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    "Fetching data from a CATMAID server\n",
    "***********************************\n",
    "With minor exceptions, ``pymaid`` covers every API endpoint of your CATMAID server\n",
    "- i.e. you should be able to get all the data that your web browser shows (and more)\n",
    "\n",
    "``pymaid`` has >50 different functions that fetch various kinds of data from\n",
    "CATMAID. Too many to cover them all - instead this tutorial will try to give\n",
    "you a flavour by showcasing a few examples.\n",
    "\n",
    "If you know what data you are after, head over to the :ref:`API reference <api>`\n",
    "and search for keywords to find the appropriate function.\n",
    "\n",
    "A quick primer\n",
    "==============\n",
    "All functions that require neurons/skeleton IDs as inputs (e.g. :func:`~pymaid.get_neuron`) accept either:\n",
    "\n",
    "1. Skeleton ID(s) (int or str)::\n",
    "\n",
    "    n = pymaid.get_neuron(16)\n",
    "    nl = pymaid.get_neuron([16, 57241])\n",
    "\n",
    "2. Neuron name(s) (str)::\n",
    "\n",
    "    n = pymaid.get_neuron('PN glomerulus DC3 57242 ML')\n",
    "    nl = pymaid.get_neuron(['PN glomerulus DC3 57242 ML', 'PN glomerulus VA6 017 DB'])\n",
    "\n",
    "3. Annotation(s) (str)::\n",
    "\n",
    "    n = pymaid.get_neuron('annotation:glomerulus VA6')\n",
    "    nl = pymaid.get_neuron(['annotation:glomerulus DC3', 'annotation:glomerulus VA6'])\n",
    "\n",
    "4. ``CatmaidNeuron`` or ``CatmaidNeuronList`` object::\n",
    "\n",
    "    n = pymaid.get_neuron(16)\n",
    "    cn_table = pymaid.get_partners(n)\n",
    "\n",
    "By default, annotations and neuron names must match exactly. You can use regex on names and annotations, using a leading ``/``::\n",
    "\n",
    "    # Get all neurons that have 'VA6' in their name\n",
    "    nl = pymaid.get_neuron('/VA6')\n",
    "\n",
    "    # Get all neurons annotated with a 'VA6'-containing annotation\n",
    "    nl = pymaid.get_neuron('annotation:/VA6')\n",
    "\n",
    "    # Get all skeleton IDs that have an annotation starting with 'AV1.R':\n",
    "    skids = pymaid.get_skids_by_annotation('/^AV1.R')\n",
    "\n",
    "\n",
    "See `here <https://medium.com/factory-mind/regex-tutorial-a-simple-cheatsheet-by-examples-649dc1c3f285>`_ for detailed explanation of regex patterns.\n",
    "\n",
    "\n",
    "Fetching neurons\n",
    "================\n",
    "One of the most basic thing is fetching neurons. First, we need to set up the connection to the CATMAID server. This should already look familiar - if not check out the `quickstart tutorial <tutorial>` first:"
   ]
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     "text": [
      "INFO  : Global CATMAID instance set. Caching is ON. (pymaid)\n"
     ]
    }
   ],
   "source": [
    "import pymaid\n",
    "rm = pymaid.connect_catmaid()"
   ]
  },
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   "source": [
    "Now that's done, we can get going. In 99% of the cases, you will use :func:`pymaid.get_neurons` to fetch skeletons for analysis and visualization."
   ]
  },
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      "                                                            \r"
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       "<div>\n",
       "<style scoped>\n",
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       "        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>type</th>\n",
       "      <th>name</th>\n",
       "      <th>skeleton_id</th>\n",
       "      <th>n_nodes</th>\n",
       "      <th>n_connectors</th>\n",
       "      <th>n_branches</th>\n",
       "      <th>n_leafs</th>\n",
       "      <th>cable_length</th>\n",
       "      <th>soma</th>\n",
       "      <th>units</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CatmaidNeuron</td>\n",
       "      <td>Uniglomerular mALT DA1 lPN 57316 2863105 ML</td>\n",
       "      <td>2863104</td>\n",
       "      <td>6774</td>\n",
       "      <td>470</td>\n",
       "      <td>280</td>\n",
       "      <td>None</td>\n",
       "      <td>1522064.50</td>\n",
       "      <td>[3245741]</td>\n",
       "      <td>1 nanometer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CatmaidNeuron</td>\n",
       "      <td>Uniglomerular mALT DA1 lPN 57382 ML</td>\n",
       "      <td>57381</td>\n",
       "      <td>8243</td>\n",
       "      <td>382</td>\n",
       "      <td>195</td>\n",
       "      <td>None</td>\n",
       "      <td>1337348.75</td>\n",
       "      <td>[3364368]</td>\n",
       "      <td>1 nanometer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CatmaidNeuron</td>\n",
       "      <td>Uniglomerular mALT DA1 lPN 61222 AJ</td>\n",
       "      <td>61221</td>\n",
       "      <td>8578</td>\n",
       "      <td>556</td>\n",
       "      <td>199</td>\n",
       "      <td>None</td>\n",
       "      <td>1353478.75</td>\n",
       "      <td>[3254492]</td>\n",
       "      <td>1 nanometer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CatmaidNeuron</td>\n",
       "      <td>Uniglomerular mALT DA1 lPN 57354 GA</td>\n",
       "      <td>57353</td>\n",
       "      <td>6985</td>\n",
       "      <td>370</td>\n",
       "      <td>225</td>\n",
       "      <td>None</td>\n",
       "      <td>1539364.25</td>\n",
       "      <td>[3254736]</td>\n",
       "      <td>1 nanometer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CatmaidNeuron</td>\n",
       "      <td>Uniglomerular mALT DA1 lPN 57324 LK JSL</td>\n",
       "      <td>57323</td>\n",
       "      <td>6223</td>\n",
       "      <td>481</td>\n",
       "      <td>256</td>\n",
       "      <td>None</td>\n",
       "      <td>1483462.50</td>\n",
       "      <td>[3251355]</td>\n",
       "      <td>1 nanometer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CatmaidNeuron</td>\n",
       "      <td>Uniglomerular mALT DA1 lPN 57358 IJA</td>\n",
       "      <td>755022</td>\n",
       "      <td>7453</td>\n",
       "      <td>515</td>\n",
       "      <td>230</td>\n",
       "      <td>None</td>\n",
       "      <td>1401414.75</td>\n",
       "      <td>[3252148]</td>\n",
       "      <td>1 nanometer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CatmaidNeuron</td>\n",
       "      <td>Uniglomerular mALT DA1 lPN 27296 BH</td>\n",
       "      <td>27295</td>\n",
       "      <td>10216</td>\n",
       "      <td>507</td>\n",
       "      <td>304</td>\n",
       "      <td>None</td>\n",
       "      <td>1806000.25</td>\n",
       "      <td>[3005291]</td>\n",
       "      <td>1 nanometer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CatmaidNeuron</td>\n",
       "      <td>Uniglomerular mlALT DA1 vPN mlALTed Milk 18114...</td>\n",
       "      <td>1811442</td>\n",
       "      <td>11769</td>\n",
       "      <td>804</td>\n",
       "      <td>378</td>\n",
       "      <td>None</td>\n",
       "      <td>2169807.25</td>\n",
       "      <td>[6838672]</td>\n",
       "      <td>1 nanometer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CatmaidNeuron</td>\n",
       "      <td>Uniglomerular mALT DA1 lPN 57312 LK</td>\n",
       "      <td>57311</td>\n",
       "      <td>5543</td>\n",
       "      <td>463</td>\n",
       "      <td>211</td>\n",
       "      <td>None</td>\n",
       "      <td>1364103.25</td>\n",
       "      <td>[3059181]</td>\n",
       "      <td>1 nanometer</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<class 'pymaid.core.CatmaidNeuronList'> of 9 neurons\n",
       "             type                                               name  \\\n",
       "0  CatmaidNeuron        Uniglomerular mALT DA1 lPN 57316 2863105 ML   \n",
       "1  CatmaidNeuron                Uniglomerular mALT DA1 lPN 57382 ML   \n",
       "2  CatmaidNeuron                Uniglomerular mALT DA1 lPN 61222 AJ   \n",
       "3  CatmaidNeuron                Uniglomerular mALT DA1 lPN 57354 GA   \n",
       "4  CatmaidNeuron            Uniglomerular mALT DA1 lPN 57324 LK JSL   \n",
       "5  CatmaidNeuron               Uniglomerular mALT DA1 lPN 57358 IJA   \n",
       "6  CatmaidNeuron                Uniglomerular mALT DA1 lPN 27296 BH   \n",
       "7  CatmaidNeuron  Uniglomerular mlALT DA1 vPN mlALTed Milk 18114...   \n",
       "8  CatmaidNeuron                Uniglomerular mALT DA1 lPN 57312 LK   \n",
       "\n",
       "  skeleton_id  n_nodes  n_connectors  n_branches n_leafs  cable_length  \\\n",
       "0     2863104     6774           470         280    None    1522064.50   \n",
       "1       57381     8243           382         195    None    1337348.75   \n",
       "2       61221     8578           556         199    None    1353478.75   \n",
       "3       57353     6985           370         225    None    1539364.25   \n",
       "4       57323     6223           481         256    None    1483462.50   \n",
       "5      755022     7453           515         230    None    1401414.75   \n",
       "6       27295    10216           507         304    None    1806000.25   \n",
       "7     1811442    11769           804         378    None    2169807.25   \n",
       "8       57311     5543           463         211    None    1364103.25   \n",
       "\n",
       "        soma        units  \n",
       "0  [3245741]  1 nanometer  \n",
       "1  [3364368]  1 nanometer  \n",
       "2  [3254492]  1 nanometer  \n",
       "3  [3254736]  1 nanometer  \n",
       "4  [3251355]  1 nanometer  \n",
       "5  [3252148]  1 nanometer  \n",
       "6  [3005291]  1 nanometer  \n",
       "7  [6838672]  1 nanometer  \n",
       "8  [3059181]  1 nanometer  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "da1 = pymaid.get_neuron('annotation:glomerulus DA1 right')\n",
    "da1"
   ]
  },
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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = da1.plot2d(lw=1.5)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "Fetching volumes\n",
    "================\n",
    "Lets also fetch a volume to put those neurons into context:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<navis.Volume(name=v14.neuropil, color=(120, 120, 120, 0.6), vertices.shape=(8997, 3), faces.shape=(18000, 3))>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vol = pymaid.get_volume('v14.neuropil')\n",
    "vol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Plot neurons'), FloatProgress(value=0.0, max=9.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# For visualization we rely on navis\n",
    "import navis\n",
    "\n",
    "# Make the volume more transparent\n",
    "vol.color = (230, 230, 230, .2)\n",
    "\n",
    "# Plot\n",
    "fig, ax = navis.plot2d([da1, vol], lw=2)\n",
    "\n",
    "# Rotate to frontal view\n",
    "ax.elev = ax.azim = -90\n",
    "ax.dist = 5"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "Obscure functions\n",
    "=================\n",
    "Fetching skeletons and volumes is pretty standard, and fetching connectivity is covered in a :ref:`separate tutorial <cn_tut>`. Let's have a look at some more obscure functions.\n",
    "\n",
    "Find out who contributed to the DA1 neurons we loaded above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                   \r"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'harrisonb': 2587,\n",
       " 'jefferis': 2031,\n",
       " 'schlegelp': 9977,\n",
       " 'koppenhaverb': 3355,\n",
       " 'ratliffj': 2177,\n",
       " 'hallouc': 3328,\n",
       " 'robertsr': 14537,\n",
       " 'masoodpanahn': 1436,\n",
       " 'kmecoval': 5203,\n",
       " 'alij': 3740,\n",
       " 'ranftp': 5930,\n",
       " 'fisherc': 2286,\n",
       " 'imtiazs': 2029,\n",
       " 'adamjohn': 2791,\n",
       " 'allredg': 1696,\n",
       " 'michaelLingelbach': 4253}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cont = pymaid.get_contributor_statistics(da1)\n",
    "\n",
    "# `cont` is a pandas.Series containing e.g. nodes contributed\n",
    "# Let's plot only people with >1000 nodes contributed\n",
    "{k: v for k, v in cont.node_contributors.items() if v > 1000}"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "Have any of these nodes been imported (as opposed to being manually placed):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fetching info: 100%|██████████| 1/1 [00:02<00:00,  2.37s/it]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'2863104': {'n_imported_treenodes': 1524},\n",
       " '57381': {'n_imported_treenodes': 502},\n",
       " '61221': {'n_imported_treenodes': 679},\n",
       " '57353': {'n_imported_treenodes': 1005},\n",
       " '57323': {'n_imported_treenodes': 951},\n",
       " '755022': {'n_imported_treenodes': 1271},\n",
       " '27295': {'n_imported_treenodes': 1161},\n",
       " '1811442': {'n_imported_treenodes': 916},\n",
       " '57311': {'n_imported_treenodes': 608}}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iinfo = pymaid.get_import_info(da1)\n",
    "iinfo"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "Looks like some parts of these neurons have been imported. \n",
    "\n",
    "Last but not least, let's check who has reviewed these neurons:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>node_id</th>\n",
       "      <th>imtiazs</th>\n",
       "      <th>masoodpanahn</th>\n",
       "      <th>kmecoval</th>\n",
       "      <th>fisherc</th>\n",
       "      <th>ratliffj</th>\n",
       "      <th>helmickl</th>\n",
       "      <th>alij</th>\n",
       "      <th>sharifin</th>\n",
       "      <th>robertsr</th>\n",
       "      <th>...</th>\n",
       "      <th>meechank</th>\n",
       "      <th>edmondsona</th>\n",
       "      <th>jamasba</th>\n",
       "      <th>schlegelp</th>\n",
       "      <th>calles</th>\n",
       "      <th>hallouc</th>\n",
       "      <th>adesinaa</th>\n",
       "      <th>lovef</th>\n",
       "      <th>mooree</th>\n",
       "      <th>skeleton_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3047214</td>\n",
       "      <td>2016-05-04 21:05:00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>...</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2863104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3047212</td>\n",
       "      <td>2016-05-04 21:05:00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>...</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2863104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3047210</td>\n",
       "      <td>2016-05-04 21:05:00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>...</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2863104</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   node_id             imtiazs masoodpanahn kmecoval fisherc ratliffj  \\\n",
       "0  3047214 2016-05-04 21:05:00          NaT      NaT     NaT      NaT   \n",
       "1  3047212 2016-05-04 21:05:00          NaT      NaT     NaT      NaT   \n",
       "2  3047210 2016-05-04 21:05:00          NaT      NaT     NaT      NaT   \n",
       "\n",
       "  helmickl alij sharifin robertsr  ... meechank edmondsona jamasba schlegelp  \\\n",
       "0      NaT  NaT      NaT      NaT  ...      NaT        NaT     NaT       NaT   \n",
       "1      NaT  NaT      NaT      NaT  ...      NaT        NaT     NaT       NaT   \n",
       "2      NaT  NaT      NaT      NaT  ...      NaT        NaT     NaT       NaT   \n",
       "\n",
       "  calles hallouc adesinaa lovef mooree skeleton_id  \n",
       "0    NaT     NaT      NaT   NaT    NaT     2863104  \n",
       "1    NaT     NaT      NaT   NaT    NaT     2863104  \n",
       "2    NaT     NaT      NaT   NaT    NaT     2863104  \n",
       "\n",
       "[3 rows x 24 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rev = pymaid.get_review_details(da1)\n",
    "\n",
    "# `rev` is a DataFrame that contains timestamps of when a user has reviewed a given node\n",
    "rev.head(3)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "Scaling things up\n",
    "=================\n",
    "So far, the examples have pulled only a small amount of data but ``pymaid`` scales really well. Let's demonstrate how to fetch all neurons in the FAFB dataset, shall we?\n",
    "\n",
    "First we need to compile a list of neurons above a certain size and with a soma using :func:`pymaid.find_neurons`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO  : Get all neurons with >= 1000 nodes (pymaid)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Make nrn'), FloatProgress(value=0.0, max=11939.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO  : Found 7920 neurons matching the search parameters (pymaid)\n"
     ]
    }
   ],
   "source": [
    "all_fafb = pymaid.find_neurons(min_size=1000, only_soma=True)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "``all_fafb`` is now a list of 7920 neurons. At the moment these neurons only consist of skeleton ID and a name - any other data such as nodes or connectors are not (yet) loaded. Note the ``NAs``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>name</th>\n",
       "      <th>skeleton_id</th>\n",
       "      <th>n_nodes</th>\n",
       "      <th>n_connectors</th>\n",
       "      <th>n_branches</th>\n",
       "      <th>n_leafs</th>\n",
       "      <th>cable_length</th>\n",
       "      <th>soma</th>\n",
       "      <th>units</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CatmaidNeuron</td>\n",
       "      <td>PD5g1_L#3 8880132 ECM Tarly family left</td>\n",
       "      <td>8880131</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>1 nanometer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CatmaidNeuron</td>\n",
       "      <td>KCaBx 12719 CF</td>\n",
       "      <td>10190852</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>1 nanometer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CatmaidNeuron</td>\n",
       "      <td>aSP-g type tract crossing alpha lobe neuron 98...</td>\n",
       "      <td>983054</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>1 nanometer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CatmaidNeuron</td>\n",
       "      <td>Uniglomerular mALT VA6 adPN 017 DB</td>\n",
       "      <td>16</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>1 nanometer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CatmaidNeuron</td>\n",
       "      <td>Multiglomerular mALT l2PN VM1+VP2+VP3+23 LTS 0...</td>\n",
       "      <td>32793</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>1 nanometer</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<class 'pymaid.core.CatmaidNeuronList'> of 5 neurons\n",
       "             type                                               name  \\\n",
       "0  CatmaidNeuron            PD5g1_L#3 8880132 ECM Tarly family left   \n",
       "1  CatmaidNeuron                                     KCaBx 12719 CF   \n",
       "2  CatmaidNeuron  aSP-g type tract crossing alpha lobe neuron 98...   \n",
       "3  CatmaidNeuron                 Uniglomerular mALT VA6 adPN 017 DB   \n",
       "4  CatmaidNeuron  Multiglomerular mALT l2PN VM1+VP2+VP3+23 LTS 0...   \n",
       "\n",
       "  skeleton_id n_nodes n_connectors n_branches n_leafs cable_length soma  \\\n",
       "0     8880131      NA           NA         NA      NA           NA   NA   \n",
       "1    10190852      NA           NA         NA      NA           NA   NA   \n",
       "2      983054      NA           NA         NA      NA           NA   NA   \n",
       "3          16      NA           NA         NA      NA           NA   NA   \n",
       "4       32793      NA           NA         NA      NA           NA   NA   \n",
       "\n",
       "         units  \n",
       "0  1 nanometer  \n",
       "1  1 nanometer  \n",
       "2  1 nanometer  \n",
       "3  1 nanometer  \n",
       "4  1 nanometer  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_fafb[:5]"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "The not-yet-available data is lazily loaded on request:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>node_id</th>\n",
       "      <th>parent_id</th>\n",
       "      <th>creator_id</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>radius</th>\n",
       "      <th>confidence</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>38680419</td>\n",
       "      <td>38680415</td>\n",
       "      <td>127</td>\n",
       "      <td>649397.0</td>\n",
       "      <td>128668.0</td>\n",
       "      <td>170760.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>5</td>\n",
       "      <td>slab</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38680424</td>\n",
       "      <td>38680419</td>\n",
       "      <td>127</td>\n",
       "      <td>649497.0</td>\n",
       "      <td>128728.0</td>\n",
       "      <td>170840.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>5</td>\n",
       "      <td>slab</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38680426</td>\n",
       "      <td>38680424</td>\n",
       "      <td>127</td>\n",
       "      <td>649733.0</td>\n",
       "      <td>128876.0</td>\n",
       "      <td>170920.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>5</td>\n",
       "      <td>slab</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>38680436</td>\n",
       "      <td>38680432</td>\n",
       "      <td>127</td>\n",
       "      <td>649945.0</td>\n",
       "      <td>129012.0</td>\n",
       "      <td>171040.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>5</td>\n",
       "      <td>slab</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>38680432</td>\n",
       "      <td>38680426</td>\n",
       "      <td>127</td>\n",
       "      <td>649901.0</td>\n",
       "      <td>128984.0</td>\n",
       "      <td>171000.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>5</td>\n",
       "      <td>slab</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    node_id  parent_id creator_id         x         y         z  radius  \\\n",
       "0  38680419   38680415        127  649397.0  128668.0  170760.0    51.0   \n",
       "1  38680424   38680419        127  649497.0  128728.0  170840.0    51.0   \n",
       "2  38680426   38680424        127  649733.0  128876.0  170920.0    51.0   \n",
       "3  38680436   38680432        127  649945.0  129012.0  171040.0    51.0   \n",
       "4  38680432   38680426        127  649901.0  128984.0  171000.0    51.0   \n",
       "\n",
       "  confidence  type  \n",
       "0          5  slab  \n",
       "1          5  slab  \n",
       "2          5  slab  \n",
       "3          5  slab  \n",
       "4          5  slab  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_fafb[0].nodes.head()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "Note how the first neuron is now the only one with more detailed info in the summary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CatmaidNeuron</td>\n",
       "      <td>PD5g1_L#3 8880132 ECM Tarly family left</td>\n",
       "      <td>8880131</td>\n",
       "      <td>3129</td>\n",
       "      <td>96</td>\n",
       "      <td>126</td>\n",
       "      <td>None</td>\n",
       "      <td>883607</td>\n",
       "      <td>[38712262]</td>\n",
       "      <td>1 nanometer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CatmaidNeuron</td>\n",
       "      <td>KCaBx 12719 CF</td>\n",
       "      <td>10190852</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>1 nanometer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CatmaidNeuron</td>\n",
       "      <td>aSP-g type tract crossing alpha lobe neuron 98...</td>\n",
       "      <td>983054</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>1 nanometer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CatmaidNeuron</td>\n",
       "      <td>Uniglomerular mALT VA6 adPN 017 DB</td>\n",
       "      <td>16</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>1 nanometer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CatmaidNeuron</td>\n",
       "      <td>Multiglomerular mALT l2PN VM1+VP2+VP3+23 LTS 0...</td>\n",
       "      <td>32793</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>1 nanometer</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<class 'pymaid.core.CatmaidNeuronList'> of 5 neurons\n",
       "             type                                               name  \\\n",
       "0  CatmaidNeuron            PD5g1_L#3 8880132 ECM Tarly family left   \n",
       "1  CatmaidNeuron                                     KCaBx 12719 CF   \n",
       "2  CatmaidNeuron  aSP-g type tract crossing alpha lobe neuron 98...   \n",
       "3  CatmaidNeuron                 Uniglomerular mALT VA6 adPN 017 DB   \n",
       "4  CatmaidNeuron  Multiglomerular mALT l2PN VM1+VP2+VP3+23 LTS 0...   \n",
       "\n",
       "  skeleton_id n_nodes n_connectors n_branches n_leafs cable_length  \\\n",
       "0     8880131    3129           96        126    None       883607   \n",
       "1    10190852      NA           NA         NA      NA           NA   \n",
       "2      983054      NA           NA         NA      NA           NA   \n",
       "3          16      NA           NA         NA      NA           NA   \n",
       "4       32793      NA           NA         NA      NA           NA   \n",
       "\n",
       "         soma        units  \n",
       "0  [38712262]  1 nanometer  \n",
       "1          NA  1 nanometer  \n",
       "2          NA  1 nanometer  \n",
       "3          NA  1 nanometer  \n",
       "4          NA  1 nanometer  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_fafb[:5]"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "So in theory, you now have about ~8k neurons to work with. But while the lazy loading is neat, it is also rather slow because it is done sequentially (i.e. one neuron at a time). If you want skeleton information (nodes + connectors) for all these neurons, you are best off triggering a full download which will use parallel threads:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_fafb.get_skeletons()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "By default, ``pymaid`` uses 100 parallel threads to fetch that data. On slow internet connections, this can cause errors. To reduce the maximum number of parallel threads, you can do this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rm.max_threads = 10"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "That's it for now. Check out the :ref:`API reference <api>` for a full list of available functions."
   ]
  }
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