input_cluster_1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook is a continuation of cluster_deep_dive.ipynb."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. 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      "application/vnd.bokehjs_load.v0+json": ""
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import important stuff here\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "\n",
    "import bokeh\n",
    "import bokeh.palettes\n",
    "from bokeh.plotting import figure, show, output_notebook\n",
    "output_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/jspanel.js', 'https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal.js', 'https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip.js', 'https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint.js', 'https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout.js', 'https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu.js', 'https://cdn.holoviz.org/panel/1.3.8/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/1.3.8/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/1.3.8/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-3.2.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.2.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.2.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.2.1.min.js\", \"https://cdn.holoviz.org/panel/1.3.8/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));",
      "application/vnd.holoviews_load.v0+json": ""
     },
     "metadata": {},
     "output_type": "display_data"
    },
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     "data": {
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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 || ((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",
      "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='p1066'>\n",
       "  <div id=\"e9d59f0e-45a0-45e4-a0e4-99e4ff6437b1\" data-root-id=\"p1066\" style=\"display: contents;\"></div>\n",
       "</div>\n",
       "<script type=\"application/javascript\">(function(root) {\n",
       "  var docs_json = {\"c7e5aeea-7ce3-48e7-b0f2-9d0f36b1936e\":{\"version\":\"3.2.1\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"panel.models.browser.BrowserInfo\",\"id\":\"p1066\"},{\"type\":\"object\",\"name\":\"panel.models.comm_manager.CommManager\",\"id\":\"p1067\",\"attributes\":{\"plot_id\":\"p1066\",\"comm_id\":\"8f92842029a54daabf5e2fa337d561ef\",\"client_comm_id\":\"7be15c13cb39477e8dd034b2ba990fb1\"}}],\"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\":\"copy_to_clipboard1\",\"properties\":[{\"name\":\"fill\",\"kind\":\"Any\",\"default\":\"none\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":null}]},{\"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\":\"c7e5aeea-7ce3-48e7-b0f2-9d0f36b1936e\",\"roots\":{\"p1066\":\"e9d59f0e-45a0-45e4-a0e4-99e4ff6437b1\"},\"root_ids\":[\"p1066\"]}];\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": "p1066"
      }
     },
     "output_type": "display_data"
    },
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       "       style='height:15px; border-radius:12px; display: inline-block; float: left'></img>\n",
       "  \n",
       "\n",
       "\n",
       "\n",
       "\n",
       "</div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import holoviews as hv\n",
    "from holoviews import opts, dim\n",
    "hv.extension('bokeh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       "        .bk-notebook-logo {\n",
       "            display: block;\n",
       "            width: 20px;\n",
       "            height: 20px;\n",
       "            background-image: url(data:image/png;base64,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);\n",
       "        }\n",
       "    </style>\n",
       "    <div>\n",
       "        <a href=\"https://bokeh.org\" target=\"_blank\" class=\"bk-notebook-logo\"></a>\n",
       "        <span id=\"d384b3a3-b638-43f1-b2d7-3c7252962ff2\">Loading BokehJS ...</span>\n",
       "    </div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\nconst JS_MIME_TYPE = 'application/javascript';\n  const HTML_MIME_TYPE = 'text/html';\n  const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n  const CLASS_NAME = 'output_bokeh rendered_html';\n\n  /**\n   * Render data to the DOM node\n   */\n  function render(props, node) {\n    const script = document.createElement(\"script\");\n    node.appendChild(script);\n  }\n\n  /**\n   * Handle when an output is cleared or removed\n   */\n  function handleClearOutput(event, handle) {\n    const cell = handle.cell;\n\n    const id = cell.output_area._bokeh_element_id;\n    const server_id = cell.output_area._bokeh_server_id;\n    // Clean up Bokeh references\n    if (id != null && id in Bokeh.index) {\n      Bokeh.index[id].model.document.clear();\n      delete Bokeh.index[id];\n    }\n\n    if (server_id !== undefined) {\n      // Clean up Bokeh references\n      const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n      cell.notebook.kernel.execute(cmd_clean, {\n        iopub: {\n          output: function(msg) {\n            const id = msg.content.text.trim();\n            if (id in Bokeh.index) {\n              Bokeh.index[id].model.document.clear();\n              delete Bokeh.index[id];\n            }\n          }\n        }\n      });\n      // Destroy server and session\n      const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n      cell.notebook.kernel.execute(cmd_destroy);\n    }\n  }\n\n  /**\n   * Handle when a new output is added\n   */\n  function handleAddOutput(event, handle) {\n    const output_area = handle.output_area;\n    const output = handle.output;\n\n    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n    if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n      return\n    }\n\n    const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n\n    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n      // store reference to embed id on output_area\n      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n    }\n    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n      const bk_div = document.createElement(\"div\");\n      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n      const script_attrs = bk_div.children[0].attributes;\n      for (let i = 0; i < script_attrs.length; i++) {\n        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n        toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\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  function register_renderer(events, OutputArea) {\n\n    function append_mime(data, metadata, element) {\n      // create a DOM node to render to\n      const 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      const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n      render(props, toinsert[toinsert.length - 1]);\n      element.append(toinsert);\n      return toinsert\n    }\n\n    /* Handle when an output is cleared or removed */\n    events.on('clear_output.CodeCell', handleClearOutput);\n    events.on('delete.Cell', handleClearOutput);\n\n    /* Handle when a new output is added */\n    events.on('output_added.OutputArea', handleAddOutput);\n\n    /**\n     * Register the mime type and append_mime function with output_area\n     */\n    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n      /* Is output safe? */\n      safe: true,\n      /* Index of renderer in `output_area.display_order` */\n      index: 0\n    });\n  }\n\n  // register the mime type if in Jupyter Notebook environment and previously unregistered\n  if (root.Jupyter !== undefined) {\n    const events = require('base/js/events');\n    const OutputArea = require('notebook/js/outputarea').OutputArea;\n\n    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n      register_renderer(events, OutputArea);\n    }\n  }\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"d384b3a3-b638-43f1-b2d7-3c7252962ff2\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\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, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\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 == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\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\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\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    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\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  };\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  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.2.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.2.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.2.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.2.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.2.1.min.js\", \"https://unpkg.com/@holoviz/panel@1.3.8/dist/panel.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\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    } else if (force !== true) {\n      const cell = $(document.getElementById(\"d384b3a3-b638-43f1-b2d7-3c7252962ff2\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));",
      "application/vnd.bokehjs_load.v0+json": ""
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import hvplot.pandas\n",
    "\n",
    "import bokeh.palettes\n",
    "from bokeh.plotting import figure, show, output_notebook\n",
    "output_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.1.0'"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from neuprint import Client\n",
    "# remove my token before making notebook public\n",
    "c = Client('neuprint.janelia.org', dataset='hemibrain:v1.2.1', token='eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJlbWFpbCI6ImdnMjExNEBjb2x1bWJpYS5lZHUiLCJsZXZlbCI6Im5vYXV0aCIsImltYWdlLXVybCI6Imh0dHBzOi8vbGgzLmdvb2dsZXVzZXJjb250ZW50LmNvbS9hLS9BT2gxNEdpb1lJLUVPLWdidGxPRTh6SmQ0eF9ZQ1Y4ZHF0YVFjWGlHeG5CMz1zOTYtYz9zej01MD9zej01MCIsImV4cCI6MTgxMDUyOTYzNH0.jv9eR0SH5RhfBdXrtp4r-dDFOhcsT8GBbE4v69ysCKs') \n",
    "c.fetch_version()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# body IDs of oviINs from Neuprint\n",
    "oviINr_bodyID = 423101189\n",
    "oviINl_bodyID = 485934965"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>key</th>\n",
       "      <th>0.0</th>\n",
       "      <th>0.05</th>\n",
       "      <th>0.1</th>\n",
       "      <th>0.5</th>\n",
       "      <th>0.75</th>\n",
       "      <th>1.0</th>\n",
       "      <th>instance</th>\n",
       "      <th>celltype</th>\n",
       "      <th>...</th>\n",
       "      <th>status</th>\n",
       "      <th>cropped</th>\n",
       "      <th>statusLabel</th>\n",
       "      <th>cellBodyFiber</th>\n",
       "      <th>somaRadius</th>\n",
       "      <th>somaLocation</th>\n",
       "      <th>roiInfo</th>\n",
       "      <th>notes</th>\n",
       "      <th>inputRois</th>\n",
       "      <th>outputRois</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>CL229_R</td>\n",
       "      <td>CL229</td>\n",
       "      <td>...</td>\n",
       "      <td>Traced</td>\n",
       "      <td>False</td>\n",
       "      <td>Roughly traced</td>\n",
       "      <td>PDM19</td>\n",
       "      <td>301.0</td>\n",
       "      <td>[23044, 14981, 11600]</td>\n",
       "      <td>{'INP': {'pre': 87, 'post': 351, 'downstream':...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>['EPA(R)', 'GOR(R)', 'IB', 'ICL(R)', 'INP', 'S...</td>\n",
       "      <td>['GOR(R)', 'IB', 'ICL(R)', 'INP', 'SCL(R)', 'S...</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1005952640</td>\n",
       "      <td>2</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>IB058_R</td>\n",
       "      <td>IB058</td>\n",
       "      <td>...</td>\n",
       "      <td>Traced</td>\n",
       "      <td>False</td>\n",
       "      <td>Roughly traced</td>\n",
       "      <td>PVL20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'INP': {'pre': 464, 'post': 1327, 'downstream...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>['ATL(R)', 'IB', 'ICL(R)', 'INP', 'PLP(R)', 'S...</td>\n",
       "      <td>['ATL(R)', 'IB', 'ICL(R)', 'INP', 'PLP(R)', 'S...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1006928515</td>\n",
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       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>CL300_R</td>\n",
       "      <td>CL300</td>\n",
       "      <td>...</td>\n",
       "      <td>Traced</td>\n",
       "      <td>False</td>\n",
       "      <td>Roughly traced</td>\n",
       "      <td>PVL13</td>\n",
       "      <td>236.0</td>\n",
       "      <td>[12083, 10523, 16816]</td>\n",
       "      <td>{'INP': {'pre': 79, 'post': 126, 'downstream':...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>['ATL(R)', 'IB', 'ICL(R)', 'INP', 'SCL(R)', 'S...</td>\n",
       "      <td>['ATL(R)', 'IB', 'ICL(R)', 'INP', 'SCL(R)', 'S...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1007260806</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>CL301_R</td>\n",
       "      <td>CL301</td>\n",
       "      <td>...</td>\n",
       "      <td>Traced</td>\n",
       "      <td>False</td>\n",
       "      <td>Roughly traced</td>\n",
       "      <td>PVL13</td>\n",
       "      <td>236.0</td>\n",
       "      <td>[13524, 10108, 16480]</td>\n",
       "      <td>{'INP': {'pre': 40, 'post': 128, 'downstream':...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>['GOR(R)', 'IB', 'ICL(R)', 'INP', 'PLP(R)', 'S...</td>\n",
       "      <td>['IB', 'ICL(R)', 'INP', 'PLP(R)', 'SCL(R)', 'S...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1008024276</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>FB5N_R</td>\n",
       "      <td>FB5N</td>\n",
       "      <td>...</td>\n",
       "      <td>Traced</td>\n",
       "      <td>False</td>\n",
       "      <td>Roughly traced</td>\n",
       "      <td>AVM08</td>\n",
       "      <td>472.5</td>\n",
       "      <td>[19178, 29711, 37312]</td>\n",
       "      <td>{'SNP(L)': {'post': 5, 'upstream': 5, 'mito': ...</td>\n",
       "      <td>SMPCREFB5_4</td>\n",
       "      <td>['CRE(-ROB,-RUB)(R)', 'CRE(R)', 'CX', 'FB', 'F...</td>\n",
       "      <td>['CRE(-ROB,-RUB)(R)', 'CRE(R)', 'CX', 'FB', 'F...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2506</th>\n",
       "      <td>987273073</td>\n",
       "      <td>2507</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>409</td>\n",
       "      <td>604</td>\n",
       "      <td>629</td>\n",
       "      <td>(PVL05)_L</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Traced</td>\n",
       "      <td>False</td>\n",
       "      <td>Roughly traced</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'SNP(R)': {'pre': 65, 'post': 52, 'downstream...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>['CRE(-ROB,-RUB)(R)', 'CRE(-RUB)(L)', 'CRE(L)'...</td>\n",
       "      <td>['CRE(-ROB,-RUB)(R)', 'CRE(-RUB)(L)', 'CRE(L)'...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2507</th>\n",
       "      <td>987842109</td>\n",
       "      <td>2508</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>23</td>\n",
       "      <td>533</td>\n",
       "      <td>780</td>\n",
       "      <td>815</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Orphan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Orphan hotknife</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'SNP(R)': {'pre': 2, 'post': 13, 'downstream'...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>['SMP(R)', 'SNP(R)']</td>\n",
       "      <td>['SMP(R)', 'SNP(R)']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2508</th>\n",
       "      <td>988567837</td>\n",
       "      <td>2509</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>58</td>\n",
       "      <td>63</td>\n",
       "      <td>FB4G_R</td>\n",
       "      <td>FB4G</td>\n",
       "      <td>...</td>\n",
       "      <td>Traced</td>\n",
       "      <td>False</td>\n",
       "      <td>Roughly traced</td>\n",
       "      <td>AVM08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'SNP(R)': {'pre': 6, 'post': 73, 'downstream'...</td>\n",
       "      <td>CRELALFB4_3</td>\n",
       "      <td>['CRE(-ROB,-RUB)(R)', 'CRE(R)', 'CX', 'FB', 'F...</td>\n",
       "      <td>['CRE(-ROB,-RUB)(R)', 'CRE(R)', 'CX', 'FB', 'F...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2509</th>\n",
       "      <td>988909130</td>\n",
       "      <td>2510</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>389</td>\n",
       "      <td>559</td>\n",
       "      <td>572</td>\n",
       "      <td>FB5V_R</td>\n",
       "      <td>FB5V</td>\n",
       "      <td>...</td>\n",
       "      <td>Traced</td>\n",
       "      <td>False</td>\n",
       "      <td>Roughly traced</td>\n",
       "      <td>AVM10</td>\n",
       "      <td>296.5</td>\n",
       "      <td>[13226, 32024, 18600]</td>\n",
       "      <td>{'SNP(R)': {'pre': 1, 'post': 28, 'downstream'...</td>\n",
       "      <td>CRELALFB5</td>\n",
       "      <td>['AB(R)', 'CRE(-ROB,-RUB)(R)', 'CRE(R)', 'CX',...</td>\n",
       "      <td>['CRE(-ROB,-RUB)(R)', 'CRE(R)', 'CX', 'FB', 'F...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2510</th>\n",
       "      <td>989228019</td>\n",
       "      <td>2511</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>389</td>\n",
       "      <td>559</td>\n",
       "      <td>572</td>\n",
       "      <td>FB5V_R</td>\n",
       "      <td>FB5V</td>\n",
       "      <td>...</td>\n",
       "      <td>Traced</td>\n",
       "      <td>False</td>\n",
       "      <td>Roughly traced</td>\n",
       "      <td>AVM10</td>\n",
       "      <td>296.5</td>\n",
       "      <td>[12516, 33587, 18648]</td>\n",
       "      <td>{'SNP(R)': {'pre': 1, 'post': 45, 'downstream'...</td>\n",
       "      <td>CRELALFB5</td>\n",
       "      <td>['CRE(-ROB,-RUB)(R)', 'CRE(R)', 'CX', 'FB', 'F...</td>\n",
       "      <td>['CRE(-ROB,-RUB)(R)', 'CRE(R)', 'CX', 'FB', 'F...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2511 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              id   key  0.0  0.05  0.1  0.5  0.75  1.0   instance celltype  \\\n",
       "0     1003215282     1    1     1    1    1     1    1    CL229_R    CL229   \n",
       "1     1005952640     2    2     1    1    2     2    2    IB058_R    IB058   \n",
       "2     1006928515     3    1     1    1    3     3    3    CL300_R    CL300   \n",
       "3     1007260806     4    2     1    1    4     4    4    CL301_R    CL301   \n",
       "4     1008024276     5    3     2    2    5     5    5     FB5N_R     FB5N   \n",
       "...          ...   ...  ...   ...  ...  ...   ...  ...        ...      ...   \n",
       "2506   987273073  2507    3     8    8  409   604  629  (PVL05)_L      NaN   \n",
       "2507   987842109  2508    3     9   23  533   780  815        NaN      NaN   \n",
       "2508   988567837  2509    2     3    4   16    58   63     FB4G_R     FB4G   \n",
       "2509   988909130  2510    2     3    4  389   559  572     FB5V_R     FB5V   \n",
       "2510   989228019  2511    2     3    4  389   559  572     FB5V_R     FB5V   \n",
       "\n",
       "      ...  status  cropped      statusLabel  cellBodyFiber  somaRadius  \\\n",
       "0     ...  Traced    False   Roughly traced          PDM19       301.0   \n",
       "1     ...  Traced    False   Roughly traced          PVL20         NaN   \n",
       "2     ...  Traced    False   Roughly traced          PVL13       236.0   \n",
       "3     ...  Traced    False   Roughly traced          PVL13       236.0   \n",
       "4     ...  Traced    False   Roughly traced          AVM08       472.5   \n",
       "...   ...     ...      ...              ...            ...         ...   \n",
       "2506  ...  Traced    False   Roughly traced            NaN         NaN   \n",
       "2507  ...  Orphan      NaN  Orphan hotknife            NaN         NaN   \n",
       "2508  ...  Traced    False   Roughly traced          AVM08         NaN   \n",
       "2509  ...  Traced    False   Roughly traced          AVM10       296.5   \n",
       "2510  ...  Traced    False   Roughly traced          AVM10       296.5   \n",
       "\n",
       "               somaLocation  \\\n",
       "0     [23044, 14981, 11600]   \n",
       "1                       NaN   \n",
       "2     [12083, 10523, 16816]   \n",
       "3     [13524, 10108, 16480]   \n",
       "4     [19178, 29711, 37312]   \n",
       "...                     ...   \n",
       "2506                    NaN   \n",
       "2507                    NaN   \n",
       "2508                    NaN   \n",
       "2509  [13226, 32024, 18600]   \n",
       "2510  [12516, 33587, 18648]   \n",
       "\n",
       "                                                roiInfo        notes  \\\n",
       "0     {'INP': {'pre': 87, 'post': 351, 'downstream':...          NaN   \n",
       "1     {'INP': {'pre': 464, 'post': 1327, 'downstream...          NaN   \n",
       "2     {'INP': {'pre': 79, 'post': 126, 'downstream':...          NaN   \n",
       "3     {'INP': {'pre': 40, 'post': 128, 'downstream':...          NaN   \n",
       "4     {'SNP(L)': {'post': 5, 'upstream': 5, 'mito': ...  SMPCREFB5_4   \n",
       "...                                                 ...          ...   \n",
       "2506  {'SNP(R)': {'pre': 65, 'post': 52, 'downstream...          NaN   \n",
       "2507  {'SNP(R)': {'pre': 2, 'post': 13, 'downstream'...          NaN   \n",
       "2508  {'SNP(R)': {'pre': 6, 'post': 73, 'downstream'...  CRELALFB4_3   \n",
       "2509  {'SNP(R)': {'pre': 1, 'post': 28, 'downstream'...    CRELALFB5   \n",
       "2510  {'SNP(R)': {'pre': 1, 'post': 45, 'downstream'...    CRELALFB5   \n",
       "\n",
       "                                              inputRois  \\\n",
       "0     ['EPA(R)', 'GOR(R)', 'IB', 'ICL(R)', 'INP', 'S...   \n",
       "1     ['ATL(R)', 'IB', 'ICL(R)', 'INP', 'PLP(R)', 'S...   \n",
       "2     ['ATL(R)', 'IB', 'ICL(R)', 'INP', 'SCL(R)', 'S...   \n",
       "3     ['GOR(R)', 'IB', 'ICL(R)', 'INP', 'PLP(R)', 'S...   \n",
       "4     ['CRE(-ROB,-RUB)(R)', 'CRE(R)', 'CX', 'FB', 'F...   \n",
       "...                                                 ...   \n",
       "2506  ['CRE(-ROB,-RUB)(R)', 'CRE(-RUB)(L)', 'CRE(L)'...   \n",
       "2507                               ['SMP(R)', 'SNP(R)']   \n",
       "2508  ['CRE(-ROB,-RUB)(R)', 'CRE(R)', 'CX', 'FB', 'F...   \n",
       "2509  ['AB(R)', 'CRE(-ROB,-RUB)(R)', 'CRE(R)', 'CX',...   \n",
       "2510  ['CRE(-ROB,-RUB)(R)', 'CRE(R)', 'CX', 'FB', 'F...   \n",
       "\n",
       "                                             outputRois  \n",
       "0     ['GOR(R)', 'IB', 'ICL(R)', 'INP', 'SCL(R)', 'S...  \n",
       "1     ['ATL(R)', 'IB', 'ICL(R)', 'INP', 'PLP(R)', 'S...  \n",
       "2     ['ATL(R)', 'IB', 'ICL(R)', 'INP', 'SCL(R)', 'S...  \n",
       "3     ['IB', 'ICL(R)', 'INP', 'PLP(R)', 'SCL(R)', 'S...  \n",
       "4     ['CRE(-ROB,-RUB)(R)', 'CRE(R)', 'CX', 'FB', 'F...  \n",
       "...                                                 ...  \n",
       "2506  ['CRE(-ROB,-RUB)(R)', 'CRE(-RUB)(L)', 'CRE(L)'...  \n",
       "2507                               ['SMP(R)', 'SNP(R)']  \n",
       "2508  ['CRE(-ROB,-RUB)(R)', 'CRE(R)', 'CX', 'FB', 'F...  \n",
       "2509  ['CRE(-ROB,-RUB)(R)', 'CRE(R)', 'CX', 'FB', 'F...  \n",
       "2510  ['CRE(-ROB,-RUB)(R)', 'CRE(R)', 'CX', 'FB', 'F...  \n",
       "\n",
       "[2511 rows x 26 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load the oviINr input connectome\n",
    "ovi_in_node_df = pd.read_csv('ovi_preprocessed/preprocessed_inputs-v1.2.1/preprocessed_nodes.csv')\n",
    "ovi_in_node_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to load the saved dataframe of oviINr presynapses\n",
    "ovi_pre_syns = pd.read_csv('oviIN_specs/ovi_pre_syns.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Input cluster 1\n",
    "Sankey of just cluster 1\n",
    "Filter out singletons and plot synaptic org at finer res of cluster 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>key</th>\n",
       "      <th>0.0</th>\n",
       "      <th>0.05</th>\n",
       "      <th>0.1</th>\n",
       "      <th>0.5</th>\n",
       "      <th>0.75</th>\n",
       "      <th>1.0</th>\n",
       "      <th>instance</th>\n",
       "      <th>celltype</th>\n",
       "      <th>...</th>\n",
       "      <th>status</th>\n",
       "      <th>cropped</th>\n",
       "      <th>statusLabel</th>\n",
       "      <th>cellBodyFiber</th>\n",
       "      <th>somaRadius</th>\n",
       "      <th>somaLocation</th>\n",
       "      <th>roiInfo</th>\n",
       "      <th>notes</th>\n",
       "      <th>inputRois</th>\n",
       "      <th>outputRois</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1003215282</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>CL229_R</td>\n",
       "      <td>CL229</td>\n",
       "      <td>...</td>\n",
       "      <td>Traced</td>\n",
       "      <td>False</td>\n",
       "      <td>Roughly traced</td>\n",
       "      <td>PDM19</td>\n",
       "      <td>301.0</td>\n",
       "      <td>[23044, 14981, 11600]</td>\n",
       "      <td>{'INP': {'pre': 87, 'post': 351, 'downstream':...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>['EPA(R)', 'GOR(R)', 'IB', 'ICL(R)', 'INP', 'S...</td>\n",
       "      <td>['GOR(R)', 'IB', 'ICL(R)', 'INP', 'SCL(R)', 'S...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1006928515</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>CL300_R</td>\n",
       "      <td>CL300</td>\n",
       "      <td>...</td>\n",
       "      <td>Traced</td>\n",
       "      <td>False</td>\n",
       "      <td>Roughly traced</td>\n",
       "      <td>PVL13</td>\n",
       "      <td>236.0</td>\n",
       "      <td>[12083, 10523, 16816]</td>\n",
       "      <td>{'INP': {'pre': 79, 'post': 126, 'downstream':...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>['ATL(R)', 'IB', 'ICL(R)', 'INP', 'SCL(R)', 'S...</td>\n",
       "      <td>['ATL(R)', 'IB', 'ICL(R)', 'INP', 'SCL(R)', 'S...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1008085163</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Traced</td>\n",
       "      <td>True</td>\n",
       "      <td>Leaves</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'SNP(L)': {'pre': 10, 'post': 9, 'downstream'...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>['ATL(L)', 'IB', 'INP', 'SMP(L)', 'SNP(L)']</td>\n",
       "      <td>['ATL(L)', 'IB', 'INP', 'SMP(L)', 'SNP(L)']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1008966615</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>CL300_R</td>\n",
       "      <td>CL300</td>\n",
       "      <td>...</td>\n",
       "      <td>Traced</td>\n",
       "      <td>False</td>\n",
       "      <td>Roughly traced</td>\n",
       "      <td>PVL13</td>\n",
       "      <td>236.0</td>\n",
       "      <td>[13492, 9603, 16400]</td>\n",
       "      <td>{'INP': {'pre': 42, 'post': 132, 'downstream':...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>['IB', 'ICL(R)', 'INP', 'PLP(R)', 'SCL(R)', 'S...</td>\n",
       "      <td>['IB', 'ICL(R)', 'INP', 'PLP(R)', 'VLNP(R)']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1011378821</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Orphan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Orphan hotknife</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'SNP(R)': {'pre': 6, 'post': 3, 'downstream':...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>['SMP(R)', 'SNP(R)']</td>\n",
       "      <td>['SMP(R)', 'SNP(R)']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2464</th>\n",
       "      <td>976964834</td>\n",
       "      <td>2465</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>246</td>\n",
       "      <td>59</td>\n",
       "      <td>110</td>\n",
       "      <td>IB022_R</td>\n",
       "      <td>IB022</td>\n",
       "      <td>...</td>\n",
       "      <td>Traced</td>\n",
       "      <td>False</td>\n",
       "      <td>Roughly traced</td>\n",
       "      <td>PDM09</td>\n",
       "      <td>356.0</td>\n",
       "      <td>[19860, 14157, 16552]</td>\n",
       "      <td>{'SNP(R)': {'pre': 193, 'post': 196, 'downstre...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>['GOR(R)', 'IB', 'ICL(R)', 'INP', 'SMP(R)', 'S...</td>\n",
       "      <td>['GOR(R)', 'IB', 'INP', 'SMP(R)', 'SNP(R)', 'S...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2470</th>\n",
       "      <td>977413163</td>\n",
       "      <td>2471</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>3</td>\n",
       "      <td>589</td>\n",
       "      <td>IB016(PDM05)_L</td>\n",
       "      <td>IB016</td>\n",
       "      <td>...</td>\n",
       "      <td>Traced</td>\n",
       "      <td>False</td>\n",
       "      <td>Roughly traced</td>\n",
       "      <td>NaN</td>\n",
       "      <td>291.0</td>\n",
       "      <td>[28607, 14752, 12104]</td>\n",
       "      <td>{'SNP(L)': {'pre': 6, 'post': 41, 'downstream'...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>['ATL(L)', 'ATL(R)', 'IB', 'ICL(L)', 'ICL(R)',...</td>\n",
       "      <td>['ATL(L)', 'IB', 'ICL(L)', 'ICL(R)', 'INP', 'S...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2471</th>\n",
       "      <td>977598865</td>\n",
       "      <td>2472</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>357</td>\n",
       "      <td>118</td>\n",
       "      <td>126</td>\n",
       "      <td>CL318_R</td>\n",
       "      <td>CL318</td>\n",
       "      <td>...</td>\n",
       "      <td>Traced</td>\n",
       "      <td>False</td>\n",
       "      <td>Roughly traced</td>\n",
       "      <td>PVL21</td>\n",
       "      <td>301.0</td>\n",
       "      <td>[6799, 12873, 16224]</td>\n",
       "      <td>{'SNP(R)': {'pre': 229, 'post': 235, 'downstre...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>['ATL(L)', 'ATL(R)', 'GOR(R)', 'IB', 'ICL(R)',...</td>\n",
       "      <td>['ATL(L)', 'ATL(R)', 'GOR(R)', 'IB', 'ICL(R)',...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2478</th>\n",
       "      <td>979001183</td>\n",
       "      <td>2479</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>477</td>\n",
       "      <td>706</td>\n",
       "      <td>692</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Orphan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Orphan hotknife</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'SNP(R)': {'pre': 5, 'post': 5, 'downstream':...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>['SMP(R)', 'SNP(R)']</td>\n",
       "      <td>['SMP(R)', 'SNP(R)']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2500</th>\n",
       "      <td>985510931</td>\n",
       "      <td>2501</td>\n",
       "      <td>1</td>\n",
       "      <td>101</td>\n",
       "      <td>89</td>\n",
       "      <td>531</td>\n",
       "      <td>777</td>\n",
       "      <td>820</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Assign</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.5assign</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'SNP(R)': {'pre': 2, 'post': 5, 'downstream':...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>['SMP(R)', 'SNP(R)']</td>\n",
       "      <td>['SMP(R)', 'SNP(R)']</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>519 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              id   key  0.0  0.05  0.1  0.5  0.75  1.0        instance  \\\n",
       "0     1003215282     1    1     1    1    1     1    1         CL229_R   \n",
       "2     1006928515     3    1     1    1    3     3    3         CL300_R   \n",
       "5     1008085163     6    1     1    1    6     6    6             NaN   \n",
       "10    1008966615    11    1     1    1    3     8    3         CL300_R   \n",
       "17    1011378821    18    1     4    5   12    12    1             NaN   \n",
       "...          ...   ...  ...   ...  ...  ...   ...  ...             ...   \n",
       "2464   976964834  2465    1    13    6  246    59  110         IB022_R   \n",
       "2470   977413163  2471    1     1    1   56     3  589  IB016(PDM05)_L   \n",
       "2471   977598865  2472    1    13    6  357   118  126         CL318_R   \n",
       "2478   979001183  2479    1    10    5  477   706  692             NaN   \n",
       "2500   985510931  2501    1   101   89  531   777  820             NaN   \n",
       "\n",
       "     celltype  ...  status  cropped      statusLabel  cellBodyFiber  \\\n",
       "0       CL229  ...  Traced    False   Roughly traced          PDM19   \n",
       "2       CL300  ...  Traced    False   Roughly traced          PVL13   \n",
       "5         NaN  ...  Traced     True           Leaves            NaN   \n",
       "10      CL300  ...  Traced    False   Roughly traced          PVL13   \n",
       "17        NaN  ...  Orphan      NaN  Orphan hotknife            NaN   \n",
       "...       ...  ...     ...      ...              ...            ...   \n",
       "2464    IB022  ...  Traced    False   Roughly traced          PDM09   \n",
       "2470    IB016  ...  Traced    False   Roughly traced            NaN   \n",
       "2471    CL318  ...  Traced    False   Roughly traced          PVL21   \n",
       "2478      NaN  ...  Orphan      NaN  Orphan hotknife            NaN   \n",
       "2500      NaN  ...  Assign      NaN        0.5assign            NaN   \n",
       "\n",
       "      somaRadius           somaLocation  \\\n",
       "0          301.0  [23044, 14981, 11600]   \n",
       "2          236.0  [12083, 10523, 16816]   \n",
       "5            NaN                    NaN   \n",
       "10         236.0   [13492, 9603, 16400]   \n",
       "17           NaN                    NaN   \n",
       "...          ...                    ...   \n",
       "2464       356.0  [19860, 14157, 16552]   \n",
       "2470       291.0  [28607, 14752, 12104]   \n",
       "2471       301.0   [6799, 12873, 16224]   \n",
       "2478         NaN                    NaN   \n",
       "2500         NaN                    NaN   \n",
       "\n",
       "                                                roiInfo notes  \\\n",
       "0     {'INP': {'pre': 87, 'post': 351, 'downstream':...   NaN   \n",
       "2     {'INP': {'pre': 79, 'post': 126, 'downstream':...   NaN   \n",
       "5     {'SNP(L)': {'pre': 10, 'post': 9, 'downstream'...   NaN   \n",
       "10    {'INP': {'pre': 42, 'post': 132, 'downstream':...   NaN   \n",
       "17    {'SNP(R)': {'pre': 6, 'post': 3, 'downstream':...   NaN   \n",
       "...                                                 ...   ...   \n",
       "2464  {'SNP(R)': {'pre': 193, 'post': 196, 'downstre...   NaN   \n",
       "2470  {'SNP(L)': {'pre': 6, 'post': 41, 'downstream'...   NaN   \n",
       "2471  {'SNP(R)': {'pre': 229, 'post': 235, 'downstre...   NaN   \n",
       "2478  {'SNP(R)': {'pre': 5, 'post': 5, 'downstream':...   NaN   \n",
       "2500  {'SNP(R)': {'pre': 2, 'post': 5, 'downstream':...   NaN   \n",
       "\n",
       "                                              inputRois  \\\n",
       "0     ['EPA(R)', 'GOR(R)', 'IB', 'ICL(R)', 'INP', 'S...   \n",
       "2     ['ATL(R)', 'IB', 'ICL(R)', 'INP', 'SCL(R)', 'S...   \n",
       "5           ['ATL(L)', 'IB', 'INP', 'SMP(L)', 'SNP(L)']   \n",
       "10    ['IB', 'ICL(R)', 'INP', 'PLP(R)', 'SCL(R)', 'S...   \n",
       "17                                 ['SMP(R)', 'SNP(R)']   \n",
       "...                                                 ...   \n",
       "2464  ['GOR(R)', 'IB', 'ICL(R)', 'INP', 'SMP(R)', 'S...   \n",
       "2470  ['ATL(L)', 'ATL(R)', 'IB', 'ICL(L)', 'ICL(R)',...   \n",
       "2471  ['ATL(L)', 'ATL(R)', 'GOR(R)', 'IB', 'ICL(R)',...   \n",
       "2478                               ['SMP(R)', 'SNP(R)']   \n",
       "2500                               ['SMP(R)', 'SNP(R)']   \n",
       "\n",
       "                                             outputRois  \n",
       "0     ['GOR(R)', 'IB', 'ICL(R)', 'INP', 'SCL(R)', 'S...  \n",
       "2     ['ATL(R)', 'IB', 'ICL(R)', 'INP', 'SCL(R)', 'S...  \n",
       "5           ['ATL(L)', 'IB', 'INP', 'SMP(L)', 'SNP(L)']  \n",
       "10         ['IB', 'ICL(R)', 'INP', 'PLP(R)', 'VLNP(R)']  \n",
       "17                                 ['SMP(R)', 'SNP(R)']  \n",
       "...                                                 ...  \n",
       "2464  ['GOR(R)', 'IB', 'INP', 'SMP(R)', 'SNP(R)', 'S...  \n",
       "2470  ['ATL(L)', 'IB', 'ICL(L)', 'ICL(R)', 'INP', 'S...  \n",
       "2471  ['ATL(L)', 'ATL(R)', 'GOR(R)', 'IB', 'ICL(R)',...  \n",
       "2478                               ['SMP(R)', 'SNP(R)']  \n",
       "2500                               ['SMP(R)', 'SNP(R)']  \n",
       "\n",
       "[519 rows x 26 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get coarse cluster 1\n",
    "cluster1 = ovi_in_node_df[ovi_in_node_df['0.0']==1]\n",
    "cluster1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sankey of cluster 1 modularity across resolutions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# take only the columns with modularity data\n",
    "ovi_HB_chunk = cluster1[['0.0','0.05','0.1','0.5','0.75','1.0']].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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          "mapbox": {
           "style": "light"
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          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
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          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "oviINr input connectome cluster1"
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from viz_functions import create_Sankey_fig\n",
    "create_Sankey_fig(ovi_HB_chunk, 'oviINr input connectome cluster1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Without NaN cell types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pre-conditions: the df may not have any NaN cell types\n",
    "pretest = cluster1.dropna(subset=['celltype'])\n",
    "pretest = pretest[['0.0','0.05','0.1','0.5','0.75','1.0']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
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          "geo": {
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           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
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          "hoverlabel": {
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          "hovermode": "closest",
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           "bgcolor": "#E5ECF6",
           "radialaxis": {
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          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
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           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
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           "zaxis": {
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            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
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          },
          "shapedefaults": {
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          "ternary": {
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            "ticks": ""
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           "baxis": {
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            "ticks": ""
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           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
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           "zerolinecolor": "white",
           "zerolinewidth": 2
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         }
        },
        "title": {
         "text": "oviINr inputs modularity data across resolutions without NaN cell types"
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from viz_functions import create_Sankey_fig\n",
    "create_Sankey_fig(pretest,'oviINr inputs modularity data across resolutions without NaN cell types')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Synaptic organization of cluster 1 at finer resolutions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def heterogeneity_score(df, col1, col2):\n",
    "    \"\"\"Compute the heterogeneity of `col1` with respect to `col2`.\n",
    "    The idea is, each column represents a different labeling of, e.g. nodes in a graph.\n",
    "    This computes the conditional entropy of the distribution of `col2` given `col1`,\n",
    "    and returns \n",
    "    \"\"\"\n",
    "    total_counts = {c: df.value_counts(c) for c in [col1, col2]}\n",
    "    \n",
    "    joint = df.value_counts([col1, col2]).reset_index().rename(columns={0:'count'})\n",
    "    joint['col1_count'] = total_counts[col1][joint[col1]].values\n",
    "    p = joint['count'] / joint['col1_count']\n",
    "    joint['conditional_entropy'] = -p * np.log2(p)\n",
    "    res = joint.groupby(col1).agg({'conditional_entropy': 'sum', col2:'count'})\n",
    "    res['heterogeneity'] = res['conditional_entropy'].where(res['conditional_entropy'] == 0,\n",
    "                                                            res['conditional_entropy'] / np.log2(res[col2]))\n",
    "#     res['heterogeneity'] = res['conditional_entropy'] / np.log2(res[col2])\n",
    "#     res['heterogeneity'].fillna(0, inplace=True)\n",
    "    res = res.rename(columns={col2:'n_'+col2})\n",
    "    res[col1+'_count'] = total_counts[col1][res.index]\n",
    "    return res\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = '0.05'\n",
    "modular_df = cluster1[['id', 'celltype', res]].copy()\n",
    "modular_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compute heterogeneity\n",
    "cluster_het = heterogeneity_score(modular_df, res, 'celltype')\n",
    "cluster_het"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the clusters with non-zero heterogeneity\n",
    "het_clusters = cluster_het[cluster_het['heterogeneity']>0]\n",
    "het_clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the rows of the dataframe corresponding to heterogenous clusters\n",
    "modular_df = modular_df[modular_df[res].isin(het_clusters.index)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ovi_pre_modules = ovi_pre_syns.merge(modular_df, how='inner', left_on='bodyId_pre', right_on='id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "from random import shuffle\n",
    "# create a color palette for the clusters\n",
    "#cmap = bokeh.palettes.turbo(ovi_in_node_df[res].max())\n",
    "cmap = bokeh.palettes.turbo(len(het_clusters.index))\n",
    "\n",
    "# Convert the tuple to a list and shuffle it\n",
    "cmap_list = list(cmap)\n",
    "random.shuffle(cmap_list)\n",
    "\n",
    "# Convert the list back to a tuple\n",
    "cmap = tuple(cmap_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# this is the official color palette for the coarse modularity for oviINs full connectome\n",
    "colormap = dict(zip(ovi_pre_modules[res].sort_values().unique(), cmap))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add the color information to the df\n",
    "ovi_pre_modules['color'] = ovi_pre_modules[res].map(colormap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Download some skeletons as DataFrames and attach columns for bodyId and color\n",
    "skeletons = []\n",
    "\n",
    "# could add more skeletons with a for loop\n",
    "s = c.fetch_skeleton(oviINr_bodyID, format='pandas')\n",
    "s['bodyId'] = oviINr_bodyID\n",
    "s['color'] = bokeh.palettes.Greys[3][1]\n",
    "skeletons.append(s)\n",
    "\n",
    "# Combine into one big table for convenient processing\n",
    "skeletons = pd.concat(skeletons, ignore_index=True)\n",
    "\n",
    "# Join parent/child nodes for plotting as line segments below.\n",
    "# (Using each row's 'link' (parent) ID, find the row with matching rowId.)\n",
    "segments = skeletons.merge(skeletons, 'inner',\n",
    "                           left_on=['bodyId', 'link'],\n",
    "                           right_on=['bodyId', 'rowId'],\n",
    "                           suffixes=['_child', '_parent'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bokeh.plotting import figure\n",
    "from bokeh.layouts import gridplot\n",
    "from bokeh.io import show\n",
    "from bokeh.io import export_png\n",
    "\n",
    "# make a pretty skeleton with synapses on it showing inputs and outputs in different colors\n",
    "#pmpre = figure(title=\"Post sites from pre-synaptic partners on oviIN_R according to ovi coarse modules\")\n",
    "pmpre = figure(width=500, height=550, title=\"Synaptic input sites on oviINr colored by oviINr input modules \" + res)\n",
    "pmpre.y_range.flipped = True\n",
    "\n",
    "pmpre.output_backend = \"svg\"\n",
    "\n",
    "# Plot skeleton segments (in 2D)\n",
    "pmpre.segment(x0='x_child', x1='x_parent',\n",
    "          y0='z_child', y1='z_parent',\n",
    "          color='color_child',\n",
    "          source=segments)\n",
    "\n",
    "# default point size is 4\n",
    "pmpre.scatter('x_post', 'z_post', color='color', legend_group=res, source=ovi_pre_modules, size=2)\n",
    "pmpre.legend.location = \"bottom_right\"\n",
    "\n",
    "pmpre.xaxis.visible = False\n",
    "pmpre.xgrid.visible = False\n",
    "\n",
    "pmpre.yaxis.visible = False\n",
    "pmpre.ygrid.visible = False\n",
    "\n",
    "pmpre.legend.title = \"module id\"\n",
    "\n",
    "\n",
    "pmpre1 = figure(width=500, height=550, title=\"Synaptic input sites on oviINr colored by oviINr input modules \" + res)\n",
    "pmpre1.y_range.flipped = True\n",
    "\n",
    "pmpre1.output_backend = \"svg\"\n",
    "\n",
    "# Plot skeleton segments (in 2D)\n",
    "pmpre1.segment(x0='y_child', x1='y_parent',\n",
    "          y0='z_child', y1='z_parent',\n",
    "          color='color_child',\n",
    "          source=segments)\n",
    "\n",
    "# default point size is 4\n",
    "pmpre1.scatter('y_post', 'z_post', color='color', legend_group=res, source=ovi_pre_modules, size=2)\n",
    "pmpre1.legend.location = \"top_left\"\n",
    "\n",
    "pmpre1.xaxis.visible = False\n",
    "pmpre1.xgrid.visible = False\n",
    "\n",
    "pmpre1.yaxis.visible = False\n",
    "pmpre1.ygrid.visible = False\n",
    "\n",
    "pmpre1.legend.title = \"module id\"\n",
    "\n",
    "# Create a gridplot with the figures\n",
    "p = gridplot([[pmpre, pmpre1]])\n",
    "\n",
    "# Show the plot\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bokeh.io import export_png\n",
    "from bokeh.io import export_svg\n",
    "from bokeh.plotting import figure\n",
    "\n",
    "import os\n",
    "\n",
    "os.chdir('/Users/ggutierr/My Drive (ggutierr@barnard.edu)/GitHub/oviIN-analyses-gabrielle/figures')\n",
    "\n",
    "# save the results to a file\n",
    "#export_svg(p, filename=\"pre_partner_syn_sites_oviINr_inputs_0cluster3_mod005_ovi_morph.svg\")\n",
    "export_png(p, filename=\"pre_partner_syn_sites_oviINr_inputs_0cluster3_mod005_ovi_morph.png\")\n",
    "\n",
    "# return to directory\n",
    "os.chdir('/Users/ggutierr/My Drive (ggutierr@barnard.edu)/GitHub/oviIN-analyses-gabrielle')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = '0.1'\n",
    "modular_df = cluster1[['id', 'celltype', res]].copy()\n",
    "modular_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compute heterogeneity\n",
    "cluster_het = heterogeneity_score(modular_df, res, 'celltype')\n",
    "cluster_het"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the clusters with non-zero heterogeneity\n",
    "het_clusters = cluster_het[cluster_het['heterogeneity']>0]\n",
    "het_clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the rows of the dataframe corresponding to heterogenous clusters\n",
    "modular_df = modular_df[modular_df[res].isin(het_clusters.index)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ovi_pre_modules = ovi_pre_syns.merge(modular_df, how='inner', left_on='bodyId_pre', right_on='id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "from random import shuffle\n",
    "# create a color palette for the clusters\n",
    "#cmap = bokeh.palettes.turbo(ovi_in_node_df[res].max())\n",
    "cmap = bokeh.palettes.turbo(len(het_clusters.index))\n",
    "\n",
    "# Convert the tuple to a list and shuffle it\n",
    "cmap_list = list(cmap)\n",
    "random.shuffle(cmap_list)\n",
    "\n",
    "# Convert the list back to a tuple\n",
    "cmap = tuple(cmap_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# this is the official color palette for the coarse modularity for oviINs full connectome\n",
    "colormap = dict(zip(ovi_pre_modules[res].sort_values().unique(), cmap))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add the color information to the df\n",
    "ovi_pre_modules['color'] = ovi_pre_modules[res].map(colormap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bokeh.plotting import figure\n",
    "from bokeh.layouts import gridplot\n",
    "from bokeh.io import show\n",
    "from bokeh.io import export_png\n",
    "\n",
    "# make a pretty skeleton with synapses on it showing inputs and outputs in different colors\n",
    "#pmpre = figure(title=\"Post sites from pre-synaptic partners on oviIN_R according to ovi coarse modules\")\n",
    "pmpre = figure(width=500, height=550, title=\"Synaptic input sites on oviINr colored by oviINr input modules \" + res)\n",
    "pmpre.y_range.flipped = True\n",
    "\n",
    "pmpre.output_backend = \"svg\"\n",
    "\n",
    "# Plot skeleton segments (in 2D)\n",
    "pmpre.segment(x0='x_child', x1='x_parent',\n",
    "          y0='z_child', y1='z_parent',\n",
    "          color='color_child',\n",
    "          source=segments)\n",
    "\n",
    "# default point size is 4\n",
    "pmpre.scatter('x_post', 'z_post', color='color', legend_group=res, source=ovi_pre_modules, size=2)\n",
    "pmpre.legend.location = \"bottom_right\"\n",
    "\n",
    "pmpre.xaxis.visible = False\n",
    "pmpre.xgrid.visible = False\n",
    "\n",
    "pmpre.yaxis.visible = False\n",
    "pmpre.ygrid.visible = False\n",
    "\n",
    "pmpre.legend.title = \"module id\"\n",
    "\n",
    "\n",
    "pmpre1 = figure(width=500, height=550, title=\"Synaptic input sites on oviINr colored by oviINr input modules \" + res)\n",
    "pmpre1.y_range.flipped = True\n",
    "\n",
    "pmpre1.output_backend = \"svg\"\n",
    "\n",
    "# Plot skeleton segments (in 2D)\n",
    "pmpre1.segment(x0='y_child', x1='y_parent',\n",
    "          y0='z_child', y1='z_parent',\n",
    "          color='color_child',\n",
    "          source=segments)\n",
    "\n",
    "# default point size is 4\n",
    "pmpre1.scatter('y_post', 'z_post', color='color', legend_group=res, source=ovi_pre_modules, size=2)\n",
    "pmpre1.legend.location = \"top_left\"\n",
    "\n",
    "pmpre1.xaxis.visible = False\n",
    "pmpre1.xgrid.visible = False\n",
    "\n",
    "pmpre1.yaxis.visible = False\n",
    "pmpre1.ygrid.visible = False\n",
    "\n",
    "pmpre1.legend.title = \"module id\"\n",
    "\n",
    "# Create a gridplot with the figures\n",
    "p = gridplot([[pmpre, pmpre1]])\n",
    "\n",
    "# Show the plot\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bokeh.io import export_png\n",
    "from bokeh.io import export_svg\n",
    "from bokeh.plotting import figure\n",
    "\n",
    "import os\n",
    "\n",
    "os.chdir('/Users/ggutierr/My Drive (ggutierr@barnard.edu)/GitHub/oviIN-analyses-gabrielle/figures')\n",
    "\n",
    "# save the results to a file\n",
    "#export_svg(p, filename=\"pre_partner_syn_sites_oviINr_inputs_0cluster3_mod01_ovi_morph.svg\")\n",
    "export_png(p, filename=\"pre_partner_syn_sites_oviINr_inputs_0cluster3_mod01_ovi_morph.png\")\n",
    "\n",
    "# return to directory\n",
    "os.chdir('/Users/ggutierr/My Drive (ggutierr@barnard.edu)/GitHub/oviIN-analyses-gabrielle')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster1[cluster1['0.1']==15]['celltype'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = '0.5'\n",
    "modular_df = cluster1[['id', 'celltype', res]].copy()\n",
    "modular_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compute heterogeneity\n",
    "cluster_het = heterogeneity_score(modular_df, res, 'celltype')\n",
    "cluster_het"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the clusters with non-zero heterogeneity\n",
    "het_clusters = cluster_het[cluster_het['heterogeneity']>0]\n",
    "het_clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# further filter by cell count\n",
    "het_clusters = het_clusters[het_clusters['0.5_count']>5]\n",
    "het_clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the rows of the dataframe corresponding to heterogenous clusters\n",
    "modular_df = modular_df[modular_df[res].isin(het_clusters.index)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ovi_pre_modules = ovi_pre_syns.merge(modular_df, how='inner', left_on='bodyId_pre', right_on='id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "from random import shuffle\n",
    "# create a color palette for the clusters\n",
    "#cmap = bokeh.palettes.turbo(ovi_in_node_df[res].max())\n",
    "cmap = bokeh.palettes.turbo(len(het_clusters.index))\n",
    "\n",
    "# Convert the tuple to a list and shuffle it\n",
    "cmap_list = list(cmap)\n",
    "random.shuffle(cmap_list)\n",
    "\n",
    "# Convert the list back to a tuple\n",
    "cmap = tuple(cmap_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# this is the official color palette for the coarse modularity for oviINs full connectome\n",
    "colormap = dict(zip(ovi_pre_modules[res].sort_values().unique(), cmap))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add the color information to the df\n",
    "ovi_pre_modules['color'] = ovi_pre_modules[res].map(colormap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bokeh.plotting import figure\n",
    "from bokeh.layouts import gridplot\n",
    "from bokeh.io import show\n",
    "from bokeh.io import export_png\n",
    "\n",
    "# make a pretty skeleton with synapses on it showing inputs and outputs in different colors\n",
    "#pmpre = figure(title=\"Post sites from pre-synaptic partners on oviIN_R according to ovi coarse modules\")\n",
    "pmpre = figure(width=500, height=550, title=\"Synaptic input sites on oviINr colored by oviINr input modules \" + res)\n",
    "pmpre.y_range.flipped = True\n",
    "\n",
    "pmpre.output_backend = \"svg\"\n",
    "\n",
    "# Plot skeleton segments (in 2D)\n",
    "pmpre.segment(x0='x_child', x1='x_parent',\n",
    "          y0='z_child', y1='z_parent',\n",
    "          color='color_child',\n",
    "          source=segments)\n",
    "\n",
    "# default point size is 4\n",
    "pmpre.scatter('x_post', 'z_post', color='color', legend_group=res, source=ovi_pre_modules, size=2)\n",
    "pmpre.legend.location = \"bottom_right\"\n",
    "\n",
    "pmpre.xaxis.visible = False\n",
    "pmpre.xgrid.visible = False\n",
    "\n",
    "pmpre.yaxis.visible = False\n",
    "pmpre.ygrid.visible = False\n",
    "\n",
    "pmpre.legend.title = \"module id\"\n",
    "\n",
    "\n",
    "pmpre1 = figure(width=500, height=550, title=\"Synaptic input sites on oviINr colored by oviINr input modules \" + res)\n",
    "pmpre1.y_range.flipped = True\n",
    "\n",
    "pmpre1.output_backend = \"svg\"\n",
    "\n",
    "# Plot skeleton segments (in 2D)\n",
    "pmpre1.segment(x0='y_child', x1='y_parent',\n",
    "          y0='z_child', y1='z_parent',\n",
    "          color='color_child',\n",
    "          source=segments)\n",
    "\n",
    "# default point size is 4\n",
    "pmpre1.scatter('y_post', 'z_post', color='color', legend_group=res, source=ovi_pre_modules, size=2)\n",
    "pmpre1.legend.location = \"top_left\"\n",
    "\n",
    "pmpre1.xaxis.visible = False\n",
    "pmpre1.xgrid.visible = False\n",
    "\n",
    "pmpre1.yaxis.visible = False\n",
    "pmpre1.ygrid.visible = False\n",
    "\n",
    "pmpre1.legend.title = \"module id\"\n",
    "\n",
    "# Create a gridplot with the figures\n",
    "p = gridplot([[pmpre, pmpre1]])\n",
    "\n",
    "# Show the plot\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = '0.75'\n",
    "modular_df = cluster1[['id', 'celltype', res]].copy()\n",
    "modular_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compute heterogeneity\n",
    "cluster_het = heterogeneity_score(modular_df, res, 'celltype')\n",
    "cluster_het"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the clusters with non-zero heterogeneity\n",
    "het_clusters = cluster_het[cluster_het['heterogeneity']>0]\n",
    "het_clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# further filter by cell count\n",
    "het_clusters = het_clusters[het_clusters['0.75_count']>5]\n",
    "het_clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the rows of the dataframe corresponding to heterogenous clusters\n",
    "modular_df = modular_df[modular_df[res].isin(het_clusters.index)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ovi_pre_modules = ovi_pre_syns.merge(modular_df, how='inner', left_on='bodyId_pre', right_on='id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "from random import shuffle\n",
    "# create a color palette for the clusters\n",
    "#cmap = bokeh.palettes.turbo(ovi_in_node_df[res].max())\n",
    "cmap = bokeh.palettes.turbo(len(het_clusters.index))\n",
    "\n",
    "# Convert the tuple to a list and shuffle it\n",
    "cmap_list = list(cmap)\n",
    "random.shuffle(cmap_list)\n",
    "\n",
    "# Convert the list back to a tuple\n",
    "cmap = tuple(cmap_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# this is the official color palette for the coarse modularity for oviINs full connectome\n",
    "colormap = dict(zip(ovi_pre_modules[res].sort_values().unique(), cmap))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add the color information to the df\n",
    "ovi_pre_modules['color'] = ovi_pre_modules[res].map(colormap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bokeh.plotting import figure\n",
    "from bokeh.layouts import gridplot\n",
    "from bokeh.io import show\n",
    "from bokeh.io import export_png\n",
    "\n",
    "# make a pretty skeleton with synapses on it showing inputs and outputs in different colors\n",
    "#pmpre = figure(title=\"Post sites from pre-synaptic partners on oviIN_R according to ovi coarse modules\")\n",
    "pmpre = figure(width=500, height=550, title=\"Synaptic input sites on oviINr colored by oviINr input modules \" + res)\n",
    "pmpre.y_range.flipped = True\n",
    "\n",
    "pmpre.output_backend = \"svg\"\n",
    "\n",
    "# Plot skeleton segments (in 2D)\n",
    "pmpre.segment(x0='x_child', x1='x_parent',\n",
    "          y0='z_child', y1='z_parent',\n",
    "          color='color_child',\n",
    "          source=segments)\n",
    "\n",
    "# default point size is 4\n",
    "pmpre.scatter('x_post', 'z_post', color='color', legend_group=res, source=ovi_pre_modules, size=2)\n",
    "pmpre.legend.location = \"bottom_right\"\n",
    "\n",
    "pmpre.xaxis.visible = False\n",
    "pmpre.xgrid.visible = False\n",
    "\n",
    "pmpre.yaxis.visible = False\n",
    "pmpre.ygrid.visible = False\n",
    "\n",
    "pmpre.legend.title = \"module id\"\n",
    "\n",
    "\n",
    "pmpre1 = figure(width=500, height=550, title=\"Synaptic input sites on oviINr colored by oviINr input modules \" + res)\n",
    "pmpre1.y_range.flipped = True\n",
    "\n",
    "pmpre1.output_backend = \"svg\"\n",
    "\n",
    "# Plot skeleton segments (in 2D)\n",
    "pmpre1.segment(x0='y_child', x1='y_parent',\n",
    "          y0='z_child', y1='z_parent',\n",
    "          color='color_child',\n",
    "          source=segments)\n",
    "\n",
    "# default point size is 4\n",
    "pmpre1.scatter('y_post', 'z_post', color='color', legend_group=res, source=ovi_pre_modules, size=2)\n",
    "pmpre1.legend.location = \"top_left\"\n",
    "\n",
    "pmpre1.xaxis.visible = False\n",
    "pmpre1.xgrid.visible = False\n",
    "\n",
    "pmpre1.yaxis.visible = False\n",
    "pmpre1.ygrid.visible = False\n",
    "\n",
    "pmpre1.legend.title = \"module id\"\n",
    "\n",
    "# Create a gridplot with the figures\n",
    "p = gridplot([[pmpre, pmpre1]])\n",
    "\n",
    "# Show the plot\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = '1.0'\n",
    "modular_df = cluster1[['id', 'celltype', res]].copy()\n",
    "modular_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compute heterogeneity\n",
    "cluster_het = heterogeneity_score(modular_df, res, 'celltype')\n",
    "cluster_het"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the clusters with non-zero heterogeneity\n",
    "het_clusters = cluster_het[cluster_het['heterogeneity']>0]\n",
    "het_clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# further filter by cell count\n",
    "het_clusters = het_clusters[het_clusters['1.0_count']>5]\n",
    "het_clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the rows of the dataframe corresponding to heterogenous clusters\n",
    "modular_df = modular_df[modular_df[res].isin(het_clusters.index)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ovi_pre_modules = ovi_pre_syns.merge(modular_df, how='inner', left_on='bodyId_pre', right_on='id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "from random import shuffle\n",
    "# create a color palette for the clusters\n",
    "#cmap = bokeh.palettes.turbo(ovi_in_node_df[res].max())\n",
    "cmap = bokeh.palettes.turbo(len(het_clusters.index))\n",
    "\n",
    "# Convert the tuple to a list and shuffle it\n",
    "cmap_list = list(cmap)\n",
    "random.shuffle(cmap_list)\n",
    "\n",
    "# Convert the list back to a tuple\n",
    "cmap = tuple(cmap_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# this is the official color palette for the coarse modularity for oviINs full connectome\n",
    "colormap = dict(zip(ovi_pre_modules[res].sort_values().unique(), cmap))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add the color information to the df\n",
    "ovi_pre_modules['color'] = ovi_pre_modules[res].map(colormap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bokeh.plotting import figure\n",
    "from bokeh.layouts import gridplot\n",
    "from bokeh.io import show\n",
    "from bokeh.io import export_png\n",
    "\n",
    "# make a pretty skeleton with synapses on it showing inputs and outputs in different colors\n",
    "#pmpre = figure(title=\"Post sites from pre-synaptic partners on oviIN_R according to ovi coarse modules\")\n",
    "pmpre = figure(width=500, height=550, title=\"Synaptic input sites on oviINr colored by oviINr input modules \" + res)\n",
    "pmpre.y_range.flipped = True\n",
    "\n",
    "pmpre.output_backend = \"svg\"\n",
    "\n",
    "# Plot skeleton segments (in 2D)\n",
    "pmpre.segment(x0='x_child', x1='x_parent',\n",
    "          y0='z_child', y1='z_parent',\n",
    "          color='color_child',\n",
    "          source=segments)\n",
    "\n",
    "# default point size is 4\n",
    "pmpre.scatter('x_post', 'z_post', color='color', legend_group=res, source=ovi_pre_modules, size=2)\n",
    "pmpre.legend.location = \"bottom_right\"\n",
    "\n",
    "pmpre.xaxis.visible = False\n",
    "pmpre.xgrid.visible = False\n",
    "\n",
    "pmpre.yaxis.visible = False\n",
    "pmpre.ygrid.visible = False\n",
    "\n",
    "pmpre.legend.title = \"module id\"\n",
    "\n",
    "\n",
    "pmpre1 = figure(width=500, height=550, title=\"Synaptic input sites on oviINr colored by oviINr input modules \" + res)\n",
    "pmpre1.y_range.flipped = True\n",
    "\n",
    "pmpre1.output_backend = \"svg\"\n",
    "\n",
    "# Plot skeleton segments (in 2D)\n",
    "pmpre1.segment(x0='y_child', x1='y_parent',\n",
    "          y0='z_child', y1='z_parent',\n",
    "          color='color_child',\n",
    "          source=segments)\n",
    "\n",
    "# default point size is 4\n",
    "pmpre1.scatter('y_post', 'z_post', color='color', legend_group=res, source=ovi_pre_modules, size=2)\n",
    "pmpre1.legend.location = \"top_left\"\n",
    "\n",
    "pmpre1.xaxis.visible = False\n",
    "pmpre1.xgrid.visible = False\n",
    "\n",
    "pmpre1.yaxis.visible = False\n",
    "pmpre1.ygrid.visible = False\n",
    "\n",
    "pmpre1.legend.title = \"module id\"\n",
    "\n",
    "# Create a gridplot with the figures\n",
    "p = gridplot([[pmpre, pmpre1]])\n",
    "\n",
    "# Show the plot\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Network graph of cluster 1 by cell type\n",
    "This isn't too revealing, mainly because there is so much tangled mess. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from neuprint import fetch_simple_connections\n",
    "\n",
    "# fetch simple connections among all the neurons in oviIN's personal connectome\n",
    "clu1_connectome = fetch_simple_connections(cluster1.id,cluster1.id,min_weight=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# replace None with string 'None' to allow it to be a node in the graph\n",
    "clu1_connectome = clu1_connectome.fillna('None')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type_pre</th>\n",
       "      <th>type_post</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">5-HTPMPV01</th>\n",
       "      <th>None</th>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLP122</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLP210</th>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLP244</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMP378</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">pC1c</th>\n",
       "      <th>SMP392</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMP394</th>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMP454</th>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMP470</th>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMP546</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2320 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      weight\n",
       "type_pre   type_post        \n",
       "5-HTPMPV01 None           57\n",
       "           PLP122         10\n",
       "           PLP210         30\n",
       "           PLP244         17\n",
       "           SMP378         13\n",
       "...                      ...\n",
       "pC1c       SMP392         13\n",
       "           SMP394         21\n",
       "           SMP454         37\n",
       "           SMP470         31\n",
       "           SMP546         13\n",
       "\n",
       "[2320 rows x 1 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clu1_type_connectome_all = clu1_connectome[['type_pre','type_post','weight']].groupby(['type_pre','type_post']).sum()\n",
    "clu1_type_connectome_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type_pre</th>\n",
       "      <th>type_post</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">CL029</th>\n",
       "      <th>SMP092</th>\n",
       "      <td>347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMP176</th>\n",
       "      <td>254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL030</th>\n",
       "      <th>SMP092</th>\n",
       "      <td>261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"9\" valign=\"top\">None</th>\n",
       "      <th>DNp27</th>\n",
       "      <td>310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MBON35</th>\n",
       "      <td>448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>None</th>\n",
       "      <td>253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMP081</th>\n",
       "      <td>677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMP148</th>\n",
       "      <td>262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMP162</th>\n",
       "      <td>203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMP176</th>\n",
       "      <td>444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMP383</th>\n",
       "      <td>330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMP512</th>\n",
       "      <td>233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMP057</th>\n",
       "      <th>IB018</th>\n",
       "      <td>352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMP081</th>\n",
       "      <th>SMP245</th>\n",
       "      <td>248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">SMP162</th>\n",
       "      <th>SMP052</th>\n",
       "      <td>207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMP383</th>\n",
       "      <td>569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMP176</th>\n",
       "      <th>MBON35</th>\n",
       "      <td>357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMP251</th>\n",
       "      <th>SMP052</th>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">SMP271</th>\n",
       "      <th>SMP092</th>\n",
       "      <td>215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMP403</th>\n",
       "      <td>344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMP272</th>\n",
       "      <th>SMP516</th>\n",
       "      <td>201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMP319</th>\n",
       "      <th>SMP081</th>\n",
       "      <td>208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMP320</th>\n",
       "      <th>SMP081</th>\n",
       "      <td>388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMP336</th>\n",
       "      <th>SMP595</th>\n",
       "      <td>233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">SMP383</th>\n",
       "      <th>CL157</th>\n",
       "      <td>274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMP037</th>\n",
       "      <td>301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMP081</th>\n",
       "      <td>419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMP313</th>\n",
       "      <td>272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">SMP403</th>\n",
       "      <th>SMP176</th>\n",
       "      <td>201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMP271</th>\n",
       "      <td>255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMP495</th>\n",
       "      <th>SMP081</th>\n",
       "      <td>211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMP554</th>\n",
       "      <th>MBON35</th>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMP594</th>\n",
       "      <th>SMP454</th>\n",
       "      <td>492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aMe24</th>\n",
       "      <th>SMP176</th>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    weight\n",
       "type_pre type_post        \n",
       "CL029    SMP092        347\n",
       "         SMP176        254\n",
       "CL030    SMP092        261\n",
       "None     DNp27         310\n",
       "         MBON35        448\n",
       "         None          253\n",
       "         SMP081        677\n",
       "         SMP148        262\n",
       "         SMP162        203\n",
       "         SMP176        444\n",
       "         SMP383        330\n",
       "         SMP512        233\n",
       "SMP057   IB018         352\n",
       "SMP081   SMP245        248\n",
       "SMP162   SMP052        207\n",
       "         SMP383        569\n",
       "SMP176   MBON35        357\n",
       "SMP251   SMP052        222\n",
       "SMP271   SMP092        215\n",
       "         SMP403        344\n",
       "SMP272   SMP516        201\n",
       "SMP319   SMP081        208\n",
       "SMP320   SMP081        388\n",
       "SMP336   SMP595        233\n",
       "SMP383   CL157         274\n",
       "         SMP037        301\n",
       "         SMP081        419\n",
       "         SMP313        272\n",
       "SMP403   SMP176        201\n",
       "         SMP271        255\n",
       "SMP495   SMP081        211\n",
       "SMP554   MBON35        221\n",
       "SMP594   SMP454        492\n",
       "aMe24    SMP176        222"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's threshold significantly\n",
    "clu1_type_connectome = clu1_type_connectome_all[clu1_type_connectome_all['weight']>200]\n",
    "clu1_type_connectome"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "clu1_type_connectome = clu1_type_connectome.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type_pre</th>\n",
       "      <th>type_post</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CL029</td>\n",
       "      <td>SMP092</td>\n",
       "      <td>347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CL029</td>\n",
       "      <td>SMP176</td>\n",
       "      <td>254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CL030</td>\n",
       "      <td>SMP092</td>\n",
       "      <td>261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>None</td>\n",
       "      <td>DNp27</td>\n",
       "      <td>310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>None</td>\n",
       "      <td>MBON35</td>\n",
       "      <td>448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>None</td>\n",
       "      <td>SMP081</td>\n",
       "      <td>677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>None</td>\n",
       "      <td>SMP148</td>\n",
       "      <td>262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>None</td>\n",
       "      <td>SMP162</td>\n",
       "      <td>203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>None</td>\n",
       "      <td>SMP176</td>\n",
       "      <td>444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>None</td>\n",
       "      <td>SMP383</td>\n",
       "      <td>330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>None</td>\n",
       "      <td>SMP512</td>\n",
       "      <td>233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>SMP057</td>\n",
       "      <td>IB018</td>\n",
       "      <td>352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>SMP081</td>\n",
       "      <td>SMP245</td>\n",
       "      <td>248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>SMP162</td>\n",
       "      <td>SMP052</td>\n",
       "      <td>207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>SMP162</td>\n",
       "      <td>SMP383</td>\n",
       "      <td>569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>SMP176</td>\n",
       "      <td>MBON35</td>\n",
       "      <td>357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>SMP251</td>\n",
       "      <td>SMP052</td>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>SMP271</td>\n",
       "      <td>SMP092</td>\n",
       "      <td>215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>SMP271</td>\n",
       "      <td>SMP403</td>\n",
       "      <td>344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>SMP272</td>\n",
       "      <td>SMP516</td>\n",
       "      <td>201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>SMP319</td>\n",
       "      <td>SMP081</td>\n",
       "      <td>208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>SMP320</td>\n",
       "      <td>SMP081</td>\n",
       "      <td>388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>SMP336</td>\n",
       "      <td>SMP595</td>\n",
       "      <td>233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>SMP383</td>\n",
       "      <td>CL157</td>\n",
       "      <td>274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>SMP383</td>\n",
       "      <td>SMP037</td>\n",
       "      <td>301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>SMP383</td>\n",
       "      <td>SMP081</td>\n",
       "      <td>419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>SMP383</td>\n",
       "      <td>SMP313</td>\n",
       "      <td>272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>SMP403</td>\n",
       "      <td>SMP176</td>\n",
       "      <td>201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>SMP403</td>\n",
       "      <td>SMP271</td>\n",
       "      <td>255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>SMP495</td>\n",
       "      <td>SMP081</td>\n",
       "      <td>211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>SMP554</td>\n",
       "      <td>MBON35</td>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>SMP594</td>\n",
       "      <td>SMP454</td>\n",
       "      <td>492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>aMe24</td>\n",
       "      <td>SMP176</td>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   type_pre type_post  weight\n",
       "0     CL029    SMP092     347\n",
       "1     CL029    SMP176     254\n",
       "2     CL030    SMP092     261\n",
       "3      None     DNp27     310\n",
       "4      None    MBON35     448\n",
       "5      None      None     253\n",
       "6      None    SMP081     677\n",
       "7      None    SMP148     262\n",
       "8      None    SMP162     203\n",
       "9      None    SMP176     444\n",
       "10     None    SMP383     330\n",
       "11     None    SMP512     233\n",
       "12   SMP057     IB018     352\n",
       "13   SMP081    SMP245     248\n",
       "14   SMP162    SMP052     207\n",
       "15   SMP162    SMP383     569\n",
       "16   SMP176    MBON35     357\n",
       "17   SMP251    SMP052     222\n",
       "18   SMP271    SMP092     215\n",
       "19   SMP271    SMP403     344\n",
       "20   SMP272    SMP516     201\n",
       "21   SMP319    SMP081     208\n",
       "22   SMP320    SMP081     388\n",
       "23   SMP336    SMP595     233\n",
       "24   SMP383     CL157     274\n",
       "25   SMP383    SMP037     301\n",
       "26   SMP383    SMP081     419\n",
       "27   SMP383    SMP313     272\n",
       "28   SMP403    SMP176     201\n",
       "29   SMP403    SMP271     255\n",
       "30   SMP495    SMP081     211\n",
       "31   SMP554    MBON35     221\n",
       "32   SMP594    SMP454     492\n",
       "33    aMe24    SMP176     222"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clu1_type_connectome"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# use networkX package to make a graph from the dataframe\n",
    "import networkx as nx\n",
    "\n",
    "#G = nx.Graph()\n",
    "#oviG = nx.from_pandas_edgelist(ovi_connectome, 'bodyId_pre', 'bodyId_post', edge_attr='weight', create_using=nx.DiGraph())\n",
    "G = nx.from_pandas_edgelist(clu1_type_connectome, 'type_pre', 'type_post', edge_attr='weight', create_using=nx.DiGraph())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# display the graph\n",
    "nx.draw(G, with_labels=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# in-degree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('SMP081', 1903),\n",
       " ('SMP176', 1121),\n",
       " ('MBON35', 1026),\n",
       " ('SMP383', 899),\n",
       " ('SMP092', 823),\n",
       " ('SMP454', 492),\n",
       " ('SMP052', 429),\n",
       " ('IB018', 352),\n",
       " ('SMP403', 344),\n",
       " ('DNp27', 310),\n",
       " ('SMP037', 301),\n",
       " ('CL157', 274),\n",
       " ('SMP313', 272),\n",
       " ('SMP148', 262),\n",
       " ('SMP271', 255),\n",
       " ('None', 253),\n",
       " ('SMP245', 248),\n",
       " ('SMP512', 233),\n",
       " ('SMP595', 233),\n",
       " ('SMP162', 203),\n",
       " ('SMP516', 201),\n",
       " ('CL029', 0),\n",
       " ('CL030', 0),\n",
       " ('SMP057', 0),\n",
       " ('SMP251', 0),\n",
       " ('SMP272', 0),\n",
       " ('SMP319', 0),\n",
       " ('SMP320', 0),\n",
       " ('SMP336', 0),\n",
       " ('SMP495', 0),\n",
       " ('SMP554', 0),\n",
       " ('SMP594', 0),\n",
       " ('aMe24', 0)]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sorted dictionary of the in-degrees of the nodes in the graph\n",
    "in_deg = G.in_degree(weight='weight')\n",
    "sorted(dict(in_deg).items(), key=lambda x: x[1], reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('SMP081', 0.15625),\n",
       " ('SMP176', 0.125),\n",
       " ('SMP092', 0.09375),\n",
       " ('MBON35', 0.09375),\n",
       " ('SMP383', 0.0625),\n",
       " ('SMP052', 0.0625),\n",
       " ('None', 0.03125),\n",
       " ('DNp27', 0.03125),\n",
       " ('SMP148', 0.03125),\n",
       " ('SMP162', 0.03125),\n",
       " ('SMP512', 0.03125),\n",
       " ('IB018', 0.03125),\n",
       " ('SMP245', 0.03125),\n",
       " ('SMP271', 0.03125),\n",
       " ('SMP403', 0.03125),\n",
       " ('SMP516', 0.03125),\n",
       " ('SMP595', 0.03125),\n",
       " ('CL157', 0.03125),\n",
       " ('SMP037', 0.03125),\n",
       " ('SMP313', 0.03125),\n",
       " ('SMP454', 0.03125),\n",
       " ('CL029', 0.0),\n",
       " ('CL030', 0.0),\n",
       " ('SMP057', 0.0),\n",
       " ('SMP251', 0.0),\n",
       " ('SMP272', 0.0),\n",
       " ('SMP319', 0.0),\n",
       " ('SMP320', 0.0),\n",
       " ('SMP336', 0.0),\n",
       " ('SMP495', 0.0),\n",
       " ('SMP554', 0.0),\n",
       " ('SMP594', 0.0),\n",
       " ('aMe24', 0.0)]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "in_deg_centr = nx.in_degree_centrality(G)\n",
    "sorted(in_deg_centr.items(), key=lambda x: x[1], reverse=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pagerank of cluster 1 cell types\n",
    "Perhaps the most important neurons in this cluster can be determined based on Pagerank done on the directed graph of cell types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "pg = nx.pagerank(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('MBON35', 0.08215642444450257)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the max pagerank value and key\n",
    "max(pg.items(), key=lambda x: x[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('MBON35', 0.08215642444450257),\n",
       " ('SMP245', 0.08201201253905656),\n",
       " ('SMP081', 0.07552229425404675),\n",
       " ('SMP176', 0.0551700473526334),\n",
       " ('SMP092', 0.05313642773284391),\n",
       " ('SMP052', 0.03723489042721181),\n",
       " ('SMP403', 0.03611077701139136),\n",
       " ('SMP271', 0.03497836448261922),\n",
       " ('IB018', 0.032958726243584785),\n",
       " ('SMP516', 0.032958726243584785),\n",
       " ('SMP595', 0.032958726243584785),\n",
       " ('SMP454', 0.032958726243584785),\n",
       " ('SMP383', 0.031266765486842416),\n",
       " ('SMP037', 0.024134505228486057),\n",
       " ('CL157', 0.023567692148003045),\n",
       " ('SMP313', 0.02352570599389319),\n",
       " ('DNp27', 0.019409636649764517),\n",
       " ('SMP148', 0.019162816373922767),\n",
       " ('None', 0.01911653757220244),\n",
       " ('SMP512', 0.019013695790601713),\n",
       " ('SMP162', 0.018859433118200618),\n",
       " ('CL029', 0.017815589034953217),\n",
       " ('CL030', 0.017815589034953217),\n",
       " ('SMP057', 0.017815589034953217),\n",
       " ('SMP251', 0.017815589034953217),\n",
       " ('SMP272', 0.017815589034953217),\n",
       " ('SMP319', 0.017815589034953217),\n",
       " ('SMP320', 0.017815589034953217),\n",
       " ('SMP336', 0.017815589034953217),\n",
       " ('SMP495', 0.017815589034953217),\n",
       " ('SMP554', 0.017815589034953217),\n",
       " ('SMP594', 0.017815589034953217),\n",
       " ('aMe24', 0.017815589034953217)]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sort the pagerank values and their keys\n",
    "sorted(pg.items(), key=lambda x: x[1], reverse=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Connectivity matrix (maybe also hierarchical clustering)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>key</th>\n",
       "      <th>0.0</th>\n",
       "      <th>0.05</th>\n",
       "      <th>0.1</th>\n",
       "      <th>0.5</th>\n",
       "      <th>0.75</th>\n",
       "      <th>1.0</th>\n",
       "      <th>instance</th>\n",
       "      <th>celltype</th>\n",
       "      <th>...</th>\n",
       "      <th>status</th>\n",
       "      <th>cropped</th>\n",
       "      <th>statusLabel</th>\n",
       "      <th>cellBodyFiber</th>\n",
       "      <th>somaRadius</th>\n",
       "      <th>somaLocation</th>\n",
       "      <th>roiInfo</th>\n",
       "      <th>notes</th>\n",
       "      <th>inputRois</th>\n",
       "      <th>outputRois</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>728</th>\n",
       "      <td>361722354</td>\n",
       "      <td>729</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>246</td>\n",
       "      <td>376</td>\n",
       "      <td>340</td>\n",
       "      <td>SMP081_R</td>\n",
       "      <td>SMP081</td>\n",
       "      <td>...</td>\n",
       "      <td>Traced</td>\n",
       "      <td>False</td>\n",
       "      <td>Roughly traced</td>\n",
       "      <td>ADM04</td>\n",
       "      <td>369.5</td>\n",
       "      <td>[23292, 32394, 7488]</td>\n",
       "      <td>{'SNP(R)': {'pre': 1047, 'post': 9802, 'downst...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>['AOTU(R)', 'CRE(-ROB,-RUB)(R)', 'CRE(R)', 'IN...</td>\n",
       "      <td>['AOTU(R)', 'CRE(-ROB,-RUB)(R)', 'CRE(R)', 'IN...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1382</th>\n",
       "      <td>5813057162</td>\n",
       "      <td>1383</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>246</td>\n",
       "      <td>450</td>\n",
       "      <td>428</td>\n",
       "      <td>SMP081_R</td>\n",
       "      <td>SMP081</td>\n",
       "      <td>...</td>\n",
       "      <td>Traced</td>\n",
       "      <td>False</td>\n",
       "      <td>Roughly traced</td>\n",
       "      <td>ADM04</td>\n",
       "      <td>357.0</td>\n",
       "      <td>[23987, 33592, 12152]</td>\n",
       "      <td>{'SNP(R)': {'pre': 876, 'post': 9447, 'downstr...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>['ATL(R)', 'CRE(-ROB,-RUB)(R)', 'CRE(R)', 'IB'...</td>\n",
       "      <td>['ATL(R)', 'CRE(-ROB,-RUB)(R)', 'CRE(R)', 'INP...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              id   key  0.0  0.05  0.1  0.5  0.75  1.0  instance celltype  \\\n",
       "728    361722354   729    1    13    6  246   376  340  SMP081_R   SMP081   \n",
       "1382  5813057162  1383    1    13    6  246   450  428  SMP081_R   SMP081   \n",
       "\n",
       "      ...  status  cropped     statusLabel  cellBodyFiber  somaRadius  \\\n",
       "728   ...  Traced    False  Roughly traced          ADM04       369.5   \n",
       "1382  ...  Traced    False  Roughly traced          ADM04       357.0   \n",
       "\n",
       "               somaLocation  \\\n",
       "728    [23292, 32394, 7488]   \n",
       "1382  [23987, 33592, 12152]   \n",
       "\n",
       "                                                roiInfo notes  \\\n",
       "728   {'SNP(R)': {'pre': 1047, 'post': 9802, 'downst...   NaN   \n",
       "1382  {'SNP(R)': {'pre': 876, 'post': 9447, 'downstr...   NaN   \n",
       "\n",
       "                                              inputRois  \\\n",
       "728   ['AOTU(R)', 'CRE(-ROB,-RUB)(R)', 'CRE(R)', 'IN...   \n",
       "1382  ['ATL(R)', 'CRE(-ROB,-RUB)(R)', 'CRE(R)', 'IB'...   \n",
       "\n",
       "                                             outputRois  \n",
       "728   ['AOTU(R)', 'CRE(-ROB,-RUB)(R)', 'CRE(R)', 'IN...  \n",
       "1382  ['ATL(R)', 'CRE(-ROB,-RUB)(R)', 'CRE(R)', 'INP...  \n",
       "\n",
       "[2 rows x 26 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster1[cluster1['celltype']=='SMP081']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "mod_ids = cluster1[cluster1['0.5']==246]['id'].to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "from neuprint import fetch_simple_connections\n",
    "\n",
    "# fetch simple connections among all the neurons in oviIN's personal connectome\n",
    "clu_res01_connectome = fetch_simple_connections(mod_ids,mod_ids,min_weight=5)\n",
    "\n",
    "# replace None with string 'None' to allow it to be a node in the graph\n",
    "clu_res01_connectome = clu_res01_connectome.fillna('None')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make a pivot table\n",
    "agg_weights_df = clu_res01_connectome.groupby(['type_pre', 'type_post'], sort=False)['weight'].sum().reset_index()\n",
    "matrix = agg_weights_df.pivot(columns='type_post', index='type_pre', values='weight')\n",
    "dtype = clu_res01_connectome['weight'].dtype\n",
    "matrix = matrix.fillna(0).astype(dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
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       "    if (root.Bokeh === undefined) {\n",
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      ],
      "text/plain": [
       ":HeatMap   [columns,index]   (value)"
      ]
     },
     "execution_count": 57,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {
       "id": "p1068"
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# used the example from the neuprint tutotial to make a heatmap\n",
    "matrix.index = matrix.index.astype(str)\n",
    "matrix.columns = matrix.columns.astype(str)\n",
    "conn_fig = matrix.hvplot.heatmap(height=600, width=700, xaxis='top', xlabel='postsynaptic', ylabel='presynaptic', clabel='synapse count', title='Connectivity among cell types', fontscale=1.5).opts(xrotation=60)\n",
    "conn_fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to save the figure\n",
    "hvplot.save(ovi_conn_fig, '/Users/ggutierr/My Drive (ggutierr@barnard.edu)/GitHub/oviIN-analyses-gabrielle/figures/oviposition_connectivity_heatmap.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>key</th>\n",
       "      <th>0.0</th>\n",
       "      <th>0.05</th>\n",
       "      <th>0.1</th>\n",
       "      <th>0.5</th>\n",
       "      <th>0.75</th>\n",
       "      <th>1.0</th>\n",
       "      <th>instance</th>\n",
       "      <th>celltype</th>\n",
       "      <th>...</th>\n",
       "      <th>status</th>\n",
       "      <th>cropped</th>\n",
       "      <th>statusLabel</th>\n",
       "      <th>cellBodyFiber</th>\n",
       "      <th>somaRadius</th>\n",
       "      <th>somaLocation</th>\n",
       "      <th>roiInfo</th>\n",
       "      <th>notes</th>\n",
       "      <th>inputRois</th>\n",
       "      <th>outputRois</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1110</th>\n",
       "      <td>576574889</td>\n",
       "      <td>1111</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>262</td>\n",
       "      <td>337</td>\n",
       "      <td>358</td>\n",
       "      <td>SMP052_R</td>\n",
       "      <td>SMP052</td>\n",
       "      <td>...</td>\n",
       "      <td>Traced</td>\n",
       "      <td>False</td>\n",
       "      <td>Roughly traced</td>\n",
       "      <td>ADM01</td>\n",
       "      <td>319.5</td>\n",
       "      <td>[20595, 30154, 6056]</td>\n",
       "      <td>{'SNP(R)': {'pre': 234, 'post': 3256, 'downstr...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>['ATL(R)', 'CRE(-ROB,-RUB)(R)', 'CRE(R)', 'GOR...</td>\n",
       "      <td>['CRE(-ROB,-RUB)(R)', 'CRE(R)', 'GOR(L)', 'GOR...</td>\n",
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       "      <th>1661</th>\n",
       "      <td>635908014</td>\n",
       "      <td>1662</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>262</td>\n",
       "      <td>337</td>\n",
       "      <td>358</td>\n",
       "      <td>SMP052_R</td>\n",
       "      <td>SMP052</td>\n",
       "      <td>...</td>\n",
       "      <td>Traced</td>\n",
       "      <td>False</td>\n",
       "      <td>Roughly traced</td>\n",
       "      <td>ADM01</td>\n",
       "      <td>345.5</td>\n",
       "      <td>[21799, 31368, 5944]</td>\n",
       "      <td>{'SNP(R)': {'pre': 236, 'post': 2884, 'downstr...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>['ATL(R)', 'CRE(-ROB,-RUB)(R)', 'CRE(R)', 'GOR...</td>\n",
       "      <td>['CRE(-ROB,-RUB)(R)', 'CRE(R)', 'GOR(L)', 'GOR...</td>\n",
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       "<p>2 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             id   key  0.0  0.05  0.1  0.5  0.75  1.0  instance celltype  ...  \\\n",
       "1110  576574889  1111    1    13   15  262   337  358  SMP052_R   SMP052  ...   \n",
       "1661  635908014  1662    1    13   15  262   337  358  SMP052_R   SMP052  ...   \n",
       "\n",
       "      status  cropped     statusLabel  cellBodyFiber  somaRadius  \\\n",
       "1110  Traced    False  Roughly traced          ADM01       319.5   \n",
       "1661  Traced    False  Roughly traced          ADM01       345.5   \n",
       "\n",
       "              somaLocation                                            roiInfo  \\\n",
       "1110  [20595, 30154, 6056]  {'SNP(R)': {'pre': 234, 'post': 3256, 'downstr...   \n",
       "1661  [21799, 31368, 5944]  {'SNP(R)': {'pre': 236, 'post': 2884, 'downstr...   \n",
       "\n",
       "     notes                                          inputRois  \\\n",
       "1110   NaN  ['ATL(R)', 'CRE(-ROB,-RUB)(R)', 'CRE(R)', 'GOR...   \n",
       "1661   NaN  ['ATL(R)', 'CRE(-ROB,-RUB)(R)', 'CRE(R)', 'GOR...   \n",
       "\n",
       "                                             outputRois  \n",
       "1110  ['CRE(-ROB,-RUB)(R)', 'CRE(R)', 'GOR(L)', 'GOR...  \n",
       "1661  ['CRE(-ROB,-RUB)(R)', 'CRE(R)', 'GOR(L)', 'GOR...  \n",
       "\n",
       "[2 rows x 26 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster1[cluster1['celltype']=='SMP052']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "mod_ids = cluster1[cluster1['0.5']==262]['id'].to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "from neuprint import fetch_simple_connections\n",
    "\n",
    "# fetch simple connections among all the neurons in oviIN's personal connectome\n",
    "clu_res01_connectome = fetch_simple_connections(mod_ids,mod_ids,min_weight=5)\n",
    "\n",
    "# replace None with string 'None' to allow it to be a node in the graph\n",
    "clu_res01_connectome = clu_res01_connectome.fillna('None')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make a pivot table\n",
    "agg_weights_df = clu_res01_connectome.groupby(['type_pre', 'type_post'], sort=False)['weight'].sum().reset_index()\n",
    "matrix = agg_weights_df.pivot(columns='type_post', index='type_pre', values='weight')\n",
    "dtype = clu_res01_connectome['weight'].dtype\n",
    "matrix = matrix.fillna(0).astype(dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "</div>\n",
       "<script type=\"application/javascript\">(function(root) {\n",
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400px));\\n}\",{\"id\":\"p1133\"},{\"id\":\"p1131\"},{\"id\":\"p1132\"}],\"margin\":0,\"sizing_mode\":\"stretch_width\",\"align\":\"start\"}}]}}],\"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\":\"copy_to_clipboard1\",\"properties\":[{\"name\":\"fill\",\"kind\":\"Any\",\"default\":\"none\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":null}]},{\"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\":\"8b4df67b-2183-4e57-b6ff-f67148e64e7c\",\"roots\":{\"p1130\":\"bb090fd5-4646-4083-ae5a-5758f77f8024\"},\"root_ids\":[\"p1130\"]}];\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>"
      ],
      "text/plain": [
       ":HeatMap   [columns,index]   (value)"
      ]
     },
     "execution_count": 62,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {
       "id": "p1130"
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# used the example from the neuprint tutotial to make a heatmap\n",
    "matrix.index = matrix.index.astype(str)\n",
    "matrix.columns = matrix.columns.astype(str)\n",
    "conn_fig = matrix.hvplot.heatmap(height=600, width=700, xaxis='top', xlabel='postsynaptic', ylabel='presynaptic', clabel='synapse count', title='Connectivity among cell types', fontscale=1.5).opts(xrotation=60)\n",
    "conn_fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Focusing on SMP052"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "modularity",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}