.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "tutorials/tutorial_examples/plot_001_afq_api.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_tutorials_tutorial_examples_plot_001_afq_api.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_tutorials_tutorial_examples_plot_001_afq_api.py:


======================================
Getting started programming with pyAFQ
======================================

There are two ways to :doc:`use pyAFQ </howto/getting_started>`: through the
command line interface, and by writing Python code. This tutorial will walk you
through the basics of the latter, using pyAFQ's Python Application Programming
Interface (API).

.. GENERATED FROM PYTHON SOURCE LINES 12-23

.. code-block:: Python

    import os.path as op

    import matplotlib.pyplot as plt
    import nibabel as nib
    import plotly
    import pandas as pd

    from AFQ.api.group import GroupAFQ
    import AFQ.data.fetch as afd
    import AFQ.viz.altair as ava








.. GENERATED FROM PYTHON SOURCE LINES 24-50

Example data
------------
pyAFQ assumes that the data is organized in a BIDS compliant directory.
To get users started with this tutorial, we will download some example
data and organize it in a BIDS compliant way (for more details on how
BIDS is used in pyAFQ, refer to :ref:`bids_tutorial`).

The following call dowloads a dataset that contains a single subject's
high angular resolution diffusion imaging (HARDI) data, collected at the
Stanford Vista Lab

.. note::
  See https://purl.stanford.edu/ng782rw8378 for details on dataset.

The data are downloaded and organized locally into a BIDS compliant
anatomical data folder (``anat``) and a diffusion-weighted imaging data
(``dwi``) folder, which are both placed in the user's home directory under::

  ``~/AFQ_data/stanford_hardi/``

The data is also placed in a derivatives directory, signifying that it has
already undergone the required preprocessing necessary for pyAFQ to run.

The clear_previous_afq is used to remove any previous runs of the afq object
stored in the `~/AFQ_data/stanford_hardi/` BIDS directory. Set it to None if
you want to use the results of previous runs.

.. GENERATED FROM PYTHON SOURCE LINES 50-53

.. code-block:: Python


    afd.organize_stanford_data(clear_previous_afq="track")








.. GENERATED FROM PYTHON SOURCE LINES 54-63

Set tractography parameters (optional)
---------------------------------------
We make create a `tracking_params` variable, which we will pass to the
GroupAFQ object which specifies that we want 25,000 seeds randomly
distributed in the white matter. We only do this to make this example 
faster and consume less space. We also set ``num_chunks`` to `True`,
which will use ray to parallelize the tracking across all cores.
This can be removed to process in serial, or set to use a particular
distribution of work by setting `n_chunks` to an integer number.

.. GENERATED FROM PYTHON SOURCE LINES 63-70

.. code-block:: Python


    tracking_params = dict(n_seeds=25000,
                           random_seeds=True,
                           rng_seed=2022,
                           trx=True,
                           num_chunks=True)








.. GENERATED FROM PYTHON SOURCE LINES 71-95

Initialize a GroupAFQ object:
-------------------------

Creates a GroupAFQ object, that encapsulates tractometry. This object can be
used to manage the entire :doc:`AFQ pipeline`, including:

- Tractography
- Registration
- Segmentation
- Cleaning
- Profiling
- Visualization

This will also create an output folder for the corresponding AFQ derivatives
in the AFQ data directory: ``AFQ_data/stanford_hardi/derivatives/afq/``

To initialize this object we will pass in the path location to our BIDS
compliant data, the name of the preprocessing pipeline we want to use, and
the tracking parameters we defined above. We will also specify the
visualization backend we want to use (see below for more details).
We will also be using plotly to generate an interactive visualization.
The value `plotly_no_gif` indicates that interactive visualizations will be
generated as html web-pages that can be opened in a browser, but not as
static gif files.

.. GENERATED FROM PYTHON SOURCE LINES 95-102

.. code-block:: Python


    myafq = GroupAFQ(
        bids_path=op.join(afd.afq_home, 'stanford_hardi'),
        preproc_pipeline='vistasoft',
        tracking_params=tracking_params,
        viz_backend_spec='plotly_no_gif')








.. GENERATED FROM PYTHON SOURCE LINES 103-125

Calculating DTI FA (Diffusion Tensor Imaging Fractional Anisotropy)
------------------------------------------------------------------
The GroupAFQ object has a method called `export`, which allows the user
to calculate various derived quantities from the data.

For example, FA can be computed using the DTI model, by explicitly
calling `myafq.export("dti_fa")`. This triggers the computation of DTI
parameters for all subjects in the dataset, and stores the results in
the AFQ derivatives directory. In addition, it calculates the FA
from these parameters and stores it in a different file in the same
directory.

.. note::

   The AFQ API computes quantities lazily. This means that DTI parameters
   are not computed until they are required. This means that the first
   line below is the one that requires time.

The result of the call to `export` is a dictionary, with the subject
IDs as keys, and the filenames of the corresponding files as values.
This means that to extract the filename corresponding to the FA of the first
subject, we can do:

.. GENERATED FROM PYTHON SOURCE LINES 125-134

.. code-block:: Python


    FA_fname = myafq.export("dti_fa")["01"]

    # We will then use `nibabel` to load the deriviative file and retrieve the
    # data array.

    FA_img = nib.load(FA_fname)
    FA = FA_img.get_fdata()








.. GENERATED FROM PYTHON SOURCE LINES 135-146

Visualize the result with Matplotlib
-------------------------------------
At this point `FA` is an array, and we can use standard Python tools to
visualize it or perform additional computations with it.

In this case we are going to take an axial slice halfway through the
FA data array and plot using a sequential color map.

.. note::

   The data array is structured as a xyz coordinate system.

.. GENERATED FROM PYTHON SOURCE LINES 146-151

.. code-block:: Python


    fig, ax = plt.subplots(1)
    ax.matshow(FA[:, :, FA.shape[-1] // 2], cmap='viridis')
    ax.axis("off")




.. image-sg:: /tutorials/tutorial_examples/images/sphx_glr_plot_001_afq_api_001.png
   :alt: plot 001 afq api
   :srcset: /tutorials/tutorial_examples/images/sphx_glr_plot_001_afq_api_001.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    (-0.5, 105.5, 80.5, -0.5)



.. GENERATED FROM PYTHON SOURCE LINES 152-164

Recognizing the bundles and calculating tract profiles:
-----------------------------------------------------
Typically, users of pyAFQ are interested in calculating not only an overall
map of the FA, but also the major white matter pathways (or bundles) and
tract profiles of tissue properties along their length. To trigger the
pyAFQ pipeline that calculates the profiles, users can call the
`export('profiles')` method:

.. note::
   Running the code below triggers the full pipeline of operations
   leading to the computation of the tract profiles. Therefore, it
   takes a little while to run (about 40 minutes, typically).

.. GENERATED FROM PYTHON SOURCE LINES 164-167

.. code-block:: Python


    myafq.export('profiles')





.. rst-class:: sphx-glr-script-out

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    (TractActor pid=4922) 
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    (TractActor pid=4923) 
    1233it [00:07, 166.46it/s]
    (TractActor pid=4921) 
    1555it [00:07, 193.15it/s]
    (TractActor pid=4922) 
    1226it [00:07, 165.80it/s]
    (TractActor pid=4923) 
    1252it [00:07, 171.89it/s]
    (TractActor pid=4921) 
    1578it [00:07, 201.28it/s]
    (TractActor pid=4922) 
    1244it [00:07, 167.35it/s]
    (TractActor pid=4923) 
    1274it [00:07, 184.93it/s]
    (TractActor pid=4921) 
    1602it [00:07, 210.42it/s]
    (TractActor pid=4922) 
    1265it [00:08, 179.20it/s]
    (TractActor pid=4923) 
    1294it [00:07, 187.73it/s]
    (TractActor pid=4921) 
    1629it [00:07, 226.60it/s]
    (TractActor pid=4922) 
    1287it [00:08, 187.70it/s]
    (TractActor pid=4923) 
    1314it [00:07, 175.35it/s]
    (TractActor pid=4921) 
    1655it [00:08, 235.31it/s]
    (TractActor pid=4922) 
    1307it [00:08, 177.09it/s]
    (TractActor pid=4923) 
    1332it [00:07, 164.88it/s]
    (TractActor pid=4921) 
    1679it [00:08, 228.50it/s]
    (TractActor pid=4922) 
    1326it [00:08, 174.24it/s]
    (TractActor pid=4923) 
    1351it [00:08, 170.90it/s]
    (TractActor pid=4922) 
    1344it [00:08, 169.75it/s]
    (TractActor pid=4923) 
    1369it [00:08, 172.42it/s]
    (TractActor pid=4921) 
    1703it [00:08, 184.54it/s]
    (TractActor pid=4922) 
    1362it [00:08, 166.15it/s]
    (TractActor pid=4923) 
    1388it [00:08, 175.49it/s]
    (TractActor pid=4921) 
    1726it [00:08, 195.09it/s]
    (TractActor pid=4922) 
    1382it [00:08, 172.63it/s]
    (TractActor pid=4923) 
    1406it [00:08, 174.67it/s]
    (TractActor pid=4921) 
    1748it [00:08, 200.34it/s]
    (TractActor pid=4922) 
    1400it [00:08, 174.63it/s]
    (TractActor pid=4921) 
    1775it [00:08, 218.33it/s]
    (TractActor pid=4922) 
    1418it [00:08, 172.08it/s]
    (TractActor pid=4923) 
    1424it [00:08, 168.54it/s]
    (TractActor pid=4921) 
    1798it [00:08, 209.94it/s]
    (TractActor pid=4923) 
    1441it [00:08, 162.36it/s]
    (TractActor pid=4921) 
    1820it [00:08, 211.70it/s]
    (TractActor pid=4922) 
    1436it [00:09, 164.62it/s]
    (TractActor pid=4923) 
    1459it [00:08, 167.10it/s]
    (TractActor pid=4921) 
    1844it [00:08, 217.18it/s]
    (TractActor pid=4922) 
    1453it [00:09, 162.88it/s]
    (TractActor pid=4923) 
    1478it [00:08, 171.84it/s]
    (TractActor pid=4921) 
    1867it [00:09, 219.30it/s]
    (TractActor pid=4922) 
    1472it [00:09, 168.45it/s]
    (TractActor pid=4923) 
    1496it [00:08, 164.91it/s]
    (TractActor pid=4921) 
    1890it [00:09, 219.85it/s]
    (TractActor pid=4922) 
    1489it [00:09, 165.35it/s]
    (TractActor pid=4923) 
    1513it [00:09, 151.09it/s]
    (TractActor pid=4921) 
    1914it [00:09, 224.64it/s]
    (TractActor pid=4922) 
    1506it [00:09, 157.86it/s]
    (TractActor pid=4923) 
    1531it [00:09, 157.55it/s]
    (TractActor pid=4921) 
    1937it [00:09, 222.33it/s]
    (TractActor pid=4922) 
    1522it [00:09, 153.60it/s]
    (TractActor pid=4923) 
    1548it [00:09, 151.29it/s]
    (TractActor pid=4921) 
    1961it [00:09, 227.43it/s]
    (TractActor pid=4922) 
    1539it [00:09, 155.64it/s]
    (TractActor pid=4923) 
    1566it [00:09, 158.46it/s]
    (TractActor pid=4921) 
    1984it [00:09, 224.84it/s]
    (TractActor pid=4922) 
    1555it [00:09, 152.71it/s]
    (TractActor pid=4923) 
    1584it [00:09, 159.88it/s]
    (TractActor pid=4921) 
    2007it [00:09, 218.67it/s]
    (TractActor pid=4922) 
    1574it [00:09, 161.13it/s]
    (TractActor pid=4923) 
    1603it [00:09, 167.70it/s]
    (TractActor pid=4921) 
    2029it [00:09, 213.75it/s]
    (TractActor pid=4922) 
    1591it [00:09, 161.85it/s]
    (TractActor pid=4923) 
    1623it [00:09, 176.22it/s]
    (TractActor pid=4921) 
    2051it [00:09, 211.29it/s]
    (TractActor pid=4922) 
    1611it [00:10, 171.16it/s]
    (TractActor pid=4923) 
    1643it [00:09, 180.11it/s]
    (TractActor pid=4921) 
    2074it [00:10, 214.53it/s]
    (TractActor pid=4922) 
    1632it [00:10, 181.97it/s]
    (TractActor pid=4923) 
    1662it [00:09, 178.28it/s]
    (TractActor pid=4921) 
    2096it [00:10, 215.71it/s]
    (TractActor pid=4922) 
    1651it [00:10, 182.41it/s]
    (TractActor pid=4923) 
    1680it [00:09, 177.52it/s]
    (TractActor pid=4921) 
    2121it [00:10, 222.68it/s]
    (TractActor pid=4922) 
    1670it [00:10, 177.30it/s]
    (TractActor pid=4921) 
    2144it [00:10, 224.12it/s]
    (TractActor pid=4922) 
    1688it [00:10, 172.15it/s]
    (TractActor pid=4923) 
    1698it [00:10, 134.66it/s]
    (TractActor pid=4921) 
    2167it [00:10, 206.75it/s]
    (TractActor pid=4923) 
    1715it [00:10, 142.69it/s]
    (TractActor pid=4921) 
    2188it [00:10, 203.87it/s]
    (TractActor pid=4922) 
    1706it [00:10, 134.37it/s]
    (TractActor pid=4923) 
    1732it [00:10, 148.54it/s]
    (TractActor pid=4921) 
    2212it [00:10, 213.23it/s]
    (TractActor pid=4922) 
    1724it [00:10, 142.05it/s]
    (TractActor pid=4923) 
    1750it [00:10, 155.36it/s]
    (TractActor pid=4921) 
    2234it [00:10, 196.05it/s]
    (TractActor pid=4922) 
    1741it [00:10, 146.57it/s]
    (TractActor pid=4923) 
    1770it [00:10, 165.74it/s]
    (TractActor pid=4922) 
    1761it [00:11, 156.30it/s]
    (TractActor pid=4923) 
    1789it [00:10, 168.73it/s]
    (TractActor pid=4921) 
    2254it [00:10, 183.29it/s]
    (TractActor pid=4922) 
    1781it [00:11, 167.62it/s]
    (TractActor pid=4923) 
    1808it [00:10, 174.19it/s]
    (TractActor pid=4921) 
    2277it [00:11, 194.07it/s]
    (TractActor pid=4922) 
    1799it [00:11, 170.57it/s]
    (TractActor pid=4923) 
    1826it [00:10, 171.19it/s]
    (TractActor pid=4921) 
    2300it [00:11, 202.54it/s]
    (TractActor pid=4922) 
    1817it [00:11, 169.43it/s]
    (TractActor pid=4923) 
    1844it [00:11, 171.30it/s]
    (TractActor pid=4921) 
    2324it [00:11, 212.91it/s]
    (TractActor pid=4922) 
    1836it [00:11, 173.64it/s]
    (TractActor pid=4923) 
    1862it [00:11, 173.72it/s]
    (TractActor pid=4921) 
    2346it [00:11, 169.42it/s]
    (TractActor pid=4922) 
    1854it [00:11, 170.37it/s]
    (TractActor pid=4923) 
    1880it [00:11, 169.85it/s]
    (TractActor pid=4921) 
    2369it [00:11, 183.46it/s]
    (TractActor pid=4922) 
    1872it [00:11, 167.60it/s]
    (TractActor pid=4923) 
    1898it [00:11, 167.45it/s]
    (TractActor pid=4921) 
    2389it [00:11, 186.10it/s]
    (TractActor pid=4922) 
    1889it [00:11, 167.09it/s]
    (TractActor pid=4923) 
    1917it [00:11, 171.38it/s]
    (TractActor pid=4921) 
    2413it [00:11, 199.27it/s]
    (TractActor pid=4922) 
    1906it [00:11, 165.63it/s]
    (TractActor pid=4921) 
    2437it [00:11, 209.01it/s]
    (TractActor pid=4922) 
    1923it [00:12, 164.85it/s]
    (TractActor pid=4923) 
    1935it [00:11, 167.20it/s]
    (TractActor pid=4921) 
    2459it [00:11, 209.73it/s]
    (TractActor pid=4922) 
    1942it [00:12, 171.28it/s]
    (TractActor pid=4923) 
    1954it [00:11, 171.79it/s]
    (TractActor pid=4921) 
    2481it [00:12, 206.91it/s]
    (TractActor pid=4922) 
    1961it [00:12, 175.97it/s]
    (TractActor pid=4923) 
    1972it [00:11, 172.62it/s]
    (TractActor pid=4921) 
    2505it [00:12, 215.94it/s]
    (TractActor pid=4922) 
    1979it [00:12, 173.21it/s]
    (TractActor pid=4923) 
    1990it [00:11, 168.63it/s]
    (TractActor pid=4921) 
    2531it [00:12, 225.00it/s]
    (TractActor pid=4922) 
    1997it [00:12, 162.36it/s]
    (TractActor pid=4923) 
    2007it [00:11, 167.54it/s]
    (TractActor pid=4922) 
    2017it [00:12, 172.22it/s]
    (TractActor pid=4923) 
    2025it [00:12, 169.11it/s]
    (TractActor pid=4921) 
    2557it [00:12, 225.30it/s]
    (TractActor pid=4922) 
    2035it [00:12, 171.24it/s]
    (TractActor pid=4921) 
    2581it [00:12, 229.35it/s]
    (TractActor pid=4923) 
    2042it [00:12, 168.45it/s]
    (TractActor pid=4923) 
    2059it [00:12, 164.88it/s]
    (TractActor pid=4921) 
    2605it [00:12, 213.11it/s]
    (TractActor pid=4922) 
    2053it [00:12, 169.75it/s]
    (TractActor pid=4923) 
    2079it [00:12, 170.61it/s]
    (TractActor pid=4921) 
    2631it [00:12, 224.62it/s]
    (TractActor pid=4922) 
    2071it [00:12, 168.87it/s]
    (TractActor pid=4923) 
    2097it [00:12, 172.49it/s]
    (TractActor pid=4921) 
    2654it [00:12, 205.94it/s]
    (TractActor pid=4922) 
    2088it [00:12, 169.09it/s]
    2107it [00:13, 175.10it/s]
    (TractActor pid=4923) 
    2117it [00:12, 178.44it/s]
    (TractActor pid=4921) 
    2677it [00:12, 209.15it/s]
    (TractActor pid=4922) 
    2125it [00:13, 176.01it/s]
    (TractActor pid=4923) 
    2135it [00:12, 173.97it/s]
    (TractActor pid=4921) 
    2703it [00:13, 221.67it/s]
    (TractActor pid=4922) 
    2143it [00:13, 174.83it/s]
    (TractActor pid=4923) 
    2153it [00:12, 164.44it/s]
    (TractActor pid=4921) 
    2726it [00:13, 214.84it/s]
    (TractActor pid=4923) 
    2170it [00:12, 160.16it/s]
    (TractActor pid=4921) 
    2749it [00:13, 215.49it/s]
    (TractActor pid=4922) 
    2161it [00:13, 161.31it/s]
    (TractActor pid=4923) 
    2187it [00:13, 153.64it/s]
    (TractActor pid=4921) 
    2771it [00:13, 214.21it/s]
    (TractActor pid=4922) 
    2179it [00:13, 166.12it/s]
    (TractActor pid=4923) 
    2204it [00:13, 157.08it/s]
    (TractActor pid=4921) 
    2794it [00:13, 218.25it/s]
    (TractActor pid=4922) 
    2196it [00:13, 159.97it/s]
    (TractActor pid=4923) 
    2223it [00:13, 163.50it/s]
    (TractActor pid=4921) 
    2816it [00:13, 202.58it/s]
    (TractActor pid=4922) 
    2215it [00:13, 166.99it/s]
    (TractActor pid=4921) 
    2843it [00:13, 219.55it/s]
    (TractActor pid=4922) 
    2232it [00:13, 149.95it/s]
    (TractActor pid=4923) 
    2240it [00:13, 143.00it/s]
    (TractActor pid=4921) 
    2866it [00:13, 222.15it/s]
    (TractActor pid=4922) 
    2248it [00:13, 144.10it/s]
    (TractActor pid=4923) 
    2256it [00:13, 145.30it/s]
    (TractActor pid=4921) 
    2891it [00:13, 226.58it/s]
    (TractActor pid=4922) 
    2268it [00:14, 157.20it/s]
    (TractActor pid=4923) 
    2276it [00:13, 157.40it/s]
    (TractActor pid=4921) 
    2916it [00:14, 230.40it/s]
    (TractActor pid=4923) 
    2293it [00:13, 158.57it/s]
    (TractActor pid=4921) 
    2941it [00:14, 234.45it/s]
    (TractActor pid=4922) 
    2285it [00:14, 152.38it/s]
    (TractActor pid=4923) 
    2313it [00:13, 169.43it/s]
    (TractActor pid=4922) 
    2306it [00:14, 166.31it/s]
    (TractActor pid=4923) 
    2331it [00:13, 164.22it/s]
    (TractActor pid=4921) 
    2965it [00:14, 220.93it/s]
    (TractActor pid=4922) 
    2326it [00:14, 173.16it/s]
    (TractActor pid=4921) 
    2988it [00:14, 210.26it/s]
    (TractActor pid=4923) 
    2348it [00:14, 130.28it/s]
    (TractActor pid=4921) 
    3010it [00:14, 209.41it/s]
    (TractActor pid=4922) 
    2344it [00:14, 128.65it/s]
    (TractActor pid=4923) 
    2364it [00:14, 135.66it/s]
    (TractActor pid=4921) 
    3032it [00:14, 212.31it/s]
    (TractActor pid=4922) 
    2361it [00:14, 137.78it/s]
    (TractActor pid=4923) 
    2380it [00:14, 141.69it/s]
    (TractActor pid=4921) 
    3056it [00:14, 216.45it/s]
    (TractActor pid=4922) 
    2377it [00:14, 142.61it/s]
    (TractActor pid=4923) 
    2398it [00:14, 151.78it/s]
    (TractActor pid=4921) 
    3080it [00:14, 222.55it/s]
    (TractActor pid=4922) 
    2395it [00:14, 150.87it/s]
    (TractActor pid=4923) 
    2416it [00:14, 157.21it/s]
    (TractActor pid=4921) 
    3104it [00:14, 224.24it/s]
    (TractActor pid=4922) 
    2412it [00:15, 155.52it/s]
    (TractActor pid=4923) 
    2436it [00:14, 166.10it/s]
    (TractActor pid=4921) 
    3129it [00:14, 229.49it/s]
    (TractActor pid=4922) 
    2433it [00:15, 170.02it/s]
    (TractActor pid=4923) 
    2453it [00:14, 158.94it/s]
    (TractActor pid=4921) 
    3155it [00:15, 237.66it/s]
    (TractActor pid=4922) 
    2451it [00:15, 163.36it/s]
    (TractActor pid=4923) 
    2474it [00:14, 171.40it/s]
    (TractActor pid=4921) 
    3181it [00:15, 242.97it/s]
    (TractActor pid=4922) 
    2470it [00:15, 167.47it/s]
    (TractActor pid=4922) 
    2488it [00:15, 168.80it/s]
    (TractActor pid=4923) 
    2492it [00:15, 131.57it/s]
    (TractActor pid=4921) 
    3206it [00:15, 170.99it/s]
    (TractActor pid=4922) 
    2510it [00:15, 183.00it/s]
    (TractActor pid=4923) 
    2512it [00:15, 146.37it/s]
    (TractActor pid=4921) 
    3229it [00:15, 183.03it/s]
    (TractActor pid=4922) 
    2535it [00:15, 200.92it/s]
    (TractActor pid=4923) 
    2532it [00:15, 158.04it/s]
    (TractActor pid=4921) 
    3251it [00:15, 190.78it/s]
    (TractActor pid=4922) 
    2556it [00:15, 203.04it/s]
    (TractActor pid=4923) 
    2553it [00:15, 170.04it/s]
    (TractActor pid=4921) 
    3272it [00:15, 194.06it/s]
    (TractActor pid=4922) 
    2577it [00:15, 187.91it/s]
    (TractActor pid=4923) 
    2572it [00:15, 162.76it/s]
    (TractActor pid=4921) 
    3293it [00:15, 198.26it/s]
    (TractActor pid=4922) 
    2597it [00:16, 170.72it/s]
    (TractActor pid=4923) 
    2592it [00:15, 170.72it/s]
    (TractActor pid=4921) 
    3317it [00:15, 208.04it/s]
    (TractActor pid=4922) 
    2615it [00:16, 170.15it/s]
    (TractActor pid=4923) 
    2610it [00:15, 158.70it/s]
    (TractActor pid=4921) 
    3339it [00:16, 201.95it/s]
    (TractActor pid=4922) 
    2634it [00:16, 172.36it/s]
    (TractActor pid=4921) 
    3363it [00:16, 211.71it/s]
    (TractActor pid=4923) 
    2630it [00:15, 166.59it/s]
    (TractActor pid=4921) 
    3385it [00:16, 207.78it/s]
    (TractActor pid=4922) 
    2652it [00:16, 158.70it/s]
    (TractActor pid=4923) 
    2648it [00:16, 156.92it/s]
    (TractActor pid=4921) 
    3407it [00:16, 201.03it/s]
    (TractActor pid=4922) 
    2669it [00:16, 158.03it/s]
    (TractActor pid=4923) 
    2665it [00:16, 151.28it/s]
    (TractActor pid=4921) 
    3428it [00:16, 203.26it/s]
    (TractActor pid=4922) 
    2688it [00:16, 166.53it/s]
    (TractActor pid=4923) 
    2685it [00:16, 163.28it/s]
    (TractActor pid=4921) 
    3455it [00:16, 220.55it/s]
    (TractActor pid=4922) 
    2708it [00:16, 174.98it/s]
    (TractActor pid=4923) 
    2706it [00:16, 173.31it/s]
    (TractActor pid=4921) 
    3478it [00:16, 222.42it/s]
    (TractActor pid=4922) 
    2726it [00:16, 175.82it/s]
    (TractActor pid=4923) 
    2726it [00:16, 172.63it/s]
    (TractActor pid=4921) 
    3501it [00:16, 214.00it/s]
    (TractActor pid=4922) 
    2745it [00:16, 178.26it/s]
    (TractActor pid=4923) 
    2745it [00:16, 175.76it/s]
    (TractActor pid=4921) 
    3525it [00:16, 217.95it/s]
    (TractActor pid=4922) 
    2763it [00:17, 176.29it/s]
    (TractActor pid=4923) 
    2763it [00:16, 174.88it/s]
    (TractActor pid=4921) 
    3547it [00:17, 218.45it/s]
    (TractActor pid=4922) 
    2781it [00:17, 169.89it/s]
    (TractActor pid=4923) 
    2781it [00:16, 168.94it/s]
    (TractActor pid=4921) 
    3569it [00:17, 214.86it/s]
    (TractActor pid=4922) 
    2799it [00:17, 167.28it/s]
    (TractActor pid=4923) 
    2799it [00:16, 166.62it/s]
    (TractActor pid=4921) 
    3591it [00:17, 216.20it/s]
    (TractActor pid=4922) 
    2816it [00:17, 159.60it/s]
    (TractActor pid=4923) 
    2816it [00:17, 158.73it/s]
    (TractActor pid=4921) 
    3614it [00:17, 218.51it/s]
    (TractActor pid=4922) 
    2837it [00:17, 169.54it/s]
    (TractActor pid=4923) 
    2837it [00:17, 168.52it/s]
    (TractActor pid=4921) 
    3639it [00:17, 226.48it/s]
    (TractActor pid=4922) 
    2858it [00:17, 175.11it/s]
    (TractActor pid=4921) 
    3662it [00:17, 225.76it/s]
    (TractActor pid=4922) 
    2876it [00:17, 170.35it/s]
    (TractActor pid=4923) 
    2858it [00:17, 173.55it/s]
    (TractActor pid=4921) 
    3685it [00:17, 215.53it/s]
    (TractActor pid=4922) 
    2896it [00:17, 176.95it/s]
    (TractActor pid=4923) 
    2876it [00:17, 170.20it/s]
    (TractActor pid=4922) 
    2916it [00:17, 182.68it/s]
    (TractActor pid=4923) 
    2896it [00:17, 176.79it/s]
    (TractActor pid=4921) 
    3707it [00:17, 204.19it/s]
    (TractActor pid=4922) 
    2935it [00:18, 183.92it/s]
    (TractActor pid=4923) 
    2916it [00:17, 182.45it/s]
    (TractActor pid=4921) 
    3731it [00:17, 213.06it/s]
    (TractActor pid=4922) 
    2954it [00:18, 183.80it/s]
    (TractActor pid=4923) 
    2935it [00:17, 183.59it/s]
    (TractActor pid=4921) 
    3753it [00:17, 212.20it/s]
    (TractActor pid=4922) 
    2973it [00:18, 177.89it/s]
    (TractActor pid=4923) 
    2954it [00:17, 183.09it/s]
    (TractActor pid=4921) 
    3777it [00:18, 217.60it/s]
    (TractActor pid=4922) 
    2991it [00:18, 168.02it/s]
    (TractActor pid=4923) 
    2973it [00:17, 177.34it/s]
    (TractActor pid=4921) 
    3799it [00:18, 215.81it/s]
    (TractActor pid=4923) 
    2991it [00:18, 167.27it/s]
    (TractActor pid=4921) 
    3821it [00:18, 215.17it/s]
    (TractActor pid=4922) 
    3008it [00:18, 162.10it/s]
    (TractActor pid=4923) 
    3008it [00:18, 162.16it/s]
    (TractActor pid=4921) 
    3844it [00:18, 217.85it/s]
    (TractActor pid=4922) 
    3025it [00:18, 162.27it/s]
    (TractActor pid=4923) 
    3025it [00:18, 162.40it/s]
    (TractActor pid=4921) 
    3866it [00:18, 215.93it/s]
    (TractActor pid=4922) 
    3044it [00:18, 168.65it/s]
    (TractActor pid=4923) 
    3044it [00:18, 168.14it/s]
    (TractActor pid=4921) 
    3888it [00:18, 202.82it/s]
    (TractActor pid=4922) 
    3062it [00:18, 171.08it/s]
    (TractActor pid=4923) 
    3062it [00:18, 170.27it/s]
    (TractActor pid=4921) 
    3914it [00:18, 217.05it/s]
    (TractActor pid=4922) 
    3081it [00:18, 174.87it/s]
    (TractActor pid=4923) 
    3081it [00:18, 173.73it/s]
    (TractActor pid=4921) 
    3936it [00:18, 217.60it/s]
    (TractActor pid=4922) 
    3100it [00:18, 177.21it/s]
    (TractActor pid=4923) 
    3100it [00:18, 176.04it/s]
    (TractActor pid=4921) 
    3958it [00:18, 217.25it/s]
    (TractActor pid=4922) 
    3121it [00:19, 184.97it/s]
    (TractActor pid=4923) 
    3121it [00:18, 184.50it/s]
    (TractActor pid=4921) 
    3983it [00:19, 223.68it/s]
    (TractActor pid=4922) 
    3140it [00:19, 181.41it/s]
    (TractActor pid=4923) 
    3140it [00:18, 180.58it/s]
    (TractActor pid=4921) 
    4006it [00:19, 222.42it/s]
    (TractActor pid=4922) 
    3160it [00:19, 184.07it/s]
    (TractActor pid=4923) 
    3160it [00:18, 183.65it/s]
    (TractActor pid=4921) 
    4029it [00:19, 218.97it/s]
    (TractActor pid=4922) 
    3181it [00:19, 189.25it/s]
    (TractActor pid=4923) 
    3181it [00:19, 190.85it/s]
    (TractActor pid=4921) 
    4051it [00:19, 212.19it/s]
    (TractActor pid=4921) 
    4074it [00:19, 211.68it/s]
    (TractActor pid=4922) 
    3200it [00:19, 135.99it/s]
    (TractActor pid=4923) 
    3201it [00:19, 137.03it/s]
    (TractActor pid=4921) 
    4097it [00:19, 210.26it/s]
    (TractActor pid=4922) 
    3221it [00:19, 149.42it/s]
    (TractActor pid=4923) 
    3221it [00:19, 148.46it/s]
    (TractActor pid=4921) 
    4119it [00:19, 197.31it/s]
    (TractActor pid=4922) 
    3239it [00:19, 154.59it/s]
    (TractActor pid=4923) 
    3239it [00:19, 153.80it/s]
    (TractActor pid=4921) 
    4142it [00:19, 206.19it/s]
    (TractActor pid=4922) 
    3257it [00:19, 160.85it/s]
    (TractActor pid=4923) 
    3257it [00:19, 160.24it/s]
    (TractActor pid=4921) 
    4163it [00:19, 198.95it/s]
    (TractActor pid=4922) 
    3275it [00:20, 159.95it/s]
    (TractActor pid=4923) 
    3275it [00:19, 159.55it/s]
    (TractActor pid=4921) 
    4184it [00:20, 199.70it/s]
    (TractActor pid=4922) 
    3292it [00:20, 160.09it/s]
    (TractActor pid=4923) 
    3292it [00:19, 159.77it/s]
    (TractActor pid=4921) 
    4209it [00:20, 211.77it/s]
    (TractActor pid=4922) 
    3311it [00:20, 164.87it/s]
    (TractActor pid=4921) 
    4231it [00:20, 210.88it/s]
    (TractActor pid=4922) 
    3328it [00:20, 164.26it/s]
    (TractActor pid=4923) 
    3311it [00:19, 164.50it/s]
    (TractActor pid=4922) 
    3345it [00:20, 165.05it/s]
    (TractActor pid=4923) 
    3328it [00:20, 162.67it/s]
    (TractActor pid=4922) 
    3362it [00:20, 165.49it/s]
    (TractActor pid=4923) 
    3345it [00:20, 163.38it/s]
    (TractActor pid=4921) 
    4253it [00:20, 150.73it/s]
    (TractActor pid=4922) 
    3379it [00:20, 163.92it/s]
    (TractActor pid=4923) 
    3362it [00:20, 164.85it/s]
    (TractActor pid=4921) 
    4275it [00:20, 164.73it/s]
    (TractActor pid=4922) 
    3396it [00:20, 160.80it/s]
    (TractActor pid=4923) 
    3379it [00:20, 163.50it/s]
    (TractActor pid=4921) 
    4299it [00:20, 179.53it/s]
    (TractActor pid=4922) 
    3414it [00:20, 165.46it/s]
    (TractActor pid=4923) 
    3396it [00:20, 159.96it/s]
    (TractActor pid=4921) 
    4319it [00:20, 178.12it/s]
    (TractActor pid=4922) 
    3431it [00:21, 165.26it/s]
    (TractActor pid=4923) 
    3414it [00:20, 164.36it/s]
    (TractActor pid=4921) 
    4345it [00:20, 198.18it/s]
    (TractActor pid=4922) 
    3452it [00:21, 176.17it/s]
    (TractActor pid=4923) 
    3431it [00:20, 165.00it/s]
    (TractActor pid=4921) 
    4370it [00:21, 210.20it/s]
    (TractActor pid=4922) 
    3470it [00:21, 175.48it/s]
    (TractActor pid=4923) 
    3452it [00:20, 175.67it/s]
    (TractActor pid=4921) 
    4392it [00:21, 207.74it/s]
    (TractActor pid=4922) 
    3489it [00:21, 175.20it/s]
    (TractActor pid=4923) 
    3470it [00:20, 175.14it/s]
    (TractActor pid=4921) 
    4415it [00:21, 213.86it/s]
    (TractActor pid=4922) 
    3507it [00:21, 164.15it/s]
    (TractActor pid=4923) 
    3489it [00:21, 174.92it/s]
    (TractActor pid=4921) 
    4437it [00:21, 212.82it/s]
    (TractActor pid=4922) 
    3526it [00:21, 170.31it/s]
    (TractActor pid=4923) 
    3507it [00:21, 163.88it/s]
    (TractActor pid=4921) 
    4462it [00:21, 222.28it/s]
    (TractActor pid=4922) 
    3544it [00:21, 170.11it/s]
    (TractActor pid=4923) 
    3526it [00:21, 169.84it/s]
    (TractActor pid=4921) 
    4490it [00:21, 236.65it/s]
    (TractActor pid=4923) 
    3544it [00:21, 169.62it/s]
    (TractActor pid=4921) 
    4517it [00:21, 245.97it/s]
    (TractActor pid=4922) 
    3562it [00:21, 161.82it/s]
    (TractActor pid=4923) 
    3562it [00:21, 161.16it/s]
    (TractActor pid=4921) 
    4542it [00:21, 244.30it/s]
    (TractActor pid=4922) 
    3580it [00:21, 164.48it/s]
    (TractActor pid=4923) 
    3579it [00:21, 163.41it/s]
    (TractActor pid=4921) 
    4567it [00:21, 240.95it/s]
    (TractActor pid=4922) 
    3597it [00:22, 160.07it/s]
    (TractActor pid=4923) 
    3596it [00:21, 161.28it/s]
    (TractActor pid=4921) 
    4592it [00:21, 217.34it/s]
    (TractActor pid=4922) 
    3619it [00:22, 176.32it/s]
    (TractActor pid=4923) 
    3617it [00:21, 173.74it/s]
    (TractActor pid=4921) 
    4616it [00:22, 222.45it/s]
    (TractActor pid=4922) 
    3639it [00:22, 180.84it/s]
    (TractActor pid=4923) 
    3638it [00:21, 180.38it/s]
    (TractActor pid=4921) 
    4639it [00:22, 210.05it/s]
    (TractActor pid=4922) 
    3660it [00:22, 186.99it/s]
    (TractActor pid=4923) 
    3658it [00:22, 185.66it/s]
    (TractActor pid=4921) 
    4663it [00:22, 217.10it/s]
    (TractActor pid=4922) 
    3679it [00:22, 171.98it/s]
    (TractActor pid=4923) 
    3677it [00:22, 172.80it/s]
    (TractActor pid=4921) 
    4689it [00:22, 224.74it/s]
    (TractActor pid=4922) 
    3697it [00:22, 168.07it/s]
    (TractActor pid=4923) 
    3695it [00:22, 170.75it/s]
    (TractActor pid=4921) 
    4712it [00:22, 218.80it/s]
    (TractActor pid=4922) 
    3715it [00:22, 166.37it/s]
    (TractActor pid=4921) 
    4736it [00:22, 224.47it/s]
    (TractActor pid=4922) 
    3733it [00:22, 168.97it/s]
    (TractActor pid=4923) 
    3713it [00:22, 164.41it/s]
    (TractActor pid=4922) 
    3751it [00:22, 162.30it/s]
    (TractActor pid=4923) 
    3730it [00:22, 165.87it/s]
    (TractActor pid=4921) 
    4759it [00:22, 205.60it/s]
    (TractActor pid=4922) 
    3770it [00:23, 168.00it/s]
    (TractActor pid=4923) 
    3747it [00:22, 164.64it/s]
    (TractActor pid=4921) 
    4785it [00:22, 216.86it/s]
    (TractActor pid=4922) 
    3788it [00:23, 170.56it/s]
    (TractActor pid=4923) 
    3764it [00:22, 162.97it/s]
    (TractActor pid=4921) 
    4808it [00:22, 214.06it/s]
    (TractActor pid=4922) 
    3806it [00:23, 161.02it/s]
    (TractActor pid=4923) 
    3784it [00:22, 168.13it/s]
    (TractActor pid=4921) 
    4830it [00:23, 206.87it/s]
    (TractActor pid=4923) 
    3801it [00:22, 168.02it/s]
    (TractActor pid=4921) 
    4854it [00:23, 213.74it/s]
    (TractActor pid=4922) 
    3825it [00:23, 164.59it/s]
    (TractActor pid=4923) 
    3818it [00:22, 165.68it/s]
    (TractActor pid=4921) 
    4877it [00:23, 216.36it/s]
    (TractActor pid=4922) 
    3844it [00:23, 169.03it/s]
    (TractActor pid=4923) 
    3838it [00:23, 175.06it/s]
    (TractActor pid=4921) 
    4899it [00:23, 206.77it/s]
    (TractActor pid=4922) 
    3862it [00:23, 158.69it/s]
    (TractActor pid=4923) 
    3857it [00:23, 179.17it/s]
    (TractActor pid=4921) 
    4920it [00:23, 193.02it/s]
    (TractActor pid=4922) 
    3879it [00:23, 159.41it/s]
    (TractActor pid=4923) 
    3879it [00:23, 188.65it/s]
    (TractActor pid=4921) 
    4940it [00:23, 194.52it/s]
    (TractActor pid=4922) 
    3896it [00:23, 160.67it/s]
    (TractActor pid=4923) 
    3898it [00:23, 186.59it/s]
    (TractActor pid=4921) 
    4960it [00:23, 193.34it/s]
    (TractActor pid=4922) 
    3916it [00:23, 169.70it/s]
    (TractActor pid=4923) 
    3924it [00:23, 207.23it/s]
    (TractActor pid=4922) 
    3934it [00:24, 167.98it/s]
    (TractActor pid=4923) 
    3946it [00:23, 210.24it/s]
    (TractActor pid=4921) 
    4980it [00:23, 175.90it/s]
    (TractActor pid=4922) 
    3952it [00:24, 168.34it/s]
    (TractActor pid=4923) 
    3969it [00:23, 214.44it/s]
    (TractActor pid=4921) 
    4999it [00:23, 179.17it/s]
    (TractActor pid=4922) 
    3969it [00:24, 168.01it/s]
    (TractActor pid=4923) 
    3991it [00:23, 203.00it/s]
    (TractActor pid=4921) 
    5019it [00:24, 183.71it/s]
    (TractActor pid=4922) 
    3986it [00:24, 165.81it/s]
    (TractActor pid=4923) 
    4014it [00:23, 210.27it/s]
    (TractActor pid=4921) 
    5038it [00:24, 165.80it/s]
    (TractActor pid=4922) 
    4003it [00:24, 164.01it/s]
    (TractActor pid=4923) 
    4036it [00:24, 204.42it/s]
    (TractActor pid=4921) 
    5056it [00:24, 165.02it/s]
    (TractActor pid=4922) 
    4021it [00:24, 167.04it/s]
    (TractActor pid=4923) 
    4059it [00:24, 209.65it/s]
    (TractActor pid=4921) 
    5075it [00:24, 170.32it/s]
    (TractActor pid=4922) 
    4038it [00:24, 155.59it/s]
    (TractActor pid=4923) 
    4081it [00:24, 208.50it/s]
    (TractActor pid=4921) 
    5093it [00:24, 161.16it/s]
    (TractActor pid=4922) 
    4058it [00:24, 164.42it/s]
    (TractActor pid=4923) 
    4102it [00:24, 208.60it/s]
    (TractActor pid=4921) 
    5111it [00:24, 163.90it/s]
    (TractActor pid=4922) 
    4075it [00:24, 159.73it/s]
    (TractActor pid=4923) 
    4123it [00:24, 204.81it/s]
    (TractActor pid=4921) 
    5129it [00:24, 167.88it/s]
    (TractActor pid=4922) 
    4092it [00:24, 161.46it/s]
    (TractActor pid=4923) 
    4145it [00:24, 207.69it/s]
    (TractActor pid=4921) 
    5150it [00:24, 176.55it/s]
    (TractActor pid=4922) 
    4109it [00:25, 163.12it/s]
    (TractActor pid=4923) 
    4166it [00:24, 202.70it/s]
    (TractActor pid=4921) 
    5168it [00:24, 176.76it/s]
    (TractActor pid=4922) 
    4126it [00:25, 157.19it/s]
    (TractActor pid=4923) 
    4187it [00:24, 203.56it/s]
    (TractActor pid=4921) 
    5186it [00:25, 172.67it/s]
    (TractActor pid=4923) 
    4208it [00:24, 200.26it/s]
    (TractActor pid=4921) 
    5209it [00:25, 187.09it/s]
    (TractActor pid=4922) 
    4143it [00:25, 155.83it/s]
    (TractActor pid=4923) 
    4229it [00:25, 184.28it/s]
    (TractActor pid=4921) 
    5235it [00:25, 205.46it/s]
    (TractActor pid=4922) 
    4159it [00:25, 152.10it/s]
    (TractActor pid=4921) 
    5257it [00:25, 208.10it/s]
    (TractActor pid=4922) 
    4175it [00:25, 150.32it/s]
    (TractActor pid=4921) 
    5283it [00:25, 220.36it/s]
    (TractActor pid=4922) 
    4192it [00:25, 154.71it/s]
    (TractActor pid=4923) 
    4248it [00:25, 129.87it/s]
    (TractActor pid=4921) 
    5306it [00:25, 222.73it/s]
    (TractActor pid=4922) 
    4211it [00:25, 164.54it/s]
    (TractActor pid=4923) 
    4264it [00:25, 132.71it/s]
    (TractActor pid=4921) 
    5329it [00:25, 223.90it/s]
    (TractActor pid=4922) 
    4228it [00:25, 160.81it/s]
    (TractActor pid=4923) 
    4281it [00:25, 140.90it/s]
    (TractActor pid=4921) 
    5356it [00:25, 234.36it/s]
    (TractActor pid=4923) 
    4300it [00:25, 152.06it/s]
    (TractActor pid=4921) 
    5380it [00:25, 230.99it/s]
    (TractActor pid=4922) 
    4245it [00:26, 114.62it/s]
    (TractActor pid=4923) 
    4318it [00:25, 156.59it/s]
    (TractActor pid=4921) 
    5404it [00:26, 226.10it/s]
    (TractActor pid=4922) 
    4260it [00:26, 121.79it/s]
    (TractActor pid=4923) 
    4339it [00:25, 170.77it/s]
    (TractActor pid=4922) 
    4276it [00:26, 130.64it/s]
    (TractActor pid=4923) 
    4359it [00:25, 178.12it/s]
    (TractActor pid=4921) 
    5427it [00:26, 214.28it/s]
    (TractActor pid=4922) 
    4294it [00:26, 141.88it/s]
    (TractActor pid=4923) 
    4378it [00:26, 168.22it/s]
    (TractActor pid=4921) 
    5450it [00:26, 215.67it/s]
    (TractActor pid=4922) 
    4311it [00:26, 146.51it/s]
    (TractActor pid=4923) 
    4399it [00:26, 176.64it/s]
    (TractActor pid=4921) 
    5477it [00:26, 230.30it/s]
    (TractActor pid=4922) 
    4331it [00:26, 159.55it/s]
    (TractActor pid=4923) 
    4418it [00:26, 172.42it/s]
    (TractActor pid=4921) 
    5505it [00:26, 241.21it/s]
    (TractActor pid=4922) 
    4351it [00:26, 169.77it/s]
    (TractActor pid=4923) 
    4436it [00:26, 167.09it/s]
    (TractActor pid=4921) 
    5530it [00:26, 239.68it/s]
    (TractActor pid=4922) 
    4369it [00:26, 172.17it/s]
    (TractActor pid=4923) 
    4456it [00:26, 175.40it/s]
    (TractActor pid=4921) 
    5555it [00:26, 221.31it/s]
    (TractActor pid=4922) 
    4387it [00:26, 164.22it/s]
    (TractActor pid=4923) 
    4477it [00:26, 183.22it/s]
    (TractActor pid=4921) 
    5582it [00:26, 233.57it/s]
    (TractActor pid=4922) 
    4408it [00:27, 176.06it/s]
    (TractActor pid=4923) 
    4499it [00:26, 193.02it/s]
    (TractActor pid=4921) 
    5609it [00:26, 242.83it/s]
    (TractActor pid=4923) 
    4519it [00:26, 194.13it/s]
    (TractActor pid=4922) 
    4426it [00:27, 162.99it/s]
    (TractActor pid=4923) 
    4539it [00:26, 187.54it/s]
    (TractActor pid=4921) 
    5634it [00:27, 167.21it/s]
    (TractActor pid=4922) 
    4446it [00:27, 173.04it/s]
    (TractActor pid=4923) 
    4559it [00:26, 190.00it/s]
    (TractActor pid=4922) 
    4464it [00:27, 172.04it/s]
    (TractActor pid=4921) 
    5655it [00:27, 172.83it/s]
    (TractActor pid=4922) 
    4486it [00:27, 184.92it/s]
    (TractActor pid=4923) 
    4579it [00:27, 187.60it/s]
    (TractActor pid=4921) 
    5676it [00:27, 180.44it/s]
    (TractActor pid=4922) 
    4507it [00:27, 191.44it/s]
    (TractActor pid=4923) 
    4598it [00:27, 173.17it/s]
    (TractActor pid=4921) 
    5697it [00:27, 187.75it/s]
    (TractActor pid=4922) 
    4527it [00:27, 188.50it/s]
    (TractActor pid=4923) 
    4617it [00:27, 177.44it/s]
    (TractActor pid=4921) 
    5725it [00:27, 211.37it/s]
    (TractActor pid=4922) 
    4548it [00:27, 192.64it/s]
    (TractActor pid=4923) 
    4635it [00:27, 162.31it/s]
    (TractActor pid=4921) 
    5749it [00:27, 215.64it/s]
    (TractActor pid=4922) 
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    {'01': '/home/runner/AFQ_data/stanford_hardi/derivatives/afq/sub-01/ses-01/sub-01_ses-01_coordsys-RASMM_trkmethod-probCSD_recogmethod-AFQ_desc-profiles_dwi.csv'}



.. GENERATED FROM PYTHON SOURCE LINES 168-186

Visualizing the bundles and calculating act profiles:
-----------------------------------------------------
The pyAFQ API provides several ways to visualize bundles and profiles.

First, we will run a function that exports an html file that contains
an interactive visualization of the bundles that are segmented.

.. note::
   By default we resample a 100 points within a bundle, however to reduce
   processing time we will only resample 50 points.

Once it is done running, it should pop a browser window open and let you
interact with the bundles.

.. note::
   You can hide or show a bundle by clicking the legend, or select a
   single bundle by double clicking the legend. The interactive
   visualization will also all you to pan, zoom, and rotate.

.. GENERATED FROM PYTHON SOURCE LINES 186-190

.. code-block:: Python


    bundle_html = myafq.export("all_bundles_figure")
    plotly.io.show(bundle_html["01"][0])




.. raw:: html
    :file: images/sphx_glr_plot_001_afq_api_002.html





.. GENERATED FROM PYTHON SOURCE LINES 191-196

We can also visualize the tract profiles in all of the bundles. These
plots show both FA (left) and MD (right) layed out anatomically.
To make this plot, it is required that you install with
`pip install pyAFQ[plot]` so that you have the necessary dependencies.


.. GENERATED FROM PYTHON SOURCE LINES 196-199

.. code-block:: Python


    fig_files = myafq.export("tract_profile_plots")["01"]





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


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.. GENERATED FROM PYTHON SOURCE LINES 200-202

.. figure:: {{ fig_files[0] }}


.. GENERATED FROM PYTHON SOURCE LINES 205-211

We can even use altair to visualize the tract profiles in all
of the bundles. We provide a more customizable interface for visualizing
the tract profiles using altair.
Again, to make this plot, it is required that you install with
`pip install pyAFQ[plot]` so that you have the necessary dependencies.


.. GENERATED FROM PYTHON SOURCE LINES 211-217

.. code-block:: Python

    profiles_df = myafq.combine_profiles()
    altair_df = ava.combined_profiles_df_to_altair_df(profiles_df)
    altair_chart = ava.altair_df_to_chart(altair_df)
    altair_chart.display()






.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    /opt/hostedtoolcache/Python/3.11.10/x64/lib/python3.11/site-packages/scipy/stats/_distn_infrastructure.py:2285: RuntimeWarning:

    invalid value encountered in multiply

    /opt/hostedtoolcache/Python/3.11.10/x64/lib/python3.11/site-packages/scipy/stats/_distn_infrastructure.py:2286: RuntimeWarning:

    invalid value encountered in multiply

    alt.VConcatChart(...)




.. GENERATED FROM PYTHON SOURCE LINES 218-220

We can check the number of streamlines per bundle, to make sure
every bundle is found with a reasonable amount of streamlines.

.. GENERATED FROM PYTHON SOURCE LINES 220-231

.. code-block:: Python


    bundle_counts = pd.read_csv(myafq.export("sl_counts")["01"], index_col=[0])
    for ind in bundle_counts.index:
        if ind == "Total Recognized":
            threshold = 1000
        else:
            threshold = 10
        if bundle_counts["n_streamlines"][ind] < threshold:
            raise ValueError((
                "Small number of streamlines found "
                f"for bundle(s):\n{bundle_counts}"))








.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (3 minutes 41.026 seconds)

**Estimated memory usage:**  1859 MB


.. _sphx_glr_download_tutorials_tutorial_examples_plot_001_afq_api.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_001_afq_api.ipynb <plot_001_afq_api.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_001_afq_api.py <plot_001_afq_api.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_001_afq_api.zip <plot_001_afq_api.zip>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_