.. _index_neuronlists:

Indexing CatmaidNeuronLists
****************************
A :class:`pymaid.CatmaidNeuronList` is a subclass of :class:`navis:navis.NeuronList` and as such behaves similar to pandas DataFrames in that it allow some fancing indexing:

To illustrate, let's first get a bunch of neurons

.. code:: ipython3

    skids = pymaid.get_neurons_in_volume('AL_L')
    nl = pymaid.get_neurons(skids)
    nl.head()




.. raw:: html

    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }
    
        .dataframe tbody tr th {
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            text-align: right;
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    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>neuron_name</th>
          <th>skeleton_id</th>
          <th>n_nodes</th>
          <th>n_connectors</th>
          <th>n_branch_nodes</th>
          <th>n_end_nodes</th>
          <th>open_ends</th>
          <th>cable_length</th>
          <th>review_status</th>
          <th>soma</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>neuron 4562948</td>
          <td>4562947</td>
          <td>2</td>
          <td>0</td>
          <td>0</td>
          <td>1</td>
          <td>1</td>
          <td>3.945403</td>
          <td>NA</td>
          <td>False</td>
        </tr>
        <tr>
          <th>1</th>
          <td>neuron 7094282 MWP Hogeweg</td>
          <td>7094281</td>
          <td>4267</td>
          <td>64</td>
          <td>56</td>
          <td>57</td>
          <td>42</td>
          <td>1537.999647</td>
          <td>NA</td>
          <td>True</td>
        </tr>
        <tr>
          <th>2</th>
          <td>neuron 8216644 NS</td>
          <td>8216643</td>
          <td>5934</td>
          <td>140</td>
          <td>163</td>
          <td>166</td>
          <td>141</td>
          <td>2330.199132</td>
          <td>NA</td>
          <td>False</td>
        </tr>
        <tr>
          <th>3</th>
          <td>Multiglomerular PN mALT 57431 IJA ECM</td>
          <td>57430</td>
          <td>5436</td>
          <td>205</td>
          <td>101</td>
          <td>106</td>
          <td>87</td>
          <td>1389.803214</td>
          <td>NA</td>
          <td>True</td>
        </tr>
        <tr>
          <th>4</th>
          <td>aSP-g tract 6725720 KMS</td>
          <td>6725719</td>
          <td>2251</td>
          <td>29</td>
          <td>79</td>
          <td>84</td>
          <td>38</td>
          <td>842.488107</td>
          <td>NA</td>
          <td>True</td>
        </tr>
      </tbody>
    </table>
    </div>



Index by attributes
+++++++++++++++++++

You can index by all :class:`pymaid.CatmaidNeuronList`/:class:`navis:navis.NeuronList` attributes that return a ``numpy.array``. For example ``n_nodes``, ``cable_length``, ``soma``, etc.

Index using node count

.. code:: ipython3

    subset = nl[nl.n_nodes > 6000]
    subset.head()




.. raw:: html

    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
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            text-align: right;
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    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>neuron_name</th>
          <th>skeleton_id</th>
          <th>n_nodes</th>
          <th>n_connectors</th>
          <th>n_branch_nodes</th>
          <th>n_end_nodes</th>
          <th>open_ends</th>
          <th>cable_length</th>
          <th>review_status</th>
          <th>soma</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>Multiglomerular bilateral PN mALT 57435 LK</td>
          <td>57434</td>
          <td>8582</td>
          <td>155</td>
          <td>161</td>
          <td>168</td>
          <td>82</td>
          <td>1415.474701</td>
          <td>NA</td>
          <td>True</td>
        </tr>
        <tr>
          <th>1</th>
          <td>PBG5-EBw-gall (right) neuron 341414 EWN AW</td>
          <td>4210786</td>
          <td>29397</td>
          <td>3107</td>
          <td>1886</td>
          <td>1955</td>
          <td>0</td>
          <td>4875.387524</td>
          <td>NA</td>
          <td>True</td>
        </tr>
        <tr>
          <th>2</th>
          <td>Multiglomerular PN mALT bilateral 57476 IJA</td>
          <td>57475</td>
          <td>6737</td>
          <td>174</td>
          <td>192</td>
          <td>196</td>
          <td>63</td>
          <td>1236.637869</td>
          <td>NA</td>
          <td>True</td>
        </tr>
        <tr>
          <th>3</th>
          <td>putative OA mALT 57480 GA FML</td>
          <td>57479</td>
          <td>8558</td>
          <td>226</td>
          <td>202</td>
          <td>207</td>
          <td>116</td>
          <td>1965.510890</td>
          <td>NA</td>
          <td>True</td>
        </tr>
        <tr>
          <th>4</th>
          <td>PN glomerulus VL1 57500 ML</td>
          <td>57499</td>
          <td>6551</td>
          <td>530</td>
          <td>303</td>
          <td>322</td>
          <td>111</td>
          <td>1854.932978</td>
          <td>NA</td>
          <td>True</td>
        </tr>
      </tbody>
    </table>
    </div>



Subset to neurons that have a soma

.. code:: ipython3

    subset = nl[nl.soma != None]
    subset.head()




.. raw:: html

    <div>
    <style scoped>
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        }
    
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            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>neuron_name</th>
          <th>skeleton_id</th>
          <th>n_nodes</th>
          <th>n_connectors</th>
          <th>n_branch_nodes</th>
          <th>n_end_nodes</th>
          <th>open_ends</th>
          <th>cable_length</th>
          <th>review_status</th>
          <th>soma</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>neuron 7094282 MWP Hogeweg</td>
          <td>7094281</td>
          <td>4267</td>
          <td>64</td>
          <td>56</td>
          <td>57</td>
          <td>42</td>
          <td>1537.999647</td>
          <td>NA</td>
          <td>True</td>
        </tr>
        <tr>
          <th>1</th>
          <td>Multiglomerular PN mALT 57431 IJA ECM</td>
          <td>57430</td>
          <td>5436</td>
          <td>205</td>
          <td>101</td>
          <td>106</td>
          <td>87</td>
          <td>1389.803214</td>
          <td>NA</td>
          <td>True</td>
        </tr>
        <tr>
          <th>2</th>
          <td>aSP-g tract 6725720 KMS</td>
          <td>6725719</td>
          <td>2251</td>
          <td>29</td>
          <td>79</td>
          <td>84</td>
          <td>38</td>
          <td>842.488107</td>
          <td>NA</td>
          <td>True</td>
        </tr>
        <tr>
          <th>3</th>
          <td>Multiglomerular bilateral PN mALT 57435 LK</td>
          <td>57434</td>
          <td>8582</td>
          <td>155</td>
          <td>161</td>
          <td>168</td>
          <td>82</td>
          <td>1415.474701</td>
          <td>NA</td>
          <td>True</td>
        </tr>
        <tr>
          <th>4</th>
          <td>PBG5-EBw-gall (right) neuron 341414 EWN AW</td>
          <td>4210786</td>
          <td>29397</td>
          <td>3107</td>
          <td>1886</td>
          <td>1955</td>
          <td>0</td>
          <td>4875.387524</td>
          <td>NA</td>
          <td>True</td>
        </tr>
      </tbody>
    </table>
    </div>



Index by skeleton ID
++++++++++++++++++++

Indexing by skeleton ID(s) uses the ``.skid`` indexer:

Index by single skeleton ID

.. code:: ipython3

    subset = nl.skid['57499']
    subset




.. raw:: html

    <div>
    <style scoped>
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    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th></th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>type</th>
          <td>&lt;class 'pymaid.core.CatmaidNeuron'&gt;</td>
        </tr>
        <tr>
          <th>neuron_name</th>
          <td>PN glomerulus VL1 57500 ML</td>
        </tr>
        <tr>
          <th>skeleton_id</th>
          <td>57499</td>
        </tr>
        <tr>
          <th>n_nodes</th>
          <td>6551</td>
        </tr>
        <tr>
          <th>n_connectors</th>
          <td>530</td>
        </tr>
        <tr>
          <th>n_branch_nodes</th>
          <td>303</td>
        </tr>
        <tr>
          <th>n_end_nodes</th>
          <td>322</td>
        </tr>
        <tr>
          <th>n_open_ends</th>
          <td>111</td>
        </tr>
        <tr>
          <th>cable_length</th>
          <td>1854.93</td>
        </tr>
        <tr>
          <th>review_status</th>
          <td>NA</td>
        </tr>
        <tr>
          <th>soma</th>
          <td>3247378</td>
        </tr>
      </tbody>
    </table>
    </div>



Index by list of skeleton IDs

.. code:: ipython3

    subset = nl.skid [[57499, 57479]]
    subset




.. raw:: html

    <div>
    <style scoped>
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    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>neuron_name</th>
          <th>skeleton_id</th>
          <th>n_nodes</th>
          <th>n_connectors</th>
          <th>n_branch_nodes</th>
          <th>n_end_nodes</th>
          <th>open_ends</th>
          <th>cable_length</th>
          <th>review_status</th>
          <th>soma</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>putative OA mALT 57480 GA FML</td>
          <td>57479</td>
          <td>8558</td>
          <td>226</td>
          <td>202</td>
          <td>207</td>
          <td>116</td>
          <td>1965.510890</td>
          <td>NA</td>
          <td>True</td>
        </tr>
        <tr>
          <th>1</th>
          <td>PN glomerulus VL1 57500 ML</td>
          <td>57499</td>
          <td>6551</td>
          <td>530</td>
          <td>303</td>
          <td>322</td>
          <td>111</td>
          <td>1854.932978</td>
          <td>NA</td>
          <td>True</td>
        </tr>
      </tbody>
    </table>
    </div>



Index by neuron name
++++++++++++++++++++

If you index a :class:`pymaid.CatmaidNeuronList` by a name (i.e. something that can't be converted into an integer), it will assumed to be a neuron name:

.. code:: ipython3

    subset = nl['PN glomerulus VL1 57500 ML']
    subset




.. raw:: html

    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
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    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>neuron_name</th>
          <th>skeleton_id</th>
          <th>n_nodes</th>
          <th>n_connectors</th>
          <th>n_branch_nodes</th>
          <th>n_end_nodes</th>
          <th>open_ends</th>
          <th>cable_length</th>
          <th>review_status</th>
          <th>soma</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>PN glomerulus VL1 57500 ML</td>
          <td>57499</td>
          <td>6551</td>
          <td>530</td>
          <td>303</td>
          <td>322</td>
          <td>111</td>
          <td>1854.932978</td>
          <td>NA</td>
          <td>True</td>
        </tr>
      </tbody>
    </table>
    </div>



Index by annotation
+++++++++++++++++++
Indexing by annotation(s) uses the :func:`~pymaid.CatmaidNeuronList.has_annotation` function. This is something unique to :class:`~pymaid.CatmaidNeuronList` and won't work with :class:`navis:navis.NeuronList`.

.. code:: ipython3

    subset = nl.has_annotation('LH_DONE', intersect=False)
    subset.head()




.. raw:: html

    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }
    
        .dataframe tbody tr th {
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    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>neuron_name</th>
          <th>skeleton_id</th>
          <th>n_nodes</th>
          <th>n_connectors</th>
          <th>n_branch_nodes</th>
          <th>n_end_nodes</th>
          <th>open_ends</th>
          <th>cable_length</th>
          <th>review_status</th>
          <th>soma</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>PN glomerulus VL1 57500 ML</td>
          <td>57499</td>
          <td>6551</td>
          <td>530</td>
          <td>303</td>
          <td>322</td>
          <td>111</td>
          <td>1854.932978</td>
          <td>NA</td>
          <td>True</td>
        </tr>
        <tr>
          <th>1</th>
          <td>Multiglomerular PN mALT 57537 LK-NM</td>
          <td>57536</td>
          <td>14654</td>
          <td>1233</td>
          <td>850</td>
          <td>906</td>
          <td>278</td>
          <td>3193.380472</td>
          <td>NA</td>
          <td>True</td>
        </tr>
        <tr>
          <th>2</th>
          <td>PN glomerulus VL1 73938 LK</td>
          <td>73937</td>
          <td>5273</td>
          <td>452</td>
          <td>259</td>
          <td>274</td>
          <td>119</td>
          <td>1610.767540</td>
          <td>NA</td>
          <td>True</td>
        </tr>
        <tr>
          <th>3</th>
          <td>AL.L(DA1) -{mALT}-&gt; CAL.L-LH.L 2319458 PN036 D...</td>
          <td>2319457</td>
          <td>10209</td>
          <td>964</td>
          <td>431</td>
          <td>458</td>
          <td>90</td>
          <td>1747.511710</td>
          <td>NA</td>
          <td>True</td>
        </tr>
        <tr>
          <th>4</th>
          <td>PN glomerulus DA2 2467660 RJVR</td>
          <td>2467659</td>
          <td>2855</td>
          <td>266</td>
          <td>54</td>
          <td>56</td>
          <td>8</td>
          <td>726.656270</td>
          <td>NA</td>
          <td>True</td>
        </tr>
      </tbody>
    </table>
    </div>



:func:`pymaid.CatmaidNeuronList.has_annotation` allows for some more sophisticated intersections and criteria. For example, leading a string with "~" (tilde), works as a *negative* indicator:

.. code:: ipython3

    subset = nl.has_annotation('~LH_DONE', intersect=False)
    subset.head()




.. raw:: html

    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }
    
        .dataframe tbody tr th {
            vertical-align: top;
        }
    
        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>neuron_name</th>
          <th>skeleton_id</th>
          <th>n_nodes</th>
          <th>n_connectors</th>
          <th>n_branch_nodes</th>
          <th>n_end_nodes</th>
          <th>open_ends</th>
          <th>cable_length</th>
          <th>review_status</th>
          <th>soma</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>neuron 4562948</td>
          <td>4562947</td>
          <td>2</td>
          <td>0</td>
          <td>0</td>
          <td>1</td>
          <td>1</td>
          <td>3.945403</td>
          <td>NA</td>
          <td>False</td>
        </tr>
        <tr>
          <th>1</th>
          <td>neuron 7094282 MWP Hogeweg</td>
          <td>7094281</td>
          <td>4267</td>
          <td>64</td>
          <td>56</td>
          <td>57</td>
          <td>42</td>
          <td>1537.999647</td>
          <td>NA</td>
          <td>True</td>
        </tr>
        <tr>
          <th>2</th>
          <td>neuron 8216644 NS</td>
          <td>8216643</td>
          <td>5934</td>
          <td>140</td>
          <td>163</td>
          <td>166</td>
          <td>141</td>
          <td>2330.199132</td>
          <td>NA</td>
          <td>False</td>
        </tr>
        <tr>
          <th>3</th>
          <td>Multiglomerular PN mALT 57431 IJA ECM</td>
          <td>57430</td>
          <td>5436</td>
          <td>205</td>
          <td>101</td>
          <td>106</td>
          <td>87</td>
          <td>1389.803214</td>
          <td>NA</td>
          <td>True</td>
        </tr>
        <tr>
          <th>4</th>
          <td>aSP-g tract 6725720 KMS</td>
          <td>6725719</td>
          <td>2251</td>
          <td>29</td>
          <td>79</td>
          <td>84</td>
          <td>38</td>
          <td>842.488107</td>
          <td>NA</td>
          <td>True</td>
        </tr>
      </tbody>
    </table>
    </div>



Index XOR (either or) by multiple annotations

.. code:: ipython3

    subset = nl.has_annotation(['LH_DONE', 'glomerulus DL1'], intersect=False)
    subset.head()




.. raw:: html

    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }
    
        .dataframe tbody tr th {
            vertical-align: top;
        }
    
        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>neuron_name</th>
          <th>skeleton_id</th>
          <th>n_nodes</th>
          <th>n_connectors</th>
          <th>n_branch_nodes</th>
          <th>n_end_nodes</th>
          <th>open_ends</th>
          <th>cable_length</th>
          <th>review_status</th>
          <th>soma</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>PN glomerulus VL1 57500 ML</td>
          <td>57499</td>
          <td>6551</td>
          <td>530</td>
          <td>303</td>
          <td>322</td>
          <td>111</td>
          <td>1854.932978</td>
          <td>NA</td>
          <td>True</td>
        </tr>
        <tr>
          <th>1</th>
          <td>Multiglomerular PN mALT 57537 LK-NM</td>
          <td>57536</td>
          <td>14654</td>
          <td>1233</td>
          <td>850</td>
          <td>906</td>
          <td>278</td>
          <td>3193.380472</td>
          <td>NA</td>
          <td>True</td>
        </tr>
        <tr>
          <th>2</th>
          <td>PN glomerulus VL1 73938 LK</td>
          <td>73937</td>
          <td>5273</td>
          <td>452</td>
          <td>259</td>
          <td>274</td>
          <td>119</td>
          <td>1610.767540</td>
          <td>NA</td>
          <td>True</td>
        </tr>
        <tr>
          <th>3</th>
          <td>AL.L(DA1) -{mALT}-&gt; CAL.L-LH.L 2319458 PN036 D...</td>
          <td>2319457</td>
          <td>10209</td>
          <td>964</td>
          <td>431</td>
          <td>458</td>
          <td>90</td>
          <td>1747.511710</td>
          <td>NA</td>
          <td>True</td>
        </tr>
        <tr>
          <th>4</th>
          <td>PN glomerulus DA2 2467660 RJVR</td>
          <td>2467659</td>
          <td>2855</td>
          <td>266</td>
          <td>54</td>
          <td>56</td>
          <td>8</td>
          <td>726.656270</td>
          <td>NA</td>
          <td>True</td>
        </tr>
      </tbody>
    </table>
    </div>



Index XAND (all required) by multiple annotations

.. code:: ipython3

    subset = nl.has_annotation(['LH_DONE', 'glomerulus DL1'], intersect=True)
    subset.head()




.. raw:: html

    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }
    
        .dataframe tbody tr th {
            vertical-align: top;
        }
    
        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>neuron_name</th>
          <th>skeleton_id</th>
          <th>n_nodes</th>
          <th>n_connectors</th>
          <th>n_branch_nodes</th>
          <th>n_end_nodes</th>
          <th>open_ends</th>
          <th>cable_length</th>
          <th>review_status</th>
          <th>soma</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>PN glomerulus DL1 5269365 GSXEJ</td>
          <td>5269364</td>
          <td>3477</td>
          <td>893</td>
          <td>167</td>
          <td>181</td>
          <td>58</td>
          <td>1228.339971</td>
          <td>NA</td>
          <td>True</td>
        </tr>
        <tr>
          <th>1</th>
          <td>PN glomerulus DL1 5305038 ARJ</td>
          <td>5305037</td>
          <td>2818</td>
          <td>898</td>
          <td>128</td>
          <td>138</td>
          <td>15</td>
          <td>1050.094319</td>
          <td>NA</td>
          <td>True</td>
        </tr>
      </tbody>
    </table>
    </div>