marimo

marimo-notebook/README.md

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+The materials contained in this download are designed to complement the Real Python tutorial [Marimo: A Reactive, Reproducible Notebook](https://realpython.com/marimo-notebook-reactive-reproducible/).
+
+You should create a new folder named marimo on your computer and place each of these files inside it. You might also consider creating a [Python virtual environment](https://realpython.com/python-virtual-environments-a-primer/) within this folder.
+
+Your download bundle contains the following files:
+
+hypotenuse_calculator.py                      - This file contains the original hypotenuse_calculator code
+hypotenuse_calculator_before_update.py        - This file contains the code before any updating is attempted.  The code has been deliberately placed out of order, however it runs cleanly.
+hypotenuse_calculator_duplicate_variable.py   - This file shows the effect of re-defining a variable. This file produces an error.
+hypotenuse_calculator_after_update.py         - This file contains the code after the `adjacent` variable was updated to `10`. This file runs cleanly.
+hypotenuse_calculator_after_deletion.py       - This file contains the code after the `opposite variable` was deleted. This file produces an error.
+
+break_even_analysis_chart_code.py             - This file contains the basic chart code.  It will produce a break-even analysis for a fixed set of input data.
+break_even_analysis_UI_elements.py            - This file contains includes the four UI interface elements to allow the plot to be adjusted. 
+break_even_analysis_solution.py               - This file contains a possible solution to the skills test. 
+
+packages.py    - This file contains the code used to demonstrate sandboxing.
+quadratic.py   - This file contains the marimo notebook version of the quadratic formula example.
+equation.py    - This file contains the Python script version of quadratic.py.
+
+simultaneous_equations.py     - This file contains the code used to demonstrate marimo's UI elements.
+simultaneous_equations_ui.py  - This file contains a possible solution to the challenge skills test.
+
+hidden_state.ipynb - This file contains a Jupyter Notebook that can be used as a starting point for the investigation of its problems. You should adjust it as instructed in the tutorial. 

marimo-notebook/break_even_analysis_UI_elements.py

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+# flake8: noqa
+
+import marimo
+
+__generated_with = "0.11.0"
+app = marimo.App(width="medium")
+
+
+@app.cell
+def _():
+    import matplotlib.pyplot as plt
+    import marimo as mo
+
+    return mo, plt
+
+
+@app.cell
+def _(ui_fixed_cost, ui_quantity, ui_selling_price, ui_unit_cost):
+    fixed_cost = int(ui_fixed_cost.value)
+    unit_cost = ui_unit_cost.value
+    selling_price = float(ui_selling_price.value)
+    upper_production_quantity = ui_quantity.value
+    return fixed_cost, selling_price, unit_cost, upper_production_quantity
+
+
+@app.cell
+def _(
+    fixed_cost,
+    plt,
+    selling_price,
+    ui_color_costs,
+    unit_cost,
+    upper_production_quantity,
+):
+    break_even_quantity = fixed_cost / (selling_price - unit_cost)
+    break_even_income = break_even_quantity * selling_price
+
+    units = range(0, upper_production_quantity + 1, 1000)
+    unit_costs = [(unit * unit_cost) + fixed_cost for unit in units]
+    sales_income = [unit * selling_price for unit in units]
+
+    plt.plot(units, unit_costs, marker="o", color=ui_color_costs.value)
+    plt.plot(units, sales_income, marker="x")
+
+    plt.xlabel("Units Produced")
+    plt.ylabel("($)")
+    plt.legend(["Total Costs", "Total Income"])
+    plt.title("Break-Even Analysis")
+
+    plt.vlines(
+        break_even_quantity,
+        ymin=0,
+        ymax=break_even_income,
+        linestyles="dashed",
+    )
+    plt.text(
+        x=break_even_quantity + 100,
+        y=int(break_even_income / 2),
+        s=int(break_even_quantity),
+    )
+    plt.grid()
+    plt.show()
+    return (
+        break_even_income,
+        break_even_quantity,
+        sales_income,
+        unit_costs,
+        units,
+    )
+
+
+@app.cell
+def _(mo):
+    ui_fixed_cost = mo.ui.radio(options=["40000", "50000"], value="50000")
+
+    ui_unit_cost = mo.ui.slider(start=2, stop=5, step=1)
+
+    ui_selling_price = mo.ui.text(value="10")
+
+    ui_quantity = mo.ui.dropdown(
+        options={"10000": 10000, "12000": 12000, "15000": 15000},
+        value="10000",
+    )
+
+    ui_disply_break_even = mo.ui.switch()
+
+    ui_color_costs = mo.ui.dropdown(
+        options={"Red": "red", "Green": "green", "Blue": "blue"}, value="Red"
+    )
+
+    mo.md(
+        f"""
+        Fixed Costs: {ui_fixed_cost}
+
+        Unit Cost Price: {ui_unit_cost}
+
+        Selling Price: {ui_selling_price}
+
+        Maximum Production Quantity: {ui_quantity}
+        """
+    )
+    return (
+        ui_color_costs,
+        ui_disply_break_even,
+        ui_fixed_cost,
+        ui_quantity,
+        ui_selling_price,
+        ui_unit_cost,
+    )
+
+
+if __name__ == "__main__":
+    app.run()

marimo-notebook/break_even_analysis_chart_code.py

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+# flake8: noqa
+
+import marimo
+
+__generated_with = "0.11.0"
+app = marimo.App(width="medium")
+
+
+@app.cell
+def _():
+    import matplotlib.pyplot as plt
+    import marimo as mo
+
+    return mo, plt
+
+
+@app.cell
+def _():
+    fixed_cost = 50000
+    unit_cost = 2
+    selling_price = 10
+    upper_production_quantity = 10000
+    return fixed_cost, selling_price, unit_cost, upper_production_quantity
+
+
+@app.cell
+def _(
+    fixed_cost,
+    plt,
+    selling_price,
+    unit_cost,
+    upper_production_quantity,
+):
+    break_even_quantity = fixed_cost / (selling_price - unit_cost)
+    break_even_income = fixed_cost + break_even_quantity * unit_cost
+
+    units = range(0, upper_production_quantity + 1, 1000)
+    unit_costs = [(x * unit_cost) + fixed_cost for x in units]
+    sales_income = [unit * selling_price for unit in units]
+
+    plt.plot(units, unit_costs, marker="o")
+    plt.plot(units, sales_income, marker="x")
+
+    plt.xlabel("Units Produced")
+    plt.ylabel("($)")
+    plt.legend(["Total Costs", "Total Income"])
+    plt.title("Break-Even Analysis")
+
+    plt.vlines(
+        break_even_quantity,
+        ymin=0,
+        ymax=break_even_income,
+        linestyles="dashed",
+    )
+    plt.text(
+        x=break_even_quantity + 100,
+        y=int(break_even_income / 2),
+        s=int(break_even_quantity),
+    )
+    plt.grid()
+    plt.show()
+    return (
+        break_even_income,
+        break_even_quantity,
+        sales_income,
+        unit_costs,
+        units,
+    )
+
+
+if __name__ == "__main__":
+    app.run()

marimo-notebook/break_even_analysis_solution.py

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+# flake8: noqa
+
+import marimo
+
+__generated_with = "0.11.0"
+app = marimo.App(width="medium")
+
+
+@app.cell
+def _():
+    import matplotlib.pyplot as plt
+    import marimo as mo
+
+    return mo, plt
+
+
+@app.cell
+def _(ui_fixed_cost, ui_quantity, ui_selling_price, ui_unit_cost):
+    fixed_cost = int(ui_fixed_cost.value)
+    unit_cost = ui_unit_cost.value
+    selling_price = float(ui_selling_price.value)
+    upper_production_quantity = ui_quantity.value
+    return fixed_cost, selling_price, unit_cost, upper_production_quantity
+
+
+@app.cell
+def _(
+    fixed_cost,
+    plt,
+    selling_price,
+    ui_break_even,
+    ui_plot_color,
+    unit_cost,
+    upper_production_quantity,
+):
+    break_even_quantity = fixed_cost / (selling_price - unit_cost)
+    break_even_income = break_even_quantity * selling_price
+
+    units = range(0, upper_production_quantity + 1, 1000)
+    total_costs = [(unit * unit_cost) + fixed_cost for unit in units]
+    sales_income = [unit * selling_price for unit in units]
+
+    plt.plot(units, total_costs, marker="o", color=ui_plot_color.value)
+    plt.plot(units, sales_income, marker="x")
+
+    plt.xlabel("Units Produced")
+    plt.ylabel("($)")
+    plt.legend(["Total Costs", "Total Income"])
+    plt.title("Break-Even Analysis")
+
+    if ui_break_even.value:
+        plt.vlines(
+            break_even_quantity,
+            ymin=100,
+            ymax=break_even_income,
+            linestyles="dashed",
+        )
+
+        plt.text(
+            x=break_even_quantity + 100,
+            y=int(break_even_income / 2),
+            s=int(break_even_quantity),
+        )
+
+    plt.grid()
+    plt.show()
+    return (
+        break_even_income,
+        break_even_quantity,
+        sales_income,
+        total_costs,
+        units,
+    )
+
+
+@app.cell
+def _(mo):
+    ui_fixed_cost = mo.ui.radio(options=["40000", "50000"], value="50000")
+
+    ui_unit_cost = mo.ui.slider(start=2, stop=5, step=1)
+
+    ui_selling_price = mo.ui.text(value="10")
+
+    ui_quantity = mo.ui.dropdown(
+        options={"10000": 10000, "12000": 12000, "15000": 15000}, value="10000"
+    )
+
+    ui_break_even = mo.ui.switch()
+
+    ui_plot_color = mo.ui.dropdown(
+        options={"Red": "red", "Green": "green", "Blue": "blue"}, value="Red"
+    )
+
+    mo.md(
+        f"""
+        Fixed Costs: {ui_fixed_cost}
+
+        Unit Cost Price: {ui_unit_cost}
+
+        Selling Price: {ui_selling_price}
+
+        Maximum Quantity: {ui_quantity}
+
+        Display Break-Even Data: {ui_break_even}
+
+        Total Costs Plot Color: {ui_plot_color}
+        """
+    )
+    return (
+        ui_break_even,
+        ui_fixed_cost,
+        ui_plot_color,
+        ui_quantity,
+        ui_selling_price,
+        ui_unit_cost,
+    )
+
+
+if __name__ == "__main__":
+    app.run()

marimo-notebook/equation.py

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+# flake8: noqa
+
+__generated_with = "0.11.0"
+
+# %%
+import marimo as mo
+
+# %%
+mo.md(
+    r"""
+    A quadratic equation is one of the form **$ax^2 + bx + c = 0$**, where a, b and c are constants, and a $\neq$ 0.
+
+    You can solve it using the *quadratic formula*:
+
+    $$x = \frac {-b \pm \sqrt{b^2 -4ac}} {2a}$$
+
+    For example, suppose you wanted to solve: **$2x^2 - 3x - 2 = 0$**
+    """
+)
+
+# %%
+a = 2
+
+# %%
+import math
+
+# %%
+b = -3
+
+# %%
+c = -2
+
+# %%
+x1 = (-b + math.sqrt(b**2 - 4 * a * c)) / (2 * a)
+
+# %%
+x2 = (-b - math.sqrt(b**2 - 4 * a * c)) / (2 * a)
+
+# %%
+print(f"x = {x1} and {x2}.")