First draft

.devcontainer/devcontainer.json

@@ -0,0 +1,35 @@
+// For format details, see https://aka.ms/devcontainer.json. For config options, see the
+// README at: https://github.com/devcontainers/templates/tree/main/src/python
+{
+  "name": "Python 3",
+  // Or use a Dockerfile or Docker Compose file. More info: https://containers.dev/guide/dockerfile
+  "image": "mcr.microsoft.com/devcontainers/python:1-3.12-bullseye",
+  "workspaceFolder": "/workspaces/dsp_workshop_dataviz_python",
+
+  // Features to add to the dev container. More info: https://containers.dev/features.
+  // "features": {},
+
+  // Use 'forwardPorts' to make a list of ports inside the container available locally.
+  // "forwardPorts": [],
+
+  // Use 'postCreateCommand' to run commands after the container is created.
+  "postCreateCommand": "pip3 install --user -r requirements.txt",
+//   "onCreateCommand": "bash .devcontainer/setup.sh",
+
+  // Configure tool-specific properties.
+  "customizations": {
+    "vscode": {
+      "extensions": [
+        "ms-toolsai.jupyter",
+        "ms-python.python",
+        "mechatroner.rainbow-csv"
+      ],
+      // Use Python from conda
+      "settings": {},
+      // Use bash
+      "terminal.integrated.defaultProfile.linux": "bash"
+    }
+  }
+  // Uncomment to connect as root instead. More info: https://aka.ms/dev-containers-non-root.
+  // "remoteUser": "root"
+}

.github/workflows/build_website.yaml

@@ -6,6 +6,9 @@ on:
   push:
     branches:
       - main
+  pull_request:
+    branches: 
+      - main
   workflow_dispatch:
 
 # This job installs dependencies, builds the website and pushes it to `gh-pages`
@@ -33,3 +36,11 @@ jobs:
           github_token: ${{ secrets.GITHUB_TOKEN }}
           publish_dir: ./_build
           # cname: website.com
+      - if: ${{ github.ref != 'refs/heads/main' }}
+        name: Save to gh-pages
+        uses: peaceiris/actions-gh-pages@v4
+        with:
+          github_token: ${{ secrets.GITHUB_TOKEN }}
+          publish_dir: ./_build
+          # hide dev version of website in subfolder
+          destination_dir: dev

.gitignore

@@ -1,2 +1,3 @@
 _build
+jupyter_execute
 *.docx

1_1_matplotlib.ipynb

@@ -0,0 +1,219 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "01419663",
+   "metadata": {},
+   "source": [
+    "# Matplotlib\n",
+    "- A comprehensive library for creating static, animated, and interactive visualizations in Python.\n",
+    "- Built on NumPy arrays and designed to work with the broader SciPy stack."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0748b014",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib as mpl\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "348c1b11",
+   "metadata": {},
+   "source": [
+    "## Basic Line Plot\n",
+    "From Getting Started."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "37ad8b55",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots()  # Create a figure containing a single Axes.\n",
+    "ax.plot([1, 2, 3, 4], [1, 4, 2, 3])  # Plot some data on the Axes.\n",
+    "plt.show()  # Show the figure."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dd74e2cc",
+   "metadata": {
+    "lines_to_next_cell": 0
+   },
+   "source": [
+    "![Anatomy of a matplotlib figure](https://matplotlib.org/stable/_images/anatomy.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2d86fcd1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots()  # Create a figure containing a single Axes.\n",
+    "ax.plot([1, 2, 3, 4], [1, 4, 2, 3])  # Plot some data on the Axes.\n",
+    "ax.grid()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0595fdfd",
+   "metadata": {},
+   "source": [
+    "Customize ticks\n",
+    "- [Ticker API](https://matplotlib.org/stable/api/ticker_api.html)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0817796f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ax.yaxis.set_major_locator(mpl.ticker.MaxNLocator(integer=True))\n",
+    "# ax.xaxis.set_major_locator(mpl.ticker.MaxNLocator(integer=True))\n",
+    "ax.get_figure()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "80d2be97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mu, sigma = 115, 15\n",
+    "x = mu + sigma * np.random.randn(10000)\n",
+    "fig, ax = plt.subplots(figsize=(5, 2.7), layout=\"constrained\")\n",
+    "# the histogram of the data\n",
+    "n, bins, patches = ax.hist(x, 50, density=True, facecolor=\"C0\", alpha=0.75)\n",
+    "\n",
+    "ax.set_xlabel(\"Length [cm]\")\n",
+    "ax.set_ylabel(\"Probability\")\n",
+    "ax.set_title(\"Aardvark lengths\\n (not really)\")\n",
+    "ax.text(75, 0.025, r\"$\\mu=115,\\ \\sigma=15$\")\n",
+    "ax.axis([55, 175, 0, 0.03])\n",
+    "ax.grid(True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d78dc9ce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ## Proteomics data example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c290244b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pathlib\n",
+    "\n",
+    "import pandas as pd\n",
+    "\n",
+    "dir_data = pathlib.Path(\"data\")\n",
+    "df = pd.read_csv(dir_data / \"proteins\" / \"proteins.csv\", index_col=0)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1787a67d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = df.iloc[0]\n",
+    "x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8e27f422",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ax = x.hist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5e525e25",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "n, bins, patches = ax.hist(x, bins=30, alpha=0.7, color=\"C0\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6360d4cd",
+   "metadata": {},
+   "source": [
+    "## Available styles\n",
+    "Choose your preferred style with it's defaults\n",
+    "[here](https://matplotlib.org/stable/gallery/style_sheets/style_sheets_reference.html)\n",
+    "\n",
+    "```python\n",
+    "plt.style.use('ggplot')\n",
+    "```\n",
+    "\n",
+    "![ggplot](https://matplotlib.org/stable/_images/sphx_glr_style_sheets_reference_008_2_00x.png)\n",
+    "![seaborn_v0_8-bright](https://matplotlib.org/stable/_images/sphx_glr_style_sheets_reference_012_2_00x.png)\n",
+    "![seaborn_v0_8-white](https://matplotlib.org/stable/_images/sphx_glr_style_sheets_reference_025_2_00x.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f32690b4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with plt.style.context(\"ggplot\"):\n",
+    "    fig, ax = plt.subplots()\n",
+    "    n, bins, patches = ax.hist(x, bins=30, alpha=0.7)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "41efb93a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "cell_metadata_filter": "-all",
+   "main_language": "python",
+   "notebook_metadata_filter": "-all"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

1_1_matplotlib.py

@@ -0,0 +1,93 @@
+# %% [markdown]
+# # Matplotlib
+# - A comprehensive library for creating static, animated, and interactive visualizations in Python.
+# - Built on NumPy arrays and designed to work with the broader SciPy stack.
+
+# %%
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+import numpy as np
+
+# %% [markdown]
+# ## Basic Line Plot
+# From Getting Started.
+
+# %%
+fig, ax = plt.subplots()  # Create a figure containing a single Axes.
+ax.plot([1, 2, 3, 4], [1, 4, 2, 3])  # Plot some data on the Axes.
+plt.show()  # Show the figure.
+
+# %% [markdown]
+# ![Anatomy of a matplotlib figure](https://matplotlib.org/stable/_images/anatomy.png)
+# %%
+fig, ax = plt.subplots()  # Create a figure containing a single Axes.
+ax.plot([1, 2, 3, 4], [1, 4, 2, 3])  # Plot some data on the Axes.
+ax.grid()
+plt.show()
+
+# %% [markdown]
+# Customize ticks
+# - [Ticker API](https://matplotlib.org/stable/api/ticker_api.html)
+
+# %%
+ax.yaxis.set_major_locator(mpl.ticker.MaxNLocator(integer=True))
+# ax.xaxis.set_major_locator(mpl.ticker.MaxNLocator(integer=True))
+ax.get_figure()
+
+# %%
+mu, sigma = 115, 15
+x = mu + sigma * np.random.randn(10000)
+fig, ax = plt.subplots(figsize=(5, 2.7), layout="constrained")
+# the histogram of the data
+n, bins, patches = ax.hist(x, 50, density=True, facecolor="C0", alpha=0.75)
+
+ax.set_xlabel("Length [cm]")
+ax.set_ylabel("Probability")
+ax.set_title("Aardvark lengths\n (not really)")
+ax.text(75, 0.025, r"$\mu=115,\ \sigma=15$")
+ax.axis([55, 175, 0, 0.03])
+ax.grid(True)
+
+# %%
+# ## Proteomics data example
+
+# %%
+import pathlib
+
+import pandas as pd
+
+dir_data = pathlib.Path("data")
+df = pd.read_csv(dir_data / "proteins" / "proteins.csv", index_col=0)
+df
+
+# %%
+x = df.iloc[0]
+x
+
+# %%
+ax = x.hist()
+
+# %%
+fig, ax = plt.subplots()
+n, bins, patches = ax.hist(x, bins=30, alpha=0.7, color="C0")
+
+
+# %% [markdown]
+# ## Available styles
+# Choose your preferred style with it's defaults
+# [here](https://matplotlib.org/stable/gallery/style_sheets/style_sheets_reference.html)
+#
+# ```python
+# plt.style.use('ggplot')
+# ```
+#
+# ![ggplot](https://matplotlib.org/stable/_images/sphx_glr_style_sheets_reference_008_2_00x.png)
+# ![seaborn_v0_8-bright](https://matplotlib.org/stable/_images/sphx_glr_style_sheets_reference_012_2_00x.png)
+# ![seaborn_v0_8-white](https://matplotlib.org/stable/_images/sphx_glr_style_sheets_reference_025_2_00x.png)
+
+# %%
+with plt.style.context("ggplot"):
+    fig, ax = plt.subplots()
+    n, bins, patches = ax.hist(x, bins=30, alpha=0.7)
+
+# %%