Merge branch 'master' into manual-dispatch

Author: fangohr

.github/workflows/deploy.yml

@@ -55,7 +55,7 @@ jobs:
 
     # Deploy the book's HTML to gh-pages branch
     - name: GitHub Pages action
-      uses: peaceiris/actions-gh-pages@bbdfb200618d235585ad98e965f4aafc39b4c501
+      uses: peaceiris/actions-gh-pages@v3
       with:
         github_token: ${{ secrets.GITHUB_TOKEN }}
         publish_dir: ./book/_build/html

.gitignore

@@ -22,3 +22,4 @@ stock.csv
 .venv
 .venv/
 book/_build
+.DS_Store

Dockerfile

@@ -1,20 +1,26 @@
-FROM debian:bullseye-slim
+FROM debian:bookworm-slim
 
-RUN apt-get update -y && apt-get install -y texlive-xetex latexmk texlive-xetex \
-	texlive-fonts-extra fonts-freefont-otf python3 python3-pip git zile wget
+#RUN apt-get update -y && apt-get install -y texlive-xetex latexmk texlive-xetex \
+#	texlive-fonts-extra fonts-freefont-otf python3 python3-pip git zile wget
 
-COPY poetry.lock pyproject.toml /opt/
+RUN apt-get update -y && apt-get install -y python3 python3-pip texlive-xetex \
+    latexmk git zile wget texlive-fonts-extra python3-venv
 
-RUN pip install poetry==1.1.4
+COPY requirements.txt /opt/
 
 WORKDIR /opt/
-RUN poetry config virtualenvs.create false
-RUN poetry install -vvv
+RUN python3 -m venv venv
+RUN . venv/bin/activate && pip install -r requirements.txt
+RUN . venv/bin/activate && pip list
+# activate venv
+ENV PATH="/opt/venv/bin:$PATH"
 
 RUN mkdir -p /io
 WORKDIR /io
 
 # Need this for one nbval and chapter 1
 RUN ln -s /usr/bin/python3 /usr/local/bin/python
 
+# supress warning when running jupyter-book in container
+ENV PYDEVD_DISABLE_FILE_VALIDATION=1
 CMD ["/bin/bash"]

Makefile

@@ -3,7 +3,7 @@ all:
 	make pdf
 
 install:
-	poetry install -vvv 
+	pip install -r requirements.txt
 
 clean:
 	cd book; rm -rf \
@@ -13,20 +13,20 @@ clean:
 		_build .ipynb_checkpoints
 
 html:
-	poetry run jupyter-book build book --builder html
+	jupyter-book build book --builder html
 
 linkcheck:
-	poetry run jupyter-book build book --builder linkcheck
+	jupyter-book build book --builder linkcheck
 
 pdf: book/*-*.ipynb
-	poetry run jupyter-book build book --builder pdflatex
+	jupyter-book build book --builder pdflatex
 
 nbval:
 	@echo "Testing all chapters (apart from 18) with --nbval"
-	poetry run pytest -v --nbval book --sanitize-with book/static/nbval_sanitize.cfg \
+	pytest -v --nbval book --sanitize-with book/static/nbval_sanitize.cfg \
 	    --ignore=book/18-environments.ipynb --ignore=book/_build
 	@echo "Testing chapter 18 with --nbval-lax"
-	poetry run pytest -v --nbval-lax book/18-environments.ipynb
+	pytest -v --nbval-lax book/18-environments.ipynb
 
 
 
@@ -44,16 +44,8 @@ docker-build:
 docker-build-nocache:
 	docker build -t python4compscience2 --no-cache .
 
-# Here we only bind the required directories and files into the docker container
-# at runtime to avoid potential conflicts with local files
-
 define DOCKER_RUN
-docker run --workdir=/io  \
-	-v $(CURDIR)/book:/io/book \
-	-v $(CURDIR)/Makefile:/io/Makefile \
-	-v $(CURDIR)/poetry.lock:/io/poetry.lock \
-	-v $(CURDIR)/pyproject.toml:/io/pyproject.toml \
-	python4compscience
+docker run --rm -v $(CURDIR):/io	python4compscience
 endef
 
 docker-bash:

Readme.md

@@ -119,7 +119,7 @@ and schools. Please send a message to the author if you do so.
 
 Hans Fangohr is a researcher and teacher (see
 [homepage](https://fangohr.github.io), [blog](https://fangohr.github.io/blog),
-[twitter](https://twitter.com/ProfCompMod)). His interests include effective
+[mastodon](https://fosstodon.org/@ProfCompMod)). His interests include effective
 software engineering for computational science and data science, researching
 computational modelling and data analysis methods, and education. He is a
 Professor at the [University of Southampton (UK)](https://www.southampton.ac.uk)

book/12-symbolic-computation.ipynb

@@ -28,7 +28,7 @@
     "For more detailed information, check these links\n",
     "\n",
     "-   A very nice introduction in the object oriented Matplotlib interface, and summary of all important ways of changing style, figure size, linewidth, etc. This is a useful reference:\n",
-    "    <https://nbviewer.ipython.org/github/jrjohansson/scientific-python-lectures/blob/master/Lecture-4-Matplotlib.ipynb>\n",
+    "    <https://github.com/jrjohansson/scientific-python-lectures/blob/master/Lecture-4-Matplotlib.ipynb>\n",
     "\n",
     "-   [Matplotlib tutorial](https://matplotlib.org/stable/tutorials/index)\n",
     "\n",
@@ -46,7 +46,7 @@
     "\n",
     "This chapter focusses on the Pylab interface, but also provides examples for the object-oriented `matplotlib.pyplot` interface.\n",
     "\n",
-    "An excellent introduction and overview of the `matplotlib.pyplot` plotting interface is available in <https://nbviewer.ipython.org/github/jrjohansson/scientific-python-lectures/blob/master/Lecture-4-Matplotlib.ipynb>."
+    "An excellent introduction and overview of the `matplotlib.pyplot` plotting interface is available in <https://github.com/jrjohansson/scientific-python-lectures/blob/master/Lecture-4-Matplotlib.ipynb>."
    ]
   },
   {
@@ -1135,7 +1135,7 @@
     "\n",
     "I would speculate that increasingly we will be using high-level plotting tools (such as pandas, xarray, holoviews) to explore data interactively.\n",
     "\n",
-    "We can see a trend in data analysis libraries that data objects can be converted to such high-level annotated data objecs (such as European XFEL's [extra-data](https://extra-data.readthedocs.io) tools which can return a labelled xarray object). Other projects combine the metadata with the data in custom made objects to then provide convenience methods (such as [Ubermag](http://ubermag.github.io)'s [discretisedfield](https://ubermag.github.io/documentation/ipynb/discretisedfield/field-matplotlib-visualisation.html) object).\n",
+    "We can see a trend in data analysis libraries that data objects can be converted to such high-level annotated data objecs (such as European XFEL's [extra-data](https://extra-data.readthedocs.io) tools which can return a labelled xarray object). Other projects combine the metadata with the data in custom made objects to then provide convenience methods (such as [Ubermag](http://ubermag.github.io)'s [discretisedfield](discretisedfield/field-matplotlib-visualisation.html) object).\n",
     "\n",
     "Will we still need to learn the basics, such as the `matplotlib.pyplot` interface? Probably yes: the very least to fine tune the plots provided by these high level libraries: \n",
     "\n"
@@ -1261,7 +1261,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.1"
+   "version": "3.9.13"
   }
  },
  "nbformat": 4,

book/14-numpy.ipynb

@@ -1043,7 +1043,7 @@
     "import scipy.fft\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "pi = scipy.pi\n",
+    "pi = np.pi\n",
     "\n",
     "signal_length = 0.5     # [seconds]\n",
     "sample_rate = 500       # sampling rate [Hz]\n",
@@ -1310,7 +1310,7 @@
    "source": [
     "Note that there are other functions to read from and write to in formats as used by IDL, Netcdf and other formats in `scipy.io`.\n",
     "\n",
-    "More → see [Scipy tutorial](https://docs.scipy.org/doc/scipy/reference/tutorial/io.html)."
+    "More → see [Scipy tutorial](https://docs.scipy.org/doc/scipy/reference/io.html)."
    ]
   }
  ],
@@ -1330,9 +1330,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.9.13"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 1
-}
+}

book/15-visualising-data.ipynb

@@ -124,7 +124,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In a way, the keys of the dictionary contain the stock labels and the values contain the actual values:"
+    "The keys of the dictionary contain the stock labels and the values contain the actual values:"
    ]
   },
   {
@@ -189,7 +189,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The Pandas Series object address the requriments above. It is similar to a dictionary, but with improvements for the given problem:\n",
+    "The Pandas Series object address the requirements above. It is similar to a dictionary, but with improvements for the given problem:\n",
     "\n",
     "* the order of the items is maintained\n",
     "* the values have to have the same type (higher execution performance)\n",
@@ -342,11 +342,11 @@
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
-    "# settings for jupyter book: svg for html version, high-resolution png for pdf\n",
-    "from IPython.display import set_matplotlib_formats\n",
-    "set_matplotlib_formats('svg', 'png')\n",
-    "import matplotlib as mpl\n",
-    "mpl.rcParams['figure.dpi'] = 400"
+    "# settings for Jupyter book: svg for html version, high-resolution png for pdf\n",
+    "import matplotlib\n",
+    "import matplotlib_inline\n",
+    "matplotlib_inline.backend_inline.set_matplotlib_formats('svg', 'png')\n",
+    "matplotlib.rcParams['figure.dpi'] = 400"
    ]
   },
   {
@@ -607,7 +607,7 @@
    "source": [
     "## Plotting data\n",
     "\n",
-    "Commonly used plots are easily accessible via the `plot()` method of the Series object. We have seen a bar plot above already. The `Series.plot()` method accepts an argument `kind` such as `kind=\"bar\"`, but there is an equivalent method `Series.plot.bar()` avaialble.\n",
+    "Commonly used plots are easily accessible via the `plot()` method of the Series object. We have seen a bar plot above already. The `Series.plot()` method accepts an argument `kind` such as `kind=\"bar\"`, but there is an equivalent method `Series.plot.bar()` available.\n",
     "\n",
     "Further examples:"
    ]
@@ -656,7 +656,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can also fetch the data from the series and drive the plotting \"manually\" ourselves:"
+    "We can also retrieve the data from the series and drive the plotting \"manually\" ourselves:"
    ]
   },
   {
@@ -667,8 +667,8 @@
    "source": [
     "import matplotlib.pyplot as plt\n",
     "\n",
-    "names = list(stock.index)\n",
-    "values = list(stock.values)\n",
+    "names = list(stock.index)  # conversion to list not necessary \n",
+    "values = list(stock.values)  # conversion to list not necessary\n",
     "\n",
     "fig, ax = plt.subplots(1, 1, figsize=(9, 3))\n",
     "ax.bar(names, values)\n",
@@ -1788,7 +1788,7 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1802,9 +1802,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6-final"
+   "version": "3.9.13"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}