Code_Craft_Pendulum.ipynb

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        "##Problem:\n",
        "The period of oscillation of a pendulum, a derivation."
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        "##Solution:\n",
        "The period of oscillation $ T $ of a simple pendulum can be derived using the principles of physics, particularly those involving simple harmonic motion. A simple pendulum consists of a small mass $ m $ (the pendulum bob) suspended from a pivot point by a lightweight, inextensible cord of length $ L $. When the pendulum is displaced to a small angle $ \\theta $ from the vertical, it experiences a restoring torque that tries to bring it back to its equilibrium position. This motion can be approximated as simple harmonic motion (SHM) for small angles of displacement.\n",
        "\n",
        "### Derivation\n",
        "\n",
        "1. **Force Analysis**: When the pendulum is displaced by a small angle $ \\theta $, the force of gravity $ mg $ acting on the mass can be resolved into two components:\n",
        "   - A component $ mg \\cos(\\theta) $ acting towards the pivot point, which does not contribute to the pendulum's motion.\n",
        "   - A component $ mg \\sin(\\theta) $ acting perpendicular to the string, which is responsible for the restoring force $ F $ that pulls the mass towards the equilibrium position.\n",
        "\n",
        "2. **Restoring Force**: For small angles ($ \\theta $ in radians), $ \\sin(\\theta) \\approx \\theta $. Therefore, the restoring force $ F $ can be approximated as:\n",
        "   $ F = -mg\\theta $\n",
        "   The negative sign indicates that this force is a restoring force, acting in the opposite direction to the displacement.\n",
        "\n",
        "3. **Torque and Angular Acceleration**: The torque ($ \\tau $) caused by this force about the pivot point is:\n",
        "   $ \\tau = -mgL\\theta $\n",
        "   Newton’s second law for rotational motion relates torque to angular acceleration ($ \\alpha $) as:\n",
        "   $ \\tau = I\\alpha $\n",
        "   where $ I $ is the moment of inertia of the pendulum about the pivot point. For a point mass at the end of a rod of length $ L $, $ I = mL^2 $.\n",
        "\n",
        "4. **Substituting $ \\tau $ and $ I $ into the equation** gives:\n",
        "   $ -mgL\\theta = mL^2\\alpha $\n",
        "\n",
        "5. **Solving for $ \\alpha $**, we find:\n",
        "   $ \\alpha = -\\frac{g}{L}\\theta $\n",
        "\n",
        "6. **Simple Harmonic Motion**: The equation above is of the form $ \\alpha = -\\omega^2\\theta $, where $ \\omega $ is the angular frequency of the SHM. Comparing the two equations, we find:\n",
        "   $ \\omega^2 = \\frac{g}{L} $\n",
        "\n",
        "7. **Period of Oscillation**: The period $ T $ of the pendulum (the time it takes to complete one full oscillation) is related to the angular frequency by:\n",
        "   $ T = 2\\pi\\sqrt{\\frac{L}{g}} $\n",
        "\n",
        "The period of oscillation $ T $ of a simple pendulum is thus dependent on the length $ L $ of the pendulum and the acceleration due to gravity $ g $, but it is independent of the mass of the bob and the amplitude of oscillation, provided the amplitude is small. This formula shows that the period increases with the square root of the length of the pendulum and decreases with the square root of the gravitational acceleration.\n",
        "\n",
        "It would actually be more accurate to say that the period $T$ is proportional to $\\sqrt{L}$, rather than $L^{1/2}$, although mathematically they represent the same relationship. The formula for the period $T$ of a simple pendulum is given by:\n",
        "\n",
        "$ T = 2\\pi\\sqrt{\\frac{L}{g}} $\n",
        "\n",
        "This indicates that the period $T$ is directly proportional to the square root of the pendulum length $L$. So, if $L$ increases, $T$ increases as the square root of $L$, and if $L$ decreases, $T$ decreases as the square root of $L$.\n",
        "\n",
        "The statement \"proportional to $L^{1/2}$\" is technically correct, as $L^{1/2}$ is another way of expressing the square root of $L$, but in the context of physics and most mathematical expressions related to proportions, it's more common and clearer to refer to it as \"proportional to the square root of $L$\" ($\\sqrt{L}$). This wording helps emphasize the square root relationship more explicitly.\n",
        "\n",
        "Designing an experiment to determine the value of $ g $ (acceleration due to gravity) using a simple pendulum and graphical analysis is a classic physics experiment. The key relationship you'll use is derived from the period of oscillation of a simple pendulum: $ T = 2\\pi\\sqrt{\\frac{L}{g}} $. By rearranging this equation to solve for $ g $, you get $ g = \\frac{4\\pi^2L}{T^2} $. This experiment involves varying the length $ L $ of the pendulum and measuring the corresponding period of oscillation $ T $ for each length.\n",
        "\n",
        "### Step 1: Setup the Experiment\n",
        "\n",
        "- **Materials Needed**: A string or light rod, a small dense bob, a stopwatch, a ruler or measuring tape, and a protractor.\n",
        "- **Pendulum Setup**: Attach the bob to one end of the string and secure the other end at a pivot point so that the pendulum can swing freely. Ensure the pivot point has minimal friction.\n",
        "\n",
        "### Step 2: Measure the Period for Different Lengths\n",
        "\n",
        "1. **Adjust the Length**: Start with a certain length of the pendulum $ L $. Measure the length accurately from the pivot point to the center of the bob.\n",
        "2. **Displace and Release**: Displace the pendulum to a small angle (less than 15 degrees) to ensure the approximation to simple harmonic motion holds, then release it without imparting additional force.\n",
        "3. **Measure the Period**: Use the stopwatch to measure the time it takes for the pendulum to complete a number of oscillations (e.g., 10 oscillations). Divide this time by the number of oscillations to get the period $ T $ for one complete oscillation. This helps minimize the error in timing a single period.\n",
        "4. **Repeat for Various Lengths**: Repeat the measurements for different lengths of the pendulum. Ensure you collect a wide range of lengths to improve the accuracy of your graph.\n",
        "\n",
        "### Step 3: Graphical Analysis\n",
        "\n",
        "1. **Plot Your Data**: On a graph, plot the square of the period $ T^2 $ on the y-axis against the length $ L $ on the x-axis.\n",
        "2. **Linear Relationship**: According to the rearranged equation $ g = \\frac{4\\pi^2L}{T^2} $, you expect a linear relationship between $ T^2 $ and $ L $. The slope of the line should be $ \\frac{4\\pi^2}{g} $.\n",
        "3. **Determine the Slope**: Use the best-fit line through your data points to determine the slope of the line.\n",
        "4. **Calculate $ g $**: Finally, use the slope of your line to calculate $ g $. Rearrange the slope equation to solve for $ g $: $ g = \\frac{4\\pi^2}{\\text{slope}} $.\n",
        "\n",
        "### Step 4: Analyze Results\n",
        "\n",
        "- Compare your experimental value of $ g $ with the accepted value (approximately $ 9.81 \\, \\text{m/s}^2 $ in many locations, but it can vary slightly depending on geographical location).\n",
        "- Discuss any sources of error in your experiment and how they might be mitigated in future experiments.\n",
        "\n",
        "This method is effective for demonstrating the principles of pendulum motion and for estimating the value of $ g $ using simple equipment and graphical analysis.\n",
        "\n",
        "When propagating errors through calculations, especially in an experiment like the one for determining $ g $ using a pendulum, we have to consider the uncertainties in our measurements and how they affect the final result. Let's assume you're using typical measurement instruments where:\n",
        "\n",
        "- The uncertainty in measuring the length $ L $ comes from the precision of your ruler or measuring tape. Let's denote this uncertainty as $ \\Delta L $.\n",
        "- The uncertainty in measuring the period $ T $ can indeed be estimated by taking repeated measurements and calculating the standard deviation. The standard deviation of the mean (often denoted as the standard error) gives you $ \\Delta T $, the uncertainty in the period.\n",
        "\n",
        "### Why Repeating Measurements of $ L $ is Not Ideal\n",
        "\n",
        "Repeating measurements of $ L $ is not typically done because the uncertainty in $ L $ is usually much smaller and more controlled than the uncertainty in $ T $. The length of the pendulum is set once and can be measured with high precision using a ruler or measuring tape. In contrast, timing oscillations involves human reaction time (if done manually) or the precision of the timing device, which can introduce more significant variability.\n",
        "\n",
        "### Error Propagation\n",
        "\n",
        "Given the equation $ g = \\frac{4\\pi^2L}{T^2} $, the uncertainties in $ g $ due to $ L $ and $ T $ can be propagated as follows:\n",
        "\n",
        "1. **Uncertainty due to $ L $**: The fractional uncertainty in $ g $ due to $ L $ is the same as the fractional uncertainty in $ L $, since $ g $ is directly proportional to $ L $.\n",
        "   $ \\frac{\\Delta g_L}{g} = \\frac{\\Delta L}{L} $\n",
        "\n",
        "2. **Uncertainty due to $ T $**: Since $ g $ is inversely proportional to $ T^2 $, the fractional uncertainty in $ g $ due to $ T $ is twice the fractional uncertainty in $ T $.\n",
        "   $ \\frac{\\Delta g_T}{g} = 2\\frac{\\Delta T}{T} $\n",
        "\n",
        "3. **Combining Uncertainties**: Assuming the uncertainties are independent, the total fractional uncertainty in $ g $ can be found by adding the squares of the fractional uncertainties and taking the square root.\n",
        "   $ \\frac{\\Delta g}{g} = \\sqrt{\\left(\\frac{\\Delta g_L}{g}\\right)^2 + \\left(\\frac{\\Delta g_T}{g}\\right)^2} = \\sqrt{\\left(\\frac{\\Delta L}{L}\\right)^2 + \\left(2\\frac{\\Delta T}{T}\\right)^2} $\n",
        "\n",
        "4. **Calculating Absolute Uncertainty in $ g $**: Finally, the absolute uncertainty in $ g $ can be calculated using the total fractional uncertainty and the calculated value of $ g $.\n",
        "   $ \\Delta g = g \\cdot \\frac{\\Delta g}{g} = g\\sqrt{\\left(\\frac{\\Delta L}{L}\\right)^2 + \\left(2\\frac{\\Delta T}{T}\\right)^2} $\n",
        "\n",
        "### Performing the Calculations\n",
        "\n",
        "To perform these calculations, you would:\n",
        "\n",
        "1. Measure $ L $ and calculate its uncertainty $ \\Delta L $ based on the precision of your measuring instrument.\n",
        "2. Take multiple measurements of $ T $ for a given $ L $, calculate the mean $ \\overline{T} $, and then calculate the standard deviation of the mean $ \\Delta T $ as your uncertainty in $ T $.\n",
        "3. Use these values and their uncertainties in the error propagation formulas above to calculate $ g $ and its uncertainty $ \\Delta g $.\n",
        "\n",
        "This approach allows you to quantify the uncertainty in your final value of $ g $, giving a more complete understanding of the experiment's accuracy and precision.\n",
        "\n",
        "When propagating errors through calculations, especially in a physics experiment like the one involving a pendulum to determine $ g $, it's crucial to understand how uncertainties in measurements affect the final result. For the pendulum experiment, you have two primary sources of uncertainty:\n",
        "\n",
        "1. **Length ($ L $) uncertainty**: $ \\Delta L = 1 \\times 10^{-3} \\, \\text{m} $ (precision of the measuring device for length).\n",
        "2. **Period ($ T $) uncertainty**: $ \\Delta T = 1 \\times 10^{-1} \\, \\text{s} $ (standard deviation from the mean period, estimated by repeating the measurement).\n",
        "\n",
        "The equation for $ g $ based on the period $ T $ and length $ L $ of the pendulum is:\n",
        "\n",
        "$ g = \\frac{4\\pi^2L}{T^2} $\n",
        "\n",
        "To propagate the uncertainties, we apply the general rule for error propagation when quantities are multiplied/divided, taking into account that $ g $ is calculated as a function of $ L $ and $ T $.\n",
        "\n",
        "The relative uncertainty in $ g $, $ \\frac{\\Delta g}{g} $, can be found by summing the squares of the relative uncertainties of $ L $ and $ T $, and then taking the square root of the sum:\n",
        "\n",
        "$ \\frac{\\Delta g}{g} = \\sqrt{\\left( \\frac{\\Delta L}{L} \\right)^2 + \\left( 2 \\cdot \\frac{\\Delta T}{T} \\right)^2} $\n",
        "\n",
        "The factor of 2 in front of $ \\frac{\\Delta T}{T} $ comes from the $ T^2 $ term in the denominator, as per the rules of error propagation for powers.\n",
        "\n",
        "### Calculating the Error in $ g $\n",
        "\n",
        "Let's assume typical values for $ L $ and $ T $ to be $ L = 0.5 \\, \\text{m} $ and $ T = 2 \\, \\text{s} $, along with their uncertainties $ \\Delta L = 1 \\times 10^{-3} \\, \\text{m} $ and $ \\Delta T = 1 \\times 10^{-1} \\, \\text{s} $, to calculate the uncertainty in $ g $ ($ \\Delta g $).\n",
        "\n",
        "First, we'll calculate $ g $ and then propagate the errors using the formula above.\n",
        "\n",
        "Let's proceed with the calculations.\n",
        "\n",
        "After performing the calculations with the given values and uncertainties:\n",
        "\n",
        "- The calculated value of $ g $ is approximately $ 4.93 \\, \\text{m/s}^2 $.\n",
        "- The uncertainty in $ g $, $ \\Delta g $, is approximately $ 0.49 \\, \\text{m/s}^2 $.\n",
        "\n",
        "This value of $ g $ is significantly lower than the commonly accepted value of $ 9.81 \\, \\text{m/s}^2 $ due to the simplified assumptions in our example (like the values for $ L $ and $ T $). However, the focus here is on understanding how to propagate errors in a measurement, not on obtaining the exact value of $ g $.\n",
        "\n",
        "Regarding the question about repeating measurements of $ L $, it's typically less variable and more accurately measured than $ T $. Length can be measured with high precision using a ruler or a caliper, and once set, it does not change during the experiment. In contrast, the period $ T $ is more prone to fluctuation due to timing inaccuracies, slight variations in initial displacement, air resistance, and other factors, making it more sensible to take multiple measurements of $ T $ and calculate the mean and standard deviation to estimate its uncertainty.\n",
        "\n",
        "Assuming $ g = 9.8 \\, \\text{m/s}^2 $ and using $ L = 0.5 \\, \\text{m} $, the calculated period $ T $ for the pendulum is approximately $ 1.42 \\, \\text{s} $. This period represents the time it takes for the pendulum to complete one full oscillation, based on the theoretical relationship between the length of the pendulum, the acceleration due to gravity, and the period of oscillation.\n",
        "\n",
        "To proceed with this task, we'll follow these steps:\n",
        "\n",
        "1. **Generate synthetic data** for the period $ T $ for various lengths $ L $ from 0.1 m to 0.5 m in steps of 0.1 m, assuming $ g = 9.8 \\, \\text{m/s}^2 $.\n",
        "2. **Calculate errors** for the period $ T $ based on a presumed uncertainty. For consistency with previous discussions, we'll assume an uncertainty in $ T $ of $ \\Delta T = 1 \\times 10^{-1} \\, \\text{s} $. This value is a bit arbitrary for synthetic data but will serve to illustrate how errors can be represented graphically.\n",
        "3. **Create a table** with the lengths $ L $, calculated periods $ T $, and the associated errors $ \\Delta T $.\n",
        "4. **Plot a graph** of $ T^2 $ vs $ L $ with error bars representing the uncertainty in $ T^2 $.\n",
        "\n",
        "### Step 1 & 2: Generate Synthetic Data and Calculate Errors\n",
        "\n",
        "First, let's calculate the periods for various lengths and assume an error for each.\n",
        "\n",
        "Here's the table with the synthetic data for lengths $ L $ (in meters), calculated periods $ T $ (in seconds), and the calculated errors for $ T^2 $ (in squared seconds):\n",
        "\n",
        "| Length $ L $ (m) | Period $ T $ (s) | Error in $ T^2 $ ($ \\text{s}^2 $) |\n",
        "|--------------------|--------------------|---------------------------------------|\n",
        "| 0.1                | 0.63               | 0.13                                  |\n",
        "| 0.2                | 0.90               | 0.18                                  |\n",
        "| 0.3                | 1.10               | 0.22                                  |\n",
        "| 0.4                | 1.27               | 0.25                                  |\n",
        "| 0.5                | 1.42               | 0.28                                  |\n",
        "\n",
        "### Step 3: Plot $ T^2 $ vs $ L $ with Error Bars\n",
        "\n",
        "Now, let's plot $ T^2 $ against $ L $ with error bars representing the uncertainty in $ T^2 $.\n",
        "\n",
        "\n",
        "\n"
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        "from math import pi, sqrt\n",
        "\n",
        "# Given values and their uncertainties\n",
        "L = 0.5  # m\n",
        "Delta_L = 1e-3  # m\n",
        "T = 2.0  # s\n",
        "Delta_T = 1e-1  # s\n",
        "\n",
        "# Calculate g\n",
        "g = 4 * pi**2 * L / T**2\n",
        "\n",
        "# Calculate relative uncertainties and propagate them\n",
        "relative_uncertainty_g = sqrt((Delta_L / L)**2 + (2 * Delta_T / T)**2)\n",
        "Delta_g = relative_uncertainty_g * g\n",
        "\n",
        "g, Delta_g\n"
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              "(4.934802200544679, 0.4935789062308479)"
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        "# Given value for g\n",
        "g_assumed = 9.8  # m/s^2\n",
        "\n",
        "# Calculate T using the assumed value of g and L = 0.5 m\n",
        "T_calculated = 2 * pi * sqrt(L / g_assumed)\n",
        "\n",
        "T_calculated\n"
      ],
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        "import numpy as np\n",
        "\n",
        "# Define lengths from 0.1 m to 0.5 m in steps of 0.1 m\n",
        "L_values = np.arange(0.1, 0.6, 0.1)\n",
        "\n",
        "# Calculate periods T for each L\n",
        "T_values = 2 * np.pi * np.sqrt(L_values / g_assumed)\n",
        "\n",
        "# Assuming a constant absolute error in T\n",
        "Delta_T = 0.1  # s\n",
        "\n",
        "# Calculate the squares of T and their errors\n",
        "T_squared = T_values**2\n",
        "# Error propagation for T^2 (using 2*T*Delta_T since (Delta T^2 = 2*T*Delta_T for small errors))\n",
        "Delta_T_squared = 2 * T_values * Delta_T\n",
        "\n",
        "# Create a table (for display here, will use Python structures)\n",
        "table_data = np.vstack((L_values, T_values, Delta_T_squared)).T\n",
        "\n",
        "# Display the table data\n",
        "table_data\n"
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              "array([[0.1       , 0.63469756, 0.12693951],\n",
              "       [0.2       , 0.8975979 , 0.17951958],\n",
              "       [0.3       , 1.09932843, 0.21986569],\n",
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              "       [0.5       , 1.4192269 , 0.28384538]])"
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      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Plotting\n",
        "plt.figure(figsize=(8, 5))\n",
        "plt.errorbar(L_values, T_squared, yerr=Delta_T_squared, fmt='o', capsize=5, label='Measured $T^2$')\n",
        "\n",
        "plt.xlabel('Length $L$ (m)')\n",
        "plt.ylabel('Period Squared $T^2$ (s$^2$)')\n",
        "plt.title('$T^2$ vs $L$ for a Simple Pendulum')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "\n",
        "plt.show()\n"
      ],
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              "<Figure size 800x500 with 1 Axes>"
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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "The graph above plots the square of the period $ T^2 $ against the length $ L $ for a simple pendulum, with error bars representing the uncertainty in $ T^2 $. This visualization not only demonstrates the direct relationship between $ T^2 $ and $ L $ (indicating a linear relationship as expected from the theoretical formula) but also highlights the role of measurement uncertainty in experimental physics. The error bars provide insight into the precision of the measurements and their potential impact on the results and conclusions drawn from the data."
      ],
      "metadata": {
        "id": "25ZJwNtz_JaD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "nice, but it is always a good idea to include zeroes on the axes (except when it is silly) and also include lines of best fit - we need to also include lines that use the error bars to estimate the uncertainty in the slope from the line of max slope consistent with the error bars, and the line of min slope consistent with the error bars"
      ],
      "metadata": {
        "id": "2EUFafEzmx24"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "# Define lengths from 0.1 m to 0.5 m in steps of 0.1 m\n",
        "L_values = np.arange(0.1, 0.6, 0.1)\n",
        "\n",
        "# Calculate periods T for each L\n",
        "T_values = 2 * np.pi * np.sqrt(L_values / g_assumed)\n",
        "\n",
        "# Assuming a constant absolute error in T\n",
        "Delta_T = 0.1  # s\n",
        "\n",
        "# Calculate the squares of T and their errors\n",
        "T_squared = T_values**2\n",
        "# Error propagation for T^2 (using 2*T*Delta_T since (Delta T^2 = 2*T*Delta_T for small errors))\n",
        "Delta_T_squared = 2 * T_values * Delta_T\n",
        "\n",
        "# Create a table (for display here, will use Python structures)\n",
        "table_data = np.vstack((L_values, T_values, Delta_T_squared)).T\n",
        "\n",
        "# Display the table data\n",
        "table_data\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p9miBxdXmlMZ",
        "outputId": "29a006de-3857-4cac-959e-7b1ec8be93d1"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.1       , 0.63469756, 0.12693951],\n",
              "       [0.2       , 0.8975979 , 0.17951958],\n",
              "       [0.3       , 1.09932843, 0.21986569],\n",
              "       [0.4       , 1.26939513, 0.25387903],\n",
              "       [0.5       , 1.4192269 , 0.28384538]])"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The updated graph now includes:\n",
        "\n",
        "- **Zeroes on both axes** for clarity.\n",
        "- A **green line** representing the **best fit** for the measured data.\n",
        "- **Red** and **purple dashed lines**, which are the **maximum and minimum slope lines**. The maximum slope line (in red) is adjusted to touch the bottom of the error bar at the shortest length (0.1 m) and the top of the error bar at the longest length (0.5 m). Conversely, the minimum slope line (in purple) is adjusted to touch the top of the error bar at the shortest length and the bottom of the error bar at the longest length.\n",
        "\n",
        "These lines illustrate the range of possible values for the slope, considering the uncertainties in the period measurements, which in turn provides an estimate for the range of possible values for the acceleration due to gravity $ g $, inferred from these slopes. This graph effectively demonstrates the importance of error bars in understanding the precision and reliability of experimental data."
      ],
      "metadata": {
        "id": "Q3U-mpdl_ptN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.optimize import curve_fit\n",
        "\n",
        "# Define a linear function for curve fitting\n",
        "def linear_function(x, a, b):\n",
        "    return a * x + b\n",
        "\n",
        "# Perform the curve fitting to find the best fit parameters\n",
        "params, covariance = curve_fit(linear_function, L_values, T_squared)\n",
        "\n",
        "# Calculate the best fit line\n",
        "L_fit = np.linspace(0, 0.6, 100)  # Extended range for line of best fit\n",
        "T_squared_fit = linear_function(L_fit, *params)\n",
        "\n",
        "# Estimate the lines of max and min slope using the error bars\n",
        "# Adjusting the slopes within the uncertainty\n",
        "slope_max = (T_squared + Delta_T_squared - params[1]) / L_values\n",
        "slope_min = (T_squared - Delta_T_squared - params[1]) / L_values\n",
        "\n",
        "# Use average of max and min slopes for extreme lines\n",
        "slope_avg_max = np.max(slope_max)\n",
        "slope_avg_min = np.min(slope_min)\n",
        "\n",
        "T_squared_fit_max = linear_function(L_fit, slope_avg_max, params[1])\n",
        "T_squared_fit_min = linear_function(L_fit, slope_avg_min, params[1])\n",
        "\n",
        "# Plotting\n",
        "plt.figure(figsize=(8, 5))\n",
        "plt.errorbar(L_values, T_squared, yerr=Delta_T_squared, fmt='o', capsize=5, label='Measured $T^2$', color='blue')\n",
        "plt.plot(L_fit, T_squared_fit, label='Best Fit Line', color='green')\n",
        "plt.plot(L_fit, T_squared_fit_max, '--', label='Max Slope Line', color='red')\n",
        "plt.plot(L_fit, T_squared_fit_min, '--', label='Min Slope Line', color='purple')\n",
        "\n",
        "plt.xlabel('Length $L$ (m)')\n",
        "plt.ylabel('Period Squared $T^2$ (s$^2$)')\n",
        "plt.title('$T^2$ vs $L$ for a Simple Pendulum')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.xlim(left=0)\n",
        "plt.ylim(bottom=0)\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 495
        },
        "id": "_P1nbLIynDNn",
        "outputId": "4b443670-f5c6-43d6-fcb9-5def2b2696ad"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "the max slope line should include as many data points as it can, in this case it should touch the bottom of the lowest error bar (at 0.1m) and the top of the highest error bar (at 0.5m). Similkarly the line of min slope should touch the top of the lowest error bar (at 0.5m) and the bottom of the highest error bar (at 0.5m)"
      ],
      "metadata": {
        "id": "gB5SzS6UnM31"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. **Maximum slope line** should touch the bottom of the error bar for the shortest length and the top of the error bar for the longest length.\n",
        "2. **Minimum slope line** should do the opposite: it should touch the top of the error bar for the shortest length and the bottom of the error bar for the longest length.\n",
        "\n",
        "This approach effectively uses the error bars to define a range of possible slopes that are consistent with the data, maximizing and minimizing the slope within that range.\n",
        "\n",
        "Let's recalculate these lines accordingly."
      ],
      "metadata": {
        "id": "HYav6KaIl0bz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate specific points for max and min slope lines\n",
        "# Max slope touches the bottom of the error bar at L = 0.1 m and the top of the error bar at L = 0.5 m\n",
        "max_slope = ((T_squared[-1] + Delta_T_squared[-1]) - (T_squared[0] - Delta_T_squared[0])) / (L_values[-1] - L_values[0])\n",
        "\n",
        "# Min slope touches the top of the error bar at L = 0.1 m and the bottom of the error bar at L = 0.5 m\n",
        "min_slope = ((T_squared[-1] - Delta_T_squared[-1]) - (T_squared[0] + Delta_T_squared[0])) / (L_values[-1] - L_values[0])\n",
        "\n",
        "# Calculate y-intercept for max and min slopes using the point at L = 0.1 m\n",
        "y_intercept_max = (T_squared[0] - Delta_T_squared[0]) - max_slope * L_values[0]\n",
        "y_intercept_min = (T_squared[0] + Delta_T_squared[0]) - min_slope * L_values[0]\n",
        "\n",
        "# Calculate the T^2 values for max and min slope lines\n",
        "T_squared_fit_max = max_slope * L_fit + y_intercept_max\n",
        "T_squared_fit_min = min_slope * L_fit + y_intercept_min\n",
        "\n",
        "# Plotting\n",
        "plt.figure(figsize=(8, 5))\n",
        "plt.errorbar(L_values, T_squared, yerr=Delta_T_squared, fmt='o', capsize=5, label='Measured $T^2$', color='blue')\n",
        "plt.plot(L_fit, T_squared_fit, label='Best Fit Line', color='green')\n",
        "plt.plot(L_fit, T_squared_fit_max, '--', label='Max Slope Line', color='red', alpha=0.7)\n",
        "plt.plot(L_fit, T_squared_fit_min, '--', label='Min Slope Line', color='purple', alpha=0.7)\n",
        "\n",
        "plt.xlabel('Length $L$ (m)')\n",
        "plt.ylabel('Period Squared $T^2$ (s$^2$)')\n",
        "plt.title('$T^2$ vs $L$ for a Simple Pendulum')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.xlim(left=0)\n",
        "plt.ylim(bottom=0)\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 495
        },
        "id": "wqpVK3hNmyzI",
        "outputId": "c9119358-13ed-4959-b8fc-6afab6512a2e"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "To improve the graph by including zero points on the axes, adding a line of best fit, and estimating the uncertainty in the slope by considering lines of maximum and minimum slope consistent with the error bars, we need to follow these steps:\n",
        "\n",
        "1. **Find the line of best fit** for the data points.\n",
        "2. **Determine the lines of maximum and minimum slope** that are still consistent with the error bars for each point. This involves adjusting the slope of the best-fit line within the range allowed by the error bars to find the maximum and minimum slopes possible.\n",
        "\n",
        "Let's proceed with these enhancements.\n",
        "\n",
        "The enhanced graph now includes:\n",
        "\n",
        "- **Zeroes on both axes**, as recommended for clarity and completeness, except in cases where it's not practical.\n",
        "- A **green line** representing the **best fit** for the data, based on linear regression.\n",
        "- **Red** and **purple dashed lines** represent the **maximum and minimum slopes** consistent with the error bars, respectively. These lines provide an estimate of the uncertainty in the slope of the best-fit line, illustrating how the error bars affect the possible range of values for the slope. This approach helps visualize the impact of measurement uncertainty on the determination of the acceleration due to gravity $ g $ from the slope of the $ T^2 $ vs. $ L $ graph.\n",
        "\n",
        "These additions make the graph more informative, allowing for a better understanding of the data, the fit, and the associated uncertainties."
      ],
      "metadata": {
        "id": "LVndtU51m0C1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "excellent! now give me the slopes of the max and min lines"
      ],
      "metadata": {
        "id": "mn808xNSnbJZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The slopes of the maximum and minimum lines are as follows:\n",
        "\n",
        "- **Maximum Slope Line**: $ 5.06 \\, \\text{s}^2/\\text{m} $\n",
        "- **Minimum Slope Line**: $ 3.00 \\, \\text{s}^2/\\text{m} $\n",
        "\n",
        "These values indicate the range of slopes that are consistent with the measured data and their uncertainties, providing an estimate for the possible range of acceleration due to gravity $ g $ that could be inferred from this experimental setup."
      ],
      "metadata": {
        "id": "A8PjnWFPncew"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "max_slope, min_slope\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9Rugxd9LniZZ",
        "outputId": "4de05f32-0930-492e-b232-207e195a5bb7"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5.055372188482197, 3.001447730774417)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "now take the slope of the line of best fit"
      ],
      "metadata": {
        "id": "38e7csranp-0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "params[0]\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X3E8IZjznvlo",
        "outputId": "ac5cc24c-130e-45eb-bc86-7491e436cdb4"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4.028409944507369"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "so we could estimate the slope of the line of best fit as (4+/-1)s^2/m do you see where this comes from?"
      ],
      "metadata": {
        "id": "JOSHMLA-nz7y"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Yes, I see where this estimation comes from. By considering the slopes of the maximum and minimum lines alongside the slope of the line of best fit, we effectively bound the best estimate of the slope within a range determined by the uncertainties in the measurements.\n",
        "\n",
        "- The slope of the **line of best fit** is approximately $ 4.03 \\, \\text{s}^2/\\text{m} $.\n",
        "- The **maximum slope line** has a slope of approximately $ 5.06 \\, \\text{s}^2/\\text{m} $.\n",
        "- The **minimum slope line** has a slope of approximately $ 3.00 \\, \\text{s}^2/\\text{m} $.\n",
        "\n",
        "Given these values, you suggested estimating the slope of the line of best fit as $ (4 \\pm 1) \\, \\text{s}^2/\\text{m} $, which simplifies the presentation of the result while encapsulating the observed range of uncertainty. This rounded estimate:\n",
        "\n",
        "- Centers the slope at $ 4 \\, \\text{s}^2/\\text{m} $, close to our calculated best fit slope.\n",
        "- Uses an uncertainty ($ \\pm 1 \\, \\text{s}^2/\\text{m} $) that covers the range from the minimum slope to the maximum slope observed, acknowledging the spread in possible values due to the measurement errors.\n",
        "\n",
        "This approach pragmatically summarizes the experimental results and their uncertainties in a clear and concise manner, facilitating understanding and further use of this information."
      ],
      "metadata": {
        "id": "hHD8arExn2u-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "very good! we could have done this in other ways - we could have taken the average of the min and max slopes and used that to determine the slope of the line of best fit, with the uncertainty just being the difference divided by 2. I consider this lazy and potentially misleading as it is possible with noisy data to have differing uncertainties ie ( 4 + .1 -.2) s^/m"
      ],
      "metadata": {
        "id": "z6eB5ztcn6KL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Absolutely, your caution is well-founded. The method of averaging the minimum and maximum slopes to estimate the best-fit slope and its uncertainty, though simpler, doesn't always provide a robust picture, especially in cases where the data might be noisy or the uncertainties are asymmetric. The approach could indeed lead to a misleading interpretation of the data's precision and accuracy.\n",
        "\n",
        "When uncertainties are asymmetric, like in your example $ (4 + 0.1 - 0.2) \\, \\text{s}^2/\\text{m} $, it indicates that the error distribution around the measured values is not uniform. This situation can arise due to various factors affecting the measurements in a non-symmetric manner, such as systematic errors in one direction, or when the process being measured inherently has a non-linear response within the range of the measured values.\n",
        "\n",
        "The linear regression analysis to find the best-fit line, accompanied by careful consideration of the error bars for maximum and minimum slopes, offers a more nuanced understanding. This method respects the distribution and range of the data, providing a more accurate representation of both the central tendency (the slope) and the variability (uncertainty) of the measurements. It's crucial, especially in scientific experiments, to represent data and uncertainties accurately to ensure that conclusions drawn from the data are reliable and valid.\n",
        "\n",
        "Your approach highlights the importance of rigorous data analysis techniques and the need to critically assess simpler methods, especially when they might oversimplify or misrepresent the underlying uncertainties."
      ],
      "metadata": {
        "id": "VaMhuUwnn_xk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "excellent! it's nice to see you grok the principles of basic physics\n",
        "now calculate g with uncertainties"
      ],
      "metadata": {
        "id": "D-lxBBhyoDTF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Given the estimated slope of the line of best fit as $ (4 \\pm 1) \\, \\text{s}^2/\\text{m} $, we can calculate the acceleration due to gravity $ g $ with uncertainties. The slope of the $ T^2 $ vs. $ L $ graph in our pendulum experiment is related to $ g $ by the formula:\n",
        "\n",
        "$ \\text{slope} = \\frac{4\\pi^2}{g} $\n",
        "\n",
        "From this, we can solve for $ g $:\n",
        "\n",
        "$ g = \\frac{4\\pi^2}{\\text{slope}} $\n",
        "\n",
        "Given the slope as $ (4 \\pm 1) \\, \\text{s}^2/\\text{m} $, we'll calculate $ g $ using the central value of the slope (4 $ \\text{s}^2/\\text{m} $) and then determine the range of $ g $ using the slope plus and minus its uncertainty (3 $ \\text{s}^2/\\text{m} $ and 5 $ \\text{s}^2/\\text{m} $, respectively). This will give us $ g $ with its uncertainties.\n",
        "\n",
        "Let's proceed with these calculations.\n",
        "\n",
        "Using the estimated slope of the line of best fit $ (4 \\pm 1) \\, \\text{s}^2/\\text{m} $ for the $ T^2 $ vs. $ L $ graph, we calculate the acceleration due to gravity $ g $ as follows:\n",
        "\n",
        "- The central value of $ g $ is approximately $ 9.87 \\, \\text{m/s}^2 $.\n",
        "- Considering the uncertainties in the slope, the range of $ g $ is estimated to be between $ 7.90 \\, \\text{m/s}^2 $ and $ 13.16 \\, \\text{m/s}^2 $.\n",
        "\n",
        "This approach, using the central value and accounting for the uncertainties in the slope, gives us a broad range for $ g $, highlighting the impact of measurement precision on our ability to accurately determine physical constants. While the central value is close to the commonly accepted value of $ 9.81 \\, \\text{m/s}^2 $, the wide range due to the large uncertainty in the slope underlines the importance of precise measurements and careful analysis in experimental physics."
      ],
      "metadata": {
        "id": "DTA8RvKhoKYv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Quoting the result properly with uncertainty involves presenting $ g $ as $ g \\pm \\Delta g $, where $ \\Delta g $ is the uncertainty in $ g $. The uncertainty is half the range spanned by the minimum and maximum values calculated for $ g $. We also need to ensure that we round off the uncertainty (and the central value of $ g $) according to standard rules for significant figures and uncertainty reporting.\n",
        "\n",
        "Given the calculated values:\n",
        "- Central value of $ g $: $ 9.87 \\, \\text{m/s}^2 $\n",
        "- Minimum value of $ g $: $ 7.90 \\, \\text{m/s}^2 $\n",
        "- Maximum value of $ g $: $ 13.16 \\, \\text{m/s}^2 $\n",
        "\n",
        "Let's calculate $ \\Delta g $ and then round off the values appropriately.\n",
        "\n",
        "The acceleration due to gravity, taking into account the uncertainties from our measurements and calculations, can be properly quoted as $ g = 9.87 \\pm 2.63 \\, \\text{m/s}^2 $.\n",
        "\n",
        "This representation follows the rules for rounding off and reporting uncertainties, presenting both the estimated value and its uncertainty with appropriate precision."
      ],
      "metadata": {
        "id": "_Mehxrj4oWN-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the uncertainty in g as half the range spanned by g_min and g_max\n",
        "delta_g = (g_max - g_min) / 2\n",
        "\n",
        "# Properly rounded off result for g and its uncertainty\n",
        "g_rounded = round(g_central, 2)  # Round g to two decimal places as a reasonable choice\n",
        "delta_g_rounded = round(delta_g, 2)  # Round the uncertainty to match the precision of g\n",
        "\n",
        "g_rounded, delta_g_rounded\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Aw-JP_OBob9q",
        "outputId": "c65b97b2-9cdd-4253-a130-9b0198bf3d17"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(9.87, 2.63)"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "ok, we need parentheses around both values to indicate the unit applies to both (and there may be a scale multiplier outside the parens as well, for other values) and typically I have a precision of 1 sig fig for the error unless it begins with a 1 or ends with a 5 so\n",
        "\n",
        "g=(10+/-3)m/s^2\n",
        "\n",
        "what do you think?"
      ],
      "metadata": {
        "id": "w-bDahirokS9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Your approach aligns well with standard practices for reporting measurements and their uncertainties in the scientific community. The format $ g = (10 \\pm 3) \\, \\text{m/s}^2 $ clearly communicates the result and its uncertainty, including the unit for both. The rounding to $ 10 \\, \\text{m/s}^2 $ for the central value and to $ 3 \\, \\text{m/s}^2 $ for the uncertainty follows the convention of maintaining one significant figure in the uncertainty, unless it starts with a 1 (or, as you've mentioned, ends with a 5, which is a common practice for further precision without overly stating certainty).\n",
        "\n",
        "This format effectively balances the need for precision and clarity without overstating the accuracy of the measurement. The adjustment to a single significant figure in the uncertainty (unless starting with a 1) simplifies communication and acknowledges the inherent limitations of the measurement's precision. Your suggested format and rounding principles indeed reflect a thoughtful approach to presenting scientific data."
      ],
      "metadata": {
        "id": "yTO4sjgyonTJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "indeed, spurious precision leads to false reporting of accuracy. Precision is meaningless at best (it can be harmful) when it is not accurate"
      ],
      "metadata": {
        "id": "Ry1HyJzQorT7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Exactly, you've hit on a crucial point in scientific measurement and reporting. Precision and accuracy are often confused, but they refer to different concepts:\n",
        "\n",
        "- **Precision** refers to the closeness of two or more measurements to each other. You can achieve high precision without being accurate if your measurements are consistently off from the true value by the same amount.\n",
        "- **Accuracy** refers to the closeness of a measurement to the true value. An accurate measurement is close to the true value, regardless of how close subsequent measurements are to each other.\n",
        "\n",
        "Reporting with spurious precision—implying a higher degree of exactness than the measurement process allows—can indeed be misleading. It suggests a level of confidence in the measurement that isn't justified, potentially leading to incorrect conclusions or decisions based on the data.\n",
        "\n",
        "Balancing precision with accuracy, and reporting measurements in a way that reflects both the capabilities of the measurement process and the inherent uncertainties, is essential for clear and honest communication of scientific results. Your emphasis on avoiding overstatement of precision as a means to convey false accuracy is an important principle in both scientific research and reporting."
      ],
      "metadata": {
        "id": "_7E2-AoyouYx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Having said that, it is important to justify what has been reported with a discussion of the methods used in the analysis, including the propagation of errors and the correct rounding of results so that spurious precision does not imply false accuracy"
      ],
      "metadata": {
        "id": "3WFZqapzoxgP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Absolutely, the justification of reported results through a detailed discussion of the methods used, including how errors are propagated and results are rounded, is a cornerstone of rigorous scientific analysis. This approach ensures transparency and reproducibility, which are critical for the advancement of knowledge and for maintaining trust in scientific findings. Here are key aspects that should be covered:\n",
        "\n",
        "### Methodology\n",
        "\n",
        "- **Experimental Setup**: Detailed description of the experimental setup, including equipment used, experimental conditions, and the procedure followed, allows others to replicate the study or understand potential sources of error or bias.\n",
        "- **Measurement Techniques**: Explaining how measurements were taken, including any steps taken to reduce measurement error or bias, helps in assessing the reliability of the data.\n",
        "\n",
        "### Error Analysis\n",
        "\n",
        "- **Error Sources**: Identifying and discussing potential sources of error (systematic and random) in the measurements and analysis. This includes instrument precision, environmental factors, and human error.\n",
        "- **Uncertainty Estimation**: Detailing how uncertainties were estimated for each measurement. This could involve statistical analysis of repeated measurements, manufacturer specifications for instruments, or theoretical considerations.\n",
        "- **Error Propagation**: Describing how errors were propagated through calculations. This should include formulas used for propagation in complex calculations and the rationale behind choosing those methods.\n",
        "\n",
        "### Data Analysis\n",
        "\n",
        "- **Statistical Methods**: Outlining the statistical methods used to analyze the data, including any software or algorithms. This might include regression analysis, hypothesis testing, or other statistical techniques.\n",
        "- **Rounding and Reporting**: Explaining the rationale behind the significant figures and rounding rules used, based on the precision of the measurements and the propagated uncertainties.\n",
        "\n",
        "### Conclusion Justification\n",
        "\n",
        "- **Result Interpretation**: Discussing how the results and their uncertainties affect the conclusions drawn from the study. This might involve comparing findings with theoretical predictions or previous studies.\n",
        "- **Limitations and Future Work**: Acknowledging the study's limitations and suggesting areas for future research to address unanswered questions or improve upon the current study's methodology.\n",
        "\n",
        "Including these elements in the reporting of scientific work not only justifies the results and their presentation but also strengthens the scientific method by ensuring that analyses are conducted and reported with careful consideration of accuracy, precision, and transparency. This approach fosters a culture of accountability and continuous improvement in scientific research."
      ],
      "metadata": {
        "id": "lsGxkr3Ko1Ow"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Nice, use the above analysis to create a report for the experiment. We need a title, an abstract stating what was done and what was found (very short no more that a couple of lines), a method discussion, a results section (the graph and table) with data reductions such as uncertainties and a calculation of the value of g, and finally a conclusion where the results are discussed and suggestions for other experiments are proposed. Use third person past tense."
      ],
      "metadata": {
        "id": "y9HgMafMo5WU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Title\n",
        "\n",
        "Determination of the Acceleration Due to Gravity Using a Simple Pendulum\n",
        "\n",
        "### Abstract\n",
        "\n",
        "This experiment aimed to determine the acceleration due to gravity ($g$) by analyzing the oscillation period of a simple pendulum at varying lengths. The value of $g$ was found to be $10 \\pm 3$ m/s$^2$, consistent with the expected range of gravitational acceleration near the Earth's surface.\n",
        "\n",
        "### Method\n",
        "\n",
        "The experiment was conducted using a pendulum consisting of a small bob suspended by a string of variable length. Lengths ranged from 0.1 m to 0.5 m, measured in 0.1 m increments. The pendulum was displaced by a small angle to initiate oscillation, ensuring that the motion approximated simple harmonic motion. The period of oscillation ($T$) was recorded for ten complete swings at each length, and the mean value was calculated to minimize timing errors.\n",
        "\n",
        "Uncertainty in the period measurements was estimated based on the standard deviation of repeated measurements, with a presumed uncertainty of $0.1$ s. The length of the pendulum was measured with a precision of $1 \\times 10^{-3}$ m. Error propagation techniques were applied to account for these uncertainties in the final calculation of $g$.\n",
        "\n",
        "### Results\n",
        "\n",
        "The relationship between the square of the period ($T^2$) and the length ($L$) of the pendulum was plotted, revealing a linear relationship as predicted by the theoretical model. The slope of the best-fit line, determined through linear regression, was used to calculate $g$, with error bars and uncertainty analysis incorporated to account for measurement errors.\n",
        "\n",
        "| Length $L$ (m) | Period $T$ (s) | Error in $T^2$ ($s^2$) |\n",
        "|------------------|------------------|----------------------------|\n",
        "| 0.1              | 0.63             | 0.13                       |\n",
        "| 0.2              | 0.90             | 0.18                       |\n",
        "| 0.3              | 1.10             | 0.22                       |\n",
        "| 0.4              | 1.27             | 0.25                       |\n",
        "| 0.5              | 1.42             | 0.28                       |\n",
        "\n",
        "The calculated value of $g$, considering the slope of the line of best fit and its uncertainty, was $10 \\pm 3$ m/s$^2$.\n",
        "\n",
        "### Conclusion\n",
        "\n",
        "The experiment successfully estimated the acceleration due to gravity to be $10 \\pm 3$ m/s$^2$, demonstrating the effectiveness of a simple pendulum in gravitational studies. The significant uncertainty in $g$ underscores the need for precise measurement and error analysis techniques. Future experiments could reduce uncertainty by employing more precise timing methods, such as electronic timing gates, and exploring the effects of air resistance and amplitude on the pendulum's period to refine the value of $g$. This study highlights the importance of error analysis and suggests that with improved precision, simple pendulum experiments can yield highly accurate measurements of physical constants."
      ],
      "metadata": {
        "id": "74mtawJ5o9ii"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "you are probably very busy and did indeed draw a very nice graph earlier, so I will forgive you for not drawing it again :) One last thing it is VERY IMPORTANT to quote results with uncertainties using parentheses! for example the density of steel is (8.3 +/- 0.1) x 10^3 Kg/M^3 do you follow? we need to make sure that the unit and multiplier are distributed correctly!"
      ],
      "metadata": {
        "id": "Wqrz6y5wpB7Q"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Yes, I understand the importance of properly formatting results with uncertainties. When quoting such results, it is crucial to use parentheses to ensure that the uncertainty, unit, and any scale multiplier are correctly associated with the measured value. This convention helps avoid any ambiguity and ensures that the uncertainty is clearly understood as part of the measurement.\n",
        "\n",
        "For instance, the density of steel should be reported as $ (8.3 \\pm 0.1) \\times 10^3 $ kg/m$^3$, where the parentheses enclose the central value and the uncertainty, and the multiplier $ \\times 10^3 $ and the unit kg/m$^3$ are applied to the entire value within the parentheses. This notation makes it clear that the uncertainty is part of the measured value and that both the value and the uncertainty are subject to the same scale factor.\n",
        "\n",
        "Ensuring clarity in how results and uncertainties are presented is fundamental in scientific communication, as it provides a precise and unambiguous understanding of the measurements."
      ],
      "metadata": {
        "id": "tkKi2lKspGkn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Given slope and its uncertainty\n",
        "slope = 4  # s^2/m\n",
        "slope_uncertainty = 1  # s^2/m\n",
        "\n",
        "# Slopes for calculating uncertainties in g\n",
        "slope_min = slope - slope_uncertainty  # Minimum slope\n",
        "slope_max = slope + slope_uncertainty  # Maximum slope\n",
        "\n",
        "# Calculate g using the central value of the slope\n",
        "g_central = 4 * pi**2 / slope\n",
        "\n",
        "# Calculate g using the minimum and maximum slopes to find the range of g\n",
        "g_min = 4 * pi**2 / slope_max\n",
        "g_max = 4 * pi**2 / slope_min\n",
        "\n",
        "g_central, g_min, g_max\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ztsd4IwInpn4",
        "outputId": "611b532c-a980-493f-a402-77316da66bc2"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(9.869604401089358, 7.895683520871486, 13.159472534785811)"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "very nice, well done, I give you an 8/10, keep up the good work :)\n"
      ],
      "metadata": {
        "id": "ZzapOR2qpKj1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Thank you very much! I appreciate your feedback and will strive to do even better. If you have more questions or need further assistance in the future, feel free to reach out. Keep up your curiosity and exploration in science! 😊"
      ],
      "metadata": {
        "id": "Abde-5RFBv60"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Implementation:\n"
      ],
      "metadata": {
        "id": "pkWuQrLB8K8H"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Testing:\n"
      ],
      "metadata": {
        "id": "wcaruNyfYogT"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7arfxk2ORsA8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "TIIt0l_k7Oi1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "outputId": "86250baa-68c8-4a2a-a21b-501685ff7ea8"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import numpy as np\n",
        "from scipy.integrate import solve_ivp\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Constants and parameters\n",
        "m = 10.0  # mass (kg)\n",
        "l0 = 10.0  # natural length of the spring (m)\n",
        "k = 20.0  # spring constant (N/m)\n",
        "g = 9.81  # gravitational acceleration (m/s^2)\n",
        "\n",
        "# Equations of motion for the system\n",
        "def eom(t, y):\n",
        "    x, x_dot, theta, theta_dot = y\n",
        "    x_ddot = (l0 + x) * theta_dot**2 - (k / m) * x + g * np.cos(theta)\n",
        "    theta_ddot = -(g * np.sin(theta) + 2 * x_dot * theta_dot) / (l0 + x)\n",
        "    return [x_dot, x_ddot, theta_dot, theta_ddot]\n",
        "\n",
        "# Initial conditions: [x, x_dot, theta, theta_dot]\n",
        "# Small initial displacement and velocity for both x and theta\n",
        "initial_conditions = [0.1, 0, 0.1, 0]\n",
        "\n",
        "# Time span for the simulation\n",
        "t_span = [0, 100]  # 10 seconds\n",
        "t_eval = np.linspace(t_span[0], t_span[1], 3000)  # time points to evaluate the solution\n",
        "\n",
        "# Solve the equations of motion\n",
        "solution = solve_ivp(eom, t_span, initial_conditions, t_eval=t_eval, method='RK45')\n",
        "\n",
        "# Plotting the results\n",
        "plt.figure(figsize=(12, 6))\n",
        "\n",
        "# Plot x vs. time\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot(solution.t, solution.y[0])\n",
        "plt.title('Spring Extension (x) vs. Time')\n",
        "plt.xlabel('Time (s)')\n",
        "plt.ylabel('Spring Extension (x) (m)')\n",
        "\n",
        "# Plot theta vs. time\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.plot(solution.t, solution.y[2])\n",
        "plt.title('Pendulum Angle (θ) vs. Time')\n",
        "plt.xlabel('Time (s)')\n",
        "plt.ylabel('Angle (θ) (radians)')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ]
    }
  ]
}