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enryHenryH
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Merge pull request #1 from biosustain/first_draft
First draft. Needs to be update with plots used in R.
2 parents 798c8e3 + 2996174 commit c93d1d7

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‎.devcontainer/devcontainer.json‎

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

‎.github/workflows/build_website.yaml‎

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

‎.gitignore‎

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_build
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jupyter_execute
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*.docx

‎1_1_matplotlib.ipynb‎

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

‎1_1_matplotlib.py‎

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

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