<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
 Data input for BIDS datasets
DataGrabber and SelectFiles are great if you are dealing with generic datasets with arbitrary organization. However, if you have decided to use Brain Imaging Data Structure (BIDS) to organize your data (or got your hands on a BIDS dataset) you can take advantage of a formal structure BIDS imposes. In this short tutorial, you will learn how to do this.
pybids - a Python API for working with BIDS datasets
pybids is a lightweight python API for querying BIDS folder structure for specific files and metadata. You can install it from PyPi
Step1:  Let's figure out what are the subject labels in this dataset
Step2:  What datatypes are included in this dataset?
Step3:  Which different data suffixes are included in this dataset?
Step4:  What are the different tasks included in this dataset?
Step5:  We can also ask for all of the data for a particular subject and one datatype.
Step6:  We can also ask for a specific subset of data. Note that we are using extension filter to get just the imaging data (BIDS allows both .nii and .nii.gz so we need to include both).
Step7:  You probably noticed that this method does not only return the file paths, but objects with relevant query fields. We can easily extract just the file paths.
Step8:  Exercise 1
Step9:  BIDSDataGrabber
Step10:  You can define static filters, that will apply to all queries, by modifying the appropriate input
Step11:  Note that by default BIDSDataGrabber will fetch nifti files matching datatype func and anat, and output them as two output fields. 
To define custom fields, simply define the arguments to pass to BIDSLayout.get as dictionary, like so
Step12:  This results in a single output field bold, which returns all files with suffix
Step13:  Exercise 2
Step14:  Iterating over subject labels
In the previous example, we demonstrated how to use pybids to "analyze" one subject. How can we scale it for all subjects? Easy - using iterables (more in Iteration/Iterables).
Step15:  Accessing additional metadata
Querying different files is nice, but sometimes you want to access more metadata. For example RepetitionTime. pybids can help with that as well
Step16:  Can we incorporate this into our pipeline? Yes, we can! To do so, let's use a Function node to use BIDSLayout in a custom way.
(More about MapNode in MapNode)
Step17:  Exercise 3
<ASSISTANT_TASK:>
Python Code:
from bids.layout import BIDSLayout
layout = BIDSLayout("/data/ds000114/")
!tree -L 4 /data/ds000114/
Explanation: Data input for BIDS datasets
DataGrabber and SelectFiles are great if you are dealing with generic datasets with arbitrary organization. However, if you have decided to use Brain Imaging Data Structure (BIDS) to organize your data (or got your hands on a BIDS dataset) you can take advantage of a formal structure BIDS imposes. In this short tutorial, you will learn how to do this.
pybids - a Python API for working with BIDS datasets
pybids is a lightweight python API for querying BIDS folder structure for specific files and metadata. You can install it from PyPi:
pip install pybids
Please note it should be already installed in the tutorial Docker image.
The layout object and simple queries
To begin working with pybids we need to initialize a layout object. We will need it to do all of our queries
End of explanation
layout.get_subjects()
Explanation: Let's figure out what are the subject labels in this dataset
End of explanation
layout.get_datatypes()
Explanation: What datatypes are included in this dataset?
End of explanation
layout.get_suffixes(datatype='func')
Explanation: Which different data suffixes are included in this dataset?
End of explanation
layout.get_tasks()
Explanation: What are the different tasks included in this dataset?
End of explanation
layout.get(subject='01', datatype="anat", session="test")
Explanation: We can also ask for all of the data for a particular subject and one datatype.
End of explanation
layout.get(subject='01', suffix='bold', extension=['.nii', '.nii.gz'])
Explanation: We can also ask for a specific subset of data. Note that we are using extension filter to get just the imaging data (BIDS allows both .nii and .nii.gz so we need to include both).
End of explanation
layout.get(subject='01', suffix='bold', extension=['.nii', '.nii.gz'], return_type='file')
Explanation: You probably noticed that this method does not only return the file paths, but objects with relevant query fields. We can easily extract just the file paths.
End of explanation
#write your solution here
from bids.layout import BIDSLayout
layout = BIDSLayout("/data/ds000114/")
layout.get(subject='02', return_type='file', task="linebisection")
Explanation: Exercise 1:
List all files for the "linebisection" task for subject 02.
End of explanation
from nipype.interfaces.io import BIDSDataGrabber
from nipype.pipeline import Node, MapNode, Workflow
from nipype.interfaces.utility import Function
bg = Node(BIDSDataGrabber(), name='bids-grabber')
bg.inputs.base_dir = '/data/ds000114'
Explanation: BIDSDataGrabber: Including pybids in your nipype workflow
This is great, but what we really want is to include this into our nipype workflows. To do this, we can import BIDSDataGrabber, which provides an Interface for BIDSLayout.get
End of explanation
bg.inputs.subject = '01'
res = bg.run()
res.outputs
Explanation: You can define static filters, that will apply to all queries, by modifying the appropriate input
End of explanation
bg.inputs.output_query = {'bolds': dict(suffix='bold')}
res = bg.run()
res.outputs
Explanation: Note that by default BIDSDataGrabber will fetch nifti files matching datatype func and anat, and output them as two output fields. 
To define custom fields, simply define the arguments to pass to BIDSLayout.get as dictionary, like so:
End of explanation
def printMe(paths):
    print("\n\nanalyzing " + str(paths) + "\n\n")
    
analyzeBOLD = Node(Function(function=printMe, input_names=["paths"],
                            output_names=[]), name="analyzeBOLD")
wf = Workflow(name="bids_demo")
wf.connect(bg, "bolds", analyzeBOLD, "paths")
wf.run()
Explanation: This results in a single output field bold, which returns all files with suffix:bold for subject:"01" 
Now, lets put it in a workflow. We are not going to analyze any data, but for demonstration purposes, we will add a couple of nodes that pretend to analyze their inputs
End of explanation
# write your solution here
from nipype.pipeline import Node, MapNode, Workflow
from nipype.interfaces.io import BIDSDataGrabber
ex2_BIDSDataGrabber = BIDSDataGrabber()
ex2_BIDSDataGrabber.inputs.base_dir = '/data/ds000114'
ex2_BIDSDataGrabber.inputs.subject = '10'
ex2_BIDSDataGrabber.inputs.output_query = {'T1w': dict(datatype='anat')}
ex2_res = ex2_BIDSDataGrabber.run()
ex2_res.outputs
Explanation: Exercise 2:
Modify the BIDSDataGrabber and the workflow to collect T1ws images for subject 10.
End of explanation
bg_all = Node(BIDSDataGrabber(), name='bids-grabber')
bg_all.inputs.base_dir = '/data/ds000114'
bg_all.inputs.output_query = {'bolds': dict(suffix='bold')}
bg_all.iterables = ('subject', layout.get_subjects()[:2])
wf = Workflow(name="bids_demo")
wf.connect(bg_all, "bolds", analyzeBOLD, "paths")
wf.run()
Explanation: Iterating over subject labels
In the previous example, we demonstrated how to use pybids to "analyze" one subject. How can we scale it for all subjects? Easy - using iterables (more in Iteration/Iterables).
End of explanation
layout.get_metadata('/data/ds000114/sub-01/ses-test/func/sub-01_ses-test_task-fingerfootlips_bold.nii.gz')
Explanation: Accessing additional metadata
Querying different files is nice, but sometimes you want to access more metadata. For example RepetitionTime. pybids can help with that as well
End of explanation
def printMetadata(path, data_dir):
    from bids.layout import BIDSLayout
    layout = BIDSLayout(data_dir)
    print("\n\nanalyzing " + path + "\nTR: "+ str(layout.get_metadata(path)["RepetitionTime"]) + "\n\n")
    
analyzeBOLD2 = MapNode(Function(function=printMetadata, input_names=["path", "data_dir"],
                             output_names=[]), name="analyzeBOLD2", iterfield="path")
analyzeBOLD2.inputs.data_dir = "/data/ds000114/"
wf = Workflow(name="bids_demo")
wf.connect(bg, "bolds", analyzeBOLD2, "path")
wf.run()
Explanation: Can we incorporate this into our pipeline? Yes, we can! To do so, let's use a Function node to use BIDSLayout in a custom way.
(More about MapNode in MapNode)
End of explanation
# write your solution here
from nipype.pipeline import Node, MapNode, Workflow
from nipype.interfaces.io import BIDSDataGrabber
ex3_BIDSDataGrabber = Node(BIDSDataGrabber(), name='bids-grabber')
ex3_BIDSDataGrabber.inputs.base_dir = '/data/ds000114'
ex3_BIDSDataGrabber.inputs.subject = '01'
ex3_BIDSDataGrabber.inputs.output_query = {'bolds': dict(suffix='bold')}
# and now modify analyzeBOLD2
def printMetadata_et(path, data_dir):
    from bids.layout import BIDSLayout
    layout = BIDSLayout(data_dir)
    print("\n\nanalyzing " + path + "\nTR: "+ 
          str(layout.get_metadata(path)["RepetitionTime"]) +
          "\nET: "+ str(layout.get_metadata(path)["EchoTime"])+ "\n\n")
    
ex3_analyzeBOLD2 = MapNode(Function(function=printMetadata_et, 
                                    input_names=["path", "data_dir"],
                                    output_names=[]), 
                           name="ex3", iterfield="path")
ex3_analyzeBOLD2.inputs.data_dir = "/data/ds000114/"
# and create a new workflow
ex3_wf = Workflow(name="ex3")
ex3_wf.connect(ex3_BIDSDataGrabber, "bolds", ex3_analyzeBOLD2, "path")
ex3_wf.run()
Explanation: Exercise 3:
Modify the printMetadata function to also print EchoTime
End of explanation
<END_TASK>