191 lines
6.7 KiB
Plaintext
191 lines
6.7 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 SynAdaptiveEnv\n",
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"from xlayers.super_core import SuperMLPv1"
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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": "supreme-basis",
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"metadata": {},
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"outputs": [],
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"source": [
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"def optimize_fn(xs, ys, test_sets):\n",
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" xs = torch.FloatTensor(xs).view(-1, 1)\n",
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" ys = torch.FloatTensor(ys).view(-1, 1)\n",
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" \n",
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" model = SuperMLPv1(1, 10, 1, torch.nn.ReLU)\n",
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" optimizer = torch.optim.Adam(\n",
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" model.parameters(),\n",
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" lr=0.01, weight_decay=1e-4, amsgrad=True\n",
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" )\n",
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" for _iter in range(100):\n",
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" preds = model(ys)\n",
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"\n",
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" optimizer.zero_grad()\n",
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" loss = torch.nn.functional.mse_loss(preds, ys)\n",
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" loss.backward()\n",
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" optimizer.step()\n",
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" \n",
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" with torch.no_grad():\n",
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" answers = []\n",
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" for test_set in test_sets:\n",
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" test_set = torch.FloatTensor(test_set).view(-1, 1)\n",
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" preds = model(test_set).view(-1).numpy()\n",
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" answers.append(preds.tolist())\n",
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" return answers\n",
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"\n",
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"def f(x):\n",
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" return np.cos( 0.5 * x + 0.)\n",
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"\n",
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"def get_data(mode):\n",
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" dataset = SynAdaptiveEnv(mode=mode)\n",
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" times, xs, ys = [], [], []\n",
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" for i, (_, t, x) in enumerate(dataset):\n",
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" times.append(t)\n",
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" xs.append(x)\n",
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" dataset.set_transform(f)\n",
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" for i, (_, _, y) in enumerate(dataset):\n",
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" ys.append(y)\n",
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" return times, xs, ys\n",
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"\n",
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"def visualize_syn(save_path):\n",
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" save_dir = (save_path / '..').resolve()\n",
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" save_dir.mkdir(parents=True, exist_ok=True)\n",
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" \n",
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" dpi, width, height = 40, 2000, 900\n",
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" figsize = width / float(dpi), height / float(dpi)\n",
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" LabelSize, LegendFontsize, font_gap = 40, 40, 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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" times, xs, ys = get_data(None)\n",
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" \n",
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" def draw_ax(cur_ax, xaxis, yaxis, xlabel, ylabel,\n",
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" alpha=0.1, color='k', linestyle='-', legend=None, plot_only=False):\n",
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" if legend is not None:\n",
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" cur_ax.plot(xaxis[:1], yaxis[:1], color=color, label=legend)\n",
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" cur_ax.plot(xaxis, yaxis, color=color, linestyle=linestyle, alpha=alpha, label=None)\n",
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" if not plot_only:\n",
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" cur_ax.set_xlabel(xlabel, fontsize=LabelSize)\n",
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" cur_ax.set_ylabel(ylabel, rotation=0, fontsize=LabelSize)\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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" cur_ax = fig.add_subplot(2, 1, 1)\n",
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" draw_ax(cur_ax, times, xs, \"time\", \"x\", alpha=1.0, legend=None)\n",
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"\n",
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" cur_ax = fig.add_subplot(2, 1, 2)\n",
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" draw_ax(cur_ax, times, ys, \"time\", \"y\", alpha=0.1, legend=\"ground truth\")\n",
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" \n",
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" train_times, train_xs, train_ys = get_data(\"train\")\n",
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" draw_ax(cur_ax, train_times, train_ys, None, None, alpha=1.0, color='r', legend=None, plot_only=True)\n",
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" \n",
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" valid_times, valid_xs, valid_ys = get_data(\"valid\")\n",
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" draw_ax(cur_ax, valid_times, valid_ys, None, None, alpha=1.0, color='g', legend=None, plot_only=True)\n",
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" \n",
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" test_times, test_xs, test_ys = get_data(\"test\")\n",
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" draw_ax(cur_ax, test_times, test_ys, None, None, alpha=1.0, color='b', legend=None, plot_only=True)\n",
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" \n",
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" # optimize MLP models\n",
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" [train_preds, valid_preds, test_preds] = optimize_fn(train_xs, train_ys, [train_xs, valid_xs, test_xs])\n",
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" draw_ax(cur_ax, train_times, train_preds, None, None,\n",
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" alpha=1.0, linestyle='--', color='r', legend=\"MLP\", plot_only=True)\n",
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" draw_ax(cur_ax, valid_times, valid_preds, None, None,\n",
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" alpha=1.0, linestyle='--', color='g', legend=None, plot_only=True)\n",
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" draw_ax(cur_ax, test_times, test_preds, None, None,\n",
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" alpha=1.0, linestyle='--', color='b', legend=None, plot_only=True)\n",
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"\n",
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" plt.legend(loc=1, fontsize=LegendFontsize)\n",
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"\n",
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" fig.savefig(save_path, dpi=dpi, bbox_inches=\"tight\", format=\"pdf\")\n",
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" plt.close(\"all\")\n",
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" # plt.show()"
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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": "shared-envelope",
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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 Desktop is at: /Users/xuanyidong/Desktop\n"
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]
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}
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],
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"source": [
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"# Visualization\n",
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"home_dir = Path.home()\n",
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"desktop_dir = home_dir / 'Desktop'\n",
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"print('The Desktop is at: {:}'.format(desktop_dir))\n",
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"visualize_syn(desktop_dir / 'tot-synthetic-v0.pdf')"
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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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