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    "# CDS: Numerical Methods -- Final Assignment\n",
    "\n",
    "- See lecture notes and documentation on Brightspace for Python and Jupyter basics. If you are stuck, try to google or get in touch via Discord.\n",
    "\n",
    "- Solutions must be submitted <font color=red>**individually**</font> via the Jupyter Hub until <font color=red>**Monday, April 4th, 23:59**</font>.\n",
    "\n",
    "- Make sure you fill in any place that says `YOUR CODE HERE` or \"YOUR ANSWER HERE\".\n",
    "\n",
    "- Remember to document your source codes (docstrings, comments where necessary) and to write it as clear as possible.\n",
    "\n",
    "- Do not forget to fully annotate all of your plots.\n",
    "\n",
    "## Submission\n",
    "\n",
    "1. make sure everything runs as expected\n",
    "2. **restart the kernel** (in the menubar, select Kernel$\\rightarrow$Restart)\n",
    "3. **run all cells** (in the menubar, select Cell$\\rightarrow$Run All)\n",
    "4. Check all outputs (Out[\\*]) for errors and **resolve them if necessary**\n",
    "5. submit your solutions  **in time (before the deadline)**"
   ]
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    "## Tight-Binding Propagation Method Module\n",
    "\n",
    "### Tight-Binding Theory\n",
    "\n",
    "Solid state theory aims to describe crystalline structures defined by periodic arrangements of atomic positions $\\vec{R}_i$ with $i= 1 \\dots n$. To model the electronic properties of such a structure, we can use the so-called tight-binding method. Here one assumes that the problem for a single atom described by the Hamiltonian $H_{at}(\\vec{r})$ has already been solved, so that the atomic wave functions $\\phi_m(\\vec{r})$ are known. The Hamiltonian of the crystalline structure is then constructed from these atomic Hamiltonians as follows \n",
    "\n",
    "\\begin{align*}\n",
    "    H(\\vec{r}) = \\sum_{i} H_{at}(\\vec{r} - \\vec{R}_i) + \\Delta V(\\vec{r}),\n",
    "\\end{align*}\n",
    "\n",
    "where $\\Delta V(\\vec{r})$ describes the changes to the atomic potentials due to the periodic arrangement. Solutions to the time-dependent Schrödinger equation $\\psi_n(\\vec{r})$ can then be approximated by linear combinations of the atomic orbitals, i.e. \n",
    "\n",
    "\\begin{align*}\n",
    "    \\psi_m(\\vec{r}) = \\sum_{i} \\, c_{i,m} \\, \\phi_m(\\vec{r}-\\vec{R}_i). \n",
    "\\end{align*}\n",
    "\n",
    "Thus, our task is to find the coefficients $c_{i,m}$, which are the eigenfunctions of the tight-binding Hamiltonian $H_{tb}$. In the basis of the atomic orbitals $H_{tb}$ is an $n \\times n$ matrix which describes the \"hopping\" of an electron from one atomic position to the other. In this description the electrons are assumed to be tightly bound to the atomic positions, hence the name of the approach. In summary, we have reduced our original problem $H(\\vec{r})$, described in a continuous space $\\vec{r}$, to a strongly discretized problem $H_{tb}$ in the space of lattice coordinates $\\vec{R}_i$.\n",
    "\n",
    "### Propagation Method\n",
    "\t\n",
    "While this reduction already helps a lot, full diagonalizations of the tight-binding matrix is still not feasible if we need to describe realistic structures with thousands of atoms. For this case we like to have a method which allows us to study the electronic properties, without the need of fully diagonalizing the tight-binding matrix. The tight-biding propagation method allows for exactly this. By analyzing the propagation of an initial electronic state through the crystalline structure we also have access to the full eigenspectrum of $H_{tb}$, without explicit diagonalization.\n",
    "\t\n",
    "### Your Goal\n",
    "    \n",
    "In the following you will setup the tight-binding Hamiltonian for a one-dimensional chain of atoms and numerically study its properties using exact diagonalization. Then you will compare it to the results obtained using the tight-binding propagation method. You will need some of the algorithms which you have implemented in the weekly assignments before. Additionally, you will need to implement a few new algorithms, which we have discussed in the last lecture. In principle there will be no need to use Numpy or Scipy (except for Numpy's array handling and a few other exceptions). However, if you encounter any problems with your own implementations of specific functionalities, you can use the Numpy and Scipy alternatives. Therefore you should be able to perform all of the following tasks in any case.\n",
    "\n",
    "Let us start by importing the necessary packages."
   ]
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   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt"
   ]
  },
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   "source": [
    "## Step 1: Crystal Lattice\n",
    "\n",
    "### Task 1.1 [3 points]\n",
    "\n",
    "In the following exercises the atomic positions of the 1D crystal lattice will be fixed to $\\vec{R}_i = x_i = i a$, with $i = 0 \\dots n-1$ and $a$ being the lattice constant.\n",
    "\n",
    "Write a simple Python function that takes the chain length $n$ as an argument and returns the atomic positions $x_i$. Set $a = 1$ for all the following exercises."
   ]
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    "def atomic_positions(n, a=1):\n",
    "    \"\"\"\n",
    "    Creates an array of atomic position in a 1D crystal lattice\n",
    "    for lattice constant a having default value a = 1.\n",
    "    \n",
    "    Args:\n",
    "        n: number of atoms in the 1D lattice string\n",
    "        a: numerical value for the lattice constant\n",
    "\n",
    "    Returns:\n",
    "        A 1D array of atomic positions.\n",
    "    \"\"\"\n",
    "    \n",
    "    return np.arange(n)*a"
   ]
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    "## Step 2: Atomic Basis Functions\n",
    "\n",
    "Our atomic basis functions will be Gaussians of the form\n",
    "$$\n",
    "\\large\n",
    "\\phi(x, \\mu, \\sigma) = \\frac{1}{\\pi^{1/4} \\sigma^{1/2}} e^{-\\frac{1}{2} \\left(\\frac{x-\\mu}{\\sigma}\\right)^2},\n",
    "$$\n",
    "\twhere $\\mu$ is their localization position and $\\sigma$ their broadenings. We also choose to have just one orbital per atom so that we can drop the index $m$ from now on. \n",
    "\t\n",
    "### Task 2.1 [4 points]\n",
    "Implement a Python function which calculates $\\phi(x, \\mu, \\sigma)$ for a whole array of arbitrary $x$, centered at given $\\mu$ with a given broadening $\\sigma$.\n",
    "\n",
    "Plot all the atomic basis functions for a chain with $n = 10$ atoms, using $\\sigma = 0.25$. I.e. plot $\\phi(x, x_i, \\sigma)$ vs $x$, for all atomic positions $x_i$ in the chain."
   ]
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def atomic_basis(x, mu, sigma):\n",
    "    \"\"\"\n",
    "    Calculates the atomic basis functions for the 1D chain of atoms.\n",
    "    \n",
    "    Args:\n",
    "        x:     array of positions to calculate the wavefunction at\n",
    "        mu:    atomic position(s) to center Gaussian wavefunction at\n",
    "        sigma: broadening constant for Gaussian function(s)\n",
    "\n",
    "    Returns:\n",
    "        An array of values for the wavefunction over the positions\n",
    "        as given by x with shape len(x) by len(mu)\n",
    "    \"\"\"\n",
    "    \n",
    "    return np.pi**(-1/4)*sigma**(-1/2)*np.exp(-1/2*(np.subtract.outer(x, mu)/sigma)**2)\n",
    "\n",
    "n = 10\n",
    "sigma = .25\n",
    "x = np.linspace(-2, 12, 1000)\n",
    "\n",
    "plt.figure()\n",
    "plt.xlabel(\"$x$\")\n",
    "plt.ylabel(\"$\\phi$\")\n",
    "\n",
    "for mu in atomic_positions(n):\n",
    "    plt.plot(x, atomic_basis(x, mu, sigma), label=\"n = \" + str(mu))\n",
    "\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
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   "source": [
    "### Task 2.2 [6 points]\n",
    "Implement a Python function to calculate numerical integrals (using for example the composite trapezoid or Simpson rule). This one should be general enough to calculate integrals $\\int_a^b f(x) dx$ for arbitrary functions $f(x)$, as you will need it for other tasks as well.\n",
    "\n",
    "Implement a simple unit test for your integration function."
   ]
  },
  {
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   "execution_count": 4,
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   "outputs": [],
   "source": [
    "def integrate(yk, x):\n",
    "    \"\"\"\n",
    "    Numerically integrates function yk over [x[0], x[-1]] using Simpson's 3/8\n",
    "    rule over the grid provided by x.\n",
    "    \n",
    "    Args:\n",
    "        yk: function of one numerical argument that returns a numeric\n",
    "            or an array of function values such that x[i] corresponds to yk[i]\n",
    "        x:  array of numerics as argument to yk\n",
    "\n",
    "    Returns:\n",
    "        A numeric value for the quadrature of yk over x with error\n",
    "        of order \n",
    "    \"\"\"\n",
    "    \n",
    "    # If yk is callable, we use it to determine the function values\n",
    "    # over array x.\n",
    "    if callable(yk):\n",
    "        yk = yk(x)\n",
    "    \n",
    "    # The distance h_i = x[i + 1] - x[i] is not necessarily constant. The choice of\n",
    "    # partitioning of the interval is subject to mathematical considerations I will\n",
    "    # not go into.\n",
    "    h = x[1:] - x[:-1]\n",
    "    \n",
    "    integral = 0\n",
    "    integral += 3/8*(x[1] - x[0])*yk[0]\n",
    "    integral += 9/8*h[1::3]@yk[1:-1:3]\n",
    "    integral += 9/8*h[2::3]@yk[2:-1:3]\n",
    "    integral += 6/8*h[ ::3]@yk[ :-1:3]\n",
    "    integral += 3/8*(x[-1] - x[-2])*yk[-1]\n",
    "    return integral"
   ]
  },
  {
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   "execution_count": 5,
   "metadata": {
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   "source": [
    "def test_integrate():\n",
    "    # Test integral 1 of f with F its primitive with integration constant 0\n",
    "    f = lambda x: x**2\n",
    "    F = lambda x: x**3/3\n",
    "    x = np.logspace(0, 3, 1000000)\n",
    "    assert np.isclose(integrate(f, x), F(x[-1]) - F(x[0]))\n",
    "    \n",
    "    # Test integral 2 of f with F its primitive with integration constant 0\n",
    "    f = lambda x: np.sin(2*x)/(2 + np.cos(2*x))\n",
    "    F = lambda x: -.5*np.log(np.cos(2*x) + 2)\n",
    "    x = np.linspace(0, 10, 1000)\n",
    "    assert np.isclose(integrate(f, x), F(x[-1]) - F(x[0]))\n",
    "    \n",
    "test_integrate()"
   ]
  },
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   "source": [
    "### Task 2.3 [2 points]\n",
    "Use your Python integration function to check the orthogonality of the Gaussian basis functions by verifying the following condition $$\\delta_{ij} = \\int_{-\\infty}^{+\\infty} \\phi(x, x_i, \\sigma) \\, \\phi(x, x_j, \\sigma) \\, dx,$$ where $\\delta_{ii} \\approx 1$ and $\\delta_{ij} \\approx 0$ for $\\sigma = 0.25$."
   ]
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   "execution_count": 6,
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "delta_00 = 1.00000   (self)\n",
      "delta_01 = 0.01832   (nearest neighbours)\n",
      "delta_02 = 0.00000\n",
      "delta_03 = 0.00000\n",
      "delta_04 = 0.00000\n",
      "delta_05 = 0.00000\n",
      "delta_06 = 0.00000\n",
      "delta_07 = 0.00000\n",
      "delta_08 = 0.00000\n",
      "delta_09 = 0.00000\n",
      "delta_10 = 0.01832   (nearest neighbours)\n",
      "delta_11 = 1.00000   (self)\n",
      "delta_12 = 0.01832   (nearest neighbours)\n",
      "delta_13 = 0.00000\n",
      "delta_14 = 0.00000\n",
      "delta_15 = 0.00000\n",
      "delta_16 = 0.00000\n",
      "delta_17 = 0.00000\n",
      "delta_18 = 0.00000\n",
      "delta_19 = 0.00000\n",
      "delta_20 = 0.00000\n",
      "delta_21 = 0.01832   (nearest neighbours)\n",
      "delta_22 = 1.00000   (self)\n",
      "delta_23 = 0.01832   (nearest neighbours)\n",
      "delta_24 = 0.00000\n",
      "delta_25 = 0.00000\n",
      "delta_26 = 0.00000\n",
      "delta_27 = 0.00000\n",
      "delta_28 = 0.00000\n",
      "delta_29 = 0.00000\n",
      "delta_30 = 0.00000\n",
      "delta_31 = 0.00000\n",
      "delta_32 = 0.01832   (nearest neighbours)\n",
      "delta_33 = 1.00000   (self)\n",
      "delta_34 = 0.01832   (nearest neighbours)\n",
      "delta_35 = 0.00000\n",
      "delta_36 = 0.00000\n",
      "delta_37 = 0.00000\n",
      "delta_38 = 0.00000\n",
      "delta_39 = 0.00000\n",
      "delta_40 = 0.00000\n",
      "delta_41 = 0.00000\n",
      "delta_42 = 0.00000\n",
      "delta_43 = 0.01832   (nearest neighbours)\n",
      "delta_44 = 1.00000   (self)\n",
      "delta_45 = 0.01832   (nearest neighbours)\n",
      "delta_46 = 0.00000\n",
      "delta_47 = 0.00000\n",
      "delta_48 = 0.00000\n",
      "delta_49 = 0.00000\n",
      "delta_50 = 0.00000\n",
      "delta_51 = 0.00000\n",
      "delta_52 = 0.00000\n",
      "delta_53 = 0.00000\n",
      "delta_54 = 0.01832   (nearest neighbours)\n",
      "delta_55 = 1.00000   (self)\n",
      "delta_56 = 0.01832   (nearest neighbours)\n",
      "delta_57 = 0.00000\n",
      "delta_58 = 0.00000\n",
      "delta_59 = 0.00000\n",
      "delta_60 = 0.00000\n",
      "delta_61 = 0.00000\n",
      "delta_62 = 0.00000\n",
      "delta_63 = 0.00000\n",
      "delta_64 = 0.00000\n",
      "delta_65 = 0.01832   (nearest neighbours)\n",
      "delta_66 = 1.00000   (self)\n",
      "delta_67 = 0.01832   (nearest neighbours)\n",
      "delta_68 = 0.00000\n",
      "delta_69 = 0.00000\n",
      "delta_70 = 0.00000\n",
      "delta_71 = 0.00000\n",
      "delta_72 = 0.00000\n",
      "delta_73 = 0.00000\n",
      "delta_74 = 0.00000\n",
      "delta_75 = 0.00000\n",
      "delta_76 = 0.01832   (nearest neighbours)\n",
      "delta_77 = 1.00000   (self)\n",
      "delta_78 = 0.01832   (nearest neighbours)\n",
      "delta_79 = 0.00000\n",
      "delta_80 = 0.00000\n",
      "delta_81 = 0.00000\n",
      "delta_82 = 0.00000\n",
      "delta_83 = 0.00000\n",
      "delta_84 = 0.00000\n",
      "delta_85 = 0.00000\n",
      "delta_86 = 0.00000\n",
      "delta_87 = 0.01832   (nearest neighbours)\n",
      "delta_88 = 1.00000   (self)\n",
      "delta_89 = 0.01832   (nearest neighbours)\n",
      "delta_90 = 0.00000\n",
      "delta_91 = 0.00000\n",
      "delta_92 = 0.00000\n",
      "delta_93 = 0.00000\n",
      "delta_94 = 0.00000\n",
      "delta_95 = 0.00000\n",
      "delta_96 = 0.00000\n",
      "delta_97 = 0.00000\n",
      "delta_98 = 0.01832   (nearest neighbours)\n",
      "delta_99 = 1.00000   (self)\n"
     ]
    }
   ],
   "source": [
    "n = 10\n",
    "sigma = .25\n",
    "\n",
    "positions = atomic_positions(n)\n",
    "infty = 10000\n",
    "x = np.linspace(-infty, infty, 1000000)\n",
    "\n",
    "def ijlabel(i, j):\n",
    "    \"\"\"\n",
    "    Returns a string label describing the relation between two states in words,\n",
    "    if they are close enough.\n",
    "    \"\"\"\n",
    "    \n",
    "    if i == j:\n",
    "        return \"   (self)\"\n",
    "    if abs(i - j) == 1:\n",
    "        return \"   (nearest neighbours)\"\n",
    "    # Default:\n",
    "    return \"\"\n",
    "\n",
    "for i in range(n):\n",
    "    for j in range(n):\n",
    "        integrand = lambda x: atomic_basis(x, positions[i], sigma)*atomic_basis(x, positions[j], sigma)\n",
    "        print(\"delta_{}{} = {:.5f}{}\".format(i, j, integrate(integrand, x), ijlabel(i, j)))\n",
    "\n",
    "# Yann had output:\n",
    "#delta_00 = 1.00000\n",
    "#delta_01 = 0.01832\n",
    "#delta_02 = 0.00000\n",
    "#delta_34 = 0.01832\n",
    "# Explanation: next neighbours migth have some overlap. Further away, no overlap at all."
   ]
  },
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   "source": [
    "## Step 3: Tight-Binding Hamiltonian\n",
    "\n",
    "The tight-binding Hamiltonian for our 1D chain should describe the hopping of an electron from all atomic positions to their nearest left and right neighbours (i.e. no long-range hopping). The resulting matrix representation in the basis of the discrete $x_i$ positions is therefore given as a tri-diagonal $n \\times n$ matrix of the form\n",
    "\n",
    "\\begin{align}\n",
    "    \\mathbf{H}_{tb} =\n",
    "    \\left( \\begin{array}{cccc}\n",
    "    0 & t & & 0\\\\\n",
    "    t & \\ddots & \\ddots &  \\\\\n",
    "      & \\ddots & \\ddots &  t \\\\\n",
    "    0 &  & t & 0\n",
    "    \\end{array}  \\right),\n",
    "\\end{align}\n",
    "\n",
    "where $t = t_{i,i\\pm1}$ is the nearest-neighbour hopping matrix element. A hopping matrix element $t_{i,j}$ is a measure for the probability of an electron to hop from site $i$ to site $j$. They are defined as\n",
    "\n",
    "\\begin{align}\n",
    "    t_{i,j} = \\int_{-\\infty}^{+\\infty} \\phi(x, x_i, \\sigma) \\, \\Delta V(x) \\, \\phi(x, x_j, \\sigma) \\, dx,\n",
    "\\end{align}\n",
    "\n",
    "with the potential fixed to\n",
    "\n",
    "\\begin{align}\n",
    "    \\Delta V(x) = \\sum_i \\frac{-1}{|x - x_i| + 0.001}.\n",
    "\\end{align}\n",
    "\n",
    "### Task 3.1 [3 points]\n",
    "Write a Python function to calculate $t_{i,j}$, using $\\sigma = 0.25$. The function should have as input the indices $i$ and $j$, and the chain length $n$. Verify that the long-range hoppings $t_{i,i\\pm2}$ and $t_{i,i\\pm3}$ are negligible compared to $t_{i,i\\pm1}$.\n",
    "\n",
    "Hint: use your integration function from task 2.2"
   ]
  },
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   "execution_count": 7,
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   "source": [
    "def hopping(i, j, n, sigma=.25):\n",
    "    \"\"\"\n",
    "    Calculates hopping matrix elements t_ij for sigma = 0.25 in a 1D\n",
    "    chain of n atoms at distance a = 1 from eachother.\n",
    "    \n",
    "    Args:\n",
    "        i:     origin site index\n",
    "        j:     destination site index\n",
    "        n:     number of atoms in the chain\n",
    "        sigma: standard deviation to the Gaussian wave functions\n",
    "\n",
    "    Returns:\n",
    "        Hopping parameter t_ij.\n",
    "    \"\"\"\n",
    "    \n",
    "    positions = atomic_positions(n)\n",
    "    \n",
    "    # This 'infinity' is large enough, as the Gaussians decay quite quickly\n",
    "    # away from the atomic positions, which we already saw in the overlap\n",
    "    # above. In fact, 99.7% of all probability mass is under the integral\n",
    "    # for x radius of 3*sigma from the centers x_i.\n",
    "    h = 1e-5\n",
    "    x = np.arange(positions[0] - 10*sigma, positions[-1] - 10*sigma, h)\n",
    "    \n",
    "    def V(x):\n",
    "         ret = np.zeros(x.shape)\n",
    "         for x_i in positions:\n",
    "             ret += -1./(np.abs(x - x_i) + 0.001)\n",
    "         return ret\n",
    "    # Instead of using a loop, one could vectorize the problem further by calculating all sum\n",
    "    # terms as elements of a len(x) by len(positions) matrix and then summing along the rows.\n",
    "    # In testing I found that this was slower than using the loop, so I commented it out.\n",
    "    # This might be due to the large memory overhead O(len(x)*len(positions)), and the fact that\n",
    "    # the len(positions) iterations already do vectorized calculations on len(x) >> len(positions)\n",
    "    # numbers, making the theoretical speed gain only plausible at larger len(positions). \n",
    "    #V = lambda x: np.sum( -1/( np.abs(np.subtract.outer(x, positions)) + 0.001 ), axis=1 )\n",
    "    \n",
    "    integrand = lambda x: atomic_basis(x, positions[i], sigma)*V(x)*atomic_basis(x, positions[j], sigma)\n",
    "    return integrate(integrand, x)"
   ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For i = 0 ...\n",
      "\tt_{i,i+1} = -0.13849173441658025\n",
      "\tt_{i,i+2} = -3.088088057066831e-06\n",
      "\tt_{i,i+3} = -1.8833562200578063e-15\n",
      "\n",
      "For i = 1 ...\n",
      "\tt_{i,i-1} = -0.13849173441658025\n",
      "\tt_{i,i+1} = -0.14871538221422848\n",
      "\tt_{i,i+2} = -3.1306987950404085e-06\n",
      "\tt_{i,i+3} = -1.945630457066332e-15\n",
      "\n",
      "For i = 2 ...\n",
      "\tt_{i,i-1} = -0.14871538221422848\n",
      "\tt_{i,i+1} = -0.15363274031153992\n",
      "\tt_{i,i-2} = -3.088088057066831e-06\n",
      "\tt_{i,i+2} = -3.152251440766849e-06\n",
      "\tt_{i,i+3} = -1.9763481552880358e-15\n",
      "\n",
      "For i = 3 ...\n",
      "\tt_{i,i-1} = -0.15363274031153992\n",
      "\tt_{i,i+1} = -0.1560583006931239\n",
      "\tt_{i,i-2} = -3.1306987950404085e-06\n",
      "\tt_{i,i+2} = -3.1616643825949025e-06\n",
      "\tt_{i,i-3} = -1.8833562200578063e-15\n",
      "\tt_{i,i+3} = -1.9857521228152284e-15\n",
      "\n",
      "For i = 4 ...\n",
      "\tt_{i,i-1} = -0.1560583006931239\n",
      "\tt_{i,i+1} = -0.15680086580653224\n",
      "\tt_{i,i-2} = -3.1522514407668485e-06\n",
      "\tt_{i,i+2} = -3.1616580341274714e-06\n",
      "\tt_{i,i-3} = -1.945630457066332e-15\n",
      "\tt_{i,i+3} = -1.9763479030784917e-15\n",
      "\n",
      "For i = 5 ...\n",
      "\tt_{i,i-1} = -0.15680086580653224\n",
      "\tt_{i,i+1} = -0.1560582807779115\n",
      "\tt_{i,i-2} = -3.1616643825949025e-06\n",
      "\tt_{i,i+2} = -3.1503943708763577e-06\n",
      "\tt_{i,i-3} = -1.9763481552880358e-15\n",
      "\tt_{i,i+3} = -9.75855063584149e-16\n",
      "\n",
      "For i = 6 ...\n",
      "\tt_{i,i-1} = -0.15605828077791148\n",
      "\tt_{i,i+1} = -0.07705640452986241\n",
      "\tt_{i,i-2} = -3.1616580341274714e-06\n",
      "\tt_{i,i+2} = -1.8615080260773555e-09\n",
      "\tt_{i,i-3} = -1.985752122815229e-15\n",
      "\tt_{i,i+3} = -1.2516261372405081e-23\n",
      "\n",
      "For i = 7 ...\n",
      "\tt_{i,i-1} = -0.07705640452986241\n",
      "\tt_{i,i+1} = -9.883210483852472e-10\n",
      "\tt_{i,i-2} = -3.1503943708763577e-06\n",
      "\tt_{i,i+2} = -8.453472394803474e-24\n",
      "\tt_{i,i-3} = -1.9763479030784917e-15\n",
      "\n",
      "For i = 8 ...\n",
      "\tt_{i,i-1} = -9.88321048385247e-10\n",
      "\tt_{i,i+1} = -7.16787287550824e-31\n",
      "\tt_{i,i-2} = -1.8615080260773555e-09\n",
      "\tt_{i,i-3} = -9.75855063584149e-16\n",
      "\n",
      "For i = 9 ...\n",
      "\tt_{i,i-1} = -7.16787287550824e-31\n",
      "\tt_{i,i-2} = -8.453472394803472e-24\n",
      "\tt_{i,i-3} = -1.2516261372405081e-23\n",
      "\n"
     ]
    }
   ],
   "source": [
    "n = 10\n",
    "\n",
    "for i in range(n):\n",
    "    print(\"For i =\", i, \"...\")\n",
    "    for r in range(1, 4):\n",
    "        if i - r >= 0:\n",
    "            print(\"\\tt_{{i,i-{}}} = {}\".format(r, hopping(i, i - r, n)))\n",
    "        if i + r < n:\n",
    "            print(\"\\tt_{{i,i+{}}} = {}\".format(r, hopping(i, i + r, n)))\n",
    "    print()"
   ]
  },
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   "source": [
    "### Task 3.2 [3 points]\n",
    "Implement a diagonalization routine for tri-diagonal matrices which returns all eigenvalues, for example using the $QR$ decomposition (it is fine to use Numpy's $\\text{qr()}$). \n",
    "\n",
    "Hint: For tri-diagonal matrices with vanishing diagonal elements, the $QR$-decomposition-based diagonalization algorithm gets trapped. To get around this you could, for example, add a diagonal $1$ to your matrix, and later subtract $1$ from each eigenvalue."
   ]
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   "execution_count": 9,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "c20cbcce0a7df50b6ae7b90c7aa35721",
     "grade": true,
     "grade_id": "cell-9d4942b717eadeb2",
     "locked": false,
     "points": 3,
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   "outputs": [],
   "source": [
    "def QREig(T, eps=1e-6, k_max=10000):\n",
    "    \"\"\"\n",
    "    Follows the method of the QR decomposition based diagonalization routine\n",
    "    for tridiagonal matrices. The matrix T is diagonalized, resulting in\n",
    "    all diagonal elements being an eigenvalue.\n",
    "    \n",
    "    Args:\n",
    "        T:     a tridiagonaliz matrix.\n",
    "        eps:   the desired accuracy.\n",
    "        k_max: maximum number of iterations after which to cut off\n",
    "    \n",
    "    Returns:\n",
    "        A one dimensional array with the eigenvalues of the matrix T.\n",
    "    \"\"\"\n",
    "    \n",
    "    e = eps + 1\n",
    "    k = 0\n",
    "    while e > eps and k < k_max:\n",
    "        k += 1\n",
    "        Q, R = np.linalg.qr(T)\n",
    "        T = np.matmul(R,Q)\n",
    "        e = np.sum(np.abs(np.diag(T, k=1)))\n",
    "    return np.diag(T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "markdown",
     "checksum": "19976946c5746804cb08c34f0bda50fc",
     "grade": false,
     "grade_id": "cell-2d8fb5c080951dd5",
     "locked": true,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "source": [
    "### Task 3.3 [3 points]\n",
    "Implement a unit test for your diagonalization routine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "d95777361c07514a97ff1458f26f4f44",
     "grade": true,
     "grade_id": "cell-001cb3c043c4e371",
     "locked": false,
     "points": 3,
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    }
   },
   "outputs": [],
   "source": [
    "def test_QREig():\n",
    "    # Test case one\n",
    "    T = np.array([\n",
    "        [1,4,0,0],\n",
    "        [3,4,1,0],\n",
    "        [0,2,3,4],\n",
    "        [0,0,1,3]\n",
    "    ])\n",
    "    # Eigenvalues are roots of λ^4 - 11*λ^3 + 25*λ^2 + 31*λ - 46.\n",
    "    eigenvalues_of_T = np.array([-1.45350244, 1., 4.65531023, 6.79819221])\n",
    "    assert np.allclose(np.sort(QREig(T)), eigenvalues_of_T)\n",
    "    \n",
    "    # Test case two\n",
    "    T = np.array([\n",
    "        [1,4,0,0],\n",
    "        [3,0,1,0],\n",
    "        [0,2,0,4],\n",
    "        [0,0,0,3]\n",
    "    ])\n",
    "    eigenvalues_of_T = np.sort(np.linalg.eig(T)[0])\n",
    "    assert np.allclose(np.sort(QREig(T)), eigenvalues_of_T)\n",
    "\n",
    "test_QREig()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "markdown",
     "checksum": "83df149b46d779a846f9de925342b681",
     "grade": false,
     "grade_id": "cell-85c89b0eb0930f2b",
     "locked": true,
     "schema_version": 3,
     "solution": false,
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    }
   },
   "source": [
    "### Task 3.4 [4 points]\n",
    "First, write a function that generates your tight-binding Hamiltonian $\\mathbf{H}_{tb}$, for a given chain length $n$. Use $t = t_{i,i\\pm1}$, as calculated in task 3.1. You can choose any $i$ near the center of the chain for the calculation of $t$, as the chain is (approximately) periodic.\n",
    "\n",
    "Second, use your diagonalization routine to calculate all the eigenvalues $E_m$, for a variety of $n=10,20,40,80,100$. Sort the resulting $E_m$ and plot them vs. $m$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "a0779b496fd41a4664bb0cdd857c70fc",
     "grade": true,
     "grade_id": "cell-764cb41c37700042",
     "locked": false,
     "points": 3,
     "schema_version": 3,
     "solution": true,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "def TBHamiltonian(n, sigma=.25):\n",
    "    \"\"\"\n",
    "    Generates the tight-binding hamiltonian H_tb for given chain length n,\n",
    "    using the approximation of constant hopping parameter in a periodic\n",
    "    chain of atoms.\n",
    "    \n",
    "    Args:\n",
    "        n:     number of atoms in the chain\n",
    "        sigma: standard deviation to the Gaussian wave functions\n",
    "\n",
    "    Returns:\n",
    "        Tight-binding hamiltonian H_tb.\n",
    "    \"\"\"\n",
    "    \n",
    "    # TODO: Comment on the weird 20% differences in hopping parameters.\n",
    "    \n",
    "    i = n//2\n",
    "    t = hopping(i, i + 1, n, sigma)\n",
    "    H_tb = (np.eye(n, n, -1) + np.eye(n, n, 1))*t\n",
    "    \n",
    "    return H_tb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "634e139137eead8808d1d8ccb793d5a5",
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     "grade_id": "cell-39ada0528e69d2e5",
     "locked": false,
     "points": 1,
     "schema_version": 3,
     "solution": true,
     "task": false
    }
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "\n",
    "for n in [10, 20, 40, 80, 100]:\n",
    "    H_tb = TBHamiltonian(n)\n",
    "    # TODO: Decide whether to implement this + 1 - 1 trick here or in QREig above.\n",
    "    E_m  = QREig(H_tb + np.eye(n)) - 1\n",
    "    plt.plot(np.arange(len(E_m)) + 1, np.sort(E_m), label=\"n = {}\".format(n))\n",
    "\n",
    "plt.legend()\n",
    "plt.title(\"Energy eigenvalues of $H_{{tb}}$ for different chain lengths $n$\")\n",
    "plt.xlabel(\"$m$\")\n",
    "plt.ylabel(\"$E_m$\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "markdown",
     "checksum": "c2b46d2fef4b0c243103a5a6f1111e2d",
     "grade": false,
     "grade_id": "cell-b7c84b8c4ed4c1be",
     "locked": true,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "source": [
    "### Task 3.5 [3 points]\n",
    "Implement a function to calculate the so-called density-of-states \n",
    "\n",
    "\\begin{align*}\n",
    "    \\rho(\\omega) = \\frac{1}{N} \\sum_i \\delta(\\omega - E_i),\n",
    "\\end{align*}\n",
    "\n",
    "for a variable energy grid $\\omega$. Do this by approximating the $\\delta$-distribution with a Gaussian. In detail, you can use your atomic orbital function $\\delta(\\omega - E_i) \\approx \\phi(\\omega, E_i, \\sigma_\\rho)$. Calculate the normalization factor $N$ such that $\\int \\rho(\\omega) dw = 1$ is fulfilled.\n",
    "\n",
    "Your function should take as input the energy grid $\\omega$, the eigenenergies $E_i$ and the broadening $\\sigma_\\rho$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "ca46cf0b09305fafb522fc0395d1e495",
     "grade": true,
     "grade_id": "cell-d7c225b7687b5a9c",
     "locked": false,
     "points": 3,
     "schema_version": 3,
     "solution": true,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "def getDOS_ED(w, Ei, sigma):\n",
    "    \"\"\"\n",
    "    Calculates the density-of-states (DOS) for energy grid w by\n",
    "    counting the occupation using a Gaussian approximation to the\n",
    "    delta function.\n",
    "    \n",
    "    Args:\n",
    "        w:     grid of energies to calculate the DOS over\n",
    "        Ei:    array of n eigenenergies for the system\n",
    "        sigma: standard deviation to the Gaussian\n",
    "\n",
    "    Returns:\n",
    "        Tight-binding hamiltonian H_tb.\n",
    "    \"\"\"\n",
    "    \n",
    "    # TODO: Kijk nog eens kritisch naar de omschrijving hierboven van de functie.\n",
    "    \n",
    "    # Luckily, the function is built in such a way it can also\n",
    "    # handle an array input as its first argument.\n",
    "    delta = atomic_basis(w, Ei, sigma)\n",
    "    \n",
    "    rho = np.sum(delta, axis=1)\n",
    "    \n",
    "    # Now normalize rho.\n",
    "    N = integrate(rho, w)\n",
    "    rho /= N\n",
    "    \n",
    "    return rho"
   ]
  },
  {
   "attachments": {
    "dosN010.png": {
     "image/png": 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"
    },
    "dosN100.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "markdown",
     "checksum": "b2049bb4d5fb56c59e7d0e742a91d3c8",
     "grade": false,
     "grade_id": "cell-7560c4658b1da5d3",
     "locked": true,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "source": [
    "### Task 3.6 [3 points]\n",
    "Use your density-of-states routine to calculate $\\rho(\\omega)$ for $n=10,20,40,80,100$ for $\\sigma_\\rho \\approx 0.005$. See below for two examples with $t \\approx -0.195$ and $n=10$ and $n=100$.\n",
    "\n",
    "Hint: if your plots look like they are smoothed out, try decreasing $\\sigma_\\rho$. If they look like there is a lot of noise, try increasing $\\sigma_\\rho$.\n",
    "\n",
    "$n = 10$ | $n = 100$\n",
    ":-: | :-:\n",
    "![dosN010.png](attachment:dosN010.png) | ![dosN100.png](attachment:dosN100.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "10cb847540f9e1998c9c1b40c5e43a7b",
     "grade": true,
     "grade_id": "cell-c3083a03553a2aa9",
     "locked": false,
     "points": 3,
     "schema_version": 3,
     "solution": true,
     "task": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sigma = .005\n",
    "w = np.linspace(-.5, .5, 1000)\n",
    "\n",
    "fig = plt.figure(figsize=(16,16))\n",
    "\n",
    "for i, n in enumerate([10, 20, 40, 80, 100]):\n",
    "    H_tb = TBHamiltonian(n)\n",
    "    # TODO: Decide whether to implement this + 1 - 1 trick here or in QREig above.\n",
    "    E_m  = QREig(H_tb + np.eye(n)) - 1\n",
    "    DOS = getDOS_ED(w, E_m, sigma)\n",
    "    \n",
    "    ax = fig.add_subplot(3, 2, i + 1)\n",
    "    ax.plot(w, DOS, label=\"n = {}\".format(n))\n",
    "    ax.set_xlabel(\"$\\\\omega$\")\n",
    "    ax.set_ylabel(\"$\\\\rho(\\\\omega)$\")\n",
    "    ax.grid()\n",
    "    ax.set_title(\"n = {}\".format(n))\n",
    "\n",
    "fig.suptitle(\"Density of states $\\\\rho(\\\\omega)$ for different chain lengths $n$\")\n",
    "fig.tight_layout()\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "markdown",
     "checksum": "69c457b9ef8fbd13ad935fe12c37c81c",
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     "locked": true,
     "schema_version": 3,
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    }
   },
   "source": [
    "## Step 4: Tight-Binding Propagation Method\n",
    "\n",
    "Now we turn to the time-dependent Schrödinger equation\n",
    "\n",
    "\\begin{align}\n",
    "    i\\hbar\\frac{\\partial}{\\partial t} \\psi(x,t) = H \\psi(x,t),\n",
    "\\end{align}\n",
    "\n",
    "which has the formal solution\n",
    "\n",
    "\\begin{align}\n",
    "    \\psi(x,t) = U(t) \\psi(x,t=0),\n",
    "\\end{align}\n",
    "\n",
    "with \n",
    "\n",
    "\\begin{align}\n",
    "    U(t) = e^{-i \\hbar H t}\n",
    "\\end{align}\n",
    "\n",
    "being the time-propagation operator. Within the propagation method we can calculate the so-called local density-of-states\n",
    "\n",
    "\\begin{align}\n",
    "    \\rho_{loc}(\\omega) = \\frac{1}{2\\pi} \\int_{-\\infty}^{+\\infty} \\, e^{i\\omega t} \\, f(t) \\ dt,\n",
    "\\end{align}\n",
    "\n",
    "with respect to an (arbitrary) initial state $\\psi(x,t=0)$, where\n",
    "\n",
    "\\begin{align}\n",
    "    f(t) &= \\int_{-\\infty}^{+\\infty} \\, \\psi^*(x,t) \\, \\psi(x,t=0) \\, dx \\\\\n",
    "         &\\approx \\int_{-\\infty}^{+\\infty} \\sum_i c_i^*(t) \\phi(x,x_i,\\sigma) \\, \\sum_j c_j(0) \\phi(x,x_j,\\sigma) \\, dx \\notag \\\\\n",
    "         &\\approx \\sum_i c_i^*(t) c_i(0). \\notag\n",
    "\\end{align}\n",
    "\n",
    "Thus, the time propagation of an initial state towards positive *and* negative times followed by a Fourier transform of $f(t)$ yields the local density-of-states. To obtain the full density-of-states we need to average $\\rho_{loc}(\\omega)$ as follows\n",
    "\n",
    "\\begin{align}\n",
    "    \\rho(\\omega) = \\lim_{S \\to \\infty} \\frac{1}{S} \\sum_p^S \\rho^{(p)}_{loc}(\\omega)\n",
    "\\end{align}\n",
    "\n",
    "over a variety of *random* initial states $p$.\n",
    "\n",
    "### Task 4.1 [3 points]\n",
    "Implement a function which calculates the exact time-propagation matrix $U(\\tau)$ for a small time-step $\\tau$ given the Hamiltonian $H$. For simplicity, set $\\hbar = 1$ in the following. \n",
    "\n",
    "Hint: Use Scipy's $\\text{expm()}$ function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "6cb01e4b3c6c192a0df3c4111b91c8fa",
     "grade": true,
     "grade_id": "cell-42a7aac3f0fa4d1b",
     "locked": false,
     "points": 3,
     "schema_version": 3,
     "solution": true,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "def getU_exact(tau, H):\n",
    "    # YOUR CODE HERE\n",
    "    raise NotImplementedError()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "markdown",
     "checksum": "79f8101b73fbb28ff9138437e9767178",
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     "grade_id": "cell-9b02ad5515424242",
     "locked": true,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "source": [
    "### Task 4.2 [3 points]\n",
    "Implement a function which performs the step-by-step time propagation given an initial state $\\vec{c}(0)$, the matrix $U(\\tau)$ and the discretized time grid $t_j$. In other words, your function should calculate \n",
    "\n",
    "$$\\vec{c}(j+1) = U(\\tau) \\cdot \\vec{c}(j)$$ \n",
    "\n",
    "for all $j$ of a given discretized time grid $t_j = j \\tau$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "dc040cc32e832b097bfb8c367f4203a1",
     "grade": true,
     "grade_id": "cell-4e444f44bf3bc9c1",
     "locked": false,
     "points": 3,
     "schema_version": 3,
     "solution": true,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "def timePropagate(U, c0, t):\n",
    "    # YOUR CODE HERE\n",
    "    raise NotImplementedError()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "markdown",
     "checksum": "61362905e7a2d19219ae21f10a417823",
     "grade": false,
     "grade_id": "cell-62bfe608c358ff6d",
     "locked": true,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "source": [
    "### Task 4.3 [4 points]\n",
    "Use both of the above functions to calculate and animate the time propagation of an initial state\n",
    "\n",
    "$$\\psi(x,t=0) = \\phi(x, x_{i=n/2}, \\sigma) \\leftrightarrow \\vec{c}(0) = [c_{i=n/2}(0) = 1, c_{i\\neq n/2}(0) = 0]$$\n",
    "\n",
    "for a $n=100$ chain. Discretize your time grid as $t_j=j\\tau$ with $j=0 \\dots 200$, and $\\tau=1.5$. Use again $a = 1$ and $\\sigma=0.25$. \n",
    "\n",
    "To plot / animate the time propagation you should plot the real-space wave function $\\psi(x,t) \\approx \\sum_i c_i(t) \\phi(x, x_i, \\sigma)$.\n",
    "\n",
    "Hint: use your function from task 3.4 to get the Hamiltonian $H$.\n",
    "\n",
    "For the animation you can use the following draft:\n",
    "```python\n",
    "# use matplotlib's animation package\n",
    "import matplotlib.pylab as plt\n",
    "import matplotlib\n",
    "import matplotlib.animation as animation\n",
    "# set the animation style to \"jshtml\" (for the use in Jupyter)\n",
    "matplotlib.rcParams['animation.html'] = 'jshtml'\n",
    "\n",
    "# create a figure for the animation\n",
    "fig = plt.figure()\n",
    "plt.grid(True)\n",
    "plt.xlim( ... )     # fix x limits\n",
    "plt.ylim( ... )     # fix y limits\n",
    "\n",
    "# Create an empty plot object and prevent its showing (we will fill it each frame)\n",
    "myPlot, = plt.plot([0], [0])\n",
    "plt.close()\n",
    "\n",
    "# This function is called each frame to generate the animation (f is the frame number)\n",
    "def animate(f):                   \n",
    "    myPlot.set_data( ... )  # update plot\n",
    "\n",
    "# Show the animation\n",
    "frames = np.arange(1, np.size(t))  # t is the time grid here\n",
    "myAnimation = animation.FuncAnimation(fig, animate, frames, interval = 20)\n",
    "myAnimation\n",
    "```"
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    "# YOUR CODE HERE\n",
    "raise NotImplementedError()"
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    "# Animate here ...\n",
    "\n",
    "# YOUR CODE HERE\n",
    "raise NotImplementedError()\n",
    "\n",
    "# Yann has an animation about an atomic orbital that starts\n",
    "# moving to left and right and then bounce back."
   ]
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   "source": [
    "### Task 4.4 [3 points]\n",
    "Implement a function which calculates the Crank-Nicolson time-propagation matrix \n",
    "\n",
    "\\begin{align*}\n",
    "    U_{CN}(\\tau) = (I - i \\tau H / 2)\\cdot(I + i \\tau H / 2)^{-1}.\n",
    "\\end{align*}\n",
    "\n",
    "Here, $I$ is the diagonal identity matrix. Use Numpy's $\\text{inv()}$ function to invert the needed expression."
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    "def getU_CN(tau, H):\n",
    "    # YOUR CODE HERE\n",
    "    raise NotImplementedError()\n",
    "\n",
    "# Yann notes that the definition of $U_{CN}(\\tau)$ here is a little\n",
    "# different from what Malte used on the slides. He recommends using\n",
    "# what is stated here."
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    "### Task 4.5 [5 points]\n",
    "Implement a function which calculates the time-propagation matrix using the Trotter-Suzuki decomposition \n",
    "\n",
    "\\begin{align*}\n",
    "    U_{TZ}(\\tau) = e^{-i\\tau H_1} \\cdot e^{-i \\tau H_2}.\n",
    "\\end{align*}\n",
    "\n",
    "In this approach you choose a decomposition of the tight-binding Hamiltonian $H = H_1 + H_2$, which allows you to analytically diagonalize $H_1$ and $H_2$ (see last lecture). From this analytic diagonalization you will be able to calculate the matrix exponentials $e^{-i\\tau H_1}$ and $e^{-i \\tau H_2}$.\n",
    "\n",
    "Write your definition of the 2x2 blocks in $e^{-i\\tau H_1}$ and $e^{-i \\tau H_2}$ in the Markdown cell below. (Double click on \"YOUR ANSWER HERE\" to open the cell, and ctrl+enter to compile.) "
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    "YOUR ANSWER HERE"
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   "source": [
    "def getU_TZ(tau, H):\n",
    "    # YOUR CODE HERE\n",
    "    raise NotImplementedError()\n",
    "\n",
    "# Yann mentions again that this is slightly different wrong what\n",
    "# is in the slides/lecture."
   ]
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   "source": [
    "### Task 4.6 [3 points]\n",
    "In your implementation of $U_{TZ}(\\tau)$ you analytically evaluate the matrix exponentials $e^{-i\\tau H_1}$ and $e^{-i \\tau H_2}$. Test your implementation by comparing your results for these matrix exponentials to those obtained using Scipy's $\\text{expm()}$ function."
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    "# YOUR CODE HERE\n",
    "raise NotImplementedError()\n",
    "\n",
    "# Yann printed\n",
    "#Biggest differences of U1 with Scipy:\n",
    "#Real: 1e-16 \n",
    "#Imag: 2.77e-17\n",
    "#    \n",
    "# and difference with U_exact in the order of 1e-1 or 1e-2."
   ]
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   "source": [
    "### Task 4.7 [6 points]\n",
    "In the next task you will need a Fourier transform to calculate the local density-of-states. Therefore you will need to implement a function that returns the Fourier transform $f(\\omega)$ of a given function $f(t)$ defined on a time grid $t$, for a given energy grid $\\omega$. I.e. it should calculate:\n",
    "\n",
    "\\begin{align}\n",
    "    f(\\omega) = \\frac{1}{2\\pi} \\int_{-\\infty}^{+\\infty} \\, e^{i\\omega t} \\, f(t) \\ dt.\n",
    "\\end{align}\n",
    "\n",
    "Hint: use your integration function from task 2.2.\n",
    "\n",
    "Then implement a unit test for your function."
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    "# YOUR CODE HERE\n",
    "raise NotImplementedError()"
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    "# Implement your unit test here ...\n",
    "\n",
    "# YOUR CODE HERE\n",
    "raise NotImplementedError()"
   ]
  },
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   "source": [
    "### Task 4.8 [3 points]\n",
    "Calculate the local density-of-states $\\rho_{loc}(\\omega)$ from the Fourier transform of $f(t)$ using all three time propagation methods: $U(\\tau)$, $U_{CN}(\\tau)$ and $U_{TZ}(\\tau)$.\n",
    "\n",
    "Start from $\\psi(x,t=0) = \\phi(x, x_{i=0}, \\sigma)$ and $\\psi(x,t=0) = \\phi(x, x_{i=n/2}, \\sigma)$, using a $n=100$ chain. Discretize your integration time grid as $t_j=j\\tau$, with $j=-150 \\dots 150$ and $\\tau=1.5$. Use again $a = 1$ and $\\sigma=0.25$.\n",
    "\n",
    "Be careful: for the Fourier transform you will need positive *and* negative time steps! Thus you will need to do two time propagations: one using $U(\\tau)$ towards positive times and one using $U(-\\tau)$ towards negative times, both starting from $\\psi(x,t=0)$."
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   "outputs": [],
   "source": [
    "# YOUR CODE HERE\n",
    "raise NotImplementedError()"
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    "# Do your own testing here ...\n",
    "\n",
    "# YOUR CODE HERE\n",
    "raise NotImplementedError()\n",
    "\n",
    "# Yann had a plot for Tau = 1.5\n",
    "# DOS: looking like a hill (\"like a dome with a peak around zero energy 0\")\n",
    "# for CN, TS and the exact one\n",
    "# a plot of f(t)\n",
    "# a plot of local DOS\n",
    "# in the title he mentiones the inital values."
   ]
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   "source": [
    "### Task 4.9 [6 points]\n",
    "Use the Trotter-Suzuki decomposition to calculate the full density-of-states by averaging over about $100$ local density-of-states you obtained from the time propagation of $100$ random initial states $\\vec{c}(0)$. To this end, you will need to make sure that each $\\vec{c}(0)$ is (a) normalized and (b) can have positive *and* negative elements. \n",
    "\n",
    "Compare this approximation to the total density-of-states to the exact one from task 3.6, which you obtained directly from the eigenvalues.\n",
    "\n",
    "Hint: don't expect the results to be the exact same. Check for the location of the peaks, and whether they have a similar order of magnitude.\n",
    "\n",
    "Hint: if you did not get the Trotter-Suzuki decomposition to work, you can instead use the exact or the Crank-Nicolson time-propagation matrix."
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   "source": [
    "# YOUR CODE HERE\n",
    "raise NotImplementedError()\n",
    "\n",
    "# Yann says the initial states do need to be negative, too."
   ]
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    "# Do your plotting here ...\n",
    "\n",
    "# YOUR CODE HERE\n",
    "raise NotImplementedError()\n",
    "\n",
    "# Yann plotted the exact diagonalisation and the TS propagation results\n",
    "# he had two plots, one peaky, one with peaks on the edges (looking a little\n",
    "# like my 1f/2f results in my bachelor internship hmmpfff)"
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