# In[16]: f = layout.get()[0] f # If we wanted to get the path of the file, we can use `.path`. # In[17]: f.path # Suppose we were interested in getting a list of tasks included in the dataset. # In[18]: layout.get_task() # We can query all of the files associated with this task. # In[19]: layout.get(task='localizer', suffix='bold', scope='raw')[:10] # Notice that there are nifti and event files. We can get the filename for the first particant's functional run # In[140]: f = layout.get(task='localizer')[0].filename f # If you want a summary of all the files in your BIDSLayout, but don't want to have to iterate BIDSFile objects and extract their entities, you can get a nice bird's-eye view of your dataset using the `to_df()` method.
- .get_image(): Returns the file contents as a nibabel image (only works for image files) - .get_df(): Get file contents as a pandas DataFrame (only works for TSV files) - .get_metadata(): Returns a dictionary of all metadata found in associated JSON files - .get_associations(): Returns a list of all files associated with this one in some way Let's explore the first file in a little more detail. f = layout.get()[0] f If we wanted to get the path of the file, we can use `.path`. f.path Suppose we were interested in getting a list of tasks included in the dataset. layout.get_task() We can query all of the files associated with this task. layout.get(task='localizer', suffix='bold', scope='raw')[:10] Notice that there are nifti and event files. We can get the filename for the first particant's functional run f = layout.get(task='localizer')[0].filename f If you want a summary of all the files in your BIDSLayout, but don't want to have to iterate BIDSFile objects and extract their entities, you can get a nice bird's-eye view of your dataset using the `to_df()` method. layout.to_df()
