Apply black formatting to all notebooks. (#95)

Author: rossbar

content/mooreslaw-tutorial.md

@@ -4,8 +4,8 @@ jupytext:
   text_representation:
     extension: .md
     format_name: myst
-    format_version: 0.12
-    jupytext_version: 1.6.0
+    format_version: 0.13
+    jupytext_version: 1.11.1
 kernelspec:
   display_name: Python 3
   language: python
@@ -16,7 +16,7 @@ kernelspec:
 
 ## What you'll do
 This guide will keep a Jupyter notebook synced _or paired_ between
-`.ipynb` and `.md`. 
+`.ipynb` and `.md`.
 
 ## What you'll learn
 - The difference between Jupyter's json format and MyST-NB's markdown
@@ -44,15 +44,15 @@ format.
 Jupyter notebooks are stored on your disk in a
 [json format](https://nbformat.readthedocs.io/en/latest/format_description.html). The json format is
 very powerful and allows you to store almost any input and output that
-Python libraries can create. The drawback is that it is hard to see and compare changes made in the notebook file when reviewing pull requests, because this means the reviewers are looking only at the raw json files. 
+Python libraries can create. The drawback is that it is hard to see and compare changes made in the notebook file when reviewing pull requests, because this means the reviewers are looking only at the raw json files.
 
-MyST-NB notebooks are stored on your disk in a 
+MyST-NB notebooks are stored on your disk in a
 [markdown](https://en.wikipedia.org/wiki/Markdown) format. The markdown
 format is a lightweight markup language. Its key design goal is
 [_readability_](https://daringfireball.net/projects/markdown/syntax#philosophy).
 The drawback is that markdown can only store the inputs of your code.
 Each time you open the notebook, you must execute the inputs to see the
-output. 
+output.
 
 > __Note:__ You should use [common mark](https://commonmark.org)
 > markdown cells. Jupyter only renders common mark markdown, but MyST-NB
@@ -64,7 +64,7 @@ output.
 Consider these two versions of the same __Simple notebook example__. You
 have three things in the notebooks:
 
-1. A markdown cell that explains the code 
+1. A markdown cell that explains the code
     ```This code calculates 2+2 and prints the output.```
 2. A code cell that shows the code
     ```python
@@ -78,10 +78,12 @@ have three things in the notebooks:
 ---
 __<center> Simple notebook example </center>__
 This code calculates 2+2 and prints the output.
-```{code-cell} ipython3
+
+```{code-cell}
 x = 2 + 2
-print('x = ', x)
+print("x = ", x)
 ```
+
 ---
 
 Here are the two Simple notebook example raw inputs side-by-side:
@@ -186,7 +188,7 @@ When you submit a Jupyter notebook to NumPy tutorials, we (the reviewers) will c
 it to a MyST-NB format. You can also submit the MyST-NB `.md` in your
 pull request.
 To keep the `.ipynb` and `.md` in sync--_or paired_--you need
-[Jupytext](https://jupytext.readthedocs.io/en/latest/index.html). 
+[Jupytext](https://jupytext.readthedocs.io/en/latest/index.html).
 
 Install `jupytext` using:
 
@@ -200,7 +202,7 @@ conda install jupytext -c conda-forge
 
 Once installed, start your `jupyter lab` or `jupyter notebook`
 session in the browser. When launching `jupyter lab` it will ask you to rebuild
-to include the Jupytext extension. 
+to include the Jupytext extension.
 
 You can pair the two formats in the classic Jupyter, Jupyter Lab,
 or the command line:
@@ -238,8 +240,8 @@ jupytext --sync notebook.ipynb
 
 ## Wrapping up
 
-In this tutorial, you saw the json `.ipynb` and MyST-NB `.md` raw code 
+In this tutorial, you saw the json `.ipynb` and MyST-NB `.md` raw code
 to create Jupyter notebooks. You can use both formats to create
 tutorials. Now you can work in either a simple text editor like VIM
 or emacs or continue building notebooks in your browser. Jupytext can
-handle pairing to keep your work in sync. 
+handle pairing to keep your work in sync.

content/pairing.md

@@ -3,8 +3,8 @@ jupytext:
   text_representation:
     extension: .md
     format_name: myst
-    format_version: 0.12
-    jupytext_version: 1.6.0
+    format_version: 0.13
+    jupytext_version: 1.11.1
 kernelspec:
   display_name: Python 3
   language: python
@@ -22,14 +22,14 @@ file types back into NumPy workspaces.
 ## What you'll do
 
 You'll learn two ways of saving and reading files--as compressed and as
-text files--that will serve most of your storage needs in NumPy. 
+text files--that will serve most of your storage needs in NumPy.
 
-* You'll create two 1D arrays and one 2D array 
+* You'll create two 1D arrays and one 2D array
 * You'll save these arrays to files
 * You'll remove variables from your workspace
 * You'll load the variables from your saved file
 * You'll compare zipped binary files to human-readable delimited files
-* You'll finish with the skills of saving, loading, and sharing NumPy arrays 
+* You'll finish with the skills of saving, loading, and sharing NumPy arrays
 
 
 ## What you'll need
@@ -39,7 +39,7 @@ text files--that will serve most of your storage needs in NumPy.
 
 Load the necessary functions using the following command.
 
-```{code-cell} ipython3
+```{code-cell}
 import numpy as np
 ```
 
@@ -63,11 +63,11 @@ In this tutorial, you will use the following Python, IPython magic, and NumPy fu
 
 Now that you have imported the NumPy library, you can make a couple of
 arrays; let's start with two 1D arrays, `x` and `y`, where `y =
-x**2`.You 
+x**2`.You
 will assign `x` to the integers from 0 to 9 using
 [`np.arange`](https://numpy.org/doc/stable/reference/generated/numpy.arange.html).
 
-```{code-cell} ipython3
+```{code-cell}
 x = np.arange(0, 10, 1)
 y = x ** 2
 print(x)
@@ -76,7 +76,7 @@ print(y)
 
 ## Save your arrays with NumPy's [`savez`](https://numpy.org/doc/stable/reference/generated/numpy.savez.html?highlight=savez#numpy.savez)
 
-Now you have two arrays in your workspace, 
+Now you have two arrays in your workspace,
 
 `x: [0 1 2 3 4 5 6 7 8 9]`
 
@@ -85,12 +85,12 @@ Now you have two arrays in your workspace,
 The first thing you will do is save them to a file as zipped arrays
 using
 [`savez`](https://numpy.org/doc/stable/reference/generated/numpy.savez.html?highlight=savez#numpy.savez).
-You will use two options to label the arrays in the file, 
+You will use two options to label the arrays in the file,
 
 1. `x_axis = x`: this option is assigning the name `x_axis` to the variable `x`
 2. `y_axis = y`: this option is assigning the name `y_axis` to the variable `y`
 
-```{code-cell} ipython3
+```{code-cell}
 np.savez("x_y-squared.npz", x_axis=x, y_axis=y)
 ```
 
@@ -103,7 +103,7 @@ name `x_y-squared.npz`. This file is a zipped binary of the two arrays,
 format](https://numpy.org/doc/stable/reference/generated/numpy.lib.format.html#module-numpy.lib.format)
 files. The NPY format is a [native binary
 format](https://en.wikipedia.org/wiki/Binary_file). You cannot read
-the numbers in a standard text editor or spreadsheet. 
+the numbers in a standard text editor or spreadsheet.
 
 1. remove `x` and `y` from the workspaec with [`del`](https://docs.python.org/3/reference/simple_stmts.html#del)
 2. load the arrays into the workspace in a dictionary with [`np.load`](https://numpy.org/doc/stable/reference/generated/numpy.load.html#numpy.load)
@@ -112,31 +112,31 @@ To see what variables are in the workspace, use the Jupyter/IPython
 "magic" command
 [`whos`](https://ipython.readthedocs.io/en/stable/interactive/magics.html#magic-whos).
 
-```{code-cell} ipython3
+```{code-cell}
 del x, y
 ```
 
-```{code-cell} ipython3
+```{code-cell}
 %whos
 ```
 
-```{code-cell} ipython3
+```{code-cell}
 load_xy = np.load("x_y-squared.npz")
 
 print(load_xy.files)
 ```
 
-```{code-cell} ipython3
+```{code-cell}
 whos
 ```
 
 ## Reassign the NpzFile arrays to `x` and `y`
 
 You've now created the dictionary with an `NpzFile`-type. The
 included files are `x_axis` and `y_axis` that you defined in your
-`savez` command. You can reassign `x` and `y` to the `load_xy` files.  
+`savez` command. You can reassign `x` and `y` to the `load_xy` files.
 
-```{code-cell} ipython3
+```{code-cell}
 x = load_xy["x_axis"]
 y = load_xy["y_axis"]
 print(x)
@@ -153,7 +153,7 @@ that is easier to share. Next, you use the
 [`savetxt`](https://numpy.org/doc/stable/reference/generated/numpy.savetxt.html#numpy.savetxt)
 to save `x` and `y` in a comma separated value file, `x_y-squared.csv`.
 The resulting csv is composed of ASCII characters. You can load the file
-back into NumPy or read it with other programs. 
+back into NumPy or read it with other programs.
 
 ## Rearrange the data into a single 2D array
 First, you have to create a single 2D array from your two 1D arrays. The
@@ -167,7 +167,7 @@ two NumPy functions to format the data:
 
 2. [`np.newaxis`](https://numpy.org/doc/stable/reference/constants.html?highlight=newaxis#numpy.newaxis): this function forces the 1D array into a 2D column vector with 10 rows and 1 column.
 
-```{code-cell} ipython3
+```{code-cell}
 array_out = np.block([x[:, np.newaxis], y[:, np.newaxis]])
 print("the output array has shape ", array_out.shape, " with values:")
 print(array_out)
@@ -181,7 +181,7 @@ You use `savetxt` with a three options to make your file easier to read:
 * `header = 'x, y'`: this option writes a header before any data that labels the columns of the csv
 * `delimiter = ','`: this option tells `savetxt` to place a comma between each column in the file
 
-```{code-cell} ipython3
+```{code-cell}
 np.savetxt("x_y-squared.csv", X=array_out, header="x, y", delimiter=",")
 ```
 
@@ -208,31 +208,29 @@ There are two features that you shoud notice here:
 1. NumPy uses `#` to ignore headings when using `loadtxt`. If you're using
 [`loadtxt`](https://numpy.org/doc/stable/reference/generated/numpy.loadtxt.html)
 with other csv files, you can skip header rows with `skiprows =
-<number_of_header_lines>`.  
+<number_of_header_lines>`.
 2. The integers were written in scientific notation. _You can_ specify
 the format of the text using the `savetxt` option, [`fmt =
 `](https://docs.python.org/3/library/string.html#formatstrings), but it
 will still be written with ASCII characters. In general, you cannot
-preserve the type of ASCII numbers as `float` or `int`. 
+preserve the type of ASCII numbers as `float` or `int`.
 
 
-Now, delete `x` and `y` again and assign them to your columns in `x-y_squared.csv`. 
+Now, delete `x` and `y` again and assign them to your columns in `x-y_squared.csv`.
 
-```{code-cell} ipython3
+```{code-cell}
 del x, y
 ```
 
-```{code-cell} ipython3
-load_xy = np.loadtxt(
-    "x_y-squared.csv", 
-    delimiter=",")
+```{code-cell}
+load_xy = np.loadtxt("x_y-squared.csv", delimiter=",")
 ```
 
-```{code-cell} ipython3
+```{code-cell}
 load_xy.shape
 ```
 
-```{code-cell} ipython3
+```{code-cell}
 x = load_xy[:, 0]
 y = load_xy[:, 1]
 print(x)
@@ -242,12 +240,12 @@ print(y)
 ## Success, but remember your types
 
 When you saved the arrays to the csv file, you did not preserve the
-`int` type. When loading the arrays back into your workspace the default process will be to load the csv file as a 2D floating point array e.g. `load_xy.dtype == 'float64'` and `load_xy.shape == (10, 2)`. 
+`int` type. When loading the arrays back into your workspace the default process will be to load the csv file as a 2D floating point array e.g. `load_xy.dtype == 'float64'` and `load_xy.shape == (10, 2)`.
 
 +++
 
 ## Wrapping up
 
-In conclusion, you can create, save, and load arrays in NumPy. Saving arrays makes sharing your work and collaboration much easier. There are other ways Python can save data to files, such as [pickle](https://docs.python.org/3/library/pickle.html), but `savez` and `savetxt` will serve most of your storage needs for future NumPy work and sharing with other people, respectively. 
+In conclusion, you can create, save, and load arrays in NumPy. Saving arrays makes sharing your work and collaboration much easier. There are other ways Python can save data to files, such as [pickle](https://docs.python.org/3/library/pickle.html), but `savez` and `savetxt` will serve most of your storage needs for future NumPy work and sharing with other people, respectively.
 
 __Next steps__: you can import data with missing values from [Importing with genfromtext](https://numpy.org/devdocs/user/basics.io.genfromtxt.html) or learn more about general NumPy IO with [Reading and Writing Files](https://numpy.org/devdocs/user/how-to-io.html).