moved to uv from conda

Author: FilippoMB

.github/workflows/ci.yml

@@ -0,0 +1,41 @@
+name: CI
+
+on:
+  pull_request:
+  push:
+    branches:
+      - main
+
+permissions:
+  contents: read
+
+jobs:
+  validate:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+
+      - name: Set up uv
+        uses: astral-sh/setup-uv@v7
+        with:
+          python-version: "3.12"
+          enable-cache: true
+
+      - name: Install dependencies
+        run: uv sync --extra notebooks --group docs --frozen
+
+      - name: Build package
+        run: uv build
+
+      - name: Smoke test package imports
+        run: |
+          uv run python - <<'PY'
+          import tsa_course.lecture1
+          import tsa_course.lecture2
+          import tsa_course.lecture8
+          import tsa_course.lecture11
+          PY
+
+      - name: Build Jupyter Book
+        run: uv run --extra notebooks --group docs jupyter-book build . --builder html

.github/workflows/deploy-book.yml

@@ -16,37 +16,38 @@ concurrency:
 jobs:
   deploy:
     runs-on: ubuntu-latest
-    defaults:
-      run:
-        shell: bash -el {0}
     steps:
       - name: Checkout repository
         uses: actions/checkout@v4
 
-      - name: Set up Conda environment
-        uses: conda-incubator/setup-miniconda@v3
+      - name: Set up uv
+        uses: astral-sh/setup-uv@v7
         with:
-          activate-environment: pytsa
-          environment-file: env.yml
-          auto-activate-base: false
+          python-version: "3.12"
+          enable-cache: true
 
-      - name: Install build tools
-        run: |
-          python -m pip install --upgrade pip
-          pip install "jupyter-book<2" ghp-import
+      - name: Install dependencies
+        run: uv sync --extra notebooks --group docs --group deploy --frozen
+
+      - name: Build package
+        run: uv build
 
-      - name: Show tool versions
+      - name: Smoke test package imports
         run: |
-          jupyter-book --version
-          ghp-import --version
+          uv run python - <<'PY'
+          import tsa_course.lecture1
+          import tsa_course.lecture2
+          import tsa_course.lecture8
+          import tsa_course.lecture11
+          PY
 
       - name: Build Jupyter Book
-        run: jupyter-book build . --builder html
+        run: uv run --extra notebooks --group docs jupyter-book build . --builder html
 
       - name: Verify build output
         run: |
           test -d _build/html
           ls -la _build/html | head -n 20
 
       - name: Deploy to GitHub Pages
-        run: ghp-import -n -p -f _build/html
+        run: uv run --group deploy ghp-import -n -p -f _build/html

.python-version

@@ -0,0 +1 @@
+3.12

README.md

@@ -158,30 +158,48 @@ You can view and execute the notebooks by clicking on the buttons below.
 
 To run the notebooks locally the recommended steps are the following:
 
-1. Download and install [Miniconda](https://docs.conda.io/projects/miniconda/en/latest/miniconda-install.html).
+1. Install [uv](https://docs.astral.sh/uv/getting-started/installation/).
 
-2. Download the [env.yml](https://github.com/FilippoMB/python-time-series-handbook/blob/main/env.yml) file.
+2. Clone the repository:
 
-3. Open the shell and navigate to the location with the `yml` file you just downloaded.
-    - If you are on Windows, open the Miniconda shell.
-
-4. Install the environment with 
-   ```{bash}
-   > conda env create -f env.yml
+   ```bash
+   git clone https://github.com/FilippoMB/python-time-series-handbook.git
+   cd python-time-series-handbook
    ```
 
-5. Activate your environment: 
-   ```{bash}
-   > conda activate pytsa
+3. Create the environment and install all dependencies:
+
+   ```bash
+   uv sync --extra notebooks
    ```
 
-6. Go to the folder with the notebooks
+4. Launch Jupyter Lab:
 
-7. Launch Jupyter Lab with the command 
-   ```{bash}
-   > jupyter lab
+   ```bash
+   uv run jupyter lab
    ```
 
+## 🛠 Build the book locally
+
+To build the Jupyter Book locally, install the notebook dependencies together with the docs tooling:
+
+```bash
+uv sync --extra notebooks --group docs
+uv run --extra notebooks --group docs jupyter-book build . --builder html
+```
+
+The build output will be written to `_build/html`.
+
+## 📦 Build the package
+
+To build the Python package artifacts:
+
+```bash
+uv build
+```
+
+This produces a wheel and a source distribution in `dist/`.
+
 ## 🎥 Notebook format and slides
 
 The notebooks are structured as a sequence of slides to be presented using [RISE](https://rise.readthedocs.io/en/latest/).
@@ -203,12 +221,12 @@ See the [RISE documentation](https://rise.readthedocs.io/en/latest/) for more in
 
 If you are using this material in your courses or in your research, please consider citing it as follows:
 
-````bibtex
+```bibtex
 @misc{bianchi2024tsbook,
   author       = {Filippo Maria Bianchi},
   title        = {Time Series Analysis with Python},
   year         = {2024},
   howpublished = {Online},
   url          = {https://github.com/FilippoMB/python-time-series-handbook}
 }
-````
+```

_config.yml

@@ -25,6 +25,18 @@ html:
   use_repository_button: true
   favicon : "logo.png"
 
+exclude_patterns:
+  - _build
+  - README.md
+  - .venv
+  - .venv/**
+  - build
+  - build/**
+  - dist
+  - dist/**
+  - .DS_Store
+  - Thumbs.db
+
 parse:
   myst_enable_extensions:
     # Defaults here: https://jupyterbook.org/en/stable/customize/config.html

env.yml

@@ -11,12 +11,12 @@ Time series analysis is a crucial discipline in data science, offering insights
 :width: 500px
 :align: center
 ```
+
 The course is designed to combine high-level theoretical knowledge with practical programming skills. Each chapter introduces key concepts of time series analysis, followed by hands-on coding sections. This structure allows you to immediately apply the theoretical concepts you learn, seeing first-hand how these translate into functional tools in data analytics. Through this process, you will gain both the knowledge to understand complex time series data and the skills to analyze and predict it effectively.
 To reinforce learning and encourage active engagement, each chapter concludes with exercises. These are designed to test your understanding and help you apply the lessons in practical contexts.
 
 Whether you are new to time series analysis or looking to refine your expertise, this course offers a broad exploration of the field, with Python as your toolkit. I hope that you will find this material both educational and entertaining, brining you a step closer to mastering time series analysis.
 
-
 ## 📖 Chapters
 
 The course is organized into the following chapters.
@@ -34,7 +34,6 @@ For this reason, the text in the notebooks is organized with bullet points.
 
 These notebooks are currently adopted in [STA-2003 Tidsrekker](https://sa.uit.no/utdanning/emner/emne?p_document_id=822793) at UiT the Arctic University of Tromsø and [062785 - Time Series Analysis](https://www11.ceda.polimi.it/schedaincarico/schedaincarico/controller/scheda_pubblica/SchedaPublic.do?&evn_default=evento&c_classe=843368&lang=IT&__pj0=0&__pj1=2ad8b3dba5bb35c2b94e513147e1e84e) at Polytechnic of Milan.
 
-
 ## 🚀 Getting started with coding
 
 You can read from here all the content of the course. However, to get your hands-on experience with coding you want to run the code snippets or the whole notebooks that you can download on <a href="https://github.com/FilippoMB/python-time-series-handbook" target="_blank">
@@ -43,30 +42,26 @@ You can read from here all the content of the course. However, to get your hands
 
 To run the code and the notebooks the recommended steps are the following:
 
-1. Download and install [Miniconda](https://docs.conda.io/projects/miniconda/en/latest/miniconda-install.html).
-
-2. Download the [env.yml](https://github.com/FilippoMB/python-time-series-handbook/blob/main/env.yml) file.
+1. Install [uv](https://docs.astral.sh/uv/getting-started/installation/).
 
-3. Open the shell and navigate to the location with the yml file you just downloaded.
-    - If you are on Windows, open the Miniconda shell.
+2. Clone the repository:
 
-4. Install the environment with 
-   ```
-   > conda env create -f env.yml
+   ```bash
+   git clone https://github.com/FilippoMB/python-time-series-handbook.git
+   cd python-time-series-handbook
    ```
 
-5. Activate your environment: 
-   ```
-   > conda activate pytsa
+3. Create the environment and install all dependencies:
+
+   ```bash
+   uv sync --extra notebooks
    ```
 
-6. Go to the folder with the notebooks
+4. Launch Jupyter Lab:
 
-7. Launch Jupyter Lab with the command 
+   ```bash
+   uv run jupyter lab
    ```
-   > jupyter lab
-   ```
-
 
 ## ⚒ Roadmap
 
@@ -81,29 +76,29 @@ If there is a specific topic you feel is missing or passages that you feel are n
 
 For the moment, I intentionally decided to leave more advanced deep learning techniques aside. There are a couple of reasons for this choice. Firstly, advanced deep learning methods rely heavily on specific knowledge and tools that are generally covered in specialized deep learning courses. This focus does not align with the introductory nature of this course, which is aimed at covering the fundamentals of time series analysis.
 
-Secondly, while deep learning opens up exciting avenues for new applications—such as NLP and analysis of video and spatio-temporal data—it primarily enhances capabilities in handling diverse data types and scaling to large datasets. However, for the core objectives of this course, which are to understand and manipulate time series data effectively, the advantages of moving from the basic neural networks introduced here to more complex deep learning models do not significantly alter the fundamental approach. 
-
+Secondly, while deep learning opens up exciting avenues for new applications—such as NLP and analysis of video and spatio-temporal data—it primarily enhances capabilities in handling diverse data types and scaling to large datasets. However, for the core objectives of this course, which are to understand and manipulate time series data effectively, the advantages of moving from the basic neural networks introduced here to more complex deep learning models do not significantly alter the fundamental approach.
 
 ## 🤝 Contributing
 
-Time series analysis with Python is designed with accessibility in mind. The material is completely open-sourced and uses only free software, based in Python. 
+Time series analysis with Python is designed with accessibility in mind. The material is completely open-sourced and uses only free software, based in Python.
 You can contribute both by adding new material, fixing typos, and suggesting edits. To do that,
 <a class="github-button" href="https://github.com/FilippoMB/python-time-series-handbook/fork" data-color-scheme="no-preference: light; light: light; dark: dark;" data-icon="octicon-repo-forked" aria-label="Fork FilippoMB/python-time-series-handbook on GitHub">Fork</a> the Github repository and submit a pull request.
 
+If you want to build the Jupyter Book itself or package the `tsa-course` library, the repository README contains the additional contributor commands.
+
 Finally, if you liked this content, please share it with others who might find it useful and give it a
 <a class="github-button" href="https://github.com/FilippoMB/python-time-series-handbook" data-color-scheme="no-preference: light; light: light; dark: dark;" data-icon="octicon-star" aria-label="Star FilippoMB/python-time-series-handbook on GitHub">Star</a> on GitHub.
 
-
 ## 📝 Citation
 
 If you are using this material in your courses or in your research, please consider citing it as follows:
 
-````bibtex
+```bibtex
 @misc{bianchi2024tsbook,
   author       = {Filippo Maria Bianchi},
   title        = {Time Series Analysis with Python},
   year         = {2024},
   howpublished = {Online},
   url          = {https://github.com/FilippoMB/python-time-series-handbook}
 }
-````
+```

notebooks/00/intro.md

@@ -858,7 +858,7 @@
    "outputs": [],
    "source": [
     "# Generate synthetic time series data\n",
-    "dates = pd.date_range(start='2010-01-01', periods=60, freq='M')  # Monthly data for 5 years\n",
+    "dates = pd.date_range(start='2010-01-01', periods=60, freq='ME')  # Monthly data for 5 years\n",
     "seas = 12 # change this and see how the plots change\n",
     "data = (np.sin(np.arange(60)*2*np.pi/seas) + \n",
     "        np.random.normal(loc=0, scale=0.2, size=60))  # Seasonal data with noise\n",

notebooks/05/arma_arima_sarima.ipynb

@@ -358,7 +358,7 @@
     "- When an AR(1) process has a unit root (i.e., $\\phi = 1$), it implies that the series is non-stationary as it becomes a random walk: $Y(t) = Y(t-1) + \\epsilon_t$ \n",
     "- The value of the series will be highly dependent on its previous values (the shocks to the series have a permanent effect).\n",
     "\n",
-    "```{info}\n",
+    "```{note}\n",
     "The term \"unit root\" describes a specific condition in the autoregressive representation of a time series, indicating that the series is **non-stationary**.\n",
     "```"
    ]
@@ -806,17 +806,17 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def get_data(tickerSymbol, period, start, end):\n",
+    "def get_data(tickerSymbol, start, end):\n",
     "\n",
     "    # Get data on the ticker\n",
     "    tickerData = yf.Ticker(tickerSymbol)\n",
     "\n",
     "    # Get the historical prices for this ticker\n",
-    "    tickerDf = tickerData.history(period=period, start=start, end=end)\n",
+    "    tickerDf = tickerData.history(start=start, end=end)\n",
     "    \n",
     "    return tickerDf\n",
     "    \n",
-    "data = get_data('GOOG', period='1d', start='2004-09-01', end='2020-08-31')"
+    "data = get_data('GOOG', start='2004-09-01', end='2020-08-31')"
    ]
   },
   {
@@ -1485,7 +1485,7 @@
    "outputs": [],
    "source": [
     "# Step 1: Get the \"training\" data (e.g., 2020-2022)\n",
-    "data2 = get_data('GOOG', period='1d', start='2019-12-31', end='2022-12-31')"
+    "data2 = get_data('GOOG', start='2019-12-31', end='2022-12-31')"
    ]
   },
   {
@@ -1500,7 +1500,7 @@
    "outputs": [],
    "source": [
     "# Get \"test\" data, for comparison (e.g., 2023)\n",
-    "data3 = get_data('GOOG', period='1d', start='2022-12-31', end='2023-12-31')\n",
+    "data3 = get_data('GOOG', start='2022-12-31', end='2023-12-31')\n",
     "test_days = len(data3)"
    ]
   },

notebooks/06/unit-root-hurst.ipynb

@@ -1247,7 +1247,7 @@
     "# generate noisy measurements\n",
     "Zx = np.zeros(n_measurements)\n",
     "for t in range(0, n_measurements):\n",
-    "    Zx[t]= Xt[t] + np.random.normal(0, np.sqrt(R), 1)"
+    "    Zx[t]= Xt[t] + np.random.normal(0, np.sqrt(R))"
    ]
   },
   {