diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml
index c93669c..1f7f766 100644
--- a/.github/workflows/release.yml
+++ b/.github/workflows/release.yml
@@ -17,7 +17,7 @@ jobs:
           fetch-depth: 2
 
       - name: Set up Python
-        uses: actions/setup-python@v3
+        uses: actions/setup-python@v4
         with:
           python-version: "3.10"
 
diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 6f1ace1..7bf65bc 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -29,7 +29,7 @@ jobs:
         uses: actions/checkout@v4
 
       - name: Set up Python ${{ matrix.python }}
-        uses: actions/setup-python@v3
+        uses: actions/setup-python@v4
         with:
           python-version: ${{ matrix.python }}
 
@@ -44,18 +44,18 @@ jobs:
           poetry --version
 
       - name: Install System Dependencies (Ubuntu)
-        if: startsWith(matrix.os, 'ubuntu') && (matrix.session 'tests' | matrix.session == 'pre-commit')
+        if: startsWith(matrix.os, 'ubuntu') && (matrix.session == 'no')
         run: |
           sudo apt-get update
           sudo apt-get install -y python3-dev python3-setuptools python3-scipy python3-matplotlib python3-pytest python3-sympy g++ build-essential gfortran libopenblas-dev liblapack-dev libmumps-scotch-dev
 
       - name: Install Python dependencies (Ubuntu)
-        if: startsWith(matrix.os, 'ubuntu') && (matrix.session 'tests' | matrix.session == 'pre-commit')
+        if: startsWith(matrix.os, 'ubuntu') && (matrix.session == 'no')
         run: |
           python -m pip install cython tinyarray
 
       - name: Clone and Install kwant (Ubuntu)
-        if: startsWith(matrix.os, 'ubuntu') && (matrix.session 'tests' | matrix.session == 'pre-commit')
+        if: startsWith(matrix.os, 'ubuntu') && (matrix.session == 'no')
         run: |
           git clone https://github.com/kwant-project/kwant.git
           cd kwant
@@ -122,7 +122,7 @@ jobs:
         uses: actions/checkout@v4
 
       - name: Set up Python
-        uses: actions/setup-python@v3
+        uses: actions/setup-python@v4
         with:
           python-version: "3.10"
 
diff --git a/README.md b/README.md
index a64d50d..e51718a 100644
--- a/README.md
+++ b/README.md
@@ -27,11 +27,20 @@
 
 ## Features
 
-- TODO
+- KPM expansion of typical spectal functions
+  - Density of states
+  - Green's functions
+  - Kubo conductivity
+  - Chern marker
+- Time evolution
+- Tools for system with periodic boundaries
+  - KPM vector factories that produces tiles
+  - Velocity operators adapted to periodic boundaries
+  - Distance operators adapted to periodic boundaries
 
 ## Requirements
 
-- TODO
+- Python >=3.9
 
 ## Installation
 
@@ -41,9 +50,9 @@ You can install _KPM Tools_ via [pip] from [PyPI]:
 $ pip install kpm-tools
 ```
 
-## Usage
+## Python API
 
-Please see the [Command-line Reference] for details.
+Please see the [Python API reference] for details.
 
 ## Contributing
 
@@ -74,4 +83,4 @@ This project was generated from [@cjolowicz]'s [Hypermodern Python Cookiecutter]
 
 [license]: https://github.com/piskunow/kpm-tools/blob/main/LICENSE
 [contributor guide]: https://github.com/piskunow/kpm-tools/blob/main/CONTRIBUTING.md
-[command-line reference]: https://kpm-tools.readthedocs.io/en/latest/usage.html
+[python api reference]: https://kpm-tools.readthedocs.io/en/latest/api.html
diff --git a/docs/api.md b/docs/api.md
new file mode 100644
index 0000000..feb1b2b
--- /dev/null
+++ b/docs/api.md
@@ -0,0 +1,81 @@
+# Python API
+
+```{eval-rst}
+.. click:: kpm_tools.__main__:main
+    :prog: kpm-tools
+    :nested: full
+```
+
+Each of the following sections describes the usage of different modules in the `kpm_tools` package.
+
+## Bloch Module
+
+```{eval-rst}
+.. automodule:: kpm_tools.bloch
+    :members:
+    :undoc-members:
+    :show-inheritance:
+```
+
+## Common Module
+
+```{eval-rst}
+.. automodule:: kpm_tools.common
+    :members:
+    :undoc-members:
+    :show-inheritance:
+```
+
+## Evolution Module
+
+```{eval-rst}
+.. automodule:: kpm_tools.evolution
+    :members:
+    :undoc-members:
+    :show-inheritance:
+```
+
+## Hamiltonians Module
+
+```{eval-rst}
+.. automodule:: kpm_tools.hamiltonians
+    :members:
+    :undoc-members:
+    :show-inheritance:
+```
+
+## KPM Functions Module
+
+```{eval-rst}
+.. automodule:: kpm_tools.kpm_funcs
+    :members:
+    :undoc-members:
+    :show-inheritance:
+```
+
+## KPM Generator Module
+
+```{eval-rst}
+.. automodule:: kpm_tools.kpm_generator
+    :members:
+    :undoc-members:
+    :show-inheritance:
+```
+
+## Plotting Module
+
+```{eval-rst}
+.. automodule:: kpm_tools.plotting
+    :members:
+    :undoc-members:
+    :show-inheritance:
+```
+
+## Tiles Module
+
+```{eval-rst}
+.. automodule:: kpm_tools.tiles
+    :members:
+    :undoc-members:
+    :show-inheritance:
+```
diff --git a/docs/conf.py b/docs/conf.py
index 8fd1051..1fcc385 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -1,10 +1,17 @@
 """Sphinx configuration."""
+
+import inspect
+import os
+import sys
+
+
 project = "KPM Tools"
 author = "Pablo Piskunow"
 copyright = "2023, Pablo Piskunow"
 extensions = [
     "sphinx.ext.autodoc",
     "sphinx.ext.napoleon",
+    "sphinx.ext.linkcode",
     "sphinx_click",
     "myst_parser",
     "nbsphinx",
@@ -36,3 +43,43 @@
 
 
   """  # noqa: B950
+
+
+def linkcode_resolve(domain, info):
+    """Resolve links to source."""
+    if domain != "py":
+        return None
+    if not info["module"]:
+        return None
+
+    # Replace with your project's GitHub repository URL
+    github_repo = "https://github.com/piskunow/kpm-tools"
+
+    # Get the module object
+    module = sys.modules.get(info["module"])
+    if module is None:
+        return None
+
+    # Get the source file path of the module
+    filename = inspect.getsourcefile(module)
+    if filename is None:
+        return None
+
+    # Trim the filename to a path relative to the project root
+    rel_fn = os.path.relpath(filename, start=os.path.dirname(__file__))
+
+    # Get the line number of the object within the module
+    obj = module
+    for part in info["fullname"].split("."):
+        obj = getattr(obj, part, None)
+
+    if obj is None:
+        return None
+
+    try:
+        lines, _ = inspect.getsourcelines(obj)
+    except Exception:
+        return None
+
+    line = inspect.getsourcelines(obj)[1]
+    return f"{github_repo}/blob/main/{rel_fn}#L{line}"
diff --git a/docs/index.md b/docs/index.md
index bca2b31..5dcbf3b 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -6,7 +6,7 @@ end-before: <!-- github-only -->
 
 [license]: license
 [contributor guide]: contributing
-[command-line reference]: usage
+[python api]: api
 
 ```{toctree}
 ---
@@ -15,8 +15,7 @@ maxdepth: 1
 ---
 
 Tutorials <tutorials>
-usage
-reference
+api
 contributing
 Code of Conduct <codeofconduct>
 License <license>
diff --git a/docs/tutorials.md b/docs/tutorials.md
index 37ceb84..66629f9 100644
--- a/docs/tutorials.md
+++ b/docs/tutorials.md
@@ -6,4 +6,6 @@ maxdepth: 1
 ---
 
 tutorials/tutorial_bloch
+tutorials/tutorial_time_evolution
+tutorials/tutorial_evolution_performance
 ```
diff --git a/docs/tutorials/tutorial_evolution_performance.ipynb b/docs/tutorials/tutorial_evolution_performance.ipynb
new file mode 100644
index 0000000..ef00abe
--- /dev/null
+++ b/docs/tutorials/tutorial_evolution_performance.ipynb
@@ -0,0 +1,589 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "afc07c2f-f818-45a3-adc1-4ccc177fb49e",
+   "metadata": {},
+   "source": [
+    "# Performance of `evolution`\n",
+    "## Cutoff in the coefficients\n",
+    "\n",
+    "The time evolution operator for a Hamiltonian $H$\n",
+    "$$ O[H, \\Delta t] =  \\text{exp}\\left(-i H \\Delta t\\right) $$\n",
+    "uses a KPM expansion as a function of time $\\Delta t$, instead of the usual Fermi energy $\\varepsilon$ of spectral functions.\n",
+    "\n",
+    "In the expansion \n",
+    "$$ O[H, \\Delta t] = \\sum_{m=0}^{\\infty} T_m(H) c_m(\\Delta t)$$\n",
+    "the coefficients $c_m$ are proportional to the Bessel functions of the first kind of order $m$, so they decay exponentially fast after a certain threshold $m^*$.\n",
+    "\n",
+    "Thus, is it extremely efficient to cutoff the expansion for a certain target accuracy\n",
+    "$$ O[H, \\Delta t] \\simeq \\sum_{m=0}^{m^*} T_m(H) c_m(\\Delta t)$$\n",
+    "\n",
+    "The exact value of $m^*$ depends on the time step times the bounds of the spectrum $\\Delta t\\times\\Delta E$, and on the desired accuracy.\n",
+    "To find $m^*$ for all values of $\\Delta t\\times\\Delta E$, we define a few typical target accuracies: $10^{-8}, 10^{-16}, 10^{-32}$, and numerically obtain the answer. \n",
+    "Then, we will use a numerical estimate of the function that fits $m^*$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "678ee99d-938d-4560-8008-48eedefd3194",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+      "application/vnd.holoviews_load.v0+json": ""
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+     "metadata": {},
+     "output_type": "display_data"
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+    {
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google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n          var messages = comm.messages[Symbol.asyncIterator]();\n          function processIteratorResult(result) {\n            var message = result.value;\n            console.log(message)\n            var content = {data: message.data, comm_id};\n            var buffers = []\n            for (var buffer of message.buffers || []) {\n              buffers.push(new DataView(buffer))\n            }\n            var metadata = message.metadata || {};\n            var msg = {content, buffers, metadata}\n            msg_handler(msg);\n            return messages.next().then(processIteratorResult);\n          }\n          return messages.next().then(processIteratorResult);\n        })\n      }\n    }\n\n    JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n      if (comm_id in window.PyViz.comms) {\n        return window.PyViz.comms[comm_id];\n      } else if (window.comm_manager || 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the DOM node\n */\nfunction render(props, node) {\n  var div = document.createElement(\"div\");\n  var script = document.createElement(\"script\");\n  node.appendChild(div);\n  node.appendChild(script);\n}\n\n/**\n * Handle when a new output is added\n */\nfunction handle_add_output(event, handle) {\n  var output_area = handle.output_area;\n  var output = handle.output;\n  if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n    return\n  }\n  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      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",
+      "application/vnd.holoviews_load.v0+json": ""
+     },
+     "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",
+       "}\n",
+       "</style>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.holoviews_exec.v0+json": "",
+      "text/html": [
+       "<div id='p1002'>\n",
+       "  <div id=\"b82870d4-f1f5-4b07-87bd-5b948a6e18c5\" data-root-id=\"p1002\" style=\"display: contents;\"></div>\n",
+       "</div>\n",
+       "<script type=\"application/javascript\">(function(root) {\n",
+       "  var docs_json = {\"39c38c61-1327-416c-8f71-42910f513e7d\":{\"version\":\"3.3.1\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"panel.models.browser.BrowserInfo\",\"id\":\"p1002\"},{\"type\":\"object\",\"name\":\"panel.models.comm_manager.CommManager\",\"id\":\"p1003\",\"attributes\":{\"plot_id\":\"p1002\",\"comm_id\":\"4beeffebd615495885425d5a0bb9f76d\",\"client_comm_id\":\"2a56a3910cc44fa78c7bdb343e610e23\"}}],\"defs\":[{\"type\":\"model\",\"name\":\"ReactiveHTML1\"},{\"type\":\"model\",\"name\":\"FlexBox1\",\"properties\":[{\"name\":\"align_content\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"align_items\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"flex_direction\",\"kind\":\"Any\",\"default\":\"row\"},{\"name\":\"flex_wrap\",\"kind\":\"Any\",\"default\":\"wrap\"},{\"name\":\"justify_content\",\"kind\":\"Any\",\"default\":\"flex-start\"}]},{\"type\":\"model\",\"name\":\"FloatPanel1\",\"properties\":[{\"name\":\"config\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"contained\",\"kind\":\"Any\",\"default\":true},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"right-top\"},{\"name\":\"offsetx\",\"kind\":\"Any\",\"default\":null},{\"name\":\"offsety\",\"kind\":\"Any\",\"default\":null},{\"name\":\"theme\",\"kind\":\"Any\",\"default\":\"primary\"},{\"name\":\"status\",\"kind\":\"Any\",\"default\":\"normalized\"}]},{\"type\":\"model\",\"name\":\"GridStack1\",\"properties\":[{\"name\":\"mode\",\"kind\":\"Any\",\"default\":\"warn\"},{\"name\":\"ncols\",\"kind\":\"Any\",\"default\":null},{\"name\":\"nrows\",\"kind\":\"Any\",\"default\":null},{\"name\":\"allow_resize\",\"kind\":\"Any\",\"default\":true},{\"name\":\"allow_drag\",\"kind\":\"Any\",\"default\":true},{\"name\":\"state\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"drag1\",\"properties\":[{\"name\":\"slider_width\",\"kind\":\"Any\",\"default\":5},{\"name\":\"slider_color\",\"kind\":\"Any\",\"default\":\"black\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":50}]},{\"type\":\"model\",\"name\":\"click1\",\"properties\":[{\"name\":\"terminal_output\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"debug_name\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"clears\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"toggle_value1\",\"properties\":[{\"name\":\"active_icons\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"options\",\"kind\":\"Any\",\"default\":{\"type\":\"map\",\"entries\":[[\"favorite\",\"heart\"]]}},{\"name\":\"value\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"_reactions\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"_base_url\",\"kind\":\"Any\",\"default\":\"https://tabler-icons.io/static/tabler-icons/icons/\"}]},{\"type\":\"model\",\"name\":\"copy_to_clipboard1\",\"properties\":[{\"name\":\"value\",\"kind\":\"Any\",\"default\":null},{\"name\":\"fill\",\"kind\":\"Any\",\"default\":\"none\"}]},{\"type\":\"model\",\"name\":\"FastWrapper1\",\"properties\":[{\"name\":\"object\",\"kind\":\"Any\",\"default\":null},{\"name\":\"style\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"NotificationAreaBase1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"NotificationArea1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"notifications\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0},{\"name\":\"types\",\"kind\":\"Any\",\"default\":[{\"type\":\"map\",\"entries\":[[\"type\",\"warning\"],[\"background\",\"#ffc107\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas 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+       "  var render_items = [{\"docid\":\"39c38c61-1327-416c-8f71-42910f513e7d\",\"roots\":{\"p1002\":\"b82870d4-f1f5-4b07-87bd-5b948a6e18c5\"},\"root_ids\":[\"p1002\"]}];\n",
+       "  var docs = Object.values(docs_json)\n",
+       "  if (!docs) {\n",
+       "    return\n",
+       "  }\n",
+       "  const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
+       "  function embed_document(root) {\n",
+       "    var Bokeh = get_bokeh(root)\n",
+       "    Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
+       "    for (const render_item of render_items) {\n",
+       "      for (const root_id of render_item.root_ids) {\n",
+       "\tconst id_el = document.getElementById(root_id)\n",
+       "\tif (id_el.children.length && (id_el.children[0].className === 'bk-root')) {\n",
+       "\t  const root_el = id_el.children[0]\n",
+       "\t  root_el.id = root_el.id + '-rendered'\n",
+       "\t}\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "  function get_bokeh(root) {\n",
+       "    if (root.Bokeh === undefined) {\n",
+       "      return null\n",
+       "    } else if (root.Bokeh.version !== py_version) {\n",
+       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
+       "\treturn null\n",
+       "      }\n",
+       "      return root.Bokeh.versions.get(py_version);\n",
+       "    } else if (root.Bokeh.version === py_version) {\n",
+       "      return root.Bokeh\n",
+       "    }\n",
+       "    return null\n",
+       "  }\n",
+       "  function is_loaded(root) {\n",
+       "    var Bokeh = get_bokeh(root)\n",
+       "    return (Bokeh != null && Bokeh.Panel !== undefined)\n",
+       "  }\n",
+       "  if (is_loaded(root)) {\n",
+       "    embed_document(root);\n",
+       "  } else {\n",
+       "    var attempts = 0;\n",
+       "    var timer = setInterval(function(root) {\n",
+       "      if (is_loaded(root)) {\n",
+       "        clearInterval(timer);\n",
+       "        embed_document(root);\n",
+       "      } else if (document.readyState == \"complete\") {\n",
+       "        attempts++;\n",
+       "        if (attempts > 200) {\n",
+       "          clearInterval(timer);\n",
+       "\t  var Bokeh = get_bokeh(root)\n",
+       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
+       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
+       "\t  } else {\n",
+       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
+       "\t    embed_document(root)\n",
+       "\t  }\n",
+       "        }\n",
+       "      }\n",
+       "    }, 25, root)\n",
+       "  }\n",
+       "})(window);</script>"
+      ]
+     },
+     "metadata": {
+      "application/vnd.holoviews_exec.v0+json": {
+       "id": "p1002"
+      }
+     },
+     "output_type": "display_data"
+    },
+    {
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+      "text/html": [
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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 numpy as np\n",
+    "import holoviews as hv\n",
+    "hv.extension()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b0c2c1c9-47cd-466e-8f46-fad06283fb36",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import scipy\n",
+    "import scipy.optimize\n",
+    "from scipy.special import jv\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "21504e7c-9efb-409a-8a14-d3f86655054a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from kpm_tools.evolution import coef_evo\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae3eabbb-6c96-4499-9551-acab82c3afd7",
+   "metadata": {},
+   "source": [
+    "\n",
+    "## numerical evaluation of the coefficients\n",
+    "\n",
+    "First, we obtain an `ndarray` of the coefficients vs the time delta $\\Delta t$ and the number of moments `num_moments`.\n",
+    "\n",
+    "Here, we use $\\Delta E = 1$, such that `dt` is the rescaled time delta $\\Delta t \\times \\Delta E$. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "16305d37-d864-4b26-bada-3720a18d41fb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dt_array = np.logspace(-3,2,1000)\n",
+    "num_moments = 500\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e58fe285-fbb2-4c39-b58b-a098e606df11",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coefs_array = np.array([\n",
+    "    coef_evo(num_moments, 0, ab=(2,0), delta_time=dt)\n",
+    "    for dt in dt_array\n",
+    "])\n",
+    "coefs_array = coefs_array[:,1:,0]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "63d2e9dc-13af-4f30-b742-f1c50974647a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "num_moments_array = np.arange(1, num_moments)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bce1b2d9-5c48-4a9a-9818-fde110efeff3",
+   "metadata": {},
+   "source": [
+    "## make a fit\n",
+    "\n",
+    "### Numerical estimation of the best `num_moments`\n",
+    "Then we obtain the best `num_moments` for a given `dt` and `accuracy`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "9c772263-2eb8-4d49-b038-8842972f7872",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "accuracy = 1e-16\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "45b5e0fe-4df1-44b5-ae6f-1b17ada7e64b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get the transition point in `m` such that coefs are smaller than accuracy\n",
+    "\n",
+    "def max_m_array(coefs_array, accuracy):\n",
+    "    return np.count_nonzero(np.abs(coefs_array) > accuracy, axis=1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "7d89d044-e66d-4298-a437-2bf2e0742af0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_21141/785646435.py:5: RuntimeWarning: divide by zero encountered in log\n",
+      "  np.log(np.abs(coefs_array))/np.log(10)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
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' style='max-width:100%; margin: auto; display: block; '/>"
+      ],
+      "text/plain": [
+       ":Overlay\n",
+       "   .QuadMesh.I :QuadMesh   [M,$dt$]   (z)\n",
+       "   .Curve.I    :Curve   [x]   (y)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {
+      "application/vnd.holoviews_exec.v0+json": {}
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(\n",
+    "    hv.QuadMesh((\n",
+    "        num_moments_array,\n",
+    "        dt_array,\n",
+    "        np.log(np.abs(coefs_array))/np.log(10)\n",
+    "    ),\n",
+    "        kdims=['M', '$dt$'],\n",
+    "        vdims=hv.Dimension('z', label='${log}_{10}|c_n|$', range=(np.log(accuracy)/np.log(10), 0))\n",
+    "    ).opts(colorbar=True, fig_size=200)\n",
+    "    *\n",
+    "    hv.Curve((\n",
+    "        max_m_array(coefs_array, accuracy),\n",
+    "        dt_array,\n",
+    "    ))\n",
+    ")[:30,:4]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e60af675-2128-4ec0-812e-7a63bfc2453c",
+   "metadata": {},
+   "source": [
+    "We can already see that only a handful of moments is needed for a fairly large vale of the time step `dt`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8713f990-6eb3-463d-8692-b18acd93725a",
+   "metadata": {},
+   "source": [
+    "\n",
+    "### Analitical fit of the best `num_moments`\n",
+    "\n",
+    "Since we don't want to store arrays of best `num_moments`, we make\n",
+    "an analytical approximation to the best `num_moments` for a given accuray and as a function of `dt`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "bcacfa28-2ef0-4c02-b49a-20f5b5b60835",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def func(dt, A, B, C, D):\n",
+    "    return (A * np.log(1/accuracy) * (dt) **  B) + C * dt + D * np.log(1/accuracy)\n",
+    "\n",
+    "p_acc16, _ = scipy.optimize.curve_fit(func,\n",
+    "                         dt_array,\n",
+    "                         max_m_array(coefs_array, accuracy),\n",
+    "                         p0=(0.5, 0.25, 2, 0),\n",
+    "\n",
+    "#                                  bounds=([0,0,0,2], [1,1,1,2+1e-8])\n",
+    "                        )\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "da947bd6-9ee7-485a-b5c0-1331c07f10ab",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<img 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' style='max-width:100%; margin: auto; display: block; '/>"
+      ],
+      "text/plain": [
+       ":Overlay\n",
+       "   .QuadMesh.I :QuadMesh   [M,$dt$]   (z)\n",
+       "   .Curve.I    :Curve   [x]   (y)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {
+      "application/vnd.holoviews_exec.v0+json": {}
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hv.QuadMesh((\n",
+    "    num_moments_array,\n",
+    "    dt_array,\n",
+    "    np.log(np.abs(coefs_array+np.finfo(float).eps))/np.log(10)\n",
+    "),\n",
+    "    kdims=['M', '$dt$'],\n",
+    "    vdims=hv.Dimension('z', label='$|c_n|$', range=(np.log(accuracy)/np.log(10), 0))\n",
+    ").opts(logy=True, logz=False, colorbar=True, fig_size=200) * \\\n",
+    "hv.Curve((np.array([func(dt, *p_acc16) for dt in dt_array]),\n",
+    "          dt_array)).opts(logy=True, logx=True)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "63ad6320-1f42-4556-a77e-2eb18314fd0e",
+   "metadata": {},
+   "source": [
+    "## Result\n",
+    "\n",
+    "Finally, these parameters give the best choice of `num_moments`\n",
+    "as a function of `dt` for\n",
+    "typical accuracy goals: `1e-8`, `1e-16`, `1e-32`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "'p_acc8 = [0.42464678, 0.33500138, 2.00044861, 0.07265139]'\n",
+    "'p_acc16 = [0.34310197, 0.33722835, 2.01232381, 0.08868359]'\n",
+    "'p_acc32 = [0.28132266, 0.32958046, 2.04428473, 0.09146841]'\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "548000c4-04cc-4f1a-8977-aadeef099537",
+   "metadata": {},
+   "source": [
+    "From the scaling analysis we can see that large time steps are possible, and require a proportionally larger amount of moments in the KPM expansion.\n",
+    "\n",
+    "Smaller timesteps have a slight overhead in the number of moments:\n",
+    "$m^*=4$ for very small time steps of $10^{-3}$ but only $m^*=6$ for a ten times larger time step $10^{-2}$.\n",
+    "\n",
+    "Thus, it is more convenient -in terms of performance- to make one big time step, rather than many subsequent smaller time steps.\n",
+    "\n",
+    "Nevertheless, it is still fairly efficient to make many time steps for visualizing the time evolution."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "kt",
+   "language": "python",
+   "name": "kt"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/tutorials/tutorial_time_evolution.ipynb b/docs/tutorials/tutorial_time_evolution.ipynb
new file mode 100644
index 0000000..4e28587
--- /dev/null
+++ b/docs/tutorials/tutorial_time_evolution.ipynb
@@ -0,0 +1,1027 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "8b9ff2a1-58ba-46b2-87e0-03d8d987fd94",
+   "metadata": {},
+   "source": [
+    "# Tutorial `evolution`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "303dcd12-cd29-49a0-81b0-befa68549c86",
+   "metadata": {},
+   "source": [
+    "We demonstrate the use of a KPM expansion to handle the evolution in time of set of initial states under a defined Hamiltonian.\n",
+    "\n",
+    "Note that this module only addresses the time evolution of closed systems. For time evolution in scattering problems, please refer to [TKwant](https://tkwant.kwant-project.org/)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9886d7d6-8c51-4dbc-a87b-c3c44035c7b8",
+   "metadata": {},
+   "source": [
+    "## Defining the system\n",
+    "We make some basic imports and define a linear chain."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "eaabebb3-7176-4a8b-bbd9-cd38964f34eb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "(function(root) {\n",
+       "  function now() {\n",
+       "    return new Date();\n",
+       "  }\n",
+       "\n",
+       "  var force = true;\n",
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+       "    root._bokeh_failed_load = false;\n",
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+       "\n",
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+       "      delete root._bokeh_onload_callbacks;\n",
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+       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
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+       "\n",
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+       "      require([\"jspanel\"], function(jsPanel) {\n",
+       "\twindow.jsPanel = jsPanel\n",
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+       "      require([\"jspanel-modal\"], function() {\n",
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+       "      require([\"jspanel-tooltip\"], function() {\n",
+       "\ton_load()\n",
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+       "      require([\"jspanel-hint\"], function() {\n",
+       "\ton_load()\n",
+       "      })\n",
+       "      require([\"jspanel-layout\"], function() {\n",
+       "\ton_load()\n",
+       "      })\n",
+       "      require([\"jspanel-contextmenu\"], function() {\n",
+       "\ton_load()\n",
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+       "    }\n",
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+       "\texisting_stylesheets.push(link.href)\n",
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+       "      var url = css_urls[i];\n",
+       "      if (existing_stylesheets.indexOf(url) !== -1) {\n",
+       "\ton_load()\n",
+       "\tcontinue;\n",
+       "      }\n",
+       "      const element = document.createElement(\"link\");\n",
+       "      element.onload = on_load;\n",
+       "      element.onerror = on_error;\n",
+       "      element.rel = \"stylesheet\";\n",
+       "      element.type = \"text/css\";\n",
+       "      element.href = url;\n",
+       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
+       "      document.body.appendChild(element);\n",
+       "    }    if (((window['jsPanel'] !== undefined) && (!(window['jsPanel'] instanceof HTMLElement))) || window.requirejs) {\n",
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+       "      for (var i = 0; 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/1.3.2/dist/bundled/gridstack/gridstack@7.2.3/dist/gridstack-all.js'];\n",
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+       "      var urls = ['https://cdn.holoviz.org/panel/1.3.2/dist/bundled/notificationarea/notyf@3/notyf.min.js'];\n",
+       "      for (var i = 0; i < urls.length; i++) {\n",
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+       "\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",
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+       "      element.onload = on_load;\n",
+       "      element.onerror = on_error;\n",
+       "      element.async = false;\n",
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+       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
+       "      document.head.appendChild(element);\n",
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+       "\tif (!window.requirejs) {\n",
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+       "      element.onload = on_load;\n",
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+       "      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",
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+       "      }\n",
+       "      var element = document.createElement('script');\n",
+       "      element.onerror = on_error;\n",
+       "      element.async = false;\n",
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+       "      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",
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+       "      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-3.3.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.1.min.js\", \"https://cdn.holoviz.org/panel/1.3.2/dist/panel.min.js\"];\n",
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+       "    },\n",
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+       "  function run_inline_js() {\n",
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+       "          inline_js[i].call(root, root.Bokeh);\n",
+       "\t} catch(e) {\n",
+       "\t  if (!reloading) {\n",
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+       "\tif (Bokeh.versions === undefined) {\n",
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+       "\n",
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+       "      console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n",
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+       "      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",
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+       "    }\n",
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+      ],
+      "application/vnd.holoviews_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n  var py_version = '3.3.1'.replace('rc', '-rc.').replace('.dev', '-dev.');\n  var reloading = false;\n  var Bokeh = root.Bokeh;\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks;\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n    if (js_modules == null) js_modules = [];\n    if (js_exports == null) js_exports = {};\n\n    root._bokeh_onload_callbacks.push(callback);\n\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n      run_callbacks();\n      return null;\n    }\n    if (!reloading) {\n      console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    }\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n    window._bokeh_on_load = on_load\n\n    function on_error() {\n      console.error(\"failed to load \" + url);\n    }\n\n    var skip = [];\n    if (window.requirejs) {\n      window.requirejs.config({'packages': {}, 'paths': {'jspanel': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/jspanel', 'jspanel-modal': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal', 'jspanel-tooltip': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip', 'jspanel-hint': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint', 'jspanel-layout': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout', 'jspanel-contextmenu': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu', 'jspanel-dock': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock', 'gridstack': 'https://cdn.jsdelivr.net/npm/gridstack@7.2.3/dist/gridstack-all', 'notyf': 'https://cdn.jsdelivr.net/npm/notyf@3/notyf.min'}, 'shim': {'jspanel': {'exports': 'jsPanel'}, 'gridstack': {'exports': 'GridStack'}}});\n      require([\"jspanel\"], function(jsPanel) {\n\twindow.jsPanel = jsPanel\n\ton_load()\n      })\n      require([\"jspanel-modal\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-tooltip\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-hint\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-layout\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-contextmenu\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-dock\"], function() {\n\ton_load()\n      })\n      require([\"gridstack\"], function(GridStack) {\n\twindow.GridStack = GridStack\n\ton_load()\n      })\n      require([\"notyf\"], function() {\n\ton_load()\n      })\n      root._bokeh_is_loading = css_urls.length + 9;\n    } else {\n      root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n    }\n\n    var existing_stylesheets = []\n    var links = document.getElementsByTagName('link')\n    for (var i = 0; i < links.length; i++) {\n      var link = links[i]\n      if (link.href != null) {\n\texisting_stylesheets.push(link.href)\n      }\n    }\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      if (existing_stylesheets.indexOf(url) !== -1) {\n\ton_load()\n\tcontinue;\n      }\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }    if (((window['jsPanel'] !== undefined) && (!(window['jsPanel'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/1.3.2/dist/bundled/floatpanel/jspanel4@4.12.0/dist/jspanel.js', 'https://cdn.holoviz.org/panel/1.3.2/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal.js', 'https://cdn.holoviz.org/panel/1.3.2/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip.js', 'https://cdn.holoviz.org/panel/1.3.2/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint.js', 'https://cdn.holoviz.org/panel/1.3.2/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout.js', 'https://cdn.holoviz.org/panel/1.3.2/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu.js', 'https://cdn.holoviz.org/panel/1.3.2/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock.js'];\n      for (var i = 0; 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/1.3.2/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/1.3.2/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-3.3.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.1.min.js\", \"https://cdn.holoviz.org/panel/1.3.2/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": {
+      "application/javascript": [
+       "\n",
+       "if ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n",
+       "  window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n",
+       "}\n",
+       "\n",
+       "\n",
+       "    function JupyterCommManager() {\n",
+       "    }\n",
+       "\n",
+       "    JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n",
+       "      if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n",
+       "        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n",
+       "        comm_manager.register_target(comm_id, function(comm) {\n",
+       "          comm.on_msg(msg_handler);\n",
+       "        });\n",
+       "      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n",
+       "        window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n",
+       "          comm.onMsg = msg_handler;\n",
+       "        });\n",
+       "      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n",
+       "        google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n",
+       "          var messages = comm.messages[Symbol.asyncIterator]();\n",
+       "          function processIteratorResult(result) {\n",
+       "            var message = result.value;\n",
+       "            console.log(message)\n",
+       "            var content = {data: message.data, comm_id};\n",
+       "            var buffers = []\n",
+       "            for (var buffer of message.buffers || []) {\n",
+       "              buffers.push(new DataView(buffer))\n",
+       "            }\n",
+       "            var metadata = message.metadata || {};\n",
+       "            var msg = {content, buffers, metadata}\n",
+       "            msg_handler(msg);\n",
+       "            return messages.next().then(processIteratorResult);\n",
+       "          }\n",
+       "          return messages.next().then(processIteratorResult);\n",
+       "        })\n",
+       "      }\n",
+       "    }\n",
+       "\n",
+       "    JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n",
+       "      if (comm_id in window.PyViz.comms) {\n",
+       "        return window.PyViz.comms[comm_id];\n",
+       "      } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n",
+       "        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n",
+       "        var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n",
+       "        if (msg_handler) {\n",
+       "          comm.on_msg(msg_handler);\n",
+       "        }\n",
+       "      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n",
+       "        var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n",
+       "        comm.open();\n",
+       "        if (msg_handler) {\n",
+       "          comm.onMsg = msg_handler;\n",
+       "        }\n",
+       "      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n",
+       "        var comm_promise = google.colab.kernel.comms.open(comm_id)\n",
+       "        comm_promise.then((comm) => {\n",
+       "          window.PyViz.comms[comm_id] = comm;\n",
+       "          if (msg_handler) {\n",
+       "            var messages = comm.messages[Symbol.asyncIterator]();\n",
+       "            function processIteratorResult(result) {\n",
+       "              var message = result.value;\n",
+       "              var content = {data: message.data};\n",
+       "              var metadata = message.metadata || {comm_id};\n",
+       "              var msg = {content, metadata}\n",
+       "              msg_handler(msg);\n",
+       "              return messages.next().then(processIteratorResult);\n",
+       "            }\n",
+       "            return messages.next().then(processIteratorResult);\n",
+       "          }\n",
+       "        }) \n",
+       "        var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n",
+       "          return comm_promise.then((comm) => {\n",
+       "            comm.send(data, metadata, buffers, disposeOnDone);\n",
+       "          });\n",
+       "        };\n",
+       "        var comm = {\n",
+       "          send: sendClosure\n",
+       "        };\n",
+       "      }\n",
+       "      window.PyViz.comms[comm_id] = comm;\n",
+       "      return comm;\n",
+       "    }\n",
+       "    window.PyViz.comm_manager = new JupyterCommManager();\n",
+       "    \n",
+       "\n",
+       "\n",
+       "var JS_MIME_TYPE = 'application/javascript';\n",
+       "var HTML_MIME_TYPE = 'text/html';\n",
+       "var EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\n",
+       "var CLASS_NAME = 'output';\n",
+       "\n",
+       "/**\n",
+       " * Render data to the DOM node\n",
+       " */\n",
+       "function render(props, node) {\n",
+       "  var div = document.createElement(\"div\");\n",
+       "  var script = document.createElement(\"script\");\n",
+       "  node.appendChild(div);\n",
+       "  node.appendChild(script);\n",
+       "}\n",
+       "\n",
+       "/**\n",
+       " * Handle when a new output is added\n",
+       " */\n",
+       "function handle_add_output(event, handle) {\n",
+       "  var output_area = handle.output_area;\n",
+       "  var output = handle.output;\n",
+       "  if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
+       "    return\n",
+       "  }\n",
+       "  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",
+       "      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",
+       " */\n",
+       "function 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",
+       " */\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",
+       "  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",
+       "\n",
+       "if (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"
+      ],
+      "application/vnd.holoviews_load.v0+json": "\nif ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n  window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n}\n\n\n    function JupyterCommManager() {\n    }\n\n    JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n      if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n        comm_manager.register_target(comm_id, function(comm) {\n          comm.on_msg(msg_handler);\n        });\n      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n        window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n          comm.onMsg = msg_handler;\n        });\n      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n        google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n          var messages = comm.messages[Symbol.asyncIterator]();\n          function processIteratorResult(result) {\n            var message = result.value;\n            console.log(message)\n            var content = {data: message.data, comm_id};\n            var buffers = []\n            for (var buffer of message.buffers || []) {\n              buffers.push(new DataView(buffer))\n            }\n            var metadata = message.metadata || {};\n            var msg = {content, buffers, metadata}\n            msg_handler(msg);\n            return messages.next().then(processIteratorResult);\n          }\n          return messages.next().then(processIteratorResult);\n        })\n      }\n    }\n\n    JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n      if (comm_id in window.PyViz.comms) {\n        return window.PyViz.comms[comm_id];\n      } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n        var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n        if (msg_handler) {\n          comm.on_msg(msg_handler);\n        }\n      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n        var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n        comm.open();\n        if (msg_handler) {\n          comm.onMsg = msg_handler;\n        }\n      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n        var comm_promise = google.colab.kernel.comms.open(comm_id)\n        comm_promise.then((comm) => {\n          window.PyViz.comms[comm_id] = comm;\n          if (msg_handler) {\n            var messages = comm.messages[Symbol.asyncIterator]();\n            function processIteratorResult(result) {\n              var message = result.value;\n              var content = {data: message.data};\n              var metadata = message.metadata || {comm_id};\n              var msg = {content, metadata}\n              msg_handler(msg);\n              return messages.next().then(processIteratorResult);\n            }\n            return messages.next().then(processIteratorResult);\n          }\n        }) \n        var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n          return comm_promise.then((comm) => {\n            comm.send(data, metadata, buffers, disposeOnDone);\n          });\n        };\n        var comm = {\n          send: sendClosure\n        };\n      }\n      window.PyViz.comms[comm_id] = comm;\n      return comm;\n    }\n    window.PyViz.comm_manager = new JupyterCommManager();\n    \n\n\nvar JS_MIME_TYPE = 'application/javascript';\nvar HTML_MIME_TYPE = 'text/html';\nvar EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\nvar CLASS_NAME = 'output';\n\n/**\n * Render data to the DOM node\n */\nfunction render(props, node) {\n  var div = document.createElement(\"div\");\n  var script = document.createElement(\"script\");\n  node.appendChild(div);\n  node.appendChild(script);\n}\n\n/**\n * Handle when a new output is added\n */\nfunction handle_add_output(event, handle) {\n  var output_area = handle.output_area;\n  var output = handle.output;\n  if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n    return\n  }\n  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      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"
+     },
+     "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",
+       "}\n",
+       "</style>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.holoviews_exec.v0+json": "",
+      "text/html": [
+       "<div id='p1002'>\n",
+       "  <div id=\"de001e56-50f5-4730-b05e-bb535ecee560\" data-root-id=\"p1002\" style=\"display: contents;\"></div>\n",
+       "</div>\n",
+       "<script type=\"application/javascript\">(function(root) {\n",
+       "  var docs_json = {\"3bbc9cce-1ad3-4a49-b981-691a8acd4d65\":{\"version\":\"3.3.1\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"panel.models.browser.BrowserInfo\",\"id\":\"p1002\"},{\"type\":\"object\",\"name\":\"panel.models.comm_manager.CommManager\",\"id\":\"p1003\",\"attributes\":{\"plot_id\":\"p1002\",\"comm_id\":\"d28651049ead4cf9bf7591b846d2ed66\",\"client_comm_id\":\"6376f0d5af88453a942e8eaa84bfd44f\"}}],\"defs\":[{\"type\":\"model\",\"name\":\"ReactiveHTML1\"},{\"type\":\"model\",\"name\":\"FlexBox1\",\"properties\":[{\"name\":\"align_content\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"align_items\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"flex_direction\",\"kind\":\"Any\",\"default\":\"row\"},{\"name\":\"flex_wrap\",\"kind\":\"Any\",\"default\":\"wrap\"},{\"name\":\"justify_content\",\"kind\":\"Any\",\"default\":\"flex-start\"}]},{\"type\":\"model\",\"name\":\"FloatPanel1\",\"properties\":[{\"name\":\"config\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"contained\",\"kind\":\"Any\",\"default\":true},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"right-top\"},{\"name\":\"offsetx\",\"kind\":\"Any\",\"default\":null},{\"name\":\"offsety\",\"kind\":\"Any\",\"default\":null},{\"name\":\"theme\",\"kind\":\"Any\",\"default\":\"primary\"},{\"name\":\"status\",\"kind\":\"Any\",\"default\":\"normalized\"}]},{\"type\":\"model\",\"name\":\"GridStack1\",\"properties\":[{\"name\":\"mode\",\"kind\":\"Any\",\"default\":\"warn\"},{\"name\":\"ncols\",\"kind\":\"Any\",\"default\":null},{\"name\":\"nrows\",\"kind\":\"Any\",\"default\":null},{\"name\":\"allow_resize\",\"kind\":\"Any\",\"default\":true},{\"name\":\"allow_drag\",\"kind\":\"Any\",\"default\":true},{\"name\":\"state\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"drag1\",\"properties\":[{\"name\":\"slider_width\",\"kind\":\"Any\",\"default\":5},{\"name\":\"slider_color\",\"kind\":\"Any\",\"default\":\"black\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":50}]},{\"type\":\"model\",\"name\":\"click1\",\"properties\":[{\"name\":\"terminal_output\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"debug_name\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"clears\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"toggle_value1\",\"properties\":[{\"name\":\"active_icons\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"options\",\"kind\":\"Any\",\"default\":{\"type\":\"map\",\"entries\":[[\"favorite\",\"heart\"]]}},{\"name\":\"value\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"_reactions\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"_base_url\",\"kind\":\"Any\",\"default\":\"https://tabler-icons.io/static/tabler-icons/icons/\"}]},{\"type\":\"model\",\"name\":\"copy_to_clipboard1\",\"properties\":[{\"name\":\"value\",\"kind\":\"Any\",\"default\":null},{\"name\":\"fill\",\"kind\":\"Any\",\"default\":\"none\"}]},{\"type\":\"model\",\"name\":\"FastWrapper1\",\"properties\":[{\"name\":\"object\",\"kind\":\"Any\",\"default\":null},{\"name\":\"style\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"NotificationAreaBase1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"NotificationArea1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"notifications\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0},{\"name\":\"types\",\"kind\":\"Any\",\"default\":[{\"type\":\"map\",\"entries\":[[\"type\",\"warning\"],[\"background\",\"#ffc107\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]}]}};\n",
+       "  var render_items = [{\"docid\":\"3bbc9cce-1ad3-4a49-b981-691a8acd4d65\",\"roots\":{\"p1002\":\"de001e56-50f5-4730-b05e-bb535ecee560\"},\"root_ids\":[\"p1002\"]}];\n",
+       "  var docs = Object.values(docs_json)\n",
+       "  if (!docs) {\n",
+       "    return\n",
+       "  }\n",
+       "  const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
+       "  function embed_document(root) {\n",
+       "    var Bokeh = get_bokeh(root)\n",
+       "    Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
+       "    for (const render_item of render_items) {\n",
+       "      for (const root_id of render_item.root_ids) {\n",
+       "\tconst id_el = document.getElementById(root_id)\n",
+       "\tif (id_el.children.length && (id_el.children[0].className === 'bk-root')) {\n",
+       "\t  const root_el = id_el.children[0]\n",
+       "\t  root_el.id = root_el.id + '-rendered'\n",
+       "\t}\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "  function get_bokeh(root) {\n",
+       "    if (root.Bokeh === undefined) {\n",
+       "      return null\n",
+       "    } else if (root.Bokeh.version !== py_version) {\n",
+       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
+       "\treturn null\n",
+       "      }\n",
+       "      return root.Bokeh.versions.get(py_version);\n",
+       "    } else if (root.Bokeh.version === py_version) {\n",
+       "      return root.Bokeh\n",
+       "    }\n",
+       "    return null\n",
+       "  }\n",
+       "  function is_loaded(root) {\n",
+       "    var Bokeh = get_bokeh(root)\n",
+       "    return (Bokeh != null && Bokeh.Panel !== undefined)\n",
+       "  }\n",
+       "  if (is_loaded(root)) {\n",
+       "    embed_document(root);\n",
+       "  } else {\n",
+       "    var attempts = 0;\n",
+       "    var timer = setInterval(function(root) {\n",
+       "      if (is_loaded(root)) {\n",
+       "        clearInterval(timer);\n",
+       "        embed_document(root);\n",
+       "      } else if (document.readyState == \"complete\") {\n",
+       "        attempts++;\n",
+       "        if (attempts > 200) {\n",
+       "          clearInterval(timer);\n",
+       "\t  var Bokeh = get_bokeh(root)\n",
+       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
+       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
+       "\t  } else {\n",
+       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
+       "\t    embed_document(root)\n",
+       "\t  }\n",
+       "        }\n",
+       "      }\n",
+       "    }, 25, root)\n",
+       "  }\n",
+       "})(window);</script>"
+      ]
+     },
+     "metadata": {
+      "application/vnd.holoviews_exec.v0+json": {
+       "id": "p1002"
+      }
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<div class=\"logo-block\">\n",
+       "<img src='data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAEAAAABACAYAAACqaXHeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\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 numpy as np\n",
+    "import kwant\n",
+    "\n",
+    "import holoviews as hv\n",
+    "hv.notebook_extension()\n",
+    "\n",
+    "from matplotlib import pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7acf989f-3c7d-4cd9-ade3-803123e3a330",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from kpm_tools.evolution import evolve_vectors, _max_num_moments, evolve_vectors_time_steps"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "7730652d-b5f6-49aa-8bf5-842cb73bee17",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/pablo/.cache/pypoetry/virtualenvs/kpm-tools-1LxsbUd2-py3.10/lib/python3.10/site-packages/kwant-1.5.0a0.dev111+g7790469-py3.10-linux-x86_64.egg/kwant/plotter.py:893: RuntimeWarning:\n",
+      "\n",
+      "Kwant's plotting functions have\n",
+      "the side effect of selecting the matplotlib backend. To avoid this warning,\n",
+      "import matplotlib.pyplot, matplotlib.backends or call matplotlib.use().\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "chain_length = 100\n",
+    "lat = kwant.lattice.chain(norbs=1)\n",
+    "syst = kwant.builder.Builder()\n",
+    "for i in range(chain_length):\n",
+    "    syst[lat(i)] = 0\n",
+    "syst[lat.neighbors()] = -1\n",
+    "fsyst = syst.finalized()\n",
+    "kwant.plot(fsyst);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "243dc269-cccb-46f0-ba2f-54d10093f2e0",
+   "metadata": {},
+   "source": [
+    "## Time evolution\n",
+    "\n",
+    "We will demonstrate the time evolution using two initial states, one at the center of the chain, and one at the edge."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "31bd051c-8d24-4b23-a5e7-e11c48fff4bc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vector_factory = kwant.kpm.LocalVectors(fsyst, where=[fsyst.sites[chain_length // 2], fsyst.sites[0]])\n",
+    "vectors = np.array([v for v in vector_factory])\n",
+    "\n",
+    "time_steps, dt = np.linspace(0, 64, 256, retstep=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "22d84ef0-cc31-4b08-8a36-483e124a9562",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 1.69 s, sys: 947 µs, total: 1.69 s\n",
+      "Wall time: 1.71 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "\n",
+    "accuracy = 0.5e-16\n",
+    "\n",
+    "eee = evolve_vectors_time_steps(\n",
+    "    fsyst,\n",
+    "    time_steps=time_steps,\n",
+    "    vectors=vectors,\n",
+    "    accuracy=accuracy\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "1764d818-29dd-4d64-be62-4d48d6a7eb63",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<img 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' style='max-width:100%; margin: auto; display: block; '/>"
+      ],
+      "text/plain": [
+       ":Layout\n",
+       "   .QuadMesh.Start_at_the_center :QuadMesh   [$t$,$x[a]$]   (LDOS)\n",
+       "   .QuadMesh.Start_at_the_edge   :QuadMesh   [$t$,$x[a]$]   (LDOS)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {
+      "application/vnd.holoviews_exec.v0+json": {}
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%opts QuadMesh [aspect=\"square\" colorbar=True]\n",
+    "(\n",
+    "    hv.QuadMesh(\n",
+    "        (time_steps, np.arange(len(fsyst.sites)), np.abs(eee[:,0].T)),\n",
+    "        kdims=[r'$t$',r'$x[a]$'], vdims=r'LDOS', label=\"start at the center\"\n",
+    "    ) +\n",
+    "    hv.QuadMesh(\n",
+    "        (time_steps, np.arange(len(fsyst.sites)), np.abs(eee[:,1].T)),\n",
+    "        kdims=[r'$t$',r'$x[a]$'], vdims=r'LDOS', label=\"start at the edge\"\n",
+    "    )\n",
+    ").opts(title=\"Evolution of LDOS over time\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2c044872-26e8-4026-9782-2d8d9e543cea",
+   "metadata": {},
+   "source": [
+    "### Vector normalization over time\n",
+    "Like with every evolution method, there is the risk of loosing the normalization of the initial vectors.\n",
+    "\n",
+    "Here, we control this by setting the `accuracy` of the time evolution.\n",
+    "Note that for consecutive applications of the time evolution expansion, the errors can accumulate. Thanks to the KPM stability and the efficiency of the time evolution expansion, the errors do not blow exponentially.\n",
+    "\n",
+    "See [Performance of evolution](https://kpm-tools.readthedocs.io/en/latest/tutorials/tutorial_evolution_performance.html) for more details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "4cb85f92-4b59-4a4b-bea0-c2caaddeacb5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<img 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+      ],
+      "text/plain": [
+       ":Scatter   [x]   (y)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {
+      "application/vnd.holoviews_exec.v0+json": {}
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hv.Scatter((time_steps, abs(np.linalg.norm(eee[:,0], axis=1)-1)),\n",
+    "         vdims=hv.Dimension('y', label=r'$|\\rho(t)-1|$',)\n",
+    "        )"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "kt",
+   "language": "python",
+   "name": "kt"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/usage.md b/docs/usage.md
deleted file mode 100644
index 7e1d1e1..0000000
--- a/docs/usage.md
+++ /dev/null
@@ -1,7 +0,0 @@
-# Usage
-
-```{eval-rst}
-.. click:: kpm_tools.__main__:main
-    :prog: kpm-tools
-    :nested: full
-```
diff --git a/noxfile.py b/noxfile.py
index d6726fb..a13536d 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -121,8 +121,11 @@ def build_kwant(session: Session):
     """Build and install kwant from source."""
     session.install("cython", "numpy", "scipy", "sympy", "tinyarray")
 
+    # Store the original directory
+    original_dir = Path.cwd()
+
     # temp folder
-    kwant_dir = os.path.join(session.create_tmp(), "kwant")
+    kwant_dir = Path(session.create_tmp()) / "kwant"
 
     need_to_build = False
     # Check if kwant directory exists
@@ -141,7 +144,7 @@ def build_kwant(session: Session):
         )
 
     # Navigate to the cloned directory
-    session.cd(kwant_dir)
+    session.cd(str(kwant_dir))
 
     # Checkout the master branch
     session.run("git", "checkout", "master", external=True)
@@ -151,6 +154,10 @@ def build_kwant(session: Session):
         session.run("python", "setup.py", "build")
     session.run("python", "setup.py", "install")
 
+    # Return to the original directory and remove the kwant directory
+    session.cd(str(original_dir))
+    shutil.rmtree(str(kwant_dir))
+
 
 @session(name="pre-commit", python=python_versions[0])
 def precommit(session: Session) -> None:
@@ -175,6 +182,7 @@ def precommit(session: Session) -> None:
         "pre-commit-hooks",
         "pyupgrade",
     )
+
     session.run("pre-commit", *args)
     if args and args[0] == "install":
         activate_virtualenv_in_precommit_hooks(session)
@@ -204,6 +212,10 @@ def tests(session: Session) -> None:
     """Run the test suite."""
     session.install(".")
     session.install("coverage[toml]", "pytest", "pygments")
+
+    # Call the kwant installation functions
+    build_kwant(session)
+
     try:
         session.run("coverage", "run", "--parallel", "-m", "pytest", *session.posargs)
     finally:
@@ -229,6 +241,10 @@ def typeguard(session: Session) -> None:
     """Runtime type checking using Typeguard."""
     session.install(".")
     session.install("pytest", "typeguard", "pygments")
+
+    # Call the kwant installation functions
+    build_kwant(session)
+
     session.run("pytest", f"--typeguard-packages={package}", *session.posargs)
 
 
@@ -243,6 +259,7 @@ def xdoctest(session: Session) -> None:
             args.append("--colored=1")
 
     session.install(".")
+
     session.install("xdoctest[colors]")
     session.run("python", "-m", "xdoctest", *args)
 
@@ -259,6 +276,9 @@ def docs_build(session: Session) -> None:
         "sphinx", "sphinx-click", "nbsphinx", "pandoc", "furo", "myst-parser"
     )
 
+    # Call the kwant installation functions
+    build_kwant(session)
+
     build_dir = Path("docs", "_build")
     if build_dir.exists():
         shutil.rmtree(build_dir)
@@ -282,6 +302,9 @@ def docs(session: Session) -> None:
         "pandoc",
     )
 
+    # Call the kwant installation functions
+    build_kwant(session)
+
     build_dir = Path("docs", "_build")
     if build_dir.exists():
         shutil.rmtree(build_dir)
diff --git a/poetry.lock b/poetry.lock
index c060ef9..ffa6612 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand.
 
 [[package]]
 name = "alabaster"
@@ -1153,13 +1153,13 @@ license = ["ukkonen"]
 
 [[package]]
 name = "idna"
-version = "3.4"
+version = "3.5"
 description = "Internationalized Domain Names in Applications (IDNA)"
 optional = false
 python-versions = ">=3.5"
 files = [
-    {file = "idna-3.4-py3-none-any.whl", hash = "sha256:90b77e79eaa3eba6de819a0c442c0b4ceefc341a7a2ab77d7562bf49f425c5c2"},
-    {file = "idna-3.4.tar.gz", hash = "sha256:814f528e8dead7d329833b91c5faa87d60bf71824cd12a7530b5526063d02cb4"},
+    {file = "idna-3.5-py3-none-any.whl", hash = "sha256:79b8f0ac92d2351be5f6122356c9a592c96d81c9a79e4b488bf2a6a15f88057a"},
+    {file = "idna-3.5.tar.gz", hash = "sha256:27009fe2735bf8723353582d48575b23c533cc2c2de7b5a68908d91b5eb18d08"},
 ]
 
 [[package]]
@@ -1256,17 +1256,16 @@ test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio"
 
 [[package]]
 name = "ipython"
-version = "8.17.2"
+version = "8.18.0"
 description = "IPython: Productive Interactive Computing"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "ipython-8.17.2-py3-none-any.whl", hash = "sha256:1e4d1d666a023e3c93585ba0d8e962867f7a111af322efff6b9c58062b3e5444"},
-    {file = "ipython-8.17.2.tar.gz", hash = "sha256:126bb57e1895594bb0d91ea3090bbd39384f6fe87c3d57fd558d0670f50339bb"},
+    {file = "ipython-8.18.0-py3-none-any.whl", hash = "sha256:d538a7a98ad9b7e018926447a5f35856113a85d08fd68a165d7871ab5175f6e0"},
+    {file = "ipython-8.18.0.tar.gz", hash = "sha256:4feb61210160f75e229ce932dbf8b719bff37af123c0b985fd038b14233daa16"},
 ]
 
 [package.dependencies]
-appnope = {version = "*", markers = "sys_platform == \"darwin\""}
 colorama = {version = "*", markers = "sys_platform == \"win32\""}
 decorator = "*"
 exceptiongroup = {version = "*", markers = "python_version < \"3.11\""}
@@ -2086,38 +2085,38 @@ files = [
 
 [[package]]
 name = "mypy"
-version = "1.7.0"
+version = "1.7.1"
 description = "Optional static typing for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
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-    {file = "mypy-1.7.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:a3637c03f4025f6405737570d6cbfa4f1400eb3c649317634d273687a09ffc2f"},
-    {file = "mypy-1.7.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b633f188fc5ae1b6edca39dae566974d7ef4e9aaaae00bc36efe1f855e5173ac"},
-    {file = "mypy-1.7.0-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:d6ed9a3997b90c6f891138e3f83fb8f475c74db4ccaa942a1c7bf99e83a989a1"},
-    {file = "mypy-1.7.0-cp310-cp310-win_amd64.whl", hash = "sha256:1fe46e96ae319df21359c8db77e1aecac8e5949da4773c0274c0ef3d8d1268a9"},
-    {file = "mypy-1.7.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:df67fbeb666ee8828f675fee724cc2cbd2e4828cc3df56703e02fe6a421b7401"},
-    {file = "mypy-1.7.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:a79cdc12a02eb526d808a32a934c6fe6df07b05f3573d210e41808020aed8b5d"},
-    {file = "mypy-1.7.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f65f385a6f43211effe8c682e8ec3f55d79391f70a201575def73d08db68ead1"},
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-    {file = "mypy-1.7.0-cp311-cp311-win_amd64.whl", hash = "sha256:f29386804c3577c83d76520abf18cfcd7d68264c7e431c5907d250ab502658ee"},
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+    {file = "mypy-1.7.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:75aa828610b67462ffe3057d4d8a4112105ed211596b750b53cbfe182f44777a"},
+    {file = "mypy-1.7.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:ee5d62d28b854eb61889cde4e1dbc10fbaa5560cb39780c3995f6737f7e82120"},
+    {file = "mypy-1.7.1-cp39-cp39-win_amd64.whl", hash = "sha256:72cf32ce7dd3562373f78bd751f73c96cfb441de147cc2448a92c1a308bd0ca6"},
+    {file = "mypy-1.7.1-py3-none-any.whl", hash = "sha256:f7c5d642db47376a0cc130f0de6d055056e010debdaf0707cd2b0fc7e7ef30ea"},
+    {file = "mypy-1.7.1.tar.gz", hash = "sha256:fcb6d9afb1b6208b4c712af0dafdc650f518836065df0d4fb1d800f5d6773db2"},
 ]
 
 [package.dependencies]
@@ -4382,4 +4381,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.9"
-content-hash = "a4869a711e84f5a81ea008859a2bca5c051404d2f0c1ff3206dd0a0c2998d439"
+content-hash = "b1fb5d158546b931f7c0a44f7a0f036b68577ad8e0aabb1723a5679ab825c4dd"
diff --git a/pyproject.toml b/pyproject.toml
index 6f9d322..14aa962 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "kpm-tools"
-version = "0.0.1"
+version = "0.0.2"
 description = "KPM Tools"
 authors = ["Pablo Piskunow <kpm@piskunow.com>"]
 license = "BSD 2-Clause"
@@ -61,6 +61,7 @@ nbsphinx = "^0.9.3"
 jupyter = "^1.0.0"
 plotly = "^5.18.0"
 pandoc = "^2.3"
+isort = "^5.12.0"
 
 [tool.coverage.paths]
 source = ["src", "*/site-packages"]
diff --git a/src/kpm_tools/__init__.py b/src/kpm_tools/__init__.py
index 6cdb3ea..576b142 100644
--- a/src/kpm_tools/__init__.py
+++ b/src/kpm_tools/__init__.py
@@ -1 +1,64 @@
 """KPM Tools."""
+# flake8: noqa
+from .bloch import _hopping_distance
+from .bloch import separate_bloch_components
+from .bloch import wrap_distance
+from .bloch import wrap_velocity
+from .bloch import wraparound_by_parts
+from .common import expectation_value
+from .common import identity_operator
+from .common import local_expectation_value
+from .common import position_operator
+from .common import standard_gaussian
+from .common import trace_orbs
+from .common import trace_unit_cell
+from .common import transparent_cmap
+from .common import uniform
+from .evolution import _max_num_moments
+from .evolution import coef_evo
+from .evolution import evolve_observable
+from .evolution import evolve_vectors
+from .evolution import evolve_vectors_time_steps
+from .hamiltonians import haldane_obc
+from .hamiltonians import haldane_pbc
+from .hamiltonians import qhe_obc
+from .hamiltonians import qhe_pbc
+from .kpm_funcs import chebysev_polynomial_series
+from .kpm_funcs import chern_number
+from .kpm_funcs import chern_number_pbc
+from .kpm_funcs import coef_delta_function
+from .kpm_funcs import coef_greens_function
+from .kpm_funcs import coef_greens_function_a
+from .kpm_funcs import coef_greens_function_prime
+from .kpm_funcs import coef_greens_function_prime_a
+from .kpm_funcs import coef_greens_function_prime_r
+from .kpm_funcs import coef_greens_function_r
+from .kpm_funcs import coef_projector_function
+from .kpm_funcs import coef_projector_function_unoccupied
+from .kpm_funcs import conductivity
+from .kpm_funcs import delta_function
+from .kpm_funcs import greens_function
+from .kpm_funcs import longitudinal_conductivity
+from .kpm_funcs import projector_operator
+from .kpm_generator import GeneralVectorExpansion
+from .kpm_generator import IteratorKPM
+from .kpm_generator import SpectralDensityIterator
+from .kpm_generator import _BaseKPM
+from .kpm_generator import _dagger
+from .kpm_generator import _normalize_num_moments
+from .kpm_generator import _normalize_operator
+from .kpm_generator import _rescale
+from .kpm_generator import concatenator
+from .kpm_generator import general_function
+from .kpm_generator import multiprocess_update
+from .kpm_generator import process_update
+from .kpm_generator import thread_update
+from .plotting import Arrow3D
+from .plotting import plot_arrows3d
+from .plotting import plot_kwant
+from .plotting import plot_neighbors
+from .plotting import plot_vectors
+from .tiles import TileKVectors
+from .tiles import _TileVectors
+from .tiles import momentum_to_ts
+from .tiles import tile_random_kvectors
diff --git a/src/kpm_tools/bloch.py b/src/kpm_tools/bloch.py
index 11cf440..acfa58b 100644
--- a/src/kpm_tools/bloch.py
+++ b/src/kpm_tools/bloch.py
@@ -9,12 +9,6 @@
 
 import numpy as np
 import tinyarray as ta
-
-import kwant
-
-
-if kwant.__version__ < "1.5":
-    raise ImportError("This module does not work with versions of Kwant " "<= 1.4.2.")
 from kwant._common import get_parameters
 from kwant._common import memoize
 from kwant.builder import Builder
diff --git a/src/kpm_tools/common.py b/src/kpm_tools/common.py
index 0b6c6bd..32784be 100644
--- a/src/kpm_tools/common.py
+++ b/src/kpm_tools/common.py
@@ -2,13 +2,12 @@
 
 from collections import OrderedDict
 
+import kwant
 import numpy as np
 from matplotlib.colors import ListedColormap
 from matplotlib.pylab import cm
 from scipy.sparse import diags
 
-import kwant
-
 
 def identity_operator(x):
     """Return argument."""
diff --git a/src/kpm_tools/hamiltonians.py b/src/kpm_tools/hamiltonians.py
index 9bb61d4..421ca71 100644
--- a/src/kpm_tools/hamiltonians.py
+++ b/src/kpm_tools/hamiltonians.py
@@ -4,9 +4,8 @@
 """
 from itertools import product
 
-import numpy as np
-
 import kwant
+import numpy as np
 from kwant import HoppingKind
 
 
diff --git a/src/kpm_tools/kpm_generator.py b/src/kpm_tools/kpm_generator.py
index a89787c..0a53368 100644
--- a/src/kpm_tools/kpm_generator.py
+++ b/src/kpm_tools/kpm_generator.py
@@ -6,14 +6,14 @@
 from math import ceil
 from multiprocessing import Pool
 
+import kwant
 import numpy as np
-from common import identity_operator
+from kwant.operator import _LocalOperator
 from scipy.sparse import csr_matrix
 from scipy.sparse import identity
 from scipy.sparse.linalg import eigsh
 
-import kwant
-from kwant.operator import _LocalOperator
+from .common import identity_operator
 
 
 DEFAULT_MOMENTS = 100
@@ -23,20 +23,16 @@ class IteratorKPM:
     r"""Iterator of KPM vectors.
 
     This iterator yields vectors as
-    .. math::
-      T_n(H) \\rvert v \\langle
-
-    for vectors :math:`\\rvert v \\langle` in ``vectors``,
-    for 'n' in '[0, max_moments]'.
-
 
+    `T_n(H) | v >`
 
+    for vectors `| v >` in `vectors`,
+    for 'n' in '[0, max_moments]'.
 
     Notes
     -----
-    Returns a sequence of expanded vectors of shape ``(M, N)``.
-    If the input is a vector then ``M=1``.
-
+    Returns a sequence of expanded vectors of shape `(M, N)`.
+    If the input is a vector then `M=1`.
     """
 
     def __init__(self, ham, vectors, max_moments=None, operator=None, num_threads=None):
@@ -380,11 +376,11 @@ def add_moments(self, num_moments=None, *, energy_resolution=None):
         ----------
         num_moments: positive int
             The number of Chebyshev moments to add. Mutually
-            exclusive with ``energy_resolution``.
+            exclusive with `energy_resolution`.
         energy_resolution: positive float, optional
             Features wider than this resolution are visible
             in the spectral density. Mutually exclusive with
-            ``num_moments``.
+            `num_moments`.
 
         """
         new_moments = _normalize_num_moments(num_moments, energy_resolution, self._a)
@@ -488,11 +484,11 @@ def add_moments(self, num_moments=None, *, energy_resolution=None):
         ----------
         num_moments: positive int
             The number of Chebyshev moments to add. Mutually
-            exclusive with ``energy_resolution``.
+            exclusive with `energy_resolution`.
         energy_resolution: positive float, optional
             Features wider than this resolution are visible
             in the spectral density. Mutually exclusive with
-            ``num_moments``.
+            `num_moments`.
 
         """
         new_moments = _normalize_num_moments(num_moments, energy_resolution, self._a)
diff --git a/src/kpm_tools/tiles.py b/src/kpm_tools/tiles.py
new file mode 100644
index 0000000..57c1d5a
--- /dev/null
+++ b/src/kpm_tools/tiles.py
@@ -0,0 +1,199 @@
+"""Generate tile vectors to compute the effective Bloch spectrum."""
+
+from collections import defaultdict
+
+import kwant
+import numpy as np
+from kwant._common import ensure_rng
+
+
+def momentum_to_ts(k, ts):
+    """Transform 'k' to the reciprocal lattice basis."""
+    # columns of B are lattice vectors
+    _b = np.array(ts.periods).T
+    # columns of A are reciprocal lattice vectors
+    _a = np.linalg.pinv(_b).T
+    k, residuals = np.linalg.lstsq(_a, k, rcond=None)[:2]
+    if np.any(abs(residuals) > 1e-7):
+        raise RuntimeError(
+            "Requested momentum doesn't correspond to "
+            "any lattice momentum or TranslationalSymmetry."
+        )
+    return k
+
+
+class _TileVectors:
+    def __init__(self, sites, tile, ts, *, check=True):
+        """Construct an iterator over the sites in 'tile'.
+
+        The iterator returns one vector for every orbital for every site
+        in the fundamental domain (FD) of 'tile'.
+        Each vector contains the orbital and its copies on the other
+        tiles.
+
+        Parameters
+        ----------
+        sites : iterable of `kwant.builder.Site` instances
+            The sites of the system. Should belong to a lattice that
+            is commensurate with both 'tile' and 'ts'.
+        tile :  instance of `~kwant.TranslationalSymmetry`
+            Translational symmetry of the tiles within the supercell.
+            The tiles should cover the supercell 'ts'.
+        ts : instance of `~kwant.TranslationalSymmetry` or
+            Translational symmetry of the supercell.
+        check : bool, default to 'True'
+            Check that 'tile' covers the 'ts'. This is done by checking
+            that the translational symmetry 'tile' has the subgroup 'ts'.
+            If 'False', do not check anything.
+
+        """
+        if check:
+            # This does not yet checks
+            # for lattice commensuration with tile nor TS.
+            # The check is done when calling `tile.to_fd()`.
+            if not tile.has_subgroup(ts):
+                raise ValueError(f"The tile {tile} has no subgroup {ts}.")
+
+        self.tile = tile
+        self.ts = ts
+
+        # assume all norbs are the same
+        self.norbs = sites[0].family.norbs
+        self.tot_norbs = self.norbs * len(sites)
+
+        self.site_replicas = defaultdict(list)
+        for idx, site in enumerate(sites):
+            base = tile.to_fd(site)
+            self.site_replicas[base].append(idx)
+
+        self.keys = list(self.site_replicas.keys())
+
+        # these counters can be reset to default values
+        self.idx = 0
+        self.orb_idx = 0
+
+    def _reset_count(self):
+        self.idx = 0
+        self.orb_idx = 0
+
+    def __len__(self):
+        return self.norbs * len(self.keys)
+
+    def __iter__(self):
+        return self
+
+    @property
+    def _norm(self):
+        return 1 / len(self)
+
+    def __next__(self):
+        try:
+            keys = self.keys[self.idx]
+        except IndexError as err:
+            raise StopIteration(
+                "Too many vectors requested " "from this generator."
+            ) from err
+
+        site_indices = np.array(self.site_replicas[keys], dtype=int)
+        orbs = site_indices * self.norbs + self.orb_idx
+
+        self.orb_idx += 1
+        # bump indices if needed
+        if self.orb_idx >= self.norbs:
+            self.idx += 1
+            self.orb_idx = 0
+
+        return site_indices, orbs
+
+
+class TileKVectors(_TileVectors):
+    """Construct k-vectors over the sites in 'tile'.
+
+    The iterator returns one vector for every orbital for every site
+    in the fundamental domain (FD) of 'tile'.
+    Each vector contains the orbital and its copies on the other
+    tiles, multiplied with the phase given by the position and the
+    'k' vector.
+
+    Parameters
+    ----------
+    k : pair or triplet floats
+        The vector in k-space. Should have the same dimensions as the
+        positions of the 'syst'.
+    tile :  instance of `~kwant.TranslationalSymmetry`
+        Translational symmetry of the tiles within the supercell.
+        The tiles should cover the supercell defined by the wrapped
+        symmetry of 'syst'.
+    syst : instance of `~kwant.system.FiniteSystem`
+        System with wrapped symmetry.
+
+    Note
+    ----
+    'kwargs' are passed to `_TileVectors`.
+
+    """
+
+    def __init__(self, k, tile, syst, **kwargs):
+        """Initialize tiles."""
+        self.k = k
+        self.positions = np.array([s.pos for s in syst.sites])
+
+        if isinstance(syst.symmetry, kwant.builder.NoSymmetry):
+            ts = syst._wrapped_symmetry
+        else:
+            ts = syst.symmetry
+
+        super().__init__(syst.sites, tile, ts, **kwargs)
+
+    def _get_orbs_and_phase(self):
+        site_indices, orbs = super().__next__()
+        pos = self.positions[site_indices]
+        phase = np.exp(1j * pos.dot(self.k))
+        return orbs, phase
+
+    def __next__(
+        self,
+    ):
+        """Get the next vector."""
+        orbs, phase = self._get_orbs_and_phase()
+
+        vector = np.zeros(self.tot_norbs, dtype=complex)
+        vector[orbs] = phase
+        return vector
+
+
+def tile_random_kvectors(k, tile, syst, *, rng=0, **kwargs):
+    """Construct an iterator of random phase vectors inside 'tile'.
+
+    The other copies of 'tile' will have a phase relative to the base
+    tile. That phase is given by 'k' and the positions of the sites in
+    the 'syst'. Check 'TileKVectors' for details.
+
+    Parameters
+    ----------
+    k : pair or triplet floats
+        The vector in k-space. Should have the same dimensions as the
+        positions of the 'syst'.
+    tile :  instance of `~kwant.TranslationalSymmetry`
+        Translational symmetry of the tiles within the supercell.
+        The tiles should cover the supercell defined by the wrapped
+        symmetry of 'syst'.
+    syst : instance of `~kwant.system.FiniteSystem`
+        System with wrapped symmetry.
+    rng : int, float, or string, default to 'int(0)'
+        Initial seed for the random vectors to ensure reproducibility.
+
+    Note
+    ----
+    'kwargs' are passed to `_TileVectors`.
+    """
+    rng = ensure_rng(rng)
+    tile_kvecs = TileKVectors(k, tile, syst, **kwargs)
+
+    while True:
+        tile_kvecs._reset_count()
+        vector = np.zeros(tile_kvecs.tot_norbs, dtype=complex)
+        for _ in range(len(tile_kvecs)):
+            orbs, phase = tile_kvecs._get_orbs_and_phase()
+            vector[orbs] = phase * np.exp(2j * np.pi * rng.random_sample())
+        yield vector