  FMRI data are noisy, complicated to acquire, and typically go through many
steps of processing before they are used in a study or clinical practice. Being
able to visualize and understand the data from the start through the completion
of processing, while being confident that each intermediate step was
successful, is challenging. AFNI's "afni_proc.py" is a tool to create and run a
processing pipeline for FMRI data. With its flexible features, "afni_proc.py"
allows users to both control and evaluate their processing at a detailed level.
It has been designed to keep users informed about all processing steps: it does
not just process the data, but first outputs a fully commented processing
script that the users can read, query, interpret and refer back to. Having this
full provenance is important for being able to understand each step of
processing; it also promotes transparency and reproducibility by keeping the
record of individual-level processing and modeling specifics in a single,
shareable place. Additionally, "afni_proc.py" creates pipelines that contain
several automatic self-checks for potential problems during runtime. The output
directory contains a dictionary of relevant quantities that can be
programmatically queried for potential issues and a systematic, interactive
quality control (QC) HTML. All of these features help users evaluate and
understand their data and processing in detail. We describe these and other
aspects of "afni_proc.py" here using a set of task-based and resting state FMRI
example commands.
