notebooks/nb01_Data-Organisation.ipynb

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    "- **Author:** [Dace Apšvalka](https://www.mrc-cbu.cam.ac.uk/people/dace.apsvalka/) \n",
    "- **Date:** February 2025  \n",
    "- **conda environment**: [mri conda environment](https://github.com/dcdace/fMRI_training/blob/main/mri_environment.yml)."
   ]
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   "source": [
    "# fMRI Data Organisation\n",
    "\n",
    "An fMRI study typically involves multiple imaging protocols and subjects, resulting in complex datasets with numerous files in various formats, ranging from simple text files to multidimensional imaging data. These files can be organized in many ways, and without a consistent approach across projects and research groups, can arise in wasted time rearranging data or rewriting scripts to match specific file formats and structures, potentially introducing errors. Adopting a common standard for data organisation provides numerous advantages: it improves understanding of the data, even for those not directly involved in the project, reduces errors, enhances compatibility with analysis software, and facilitates the development of automated tools. In essence, it helps **make data FAIR** (Findable, Accessible, Interoperable, and Reusable).\n",
    "\n",
    "The [Brain Imaging Data Structure (BIDS)](https://bids.neuroimaging.io/), developed since 2015, is a community-driven standard for organising data and metadata across various neuroscience modalities. Converting your fMRI project's data into BIDS format enables easier sharing, reuse, and application of automated pipelines and quality assurance protocols, such as [BIDS Apps](https://bids-apps.neuroimaging.io/) or your/your group's own pipelines.\n",
    "\n",
    "Converting raw data into the BIDS format may initially appear challenging. While there are various conversion tools available (see a list [here](https://bids.neuroimaging.io/benefits#converters)), the range of options can make it hard to decide which tool to use. We have chosen [HeuDiConv](https://joss.theoj.org/papers/10.21105/joss.05839) which in fact predates the BIDS format and is a flexible, general-purpose converter for organising brain imaging data into structured directory layouts. \n",
    "\n",
    "In this tutorial, we will walk you through the process of converting fMRI data into BIDS format using HeuDiConv and show you how to interact with the resulting BIDS dataset using [PyBIDS](https://bids-standard.github.io/pybids/).\n",
    "\n",
    "!***For a more detailed tutorial on BIDS conversion see: [https://github.com/MRC-CBU/BIDS_conversion/tree/main/MRI](https://github.com/MRC-CBU/BIDS_conversion/tree/main/MRI).***\n",
    "\n",
    "Here are two recommended short videos to help you better understand the basics of fMRI terminology and data structure."
   ]
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       "\n",
       "<div style=\"width: 1200px; margin: 0; display: flex; justify-content: space-around;\">\n",
       "    <div style=\"text-align: center;\">\n",
       "        <h3>fMRI Data Structure & Terminology (7 min)</h3>\n",
       "        <iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/OuRdQJMU5ro\" frameborder=\"0\" allow=\"picture-in-picture\" allowfullscreen></iframe>\n",
       "    </div>\n",
       "    <div style=\"text-align: center;\">\n",
       "        <h3>Brain imaging data structure (11 min)</h3>\n",
       "        <iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/5H6XaJLp2V8\" frameborder=\"0\" allow=\"picture-in-picture\" allowfullscreen></iframe>\n",
       "    </div>\n",
       "</div>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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   "source": [
    "from IPython.display import display, HTML\n",
    "\n",
    "display(HTML('''\n",
    "<div style=\"width: 1200px; margin: 0; display: flex; justify-content: space-around;\">\n",
    "    <div style=\"text-align: center;\">\n",
    "        <h3>fMRI Data Structure & Terminology (7 min)</h3>\n",
    "        <iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/OuRdQJMU5ro\" frameborder=\"0\" allow=\"picture-in-picture\" allowfullscreen></iframe>\n",
    "    </div>\n",
    "    <div style=\"text-align: center;\">\n",
    "        <h3>Brain imaging data structure (11 min)</h3>\n",
    "        <iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/5H6XaJLp2V8\" frameborder=\"0\" allow=\"picture-in-picture\" allowfullscreen></iframe>\n",
    "    </div>\n",
    "</div>\n",
    "'''))"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----------\n",
    "\n",
    "**Table of contents**    \n",
    "1. Create a project folder  \n",
    "2. Workshop's Example Data \n",
    "3. Retrieving the DICOM files    \n",
    "4. Converting to BIDS using HeuDiConv    \n",
    "4.1. Step 1: Discovering your scans    \n",
    "4.2. Step 2: Creating a heuristic file    \n",
    "4.3. Step 3: Converting the data    \n",
    "4.4. 'To Do' - additional information to check and add    \n",
    "4.5. Validate BIDS structure    \n",
    "5. Downloading BIDS repository from OpenNeuro\n",
    "6. PyBIDS    \n",
    "6.1. Querying the BIDSLayout    \n",
    "6.2. Filtering files by entities    \n",
    "6.3. Filtering by metadata   \n",
    "6.4. Other `return_type` values    \n",
    "6.5. The `BIDSFile`   \n",
    "6.6. `.tsv` files   \n",
    "6.7. Filename parsing    \n",
    "6.8. Report generation   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------------\n",
    "\n",
    "## Create a project folder\n",
    "\n",
    "There is no universally agreed standard, but here is a recommended folder structure for your fMRI project.\n",
    "\n",
    "```bash\n",
    "# ======================================================================\n",
    "# Recommended directory structure for an fMRI project\n",
    "# ======================================================================\n",
    "\n",
    "# project_name                     \n",
    "#    └── code\n",
    "#        └── task\n",
    "#        └── preprocessing\n",
    "#        └── analysis\n",
    "#    └── data           # BIDS data and derivatives\n",
    "#    └── documents      # Protocols, reports, and other documentation\n",
    "#    └── results        # Analysis results, figures, and summary outputs\n",
    "#    └── scratch        # Temporary files and intermediate results\n",
    "#    └── logs           # Log files, error reports, and processing records\n",
    "\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can create the folder structure manually or use a simple `command-line` command like this:\n",
    "```bash\n",
    "mkdir -p My_fMRI_Project/{code/{task,preprocessing,analysis},data,documents,results,scratch,logs}\n",
    "```\n",
    "\n",
    "We can run command-line commands directly within the notebook by adding an '!' sign at the beginning:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pwd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or you can run several lines by adding `%%bash` at the beginning. Like in the example below, where I create the project folder and display its content. "
   ]
  },
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   "cell_type": "code",
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   "source": [
    "%%bash\n",
    "\n",
    "# change the current working directory to where you want to create the project folder\n",
    "cd /imaging/correia/da05/workshops/2025-CBU\n",
    "\n",
    "echo \"Current working directory: $(pwd)\"\n",
    "\n",
    "# create the project folder\n",
    "mkdir -p FaceRecognition/{code/{task,preprocessing,analysis},data,documents,results,scratch,logs}\n",
    "\n",
    "# list the contents of the project folder\n",
    "tree FaceRecognition -L 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Workshop's Example Data\n",
    "\n",
    "You can obtain an example data from https://cloud.mrc-cbu.cam.ac.uk/index.php/s/gUJZ6Ehli92Sm6X **~6GB**  - **Ask [me](https://www.mrc-cbu.cam.ac.uk/people/dace.apsvalka/) for a password!**\n",
    "\n",
    "The example data contain the following:\n",
    "\n",
    "- `mridata `- raw data from the MRI scanner, DICOM format (sub-15, 2 dummy scans removed). To be converted to BIDS  \n",
    "- `atlases` - Julich probabilistic atlas  \n",
    "- `sub-15_task-files` - .tsv event files filled with the onset times etc. Need to be copied in the FaceRecognition/data/sub-15/ses-mri/func after the BIDS conversion\n",
    "- `FaceRecognition` - the fMRI project folder. Contains:  \n",
    "  - `data/derivatives` - all subjects' mriqc derivatives and sub-15 fmriprep derivatives \n",
    "  - `results/first-level` - all subjects first-level results \n",
    "\n",
    "\n",
    "*Example subject is sub-15 because it is the same subject used for demonstrating M/EEG and fMRI analysing using the SPM12 software package: [Henson et al., 2019](https://pubmed.ncbi.nlm.nih.gov/31068770/)*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!tree example_data/ -L 4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Retrieving the DICOM files\n",
    "\n",
    "`DICOM` files are the raw imaging files generated by the MRI scanner. Typically, they are stored on an MRI data server. Here, at the CBU, each imaging project is assigned a unique code. By knowing the project's code, we can locate the raw DICOM files on our server.\n",
    "\n",
    "For this tutorial, we have copied one subject's DICOM data from our server to the `mridata` directory (and we removed two dummy scans to comply with OpenNeuro version of this dataset)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each `Series###` folder contains DICOM files of the particular scan. The name of the folder is the same as what a radiographer named the scan in the MRI console. Usually, the name is very indicative of what type of scan it is. In the example above, we acquired a T1w anatomical/structural scan (**MPRAGE**), nine functional scans (**BOLD**), and two **field maps**. The *Series_001_CBU_Localiser* scan is a positional scan for the MRI and can be ignored.\n",
    "\n",
    "Each of these folders contains DICOM (`.dcm`) files, typically, one file per slice for structural scans or one file per volume for functional scans. These files contain a header with the metadata (e.g., acquisition parameters) and the actual image itself. DICOM is a standard format for any medical image, not just the brain. To work with the brain images, we **need to convert the DICOM files to NIfTI format** which is a cross-platform and cross-software standard for brain images. Along with having the files in NIfTI format, we need to **name and organise them according to BIDS** standard. For that, we will use HeuDiConv."
   ]
  },
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xDSSBSiQSiUQikQRqBCjuFiu/853vHFx11VWDCy64oBCDUOBtD0xpt4D53e9+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kmgOsALP8tNJJqgaBPMJmWTlInAhNuGIFDjwltMXE1FnbAzKZhYxoGw5gUZ55EB5TbpmBhA2Qh9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\nz3t3EF+kvxVINHEkbwIDYQZkWeQrymNdmQ0wRBOCRItAyHOvvfYq55Qvd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7RIj43ZUDlF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6sfQ9xJEaD0YdeFpnMULjwd/XUbswrzp2YgoQQtqT/ZAYDyDwo+tHB9lwTjnYKcAeftzYdBb8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\ncnHZZZdNrr322vVqPyLhiCOOWBENCGyfGPn+978/ee973zv5zGc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UbYHMhq3q7elB2sS2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TasoybayzlTLY866qjJvffeW9JQn+h30vbZdez45z//uXxfQ5phY9fV+bufkPDXrp7ail/1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\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4LEtXnNoQ4FmPVe3Xj67+2fR/UiG3MoKosjRRBptYMhYsNfZZ9s51bRaQAiqRSCR6YoiA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\nz0kiBVQikUj0RAqo8YBoI6Qr+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    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Converting to BIDS using HeuDiConv\n",
    "\n",
    "HeudiConv is a Python-based comand-line tool. To use it, you would either install *heudiconv* and *dcm2niix* packages locally (`pip install heudiconv dcm2niix`) or use Docker (or Apptainer/Singularity) container image (`docker pull nipy/heudiconv`).\n",
    "\n",
    "Here we have *heudiconv* (and the required *dcm2niix*, for dicom to nifti conversion) included in the `mri` conda environment that we are using for this workshop. \n",
    "\n",
    "HeuDiConv does the following:\n",
    "\n",
    "- converts DICOM (.dcm) files to NIfTI format (`.nii` or `.nii.gz`);\n",
    "- generates their corresponding metadata files (`.json`);\n",
    "- renames the files and organises them in folders following BIDS specification;\n",
    "- generates several other `.json` and `.tsv` files required for BIDS datasets.\n",
    "\n",
    "The final result of DICOM Series being converted into BIDS for our example subject above would be this:\n",
    "\n",
    "![dicom_bids.png](attachment:a4ab1e14-4ac3-44f6-94b8-d60f76dfbd17.png)\n",
    "\n",
    "HeuDiConv needs information on how to translate your specific DICOMs into BIDS. This information is provided in a [heuristic file](https://heudiconv.readthedocs.io/en/latest/heuristics.html) that the user creates. To create the heuristic file, you need to know what scans you have, which ones you want to convert (you don't have to convert all scans, only the ones you need for your project), and how to uniquely identify each scan based on its metadata.\n",
    "\n",
    "As such, **converting DICOM data to BIDS using HeuDiConv involves 3 main steps**:\n",
    "\n",
    " 1. Discovering what DICOM series (scans) there are in your data\n",
    " 2. Creating a heuristic file specifying how to translate the DICOMs into BIDS\n",
    " 3. Converting the data"
   ]
  },
  {
   "attachments": {
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     "image/png": 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Asczgg2PWxuiYRycwD+NztnceIs/B3OCc2MesusGvNq7mVw+71OY3vUDi/B5Ne362xwg/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\nR/9cer3+f4LdLlzTXzgfAAAAAElFTkSuQmCC\n"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1: Discovering your scans\n",
    "\n",
    "First, you need to know what scans there are in your MRI data and how to uniquely identify them using their metadata. While you could manually check the metadata of each DICOM file, this is not very convenient. Instead, you can 'ask' HeuDiConv to perform the scan discovery for you. By running HeuDiConv without specifying NIfTI conversion or a heuristic file, it will generate a DICOM info table that lists all scans and their metadata. It will look something like this:\n",
    "\n",
    "![dicom_info.png](attachment:01584b59-8169-4416-accd-52591d8b068d.png)\n",
    "\n",
    "The column names are metadata fields and rows contain their corresponding values. To generate such a table, you would run the following command in the terminal:\n",
    "\n",
    "`heudiconv --files mridata/CBU090962_MR09029/*/*/*.dcm --outdir FaceRecognition/scratch/dicom_discovery --heuristic convertall --subjects '15' --converter none --bids --overwrite`\n",
    "\n",
    "However, command lines are error-prone and not easily reproducible. Instead, we recommend writing a simple bash script like this [code-examples/step01_dicom_discover.sh](../code-examples/step01_dicom_discover.sh) \n",
    "\n",
    "**Example script:**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```bash\n",
    "#!/bin/bash\n",
    "\n",
    "# Path to the raw DICOM files\n",
    "DICOM_PATH='/imaging/correia/da05/workshops/2025-CBU/example_data/mridata/CBU090962_MR09029'\n",
    "\n",
    "# Location of the output data (it will be created if it doesn't exist)\n",
    "OUTPUT_PATH='/imaging/correia/da05/workshops/2025-CBU/example_data/FaceRecognition/scratch/dicom_discovery'\n",
    "\n",
    "# Subject ID\n",
    "SUBJECT_ID='15'\n",
    "\n",
    "# ------------------------------------------------------------\n",
    "# Run the heudiconv\n",
    "# ------------------------------------------------------------\n",
    "heudiconv \\\n",
    "--files \"${DICOM_PATH}\"/*/*/*.dcm \\\n",
    "--outdir \"${OUTPUT_PATH}\" \\\n",
    "--heuristic convertall \\\n",
    "--subjects \"${SUBJECT_ID}\" \\\n",
    "--converter none \\\n",
    "--bids \\\n",
    "--overwrite\n",
    "# ------------------------------------------------------------\n",
    "\n",
    "# Copy the dicominfo.tsv file from a hidden directory to the output directory\n",
    "cp \"${OUTPUT_PATH}\"/.heudiconv/\"${SUBJECT_ID}\"/info/dicominfo.tsv \"${OUTPUT_PATH}\"\n",
    "# ------------------------------------------------------------\n",
    "\n",
    "# HeudiConv parameters:\n",
    "# --files: Files or directories containing files to process\n",
    "# --outdir: Output directory\n",
    "# --heuristic: Name of a known heuristic or path to the Python script containing heuristic\n",
    "# --subjects: Subject ID\n",
    "# --converter : dicom to nii converter (dcm2niix or none)\n",
    "# --bids: Flag for output into BIDS structure\n",
    "# --overwrite: Flag to overwrite existing files\n",
    "#\n",
    "# For a full list of parameters, see: https://heudiconv.readthedocs.io/en/latest/usage.html\n",
    "\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's run this script to discover which DICOMs our example subject has.\n",
    "\n",
    "Normally, we would execute bash scripts from the terminal. However, we can also run command-line commands directly within the notebook by adding an '!' sign at the beginning. **After running it, it's a good idea to comment it out to avoid accidentally running it again when it's not needed.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!../code-examples/step01_dicom_discover.sh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!tree -a ../example_data/FaceRecognition/scratch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After running the script, the table that we are interested in will be located at *`OUTPUT_PATH/.heudiconv/[subject ID]/info/dicominfo.tsv`*. The .heudiconv directory is a hidden directory and you might not be able to see it in your file system unless you copy it to an unhidden directory which we in fact did (notice the last line in the script). \n",
    "\n",
    "\n",
    "Now, you can open the [`dicominfo.tsv`](../example_data/FaceRecognition/scratch/dicom_discovery/dicominfo.tsv) file and keep it open for the next step - creating a heuristic file. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: Creating a heuristic file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The heuristic file must be a `Python` file. You can create and edit Python files in any text editor. You can name the file anything you want. For example, `bids_heuristic.py` like we have named our file which is available here: [code-examples/bids_heuristic.py](../code-examples/bids_heuristic.py).\n",
    "\n",
    "We need to specify the conversion template for each DICOM series. The template can be anything you want. In this particular case, we want it to be in BIDS format. Therefore, for each of our scan types, we need to consult BIDS specification. A good starting point is to look at the BIDS starter kit [folders](https://bids-standard.github.io/bids-starter-kit/folders_and_files/folders.html) and [filenames](https://bids-standard.github.io/bids-starter-kit/folders_and_files/files.html). (See the full BIDS specification for MRI [here](https://bids-specification.readthedocs.io/en/stable/modality-specific-files/magnetic-resonance-imaging-data.html).)\n",
    "\n",
    "Following these guidelines, we define the template (folder structure and filenames) for our anatomical, fieldmap, and functional scans. (We don't need to worry about the BIDS-required metadata, HeuDiConv will generate the required metadata for us.)\n",
    "\n",
    "```python\n",
    "    # The structural/anatomical scan\n",
    "    anat = create_key('sub-{subject}/ses-mri/anat/sub-{subject}_ses-mri_T1w')\n",
    "    \n",
    "    # The fieldmap scans\n",
    "    fmap_mag = create_key('sub-{subject}/ses-mri/fmap/sub-{subject}_ses-mri_acq-func_magnitude')\n",
    "    fmap_phase = create_key('sub-{subject}/ses-mri/fmap/sub-{subject}_ses-mri_acq-func_phasediff')\n",
    "    \n",
    "    # The functional scans\n",
    "    # You need to specify the task name in the filename. It must be a single string of letters WITHOUT spaces, underscores, or dashes!\n",
    "    func_task = create_key('sub-{subject}/ses-mri/func/sub-{subject}_ses-mri_task-facerecognition_run-{item:02d}_bold')\n",
    "```\n",
    "\n",
    "A couple of important points to note:\n",
    "\n",
    "- For the fielmaps, we add a key-value pair `acq-func` to specify that these fieldmaps are intended for the functional images - to correct the functional images for susceptibility distortions. You will see how this key-value is relevant later.\n",
    "\n",
    "- For the functional scans, you have to specify the task name. In our case, the task was *Face Recognition*. The task name must be a **single string** of letters without spaces, underscores, or dashes!\n",
    "\n",
    "- In this example project, we had multiple functional scans with identical parameters and task names. They correspond to multiple runs of our task. Therefore, we add a `run` key and its value will be added automatically as a 2-digit integer (*run-01*, *run-02* etc.).\n",
    "\n",
    "Next, we create a dictionary `info` with our template names as keys and empty lists for their values. The values will be filled in in the next step.\n",
    "\n",
    "```python\n",
    "    info = {\n",
    "        anat: [],\n",
    "        fmap_mag: [],\n",
    "        fmap_phase: [],\n",
    "        func_task: []\n",
    "        }\n",
    "```\n",
    "A `for loop` loops through all our DICOM series that are found in the `RAW_PATH` that we specify in our HeuDiConv parameters. Here we need to specify the criteria for associating DICOM series with their respective outputs. Now you need to look at the *dicominfo.tsv* table that we generated in the previous step.\n",
    "\n",
    "- We only have one DICOM series with *MPRAGE* in its protocol name. Therefore, for the anatomical scan, we don't need to specify any additional criteria.\n",
    "\n",
    "- We have two fieldmaps. One of them should be a *magnitude* image and the other one a *phase* image. Both have *FieldMapping* in their protocol name therefore we need to define additional criteria to distinguish between them. They differ in the `dim3` parameter. If in doubt, ask your radiographer, but typically, the *magnitude* image has more slices (higher `dim3`).\n",
    "\n",
    "- Our 9 functional scans are the only ones with more than one volume (`dim4`). They all are different runs of the same task, therefore we don't need any other distinguishing criteria for them, just the `dim4`. We specify here `dim4 > 100` because it could happen that not all participants and not all runs had exactly 210 volumes collected. In addition, sometimes it happens that a run is cancelled due to some problem and we want to discard any run with less than 100 volumes.\n",
    "\n",
    "```python\n",
    "# Loop through all the DICOM series and assign them to the appropriate conversion key.\n",
    "    for s in seqinfo:\n",
    "        # Uniquelly identify each series\n",
    "        \n",
    "        # Structural\n",
    "        if \"MPRAGE\" in s.series_id:\n",
    "            info[anat].append(s.series_id)\n",
    "            \n",
    "        # Field map Magnitude (the fieldmap with the largest dim3 is the magnitude, the other is the phase)\n",
    "        if 'FieldMapping' in s.series_id and s.series_files == 66:\n",
    "            info[fmap_mag].append(s.series_id)\n",
    "            \n",
    "        # Field map PhaseDiff\n",
    "        if 'FieldMapping' in s.series_id and s.series_files == 33:\n",
    "            info[fmap_phase].append(s.series_id)\n",
    "\n",
    "        # Functional Bold\n",
    "        if s.dim4 > 100:\n",
    "           info[func_task].append(s.series_id)\n",
    "            \n",
    "    # Return the dictionary\n",
    "    return info\n",
    "```\n",
    "Finally, we add `POPULATE_INTENDED_FOR_OPTS` dictionary to our heuristic file. This will automatically add an `IntendedFor` field in the fieldmap .json files telling for which images the fieldmaps should be used to correct for susceptibility distortion. Typically, these are the functional images. There are several options how you can specify for which images the fieldmaps are intended for. See full information [here](https://heudiconv.readthedocs.io/en/latest/heuristics.html#populate-intended-for-opts). In our example, we specify to look for a `modality acquisition label`. It checks for what modality (e.g., *anat*, *func*, *dwi*) each fieldmap is intended by checking the `acq-` label in the fieldmap filename and finding corresponding modalities. For example, `acq-func` will be matched with the `func` modality. That's why when defining the template for the `fmap_mag` and `fmap_phase` we added `acq-func` in their filenames.\n",
    "\n",
    "```python\n",
    "POPULATE_INTENDED_FOR_OPTS = {\n",
    "    'matching_parameters': ['ModalityAcquisitionLabel'],\n",
    "    'criterion': 'Closest'\n",
    "}\n",
    "```\n",
    "\n",
    "Other, more advanced functions can be added to the heuristic file as well, such as input file filtering. For full information, see the [documentation](https://heudiconv.readthedocs.io/en/latest/heuristics.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3: Converting the data\n",
    "\n",
    "Once our heuristic file is created, we are ready to convert our raw DICOM data to BIDS.\n",
    "\n",
    "To do that, we need to make three changes in our *code-examples/step01_dicom_discover.sh* bash script that we wrote in the first step. We named the edited script [code-examples/step02_dicom_to_bids.sh](../code-examples/step02_dicom_to_bids.sh).\n",
    "\n",
    "- We need to change the output path because we don't want to put this result in the *scratch* directory but in the final *data* directory\n",
    "\n",
    "- We need to change the `--heuristic` parameter specifying the path to our newly created heuristic file\n",
    "\n",
    "- We need to change the `--converter` parameter to `dcm2niix` because now we want the DICOM files to be converted to NIfTI format.\n",
    "\n",
    "Once these changes are saved, you can run the script from the terminal:\n",
    "\n",
    "\n",
    "`./code-examples/step02_dicom_to_bids.sh`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!../code-examples/step02_dicom_to_bids.sh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!tree ../example_data/FaceRecognition/data --prune -I 'derivatives'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 'To Do' - additional information to check and add\n",
    "\n",
    "Once you have converted the DICOMs to BIDS, there are some details you'll need to fill in yourself to ensure the dataset is fully BIDS-compliant. HeuDiConv marks any 'missing' information with `'To Do'` placeholders."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Events\n",
    "\n",
    "HeuDiConv generates the `events.tsv` file for each functional run. The files are just a template that you need to fill with the actual data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "events_file_empty = pd.read_csv('../example_data/FaceRecognition/data/sub-15/ses-mri/func/sub-15_ses-mri_task-facerecognition_run-01_events.tsv', sep='\\t')\n",
    "events_file_empty .head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For tasks (i.e, not just resting state), you need to provide minimal details of *onset*, *duration*, and *trial type* for each trial in that run (in addition, you can add other details that are specific to your experiment). This data should come from the outputs of your experimental script, so make sure your task is programmed in a way that allows you to easily retrieve the necessary trial and timing information."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "**For our example subject, we have prepared the final events files, and you will need to copy them into the subject's 'func' folder.** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # remove the existing events files\n",
    "# !rm -f ../example_data/FaceRecognition/data/sub-15/ses-mri/func/sub-15_ses-mri_task-facerecognition_run-*.tsv\n",
    "\n",
    "# # copy the prepared events\n",
    "# !cp -r ../example_data/sub-15_task-files/* ../example_data/FaceRecognition/data/sub-15/ses-mri/func"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "events_file_filled = pd.read_csv('../example_data/FaceRecognition/data/sub-15/ses-mri/func/sub-15_ses-mri_task-facerecognition_run-01_events.tsv', sep='\\t')\n",
    "events_file_filled.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dataset description\n",
    "\n",
    "`dataset_description.json`\n",
    "\n",
    "A brief description of your dataset. \n",
    "\n",
    "```json\n",
    "{\n",
    "  \"Acknowledgements\": \"TODO: whom you want to acknowledge\",\n",
    "  \"Authors\": [\n",
    "    \"TODO:\",\n",
    "    \"First1 Last1\",\n",
    "    \"First2 Last2\",\n",
    "    \"...\"\n",
    "  ],\n",
    "  \"BIDSVersion\": \"1.8.0\",\n",
    "  \"DatasetDOI\": \"TODO: eventually a DOI for the dataset\",\n",
    "  \"Funding\": [\n",
    "    \"TODO\",\n",
    "    \"GRANT #1\",\n",
    "    \"GRANT #2\"\n",
    "  ],\n",
    "  \"HowToAcknowledge\": \"TODO: describe how to acknowledge -- either cite a corresponding paper, or just in acknowledgement section\",\n",
    "  \"License\": \"TODO: choose a license, e.g. PDDL (http://opendatacommons.org/licenses/pddl/)\",\n",
    "  \"Name\": \"TODO: name of the dataset\",\n",
    "  \"ReferencesAndLinks\": [\n",
    "    \"TODO\",\n",
    "    \"List of papers or websites\"\n",
    "  ]\n",
    "}\n",
    "```\n",
    "\n",
    "See a complete description for this dataset here: https://openneuro.org/datasets/ds000117/versions/1.1.0/file-display/dataset_description.json"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Participants\n",
    "\n",
    "`participants.json`\n",
    "\n",
    "```json\n",
    "{\n",
    "  \"participant_id\": {\n",
    "    \"Description\": \"Participant identifier\"\n",
    "  },\n",
    "  \"age\": {\n",
    "    \"Description\": \"Age in years (TODO - verify) as in the initial session, might not be correct for other sessions\"\n",
    "  },\n",
    "  \"sex\": {\n",
    "    \"Description\": \"self-rated by participant, M for male/F for female (TODO: verify)\"\n",
    "  },\n",
    "  \"group\": {\n",
    "    \"Description\": \"(TODO: adjust - by default everyone is in control group)\"\n",
    "  }\n",
    "}\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Task information\n",
    "\n",
    "`task-facerecognition_bold.json`\n",
    "\n",
    "Could add full task name and a Cognitive Atlas ID if it exists. \n",
    "```json\n",
    "{\n",
    " ...\n",
    "  \"TaskName\": \"TODO: full task name for facerecognition\",\n",
    " ...\n",
    " ```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### README\n",
    "\n",
    "*\"TODO: Provide description for the dataset -- basic details about the study, possibly pointing to pre-registration (if public or embargoed)\"*\n",
    "\n",
    "See an example for the OpenNeuro version of this dataset https://openneuro.org/datasets/ds000117/versions/1.1.0/file-display/README"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Validate BIDS structure\n",
    "\n",
    "Once we have our BIDS dataset, we can use an [online BIDS validator](https://bids-standard.github.io/bids-validator/) to check if our dataset confirms with BIDS standard and what additional information we might need to include in your dataset's metadata. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Downloading BIDS repository from OpenNeuro\n",
    "\n",
    "Your workshop example materials, for data-saving purposes, allow you to only use single subject's BIDS data. If you like, you can download the whole BIDS dataset from the OpenNeuro. Here is an example script for doing that: [/code-examples/download_openneuro_bids_dataset.py](../code-examples/download_openneuro_bids_dataset.py). \n",
    "\n",
    "If restricting the download to only include the mri session and only the modalities we use in this workshop, the download size will be **~5GB**. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PyBIDS\n",
    "\n",
    "`PyBids` is a Python module to interface with datasets conforming BIDS. See the [documentation](https://bids-standard.github.io/pybids/) and [paper](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7409983/) for more info. \n",
    "\n",
    "**Let's explore some of the functionality of pybids.layout.** The material is adapted from https://github.com/bids-standard/pybids/tree/master/examples\n",
    "\n",
    "I will here use the whole BIDS dataset that I have prepared. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bids.layout import BIDSLayout\n",
    "\n",
    "fmri_data_dir = '/imaging/correia/da05/workshops/FaceRecognition/data/'\n",
    "\n",
    "# Initialize the layout\n",
    "layout = BIDSLayout(fmri_data_dir)\n",
    "\n",
    "# Print some basic information about the layout\n",
    "layout"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Querying the BIDSLayout\n",
    "The main method for querying `BIDSLayout` is `.get()`.\n",
    "\n",
    "If we call `.get()` with no additional arguments, we get back a list of all the BIDS files in our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_files = layout.get()\n",
    "print(\"There are {} files in the layout.\".format(len(all_files)))\n",
    "print(\"\\nThe first 5 files are:\")\n",
    "all_files[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The returned object is a list of `BIDSFile` objects. \n",
    "\n",
    "We can also get just filenames."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "layout.get(return_type='filename')[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also get such information as\n",
    "* all `subject` IDs\n",
    "* all `task` names\n",
    "* dataset `description`\n",
    "* the BOLD repetition time TR\n",
    "* how many `runs` there are"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "layout.get_subjects()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "layout.get_tasks()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "layout.get_dataset_description()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also convert layout to a data frame, which might be easier to read:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = layout.to_df()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "TR = layout.get_tr()\n",
    "print(TR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, with a more bit programming, we can check the number of runs per participant, and number of volumes per run. To calculate the number of volumes, we need to use the `nibabel` package (which is explained more in next Notebook) to load an image: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for sID in layout.get_subjects(): \n",
    "    volumes_per_run = []\n",
    "    for rID in layout.get_runs(subject=sID):\n",
    "        # Get the metadata for the corresponding BOLD file\n",
    "        bold_file = layout.get(subject=sID, run=rID, extension='nii.gz', suffix='bold', return_type='filename')[0]\n",
    "        metadata = layout.get_metadata(bold_file)\n",
    "        num_volumes = metadata.get(\"dcmmeta_shape\", [])[3] if \"dcmmeta_shape\" in metadata else None\n",
    "        volumes_per_run.append(num_volumes)\n",
    "    print(f\"Subject {sID}: Number of volumes per run: {volumes_per_run}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You might notice that sub-10 had fewer volumes in their last run (e.g, due to scanner stopping). This is not a problem for later analysis, but worth knowing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filtering files by entities\n",
    "We can pass any BIDS-defined entities (keywords) to `.get()` method. For example, here's how we would retrieve all BOLD runs with `.nii.gz` extensions for subject `15`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Retrieve filenames of all BOLD runs for subject\n",
    "layout.get(subject='15', extension='nii.gz', suffix='bold', return_type='filename')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All of the entities are found in the names of BIDS files. For example `sub-01_task-facerecognition_run-01_bold.nii.gz` has entities: **subject**, **task**, **run**, **suffix**, **extension**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can get the list of all availabe entities by `layout.get_entities()`.\n",
    "\n",
    "Here are a few of the most common entities:\n",
    "\n",
    "* `suffix`: The part of a BIDS filename just before the extension (e.g., 'bold', 'events', 'T1w', etc.).\n",
    "* `subject`: The subject label\n",
    "* `session`: The session label\n",
    "* `run`: The run index\n",
    "* `task`: The task name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filtering by metadata\n",
    "Sometimes we want to search for files not just by their names, but also based on metadata defined in JSON files. We can use any key that appears in a JSON file in our project as an argument to `.get()`. This can be combined with any number of core BIDS entities (like *subject*, *run*, etc.).\n",
    "\n",
    "For example, let's retrieve the subject's `SpacingBetweenSlices` (measured from the center of each slice to the next, in mm)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "layout.get_SpacingBetweenSlices(subject='15', suffix='bold')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**==================================================================================================**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Other `return_type` values\n",
    "We can also ask `get()` to return unique values (or IDs) of particular entities. For example, we want to know which subjects had a fieldmap acquired. We can request that information by setting `return_type='id'` - to get subject IDs. When using this option, we also need to specify a `target` entity for the ID (in this case, subject). This combination tells the `BIDSLayout` to return the unique values for the specified `target` entity. \n",
    "\n",
    "For example, in the next example, we ask for all of the unique subject IDs that have at least one file with a `phasediff` (fieldmap) suffix. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ask get() to return the ids of subjects that have phasediff (fieldmap_ files)\n",
    "\n",
    "layout.get(return_type='id', target='subject', suffix='phasediff')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If our `target` is a BIDS entity that corresponds to a particular directory in the BIDS specification (e.g., `session`) we can also use `return_type='dir'` to get all matching subdirectories. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "layout.get(return_type='dir', target='session')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The `BIDSFile`\n",
    "When you call `.get()` on a `BIDSLayout`, the default returned values are objects of class `BIDSFile`. A `BIDSFile` is a lightweight container for individual files in a BIDS dataset. It provides easy access to a variety of useful attributes and methods. Let's take a closer look. First, let's pick a random file from our existing `layout`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pick the 7th file in the dataset\n",
    "bf = layout.get(subject='15', extension='nii.gz', suffix='bold')[0]\n",
    "# Print it\n",
    "bf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are some of the attributes and methods available to us in a `BIDSFile` (note that some of these are only available for certain subclasses of `BIDSFile`; e.g., you can't call `get_image()` on a `BIDSFile` that doesn't correspond to an image file!):\n",
    "* `.path`: The full path of the associated file\n",
    "* `.filename`: The associated file's filename (without directory)\n",
    "* `.dirname`: The directory containing the file\n",
    "* `.get_entities()`: Returns information about entities associated with this `BIDSFile` (optionally including metadata)\n",
    "* `.get_image()`: Returns the file contents as a nibabel image (only works for image files)\n",
    "* `.get_df()`: Get file contents as a pandas DataFrame (only works for TSV files)\n",
    "* `.get_metadata()`: Returns a dictionary of all metadata found in associated JSON files\n",
    "* `.get_associations()`: Returns a list of all files associated with this one in some way\n",
    "\n",
    "Let's see some of these in action."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Print all the entities associated with this file, and their values\n",
    "bf.get_entities()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Print first 30 metadata items associated with this file\n",
    "file_metadata = bf.get_metadata()\n",
    "\n",
    "{k: file_metadata[k] for k in list(file_metadata)[:30]}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`.get_image()`: Returns the file contents as a `nibabel` image (only works for image files). We can then display the image, for example, using `OrthoSlicer3D`.   \n",
    "\n",
    "**Note:** When using `orthoview()` in notebook, don't forget to close figures afterward again or use %matplotlib inline again, otherwise, you cannot plot any other figures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "bf.get_image().orthoview()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `.tsv` files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In cases where a file has a `.tsv.gz` or `.tsv` extension, it will automatically be created as a `BIDSDataFile`, and we can easily grab the contents as a `DataFrame`.\n",
    "\n",
    "Let's look at the first `events` file from our layout."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the first events file\n",
    "evfile = layout.get(suffix='events')[0]\n",
    "\n",
    "# Get contents as a DataFrame and show the first few rows\n",
    "df = evfile.get_df()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the `participants` information. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "participants = layout.get(suffix='participants', extension='tsv')[0]\n",
    "\n",
    "df = participants.get_df()\n",
    "df.sort_values(by=['participant_id'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filename parsing\n",
    "Let’s say you have a filename and want to manually extract BIDS entities from it. . The `parse_file_entities` method provides the facility:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "layout.parse_file_entities('sub-15/ses-mri/func/sub-15_ses-mri_task-facerecognition_run-04_bold.nii.gz')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can do the same for `BIDSFile` object that we defined earlier. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "layout.parse_file_entities(bf.path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "layout.parse_file_entities(bf.filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Report generation\n",
    "`PyBIDS` also allows you to automatically create data acquisition reports based on the available `image` and `meta-data` information. This enables a new level of standardisation and transparency. FAIR-ness, meta-analyses, etc. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the BIDSReport function from the reports submodule\n",
    "from bids.reports import BIDSReport"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we only need to apply the `BIDSReport` function to our `layout` and generate our report. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize a report for the dataset\n",
    "report = BIDSReport(layout)\n",
    "\n",
    "# Method generate returns a Counter of unique descriptions across subjects\n",
    "try:\n",
    "    descriptions = report.generate()\n",
    "    pub_description = descriptions.most_common()[0][0]\n",
    "    print(pub_description)\n",
    "except IndexError:\n",
    "    print('Sorry, it seems that the dataset is not complete and report cannot be generated.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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