
prtden 0
############################################################################################
# SECTION: gstate
############################################################################################
tsmear 0.04
optdriver 7
############################################################################################
# STRUCTURE
############################################################################################
natom 1
ntypat 1
typat 1
znucl 13
xred 0.0000000000 0.0000000000 0.0000000000
acell 1.0 1.0 1.0
rprim
0.0000000000 3.7500000000 3.7500000000
3.7500000000 0.0000000000 3.7500000000
3.7500000000 3.7500000000 0.0000000000" ], "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print(flow[-1][-1])\n", "flow[-1][-1].input" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "The input does not contain any `irdwfk` or `getwfk` variable. AbiPy will add the required `ird*` \n", "variables at runtime and create symbolic links to connect this task to its parents.\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As usual, it is much easier to understand what is happening if we plot a graph\n", "with the individual tasks and their connections.\n", "Since there are several nodes in our graph, we mainly focus on the EPH part.\n", "\n", "Let's have a look at the parents of the last `EphTask` with:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "\n", "\n", "\n", "\n", "\n", "\n", "task\n", "\n", "EphTask, node_id=345856, workdir=flow_eph_al/w2/t2\n", "clusterw2\n", "\n", "EphTask (w2_t2)\n", "\n", "\n", "w0_t4\n", "\n", "w0_t4\n", "NscfTask\n", "\n", "\n", "w2_t2\n", "\n", "w2_t2\n", "EphTask\n", "\n", "\n", "w0_t4->w2_t2\n", "\n", "\n", "WFK\n", "\n", "\n", "w1\n", "\n", "w1\n", "PhononWork\n", "\n", "\n", "w1->w2_t2\n", "\n", "\n", "DDB+DVDB\n", "\n", "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "flow[-1][-1].get_graphviz()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We do not repeat here the detailed description of the Flow because it is very similar in spirit\n", "to what has been already done in the [previous EPH lesson](https://nbviewer.jupyter.org/github/abinit/abitutorials/blob/master/abitutorials/sigeph/lesson_sigeph.ipynb).\n", "Hopefully, you are already familiar with the AbiPy philosophy.\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ngkpt in EPH tasks: [[16, 16, 16], [24, 24, 24], [32, 32, 32]]\n" ] } ], "source": [ "print(\"ngkpt in EPH tasks:\", [task.input[\"ngkpt\"] for task in flow[2]])" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
ngkptclass
w2_t0[16, 16, 16]EphTask
w2_t1[24, 24, 24]EphTask
w2_t2[32, 32, 32]EphTask
\n", "
" ], "text/plain": [ " ngkpt class\n", "w2_t0 [16, 16, 16] EphTask\n", "w2_t1 [24, 24, 24] EphTask\n", "w2_t2 [32, 32, 32] EphTask" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "flow[2].get_vars_dataframe(\"ngkpt\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For completeness, we show the entire flow with the different connections:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "\n", "\n", "\n", "\n", "\n", "\n", "flow\n", "\n", "Flow, node_id=345822, workdir=flow_eph_al\n", "clusterw0\n", "\n", "BandStructureWork (w0)\n", "\n", "clusterw1\n", "\n", "PhononWork (w1)\n", "\n", "clusterw2\n", "\n", "Work (w2)\n", "\n", "\n", "w0_t0\n", "\n", "w0_t0\n", "ScfTask\n", "\n", "\n", "w0_t1\n", "\n", "w0_t1\n", "NscfTask\n", "\n", "\n", "w0_t0->w0_t1\n", "\n", "\n", "DEN\n", "\n", "\n", "w0_t2\n", "\n", "w0_t2\n", "NscfTask\n", "\n", "\n", "w0_t0->w0_t2\n", "\n", "\n", "DEN\n", "\n", "\n", "w0_t3\n", "\n", "w0_t3\n", "NscfTask\n", "\n", "\n", "w0_t0->w0_t3\n", "\n", "\n", "DEN\n", "\n", "\n", "w0_t4\n", "\n", "w0_t4\n", "NscfTask\n", "\n", "\n", "w0_t0->w0_t4\n", "\n", "\n", "DEN\n", "\n", "\n", "w1_t0\n", "\n", "w1_t0\n", "PhononTask\n", "\n", "\n", "w0_t0->w1_t0\n", "\n", "\n", "WFK\n", "\n", "\n", "w1_t1\n", "\n", "w1_t1\n", "PhononTask\n", "\n", "\n", "w0_t0->w1_t1\n", "\n", "\n", "WFK\n", "\n", "\n", "w1_t2\n", "\n", "w1_t2\n", "PhononTask\n", "\n", "\n", "w0_t0->w1_t2\n", "\n", "\n", "WFK\n", "\n", "\n", "w1_t3\n", "\n", "w1_t3\n", "PhononTask\n", "\n", "\n", "w0_t0->w1_t3\n", "\n", "\n", "WFK\n", "\n", "\n", "w1_t4\n", "\n", "w1_t4\n", "PhononTask\n", "\n", "\n", "w0_t0->w1_t4\n", "\n", "\n", "WFK\n", "\n", "\n", "w1_t5\n", "\n", "w1_t5\n", "PhononTask\n", "\n", "\n", "w0_t0->w1_t5\n", "\n", "\n", "WFK\n", "\n", "\n", "w1_t6\n", "\n", "w1_t6\n", "PhononTask\n", "\n", "\n", "w0_t0->w1_t6\n", "\n", "\n", "WFK\n", "\n", "\n", "w1_t7\n", "\n", "w1_t7\n", "PhononTask\n", "\n", "\n", "w0_t0->w1_t7\n", "\n", "\n", "WFK\n", "\n", "\n", "w1_t8\n", "\n", "w1_t8\n", "PhononTask\n", "\n", "\n", "w0_t0->w1_t8\n", "\n", "\n", "WFK\n", "\n", "\n", "w1_t9\n", "\n", "w1_t9\n", "PhononTask\n", "\n", "\n", "w0_t0->w1_t9\n", "\n", "\n", "WFK\n", "\n", "\n", "w1_t10\n", "\n", "w1_t10\n", "PhononTask\n", "\n", "\n", "w0_t0->w1_t10\n", "\n", "\n", "WFK\n", "\n", "\n", "w1_t11\n", "\n", "w1_t11\n", "PhononTask\n", "\n", "\n", "w0_t0->w1_t11\n", "\n", "\n", "WFK\n", "\n", "\n", "w1_t12\n", "\n", "w1_t12\n", "PhononTask\n", "\n", "\n", "w0_t0->w1_t12\n", "\n", "\n", "WFK\n", "\n", "\n", "w1_t13\n", "\n", "w1_t13\n", "PhononTask\n", "\n", "\n", "w0_t0->w1_t13\n", "\n", "\n", "WFK\n", "\n", "\n", "w2_t0\n", "\n", "w2_t0\n", "EphTask\n", "\n", "\n", "w0_t2->w2_t0\n", "\n", "\n", "WFK\n", "\n", "\n", "w2_t1\n", "\n", "w2_t1\n", "EphTask\n", "\n", "\n", "w0_t3->w2_t1\n", "\n", "\n", "WFK\n", "\n", "\n", "w2_t2\n", "\n", "w2_t2\n", "EphTask\n", "\n", "\n", "w0_t4->w2_t2\n", "\n", "\n", "WFK\n", "\n", "\n", "DDB (w1)\n", "\n", "DDB (w1)\n", "\n", "\n", "DDB (w1)->w2_t0\n", "\n", "\n", "\n", "\n", "DDB (w1)->w2_t1\n", "\n", "\n", "\n", "\n", "DDB (w1)->w2_t2\n", "\n", "\n", "\n", "\n", "DVDB (w1)\n", "\n", "DVDB (w1)\n", "\n", "\n", "DVDB (w1)->w2_t0\n", "\n", "\n", "\n", "\n", "DVDB (w1)->w2_t1\n", "\n", "\n", "\n", "\n", "DVDB (w1)->w2_t2\n", "\n", "\n", "\n", "\n", "\n", "__0:clusterw1->DDB (w1)\n", "\n", "\n", "\n", "\n", "\n", "__1:clusterw1->DVDB (w1)\n", "\n", "\n", "\n", "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "flow.get_graphviz()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "You may wonder why we have so many tasks especially if you are used to the multi-dataset philosophy of Abinit.\n", "Indeed if you are a `ndtset` master, it should not be so difficult for you\n", "to write a *single input file* that performs the same kind of convergence study.\n", "Still you have to consider that the multi-dataset approach does not scale well: \n", "this kind of calculation has several independent steps \n", "that could be executed in parallel whereas abinit datasets are executed sequentially. \n", "This makes a huge difference when running on clusters with hundreds or thousands of CPUs because \n", "the *time to solution* can be considerably reduced with this kind of parallelism.\n", "
\n", "\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can generate the directories and the input files of the `Flow` with:\n", "\n", " flow.build_and_pickle_dump()\n", "\n", "and then use the `abirun.py` script to launch the entire calculation:\n", "\n", " abirun.py flow_eph_al scheduler\n", " \n", "You may want to run this example in the terminal if you've already installed and configured AbiPy and Abinit on your machine. The calculation requires ~12 minutes on a poor 1.7 GHz Intel Core i5 (50% of the time \n", "is spent in the last task to compute the phonon linewidths with the 32x32x32 k-mesh)\n", "\n", "If you prefer to skip this part, jump to next section in which we focus on the post-processing of the results.\n", "Note that most of the output files are already available in the [github repository](https://github.com/abinit/abitutorials)\n", "so it is possible to try the AbiPy post-processing tools without having to run the flow.\n", "Some DVDB, DDB, PHDOS, and PBSTS files are missing, but their absence will not prevent running the present tutorial to the end.\n", "In particular, one can use the command line and the commands:\n", "\n", " abiopen.py FILE\n", " \n", "to open the file inside ipython,\n", "\n", " abiopen.py ut_A2F.nc --expose\n", " \n", "to visualize the EPH results and finally,\n", "\n", " abicomp.py a2f flow_ep_al/\n", " \n", "to compare multiple `A2F.nc` files with the robot and ipython." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Electronic properties\n", "[[back to top](#top)]\n", "\n", "Let's focus on the electronic properties first." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "flow_eph_al//w0/t0/outdata/out_GSR.nc\n", "flow_eph_al//w0/t1/outdata/out_GSR.nc\n", "flow_eph_al//w0/t2/outdata/out_GSR.nc\n", "flow_eph_al//w0/t3/outdata/out_GSR.nc\n", "flow_eph_al//w0/t4/outdata/out_GSR.nc\n" ] } ], "source": [ "!find flow_eph_al/ -name \"out_GSR.nc\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The task `w0/t0` computed the electronic band structure on a high-symmetry k-path.\n", "Let's plot the bands with:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "with abilab.abiopen(\"flow_eph_al/w0/t1/outdata/out_GSR.nc\") as gsr:\n", " ebands_kpath = gsr.ebands\n", " \n", "# NB: Fermi level set to zero\n", "ebands_kpath.plot();" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#print(ebands_kpath)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To better understand what’s happening at $\\ee_F$, we can plot the bandwidths with:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ebands_kpath.boxplot();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There is only one band **index** crossing the Fermi level if we exclude a tiny portion with band index 2 (see the region close to the W point).\n", "As phonon lifetimes are very sensitive to the Fermi surface sampling, \n", "it is a good idea to analyze the convergence of the electronic DOS, \n", "in particular the behavior in the region around $\\ee_F$.\n", "\n", "Let's use the `GsrRobot` to load all the `GSR` files produced by the `w0` work:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
    \n", "
  1. flow_eph_al/w0/t0/outdata/out_GSR.nc
  2. \n", "
  3. flow_eph_al/w0/t1/outdata/out_GSR.nc
  4. \n", "
  5. flow_eph_al/w0/t2/outdata/out_GSR.nc
  6. \n", "
  7. flow_eph_al/w0/t3/outdata/out_GSR.nc
  8. \n", "
  9. flow_eph_al/w0/t4/outdata/out_GSR.nc
  10. \n", "
" ], "text/plain": [ "Label Relpath\n", "------------------------------------ ------------------------------------\n", "flow_eph_al/w0/t0/outdata/out_GSR.nc flow_eph_al/w0/t0/outdata/out_GSR.nc\n", "flow_eph_al/w0/t1/outdata/out_GSR.nc flow_eph_al/w0/t1/outdata/out_GSR.nc\n", "flow_eph_al/w0/t2/outdata/out_GSR.nc flow_eph_al/w0/t2/outdata/out_GSR.nc\n", "flow_eph_al/w0/t3/outdata/out_GSR.nc flow_eph_al/w0/t3/outdata/out_GSR.nc\n", "flow_eph_al/w0/t4/outdata/out_GSR.nc flow_eph_al/w0/t4/outdata/out_GSR.nc" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gsr_robot = abilab.GsrRobot.from_dir_glob(\"flow_eph_al/w0/t*/\")\n", "gsr_robot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In total, we have 5 `GSR` files but `w0/t1` computed the energies along the k-path and the \n", "DOS **requires** a homogeneous sampling.\n", "Let's remove the file for which this is not the case from the robot with:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
    \n", "
  1. flow_eph_al/w0/t0/outdata/out_GSR.nc
  2. \n", "
  3. flow_eph_al/w0/t2/outdata/out_GSR.nc
  4. \n", "
  5. flow_eph_al/w0/t3/outdata/out_GSR.nc
  6. \n", "
  7. flow_eph_al/w0/t4/outdata/out_GSR.nc
  8. \n", "
" ], "text/plain": [ "Label Relpath\n", "------------------------------------ ------------------------------------\n", "flow_eph_al/w0/t0/outdata/out_GSR.nc flow_eph_al/w0/t0/outdata/out_GSR.nc\n", "flow_eph_al/w0/t2/outdata/out_GSR.nc flow_eph_al/w0/t2/outdata/out_GSR.nc\n", "flow_eph_al/w0/t3/outdata/out_GSR.nc flow_eph_al/w0/t3/outdata/out_GSR.nc\n", "flow_eph_al/w0/t4/outdata/out_GSR.nc flow_eph_al/w0/t4/outdata/out_GSR.nc" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gsr_robot.pop_label(\"flow_eph_al/w0/t1/outdata/out_GSR.nc\")\n", "gsr_robot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and use a small lambda function to change the labels associated to the files \n", "so that we have the number of k-points in the IBZ:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "OrderedDict([('nkpt: 29', 'flow_eph_al/w0/t0/outdata/out_GSR.nc'),\n", " ('nkpt: 145', 'flow_eph_al/w0/t2/outdata/out_GSR.nc'),\n", " ('nkpt: 413', 'flow_eph_al/w0/t3/outdata/out_GSR.nc'),\n", " ('nkpt: 897', 'flow_eph_al/w0/t4/outdata/out_GSR.nc')])" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gsr_robot.remap_labels(lambda gsr: \"nkpt: %d\" % gsr.nkpt)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can finally compare the electronic DOS obtained with the different k-meshes:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "gsr_robot.combiplot_edos(xlims=[-15, +5]);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Clearly, the 8x8x8 k-mesh used to compute the density is not enough to converge the DOS at $\\ee_F$.\n", "Remember, however, that we have decided to use a minimalistic sampling in the GS/DFPT run \n", "to keep the calculation manageable.\n", "In real life, one should use a much denser k-sampling for the GS/DFPT and this is particularly \n", "true if we are trying to relax the structure.\n", "Let's forget about this technical point and focus on the DOS obtained with the other two k-meshes.\n", "\n", "As you can see, even if 145 k-points in the IBZ are not enough, the DOS is becoming *smoother*\n", "and starts to resemble the one of the free-electron gas (as a matter of fact the band dispersion of Al is not\n", "that different from the ideal free-electron model provided that BZ folding is taken into account).\n", "\n", "Visual inspection suggests that the k-sampling becomes *acceptable* at and beyond 24x24x24 (413 nkpt).\n", "\n", "
\n", "The convergence of the DOS at the Fermi level does not necessarly imply convergence in the final EPH results. \n", "This is something that should be checked explicitly by looking at the behaviour of the final observables\n", "as a function of the input parameters.\n", "
" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": true } }, "source": [ "In the introduction, we mentioned that there are two factors governing the strengh of the E-PH coupling in metals:\n", "the behaviour in (k, q)-space of the matrix elements and the *geometrical* contribution due to the Fermi surface.\n", "\n", "\\begin{equation}\n", " N(\\qq) = \\sum_{mn\\kk} \\delta(\\ee_{\\kpq m}) \\delta(\\ee_{\\kk n})\n", "\\end{equation}\n", "\n", "To understand the *geometrical* contribution, it is useful to visualize the Fermi surface.\n", "Unfortunately, graphical applications usually require KS eigenvalues on a \n", "$\\Gamma-$centered k-mesh in the *full* BZ whereas ab-initio codes usually work with KS states \n", "in the *irreducible* wedge.\n", "\n", "Fortunately, we can use AbiPy to reconstruct the KS eigenvalues in the full BZ:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Select the last GSR file in thr robot.\n", "eb3d = gsr_robot.abifiles[-1].ebands.get_ebands3d()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and then use matplotlib to visualize the Fermi energy isosurfaces:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "eb3d.plot_isosurfaces();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that, at present, the matplotlib version is only able to display isosurfaces \n", "in the unit cell of the reciprocal lattice.\n", "To visualize isosurfaces in the first BZ, one can export the data \n", "into BXSF format and then call [xcrysden](http://www.xcrysden.org/) with:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#eb3d.xcrysden_view()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is the image you should get with xcrysden: \n", "\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Vibrational properties\n", "[[back to top](#top)]\n", "\n", "Now we turn our attention to the vibrational properties.\n", "AbiPy has already merged all the independent atomic perturbations in `flow_eph_al/w1/outdata/out_DDB`:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "flow_eph_al//w0/t0/outdata/out_DDB\n", "flow_eph_al//w1/outdata/out_DDB\n" ] } ], "source": [ "!find flow_eph_al/ -name \"out_DDB\"" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/Users/gmatteo/git_repos/abitutorials/abitutorials/eph_al/flow_eph_al/w1/outdata/out_DDB\n", "DDB file merged by PhononWork on Sun Mar 18 14:41:09 2018\n", "14\n", "/Users/gmatteo/git_repos/abitutorials/abitutorials/eph_al/flow_eph_al/w1/t0/outdata/out_DDB\n", "/Users/gmatteo/git_repos/abitutorials/abitutorials/eph_al/flow_eph_al/w1/t1/outdata/out_DDB\n", "/Users/gmatteo/git_repos/abitutorials/abitutorials/eph_al/flow_eph_al/w1/t2/outdata/out_DDB\n", "/Users/gmatteo/git_repos/abitutorials/abitutorials/eph_al/flow_eph_al/w1/t3/outdata/out_DDB\n", "/Users/gmatteo/git_repos/abitutorials/abitutorials/eph_al/flow_eph_al/w1/t4/outdata/out_DDB\n", "/Users/gmatteo/git_repos/abitutorials/abitutorials/eph_al/flow_eph_al/w1/t5/outdata/out_DDB\n", "/Users/gmatteo/git_repos/abitutorials/abitutorials/eph_al/flow_eph_al/w1/t6/outdata/out_DDB\n", "/Users/gmatteo/git_repos/abitutorials/abitutorials/eph_al/flow_eph_al/w1/t7/outdata/out_DDB\n", "/Users/gmatteo/git_repos/abitutorials/abitutorials/eph_al/flow_eph_al/w1/t8/outdata/out_DDB\n", "/Users/gmatteo/git_repos/abitutorials/abitutorials/eph_al/flow_eph_al/w1/t9/outdata/out_DDB\n", "/Users/gmatteo/git_repos/abitutorials/abitutorials/eph_al/flow_eph_al/w1/t10/outdata/out_DDB\n", "/Users/gmatteo/git_repos/abitutorials/abitutorials/eph_al/flow_eph_al/w1/t11/outdata/out_DDB\n", "/Users/gmatteo/git_repos/abitutorials/abitutorials/eph_al/flow_eph_al/w1/t12/outdata/out_DDB\n", "/Users/gmatteo/git_repos/abitutorials/abitutorials/eph_al/flow_eph_al/w1/t13/outdata/out_DDB\n" ] } ], "source": [ "!cat flow_eph_al/w1/outdata/mrgddb.stdin" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the same directory, we have the `DVDB` file containing the independent DFPT potentials" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "flow_eph_al//w1/outdata/out_DVDB\n" ] } ], "source": [ "!find flow_eph_al/ -name \"out_DVDB\"" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#!cat flow_eph_al//w1/outdata/mrgdvdb.stdin" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's open the `DDB` file computed on the 4x4x4 q-mesh with:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "================================= File Info =================================\n", "Name: out_DDB\n", "Directory: /Users/gmatteo/git_repos/abitutorials/abitutorials/eph_al/flow_eph_al/w1/outdata\n", "Size: 42.10 kb\n", "Access Time: Mon Aug 13 15:11:37 2018\n", "Modification Time: Sun Mar 18 14:41:09 2018\n", "Change Time: Sun Mar 18 14:41:09 2018\n", "\n", "================================= Structure =================================\n", "Full Formula (Al1)\n", "Reduced Formula: Al\n", "abc : 2.806386 2.806386 2.806386\n", "angles: 60.000000 60.000000 60.000000\n", "Sites (1)\n", " # SP a b c\n", "--- ---- --- --- ---\n", " 0 Al 0 0 0\n", "\n", "Abinit Spacegroup: spgid: 0, num_spatial_symmetries: 48, has_timerev: True, symmorphic: False\n", "\n", "================================== DDB Info ==================================\n", "\n", "Number of q-points in DDB: 8\n", "guessed_ngqpt: [4 4 4] (guess for the q-mesh divisions made by AbiPy)\n", "Has total energy: False, Has forces: False\n", "Has stress tensor: False\n", "\n", "Has (at least one) atomic pertubation: True\n", "Has (at least one diagonal) electric-field perturbation: False\n", "Has (at least one) Born effective charge: True\n", "Has (all) strain terms: False\n", "Has (all) internal strain terms: False\n", "Has (all) piezoelectric terms: False\n" ] } ], "source": [ "ddb = abilab.abiopen(\"flow_eph_al/w1/outdata/out_DDB\")\n", "print(ddb)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and then invoke `anaddb` to compute phonon bands and DOS:" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": true }, "outputs": [], "source": [ "phbst, phdos = ddb.anaget_phbst_and_phdos_files()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally we plot the results with:" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAakAAAESCAYAAABHISrtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvFvnyVgAAIABJREFUeJzsnWdYFFcXgN8FAamKCCpYsI6IvfeCGruxFzTFGDXGHnvvMTGamNhrbLHEXmOLvTdsoKOIggIiIihFabvfj2VXOgvsLJBv3ufhAebO3Htmyz1zzz1FoVKpkJGRkZGRyY0Y5bQAMjIyMjIyaSErKRkZGRmZXIuspGRkZGRkci2ykpKRkZGRybXISkpGRkZGJtciKykZGRkZmVyLrKRkZGRkZHItspKSkZGRkcm1yEpKRkZGRibXIispGRkZGZlci6ykZGRkZGRyLfmk7FwQBHdgGmACLBFFcXmy9urAOsAGOA98J4pinCAIXwE/AUEJpx4RRXGqDkOaAXWAQCBeP3chIyOTDGOgGHADiE6lXf4e5j0yek+TYMi5XTIlJQiCEzAfqIX6pi8LgnBGFEWvRKdtBb4VRfGqIAjrgUHASqA28IMoitszOWwd4EL2pZeRkdGBJsDFVI7L38O8S1rvqRZDz+1SrqRaAadFUXwLIAjCbqAHMCfh/1KAuSiKVxPO3wjMRn0jdYDygiBMAe4CI0RRDNVhzECA0NBIlErDZ3e3s7MiJCTCINdnd6zsYsjx9TlWbniNc+p+9NWXkZECW1tLSPi+pUKq38Oc/sxmlrwmL2RdZh3e08QYdG6XUkk5kvSGA4G6GbQXT/T3IuAy8COwDOinw5jxAEqlKkeUlGZsQ12fU/eYE+Prc6zc8Brn1P3oua+0THlpfg9z+jObWfKavJA9mdesWVNs8eLFyd/XMFEUwxL9b9C5XUolZQQkfrUUgFKXdlEUu2oOCoKwEHiamYHt7KwyK6vesLe3Ntj12R0ruxhyfH2OlRte45y6H0P2ldr3MKc/s5klr8kL2ZN5x44dqZlpZwOzEv1v0LldSiX1ErV9U0NRICBZe7Hk7YIgFAC+EUXxt4TjCiAuMwOHhETkyBOQvb01wcHhBrk+u2NlF0OOr8+xcsNrnFP3o6++jIwUOj0IJv8e5vRnNrPkNXkh6zJr3tM+ffo0Wbx48ctkzWHJ/jfo3C6lC/opoKUgCPaCIFgA3YFjmkZRFH2Bj4IgNEo49AXwDxABTBAEoV7C8eHAPgnllJGRkZEBBg8e/FIUxefJfpIrKYPO7ZIpKVEU/YGpwBngDrBNFMXrgiAcFQShdsJp/YDfBEF4BFgBf4iiGA/0AlYKgvAQtQfJBKnklJGRkZHRHUPP7ZLGSYmiuA3YluxY+0R/3yXphpvm+AWgppSyycjIyMhkDUPO7XLGCRkZGcmJiori+fPnOS2GTB5EVlIyMjKS891331CuXDmCgl7ltCgyeQxZScnIyEiOv78/8fHxspKSyTSykpKRkZGRybXISkpGRkZGJtciKykZGRkZmVyLrKRkZGRkZHItspKSkZGRkcm1yEpKRkZGRibXIispGRkZGZlci6ykZGRkZGRyLbKSkpGRkZHJtchKSkZGRkYm1yIrKRkZGclRKBQAqFR5rxy7TM4iaakOGZnsoFKp8PLyRKX6iKVlIZydS2snu7yGSqXi5csX+Pn5Ymtria1tUYoVc8xpsQyG5n1TKpUZnCkjkxRZScnkSkJD3zJ48ADOnTujPebo6IS7+xd8990wbGwK5KB0uhMVFcWmTRvYvHkDT596J2kThIp89dU39O//Nfnz588hCQ2DkZFaSUVEROSwJDJ5Ddncpwd8fJ4ydeoEevTowYwZU7h//25Oi5SniY+P5+uv+3Hu3BkKFixIkyZNKFzYnoAAfxYt+on69Wty/Pg/OS1mhly/fo1mzeozc+YUnj71pmDBgtStW5+GDRtiY1MAUXzElCkTaNGiIbdu3chpcTMkKiqKLVs2MmnS2ExfW7lyNQCGDx/Crl07iImJ0bd4Mv9RZCWVTQ4c2IubWyPWrl3Fnj17WLVqGa1aNWX69MnExcXltHh5kq1bN3HlyiWKFCnKmTOXOX/+PJ6e3uzde5i6devz5k0wX3zRmz/++DWnRU2TQ4f2061bB3x9n+Pi4sqWLTvx8vLh8OETXLp0iYcPfdiwYSsVKgg8ferN55+3Y8+ev3Na7DS5ffsmzZs3YOzYkZw8eTzT10+fPouGDRsSGBjAsGGDqVKlPIMHf83vvy/m5Mlj+Pu/lPerZFLFeNasWTktgz4pCIz+8CEGQ3zeL148z9dfuxMdHU2XLt2YNWsmNja23Lt3hxs3ruHj40379p0wMtLtWcDS0oyoKN2eMDNzrhRINX54+Hu+/LIPHz584Pffl1OnTj3tWCVLlqJv3/6Ym1tw4cJZzp8/i0KhoGHDxpLIndV73LVrB999N5D4+Hi++WYQf/65FUGoqP0cWFqa8fFjHBUqCPTr9xVhYaHcunWDo0cPUb58BSpWrCTJ/WS1r6tXL9OzZxeCg1/j4lKJiROnUrNmdYDfgbBUukrxPcyf35yhQwdhZ1cUX9/nvHjxgkePHnLhwjn27t3F6tXLWbt2Ff/+e4J79+4QGBhAXFwsBQoUxNTUVC/3l1ly+juWFbIqs0KhwMLCFNJ+T3MMeU8qi7x//44hQ74hLi6OoUNHMHv2fOztrXFza0+XLj1wd+/Bvn17KFWqNFOmzMhpcfMMO3b8xdu3b6lbtz6dOnVJ0a5QKBgxYjRFixZlxIjvWLjwR4oVc6Rfvy9zQNqUXL16hdGjh6FSqZg0aRpjxoxP19nDzMyMn3/+lSJFivLTT/MYNmwwBQva0ry5mwGlThs/P1+++qovUVGR9OjRmyVLlpM/v1mW+sqXLx/u7l/g7v4Fjx49xMPjFl5eD/Dy8sTT8z5v377lypVLXLlySXuNQqHA2bk0lSpVplIlV1xdq+DqWpmSJUvlWScamcwhK6ks8tNP8wgOfk2dOvWYMWNOkrb69RuwefN2evTozJIli2jUqAnNmrXIIUnzDkqlkg0b1gLw3XfD052EevbsQ2xsLKNHD2PixB8QhIrUrl3XUKKmSkCAPwMGuBMbG8vgwUP54YcJOl87Zsx43r17x8qVS/nuu284ffoSjo5OEkqbMfHx8Qwe/DWhoaG0bt2GpUtXYWxsrJe+K1Z0oWJFF+3/KpWK16+D8PT8pLS8vDx58kTk2TMfnj3z4ciRg9rzra1tcHWtjKtrZcqXFyhSpChFihShSJGiODgUwcwsa4pUJvchK6ks4Ofny59/rsPY2JiFC39L9YvbuHFTxo+fzM8/z2fcuFGcO3cVCwuLHJA273D58kWePvXG0dGJtm3bZ3i+u/sX3L9/l/Xr1yR4Al7B2trGAJKmRKVSMXr0MEJCQmje3I1Zs+Zn6nqFQsHMmXN59MiLM2f+ZfDgAezff5R8+XLuK7p27Upu376Fo6MTy5ev0ZuCSg2FQpGgaIri5tZKezwmJoYnTx7j5fUgQYE94MGD+7x5E8zVq5e5evVyqv3Z2tomKKyiGBkpyJ8/PzNmzKVcufKS3YOMNMhKKgssX/478fHx9OzZB1fXymmeN3LkDxw8uI+HD71YseIPxo2bZEAp8x4HD+4D1KskXSfnOXMWcOvWDe7c8WD27BksWrREShHTZMuWjZw9expbW1uWLl2dJeViZGTE8uVrcXNrxPXrV1m9egXDho2UQNqMCQ4O5ueffwRg4cJfKVjQNkfkMDU11a6Yevb8dDwoKAhPz/s8eHAfX99nBAW9IigoiKCgVwQHvyY0NJTQ0FAePXqovebYsaNcvnyLsmXLyabCPITiP+ZR4ww8CwmJQKmU5r7evg2hWrWKREdHc+HCdQShorbN3t6a4ODwJOdfvnyRLl3aY21tw82b97C1LZRm36ldr49zpUDf48fHx1O1qkBw8Gv+/fcCVapU03mshw+9aN26KTExMezZc4gmTZrpRW5dz33z5g316lUnPPw9q1dvoGvXHtnq9/Tpk/Tp0x0LCwvOnbtKqVLO2ZZRFxL3NW3aRNasWUnr1m34669dSc4zMlJgZ2cFUBp4nkpXzqTyPTTUZzY+Pp6QkBCCgl5x/vxZZs+elqS9ePESNG3anFq16lC9ek0qVnTBxMQkRT85/R3LClmVWYf3NMeQXdAzya5dO4iOjsbNrVUSBZUWDRs2plmzFoSHv2fZst8NIGHe5Pr1qwQHv6ZUKWcqV66aqWtdXCpp93+mTBlvcNf/n3+eT3j4e9zcWtGlS/ds9+fm1pquXbsTFRXF5Mnj9CBh5njxwo+NG9ejUCiYMmWmwcfPLsbGxjg4OFClSlWGDRuJj08Ay5atpmvX7hQqVIiXL1+wbdsWxo4dScuWjSlb1on27VsxadJY/vxzHVevXiY09G1O34ZMArK5LxOoVCq2bNkIQP/+X+t83ZQpMzh37gzr1q1i6NARFC5cWBoB8zD//nsSgHbtOmbJFDNs2Ch27PgLUXzEpk3rGThwiL5FTBUvL0+2bPkTY2Nj5sxZoDcz0ty5P3Pq1ElOnTrB2bOnDertt2TJYmJiYujWrWe65uy8gpWVFb169aVXr74olUru37/LpUsXuXPnFh4et/H1fc7Nm9e5efN6kuscHR0pX16gYsVKuLhUonz5CpQsWQp7ewedw0pkso+spDLBvXt3ePxYpHBhe9q0aafzdTVq1KJVq884deoEmzdvyJTX1/8LZ8+eBqBFi5ZZut7MzIxZs+bz9dfu/PzzfLp165muaVVf/PTTPJRKJQMHDqZCBUFv/To4ODBq1A/MmzeLOXNm0LRpc4NMjG/evGHXru0AjB07UfLxDI2RkRHVqtWgWrUa2mNv34Zw544HDx968eiRF48ePeTx40cEBAQQEBCQJDUXqD9rTk7FKVGiZJKfokWLUbiwPfb2Dtja2sqKTE/ISioTHDp0AIBOnT5P1YadHkOGDOPUqRNs2LCWYcNGyS6yiXjz5g3379/FzMyM+vUbZrmfdu060KRJMy5cOMfvv//KrFnz9ChlSu7fv8uxY0cwNzdnzBj9P3gMGjSUDRvW8uDBPXbv3kmvXn31PkZyNm1az8ePH2ndug3ly1eQfLzcQKFCdri5tUriVRgfH09kZAiXLt1AFB/y6JEX3t7evHzpx9u3b/HxeYqPz9M0+zQ2NsbOrjD29g7Y29trlZe9vQOFCxemQIGC2NjYYGNTIOG3DdbWNjnqzZlbkV8RHVGpVBw+rFFSKYNMM6Jp0+a4uLjy8KEn+/fvoXdvd32LmGe5cOEsKpWKevUaYm5unuV+FAoFM2bMoXXrZqxfv5qBAwdTokRJPUqalF9++QmAr74aiIODg977Nzc3Z9KkaYwcOZQFC+bSuXNXSRPRRkdHJ4lT+3/G2NiYsmXLYmPjQLt2HZK0RURE8PLlC1688OXFixe8eOHHixd+vH4dRHDwa968ecO7d2G8fh3E69dBmRrX0tJKq7QSKzAbm4JJlJmFhQWWlpZYWFhgbm6BhYUFFhaWfPjgwIcPSiwsLDEzM/tPeDFKqqQEQXAHpgEmwBJRFJcna68OrANsgPPAd6IoxiVqrwFcFUUxx5cdDx964ePzFDs7uyw97SsUCoYM+Z7Ro4exYcMaWUkl4tq1KwA0btwk231Vq1aDbt16sHfvbn76aR7Ll6/Jdp+p8fChl3YVNWzYKEnGALU7/sqVy3j40JONG9dJqjz27t1LcPBrKlWqTOPGTSUbJ69jZWWVIhg5OdHR0bx5E8ybN8EEB78mODg44ec1b94EEx7+nvfv3/Pu3Tvt3+Hh74mMjCAyMoLAwIBsy2lsbIy1tTX29g44OBTBxMSEZ898GDhwMN9++122Vm2GnNslU1KCIDgB84FaQDRwWRCEM6IoeiU6bSvwrSiKVwVBWA8MAlYmXG8BLAVyJnFXMg4d2g+oN/az+uZ26dKd6dMn4+FxGy8vTypVctWniHmWGzfUG9Z169bXS3+TJ8/g0KED7N69k6FDR1C5chW99JuYtWtXAtCnTz+KFCmi9/41GBsbM23aTPr168WSJYvo1+9LyQKW161bB8BXX33zn3gCz0k0+1ZOTsV1vkapVBIREZ5Meb3j3bt3WiWmPh5OVFQkHz58ICoqkqioqISfSD5+/EBERCRRUZHExsYSFhZGWFgYT5481o4zY8YUKlWqTNOmzbN0b4ae26VcSbUCToui+DZBsN1AD2BOwv+lAHNRFK8mnL8RmE3CjQCLgSVAIwll1BlNaYiOHTtnuQ8LCwu6devJpk3r2b59C3Pn/qQv8fIsEREReHk9wNjYOMlmdnYoVcqZb74ZxOrVK5g7dwY7d+7TS78aQkJC2L17J6DeN5KaVq3aUK9eA65du8Ly5X8wadK0jC/KJM+e+XD69GnMzc3p1i39OC8ZaTAyMkow8RWgePESWeojcZxUbGws7969Izj4NZs2rdeacgEmTBjDxYs3svrAbdC5XUr3E0cgMNH/gUBxXdoFQegMWIiiuFtC+XQmODiYBw/ukT9/fho2zJ5Jql+/L4BP8VaZISwslD17/mb58j94+fJltuTILdy5c5v4+HgqV66KpaWl3vodPXo81tY2nDnzL+fPnwXgwYP7LFy4kCNHDvHhw4cs971160Y+fvxIy5atDZJmR6FQMG3abABWrVrOq1fqr83Vq1fYv3+/XkpcbNq0AVDvtxYoUDDb/cnkHGFhody968GxY0fYvn0rGzas5eFDryTn+Pg85e3blLFga9asKS4IgnOyn+QfCIPO7VKupIyAxN8eBaDMqF0QhKKobZ2tyCIJkdN64+TJQwA0bdqUEiXs0z3X3t463fZWrZpStWpV7t27x5UrZ+iZONdLGtd//PiRefPmsXjxYj5+/AiAh8d1Dhw4kJnb0DsZ3asueHndAaBJk0bp9pfZseztrZkyZTKTJ09mzpxp7N+/n969uxAcHAxAwYIFmTdvHkOHDk3XVTj5uLGxsWzcqDaLjR8/NsuvQWav69ixNZ9//jkHDhxg/PiRzJ8/n+7dOxIbG4u7uztr1qzJkpJXqVT89ttvrFy5FIDhw4fq5X2F1L+H+urbUOQGeVUqFREREbx790778/r1a169UqeCevXqFa9evcLX1xcfHx/evXuXZl92dna4ubkxYMAAXF3LpmjfsWPHhVQumw3MSvS/Qed2KZXUSyDxsqMoEJCsvVgq7R0BO+C8IKjjTgRBuAM0EUVRp3wf+k6LdOjQUQAaNGiabsoRXVOS9O7dj3v37rFy5WqaN2+b7vUhISH0799LW7nVxaUSDx96cfToUby8fLC3VyvN+Ph41qxZyd9/q2Nc+vRx59tvv5MsKai+UsacO6f+TlSuXCPN/rI6lrv7N6xcuZp79+5RpkwZQJ0LrlIlV+7c8WD48OEcP36K5cvXpOo1l9q4+/fvwd/fn/LlK1CjRoMsyZXV+5k7dyHnz5/n+PHjHD/+qfDgtm3b8PC4w59/bqVMmXI69xcVFcW4caO0psuZM2dSsWL1DGVLlEInXXIqLZK+kFLeuLg4Xr8OIiDAn8DAAAID1TFZgYH+vHr1irCwMK1DRUREOEqlMuNOE7CwsKRUKeeEn1KUKuVMmTJlcXWtgoNDEe1+Y+J707ynffr0abJ48eLkZprk9aUMOrdLlrsvYXPtIlAXiAQuA4NFUbye6JwHwBBRFC8JgrAGeCKK4i/J+lGJoqjrLq4zes7dp1KpqFatIq9eBXLmzOV0I/B1/VC/fRtClSoViI+P5969x1r35eTXv3//js6d2+Hl9YDixUuwatUG6tatR79+PTl58jjz5//MoEFDUSqVjBkznO3btyYZp3dvd/74Y6Ukm+D6+AIrlUpcXEoTGhrKzZv3KVmylN7H8vLyxN29BwEB/lSvXoOTJ0+gUplx+PBBRo8exvv37/jss7Zs3LgthX0+tXHbtWvJrVs3WLjwN77+emCWZMrO/Zw9e5qBA78kPPw9bdu2Z8GC+fTs2Qtv7ydYW9swfPgounXrSYkSJdNcIb59G8KJE8dYvPhnfH2fY2FhybJlqxkwoJ9OcuX23H36IqvyxsTE8OpVIAEB/gk/auWT+Pfr10GZVDwWWFvbaN3Q7ewK4+BQBAcHB238lZNTcWrVqoJKlXnX88zk7jP03C7ZSkoURX9BEKYCZ1B7cawTRfG6IAhHgRmiKN4E+gFrBUGwAW4Df0glT1Z5/Fjk1atA7O0d9OaNV6iQHS1atOTEiWMcOrQv1RQ+SqWSQYO+xsvrAWXLlmP//qMUKVIUUCufkyePs3PndgYNGsqqVcvZvn0r5ubmLFu2GoARI4ayc+c2GjVqQp8+/fQit77x9X1OaGgo9vYOksUzVarkyrVrd/D3f0mpUs4ULlyQ4OBwOnbsTJkyZenWrQMnThxjxozJ/PjjL+n2dfv2TW7dukGBAgXp2bOPJPJmRPPmbty9+4iQkDeULFkKBwcbjh8/w6hRwzh8+AALFsxlwYK5mJiYULCgLQqFQjthacxGUVGR2v5cXFxZuXKd7GmqIx8+fEi08vHX/lYrIPXfwcGvM+xHoVDg4FCEYsUcKVbMEUdHR+3fRYsWw9bWVquUrK1tdE4eYIgHAUPP7ZLGSYmiuA3YluxY+0R/30WtjdPrI0d9YS9cOAuog3H1uSLp2rUHJ04cY+/e3akqqRUrlnLmzL/Y2dmxc+c+rYIC+OyzdhQoUIB79+5w6tRxFi5U1y5atWqDNvAwMjKSkSOHMmXKBNq0aWeQFEGZ5cGD+wBUrVpNUpdnMzMzypRJaX+vVMmVLVt20qVLe9atW02zZm7pprtas0btnNS//1d6dfLILFZWVlhZfTK3WVvbsH79Zs6dO8OmTRu4cuUib9++TXOytLCwoHr1mvTu7U6PHr0znT3lv0pkZGQipePP+/chPHnyjMBAf/z9/QkM9E/V2SA5RkZGFC1aLEH5OCX6caRoUUecnJwoUqRonn7dDTm3yxknMuDaNbUXZcOGjfXab5s27TE3N+fGjWu8eOGXZCVx794dfvxR7c31++8rUpjB8ufPT+/evVmzZg3u7mrHi88/75YkMr53b3d27/6b8+fPsGbNSiZOnKpX+fWBp+c9gExnPdcnderUY+rUWcyaNZVRo4Zy7ty1VOOeXr0K5ODBfRgZGfHNN4NyQNL0USgUNG/upk1E++HDB96/f6f1/NP8trKywtLS6v8ur1xERHhCLj716sff/2WyVZA/YWHJt15SYmJikmz145Tkt6OjE/b2DnJ6Iz0iv5LpoFKptNkQ6tVroNe+raysaNOmHfv372Xfvj2MHDkGUJv5xo8fTVxcHN98M4jPPkv9yf7LL79kzZpP2RSmTZuVpF2hUDBu3CTOnz/D2rWrGDZsVJKn79yAZiUlRbBtZvjuu2GcOXOKc+fOMGfO9FSzVGzYsJa4uDg6deoiaaolfWFubp6tFFN5jejoaJ4+9ebp0yc8feqNr+/zJHtC4eHvM+zD1NQ0yeqnXLnSFCxYOIkisre3/79T8DmNrKTS4cULP169CqRQoUKSJNvs2rVngpLarVVS27dvxcPjNkWLFkuheBLTsOGn1Ez16jVItTBe/foNqF27LjdvXufw4QO5bm8qtygpIyMjfvllCU2a1GXXrh188cUA6tf/9FASERHBn3+q3c6HDBmWU2LKJOPZMx/++mszly6d5/79e8TExKR5bv78+bXKR6OINL+dnJwoVswJOzu7JGbnvObo8V9FVlLpoFlF1alTT5I9Eze3VtjYFMDT8z6PH4uYmVVg3jx1kblZs+ZhZZV2jEZMTAwmJibExsamm97E3f0Lbt68zs6d23KVkgoJCSEgwB8LC0ucncvktDg4O5dm+PDRLF78M1OmjOfUqfPatr/+2sS7d2HUrVufunXr5aCUMqC2cKxYsZR582YSHx8PqC0HZcqUpXz5CpQpUw5n59IUL15cuwqytS0kp3rKo8hKKh00+1F16+rX1KfBzMyMDh06sX37Vvbt283p05aEhIRQt279DEuQHzhwgNjYWAAuXbrA+PGTUz2vc+cuTJ06gUuXLqTY+8pJPD3Vq6hKlVwli+XKLCNGjGH79q08eHCPgwf3MWjQ18TExLBqlTp35vDho3NYQpno6GiGDPmGo0fVAfY9evSmZ88+1KpVGxubAjksnYwUyMbVdLh+XZr9qMRoyo3v3r2TX3/9FYDJk6dn+NS3ZcsW7d83blxL0+ZuY1OA1q3VAcPHjh3Rh8h6IbeY+hJjYWGhLUi5cOGPxMXFsXbtKvz9X1KhgsBnn7XNoAcZqRk/fjRHjx6iQIGCbN68gxUr1tKiRUtZQf2HkZVUGrx7F8ajRw8xNTWlWrXqko3TuHFTbG1t8fV9TlhYGI0aNaFRo/TzA4aHv+fEiRMoFArKli1HbGwsZ878m+b5GrfqY8f+0avs2eHBg5z37EuNvn37U6qUM97eT5g/fz6LFqmTAM+ePV/eMM9hPDxusWPHX5ibm7Nnz0Hatm2f8UUyeR75W5cGd++qc8pVqVJV0iq6JiYmSTz4JkyYkuE1p06dICYmhnr1GtC//9cAHDt2NM3zW7X6DGNjY65cuci7dxm72RqCR48eAuo0T7kJExMTbdn0WbNmERkZQdu2HWjZ8rMclkzm9OlTgLo8StWq0j04yuQuZCWVBnfu3AagevWako9lbf3JQUKXL9/hwwcB6NChE23bqhXcqVPHiYuLS/V8W9tC1KvXgLi4OG1G8JwkPj4eb291fRtBqCj5eDduXOPbb7/il18WpPkaJSZ5TNy7d2FERERIJV6mOXr0MAMG9GfjxvWoVCru3vVgw4a1nD59SutIoAv379/lzz/XcezYUe3+Zm5G896ZmuaKEnMyBkJWUmng4aFWUvqqcZQeoihq/16zZkW65759G6ItwNi+fSfKli1P+fIVCAsL4/r1q2le16xZCwAuXDinB4mzx4sXfnz8+JGiRYtJvpcQEOBPr15dOXhwH7/8soAhQ4YQFRWV7jXz588CIF++fNjbO3DlyiUGDOiXqVxrUnHz5nUGDOjHkSMHmTBhDEZGRrRu3YxJk8bSp0832rdvSUCAf7rDaKiBAAAgAElEQVR9fPz4keHDh9CyZRMmTvyBL7/sQ4sWDbl+/Xq61+U0TZo0A2DLlk1cuXIph6WRMRSykkoDzUqqRo1ako4TEhLC5cufsuMvXvyzdmxQu9vevn2T6dMnUb9+DSpWLA1A7dq1tZ56bdqobfPpmfw05cAvXjyf5jmG4vHjRwBUqCD9Kur33xcTGRlB0aLFUCgUbNiwgdKli9GyZRNmz57OzZvXkyif48f/Yd++PZibm/P48WMOHTpG4cKFOXfuDOvWrZJc3oxYsGAuKpUKR0cn7bGCBQvSo0dvHB2d8PC4Tfv2rdJUVPHx8Qwd+i1//70dMzMzevTojbNzaR4/FnFzc+P164zzzuUUDRs2pmfPPkRFRdK9eyfmzJlBRIQcx/RfR1ZSqfD69Wv8/V9iaWkleVG7Y8eOEB8fj5tbK4YOHUpMTAx9+3Zn4cIfmT59EnXqVKNtWzdWr16Bj89T8uXLR5kyZZk69VOao1at1PslZ8+m7TxRvXpNLC2t8PZ+QmBgQJrnGYLHjzWmPkHScaKiovj77x0A/P33fqZNm02JEiUwNjbm/v27LF/+O+3bt6JGjUpMmjSWefNmMXjw1wCMHz+F0qVLU6ZMORYvVtda+vHHOTk6ifv4eHPhwjksLCw4d+4KQ4YMo0ePHpw/f40VK9Zy+vRFateuS0CAP+7uPVN4fKpUKiZOHMuRIwexsSnAP/+cZsWKtVy4cJ1ateoQGRnJqVPH0xg9d/DHHysZOnQEcXFxLFu2hPr1a7Jz57ZcscqVkQZZSaXCnTu3AKhWrbrkMTwa012nTl347bffcHNrRUhICIsW/cTq1Svw83uOg0MRhgz5nqNHT/H8+SuuXvWgS5cu2j5q166LpaUVjx49xN8/9Yq9JiYmNGyortac06spzUqqfHlpldTZs6eJjIygZs1aVKzowogRo/Hz88Pb+yW7dx9k8OChODkVJzAwgA0b1vLHH7/y4cMHvvzyG77/foS2n3btOtCmTTuioqJYuvRXSWVOj8OH1bFBHTp0pkCBgsydu4Bdu3ZRtKi6dE+hQnZs3bqTsmXL4eX1gAEDvkiShWHhwh/ZvHkDZmZmbN26U+v+r1lRAel6ieYGjI2NmT17PseOnaZWrdq8fh3EiBHf0aFDay5cOKeXKsUyuQtZSaWCZj9KaqeJsLBQzp8/i7GxMW3bdsDMzIxt23azYcNWvv9+JBMnTuXAgX+4e/cRc+f+RO3adVPdNDY1NdXa69ObZDTxXpoCijmFRklJ7TTxzz+HAfXeXWIsLCxo2rQ58+b9zO3bnhw/foaxYycyfPho9u49zKJFS1K4m0+YoF65bty4njdv3kgqd1qkdT+JKVTIju3b91C4sD3nz5+hf/9ePH4sMmvWNBYv/hkjIyNWr/6T+vUbJrnOzU1dLPXcudOZcr7IKWrWrM2RI6dYunQVDg5FuHXrBt27d6JDh9Zs3vwnoaEZZyuXyRvISioV7t1Tu59Xry6t08SxY0eJi4ujUaOm2NnZAeo8ch07dmbWrHmMHTuRBg0a6bSaa9GiJZC+kqpVqw4At27d1IP0WUOlUmnNfVLuScXFxXHy5DEA2rXrmOZ5CoWCGjVqMXHiVGbMmKPdu0tOlSpVad26DdHR0SmKSxqCoKBX3Lp1g/z582sznaeFs3Npdu7cS6FChTh79jSNG9dhxYo/MDY2ZvnyNbRvn/L1KF26DGXLliUsLAwPj1tS3YZeMTIyondvd65evc3kydOxtbXl5s3rjBs3isqVy9O/fy/27Pk7V3lmymQeWUmlgqfnA0D6QNPDhw8A0KnT59nuS6Okzp07k6abdbVqNTAyMsLT836GHm5SERgYQEREOHZ2dlrFLAUeHrd4+/atNp+bPhgw4FsANm3aYPDVhiZGqGnT5jrVsqpSpRqnT1+iQ4fOFC5cmDp16rFr1wG6d++V5jVt27ZNMlZewcrKmjFjxnPr1gP++GMlzZu7ER8fz4kTxxg69FsqVy7HkCEDOHbsaLpJaGVyJ7KSSkZo6FsCAvwxNzendGnpEp++f/+Os2dPY2RklO6Tvq44O5emTJmyvH//Ls2VkpWVFS4ursTFxXHv3t1sj5kVRNEwnn2XLqk9JtNLvptZWrRoRcmSpfDze25wF2jNPmJm7sfR0Yk//9yKl5cPR46cTHOVqKFNmzZA7t+XSgsrK2v69OnH33/v5969xyxY8At16tQjKiqKffv28OWXfahcuRw//DCC27dzzpogkzlkJZUMzSrKxaWSpE4TJ04cIyYmhgYNGuHg4KCXPjX7CmfOpP0k/MnklzP7Uk+fPgGgXDn9lz5JzIUL6kk9o4k5MxgbG9O9u7rI5L59u/XWb0aoVCqtkmrUSH/3k5wWLVpgYmKCh8etPL+n4+DgwMCBQzhy5CQ3b95n2rTZVKpUmbCwMLZu3UTbtm58+WWfXO1yL6NGVlLJ8PJSKylXV2kTnx44sBeAjh07661PjckvPVf02rVzVkk9e+YDkGo5d30RHR3NjRuaisrp50HMLF26qLPTHzq032Cmo2fPnhIYGICdnZ2kaaSsrKyoV68BSqUyV2Qm0RclS5Zi5MgxnD17mQsXricUALXm2LGjdOjQKk2PWJncgaykkqFZSVWqVFmyMcLCQjl9+hRGRkZ06tRVb/1qnCzu3r2TZlZ0zUrq5s2cyS7g4/MUQFJTqofHLT5+/IiLSyUKFy6s175dXCrh4lKJsLAwLl40TPaOy5fVpsWGDZtInuS2efOMHXDyMoJQkZkz53Lp0g2qVauBr+9zevXqIgcF52JkJZUMjZKSciV15MghYmNjady4md5MfaC2ydeoUYv4+HiuXr2c6jlly5bD2tqGV68CCQoK0tvYuqJRUlKupK5fvwZIVwdMk3375EnDBL7euKG+n3r16ks+1qfV+On/dMxRsWKO7Nq1n4oVXXjy5DFTp07MaZFk0kBWUomIi4tDFNXZuV1dXSUbZ9++PQB07dpd7303aaLes9DsySTHyMgIV1f1KlFTeNBQxMbG4ufni0KhwNm5tGTjaFaJGtOmvmnVSu1gcPLkCYNM5BolVbt2XcnHcnWtTOHC9gQE+PP4sZjxBXmYggVtWb9+CyYmJmzfvpVmzRowffokDhzYm2PerzIpkZVUIry9nxAdHU3Jks5YW9tIMkZQUBAXL57DxMSEDh3SDsrMKo0bq4N608sqock0oFk1GooXL/yIj4/H0dGJ/PnzSzKGSqXSKqk6daQp9V6zZm3s7Ozw83vOkyePJRlDQ2joW7y9n5A/f36D1N4yMjLSxmGlt7f5X6F8+QrMmbMAgIcPPVm9egWDBn2Ns3NR/P3TT9QrYxhkJZUIjdNEpUrSraIOHdqHUqnEza0VBQva6r3/2rXrYmZmxoMH93j7NiTVczSmTE/Pe3ofPz2ePZPe1Ofr+5w3b4Kxs7OTbN/L2NiYFi3UnpRSm/w0Di5Vq1Y3WImKT0rqtEHGywkePLjPzJlTadmyCdOmpW7qe/78uWGFkkkVWUkl4tN+lHROExpTn6ZsvL4xNzfXriAuXbqY6jmalZSmhLuh+OQ0IZ2S+mTqq4tCoZBsnNat1SY/qROyJr4fQ9GsmVpJXb58kY8fPxpsXEPh5eVJu3ZurFy5lPv376JSqRCEinTr1pOJE6eydu1Grl69TaNGjXJaVBkgX04LkJvQ7NFI5TTx4oUfN25cw9zcXFteQwoaN27KxYvnuXTpfKrZLATBBWNjY7y9n/DhwwfMzc0lkyUxhvDsM9T+TfPmbigUCm7cuEZUVBQWFhaSjHPjhnolZUglVaRIEVxdq+DpeZ/r16/qNSA6N3Du3Bmio6NxcXFl3ryfqFWrjmTvn0z2kVdSiXj40AuQzty3a5e6bMRnn7XDyspKkjHgU8BnWvtS+fPnp3z5CiiVSh498pJMjuQYIkZKk21Dqv0oDba2hXB1rUJMTIxkMWfx8fHaPHp16hhOSYFuuSDzKlFRkQC0adOOJk2ayQoqlyMrqQTev39HYGAA+fPnp1QpZ733r1QqtYlJ+/btr/f+E1OjRs2Eon1imhm7NatFQ5r8pHY///jxI15eD1AoFFStWl2SMRLTqJG6zLwmBZO+efrUm4iIcJycilOkSFFJxkiL//K+lCb3oUZZyeRuZCWVgMZLq1y5CpKkQ7py5RK+vs9xdHTSlnKXClNTU2rWrA18Mn8l55OSMozzRGxsLC9e+KFQKCR5CAC1d1ZcXBzly1eQdKWqQZPN4vLl1Pf+ssvdux4ABlG4yalbtz7m5uZ4et7PkXg6KbGxKQBAWFhYDksiowuS7kkJguAOTANMgCWiKC5P1l4dWAfYAOeB70RRjBMEoQmwBDAFngFfiaIYKqWsmpiQChWkKcS3bdsWAPr0cZe8kCKoa0ddunSBq1cv065dhxTtlSqp0+toEr5KjZ/fc+Lj4ylevIRk7ud376pLrBhqUm/QoCEKhYLbt29Ksi+lKRlTrZrhlVT+/Plp2LAx//57knPnTtOrV1+DyyAVGq/ad+9kJZVVDDm3S7aSEgTBCZgPNAaqA4MFQUieeGwrMFwUxQqAAhiUcPxP4AtRFKsAXsB4qeTU8Ck7t/6VVEhIiLYCb58+SU19KpUKPz9fLl26gJ+fn97G1BQ4vH79SqrtguACfCpAKDWa/SgpPfv0Oal7ez/h7t276QbrFixoS+XKVSXbl9Io3ZxQUvDJ5JfbS8pnFltbtZIKDZX0ufc/i6HndinNfa2A06IovhVFMRLYDfTQNAqCUAowF0XxasKhjUDPhL9dRFH0EgTBBHACJP80aSZrKUpIbN68gY8fP9Kq1WfaTAsfP35kxYql1K1bjdq1q9C1awecnZ2ZMWMKSqUy22PWrl0HIyMj7t69k2r0vKOjE5aWVrx588YglWYNkQ7p06Se9WKVHz9+ZODAL2nYsBbVq1fH3b1Hum7YDRuq3ZT1bfJTKpXcv682xVatKm3xzbRo21a9Aj9x4jgfPnzIERmkQLOSCguTlVQWMejcnqGSEgShqiAIMwVB2CYIwhZBEKYKgqDLcsMRCEz0fyBQXJd2URRjBUGoArwEWgA7dBgvW2jMffouaR4TE8OGDWsBGDJkGKBOgNq0aT1mzZqKr+9zChUqRK1adciXLx+rVi1j2bLfsz2utbUNrq5ViIuLS7XSqkKhQPM2Pnkiffqb58+fAUiWDik6OppHj7xQKBTZyswwceIPHDq0HwsLS6ysrPj335PMnj0tzfPr1lXn09N3wt6nT72JjIzA0dEJe3t7vfatK6VKOVO9eg2ioiLzXCHE9ChUSF1s09/fX67am4w1a9YUFwTBOdlPwWSnGXRuT3NPShCEwsBKoBJwCrgOxAKlgV2CIDwERoqimNauqhGQ2FaiAJS6touieB8oIgjCEGAn0DCjm9FgZ5e5TfPIyEj8/HwxMTGhTp2qmJiYZOr6xNjbWyf5f+vWrQQFvaJy5cp0796JVatWMWrUKGJjY3F1dWXBggW0b98eY2NjDh48yOeff86iRQv4/vtBFCtWLFNjJad586bcv3+XBw9u06VLyrisqlWrcPv2LQICnmNv3zbb95oer16pU8xUqVIxU9fpOtbNmyKxsbFUrFiR0qWz9rpdu3aN7du3kj9/fi5fvoRSqaROnTps3LieCRPGUqFCyhpYbdqoTWIeHrews7PUOUt5Rvdz4oR6ZV+nTu0Mz83K66lrX3379uHOHQ9OnDjM11+7Z6qv1L6H+pQ1qxQubEW1atW4e/cuDRvW5LPPPqNXr160b98+RQB4bpA3s2RH5h07dqTmqjobmJXof4PO7ek5TvwJLBRFMVX/WkEQmgPrgbTKyr4EEhfzKQoEJGsvlrxdEIT8QFtRFPcnHN8KLE5HzhSEhESgVOqe+FOzl1G2bDnCwj4CWYuyt7e3Jjj4U8r/uLg4Zs+eA8DAgd/xww8TWLJkEQCDBw9l+vQ5mJmZ8fat2hzXuXNn2rXryD//HGbmzDnMn79Q57FSo2rVWgCcPn2WIUNGpWgvWVJtert1606GfWVl/MQ8farekypY0EGSsc6eVZezcHWtmu656fU1adIUQL3idXQsg729Ne7uX7Bly0YmTpzMmjUbU1xjamqDk1Nx/P1fcvHiDZ3qPelyPxcuqPcSK1asnOX7ySyp9eXm1g6YyMGDh/Dze425uTlGRgqdHgSTfw/1KWt2+fXXZXz77Vc8e+bD5s2b2bx5M3379uf331doz8lN8upKVmXWvKd9+vRpsnjx4uQFtpJ7mBh0bk/vsa9zWgoKQBTFs0B6FftOAS0FQbAXBMEC6A4cS3S9L/BREARN7pEvgH9Qr9aWC4JQK+F4L0AaH1+tLOqn1vLl9es0sXv3Try9n1CqlDPXrl1hyZJFGBsb88cfK5k372fMzMxSXDNhgnqi3Lz5z2xXR9WYom7cuE58fHyKdo25TxSlNfepVCp8fX0BdQE6Kciu08T9+/c4e/Y0VlbWfP/9CO3xsWMnYmJiwsGD+9MsjqfJBqFPk19OevYlRmPyi4yM4OjRQzkqiz6pUqUaly/f4t9/L2pDIrZv38rw4UP+r2tLDR48+KUois+T/SRXUgad29NTUrcFQRiYoP1SRRTFNHf4RVH0B6YCZ4A7wDZRFK8LgnBUEITaCaf1A34TBOERYAX8IYpiPNAbWCMIwh3UG3LfZnQj2UEK9/N378KYO3cmoK7ztGPHX5ibm7N583b69OmX5nWurpVp1qwF0dHR7N27K1syFCvmSMmSzkREhGuT5yZG4yQitYdfSEgIUVGRWFvbUKBAcvO2frh37y6QdaeJ7ds/hQjY2hbSHnd0dKJjx84olUq2bPkz1Wtr1VJ/nPXl4ZcbnCYS07//1wCsW7cqZwXRM8bGxlSpUpXz568xcOBgTExM+Pvv7fTt24PISDnQNy0MPbenZ+6bh9pt8CdBEDYDK0RRfJrJm9kGbEt2rH2iv+8CKfK9iKJ4EaiV/LhUaFZS+nSamDFjCsHBr7G0tMTT8z62trZs3fq3Tul6+vf/inPnzrB162a++WZwthKl1qtXHz+/51y7doUqVaolaStRoiTm5uYEBb0iLCxUkqzsAC9efFpFSZH0NSYmhocPPYFPyXMzQ3R0NHv2/A1A375fpGgfMGAQ+/btYcuWTYwbN5l8+ZJ+bfS9kvLxeUpERDjFijnqtShmVunevRfz5s3k1q2bXLt2lQYNpCkmmVOYm5uzYMEiBg4cQvfunbh27QqtWzfl99+XULNmQ4PENeY1DDm3p7mSEkVxjyiKbYE6wAfgbIKmlC4zag6hb/fzdetWaVMgRUZGUrKkM0eOnNI5n1zbth0oVKgQnp73tWafrPIpXupqijYjIyOtifPxY+nqIvn5qZVUiRIlJen/0SMvYmJitFWHM8vx40cJDQ2lcuWqVKmS0jOwXr0GlCtXnuDg15w/fyZFe5Uq1TA1NeXxY1Evbs2aTBM5berTYGlpyYAB6gfeqVMn6CVEIjdSrlx59u49RLly5fH2fkKHDh2oUqU8o0Z9z5EjhwgJSb30jYy0ZOiKlGCTnAY4A6uBQYIgSFvpzYBER0fz/PkzjIyMshXDExr6lt27d9OnTzemTJmgPV6/fkOOHDlJuXLlde7LzMyMnj37ALB16+YsywRQq5a6Ou3t2ynd0AHKlSsHgI+Pd7bGSQ9NkHKpUtLsR2U36FWTDcTdPfWcigqFgh49egOwa9fOFO1mZmbaLBe3b9/MkgyJMXTmDF0YMeIHnJyKc+/eHebMmZHT4khG2bLlOXv2CjNmzMXZ2Zk3b96wfftWBgzoh4tLaRo0qMmoUd+zZctGbt++KbuwG4DMBPOWRL2qqgL8Z5TU8+fPUCqVlCxZKkvpevbu3UWlSmURBGd69uypjScxMTFh6tSZ7Nt3hCJFimS6X43Zad++3dkKpKxY0QULC0v8/HwJDg5O0a7JAKEJtpUCzUpKKqeJT5N65vdvAgL8OXPmX0xNTenWrWea52na/vnncKoTk+Zh4ObN7O9L5RanicSEhr7FxUVdHUCTPeW/iqmpKcOHj8LHx4dz564yZcoMGjRoRP78+Xn61Jvt27cyduxI2rZ1o0wZR2rVqoy7ew9mz57OX39t5syZfxHFR//XDhj6JN3cfYIgmPFpc6sS6sjh1qIoPpNeNMOgmZzLli2X6WtHjhzKjh1/af83MTHB1rYQnTp1YcyY8dnaT6hUyZXq1Wtw544H//xzON0JND2MjY2pXr0Gly9f5Pbtm7Rp0y5Ju+a+nz6VciX1HIASJaTy7Mu6eWzHjr9QqVQJJla7NM9zdi5N3br1uX79Kv/8c1i70tVQp05dVq9enu19KaVSqXUCya7TRGxsLKtXr+DSpfOUK1ee4cPHZPqBSaVSsWXLRqZOnUB0dDRAtuII8xIKhQIXl0q4uFRi9OhxxMTEaGts3b59k0ePHuLt/YQXL/x48cKPU6dOpOjD2toGR0dHihVzxNHRSfvbycmJYsWccHR0xMamgKQFOvM66QXzrgT6ACLqoN4doihGG0owQ6GZnDOrpMaNG6VVUBUqVGTjxr9o0KCmXuMq+vb9gjt3PNi2bWuWlRRAzZq1uXz5Ih4eKZWUxsQp5UrqxQu1uU+KlVRMTAxeXmqnidT2k9JDqVSybZt679DdPaXDRHJ69OjN9etX2bVrRwolpck67+FxG6VSqXNQb3KePVM7TRQtWixLK3ANERERfPVVXy5cOAfAv/+e5MCBfRw6dFznLPTx8fGMGzeKv/5Sm5y7du3O6NHjcXWVpt5absfU1JQaNWpRo8anff/Y2FiePfNBFB/y6NFDnj9/RmBgAAEB/gQE+BMe/h5RfJ9uImcLC8sEReaEkZGCli1b8803gzE1NTXEbeV60ltJmQGtRFFMfTPjP4JmL6ZMGd2V1K5dO9i8We2O/Nln7di6NeU+hT7o2rU7M2ZM5sKFs7x44ZdlxwPNBKopCJgYjZJ69swHlUql9yc6pVKZSEnp33FCFB8SExNDmTJltSUYdOXixfP4+T3Hyam4TuVTOnfuwtSpEzh//ixBQa+S1HhyciqOg0MRXr8Owsfnaab2IBOjr6SykyeP48KFc9jbOzBt2iy2bt3EjRvX6NWrC6dOnc/QwSQ+Pp6RI4eya9cOLCwsWLTod+2+nMwnTExMqFBBoEIFgU6duiRpU6lUhIWF4u/vT2CgPwEBAdrfL174aeuQRUVF4u39BG/vJ4C6cnDduvW139v/d9Lz7vtGo6AEQeghCMI8QRAsBEH47+TsJ/MrqdDQt4werc7BV7Gii2QKCtSJMDt06IRKpWLnzm0ZX5AGmjgezVN+8jEKFSpEVFQkQUGvsiVvagQHvyY6OppChQphZaX/FDPZmdS3bVOvEPr06aeTm3GhQna0bPkZSqWSAwf2JmlTKBSJHgayvi+lD6eJvXt3sXPnNszNzdm79zB9+/Znx449VK5clWfPfJI49qTF1KkTEhSUJTt27JMVVBZQKBTY2haicuUqtG7dlrZt21O4sD2vXwdp3+fU6N3bnerVaxpQ0tyNLglmJwFDUUcHmwMzBUGYLrVghkKjpHT17BswoD+xsbGYmppy8KD0JQw0DhRbtmwkJiYmS30UK+ZI0aLFeP/+Xap7T5pVpBT7UobKNJHZ/ZugoFccPnwQhUKRqUrJPXr0AtDGVSVG8zCQHQ+/+/c1+1FZU1K+vs8ZP34MAHPmLNDG/llb27B69QbMzc3ZuXMbhw4dSLOP9evXsGHDWkxNTdm+fTf16/+34qIMjVKpZO7cmdSqVZkpUyZw+vQpIiLCKVmyFG3bduCHH8azdu1GLl68QUDAW5YuXZVlc/F/EV1eiT5AeyBSFMUQoD6QuUyTuZTw8Pe8fh2EmZkZTk7FMzzfw+OWtiTDrFnzKFhQmuwJiWnSpBkVK7oQGBjArl1ZTwaf3lO+lPtSUjtNfKpeWy2DM5OycuUyYmJiaN++U6YUaOvWbbGyssbD43YKt33Na5yWu39GKJXKbK0M4+LiGDr0W8LD39OhQ2e+/HJAkvby5SswY8ZcAMaNG0lgYECKPq5du8q0aRMBWLJkOQ0aNEpxjkzmOHz4AEuX/kZMTAxt2rRj2bLV3L37iJs377N583YmTZrO5593o0IFIUWguIxuSio2scNEQh6nWOlEMhyJaxzp8uSiMfM5Ojrx7bffSSqbBiMjI0aO/AGA3377JcvpWj5t7KecQKVUUoZymsjMpO7v/5KNG9cDMHr02EyNaW5uTocOnQDYsydp2qrq1WugUCjw9LyfpbCB5899CA9/T9GixShaNP1M7qnx449zuHnzOsWKOfLrr3+kur/4zTeDcHNrRWhoKN99N5C4uDhtW2hoKEOHDiQ+Pp5hw0bJJj49kS+f2huyRo2abNmyk169+lKsmGMOS5V30EVJvRAEoQOgEgTBTBCEqYCvxHIZhE+mvoz3ozw8bvHwoRcACxYsklSu5HTp0h0XF1f8/HyZN29mlvr4ZIoyrJKSMtuEKD4kOjqasmXL6ew0oVQq+eGHEURFRdKhQ+cs5frTeFru2fN3ksq91tY2VKzoQlxcnDb3Xma4cyfrrvQbN65n2bIlGBsbs3LluiT5BxOjUChYunQ1RYoU5cqVS4wZMxylUolKpeLbb7/l5csX1KpVmylT/rsBu4ZG84CWWvFRmYzRRUkNB34AqgKRQDtgmJRCGYrMOE2MGaPOjO3o6ES7dh0klSs5+fLlY+nSleTLl4/169fw9df9Mp3GqFq1GhgZGaX6lP/Jw08KJSVdtonMTupnzvxL8+bNOXPmX2xtbfnpp0xVgNHSpEkz7O0d8PF5qjU3avhk8su888Sn+9FNcf7993ZmzJjBsGGDmTBBvQ/144+/0LBh43Svs7e3Z8OGLVhYWLBz5zY6dGjNyJFD2bt3L9bWNqxateH/JhbKEBQvrt5KePnyZZKHGhnd0D/Q9iYAACAASURBVCUtUoAoii0BG8BWFMXGoij6SS+a9OiqpAIC/LVZxOfN+0lyuVKjatXq/PbbMkxMTDh69BAdOnTI1JOZlZUVgpD6U35iN3R952XT7EmVLOms134h8aSesSfUjh1/0adPNy5cuICNTQE2bNia5TikfPny0bVrdyClA8UnJZV55wmNwqtePX0lpVKp+PHHOQwfPoS5c+eya9cO8uXLx6JFv2tz7GVEnTr12LZtNw4ORbh164bWe/TXX//QOY5KRjcKFCiIpaUVkZERcsn6LJCmkhIEYb0gCNpAEFEUo0RRDE/UXkwQhNRrF+QRdI2RmjlzKgA2NjZ07Pi55HKlRe/e7ly4cJ2SJZ3x9vbmxx9nZ+r6mjXVQYh37iQ1+VlZWePgUITo6Og0ayZlhfj4eG1/xYuX0Fu/GnSd1Hfu3MaoUd+jUqkYO3Ys167doVGjJulekxEak9++fXuS1OrKqvNEZjJN/PzzfG3xzM6dOzNhwhQuXLiWwlEiIxo2bMzlyzeZO3cBQ4eO4OjRo3z+ebdM9SGTMQqFgtKlywDQsmUTpk2bqJccj/8vpLeSWgocFgThb0EQvhQEoaEgCI0FQfhKEIQdwBFgmWHE1D8qlYqnTzNOiaRUKjl27AigVhI5TZkyZVm/fhPGxsasXbuKq1cv63ytxq1ZMxkm7xf0uy8VGBhAXFwcDg5FMDc311u/AB8/fuThQ08UCkW6mSbOnz/L6NHDUKlUTJ06k0WLFmFnl3b6I12pUaMWpUuX4fXrIC5ePK89nlGuxLR4+tSbyMgIHB2d0k2ntXnzn/z660KMjY1Zt24TBw4cYNy4SZQtm7XgYRubAgwZMozZs+fTrl27jC+QyRKTJ0/Dysqaly9fsGbNStq2ddMmBJBJn/SCee+gTii7B+iEOgP6CtTl4vcBtfNyNorg4GDCw99ToEDBdCetjRvXEx0djUJhxOTJuWMzuVq1GkyePBmVSsWoUd/rbPbT7N1oYnESI4WSktJp4uFDT2JjYylfvkKaQcI+Pt58++2XxMfHM3z4aEaNypwnX3ooFIokDhQaNLkSIXMmvzt3bgPp70ddvnyRSZPU9/Drr0vp3LlrpuWWyRlatWpDx45JC5kXLmyfQ9LkLdLdkxJFUSWK4k5RFHuKolhFFMWqCX/vTK8qb17gk/t5mXRTAWmqkdasWQsrKyuDyKYL06dPx8XFlWfPfHQ2+7m4uGJsbIwoPkqh2DTmiOfP9Zc7+OXLFwCUKCGFqS/9zAzR0dEMHPgVYWFhtGnTjmnTZuldBk1g7+HDB5M4o2TFeSIj02VwcDCDBn1NXFwc338/MlMByDI5j5eXpzbX55Ah33P16m3at++Yw1LlDf5vw5qfP/cBPk3OqfH+/Xu8vdX7Vvp8CtcHpqamLF26Umv20+Wp3dzcHEFwQalU4ul5P0mbZrNcs/rRBwEB/gA4ORl+P2r+/Nl4et6nVClnVqxYK0kEf9my5alevQYREeGcOvUp+8inwGndDQ3pBfGqVCpGj/6e4ODXNGzYmOnTM7cXKZOzxMfH8/PP8wD1lsHcuT9lKlfo/zv/x0pKvWJIz5Pp998XAyry589P27a5ryBx1arVGTJEvd8ybtzoJIGZaV+jzsyQfF9KE8vh6/tcb/K9fKlxmsg4m0dm8fDQmMdSevadPn2KVauWYWxszKpV67NUrVdXNAGvmkzhkDhX4i2dvCXj4+MTpUNKqXQ3bFjDyZPHKViwIMuXr5HLmecxVqxYyrFjR7GxKaANzJfRnf97JeXsnPZKavdudfLYxo2bGUSmrDB+/GSKFy/Bgwf3tKbJ9PikpJImuCxVqjSgVlL6iuXw91eb+xwd9aukIiLCefTIi3z58qVwmggPf8+YMcMBmDhxqrYYoVT06NEbMzMzzpz5V7sKLVZMXT8oPPy9NrN1enh5PSAqKoqSJZ0pXLhwkrZnz3y0lXAXL16qU/oumdxDUFAQv/66EIDVq9dTvnyFHJYo76FLgtk9giC0MoQwhkSzYnB2Lp1qe1BQkDa32fjxkwwlVqaxtLTUZsD46af5GbqQa57Uk6+kChUqhKWlFeHh7/UWy6Ex9+l7JXXz5g2USiVVqlTFwsIiSduPP84hMDCAmjVrMWLEGL2OmxqFCtnRsePnqFQq/vprk/Z4ZuKlrl+/CkDduvWSHFepVEyYMIYPHz7QrVtPOnXKufAHmayxYMEcIiMjaNOmHS1bfpbT4uRJdFlJ7QWmC4LwWBCEcYIgpJ5vJY/h66tZSaWupFasWAqog2ATFznLjbRp04727TsRFRWZYRkGV9fKGBkZIYr/a++8w6Mqs8f/mRRKEpIACSW0SHspggGRgBQBARXFhggr1lXRdd2fBdRdwfZd2+pid1nbuq4K2MBdBERKKFZERCXgK90EAqYQIKGl/f64cyeTySSZTGbmzkzO53nyZO5727l37rznvuec95xtnDhxwtFus9l87pcyzX2+fvuv7NSHVGn/7rtv+de/XiMyMpI5c14MmFnsuut+D8C7775NSYmR1rK2Gl6umNeTnl412/hHH73P2rUZtGzZkr/+1ZpJ5IL3fPvtN8yb9zaRkZE88shjVosTsniSceJdrfU5wMVAG+BbpdTbSqnBfpfOTxQVHSUvL49mzZpVKVznzNKl/wOqdxzByuOPP0VsbBzLln3C6tUratwuNjaWHj16UlpayrZtmVXW+dIvdeTIYY4ePULz5s1rzCPnLd98U11JlZSUMGPGHVRUVPCHP/yJvn1P9+k5ayM9fSg9eyp+++0gy5cvA+pXtsPd9RQU5PPgg38B4KGHHiU5WcKVQ42HH54NGD7HIUMG0qZNvNu/888fTX5+vsXSBi8e+aSUUhFAD6AnRjXf34B/KKVCMsxo9+7KoAl3UV8nTpxwjCZuvPGWgMrmLSkpHZgxwyix8NBDs2oNoujXz/BLuRZeM0dSZg2ohuA8ivJltd/S0lJHuRHnTn3u3JfYunULnTunMnNmYM2zNpuNa665HoA333wNMIJaIiIi2Lp1S62Z67Ozs9i/fx8JCYmO2k8AjzzyAHl5eQwbNkLCzUOUkhLP6r9t2vQda9eu9rM0oYsnPqlHgSzgXuA9oLvWegZwDkby2ZCjrsi+t956g4qKCqKjoxk7NnTsyDfffCtduqSi9c+1zmavaVKvmQTWFyOp/fv9Y+ozggyK6dIl1TEK3rNnN3PmGOawp59+tpqfKhBMnTqN2Ng41q9fyw8/fE9cXBz9+59BWVmZw5znDnPdWWcNdrwwffHFeubPf4cmTZrw9NPP+VTJC4Fj0qQrq7UlJCTSp8/p9O3bj379zqB//zRmzLiPCy+82M0RBPBsJNUGmKC1HqG1nq+1LgHQWhcDIVlKvq6gifffN4oLnn56zel2gpGmTZs6ito99dRjHD5c6HY7cwJszSOphk/orQw/9+0cKVd/lGtwwejR5/r0fJ6SkJDoyJ338svPA5VRoc5pk1xxvZ4TJ04wc+YdANx550y6d/cu3ZFgPePGnVet7ZlnXmDNmi/JyPiCVavWs3LlOu67bxZNmza1QMLQwBMl9S6QoJQaaf8boZQ6UykVr7X+zN8C+oPK8PPqSqq8vNzhq5k6NfTMLBdddDFDhw6joKCAuXNfdLuNGba9bVtmlZL0ZqZyXwROmFGGKSkdGnwsZ9atWwPgqBj7wQcLWLNmNYmJiZYHF9xyy21ERUXxv/99zO7duxg+fCQAn3++tsZ9zOsZMsS4nmeffYqdO3fQs6cKSHSi4B/Ky8v55JPFjmWbzcbDDz9maYLqUMUTJfUMsBoj4exz9s+LgF1KqZC847UpqeXLl1FaWorNZmPatGsCLVqDsdlszJr1MACvvDKXgoLqDtm4uBZ069adkpISfv55q6PdzLGXnZ1VJbO3N/gj+/mpU6dYt87o8MeMGUtubi4PPGD4nx555HHLgwtSUjpwxRVTKC8v529/e4z09KFERUXxww+b3Yb17927hx07thMfn8CgQWeRmbmFF198DoC///0FebsOYW677SZHgdL77pvF/v0F3Hbbn8R06wWeKKm9wFit9Rla64HAMOALIA3wrkysxdQ2kXf+/LcBI9KtSZMmAZXLVwwenM7o0edSXFzkCKV3xfRLOc+XiomJoU2btpSUlDjmiHmLqaR86ZPasOFrjh0rpnfvPqSkdGD27Hs5dOgQI0eOZurUaT47T0OYOfPPNGnShIULP2Dnzu2kpw+lvLycVauqR1xmZKwCYOTIUQDcddcfKS0t5YYbbmLIkNCIKhUqOXSogBdeeJbLLruQhQs/BOCdd95jxoz7JEtIA/BESXXVWq8xF7TWG4CeWmvfFR4KICUlJezbl4XNZnObnXvDhm8AOOec0YEWzafce+/9ALz++ivk5eVVW9+vX11+qT0NOn+lkvKduW/lSsO6PHr0WJYsWcyiRR8RExPD3/8ePMEFnTt34fe/nw7AI488yPnnG1Wcly79pNq2q1YZ1zNmzFj+8Y8X2Lz5e7vyfThg8goN5/jx47z44nMMHpzGo48+xBdfrCcyMpInn5zD+PFS/qShRHmwTYlSarzpf1JKjQdOKaWSgVprTCulrgJm27d7Tmv9ssv6NOB1jKq/64BbtdalSqlhwLNAEyAf+L3W2iczTE1TVocOHauZU44cOeIwj11//Y2+OJ1lnHnmWYwbdx4rViznn/98qVrHZ/qltmypWqW3S5dUvv32G379da/XhQHLysocIzFfpUQqKytj0SLj7XTgwDO5884/AnD//Q/WGABjFXfeOYP5899h/fo1jBtnRIeuWrWC48ePO+pq5efns3r1SiIiIkhOTuaee+4EYM6c5/2aa1DwLatXr+Tuu//kyK4ycuRorr32es4+e0S1FFfhRCD7dk9GUn8AXldK7VVK/Qq8DNwK3APUmCxOKdUBeAwYjmEanK6U6uOy2TvA7VrrnoANuNne/i5wk9Y6zf75BQ/k9Ija/FFmktCmTZuGXGSfO+666x4A/vOff1Wbq9O3bz8Atm3bWsX/VDmh1/sIv9zc3ygpKSEpKclnxQ4//3wdOTn76dSpM3PnvsThw4WMH38+N9/8B58c35e0atWav/71CQDmzHmKXr16c+xYMUuXVjrSP/74Q0pKSjj77OHcf/+9lJWVceutt0vqnBDj6aefcCio1NTTeOONt7j44svCXUEFtG/3REl1AE4DLgEmAL201j9ore911Z4ujAVWa60L7OHqHwJXmCuVUl2A5lprcxLJv4HJSqmmwGyttfmK/yPgs6p5tSmpxYs/tsvW21ens5RBgwYzaNBgCgsLee+9eVXWtW7dmvbtUzh2rNhRtgR8M6HXrCPlyxId8+YZLxDNmjXnu+++JSWlA88/PzdozHyuTJlyFeeddwGHDxdy+PBhAF555WUqKirsef4M32d2dhZZWb8yYMBAMfOFAKWlpWzdmsnixR/z/PNzqjx/e/bsZs2a0J6U++qrr3ZUSqW6/CW6bBbQvt0Tc9/jWuv/Apvr3LIqKUCO03IOMLiO9R211icxtLCZ6eJh4OP6nLh165qLE/72m/HW07dvL5KTq1Z03bp1CwCTJ0+qts5TvN3Pm/092fbee2dy5ZVX8sYb/+See+6skmFj4MABLFmyn6ysnQwZYpS86NfPyHpw8OD+Oo9f0/qjRw2T6WmndWnw/QDYvHkz//3vImw2G9u3a+Lj4/n002X06pXq1fF8fY9rYsGCeaSnp/PLL78QGRnJ5s3fs3nz1xw5coQtW34kKiqKPXt2k5qaytKlS2jXzruy9r64x/44lrvfoS+P72/KysrIzc1i48aNjr/NmzdXKXDpzOWXX860aVf6zHrgLQ17Zhesd9P8CEY/bBLQvt0TJfWTUmoWsB4oMhu11pvq2C8CcK75YAPKPV2vlGoCvGWX8XEP5HSQn19Eebn7chPbtv0CQHJyCrm5Rx3tO3fucFSrveKKaVXWeUpycguv9vNmf0+3HT58LJ06dWb79u3Mm/ch551X6cjt0aM3sISvvtrAqFHnAxAXZ3SUu3fvqfX4tZ1/27Yd9m3aeXU/ysvLWbp0MfPnv8vu3bvIytrrqMuUlJTEvHkf0q5dqt+/o4Z+nxDJe+99zKRJEx2VoCdMmOAohVJaWkr37j1YsGAhkZGxljxz3hwrIsJW64ugievv0Jey+pqKigr27t3D5s2b2LTpOzZv3sRPP/3gNqVV586p9OnTh9TUrnTt2g2lepGWNpDmzZtTVFRKUZF11+jtPTa/06lTp46YM2eOa1Cca1aAgPbtniipdPvfTU5tFUDNhZgMsgFnz3s7YL/L+vbu1iul4oD/YTjWLjGzXPiCmlIivfnm64CROaB16/CxJ0dFRXHTTbfy0EP389pr/6yipE4/3fBLbdlSWaU3JaUDNpuNnJz9lJSUEB1da2yMW8w6Ut6Y+xYseJdZs+7j6NEj1daNG3c+Tz31TEjVVOrQoSPLlq3igQf+wvvvz3f4/2w2G1dffT0PPvgICQmu1hQhEJjmubVrM/jqq88pKCiotk3Hjp0444wBpKUN4IwzBnDGGWk+T5gcTEyfPj17+vTpe+rYLKB9e51KSmvtbejUSuBhexRgMTAJmO503L1KqRNKqWFa6y+Aa4Bl9tXvADswIkLqLm3qIebbElT3Sa1blwG4L98d6lx11dU88cT/sW5dBr/+utcRHGFmCs/M3OLYtkmTJrRt244DB3LIydnv2LY+VCaX9Tz8vLy8nGuumcqKFZ/aW2x07dqV00/vT9++vbj88t/VWkU5mGnZshUvvfQKs2c/wrx5/yEhIZZLL50SVi9DoUJ2dhYffLCADz5YUK0gZVJSEmlpAxkw4EwGDBjIueeOxGaz1nQXpAS0b69TSdk135NAb2Ay8AQwQ2tdVNt+Wut9djNhBka44eta6w1KqaXAg1rrjcA04DWlVDywCXhBKTUAI0hjK7BJKQWwX2vd4Prthw4VUFxcRHx8AomJLaus27XLCB6YMGFiQ08TdCQkJDJhwkQWLvyABQvedcyhSk3tSkxMLPv376OgIJ9WrQxTX8eOnThwIIfs7CyvlJQZ7VSfEc9FF41n48YNgJGl/e23FzhSKgWzmag+tGvXjrvvvjdsridUqKio4Jtvvubll5/js88+dZhbExISGTlyFKNGjWHEiHPo0iW1SiCEfE/uCXTf7om57wUMx1db4ARG3PurwFUeXMw8YJ5L2wSnzz9Q1eEG8D2GDdPnZGX9ClBtEu+OHds5deokYJQDD0emTbvWoaTMGfCRkZH07t2H7777lszMLYwYYSRE7dSpExs3bnBE6dUX09znaUqkW275vUNB3XTTLTz++NNenVcQXMnIWMXTTz/heL6aNm3KBRdcyNSp0xg5cjRRUZ50gYIrgezbPQlBH6C1ngWUaK2PYWjIkLSJ/fqreyU1b54RDhwfn0B8fHhOpBw2bASdO6eSnZ3F+vWVCU/N+WDOfqmOHStz+NWX48ePk5eXR3R0NG3atK1z+4ULP3BM0p027VpRUIJP2LVrB9dcM4UpUy5j48YNJCYmcvfd97Jp01ZeffXfjBkzThRUiOCJknLNNBpJ1UiOkMHM7t25c1UltWaNkUMtkNVcA01ERAS/+52R386ccwTOfilnJWWMgLxRUjk5hqmvffsUtwUlnSkqKuKOO24DoHfvPjz77Ev1Pp8guPLCC88wZMhAli9fRlxcC2bPfoRNm7by5z/PtjwJsVB/PHmVWKeU+hvQXCl1Hkahwwz/iuUfsrIMJVXd3GeETDtHvoUjU6Zcxd/+9hjLli2hqOgocXEt3Eb4depkKCnTPFofnCvy1sVtt93EyZMniYqKYuHC6rntBMFTsrJ+Ze7cF/nii88dpXYAvvpqE23b1j2iF4IXT0ZS92HMjzqMkQrjR4yUSCFHpU+qi1PbXk6cMCbnTZlSp5stpOnYsRPp6UM5efIky5cbwTa9e/d1TJI1a0s1xNznafbz3bt38emnSwGjuJ9EugnesHfvHu6663bS09N4/fVXqiioGTPuEwUVBngSgl4C/NX+F9K4C5yYP/9dAOLi4hpFR3nppZfzzTdf8d//LmLSpCuJjY2la9du7Ny5A61/pl+//nTsaCiYffuyqaioqFfqocqUSLUrqbvv/hMAiYmJzJz5Zy+vRmisHDt2jGeffZq5c1/k1KlTREREcPnlk7nhhpvp06ePJOkNIzwJQR+KMSu4FU6RGVrrkMrAWlFR4QiccPZJrV5t1Pnp1cs1P2J4ctFFl3D//feyevUKjhw5THx8An379mPnzh1kZv5Ev379iYtrQWJiIoWFheTl5dXLjl+Z/bzmOVIHDx7kyy8/B4y33bp8V4LgzK5dO7nhhqsdo6ZJk67knnv+TNeu3S2WTPAHnvQOrwCLgTuAPzn9hRQFBQUcO1ZMfHxClRn+v/xipEkaNWqMVaIFlLZt2zF06DBOnTrlMLeZfqmqwROmya9+filzjlRtSuqee+6goqKC2NjYoMxiLgQvBw8eYNKkiWzblkm3bt1ZunQlc+e+LgoqjPEkcKJUa/2M3yXxM+6CJoqKihx5tiZPnmqJXFZwySWX8+WXn/O//y3iyit/5zbzRMeOndiy5Ueys7MYMOBMj4+dk2PklUxJSXG7vrS0lBUrlgNw881/kFGU4DHFxcVcffUU9u3L5swzz+L99xeJWa8R4EkPsUUp1c/vkvgZd/6oxYsXARAdHc1pp9WVijB8uPDCi4mIiCAjYxWHDxc6aktt2fKjYzZ+ZYRf/YInKkPQ3Y+k5s59kbKyMiIjIx2ZLwTBE+6664/88MP3dOmSyttvvycKqpHgUfl44Dul1Hal1I/mn78F8zWmP6pLl8rIPtPcZZq2Ggtt2rQhPX0oJSUlZGSson37FFq1akVhYaHDXOeNua+4uJjCwkKaNGlC69buy068+eZrAKSnD5XJlILHLF78MR9/vJDY2Djmz/8orIsKClXxREnNAsZhZEEPWZ+UO3PfDz8YJbIGDTrLEpmsZPx4Y07YZ599is1mc4ymTL+UNxN6DxwwgibatUtxGxG4c+cOxzyqWbMe8l54oVGRn5/PfffdDcCDD/4f3bv3sFgiIZDUqKSUUp0BtNZrXf+AkEsN7G6O1MGDBwAj4q2xYU5cXrXqM0pLS51MfqaSMkLI62Pu27/fjOxz74968slHASMr+FlnpXsnuNDouP/+meTl5TFs2Aiuu+73VosjBJjaRlKOiolKqY9c1tWrCGEw4OqT+v777xy1fcaNO88yuayie/cedO3ajUOHDrFx44ZqwROmuc9MFusJleHn7pVURsZKACZObHwvBYJ3LFmymEWLPiImJoZnn31JAm0aIbV94872GteoAr9kKfcXznOkzIAAM6lpy5atGq1vxDT5LV++zCnRrOFuTEpKonnz5hQWFnpcadRUUu6CJrZu3cqRI0Yhw7vumtlg2YXwp6Agn3vvvQswzMOuNeCExkFtSqqihs/uloMac45UQkKiY47UF18Yk0l7924ck3jdMX68UTL+s8+W0aNHT6Kjo9m9exdFRUex2WyOrBGemvzMoIv27dtXW/f8838HICkp2auKvULjY/bsP5Ob+xtDhpzNjTfeYrU4gkV4OpIKadwFTezaZSSVHTXqXEtkCgbS04cSH5/A9u2/kJ2dhVK9AWPUA87BE55F+NU2kjJNfeGexFfwDRkZq/jww/do1qwZzz0nZr7GTG3ffIRSqqVSqhUQaX42lwMkn08wS3SYSqqwsJDi4mIAJk++0jK5rCY6OpoxYwwlnZGxqlrZjkolle3R8WoKnNi5cweFhYWAkUxWEGrj5MmT/OUvxnMyc6akO2rs1Kak+gF59r9+QL7TckgVXnLN2bdokREH0rRp00Zveho9eiwAa9eurhY8YaY2Ms14dVFT3r5XX50LQGJiS7p0SW2wzEJ4s3PnDnbt2gnArbfebrE0gtXUGDGgtQ6b8bWruS8jw0gq62l583DmnHNGA7B+/TpuuOFmoHIkZfqkPFFSp06dIjf3NyIjI6tV5F250kiDNGTIUJ/JLYQv27drALp27UaTJk0slkawmrBRRLXhOkfKnAt0xhkDLJMpWEhJ6UDPnori4iJKSkoA2LYtk7KysnqNpA4cMHL2tWnTlsjISmvwqVOnHBOCr7vuRl+LL4QZH330PrfeajwnV111rcXSCMFAI1NSxkjKnMQ7btx4y2QKJswM8Js2fUtKSgeOHTvGnj27HErKLGRYGzX5o95/fx4VFRVERUUxenTjDVIRPOOFF56hrKyMG2+czu2332G1OEIQEPZKqqKiwklJdSIra69jxHDBBROtFC1oMJXUmjVV/VKmksrJ2e9IPFsTZkok18i+996bD0DPnkoitIQaqaioYMmSxfzyi2Hqu/32O+V5EYBGoKTy8/M5duyYY47Uxx8vBCA2NpaYmBiLpQsOhg4dTnR0NN9/v4lu3YxIqi1bfiI2NpbExEROnjxJfn5+rceoaST1008/AJJlQqiZvLw8rrzyUm64YRplZWVcffV1dVZ2FhoPYa+kzKAJM0hi/fq1ABJl5kRsbCzp6UOrjJbM4ImUFDN4onaTn7sSHVlZezl27BgA114r/ijBPf/3fw+wdm0GiYmJPPbY3/j735+3WiQhiAh7JWXO8TH9Udu2GRNVBw5sfJnPa2PkyFEAHDhwEHAOQzdGRvv21R48YY6knLNNvP32WwC0aNGiXiXohcZDcXExixf/F4CFC5dIIUyhGmH/NJgJUs2s3rm5uQCcf/4Ey2QKRoYNGwHATz9tJiYmhv3791FQkO80kqpLSVUvG2+GnpsZ1gXBleXLl1JcXMSZZw7i9NPlORGq0wiUlDGS6tChE5mZWygvNzKfjxkz1kqxgo60tIHExMSyc+cOunUz6vVkZm6hQwfPwtDNEPT27St9Utu3/wI0zlIogmeYJsSvkQAAFyFJREFUiZ4nTWq8mV+E2gl7JWWa+zp27MgnnxjVR+Lj4xtt5vOaiI6Odky2TUw0kvBmZv7kURh6WVmZQ0m1a2eY+37+eRsnT54EYOrUaX6TWwhdiouLWbNmNQATJ15qsTRCsBL2Sso093Xo0NGR+Vxygbnn7LMNk5+pXJzD0GsbSeXm/kZZWRlJSck0bdoUgHfeMfxRiYktiY+P96fYQoiybt0aTp48ycCBZ9K2bTurxRGCFL8OJ5RSVwGzgWjgOa31yy7r04DXgXhgHXCr1rrUaf1fgTKt9cPeylA5kurkSLcyePAQbw8X1gwfbigpM0PEli0/OWo/mYER7qgs0VFp6luzZhUAAwYM9IusQuizfPlSAM47T/zDoUYg+3a/jaSUUh2Ax4DhQBowXSnlWrzpHeB2rXVPjNIgN9v3TVBKvQHMaIgMx48fJy8vl+joaJKSkikoKABgwoSLGnLYsKV//zTi4lo4lM4vv/xM69ZGVF5Ozj7Ky8vd7udujtTu3bsAuPTSy/0pshCilJeX89lnnwKipEKNQPft/jT3jQVWa60LtNbFwIfAFeZKpVQXoLnW+mt707+ByfbPlwDbgTkNEcB57s7GjRuoqKjAZrMxZMjZDTls2BIVFeXwSyUnt6GkpITs7Cxat25NSUmJIzLSlcpsE4aSyszc4sjqcdllk93uIzRutm3bSl5eLikpHRp14dFg49VXX+2olEp1+Ut02Sygfbs/zX0pQI7Tcg4wuI71HQG01v8BUEo97M2JW7eOA+DHH42R02mndSEjY7l9XWvatk3w5rAekZzcImD7N/Rc7hg/fiwrV35GbGwMubmQlbWDTp06kZ+fz/Hjh0hOrvTnmecvLMwDoEePriQnt2DJEiOrR6tWrejUyTfzo3x5rVbfY18fN1iPZf4O3R1/y5bvABgzZjRt2gSvz9Jf378/aYjMCxYsWO+m+RHgYaflgPbt/lRSEVQtM28Dyuux3mvy84soL68gM9MIgW7Tpj1r164DoFu3HuTmHvXFaaqRnNyiQceuz/4NPVdNpKUZz9qhQ0aRwi+/3EDbtu2BzWRmbic1tVe18+/YsRuA+PjW5OYeZfnyzwDo1auPT2T05bUGwz226np8dayICJtbBeSK+Tt0d/wVK4yovgEDBvvt99hQ/PX9+xNvZTa/06lTp46YM2eOayhvoevmBLBv96eSygZGOC23A/a7rG9fy/qGC5BdOZH3s8+WATB06DBfniLs6NfvDFq0iOfwYeO5zMzcQvfuxuipptRIlWXjDXPfzp07ABg79jx/iyuEIBUVFXz11ReA/B6DjenTp2dPnz59Tx2bBbRv96dPaiVwrlIqWSkVA0wCPjVXaq33AieUUuZTeg2wzJcCmHN72rdP4fDhwwBcfLHMx6iNyMjIKsUJt251nivlPgzdOdtEbm6uI1/f5MlT/SytEIrs2rWD3NzfSEpKdiQ0FkKKgPbtflNSWut9wCwgA9gMzNNab1BKLVVKDbJvNg14Vin1MxAHvOBLGczw8/x8wzcVERHB6af39+UpwpL0dENJNWnShPz8fGJjY4HKQBRnKioqqkzk/eADozRH8+YxtG3bttr2gvDVV18CxijKZrNZLI1QXwLdt/t1npTWeh4wz6VtgtPnH6jqcHPd/+GGnN+cyGvOj0pKSmrI4RoN6elG9GNkZBRwiuPHjZGRu5FUQUEBJ0+eJCEhkbi4OFasMAJUTBOhILiybVsmAAMHDqpjSyFYCWTfHrYZJyoqKhzmvh07tgM4ctIJtZOWNoCmTZs6lJM5EnWXdaLS1Gf4o7ZuNbKnDx8+KgCSCqGIWZusTZs2FksihAJhq6Ty8/M5ceIEiYmJ/PrrHqDSjCXUTtOmTRkw4EzHslmTKydnP2VlZVW2NU2A7dq158SJExw6dAiAK68Uf5TgHnO+XevWrS2WRAgFwlZJOefsM4MmzjvvAitFCimcFfq2bVtJSkqmrKyM3NzfqmxXmW2iA0uWGHWBoqKiHGXoBcGVXbuM6M/U1NMslkQIBcJWSZlBE7GxcY5ME86jA6F2nCP8du3a6Shm6JoN3TnbxCefLAYqqyALgiu7d+9i375s4uMT6Nw51WpxhBAgbJWUOZI6fvw4YGTjloqfnjNo0GBH5FV5eTktWhiz2F39Us4jqe+/N7IInHVWegAlFUKJ9983oj/Hjh0n5XIEjwjbXtscSRUWGj6S007raqU4IUdCQiJ9+lSa7CIiIoHqIynn5LIHDx4AYMKEiQGSUgglDh06xJtvvgbA9dffZLE0QqgQtkrK7ExNJTVoUI3RkEINpKdXljQ5ccIYkbqW7DADJ4qLjzmCKsaNk0wTQnUefPBBCgoKGDp0mAQxCR4TxkrKMPcVFRUBMHbseCvFCUmcs8WbUXuu5r6cHGMi74YNRsLjhIREmjRpEiAJhVDi3XffBeDRR5+USbyCx4StkjLNfRUVRp7D4cNHWilOSOL8tmsqJ2dz39GjRygqOkpMTAwbN24AoEePnoEVUggJSkpKOHToEJGRkZL1RagXYamkTp06xW+/HXS8rbVoES9OWi9o3z7FEYFlBqA4j6RM01/79imOCdPDho1AEFw5cuQIAC1atJBRlFAvwlJJmQ580+zUpUsXK8UJaZxD0W02GwcPHqC01KgCbSqstm3bObKmX3rppMALKQQ9R44YcxXj4/1Xy00IT8JaSZnlztPSZH6Utzib/GJiYikvL3fcX7NEh0lkZKRM4hXccvSoOZIK3gKHQnASlkrKdOabb/xjxpxrpTghjXPwhGkyNRPNmkrq0CEjt1/btu0CLJ0QKpjmvvh4UVJC/QhLJXXwoKGkzKAJKb7nPd279yAhwTDRnDx5AqjMMmH6pPLyjPLx/fqdYYGEQiggSkrwlrBUUgcOHHB8jomJoVmzZhZKE9rYbDYGDzbmS504YSgp0xdlzpEyR1JjxoyzQEIhFDB9UmLuE+pLmCqpHMdnySPXcFwnXpojKPO/aVa99NLLAiuYEDKY1ZpjYmItlkQINcJSSZmOfYD+/dMslCQ8cM3FZ/qinCv1Nm/enJYtWwVULiF0MEfhzZuLVUOoH2GppMzACYBRo0ZbKEl4kJY2sEpy3v3793H8+HEOHTrkmPPSpUuqRdIJoYCZVqtZs+YWSyKEGmGppMyKsgDjx0+oZUvBE5o3b063bpXl4HNy9jsi/EwlNWiQZD4XaqZyJCVKSqgfYamkTJo2bUZiYqLVYoQFzpkkDhzI4ddffwUq56JdeKFkPhdqxsxYIiMpob6EtZIyC/UJDcc592FpaSmbNm1yLNtsNs45R8yqQs2YIymJtBXqS1grqb59+1ktQthghqGbfP31147PLVu2lNyIQq2YPikx9wn1JayV1IgR51gtQtjQrl37KnnXvvnmG8fnnj17WSGSEEJUmvtkJCXUj7BWUhMmXGS1CGFFnz59HZ/NUiggLwNC3ZjZSsQnJdSXsFVSkZGRtGsnPilfcs45o9y2X3755MAKIoQc5oTvqKhIiyURQo2wVVJJSclWixB2jBt3frW2yMjIKuHpguAOM4+m1JIS6kvYKiln05TgG/r0Ob1aJ9O+fYpF0gihhCgpwVvCVklJuXjfExUVRXJymyptaWkDLZJGCCVESQneErZKasIEmVzqD/r3r1qOY/z46iZAQXDFVFIgSkqoH36d3KKUugqYDUQDz2mtX3ZZnwa8DsQD64BbtdalSqnOwDtAG0AD07TWRZ6e12aziZ/ET5x33gWsXPmZY/miiy6xUBohVCgvN5SUcw5IIXQJZN/utydGKdUBeAwYDqQB05VSfVw2ewe4XWvdE+MV62Z7+z+Af2itewEbgQfqc26Zi+E/nJVSVFQUcXFxFkojhA5i7gsXAt23+/O1ZiywWmtdoLUuBj4ErjBXKqW6AM211mbqgn8Dk5VS0cBI+/aO9vqc2NVvIviO1q2THG/DrVsnWSyNECqITyo0ePXVVzsqpVJd/lwToAa0b/enuS8FyHFazgEG17G+I5AEHNFal7q0e8wFF5xPcnKLegvsCxp63vrsb9U1tm/fnn379jFhwgUBk8GX5wmGe2zV9QTyWK1bV46yo6KMF5uWLWMte269IZRkNWmIzAsWLFjvpvkR4GGn5YD27f5UUhGYY3wDG1DuwXrXdlz2q5Pp0/9Ebu7R+uziE5KTWzTovPXZv6HnagirV3/J+vUrmDhxckBk8OW1BsM9tup6fHWsiAhbFQVUE/n5RQ5f1KlTRr90+PBxy57b+mLlb8xbvJXZ/E6nTp06Ys6cOdkuqwtdNyeAfbs/lVQ2MMJpuR2w32V9ezfrfwMSlFKRWusy+zbO+wkW07JlS2688caQ+wEL1iHmvtBg+vTp2dOnT99Tx2YB7dv96ZNaCZyrlEpWSsUAk4BPzZVa673ACaXUMHvTNcAyrXUJsB6YYm+/FljmRzkFQfAzppKS6L6wIKB9u9+eGK31PmAWkAFsBuZprTcopZYqpQbZN5sGPKuU+hmIA16wt9+GETGyFUNjz/aXnIIg+B+zOKaMpEKfQPftfp0npbWeB8xzaZvg9PkHqjrczPa9wCh/yiYIQuCQybzhRSD7dhl7C4Lgd8QnJXiLKClBEPyOKCnBW0RJCYIQAEwlZbEYQsghSkoQBL8jIynBW0RJCYLgdyQEXfAWeWIEQfA7ZuYJGUkJ9UWUlCAIfkfMfYK3iJISBMHviJISvEWUlCAIficxMdH+v6XFkgihhl8zTgiCIAA899zL5Ofvp0uXVKtFEUIMUVKCIPidTp06M3BgX8mcL9QbMfcJgiAIQYsoKUEQBCFoESUlCIIgBC2ipARBEISgRZSUIAiCELSIkhIEQRCClnALQY8EiIiwblZ7Q89dn/2tvM5An9+X5wqGe2zV9fjiWE7bRNawSY2/Q6uf2foSavKCdzJ78J1ahq2yrHNYMBxYb7UQgtBIGAF87qZdfoehS03fqWWEm5JqCpwF5ABlFssiCOFKJNAe+BY46Wa9/A5Dj7q+U8sINyUlCIIghBESOCEIgiAELaKkBEEQhKBFlJQgCIIQtIiSEgRBEIIWUVKCIAhC0CJKShAEQQhaREkJgiAIQUu4pUUKKEqpgUAGcK7WeqO9LQn4Bvh/WuslNez3KbBMa/28fbknoIEntNb329vaAFlAG631YXtbKrBFax3ndKwpwMvAFK31Kr9cqEW4u14vj1MBJGut85zargBu11qPsi979Z1YdD3VjuPtc+DJvbG3efWsO+1/FTAbiAae01q/7KmM/kQpFQ98CVyktd6jlBoLPAM0B97TWs+2b5cGvA7EA+uAW7XWpRbI+xBwpX1xidb63mCXuaHISKoBaK03AfcCHyilWiqlooD3gX/V8aNdBox2Wp4ILAYucWobA3xRW2eolLoFmAOMDTcFZQE++U6sIBDPQQOedZRSHYDHMNIlpQHTlVJ9/CFnfVBKpWOkAOppX24O/AvjO+8NnKWUusC++TsYirsnYANutkDescB4YADGfTxTKfW7YJbZF4SNklJK7VFKHVdKFTn9PeDv82qtX8F40P8FPAkUAo/XsdsyYKRSyrz/E+37tlBKdbO3nQvU+ONXSv0ZuAsYrrXe7P0V1A+r7nMAaPB3YgWBfA68fNYBxgKrtdYFWuti4EPgCr8J6jk3A38E9tuXBwPbtda77SOOd4DJSqkuQHOt9df27f4NTA60sBhppmZorU9prUuAbRgKNphlbjDhZu6bqLVeacF5bwU2Y7zhnK61rjXXlNb6F6XUIaC/UmovoICvgaXAxcCzGB3iHHf7K6WeAu4B/qi13uOri6gHVt1nv9HQ78QKLHoO6vWs20nB6GBNcjAUgqVorW8CUEqZTe7k7FhLe0DRWmean5VSPTDMfi8SxDL7grAZSVmMAloAicCZHu6zDBgFXACs0FqXA58A4+0+hwqt9c9u9osF+gETgCeVUgMaJnqjwF1HGkH15KfefidW4KvnwNN7Y+LNsx7hch4bUO6pgAGkJjmDSn6lVF9gBcYLyi5CQOaGIEqqgdidxwsxTC53AQuUUu082HUZMBK4CKMjBFiF8YY6lprNSseBi7XWy4AngIVKqVbeX0GjIA9o7dLWFsh3afP2O7ECXz0Hnt6bhjzr2RgZtk3aUWliCyZqkjNo5FdKDcN4Jv+stX6rFtmCRuaGIkqqASilIoH3gMVa6/la6zeBT4H37OtqIwPD+XkOsBxAa30c+A64nZo7xHK7PRoMv8BWYL6TL0WozjLg/5n3SCnVErgOw5TnjLffiRX46jnw6N408FlfCZyrlEpWSsUAk+z7BhvfAEop1d1+TVdhRHzuBU7YFQTANRj3LaAopToBHwNXaa0XhILMvkA6tobxNIbZZYZT2x+BVtThUNZaHwO2Gx+rRIstAXoAa+o6ud0fcC1GVM+j9RE8hIh1CdIoUkr1q+cx7gCaAVuUUj9ihOO+B7zlvJEvvhMP8MX1VKGBz4FH94aGPev7gFkYLwGbgXla6w31lNPvaK1PANcDH2Eo/Z8xgjwApgHPKqV+BuKAFywQcSbGd/WMUmqzUmozhrzXE7wyN5iwqSellNoD3BRuDv1gQ+6zIAiBREZSgiAIQtAiSkoQBEEIWsLG3CcIgiCEHzKSEgRBEIIWUVKCIAhC0CJKShAEQQhaREkJgiAIQYsoKUEQBCFoESXViFFKfaqUusNpuadSqkIp9bhTWxul1EmlVIIf5XhTKfVPN+2TlFI/eHnMBKXU6oZLJwiClYiSatwES6G/l4Hf2YvOOTPdvs4bWhIE5SAEQWgY4VZPSqgfy4CHlFIR9rIUE4H7MbJbd9Na78Sp0J9S6iL7+iZAG+AtrfUDSql5wHda6zn27f4AjNJaT1FKTcQoG94EOAbM1Fp/5SyE1nqjUkpjFMJ7236MVGAQcJl9+Wzgbxj548qAR7TWn9jX/QUjKWopRu6964E3geb2/GZnAmdj5J+LAU4Bs7XWnyqlrgdutB/3sNbaWWkLgmAxMpJqxGitfwHMQn8tqV7oD+xKSillw0guep3WehAwBPiLvXzDaxiKweR64DV7YbbHgQla6wEYI6OFSqlYN+L8A0NZmNwM/Edrfcwu25vANVrrgRgjvblKqc5KqYvt5xuqtT4d2I2RsfwG4LjWOg2j9tGHwB1a6/4YCu0dpdRp9nP1xVCqoqAEIciQkZRgFvr7DXuhP6XUJ8AflVKLcCr0Zx8VXaSUugoj47YNYwSyBmimlBqEMVpKxqh58weMmjarnKqflgPdAVdf0wLgaXup9r0YisRUGkPtx/nY6TgVQH+MOk8faK0PAWit77bLmup07HRgh9b6G/s2mUqpL+zXXQH8qLU+Us/7JghCABAlJSzDGMGcwKhVA4aCeR2nQn/20c/3wCJgPfAv4FLAprWuUEq9gVEu4iTwhr0tEliltZ5insxeE6da8TWt9Qml1L+B3wPfAlu01tvtqyOBbVrrdKfjpAC5GD6zCqf2RIyRkzORVK9AGwFEY5j+imq/RYIgWIWY+wRPC/31AOIxfDmLMUYhTTEUAMC/MUyEkzFMc2Aou/FKqV4ASqkJwI+Aa4CEyVxgKob57iWn9q+BHkqpkfbjpGH4njpgFNS7XCkVb9/2YeBuDP9UpN1M+RXQSyk12L5/X4wKvGs8uD+CIFiIKKlGTj0K/f2IUVL9Z6XUNowgi60Ypju01geATRims/32tq0YfqgF9lDyv2KUPHc7ctFa78Io2tYPp8qwWutcjGquT9uP8zaGf2qP1nophlL8Qin1E0aZ7FlADrAByMQYRU0GXrRvMw+4we6TEwQhiJEs6IIgCELQIiMpQRAEIWgRJSUIgiAELaKkBEEQhKBFlJQgCIIQtIiSEgRBEIIWUVKCIAhC0CJKShAEQQhaREkJgiAIQcv/B3gZL4Ci0WNdAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "phbst.phbands.plot_with_phdos(phdos);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The vibrational spectrum seems OK but remember that we are *enforcing* the acoustic sum rule\n", "with `asr`.\n", "Since our input parameters are underconverged, its a good idea to compare the spectrum with/without `asr`:" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ph_plotter = ddb.anacompare_asr()" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ph_plotter.combiplot(units=\"cm-1\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "Not so good! The breaking of the ASR is quite large.\n", "This is mainly due to the use of a too small value for `ecut`.\n", "In real life, we should increase `ecut` and rerun the DFPT part but since this is a tutorial\n", "aiming at showing how to perform EPH calculations, we ignore this convergence issue \n", "keeping in mind that we should redo all our calculations with larger ecut before submitting the final\n", "version of the paper!\n", "
\n", "\n", "We can now finally turn our attention to the phonon linewidths and the Eliashberg function. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Phonon linewidths and Eliashberg function\n", "[[back to top](#top)]\n", "\n", "We have generated a pair of `DDB`-`DVDB` files on a 4x4x4 q-mesh\n", "and **three** `WFK` files with a much denser k-sampling (x16, x24, x32).\n", "In total we have three EPH calculations done with different k-meshes to analyze.\n", "\n", "Let's focus on the output files produced with the 16x16x16 k-mesh by the first `EphTask` in `w2/t0`:" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "out_A2F.nc out_NOINTP_A2FW out_PHDOS.nc\n", "out_A2FW out_NOINTP_PH_A2FW out_PHDOS_by_atom\n", "out_A2FW_QPTOPT3_A2FW out_OUT.nc out_PHDOS_msqd\n", "out_A2FW_QPTOPT3_PH_A2FW out_PHBANDS.agr out_PHGAMMA\n", "out_EBANDS.agr out_PHBST.nc out_PH_A2FW\n", "out_EDOS out_PHDOS out_THERMO\n" ] } ], "source": [ "!ls flow_eph_al/w2/t0/outdata" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The most important results are stored in:\n", " \n", "* *out_A2F.nc*: main results in netcdf format\n", "* *out_PHDOS.nc*: phonon DOS and projections over atoms and directions\n", "* *out_PBSTS.nc*: phonon band structure along the q-path\n", "\n", "
\n", "There is also a bunch of text files with the same results in text format if you are a gnuplot/xmgrace aficionado...\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's get a quick summary of the most important results with:" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "================================= File Info =================================\n", "Name: out_A2F.nc\n", "Directory: /Users/gmatteo/git_repos/abitutorials/abitutorials/eph_al/flow_eph_al/w2/t0/outdata\n", "Size: 254.68 kb\n", "Access Time: Mon Aug 13 15:11:59 2018\n", "Modification Time: Sun Mar 18 14:42:28 2018\n", "Change Time: Sun Mar 18 14:42:28 2018\n", "\n", "================================= Structure =================================\n", "Full Formula (Al1)\n", "Reduced Formula: Al\n", "abc : 2.806386 2.806386 2.806386\n", "angles: 60.000000 60.000000 60.000000\n", "Sites (1)\n", " # SP a b c\n", "--- ---- --- --- ---\n", " 0 Al 0 0 0\n", "\n", "Abinit Spacegroup: spgid: 0, num_spatial_symmetries: 48, has_timerev: True, symmorphic: False\n", "\n", "============================== Electronic Bands ==============================\n", "Number of electrons: 3.0, Fermi level: 7.166 (eV)\n", "nsppol: 1, nkpt: 145, mband: 4, nspinor: 1, nspden: 1\n", "smearing scheme: gaussian, tsmear_eV: 1.088, occopt: 7\n", "\n", "================================ Phonon Bands ================================\n", "Number of q-points: 198\n", "Atomic mass units: {13.0: 26.981539}\n", "Has non-analytical contribution for q --> 0: False\n", "\n", "============================== E-PH calculation ==============================\n", "K-mesh for electrons:\n", "mpdivs: [16 16 16] with shifts [[0. 0. 0.]] and kptopt: 1\n", "\n", "a2f(w) on the [8 8 8] q-mesh (ddb_ngqpt|eph_ngqpt)\n", "Isotropic lambda: 0.25, omega_log: 0.030 (eV), 343.094 (K)\n", "\n", "a2f(w) Fourier interpolated on the [24 24 24] q-mesh (ph_ngqpt)\n", "Isotropic lambda: 0.26, omega_log: 0.027 (eV), 314.268 (K)\n" ] } ], "source": [ "a2fnc = abilab.abiopen(\"flow_eph_al/w2/t0/outdata/out_A2F.nc\")\n", "print(a2fnc)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the `E-PH calculation` we have the value of $\\lambda$ and $\\omega_{log}$ computed\n", "with a 16x16x16 k-mesh for electrons and two q-meshes.\n", "...\n", "\n", "The value of $\\lambda$ is smaller (almost a factor 2) with respect to other values\n", "reported in the literature, likely due to the coarse 12x12x12 k-sampling.\n", "We will investigate this problem in the next section.\n", "For the time being, we prefer to focus on the visualization of the results with AbiPy.\n", "\n", "Let's use matplotlib to plot the Eliashberg function obtained with the two q-meshes:" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "a2fnc.plot_a2f_interpol();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This Eliashberg function obtained on the [24, 24, 24] q-mesh looks nicer, in particular \n", "we see the appearance of Van Hove singularities.\n", "As expected, the integral $\\lambda(\\omega)$ is less sensitive to the interpolation in q-space.\n", "We conclude that the fact that our $\\lambda$ is too small when compared with other ab-initio calculations ($\\lambda \\approx 0.4$)\n", "is not related to the q-sampling but to the *quality* of our phonon linewidths that in turn is related \n", "to the description of the FS." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also visualize the phonon linewidths with a scatter plot: " ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "a2fnc.plot_with_a2f(units=\"meV\", what=\"gamma\");\n", "#a2fnc.plot(units=\"meV\", what=\"gamma\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To plot the band energies used in the calculation:" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": true, "run_control": { "marked": true } }, "outputs": [], "source": [ "#a2fnc.plot_ebands();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The notation $\\alpha^2 F(\\omega)$ was introduced because the Eliashberg function \n", "is usually proportional to the phonon DOS $F(\\omega)$.\n", "There are, however, exceptions so it is useful to plot $\\alpha^2F(\\omega)$, $F(\\omega)$ and their ratio $\\alpha^2$.\n", "\n", "It is just a matter of passing the phonon DOS computed from the `DDB` file in the previous section \n", "to the `plot_a2` method:" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "a2fnc.a2f_qcoarse.plot_a2(phdos=phdos);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To conclude this section: our results for $\\lambda$ and $\\alpha^2F(\\omega)$ look reasonable \n", "but we are still far from the results reported in previous works.\n", "Perhaps we are not completely converged and we should analyze in more detail \n", "what happens if we increase the k-point sampling.\n", "Fortunately we have already computed these results in our Flow so it is just a matter of using\n", "AbiPy to compare multiple calculations." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using the A2FRobot for convergence studies\n", "[[back to top](#top)]\n", "\n", "\n", "In this section, we use the `A2fRobot` to analyze the convergence behaviour of our results\n", "with respect to the k-point sampling in the double-delta integral.\n", "\n", "Let's ask our robot to open all the `A2F` files located within the `flow_eph_al/` directory." ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
    \n", "
  1. w2/t0/outdata/out_A2F.nc
  2. \n", "
  3. w2/t1/outdata/out_A2F.nc
  4. \n", "
  5. w2/t2/outdata/out_A2F.nc
  6. \n", "
" ], "text/plain": [ "Label Relpath\n", "------------------------ ------------------------------------\n", "w2/t0/outdata/out_A2F.nc flow_eph_al/w2/t0/outdata/out_A2F.nc\n", "w2/t1/outdata/out_A2F.nc flow_eph_al/w2/t1/outdata/out_A2F.nc\n", "w2/t2/outdata/out_A2F.nc flow_eph_al/w2/t2/outdata/out_A2F.nc" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "robot = abilab.A2fRobot.from_dir(\"flow_eph_al/\")\n", "robot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We know that all these calculations have been done with different values of `nkpt`.\n", "Let's change the labels of the files by replacing file paths with more informative strings:" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "OrderedDict([('nkpt: 145', 'w2/t0/outdata/out_A2F.nc'),\n", " ('nkpt: 413', 'w2/t1/outdata/out_A2F.nc'),\n", " ('nkpt: 897', 'w2/t2/outdata/out_A2F.nc')])" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "robot.remap_labels(lambda ncfile: \"nkpt: %d\" % ncfile.nkpt)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and print a pandas `DataFrame` with the parameters of the calculation can help to understand the data:" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
nsppolnspinornspdennbandnkptddb_nqbzeph_nqbz_fineph_nqbzeph_intmetheph_fsewineph_fsmeareph_extraeleph_fermie
nkpt: 1451114145645121382420.0293990.010.00.0
nkpt: 4131114413645121382420.0293990.010.00.0
nkpt: 8971114897645121382420.0293990.010.00.0
\n", "
" ], "text/plain": [ " nsppol nspinor nspden nband nkpt ddb_nqbz eph_nqbz_fine \\\n", "nkpt: 145 1 1 1 4 145 64 512 \n", "nkpt: 413 1 1 1 4 413 64 512 \n", "nkpt: 897 1 1 1 4 897 64 512 \n", "\n", " ph_nqbz eph_intmeth eph_fsewin eph_fsmear eph_extrael \\\n", "nkpt: 145 13824 2 0.029399 0.01 0.0 \n", "nkpt: 413 13824 2 0.029399 0.01 0.0 \n", "nkpt: 897 13824 2 0.029399 0.01 0.0 \n", "\n", " eph_fermie \n", "nkpt: 145 0.0 \n", "nkpt: 413 0.0 \n", "nkpt: 897 0.0 " ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "robot.get_params_dataframe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As usual, it is much easier to analyze the convergence of scalar quantities.\n", "Since we are mainly interested in $T_c$-related properties, it makes sense \n", "to print a table with the value of $\\lambda$ and $\\omega_{log}$ extracted from the different calculations:" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
lambda_qcoarseomegalog_qcoarselambda_qintpomegalog_qintp
nkpt: 1450.2482420.0295660.2621110.027082
nkpt: 4130.3733530.0291480.4015130.025940
nkpt: 8970.3866030.0287700.4172100.025479
\n", "
" ], "text/plain": [ " lambda_qcoarse omegalog_qcoarse lambda_qintp omegalog_qintp\n", "nkpt: 145 0.248242 0.029566 0.262111 0.027082\n", "nkpt: 413 0.373353 0.029148 0.401513 0.025940\n", "nkpt: 897 0.386603 0.028770 0.417210 0.025479" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "robot.get_dataframe(with_params=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This table gives the values integrated on the `eph_ngqpt` mesh as well as the values obtained\n", "by Fourier interpolating the results on the `ph_ngqpt` mesh.\n", "Note the **big jump** in $\\lambda$ when we go from the 18x18x18 k-mesh to the 36x36x36 k-mesh (~0.2 --> ~0.4).\n", "This clearly shows that our initial estimate for $\\lambda$ obtained with a 18x18x18 k-mesh was really bad!\n", "(Did I tell you that these calculations are very sensitive to the k-point sampling?)\n", "\n", "If you prefer figures instead of tables with numbers, just use:" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "robot.plot_a2fdata_convergence(sortby=\"nkpt\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When converging with respect to the number of k-points, it is common to plot the\n", "physical results as function of $\\frac{1}{N_{kpt}}$.\n", "Let's define a small function that tells our robot how to sort the results:" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def inv_nkpt(a2f_file):\n", " \"\"\"$\\dfrac{1}{N_k}$\"\"\"\n", " return 1/ a2f_file.nkpt\n", " \n", "robot.plot_a2fdata_convergence(sortby=inv_nkpt); #, hue=\"eph_fsmear\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "At this point, our estimate for $\\lambda$ should be somewhere in [0.39, 0.42] \n", "that compares much better with the value of 0.44 reported by Savrasov.\n", "__Most importantly__, our results started to converge (although slowly).\n", "Now we know that a serious calculation of the phonon linewidths of Al\n", "would require something around 32x32x32 k-points (this is indeed the mesh used by Savrasov in their paper)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So far, we have been focusing on $\\lambda$ but what about the convergence of $\\alpha^2F(\\omega)$?" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "robot.plot_a2f_convergence();" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "================================= File Info =================================\n", "Name: out_A2F.nc\n", "Directory: /Users/gmatteo/git_repos/abitutorials/abitutorials/eph_al/flow_eph_al/w2/t2/outdata\n", "Size: 331.68 kb\n", "Access Time: Mon Aug 13 15:12:05 2018\n", "Modification Time: Sun Mar 18 14:49:37 2018\n", "Change Time: Sun Mar 18 14:49:37 2018\n", "\n", "================================= Structure =================================\n", "Full Formula (Al1)\n", "Reduced Formula: Al\n", "abc : 2.806386 2.806386 2.806386\n", "angles: 60.000000 60.000000 60.000000\n", "Sites (1)\n", " # SP a b c\n", "--- ---- --- --- ---\n", " 0 Al 0 0 0\n", "\n", "Abinit Spacegroup: spgid: 0, num_spatial_symmetries: 48, has_timerev: True, symmorphic: False\n", "\n", "============================== Electronic Bands ==============================\n", "Number of electrons: 3.0, Fermi level: 7.166 (eV)\n", "nsppol: 1, nkpt: 897, mband: 4, nspinor: 1, nspden: 1\n", "smearing scheme: gaussian, tsmear_eV: 1.088, occopt: 7\n", "\n", "================================ Phonon Bands ================================\n", "Number of q-points: 198\n", "Atomic mass units: {13.0: 26.981539}\n", "Has non-analytical contribution for q --> 0: False\n", "\n", "============================== E-PH calculation ==============================\n", "K-mesh for electrons:\n", "mpdivs: [32 32 32] with shifts [[0. 0. 0.]] and kptopt: 1\n", "\n", "a2f(w) on the [8 8 8] q-mesh (ddb_ngqpt|eph_ngqpt)\n", "Isotropic lambda: 0.39, omega_log: 0.029 (eV), 333.858 (K)\n", "\n", "a2f(w) Fourier interpolated on the [24 24 24] q-mesh (ph_ngqpt)\n", "Isotropic lambda: 0.42, omega_log: 0.025 (eV), 295.675 (K)\n" ] } ], "source": [ "print(robot.abifiles[-1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Other post-processing tools:" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#robot.gridplot_a2f(tight_layout=True);" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#robot.plot_a2f_convergence(sortby=\"nkpt\", hue=\"eph_fsmear\", tight_layout=True);" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#robot.plot_lambda_convergence(what=\"gamma\", sortby=\"nkpt\", hue=\"eph_fsmear\", tight_layout=True);" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#robot.abifiles[-1].plot_a2f_interpol();" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#robot.abifiles[-1].a2f_qcoarse.plot_tc_vs_mustar(start=0.1, stop=0.2);" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#robot.gridplot_phbands_with_a2f(units=\"meV\");" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#robot.plot_a2fdata_convergence(sortby=\"nkpt\", hue=None);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Back to the main [Index](../index.ipynb)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" }, "latex_envs": { "bibliofile": "biblio.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": true, "eqNumInitial": 0 } }, "nbformat": 4, "nbformat_minor": 2 }