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How to view changes to an OpenNeuro dataset draft

OpenNeuro has implemented a data retention policy, stating that datasets that have been in draft state for greater than 28 days may be reverted to the latest snapshot. Unfortunately, we don't currently have an interface for viewing what changes have been made since the last snapshot, so users may not know whether they want to create a new snapshot or not.

This gist shows two ways to view the changes using the OpenNeuro CLI. We will use ds000001 as an example.

Download and diff

The easy but slow approach would be to use the CLI to download two copies of your dataset, the most recent tag and the draft, and run diff -r on the pair:

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<!--
Initially provided by John Harwell on the HCP user list [0].

Modified by Chris Markiewicz based on example wb.spec files.

[0] https://groups.google.com/a/humanconnectome.org/g/hcp-users/c/EGuwdaTVFuw/m/Pir-LpbHAQAJ
-->
<!ELEMENT CaretSpecFile (MetaData?, DataFile+)>
<!ATTLIST CaretSpecFile
    Version ( 1.0 ) #REQUIRED

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import os
import nibabel as nb
import numpy as np

# Create an image with a "plausible" range of fMRI values
img = nb.Nifti1Image(np.random.uniform(500, 2000, (256,256,256,1)), np.eye(4))
# Verify numpy's giving us float64
assert img.get_data_dtype() == np.float64

# Quantize to 16 bits and reload

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Contemporary Python Packaging

This document lays out a set of Python packaging practices. I don't claim they are best practices, but they fit my needs, and might fit yours.

Validity

This document has been superseded as of January 2023.

This was written in July 2020, superseding this gist from 2019.

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{
  "Name": "NARPS",
  "Description": "Basic NARPS model",
  "Input": {
    "task": "MGT"
  },
  "Steps": [
    {
      "Level": "run",
      "Transformations": [

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Contemporary Python Packaging

This document lays out a set of Python packaging practices. I don't claim they are best practices, but they fit my needs, and might fit yours.

Validity

This document has been superseded as of July 2020.

This was written in July 2019. As of this writing Python 2.7 and Python 3.5 still have not

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Partially-parametric permutation testing

Cluster thresholding in multi-voxel pattern analysis (MVPA) is an open problem, as many figures of merit are possible, few (if any) of which have been sufficiently analyzed to permit a parametric solution or a guarantee of compatibility with pre-computed simulations. Stelzer, et al. 2012 represents probably the most conservative approach, constructing voxel-wise and then cluster-wise null distributions at the group level, based on permuting the training labels at the individual level.

In Mapping the cortical representation of speech sounds in a syllable repetition task, we adapted this approach to skip the voxel-wise null distribution,

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name: base
channels:
  - conda-forge
  - defaults
dependencies:
  - apptools=4.4.0=py27_0
  - ca-certificates=2018.4.16=0
  - cairo=1.14.6=0
  - conda=4.5.4=py27_0
  - conda-env=2.6.0=0

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import traitlets

class _Undefined(object):
    obj = None
    def __new__(cls):
        if cls.obj is None:
            cls.obj = object.__new__(cls)
        return cls.obj

class _UseDefault(_Undefined):

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#!/usr/bin/env python3
import sys
import os
import re
import argparse
import subprocess
import tempfile
from functools import partial

__version__ = 0.1