add jupytext synced notebooks

_episodes_md/00-before-we-start.md

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+---
+jupyter:
+  jupytext:
+    formats: ipynb,md
+    text_representation:
+      extension: .md
+      format_name: markdown
+      format_version: '1.3'
+      jupytext_version: 1.13.8
+  kernelspec:
+    display_name: Python 3
+    language: python
+    name: python3
+---
+
+This overview have been adapted from the [Data Carpentry Python Lessons with Ecological Data](https://datacarpentry.org/python-ecology-lesson/).
+
+INCLUDE WHAT TO EXPECT IN COMING NOTEBOOKS
+
+## Why Python?
+
+Python is rapidly becoming the standard language for data analysis, visualization and automated workflow building.
+It is a free and open-source software that is relatively easy to pick up by new programmers and is available on multiple operating systems.
+In addition, with Python packages such as `Jupyter` one can keep an interactive code journal of analysis - this is what we'll be using in the workshop.
+Using Jupyter notebooks allows you to keep a record of all the steps in your analysis, enabling reproducibility and ease of code sharing.
+
+Another advantage of Python is that it is maintained by a large user-base.
+Anyone can easily make their own Python packages for others to use.
+Therefore, there exists a *very* large codebase for you to take advantage of for your neuroimaging analysis; from basic statistical analysis, to brain visualization tools, to advanced machine learning and multivariate methods!
+
+## Knowing your way around Anaconda and Jupyter
+
+The [Anaconda][anaconda] distribution of Python includes a lot of its popular packages, such as the IPython console, and Jupyter Notebook.
+Have a quick look around the Anaconda Navigator.
+You can launch programs from the Navigator or use the command line.
+
+The [Jupyter notebook](https://jupyter.org) is an open-source web application that allows you to create
+and share documents that allow one to create documents that combine code, graphs, and narrative text.
+If you have not worked with Jupyter notebooks before, you might start with the [tutorial from Jupyter.org](https://jupyter.org/try) called “Try Classic Notebook”.
+
+Anaconda also comes with a package manager called [conda](https://conda.io/docs/), which makes it easy to install and update additional packages.
+
+## Organizing your working directory
+
+Using a consistent folder structure across your projects will help you keep things organized, and will also make it easy to find/file things in the future.
+This can be especially helpful when you have multiple projects.
+In general, you may wish to create separate directories for your scripts, data, and documents.
+
+This lesson uses the following folders to keep things organized:
+
+- **`data/`**: Use this folder to store your raw data. For the sake of transparency and provenance,
+you should always keep a copy of your **raw data**. If you need to cleanup data, do it
+programmatically (_i.e._with scripts) and make sure to separate cleaned up data from the raw data.
+For example, you can store raw data in files `./data/raw/` and clean data in `./data/clean/`.
+
+- **`code/`**: Use this folder to store your (Python) scripts for data cleaning, analysis, and
+plotting that you use in this particular project. This folder contains all of the Jupyter notebooks used in the lesson.
+
+- **`files/`**: Use this folder to store any miscellaneous outlines, drafts, and other text. This folder contains a set of Powerpoint slides that will be used for parts of the lesson.
+
+## How to learn more after the workshop?
+
+The material we cover during this workshop will give you an initial taste of how you can use Python
+to analyze MRI data for your own research.
+However, you will need to learn more to do advanced operations such as working with specific MRI modalities, cleaning your dataset, using statistical methods, or creating beautiful graphics.
+The best way to become proficient and efficient at Python, as with any other tool, is to use it to address your actual research questions.
+As a beginner, it can feel daunting to have to write a script from scratch, and given that many people make their code available online, modifying existing code to suit your purpose might make it easier for you to get started.
+
+## Seeking help
+
+- check under the *Help* menu
+- type `help()`
+- type `?object` or `help(object)` to get information about an object
+- [Python documentation][python-docs]
+- [NiBabel documentation][nibabel-docs]
+
+Finally, a generic Google or internet search "Python task" will often either send you to the
+appropriate module documentation or a helpful forum where someone else has already asked your
+question.
+
+I am stuck... I get an error message that I don’t understand.
+Start by googling the error message. However, this doesn’t always work very well, because often,
+package developers rely on the error catching provided by Python. You end up with general error
+messages that might not be very helpful to diagnose a problem (e.g. "subscript out of bounds"). If
+the message is very generic, you might also include the name of the function or package you’re using
+in your query.
+
+You can also check [Stack Overflow][stack-overflow]. Most questions have already been answered, but the challenge is to use the right words in the search to find the answers.
+
+Another helpful resource specific to the neuroimaging community is [NeuroStars](https://neurostars.org/).
+
+[anaconda]: https://www.anaconda.com
+[nibabel-docs]: https://nipy.org/nibabel/manual.html#manual
+[python-docs]: https://www.python.org/doc
+[stack-overflow]: https://stackoverflow.com

_episodes_md/01-scanner-to-computer.md

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+---
+jupyter:
+  jupytext:
+    formats: ipynb,md
+    text_representation:
+      extension: .md
+      format_name: markdown
+      format_version: '1.3'
+      jupytext_version: 1.13.8
+  kernelspec:
+    display_name: Python 3
+    language: python
+    name: python3
+---
+
+# From the scanner to our computer
+
+SMALL INTRO BLURB ABOUT DIFFERENT MR SCANS
+
+## Neuroimaging file formats
+
+|Format Name | File Extension | Origin |
+|---|---|---|
+| DICOM | none | ACR/NEMA Consortium |
+| Analyze | .img/.hdr | Analyze Software, Mayo Clinic |
+| NIfTI | .nii | Neuroimaging Informatics Technology Initiative |
+| MINC | .mnc | Montreal Neurological Institute |
+| NRRD | .nrrd | |
+
+From the MRI scanner, images are initially collected in the DICOM format and can be converted to these other formats to make working with the data easier.
+
+<img src="../fig/dicom_to_nifti.png" alt="Drawing" align="middle" width="600px"/>
+
+Let's download some example DICOM data to see what it looks like.
+This data was generously shared publicly by the [Princeton Handbook for Reproducible Neuroimaging](https://brainhack-princeton.github.io/handbook/).
+
+```bash
+
+wget https://zenodo.org/record/3677090/files/0219191_mystudy-0219-1114.tar.gz -O data/0219191_mystudy-0219-1114.tar.gz
+mkdir -p ../data/dicom_examples
+tar -xvzf ../data/0219191_mystudy-0219-1114.tar.gz -C ../data/dicom_examples
+gzip -d ../data/dicom_examples/0219191_mystudy-0219-1114/dcm/*dcm.gz
+rm ../data/0219191_mystudy-0219-1114.tar.gz
+```
+
+NIfTI is one of the most ubiquitous file formats for storing neuroimaging data.
+If you're interested in learning more about NIfTI images, we highly recommend [this blog post about the NIfTI format](http://brainder.org/2012/09/23/the-nifti-file-format/).
+We can convert our DICOM data to NIfTI using [dcm2niix](https://github.com/rordenlab/dcm2niix).
+
+We can learn how to run `dcm2niix` by taking a look at its help menu.
+
+```bash
+
+dcm2niix -help
+```
+
+### Converting DICOM to NIfTI
+
+<!-- #region solution2="hidden" solution2_first=true -->
+**EXERCISE:** Convert the Princeton DICOM data to NIfTI
+<!-- #endregion -->
+
+```python
+
+```
+
+```bash solution2="hidden"
+
+mkdir -p ../data/dicom_examples/nii
+dcm2niix \
+    -z y \
+    -o ../data/dicom_examples/nii \
+    ../data/dicom_examples/0219191_mystudy-0219-1114/dcm
+```