153 lines
4.9 KiB
Plaintext
153 lines
4.9 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "filled-multiple",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"The root path: /Users/xuanyidong/Desktop/AutoDL-Projects\n",
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"The library path: /Users/xuanyidong/Desktop/AutoDL-Projects/lib\n"
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]
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}
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],
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"source": [
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"import os, sys\n",
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"import torch\n",
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"from pathlib import Path\n",
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"import numpy as np\n",
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"import matplotlib\n",
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"from matplotlib import cm\n",
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"matplotlib.use(\"agg\")\n",
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"import matplotlib.pyplot as plt\n",
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"import matplotlib.ticker as ticker\n",
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"\n",
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"\n",
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"__file__ = os.path.dirname(os.path.realpath(\"__file__\"))\n",
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"root_dir = (Path(__file__).parent / \"..\").resolve()\n",
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"lib_dir = (root_dir / \"lib\").resolve()\n",
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"print(\"The root path: {:}\".format(root_dir))\n",
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"print(\"The library path: {:}\".format(lib_dir))\n",
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"assert lib_dir.exists(), \"{:} does not exist\".format(lib_dir)\n",
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"if str(lib_dir) not in sys.path:\n",
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" sys.path.insert(0, str(lib_dir))\n",
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"\n",
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"from datasets import ConstantGenerator, SinGenerator, SyntheticDEnv\n",
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"from datasets import DynamicQuadraticFunc\n",
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"from datasets.synthetic_example import create_example_v1"
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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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"id": "detected-second",
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"metadata": {},
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"outputs": [],
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"source": [
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"def draw_fig(save_dir, timestamp, xaxis, yaxis):\n",
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" save_path = save_dir / '{:04d}'.format(timestamp)\n",
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" # print('Plot the figure at timestamp-{:} into {:}'.format(timestamp, save_path))\n",
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" dpi, width, height = 40, 1500, 1500\n",
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" figsize = width / float(dpi), height / float(dpi)\n",
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" LabelSize, LegendFontsize, font_gap = 80, 80, 5\n",
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"\n",
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" fig = plt.figure(figsize=figsize)\n",
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" \n",
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" cur_ax = fig.add_subplot(1, 1, 1)\n",
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" cur_ax.scatter(xaxis, yaxis, color=\"k\", s=10, alpha=0.9, label=\"Timestamp={:02d}\".format(timestamp))\n",
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" cur_ax.set_xlabel(\"X\", fontsize=LabelSize)\n",
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" cur_ax.set_ylabel(\"f(X)\", rotation=0, fontsize=LabelSize)\n",
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" cur_ax.set_xlim(-6, 6)\n",
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" cur_ax.set_ylim(-40, 40)\n",
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" for tick in cur_ax.xaxis.get_major_ticks():\n",
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" tick.label.set_fontsize(LabelSize - font_gap)\n",
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" tick.label.set_rotation(10)\n",
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" for tick in cur_ax.yaxis.get_major_ticks():\n",
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" tick.label.set_fontsize(LabelSize - font_gap)\n",
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" \n",
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" plt.legend(loc=1, fontsize=LegendFontsize)\n",
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" fig.savefig(str(save_path) + '.pdf', dpi=dpi, bbox_inches=\"tight\", format=\"pdf\")\n",
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" fig.savefig(str(save_path) + '.png', dpi=dpi, bbox_inches=\"tight\", format=\"png\")\n",
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" plt.close(\"all\")\n",
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"\n",
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"\n",
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"def visualize_env(save_dir):\n",
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" save_dir.mkdir(parents=True, exist_ok=True)\n",
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" dynamic_env, function = create_example_v1(100, num_per_task=500)\n",
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" \n",
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" additional_xaxis = np.arange(-6, 6, 0.1)\n",
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" for timestamp, dataset in dynamic_env:\n",
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" num = dataset.shape[0]\n",
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" # timeaxis = (torch.zeros(num) + timestamp).numpy()\n",
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" xaxis = dataset[:,0].numpy()\n",
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" xaxis = np.concatenate((additional_xaxis, xaxis))\n",
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" # compute the ground truth\n",
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" function.set_timestamp(timestamp)\n",
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" yaxis = function(xaxis)\n",
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" draw_fig(save_dir, timestamp, xaxis, yaxis)\n",
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"\n",
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"home_dir = Path.home()\n",
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"desktop_dir = home_dir / 'Desktop'\n",
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"vis_save_dir = desktop_dir / 'vis-synthetic'\n",
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"visualize_env(vis_save_dir)"
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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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"id": "rapid-uruguay",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"ffmpeg -y -i /Users/xuanyidong/Desktop/vis-synthetic/%04d.png -pix_fmt yuv420p -vf fps=2 -vf scale=1000:1000 -vb 5000k /Users/xuanyidong/Desktop/vis-synthetic/vis.mp4\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"0"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# Plot the data\n",
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"cmd = 'ffmpeg -y -i {:}/%04d.png -pix_fmt yuv420p -vf fps=2 -vf scale=1000:1000 -vb 5000k {:}/vis.mp4'.format(vis_save_dir, vis_save_dir)\n",
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"print(cmd)\n",
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"os.system(cmd)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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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": "3.8.8"
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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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