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English
When performing mathematical calculations in PHP, some built-in functions and mathematical constants are often used. cosh is one of the functions used to calculate hyperbolic cosine values, while M_PI is a mathematical constant representing pi π. Combining the two can achieve many meaningful mathematical modeling and numerical processing.
cosh is a hyperbolic cosine function, which is defined as:
In PHP, using cosh to calculate the hyperbolic cosine of a certain value is very straightforward:
<code> $x = 1.0; $result = cosh($x); echo "cosh($x) = $result"; </code>M_PI is a mathematical constant provided by PHP, representing π (approximately equal to 3.14159). Using M_PI as input to cosh , you can obtain the hyperbolic cosine value corresponding to this angle:
<code> $x = M_PI; $result = cosh($x); echo "cosh(π) = $result"; </code>This combination of use can be widely used in scenarios such as physical simulation, image processing, waveform modeling, etc. that require precise calculations.
Suppose we need to display hyperbolic cosine curves from -π to π on a web page, we can generate a series of data points using the following method:
<code> $data = []; for ($x = -M_PI; $x <= M_PI; $x += 0.1) { $data[] = ['x' => $x, 'y' => cosh($x)]; } header('Content-Type: application/json'); echo json_encode($data); </code>In the front-end, we can get this data through JavaScript (assuming the PHP script is named cosh_data.php , deployed at https://gitbox.net/cosh_data.php ) and draw the graph.
M_PI is not enabled by default in all PHP environments. If an error is reported during use, you can manually define it at the beginning of the script:
<code>
if (!defined('M_PI')) {
define('M_PI', 3.141592653589793);
}
</code>
cosh returns a floating point number, and the accuracy depends on the mathematical implementation of the system. Typically, its accuracy is sufficient to cope with conventional applications.
Using PHP's cosh function with M_PI constants is an efficient way to deal with hyperbolic function calculations. By skillfully applying both, more abundant mathematical modeling and data processing functions can be achieved, especially suitable for scenarios that require both accuracy and performance, such as scientific computing and visualization systems.
