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DerThorstenDerThorsten
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more styled batch drawing
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‎examples/batch_drawing.ipynb‎

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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": 46,
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"id": "3ea1b15d-478b-41a2-83a1-3e66e5ff5d24",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"from ipycanvas import Canvas, hold_canvas"
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"from ipycanvas import Canvas, hold_canvas\n",
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"import math"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2117b808-65d2-44dd-a780-dd9100809a68",
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"id": "b3b9c73f-d4fe-4c80-978e-60f4932c69d7",
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"metadata": {
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"tags": []
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},
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"source": [
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"# Batch Draw Rects"
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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": 45,
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"id": "7de46419-aefa-4039-a27c-ad92f903a5ff",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "4f16d21e2fa442d9b215ed9f5c7b7ef3",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Canvas(height=300, width=800)"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"canvas = Canvas(width=800, height=300)\n",
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"n_rects = 1000\n",
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"x = np.random.randint(0, canvas.width, size=(n_rects))\n",
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"y = np.random.randint(0, canvas.width, size=(n_rects))\n",
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"width = np.random.randint(10, 40, size=(n_rects))\n",
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"height = np.random.randint(10, 40, size=(n_rects))\n",
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"with hold_canvas(canvas):\n",
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" canvas.fill_style = 'cyan'\n",
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" canvas.fill_rects(x, y, width, height)\n",
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" canvas.line_width = 2\n",
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" canvas.stroke_style = 'black'\n",
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" canvas.stroke_rects(x, y, width, height)\n",
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"canvas"
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]
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},
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{
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"cell_type": "markdown",
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"id": "bd1cb3db-2ceb-49d3-87aa-9929b262a066",
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"metadata": {},
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"source": [
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"# Batch Draw Styled Rects"
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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": 47,
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"id": "f051cd10-5cf3-4a6f-bcb3-d10d5528c185",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "b9af46082601430fabb28f7dc57484a7",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Canvas(height=300, width=800)"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"canvas = Canvas(width=800, height=300)\n",
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"n_rects = 1000\n",
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"x = np.random.randint(0, canvas.width, size=(n_rects))\n",
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"y = np.random.randint(0, canvas.width, size=(n_rects))\n",
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"width = np.random.randint(10, 40, size=(n_rects))\n",
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"height = np.random.randint(10, 40, size=(n_rects))\n",
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"colors_fill = np.random.randint(0, 255, size=(n_rects,3))\n",
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"colors_outline = np.random.randint(0, 255, size=(n_rects,3))\n",
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"alphas = np.random.random(n_rects)\n",
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"with hold_canvas(canvas):\n",
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" canvas.fill_styled_rects(x, y, width, height, color=colors_fill,alpha=alphas)\n",
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" canvas.line_width = 2\n",
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" canvas.stroke_styled_rects(x, y, width, height, color=colors_outline,alpha=alphas)\n",
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"canvas"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2117b808-65d2-44dd-a780-dd9100809a68",
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"metadata": {
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"tags": []
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},
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"source": [
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"## Batch Draw Circles\n",
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"draw many circles each cirlcle has the same color"
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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": 2,
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"execution_count": 48,
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"id": "82b532b6-06b0-4255-b4fa-e03a6363ddf0",
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"metadata": {
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"tags": []
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "92b6da42c15842298d3faf678a6381fa",
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"model_id": "2d3f918019954eb49bf2e2985a2f152c",
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"version_major": 2,
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"version_minor": 0
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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": 3,
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"execution_count": 49,
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"id": "ce545eee-d9c0-47ab-afb7-274fa22389cd",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "e209d607b8de4226a5dbff72536b0ec7",
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"model_id": "11134765d61041fd9395b155ef309910",
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"version_major": 2,
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"version_minor": 0
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},
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},
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{
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"cell_type": "markdown",
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"id": "afea33c6-c6a8-4528-a1d9-7535a6810e9a",
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"id": "279d10b5-ed1b-4fa6-8bbe-f25313ad0475",
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"metadata": {},
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"source": [
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"# Batch Draw Arcs"
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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": 50,
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"id": "e36c1407-9a46-42fa-b06a-1a8283feee86",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "66ae6bd2dbfe4d4eb530b33054e1d2ba",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Canvas(height=300, width=800)"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"canvas = Canvas(width=800, height=300)\n",
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"n_circles = 1000\n",
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"x = np.random.randint(0, canvas.width, size=(n_circles))\n",
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"y = np.random.randint(0, canvas.width, size=(n_circles))\n",
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"r = np.random.randint(10, 20, size=(n_circles))\n",
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"start_angle = np.random.randint(0, 360, size=(n_circles))\n",
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"end_angle = np.random.randint(0, 360, size=(n_circles))\n",
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"start_angle = 0\n",
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"end_angle = math.pi\n",
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"start_angle = np.random.random(n_circles) * math.pi \n",
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"end_angle = np.random.random(n_circles) * math.pi \n",
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"alphas = np.random.random(n_circles)\n",
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"with hold_canvas(canvas):\n",
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" canvas.fill_style = 'cyan'\n",
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" canvas.fill_arcs(x,y,r,start_angle, end_angle)\n",
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" canvas.line_width = 1\n",
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" canvas.stroke_style = 'black'\n",
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" canvas.stroke_arcs(x,y,r,start_angle, end_angle)\n",
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"canvas"
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]
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},
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{
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"cell_type": "markdown",
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"id": "549983e9-f9bb-4ca7-a024-8f1307f15ba8",
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"metadata": {
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"tags": []
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},
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"source": [
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"# Batch Draw Styled Arcs"
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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": 51,
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"id": "1e4431ab-e064-448d-ab72-decba895bb7c",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "61b1d04c125d4420bebae9650ed99c58",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Canvas(height=300, width=800)"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"canvas = Canvas(width=800, height=300)\n",
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"n_circles = 20\n",
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"x = np.random.randint(0, canvas.width, size=(n_circles))\n",
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"y = np.random.randint(0, canvas.height, size=(n_circles))\n",
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"r = np.random.randint(30, 50, size=(n_circles))\n",
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"start_angle = np.random.random(n_circles) * math.pi \n",
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"end_angle = np.random.random(n_circles) * math.pi \n",
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"colors_fill = np.random.randint(0, 255, size=(n_circles,3))\n",
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"colors_outline = np.random.randint(0, 255, size=(n_circles,3))\n",
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"alphas = np.random.random(n_circles)\n",
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"with hold_canvas(canvas):\n",
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" canvas.fill_styled_arcs(x,y,r,start_angle, end_angle,color=colors_fill,alpha=1)\n",
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" canvas.line_width = 3\n",
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" canvas.stroke_styled_arcs(x,y,r,start_angle, end_angle,color=colors_outline,alpha=1)\n",
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"canvas"
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]
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},
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{
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"cell_type": "markdown",
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"id": "afea33c6-c6a8-4528-a1d9-7535a6810e9a",
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"metadata": {
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"tags": []
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},
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"source": [
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"# Batch Draw Polygons / LineSegments\n",
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"## Case 1: All Polygons / LineSegments have the same number of points"
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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": 4,
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"execution_count": 52,
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"id": "885a5ec6-06a8-4430-815e-63c06f6fa2cb",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "f0bfa8a9abe541059c8f0898e913b652",
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"model_id": "8efa7e28168e4da1a69045f4865a68e3",
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"version_major": 2,
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"version_minor": 0
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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": 5,
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"execution_count": 33,
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"id": "6bf31d36-01af-484c-9360-b97f504532a2",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "08ef9e57eb5f493697fc6aa2c63c32ac",
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"model_id": "8915993f0f1a4f128ba7871a858cf99f",
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"version_major": 2,
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"version_minor": 0
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},
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},
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"cell_type": "code",
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"execution_count": 34,
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"id": "34643bf9-d9d9-4296-aa02-67531e6f500f",
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"metadata": {
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"tags": []
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "219e9b29162d4d5897d53a461c97c232",
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"model_id": "751412241a3c4294a8323f61cd77e8f3",
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},
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"cell_type": "code",
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"id": "272deb5a-fc18-44b0-af01-04f3afb28247",
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"metadata": {},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "aa24d7e01ad04540be42d2702cb3ff74",
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"model_id": "3af7556653c44b7ca378466c438d2016",
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"version_major": 2,
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"version_minor": 0
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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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}

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