diff --git a/.github/workflows/a11y.yml b/.github/workflows/a11y.yml index 6490b2b..c8d1680 100644 --- a/.github/workflows/a11y.yml +++ b/.github/workflows/a11y.yml @@ -2,66 +2,63 @@ name: Accessibility Checks on: push: + branches: + - main + pull_request: workflow_dispatch: -env: - PRODUCTION_URL: "https://ds100.org/debugging-guide/" - SITE_SUBDIR: "debugging-guide" - jobs: - axe-audit: + axe-check: runs-on: ubuntu-latest - steps: - - name: Checkout repository + - name: Checkout code uses: actions/checkout@v4 - # Setup Node.js and install Axe and http-server - - name: Set up Node.js + # 1. Setup Python & Build the Book + - name: Set up Python + uses: actions/setup-python@v4 + with: + python-version: '3.11' + cache: 'pip' + + - name: Install Python dependencies + run: pip install -r requirements.txt + + - name: Build Jupyter Book + run: jupyter-book build --html + + # 2. Setup Node environment for Axe + - name: Setup Node.js uses: actions/setup-node@v4 with: node-version: '20' - - name: Install Axe and Server + + - name: Install Axe CLI and HTTP Server run: npm install -g @axe-core/cli http-server - # Start Local Server - - name: Start Local Server - run: | - # Mimic the production path structure exactly with - # the local server - mkdir -p public/${{ env.SITE_SUBDIR }} - cp -r docs/* public/${{ env.SITE_SUBDIR }}/ - - # Serve the 'public' folder on port 3000 - npx http-server ./public -p 3000 > /dev/null 2>&1 & - - sleep 5 + - name: Get Matching Chrome version + run: npx browser-driver-manager install chrome - # Run Axe Scan - - name: Run Accessibility Scan - id: axe-scan + # 3. Serve, Discover URLs, and Run Axe + - name: Run Axe Checks run: | - # Swap the Production URL for Localhost + Subdirectory - LOCAL_URL="http://localhost:3000/${{ env.SITE_SUBDIR }}/" + # A. Start a local server in the background + npx http-server ./_build/html -p 8080 -s & + sleep 5 - URLS=$(cat docs/sitemap.xml | \ - sed -n 's/.*\(.*\)<\/loc>.*/\1/p' | \ - sed "s|${{ env.PRODUCTION_URL }}|$LOCAL_URL|" | \ - tr '\n' ' ') - - echo "Scanning the following pages:" - echo "$URLS" - - # Run Axe + # B. Generate the URL list from the actual build artifacts + cd _build/html + URLS=$(find . -name "*.html" -not -path "*/build/*" | sed 's|^\./||' | sed 's|^|http://localhost:8080/|' | tr '\n' ' ') + + # C. Run Axe CLI on the discovered URLs axe $URLS \ --tags wcag2a,wcag2aa,wcag21a,wcag21aa \ --save axe-report.json \ --exit - # Upload Report even if previous step fails - name: Upload Accessibility Report if: always() uses: actions/upload-artifact@v4 with: name: axe-report - path: axe-report.json + path: _build/html/axe-report.json \ No newline at end of file diff --git a/.github/workflows/deploy.yml b/.github/workflows/deploy.yml new file mode 100644 index 0000000..5dc12ea --- /dev/null +++ b/.github/workflows/deploy.yml @@ -0,0 +1,48 @@ +# This file was created automatically with `jupyter-book init --gh-pages` 🪄 💚 +# Ensure your GitHub Pages settings for this repository are set to deploy with **GitHub Actions**. + +name: Jupyter Book (via myst) GitHub Pages Deploy +on: + push: + # Runs on pushes targeting the default branch + branches: [main] +env: + # `BASE_URL` determines, relative to the root of the domain, the URL that your site is served from. + # E.g., if your site lives at `https://mydomain.org/myproject`, set `BASE_URL=/myproject`. + # If, instead, your site lives at the root of the domain, at `https://mydomain.org`, set `BASE_URL=''`. + BASE_URL: /${{ github.event.repository.name }} + +# Sets permissions of the GITHUB_TOKEN to allow deployment to GitHub Pages +permissions: + contents: read + pages: write + id-token: write +# Allow only one concurrent deployment, skipping runs queued between the run in-progress and latest queued. +# However, do NOT cancel in-progress runs as we want to allow these production deployments to complete. +concurrency: + group: 'pages' + cancel-in-progress: false +jobs: + deploy: + environment: + name: github-pages + url: ${{ steps.deployment.outputs.page_url }} + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - name: Setup Pages + uses: actions/configure-pages@v3 + - uses: actions/setup-node@v4 + with: + node-version: 18.x + - name: Install Jupyter Book (via myst) + run: npm install -g jupyter-book + - name: Build HTML Assets + run: jupyter-book build --html + - name: Upload artifact + uses: actions/upload-pages-artifact@v3 + with: + path: './_build/html' + - name: Deploy to GitHub Pages + id: deployment + uses: actions/deploy-pages@v4 \ No newline at end of file diff --git a/.gitignore b/.gitignore index 04c0b79..69e762e 100644 --- a/.gitignore +++ b/.gitignore @@ -1,10 +1,10 @@ /.quarto/ # Notebook Checkpoints -.ipynb_checkpoints/ +**/.ipynb_checkpoints/ # macOS files -.DS_Store +**/.DS_Store __MACOSX/ # Site build @@ -16,3 +16,8 @@ _site/* __pycache__/ **/*.quarto_ipynb + +# MyST build outputs +_build + +venv/ diff --git a/README.md b/README.md index 2db8d8e..02df7fd 100644 --- a/README.md +++ b/README.md @@ -1,132 +1,91 @@ # Debugging Guide -[![pages-build-deployment](https://github.com/DS-100/debugging-guide/actions/workflows/pages/pages-build-deployment/badge.svg)](https://github.com/DS-100/debugging-guide/actions/workflows/pages/pages-build-deployment) [![Accessibility Checks](https://github.com/DS-100/debugging-guide/actions/workflows/a11y.yml/badge.svg)](https://github.com/DS-100/debugging-guide/actions/workflows/a11y.yml) +[![Accessibility Checks](https://github.com/DS-100/debugging-guide/actions/workflows/a11y.yml/badge.svg)](https://github.com/DS-100/debugging-guide/actions/workflows/a11y.yml) -Website link: https://ds100.org/debugging-guide/ +This was built with [Jupyter Book (MyST flavor)](https://jupyterbook.org/en/stable/intro.html). -## Accessibility Checks +## Structure -Accessibility checks run whenever you push changes to the repository. View results in the Actions tab. You can also download reports generated by individual action runs. +Only two things need to be edited. +- `myst.yml`: Configuration information about the textbook. Modify this file for things like: + - changing the logo or favicon; + - adding or removing launch buttons; + - changing information about the book. + - table of contents (section, chapter numbering and order) +- `content/`: Content of the textbook. All the notebooks with section and chapter content go here. Modify these files to change the content of the sections. -If you'd like to run checks locally, first ensure you have quarto and Node.js installed. Then run `quarto preview --render all` which will preview the site at http://localhost:xxxx and generate `docs/sitemap.xml`. If the sitemap already exists, you can skip the `--render all` flag. Note the port number. From a second terminal, run the following with the correct port number: -``` -axe $(cat docs/sitemap.xml | grep -o "[^<]*" | sed 's///' | sed 's|https://ds100.org/debugging-guide|http://localhost:xxxx|') \ - --tags wcag2a,wcag2aa,wcag21a,wcag21aa \ - --exit -``` - -## Repo Setup - -### Quarto Set-Up +## Maintaining the Textbook +This section details how to maintain the textbook. -Begin by [installing Quarto](https://quarto.org/docs/get-started/). +### One-time Setup +Follow these steps the first time you set up a computer to modify and maintain the textbook. +1. Create a local copy of this repo by running `git clone https://github.com/DS-100/debugging-guide.git` from the command line in whichever folder you want to contain the textbook. +2. Next, you need to install all the required packages. Either of the commands `pip install -r requirements.txt` or `conda install --file requirements.txt` should work. If you have a Windows device, it's preferable to run this in an Anaconda Prompt terminal. This should install the packages used to build the textbook and execute the notebooks. If you use pip to install the packages, I recommend using a python virtual environment: -Texts can be authored in Quarto using VSCode, JupyterLab, or classic Jupyter Notebook. We suggest using [VSCode](https://quarto.org/docs/get-started/hello/vscode.html) - -### Cloning the Repo -After you've installed quarto, go into your terminal on your *local* device and type +```bash +python3 -m venv venv # create virtual environment +source venv/bin/activate # activate the virtual environment +pip install -r requirements.txt # as above +``` +If you use a virutal environment, make sure to activate it before updating the textbook by first running `source venv/bin/activate` at the top level of this repo. You can deactivate the environment with `deactivate`. If you choose to use a virtual environment not named venv, ensure that you add this to the `.gitignore` so it is NOT tracked by git. -`git clone https://github.com/DS-100/debugging-guide.git` +### Updating the Textbook +These steps detail the process you should go through every time you update the textbook. Remember to activate your virtual environment first if you are using one. +1. **Pull:** `cd` into `debugging-guide/`, your local copy of the repository and `git pull origin main` to collect any updates which may have been pushed to the remote copy by other collaborators. +2. **Update:** Make any changes you wish to make. This should (likely) only consist of changes to `_myst.yml` and the files in `content/`. + - If you added new sections or chapters, update the `toc` in `myst.yml` to ensure your changes are included. +3. **Build and Check:** `cd` into the top level directory of this repository and run `jupyter book start` to build the book and serve it on localhost. View what the textbook looks like with any changes you've made. Make sure nothing is broken and the changes are as you want them. +6. **Push:** Stage any changes you made (i.e. using `git add [file]`, `git add -u`, `git add .`, etc.), commit your changes with `git commit -m "[description]"` (please include a useful description of any changes you made), and push to the repository with `git push origin main`. Deployment will happen automatically via GitHub Actions. -If you're unable to clone the repo, please contact the head/infra TAs to make sure you have read/write access to this repository. +### Deployment +The textbook is hosted on GitHub Pages and deployed automatically everytime you push to the main branch via [this workflow](https://github.com/DS-100/debugging-guide/actions/workflows/deploy.yml). -### Environment Setup -This section will ensure that you have all the necessary packages to render the website and is identical to the corresponding section in the [course-notes repo](https://github.com/DS-100/course-notes). We recommend using Conda ([download here](https://www.anaconda.com/download)) to keep track of your environment. +## Helpful Things +The [Jupyter Book](https://jupyterbook.org/stable/) website has lots of information about Jupyter Book. This textbook uses Jupyter Book v2 built on [MyST](https://mystmd.org/). -Run the following in your terminal within the main directory: +If changes you've made aren't showing up the HTML after building, sometimes deleting `_build` and then building again helps. You can also run `jupyter book clean`. -``` -conda create --name data100quarto -conda activate data100quarto -pip3 install -r requirements.txt # will take a while -ipython kernel install --user -``` +Some cells are hidden with tags like `remove-input` or `remove-cell`. -If you get an `error: externally-managed-environment` on the third line, run `pip3 install -r requirements.txt --break-system-packages`. You may also run into issues if you're running on Apple silicon. I fixed this by running `conda install -c conda-forge scikit-learn==1.2.2 scikit-image==0.25.2` before `pip3 install -r requirements.txt`. +### Images -Remember to always activate the right environment before running anything with `conda activate data100quarto`. +All images must have alt text. [More on images and figures in MyST.](https://mystmd.org/guide/figures) +### Links +Links to the internet should be done as they are usually done in Markdown. However, to cross-link to other pages of the textbook, there is an internal linking system that should be used instead (since it is robust to file structure changes). This system is described [here](https://mystmd.org/guide/cross-references#targeting-headers). -## Repo Organization -This website uses Quarto to render pages. The main index can be found in `_quarto.yml`, and subpages are organized under the `contents` section. Each subpage has it's own folder and `.md` (markdown) file. Note that unlike the [Course Notes repo](https://github.com/DS-100/course-notes), the debugging guide rarely runs any code, so we rely on `.md` files rather than `.qmd` to save us the trouble of converting from `.qmd` to `.ipynb` for editing, then back to `.qmd` to render the website. Instead we can make edits directly to the `.md` file. - -## Creating a new section -To start a new document, create an empty folder `topic_name` and create an empty markdown file `topic_name.md`. Start each document like so: - +For example, Section 12.4 Exercise 3 of the [Stat 88 Textbook](https://github.com/stat88/textbook) contains a link to Section 12.2. +1. The flag `(ch12.2)=` was added *before* the primary header of the notebook. ``` ---- -title: INSERT_TOPIC_TITLE -format: - html: - toc-location: right - code-fold: false -jupyter: - jupytext: - text_representation: - extension: .qmd - format_name: quarto - format_version: '1.0' - jupytext_version: 1.16.1 - kernelspec: - display_name: Python 3 (ipykernel) - language: python - name: python3 ---- +(ch12.2)= +## The Distribution of the Estimated Slope ## ``` - -Now, the notebook is ready for writing content. In VSCode, you can activate a live preview of markdown files by clicking the button on the upper right-hand corner. - -![](assets/images/readme/md_preview.png) - -Note that clicking on the quarto `Preview` button does not generate a *live* preview but rather a static one. Hence, we click the button on its right. - -## Document Formatting - -**Always `git pull` before making any new changes**. - -#### Formatting Images - -To give you the most control when inserting images, we use html with the following format to center images/figs and control their size: - -```
ALT TEXT HERE
``` - -For example, `
description of image
`. - -In some instances, the images may be completely described in the text of the page. In these cases, you may leave the alt text blank i.e. `alt=""`, **but alt text must still be included to meet our legal obligations.** - -## Generating Output + Rendering Website -After making edits to the `.md` files, ensure that the right documents are un-commented under `_quarto.yml`'s `chapter` section. Then, make sure you're using the right environment, and run `quarto render` in the main folder to render the entire website. - -To preview your changes, run `quarto preview`. - -Not all files in this repository need to be rendered. For example, images used in this README are not used in the website and as such do not need to be included in the rendered website. To ensure these files are not rendered, they are placed in the folder `assets/images/_readme/` which is not rendered because the folder begins with an underscore `_`. Read more about this [here](https://quarto.org/docs/websites/index.html#render-targets). - -## Pushing to Github -Once you're satisfied and have *thoroughly tested* your changes, you can push your edits onto Github. There are two ways to do this: - -1. **Command Line**. Navigate to the main `debugging-guide` folder if you haven't already and make sure you're in the right environment. - +2. The link referencing Section 12.2 is created like so: ``` -git add . # this makes sure git tracks any new files created -git commit -m # please commit with an informative message -git push # push your changes to github +**3.** +Refer to the regression of active pulse rate on resting pulse rate in [Section 12.2](ch12.2). Here are the estimated values again, along with some additional data. ``` -2. **Github Desktop** ([download](https://desktop.github.com/)). This is the GUI version of the command line. - - * Choose the files you want to commit on the left hand menu. By default, github will select all changes/new files. ![](assets/images/readme/git_files.png) - * Write an informative comment on the bottom left hand corner and click "commit to main" once finished ![](assets/images/readme/git_comment.png) - * Push your changes using the button on the top right. +### Hidden Cells -Once your changes are pushed, you can see the progress of your build in the ["Actions" tab](https://github.com/DS-100/debugging-guide/actions). The Actions tab monitors all workflows. Currently there are two workflows: "pages-build-deployment" which builds and deploys the website, and "Accessibility Checks." If both checks complete successfully, you will see a green checkmark next to each workflow's most recent run on the Actions page as well as a green checkmark on the main repo page for the associated commit. Yellow/orange indicates that checks are still running, meaning that the website may not be deployed yet. Red means that at least one check was unable to complete successfully. You should always investigate if you see a red mark; It's possible that the website deployed successfully but the accessibility checks failed, or vice versa. +[Use tags to hide cells and/or their output](https://mystmd.org/guide/interactive-notebooks#notebooks-cell-visibility) -The website should take less than 5 minutes to deploy, and you will see the changes reflected in the [website](https://ds100.org/debugging-guide/). +I wasn't able to figure out a quick dropdown to hide the cell before it is clicked, so this is what I ended up with. In a markdown cell, I first put the code that I want to hide in a dropdown [directive](https://mystmd.org/guide/directives): +````{dropdown} Click to see the code +:open: false +```python +import pandas as pd +import numpy as np +import urllib.request +import os.path +import zipfile +``` +```` -## Other Quarto Resources - -[Quick Start Guide](https://quarto.org/docs/get-started/) - -[Comprehensive Guide](https://quarto.org/docs/guide/) +Then I put the code I want to execute (and see its output on the website) in a code cell with the `remove-input` tag added. You can add tags via VSCode or like so: -[Markdown in Quarto](https://quarto.org/docs/authoring/markdown-basics.html) +```{code-cell} python +:tags: [remove-input] +print("This will show output with no input!") +``` \ No newline at end of file diff --git a/_quarto.yml b/_quarto.yml deleted file mode 100644 index 78ac869..0000000 --- a/_quarto.yml +++ /dev/null @@ -1,48 +0,0 @@ -project: - type: website - output-dir: docs - -website: - site-url: "https://ds100.org/debugging-guide/" - page-footer: - center: - - text: Accessibility - href: "https://dap.berkeley.edu/get-help/report-web-accessibility-issue" - - text: Nondiscrimination - href: "https://ophd.berkeley.edu/policies-and-procedures/nondiscrimination-policy-statement" - favicon: assets/images/data100_logo.png - title: "Data 100 Debugging Guide" - image: assets/images/data100_logo.png - repo-url: https://github.com/DS-100/debugging-guide - search: - location: sidebar - type: textbox - sidebar: - logo: "assets/images/data100_logo.png" - style: "docked" - title: "Data 100 Debugging Guide" - logo-alt: "Data 100 logo" - tools: - - icon: github - href: https://github.com/DS-100/debugging-guide - contents: - - index.md - - jupyter101/jupyter101.md - - jupyter_datahub/jupyter_datahub.md - - autograder_gradescope/autograder_gradescope.md - - pandas/pandas.md - - regex/regex.md - - visualizations/visualizations.md - - projA1/projA1.md - - projA2/projA2.md - - sql/sql.md -format: - html: - theme: cosmos - toc: true - toc-depth: 5 - toc-expand: 3 # depth to expand index - callout-icon: false - number-sections: false - include-after-body: - - assets/scripts/a11y-fixes.html diff --git a/autograder_gradescope/autograder_gradescope.md b/content/autograder_gradescope.md similarity index 75% rename from autograder_gradescope/autograder_gradescope.md rename to content/autograder_gradescope.md index 31efb7b..0818eba 100644 --- a/autograder_gradescope/autograder_gradescope.md +++ b/content/autograder_gradescope.md @@ -1,26 +1,11 @@ --- title: Autograder and Gradescope / Pensieve -format: - html: - toc-location: right - code-fold: false -jupyter: - jupytext: - text_representation: - extension: .qmd - format_name: quarto - format_version: "1.0" - jupytext_version: 1.16.1 - kernelspec: - display_name: Python 3 (ipykernel) - language: python - name: python3 --- -
- Citation - Many of these common questions were taken and modified from the UC San Diego course DSC 10: Principles of Data Science and their [debugging guide](https://dsc10.com/debugging/). -
+:::{seealso} Citation + +Many of these common questions were taken and modified from the UC San Diego course DSC 10: Principles of Data Science and their [debugging guide](https://dsc10.com/debugging/). +::: ## Autograder @@ -28,16 +13,24 @@ jupyter: When you pass a test, you'll see a nice message and a cute emoji! -
Screenshot of a grader cell for question q2a with cell ouput 'q2a passed'
+```{image} images/passing_autograder.png +:width: 200 +:alt: Screenshot of a grader cell for question q2a with cell ouput 'q2a passed' +``` When you don't, however, the message can be a little confusing. -
Example of a cell output for a failing autograder test showing what test failed, what the code that was evaluated was, the expected output, and an error that was raised during the course of the test.
-
+```{image} images/autograder_error.png +:width: 600 +:alt: Example of a cell output for a failing autograder test showing what test failed, what the code that was evaluated was, the expected output, and an error that was raised during the course of the test. +``` The best course of action is to find the test case that failed and use that as a starting point to debug your code. -
An annotated version of the previous image which includes the note 'The most useful error message is usually at the bottom'
+```{image} images/autograder_error_annot.png +:width: 700 +:alt: An annotated version of the previous image which includes the note 'The most useful error message is usually at the bottom' +``` In the example above, we see that the test case in green, `max_swing in set(bus['name'])`, is not passing. The actual output (in blue) is often hard to parse, so the best course of action is to: @@ -52,11 +45,11 @@ In the example above, we see that the test case in green, `max_swing in set(bus[ ### Why do I get an error saying "`grader is not defined`"? -If it has been a while since you’ve worked on an assignment, the kernel will shut itself down to preserve memory. When this happens, all of your variables are forgotten, including the grader. That’s OK. The easiest way to fix this is by [restarting your kernel and rerunning all the cells](https://ds100.org/debugging-guide/jupyter101/jupyter101.html#restarting-kernel). To do this, in the top left menu, click `Kernel` -> `Restart and Run All Cells`. +If it has been a while since you’ve worked on an assignment, the kernel will shut itself down to preserve memory. When this happens, all of your variables are forgotten, including the grader. That’s OK. The easiest way to fix this is by [restarting your kernel and rerunning all the cells](#restarting-kernel). To do this, in the top left menu, click `Kernel` -> `Restart and Run All Cells`. ### I’m positive I have the right answer, but the test fails. Is there a mistake in the test? -While you might see the correct answer displayed as the result of the cell, chances are your solution isn’t being stored in the answer variable. Make sure you are assigning the result to the answer variable and that there are no typos in the variable name. Finally, [restart your kernel and run all the cells in order](https://ds100.org/debugging-guide/jupyter101/jupyter101.html#restarting-kernel): `Kernel` -> `Restart and Run All Cells`. +While you might see the correct answer displayed as the result of the cell, chances are your solution isn’t being stored in the answer variable. Make sure you are assigning the result to the answer variable and that there are no typos in the variable name. Finally, [restart your kernel and run all the cells in order](#restarting-kernel): `Kernel` -> `Restart and Run All Cells`. ### Why does the last `grader.export` cell fail if all previous tests passed? @@ -64,7 +57,7 @@ This can happen if you “overwrite” a variable that is used in a question. Fo ### Why does a notebook test fail now when it passed before, and I didn’t change my code? -You probably ran your notebook out of order. [Re-run all previous cells](https://ds100.org/debugging-guide/jupyter101/jupyter101.html#running-cells) in order, which is how your code will be graded. +You probably ran your notebook out of order. [Re-run all previous cells](#running-cells) in order, which is how your code will be graded. ## Gradescope/Pensieve @@ -86,6 +79,7 @@ Find the first cell that raises an error. Make sure that all of the variables us This happens when you try to access a variable that has not been defined yet. Since the autograder runs all the cells in-order, if you happened to define a variable in a cell further down and accessed it before that cell, the autograder will likely throw this error. Another reason this could occur is because the notebook was not saved before the autograder tests are run. When in doubt, it is good practice to restart your kernel, run all the cells again, and save the notebook before running the cell that causes this error. +(multiple-nameError)= ### Why do I see multiple `NameError: name ___ is not defined` errors for all questions after a certain point on Gradescope/Pensieve? This can happen if you import external packages when you are not instructed to do so. The base image on Gradescope/Pensieve includes only a specific list of pre-installed packages, defined by the course staff in the autograder. If you import a package that is not on this list, it will not be available to the autograder, even if you download the package correctly on your local Jupyter notebook environment. As a result, it will first encounter a `ModuleNotFoundError` when trying to import the package (though this error may not be shown to you), and then a `NameError` for every question afterward. Although this behavior is unintuitive, it’s a limitation of the autograder—only course staff can view certain output messages from your notebook to prevent cheating. If you encounter this issue, review your notebook’s imports and ensure you are not using any packages not included in the autograder environment. @@ -94,13 +88,19 @@ This can happen if you import external packages when you are not instructed to d If your Gradescope submission page has been stuck running on this page for a while: -
Autograder Results. The autograder hasn't finish running yet.
+```{image} images/gradescope_loading.png +:width: 359 +:alt: Autograder Results. The autograder hasn't finish running yet. +``` or if it times out: -
Autograder Results. In a red box 'The autograder failed to respond in the expected amount of time. If the autograder continues to fail, contact your course staff for help in debugging this issue. Make sure to include a link to this page so they can help you most effectively.'
+```{image} images/gradescope_timeout.png +:width: 650 +:alt: Autograder Results. In a red box 'The autograder failed to respond in the expected amount of time. If the autograder continues to fail, contact your course staff for help in debugging this issue. Make sure to include a link to this page so they can help you most effectively. +``` -it means that the Gradescope autograder failed to execute in the expected amount of time. TThis could be due to an inefficiency in your code or an issue on Gradescope's end, so we recommend resubmitting and allowing the autograder to rerun. If the issue persists after a few attempts, you may need to investigate your code for inefficiencies. +it means that the Gradescope autograder failed to execute in the expected amount of time. This could be due to an inefficiency in your code or an issue on Gradescope's end, so we recommend resubmitting and allowing the autograder to rerun. If the issue persists after a few attempts, you may need to investigate your code for inefficiencies. For example, if you rerun all cells in your Jupyter notebook and notice that some cells take significantly longer to run than others, you might need to optimize those cells. Keep in mind that the time it takes to run all tests in your Jupyter notebook is not equivalent to the time it takes on Gradescope/Pensieve. 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a/docs/regex/images/updated_greedy.png b/content/images/updated_greedy.png similarity index 100% rename from docs/regex/images/updated_greedy.png rename to content/images/updated_greedy.png diff --git a/docs/jupyter_datahub/zip_download.png b/content/images/zip_download.png similarity index 100% rename from docs/jupyter_datahub/zip_download.png rename to content/images/zip_download.png diff --git a/docs/jupyter_datahub/zip_left_menu.png b/content/images/zip_left_menu.png similarity index 100% rename from docs/jupyter_datahub/zip_left_menu.png rename to content/images/zip_left_menu.png diff --git a/index.md b/content/index.md similarity index 79% rename from index.md rename to content/index.md index ab16130..ffdecbb 100644 --- a/index.md +++ b/content/index.md @@ -4,7 +4,7 @@ This text offers pointers for keyboard shortcuts or common mistakes that accompa Inspiration for this guide was taken from the UC San Diego course DSC 10: Principles of Data Science and their [debugging guide](https://dsc10.com/debugging/). -If you spot any typos or would like to suggest any changes, please email us at **data100.instructors@berkeley.edu**. +If you spot any typos or would like to suggest any changes, please email us at **data100.instructors@berkeley.edu** or [file an issue on our GitHub](https://github.com/DS-100/debugging-guide/issues/new). ## Authors In alphabetical order, Yash Dave, Emrie Loh, Lillian Weng, and Wesley Zheng. diff --git a/jupyter101/jupyter101.md b/content/jupyter101.md similarity index 71% rename from jupyter101/jupyter101.md rename to content/jupyter101.md index 8c8fb57..318d7d9 100644 --- a/jupyter101/jupyter101.md +++ b/content/jupyter101.md @@ -1,23 +1,8 @@ --- title: Jupyter 101 -format: - html: - toc-location: right - code-fold: false -jupyter: - jupytext: - text_representation: - extension: .qmd - format_name: quarto - format_version: '1.0' - jupytext_version: 1.16.1 - kernelspec: - display_name: Python 3 (ipykernel) - language: python - name: python3 --- -::: {.callout-note collapse="false"} +::: {note} If you're using a MacBook, replace `ctrl` with `cmd`. ::: @@ -31,6 +16,7 @@ For the following commands, make sure you're in command mode. You can enter this - `m`: convert a cell to markdown (text cell) - `y`: convert a cell to code +(running-cells)= ## Running Cells For individual cells, @@ -45,12 +31,17 @@ To run all cells in a notebook: - `Run All Above Selected Cell`: this runs every cell above the selected cell - `Run Selected Cell and All Below`: this runs the selected cell and all cells below - `Run All`: this runs every cell in the notebook from top-to-bottom - -
Gif showing exactly where to click to run all cells
+```{image} images/run_all.gif +:width: 500 +:alt: "Gif showing exactly where to click to run all cells +``` + +(saving-your-notebook)= ## Saving your notebook Jupyter autosaves your work, but there can be a delay. As such, it's a good idea to save your work as often as you remember and especially before submitting assignments. To do so, press `ctrl` + `s`. +(restarting-kernel)= ## Restarting Kernel In the menu bar on the left, click `Kernel`. From here, you have several options. The ones we use most commonly are: @@ -58,9 +49,15 @@ In the menu bar on the left, click `Kernel`. From here, you have several options - `Restart Kernel and Run up to Selected Cell` - `Restart Kernel and Run All Cells` -
Gif showing exactly where to click to access the mostly frequently used kernel options.
+```{image} images/kernel.gif +:width: 500 +:alt: Gif showing exactly where to click to access the mostly frequently used kernel options. +``` ## Automatically Closing Brackets Many IDEs like VSCode have a functionality that automatically closes brackets. For example, pressing `(`, `{`, or `[` would automatically add the second bracket at the other end `)`, `}`, and `]`, respectively. Datahub does not have this functionality turned on by default, but you can do so by going into `Settings` -> `Auto Close Brackets`. If you see a check mark to the left of `Auto Close Brackets`, then it's enabled. -
Gif showing how to toggle autoclose bracket
+```{image} images/autoclose_brackets.gif +:width: 500 +:alt: Gif showing how to toggle autoclose bracket +``` diff --git a/jupyter_datahub/jupyter_datahub.md b/content/jupyter_datahub.md similarity index 73% rename from jupyter_datahub/jupyter_datahub.md rename to content/jupyter_datahub.md index 9a25704..80a1dfc 100644 --- a/jupyter_datahub/jupyter_datahub.md +++ b/content/jupyter_datahub.md @@ -1,20 +1,5 @@ --- title: Jupyter / Datahub -format: - html: - toc-location: right - code-fold: false -jupyter: - jupytext: - text_representation: - extension: .qmd - format_name: quarto - format_version: "1.0" - jupytext_version: 1.16.1 - kernelspec: - display_name: Python 3 (ipykernel) - language: python - name: python3 --- ## My kernel died, restarted, or is very slow @@ -27,13 +12,16 @@ Jupyterhub connects you to an external container to run your code. That connecti When you see a message like this: -
Screenshot of dead kernel messaging
+```{image} images/kernel_die.png +:width: 500 +:alt: Screenshot of dead kernel messaging +``` 1. Either press the "Ok" button or reload the page -2. [Restart your kernel](https://ds100.org/debugging-guide/jupyter101/jupyter101.html#restarting-kernel) -3. [Rerun your cells](https://ds100.org/debugging-guide/jupyter101/jupyter101.html#running-cells) +2. [Restart your kernel](#restarting-kernel) +3. [Rerun your cells](#running-cells) -Note that you may lose some recent work if your kernel restarted when you were in the middle of editing a cell. As such, we recommend [saving your work](https://ds100.org/debugging-guide/jupyter101/jupyter101.html#saving-your-notebook) as often as possible. +Note that you may lose some recent work if your kernel restarted when you were in the middle of editing a cell. As such, we recommend [saving your work](#saving-your-notebook) as often as possible. If this does not fix the issue, it could be a problem with your code, usually the last cell that executed before your kernel crashed. Double check your logic, and feel free to make a private post on Ed if you're stuck! @@ -45,17 +33,24 @@ We set some cells to read-only mode prevent accidental modification. That said, 2. Click setting on the top right corner 3. Under "Common Tools", you can toggle between "Editable" (can edit the cell) and "Read-Only" (cannot edit the cell) -
Gif showing how to toggle between 'Editable' and 'Read-Only' for a cell
+```{image} images/toggle_edit.gif +:width: 500 +:alt: Gif showing how to toggle between 'Editable' and 'Read-Only' for a cell +``` ## My text cell looks like code If you double-click on a text (markdown) cell, it'll appear in its raw format. To fix this, simply run the cell. If this doesn't fix the problem, check out the commonly asked question below. +(cell-type-change)= ## My text cell changed to a code cell / My code cell changed to a text cell Sometimes, a text (markdown) cell was changed to a code cell, or a code cell can't be run because it's been changed to a text (markdown) or raw cell. To fix this, toggle the desired cell type in the top bar. -
Gif showing how to toggle the cell type from code to text and back again.
+```{image} images/toggle_cell_type.gif +:width: 700 +:alt: Gif showing how to toggle the cell type from code to text and back again. +``` ## Why does running a particular cell cause my kernel to die? @@ -65,31 +60,43 @@ If one particular cell seems to cause your kernel to die, this is likely because Suppose you’re working on Lab 5. One solution is to go directly to DataHub and rename your lab05 folder to something else, like lab05-old. Then, click the Lab 5 link on the course website again, and it’ll bring you to a brand-new version of Lab 5. You can then copy your work from your old Lab 5 to this new one, which should have the original version of the assignment. -Alternatively, you can access this [public repo](https://github.com/DS-100/fa25-student) and navigate to a blank copy of the assignment you were working on. In the case of Lab 5 for example, the notebook would be located at `lab/lab05/lab05.ipynb`. You can then check and copy over the contents of the deleted cell into a new cell in your existing notebook. +Alternatively, you can access this [public repo](https://github.com/DS-100/sp26-student) and navigate to a blank copy of the assignment you were working on. In the case of Lab 5 for example, the notebook would be located at `lab/lab05/lab05.ipynb`. You can then check and copy over the contents of the deleted cell into a new cell in your existing notebook. ## "Click here to download zip file" is not working When this happens, you can download the zip file through the menu on the left. -
Left menu showing files with the zip file highlighted by a red box.
+```{image} images/zip_left_menu.png +:width: 400 +:alt: Left menu showing files with the zip file highlighted by a red box. +``` Right click on the generated zip file and click "Download". -
After right/force clicking on the zip file, click 'Download' which is highlighted in this screenshot with a red box.
+```{image} images/zip_download.png +:width: 500 +:alt: After right/force clicking on the zip file, click 'Download' which is highlighted in this screenshot with a red box. +``` ## Autograder could not locate my PDF Sometimes when running the `grader.export(run_tests=True)` cell at the end of the notebook, you run into an error where the autograder could not locate the PDF: -
Screenshot showing 'UserWarning: Could not locate a PDF to include'
+```{image} images/pdf_not_located.png +:width: 700 +:alt: Screenshot showing 'UserWarning: Could not locate a PDF to include' +``` -To fix this, make sure you have not accidentally converted any cells to `raw` type. All cells should either be `markdown` or `code` cells. You can easily convert cells by following [these instructions](https://ds100.org/debugging-guide/jupyter_datahub/jupyter_datahub.html#my-text-cell-changed-to-a-code-cell-my-code-cell-changed-to-a-text-cell) earlier in the debugging guide. +To fix this, make sure you have not accidentally converted any cells to `raw` type. All cells should either be `markdown` or `code` cells. You can easily convert cells by following [these instructions](#cell-type-change) earlier in the debugging guide. ## I can’t export my assignment as a PDF due to a `LatexFailed` error Occasionally when running the `grader.export(run_tests=True)` cell at the end of the notebook, you run into an error where the PDF failed to generate: -
Screenshot of a latex error that begins with 'LatexFailed: PDF creating failed, captured latex output:...'
+```{image} images/LatexError.png +:width: 500 +:alt: Screenshot of a latex error that begins with 'LatexFailed: PDF creating failed, captured latex output:...' +``` Converting a Jupyter notebook to a PDF involves formatting some of the markdown text in [LaTeX](https://www.latex-project.org/). However, this process will fail if your free response answers have (unresolved) LaTeX characters like `\n`, `$`, or `$$`. There are several ways to resolve this: diff --git a/pandas/pandas.md b/content/pandas.md similarity index 84% rename from pandas/pandas.md rename to content/pandas.md index 526b356..2af8512 100644 --- a/pandas/pandas.md +++ b/content/pandas.md @@ -1,24 +1,21 @@ --- title: Pandas -format: - html: - toc-location: right - code-fold: false -jupyter: python3 --- ## Understanding `pandas` errors `pandas` errors can look red, scary, and very long. Fortunately, we don't need to understand the entire thing! The most important parts of an error message are at the **top**, which tells you which line of code is causing the issue, and at the **bottom**, which tells you exactly what the error message is. -
A screenshot of an error output by running a pandas command. The screenshot is annotated, with the first annotation reading 'The arrow points to the line of code that is erroring' next to a green arrow in the error output. Also highlighted and annotated is the last line of the error message, 'The most useful error messages are usually at the bottom'
-
+```{image} images/pandas_error.png +:width: 700 +:alt: A screenshot of an error output by running a pandas command. The screenshot is annotated, with the first annotation reading 'The arrow points to the line of code that is erroring' next to a green arrow in the error output. Also highlighted and annotated is the last line of the error message, 'The most useful error messages are usually at the bottom' +``` This note is (mostly) structured around the error messages that show up at the bottom. ## My code is taking a really long time to run It is normal for a cell to take a few seconds -- sometimes a few minutes -- to run. If it's is taking too long, however, you have several options: -1. Try restarting the kernel. Sometimes, Datahub glitches or lags, causing the code to run slower than expected. [Restarting the kernel](https://ds100.org/debugging-guide/jupyter101/jupyter101.html#restarting-kernel) should fix this problem, but if the cell is still taking a while to run, it is likely a problem with your code. +1. Try restarting the kernel. Sometimes, Datahub glitches or lags, causing the code to run slower than expected. [Restarting the kernel](#restarting-kernel) should fix this problem, but if the cell is still taking a while to run, it is likely a problem with your code. 2. Scrutinize your code. Am I using too many for loops? Is there a repeated operation that I can substitute with a `pandas` function? ## Why is it generally better avoid using loops or list comprehensions when possible? @@ -31,6 +28,7 @@ This error usually happens when we have a `DataFame` called `df`, and we're tryi ## TypeErrors +(typeError-object-not-callable)= ### `TypeError: '___' object is not callable` This often happens when you use a default keyword (like `str`, `list`, `range`, `bool`, `sum`, or `max`) as a variable name, for instance: @@ -38,10 +36,11 @@ This often happens when you use a default keyword (like `str`, `list`, `range`, These errors can be tricky because they don’t error on their own but cause problems when we try to use the name `sum` (for example) later on in the notebook. -To fix the issue, identify any such lines of code (Ctrl+F on "`sum =`" for example), change your variable names to be something more informative, and [restart your notebook](https://ds100.org/debugging-guide/jupyter101/jupyter101.html#restarting-kernel). +To fix the issue, identify any such lines of code (Ctrl+F on "`sum =`" for example), change your variable names to be something more informative, and [restart your notebook](#restarting-kernel). Python keywords like `str` and `list` appear in green text, so be on the lookout if any of your variable names appear in green! +(convert-str-float)= ### `TypeError: could not convert string to a float` This error often occurs when we try to do math operations (ie. `sum`, `average`, `min`, `max`) on a `DataFrame` column or `Series` that contains strings instead of numbers (note that we can do math operations with booleans; Python treats `True` as 1 and `False` as 0). @@ -53,8 +52,9 @@ Related to the above (but distinct), you may run into this error when performing Working with the `elections` dataset for example, - elections.groupby('Year').agg('mean') - +```python +elections.groupby('Year').agg('mean') +``` would error because `pandas` cannot compute the mean of the names of presidents. There are three ways to get around this: 1. Select only the numeric columns you are interested in before applying the aggregation function. In the above case, both `elections.groupby('Year')['Popular Vote']` or `elections['Popular vote'].groupby('Year')` would work. @@ -64,16 +64,19 @@ would error because `pandas` cannot compute the mean of the names of presidents. ### `TypeError: 'NoneType' object is not subscriptable` / `AttributeError: 'NoneType' object has no attribute 'shape'` This usually occurs as you assign a `None` value to a variable, then try to either index into or access some attribute of that variable. For Python functions like `append` and `extend`, you do not need to do any variable assignment because they mutate the variable directly and return `None`. Assigning `None` tends to happen as a result of code like: - some_list = some_list.append(element) +```python +some_list = some_list.append(element) +``` In contrast, an operation like `np.append` does not mutate the variable in place and, instead, returns a copy. In these cases, (re)assignment is necessary: - some_array = np.append(some_array, element) +```python +some_array = np.append(some_array, element) +``` ### `TypeError: 'int'/'float' object is not subscriptable` This occurs when you try and index into an integer or other numeric `Python` data type. It can be confusing to debug amidst a muddle of code, but you can use the error message to identify which variable is causing this error. Using `type(var_name)` to check the data type of the variable in question can be a good starting point. - ## IndexErrors ### `IndexError: invalid index to scalar variable.` @@ -81,14 +84,18 @@ This error is similar to the last `TypeError` in the previous section. However, For a concrete example, if you defined - numpy_arr = np.array([1]) +```python +numpy_arr = np.array([1]) +``` and indexed into it twice (`numpy_arr[0][0]`), you would get the above error. Unlike a Python integer whose type is `int`, `type(numpy_arr[0])` returns the `NumPy` version of an integer, `numpy.int64`. Additionally, you can check the data type by accessing the `.dtype` attribute of `NumPy` array (`numpy_arr.dtype`) or scalar variable (`numpy_arr[0].dtype`). ### `IndexError: index _ is out of bounds for axis _ with size _` This error usually happens when you try to index a value that's greater than the size of the array/list/`DataFrame`/`Series`. For example, - some_list = [2, 4, 6, 8] +```python +some_list = [2, 4, 6, 8] +``` `some_list` has a length of 4. Trying `some_list[6]` will error because index 6 is greater than the length of the array. Note that `some_list[4]` will also cause an `IndexError` because Python and `pandas` uses zero indexing, which means that the first element has index 0, the second element has index 1, etc.; `some_list[4]` would grab the fifth element, which is impossible when the list only has 4 elements. @@ -97,7 +104,7 @@ This error usually happens when you try to index a value that's greater than the ### `ValueError: Truth value of a Series is ambiguous` This error could occur when you apply Python logical operators (`or`, `and`, `not`), which only operate on a single boolean values, to `NumPy` arrays or `Series` objects, which can contain multiple values. The fix is to use bitwise operators `|`, `&`, `~` , respectively, to allow for element-wise comparisons between values in arrays or `Series`. -Alternatively, these errors could emerge due to overwriting Python keywords like `bool` and `sum` that may be used in the autograder tests, similar to what's described [here](https://ds100.org/debugging-guide/pandas/pandas.html#typeerror-___-object-is-not-callable). You should follow a similar procedure of identifying the line of code erroring, checking if you've overwritten any Python keywords using Ctrl+F, and renaming those variables to something more informative before restarting your kernel and running the erroring tests again. +Alternatively, these errors could emerge due to overwriting Python keywords like `bool` and `sum` that may be used in the autograder tests, similar to what's described [here](#typeError-object-not-callable). You should follow a similar procedure of identifying the line of code erroring, checking if you've overwritten any Python keywords using Ctrl+F, and renaming those variables to something more informative before restarting your kernel and running the erroring tests again. ### `ValueError: Can only compare identically-labeled Series objects` As the message would suggest, this error occurs when comparing two `Series` objects that have different lengths. You can double check the lengths of the `Series` using `len(series_name)` or `series_name.size`. diff --git a/projA1/projA1.md b/content/projA1.md similarity index 90% rename from projA1/projA1.md rename to content/projA1.md index 3883dcd..d5d42ec 100644 --- a/projA1/projA1.md +++ b/content/projA1.md @@ -1,17 +1,12 @@ --- title: Project A1 Common Questions -format: - html: - toc-location: right - code-fold: false -jupyter: python3 --- ## Question 6 ### `TypeError: could not convert string to float: 'SF'` -Type errors like these usually stem from applying a numeric aggregation function to a non-numeric column as described in the `pandas` [çsection](https://ds100.org/debugging-guide/pandas/pandas.html#typeerror-could-not-convert-string-to-numeric) of the debugging guide. +Type errors like these usually stem from applying a numeric aggregation function to a non-numeric column as described in the `pandas` [section](#convert-str-float) of the debugging guide. Aggregation functions like `np.median` and `np.mean` are only well-defined for columns with numeric types like `int` and `float`. Your code is likely trying to aggregate across all columns in `training_data`, including those of type `str`. Instead of aggregating across the entire `DataFrame`, try just selecting the relevant columns. diff --git a/projA2/projA2.md b/content/projA2.md similarity index 88% rename from projA2/projA2.md rename to content/projA2.md index 26656ab..2d8e7fb 100644 --- a/projA2/projA2.md +++ b/content/projA2.md @@ -1,9 +1,5 @@ --- title: Project A2 Common Questions -format: - html: - code-fold: false -jupyter: python3 --- ## Questions 5d and 5f @@ -20,9 +16,12 @@ Question 5 is a challenging question that mirrors a lot of data science work in Your model is likely overfitting to the training data and does not generalize to the test set. Recall the bias-variance tradeoff discussed in lecture. As you add more features and make your model more complex, it is expected that your training error will decrease. Your validation and test error may also decrease initially, but if your model is too complex, you end up with high validation and test RMSE. -
Plot showing a conceptual understanding of underfitting and overfitting. Model complexity is on the x-axis and Error/Variance is on the y axis. As you increase model complexity, Variance increases and training error decreases. Above the curves that represent Variance and Training Error, a third curve labeled Validtion Error exists in a U-shape. The chosen complexity level is represented by a dashed vertical line that intersections with the lowest y-value for the Validation Error curve. To the left of the dashed line is underfitting and to the right of the dashed line is overfitting.)
+```{image} images/under_overfit.png +:width: 500 +:alt: Plot showing a conceptual understanding of underfitting and overfitting. Model complexity is on the x-axis and Error/Variance is on the y axis. As you increase model complexity, Variance increases and training error decreases. Above the curves that represent Variance and Training Error, a third curve labeled Validtion Error exists in a U-shape. The chosen complexity level is represented by a dashed vertical line that intersections with the lowest y-value for the Validation Error curve. To the left of the dashed line is underfitting and to the right of the dashed line is overfitting. +``` -To decrease model complexity, consider visualizing the relationship between the features you've chosen with the `(Log) Sale Price` and removing features that are not highly correlated. Removing outliers can also help your model generalize better and prevent it from fitting to noise in the data. Methods like cross-validation allow you to get a better sense of where you lie along the validation error curve. Feel free to take a look at the [code used in Lecture 16](https://ds100.org/fa24/resources/assets/lectures/lec16/lec16.html) if you're confused on how to implement cross-validation. +To decrease model complexity, consider visualizing the relationship between the features you've chosen with the `(Log) Sale Price` and removing features that are not highly correlated. Removing outliers can also help your model generalize better and prevent it from fitting to noise in the data. Methods like cross-validation allow you to get a better sense of where you lie along the validation error curve. Feel free to take a look at this [code](https://ds100.org/fa24/resources/assets/lectures/lec16/lec16.html) if you're confused on how to implement cross-validation. ### `ValueError: Per-column arrays must each be 1-dimensional` @@ -104,7 +103,7 @@ Check the return type of all functions you end up using. For example, `np.append Note that in the function body, the skeleton code includes: -``` +```python # DO NOT MODIFY THESE TWO LINES if subset_df.shape[0] == 0: return -1 @@ -116,7 +115,7 @@ The above two lines of code are included to avoid dividing by 0 when computing ` ### I don't have many Gradescope/Pensieve submissions left -If you're almost out of Gradescope submissions, try using k-fold cross-validation to check the accuracy of your model. Results from cross-validation will be closer to the test set accuracy than results from the training data. Feel free to take a look at the [code used in Lecture 16](https://ds100.org/fa24/resources/assets/lectures/lec16/lec16.html) if you're confused on how to implement cross-validation. +If you're almost out of Gradescope submissions, try using k-fold cross-validation to check the accuracy of your model. Results from cross-validation will be closer to the test set accuracy than results from the training data. Feel free to take a look at this [code](https://ds100.org/fa24/resources/assets/lectures/lec16/lec16.html) if you're confused on how to implement cross-validation. ### "Wrong number of lines ( ** instead of ** )" @@ -124,7 +123,7 @@ This occurs when you remove outliers when preprocessing the testing data. _Pleas ### My code runs locally, but it fails for every question after a certain point -See [Why do I see multiple NameError: name `___` is not defined errors for all questions after a certain point on Gradescope/Pensieve?](https://ds100.org/debugging-guide/autograder_gradescope/autograder_gradescope.html#why-do-i-see-multiple-nameerror-name-___-is-not-defined-errors-for-all-questions-after-a-certain-point-on-gradescopepensieve) +See [Why do I see multiple NameError: name `___` is not defined errors for all questions after a certain point on Gradescope/Pensieve?](#multiple-nameError) - ### `Cannot compare values of type __ and type __` This error often occurs when a string-type column (ie. `CHAR` or `VARCHAR`) is compared with a numeric-type column (ie. `INT`, `BIGINT`, `FLOAT`, `REAL`). If you're interested in learning more about SQL datatypes, check out [this link](https://www.w3schools.com/sql/sql_datatypes.asp)! To debug, start by checking the type of the column(s) you're working with; you can either scroll up to the cell (or make a new cell) with the code: -``` +```python %%sql SELECT * FROM sqlite_master WHERE type='table'; ``` which outputs the schema for each table. If we're working with the IMDB database, we get the following output: -
Schema for the IMDB database
- +```{image} images/imdb_schema.png +:width: 700 +:alt: Schema for the IMDB database +``` We can see that the table `Title` contains the columns diff --git a/visualizations/visualizations.md b/content/visualizations.md similarity index 82% rename from visualizations/visualizations.md rename to content/visualizations.md index 5bcadb7..d196fc0 100644 --- a/visualizations/visualizations.md +++ b/content/visualizations.md @@ -1,29 +1,26 @@ --- title: Visualizations -format: - html: - toc-location: right - code-fold: false -jupyter: python3 --- Visualizations are how data scientists use to communicate their insights to the world. In the process of making a [good visualization](https://ds100.org/course-notes/visualization_2/visualization_2.html#harnessing-context), be it while adding a legend or setting the x-axis label, we may run into some errors. Let's take a look at how to resolve them below! - ### My legend’s labels don’t match up / my legend isn’t displaying properly If you simply add `plt.legend()` after your plotting line of code, you should see a legend; seaborn will sometimes automatically populate the legend. However, if you’re plotting multiple lines or sets of points on a single plot, the labels in the legend may not correctly line up with what’s shown. Make sure to pass in the `label` argument into the `sns` plotting function with the label you want associated with that individual plot. For example, -``` +```python sns.histplot(means_arr, label = 'simulated values') # informative label name plt.title('Simulated values') plt.plot(original, 10, 'bo', label = 'original test statistic') # informative label name plt.legend(loc = 'upper left') # can specify location of legend ``` -
Histogram titled 'Simulated values of correlated coefficients'. The original test statistic is shown in the center of the distribution which ranges from just over 0.1 to just under 0.3. The distribution is roughly normal.
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+ +```{image} images/example_label_plot.png +:width: 500 +:alt: Histogram titled 'Simulated values of correlated coefficients'. The original test statistic is shown in the center of the distribution which ranges from just over 0.1 to just under 0.3. The distribution is roughly normal. +``` ## The y-axis of my `histplot` shows the count, not the density @@ -32,7 +29,7 @@ Look into the `sns.histplot` [documentation](https://seaborn.pydata.org/generate ## I’m having trouble labeling the axes/title of my graph To label the axes and title of a graph, we use the following syntax: -``` +```python plt.xlabel(“x axis name”) plt.ylabel(“y axis name”) plt.title(“graph title”) @@ -42,12 +39,13 @@ Where `plt.xlabel`, `plt.ylabel`, and `plt.title` are matplotlib functions that However, we often see students use the following incorrect syntax to try and label their plot: -``` +```python plt.xlabel = "x name" plt.ylabel = "y name" plt.title = "graph title" ``` -Now, instead of `plt.xlabel`, `plt.ylabel`, and `plt.title` being functions, they are strings. Trying to call one of the labelling function using the correct syntax in the next few cells (ie.` plt.xlabel(“x name”)`) will result in a `TypeError: str object is not callable`. If this happens to you, comb through your notebook and look for places when you used the incorrect syntax. After fixing it, [restart your kernel](https://ds100.org/debugging-guide/jupyter101/jupyter101.html#restarting-kernel) and [rerun your cells](https://ds100.org/debugging-guide/jupyter101/jupyter101.html#running-cells). + +Now, instead of `plt.xlabel`, `plt.ylabel`, and `plt.title` being functions, they are strings. Trying to call one of the labelling function using the correct syntax in the next few cells (ie.` plt.xlabel(“x name”)`) will result in a `TypeError: str object is not callable`. If this happens to you, comb through your notebook and look for places when you used the incorrect syntax. After fixing it, [restart your kernel](#restarting-kernel) and [rerun your cells](#running-cells). ## My `sns.lineplot` has an unwanted shaded region around the solid lines. >Note: the following examples are taken from `sns.lineplot`'s [documentation](https://seaborn.pydata.org/generated/seaborn.lineplot.html). @@ -63,8 +61,10 @@ Now, instead of `plt.xlabel`, `plt.ylabel`, and `plt.title` being functions, the will give us a clean plot because each year corresponds to a single "Number of Flights in May" value. -
A line plot is shown with year on the x-axis and May on the y-axis.
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+```{image} images/normal_lineplot.png +:width: 500 +:alt: A line plot is shown with year on the x-axis and May on the y-axis. +``` When each x value has multiple y values, `sns.lineplot` will automatically plot a shaded region around the solid line, where the solid line is the mean of the y values for that x value (think of a groupby on x aggregated by `.mean()` on y) and the shaded region is the 95% confidence interval (read more about confidence intervals in the [Data 8 textbook](https://inferentialthinking.com/chapters/13/3/Confidence_Intervals.html?highlight=confidence+intervals)). For example, the table @@ -80,7 +80,9 @@ When each x value has multiple y values, `sns.lineplot` will automatically plot will plot a lineplot with a shaded region. -
The same lineplot as above is shown again with the 95% confidence interval shaded in around the line itself.
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+```{image} images/shaded_lineplot.png +:width: 500 +:alt: The same lineplot as above is shown again with the 95% confidence interval shaded in around the line itself. +``` If you do not want the shaded region, aggregate the data such that there is only one y-value for a given x-value; then, make the plot. \ No newline at end of file diff --git a/docs/assets/images/data100_logo.png b/docs/assets/images/data100_logo.png deleted file mode 100644 index b30e64d..0000000 Binary files a/docs/assets/images/data100_logo.png and /dev/null differ diff --git a/docs/autograder_gradescope/autograder_error.png b/docs/autograder_gradescope/autograder_error.png deleted file mode 100644 index f8a068d..0000000 Binary files a/docs/autograder_gradescope/autograder_error.png and /dev/null differ diff --git a/docs/autograder_gradescope/autograder_error_annot.png b/docs/autograder_gradescope/autograder_error_annot.png deleted file mode 100644 index b7b442e..0000000 Binary files a/docs/autograder_gradescope/autograder_error_annot.png and /dev/null differ diff --git a/docs/autograder_gradescope/autograder_gradescope.html b/docs/autograder_gradescope/autograder_gradescope.html deleted file mode 100644 index 53777d0..0000000 --- a/docs/autograder_gradescope/autograder_gradescope.html +++ /dev/null @@ -1,781 +0,0 @@ - - - - - - - - - -Autograder and Gradescope / Pensieve – Data 100 Debugging Guide - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Autograder

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Understanding autograder error messages

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When you pass a test, you’ll see a nice message and a cute emoji!

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When you don’t, however, the message can be a little confusing.

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The best course of action is to find the test case that failed and use that as a starting point to debug your code.

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In the example above, we see that the test case in green, max_swing in set(bus['name']), is not passing. The actual output (in blue) is often hard to parse, so the best course of action is to:

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  1. Make a new (temporary) cell after the grader.check(...) cell. Please do not make a new cell in between the given code cell and the grader.check(...) cell, as it could mess with the results.

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  2. Copy and paste the failing test case into your temporary cell and run it.

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    2. If it’s not giving you an error, it’ll likely give you an output like False. This means that your code does not cause an error (yay!), but it returns an incorrect output. In these casses, inspect each individual element of the test case. The example above checks if max_swing is in set(bus['name']), so it might be a good idea to display both variables and do a visual check.
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  3. After your grader.check(...) passes, feel free to delete the temporary cell.

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Why do I get an error saying “grader is not defined”?

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If it has been a while since you’ve worked on an assignment, the kernel will shut itself down to preserve memory. When this happens, all of your variables are forgotten, including the grader. That’s OK. The easiest way to fix this is by restarting your kernel and rerunning all the cells. To do this, in the top left menu, click Kernel -> Restart and Run All Cells.

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I’m positive I have the right answer, but the test fails. Is there a mistake in the test?

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While you might see the correct answer displayed as the result of the cell, chances are your solution isn’t being stored in the answer variable. Make sure you are assigning the result to the answer variable and that there are no typos in the variable name. Finally, restart your kernel and run all the cells in order: Kernel -> Restart and Run All Cells.

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Why does the last grader.export cell fail if all previous tests passed?

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This can happen if you “overwrite” a variable that is used in a question. For instance, say Question 1 asks you to store your answer in a variable named stat and, later on in the notebook, you change the value of stat; the test right after Question 1 will pass, but the test at the end of the notebook will fail. It is good programming practice to give your variables informative names and to avoid repeating the same variable name for more than one purpose.

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Why does a notebook test fail now when it passed before, and I didn’t change my code?

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You probably ran your notebook out of order. Re-run all previous cells in order, which is how your code will be graded.

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Gradescope/Pensieve

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When submitting to Gradescope or Pensieve, there are often unexpected errors that make students lose more points than expected. Thus, it is imperative that you stay on the submission page until the autograder finishes running, and the results are displayed.

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Why did a Gradescope/Pensieve test fail when all the Jupyter notebook’s tests passed?

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This can happen if you’re running your notebook’s cells out of order. The autograder runs your notebook from top-to-bottom. If you’re defining a variable at the bottom of your notebook and using it at the top, the Gradescope/Pensieve autograder will fail because it doesn’t recognize the variable when it encounters it.

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Another scenario where this can happen is if you defined a variable in an earlier cell but later changed its name or deleted it. While your current session’s kernel will “remember” the variable, the Gradescope/Pensieve autograder will not, because it runs the notebook from top to bottom with no variables pre-stored. As a result, it will not recognize the variable if it has been deleted or renamed, and will throw an error.

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The third scenario where this can happen is if you do not save your notebook before running the final export cell, which generates a zip file containing your notebook and the necessary files. If you run the export cell without saving, the autograder will not have access to the most recent version of your notebook, which can lead to unexpected errors. It will rely on the state of the notebook at the time of the last save, so any changes made afterward will not be reflected in the autograder’s execution.

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This is why we recommend first saving and then going into the top left menu and clicking Kernel -> Restart -> Run All. The autograder “forgets” all of the variables and runs the notebook from top-to-bottom like the Gradescope/Pensieve autograder does. This will highlight any issues.

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Find the first cell that raises an error. Make sure that all of the variables used in that cell have been defined above that cell, and not below.

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Why do I get a NameError: name ___ is not defined when I run a grader check?

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This happens when you try to access a variable that has not been defined yet. Since the autograder runs all the cells in-order, if you happened to define a variable in a cell further down and accessed it before that cell, the autograder will likely throw this error. Another reason this could occur is because the notebook was not saved before the autograder tests are run. When in doubt, it is good practice to restart your kernel, run all the cells again, and save the notebook before running the cell that causes this error.

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Why do I see multiple NameError: name ___ is not defined errors for all questions after a certain point on Gradescope/Pensieve?

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This can happen if you import external packages when you are not instructed to do so. The base image on Gradescope/Pensieve includes only a specific list of pre-installed packages, defined by the course staff in the autograder. If you import a package that is not on this list, it will not be available to the autograder, even if you download the package correctly on your local Jupyter notebook environment. As a result, it will first encounter a ModuleNotFoundError when trying to import the package (though this error may not be shown to you), and then a NameError for every question afterward. Although this behavior is unintuitive, it’s a limitation of the autograder—only course staff can view certain output messages from your notebook to prevent cheating. If you encounter this issue, review your notebook’s imports and ensure you are not using any packages not included in the autograder environment.

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My autograder keeps running/timed out

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If your Gradescope submission page has been stuck running on this page for a while:

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it means that the Gradescope autograder failed to execute in the expected amount of time. TThis could be due to an inefficiency in your code or an issue on Gradescope’s end, so we recommend resubmitting and allowing the autograder to rerun. If the issue persists after a few attempts, you may need to investigate your code for inefficiencies.

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For example, if you rerun all cells in your Jupyter notebook and notice that some cells take significantly longer to run than others, you might need to optimize those cells. Keep in mind that the time it takes to run all tests in your Jupyter notebook is not equivalent to the time it takes on Gradescope/Pensieve. However, it is proportional—if your notebook runs slowly on Datahub, it will likely run even more slowly on Gradescope/Pensieve, as the difference in runtime is amplified.

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About

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This text offers pointers for keyboard shortcuts or common mistakes that accompany the coursework in the Summer 2025 Edition of the UC Berkeley course Data 100: Principles and Techniques of Data Science.

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Inspiration for this guide was taken from the UC San Diego course DSC 10: Principles of Data Science and their debugging guide.

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If you spot any typos or would like to suggest any changes, please email us at data100.instructors@berkeley.edu.

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In alphabetical order, Yash Dave, Emrie Loh, Lillian Weng, and Wesley Zheng.

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For the following commands, make sure you’re in command mode. You can enter this mode by pressing esc.

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Saving your notebook

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Jupyter autosaves your work, but there can be a delay. As such, it’s a good idea to save your work as often as you remember and especially before submitting assignments. To do so, press ctrl + s.

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In the menu bar on the left, click Kernel. From here, you have several options. The ones we use most commonly are:

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Many IDEs like VSCode have a functionality that automatically closes brackets. For example, pressing (, {, or [ would automatically add the second bracket at the other end ), }, and ], respectively. Datahub does not have this functionality turned on by default, but you can do so by going into Settings -> Auto Close Brackets. If you see a check mark to the left of Auto Close Brackets, then it’s enabled.

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Jupyterhub connects you to an external container to run your code. That connection could be slow/severed because:

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If this does not fix the issue, it could be a problem with your code, usually the last cell that executed before your kernel crashed. Double check your logic, and feel free to make a private post on Ed if you’re stuck!

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We set some cells to read-only mode prevent accidental modification. That said, do not modify the given cells unless instructed to do so. If instructed, to make the cell writeable,

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If you double-click on a text (markdown) cell, it’ll appear in its raw format. To fix this, simply run the cell. If this doesn’t fix the problem, check out the commonly asked question below.

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My text cell changed to a code cell / My code cell changed to a text cell

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Sometimes, a text (markdown) cell was changed to a code cell, or a code cell can’t be run because it’s been changed to a text (markdown) or raw cell. To fix this, toggle the desired cell type in the top bar.

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Why does running a particular cell cause my kernel to die?

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If one particular cell seems to cause your kernel to die, this is likely because the computer is trying to use more memory than it has available. For instance: your code is trying to create a gigantic array. To prevent the entire server from crashing, the kernel will “die”. This is an indication that there is a mistake in your code that you need to fix.

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I accidentally deleted something in a cell that was provided to me – how do I get it back?

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Suppose you’re working on Lab 5. One solution is to go directly to DataHub and rename your lab05 folder to something else, like lab05-old. Then, click the Lab 5 link on the course website again, and it’ll bring you to a brand-new version of Lab 5. You can then copy your work from your old Lab 5 to this new one, which should have the original version of the assignment.

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Alternatively, you can access this public repo and navigate to a blank copy of the assignment you were working on. In the case of Lab 5 for example, the notebook would be located at lab/lab05/lab05.ipynb. You can then check and copy over the contents of the deleted cell into a new cell in your existing notebook.

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“Click here to download zip file” is not working

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When this happens, you can download the zip file through the menu on the left.

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Right click on the generated zip file and click “Download”.

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Autograder could not locate my PDF

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Sometimes when running the grader.export(run_tests=True) cell at the end of the notebook, you run into an error where the autograder could not locate the PDF:

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To fix this, make sure you have not accidentally converted any cells to raw type. All cells should either be markdown or code cells. You can easily convert cells by following these instructions earlier in the debugging guide.

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I can’t export my assignment as a PDF due to a LatexFailed error

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Occasionally when running the grader.export(run_tests=True) cell at the end of the notebook, you run into an error where the PDF failed to generate:

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-Screenshot of a latex error that begins with 'LatexFailed: PDF creating failed, captured latex output:...' -
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Converting a Jupyter notebook to a PDF involves formatting some of the markdown text in LaTeX. However, this process will fail if your free response answers have (unresolved) LaTeX characters like \n, $, or $$. There are several ways to resolve this:

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  1. Export the notebook as a PDF: In the upper left hand menu, go to File -> Save and Export Notebook As -> PDF. Upload this file to Gradescope under the “Submit PDF” option.
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  2. Print the notebook from HTML: In the upper left hand menu, go to File -> Save and Export Notebook As -> HTML. In the new tab that will open up, print the website by typing ctrl + p (Windows) or cmd + p (Mac).
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  3. Take screenshots: If you’re short on time, your best bet is to take screenshots of your free response answers. When submitting to Gradescope, choose the “Submit Images” options instead of the “Submit PDF” option.
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  4. Removing special LaTeX characters: If you have more time and would like the Datahub-generated PDF, please remove any special LaTeX characters from your free response answers.
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If you use an alternate form of submission listed above, you don’t need to worry if you can’t select pages or if the selection doesn’t align. We’ll manually look through your submission when grading, and will account for that.

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I can’t open Jupyter: HTTP ERROR 431

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If this happens, try clearing your browser cache or opening Datahub in an incognito window.

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Datahub is not loading

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If your link to Datahub is not loading, go to https://data100.datahub.berkeley.edu/hub/home and restart your server.

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Pandas

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Understanding pandas errors

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pandas errors can look red, scary, and very long. Fortunately, we don’t need to understand the entire thing! The most important parts of an error message are at the top, which tells you which line of code is causing the issue, and at the bottom, which tells you exactly what the error message is.

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-A screenshot of an error output by running a pandas command. The screenshot is annotated, with the first annotation reading 'The arrow points to the line of code that is erroring' next to a green arrow in the error output. Also highlighted and annotated is the last line of the error message, 'The most useful error messages are usually at the bottom' -
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This note is (mostly) structured around the error messages that show up at the bottom.

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My code is taking a really long time to run

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It is normal for a cell to take a few seconds – sometimes a few minutes – to run. If it’s is taking too long, however, you have several options:

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  1. Try restarting the kernel. Sometimes, Datahub glitches or lags, causing the code to run slower than expected. Restarting the kernel should fix this problem, but if the cell is still taking a while to run, it is likely a problem with your code.
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Why is it generally better avoid using loops or list comprehensions when possible?

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In one word: performance. NumPy and pandas functions are optimized to handle large amounts of data in an efficient manner. Even for simple operations, like the elementwise addition of two arrays, NumPy arrays are much faster and scale better (feel free to experiment with this yourself using %%time). This is why we encourage you to vectorize your code (ie. using NumPy arrays, Series, or DataFrames instead of Python lists) and use in-built NumPy/pandas functions wherever possible.

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KeyErrors

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KeyError: 'column_name'

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This error usually happens when we have a DataFame called df, and we’re trying to do an operation on a column 'column_name' that does not exist. If you encounter this error, double check that you’re operating on the right column. It might be a good idea to display df and see what it looks like. You could also call df.columns to list all the columns in df.

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TypeErrors

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TypeError: '___' object is not callable

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This often happens when you use a default keyword (like str, list, range, bool, sum, or max) as a variable name, for instance:

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sum = 1 + 2 + 3
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These errors can be tricky because they don’t error on their own but cause problems when we try to use the name sum (for example) later on in the notebook.

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To fix the issue, identify any such lines of code (Ctrl+F on “sum =” for example), change your variable names to be something more informative, and restart your notebook.

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Python keywords like str and list appear in green text, so be on the lookout if any of your variable names appear in green!

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TypeError: could not convert string to a float

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This error often occurs when we try to do math operations (ie. sum, average, min, max) on a DataFrame column or Series that contains strings instead of numbers (note that we can do math operations with booleans; Python treats True as 1 and False as 0).

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Double check that the column you’re interested in is a numerical type (int, float, or double). If it looks like a number, but you’re still getting this error, you can use .astype(...) (documentation) to change the datatype of a DataFrame or Series.

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TypeError: Could not convert <string> to numeric

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Related to the above (but distinct), you may run into this error when performing a numeric aggregation function (like mean or sum functions that take integer arguments) after doing a groupby operation on a DataFrame with non-numeric columns.

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Working with the elections dataset for example,

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elections.groupby('Year').agg('mean')
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would error because pandas cannot compute the mean of the names of presidents. There are three ways to get around this:

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  1. Select only the numeric columns you are interested in before applying the aggregation function. In the above case, both elections.groupby('Year')['Popular Vote'] or elections['Popular vote'].groupby('Year') would work.
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  2. Setting the numeric_only argument to True in the .agg call, thereby applying the aggregation function only to numeric columns. For example, elections.groupby('Year').agg('mean', numeric_only=True).
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  3. Passing in a dictionary to .agg where you specify the column you are applying a particular aggregation function to. Continuing the same example, this looks like elections.groupby('Year').agg({'Popular vote' : 'mean').
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TypeError: 'NoneType' object is not subscriptable / AttributeError: 'NoneType' object has no attribute 'shape'

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This usually occurs as you assign a None value to a variable, then try to either index into or access some attribute of that variable. For Python functions like append and extend, you do not need to do any variable assignment because they mutate the variable directly and return None. Assigning None tends to happen as a result of code like:

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some_list = some_list.append(element)
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In contrast, an operation like np.append does not mutate the variable in place and, instead, returns a copy. In these cases, (re)assignment is necessary:

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some_array = np.append(some_array, element)
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TypeError: 'int'/'float' object is not subscriptable

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This occurs when you try and index into an integer or other numeric Python data type. It can be confusing to debug amidst a muddle of code, but you can use the error message to identify which variable is causing this error. Using type(var_name) to check the data type of the variable in question can be a good starting point.

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IndexErrors

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IndexError: invalid index to scalar variable.

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This error is similar to the last TypeError in the previous section. However, it is slightly different in that scalar variables come up in the context of NumPy data types which have slightly different attributes.

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For a concrete example, if you defined

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numpy_arr = np.array([1])
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and indexed into it twice (numpy_arr[0][0]), you would get the above error. Unlike a Python integer whose type is int, type(numpy_arr[0]) returns the NumPy version of an integer, numpy.int64. Additionally, you can check the data type by accessing the .dtype attribute of NumPy array (numpy_arr.dtype) or scalar variable (numpy_arr[0].dtype).

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IndexError: index _ is out of bounds for axis _ with size _

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This error usually happens when you try to index a value that’s greater than the size of the array/list/DataFrame/Series. For example,

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some_list = [2, 4, 6, 8]
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some_list has a length of 4. Trying some_list[6] will error because index 6 is greater than the length of the array. Note that some_list[4] will also cause an IndexError because Python and pandas uses zero indexing, which means that the first element has index 0, the second element has index 1, etc.; some_list[4] would grab the fifth element, which is impossible when the list only has 4 elements.

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ValueErrors

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ValueError: Truth value of a Series is ambiguous

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This error could occur when you apply Python logical operators (or, and, not), which only operate on a single boolean values, to NumPy arrays or Series objects, which can contain multiple values. The fix is to use bitwise operators |, &, ~ , respectively, to allow for element-wise comparisons between values in arrays or Series.

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Alternatively, these errors could emerge due to overwriting Python keywords like bool and sum that may be used in the autograder tests, similar to what’s described here. You should follow a similar procedure of identifying the line of code erroring, checking if you’ve overwritten any Python keywords using Ctrl+F, and renaming those variables to something more informative before restarting your kernel and running the erroring tests again.

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ValueError: Can only compare identically-labeled Series objects

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As the message would suggest, this error occurs when comparing two Series objects that have different lengths. You can double check the lengths of the Series using len(series_name) or series_name.size.

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ValueError: -1 is not in range / KeyError: -1

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This error occurs when you try and index into a Series or DataFrame as you would a Python list. Unlike a list where passing an index of -1 gives the last element, pandas interprets df[-1] as an attempt to find the row corresponding to index -1 (that is, df.loc[-1]). If your intention is to pick out the last row in df, consider using integer-position based indexing by doing df.iloc[-1]. In general, to avoid ambiguity in these cases, it is also good practice to write out both the row and column indices you want with df.iloc[-1, :].

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Question 6

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TypeError: could not convert string to float: 'SF'

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Type errors like these usually stem from applying a numeric aggregation function to a non-numeric column as described in the pandas çsection of the debugging guide.

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Aggregation functions like np.median and np.mean are only well-defined for columns with numeric types like int and float. Your code is likely trying to aggregate across all columns in training_data, including those of type str. Instead of aggregating across the entire DataFrame, try just selecting the relevant columns.

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TypeError: unhashable type: 'Series'

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This error can occur if you try and use Python’s in to check whether values in a Series are contained in a list. If you’re trying to perform boolean filtering in this manner, you should look into the .isin (documentation) function as introduced in HW 2.

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Question 7

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I’m not sure how to use sklearn to do One Hot Encoding

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A good starting point is to revisit the One Hot Encoding question in Lab 7. It’s recommended you look through this portion of the walkthrough, so you have a good understanding of how to use the OneHotEncoder object. Pay attention to what each variable represents and the expected outputs of the functions used. Can you map the logic from the lab to this project? A nice way to start is to make a new cell and experiment with examples from the documentation.

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My OHE columns contain a lot of NaN values

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This may happen if you try and merge the OHE columns with the training_data table without making sure both DataFrame have the same index values. Look into the pd.merge documentation for ways to resolve this.

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Questions 5d and 5f

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General Debugging Tips

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Question 5 is a challenging question that mirrors a lot of data science work in the real world: cleaning, exploring, and transforming data; fitting a model, working with a pre-defined pipeline and evaluating your model’s performance. Here are some general debugging tips to make the process easier:

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  • Separate small tasks into helper functions, especially if you will execute them multiple times. For example, a helper function that one-hot encodes a categorical variable may be helpful as you could perform it on multiple such columns. If you’re parsing a column with RegEx, it also might be a good idea to separate it to a helper function. This allows you to verify that you’re not making errors in these small tasks and prevents unknown bugs from appearing.
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My training RMSE is low, but my validation/test RMSE is high

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Your model is likely overfitting to the training data and does not generalize to the test set. Recall the bias-variance tradeoff discussed in lecture. As you add more features and make your model more complex, it is expected that your training error will decrease. Your validation and test error may also decrease initially, but if your model is too complex, you end up with high validation and test RMSE.

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-Plot showing a conceptual understanding of underfitting and overfitting. Model complexity is on the x-axis and Error/Variance is on the y axis. As you increase model complexity, Variance increases and training error decreases. Above the curves that represent Variance and Training Error, a third curve labeled Validtion Error exists in a U-shape. The chosen complexity level is represented by a dashed vertical line that intersections with the lowest y-value for the Validation Error curve. To the left of the dashed line is underfitting and to the right of the dashed line is overfitting.) -
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To decrease model complexity, consider visualizing the relationship between the features you’ve chosen with the (Log) Sale Price and removing features that are not highly correlated. Removing outliers can also help your model generalize better and prevent it from fitting to noise in the data. Methods like cross-validation allow you to get a better sense of where you lie along the validation error curve. Feel free to take a look at the code used in Lecture 16 if you’re confused on how to implement cross-validation.

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ValueError: Per-column arrays must each be 1-dimensional

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If you’re passing the tests for question 5d but getting this error in question 5f, then your Y variable is likely a DataFrame, not a Series. sklearn models like LinearRegression expect X to be a 2D datatype (ie. DataFrame, 2D NumPy array) and Y to be a 1D datatype (ie. Series, 1D NumPy array).

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KeyError: 'Sale Price'/KeyError: 'Log Sale Price'

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KeyErrors are raised when a column name does not exist in your DataFrame. You could be getting this error because:

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Value Error: could not convert string to float

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This error occurs if your final design matric contains non-numeric columns. For example, if you simply run X = data.drop(columns = ["Log Sale Price", "Sale Price"]), all the non-numeric columns of data are still included in X and you will see this error message. The fit function of a lm.LinearRegression object can take a pandas DataFrame as the X argument, but requires that the DataFrame is only composed of numeric values.

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ValueError: Input X contains infinity or a value too large for dtype('float64')

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The reason why your X data contains infinity is likely because you are taking the logarithm of 0 somewhere in your code. To prevent this, try:

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ValueError: Input X contains NaN

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The reason why your design matrix X contains NaN values is likely because you take the log of a negative number somewhere in your code. To prevent this, try:

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ValueError: The feature names should match those that were passed during fit

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This error is followed by one or both of the following:

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Feature names unseen at fit time:
-- FEATURE NAME 1
-- FEATURE NAME 2
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-- FEATURE NAME 1
-- FEATURE NAME 2
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This error occurs if the columns/features you’re passing in for the test dataset aren’t the same as the features you used to train the model. sklearn’s models expect the testing data’s column names to match the training data’s. The features listed under Feature names unseen at fit time are columns that were present in the training data but not the testing data, and features listed under Feature names seen at fit time, yet now missing were present in the testing data but not the training data.

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Potential causes for this error:

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  • Your preprocessing for X is different for training and testing. Double-check your code in feature_engine_final! Besides removing any references to 'Sale Price' and code that would remove rows from the test set, your preprocessing should be the same.

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-- Data100_A
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ValueError: operands could not be broadcast together with shapes ...

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This error occurs when you attempt to perform an operation on two NumPy arrays with mismatched dimensions. For example, np.ones(100000) - np.ones(1000000) is not defined since you cannot perform elementwise addition on arrays with different lengths. Use the error traceback to identify which line is erroring, and print out the shape of the arrays on the line before using .shape.

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TypeError: NoneType is not subscriptable

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This error occurs when a NoneType variable is being accessed like a class, for example None.some_function(). It may be difficult to identify where the NoneType is coming from, but here are some possible causes:

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  • Check that your helper functions always end with a return statement and that the result is expected!
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Check the return type of all functions you end up using. For example, np.append(arr_1, arr_2) returns a NumPy array. In contrast, Python’s .append function mutates the iterable and returns None. If you are unsure of what data type is being returned, looking up the documentation of the function , adding print statements, using type(some_function(input) are all useful ways to debug your code.

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Question 6

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I’m getting negative values for the prop_overest plot

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Note that in the function body, the skeleton code includes:

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# DO NOT MODIFY THESE TWO LINES
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The above two lines of code are included to avoid dividing by 0 when computing prop_overest in the case that subset_df does not have any rows. When interpreting the plots, you can disregard the negative portions as you know they are invalid values corresponding to this edge case. You can verify this by observing that your RMSE plot does not display the corresponding intervals.

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Gradescope/Pensieve

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I don’t have many Gradescope/Pensieve submissions left

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If you’re almost out of Gradescope submissions, try using k-fold cross-validation to check the accuracy of your model. Results from cross-validation will be closer to the test set accuracy than results from the training data. Feel free to take a look at the code used in Lecture 16 if you’re confused on how to implement cross-validation.

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“Wrong number of lines ( ** instead of ** )”

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This occurs when you remove outliers when preprocessing the testing data. Please do not remove any outliers from your test set. You may only remove outliers in training data.

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My code runs locally, but it fails for every question after a certain point

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See Why do I see multiple NameError: name ___ is not defined errors for all questions after a certain point on Gradescope/Pensieve?

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RegEx syntax can be incredibly confusing, so we highly encourage using sources like the Data 100 Exam reference sheet (you can find this under the “Exam Resources” section on our Resources page) or websites like regex101.com to help build your understanding.

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How to Interpret regex101

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Regex101 is a great tool that helps you visually interact with RegEx patterns. Let’s take a look at its components with a simple example.

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Example 1: Basic

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  1. Flavor: Regular expressions work slightly differently depending on the programming language you use. In Data 100, we only use the Python flavor. By default, regex101 opens on the PCRE2 flavor, so make sure to change to Python before experimenting.
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  2. Regular Expression: This is where the RegEx expression goes. For this example, our pattern is Data 100. In Python, we denote it as a string r"Data 100" with the prefix r to indicate that this is a RegEx expression, not a normal Python string. In regex101, because we changed to the Python flavor, we don’t need to type out the r" at the start or the " at the end, as that’s already set up for us.
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  3. Explanation: This portion of the website explains each component of the pattern above. Since it does not contain any special characters, Data 100 will match any portion of a string containing Data 100.
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  4. Test String: This is where you can try out different inputs and see if they match the RegEx pattern. Of the 4 example sentences, we see that only the first sentence contains characters that match the pattern, highlighted in blue. (Note that while sentence 3 does contain data 100, RegEx is sensitive to capitalization. d and D are different characters)
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Example 2: Greedy

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For this example, let’s replace the 100 in our original expression with \d+ so that our pattern is Data \d+

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\d and + are both special operators, and the explanation on the top right (boxed in red) tells us what they do:

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  • \d matches digits, or any number between 0 and 9. It’s equivalent to [0-9].
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  • + matches the previous token \(\geq 1\) times. It is a greedy operation, meaning it will match as many characters as possible.
    • -
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Altogether, the expression \d+ will match any digit one or more times. Look at each match under “Match Information”. Can you see why they align with Data \d+?

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Example 3: Capturing Groups

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Let’s say we’re given a body of text with dates formatted as DD/Month/YYYY (ie. 04/Jan/2014), and we’re interested in extracting the dates. An expression like r"\d+\/\w+\/\d+" would match any string with the DD/Month/YYYY format:

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  • the first \d+ matches DD patterns (ie. 04)
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  • \/ matches the / separator. Since / is a special operator in RegEx, we need to escape it with \ to get the literal character.
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  • \w+ in the middle matches Month patterns we’re interested in (ie. Jan, January)
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  • lastly, \d+ matches YYYY patterns (ie. 2014)
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That’s great! This pattern will match the entirety of DD/Month/YYYY, but what if we want to access DD individually? What about YYYY? This is where capturing groups comes in handy. Capturing groups are RegEx expressions surrounded by parenthesis () that are used to remember the text they match so that it can be referenced later. Putting capturing groups around \d+ and \w+ to get r"(\d+)\/(\w+)\/(\d+)"gives us the following:

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  • The “Explanation” section now shows an explanation for each of the 3 capturing groups.
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  • In our test strings, the portion matching the RegEx expression is highlighted in blue per usual. Additionally, each capturing group is highlighted with a particular color: green, orange, and purple.
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  • These colored highlights correspond to their match/group under “Match Information”. “Match #” (light blue) shows the entire portion that matches the expression while “Group #” shows the match per group.
    • -
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How do I access captured groups?

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To access each group, we use the following syntax:

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target_string = "Today's date is 01/March/2024."
-result = re.search(r"(\d+)\/(\w+)\/(\d+)", target_string)
-
-result # re.Match object
-result.groups() # all captured groups: ('01', 'March', '2024')
-result.group(0) # '01/March/2024', the full match
-result.group(1) # '01', the first captured group
-result.group(2) # 'March', the second captured group
-result.group(3) # '2024', the third captured group
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RegEx Misconceptions & General Errors

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I’m certain my RegEx pattern in .str.replace is correct, but I’m not passing the grader check.

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Here’s the skeleton from the exam reference sheet:

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s.str.replace(pat, repl, regex=False)

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Notice how the regex= argument has a default value of False, causing pandas to treat pat like a normal Python string. Make sure to set regex=True if you’re using RegEx!

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My RegEx pattern matches the test cases on regex101, but is not working in pandas with .str.findall / .str.extractall / .str.extract.

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The most likely reason for this is forgetting to include the r before the string with your regular expression. Without including the r in .str.findall(r".*"), pandas will not interpret your pattern as a regex expression and will only match it literally.

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Value Error: pattern contains no capture groups

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These errors usually occur when using s.str.extract or s.str.extractall. Read more about it in the RegEx course notes. This error means that your RegEx pattern does not match anything in the given Series of strings. To debug this, try putting your pattern into regex101.com and use example strings from the Series as test cases.

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When do I need to escape characters?

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The special characters in RegEx are: . ^ $ * + ? ] [ \ | ( ) { } }

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If you want to match exactly those characters in a RegEx expression, you need to “escape” them by preceding them with a backslash \. However, the rules around this can change in the context of character classes.

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For example, the pattern r"[.]" matches '.', the literal period. In this context, it is not treated as a special character. The hyphen, while not included in the list of special characters, also changes its behavior depending on its position in a character class. It can be used to specify a range of characters (e.g. r"[0-9]") based on their Unicode values, or match a literal '-' if it does not have two adjacent characters (e.g. r"[-09]" matches -, 0, 9). To be on the safer side, you could escape - like in r"[0\-9]" to achieve the same result.

-

Finally, it’s generally good practice to escape both single and double quotes for greater readability. Technically, patterns like r"'(.\*)'" and r'"(.\*)"' do work as you’d expect, but you can already see how confusing it is to decipher what’s going on. Escaping the quotes inside the pattern does not affect what matches you get, but makes it easier to figure out what the intended match was.

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The three uses of ^

-

The ^ character can be tricky to wrap your head around given how its function changes depending on the context:

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    -
  1. If used at the start of a pattern, like in r"^\w", it means that a lowercase letter must begin the string in order for a match to occur.
    2. -
  2. If included at the start of a character class, like in r"[^abc]", it negates all the characters in that class and will match with any other character – in the above example, any character that is not a, b, c.
    3. -
  3. Finally, if escaped as in r"\^" it is treated as a literal and will match any instance of ^.
    4. -
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What’s the difference between all the re functions?

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The exam reference sheets give a few re functions, but how can you determine which one to use?

-
-Screenshot of the exam reference sheet showing re.match(), re.search(), re.findall(), and re.sub() -
-


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re.match and re.search only return one instance of a match between string and pattern (or None if there’s no match) - re.match only considers characters at the beginning of a string - re.search considers characters anywhere in the string - For example: ``` pattern = r”Data 100” example1 = “Data 100 is the best!” example2 = “I love Data 100!”

-

re.match(pattern, example1).group(0) # matches “Data 100” re.match(pattern, example2) # does not match “Data 100” because it’s not at the beginning of a string; returns None

-

re.search(pattern, example1).group(0) # matches “Data 100” re.search(pattern, example2).group(0) # matches “Data 100” ```

-

If, instead, you’re interested in finding all matches between the given string and pattern, re.findall will find them all, returning the matches in a list.

-
re.findall(r'\d+', 'Data 100, Data 8, Data 101') 
-# returns a list of strings: ['100', '8', '101']
-
-re.findall(r'\d+', 'Data science is great')  
-# no matches found, returns empty list: []
-

re.sub will find them all and replace it with a string of your choice.

-
re.sub(r'\d+', 'panda', 'Data 100, Data 8, Data 101') 
-# returns 'Data panda, Data panda, Data panda'
-
-re.sub(r'\d+', 'panda', 'Data science is great')  
-# no matches found, returns the original string "Data science is great"
-
-
-

What’s the difference between re functions and pd.Series.str functions?

-

Generally, all the pd.Series.str functions are used when you want to apply a Python or RegEx string function to a Series of strings. In contrast, re functions are applied to string objects. The reference sheet gives a great overview of the different use cases of each of the pd.Series.str functions.

- - -
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- - -
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Refer to the image below if you need help identifying the most important parts (the red and blue text).\n\n\n\nSometimes, SQL errors may show up as a long string without any red highlight, for example:\n(duckdb.duckdb.BinderException) Binder Error: Referenced column \"some_column\" not found in FROM clause! LINE 1: SELECT some_column ^ ...\nNote that this is the same error as the one above – it’s just not formatted nicely. 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Note that this error could be formatted as one long string without any indents or newlines, so it may be harder than typical Python errors to parse.\n\n\n\nReferenced column \"{some_string}\" not found\nThere are 2 potential causes for this error:\n\nThe column you’re looking for does not exist in the table. Make sure that the column names are spelled and capatalized exactly as shown in the assignment. If you are trying to reference a column that is not in the current table, consider using JOIN to be able to access these columns. You can reference the SQL II course notes for a refresher on how to use the JOIN clause.\nYou used double quotes \"\" around some_string instead of single quotes ''. Unlike Python, SQL differentiates double and single quotes: double quotes indicate something that exists within the database, like tables and column names; single quotes indicate strings.\n\n\n\n\nCannot compare values of type __ and type __\nThis error often occurs when a string-type column (ie. 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The actual output (in blue) is often hard to parse, so the best course of action is to:\n\nMake a new (temporary) cell after the grader.check(...) cell. Please do not make a new cell in between the given code cell and the grader.check(...) cell, as it could mess with the results.\nCopy and paste the failing test case into your temporary cell and run it.\n\nIf it’s giving you an error like in the example above, look at the last line of the error and use the Debugging Guide’s search functionality in the top left menu to find the corresponding guide.\nIf it’s not giving you an error, it’ll likely give you an output like False. This means that your code does not cause an error (yay!), but it returns an incorrect output. In these casses, inspect each individual element of the test case. The example above checks if max_swing is in set(bus['name']), so it might be a good idea to display both variables and do a visual check.\nIf you’re still having issues, post on Ed!\n\nAfter your grader.check(...) passes, feel free to delete the temporary cell.\n\n\n\nWhy do I get an error saying “grader is not defined”?\nIf it has been a while since you’ve worked on an assignment, the kernel will shut itself down to preserve memory. When this happens, all of your variables are forgotten, including the grader. That’s OK. The easiest way to fix this is by restarting your kernel and rerunning all the cells. To do this, in the top left menu, click Kernel -> Restart and Run All Cells.\n\n\nI’m positive I have the right answer, but the test fails. Is there a mistake in the test?\nWhile you might see the correct answer displayed as the result of the cell, chances are your solution isn’t being stored in the answer variable. Make sure you are assigning the result to the answer variable and that there are no typos in the variable name. Finally, restart your kernel and run all the cells in order: Kernel -> Restart and Run All Cells.\n\n\nWhy does the last grader.export cell fail if all previous tests passed?\nThis can happen if you “overwrite” a variable that is used in a question. For instance, say Question 1 asks you to store your answer in a variable named stat and, later on in the notebook, you change the value of stat; the test right after Question 1 will pass, but the test at the end of the notebook will fail. It is good programming practice to give your variables informative names and to avoid repeating the same variable name for more than one purpose.\n\n\nWhy does a notebook test fail now when it passed before, and I didn’t change my code?\nYou probably ran your notebook out of order. Re-run all previous cells in order, which is how your code will be graded.", - "crumbs": [ - "Autograder and Gradescope / Pensieve" - ] - }, - { - "objectID": "autograder_gradescope/autograder_gradescope.html#gradescopepensieve", - "href": "autograder_gradescope/autograder_gradescope.html#gradescopepensieve", - "title": "Autograder and Gradescope / Pensieve", - "section": "Gradescope/Pensieve", - "text": "Gradescope/Pensieve\nWhen submitting to Gradescope or Pensieve, there are often unexpected errors that make students lose more points than expected. Thus, it is imperative that you stay on the submission page until the autograder finishes running, and the results are displayed.\n\nWhy did a Gradescope/Pensieve test fail when all the Jupyter notebook’s tests passed?\nThis can happen if you’re running your notebook’s cells out of order. The autograder runs your notebook from top-to-bottom. If you’re defining a variable at the bottom of your notebook and using it at the top, the Gradescope/Pensieve autograder will fail because it doesn’t recognize the variable when it encounters it.\nAnother scenario where this can happen is if you defined a variable in an earlier cell but later changed its name or deleted it. While your current session’s kernel will “remember” the variable, the Gradescope/Pensieve autograder will not, because it runs the notebook from top to bottom with no variables pre-stored. As a result, it will not recognize the variable if it has been deleted or renamed, and will throw an error.\nThe third scenario where this can happen is if you do not save your notebook before running the final export cell, which generates a zip file containing your notebook and the necessary files. If you run the export cell without saving, the autograder will not have access to the most recent version of your notebook, which can lead to unexpected errors. It will rely on the state of the notebook at the time of the last save, so any changes made afterward will not be reflected in the autograder’s execution.\nThis is why we recommend first saving and then going into the top left menu and clicking Kernel -> Restart -> Run All. The autograder “forgets” all of the variables and runs the notebook from top-to-bottom like the Gradescope/Pensieve autograder does. This will highlight any issues.\nFind the first cell that raises an error. Make sure that all of the variables used in that cell have been defined above that cell, and not below.\n\n\nWhy do I get a NameError: name ___ is not defined when I run a grader check?\nThis happens when you try to access a variable that has not been defined yet. Since the autograder runs all the cells in-order, if you happened to define a variable in a cell further down and accessed it before that cell, the autograder will likely throw this error. Another reason this could occur is because the notebook was not saved before the autograder tests are run. When in doubt, it is good practice to restart your kernel, run all the cells again, and save the notebook before running the cell that causes this error.\n\n\nWhy do I see multiple NameError: name ___ is not defined errors for all questions after a certain point on Gradescope/Pensieve?\nThis can happen if you import external packages when you are not instructed to do so. The base image on Gradescope/Pensieve includes only a specific list of pre-installed packages, defined by the course staff in the autograder. If you import a package that is not on this list, it will not be available to the autograder, even if you download the package correctly on your local Jupyter notebook environment. As a result, it will first encounter a ModuleNotFoundError when trying to import the package (though this error may not be shown to you), and then a NameError for every question afterward. Although this behavior is unintuitive, it’s a limitation of the autograder—only course staff can view certain output messages from your notebook to prevent cheating. If you encounter this issue, review your notebook’s imports and ensure you are not using any packages not included in the autograder environment.\n\n\nMy autograder keeps running/timed out\nIf your Gradescope submission page has been stuck running on this page for a while:\n\n\n\nor if it times out:\n\n\n\nit means that the Gradescope autograder failed to execute in the expected amount of time. TThis could be due to an inefficiency in your code or an issue on Gradescope’s end, so we recommend resubmitting and allowing the autograder to rerun. If the issue persists after a few attempts, you may need to investigate your code for inefficiencies.\nFor example, if you rerun all cells in your Jupyter notebook and notice that some cells take significantly longer to run than others, you might need to optimize those cells. Keep in mind that the time it takes to run all tests in your Jupyter notebook is not equivalent to the time it takes on Gradescope/Pensieve. However, it is proportional—if your notebook runs slowly on Datahub, it will likely run even more slowly on Gradescope/Pensieve, as the difference in runtime is amplified.\nIt is your responsibility to ensure that the autograder runs properly, and if it still fails, you should follow up by making a private Ed post.", - "crumbs": [ - "Autograder and Gradescope / Pensieve" - ] - }, - { - "objectID": "regex/regex.html", - "href": "regex/regex.html", - "title": "RegEx", - "section": "", - "text": "RegEx syntax can be incredibly confusing, so we highly encourage using sources like the Data 100 Exam reference sheet (you can find this under the “Exam Resources” section on our Resources page) or websites like regex101.com to help build your understanding.", - "crumbs": [ - "RegEx" - ] - }, - { - "objectID": "regex/regex.html#how-to-interpret-regex101", - "href": "regex/regex.html#how-to-interpret-regex101", - "title": "RegEx", - "section": "How to Interpret regex101", - "text": "How to Interpret regex101\nRegex101 is a great tool that helps you visually interact with RegEx patterns. Let’s take a look at its components with a simple example.\n\nExample 1: Basic\n\n\n\n\n\nFlavor: Regular expressions work slightly differently depending on the programming language you use. In Data 100, we only use the Python flavor. By default, regex101 opens on the PCRE2 flavor, so make sure to change to Python before experimenting.\nRegular Expression: This is where the RegEx expression goes. For this example, our pattern is Data 100. In Python, we denote it as a string r\"Data 100\" with the prefix r to indicate that this is a RegEx expression, not a normal Python string. In regex101, because we changed to the Python flavor, we don’t need to type out the r\" at the start or the \" at the end, as that’s already set up for us.\nExplanation: This portion of the website explains each component of the pattern above. Since it does not contain any special characters, Data 100 will match any portion of a string containing Data 100.\nTest String: This is where you can try out different inputs and see if they match the RegEx pattern. Of the 4 example sentences, we see that only the first sentence contains characters that match the pattern, highlighted in blue. (Note that while sentence 3 does contain data 100, RegEx is sensitive to capitalization. d and D are different characters)\nMatch Information: Each match between the RegEx expression and test strings is shown here.\n\n\n\nExample 2: Greedy\nFor this example, let’s replace the 100 in our original expression with \\d+ so that our pattern is Data \\d+\n\n\n\n\n\\d and + are both special operators, and the explanation on the top right (boxed in red) tells us what they do:\n\n\\d matches digits, or any number between 0 and 9. It’s equivalent to [0-9].\n+ matches the previous token \\(\\geq 1\\) times. It is a greedy operation, meaning it will match as many characters as possible.\n\nAltogether, the expression \\d+ will match any digit one or more times. Look at each match under “Match Information”. Can you see why they align with Data \\d+?\n\n\nExample 3: Capturing Groups\nLet’s say we’re given a body of text with dates formatted as DD/Month/YYYY (ie. 04/Jan/2014), and we’re interested in extracting the dates. An expression like r\"\\d+\\/\\w+\\/\\d+\" would match any string with the DD/Month/YYYY format:\n\nthe first \\d+ matches DD patterns (ie. 04)\n\\/ matches the / separator. Since / is a special operator in RegEx, we need to escape it with \\ to get the literal character.\n\\w+ in the middle matches Month patterns we’re interested in (ie. Jan, January)\nlastly, \\d+ matches YYYY patterns (ie. 2014)\n\nThat’s great! This pattern will match the entirety of DD/Month/YYYY, but what if we want to access DD individually? What about YYYY? This is where capturing groups comes in handy. Capturing groups are RegEx expressions surrounded by parenthesis () that are used to remember the text they match so that it can be referenced later. Putting capturing groups around \\d+ and \\w+ to get r\"(\\d+)\\/(\\w+)\\/(\\d+)\"gives us the following:\n\n\n\n\n\nThe “Explanation” section now shows an explanation for each of the 3 capturing groups.\nIn our test strings, the portion matching the RegEx expression is highlighted in blue per usual. Additionally, each capturing group is highlighted with a particular color: green, orange, and purple.\nThese colored highlights correspond to their match/group under “Match Information”. “Match #” (light blue) shows the entire portion that matches the expression while “Group #” shows the match per group.\n\n\nHow do I access captured groups?\nTo access each group, we use the following syntax:\ntarget_string = \"Today's date is 01/March/2024.\"\nresult = re.search(r\"(\\d+)\\/(\\w+)\\/(\\d+)\", target_string)\n\nresult # re.Match object\nresult.groups() # all captured groups: ('01', 'March', '2024')\nresult.group(0) # '01/March/2024', the full match\nresult.group(1) # '01', the first captured group\nresult.group(2) # 'March', the second captured group\nresult.group(3) # '2024', the third captured group", - "crumbs": [ - "RegEx" - ] - }, - { - "objectID": "regex/regex.html#regex-misconceptions-general-errors", - "href": "regex/regex.html#regex-misconceptions-general-errors", - "title": "RegEx", - "section": "RegEx Misconceptions & General Errors", - "text": "RegEx Misconceptions & General Errors\n\nI’m certain my RegEx pattern in .str.replace is correct, but I’m not passing the grader check.\nHere’s the skeleton from the exam reference sheet:\ns.str.replace(pat, repl, regex=False)\nNotice how the regex= argument has a default value of False, causing pandas to treat pat like a normal Python string. Make sure to set regex=True if you’re using RegEx!\n\n\nMy RegEx pattern matches the test cases on regex101, but is not working in pandas with .str.findall / .str.extractall / .str.extract.\nThe most likely reason for this is forgetting to include the r before the string with your regular expression. Without including the r in .str.findall(r\".*\"), pandas will not interpret your pattern as a regex expression and will only match it literally.\n\n\nValue Error: pattern contains no capture groups\nThese errors usually occur when using s.str.extract or s.str.extractall. Read more about it in the RegEx course notes. This error means that your RegEx pattern does not match anything in the given Series of strings. To debug this, try putting your pattern into regex101.com and use example strings from the Series as test cases.\n\n\nWhen do I need to escape characters?\nThe special characters in RegEx are: . ^ $ * + ? ] [ \\ | ( ) { } }\nIf you want to match exactly those characters in a RegEx expression, you need to “escape” them by preceding them with a backslash \\. However, the rules around this can change in the context of character classes.\nFor example, the pattern r\"[.]\" matches '.', the literal period. In this context, it is not treated as a special character. The hyphen, while not included in the list of special characters, also changes its behavior depending on its position in a character class. It can be used to specify a range of characters (e.g. r\"[0-9]\") based on their Unicode values, or match a literal '-' if it does not have two adjacent characters (e.g. r\"[-09]\" matches -, 0, 9). To be on the safer side, you could escape - like in r\"[0\\-9]\" to achieve the same result.\nFinally, it’s generally good practice to escape both single and double quotes for greater readability. Technically, patterns like r\"'(.\\*)'\" and r'\"(.\\*)\"' do work as you’d expect, but you can already see how confusing it is to decipher what’s going on. Escaping the quotes inside the pattern does not affect what matches you get, but makes it easier to figure out what the intended match was.\n\n\nThe three uses of ^\nThe ^ character can be tricky to wrap your head around given how its function changes depending on the context:\n\nIf used at the start of a pattern, like in r\"^\\w\", it means that a lowercase letter must begin the string in order for a match to occur.\nIf included at the start of a character class, like in r\"[^abc]\", it negates all the characters in that class and will match with any other character – in the above example, any character that is not a, b, c.\nFinally, if escaped as in r\"\\^\" it is treated as a literal and will match any instance of ^.\n\n\n\nWhat’s the difference between all the re functions?\nThe exam reference sheets give a few re functions, but how can you determine which one to use?\n\n\n\n\nre.match and re.search only return one instance of a match between string and pattern (or None if there’s no match) - re.match only considers characters at the beginning of a string - re.search considers characters anywhere in the string - For example: ``` pattern = r”Data 100” example1 = “Data 100 is the best!” example2 = “I love Data 100!”\nre.match(pattern, example1).group(0) # matches “Data 100” re.match(pattern, example2) # does not match “Data 100” because it’s not at the beginning of a string; returns None\nre.search(pattern, example1).group(0) # matches “Data 100” re.search(pattern, example2).group(0) # matches “Data 100” ```\nIf, instead, you’re interested in finding all matches between the given string and pattern, re.findall will find them all, returning the matches in a list.\nre.findall(r'\\d+', 'Data 100, Data 8, Data 101') \n# returns a list of strings: ['100', '8', '101']\n\nre.findall(r'\\d+', 'Data science is great') \n# no matches found, returns empty list: []\nre.sub will find them all and replace it with a string of your choice.\nre.sub(r'\\d+', 'panda', 'Data 100, Data 8, Data 101') \n# returns 'Data panda, Data panda, Data panda'\n\nre.sub(r'\\d+', 'panda', 'Data science is great') \n# no matches found, returns the original string \"Data science is great\" \n\n\nWhat’s the difference between re functions and pd.Series.str functions?\nGenerally, all the pd.Series.str functions are used when you want to apply a Python or RegEx string function to a Series of strings. In contrast, re functions are applied to string objects. The reference sheet gives a great overview of the different use cases of each of the pd.Series.str functions.", - "crumbs": [ - "RegEx" - ] - }, - { - "objectID": "jupyter_datahub/jupyter_datahub.html", - "href": "jupyter_datahub/jupyter_datahub.html", - "title": "Jupyter / Datahub", - "section": "", - "text": "Jupyterhub connects you to an external container to run your code. That connection could be slow/severed because:\n\nyou haven’t made any changes to the notebook for a while\na cell took too much time to run\na cell took up too many resources to compute\n\nWhen you see a message like this:\n\n\n\n\nEither press the “Ok” button or reload the page\nRestart your kernel\nRerun your cells\n\nNote that you may lose some recent work if your kernel restarted when you were in the middle of editing a cell. As such, we recommend saving your work as often as possible.\nIf this does not fix the issue, it could be a problem with your code, usually the last cell that executed before your kernel crashed. Double check your logic, and feel free to make a private post on Ed if you’re stuck!", - "crumbs": [ - "Jupyter / Datahub" - ] - }, - { - "objectID": "jupyter_datahub/jupyter_datahub.html#my-kernel-died-restarted-or-is-very-slow", - "href": "jupyter_datahub/jupyter_datahub.html#my-kernel-died-restarted-or-is-very-slow", - "title": "Jupyter / Datahub", - "section": "", - "text": "Jupyterhub connects you to an external container to run your code. That connection could be slow/severed because:\n\nyou haven’t made any changes to the notebook for a while\na cell took too much time to run\na cell took up too many resources to compute\n\nWhen you see a message like this:\n\n\n\n\nEither press the “Ok” button or reload the page\nRestart your kernel\nRerun your cells\n\nNote that you may lose some recent work if your kernel restarted when you were in the middle of editing a cell. As such, we recommend saving your work as often as possible.\nIf this does not fix the issue, it could be a problem with your code, usually the last cell that executed before your kernel crashed. Double check your logic, and feel free to make a private post on Ed if you’re stuck!", - "crumbs": [ - "Jupyter / Datahub" - ] - }, - { - "objectID": "jupyter_datahub/jupyter_datahub.html#i-cant-edit-a-cell", - "href": "jupyter_datahub/jupyter_datahub.html#i-cant-edit-a-cell", - "title": "Jupyter / Datahub", - "section": "I can’t edit a cell", - "text": "I can’t edit a cell\nWe set some cells to read-only mode prevent accidental modification. That said, do not modify the given cells unless instructed to do so. If instructed, to make the cell writeable,\n\nClick the cell\nClick setting on the top right corner\nUnder “Common Tools”, you can toggle between “Editable” (can edit the cell) and “Read-Only” (cannot edit the cell)", - "crumbs": [ - "Jupyter / Datahub" - ] - }, - { - "objectID": "jupyter_datahub/jupyter_datahub.html#my-text-cell-looks-like-code", - "href": "jupyter_datahub/jupyter_datahub.html#my-text-cell-looks-like-code", - "title": "Jupyter / Datahub", - "section": "My text cell looks like code", - "text": "My text cell looks like code\nIf you double-click on a text (markdown) cell, it’ll appear in its raw format. To fix this, simply run the cell. If this doesn’t fix the problem, check out the commonly asked question below.", - "crumbs": [ - "Jupyter / Datahub" - ] - }, - { - "objectID": "jupyter_datahub/jupyter_datahub.html#my-text-cell-changed-to-a-code-cell-my-code-cell-changed-to-a-text-cell", - "href": "jupyter_datahub/jupyter_datahub.html#my-text-cell-changed-to-a-code-cell-my-code-cell-changed-to-a-text-cell", - "title": "Jupyter / Datahub", - "section": "My text cell changed to a code cell / My code cell changed to a text cell", - "text": "My text cell changed to a code cell / My code cell changed to a text cell\nSometimes, a text (markdown) cell was changed to a code cell, or a code cell can’t be run because it’s been changed to a text (markdown) or raw cell. To fix this, toggle the desired cell type in the top bar.", - "crumbs": [ - "Jupyter / Datahub" - ] - }, - { - "objectID": "jupyter_datahub/jupyter_datahub.html#why-does-running-a-particular-cell-cause-my-kernel-to-die", - "href": "jupyter_datahub/jupyter_datahub.html#why-does-running-a-particular-cell-cause-my-kernel-to-die", - "title": "Jupyter / Datahub", - "section": "Why does running a particular cell cause my kernel to die?", - "text": "Why does running a particular cell cause my kernel to die?\nIf one particular cell seems to cause your kernel to die, this is likely because the computer is trying to use more memory than it has available. For instance: your code is trying to create a gigantic array. To prevent the entire server from crashing, the kernel will “die”. This is an indication that there is a mistake in your code that you need to fix.", - "crumbs": [ - "Jupyter / Datahub" - ] - }, - { - "objectID": "jupyter_datahub/jupyter_datahub.html#i-accidentally-deleted-something-in-a-cell-that-was-provided-to-me-how-do-i-get-it-back", - "href": "jupyter_datahub/jupyter_datahub.html#i-accidentally-deleted-something-in-a-cell-that-was-provided-to-me-how-do-i-get-it-back", - "title": "Jupyter / Datahub", - "section": "I accidentally deleted something in a cell that was provided to me – how do I get it back?", - "text": "I accidentally deleted something in a cell that was provided to me – how do I get it back?\nSuppose you’re working on Lab 5. One solution is to go directly to DataHub and rename your lab05 folder to something else, like lab05-old. Then, click the Lab 5 link on the course website again, and it’ll bring you to a brand-new version of Lab 5. You can then copy your work from your old Lab 5 to this new one, which should have the original version of the assignment.\nAlternatively, you can access this public repo and navigate to a blank copy of the assignment you were working on. In the case of Lab 5 for example, the notebook would be located at lab/lab05/lab05.ipynb. You can then check and copy over the contents of the deleted cell into a new cell in your existing notebook.", - "crumbs": [ - "Jupyter / Datahub" - ] - }, - { - "objectID": "jupyter_datahub/jupyter_datahub.html#click-here-to-download-zip-file-is-not-working", - "href": "jupyter_datahub/jupyter_datahub.html#click-here-to-download-zip-file-is-not-working", - "title": "Jupyter / Datahub", - "section": "“Click here to download zip file” is not working", - "text": "“Click here to download zip file” is not working\nWhen this happens, you can download the zip file through the menu on the left.\n\n\n\nRight click on the generated zip file and click “Download”.", - "crumbs": [ - "Jupyter / Datahub" - ] - }, - { - "objectID": "jupyter_datahub/jupyter_datahub.html#autograder-could-not-locate-my-pdf", - "href": "jupyter_datahub/jupyter_datahub.html#autograder-could-not-locate-my-pdf", - "title": "Jupyter / Datahub", - "section": "Autograder could not locate my PDF", - "text": "Autograder could not locate my PDF\nSometimes when running the grader.export(run_tests=True) cell at the end of the notebook, you run into an error where the autograder could not locate the PDF:\n\n\n\nTo fix this, make sure you have not accidentally converted any cells to raw type. All cells should either be markdown or code cells. You can easily convert cells by following these instructions earlier in the debugging guide.", - "crumbs": [ - "Jupyter / Datahub" - ] - }, - { - "objectID": "jupyter_datahub/jupyter_datahub.html#i-cant-export-my-assignment-as-a-pdf-due-to-a-latexfailed-error", - "href": "jupyter_datahub/jupyter_datahub.html#i-cant-export-my-assignment-as-a-pdf-due-to-a-latexfailed-error", - "title": "Jupyter / Datahub", - "section": "I can’t export my assignment as a PDF due to a LatexFailed error", - "text": "I can’t export my assignment as a PDF due to a LatexFailed error\nOccasionally when running the grader.export(run_tests=True) cell at the end of the notebook, you run into an error where the PDF failed to generate:\n\n\n\nConverting a Jupyter notebook to a PDF involves formatting some of the markdown text in LaTeX. However, this process will fail if your free response answers have (unresolved) LaTeX characters like \\n, $, or $$. There are several ways to resolve this:\n\nExport the notebook as a PDF: In the upper left hand menu, go to File -> Save and Export Notebook As -> PDF. Upload this file to Gradescope under the “Submit PDF” option.\nPrint the notebook from HTML: In the upper left hand menu, go to File -> Save and Export Notebook As -> HTML. In the new tab that will open up, print the website by typing ctrl + p (Windows) or cmd + p (Mac).\nTake screenshots: If you’re short on time, your best bet is to take screenshots of your free response answers. When submitting to Gradescope, choose the “Submit Images” options instead of the “Submit PDF” option.\nRemoving special LaTeX characters: If you have more time and would like the Datahub-generated PDF, please remove any special LaTeX characters from your free response answers.\n\nIf you use an alternate form of submission listed above, you don’t need to worry if you can’t select pages or if the selection doesn’t align. We’ll manually look through your submission when grading, and will account for that.", - "crumbs": [ - "Jupyter / Datahub" - ] - }, - { - "objectID": "jupyter_datahub/jupyter_datahub.html#i-cant-open-jupyter-http-error-431", - "href": "jupyter_datahub/jupyter_datahub.html#i-cant-open-jupyter-http-error-431", - "title": "Jupyter / Datahub", - "section": "I can’t open Jupyter: HTTP ERROR 431", - "text": "I can’t open Jupyter: HTTP ERROR 431\nIf this happens, try clearing your browser cache or opening Datahub in an incognito window.", - "crumbs": [ - "Jupyter / Datahub" - ] - }, - { - "objectID": "jupyter_datahub/jupyter_datahub.html#datahub-is-not-loading", - "href": "jupyter_datahub/jupyter_datahub.html#datahub-is-not-loading", - "title": "Jupyter / Datahub", - "section": "Datahub is not loading", - "text": "Datahub is not loading\nIf your link to Datahub is not loading, go to https://data100.datahub.berkeley.edu/hub/home and restart your server.", - "crumbs": [ - "Jupyter / Datahub" - ] - }, - { - "objectID": "jupyter101/jupyter101.html", - "href": "jupyter101/jupyter101.html", - "title": "Jupyter 101", - "section": "", - "text": "Note\n\n\n\n\n\nIf you’re using a MacBook, replace ctrl with cmd.", - "crumbs": [ - "Jupyter 101" - ] - }, - { - "objectID": "jupyter101/jupyter101.html#shortcuts-for-cells", - "href": "jupyter101/jupyter101.html#shortcuts-for-cells", - "title": "Jupyter 101", - "section": "Shortcuts for Cells", - "text": "Shortcuts for Cells\nFor the following commands, make sure you’re in command mode. You can enter this mode by pressing esc.\n\na: create a cell above\nb: create a cell below\ndd: delete current cell\nm: convert a cell to markdown (text cell)\ny: convert a cell to code", - "crumbs": [ - "Jupyter 101" - ] - }, - { - "objectID": "jupyter101/jupyter101.html#running-cells", - "href": "jupyter101/jupyter101.html#running-cells", - "title": "Jupyter 101", - "section": "Running Cells", - "text": "Running Cells\nFor individual cells,\n\nctrl + return: run the current cell\nshift + return: run the current cell and move to the next cell\n\nTo run all cells in a notebook:\n\nIn the menu bar on the left, click Run. From here, you have several options. The ones we use most commonly are:\n\nRun All Above Selected Cell: this runs every cell above the selected cell\nRun Selected Cell and All Below: this runs the selected cell and all cells below\nRun All: this runs every cell in the notebook from top-to-bottom", - "crumbs": [ - "Jupyter 101" - ] - }, - { - "objectID": "jupyter101/jupyter101.html#saving-your-notebook", - "href": "jupyter101/jupyter101.html#saving-your-notebook", - "title": "Jupyter 101", - "section": "Saving your notebook", - "text": "Saving your notebook\nJupyter autosaves your work, but there can be a delay. As such, it’s a good idea to save your work as often as you remember and especially before submitting assignments. To do so, press ctrl + s.", - "crumbs": [ - "Jupyter 101" - ] - }, - { - "objectID": "jupyter101/jupyter101.html#restarting-kernel", - "href": "jupyter101/jupyter101.html#restarting-kernel", - "title": "Jupyter 101", - "section": "Restarting Kernel", - "text": "Restarting Kernel\nIn the menu bar on the left, click Kernel. From here, you have several options. The ones we use most commonly are:\n\nRestart Kernel...\nRestart Kernel and Run up to Selected Cell\nRestart Kernel and Run All Cells", - "crumbs": [ - "Jupyter 101" - ] - }, - { - "objectID": "jupyter101/jupyter101.html#automatically-closing-brackets", - "href": "jupyter101/jupyter101.html#automatically-closing-brackets", - "title": "Jupyter 101", - "section": "Automatically Closing Brackets", - "text": "Automatically Closing Brackets\nMany IDEs like VSCode have a functionality that automatically closes brackets. For example, pressing (, {, or [ would automatically add the second bracket at the other end ), }, and ], respectively. Datahub does not have this functionality turned on by default, but you can do so by going into Settings -> Auto Close Brackets. If you see a check mark to the left of Auto Close Brackets, then it’s enabled.", - "crumbs": [ - "Jupyter 101" - ] - }, - { - "objectID": "projA2/projA2.html", - "href": "projA2/projA2.html", - "title": "Project A2 Common Questions", - "section": "", - "text": "Question 5 is a challenging question that mirrors a lot of data science work in the real world: cleaning, exploring, and transforming data; fitting a model, working with a pre-defined pipeline and evaluating your model’s performance. Here are some general debugging tips to make the process easier:\n\nSeparate small tasks into helper functions, especially if you will execute them multiple times. For example, a helper function that one-hot encodes a categorical variable may be helpful as you could perform it on multiple such columns. If you’re parsing a column with RegEx, it also might be a good idea to separate it to a helper function. This allows you to verify that you’re not making errors in these small tasks and prevents unknown bugs from appearing.\nFeel free to make new cells to play with the data! As long as you delete them afterward, it will not affect the autograder.\nThe feature_engine_final function looks daunting at first, but start small. First, try and implement a model with a single feature to get familiar with how the pipeline works, then slowly experiment with adding one feature at a time and see how that affects your training RMSE.\n\n\n\n\nYour model is likely overfitting to the training data and does not generalize to the test set. Recall the bias-variance tradeoff discussed in lecture. As you add more features and make your model more complex, it is expected that your training error will decrease. Your validation and test error may also decrease initially, but if your model is too complex, you end up with high validation and test RMSE.\n\n\n\nTo decrease model complexity, consider visualizing the relationship between the features you’ve chosen with the (Log) Sale Price and removing features that are not highly correlated. Removing outliers can also help your model generalize better and prevent it from fitting to noise in the data. Methods like cross-validation allow you to get a better sense of where you lie along the validation error curve. Feel free to take a look at the code used in Lecture 16 if you’re confused on how to implement cross-validation.\n\n\n\nIf you’re passing the tests for question 5d but getting this error in question 5f, then your Y variable is likely a DataFrame, not a Series. sklearn models like LinearRegression expect X to be a 2D datatype (ie. DataFrame, 2D NumPy array) and Y to be a 1D datatype (ie. Series, 1D NumPy array).\n\n\n\nKeyErrors are raised when a column name does not exist in your DataFrame. You could be getting this error because:\n\nThe test set does not contain a \"(Log) Sale Price\" as that’s what we’re trying to predict. Make sure you only reference the \"(Log) Sale Price\" column when working with training data (is_test_set=False).\nYou dropped the \"Sale Price\" column twice in your preprocessing code.\n\n\n\n\nThis error occurs if your final design matric contains non-numeric columns. For example, if you simply run X = data.drop(columns = [\"Log Sale Price\", \"Sale Price\"]), all the non-numeric columns of data are still included in X and you will see this error message. The fit function of a lm.LinearRegression object can take a pandas DataFrame as the X argument, but requires that the DataFrame is only composed of numeric values.\n\n\n\nThe reason why your X data contains infinity is likely because you are taking the logarithm of 0 somewhere in your code. To prevent this, try:\n\nAdding a small number to the features that you want to perform the log transformation on so that all values are positive and greater than 0. Note that whatever value you add to your train data should also be added to your test data.\nRemoving zeroes before taking the logarithm. Note that this is only possible on the training data as you cannot drop rows from the test set.\n\n\n\n\nThe reason why your design matrix X contains NaN values is likely because you take the log of a negative number somewhere in your code. To prevent this, try:\n\nShifting the range of values for features that you want to perform the logging operation on to positive values greater than 0. Note that whatever value you add to your train data should also be added to your test data.\nRemoving negative values before taking the log. Note that this is only possible on the training data as you cannot drop rows from the test set.\n\n\n\n\nThis error is followed by one or both of the following:\nFeature names unseen at fit time:\n- FEATURE NAME 1\n- FEATURE NAME 2\n ...\n\nFeature names seen at fit time, yet now missing\n- FEATURE NAME 1\n- FEATURE NAME 2\n ...\nThis error occurs if the columns/features you’re passing in for the test dataset aren’t the same as the features you used to train the model. sklearn’s models expect the testing data’s column names to match the training data’s. The features listed under Feature names unseen at fit time are columns that were present in the training data but not the testing data, and features listed under Feature names seen at fit time, yet now missing were present in the testing data but not the training data.\nPotential causes for this error:\n\nYour preprocessing for X is different for training and testing. Double-check your code in feature_engine_final! Besides removing any references to 'Sale Price' and code that would remove rows from the test set, your preprocessing should be the same.\nSome one-hot-encoded categories are present in training but not in testing (or vice versa). For example, let’s say that the feature \"Data100\" has categories “A”, “B”, “C”, and “D”. If “A”, “B”, and “C” are present in the training data, but “B”, “C”, and “D” are present in the testing data, you will get this error:\nThe feature names should match those that were passed during fit. Feature names unseen at fit time:\n- Data100_D\n ...\n\nFeature names seen at fit time, yet now missing\n- Data100_A\n\n\n\n\nThis error occurs when you attempt to perform an operation on two NumPy arrays with mismatched dimensions. For example, np.ones(100000) - np.ones(1000000) is not defined since you cannot perform elementwise addition on arrays with different lengths. Use the error traceback to identify which line is erroring, and print out the shape of the arrays on the line before using .shape.\n\n\n\nThis error occurs when a NoneType variable is being accessed like a class, for example None.some_function(). It may be difficult to identify where the NoneType is coming from, but here are some possible causes:\n\nCheck that your helper functions always end with a return statement and that the result is expected!\npandas’ inplace= argument allows us to simplify code; instead of reassigning df = df.an_operation(inplace=False), you can choose to shorten the operation as df.an_operation(inplace=True). Note that any inplace=True argument modifies the DataFrame and returns nothing (read more about it in this stack overflow post). Both df = df.an_operation(inplace=True) and df.an_operation(inplace=True).another_operation() will result in this TypeError.\n\nCheck the return type of all functions you end up using. For example, np.append(arr_1, arr_2) returns a NumPy array. In contrast, Python’s .append function mutates the iterable and returns None. If you are unsure of what data type is being returned, looking up the documentation of the function , adding print statements, using type(some_function(input) are all useful ways to debug your code.", - "crumbs": [ - "Project A2 Common Questions" - ] - }, - { - "objectID": "projA2/projA2.html#questions-5d-and-5f", - "href": "projA2/projA2.html#questions-5d-and-5f", - "title": "Project A2 Common Questions", - "section": "", - "text": "Question 5 is a challenging question that mirrors a lot of data science work in the real world: cleaning, exploring, and transforming data; fitting a model, working with a pre-defined pipeline and evaluating your model’s performance. Here are some general debugging tips to make the process easier:\n\nSeparate small tasks into helper functions, especially if you will execute them multiple times. For example, a helper function that one-hot encodes a categorical variable may be helpful as you could perform it on multiple such columns. If you’re parsing a column with RegEx, it also might be a good idea to separate it to a helper function. This allows you to verify that you’re not making errors in these small tasks and prevents unknown bugs from appearing.\nFeel free to make new cells to play with the data! As long as you delete them afterward, it will not affect the autograder.\nThe feature_engine_final function looks daunting at first, but start small. First, try and implement a model with a single feature to get familiar with how the pipeline works, then slowly experiment with adding one feature at a time and see how that affects your training RMSE.\n\n\n\n\nYour model is likely overfitting to the training data and does not generalize to the test set. Recall the bias-variance tradeoff discussed in lecture. As you add more features and make your model more complex, it is expected that your training error will decrease. Your validation and test error may also decrease initially, but if your model is too complex, you end up with high validation and test RMSE.\n\n\n\nTo decrease model complexity, consider visualizing the relationship between the features you’ve chosen with the (Log) Sale Price and removing features that are not highly correlated. Removing outliers can also help your model generalize better and prevent it from fitting to noise in the data. Methods like cross-validation allow you to get a better sense of where you lie along the validation error curve. Feel free to take a look at the code used in Lecture 16 if you’re confused on how to implement cross-validation.\n\n\n\nIf you’re passing the tests for question 5d but getting this error in question 5f, then your Y variable is likely a DataFrame, not a Series. sklearn models like LinearRegression expect X to be a 2D datatype (ie. DataFrame, 2D NumPy array) and Y to be a 1D datatype (ie. Series, 1D NumPy array).\n\n\n\nKeyErrors are raised when a column name does not exist in your DataFrame. You could be getting this error because:\n\nThe test set does not contain a \"(Log) Sale Price\" as that’s what we’re trying to predict. Make sure you only reference the \"(Log) Sale Price\" column when working with training data (is_test_set=False).\nYou dropped the \"Sale Price\" column twice in your preprocessing code.\n\n\n\n\nThis error occurs if your final design matric contains non-numeric columns. For example, if you simply run X = data.drop(columns = [\"Log Sale Price\", \"Sale Price\"]), all the non-numeric columns of data are still included in X and you will see this error message. The fit function of a lm.LinearRegression object can take a pandas DataFrame as the X argument, but requires that the DataFrame is only composed of numeric values.\n\n\n\nThe reason why your X data contains infinity is likely because you are taking the logarithm of 0 somewhere in your code. To prevent this, try:\n\nAdding a small number to the features that you want to perform the log transformation on so that all values are positive and greater than 0. Note that whatever value you add to your train data should also be added to your test data.\nRemoving zeroes before taking the logarithm. Note that this is only possible on the training data as you cannot drop rows from the test set.\n\n\n\n\nThe reason why your design matrix X contains NaN values is likely because you take the log of a negative number somewhere in your code. To prevent this, try:\n\nShifting the range of values for features that you want to perform the logging operation on to positive values greater than 0. Note that whatever value you add to your train data should also be added to your test data.\nRemoving negative values before taking the log. Note that this is only possible on the training data as you cannot drop rows from the test set.\n\n\n\n\nThis error is followed by one or both of the following:\nFeature names unseen at fit time:\n- FEATURE NAME 1\n- FEATURE NAME 2\n ...\n\nFeature names seen at fit time, yet now missing\n- FEATURE NAME 1\n- FEATURE NAME 2\n ...\nThis error occurs if the columns/features you’re passing in for the test dataset aren’t the same as the features you used to train the model. sklearn’s models expect the testing data’s column names to match the training data’s. The features listed under Feature names unseen at fit time are columns that were present in the training data but not the testing data, and features listed under Feature names seen at fit time, yet now missing were present in the testing data but not the training data.\nPotential causes for this error:\n\nYour preprocessing for X is different for training and testing. Double-check your code in feature_engine_final! Besides removing any references to 'Sale Price' and code that would remove rows from the test set, your preprocessing should be the same.\nSome one-hot-encoded categories are present in training but not in testing (or vice versa). For example, let’s say that the feature \"Data100\" has categories “A”, “B”, “C”, and “D”. If “A”, “B”, and “C” are present in the training data, but “B”, “C”, and “D” are present in the testing data, you will get this error:\nThe feature names should match those that were passed during fit. Feature names unseen at fit time:\n- Data100_D\n ...\n\nFeature names seen at fit time, yet now missing\n- Data100_A\n\n\n\n\nThis error occurs when you attempt to perform an operation on two NumPy arrays with mismatched dimensions. For example, np.ones(100000) - np.ones(1000000) is not defined since you cannot perform elementwise addition on arrays with different lengths. Use the error traceback to identify which line is erroring, and print out the shape of the arrays on the line before using .shape.\n\n\n\nThis error occurs when a NoneType variable is being accessed like a class, for example None.some_function(). It may be difficult to identify where the NoneType is coming from, but here are some possible causes:\n\nCheck that your helper functions always end with a return statement and that the result is expected!\npandas’ inplace= argument allows us to simplify code; instead of reassigning df = df.an_operation(inplace=False), you can choose to shorten the operation as df.an_operation(inplace=True). Note that any inplace=True argument modifies the DataFrame and returns nothing (read more about it in this stack overflow post). Both df = df.an_operation(inplace=True) and df.an_operation(inplace=True).another_operation() will result in this TypeError.\n\nCheck the return type of all functions you end up using. For example, np.append(arr_1, arr_2) returns a NumPy array. In contrast, Python’s .append function mutates the iterable and returns None. If you are unsure of what data type is being returned, looking up the documentation of the function , adding print statements, using type(some_function(input) are all useful ways to debug your code.", - "crumbs": [ - "Project A2 Common Questions" - ] - }, - { - "objectID": "projA2/projA2.html#question-6", - "href": "projA2/projA2.html#question-6", - "title": "Project A2 Common Questions", - "section": "Question 6", - "text": "Question 6\n\nI’m getting negative values for the prop_overest plot\nNote that in the function body, the skeleton code includes:\n# DO NOT MODIFY THESE TWO LINES\n if subset_df.shape[0] == 0:\n return -1\nThe above two lines of code are included to avoid dividing by 0 when computing prop_overest in the case that subset_df does not have any rows. When interpreting the plots, you can disregard the negative portions as you know they are invalid values corresponding to this edge case. You can verify this by observing that your RMSE plot does not display the corresponding intervals.", - "crumbs": [ - "Project A2 Common Questions" - ] - }, - { - "objectID": "projA2/projA2.html#gradescopepensieve", - "href": "projA2/projA2.html#gradescopepensieve", - "title": "Project A2 Common Questions", - "section": "Gradescope/Pensieve", - "text": "Gradescope/Pensieve\n\nI don’t have many Gradescope/Pensieve submissions left\nIf you’re almost out of Gradescope submissions, try using k-fold cross-validation to check the accuracy of your model. Results from cross-validation will be closer to the test set accuracy than results from the training data. Feel free to take a look at the code used in Lecture 16 if you’re confused on how to implement cross-validation.\n\n\n“Wrong number of lines ( ** instead of ** )”\nThis occurs when you remove outliers when preprocessing the testing data. Please do not remove any outliers from your test set. You may only remove outliers in training data.\n\n\nMy code runs locally, but it fails for every question after a certain point\nSee Why do I see multiple NameError: name ___ is not defined errors for all questions after a certain point on Gradescope/Pensieve?", - "crumbs": [ - "Project A2 Common Questions" - ] - }, - { - "objectID": "visualizations/visualizations.html", - "href": "visualizations/visualizations.html", - "title": "Visualizations", - "section": "", - "text": "Visualizations are how data scientists use to communicate their insights to the world. In the process of making a good visualization, be it while adding a legend or setting the x-axis label, we may run into some errors. Let’s take a look at how to resolve them below!", - "crumbs": [ - "Visualizations" - ] - }, - { - "objectID": "visualizations/visualizations.html#the-y-axis-of-my-histplot-shows-the-count-not-the-density", - "href": "visualizations/visualizations.html#the-y-axis-of-my-histplot-shows-the-count-not-the-density", - "title": "Visualizations", - "section": "The y-axis of my histplot shows the count, not the density", - "text": "The y-axis of my histplot shows the count, not the density\nLook into the sns.histplot documentation and see what arguments the stat parameter takes in. By default, stat=count, and the number of elements in each histogram bin is the y axis. But if you wanted to normalize the distribution such that the total area is 1, consider passing stat=density into the plot function.", - "crumbs": [ - "Visualizations" - ] - }, - { - "objectID": "visualizations/visualizations.html#im-having-trouble-labeling-the-axestitle-of-my-graph", - "href": "visualizations/visualizations.html#im-having-trouble-labeling-the-axestitle-of-my-graph", - "title": "Visualizations", - "section": "I’m having trouble labeling the axes/title of my graph", - "text": "I’m having trouble labeling the axes/title of my graph\nTo label the axes and title of a graph, we use the following syntax:\nplt.xlabel(“x axis name”) \nplt.ylabel(“y axis name”) \nplt.title(“graph title”) \nWhere plt.xlabel, plt.ylabel, and plt.title are matplotlib functions that we call.\nHowever, we often see students use the following incorrect syntax to try and label their plot:\nplt.xlabel = \"x name\"\nplt.ylabel = \"y name\"\nplt.title = \"graph title\"\nNow, instead of plt.xlabel, plt.ylabel, and plt.title being functions, they are strings. Trying to call one of the labelling function using the correct syntax in the next few cells (ie.plt.xlabel(“x name”)) will result in a TypeError: str object is not callable. If this happens to you, comb through your notebook and look for places when you used the incorrect syntax. After fixing it, restart your kernel and rerun your cells.", - "crumbs": [ - "Visualizations" - ] - }, - { - "objectID": "visualizations/visualizations.html#my-sns.lineplot-has-an-unwanted-shaded-region-around-the-solid-lines.", - "href": "visualizations/visualizations.html#my-sns.lineplot-has-an-unwanted-shaded-region-around-the-solid-lines.", - "title": "Visualizations", - "section": "My sns.lineplot has an unwanted shaded region around the solid lines.", - "text": "My sns.lineplot has an unwanted shaded region around the solid lines.\n\nNote: the following examples are taken from sns.lineplot’s documentation.\n\nsns.lineplot gives us a clean line when each x value has one y value. For example, the table\n\n\n\nYear\nMay\n\n\n\n\n1948\n120\n\n\n1949\n122\n\n\n1950\n123\n\n\n\\(\\vdots\\)\n\\(\\vdots\\)\n\n\n\nwill give us a clean plot because each year corresponds to a single “Number of Flights in May” value.\n\n\n\n\nWhen each x value has multiple y values, sns.lineplot will automatically plot a shaded region around the solid line, where the solid line is the mean of the y values for that x value (think of a groupby on x aggregated by .mean() on y) and the shaded region is the 95% confidence interval (read more about confidence intervals in the Data 8 textbook). For example, the table\n\n\n\nYear\nMay\n\n\n\n\n1948\n115\n\n\n1948\n120\n\n\n1948\n125\n\n\n1949\n118\n\n\n1949\n122\n\n\n1949\n126\n\n\n\\(\\vdots\\)\n\\(\\vdots\\)\n\n\n\nwill plot a lineplot with a shaded region.\n\n\n\n\nIf you do not want the shaded region, aggregate the data such that there is only one y-value for a given x-value; then, make the plot.", - "crumbs": [ - "Visualizations" - ] - }, - { - "objectID": "index.html", - "href": "index.html", - "title": "About", - "section": "", - "text": "This text offers pointers for keyboard shortcuts or common mistakes that accompany the coursework in the Summer 2025 Edition of the UC Berkeley course Data 100: Principles and Techniques of Data Science.\nInspiration for this guide was taken from the UC San Diego course DSC 10: Principles of Data Science and their debugging guide.\nIf you spot any typos or would like to suggest any changes, please email us at data100.instructors@berkeley.edu.\n\n\nIn alphabetical order, Yash Dave, Emrie Loh, Lillian Weng, and Wesley Zheng.", - "crumbs": [ - "About" - ] - }, - { - "objectID": "index.html#authors", - "href": "index.html#authors", - "title": "About", - "section": "", - "text": "In alphabetical order, Yash Dave, Emrie Loh, Lillian Weng, and Wesley Zheng.", - "crumbs": [ - "About" - ] - }, - { - "objectID": "pandas/pandas.html", - "href": "pandas/pandas.html", - "title": "Pandas", - "section": "", - "text": "pandas errors can look red, scary, and very long. Fortunately, we don’t need to understand the entire thing! The most important parts of an error message are at the top, which tells you which line of code is causing the issue, and at the bottom, which tells you exactly what the error message is.\n\n\n\n\nThis note is (mostly) structured around the error messages that show up at the bottom.", - "crumbs": [ - "Pandas" - ] - }, - { - "objectID": "pandas/pandas.html#understanding-pandas-errors", - "href": "pandas/pandas.html#understanding-pandas-errors", - "title": "Pandas", - "section": "", - "text": "pandas errors can look red, scary, and very long. Fortunately, we don’t need to understand the entire thing! The most important parts of an error message are at the top, which tells you which line of code is causing the issue, and at the bottom, which tells you exactly what the error message is.\n\n\n\n\nThis note is (mostly) structured around the error messages that show up at the bottom.", - "crumbs": [ - "Pandas" - ] - }, - { - "objectID": "pandas/pandas.html#my-code-is-taking-a-really-long-time-to-run", - "href": "pandas/pandas.html#my-code-is-taking-a-really-long-time-to-run", - "title": "Pandas", - "section": "My code is taking a really long time to run", - "text": "My code is taking a really long time to run\nIt is normal for a cell to take a few seconds – sometimes a few minutes – to run. If it’s is taking too long, however, you have several options:\n\nTry restarting the kernel. Sometimes, Datahub glitches or lags, causing the code to run slower than expected. Restarting the kernel should fix this problem, but if the cell is still taking a while to run, it is likely a problem with your code.\nScrutinize your code. Am I using too many for loops? Is there a repeated operation that I can substitute with a pandas function?", - "crumbs": [ - "Pandas" - ] - }, - { - "objectID": "pandas/pandas.html#why-is-it-generally-better-avoid-using-loops-or-list-comprehensions-when-possible", - "href": "pandas/pandas.html#why-is-it-generally-better-avoid-using-loops-or-list-comprehensions-when-possible", - "title": "Pandas", - "section": "Why is it generally better avoid using loops or list comprehensions when possible?", - "text": "Why is it generally better avoid using loops or list comprehensions when possible?\nIn one word: performance. NumPy and pandas functions are optimized to handle large amounts of data in an efficient manner. Even for simple operations, like the elementwise addition of two arrays, NumPy arrays are much faster and scale better (feel free to experiment with this yourself using %%time). This is why we encourage you to vectorize your code (ie. using NumPy arrays, Series, or DataFrames instead of Python lists) and use in-built NumPy/pandas functions wherever possible.", - "crumbs": [ - "Pandas" - ] - }, - { - "objectID": "pandas/pandas.html#keyerrors", - "href": "pandas/pandas.html#keyerrors", - "title": "Pandas", - "section": "KeyErrors", - "text": "KeyErrors\n\nKeyError: 'column_name'\nThis error usually happens when we have a DataFame called df, and we’re trying to do an operation on a column 'column_name' that does not exist. If you encounter this error, double check that you’re operating on the right column. It might be a good idea to display df and see what it looks like. You could also call df.columns to list all the columns in df.", - "crumbs": [ - "Pandas" - ] - }, - { - "objectID": "pandas/pandas.html#typeerrors", - "href": "pandas/pandas.html#typeerrors", - "title": "Pandas", - "section": "TypeErrors", - "text": "TypeErrors\n\nTypeError: '___' object is not callable\nThis often happens when you use a default keyword (like str, list, range, bool, sum, or max) as a variable name, for instance:\nsum = 1 + 2 + 3\nThese errors can be tricky because they don’t error on their own but cause problems when we try to use the name sum (for example) later on in the notebook.\nTo fix the issue, identify any such lines of code (Ctrl+F on “sum =” for example), change your variable names to be something more informative, and restart your notebook.\nPython keywords like str and list appear in green text, so be on the lookout if any of your variable names appear in green!\n\n\nTypeError: could not convert string to a float\nThis error often occurs when we try to do math operations (ie. sum, average, min, max) on a DataFrame column or Series that contains strings instead of numbers (note that we can do math operations with booleans; Python treats True as 1 and False as 0).\nDouble check that the column you’re interested in is a numerical type (int, float, or double). If it looks like a number, but you’re still getting this error, you can use .astype(...) (documentation) to change the datatype of a DataFrame or Series.\n\n\nTypeError: Could not convert <string> to numeric\nRelated to the above (but distinct), you may run into this error when performing a numeric aggregation function (like mean or sum functions that take integer arguments) after doing a groupby operation on a DataFrame with non-numeric columns.\nWorking with the elections dataset for example,\nelections.groupby('Year').agg('mean')\nwould error because pandas cannot compute the mean of the names of presidents. There are three ways to get around this:\n\nSelect only the numeric columns you are interested in before applying the aggregation function. In the above case, both elections.groupby('Year')['Popular Vote'] or elections['Popular vote'].groupby('Year') would work.\nSetting the numeric_only argument to True in the .agg call, thereby applying the aggregation function only to numeric columns. For example, elections.groupby('Year').agg('mean', numeric_only=True).\nPassing in a dictionary to .agg where you specify the column you are applying a particular aggregation function to. Continuing the same example, this looks like elections.groupby('Year').agg({'Popular vote' : 'mean').\n\n\n\nTypeError: 'NoneType' object is not subscriptable / AttributeError: 'NoneType' object has no attribute 'shape'\nThis usually occurs as you assign a None value to a variable, then try to either index into or access some attribute of that variable. For Python functions like append and extend, you do not need to do any variable assignment because they mutate the variable directly and return None. Assigning None tends to happen as a result of code like:\nsome_list = some_list.append(element)\nIn contrast, an operation like np.append does not mutate the variable in place and, instead, returns a copy. In these cases, (re)assignment is necessary:\nsome_array = np.append(some_array, element)\n\n\nTypeError: 'int'/'float' object is not subscriptable\nThis occurs when you try and index into an integer or other numeric Python data type. It can be confusing to debug amidst a muddle of code, but you can use the error message to identify which variable is causing this error. Using type(var_name) to check the data type of the variable in question can be a good starting point.", - "crumbs": [ - "Pandas" - ] - }, - { - "objectID": "pandas/pandas.html#indexerrors", - "href": "pandas/pandas.html#indexerrors", - "title": "Pandas", - "section": "IndexErrors", - "text": "IndexErrors\n\nIndexError: invalid index to scalar variable.\nThis error is similar to the last TypeError in the previous section. However, it is slightly different in that scalar variables come up in the context of NumPy data types which have slightly different attributes.\nFor a concrete example, if you defined\nnumpy_arr = np.array([1])\nand indexed into it twice (numpy_arr[0][0]), you would get the above error. Unlike a Python integer whose type is int, type(numpy_arr[0]) returns the NumPy version of an integer, numpy.int64. Additionally, you can check the data type by accessing the .dtype attribute of NumPy array (numpy_arr.dtype) or scalar variable (numpy_arr[0].dtype).\n\n\nIndexError: index _ is out of bounds for axis _ with size _\nThis error usually happens when you try to index a value that’s greater than the size of the array/list/DataFrame/Series. For example,\nsome_list = [2, 4, 6, 8]\nsome_list has a length of 4. Trying some_list[6] will error because index 6 is greater than the length of the array. Note that some_list[4] will also cause an IndexError because Python and pandas uses zero indexing, which means that the first element has index 0, the second element has index 1, etc.; some_list[4] would grab the fifth element, which is impossible when the list only has 4 elements.", - "crumbs": [ - "Pandas" - ] - }, - { - "objectID": "pandas/pandas.html#valueerrors", - "href": "pandas/pandas.html#valueerrors", - "title": "Pandas", - "section": "ValueErrors", - "text": "ValueErrors\n\nValueError: Truth value of a Series is ambiguous\nThis error could occur when you apply Python logical operators (or, and, not), which only operate on a single boolean values, to NumPy arrays or Series objects, which can contain multiple values. The fix is to use bitwise operators |, &, ~ , respectively, to allow for element-wise comparisons between values in arrays or Series.\nAlternatively, these errors could emerge due to overwriting Python keywords like bool and sum that may be used in the autograder tests, similar to what’s described here. You should follow a similar procedure of identifying the line of code erroring, checking if you’ve overwritten any Python keywords using Ctrl+F, and renaming those variables to something more informative before restarting your kernel and running the erroring tests again.\n\n\nValueError: Can only compare identically-labeled Series objects\nAs the message would suggest, this error occurs when comparing two Series objects that have different lengths. You can double check the lengths of the Series using len(series_name) or series_name.size.\n\n\nValueError: -1 is not in range / KeyError: -1\nThis error occurs when you try and index into a Series or DataFrame as you would a Python list. Unlike a list where passing an index of -1 gives the last element, pandas interprets df[-1] as an attempt to find the row corresponding to index -1 (that is, df.loc[-1]). If your intention is to pick out the last row in df, consider using integer-position based indexing by doing df.iloc[-1]. In general, to avoid ambiguity in these cases, it is also good practice to write out both the row and column indices you want with df.iloc[-1, :].", - "crumbs": [ - "Pandas" - ] - } -] \ No newline at end of file diff --git a/docs/site_libs/bootstrap/bootstrap-d279e387f5997211c97d65d1670dc95e.min.css b/docs/site_libs/bootstrap/bootstrap-d279e387f5997211c97d65d1670dc95e.min.css deleted file mode 100644 index ee8ace6..0000000 --- a/docs/site_libs/bootstrap/bootstrap-d279e387f5997211c97d65d1670dc95e.min.css +++ /dev/null @@ -1,12 +0,0 @@ -/*! - * Bootstrap v5.3.1 (https://getbootstrap.com/) - * Copyright 2011-2023 The Bootstrap Authors - * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE) - */:root,[data-bs-theme=light]{--bs-blue: #0d6efd;--bs-indigo: #6610f2;--bs-purple: #6f42c1;--bs-pink: #d63384;--bs-red: #dc3545;--bs-orange: #fd7e14;--bs-yellow: #ffc107;--bs-green: #198754;--bs-teal: #20c997;--bs-cyan: #0dcaf0;--bs-black: #000;--bs-white: #ffffff;--bs-gray: 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1rem;--bs-nav-link-padding-y: 0.5rem;--bs-nav-link-font-weight: ;--bs-nav-link-color: #0d6efd;--bs-nav-link-hover-color: rgb(10.4, 88, 202.4);--bs-nav-link-disabled-color: rgba(33, 37, 41, 0.75);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding-left:0;margin-bottom:0;list-style:none}.nav-link{display:block;padding:var(--bs-nav-link-padding-y) var(--bs-nav-link-padding-x);font-size:var(--bs-nav-link-font-size);font-weight:var(--bs-nav-link-font-weight);color:var(--bs-nav-link-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background:none;border:0;transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out}@media(prefers-reduced-motion: reduce){.nav-link{transition:none}}.nav-link:hover,.nav-link:focus{color:var(--bs-nav-link-hover-color)}.nav-link:focus-visible{outline:0;box-shadow:0 0 0 .25rem rgba(13,110,253,.25)}.nav-link.disabled,.nav-link:disabled{color:var(--bs-nav-link-disabled-color);pointer-events:none;cursor:default}.nav-tabs{--bs-nav-tabs-border-width: 1px;--bs-nav-tabs-border-color: #dee2e6;--bs-nav-tabs-border-radius: 0.25rem;--bs-nav-tabs-link-hover-border-color: #e9ecef #e9ecef #dee2e6;--bs-nav-tabs-link-active-color: #000;--bs-nav-tabs-link-active-bg: #ffffff;--bs-nav-tabs-link-active-border-color: #dee2e6 #dee2e6 #ffffff;border-bottom:var(--bs-nav-tabs-border-width) solid var(--bs-nav-tabs-border-color)}.nav-tabs .nav-link{margin-bottom:calc(-1*var(--bs-nav-tabs-border-width));border:var(--bs-nav-tabs-border-width) solid rgba(0,0,0,0);border-top-left-radius:var(--bs-nav-tabs-border-radius);border-top-right-radius:var(--bs-nav-tabs-border-radius)}.nav-tabs .nav-link:hover,.nav-tabs .nav-link:focus{isolation:isolate;border-color:var(--bs-nav-tabs-link-hover-border-color)}.nav-tabs .nav-link.active,.nav-tabs .nav-item.show 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solid var(--bs-accordion-border-color)}.accordion-item:first-of-type{border-top-left-radius:var(--bs-accordion-border-radius);border-top-right-radius:var(--bs-accordion-border-radius)}.accordion-item:first-of-type .accordion-button{border-top-left-radius:var(--bs-accordion-inner-border-radius);border-top-right-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:not(:first-of-type){border-top:0}.accordion-item:last-of-type{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-item:last-of-type .accordion-button.collapsed{border-bottom-right-radius:var(--bs-accordion-inner-border-radius);border-bottom-left-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:last-of-type .accordion-collapse{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0;border-radius:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}.accordion-flush .accordion-item .accordion-button,.accordion-flush .accordion-item .accordion-button.collapsed{border-radius:0}[data-bs-theme=dark] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='rgb%28109.8, 168, 253.8%29'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='rgb%28109.8, 168, 253.8%29'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 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var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, ">") /* rtl: var(--bs-breadcrumb-divider, ">") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.75rem;--bs-pagination-padding-y: 0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #0d6efd;--bs-pagination-bg: #ffffff;--bs-pagination-border-width: 1px;--bs-pagination-border-color: #dee2e6;--bs-pagination-border-radius: 0.25rem;--bs-pagination-hover-color: rgb(10.4, 88, 202.4);--bs-pagination-hover-bg: #f8f9fa;--bs-pagination-hover-border-color: #dee2e6;--bs-pagination-focus-color: rgb(10.4, 88, 202.4);--bs-pagination-focus-bg: #e9ecef;--bs-pagination-focus-box-shadow: 0 0 0 0.25rem rgba(13, 110, 253, 0.25);--bs-pagination-active-color: #ffffff;--bs-pagination-active-bg: #0d6efd;--bs-pagination-active-border-color: #0d6efd;--bs-pagination-disabled-color: rgba(33, 37, 41, 0.75);--bs-pagination-disabled-bg: #e9ecef;--bs-pagination-disabled-border-color: #dee2e6;display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(1px*-1)}.page-item:first-child .page-link{border-top-left-radius:var(--bs-pagination-border-radius);border-bottom-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-top-right-radius:var(--bs-pagination-border-radius);border-bottom-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: 0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: 0.5rem}.pagination-sm{--bs-pagination-padding-x: 0.5rem;--bs-pagination-padding-y: 0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius: 0.2em}.badge{--bs-badge-padding-x: 0.65em;--bs-badge-padding-y: 0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight: 700;--bs-badge-color: #ffffff;--bs-badge-border-radius: 0.25rem;display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:var(--bs-badge-border-radius)}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: 1px solid var(--bs-alert-border-color);--bs-alert-border-radius: 0.25rem;--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:1rem}}.progress,.progress-stacked{--bs-progress-height: 1rem;--bs-progress-font-size:0.75rem;--bs-progress-bg: #e9ecef;--bs-progress-border-radius: 0.25rem;--bs-progress-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075);--bs-progress-bar-color: #ffffff;--bs-progress-bar-bg: #0d6efd;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media(prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: #212529;--bs-list-group-bg: #ffffff;--bs-list-group-border-color: #dee2e6;--bs-list-group-border-width: 1px;--bs-list-group-border-radius: 0.25rem;--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: 0.5rem;--bs-list-group-action-color: rgba(33, 37, 41, 0.75);--bs-list-group-action-hover-color: #000;--bs-list-group-action-hover-bg: #f8f9fa;--bs-list-group-action-active-color: #212529;--bs-list-group-action-active-bg: #e9ecef;--bs-list-group-disabled-color: rgba(33, 37, 41, 0.75);--bs-list-group-disabled-bg: #ffffff;--bs-list-group-active-color: #ffffff;--bs-list-group-active-bg: #0d6efd;--bs-list-group-active-border-color: 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". ";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item:first-child{border-top-left-radius:inherit;border-top-right-radius:inherit}.list-group-item:last-child{border-bottom-right-radius:inherit;border-bottom-left-radius:inherit}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1*var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media(min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-sm>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-md>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-lg>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xxl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush{border-radius:0}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #000;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23000'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e");--bs-btn-close-opacity: 0.5;--bs-btn-close-hover-opacity: 0.75;--bs-btn-close-focus-shadow: 0 0 0 0.25rem rgba(13, 110, 253, 0.25);--bs-btn-close-focus-opacity: 1;--bs-btn-close-disabled-opacity: 0.25;--bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:rgba(0,0,0,0) var(--bs-btn-close-bg) center/1em auto no-repeat;border:0;border-radius:.25rem;opacity:var(--bs-btn-close-opacity)}.btn-close:hover{color:var(--bs-btn-close-color);text-decoration:none;opacity:var(--bs-btn-close-hover-opacity)}.btn-close:focus{outline:0;box-shadow:var(--bs-btn-close-focus-shadow);opacity:var(--bs-btn-close-focus-opacity)}.btn-close:disabled,.btn-close.disabled{pointer-events:none;user-select:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;-o-user-select:none;opacity:var(--bs-btn-close-disabled-opacity)}.btn-close-white{filter:var(--bs-btn-close-white-filter)}[data-bs-theme=dark] .btn-close{filter:var(--bs-btn-close-white-filter)}.toast{--bs-toast-zindex: 1090;--bs-toast-padding-x: 0.75rem;--bs-toast-padding-y: 0.5rem;--bs-toast-spacing: 1.5rem;--bs-toast-max-width: 350px;--bs-toast-font-size:0.875rem;--bs-toast-color: ;--bs-toast-bg: rgba(255, 255, 255, 0.85);--bs-toast-border-width: 1px;--bs-toast-border-color: rgba(0, 0, 0, 0.175);--bs-toast-border-radius: 0.25rem;--bs-toast-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);--bs-toast-header-color: rgba(33, 37, 41, 0.75);--bs-toast-header-bg: rgba(255, 255, 255, 0.85);--bs-toast-header-border-color: rgba(0, 0, 0, 0.175);width:var(--bs-toast-max-width);max-width:100%;font-size:var(--bs-toast-font-size);color:var(--bs-toast-color);pointer-events:auto;background-color:var(--bs-toast-bg);background-clip:padding-box;border:var(--bs-toast-border-width) solid var(--bs-toast-border-color);box-shadow:var(--bs-toast-box-shadow);border-radius:var(--bs-toast-border-radius)}.toast.showing{opacity:0}.toast:not(.show){display:none}.toast-container{--bs-toast-zindex: 1090;position:absolute;z-index:var(--bs-toast-zindex);width:max-content;width:-webkit-max-content;width:-moz-max-content;width:-ms-max-content;width:-o-max-content;max-width:100%;pointer-events:none}.toast-container>:not(:last-child){margin-bottom:var(--bs-toast-spacing)}.toast-header{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;padding:var(--bs-toast-padding-y) var(--bs-toast-padding-x);color:var(--bs-toast-header-color);background-color:var(--bs-toast-header-bg);background-clip:padding-box;border-bottom:var(--bs-toast-border-width) solid var(--bs-toast-header-border-color);border-top-left-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width));border-top-right-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width))}.toast-header .btn-close{margin-right:calc(-0.5*var(--bs-toast-padding-x));margin-left:var(--bs-toast-padding-x)}.toast-body{padding:var(--bs-toast-padding-x);word-wrap:break-word}.modal{--bs-modal-zindex: 1055;--bs-modal-width: 500px;--bs-modal-padding: 1rem;--bs-modal-margin: 0.5rem;--bs-modal-color: ;--bs-modal-bg: #ffffff;--bs-modal-border-color: rgba(0, 0, 0, 0.175);--bs-modal-border-width: 1px;--bs-modal-border-radius: 0.5rem;--bs-modal-box-shadow: 0 0.125rem 0.25rem rgba(0, 0, 0, 0.075);--bs-modal-inner-border-radius: calc(0.5rem - 1px);--bs-modal-header-padding-x: 1rem;--bs-modal-header-padding-y: 1rem;--bs-modal-header-padding: 1rem 1rem;--bs-modal-header-border-color: #dee2e6;--bs-modal-header-border-width: 1px;--bs-modal-title-line-height: 1.5;--bs-modal-footer-gap: 0.5rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color: #dee2e6;--bs-modal-footer-border-width: 1px;position:fixed;top:0;left:0;z-index:var(--bs-modal-zindex);display:none;width:100%;height:100%;overflow-x:hidden;overflow-y:auto;outline:0}.modal-dialog{position:relative;width:auto;margin:var(--bs-modal-margin);pointer-events:none}.modal.fade .modal-dialog{transition:transform .3s ease-out;transform:translate(0, -50px)}@media(prefers-reduced-motion: reduce){.modal.fade .modal-dialog{transition:none}}.modal.show .modal-dialog{transform:none}.modal.modal-static .modal-dialog{transform:scale(1.02)}.modal-dialog-scrollable{height:calc(100% - var(--bs-modal-margin)*2)}.modal-dialog-scrollable .modal-content{max-height:100%;overflow:hidden}.modal-dialog-scrollable .modal-body{overflow-y:auto}.modal-dialog-centered{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;min-height:calc(100% - var(--bs-modal-margin)*2)}.modal-content{position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;width:100%;color:var(--bs-modal-color);pointer-events:auto;background-color:var(--bs-modal-bg);background-clip:padding-box;border:var(--bs-modal-border-width) solid var(--bs-modal-border-color);border-radius:var(--bs-modal-border-radius);outline:0}.modal-backdrop{--bs-backdrop-zindex: 1050;--bs-backdrop-bg: #000;--bs-backdrop-opacity: 0.5;position:fixed;top:0;left:0;z-index:var(--bs-backdrop-zindex);width:100vw;height:100vh;background-color:var(--bs-backdrop-bg)}.modal-backdrop.fade{opacity:0}.modal-backdrop.show{opacity:var(--bs-backdrop-opacity)}.modal-header{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between;padding:var(--bs-modal-header-padding);border-bottom:var(--bs-modal-header-border-width) solid 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.modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-sm-down .modal-header,.modal-fullscreen-sm-down .modal-footer{border-radius:0}.modal-fullscreen-sm-down .modal-body{overflow-y:auto}}@media(max-width: 767.98px){.modal-fullscreen-md-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-md-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-md-down .modal-header,.modal-fullscreen-md-down .modal-footer{border-radius:0}.modal-fullscreen-md-down .modal-body{overflow-y:auto}}@media(max-width: 991.98px){.modal-fullscreen-lg-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-lg-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-lg-down .modal-header,.modal-fullscreen-lg-down .modal-footer{border-radius:0}.modal-fullscreen-lg-down .modal-body{overflow-y:auto}}@media(max-width: 1199.98px){.modal-fullscreen-xl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xl-down 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0.4rem;z-index:var(--bs-tooltip-zindex);display:block;margin:var(--bs-tooltip-margin);font-family:system-ui,-apple-system,"Segoe UI",Roboto,"Helvetica Neue","Noto Sans","Liberation Sans",Arial,sans-serif,"Apple Color Emoji","Segoe UI Emoji","Segoe UI Symbol","Noto Color Emoji";font-style:normal;font-weight:400;line-height:1.5;text-align:left;text-align:start;text-decoration:none;text-shadow:none;text-transform:none;letter-spacing:normal;word-break:normal;white-space:normal;word-spacing:normal;line-break:auto;font-size:var(--bs-tooltip-font-size);word-wrap:break-word;opacity:0}.tooltip.show{opacity:var(--bs-tooltip-opacity)}.tooltip .tooltip-arrow{display:block;width:var(--bs-tooltip-arrow-width);height:var(--bs-tooltip-arrow-height)}.tooltip .tooltip-arrow::before{position:absolute;content:"";border-color:rgba(0,0,0,0);border-style:solid}.bs-tooltip-top .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^=top] 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.accordion-header{font-size:1.65rem}}.accordion .accordion-icon:not(:empty){margin-right:.75rem;display:flex}.accordion .accordion-button:not(.collapsed){box-shadow:none}.accordion 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#ffffff;--quarto-scss-export-nav-pills-link-active-bg: #0d6efd;--quarto-scss-export-nav-pills-link-active-color: #ffffff;--quarto-scss-export-nav-underline-link-active-color: #000;--quarto-scss-export-navbar-padding-x: ;--quarto-scss-export-navbar-light-contrast: #ffffff;--quarto-scss-export-navbar-dark-contrast: #ffffff;--quarto-scss-export-navbar-light-icon-color: rgba(255, 255, 255, 0.75);--quarto-scss-export-navbar-dark-icon-color: rgba(255, 255, 255, 0.75);--quarto-scss-export-dropdown-color: #212529;--quarto-scss-export-dropdown-bg: #ffffff;--quarto-scss-export-dropdown-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-divider-bg: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-link-color: #212529;--quarto-scss-export-dropdown-link-hover-color: #212529;--quarto-scss-export-dropdown-link-hover-bg: #f8f9fa;--quarto-scss-export-dropdown-link-active-bg: #0d6efd;--quarto-scss-export-dropdown-link-active-color: #ffffff;--quarto-scss-export-dropdown-link-disabled-color: rgba(33, 37, 41, 0.5);--quarto-scss-export-dropdown-header-color: #6c757d;--quarto-scss-export-dropdown-dark-color: #dee2e6;--quarto-scss-export-dropdown-dark-bg: #343a40;--quarto-scss-export-dropdown-dark-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-dark-divider-bg: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-dark-box-shadow: ;--quarto-scss-export-dropdown-dark-link-color: #dee2e6;--quarto-scss-export-dropdown-dark-link-hover-color: #ffffff;--quarto-scss-export-dropdown-dark-link-hover-bg: rgba(255, 255, 255, 0.15);--quarto-scss-export-dropdown-dark-link-active-color: #ffffff;--quarto-scss-export-dropdown-dark-link-active-bg: #0d6efd;--quarto-scss-export-dropdown-dark-link-disabled-color: #adb5bd;--quarto-scss-export-dropdown-dark-header-color: #adb5bd;--quarto-scss-export-pagination-color: #0d6efd;--quarto-scss-export-pagination-bg: #ffffff;--quarto-scss-export-pagination-border-color: #dee2e6;--quarto-scss-export-pagination-focus-color: rgb(10.4, 88, 202.4);--quarto-scss-export-pagination-focus-bg: #e9ecef;--quarto-scss-export-pagination-hover-color: rgb(10.4, 88, 202.4);--quarto-scss-export-pagination-hover-bg: #f8f9fa;--quarto-scss-export-pagination-hover-border-color: #dee2e6;--quarto-scss-export-pagination-active-color: #ffffff;--quarto-scss-export-pagination-active-bg: #0d6efd;--quarto-scss-export-pagination-active-border-color: #0d6efd;--quarto-scss-export-pagination-disabled-color: rgba(33, 37, 41, 0.75);--quarto-scss-export-pagination-disabled-bg: #e9ecef;--quarto-scss-export-pagination-disabled-border-color: #dee2e6;--quarto-scss-export-card-title-color: ;--quarto-scss-export-card-subtitle-color: ;--quarto-scss-export-card-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-card-box-shadow: ;--quarto-scss-export-card-cap-color: ;--quarto-scss-export-card-height: ;--quarto-scss-export-card-color: ;--quarto-scss-export-card-bg: #ffffff;--quarto-scss-export-accordion-color: #212529;--quarto-scss-export-accordion-bg: #ffffff;--quarto-scss-export-accordion-border-color: #dee2e6;--quarto-scss-export-accordion-button-color: #212529;--quarto-scss-export-accordion-button-bg: #ffffff;--quarto-scss-export-accordion-button-active-bg: rgb(206.6, 226, 254.6);--quarto-scss-export-accordion-button-active-color: rgb(5.2, 44, 101.2);--quarto-scss-export-accordion-button-focus-border-color: rgb(134, 182.5, 254);--quarto-scss-export-accordion-icon-color: #212529;--quarto-scss-export-accordion-icon-active-color: rgb(5.2, 44, 101.2);--quarto-scss-export-tooltip-color: #ffffff;--quarto-scss-export-tooltip-bg: #000;--quarto-scss-export-tooltip-margin: ;--quarto-scss-export-tooltip-arrow-color: ;--quarto-scss-export-form-feedback-tooltip-line-height: ;--quarto-scss-export-popover-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-popover-header-bg: #e9ecef;--quarto-scss-export-popover-body-color: #212529;--quarto-scss-export-popover-arrow-color: #ffffff;--quarto-scss-export-popover-arrow-outer-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-toast-color: ;--quarto-scss-export-toast-background-color: rgba(255, 255, 255, 0.85);--quarto-scss-export-toast-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-toast-header-color: rgba(33, 37, 41, 0.75);--quarto-scss-export-toast-header-background-color: rgba(255, 255, 255, 0.85);--quarto-scss-export-toast-header-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-badge-color: #ffffff;--quarto-scss-export-modal-content-color: ;--quarto-scss-export-modal-content-bg: #ffffff;--quarto-scss-export-modal-content-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-modal-backdrop-bg: #000;--quarto-scss-export-modal-header-border-color: #dee2e6;--quarto-scss-export-modal-footer-bg: ;--quarto-scss-export-modal-footer-border-color: #dee2e6;--quarto-scss-export-progress-bg: #e9ecef;--quarto-scss-export-progress-bar-color: #ffffff;--quarto-scss-export-progress-bar-bg: #0d6efd;--quarto-scss-export-list-group-color: #212529;--quarto-scss-export-list-group-bg: #ffffff;--quarto-scss-export-list-group-border-color: #dee2e6;--quarto-scss-export-list-group-hover-bg: #f8f9fa;--quarto-scss-export-list-group-active-bg: #0d6efd;--quarto-scss-export-list-group-active-color: #ffffff;--quarto-scss-export-list-group-active-border-color: #0d6efd;--quarto-scss-export-list-group-disabled-color: rgba(33, 37, 41, 0.75);--quarto-scss-export-list-group-disabled-bg: #ffffff;--quarto-scss-export-list-group-action-color: rgba(33, 37, 41, 0.75);--quarto-scss-export-list-group-action-hover-color: #000;--quarto-scss-export-list-group-action-active-color: #212529;--quarto-scss-export-list-group-action-active-bg: #e9ecef;--quarto-scss-export-thumbnail-bg: #ffffff;--quarto-scss-export-thumbnail-border-color: #dee2e6;--quarto-scss-export-figure-caption-color: rgba(33, 37, 41, 0.75);--quarto-scss-export-breadcrumb-font-size: ;--quarto-scss-export-breadcrumb-bg: ;--quarto-scss-export-breadcrumb-divider-color: rgba(33, 37, 41, 0.75);--quarto-scss-export-breadcrumb-active-color: rgba(33, 37, 41, 0.75);--quarto-scss-export-breadcrumb-border-radius: ;--quarto-scss-export-carousel-control-color: #ffffff;--quarto-scss-export-carousel-indicator-active-bg: #ffffff;--quarto-scss-export-carousel-caption-color: #ffffff;--quarto-scss-export-carousel-dark-indicator-active-bg: #000;--quarto-scss-export-carousel-dark-caption-color: #000;--quarto-scss-export-btn-close-color: #000;--quarto-scss-export-offcanvas-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-offcanvas-bg-color: #ffffff;--quarto-scss-export-offcanvas-color: #212529;--quarto-scss-export-offcanvas-backdrop-bg: #000;--quarto-scss-export-code-color-dark: white;--quarto-scss-export-kbd-color: #ffffff;--quarto-scss-export-kbd-bg: #212529;--quarto-scss-export-nested-kbd-font-weight: ;--quarto-scss-export-pre-bg: #f8f9fa;--quarto-scss-export-pre-color: #000;--quarto-scss-export-bslib-page-sidebar-title-bg: #517699;--quarto-scss-export-bslib-page-sidebar-title-color: #ffffff;--quarto-scss-export-bslib-sidebar-bg: rgba(var(--bs-emphasis-color-rgb, 0, 0, 0), 0.05);--quarto-scss-export-bslib-sidebar-toggle-bg: rgba(var(--bs-emphasis-color-rgb, 0, 0, 0), 0.1);--quarto-scss-export-sidebar-color: rgb(89.25, 89.25, 89.25);--quarto-scss-export-sidebar-hover-color: rgba(9.75, 82.5, 189.75, 0.8);--quarto-scss-export-sidebar-disabled-color: rgba(89.25, 89.25, 89.25, 0.75);--quarto-scss-export-valuebox-bg-primary: #0d6efd;--quarto-scss-export-valuebox-bg-secondary: #6c757d;--quarto-scss-export-valuebox-bg-success: #198754;--quarto-scss-export-valuebox-bg-info: #0dcaf0;--quarto-scss-export-valuebox-bg-warning: #ffc107;--quarto-scss-export-valuebox-bg-danger: #dc3545;--quarto-scss-export-valuebox-bg-light: #f8f9fa;--quarto-scss-export-valuebox-bg-dark: #212529;--quarto-scss-export-mermaid-bg-color: #ffffff;--quarto-scss-export-mermaid-edge-color: #6c757d;--quarto-scss-export-mermaid-node-fg-color: #212529;--quarto-scss-export-mermaid-fg-color: #212529;--quarto-scss-export-mermaid-fg-color--lighter: rgb(55.7432432432, 62.5, 69.2567567568);--quarto-scss-export-mermaid-fg-color--lightest: rgb(78.4864864865, 88, 97.5135135135);--quarto-scss-export-mermaid-label-bg-color: #ffffff;--quarto-scss-export-mermaid-label-fg-color: #0d6efd;--quarto-scss-export-mermaid-node-bg-color: rgba(13, 110, 253, 0.1);--quarto-scss-export-code-block-border-left-color: #dee2e6;--quarto-scss-export-callout-color-note: #0d6efd;--quarto-scss-export-callout-color-tip: #198754;--quarto-scss-export-callout-color-important: #dc3545;--quarto-scss-export-callout-color-caution: #fd7e14;--quarto-scss-export-callout-color-warning: #ffc107} \ No newline at end of file diff --git a/docs/site_libs/bootstrap/bootstrap-icons.css b/docs/site_libs/bootstrap/bootstrap-icons.css deleted file mode 100644 index 82b40f5..0000000 --- a/docs/site_libs/bootstrap/bootstrap-icons.css +++ /dev/null @@ -1,2106 +0,0 @@ -/*! - * Bootstrap Icons v1.13.1 (https://icons.getbootstrap.com/) - * Copyright 2019-2024 The Bootstrap Authors - * Licensed under MIT (https://github.com/twbs/icons/blob/main/LICENSE) - */ - -@font-face { - font-display: block; - font-family: "bootstrap-icons"; - src: -url("./bootstrap-icons.woff?e34853135f9e39acf64315236852cd5a") format("woff"); -} - -.bi::before, -[class^="bi-"]::before, -[class*=" bi-"]::before { - display: inline-block; - font-family: bootstrap-icons !important; - font-style: normal; - font-weight: normal !important; - font-variant: normal; - text-transform: none; - line-height: 1; - vertical-align: -.125em; - -webkit-font-smoothing: antialiased; - -moz-osx-font-smoothing: grayscale; -} - -.bi-123::before { content: "\f67f"; } -.bi-alarm-fill::before { content: "\f101"; } -.bi-alarm::before { content: "\f102"; } -.bi-align-bottom::before { content: "\f103"; } -.bi-align-center::before 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{ content: "\f4e3"; } -.bi-phone-landscape::before { content: "\f4e4"; } -.bi-phone-vibrate-fill::before { content: "\f4e5"; } -.bi-phone-vibrate::before { content: "\f4e6"; } -.bi-phone::before { content: "\f4e7"; } -.bi-pie-chart-fill::before { content: "\f4e8"; } -.bi-pie-chart::before { content: "\f4e9"; } -.bi-pin-angle-fill::before { content: "\f4ea"; } -.bi-pin-angle::before { content: "\f4eb"; } -.bi-pin-fill::before { content: "\f4ec"; } -.bi-pin::before { content: "\f4ed"; } -.bi-pip-fill::before { content: "\f4ee"; } -.bi-pip::before { content: "\f4ef"; } -.bi-play-btn-fill::before { content: "\f4f0"; } -.bi-play-btn::before { content: "\f4f1"; } -.bi-play-circle-fill::before { content: "\f4f2"; } -.bi-play-circle::before { content: "\f4f3"; } -.bi-play-fill::before { content: "\f4f4"; } -.bi-play::before { content: "\f4f5"; } -.bi-plug-fill::before { content: "\f4f6"; } -.bi-plug::before { content: "\f4f7"; } -.bi-plus-circle-dotted::before { content: "\f4f8"; } 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content: "\f522"; } -.bi-rulers::before { content: "\f523"; } -.bi-save-fill::before { content: "\f524"; } -.bi-save::before { content: "\f525"; } -.bi-save2-fill::before { content: "\f526"; } -.bi-save2::before { content: "\f527"; } -.bi-scissors::before { content: "\f528"; } -.bi-screwdriver::before { content: "\f529"; } -.bi-search::before { content: "\f52a"; } -.bi-segmented-nav::before { content: "\f52b"; } -.bi-server::before { content: "\f52c"; } -.bi-share-fill::before { content: "\f52d"; } -.bi-share::before { content: "\f52e"; } -.bi-shield-check::before { content: "\f52f"; } -.bi-shield-exclamation::before { content: "\f530"; } -.bi-shield-fill-check::before { content: "\f531"; } -.bi-shield-fill-exclamation::before { content: "\f532"; } -.bi-shield-fill-minus::before { content: "\f533"; } -.bi-shield-fill-plus::before { content: "\f534"; } -.bi-shield-fill-x::before { content: "\f535"; } -.bi-shield-fill::before { content: "\f536"; } -.bi-shield-lock-fill::before { content: 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content: "\f574"; } -.bi-sort-down::before { content: "\f575"; } -.bi-sort-numeric-down-alt::before { content: "\f576"; } -.bi-sort-numeric-down::before { content: "\f577"; } -.bi-sort-numeric-up-alt::before { content: "\f578"; } -.bi-sort-numeric-up::before { content: "\f579"; } -.bi-sort-up-alt::before { content: "\f57a"; } -.bi-sort-up::before { content: "\f57b"; } -.bi-soundwave::before { content: "\f57c"; } -.bi-speaker-fill::before { content: "\f57d"; } -.bi-speaker::before { content: "\f57e"; } -.bi-speedometer::before { content: "\f57f"; } -.bi-speedometer2::before { content: "\f580"; } -.bi-spellcheck::before { content: "\f581"; } -.bi-square-fill::before { content: "\f582"; } -.bi-square-half::before { content: "\f583"; } -.bi-square::before { content: "\f584"; } -.bi-stack::before { content: "\f585"; } -.bi-star-fill::before { content: "\f586"; } -.bi-star-half::before { content: "\f587"; } -.bi-star::before { content: "\f588"; } -.bi-stars::before { content: "\f589"; } 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content: "\f5b4"; } -.bi-telephone-forward-fill::before { content: "\f5b5"; } -.bi-telephone-forward::before { content: "\f5b6"; } -.bi-telephone-inbound-fill::before { content: "\f5b7"; } -.bi-telephone-inbound::before { content: "\f5b8"; } -.bi-telephone-minus-fill::before { content: "\f5b9"; } -.bi-telephone-minus::before { content: "\f5ba"; } -.bi-telephone-outbound-fill::before { content: "\f5bb"; } -.bi-telephone-outbound::before { content: "\f5bc"; } -.bi-telephone-plus-fill::before { content: "\f5bd"; } -.bi-telephone-plus::before { content: "\f5be"; } -.bi-telephone-x-fill::before { content: "\f5bf"; } -.bi-telephone-x::before { content: "\f5c0"; } -.bi-telephone::before { content: "\f5c1"; } -.bi-terminal-fill::before { content: "\f5c2"; } -.bi-terminal::before { content: "\f5c3"; } -.bi-text-center::before { content: "\f5c4"; } -.bi-text-indent-left::before { content: "\f5c5"; } -.bi-text-indent-right::before { content: "\f5c6"; } -.bi-text-left::before { content: "\f5c7"; } 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content: "\f5f3"; } -.bi-type-italic::before { content: "\f5f4"; } -.bi-type-strikethrough::before { content: "\f5f5"; } -.bi-type-underline::before { content: "\f5f6"; } -.bi-type::before { content: "\f5f7"; } -.bi-ui-checks-grid::before { content: "\f5f8"; } -.bi-ui-checks::before { content: "\f5f9"; } -.bi-ui-radios-grid::before { content: "\f5fa"; } -.bi-ui-radios::before { content: "\f5fb"; } -.bi-umbrella-fill::before { content: "\f5fc"; } -.bi-umbrella::before { content: "\f5fd"; } -.bi-union::before { content: "\f5fe"; } -.bi-unlock-fill::before { content: "\f5ff"; } -.bi-unlock::before { content: "\f600"; } -.bi-upc-scan::before { content: "\f601"; } -.bi-upc::before { content: "\f602"; } -.bi-upload::before { content: "\f603"; } -.bi-vector-pen::before { content: "\f604"; } -.bi-view-list::before { content: "\f605"; } -.bi-view-stacked::before { content: "\f606"; } -.bi-vinyl-fill::before { content: "\f607"; } -.bi-vinyl::before { content: "\f608"; } -.bi-voicemail::before { content: "\f609"; } -.bi-volume-down-fill::before { content: "\f60a"; } -.bi-volume-down::before { content: "\f60b"; } -.bi-volume-mute-fill::before { content: "\f60c"; } -.bi-volume-mute::before { content: "\f60d"; } -.bi-volume-off-fill::before { content: "\f60e"; } -.bi-volume-off::before { content: "\f60f"; } -.bi-volume-up-fill::before { content: "\f610"; } -.bi-volume-up::before { content: "\f611"; } -.bi-vr::before { content: "\f612"; } -.bi-wallet-fill::before { content: "\f613"; } -.bi-wallet::before { content: "\f614"; } -.bi-wallet2::before { content: "\f615"; } -.bi-watch::before { content: "\f616"; } -.bi-water::before { content: "\f617"; } -.bi-whatsapp::before { content: "\f618"; } -.bi-wifi-1::before { content: "\f619"; } -.bi-wifi-2::before { content: "\f61a"; } -.bi-wifi-off::before { content: "\f61b"; } -.bi-wifi::before { content: "\f61c"; } -.bi-wind::before { content: "\f61d"; } -.bi-window-dock::before { content: "\f61e"; } -.bi-window-sidebar::before { content: 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content: "\f64a"; } -.bi-pin-map-fill::before { content: "\f64b"; } -.bi-pin-map::before { content: "\f64c"; } -.bi-plus-lg::before { content: "\f64d"; } -.bi-question-lg::before { content: "\f64e"; } -.bi-recycle::before { content: "\f64f"; } -.bi-reddit::before { content: "\f650"; } -.bi-safe-fill::before { content: "\f651"; } -.bi-safe2-fill::before { content: "\f652"; } -.bi-safe2::before { content: "\f653"; } -.bi-sd-card-fill::before { content: "\f654"; } -.bi-sd-card::before { content: "\f655"; } -.bi-skype::before { content: "\f656"; } -.bi-slash-lg::before { content: "\f657"; } -.bi-translate::before { content: "\f658"; } -.bi-x-lg::before { content: "\f659"; } -.bi-safe::before { content: "\f65a"; } -.bi-apple::before { content: "\f65b"; } -.bi-microsoft::before { content: "\f65d"; } -.bi-windows::before { content: "\f65e"; } -.bi-behance::before { content: "\f65c"; } -.bi-dribbble::before { content: "\f65f"; } -.bi-line::before { content: "\f660"; } -.bi-medium::before { 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H{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return Xi}static get DefaultType(){return Yi}static get NAME(){return Vi}show(t){if(!this._config.isVisible)return void g(t);this._append();const e=this._getElement();this._config.isAnimated&&d(e),e.classList.add(Ki),this._emulateAnimation((()=>{g(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Ki),this._emulateAnimation((()=>{this.dispose(),g(t)}))):g(t)}dispose(){this._isAppended&&(N.off(this._element,Qi),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=r(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),N.on(t,Qi,(()=>{g(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){_(t,this._getElement(),this._config.isAnimated)}}const Gi=".bs.focustrap",Ji=`focusin${Gi}`,Zi=`keydown.tab${Gi}`,tn="backward",en={autofocus:!0,trapElement:null},nn={autofocus:"boolean",trapElement:"element"};class sn extends H{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return en}static get DefaultType(){return nn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),N.off(document,Gi),N.on(document,Ji,(t=>this._handleFocusin(t))),N.on(document,Zi,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,N.off(document,Gi))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=z.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===tn?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?tn:"forward")}}const on=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",rn=".sticky-top",an="padding-right",ln="margin-right";class cn{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,an,(e=>e+t)),this._setElementAttributes(on,an,(e=>e+t)),this._setElementAttributes(rn,ln,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,an),this._resetElementAttributes(on,an),this._resetElementAttributes(rn,ln)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&F.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=F.getDataAttribute(t,e);null!==i?(F.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(o(t))e(t);else for(const i of z.find(t,this._element))e(i)}}const hn=".bs.modal",dn=`hide${hn}`,un=`hidePrevented${hn}`,fn=`hidden${hn}`,pn=`show${hn}`,mn=`shown${hn}`,gn=`resize${hn}`,_n=`click.dismiss${hn}`,bn=`mousedown.dismiss${hn}`,vn=`keydown.dismiss${hn}`,yn=`click${hn}.data-api`,wn="modal-open",An="show",En="modal-static",Tn={backdrop:!0,focus:!0,keyboard:!0},Cn={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class On extends W{constructor(t,e){super(t,e),this._dialog=z.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new cn,this._addEventListeners()}static get Default(){return Tn}static get DefaultType(){return Cn}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||N.trigger(this._element,pn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(wn),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(N.trigger(this._element,dn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(An),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){N.off(window,hn),N.off(this._dialog,hn),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Ui({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=z.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),d(this._element),this._element.classList.add(An),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,N.trigger(this._element,mn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){N.on(this._element,vn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():this._triggerBackdropTransition())})),N.on(window,gn,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),N.on(this._element,bn,(t=>{N.one(this._element,_n,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(wn),this._resetAdjustments(),this._scrollBar.reset(),N.trigger(this._element,fn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(N.trigger(this._element,un).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(En)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(En),this._queueCallback((()=>{this._element.classList.remove(En),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=p()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=p()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=On.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}N.on(document,yn,'[data-bs-toggle="modal"]',(function(t){const e=z.getElementFromSelector(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),N.one(e,pn,(t=>{t.defaultPrevented||N.one(e,fn,(()=>{a(this)&&this.focus()}))}));const i=z.findOne(".modal.show");i&&On.getInstance(i).hide(),On.getOrCreateInstance(e).toggle(this)})),R(On),m(On);const xn=".bs.offcanvas",kn=".data-api",Ln=`load${xn}${kn}`,Sn="show",Dn="showing",$n="hiding",In=".offcanvas.show",Nn=`show${xn}`,Pn=`shown${xn}`,Mn=`hide${xn}`,jn=`hidePrevented${xn}`,Fn=`hidden${xn}`,Hn=`resize${xn}`,Wn=`click${xn}${kn}`,Bn=`keydown.dismiss${xn}`,zn={backdrop:!0,keyboard:!0,scroll:!1},Rn={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class qn extends W{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return zn}static get DefaultType(){return Rn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||N.trigger(this._element,Nn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new cn).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Dn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add(Sn),this._element.classList.remove(Dn),N.trigger(this._element,Pn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(N.trigger(this._element,Mn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add($n),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove(Sn,$n),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new cn).reset(),N.trigger(this._element,Fn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new Ui({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():N.trigger(this._element,jn)}:null})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_addEventListeners(){N.on(this._element,Bn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():N.trigger(this._element,jn))}))}static jQueryInterface(t){return this.each((function(){const e=qn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}N.on(document,Wn,'[data-bs-toggle="offcanvas"]',(function(t){const e=z.getElementFromSelector(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),l(this))return;N.one(e,Fn,(()=>{a(this)&&this.focus()}));const i=z.findOne(In);i&&i!==e&&qn.getInstance(i).hide(),qn.getOrCreateInstance(e).toggle(this)})),N.on(window,Ln,(()=>{for(const t of z.find(In))qn.getOrCreateInstance(t).show()})),N.on(window,Hn,(()=>{for(const t of z.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&qn.getOrCreateInstance(t).hide()})),R(qn),m(qn);const Vn={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Kn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Qn=/^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i,Xn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Kn.has(i)||Boolean(Qn.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Yn={allowList:Vn,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"
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")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},Gn)}_setContent(t,e,i){const n=z.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?o(e)?this._putElementInTemplate(r(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)Xn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return g(t,[this])}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const Zn=new Set(["sanitize","allowList","sanitizeFn"]),ts="fade",es="show",is=".modal",ns="hide.bs.modal",ss="hover",os="focus",rs={AUTO:"auto",TOP:"top",RIGHT:p()?"left":"right",BOTTOM:"bottom",LEFT:p()?"right":"left"},as={allowList:Vn,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,6],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'',title:"",trigger:"hover focus"},ls={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class cs extends W{constructor(t,e){if(void 0===vi)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,e),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return as}static get DefaultType(){return ls}static get NAME(){return"tooltip"}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(){this._isEnabled&&(this._activeTrigger.click=!this._activeTrigger.click,this._isShown()?this._leave():this._enter())}dispose(){clearTimeout(this._timeout),N.off(this._element.closest(is),ns,this._hideModalHandler),this._element.getAttribute("data-bs-original-title")&&this._element.setAttribute("title",this._element.getAttribute("data-bs-original-title")),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this._isWithContent()||!this._isEnabled)return;const t=N.trigger(this._element,this.constructor.eventName("show")),e=(c(this._element)||this._element.ownerDocument.documentElement).contains(this._element);if(t.defaultPrevented||!e)return;this._disposePopper();const i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),N.trigger(this._element,this.constructor.eventName("inserted"))),this._popper=this._createPopper(i),i.classList.add(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.on(t,"mouseover",h);this._queueCallback((()=>{N.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!N.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented){if(this._getTipElement().classList.remove(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.off(t,"mouseover",h);this._activeTrigger.click=!1,this._activeTrigger[os]=!1,this._activeTrigger[ss]=!1,this._isHovered=null,this._queueCallback((()=>{this._isWithActiveTrigger()||(this._isHovered||this._disposePopper(),this._element.removeAttribute("aria-describedby"),N.trigger(this._element,this.constructor.eventName("hidden")))}),this.tip,this._isAnimated())}}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove(ts,es),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute("id",i),this._isAnimated()&&e.classList.add(ts),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new Jn({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{".tooltip-inner":this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains(ts)}_isShown(){return this.tip&&this.tip.classList.contains(es)}_createPopper(t){const e=g(this._config.placement,[this,t,this._element]),i=rs[e.toUpperCase()];return 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QuartoAxeReporter { - constructor(axeResult, options) { - super(axeResult, options); - } - - report() { - console.log(JSON.stringify(this.axeResult, null, 2)); - } -} - -class QuartoAxeConsoleReporter extends QuartoAxeReporter { - constructor(axeResult, options) { - super(axeResult, options); - } - - report() { - for (const violation of this.axeResult.violations) { - console.log(violation.description); - for (const node of violation.nodes) { - for (const target of node.target) { - console.log(target); - console.log(document.querySelector(target)); - } - } - } - } -} - -class QuartoAxeDocumentReporter extends QuartoAxeReporter { - constructor(axeResult, options) { - super(axeResult, options); - } - - createViolationElement(violation) { - const violationElement = document.createElement("div"); - - const descriptionElement = document.createElement("div"); - descriptionElement.className = "quarto-axe-violation-description"; - descriptionElement.innerText = `${violation.impact.replace(/^[a-z]/, match => match.toLocaleUpperCase())}: ${violation.description}`; - violationElement.appendChild(descriptionElement); - - const helpElement = document.createElement("div"); - helpElement.className = "quarto-axe-violation-help"; - helpElement.innerText = violation.help; - violationElement.appendChild(helpElement); - - const nodesElement = document.createElement("div"); - nodesElement.className = "quarto-axe-violation-nodes"; - violationElement.appendChild(nodesElement); - const nodeElement = document.createElement("div"); - nodeElement.className = "quarto-axe-violation-selector"; - for (const node of violation.nodes) { - for (const target of node.target) { - const targetElement = document.createElement("span"); - targetElement.className = "quarto-axe-violation-target"; - targetElement.innerText = target; - nodeElement.appendChild(targetElement); - nodeElement.addEventListener("mouseenter", () => { - const element = document.querySelector(target); - if (element) { - element.scrollIntoView({ behavior: "smooth", block: "center" }); - element.classList.add("quarto-axe-hover-highlight"); - setTimeout(() => { - element.style.border = ""; - }, 2000); - } - }); - nodeElement.addEventListener("mouseleave", () => { - const element = document.querySelector(target); - if (element) { - element.classList.remove("quarto-axe-hover-highlight"); - } - }); - nodeElement.addEventListener("click", () => { - console.log(document.querySelector(target)); - }); - nodeElement.appendChild(targetElement); - } - nodesElement.appendChild(nodeElement); - } - return violationElement; - } - - report() { - const violations = this.axeResult.violations; - const reportElement = document.createElement("div"); - reportElement.className = "quarto-axe-report"; - if (violations.length === 0) { - const noViolationsElement = document.createElement("div"); - noViolationsElement.className = "quarto-axe-no-violations"; - noViolationsElement.innerText = "No axe-core violations found."; - reportElement.appendChild(noViolationsElement); - } - violations.forEach((violation) => { - reportElement.appendChild(this.createViolationElement(violation)); - }); - document.querySelector("main").appendChild(reportElement); - } -} - -const reporters = { - json: QuartoAxeJsonReporter, - console: QuartoAxeConsoleReporter, - document: QuartoAxeDocumentReporter, -}; - -class QuartoAxeChecker { - constructor(opts) { - this.options = opts; - } - async init() { - const axe = (await import("https://cdn.skypack.dev/pin/axe-core@v4.10.3-aVOFXWsJaCpVrtv89pCa/mode=imports,min/optimized/axe-core.js")).default; - const result = await axe.run({ - exclude: [ - // https://github.com/microsoft/tabster/issues/288 - // MS has claimed they won't fix this, so we need to add an exclusion to - // all tabster elements - "[data-tabster-dummy]" - ], - preload: { assets: ['cssom'], timeout: 50000 } - }); - const reporter = this.options === true ? new QuartoAxeConsoleReporter(result) : new reporters[this.options.output](result, this.options); - reporter.report(); - } -} - -export async function init() { - const opts = document.querySelector("#quarto-axe-checker-options"); - if (opts) { - const jsonOptions = JSON.parse(atob(opts.textContent)); - const checker = new QuartoAxeChecker(jsonOptions); - await checker.init(); - } -} \ No newline at end of file diff --git a/docs/site_libs/quarto-html/popper.min.js b/docs/site_libs/quarto-html/popper.min.js deleted file mode 100644 index e3726d7..0000000 --- a/docs/site_libs/quarto-html/popper.min.js +++ /dev/null @@ -1,6 +0,0 @@ -/** - * @popperjs/core v2.11.7 - MIT License - */ - -!function(e,t){"object"==typeof exports&&"undefined"!=typeof module?t(exports):"function"==typeof define&&define.amd?define(["exports"],t):t((e="undefined"!=typeof globalThis?globalThis:e||self).Popper={})}(this,(function(e){"use strict";function t(e){if(null==e)return window;if("[object Window]"!==e.toString()){var t=e.ownerDocument;return 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b/docs/site_libs/quarto-html/quarto-syntax-highlighting-587c61ba64f3a5504c4d52d930310e48.css deleted file mode 100644 index 070962e..0000000 --- a/docs/site_libs/quarto-html/quarto-syntax-highlighting-587c61ba64f3a5504c4d52d930310e48.css +++ /dev/null @@ -1,236 +0,0 @@ -/* quarto syntax highlight colors */ -:root { - --quarto-hl-ot-color: #003B4F; - --quarto-hl-at-color: #657422; - --quarto-hl-ss-color: #20794D; - --quarto-hl-an-color: #5E5E5E; - --quarto-hl-fu-color: #4758AB; - --quarto-hl-st-color: #20794D; - --quarto-hl-cf-color: #003B4F; - --quarto-hl-op-color: #5E5E5E; - --quarto-hl-er-color: #AD0000; - --quarto-hl-bn-color: #AD0000; - --quarto-hl-al-color: #AD0000; - --quarto-hl-va-color: #111111; - --quarto-hl-bu-color: inherit; - --quarto-hl-ex-color: inherit; - --quarto-hl-pp-color: #AD0000; - --quarto-hl-in-color: #5E5E5E; - --quarto-hl-vs-color: #20794D; - --quarto-hl-wa-color: #5E5E5E; - --quarto-hl-do-color: #5E5E5E; - --quarto-hl-im-color: #00769E; - --quarto-hl-ch-color: #20794D; - --quarto-hl-dt-color: #AD0000; - --quarto-hl-fl-color: #AD0000; - --quarto-hl-co-color: #5E5E5E; - --quarto-hl-cv-color: #5E5E5E; - --quarto-hl-cn-color: #8f5902; - --quarto-hl-sc-color: #5E5E5E; - --quarto-hl-dv-color: #AD0000; - --quarto-hl-kw-color: #003B4F; -} - -/* other quarto variables */ -:root { - --quarto-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace; -} - -/* syntax highlight based on Pandoc's rules */ -pre > code.sourceCode > span { - color: #003B4F; -} - -code.sourceCode > span { - color: #003B4F; -} - -div.sourceCode, -div.sourceCode pre.sourceCode { - color: #003B4F; -} - -/* Normal */ -code span { - color: #003B4F; -} - -/* Alert */ -code span.al { - color: #AD0000; - font-style: inherit; -} - -/* Annotation */ -code span.an { - color: #5E5E5E; - font-style: inherit; -} - -/* Attribute */ -code span.at { - color: #657422; - font-style: inherit; -} - -/* BaseN */ -code span.bn { - color: #AD0000; - font-style: inherit; -} - -/* BuiltIn */ -code span.bu { - font-style: inherit; -} - -/* ControlFlow */ -code span.cf { - color: #003B4F; - font-weight: bold; - font-style: inherit; -} - -/* Char */ -code span.ch { - color: #20794D; - font-style: inherit; -} - -/* Constant */ -code span.cn { - color: #8f5902; - font-style: inherit; -} - -/* Comment */ -code span.co { - color: #5E5E5E; - font-style: inherit; -} - -/* CommentVar */ -code span.cv { - color: #5E5E5E; - font-style: italic; -} - -/* Documentation */ -code span.do { - color: #5E5E5E; - font-style: italic; -} - -/* DataType */ -code span.dt { - color: #AD0000; - font-style: inherit; -} - -/* DecVal */ -code span.dv { - color: #AD0000; - font-style: inherit; -} - -/* Error */ -code span.er { - color: #AD0000; - font-style: inherit; -} - -/* Extension */ -code span.ex { - font-style: inherit; -} - -/* Float */ -code span.fl { - color: #AD0000; - font-style: inherit; -} - -/* Function */ -code span.fu { - color: #4758AB; - font-style: inherit; -} - -/* Import */ -code span.im { - color: #00769E; - font-style: inherit; -} - -/* Information */ -code span.in { - color: #5E5E5E; - font-style: inherit; -} - -/* Keyword */ -code span.kw { - color: #003B4F; - font-weight: bold; - font-style: inherit; -} - -/* Operator */ -code span.op { - color: #5E5E5E; - font-style: inherit; -} - -/* Other */ -code span.ot { - color: #003B4F; - font-style: inherit; -} - -/* Preprocessor */ -code span.pp { - color: #AD0000; - font-style: inherit; -} - -/* SpecialChar */ -code span.sc { - color: #5E5E5E; - font-style: inherit; -} - -/* SpecialString */ -code span.ss { - color: #20794D; - font-style: inherit; -} - -/* String */ -code span.st { - color: #20794D; - font-style: inherit; -} - -/* Variable */ -code span.va { - color: #111111; - font-style: inherit; -} - -/* VerbatimString */ -code span.vs { - color: #20794D; - font-style: inherit; -} - -/* Warning */ -code span.wa { - color: #5E5E5E; - font-style: italic; -} - -.prevent-inlining { - content: " { - // Find any conflicting margin elements and add margins to the - // top to prevent overlap - const marginChildren = window.document.querySelectorAll( - ".column-margin.column-container > *, .margin-caption, .aside" - ); - - let lastBottom = 0; - for (const marginChild of marginChildren) { - if (marginChild.offsetParent !== null) { - // clear the top margin so we recompute it - marginChild.style.marginTop = null; - const top = marginChild.getBoundingClientRect().top + window.scrollY; - if (top < lastBottom) { - const marginChildStyle = window.getComputedStyle(marginChild); - const marginBottom = parseFloat(marginChildStyle["marginBottom"]); - const margin = lastBottom - top + marginBottom; - marginChild.style.marginTop = `${margin}px`; - } - const styles = window.getComputedStyle(marginChild); - const marginTop = parseFloat(styles["marginTop"]); - lastBottom = top + marginChild.getBoundingClientRect().height + marginTop; - } - } -}; - -window.document.addEventListener("DOMContentLoaded", function (_event) { - // Recompute the position of margin elements anytime the body size changes - if (window.ResizeObserver) { - const resizeObserver = new window.ResizeObserver( - throttle(() => { - layoutMarginEls(); - if ( - window.document.body.getBoundingClientRect().width < 990 && - isReaderMode() - ) { - quartoToggleReader(); - } - }, 50) - ); - resizeObserver.observe(window.document.body); - } - - const tocEl = window.document.querySelector('nav.toc-active[role="doc-toc"]'); - const sidebarEl = window.document.getElementById("quarto-sidebar"); - const leftTocEl = window.document.getElementById("quarto-sidebar-toc-left"); - const marginSidebarEl = window.document.getElementById( - "quarto-margin-sidebar" - ); - // function to determine whether the element has a previous sibling that is active - const prevSiblingIsActiveLink = (el) => { - const sibling = el.previousElementSibling; - if (sibling && sibling.tagName === "A") { - return sibling.classList.contains("active"); - } else { - return false; - } - }; - - // dispatch for htmlwidgets - // they use slideenter event to trigger resize - function fireSlideEnter() { - const event = window.document.createEvent("Event"); - event.initEvent("slideenter", true, true); - window.document.dispatchEvent(event); - } - - const tabs = window.document.querySelectorAll('a[data-bs-toggle="tab"]'); - tabs.forEach((tab) => { - tab.addEventListener("shown.bs.tab", fireSlideEnter); - }); - - // dispatch for shiny - // they use BS shown and hidden events to trigger rendering - function distpatchShinyEvents(previous, current) { - if (window.jQuery) { - if (previous) { - window.jQuery(previous).trigger("hidden"); - } - if (current) { - window.jQuery(current).trigger("shown"); - } - } - } - - // tabby.js listener: Trigger event for htmlwidget and shiny - document.addEventListener( - "tabby", - function (event) { - fireSlideEnter(); - distpatchShinyEvents(event.detail.previousTab, event.detail.tab); - }, - false - ); - - // Track scrolling and mark TOC links as active - // get table of contents and sidebar (bail if we don't have at least one) - const tocLinks = tocEl - ? [...tocEl.querySelectorAll("a[data-scroll-target]")] - : []; - const makeActive = (link) => tocLinks[link].classList.add("active"); - const removeActive = (link) => tocLinks[link].classList.remove("active"); - const removeAllActive = () => - [...Array(tocLinks.length).keys()].forEach((link) => removeActive(link)); - - // activate the anchor for a section associated with this TOC entry - tocLinks.forEach((link) => { - link.addEventListener("click", () => { - if (link.href.indexOf("#") !== -1) { - const anchor = link.href.split("#")[1]; - const heading = window.document.querySelector( - `[data-anchor-id="${anchor}"]` - ); - if (heading) { - // Add the class - heading.classList.add("reveal-anchorjs-link"); - - // function to show the anchor - const handleMouseout = () => { - heading.classList.remove("reveal-anchorjs-link"); - heading.removeEventListener("mouseout", handleMouseout); - }; - - // add a function to clear the anchor when the user mouses out of it - heading.addEventListener("mouseout", handleMouseout); - } - } - }); - }); - - const sections = tocLinks.map((link) => { - const target = link.getAttribute("data-scroll-target"); - if (target.startsWith("#")) { - return window.document.getElementById(decodeURI(`${target.slice(1)}`)); - } else { - return window.document.querySelector(decodeURI(`${target}`)); - } - }); - - const sectionMargin = 200; - let currentActive = 0; - // track whether we've initialized state the first time - let init = false; - - const updateActiveLink = () => { - // The index from bottom to top (e.g. reversed list) - let sectionIndex = -1; - if ( - window.innerHeight + window.pageYOffset >= - window.document.body.offsetHeight - ) { - // This is the no-scroll case where last section should be the active one - sectionIndex = 0; - } else { - // This finds the last section visible on screen that should be made active - sectionIndex = [...sections].reverse().findIndex((section) => { - if (section) { - return window.pageYOffset >= section.offsetTop - sectionMargin; - } else { - return false; - } - }); - } - if (sectionIndex > -1) { - const current = sections.length - sectionIndex - 1; - if (current !== currentActive) { - removeAllActive(); - currentActive = current; - makeActive(current); - if (init) { - window.dispatchEvent(sectionChanged); - } - init = true; - } - } - }; - - const inHiddenRegion = (top, bottom, hiddenRegions) => { - for (const region of hiddenRegions) { - if (top <= region.bottom && bottom >= region.top) { - return true; - } - } - return false; - }; - - const categorySelector = "header.quarto-title-block .quarto-category"; - const activateCategories = (href) => { - // Find any categories - // Surround them with a link pointing back to: - // #category=Authoring - try { - const categoryEls = window.document.querySelectorAll(categorySelector); - for (const categoryEl of categoryEls) { - const categoryText = categoryEl.textContent; - if (categoryText) { - const link = `${href}#category=${encodeURIComponent(categoryText)}`; - const linkEl = window.document.createElement("a"); - linkEl.setAttribute("href", link); - for (const child of categoryEl.childNodes) { - linkEl.append(child); - } - categoryEl.appendChild(linkEl); - } - } - } catch { - // Ignore errors - } - }; - function hasTitleCategories() { - return window.document.querySelector(categorySelector) !== null; - } - - function offsetRelativeUrl(url) { - const offset = getMeta("quarto:offset"); - return offset ? offset + url : url; - } - - function offsetAbsoluteUrl(url) { - const offset = getMeta("quarto:offset"); - const baseUrl = new URL(offset, window.location); - - const projRelativeUrl = url.replace(baseUrl, ""); - if (projRelativeUrl.startsWith("/")) { - return projRelativeUrl; - } else { - return "/" + projRelativeUrl; - } - } - - // read a meta tag value - function getMeta(metaName) { - const metas = window.document.getElementsByTagName("meta"); - for (let i = 0; i < metas.length; i++) { - if (metas[i].getAttribute("name") === metaName) { - return metas[i].getAttribute("content"); - } - } - return ""; - } - - async function findAndActivateCategories() { - // Categories search with listing only use path without query - const currentPagePath = offsetAbsoluteUrl( - window.location.origin + window.location.pathname - ); - const response = await fetch(offsetRelativeUrl("listings.json")); - if (response.status == 200) { - return response.json().then(function (listingPaths) { - const listingHrefs = []; - for (const listingPath of listingPaths) { - const pathWithoutLeadingSlash = listingPath.listing.substring(1); - for (const item of listingPath.items) { - const encodedItem = encodeURI(item); - if ( - encodedItem === currentPagePath || - encodedItem === currentPagePath + "index.html" - ) { - // Resolve this path against the offset to be sure - // we already are using the correct path to the listing - // (this adjusts the listing urls to be rooted against - // whatever root the page is actually running against) - const relative = offsetRelativeUrl(pathWithoutLeadingSlash); - const baseUrl = window.location; - const resolvedPath = new URL(relative, baseUrl); - listingHrefs.push(resolvedPath.pathname); - break; - } - } - } - - // Look up the tree for a nearby linting and use that if we find one - const nearestListing = findNearestParentListing( - offsetAbsoluteUrl(window.location.pathname), - listingHrefs - ); - if (nearestListing) { - activateCategories(nearestListing); - } else { - // See if the referrer is a listing page for this item - const referredRelativePath = offsetAbsoluteUrl(document.referrer); - const referrerListing = listingHrefs.find((listingHref) => { - const isListingReferrer = - listingHref === referredRelativePath || - listingHref === referredRelativePath + "index.html"; - return isListingReferrer; - }); - - if (referrerListing) { - // Try to use the referrer if possible - activateCategories(referrerListing); - } else if (listingHrefs.length > 0) { - // Otherwise, just fall back to the first listing - activateCategories(listingHrefs[0]); - } - } - }); - } - } - if (hasTitleCategories()) { - findAndActivateCategories(); - } - - const findNearestParentListing = (href, listingHrefs) => { - if (!href || !listingHrefs) { - return undefined; - } - // Look up the tree for a nearby linting and use that if we find one - const relativeParts = href.substring(1).split("/"); - while (relativeParts.length > 0) { - const path = relativeParts.join("/"); - for (const listingHref of listingHrefs) { - if (listingHref.startsWith(path)) { - return listingHref; - } - } - relativeParts.pop(); - } - - return undefined; - }; - - const manageSidebarVisiblity = (el, placeholderDescriptor) => { - let isVisible = true; - let elRect; - - return (hiddenRegions) => { - if (el === null) { - return; - } - - // Find the last element of the TOC - const lastChildEl = el.lastElementChild; - - if (lastChildEl) { - // Converts the sidebar to a menu - const convertToMenu = () => { - for (const child of el.children) { - child.style.opacity = 0; - child.style.overflow = "hidden"; - child.style.pointerEvents = "none"; - } - - nexttick(() => { - const toggleContainer = window.document.createElement("div"); - toggleContainer.style.width = "100%"; - toggleContainer.classList.add("zindex-over-content"); - toggleContainer.classList.add("quarto-sidebar-toggle"); - toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom - toggleContainer.id = placeholderDescriptor.id; - toggleContainer.style.position = "fixed"; - - const toggleIcon = window.document.createElement("i"); - toggleIcon.classList.add("quarto-sidebar-toggle-icon"); - toggleIcon.classList.add("bi"); - toggleIcon.classList.add("bi-caret-down-fill"); - - const toggleTitle = window.document.createElement("div"); - const titleEl = window.document.body.querySelector( - placeholderDescriptor.titleSelector - ); - if (titleEl) { - toggleTitle.append( - titleEl.textContent || titleEl.innerText, - toggleIcon - ); - } - toggleTitle.classList.add("zindex-over-content"); - toggleTitle.classList.add("quarto-sidebar-toggle-title"); - toggleContainer.append(toggleTitle); - - const toggleContents = window.document.createElement("div"); - toggleContents.classList = el.classList; - toggleContents.classList.add("zindex-over-content"); - toggleContents.classList.add("quarto-sidebar-toggle-contents"); - for (const child of el.children) { - if (child.id === "toc-title") { - continue; - } - - const clone = child.cloneNode(true); - clone.style.opacity = 1; - clone.style.pointerEvents = null; - clone.style.display = null; - toggleContents.append(clone); - } - toggleContents.style.height = "0px"; - const positionToggle = () => { - // position the element (top left of parent, same width as parent) - if (!elRect) { - elRect = el.getBoundingClientRect(); - } - toggleContainer.style.left = `${elRect.left}px`; - toggleContainer.style.top = `${elRect.top}px`; - toggleContainer.style.width = `${elRect.width}px`; - }; - positionToggle(); - - toggleContainer.append(toggleContents); - el.parentElement.prepend(toggleContainer); - - // Process clicks - let tocShowing = false; - // Allow the caller to control whether this is dismissed - // when it is clicked (e.g. sidebar navigation supports - // opening and closing the nav tree, so don't dismiss on click) - const clickEl = placeholderDescriptor.dismissOnClick - ? toggleContainer - : toggleTitle; - - const closeToggle = () => { - if (tocShowing) { - toggleContainer.classList.remove("expanded"); - toggleContents.style.height = "0px"; - tocShowing = false; - } - }; - - // Get rid of any expanded toggle if the user scrolls - window.document.addEventListener( - "scroll", - throttle(() => { - closeToggle(); - }, 50) - ); - - // Handle positioning of the toggle - window.addEventListener( - "resize", - throttle(() => { - elRect = undefined; - positionToggle(); - }, 50) - ); - - window.addEventListener("quarto-hrChanged", () => { - elRect = undefined; - }); - - // Process the click - clickEl.onclick = () => { - if (!tocShowing) { - toggleContainer.classList.add("expanded"); - toggleContents.style.height = null; - tocShowing = true; - } else { - closeToggle(); - } - }; - }); - }; - - // Converts a sidebar from a menu back to a sidebar - const convertToSidebar = () => { - for (const child of el.children) { - child.style.opacity = 1; - child.style.overflow = null; - child.style.pointerEvents = null; - } - - const placeholderEl = window.document.getElementById( - placeholderDescriptor.id - ); - if (placeholderEl) { - placeholderEl.remove(); - } - - el.classList.remove("rollup"); - }; - - if (isReaderMode()) { - convertToMenu(); - isVisible = false; - } else { - // Find the top and bottom o the element that is being managed - const elTop = el.offsetTop; - const elBottom = - elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight; - - if (!isVisible) { - // If the element is current not visible reveal if there are - // no conflicts with overlay regions - if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) { - convertToSidebar(); - isVisible = true; - } - } else { - // If the element is visible, hide it if it conflicts with overlay regions - // and insert a placeholder toggle (or if we're in reader mode) - if (inHiddenRegion(elTop, elBottom, hiddenRegions)) { - convertToMenu(); - isVisible = false; - } - } - } - } - }; - }; - - const tabEls = document.querySelectorAll('a[data-bs-toggle="tab"]'); - for (const tabEl of tabEls) { - const id = tabEl.getAttribute("data-bs-target"); - if (id) { - const columnEl = document.querySelector( - `${id} .column-margin, .tabset-margin-content` - ); - if (columnEl) - tabEl.addEventListener("shown.bs.tab", function (event) { - const el = event.srcElement; - if (el) { - const visibleCls = `${el.id}-margin-content`; - // walk up until we find a parent tabset - let panelTabsetEl = el.parentElement; - while (panelTabsetEl) { - if (panelTabsetEl.classList.contains("panel-tabset")) { - break; - } - panelTabsetEl = panelTabsetEl.parentElement; - } - - if (panelTabsetEl) { - const prevSib = panelTabsetEl.previousElementSibling; - if ( - prevSib && - prevSib.classList.contains("tabset-margin-container") - ) { - const childNodes = prevSib.querySelectorAll( - ".tabset-margin-content" - ); - for (const childEl of childNodes) { - if (childEl.classList.contains(visibleCls)) { - childEl.classList.remove("collapse"); - } else { - childEl.classList.add("collapse"); - } - } - } - } - } - - layoutMarginEls(); - }); - } - } - - // Manage the visibility of the toc and the sidebar - const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, { - id: "quarto-toc-toggle", - titleSelector: "#toc-title", - dismissOnClick: true, - }); - const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, { - id: "quarto-sidebarnav-toggle", - titleSelector: ".title", - dismissOnClick: false, - }); - let tocLeftScrollVisibility; - if (leftTocEl) { - tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, { - id: "quarto-lefttoc-toggle", - titleSelector: "#toc-title", - dismissOnClick: true, - }); - } - - // Find the first element that uses formatting in special columns - const conflictingEls = window.document.body.querySelectorAll( - '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]' - ); - - // Filter all the possibly conflicting elements into ones - // the do conflict on the left or ride side - const arrConflictingEls = Array.from(conflictingEls); - const leftSideConflictEls = arrConflictingEls.filter((el) => { - if (el.tagName === "ASIDE") { - return false; - } - return Array.from(el.classList).find((className) => { - return ( - className !== "column-body" && - className.startsWith("column-") && - !className.endsWith("right") && - !className.endsWith("container") && - className !== "column-margin" - ); - }); - }); - const rightSideConflictEls = arrConflictingEls.filter((el) => { - if (el.tagName === "ASIDE") { - return true; - } - - const hasMarginCaption = Array.from(el.classList).find((className) => { - return className == "margin-caption"; - }); - if (hasMarginCaption) { - return true; - } - - return Array.from(el.classList).find((className) => { - return ( - className !== "column-body" && - !className.endsWith("container") && - className.startsWith("column-") && - !className.endsWith("left") - ); - }); - }); - - const kOverlapPaddingSize = 10; - function toRegions(els) { - return els.map((el) => { - const boundRect = el.getBoundingClientRect(); - const top = - boundRect.top + - document.documentElement.scrollTop - - kOverlapPaddingSize; - return { - top, - bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize, - }; - }); - } - - let hasObserved = false; - const visibleItemObserver = (els) => { - let visibleElements = [...els]; - const intersectionObserver = new IntersectionObserver( - (entries, _observer) => { - entries.forEach((entry) => { - if (entry.isIntersecting) { - if (visibleElements.indexOf(entry.target) === -1) { - visibleElements.push(entry.target); - } - } else { - visibleElements = visibleElements.filter((visibleEntry) => { - return visibleEntry !== entry; - }); - } - }); - - if (!hasObserved) { - hideOverlappedSidebars(); - } - hasObserved = true; - }, - {} - ); - els.forEach((el) => { - intersectionObserver.observe(el); - }); - - return { - getVisibleEntries: () => { - return visibleElements; - }, - }; - }; - - const rightElementObserver = visibleItemObserver(rightSideConflictEls); - const leftElementObserver = visibleItemObserver(leftSideConflictEls); - - const hideOverlappedSidebars = () => { - marginScrollVisibility(toRegions(rightElementObserver.getVisibleEntries())); - sidebarScrollVisiblity(toRegions(leftElementObserver.getVisibleEntries())); - if (tocLeftScrollVisibility) { - tocLeftScrollVisibility( - toRegions(leftElementObserver.getVisibleEntries()) - ); - } - }; - - window.quartoToggleReader = () => { - // Applies a slow class (or removes it) - // to update the transition speed - const slowTransition = (slow) => { - const manageTransition = (id, slow) => { - const el = document.getElementById(id); - if (el) { - if (slow) { - el.classList.add("slow"); - } else { - el.classList.remove("slow"); - } - } - }; - - manageTransition("TOC", slow); - manageTransition("quarto-sidebar", slow); - }; - const readerMode = !isReaderMode(); - setReaderModeValue(readerMode); - - // If we're entering reader mode, slow the transition - if (readerMode) { - slowTransition(readerMode); - } - highlightReaderToggle(readerMode); - hideOverlappedSidebars(); - - // If we're exiting reader mode, restore the non-slow transition - if (!readerMode) { - slowTransition(!readerMode); - } - }; - - const highlightReaderToggle = (readerMode) => { - const els = document.querySelectorAll(".quarto-reader-toggle"); - if (els) { - els.forEach((el) => { - if (readerMode) { - el.classList.add("reader"); - } else { - el.classList.remove("reader"); - } - }); - } - }; - - const setReaderModeValue = (val) => { - if (window.location.protocol !== "file:") { - window.localStorage.setItem("quarto-reader-mode", val); - } else { - localReaderMode = val; - } - }; - - const isReaderMode = () => { - if (window.location.protocol !== "file:") { - return window.localStorage.getItem("quarto-reader-mode") === "true"; - } else { - return localReaderMode; - } - }; - let localReaderMode = null; - - const tocOpenDepthStr = tocEl?.getAttribute("data-toc-expanded"); - const tocOpenDepth = tocOpenDepthStr ? Number(tocOpenDepthStr) : 1; - - // Walk the TOC and collapse/expand nodes - // Nodes are expanded if: - // - they are top level - // - they have children that are 'active' links - // - they are directly below an link that is 'active' - const walk = (el, depth) => { - // Tick depth when we enter a UL - if (el.tagName === "UL") { - depth = depth + 1; - } - - // It this is active link - let isActiveNode = false; - if (el.tagName === "A" && el.classList.contains("active")) { - isActiveNode = true; - } - - // See if there is an active child to this element - let hasActiveChild = false; - for (const child of el.children) { - hasActiveChild = walk(child, depth) || hasActiveChild; - } - - // Process the collapse state if this is an UL - if (el.tagName === "UL") { - if (tocOpenDepth === -1 && depth > 1) { - // toc-expand: false - el.classList.add("collapse"); - } else if ( - depth <= tocOpenDepth || - hasActiveChild || - prevSiblingIsActiveLink(el) - ) { - el.classList.remove("collapse"); - } else { - el.classList.add("collapse"); - } - - // untick depth when we leave a UL - depth = depth - 1; - } - return hasActiveChild || isActiveNode; - }; - - // walk the TOC and expand / collapse any items that should be shown - if (tocEl) { - updateActiveLink(); - walk(tocEl, 0); - } - - // Throttle the scroll event and walk peridiocally - window.document.addEventListener( - "scroll", - throttle(() => { - if (tocEl) { - updateActiveLink(); - walk(tocEl, 0); - } - if (!isReaderMode()) { - hideOverlappedSidebars(); - } - }, 5) - ); - window.addEventListener( - "resize", - throttle(() => { - if (tocEl) { - updateActiveLink(); - walk(tocEl, 0); - } - if (!isReaderMode()) { - hideOverlappedSidebars(); - } - }, 10) - ); - hideOverlappedSidebars(); - highlightReaderToggle(isReaderMode()); -}); - -tabsets.init(); -axe.init(); - -function throttle(func, wait) { - let waiting = false; - return function () { - if (!waiting) { - func.apply(this, arguments); - waiting = true; - setTimeout(function () { - waiting = false; - }, wait); - } - }; -} - -function nexttick(func) { - return setTimeout(func, 0); -} diff --git a/docs/site_libs/quarto-html/tabsets/tabsets.js b/docs/site_libs/quarto-html/tabsets/tabsets.js deleted file mode 100644 index 51345d0..0000000 --- a/docs/site_libs/quarto-html/tabsets/tabsets.js +++ /dev/null @@ -1,95 +0,0 @@ -// grouped tabsets - -export function init() { - window.addEventListener("pageshow", (_event) => { - function getTabSettings() { - const data = localStorage.getItem("quarto-persistent-tabsets-data"); - if (!data) { - localStorage.setItem("quarto-persistent-tabsets-data", "{}"); - return {}; - } - if (data) { - return JSON.parse(data); - } - } - - function setTabSettings(data) { - localStorage.setItem( - "quarto-persistent-tabsets-data", - JSON.stringify(data) - ); - } - - function setTabState(groupName, groupValue) { - const data = getTabSettings(); - data[groupName] = groupValue; - setTabSettings(data); - } - - function toggleTab(tab, active) { - const tabPanelId = tab.getAttribute("aria-controls"); - const tabPanel = document.getElementById(tabPanelId); - if (active) { - tab.classList.add("active"); - tabPanel.classList.add("active"); - } else { - tab.classList.remove("active"); - tabPanel.classList.remove("active"); - } - } - - function toggleAll(selectedGroup, selectorsToSync) { - for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) { - const active = selectedGroup === thisGroup; - for (const tab of tabs) { - toggleTab(tab, active); - } - } - } - - function findSelectorsToSyncByLanguage() { - const result = {}; - const tabs = Array.from( - document.querySelectorAll(`div[data-group] a[id^='tabset-']`) - ); - for (const item of tabs) { - const div = item.parentElement.parentElement.parentElement; - const group = div.getAttribute("data-group"); - if (!result[group]) { - result[group] = {}; - } - const selectorsToSync = result[group]; - const value = item.innerHTML; - if (!selectorsToSync[value]) { - selectorsToSync[value] = []; - } - selectorsToSync[value].push(item); - } - return result; - } - - function setupSelectorSync() { - const selectorsToSync = findSelectorsToSyncByLanguage(); - Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => { - Object.entries(tabSetsByValue).forEach(([value, items]) => { - items.forEach((item) => { - item.addEventListener("click", (_event) => { - setTabState(group, value); - toggleAll(value, selectorsToSync[group]); - }); - }); - }); - }); - return selectorsToSync; - } - - const selectorsToSync = setupSelectorSync(); - for (const [group, selectedName] of Object.entries(getTabSettings())) { - const selectors = selectorsToSync[group]; - // it's possible that stale state gives us empty selections, so we explicitly check here. - if (selectors) { - toggleAll(selectedName, selectors); - } - } - }); -} diff --git a/docs/site_libs/quarto-html/tippy.css b/docs/site_libs/quarto-html/tippy.css deleted file mode 100644 index e6ae635..0000000 --- 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a/docs/site_libs/quarto-search/quarto-search.js b/docs/site_libs/quarto-search/quarto-search.js deleted file mode 100644 index d788a95..0000000 --- a/docs/site_libs/quarto-search/quarto-search.js +++ /dev/null @@ -1,1290 +0,0 @@ -const kQueryArg = "q"; -const kResultsArg = "show-results"; - -// If items don't provide a URL, then both the navigator and the onSelect -// function aren't called (and therefore, the default implementation is used) -// -// We're using this sentinel URL to signal to those handlers that this -// item is a more item (along with the type) and can be handled appropriately -const kItemTypeMoreHref = "0767FDFD-0422-4E5A-BC8A-3BE11E5BBA05"; - -window.document.addEventListener("DOMContentLoaded", function (_event) { - // Ensure that search is available on this page. If it isn't, - // should return early and not do anything - var searchEl = window.document.getElementById("quarto-search"); - if (!searchEl) return; - - const { autocomplete } = window["@algolia/autocomplete-js"]; - - let quartoSearchOptions = {}; - let language = {}; - const searchOptionEl = window.document.getElementById( - "quarto-search-options" - ); - if (searchOptionEl) { - const jsonStr = searchOptionEl.textContent; - quartoSearchOptions = JSON.parse(jsonStr); - language = quartoSearchOptions.language; - } - - // note the search mode - if (quartoSearchOptions.type === "overlay") { - searchEl.classList.add("type-overlay"); - } else { - searchEl.classList.add("type-textbox"); - } - - // Used to determine highlighting behavior for this page - // A `q` query param is expected when the user follows a search - // to this page - const currentUrl = new URL(window.location); - const query = currentUrl.searchParams.get(kQueryArg); - const showSearchResults = currentUrl.searchParams.get(kResultsArg); - const mainEl = window.document.querySelector("main"); - - // highlight matches on the page - if (query && mainEl) { - // perform any highlighting - highlight(escapeRegExp(query), mainEl); - - // fix up the URL to remove the q query param - const replacementUrl = new URL(window.location); - replacementUrl.searchParams.delete(kQueryArg); - window.history.replaceState({}, "", replacementUrl); - } - - // function to clear highlighting on the page when the search query changes - // (e.g. if the user edits the query or clears it) - let highlighting = true; - const resetHighlighting = (searchTerm) => { - if (mainEl && highlighting && query && searchTerm !== query) { - clearHighlight(query, mainEl); - highlighting = false; - } - }; - - // Clear search highlighting when the user scrolls sufficiently - const resetFn = () => { - resetHighlighting(""); - window.removeEventListener("quarto-hrChanged", resetFn); - window.removeEventListener("quarto-sectionChanged", resetFn); - }; - - // Register this event after the initial scrolling and settling of events - // on the page - window.addEventListener("quarto-hrChanged", resetFn); - window.addEventListener("quarto-sectionChanged", resetFn); - - // Responsively switch to overlay mode if the search is present on the navbar - // Note that switching the sidebar to overlay mode requires more coordinate (not just - // the media query since we generate different HTML for sidebar overlays than we do - // for sidebar input UI) - const detachedMediaQuery = - quartoSearchOptions.type === "overlay" ? "all" : "(max-width: 991px)"; - - // If configured, include the analytics client to send insights - const plugins = configurePlugins(quartoSearchOptions); - - let lastState = null; - const { setIsOpen, setQuery, setCollections } = autocomplete({ - container: searchEl, - detachedMediaQuery: detachedMediaQuery, - defaultActiveItemId: 0, - panelContainer: "#quarto-search-results", - panelPlacement: quartoSearchOptions["panel-placement"], - debug: false, - openOnFocus: true, - plugins, - classNames: { - form: "d-flex", - }, - placeholder: language["search-text-placeholder"], - translations: { - clearButtonTitle: language["search-clear-button-title"], - detachedCancelButtonText: language["search-detached-cancel-button-title"], - submitButtonTitle: language["search-submit-button-title"], - }, - initialState: { - query, - }, - getItemUrl({ item }) { - return item.href; - }, - onStateChange({ state }) { - // If this is a file URL, note that - - // Perhaps reset highlighting - resetHighlighting(state.query); - - // If the panel just opened, ensure the panel is positioned properly - if (state.isOpen) { - if (lastState && !lastState.isOpen) { - setTimeout(() => { - positionPanel(quartoSearchOptions["panel-placement"]); - }, 150); - } - } - - // Perhaps show the copy link - showCopyLink(state.query, quartoSearchOptions); - - lastState = state; - }, - reshape({ sources, state }) { - return sources.map((source) => { - try { - const items = source.getItems(); - - // Validate the items - validateItems(items); - - // group the items by document - const groupedItems = new Map(); - items.forEach((item) => { - const hrefParts = item.href.split("#"); - const baseHref = hrefParts[0]; - const isDocumentItem = hrefParts.length === 1; - - const items = groupedItems.get(baseHref); - if (!items) { - groupedItems.set(baseHref, [item]); - } else { - // If the href for this item matches the document - // exactly, place this item first as it is the item that represents - // the document itself - if (isDocumentItem) { - items.unshift(item); - } else { - items.push(item); - } - groupedItems.set(baseHref, items); - } - }); - - const reshapedItems = []; - let count = 1; - for (const [_key, value] of groupedItems) { - const firstItem = value[0]; - reshapedItems.push({ - ...firstItem, - type: kItemTypeDoc, - }); - - const collapseMatches = quartoSearchOptions["collapse-after"]; - const collapseCount = - typeof collapseMatches === "number" ? collapseMatches : 1; - - if (value.length > 1) { - const target = `search-more-${count}`; - const isExpanded = - state.context.expanded && - state.context.expanded.includes(target); - - const remainingCount = value.length - collapseCount; - - for (let i = 1; i < value.length; i++) { - if (collapseMatches && i === collapseCount) { - reshapedItems.push({ - target, - title: isExpanded - ? language["search-hide-matches-text"] - : remainingCount === 1 - ? `${remainingCount} ${language["search-more-match-text"]}` - : `${remainingCount} ${language["search-more-matches-text"]}`, - type: kItemTypeMore, - href: kItemTypeMoreHref, - }); - } - - if (isExpanded || !collapseMatches || i < collapseCount) { - reshapedItems.push({ - ...value[i], - type: kItemTypeItem, - target, - }); - } - } - } - count += 1; - } - - return { - ...source, - getItems() { - return reshapedItems; - }, - }; - } catch (error) { - // Some form of error occurred - return { - ...source, - getItems() { - return [ - { - title: error.name || "An Error Occurred While Searching", - text: - error.message || - "An unknown error occurred while attempting to perform the requested search.", - type: kItemTypeError, - }, - ]; - }, - }; - } - }); - }, - navigator: { - navigate({ itemUrl }) { - if (itemUrl !== offsetURL(kItemTypeMoreHref)) { - window.location.assign(itemUrl); - } - }, - navigateNewTab({ itemUrl }) { - if (itemUrl !== offsetURL(kItemTypeMoreHref)) { - const windowReference = window.open(itemUrl, "_blank", "noopener"); - if (windowReference) { - windowReference.focus(); - } - } - }, - navigateNewWindow({ itemUrl }) { - if (itemUrl !== offsetURL(kItemTypeMoreHref)) { - window.open(itemUrl, "_blank", "noopener"); - } - }, - }, - getSources({ state, setContext, setActiveItemId, refresh }) { - return [ - { - sourceId: "documents", - getItemUrl({ item }) { - if (item.href) { - return offsetURL(item.href); - } else { - return undefined; - } - }, - onSelect({ - item, - state, - setContext, - setIsOpen, - setActiveItemId, - refresh, - }) { - if (item.type === kItemTypeMore) { - toggleExpanded(item, state, setContext, setActiveItemId, refresh); - - // Toggle more - setIsOpen(true); - } - }, - getItems({ query }) { - if (query === null || query === "") { - return []; - } - - const limit = quartoSearchOptions.limit; - if (quartoSearchOptions.algolia) { - return algoliaSearch(query, limit, quartoSearchOptions.algolia); - } else { - // Fuse search options - const fuseSearchOptions = { - isCaseSensitive: false, - shouldSort: true, - minMatchCharLength: 2, - limit: limit, - }; - - return readSearchData().then(function (fuse) { - return fuseSearch(query, fuse, fuseSearchOptions); - }); - } - }, - templates: { - noResults({ createElement }) { - const hasQuery = lastState.query; - - return createElement( - "div", - { - class: `quarto-search-no-results${ - hasQuery ? "" : " no-query" - }`, - }, - language["search-no-results-text"] - ); - }, - header({ items, createElement }) { - // count the documents - const count = items.filter((item) => { - return item.type === kItemTypeDoc; - }).length; - - if (count > 0) { - return createElement( - "div", - { class: "search-result-header" }, - `${count} ${language["search-matching-documents-text"]}` - ); - } else { - return createElement( - "div", - { class: "search-result-header-no-results" }, - `` - ); - } - }, - footer({ _items, createElement }) { - if ( - quartoSearchOptions.algolia && - quartoSearchOptions.algolia["show-logo"] - ) { - const libDir = quartoSearchOptions.algolia["libDir"]; - const logo = createElement("img", { - src: offsetURL( - `${libDir}/quarto-search/search-by-algolia.svg` - ), - class: "algolia-search-logo", - }); - return createElement( - "a", - { href: "http://www.algolia.com/" }, - logo - ); - } - }, - - item({ item, createElement }) { - return renderItem( - item, - createElement, - state, - setActiveItemId, - setContext, - refresh, - quartoSearchOptions - ); - }, - }, - }, - ]; - }, - }); - - window.quartoOpenSearch = () => { - setIsOpen(false); - setIsOpen(true); - focusSearchInput(); - }; - - document.addEventListener("keyup", (event) => { - const { key } = event; - const kbds = quartoSearchOptions["keyboard-shortcut"]; - const focusedEl = document.activeElement; - - const isFormElFocused = [ - "input", - "select", - "textarea", - "button", - "option", - ].find((tag) => { - return focusedEl.tagName.toLowerCase() === tag; - }); - - if ( - kbds && - kbds.includes(key) && - !isFormElFocused && - !document.activeElement.isContentEditable - ) { - event.preventDefault(); - window.quartoOpenSearch(); - } - }); - - // Remove the labeleledby attribute since it is pointing - // to a non-existent label - if (quartoSearchOptions.type === "overlay") { - const inputEl = window.document.querySelector( - "#quarto-search .aa-Autocomplete" - ); - if (inputEl) { - inputEl.removeAttribute("aria-labelledby"); - } - } - - function throttle(func, wait) { - let waiting = false; - return function () { - if (!waiting) { - func.apply(this, arguments); - waiting = true; - setTimeout(function () { - waiting = false; - }, wait); - } - }; - } - - // If the main document scrolls dismiss the search results - // (otherwise, since they're floating in the document they can scroll with the document) - window.document.body.onscroll = throttle(() => { - // Only do this if we're not detached - // Bug #7117 - // This will happen when the keyboard is shown on ios (resulting in a scroll) - // which then closed the search UI - if (!window.matchMedia(detachedMediaQuery).matches) { - setIsOpen(false); - } - }, 50); - - if (showSearchResults) { - setIsOpen(true); - focusSearchInput(); - } -}); - -function configurePlugins(quartoSearchOptions) { - const autocompletePlugins = []; - const algoliaOptions = quartoSearchOptions.algolia; - if ( - algoliaOptions && - algoliaOptions["analytics-events"] && - algoliaOptions["search-only-api-key"] && - algoliaOptions["application-id"] - ) { - const apiKey = algoliaOptions["search-only-api-key"]; - const appId = algoliaOptions["application-id"]; - - // Aloglia insights may not be loaded because they require cookie consent - // Use deferred loading so events will start being recorded when/if consent - // is granted. - const algoliaInsightsDeferredPlugin = deferredLoadPlugin(() => { - if ( - window.aa && - window["@algolia/autocomplete-plugin-algolia-insights"] - ) { - window.aa("init", { - appId, - apiKey, - useCookie: true, - }); - - const { createAlgoliaInsightsPlugin } = - window["@algolia/autocomplete-plugin-algolia-insights"]; - // Register the insights client - const algoliaInsightsPlugin = createAlgoliaInsightsPlugin({ - insightsClient: window.aa, - onItemsChange({ insights, insightsEvents }) { - const events = insightsEvents.flatMap((event) => { - // This API limits the number of items per event to 20 - const chunkSize = 20; - const itemChunks = []; - const eventItems = event.items; - for (let i = 0; i < eventItems.length; i += chunkSize) { - itemChunks.push(eventItems.slice(i, i + chunkSize)); - } - // Split the items into multiple events that can be sent - const events = itemChunks.map((items) => { - return { - ...event, - items, - }; - }); - return events; - }); - - for (const event of events) { - insights.viewedObjectIDs(event); - } - }, - }); - return algoliaInsightsPlugin; - } - }); - - // Add the plugin - autocompletePlugins.push(algoliaInsightsDeferredPlugin); - return autocompletePlugins; - } -} - -// For plugins that may not load immediately, create a wrapper -// plugin and forward events and plugin data once the plugin -// is initialized. This is useful for cases like cookie consent -// which may prevent the analytics insights event plugin from initializing -// immediately. -function deferredLoadPlugin(createPlugin) { - let plugin = undefined; - let subscribeObj = undefined; - const wrappedPlugin = () => { - if (!plugin && subscribeObj) { - plugin = createPlugin(); - if (plugin && plugin.subscribe) { - plugin.subscribe(subscribeObj); - } - } - return plugin; - }; - - return { - subscribe: (obj) => { - subscribeObj = obj; - }, - onStateChange: (obj) => { - const plugin = wrappedPlugin(); - if (plugin && plugin.onStateChange) { - plugin.onStateChange(obj); - } - }, - onSubmit: (obj) => { - const plugin = wrappedPlugin(); - if (plugin && plugin.onSubmit) { - plugin.onSubmit(obj); - } - }, - onReset: (obj) => { - const plugin = wrappedPlugin(); - if (plugin && plugin.onReset) { - plugin.onReset(obj); - } - }, - getSources: (obj) => { - const plugin = wrappedPlugin(); - if (plugin && plugin.getSources) { - return plugin.getSources(obj); - } else { - return Promise.resolve([]); - } - }, - data: (obj) => { - const plugin = wrappedPlugin(); - if (plugin && plugin.data) { - plugin.data(obj); - } - }, - }; -} - -function validateItems(items) { - // Validate the first item - if (items.length > 0) { - const item = items[0]; - const missingFields = []; - if (item.href == undefined) { - missingFields.push("href"); - } - if (!item.title == undefined) { - missingFields.push("title"); - } - if (!item.text == undefined) { - missingFields.push("text"); - } - - if (missingFields.length === 1) { - throw { - name: `Error: Search index is missing the ${missingFields[0]} field.`, - message: `The items being returned for this search do not include all the required fields. Please ensure that your index items include the ${missingFields[0]} field or use index-fields in your _quarto.yml file to specify the field names.`, - }; - } else if (missingFields.length > 1) { - const missingFieldList = missingFields - .map((field) => { - return `${field}`; - }) - .join(", "); - - throw { - name: `Error: Search index is missing the following fields: ${missingFieldList}.`, - message: `The items being returned for this search do not include all the required fields. Please ensure that your index items includes the following fields: ${missingFieldList}, or use index-fields in your _quarto.yml file to specify the field names.`, - }; - } - } -} - -let lastQuery = null; -function showCopyLink(query, options) { - const language = options.language; - lastQuery = query; - // Insert share icon - const inputSuffixEl = window.document.body.querySelector( - ".aa-Form .aa-InputWrapperSuffix" - ); - - if (inputSuffixEl) { - let copyButtonEl = window.document.body.querySelector( - ".aa-Form .aa-InputWrapperSuffix .aa-CopyButton" - ); - - if (copyButtonEl === null) { - copyButtonEl = window.document.createElement("button"); - copyButtonEl.setAttribute("class", "aa-CopyButton"); - copyButtonEl.setAttribute("type", "button"); - copyButtonEl.setAttribute("title", language["search-copy-link-title"]); - copyButtonEl.onmousedown = (e) => { - e.preventDefault(); - e.stopPropagation(); - }; - - const linkIcon = "bi-clipboard"; - const checkIcon = "bi-check2"; - - const shareIconEl = window.document.createElement("i"); - shareIconEl.setAttribute("class", `bi ${linkIcon}`); - copyButtonEl.appendChild(shareIconEl); - inputSuffixEl.prepend(copyButtonEl); - - const clipboard = new window.ClipboardJS(".aa-CopyButton", { - text: function (_trigger) { - const copyUrl = new URL(window.location); - copyUrl.searchParams.set(kQueryArg, lastQuery); - copyUrl.searchParams.set(kResultsArg, "1"); - return copyUrl.toString(); - }, - }); - clipboard.on("success", function (e) { - // Focus the input - - // button target - const button = e.trigger; - const icon = button.querySelector("i.bi"); - - // flash "checked" - icon.classList.add(checkIcon); - icon.classList.remove(linkIcon); - setTimeout(function () { - icon.classList.remove(checkIcon); - icon.classList.add(linkIcon); - }, 1000); - }); - } - - // If there is a query, show the link icon - if (copyButtonEl) { - if (lastQuery && options["copy-button"]) { - copyButtonEl.style.display = "flex"; - } else { - copyButtonEl.style.display = "none"; - } - } - } -} - -/* Search Index Handling */ -// create the index -var fuseIndex = undefined; -var shownWarning = false; - -// fuse index options -const kFuseIndexOptions = { - keys: [ - { name: "title", weight: 20 }, - { name: "section", weight: 20 }, - { name: "text", weight: 10 }, - ], - ignoreLocation: true, - threshold: 0.1, -}; - -async function readSearchData() { - // Initialize the search index on demand - if (fuseIndex === undefined) { - if (window.location.protocol === "file:" && !shownWarning) { - window.alert( - "Search requires JavaScript features disabled when running in file://... URLs. In order to use search, please run this document in a web server." - ); - shownWarning = true; - return; - } - const fuse = new window.Fuse([], kFuseIndexOptions); - - // fetch the main search.json - const response = await fetch(offsetURL("search.json")); - if (response.status == 200) { - return response.json().then(function (searchDocs) { - searchDocs.forEach(function (searchDoc) { - fuse.add(searchDoc); - }); - fuseIndex = fuse; - return fuseIndex; - }); - } else { - return Promise.reject( - new Error( - "Unexpected status from search index request: " + response.status - ) - ); - } - } - - return fuseIndex; -} - -function inputElement() { - return window.document.body.querySelector(".aa-Form .aa-Input"); -} - -function focusSearchInput() { - setTimeout(() => { - const inputEl = inputElement(); - if (inputEl) { - inputEl.focus(); - } - }, 50); -} - -/* Panels */ -const kItemTypeDoc = "document"; -const kItemTypeMore = "document-more"; -const kItemTypeItem = "document-item"; -const kItemTypeError = "error"; - -function renderItem( - item, - createElement, - state, - setActiveItemId, - setContext, - refresh, - quartoSearchOptions -) { - switch (item.type) { - case kItemTypeDoc: - return createDocumentCard( - createElement, - "file-richtext", - item.title, - item.section, - item.text, - item.href, - item.crumbs, - quartoSearchOptions - ); - case kItemTypeMore: - return createMoreCard( - createElement, - item, - state, - setActiveItemId, - setContext, - refresh - ); - case kItemTypeItem: - return createSectionCard( - createElement, - item.section, - item.text, - item.href - ); - case kItemTypeError: - return createErrorCard(createElement, item.title, item.text); - default: - return undefined; - } -} - -function createDocumentCard( - createElement, - icon, - title, - section, - text, - href, - crumbs, - quartoSearchOptions -) { - const iconEl = createElement("i", { - class: `bi bi-${icon} search-result-icon`, - }); - const titleEl = createElement("p", { class: "search-result-title" }, title); - const titleContents = [iconEl, titleEl]; - const showParent = quartoSearchOptions["show-item-context"]; - if (crumbs && showParent) { - let crumbsOut = undefined; - const crumbClz = ["search-result-crumbs"]; - if (showParent === "root") { - crumbsOut = crumbs.length > 1 ? crumbs[0] : undefined; - } else if (showParent === "parent") { - crumbsOut = crumbs.length > 1 ? crumbs[crumbs.length - 2] : undefined; - } else { - crumbsOut = crumbs.length > 1 ? crumbs.join(" > ") : undefined; - crumbClz.push("search-result-crumbs-wrap"); - } - - const crumbEl = createElement( - "p", - { class: crumbClz.join(" ") }, - crumbsOut - ); - titleContents.push(crumbEl); - } - - const titleContainerEl = createElement( - "div", - { class: "search-result-title-container" }, - titleContents - ); - - const textEls = []; - if (section) { - const sectionEl = createElement( - "p", - { class: "search-result-section" }, - section - ); - textEls.push(sectionEl); - } - const descEl = createElement("p", { - class: "search-result-text", - dangerouslySetInnerHTML: { - __html: text, - }, - }); - textEls.push(descEl); - - const textContainerEl = createElement( - "div", - { class: "search-result-text-container" }, - textEls - ); - - const containerEl = createElement( - "div", - { - class: "search-result-container", - }, - [titleContainerEl, textContainerEl] - ); - - const linkEl = createElement( - "a", - { - href: offsetURL(href), - class: "search-result-link", - }, - containerEl - ); - - const classes = ["search-result-doc", "search-item"]; - if (!section) { - classes.push("document-selectable"); - } - - return createElement( - "div", - { - class: classes.join(" "), - }, - linkEl - ); -} - -function createMoreCard( - createElement, - item, - state, - setActiveItemId, - setContext, - refresh -) { - const moreCardEl = createElement( - "div", - { - class: "search-result-more search-item", - onClick: (e) => { - // Handle expanding the sections by adding the expanded - // section to the list of expanded sections - toggleExpanded(item, state, setContext, setActiveItemId, refresh); - e.stopPropagation(); - }, - }, - item.title - ); - - return moreCardEl; -} - -function toggleExpanded(item, state, setContext, setActiveItemId, refresh) { - const expanded = state.context.expanded || []; - if (expanded.includes(item.target)) { - setContext({ - expanded: expanded.filter((target) => target !== item.target), - }); - } else { - setContext({ expanded: [...expanded, item.target] }); - } - - refresh(); - setActiveItemId(item.__autocomplete_id); -} - -function createSectionCard(createElement, section, text, href) { - const sectionEl = createSection(createElement, section, text, href); - return createElement( - "div", - { - class: "search-result-doc-section search-item", - }, - sectionEl - ); -} - -function createSection(createElement, title, text, href) { - const descEl = createElement("p", { - class: "search-result-text", - dangerouslySetInnerHTML: { - __html: text, - }, - }); - - const titleEl = createElement("p", { class: "search-result-section" }, title); - const linkEl = createElement( - "a", - { - href: offsetURL(href), - class: "search-result-link", - }, - [titleEl, descEl] - ); - return linkEl; -} - -function createErrorCard(createElement, title, text) { - const descEl = createElement("p", { - class: "search-error-text", - dangerouslySetInnerHTML: { - __html: text, - }, - }); - - const titleEl = createElement("p", { - class: "search-error-title", - dangerouslySetInnerHTML: { - __html: ` ${title}`, - }, - }); - const errorEl = createElement("div", { class: "search-error" }, [ - titleEl, - descEl, - ]); - return errorEl; -} - -function positionPanel(pos) { - const panelEl = window.document.querySelector( - "#quarto-search-results .aa-Panel" - ); - const inputEl = window.document.querySelector( - "#quarto-search .aa-Autocomplete" - ); - - if (panelEl && inputEl) { - panelEl.style.top = `${Math.round(panelEl.offsetTop)}px`; - if (pos === "start") { - panelEl.style.left = `${Math.round(inputEl.left)}px`; - } else { - panelEl.style.right = `${Math.round(inputEl.offsetRight)}px`; - } - } -} - -/* Highlighting */ -// highlighting functions -function highlightMatch(query, text) { - if (text) { - const start = text.toLowerCase().indexOf(query.toLowerCase()); - if (start !== -1) { - const startMark = ""; - const endMark = ""; - - const end = start + query.length; - text = - text.slice(0, start) + - startMark + - text.slice(start, end) + - endMark + - text.slice(end); - const startInfo = clipStart(text, start); - const endInfo = clipEnd( - text, - startInfo.position + startMark.length + endMark.length - ); - text = - startInfo.prefix + - text.slice(startInfo.position, endInfo.position) + - endInfo.suffix; - - return text; - } else { - return text; - } - } else { - return text; - } -} - -function clipStart(text, pos) { - const clipStart = pos - 50; - if (clipStart < 0) { - // This will just return the start of the string - return { - position: 0, - prefix: "", - }; - } else { - // We're clipping before the start of the string, walk backwards to the first space. - const spacePos = findSpace(text, pos, -1); - return { - position: spacePos.position, - prefix: "", - }; - } -} - -function clipEnd(text, pos) { - const clipEnd = pos + 200; - if (clipEnd > text.length) { - return { - position: text.length, - suffix: "", - }; - } else { - const spacePos = findSpace(text, clipEnd, 1); - return { - position: spacePos.position, - suffix: spacePos.clipped ? "…" : "", - }; - } -} - -function findSpace(text, start, step) { - let stepPos = start; - while (stepPos > -1 && stepPos < text.length) { - const char = text[stepPos]; - if (char === " " || char === "," || char === ":") { - return { - position: step === 1 ? stepPos : stepPos - step, - clipped: stepPos > 1 && stepPos < text.length, - }; - } - stepPos = stepPos + step; - } - - return { - position: stepPos - step, - clipped: false, - }; -} - -// removes highlighting as implemented by the mark tag -function clearHighlight(searchterm, el) { - const childNodes = el.childNodes; - for (let i = childNodes.length - 1; i >= 0; i--) { - const node = childNodes[i]; - if (node.nodeType === Node.ELEMENT_NODE) { - if ( - node.tagName === "MARK" && - node.innerText.toLowerCase() === searchterm.toLowerCase() - ) { - el.replaceChild(document.createTextNode(node.innerText), node); - } else { - clearHighlight(searchterm, node); - } - } - } -} - -function escapeRegExp(string) { - return string.replace(/[.*+?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string -} - -// highlight matches -function highlight(term, el) { - const termRegex = new RegExp(term, "ig"); - const childNodes = el.childNodes; - - // walk back to front avoid mutating elements in front of us - for (let i = childNodes.length - 1; i >= 0; i--) { - const node = childNodes[i]; - - if (node.nodeType === Node.TEXT_NODE) { - // Search text nodes for text to highlight - const text = node.nodeValue; - - let startIndex = 0; - let matchIndex = text.search(termRegex); - if (matchIndex > -1) { - const markFragment = document.createDocumentFragment(); - while (matchIndex > -1) { - const prefix = text.slice(startIndex, matchIndex); - markFragment.appendChild(document.createTextNode(prefix)); - - const mark = document.createElement("mark"); - mark.appendChild( - document.createTextNode( - text.slice(matchIndex, matchIndex + term.length) - ) - ); - markFragment.appendChild(mark); - - startIndex = matchIndex + term.length; - matchIndex = text.slice(startIndex).search(new RegExp(term, "ig")); - if (matchIndex > -1) { - matchIndex = startIndex + matchIndex; - } - } - if (startIndex < text.length) { - markFragment.appendChild( - document.createTextNode(text.slice(startIndex, text.length)) - ); - } - - el.replaceChild(markFragment, node); - } - } else if (node.nodeType === Node.ELEMENT_NODE) { - // recurse through elements - highlight(term, node); - } - } -} - -/* Link Handling */ -// get the offset from this page for a given site root relative url -function offsetURL(url) { - var offset = getMeta("quarto:offset"); - return offset ? offset + url : url; -} - -// read a meta tag value -function getMeta(metaName) { - var metas = window.document.getElementsByTagName("meta"); - for (let i = 0; i < metas.length; i++) { - if (metas[i].getAttribute("name") === metaName) { - return metas[i].getAttribute("content"); - } - } - return ""; -} - -function algoliaSearch(query, limit, algoliaOptions) { - const { getAlgoliaResults } = window["@algolia/autocomplete-preset-algolia"]; - - const applicationId = algoliaOptions["application-id"]; - const searchOnlyApiKey = algoliaOptions["search-only-api-key"]; - const indexName = algoliaOptions["index-name"]; - const indexFields = algoliaOptions["index-fields"]; - const searchClient = window.algoliasearch(applicationId, searchOnlyApiKey); - const searchParams = algoliaOptions["params"]; - const searchAnalytics = !!algoliaOptions["analytics-events"]; - - return getAlgoliaResults({ - searchClient, - queries: [ - { - indexName: indexName, - query, - params: { - hitsPerPage: limit, - clickAnalytics: searchAnalytics, - ...searchParams, - }, - }, - ], - transformResponse: (response) => { - if (!indexFields) { - return response.hits.map((hit) => { - return hit.map((item) => { - return { - ...item, - text: highlightMatch(query, item.text), - }; - }); - }); - } else { - const remappedHits = response.hits.map((hit) => { - return hit.map((item) => { - const newItem = { ...item }; - ["href", "section", "title", "text", "crumbs"].forEach( - (keyName) => { - const mappedName = indexFields[keyName]; - if ( - mappedName && - item[mappedName] !== undefined && - mappedName !== keyName - ) { - newItem[keyName] = item[mappedName]; - delete newItem[mappedName]; - } - } - ); - newItem.text = highlightMatch(query, newItem.text); - return newItem; - }); - }); - return remappedHits; - } - }, - }); -} - -let subSearchTerm = undefined; -let subSearchFuse = undefined; -const kFuseMaxWait = 125; - -async function fuseSearch(query, fuse, fuseOptions) { - let index = fuse; - // Fuse.js using the Bitap algorithm for text matching which runs in - // O(nm) time (no matter the structure of the text). In our case this - // means that long search terms mixed with large index gets very slow - // - // This injects a subIndex that will be used once the terms get long enough - // Usually making this subindex is cheap since there will typically be - // a subset of results matching the existing query - if (subSearchFuse !== undefined && query.startsWith(subSearchTerm)) { - // Use the existing subSearchFuse - index = subSearchFuse; - } else if (subSearchFuse !== undefined) { - // The term changed, discard the existing fuse - subSearchFuse = undefined; - subSearchTerm = undefined; - } - - // Search using the active fuse - const then = performance.now(); - const resultsRaw = await index.search(query, fuseOptions); - const now = performance.now(); - - const results = resultsRaw.map((result) => { - const addParam = (url, name, value) => { - const anchorParts = url.split("#"); - const baseUrl = anchorParts[0]; - const sep = baseUrl.search("\\?") > 0 ? "&" : "?"; - anchorParts[0] = baseUrl + sep + name + "=" + value; - return anchorParts.join("#"); - }; - - return { - title: result.item.title, - section: result.item.section, - href: addParam(result.item.href, kQueryArg, query), - text: highlightMatch(query, result.item.text), - crumbs: result.item.crumbs, - }; - }); - - // If we don't have a subfuse and the query is long enough, go ahead - // and create a subfuse to use for subsequent queries - if ( - now - then > kFuseMaxWait && - subSearchFuse === undefined && - resultsRaw.length < fuseOptions.limit - ) { - subSearchTerm = query; - subSearchFuse = new window.Fuse([], kFuseIndexOptions); - resultsRaw.forEach((rr) => { - subSearchFuse.add(rr.item); - }); - } - return results; -} diff --git a/docs/sitemap.xml b/docs/sitemap.xml deleted file mode 100644 index c04a98e..0000000 --- a/docs/sitemap.xml +++ /dev/null @@ -1,43 +0,0 @@ - - - - https://ds100.org/debugging-guide/sql/sql.html - 2025-12-17T22:30:00.413Z - - - https://ds100.org/debugging-guide/projA1/projA1.html - 2025-12-17T22:30:00.413Z - - - https://ds100.org/debugging-guide/autograder_gradescope/autograder_gradescope.html - 2025-12-17T22:30:00.404Z - - - https://ds100.org/debugging-guide/regex/regex.html - 2025-12-17T22:30:00.413Z - - - https://ds100.org/debugging-guide/jupyter_datahub/jupyter_datahub.html - 2025-12-17T22:30:00.412Z - - - https://ds100.org/debugging-guide/jupyter101/jupyter101.html - 2025-12-17T22:30:00.412Z - - - https://ds100.org/debugging-guide/projA2/projA2.html - 2025-12-17T22:30:00.413Z - - - https://ds100.org/debugging-guide/visualizations/visualizations.html - 2025-12-17T22:30:00.414Z - - - https://ds100.org/debugging-guide/index.html - 2025-12-17T22:30:00.412Z - - - https://ds100.org/debugging-guide/pandas/pandas.html - 2025-12-17T22:30:00.412Z - - diff --git a/docs/sql/sql.html b/docs/sql/sql.html deleted file mode 100644 index 5a6141c..0000000 --- a/docs/sql/sql.html +++ /dev/null @@ -1,844 +0,0 @@ - - - - - - - - - -SQL Common Errors – Data 100 Debugging Guide - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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SQL Common Errors

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Common Misconceptions

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How to read SQL Errors

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Because Jupyter notebooks run Python natively, we need to import extra modules like sql, sqlalchemy, and duckdb in order to run SQL queries within our notebook. This results in slightly more complex error messages than normal Python. Refer to the image below if you need help identifying the most important parts (the red and blue text).

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-Screenshot of a SQL Error message with the most important parts annotated. After 'Original error message from DB driver' is the highlighted error type followed by a description of why that error occured. Next in the error message is the line of code that caused the error. -
-

Sometimes, SQL errors may show up as a long string without any red highlight, for example:

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(duckdb.duckdb.BinderException) Binder Error: Referenced column "some_column" not found in FROM clause! LINE 1: SELECT some_column ^ ...

-

Note that this is the same error as the one above – it’s just not formatted nicely. Try your best to parse it; you can even copy the error into a new cell and manually indent sentences if it helps with readability!

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Comments in SQL

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Unlike Python, which uses #... or triple quotes """...""" to indicate a comment, SQL comments take the following forms:

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-- This is a single-line or in-line comment
-
-/* 
-  This is a 
-  multi-line 
-  comment 
-*/
-

Because you are still coding within a Python notebook, the colors that Jupyter chooses for your SQL queries may not match up to what you expect; using #... will be green text like Python comments, but running the code will give you:

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Parser Error: syntax error at or near...

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Using -- or /* ... */ may not color-code the text correctly, but SQL will still read it as a comment!

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Filtering with multiple conditions

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When you are using more than one condition in filtering clauses like WHERE or HAVING, it’s important consider that the ordering of ORs and ANDs will affect your SQL output.

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In SQL, the operator precedence typically evaluates AND operations before OR operations, and so incorrectly ordered conditions can lead to unexpected results. To ensure your conditions are evaluated in the intended order, use parentheses to group them explicitly.

-

For example, consider the following query condition:

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WHERE A > 500 AND B > 200 OR C < 50

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By default, is will be evaluated as

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WHERE (A > 500 AND B > 200) OR C < 50

-

because AND has a higher precedence than OR. To evaluate it differently, add parentheses to specify the order of operations:

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WHERE A > 500 AND (B > 200 OR C < 50)

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Truncated Display Limit

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Our Jupyter Notebooks are set to only display the first 10 rows of a table. Otherwise, your notebook would likely crash with the weight of displaying >1000 rows. You’ll likely see a

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Truncated to displaylimit of 10

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printed right below your SQL query if your table contains more than 10 rows.

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Common Errors

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NameError: name ‘[res_q#]’ is not defined

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This occurs when you have an error in your SQL code, causing the cell after to fail. Because we’re working with Jupyter Notebooks, some SQL errors are not highlighted in red like Python errors usually are. Check the output under your %%sql cell to see if it ran smoothly. Any text below

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Running query in 'duckdb:///data/{database_name}.db

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is an error message that you can read to debug your code. Note that this error could be formatted as one long string without any indents or newlines, so it may be harder than typical Python errors to parse.

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Referenced column "{some_string}" not found

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There are 2 potential causes for this error:

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    -
  1. The column you’re looking for does not exist in the table. Make sure that the column names are spelled and capatalized exactly as shown in the assignment. If you are trying to reference a column that is not in the current table, consider using JOIN to be able to access these columns. You can reference the SQL II course notes for a refresher on how to use the JOIN clause.
    2. -
  2. You used double quotes "" around some_string instead of single quotes ''. Unlike Python, SQL differentiates double and single quotes: double quotes indicate something that exists within the database, like tables and column names; single quotes indicate strings.
    3. -
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Cannot compare values of type __ and type __

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This error often occurs when a string-type column (ie. CHAR or VARCHAR) is compared with a numeric-type column (ie. INT, BIGINT, FLOAT, REAL). If you’re interested in learning more about SQL datatypes, check out this link!

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To debug, start by checking the type of the column(s) you’re working with; you can either scroll up to the cell (or make a new cell) with the code:

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%%sql
-SELECT * FROM sqlite_master WHERE type='table';
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which outputs the schema for each table. If we’re working with the IMDB database, we get the following output:

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-Schema for the IMDB database -
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We can see that the table Title contains the columns

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  • tconst of type BIGINT
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  • titleType of type VARCHAR
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  • primaryTitle of type VARCHAR
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  • and so on…
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It’s a good idea to double check the types of the columns involved by looking at the schema before performing numerical comparisons and when deciding whether to CAST a particular column.

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My query runs but I’m not passing the tests

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For a lot of questions in the homework, we limit our focus to movie titles. Double check that you are filtering your results appropriately. More generally, if you find your queries aren’t erroring, but are failing the tests due to some logical error, a good starting point is always reading the question again and making a checklist of all the conditions you need to implement.

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- - - - - - \ No newline at end of file diff --git a/docs/styles.scss b/docs/styles.scss deleted file mode 100644 index 5bcc5f4..0000000 --- a/docs/styles.scss +++ /dev/null @@ -1,124 +0,0 @@ -/*-- scss:defaults --*/ - -// Google Fonts -@import url('https://fonts.googleapis.com/css2?family=Montserrat:wght@300;400;500;600&family=Cinzel:wght@400;500&display=swap'); - -// Colors -$white: #fff !default; -$gray-100: #f8f9fa !default; -$gray-200: #f8f5f0 !default; -$gray-300: #dfd7ca !default; -$gray-400: #ced4da !default; -$gray-500: #98978b !default; -$gray-600: #8e8c84 !default; -$gray-700: #495057 !default; -$gray-800: #3e3f3a !default; -$gray-900: #212529 !default; -$black: #000 !default; - -$blue: #0e6876 !default; -$indigo: #6610f2 !default; -$purple: #6f42c1 !default; -$pink: #e83e8c !default; -$red: #d9534f !default; -$orange: #f47c3c !default; -$yellow: #ffc107 !default; -$green: #93c54b !default; -$teal: #20c997 !default; -$cyan: #29abe0 !default; - -$primary: $blue !default; -$secondary: $gray-600 !default; -$success: $green !default; -$info: $cyan !default; -$warning: $orange !default; -$danger: $red !default; -$light: $gray-200 !default; -$dark: $gray-800 !default; - -// Body - -$body-color: $gray-700 !default; - -// Fonts - -$font-family-sans-serif: 'Helvetica', sans-serif; -$headings-font-weight: 00 !default; - - -// /* css styles */ - -// .dropcap { -// float: left; -// font-family: Georgia; -// font-size: 75px; -// line-height: 60px; -// padding-top: 4px; -// padding-right: 8px; -// padding-left: 3px; -// } - -// h1 { -// color: $primary; -// } - -// h2 { -// color: $primary; -// } - -// .v-center-container { -// display: flex; -// justify-content: center; -// align-items: center; -// height: 90%; -// } - -// /* homepage */ - -// .content-block { -// padding-top: 20px; -// padding-bottom: 10px; -// margin-left: 30px; -// margin-right: 30px; -// } - -// .banner-text { -// color: $primary; -// font-size: clamp(1.8em, 6vw, 4em); -// line-height: .9; -// padding-top: 1.1em; -// } - -// @media (min-width: 900px) { -// .content-block { -// margin-left: 50px; -// margin-right: 50px; -// } -// } - -// @media (min-width: 1200px) { -// .content-block { -// max-width: 1100px; -// margin-left: auto; -// margin-right: auto; -// } -// } - -// @media (max-width: 768px) { -// #banner { -// order: 2; -// margin: 0em 1em 0em 1em !important; -// padding-top: 0em; -// } -// .banner-text { -// padding-top: 0em; -// } -// #hex { -// order: 1; -// padding: 1.5em 3em 0em 3em; -// } -// } - -#quarto-document-content { - width: 2000px; -} \ No newline at end of file diff --git a/docs/visualizations/visualizations.html b/docs/visualizations/visualizations.html deleted file mode 100644 index 2f8a18b..0000000 --- a/docs/visualizations/visualizations.html +++ /dev/null @@ -1,827 +0,0 @@ - - - - - - - - - -Visualizations – Data 100 Debugging Guide - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Visualizations

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Visualizations are how data scientists use to communicate their insights to the world. In the process of making a good visualization, be it while adding a legend or setting the x-axis label, we may run into some errors. Let’s take a look at how to resolve them below!

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My legend’s labels don’t match up / my legend isn’t displaying properly

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If you simply add plt.legend() after your plotting line of code, you should see a legend; seaborn will sometimes automatically populate the legend. However, if you’re plotting multiple lines or sets of points on a single plot, the labels in the legend may not correctly line up with what’s shown.

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Make sure to pass in the label argument into the sns plotting function with the label you want associated with that individual plot. For example,

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sns.histplot(means_arr, label = 'simulated values') # informative label name
-plt.title('Simulated values') 
-plt.plot(original, 10, 'bo', label = 'original test statistic') # informative label name
-plt.legend(loc = 'upper left') # can specify location of legend
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-Histogram titled 'Simulated values of correlated coefficients'. The original test statistic is shown in the center of the distribution which ranges from just over 0.1 to just under 0.3. The distribution is roughly normal. -
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The y-axis of my histplot shows the count, not the density

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Look into the sns.histplot documentation and see what arguments the stat parameter takes in. By default, stat=count, and the number of elements in each histogram bin is the y axis. But if you wanted to normalize the distribution such that the total area is 1, consider passing stat=density into the plot function.

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I’m having trouble labeling the axes/title of my graph

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To label the axes and title of a graph, we use the following syntax:

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plt.xlabel(“x axis name”) 
-plt.ylabel(“y axis name”) 
-plt.title(“graph title”)
-

Where plt.xlabel, plt.ylabel, and plt.title are matplotlib functions that we call.

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However, we often see students use the following incorrect syntax to try and label their plot:

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plt.xlabel = "x name"
-plt.ylabel = "y name"
-plt.title = "graph title"
-

Now, instead of plt.xlabel, plt.ylabel, and plt.title being functions, they are strings. Trying to call one of the labelling function using the correct syntax in the next few cells (ie.plt.xlabel(“x name”)) will result in a TypeError: str object is not callable. If this happens to you, comb through your notebook and look for places when you used the incorrect syntax. After fixing it, restart your kernel and rerun your cells.

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My sns.lineplot has an unwanted shaded region around the solid lines.

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Note: the following examples are taken from sns.lineplot’s documentation.

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sns.lineplot gives us a clean line when each x value has one y value. For example, the table

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YearMay
1948120
1949122
1950123
\(\vdots\)\(\vdots\)
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will give us a clean plot because each year corresponds to a single “Number of Flights in May” value.

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-A line plot is shown with year on the x-axis and May on the y-axis. -
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-

When each x value has multiple y values, sns.lineplot will automatically plot a shaded region around the solid line, where the solid line is the mean of the y values for that x value (think of a groupby on x aggregated by .mean() on y) and the shaded region is the 95% confidence interval (read more about confidence intervals in the Data 8 textbook). For example, the table

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YearMay
1948115
1948120
1948125
1949118
1949122
1949126
\(\vdots\)\(\vdots\)
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will plot a lineplot with a shaded region.

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-The same lineplot as above is shown again with the 95% confidence interval shaded in around the line itself. -
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If you do not want the shaded region, aggregate the data such that there is only one y-value for a given x-value; then, make the plot.

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file: content/index.md + - file: content/jupyter101.md + - file: content/jupyter_datahub.md + - file: content/autograder_gradescope.md + - file: content/pandas.md + - file: content/regex.md + - file: content/visualizations.md + - file: content/projA1.md + - file: content/projA2.md + - file: content/sql.md + github: https://github.com/DS-100/debugging-guide +site: + template: book-theme + options: + favicon: assets/images/data100_logo.png + logo: assets/images/data100_logo.png + logo_text: "Data 100 Debugging Guide" diff --git a/pandas/pandas_error.png b/pandas/pandas_error.png deleted file mode 100644 index 5ecc8d7..0000000 Binary files a/pandas/pandas_error.png and /dev/null differ diff --git a/projA2/under_overfit.png b/projA2/under_overfit.png deleted file mode 100644 index 4d2fae0..0000000 Binary files a/projA2/under_overfit.png and /dev/null differ diff --git a/regex/images/basic.png b/regex/images/basic.png deleted file mode 100644 index 411e0ba..0000000 Binary files a/regex/images/basic.png and /dev/null differ diff --git a/regex/images/capturing_groups.png b/regex/images/capturing_groups.png deleted file mode 100644 index f109a67..0000000 Binary files a/regex/images/capturing_groups.png and /dev/null differ diff --git a/regex/images/greedy.png b/regex/images/greedy.png deleted file mode 100644 index 174417f..0000000 Binary files a/regex/images/greedy.png and /dev/null differ diff --git a/regex/images/ref_sheet_re_methods.png b/regex/images/ref_sheet_re_methods.png deleted file mode 100644 index b945735..0000000 Binary files a/regex/images/ref_sheet_re_methods.png and /dev/null differ diff --git a/regex/images/updated_basic.png b/regex/images/updated_basic.png deleted file mode 100644 index 4a17b6b..0000000 Binary files a/regex/images/updated_basic.png and /dev/null differ diff --git a/regex/images/updated_capturing_groups.png b/regex/images/updated_capturing_groups.png deleted file mode 100644 index 83d1636..0000000 Binary files a/regex/images/updated_capturing_groups.png and /dev/null differ diff --git a/regex/images/updated_greedy.png b/regex/images/updated_greedy.png deleted file mode 100644 index 0992bf6..0000000 Binary files a/regex/images/updated_greedy.png and /dev/null differ diff --git a/requirements.txt b/requirements.txt index 4611fb8..ec436f1 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,27 +1 @@ -setuptools==78.1.1 -SQLAlchemy==2.0.16 -folium==0.14.0 -ipywidgets==8.0.7 -jupysql==0.8.0 -jupyterlab==4.0.4 -matplotlib==3.7.1 -mdit-py-plugins==0.4.0 -numpy<2 -otter-grader==5.4.0 -pandas==2.0.2 -plotly==5.13.1 -requests==2.28.2 -scikit-image==0.25.2 -scikit-learn==1.2.2 -scipy==1.12.0 -seaborn==0.12.2 -statsmodels==0.14.0 -ypy-websocket==0.12.1 -torch==2.8.0 -transformers==4.37.1 -duckdb==0.10.1 -duckdb_engine==0.11.2 -ipykernel==6.30.1 -notebook==7.0.8 -nbconvert==7.16.6 -gdown==5.2.0 \ No newline at end of file +jupyter-book>2 \ No newline at end of file diff --git a/sql/images/imdb_schema.png b/sql/images/imdb_schema.png deleted file mode 100644 index 4af69be..0000000 Binary files a/sql/images/imdb_schema.png and /dev/null differ diff --git a/sql/images/sql_error.png b/sql/images/sql_error.png deleted file mode 100644 index c64d301..0000000 Binary files a/sql/images/sql_error.png and /dev/null differ diff --git a/visualizations/images/example_label_plot.png b/visualizations/images/example_label_plot.png deleted file mode 100644 index b8da273..0000000 Binary files a/visualizations/images/example_label_plot.png and /dev/null differ diff --git a/visualizations/images/normal_lineplot.png b/visualizations/images/normal_lineplot.png deleted file mode 100644 index ef9ff34..0000000 Binary files a/visualizations/images/normal_lineplot.png and /dev/null differ diff --git a/visualizations/images/shaded_lineplot.png b/visualizations/images/shaded_lineplot.png deleted file mode 100644 index cc055de..0000000 Binary files a/visualizations/images/shaded_lineplot.png and /dev/null differ