Merge branch 'master' into FIX_notebook_kernel

Author: Shotgunosine

Dockerfile

@@ -1,5 +1,5 @@
 # Generated by Neurodocker version 0.4.2-dev
-# Timestamp: 2018-10-09 20:28:32 UTC
+# Timestamp: 2018-10-09 19:14:50 UTC
 # 
 # Thank you for using Neurodocker. If you discover any issues
 # or ways to improve this software, please submit an issue or
@@ -126,7 +126,7 @@ RUN export PATH="/opt/miniconda-latest/bin:$PATH" \
     && bash -c "source activate neuro \
     &&   pip install --no-cache-dir  \
              https://github.com/nipy/nipype/tarball/master \
-             https://github.com/INCF/pybids/tarball/master \
+             https://github.com/INCF/pybids/tarball/0.6.5 \
              nilearn \
              datalad[full] \
              nipy \
@@ -234,7 +234,7 @@ RUN echo '{ \
     \n        ], \
     \n        "pip_install": [ \
     \n          "https://github.com/nipy/nipype/tarball/master", \
-    \n          "https://github.com/INCF/pybids/tarball/master", \
+    \n          "https://github.com/INCF/pybids/tarball/0.6.5", \
     \n          "nilearn", \
     \n          "datalad[full]", \
     \n          "nipy", \

Singularity

@@ -1,5 +1,5 @@
 # Generated by Neurodocker version 0.4.2-dev
-# Timestamp: 2018-10-09 20:28:34 UTC
+# Timestamp: 2018-10-09 19:14:51 UTC
 # 
 # Thank you for using Neurodocker. If you discover any issues
 # or ways to improve this software, please submit an issue or
@@ -116,7 +116,7 @@ sync && conda clean -tipsy && sync
 bash -c "source activate neuro
   pip install --no-cache-dir  \
       https://github.com/nipy/nipype/tarball/master \
-      https://github.com/INCF/pybids/tarball/master \
+      https://github.com/INCF/pybids/tarball/0.6.5 \
       nilearn \
       datalad[full] \
       nipy \
@@ -228,7 +228,7 @@ echo '{
 \n        ],
 \n        "pip_install": [
 \n          "https://github.com/nipy/nipype/tarball/master",
-\n          "https://github.com/INCF/pybids/tarball/master",
+\n          "https://github.com/INCF/pybids/tarball/0.6.5",
 \n          "nilearn",
 \n          "datalad[full]",
 \n          "nipy",

docs/notebooks/resources_resources.html

@@ -8451,15 +8451,15 @@
 }
 /* Flexible box model classes */
 /* Taken from Alex Russell http://infrequently.org/2009/08/css-3-progress/ */
-/* This file is a compatability layer.  It allows the usage of flexible box
+/* This file is a compatability layer.  It allows the usage of flexible box 
 model layouts accross multiple browsers, including older browsers.  The newest,
 universal implementation of the flexible box model is used when available (see
-`Modern browsers` comments below).  Browsers that are known to implement this
+`Modern browsers` comments below).  Browsers that are known to implement this 
 new spec completely include:
 
     Firefox 28.0+
     Chrome 29.0+
-    Internet Explorer 11+
+    Internet Explorer 11+ 
     Opera 17.0+
 
 Browsers not listed, including Safari, are supported via the styling under the
@@ -11233,7 +11233,7 @@
   background: #f7f7f7;
   border-top: 1px solid #cfcfcf;
   border-bottom: 1px solid #cfcfcf;
-  /* This injects handle bars (a short, wide = symbol) for
+  /* This injects handle bars (a short, wide = symbol) for 
         the resize handle. */
 }
 div#pager .ui-resizable-handle::after {
@@ -11676,7 +11676,7 @@
 .highlight .il { color: #666666 } /* Literal.Number.Integer.Long */
     </style>
 <style type="text/css">
-
+    
 /* Temporary definitions which will become obsolete with Notebook release 5.0 */
 .ansi-black-fg { color: #3E424D; }
 .ansi-black-bg { background-color: #3E424D; }
@@ -11730,14 +11730,14 @@
   div.cell {
     display: block;
     page-break-inside: avoid;
-  }
-  div.output_wrapper {
+  } 
+  div.output_wrapper { 
     display: block;
-    page-break-inside: avoid;
+    page-break-inside: avoid; 
   }
-  div.output {
+  div.output { 
     display: block;
-    page-break-inside: avoid;
+    page-break-inside: avoid; 
   }
 }
 </style>
@@ -11775,16 +11775,6 @@
   ga('create', 'UA-92929997-1', 'auto');
   ga('send', 'pageview');
 
-</script>
-<script>
-  (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
-  (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
-  m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
-  })(window,document,'script','https://www.google-analytics.com/analytics.js','ga');
-
-  ga('create', 'UA-92929997-1', 'auto');
-  ga('send', 'pageview');
-
 </script>
 </head>
 <body>
@@ -11796,14 +11786,15 @@
 <div class="inner_cell">
 <div class="text_cell_render border-box-sizing rendered_html">
 <h1 id="Helpful-Resources">Helpful Resources<a class="anchor-link" href="#Helpful-Resources">&#182;</a></h1><h2 id="Learn-more-about-Nipype">Learn more about Nipype<a class="anchor-link" href="#Learn-more-about-Nipype">&#182;</a></h2><ul>
-<li><a href="http://nipype.readthedocs.io/en/latest/">Nipype homepage</a>: This is the best place to learn all you need to know about Nipype. For beginner's I recommend to check out the <a href="http://nipype.readthedocs.io/en/latest/quickstart.html">Quickstart</a> section.</li>
+<li><a href="http://nipype.readthedocs.io/en/latest/">Nipype homepage</a>: This is the best place to learn all you need to know about Nipype. For beginner's, I recommend to check out the <a href="http://nipype.readthedocs.io/en/latest/quickstart.html">Quickstart</a> section.</li>
 <li><a href="http://miykael.github.io/nipype-beginner-s-guide/">Beginner's Guide</a>: This beginner's guide is an in-depth step by step tutorial to Nipype.</li>
 </ul>
 <h2 id="Neuroimaging">Neuroimaging<a class="anchor-link" href="#Neuroimaging">&#182;</a></h2><ul>
 <li><a href="http://www.fmri4newbies.com/tutorials/">fMRI 4 Newbies</a>: A crash course in brain imaging.</li>
 <li><a href="https://neurostars.org/">Neurostars.org</a>: If you have any questions about Neuroinformatics, this is the place to go! </li>
 <li><a href="http://imaging.mrc-cbu.cam.ac.uk/imaging/DesignEfficiency">Design efficiency in FMRI</a>: A nice and detailed guide on how to design a good fMRI study.</li>
 </ul>
+<h2 id="Open-Science-Resources">Open Science Resources<a class="anchor-link" href="#Open-Science-Resources">&#182;</a></h2><p><a href="https://twitter.com/aya_ben_yakov">Aya Ben-Yakov</a> put together a very <a href="http://www.mrc-cbu.cam.ac.uk/openscience/resources/">nice and comprehensive list</a> of various tools and resources about open and reproducible science.</p>
 <h2 id="Learn-Python">Learn Python<a class="anchor-link" href="#Learn-Python">&#182;</a></h2><ul>
 <li><a href="http://python.swaroopch.com/">A Byte of Python</a>: A very nice introduction to Python in general.</li>
 <li><a href="http://nbviewer.jupyter.org/gist/rpmuller/5920182">A Crash Course in Python for Scientists</a>: a very good introduction to Python and scientific programming (e.g. Numpy, Scipy, Matplotlib)</li>
@@ -11829,7 +11820,7 @@ <h2 id="Learn-Unix-Shell">Learn Unix Shell<a class="anchor-link" href="#Learn-Un
   </div>
 </body><div class="h3" style="right:0;bottom:0;left:0;padding:1rem;text-align:center;"><p style="white-space:pre"><a href="https://miykael.github.io/nipype_tutorial/">Home</a>&emsp;|&emsp;<a href="https://github.com/miykael/nipype_tutorial">github</a>&emsp;|&emsp;<a href="http://nipype.readthedocs.io">Nipype</a></p></div>
 
-
+ 
 
 
 </html>

generate.sh

@@ -17,7 +17,7 @@ generate_docker() {
              conda_install="python=3.6 pytest jupyter jupyterlab jupyter_contrib_nbextensions
                             traits pandas matplotlib scikit-learn scikit-image seaborn nbformat nb_conda" \
              pip_install="https://github.com/nipy/nipype/tarball/master
-                          https://github.com/INCF/pybids/tarball/master
+                          https://github.com/INCF/pybids/tarball/0.6.5
                           nilearn datalad[full] nipy duecredit nbval" \
              create_env="neuro" \
              activate=True \
@@ -55,7 +55,7 @@ generate_singularity() {
              conda_install="python=3.6 pytest jupyter jupyterlab jupyter_contrib_nbextensions
                             traits pandas matplotlib scikit-learn scikit-image seaborn nbformat nb_conda" \
              pip_install="https://github.com/nipy/nipype/tarball/master
-                          https://github.com/INCF/pybids/tarball/master
+                          https://github.com/INCF/pybids/tarball/0.6.5
                           nilearn datalad[full] nipy duecredit nbval" \
              create_env="neuro" \
              activate=True \

notebooks/advanced_create_interfaces.ipynb

@@ -76,9 +76,17 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import nibabel as nb\n",
-    "%matplotlib inline\n",
-    "nb.load('/data/ds000114/sub-01/ses-test/anat/sub-01_ses-test_T1w.nii.gz').orthoview();"
+    "from nilearn.plotting import plot_anat\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_anat('/data/ds000114/sub-01/ses-test/anat/sub-01_ses-test_T1w.nii.gz', dim=-1);"
    ]
   },
   {
@@ -123,7 +131,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "nb.load('/data/ds000114/sub-01/ses-test/anat/sub-01_ses-test_T1w_bet.nii.gz').orthoview();"
+    "plot_anat('/data/ds000114/sub-01/ses-test/anat/sub-01_ses-test_T1w_bet.nii.gz', dim=-1);"
    ]
   },
   {
@@ -162,7 +170,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "nb.load(res.outputs.out_file).orthoview();"
+    "plot_anat(res.outputs.out_file, dim=-1);"
    ]
   },
   {
@@ -686,7 +694,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "nb.load(result.outputs.out_file).orthoview()"
+    "plot_anat(result.outputs.out_file, dim=-1);"
    ]
   },
   {
@@ -765,7 +773,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "nb.load(orig_image).orthoview()"
+    "plot_anat(orig_image, dim=-1);"
    ]
   },
   {
@@ -774,7 +782,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "nb.load('translated.nii.gz').orthoview()"
+    "plot_anat('translated.nii.gz', dim=-1);"
    ]
   },
   {
@@ -857,7 +865,7 @@
    "outputs": [],
    "source": [
     "# Plot the result\n",
-    "nb.load('translated_functioninterface.nii.gz').orthoview()"
+    "plot_anat('translated_functioninterface.nii.gz', dim=-1);"
    ]
   },
   {
@@ -1096,7 +1104,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "nb.load(result.outputs.out_file).orthoview()"
+    "plot_anat(result.outputs.out_file, dim=-1);"
    ]
   },
   {

notebooks/advanced_spmmcr.ipynb

@@ -16,7 +16,7 @@
    "outputs": [],
    "source": [
     "from nipype.interfaces import spm\n",
-    "matlab_cmd = '/opt/spm12-dev/run_spm12.sh /opt/matlabmcr-2018a/v94/ script'\n",
+    "matlab_cmd = '/opt/spm12-dev/run_spm12.sh /opt/matlabmcr-2018b/v95/ script'\n",
     "spm.SPMCommand.set_mlab_paths(matlab_cmd=matlab_cmd, use_mcr=True)"
    ]
   },

notebooks/basic_data_input_bids.ipynb

@@ -25,7 +25,7 @@
    "metadata": {},
    "source": [
     "## The `layout` object and simple queries\n",
-    "To begin working with pubids we need to initialize a layout object. We will need it to do all of our queries"
+    "To begin working with pybids we need to initialize a layout object. We will need it to do all of our queries"
    ]
   },
   {
@@ -34,7 +34,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from bids.grabbids import BIDSLayout\n",
+    "from bids.layout import BIDSLayout\n",
     "layout = BIDSLayout(\"/data/ds000114/\")"
    ]
   },
@@ -187,10 +187,10 @@
    },
    "outputs": [],
    "source": [
-    "from bids.grabbids import BIDSLayout\n",
+    "from bids.layout import BIDSLayout\n",
     "layout = BIDSLayout(\"/data/ds000114/\")\n",
     "\n",
-    "[f.filename for f in layout.get(subject='02', task=\"linebisection\")]"
+    "layout.get(subject='02', return_type='file', task=\"linebisection\")"
    ]
   },
   {
@@ -209,7 +209,7 @@
    "source": [
     "def get_niftis(subject_id, data_dir):\n",
     "    # Remember that all the necessary imports need to be INSIDE the function for the Function Interface to work!\n",
-    "    from bids.grabbids import BIDSLayout\n",
+    "    from bids.layout import BIDSLayout\n",
     "    \n",
     "    layout = BIDSLayout(data_dir)\n",
     "    \n",
@@ -330,7 +330,7 @@
     "from nipype.interfaces.utility import IdentityInterface, Function\n",
     "\n",
     "def get_T1w(subject_id, data_dir):\n",
-    "    from bids.grabbids import BIDSLayout\n",
+    "    from bids.layout import BIDSLayout\n",
     "    \n",
     "    layout = BIDSLayout(data_dir)\n",
     "    \n",
@@ -399,7 +399,7 @@
    "outputs": [],
    "source": [
     "def printMetadata(path, data_dir):\n",
-    "    from bids.grabbids import BIDSLayout\n",
+    "    from bids.layout import BIDSLayout\n",
     "    layout = BIDSLayout(data_dir)\n",
     "    print(\"\\n\\nanalyzing \" + path + \"\\nTR: \"+ str(layout.get_metadata(path)[\"RepetitionTime\"]) + \"\\n\\n\")\n",
     "    \n",
@@ -454,7 +454,7 @@
     "def get_niftis(subject_id, data_dir):\n",
     "    # Remember that all the necesary imports need to be INSIDE the function \n",
     "    # for the Function Interface to work!\n",
-    "    from bids.grabbids import BIDSLayout\n",
+    "    from bids.layout import BIDSLayout\n",
     "    \n",
     "    layout = BIDSLayout(data_dir)\n",
     "    \n",
@@ -481,7 +481,7 @@
    "source": [
     "# and now modify analyzeBOLD2\n",
     "def printMetadata_et(path, data_dir):\n",
-    "    from bids.grabbids import BIDSLayout\n",
+    "    from bids.layout import BIDSLayout\n",
     "    layout = BIDSLayout(data_dir)\n",
     "    print(\"\\n\\nanalyzing \" + path + \"\\nTR: \"+ \n",
     "          str(layout.get_metadata(path)[\"RepetitionTime\"]) +\n",

notebooks/basic_data_output.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "# Data Output\n",
     "\n",
-    "Similarly important to data input is data output. Using a data output module allows you to restructure and rename computed output and to spatial differentiate relevant output files from the temporary computed intermediate files in the working directory. Nipype provides the following modules to handle data stream output:\n",
+    "Similarly important to data input is data output. Using a data output module allows you to restructure and rename computed output and to spatially differentiate relevant output files from the temporary computed intermediate files in the working directory. Nipype provides the following modules to handle data stream output:\n",
     "\n",
     "    DataSink\n",
     "    JSONFileSink\n",
@@ -274,7 +274,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "As we can see, there is way too much content that we might not really care about. To relocate and rename all the files that are relevant to you, you can use `DataSink`?"
+    "As we can see, there is way too much content that we might not really care about. To relocate and rename all the files that are relevant to you, you can use `DataSink`."
    ]
   },
   {
@@ -368,7 +368,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This is already much better. But what if you want to rename the output files to represent something a bit readable. For this `DataSink` has the `substitution` input field.\n",
+    "This is already much better. But what if you want to rename the output files to represent something a bit more readable. For this `DataSink` has the `substitution` input field.\n",
     "\n",
     "For example, let's assume we want to get rid of the string `'task-fingerfootlips'` and `'bold_mcf'` and that we want to rename the mean file, as well as adapt the file ending of the motion parameter file:"
    ]
@@ -413,15 +413,15 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Cool, much more clearly!"
+    "Cool, much clearer filenames!"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Exercise 1\n",
-    "Create a simple workflow for skullstriping with FSL, the first node should use `BET` interface and the second node will be a ``DataSink``. Test two method of connecting the nodes and check the content of the output directory."
+    "Create a simple workflow for skullstriping with FSL, the first node should use `BET` interface and the second node will be a ``DataSink``. Test two methods of connecting the nodes and check the content of the output directory."
    ]
   },
   {

notebooks/basic_interfaces.ipynb

@@ -127,8 +127,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%%bash\n",
-    "bet"
+    "!bet"
    ]
   },
   {