Calculating Gauss Quadrature Weights

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Gauss Quadrature Weights Calculator

Precise Numerical Integration Made Easy

Gauss Quadrature Calculator

Select the number of points for the quadrature rule (e.g., 2, 3, 4, 5). Higher points increase accuracy but also complexity.
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Calculation Results

N/A
Integration Interval N/A
Approximate Integral Value N/A
Sum of Weights N/A

Gauss-Legendre quadrature approximates the integral $\int_{-1}^{1} f(x) dx \approx \sum_{i=1}^{n} w_i f(x_i)$. The calculator provides the weights ($w_i$) and abscissas ($x_i$) for a given number of points ($n$). The interval is standardized to $[-1, 1]$ for Legendre polynomials.

Points (xi) Weights (wi)
Gauss-Legendre Quadrature Points and Weights
Point Index (i) Abscissa (xi) Weight (wi)
Enter number of points and click Calculate.

What is Gauss Quadrature Weights?

Gauss Quadrature Weights, often discussed in the context of Gauss-Legendre quadrature, are fundamental coefficients used in numerical integration techniques. These weights, alongside corresponding abscissas (points), allow for the highly accurate approximation of definite integrals over a specified interval, typically $[-1, 1]$. Unlike simpler methods like the Trapezoidal Rule or Simpson's Rule, which use equally spaced points, Gauss-Legendre quadrature strategically selects non-uniformly spaced points and associated weights to achieve superior accuracy with fewer function evaluations. This makes Gauss Quadrature Weights indispensable in fields requiring precise numerical computation, such as physics, engineering, and advanced mathematics.

Who Should Use Gauss Quadrature Weights?

Professionals and students engaged in computational mathematics, scientific simulation, and data analysis frequently utilize techniques involving Gauss Quadrature Weights. This includes:

  • Engineers: For calculating moments, stresses, or fluid dynamics parameters where analytical integration is difficult.
  • Physicists: To solve complex integrals arising in quantum mechanics, electromagnetism, and statistical physics.
  • Mathematicians: For developing and analyzing numerical algorithms and solving differential equations.
  • Data Scientists: When dealing with probability distributions or complex function approximations.
  • Students: Learning numerical methods in calculus and applied mathematics courses.

Common Misconceptions

A common misconception is that Gauss-Legendre quadrature is overly complex for general use. While the derivation of the points and weights can be mathematically involved, using pre-calculated tables or dedicated calculators (like this one) simplifies the application significantly. Another misconception is that it only works for integrals from -1 to 1. While the standard Gauss-Legendre method is defined on this interval, a simple linear transformation allows it to be applied to any finite interval. Understanding the role of Gauss Quadrature Weights in achieving high accuracy with fewer points is key to appreciating its power.

Gauss Quadrature Weights Formula and Mathematical Explanation

The core idea behind Gauss-Legendre quadrature is to find a set of $n$ points $x_1, x_2, \dots, x_n$ and corresponding weights $w_1, w_2, \dots, w_n$ such that the approximation

$$ \int_{-1}^{1} f(x) dx \approx \sum_{i=1}^{n} w_i f(x_i) $$

is exact for all polynomials of degree up to $2n-1$. The points $x_i$ are the roots of the $n$-th degree Legendre polynomial, $P_n(x)$, and the weights $w_i$ are determined by the formula:

$$ w_i = \frac{2}{(1 – x_i^2) [P_n'(x_i)]^2} $$

Alternatively, weights can be calculated using associated Lagrange polynomials. For practical purposes, these points and weights are often pre-computed and tabulated.

Derivation Steps (Conceptual)

  1. Define the Problem: We seek to approximate $\int_a^b f(x) dx$. For Gauss-Legendre, we first transform the interval $[a, b]$ to $[-1, 1]$. The standard formula assumes the interval is already $[-1, 1]$.
  2. Choose the Degree: Select the number of points, $n$. This determines the highest degree of polynomial the quadrature rule will integrate exactly ($2n-1$).
  3. Identify Points (Abscissas): The $n$ abscissas ($x_i$) are the roots of the $n$-th Legendre polynomial, $P_n(x)$.
  4. Calculate Weights: The weights ($w_i$) are calculated based on the abscissas and the derivative of the Legendre polynomial. The formula ensures that the quadrature rule integrates polynomials up to degree $2n-1$ exactly.
  5. Apply the Rule: The integral is approximated by the weighted sum $\sum_{i=1}^{n} w_i f(x_i)$.

Variables Table

Variable Meaning Unit Typical Range
$n$ Number of quadrature points Dimensionless Integers (e.g., 2 to 10 for common use)
$x_i$ Abscissa (integration point) Depends on original interval (standardized to dimensionless for $[-1, 1]$) $[-1, 1]$ for standard Legendre
$w_i$ Gauss Quadrature Weight Depends on original interval (standardized to dimensionless for $[-1, 1]$) Positive values, sum is typically 2 for $[-1, 1]$ interval
$f(x)$ Function to be integrated Varies Varies
$\int_{-1}^{1} f(x) dx$ Definite integral over the standard interval Depends on $f(x)$ units Varies
$\sum_{i=1}^{n} w_i f(x_i)$ Approximation of the integral Depends on $f(x)$ units Varies

Practical Examples (Real-World Use Cases)

Let's illustrate with two examples using our Gauss Quadrature Weights calculator.

Example 1: Integrating a Simple Polynomial

Problem: Approximate the integral $\int_{-1}^{1} x^3 dx$. Analytically, this integral is $\left[ \frac{x^4}{4} \right]_{-1}^{1} = \frac{1^4}{4} – \frac{(-1)^4}{4} = \frac{1}{4} – \frac{1}{4} = 0$. Let's see how Gauss quadrature performs.

Inputs:

  • Number of Points ($n$): 2

Calculator Output:

  • Abscissas ($x_i$): $\approx -0.57735$, $\approx 0.57735$
  • Weights ($w_i$): $\approx 1.00000$, $\approx 1.00000$
  • Sum of Weights: $1.0 + 1.0 = 2.0$
  • Approximate Integral Value: $w_1 f(x_1) + w_2 f(x_2) = 1.0 \times (-0.57735)^3 + 1.0 \times (0.57735)^3 = 1.0 \times (-0.19245) + 1.0 \times (0.19245) = 0$

Interpretation: Using only $n=2$ points, Gauss-Legendre quadrature yields the exact result of 0. This is because $x^3$ is a polynomial of degree $3$, and for $n=2$, the rule is exact for polynomials up to degree $2n-1 = 2(2)-1 = 3$. This demonstrates the high accuracy of Gauss Quadrature Weights.

Example 2: Integrating a Transcendental Function

Problem: Approximate the integral $\int_{-1}^{1} e^x dx$. Analytically, this integral is $\left[ e^x \right]_{-1}^{1} = e^1 – e^{-1} \approx 2.71828 – 0.36788 = 2.35040$.

Inputs:

  • Number of Points ($n$): 4

Calculator Output (simulated, using actual tables):

  • Abscissas ($x_i$): $\approx \pm 0.38537$, $\approx \pm 0.90618$
  • Weights ($w_i$): $\approx 0.55476$, $\approx 0.18945$ (note the symmetry $w_i$ for symmetric $x_i$)
  • Sum of Weights: $2 \times 0.55476 + 2 \times 0.18945 = 1.10952 + 0.3789 = 1.48842$ (Wait, sum of weights should be 2. The formula for weights needs to be precise. Let's assume correct pre-calculated weights sum to 2.)
    *Correct weights for n=4: $w_1=w_4 \approx 0.34785$, $w_2=w_3 \approx 0.65215$. Sum: $2(0.34785) + 2(0.65215) = 0.6957 + 1.3043 = 2.0000$*
  • Approximate Integral Value: $w_1(e^{x_1} + e^{-x_1}) + w_2(e^{x_2} + e^{-x_2})$ $\approx 0.34785 (e^{-0.90618} + e^{0.90618}) + 0.65215 (e^{-0.38537} + e^{0.38537})$ $\approx 0.34785 (0.40414 + 2.47167) + 0.65215 (0.68014 + 1.46517)$ $\approx 0.34785 (2.87581) + 0.65215 (2.14531)$ $\approx 0.99947 + 1.39947 \approx 2.39894$

Interpretation: With $n=4$ points, the approximation $2.39894$ is quite close to the exact value $2.35040$. Increasing $n$ further (e.g., to $n=5$ or $n=6$) would yield even greater accuracy, highlighting the efficiency of using carefully chosen Gauss Quadrature Weights.

How to Use This Gauss Quadrature Weights Calculator

Our calculator is designed for simplicity and accuracy. Follow these steps to leverage its capabilities:

  1. Select Number of Points (n): In the 'Number of Points (n)' input field, choose the desired number of quadrature points. Common values range from 2 to 10. A higher number generally leads to greater accuracy but requires more computation.
  2. Click 'Calculate Weights': Press the 'Calculate Weights' button. The calculator will compute the abscissas ($x_i$) and weights ($w_i$) for the specified number of points using pre-defined algorithms for Legendre polynomials.
  3. Review the Results:
    • Main Result: Displays the sum of weights, which should be 2 for the standard $[-1, 1]$ interval.
    • Intermediate Results: Show the integration interval $[-1, 1]$, the calculated approximate integral value (if a function $f(x)$ were provided and evaluated at the points), and the sum of the calculated weights.
    • Table: A detailed table lists each point index, its corresponding abscissa ($x_i$), and its weight ($w_i$).
    • Chart: Visualizes the abscissas and weights, showing their distribution and magnitude.
  4. Interpret the Output: The calculated weights and points are ready to be used in your numerical integration formulas. The table and chart provide a clear overview of the quadrature rule's parameters.
  5. Use 'Copy Results': Click 'Copy Results' to copy all calculated data (main result, intermediates, table data) to your clipboard for use in reports or other applications.
  6. Use 'Reset': Click 'Reset' to return the calculator to its default state (e.g., 2 points).

Decision-Making Guidance: Choose $n$ based on the required accuracy and the complexity of the function being integrated. For polynomials, $n$ such that $2n-1$ is greater than or equal to the polynomial's degree guarantees an exact result. For other functions, start with a moderate $n$ (e.g., 4 or 5) and increase it if the approximation is insufficient.

Key Factors That Affect Gauss Quadrature Results

While Gauss-Legendre quadrature is powerful, several factors influence the accuracy and applicability of its results, especially when extending beyond the standard $[-1, 1]$ interval or when dealing with complex functions.

  • Number of Points ($n$): This is the primary factor. As $n$ increases, the accuracy generally improves dramatically, as the rule becomes exact for higher-degree polynomials. However, computational cost also increases. Selecting an appropriate $n$ balances accuracy needs with resource constraints. This relates directly to the precision of the Gauss Quadrature Weights.
  • Function Behavior ($f(x)$): The smoothness and behavior of the function $f(x)$ significantly impact convergence. Functions with singularities, rapid oscillations, or sharp discontinuities within the integration interval can challenge even high-order Gauss quadrature. The approximation error is often related to the derivatives of $f(x)$.
  • Interval Transformation: The standard Gauss-Legendre rule applies to $[-1, 1]$. To integrate over $[a, b]$, a linear transformation $x = \frac{b-a}{2}u + \frac{a+b}{2}$ is used, where $u$ is the variable in $[-1, 1]$. The Jacobian of this transformation, $\frac{b-a}{2}$, must be multiplied by the result obtained using the standard rule. Incorrect transformation can lead to significant errors.
  • Precision of Weights and Points: The accuracy of the pre-calculated Gauss Quadrature Weights and abscissas themselves is critical. Using high-precision values is essential for achieving high accuracy in the final integral approximation. Our calculator uses standard high-precision values.
  • Computational Precision: The floating-point arithmetic used in computers has limitations. For very high $n$ or functions involving extreme values, accumulated round-off errors might affect the final result, even if the theoretical quadrature rule is exact.
  • Nature of the Integral: Improper integrals (infinite limits or singularities) require special treatment, often involving transformations or different quadrature schemes (like Gauss-Laguerre or Gauss-Hermite). Standard Gauss-Legendre is not directly applicable without modification.
  • Choice of Quadrature Method: While Gauss-Legendre is popular, other Gauss-type quadratures (e.g., Chebyshev, Hermite, Laguerre) are tailored for different weight functions or intervals and might be more suitable for specific problems.

Frequently Asked Questions (FAQ)

What is the main advantage of Gauss Quadrature over Trapezoidal Rule?

Gauss Quadrature achieves significantly higher accuracy for a given number of function evaluations compared to methods like the Trapezoidal Rule or Simpson's Rule. This is because it uses strategically chosen, non-uniform points and weights, optimized to integrate polynomials exactly up to a high degree ($2n-1$).

Can Gauss Quadrature be used for integrals on intervals other than [-1, 1]?

Yes. Any finite interval $[a, b]$ can be transformed linearly to $[-1, 1]$. The integral $\int_a^b f(x) dx$ can be rewritten using $x = \frac{b-a}{2}u + \frac{a+b}{2}$ and evaluated using the standard Gauss-Legendre points and weights, remembering to multiply the result by the Jacobian determinant $\frac{b-a}{2}$.

How are the points and weights for Gauss Quadrature determined?

The points (abscissas) are the roots of the $n$-th Legendre polynomial ($P_n(x)$), and the weights are derived from these roots and the derivative of the Legendre polynomial using a specific formula. This mathematical process ensures the quadrature rule's high accuracy.

Is there a limit to the number of points (n) I can use?

Theoretically, $n$ can be any positive integer. Practically, the complexity and computational cost increase with $n$. For most applications, values of $n$ between 2 and 10 provide excellent accuracy. Extremely high $n$ might lead to diminishing returns or numerical instability.

What if my function has a singularity?

Standard Gauss-Legendre quadrature assumes the function is continuous and well-behaved on the interval. For functions with singularities or discontinuities, you might need to use specialized quadrature rules (like Gauss-Jacobi for certain weight functions) or numerical techniques like singularity removal or adaptive quadrature.

How precise are the results from this calculator?

This calculator uses standard, high-precision values for Gauss-Legendre points and weights. The accuracy of the final approximated integral value depends on the chosen $n$ and the nature of the function $f(x)$ being integrated.

What is the "Sum of Weights" result?

For the standard Gauss-Legendre quadrature on the interval $[-1, 1]$, the sum of all weights $w_i$ should equal 2. This serves as a basic check that the weights have been correctly calculated or applied.

Can I use these weights for integration involving other weight functions?

No, these are specifically Gauss-Legendre weights designed for integrating functions multiplied by a weight function of 1 over $[-1, 1]$. Other Gauss-type quadratures (Gauss-Chebyshev, Gauss-Hermite, Gauss-Laguerre, Gauss-Jacobi) use different points and weights tailored for specific weight functions ($w(x)$) or intervals.

Related Tools and Internal Resources

// Pre-computed points and weights for Gauss-Legendre Quadrature // Source: Abramowitz and Stegun, Handbook of Mathematical Functions var gaussData = { 2: { points: [-0.5773502691896257645091411, 0.5773502691896257645091411], weights: [1.0, 1.0] }, 3: { points: [-0.7745966692414833798940923, 0.0, 0.7745966692414833798940923], weights: [0.5555555555555555555555556, 0.8888888888888888888888889, 0.5555555555555555555555556] }, 4: { points: [-0.8611363115940525752239465, -0.3399810435848562648025470, 0.3399810435848562648025470, 0.8611363115940525752239465], weights: [0.3478548452399522731807999, 0.6521451547600477268192001, 0.6521451547600477268192001, 0.3478548452399522731807999] }, 5: { points: [-0.9061798459386639925990317, -0.5384693101056830934672438, 0.0, 0.5384693101056830934672438, 0.9061798459386639925990317], weights: [0.2369268850549825444350733, 0.4786286714993437954991043, 0.5688888888888888888888889, 0.4786286714993437954991043, 0.2369268850549825444350733] }, 6: { points: [-0.9324695142031520278460571, -0.6612093864662645024273250, -0.2386191860588075010172918, 0.2386191860588075010172918, 0.6612093864662645024273250, 0.9324695142031520278460571], weights: [0.1713244923791703450154227, 0.3607709329546018886295177, 0.4679139345726916154447629, 0.4679139345726916154447629, 0.3607709329546018886295177, 0.1713244923791703450154227] }, 7: { points: [-0.9491079123427585970257459, -0.7415311855710945596706711, -0.4070044063479028579731613, 0.0, 0.4070044063479028579731613, 0.7415311855710945596706711, 0.9491079123427585970257459], weights: [0.1294849673170752214505242, 0.2797053914128144196849534, 0.4172537047407599417047652, 0.4444444444444444444444444, 0.4172537047407599417047652, 0.2797053914128144196849534, 0.1294849673170752214505242] }, 8: { points: [-0.9576915343694186611319914, -0.7690967970044750813352240, -0.4248893067874989477946099, -0.1058732264713344298319477, 0.1058732264713344298319477, 0.4248893067874989477946099, 0.7690967970044750813352240, 0.9576915343694186611319914], weights: [0.1012285362844597595434432, 0.2223810343743116332617520, 0.3137066458654541994043414, 0.3626837833783619351048897, 0.3626837833783619351048897, 0.3137066458654541994043414, 0.2223810343743116332617520, 0.1012285362844597595434432] }, 9: { points: [-0.9657701767600403400357958, -0.7871511074977979889192328, -0.4750060471111634911778836, -0.1511794918127678514153856, 0.0, 0.1511794918127678514153856, 0.4750060471111634911778836, 0.7871511074977979889192328, 0.9657701767600403400357958], weights: [0.0810264750808413877693792, 0.1757078141374151949827786, 0.2587112198942129993136977, 0.3098936123984151860113291, 0.3273268396828898965179786, 0.3098936123984151860113291, 0.2587112198942129993136977, 0.1757078141374151949827786, 0.0810264750808413877693792] }, 10: { points: [-0.9705940553549997397666689, -0.7976602093556723453484431, -0.5352509880946651208890154, -0.2497418477770704727224941, 0.0, 0.2497418477770704727224941, 0.5352509880946651208890154, 0.7976602093556723453484431, 0.9705940553549997397666689], weights: [0.0647424833665276358779417, 0.1425853345723422093468184, 0.2215461816315143695719263, 0.2844444444444444444444444, 0.2955242247110514651131448, 0.2844444444444444444444444, 0.2215461816315143695719263, 0.1425853345723422093468184, 0.0647424833665276358779417] } }; var chart = null; // Global variable for chart object function calculateGaussQuadrature() { var numPointsInput = document.getElementById("numPoints"); var numPointsError = document.getElementById("numPointsError"); var resultsDiv = document.getElementById("results"); var mainResultSpan = document.getElementById("mainResult"); var intervalResultSpan = document.getElementById("intervalResult"); var integralValueResultSpan = document.getElementById("integralValueResult"); var sumWeightsResultSpan = document.getElementById("sumWeightsResult"); var tableBody = document.getElementById("resultsTableBody"); // Clear previous error messages numPointsError.textContent = ""; numPointsError.style.display = "none"; resultsDiv.style.display = "none"; tableBody.innerHTML = ""; // Clear previous table data var n = parseInt(numPointsInput.value); // Input validation if (isNaN(n) || n 10) { numPointsError.textContent = "Please enter a number between 1 and 10."; numPointsError.style.display = "block"; return; } if (!gaussData[n]) { numPointsError.textContent = "Data for this number of points is not available."; numPointsError.style.display = "block"; return; } var points = gaussData[n].points; var weights = gaussData[n].weights; // Calculate sum of weights var sumWeights = weights.reduce(function(sum, weight) { return sum + weight; }, 0); // Calculate main result (sum of weights, should be ~2 for [-1,1]) var mainResult = sumWeights.toFixed(5); // Display with reasonable precision // Populate results section intervalResultSpan.textContent = "[-1, 1]"; // For demonstration, we can't evaluate an arbitrary f(x) here. // We'll show a placeholder or the sum of weights as an indicator. integralValueResultSpan.textContent = "Requires f(x)"; sumWeightsResultSpan.textContent = sumWeights.toFixed(5); // Populate table for (var i = 0; i < n; i++) { var row = tableBody.insertRow(); var cellIndex = row.insertCell(0); var cellPoint = row.insertCell(1); var cellWeight = row.insertCell(2); cellIndex.textContent = i + 1; cellPoint.textContent = points[i].toFixed(10); // High precision cellWeight.textContent = weights[i].toFixed(10); // High precision } // Display results section resultsDiv.style.display = "block"; mainResultSpan.textContent = mainResult; // Display Sum of Weights as main result updateChart(points, weights); } function updateChart(points, weights) { var ctx = document.getElementById("gaussChart").getContext("2d"); if (chart) { chart.destroy(); // Destroy previous chart instance if it exists } chart = new Chart(ctx, { type: 'bar', // Using bar chart to represent points and weights distinctly data: { labels: points.map(function(p, i) { return "P" + (i + 1); }), // Labels for points datasets: [{ label: 'Abscissas (xᵢ)', data: points, backgroundColor: 'rgba(31, 119, 180, 0.6)', // Blueish for points borderColor: 'rgba(31, 119, 180, 1)', borderWidth: 1, yAxisID: 'y-axis-points' }, { label: 'Weights (wᵢ)', data: weights, backgroundColor: 'rgba(255, 127, 14, 0.6)', // Orangish for weights borderColor: 'rgba(255, 127, 14, 1)', borderWidth: 1, yAxisID: 'y-axis-weights' }] }, options: { responsive: true, maintainAspectRatio: true, // Maintain aspect ratio for better control scales: { x: { title: { display: true, text: 'Point Index' } }, 'y-axis-points': { type: 'linear', position: 'left', title: { display: true, text: 'Abscissa Value' }, grid: { drawOnChartArea: false, // Only draw grid for the primary y-axis }, ticks: { callback: function(value) { return value.toFixed(4); } } }, 'y-axis-weights': { type: 'linear', position: 'right', title: { display: true, text: 'Weight Value' }, grid: { drawOnChartArea: false, // Don't draw grid lines for the secondary axis }, ticks: { callback: function(value) { return value.toFixed(4); } } } }, plugins: { tooltip: { callbacks: { label: function(context) { var label = context.dataset.label || ''; if (label) { label += ': '; } if (context.parsed.y !== null) { label += context.parsed.y.toFixed(10); } return label; } } } } } }); } function resetCalculator() { document.getElementById("numPoints").value = 2; document.getElementById("numPointsError").textContent = ""; document.getElementById("numPointsError").style.display = "none"; document.getElementById("results").style.display = "none"; document.getElementById("resultsTableBody").innerHTML = 'Enter number of points and click Calculate.'; if (chart) { chart.destroy(); chart = null; } // Reset chart canvas content visually var canvas = document.getElementById("gaussChart"); var ctx = canvas.getContext("2d"); ctx.clearRect(0, 0, canvas.width, canvas.height); // Add a placeholder message on the canvas if desired ctx.font = "16px Arial"; ctx.fillStyle = "#6c757d"; ctx.textAlign = "center"; ctx.fillText("Chart will appear after calculation.", canvas.width/2, canvas.height/2); } function copyResults() { var numPoints = document.getElementById("numPoints").value; var n = parseInt(numPoints); var points = gaussData[n] ? gaussData[n].points : []; var weights = gaussData[n] ? gaussData[n].weights : []; var sumWeights = weights.length > 0 ? weights.reduce(function(sum, weight) { return sum + weight; }, 0).toFixed(5) : "N/A"; var interval = "[-1, 1]"; var approxIntegral = "Requires f(x)"; var resultText = "Gauss Quadrature Weights Calculation:\n\n"; resultText += "Number of Points (n): " + numPoints + "\n"; resultText += "———————————-\n"; resultText += "Main Result (Sum of Weights): " + sumWeights + "\n"; resultText += "Integration Interval: " + interval + "\n"; resultText += "Approximate Integral Value: " + approxIntegral + "\n"; resultText += "Sum of Weights: " + sumWeights + "\n"; resultText += "\nKey Assumptions:\n"; resultText += "- Standard Gauss-Legendre Quadrature used.\n"; resultText += "- Integration interval standardized to [-1, 1].\n\n"; resultText += "Points and Weights:\n"; resultText += "Index\tAbscissa (xᵢ)\t\tWeight (wᵢ)\n"; resultText += "———————————-\n"; for (var i = 0; i < points.length; i++) { resultText += (i + 1) + "\t" + points[i].toFixed(10) + "\t" + weights[i].toFixed(10) + "\n"; } try { navigator.clipboard.writeText(resultText).then(function() { var copyStatus = document.getElementById("copyStatus"); copyStatus.style.visibility = 'visible'; copyStatus.style.opacity = '1'; setTimeout(function() { copyStatus.style.opacity = '0'; copyStatus.style.visibility = 'hidden'; }, 2000); }); } catch (err) { console.error("Clipboard API not available or failed: ", err); alert("Failed to copy results. Please copy manually."); } } // Initial calculation on load document.addEventListener('DOMContentLoaded', function() { calculateGaussQuadrature(); // Perform calculation with default value // Add placeholder text to canvas if calculation hasn't happened yet var canvas = document.getElementById("gaussChart"); var ctx = canvas.getContext("2d"); ctx.font = "16px Arial"; ctx.fillStyle = "#6c757d"; ctx.textAlign = "center"; ctx.fillText("Chart will appear after calculation.", canvas.width/2, canvas.height/2); });

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