# Copyright 2026 Marimo. All rights reserved.
# /// script
# requires-python = ">=3.12"
# dependencies = [
#     "marimo",
#     "matplotlib==3.10.1",
#     "numpy==2.2.4",
# ]
# ///

import marimo

__generated_with = "0.23.6"
app = marimo.App()


@app.cell(hide_code=True)
def _(mo):
    mo.md("""
    # How marimo notebooks run

    Reactive execution is based on a single rule: when a cell is run, all other
    cells that reference any of the global variables it defines run
    automatically.

    To provide reactive execution, marimo creates a dataflow graph out of your
    cells.
    """)
    return


@app.cell(hide_code=True)
def _(mo):
    mo.md(r"""
    **Tip: disabling automatic execution.**

    marimo lets you disable automatic execution: in the notebook
    footer, change "On Cell Change" to "lazy".

    When the runtime is lazy, after running a cell, marimo marks its
    descendants as stale instead of automatically running them. The lazy
    runtime puts you in control over when cells are run, while still giving
    guarantees about the notebook state.
    """)
    return


@app.cell(hide_code=True)
def _(mo):
    mo.md("""
    ## References and definitions

    A marimo notebook is a directed acyclic graph in which nodes represent
    cells and edges represent data dependencies. marimo creates this graph by
    analyzing each cell (without running it) to determine its

    - references ("refs"), the global variables it reads but doesn't define;
    - definitions ("defs"), the global variables it defines.

    There is an edge from one cell to another if the latter cell references any
    global variables defined by the former cell.

    The rule for reactive execution can be restated in terms of the graph: when
    a cell is run, its descendants are run automatically.
    """)
    return


@app.cell(hide_code=True)
def _(mo):
    mo.md("""
    ### Example

    The next four cells plot a sine wave with a given period and amplitude.
    Each cell is labeled with its refs and defs.
    """)
    return


@app.cell(hide_code=True)
def _(mo):
    mo.accordion(
        {
            "Tip: inspecting refs and defs": f"""
            Use `mo.refs()` and `mo.defs()` to inspect the refs and defs of any
            given cell. This can help with debugging complex notebooks.

            For example, here are the refs and defs of this cell:

            {mo.as_html({"refs": mo.refs(), "defs": mo.defs()})}
            """
        }
    )
    return


@app.cell
def _(amplitude, mo, period, plot_wave):
    mo.md(f"""
    {mo.as_html(plot_wave(amplitude, period))}

    - `refs: {mo.refs()}`
    - `defs: {mo.defs()}`
    """)
    return


@app.cell
def _(mo):
    period = 2 * 3.14159

    mo.md(
        f"""
        - `refs: {mo.refs()}`
        - `defs: {mo.defs()}`
        """
    )
    return (period,)


@app.cell
def _(mo):
    amplitude = 1

    mo.md(
        f"""
        - `refs: {mo.refs()}`
        - `defs: {mo.defs()}`
        """
    )
    return (amplitude,)


@app.cell
def _(mo, np, plt):
    def plot_wave(amplitude, period):
        x = np.linspace(0, 2 * np.pi, 256)
        plt.plot(x, amplitude * np.sin(2 * np.pi / period * x))
        plt.xlim(0, 2 * np.pi)
        plt.ylim(-2, 2)
        plt.xticks(
            [0, np.pi / 2, np.pi, 3 * np.pi / 2, 2 * np.pi],
            [0, r"$\pi/2$", r"$\pi$", r"$3\pi/2$", r"$2\pi$"],
        )
        plt.yticks([-2, -1, 0, 1, 2])
        plt.gcf().set_size_inches(6.5, 2.4)
        return plt.gca()

    mo.md(
        f"""
        - `refs: {mo.refs()}`
        - `defs: {mo.defs()}`
        """
    )
    return (plot_wave,)


@app.cell(hide_code=True)
def _(mo):
    mo.md("""
    🌊 **Try it!** In the above cells, try changing the value `period` or
    `amplitude`, then click the run button ( ▷ ) to register your changes.
    See what happens to the sine wave.
    """)
    return


@app.cell(hide_code=True)
def _(mo):
    mo.md("""
    Here is the dataflow graph for the cells that make the sine wave plot, plus
    the cells that import libraries. Each cell is labeled with its defs.

    ```
                       +------+               +-----------+
           +-----------| {mo} |-----------+   | {np, plt} |
           |           +---+--+           |   +----+------+
           |               |              |        |
           |               |              |        |
           v               v              v        v
      +----------+   +-------------+   +--+----------+
      | {period} |   | {amplitude} |   | {plot_wave} |
      +---+------+   +-----+-------+   +------+------+
          |                |                  |
          |                v                  |
          |              +----+               |
          +------------> | {} | <-------------+
                         +----+
    ```

    The last cell, which doesn't define anything, produces the plot.
    """)
    return


@app.cell(hide_code=True)
def _(mo):
    mo.md("""
    ## Dataflow programming

    marimo's runtime rule has some important consequences that may seem
    surprising if you are not used to dataflow programming. We list these
    below.
    """)
    return


@app.cell(hide_code=True)
def _(mo):
    mo.md("""
    ### Execution order is not cell order

    The order in which cells are executed is determined entirely by the
    dataflow graph. This makes marimo notebooks more reproducible than
    traditional notebooks. It also lets you place boilerplate, like
    imports or long markdown strings, at the bottom of the editor.
    """)
    return


@app.cell(hide_code=True)
def _(mo):
    mo.md("""
    ### Global variable names must be unique

    Every global variable can be defined by only one cell. Without this
    constraint, there would be no way for marimo to know which order to
    execute cells in.

    If you violate this constraint, marimo provides a helpful
    error message, like below:
    """)
    return


@app.cell
def _():
    planet = "Mars"
    planet
    return


@app.cell
def _():
    planet = "Earth"
    planet
    return


@app.cell(hide_code=True)
def _(mo):
    mo.md("""
    **🌊 Try it!** In the previous cell, change the name `planet` to `home`,
    then run the cell.
    """)
    return


@app.cell(hide_code=True)
def _(mo):
    mo.md("""
    Because defs must be unique, global variables cannot be modified with
    operators like `+=` or `-=` in cells other than the one that created
    them; these operators count as redefinitions of a name.

    **🌊 Try it!** Get rid of the following errors by merging the next two
    cells into a single cell.
    """)
    return


@app.cell
def _():
    count = 0
    return


@app.cell
def _():
    count += 1
    return


@app.cell(hide_code=True)
def _(mo):
    mo.md("""
    ### Underscore-prefixed variables are local to cells

    Global variables prefixed with an underscore are "private" to the cells
    that define them. This means that multiple cells can define the same
    underscore-prefixed name, and one cell's private variables won't be
    made available to other cells.

    **Example**.
    """)
    return


@app.cell
def _():
    _private_variable, _ = 1, 2
    _private_variable, _
    return


@app.cell
def _():
    _private_variable, _ = 3, 4
    _private_variable, _
    return


@app.cell
def _():
    # `_private_variable` and `_` are not defined in this cell
    _private_variable, _
    return


@app.cell(hide_code=True)
def _(mo):
    mo.md("""
    ### Deleting a cell deletes its variables

    Deleting a cell deletes its global variables and
    then runs all cells that reference them. This prevents severe bugs that
    can arise when state has been deleted from the editor but not from the
    program memory.
    """)
    return


@app.cell
def _(mo):
    to_be_deleted = "variable still exists"

    mo.md(
        """
        🌊 **Try it!**

        Delete this cell by clicking the trash bin icon.
        """
    )
    return (to_be_deleted,)


@app.cell
def _(to_be_deleted):
    to_be_deleted
    return


@app.cell(hide_code=True)
def _(mo):
    mo.md("""
    ### Cycles are not allowed

    Cycles among cells are not allowed. For example:
    """)
    return


@app.cell
def _(two):
    one = two - 1
    return (one,)


@app.cell
def _(one):
    two = one + 1
    return (two,)


@app.cell(hide_code=True)
def _(mo):
    mo.md("""
    ### marimo doesn't track attributes

    marimo only tracks global variables. Writing object attributes does not
    trigger reactive execution.

    **🌊 Example**. Change the value of `state.number` in the next cell, then
    run the cell. Notice how the subsequent cell isn't updated.
    """)
    return


@app.cell
def _(state):
    state.number = 1
    return


@app.cell
def _(state):
    state.number
    return


@app.cell
def _():
    class namespace:
        pass

    state = namespace()
    state.number = 0
    return (state,)


@app.cell(hide_code=True)
def _(mo):
    mo.accordion(
        {
            "Why not track attributes?": """
            marimo can't reliably trace attributes
            to cells that define them. For example, attributes are routinely
            created or modified by library code.
            """
        }
    )
    return


@app.cell(hide_code=True)
def _(mo):
    mo.md("""
    ### marimo doesn't track mutations

    In Python, it's impossible to know whether code will
    mutate an object without running it. So: mutations (such as
    appending to a list) will not trigger reactive execution.
    """)
    return


@app.cell(hide_code=True)
def _(mo):
    mo.accordion(
        {
            "Tip (advanced): mutable state": (
                """
            You can use the fact that marimo does not track attributes or
            mutations to implement mutable state in marimo. An example of
            this is shown in the `ui` tutorial.
            """
            )
        }
    )
    return


@app.cell(hide_code=True)
def _(mo):
    mo.md("""
    ## Best practices

    The constraints marimo puts on your notebooks are all natural consequences
    of the fact that marimo programs are directed acyclic graphs. As long as
    you keep this fact in mind, you'll quickly adapt to the marimo way of
    writing notebooks.

    Ultimately, these constraints will enable you to create powerful notebooks
    and apps, and they'll encourage you to write clean, reproducible code.

    Follow these tips to stay on the marimo way:
    """)
    return


@app.cell
def _(mo, tips):
    mo.accordion(tips)
    return


@app.cell(hide_code=True)
def _(mo):
    mo.md("""
    ## What's next?

    Check out the tutorial on interactivity for a tour of UI elements:

    ```
    marimo tutorial ui
    ```
    """)
    return


@app.cell
def _():
    import matplotlib.pyplot as plt
    import numpy as np

    return np, plt


@app.cell(hide_code=True)
def _():
    tips = {
        "Use global variables sparingly": (
            """
            Keep the number of global variables in your program small to avoid
            name collisions across cells. Keep the number of global variables
            defined by any one cell small to make sure that the units of
            reactive execution are small.
            """
        ),
        "Use descriptive names": (
            """
            Use descriptive variable names, especially for global variables.
            This will help you minimize name clashes, and will also result in
            better code.
            """
        ),
        "Use functions": (
            """
            Encapsulate logic into functions to avoid polluting the global
            namespace with temporary or intermediate variables.
            """
        ),
        "Minimize mutations": (
            """
            We saw earlier that marimo cannot track object mutations. So try
            to only mutate an object in the cell that creates it, or create
            new objects instead of mutating existing ones.

            For example, don't do this:

            ```python3
            # a cell
            numbers = [1, 2, 3]
            ```

            ```python3
            # another cell
            numbers.append(4)
            ```

            Instead, prefer

            ```python3
            # a cell
            numbers = [1, 2, 3]
            numbers.append(4)
            ```

            or

            ```python3
            # a cell
            numbers = [1, 2, 3]
            ```

            ```python3
            # another cell
            more_numbers = numbers + [4]
            ```
            """
        ),
        "Write idempotent cells": (
            """
            Write cells whose outputs and behavior are the same when given
            the same inputs (refs); such cells are called _idempotent_. This will
            help you avoid bugs, and let you cache expensive intermediate
            computations (see the next tip).
            """
        ),
        "Cache intermediate computations with `@mo.cache`": (
            """
            Use `mo.cache` to cache the return value of expensive functions.
            You can do this if you abstract complex logic into idempotent
            functions, following earlier tips.

            For example:

            ```python3
            import marimo as mo

            @mo.cache
            def compute_prediction(problem_parameters):
              ...
            ```

            Whenever `compute_predictions` is called with a value of
            `problem_parameters` it has not seen, it will compute the predictions
            and store them in a cache. The next time it is called with the same
            parameters, instead of recomputing the predictions, it will just
            fetch the previously computed ones from the cache.

            If you are familiar with `functools.cache`, `mo.cache` is
            similar but more robust, with the cache persisting even
            if the cell defining the function is re-run.
            """
        ),
    }
    return (tips,)


@app.cell
def _():
    import marimo as mo

    return (mo,)


if __name__ == "__main__":
    app.run()
