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    "# Tutorial Tiles\n",
    "\n",
    "Tiles is a module to compute vectors that respect a certain symmetry, the symmetry of a superlattice.\n",
    "The only requirement for computations to make sense, is that the system is closed and has periodic boundaries, in which case it constitutes a superlattice.\n",
    "\n",
    "These vectors are used to compute momentum-resolved spectral densities, or expectation values of any operator acting on the closed system.\n",
    "\n",
    "The advantage of using `tiles` instad of local vectors, is that we can do Brillouin zone-unfolding; a procedure to compute the unfolded bands of superlattices.\n",
    "\n",
    "Another benefit is that we can compute momentum-resolved spectral densities, even when the translation symmetry is broken or the system is disordered.\n",
    "\n",
    "For example, if we would like to simulate the ARPES measurement of a non-pristine crystalline surface, the momentum components of a moire superlattice, then can use `tiles` to compute them.\n"
   ]
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   "source": [
    "from kpm_tools.tiles import (\n",
    "    tile_random_kvectors, TileKVectors,\n",
    "    momentum_to_ts\n",
    ")\n",
    "from kpm_tools.kpm_generator import SpectralDensityIterator\n"
   ]
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i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    if (((window['GridStack'] !== undefined) && (!(window['GridStack'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/1.3.6/dist/bundled/gridstack/gridstack@7.2.3/dist/gridstack-all.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    if (((window['Notyf'] !== undefined) && (!(window['Notyf'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/1.3.6/dist/bundled/notificationarea/notyf@3/notyf.min.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    var existing_scripts = []\n    var scripts = document.getElementsByTagName('script')\n    for (var i = 0; i < scripts.length; i++) {\n      var script = scripts[i]\n      if (script.src != null) {\n\texisting_scripts.push(script.src)\n      }\n    }\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (var i = 0; i < js_modules.length; i++) {\n      var url = js_modules[i];\n      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (const name in js_exports) {\n      var url = js_exports[name];\n      if (skip.indexOf(url) >= 0 || root[name] != null) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onerror = on_error;\n      element.async = false;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      element.textContent = `\n      import ${name} from \"${url}\"\n      window.${name} = ${name}\n      window._bokeh_on_load()\n      `\n      document.head.appendChild(element);\n    }\n    if (!js_urls.length && !js_modules.length) {\n      on_load()\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.2.min.js\", \"https://cdn.holoviz.org/panel/1.3.6/dist/panel.min.js\"];\n  var js_modules = [];\n  var js_exports = {};\n  var css_urls = [];\n  var inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n  ];\n\n  function run_inline_js() {\n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n\ttry {\n          inline_js[i].call(root, root.Bokeh);\n\t} catch(e) {\n\t  if (!reloading) {\n\t    throw e;\n\t  }\n\t}\n      }\n      // Cache old bokeh versions\n      if (Bokeh != undefined && !reloading) {\n\tvar NewBokeh = root.Bokeh;\n\tif (Bokeh.versions === undefined) {\n\t  Bokeh.versions = new Map();\n\t}\n\tif (NewBokeh.version !== Bokeh.version) {\n\t  Bokeh.versions.set(NewBokeh.version, NewBokeh)\n\t}\n\troot.Bokeh = Bokeh;\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    }\n    root._bokeh_is_initializing = false\n  }\n\n  function load_or_wait() {\n    // Implement a backoff loop that tries to ensure we do not load multiple\n    // versions of Bokeh and its dependencies at the same time.\n    // In recent versions we use the root._bokeh_is_initializing flag\n    // to determine whether there is an ongoing attempt to initialize\n    // bokeh, however for backward compatibility we also try to ensure\n    // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n    // before older versions are fully initialized.\n    if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n      root._bokeh_is_initializing = false;\n      root._bokeh_onload_callbacks = undefined;\n      console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n      load_or_wait();\n    } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n      setTimeout(load_or_wait, 100);\n    } else {\n      root._bokeh_is_initializing = true\n      root._bokeh_onload_callbacks = []\n      var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n      if (!reloading && !bokeh_loaded) {\n\troot.Bokeh = undefined;\n      }\n      load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n\trun_inline_js();\n      });\n    }\n  }\n  // Give older versions of the autoload script a head-start to ensure\n  // they initialize before we start loading newer version.\n  setTimeout(load_or_wait, 100)\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "          comm.on_msg(msg_handler);\n",
       "        });\n",
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       "        comm.open();\n",
       "        if (msg_handler) {\n",
       "          comm.onMsg = msg_handler;\n",
       "        }\n",
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       "      window.PyViz.comms[comm_id] = comm;\n",
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       "\n",
       "\n",
       "var JS_MIME_TYPE = 'application/javascript';\n",
       "var HTML_MIME_TYPE = 'text/html';\n",
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       "var CLASS_NAME = 'output';\n",
       "\n",
       "/**\n",
       " * Render data to the DOM node\n",
       " */\n",
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       "\n",
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       "  var output = handle.output;\n",
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       "    return\n",
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       "  var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
       "  var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
       "  if (id !== undefined) {\n",
       "    var nchildren = toinsert.length;\n",
       "    var html_node = toinsert[nchildren-1].children[0];\n",
       "    html_node.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "    var scripts = [];\n",
       "    var nodelist = html_node.querySelectorAll(\"script\");\n",
       "    for (var i in nodelist) {\n",
       "      if (nodelist.hasOwnProperty(i)) {\n",
       "        scripts.push(nodelist[i])\n",
       "      }\n",
       "    }\n",
       "\n",
       "    scripts.forEach( function (oldScript) {\n",
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       "    }\n",
       "    output_area._hv_plot_id = id;\n",
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       "      window.PyViz.plot_index[id] = Bokeh.index[id];\n",
       "    } else {\n",
       "      window.PyViz.plot_index[id] = null;\n",
       "    }\n",
       "  } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
       "    var bk_div = document.createElement(\"div\");\n",
       "    bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "    var script_attrs = bk_div.children[0].attributes;\n",
       "    for (var i = 0; i < script_attrs.length; i++) {\n",
       "      toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
       "    }\n",
       "    // store reference to server id on output_area\n",
       "    output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
       "  }\n",
       "}\n",
       "\n",
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       " * Handle when an output is cleared or removed\n",
       " */\n",
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       "  if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n",
       "  var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n",
       "  if (server_id !== null) {\n",
       "    comm.send({event_type: 'server_delete', 'id': server_id});\n",
       "    return;\n",
       "  } else if (comm !== null) {\n",
       "    comm.send({event_type: 'delete', 'id': id});\n",
       "  }\n",
       "  delete PyViz.plot_index[id];\n",
       "  if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n",
       "    var doc = window.Bokeh.index[id].model.document\n",
       "    doc.clear();\n",
       "    const i = window.Bokeh.documents.indexOf(doc);\n",
       "    if (i > -1) {\n",
       "      window.Bokeh.documents.splice(i, 1);\n",
       "    }\n",
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       "}\n",
       "\n",
       "/**\n",
       " * Handle kernel restart event\n",
       " */\n",
       "function handle_kernel_cleanup(event, handle) {\n",
       "  delete PyViz.comms[\"hv-extension-comm\"];\n",
       "  window.PyViz.plot_index = {}\n",
       "}\n",
       "\n",
       "/**\n",
       " * Handle update_display_data messages\n",
       " */\n",
       "function handle_update_output(event, handle) {\n",
       "  handle_clear_output(event, {cell: {output_area: handle.output_area}})\n",
       "  handle_add_output(event, handle)\n",
       "}\n",
       "\n",
       "function register_renderer(events, OutputArea) {\n",
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       "    metadata,\n",
       "    CLASS_NAME,\n",
       "    EXEC_MIME_TYPE\n",
       "    );\n",
       "    this.keyboard_manager.register_events(toinsert);\n",
       "    // Render to node\n",
       "    var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "    render(props, toinsert[0]);\n",
       "    element.append(toinsert);\n",
       "    return toinsert\n",
       "  }\n",
       "\n",
       "  events.on('output_added.OutputArea', handle_add_output);\n",
       "  events.on('output_updated.OutputArea', handle_update_output);\n",
       "  events.on('clear_output.CodeCell', handle_clear_output);\n",
       "  events.on('delete.Cell', handle_clear_output);\n",
       "  events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n",
       "\n",
       "  OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "    safe: true,\n",
       "    index: 0\n",
       "  });\n",
       "}\n",
       "\n",
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       "    var events = require('base/js/events');\n",
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       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
       "    }\n",
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       "  }\n",
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scripts.forEach( function (oldScript) {\n      var newScript = document.createElement(\"script\");\n      var attrs = [];\n      var nodemap = oldScript.attributes;\n      for (var j in nodemap) {\n        if (nodemap.hasOwnProperty(j)) {\n          attrs.push(nodemap[j])\n        }\n      }\n      attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n      newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n      oldScript.parentNode.replaceChild(newScript, oldScript);\n    });\n    if (JS_MIME_TYPE in output.data) {\n      toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n    }\n    output_area._hv_plot_id = id;\n    if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n      window.PyViz.plot_index[id] = Bokeh.index[id];\n    } else {\n      window.PyViz.plot_index[id] = null;\n    }\n  } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n    var bk_div = document.createElement(\"div\");\n    bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n    var script_attrs = bk_div.children[0].attributes;\n    for (var i = 0; i < script_attrs.length; i++) {\n      toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n    }\n    // store reference to server id on output_area\n    output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n  }\n}\n\n/**\n * Handle when an output is cleared or removed\n */\nfunction handle_clear_output(event, handle) {\n  var id = handle.cell.output_area._hv_plot_id;\n  var server_id = handle.cell.output_area._bokeh_server_id;\n  if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n  var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n  if (server_id !== null) {\n    comm.send({event_type: 'server_delete', 'id': server_id});\n    return;\n  } else if (comm !== null) {\n    comm.send({event_type: 'delete', 'id': id});\n  }\n  delete PyViz.plot_index[id];\n  if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n    var doc = window.Bokeh.index[id].model.document\n    doc.clear();\n    const i = window.Bokeh.documents.indexOf(doc);\n    if (i > -1) {\n      window.Bokeh.documents.splice(i, 1);\n    }\n  }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n  delete PyViz.comms[\"hv-extension-comm\"];\n  window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction handle_update_output(event, handle) {\n  handle_clear_output(event, {cell: {output_area: handle.output_area}})\n  handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n  function append_mime(data, metadata, element) {\n    // create a DOM node to render to\n    var toinsert = this.create_output_subarea(\n    metadata,\n    CLASS_NAME,\n    EXEC_MIME_TYPE\n    );\n    this.keyboard_manager.register_events(toinsert);\n    // Render to node\n    var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n    render(props, toinsert[0]);\n    element.append(toinsert);\n    return toinsert\n  }\n\n  events.on('output_added.OutputArea', handle_add_output);\n  events.on('output_updated.OutputArea', handle_update_output);\n  events.on('clear_output.CodeCell', handle_clear_output);\n  events.on('delete.Cell', handle_clear_output);\n  events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n  OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n    safe: true,\n    index: 0\n  });\n}\n\nif (window.Jupyter !== undefined) {\n  try {\n    var events = require('base/js/events');\n    var OutputArea = require('notebook/js/outputarea').OutputArea;\n    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n      register_renderer(events, OutputArea);\n    }\n  } catch(err) {\n  }\n}\n"
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>*[data-root-id],\n",
       "*[data-root-id] > * {\n",
       "  box-sizing: border-box;\n",
       "  font-family: var(--jp-ui-font-family);\n",
       "  font-size: var(--jp-ui-font-size1);\n",
       "  color: var(--vscode-editor-foreground, var(--jp-ui-font-color1));\n",
       "}\n",
       "\n",
       "/* Override VSCode background color */\n",
       ".cell-output-ipywidget-background:has(\n",
       "    > .cell-output-ipywidget-background > .lm-Widget > *[data-root-id]\n",
       "  ),\n",
       ".cell-output-ipywidget-background:has(> .lm-Widget > *[data-root-id]) {\n",
       "  background-color: transparent !important;\n",
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       "5nK5CQAQMtXznCRJePpEbwOAZhzHX4ix/wHzzC/tu64gcwAAAABJRU5ErkJggg=='\n",
       "       style='height:15px; border-radius:12px; display: inline-block; float: left'></img>\n",
       "  \n",
       "\n",
       "\n",
       "</div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot, matplotlib.backends\n",
    "import holoviews as hv\n",
    "hv.extension()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bb7583fb-dd34-46a3-a6e8-09d7264e2ab8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5c0635bc-dceb-41d3-9c0f-27156f90fbfe",
   "metadata": {},
   "outputs": [],
   "source": [
    "import kwant\n",
    "from concurrent.futures import ProcessPoolExecutor\n",
    "from kpm_tools.kpm_generator import _BaseKPM\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2058ba6-3d20-4156-80b0-e20d8fe6e9b3",
   "metadata": {},
   "source": [
    "We define globally the `honeycomb` lattice, in part to define a `tile`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2ec765b7-b2f9-4d4a-9c1d-d329de20226f",
   "metadata": {},
   "outputs": [],
   "source": [
    "honeycomb = kwant.lattice.honeycomb(norbs=1, name=[\"a\", \"b\"])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "771661d6-0349-4abc-b5d1-43b5cb0a7964",
   "metadata": {},
   "source": [
    "However, we don't need to restrict the tiles to one unitcell of the lattice, it could span several unitcells, as long as it's commensurate with the superlattice plus the periodic boundary conditions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f211dbd7-57f2-485b-bac3-e4cd0a78dccc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_superlattice(tags_1=[1, 0], tags_2=[0, 1], disorder=0):\n",
    "    def disorder_onsite(site, disorder):\n",
    "        return disorder * kwant.digest.uniform(bytes(str(site), \"UTF8\"))\n",
    "    period_1 = honeycomb.prim_vecs.T @ tags_1\n",
    "    period_2 = honeycomb.prim_vecs.T @ tags_2\n",
    "    symm = kwant.TranslationalSymmetry(period_1, period_2)\n",
    "    builder = kwant.Builder(symmetry=symm)\n",
    "\n",
    "    builder[honeycomb.shape(lambda s: True, (0, 0))] = disorder_onsite\n",
    "\n",
    "    builder[honeycomb.neighbors()] = 1\n",
    "\n",
    "    return kwant.wraparound.wraparound(builder).finalized()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "58d1c852-9565-4973-9cab-b63a377dab40",
   "metadata": {},
   "outputs": [],
   "source": [
    "fsyst = make_superlattice(tags_1=[3, 0], tags_2=[0, 1], disorder=0.5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4738437a-e74c-46a0-b197-18a3081e3560",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kwant.plot(fsyst);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ce2b9a9b-7e6d-49ef-bc36-5918e19f748a",
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {'k_x': 0, 'k_y': 0, 'disorder': 0}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc77edbe-0e11-4694-b061-f33cf9b3c0b9",
   "metadata": {},
   "source": [
    "The bounds should be fixed, since we will iterate in reciprocal space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "524a0c3c-6328-49b9-8ae9-db6d25296739",
   "metadata": {},
   "outputs": [],
   "source": [
    "def init_kpm_params(fsyst, params):\n",
    "    num_moments = 300\n",
    "    kpm_params = {\n",
    "        \"num_moments\": num_moments,\n",
    "        \"num_vectors\": 8,\n",
    "        \"rng\": 0,\n",
    "    }\n",
    "\n",
    "    bounds = _BaseKPM(\n",
    "        fsyst,\n",
    "        params=params,\n",
    "        num_vectors=1,\n",
    "        num_moments=2,\n",
    "    ).bounds\n",
    "\n",
    "    kpm_params[\"bounds\"] = bounds\n",
    "\n",
    "\n",
    "    energies = (\n",
    "        kwant.kpm._chebyshev_nodes(2 * num_moments) * (bounds[1] - bounds[0]) / 2\n",
    "        + (bounds[1] + bounds[0]) / 2\n",
    "    )\n",
    "    return kpm_params, energies\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9da2a896-dcab-4454-9bee-32e45a601728",
   "metadata": {},
   "source": [
    "### Define the k-path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0b8bb1e8-eeb7-42af-b5ce-f3cb6db233d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_path_segment(p1, p2, num_k):\n",
    "    return (\n",
    "        p1[np.newaxis]\n",
    "        + np.linspace(0, 1, num_k, endpoint=False)[:, np.newaxis]\n",
    "        * (p2 - p1)[np.newaxis]\n",
    "    )\n",
    "\n",
    "def make_path(points, num_k):\n",
    "    k_path = np.zeros((0, *points[0].shape))\n",
    "    for idx in range(len(points)-1):\n",
    "        p1 = points[idx]\n",
    "        p2 = points[idx+1]\n",
    "        k_path = np.concatenate([k_path, make_path_segment(p1, p2, num_k)], axis=0)\n",
    "    return k_path\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "045e02ab-e776-4f8c-a625-d8b7a7d19722",
   "metadata": {},
   "outputs": [],
   "source": [
    "p_gamma = np.array([0 ,0])\n",
    "p_m = np.array([1/2,-1/(2*np.sqrt(3))])\n",
    "p_K = np.array([2/3, 0])\n",
    "p_Kp = p_K + 2 * (p_m - p_K)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2e523a23-013c-4bac-9bd9-2069a17b958b",
   "metadata": {},
   "outputs": [],
   "source": [
    "points = np.array([p_gamma, p_m, p_K, p_gamma, p_Kp]) * 2 * np.pi\n",
    "num_k = 50\n",
    "\n",
    "points = points\n",
    "\n",
    "k_path = make_path(points, num_k)\n",
    "num_k_full = len(k_path)\n",
    "\n",
    "k_path_lengths = np.cumsum(np.append(0, np.linalg.norm(np.diff(k_path, axis=0), axis=1)))\n",
    "\n",
    "xticks = [(k_path_lengths[num_k * 0], '$\\Gamma$'),\n",
    "          (k_path_lengths[num_k * 1], '$M$'),\n",
    "          (k_path_lengths[num_k * 2], '$K$'),\n",
    "          (k_path_lengths[num_k * 3], '$\\Gamma$'),\n",
    "          (k_path_lengths[num_k * 4-1], \"$K'$\"),\n",
    "         ]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8996547d-f0fa-4442-8aab-d2b072e9695b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def density_in_k(k, tile):\n",
    "    kvecs = tile_random_kvectors(k, tile, fsyst)\n",
    "\n",
    "    # update boundary conditions to be periodic like `k`\n",
    "    k_in_ts = momentum_to_ts(k, fsyst._wrapped_symmetry)\n",
    "    params.update(dict(zip(fsyst._momentum_names, k_in_ts)))\n",
    "\n",
    "    spectrum_k = SpectralDensityIterator(\n",
    "        fsyst,\n",
    "        params=params,\n",
    "        vector_factory=kvecs,\n",
    "        **kpm_params,\n",
    "    )\n",
    "\n",
    "    return spectrum_k.densities\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "807a4c8a-a6f6-4d58-940d-e8023eae3d91",
   "metadata": {},
   "source": [
    "### Typical k-path plot\n",
    "Here, we obtain the Brillouin zone-unfolded typical k-path for graphene or the honeycomb lattice.\n",
    "\n",
    "Notice that the `tile` controls the translational symmetry of the KPM vectors used in the expansion, and it has no effect that the superlattice is skewed, since the periodic boundaries are take periodic in `k`, just like the vectors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9cef7201-f90c-4ef0-bac6-1491b7ee9713",
   "metadata": {},
   "outputs": [],
   "source": [
    "params[\"disorder\"] = 0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bb89c79f-8d64-4332-bee6-7f3abcf95851",
   "metadata": {},
   "outputs": [],
   "source": [
    "kpm_params, energies = init_kpm_params(fsyst, params)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1fb80ad3-cb3d-404e-8fb9-f29afbce0797",
   "metadata": {},
   "outputs": [],
   "source": [
    "tile = kwant.TranslationalSymmetry(*honeycomb.prim_vecs)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d56aab9a-723d-423d-8ee3-dfbaac7bd039",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 142 ms, sys: 53.7 ms, total: 196 ms\n",
      "Wall time: 29.5 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "with ProcessPoolExecutor() as executor:\n",
    "    densities_unfolded = np.array(list(executor.map(\n",
    "        density_in_k,\n",
    "        k_path,\n",
    "        [tile] * num_k_full,\n",
    "    )))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "91a4c605-d275-48ba-b966-bc71337e2942",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 138 ms, sys: 36.7 ms, total: 174 ms\n",
      "Wall time: 21.7 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "with ProcessPoolExecutor() as executor:\n",
    "    densities_folded = np.array(list(executor.map(\n",
    "        density_in_k,\n",
    "        k_path,\n",
    "        [fsyst._wrapped_symmetry] * num_k_full,\n",
    "    )))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c1901035-50aa-43be-b92e-1a7cacc910bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img 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' style='max-width:100%; margin: auto; display: block; '/>"
      ],
      "text/plain": [
       ":Layout\n",
       "   .QuadMesh.I  :QuadMesh   [$k$,$\\epsilon$]   ($\\rho(\\epsilon, k)$)\n",
       "   .QuadMesh.II :QuadMesh   [$k$,$\\epsilon$]   ($\\rho(\\epsilon, k)$)"
      ]
     },
     "execution_count": 20,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {}
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_kpath_unfolded = hv.QuadMesh(\n",
    "    (k_path_lengths, energies, densities_unfolded.T.real),\n",
    "    kdims=[r'$k$', r'$\\epsilon$'],\n",
    "    vdims=hv.Dimension(r'$\\rho(\\epsilon, k)$', range=(0, 50)),\n",
    ").opts(\n",
    "    xticks=xticks,\n",
    "    colorbar=True,\n",
    "    cmap='bone_r',\n",
    "    title=\"k-path unfolded\",\n",
    ")\n",
    "\n",
    "plot_kpath_folded = hv.QuadMesh(\n",
    "    (k_path_lengths, energies, densities_folded.T.real),\n",
    "    kdims=[r'$k$', r'$\\epsilon$'],\n",
    "    vdims=hv.Dimension(r'$\\rho(\\epsilon, k)$', range=(0, 50)),\n",
    ").opts(\n",
    "    xticks=xticks,\n",
    "    colorbar=True,\n",
    "    cmap='bone_r',\n",
    "    title=\"k-path folded\",\n",
    ")\n",
    "\n",
    "plot_kpath_folded + plot_kpath_unfolded\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "852d4b7b-a921-40b6-9b71-6124aa100635",
   "metadata": {},
   "source": [
    "### Zone-unfolded k-path plot\n",
    "Now we can add noise to the system. This will break the translational symmetry of the lattice, and only the translational symmetry of the superlattice will remain unbroken.\n",
    "\n",
    "In this case, we expect to see some avoided crossing evidencing the zone-unfolding procedure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "34ea44ad-63b8-43b8-89a6-d8452127a743",
   "metadata": {},
   "outputs": [],
   "source": [
    "params[\"disorder\"] = 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2c1e9291-7f0e-47f5-a8ac-2f5cfcb49947",
   "metadata": {},
   "outputs": [],
   "source": [
    "kpm_params, energies = init_kpm_params(fsyst, params)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e72537f8-8629-42f2-989d-5a586bbb159a",
   "metadata": {},
   "outputs": [],
   "source": [
    "tile = kwant.TranslationalSymmetry(*honeycomb.prim_vecs)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f1e03450-3e0d-4de8-8233-8173f0076a84",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 134 ms, sys: 104 ms, total: 238 ms\n",
      "Wall time: 25.5 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "with ProcessPoolExecutor() as executor:\n",
    "    densities_unfolded = np.array(list(executor.map(\n",
    "        density_in_k,\n",
    "        k_path,\n",
    "        [tile] * num_k_full,\n",
    "    )))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3b8f3141-b8b1-4e0b-87f0-aecba03ef934",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 147 ms, sys: 35.5 ms, total: 183 ms\n",
      "Wall time: 21 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "with ProcessPoolExecutor() as executor:\n",
    "    densities_folded = np.array(list(executor.map(\n",
    "        density_in_k,\n",
    "        k_path,\n",
    "        [fsyst._wrapped_symmetry] * num_k_full,\n",
    "    )))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c926d1ec-8450-463c-806c-9b04f9c7a517",
   "metadata": {},
   "outputs": [
    {
     "data": {
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' style='max-width:100%; margin: auto; display: block; '/>"
      ],
      "text/plain": [
       ":Layout\n",
       "   .QuadMesh.I  :QuadMesh   [$k$,$\\epsilon$]   ($\\rho(\\epsilon, k)$)\n",
       "   .QuadMesh.II :QuadMesh   [$k$,$\\epsilon$]   ($\\rho(\\epsilon, k)$)"
      ]
     },
     "execution_count": 26,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {}
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_kpath_unfolded = hv.QuadMesh(\n",
    "    (k_path_lengths, energies, densities_unfolded.T.real),\n",
    "    kdims=[r'$k$', r'$\\epsilon$'],\n",
    "    vdims=hv.Dimension(r'$\\rho(\\epsilon, k)$', range=(0, 50)),\n",
    ").opts(\n",
    "    xticks=xticks,\n",
    "    colorbar=True,\n",
    "    cmap='bone_r',\n",
    "    title=\"k-path unfolded\",\n",
    ")\n",
    "\n",
    "plot_kpath_folded = hv.QuadMesh(\n",
    "    (k_path_lengths, energies, densities_folded.T.real),\n",
    "    kdims=[r'$k$', r'$\\epsilon$'],\n",
    "    vdims=hv.Dimension(r'$\\rho(\\epsilon, k)$', range=(0, 50)),\n",
    ").opts(\n",
    "    xticks=xticks,\n",
    "    colorbar=True,\n",
    "    cmap='bone_r',\n",
    "    title=\"k-path folded\",\n",
    ")\n",
    "\n",
    "plot_kpath_folded + plot_kpath_unfolded\n"
   ]
  }
 ],
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