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f6dfc5361e
* updated MicroPython to latest in order to have `globals` API available * reduced code around helpers for both MicroPython and Pyodide as now these are more aligned * updated all dependencies and brought in latest [coincident/window](https://github.com/WebReflection/coincident#coincidentwindow) goodness to any `xworker`, preserving the `sync` previous behavior * using [@ungap/structured-clone/json](https://github.com/ungap/structured-clone#tojson) as *coincident* default `parse` and `stringify` utility to allow recursive and more complex data to travel back from the *Worker* (forward data is still fully [structured clone algorithm compatible](https://developer.mozilla.org/en-US/docs/Web/API/Web_Workers_API/Structured_clone_algorithm)) * renamed all *plugin/s* references to *custom/s* as plugin as a word was too misleading * changed *custom types* helpers logic to allow any single node to have its own version of the interpreter wrapper, and all the extra fields it carries with it, including a way to augment every interpreter execution, among as every worker code execution * created a `custom` folder where I've landed the very first `pyscript.js` custom type * created an exhaustive test page to demonstrate the current abilities of *PyScript Next* among its ability to expose utilities that can be used to create *PyScript* plugins
60 lines
2.1 KiB
HTML
60 lines
2.1 KiB
HTML
<!doctype html>
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<html lang="en">
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<head>
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<meta charset="UTF-8" />
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<meta name="viewport" content="width=device-width,initial-scale=1.0" />
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<title>python</title>
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<link rel="stylesheet" href="style.css" />
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<script defer src="./counter.js"></script>
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<script type="module" src="../core.js"></script>
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</head>
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<body>
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<script type="pyodide" config="./config.toml">
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import matplotlib.pyplot as plt
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import matplotlib.tri as tri
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import numpy as np
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import base64
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import io
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import js
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# First create the x and y coordinates of the points.
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n_angles = 36
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n_radii = 8
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min_radius = 0.25
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radii = np.linspace(min_radius, 0.95, n_radii)
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angles = np.linspace(0, 2 * np.pi, n_angles, endpoint=False)
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angles = np.repeat(angles[..., np.newaxis], n_radii, axis=1)
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angles[:, 1::2] += np.pi / n_angles
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x = (radii * np.cos(angles)).flatten()
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y = (radii * np.sin(angles)).flatten()
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z = (np.cos(radii) * np.cos(3 * angles)).flatten()
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# Create the Triangulation; no triangles so Delaunay triangulation created.
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triang = tri.Triangulation(x, y)
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# Mask off unwanted triangles.
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triang.set_mask(np.hypot(x[triang.triangles].mean(axis=1),
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y[triang.triangles].mean(axis=1))
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< min_radius)
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fig1, ax1 = plt.subplots()
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ax1.set_aspect('equal')
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tpc = ax1.tripcolor(triang, z, shading='flat')
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fig1.colorbar(tpc)
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ax1.set_title('tripcolor of Delaunay triangulation, flat shading')
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buf = io.BytesIO()
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plt.savefig(buf, format='png')
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buf.seek(0)
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img = js.document.createElement("img")
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img.style.transform = "scale(.5)"
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img.src = 'data:image/png;base64,' + base64.b64encode(buf.read()).decode('UTF-8')
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js.document.currentScript.target.appendChild(img)
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</script>
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</body>
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</html>
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