Merge pull request #1 from jsdodge/dev

Update notebooks to use f-strings instead of format method

Author: jsdodge

.gitignore

@@ -1,22 +1,26 @@
+# From macOS.gitignore
 .DS_Store
-.ipynb_checkpoints
+
+# From Emacs.gitignore
 *~
-*-eps-converted-to.pdf
-*.aux
-*.bbl
-*.blg
-*.log
-*.synctex.gz
-Lanczos3.dat
-Norris.dat
-download
-import-file-test.ipynb
-**/Untitled.ipynb
-student_feedback
 
+# From JupyterNotebooks.gitignore
+.ipynb_checkpoints
+
+# From JetBrains.gitignore
 # User-specific stuff
 .idea/**/workspace.xml
 .idea/**/tasks.xml
 .idea/**/usage.statistics.xml
 .idea/**/dictionaries
 .idea/**/shelf
+
+# Local
+Lanczos3.dat
+Norris.dat
+download
+import-file-test.ipynb
+**/Untitled.ipynb
+student_feedback
+**/www.itl.nist.gov/
+mindevfit.eps

.idea/data-analysis-python.iml

@@ -1 +1 @@
-This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
+This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.

.idea/misc.xml

@@ -1,8 +1,8 @@
-# data-analysis-python
+# Data analysis with Python
 
-This repository contains instructional materials for learning data analysis for applications in the physical sciences. They are in the form of [Jupyter](https://jupyter-notebook.readthedocs.io/en/latest/) notebooks using the [Python](https://docs.python.org/3/tutorial/index.html) programming language. A suitable textbook to accompany these materials is [*Measurements and their Uncertainties*](http://www.oupcanada.com/catalog/9780199566334.html), by Hughes and Hase.
+This repository contains instructional materials for learning data analysis for applications in the physical sciences. They are in the form of [Jupyter](https://jupyter-notebook.readthedocs.io/en/latest/) notebooks using the [Python](https://docs.python.org/3/tutorial/index.html) programming language. A suitable textbook to accompany these materials is [*Measurements and their Uncertainties*](https://www.oupcanada.com/catalog/9780199566334.html), by Hughes and Hase.
 
-These materials are a work in progress and I welcome feedback at <[email protected]>.
+These materials are a work in progress and I welcome feedback at [[email protected]](mailto:[email protected]).
 
 ![Curve-fitting, XKCD](https://imgs.xkcd.com/comics/curve_fitting.png)
 
@@ -11,8 +11,8 @@ Image credit: *Curve-fitting*, by [XKCD](https://xkcd.com/2048/)
 ## How to use these materials
 You should be able to read, run, and modify the notebooks in this repository following any one of the following methods. 
 
-If you are an SFU student, you will probably want to use Syzygy, which you can sign in to with your SFU Computing Account. [Syzygy](https://sfu.syzygy.ca/) is a cloud-based Jupyter notebook server hosted by [Compute Canada](https://www.computecanada.ca/). To upload the notebooks into your SFU Syzygy account: 
-* Use this [link](https://sfu.syzygy.ca/jupyter/hub/user-redirect/git-pull?repo=https://gitlab.phys.sfu.ca/physcrs/course-material-phys233.git&branch=master), which should upload the contents of all the folders in the PHYS 233 repository directly to your syzygy account. Syzygy will prompt you to log in if you are not already logged in. You can also use this link to upload changes to the notebooks that occur during the course.
+If you are an SFU student, you will probably want to use Syzygy, which you can sign in to with your SFU Computing Account. [Syzygy](https://sfu.syzygy.ca/) is a cloud-based Jupyter notebook server hosted by the [Digital Research Alliance of Canada](https://alliancecan.ca/en). To upload the notebooks into your SFU Syzygy account: 
+* Use this [link](https://sfu.syzygy.ca/jupyter/hub/user-redirect/git-pull?repo=https://github.com/jsdodge/data-analysis-python.git&branch=main), which should upload the contents of all the folders in the PHYS 233 repository directly to your syzygy account. Syzygy will prompt you to log in if you are not already logged in. You can also use this link to upload changes to the notebooks that occur during the course.
 * Or download the folder as a .zip file, unpack it into a folder, and upload the files to sfu.syzygy.ca.
 
 Within the Syzygy interface, open the `.ipynb` notebook files in the `notebooks` directory to read the documentation and run the code.
@@ -26,4 +26,4 @@ Or you can copy the notebooks into the [Google Colab](https://colab.research.goo
 These materials were written and tested in Python 3.7, including the [NumPy](https://docs.scipy.org/doc/numpy/reference/index.html) (1.17), [matplotlib](https://matplotlib.org/users/index.html) (3.1), and [SciPy](https://docs.scipy.org/doc/scipy/reference/tutorial/index.html) (1.3) packages. There are many more packages available for doing data analysis in Python, but these three include everything a practicing physicist needs to get started.
 
 ## License
-The notebook text is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. See more at [Creative Commons](http://creativecommons.org/licenses/by-nc-nd/4.0/). The notebook code is open source under the [MIT License](https://opensource.org/licenses/MIT).
+The notebook text is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. See more at [Creative Commons](https://creativecommons.org/licenses/by-nc-nd/4.0/). The notebook code is open source under the [MIT License](https://opensource.org/licenses/MIT).

LICENSE-TEXT

@@ -4,6 +4,7 @@ name: pydata
 channels:
   - defaults
 dependencies:
+  - jupyter
   - jupyterlab
   - matplotlib
   - notebook