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-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "ed3fcfff-8c58-422e-b072-39459a6e80bb",
- "metadata": {},
- "source": [
- "## B. Petek, D. Nabergoj, E. štrumbelj: A General Approach to Visualizing Uncertainty in Statistical Graphics"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "90b948aa-fc2e-40e0-af16-60e33115c4bf",
- "metadata": {},
- "source": [
- "This Jupyter notebook contains examples of using the $bootplot$ library (https://github.com/davidnabergoj/bootplot) to generate statistical graphics with visualized uncertainty. The resulting images are featured in the paper A General Approach to Visualizing Uncertainty in Statistical Graphics."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "ef1989bf-691f-4a78-a185-8a1ba40cf460",
- "metadata": {},
- "source": [
- "To run this notebook, make sure that the datasets $penguins.csv$ and $trajectories.csv$ and an image $map.jpg$ are present in the working directory. Both files are available in the GitHub repository."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "bd5685a9-c9be-4a8d-9802-1fac15f07808",
- "metadata": {},
- "source": [
- "First, we install the required libraries. Note that we use the \"TkCairo\" Matplotlib backend due to a known antialiasing issue affecting some backends. While $bootplot$ can be used without antialiasing if the DPI is sufficiently high, we enable antialiasing here to avoid the issue altogether."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "e879e24b-1f32-40eb-811e-47829db63fa9",
- "metadata": {},
- "outputs": [],
- "source": [
- "# Computing\n",
- "import numpy as np\n",
- "import pandas as pd\n",
- "from scipy import stats\n",
- "from sklearn.linear_model import LinearRegression\n",
- "from sklearn.preprocessing import PolynomialFeatures\n",
- "\n",
- "# Plotting\n",
- "import matplotlib\n",
- "matplotlib.use(\"TkCairo\")\n",
- "import matplotlib.pyplot as plt\n",
- "import seaborn as sns\n",
- "from matplotlib.patches import Patch\n",
- "from matplotlib import font_manager\n",
- "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
- "#from bootplotSecond.bootplot.base import bootplot\n",
- "from bootplot import bootplot\n",
- "from pywaffle import Waffle\n",
- "\n",
- "# Image processing\n",
- "import matplotlib.image as mpimg\n",
- "import cv2\n",
- "from PIL import Image\n",
- "\n",
- "# Display tools\n",
- "from IPython.display import display"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "b2a3e5db-f02f-4cd1-b51c-d9edcdfcaefc",
- "metadata": {},
- "source": [
- "Next, we define a helper function to save and display images side by side, allowing for easy visual comparison."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "cf51a5df-fe98-4e03-84ef-3b5c6909c6e3",
- "metadata": {},
- "outputs": [],
- "source": [
- "# Combine and show two images one next to each other.\n",
- "def show_images(image_paths, output_path=\"\", above=False):\n",
- " if len(image_paths) > 1:\n",
- " images = [cv2.cvtColor(cv2.imread(path), cv2.COLOR_BGR2RGB) for path in image_paths]\n",
- " above = 0 if above else 1\n",
- " combined = np.concatenate(images, axis=above)\n",
- " Image.fromarray(combined).save(output_path)\n",
- " else:\n",
- " output_path=image_paths[0]\n",
- " with Image.open(output_path) as img:\n",
- " display(img)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "180a4e93-bc8d-4b16-9901-fcb69a9dbebc",
- "metadata": {},
- "source": [
- "Several image parameters, such as dots per inch (DPI) and font sizes, remain constant across examples. We define these as global settings for convenience.\n",
- "\n",
- "Since the bootplot library operates directly on pixels, the output image dimensions must be specified in pixels. The relationship between inches and pixels is given by:\n",
- "\n",
- "$$\\text{pixels} = \\text{DPI} \\times \\text{inches}$$\n",
- "\n",
- "For example, the default bootplot output is $512×512$ pixels, which corresponds to a $5.12×5.12$ inch image when the DPI is set to $100$."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "1d0cadfa-ca5e-40db-8d52-d7c4422891f9",
- "metadata": {},
- "outputs": [],
- "source": [
- "dpi = 300\n",
- "plt.rcParams['figure.dpi'] = dpi\n",
- "plt.rcParams['ytick.labelsize'] = 12\n",
- "plt.rcParams['xtick.labelsize'] = 12\n",
- "plt.rcParams['axes.labelsize'] = 12\n",
- "plt.rcParams['legend.fontsize'] = 12\n",
- "plt.rcParams['font.size'] = 12"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "fc9ed81e-319a-416d-9de1-2339e6b16cb0",
- "metadata": {},
- "source": [
- "We use the Palmer Penguins dataset, which includes both categorical and numerical variables. The dataset was accessed on November 11, 2024, from https://github.com/allisonhorst/palmerpenguins.\n",
- "\n",
- "Below, we load and prepare the dataset as a pandas DataFrame. Note that $bootplot$ also accepts input in the form of NumPy arrays."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "7ba37880-a1d8-4184-95a5-10a68986bc4b",
- "metadata": {},
- "outputs": [
- {
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