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add JuliaSets
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‎short_tutorials/JuliaSets.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "8607eb0e",
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"metadata": {},
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"source": [
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"# Julia Sets"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "87bd1a0f",
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"metadata": {},
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"outputs": [],
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"source": [
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"%matplotlib inline\n",
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"from matplotlib import pyplot as plt\n",
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"import numpy as np"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3f5a75af",
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"metadata": {},
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"outputs": [],
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"source": [
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"def julia(c, size=256, center=(0.0, 0.0), zoom=1.0, iters=256):\n",
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" x, y = np.meshgrid(\n",
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" np.linspace(-1, 1, size)/zoom + center[0], \n",
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" np.linspace(-1, 1, size)/zoom + center[1], \n",
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" )\n",
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" z = x + 1j * y\n",
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" im = np.zeros(z.shape)\n",
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" ix = np.ones(z.shape, dtype=bool)\n",
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" for i in range(iters):\n",
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" z[ix] = z[ix] ** 2 + c\n",
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" ix = np.abs(z) < 2\n",
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" im += ix\n",
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" return im"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "79556895",
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"metadata": {},
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"outputs": [],
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"source": [
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"plt.imshow(julia(-0.4+0.6j), cmap='magma')\n",
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"plt.axis(False);"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a43baa42",
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"metadata": {},
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"outputs": [],
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"source": [
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"plt.imshow(julia(-0.4+0.6j, center=(0.34, -0.30), zoom=10000.0), cmap='magma')\n",
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"plt.axis(False);"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3c63ce73",
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"metadata": {},
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"outputs": [],
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"source": [
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"c = (\n",
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" -0.4 + 0.6j, \n",
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" -0.74543 + 0.11301j, \n",
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" -0.75 + 0.11j, \n",
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" -0.1 + 0.651j,\n",
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" -0.835 - 0.2321j,\n",
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" -0.70176 - 0.3842j,\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "cfc85940",
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"metadata": {},
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"outputs": [],
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"source": [
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"noise_level = 5.0\n",
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"\n",
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"fig, ax = plt.subplots(3, 2, figsize=(10, 16))\n",
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"for c_, a in zip(c, ax.flatten()):\n",
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" img = julia(c_, zoom=0.5) \n",
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" img += np.random.randn(*img.shape) * noise_level\n",
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" a.imshow(img, cmap='magma')\n",
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" a.axis(False)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b01e70d9",
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"metadata": {},
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"source": [
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"# Image processing"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b4069a34",
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"metadata": {},
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"outputs": [],
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"source": [
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"from skimage import data\n",
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"from skimage import filters"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "39bc25c7",
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"metadata": {},
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"outputs": [],
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"source": [
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"from skimage.morphology import disk\n",
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"from skimage import restoration"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "aa225a15",
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"metadata": {},
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"outputs": [],
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"source": [
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"noise_level = 50.0\n",
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"img = julia(-0.4+0.6j, size=200)\n",
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"noise_img = img + np.random.randn(*img.shape) * noise_level\n",
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"median_img = filters.median(noise_img, disk(3))\n",
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"tv_img = restoration.denoise_tv_chambolle(noise_img, weight=20.0)\n",
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"wavelet_img = restoration.denoise_wavelet(noise_img)\n",
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"gaussian_img = filters.gaussian(noise_img, sigma=1.8)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d60ecafd",
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"metadata": {
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"scrolled": false
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},
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"outputs": [],
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"source": [
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"fig, ax = plt.subplots(3, 2, figsize=(12, 18))\n",
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"for a, (im, title) in zip(\n",
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" ax.flatten(),\n",
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" ((img, 'original'), \n",
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" (noise_img, 'original+noise'),\n",
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" (gaussian_img, 'gaussian'),\n",
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" (median_img, 'median'), \n",
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" (wavelet_img, 'wavelet'),\n",
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" (tv_img, 'tv'), )):\n",
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" a.imshow(im, cmap='magma', vmin=0, vmax=255)\n",
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" a.axis(False)\n",
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" a.set_title(title)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b495345a",
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"metadata": {},
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"source": [
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"# DataJoint Pipeline"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9015c43e",
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"metadata": {},
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"outputs": [],
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"source": [
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"import datajoint as dj"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "62067735",
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"metadata": {},
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"outputs": [],
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"source": [
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"schema = dj.Schema('julia')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "4c4ef4a9",
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"metadata": {},
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"outputs": [],
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"source": [
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"img.max()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "5630641b",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "benv",
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"language": "python",
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"name": "benv"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

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