.. 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 ` 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 `: 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 54-61 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. .. GENERATED FROM PYTHON SOURCE LINES 61-67 .. code-block:: Python tracking_params = dict(n_seeds=25000, random_seeds=True, rng_seed=2022, trx=True) .. GENERATED FROM PYTHON SOURCE LINES 68-92 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 92-99 .. 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 100-122 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 122-131 .. 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 132-143 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 143-148 .. 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 149-161 Recognizing the bundles and calculating act 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). .. 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17684it [01:21, 207.60it/s] 17707it [01:22, 213.59it/s] 17731it [01:22, 218.51it/s] 17757it [01:22, 229.98it/s] 17783it [01:22, 238.01it/s] 17808it [01:22, 237.26it/s] 17832it [01:22, 224.23it/s] 17855it [01:22, 211.51it/s] 17884it [01:22, 230.16it/s] 17908it [01:22, 230.30it/s] 17932it [01:22, 231.88it/s] 17949it [01:23, 216.15it/s] {'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 165-183 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 183-187 .. 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 188-193 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 193-196 .. code-block:: Python fig_files = myafq.export("tract_profile_plots")["01"] .. rst-class:: sphx-glr-script-out .. code-block:: none 0%| | 0/28 [00:00` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_001_afq_api.py ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_