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wip example notebook

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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": "Mq5iNIZ9xWxt",
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+ "metadata": {
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+ "id": "Mq5iNIZ9xWxt"
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+ },
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+ "source": [
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+ "# Empty Submission Example for S23DR Challenge\n",
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+ "\n",
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+ "### Helpful Links\n",
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+ "[Challenge Page](https://huggingface.co/spaces/usm3d/S23DR) \n",
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+ "[Workshop Page](usm3d.github.io) \n",
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+ "\n",
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+ "[HoHo Train Set](https://huggingface.co/datasets/usm3d/hoho-train-set) \n",
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+ "[Handcrafted Baseline Solution](https://huggingface.co/usm3d/handcrafted_baseline_submission) \n",
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+ " "
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+ ]
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+ },
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+ {
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+ "cell_type": "markdown",
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+ "id": "dua8UJOoxiDi",
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+ "metadata": {
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+ "id": "dua8UJOoxiDi"
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+ },
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+ "source": [
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+ "## Setup\n",
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+ "\n",
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+ "We'll start by checking if we are running to Google Colab (and if we are setting `IN_COLAB = True` and installing the [hoho tools](https://huggingface.co/usm3d/tools))."
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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": 1,
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+ "id": "ItDDqoXop8bb",
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+ "metadata": {
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+ "colab": {
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+ "base_uri": "https://localhost:8080/"
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+ },
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+ "id": "ItDDqoXop8bb",
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+ "outputId": "0c9d26a7-bf79-4452-c772-d5579a9cb2a9"
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+ },
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+ "outputs": [],
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+ "source": [
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+ "try:\n",
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+ " import google.colab\n",
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+ " IN_COLAB = True\n",
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+ "except:\n",
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+ " IN_COLAB = False\n",
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+ "\n",
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+ "if IN_COLAB:\n",
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+ " !pip install git+http://hf.co/usm3d/tools.git"
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+ ]
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+ },
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+ {
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+ "cell_type": "markdown",
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+ "id": "2tHX74Z-x1cU",
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+ "metadata": {
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+ "id": "2tHX74Z-x1cU"
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+ },
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+ "source": [
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+ "We need to be logged into HF for this to work because the training dataset is gated. If you haven't already please go to the [dastaset page](https://huggingface.co/datasets/usm3d/hoho-train-set) to agree to our terms and request access to the dataset."
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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": "zq_ljluLqzzv",
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+ "metadata": {
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+ "colab": {
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+ "base_uri": "https://localhost:8080/"
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+ },
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+ "id": "zq_ljluLqzzv",
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+ "outputId": "b66806f1-b88a-47e0-8194-79515b73fa23"
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+ },
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+ "outputs": [],
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+ "source": [
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+ "if IN_COLAB:\n",
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+ " !huggingface-cli login"
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+ ]
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+ },
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+ {
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+ "cell_type": "markdown",
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+ "id": "Xf2PY79fywa5",
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+ "metadata": {
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+ "id": "Xf2PY79fywa5"
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+ },
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+ "source": [
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+ "## Data Download, Analysis, and Visualization"
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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": "e171b1ec-e861-4349-98fd-2eac4d080ff5",
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+ "metadata": {
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+ "id": "e171b1ec-e861-4349-98fd-2eac4d080ff5"
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+ },
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+ "outputs": [],
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+ "source": [
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+ "import hoho\n",
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+ "from hoho import *\n",
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+ "\n",
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+ "import numpy as np\n",
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+ "import matplotlib.pyplot as plt\n",
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+ "from pathlib import Path\n",
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+ "from collections import Counter\n",
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+ "import itertools\n",
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+ "import datasets\n",
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+ "import trimesh\n",
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+ "from tqdm.notebook import tqdm\n",
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+ "import webdataset as wds\n",
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+ "import sys"
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+ ]
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+ },
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+ {
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+ "cell_type": "markdown",
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+ "id": "83649a4c-fde7-4051-ba71-e596d382e76a",
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+ "metadata": {
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+ "id": "83649a4c-fde7-4051-ba71-e596d382e76a"
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+ },
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+ "source": [
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+ "### Load the hoho package and point to the data folder\n",
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+ "\n",
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+ "We download only one shard of the data"
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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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+ "id": "ffffc234",
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+ "metadata": {
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+ "colab": {
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+ "base_uri": "https://localhost:8080/"
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+ },
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+ "id": "ffffc234",
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+ "outputId": "e969db58-e88e-457a-eee2-14e65c8117fb"
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+ },
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+ "outputs": [
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+ {
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+ "name": "stderr",
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+ "output_type": "stream",
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+ "text": [
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+ "/Users/jack/dev/USM3D/comp/tools/hoho/hoho.py:309: UserWarning: streaming isn't using with 'all': changing `split` to 'train'\n",
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+ " warnings.warn('streaming isn\\'t using with \\'all\\': changing `split` to \\'train\\'')\n",
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+ "/Users/jack/dev/USM3D/comp/tools/hoho/hoho.py:310: UserWarning: no tarfiles found in data/usm-training-data/data/val.\n",
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+ " warnings.warn(msg)\n"
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+ ]
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+ },
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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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+ "/Users/jack/dev/USM3D/comp/empty_submission\n",
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+ "total 104\n",
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+ "-rw-r--r-- 1 jack staff 1.5K Apr 26 12:51 .gitattributes\n",
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+ "-rw-r--r-- 1 jack staff 855B Apr 26 12:51 README.md\n",
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+ "drwxr-xr-x 10 jack staff 320B Apr 26 12:55 \u001b[34m..\u001b[m\u001b[m\n",
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+ "drwxr-xr-x 3 jack staff 96B Apr 26 15:06 \u001b[34mdata\u001b[m\u001b[m\n",
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+ "-rw-r--r-- 1 jack staff 5.8K Apr 26 15:42 submission.parquet\n",
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+ "-rw-r--r-- 1 jack staff 2.3K Apr 26 15:50 script.py\n",
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+ "drwxr-xr-x 15 jack staff 480B Apr 26 15:50 \u001b[34m.git\u001b[m\u001b[m\n",
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+ "-rw-r--r-- 1 jack staff 32K Apr 26 18:26 example_notebook.ipynb\n",
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+ "drwxr-xr-x 9 jack staff 288B Apr 28 10:32 \u001b[34m.\u001b[m\u001b[m\n",
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+ "Using data/usm-training-data/data as the data directory (we are running locally)\n",
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+ "------------ Loading dataset------------ \n",
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+ "params.json not found (this means we probably aren't in the test env). Using example params.\n",
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+ "{'competition_id': 'usm3d/S23DR', 'competition_type': 'script', 'metric': 'custom', 'token': 'hf_**********************************', 'team_id': 'local-test-team_id', 'submission_id': 'local-test-submission_id', 'submission_id_col': '__key__', 'submission_cols': ['__key__', 'wf_edges', 'wf_vertices', 'edge_semantics'], 'submission_rows': 180, 'output_path': '.', 'submission_repo': '<THE HF MODEL ID of THIS REPO', 'time_limit': 7200, 'dataset': 'usm3d/usm-test-data-x', 'submission_filenames': ['submission.parquet']}\n",
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+ "------------ Now you can do your solution ---------------\n"
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+ ]
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+ },
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+ {
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+ "name": "stderr",
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+ "output_type": "stream",
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+ "text": [
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+ "0it [00:00, ?it/s]"
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+ ]
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+ },
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+ {
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+ "name": "stderr",
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+ "output_type": "stream",
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+ "text": [
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+ "2it [00:34, 17.31s/it]\n"
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+ ]
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+ },
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+ {
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+ "ename": "KeyboardInterrupt",
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+ "evalue": "",
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+ "output_type": "error",
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+ "traceback": [
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+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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+ "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
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+ "Cell \u001b[0;32mIn[4], line 47\u001b[0m\n\u001b[1;32m 45\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m------------ Now you can do your solution ---------------\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 46\u001b[0m solution \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m---> 47\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, sample \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(tqdm(dataset)):\n\u001b[1;32m 48\u001b[0m \u001b[38;5;66;03m# replace this with your solution\u001b[39;00m\n\u001b[1;32m 49\u001b[0m pred_vertices, pred_edges \u001b[38;5;241m=\u001b[39m empty_solution(sample)\n\u001b[1;32m 51\u001b[0m solution\u001b[38;5;241m.\u001b[39mappend({\n\u001b[1;32m 52\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m__key__\u001b[39m\u001b[38;5;124m'\u001b[39m: sample[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m__key__\u001b[39m\u001b[38;5;124m'\u001b[39m], \n\u001b[1;32m 53\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwf_vertices\u001b[39m\u001b[38;5;124m'\u001b[39m: pred_vertices\u001b[38;5;241m.\u001b[39mtolist(),\n\u001b[1;32m 54\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwf_edges\u001b[39m\u001b[38;5;124m'\u001b[39m: pred_edges\n\u001b[1;32m 55\u001b[0m })\n",
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+ "File \u001b[0;32m~/miniconda3/envs/d2/lib/python3.10/site-packages/tqdm/std.py:1181\u001b[0m, in \u001b[0;36mtqdm.__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1178\u001b[0m time \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_time\n\u001b[1;32m 1180\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1181\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m obj \u001b[38;5;129;01min\u001b[39;00m iterable:\n\u001b[1;32m 1182\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m obj\n\u001b[1;32m 1183\u001b[0m \u001b[38;5;66;03m# Update and possibly print the progressbar.\u001b[39;00m\n\u001b[1;32m 1184\u001b[0m \u001b[38;5;66;03m# Note: does not call self.update(1) for speed optimisation.\u001b[39;00m\n",
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+ "File \u001b[0;32m~/miniconda3/envs/d2/lib/python3.10/site-packages/webdataset/pipeline.py:70\u001b[0m, in \u001b[0;36mDataPipeline.iterator\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 68\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Create an iterator through the entire dataset, using the given number of repetitions.\"\"\"\u001b[39;00m\n\u001b[1;32m 69\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m _ \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrepetitions):\n\u001b[0;32m---> 70\u001b[0m \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39miterator1()\n",
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+ "File \u001b[0;32m~/miniconda3/envs/d2/lib/python3.10/site-packages/webdataset/filters.py:302\u001b[0m, in \u001b[0;36m_map\u001b[0;34m(data, f, handler)\u001b[0m\n\u001b[1;32m 300\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_map\u001b[39m(data, f, handler\u001b[38;5;241m=\u001b[39mreraise_exception):\n\u001b[1;32m 301\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Map samples.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 302\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m sample \u001b[38;5;129;01min\u001b[39;00m data:\n\u001b[1;32m 303\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 304\u001b[0m result \u001b[38;5;241m=\u001b[39m f(sample)\n",
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+ "File \u001b[0;32m~/miniconda3/envs/d2/lib/python3.10/site-packages/webdataset/filters.py:302\u001b[0m, in \u001b[0;36m_map\u001b[0;34m(data, f, handler)\u001b[0m\n\u001b[1;32m 300\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_map\u001b[39m(data, f, handler\u001b[38;5;241m=\u001b[39mreraise_exception):\n\u001b[1;32m 301\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Map samples.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 302\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m sample \u001b[38;5;129;01min\u001b[39;00m data:\n\u001b[1;32m 303\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 304\u001b[0m result \u001b[38;5;241m=\u001b[39m f(sample)\n",
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+ "File \u001b[0;32m~/miniconda3/envs/d2/lib/python3.10/site-packages/webdataset/tariterators.py:219\u001b[0m, in \u001b[0;36mgroup_by_keys\u001b[0;34m(data, keys, lcase, suffixes, handler)\u001b[0m\n\u001b[1;32m 203\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Group tarfile contents by keys and yield samples.\u001b[39;00m\n\u001b[1;32m 204\u001b[0m \n\u001b[1;32m 205\u001b[0m \u001b[38;5;124;03mArgs:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 216\u001b[0m \u001b[38;5;124;03m iterator over samples.\u001b[39;00m\n\u001b[1;32m 217\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 218\u001b[0m current_sample \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 219\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m filesample \u001b[38;5;129;01min\u001b[39;00m data:\n\u001b[1;32m 220\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 221\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(filesample, \u001b[38;5;28mdict\u001b[39m)\n",
200
+ "File \u001b[0;32m~/miniconda3/envs/d2/lib/python3.10/site-packages/webdataset/tariterators.py:177\u001b[0m, in \u001b[0;36mtar_file_expander\u001b[0;34m(data, handler, select_files, rename_files)\u001b[0m\n\u001b[1;32m 175\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(source, \u001b[38;5;28mdict\u001b[39m)\n\u001b[1;32m 176\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstream\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m source\n\u001b[0;32m--> 177\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m sample \u001b[38;5;129;01min\u001b[39;00m tar_file_iterator(\n\u001b[1;32m 178\u001b[0m source[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstream\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[1;32m 179\u001b[0m handler\u001b[38;5;241m=\u001b[39mhandler,\n\u001b[1;32m 180\u001b[0m select_files\u001b[38;5;241m=\u001b[39mselect_files,\n\u001b[1;32m 181\u001b[0m rename_files\u001b[38;5;241m=\u001b[39mrename_files,\n\u001b[1;32m 182\u001b[0m ):\n\u001b[1;32m 183\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m (\n\u001b[1;32m 184\u001b[0m \u001b[38;5;28misinstance\u001b[39m(sample, \u001b[38;5;28mdict\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m sample \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfname\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m sample\n\u001b[1;32m 185\u001b[0m )\n\u001b[1;32m 186\u001b[0m sample[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__url__\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m url\n",
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+ "File \u001b[0;32m~/miniconda3/envs/d2/lib/python3.10/site-packages/webdataset/tariterators.py:142\u001b[0m, in \u001b[0;36mtar_file_iterator\u001b[0;34m(fileobj, skip_meta, handler, select_files, rename_files)\u001b[0m\n\u001b[1;32m 140\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m select_files \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m select_files(fname):\n\u001b[1;32m 141\u001b[0m \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[0;32m--> 142\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[43mstream\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mextractfile\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtarinfo\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 143\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mdict\u001b[39m(fname\u001b[38;5;241m=\u001b[39mfname, data\u001b[38;5;241m=\u001b[39mdata)\n\u001b[1;32m 144\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m result\n",
202
+ "File \u001b[0;32m~/miniconda3/envs/d2/lib/python3.10/tarfile.py:689\u001b[0m, in \u001b[0;36m_FileInFile.read\u001b[0;34m(self, size)\u001b[0m\n\u001b[1;32m 687\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data:\n\u001b[1;32m 688\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfileobj\u001b[38;5;241m.\u001b[39mseek(offset \u001b[38;5;241m+\u001b[39m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mposition \u001b[38;5;241m-\u001b[39m start))\n\u001b[0;32m--> 689\u001b[0m b \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfileobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlength\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 690\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(b) \u001b[38;5;241m!=\u001b[39m length:\n\u001b[1;32m 691\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m ReadError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124munexpected end of data\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
203
+ "File \u001b[0;32m~/miniconda3/envs/d2/lib/python3.10/tarfile.py:526\u001b[0m, in \u001b[0;36m_Stream.read\u001b[0;34m(self, size)\u001b[0m\n\u001b[1;32m 524\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Return the next size number of bytes from the stream.\"\"\"\u001b[39;00m\n\u001b[1;32m 525\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m size \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 526\u001b[0m buf \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43msize\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 527\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpos \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(buf)\n\u001b[1;32m 528\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m buf\n",
204
+ "File \u001b[0;32m~/miniconda3/envs/d2/lib/python3.10/tarfile.py:544\u001b[0m, in \u001b[0;36m_Stream._read\u001b[0;34m(self, size)\u001b[0m\n\u001b[1;32m 542\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuf \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 543\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 544\u001b[0m buf \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfileobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbufsize\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 545\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m buf:\n\u001b[1;32m 546\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m\n",
205
+ "File \u001b[0;32m~/miniconda3/envs/d2/lib/python3.10/site-packages/webdataset/gopen.py:87\u001b[0m, in \u001b[0;36mPipe.read\u001b[0;34m(self, *args, **kw)\u001b[0m\n\u001b[1;32m 85\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mread\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkw):\n\u001b[1;32m 86\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Wrap stream.read and checks status.\"\"\"\u001b[39;00m\n\u001b[0;32m---> 87\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkw\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 88\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcheck_status()\n\u001b[1;32m 89\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
206
+ "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
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+ ]
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+ }
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+ ],
210
+ "source": [
211
+ "# %load script.py\n",
212
+ "### This is example of the script that will be run in the test environment.\n",
213
+ "### Some parts of the code are compulsory and you should NOT CHANGE THEM.\n",
214
+ "### They are between '''---compulsory---''' comments.\n",
215
+ "### You can change the rest of the code to define and test your solution.\n",
216
+ "### However, you should not change the signature of the provided function.\n",
217
+ "### The script would save \"submission.parquet\" file in the current directory.\n",
218
+ "### You can use any additional files and subdirectories to organize your code.\n",
219
+ "\n",
220
+ "'''---compulsory---'''\n",
221
+ "import hoho; hoho.setup() # YOU MUST CALL hoho.setup() BEFORE ANYTHING ELSE\n",
222
+ "'''---compulsory---'''\n",
223
+ "\n",
224
+ "from pathlib import Path\n",
225
+ "from tqdm import tqdm\n",
226
+ "import pandas as pd\n",
227
+ "import numpy as np\n",
228
+ "\n",
229
+ "\n",
230
+ "def empty_solution(sample):\n",
231
+ " '''Return a minimal valid solution, i.e. 2 vertices and 1 edge.'''\n",
232
+ " return np.zeros((2,3)), [(0, 1)]\n",
233
+ "\n",
234
+ "\n",
235
+ "if __name__ == \"__main__\":\n",
236
+ " print (\"------------ Loading dataset------------ \")\n",
237
+ " params = hoho.get_params()\n",
238
+ " \n",
239
+ " # by default it is usually better to use `get_dataset()` like this\n",
240
+ " # \n",
241
+ " # dataset = hoho.get_dataset(split='all')\n",
242
+ " # \n",
243
+ " # but in this case (because we don't do anything with the sample \n",
244
+ " # anyway) we set `decode=None`. We can set the `split` argument \n",
245
+ " # to 'train' or 'val' ('all' defaults back to 'train') if we are \n",
246
+ " # testing ourselves locally. \n",
247
+ " # \n",
248
+ " # dataset = hoho.get_dataset(split='val', decode=None)\n",
249
+ " #\n",
250
+ " # On the test server *`split` must be set to 'all'* \n",
251
+ " # to compute both the public and private leaderboards.\n",
252
+ " # \n",
253
+ " dataset = hoho.get_dataset(split='all', decode=None)\n",
254
+ " \n",
255
+ " print('------------ Now you can do your solution ---------------')\n",
256
+ " solution = []\n",
257
+ " for i, sample in enumerate(tqdm(dataset)):\n",
258
+ " # replace this with your solution\n",
259
+ " pred_vertices, pred_edges = empty_solution(sample)\n",
260
+ " \n",
261
+ " solution.append({\n",
262
+ " '__key__': sample['__key__'], \n",
263
+ " 'wf_vertices': pred_vertices.tolist(),\n",
264
+ " 'wf_edges': pred_edges\n",
265
+ " })\n",
266
+ " print('------------ Saving results ---------------')\n",
267
+ " sub = pd.DataFrame(solution, columns=[\"__key__\", \"wf_vertices\", \"wf_edges\"])\n",
268
+ " sub.to_parquet(Path(params['output_path']) / \"submission.parquet\")\n",
269
+ " print(\"------------ Done ------------ \")"
270
+ ]
271
+ },
272
+ {
273
+ "cell_type": "code",
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+ "execution_count": null,
275
+ "id": "f077cbd7",
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+ "metadata": {},
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+ "outputs": [],
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+ "source": []
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+ "cell_type": "code",
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+ "id": "b65ed78e",
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+ "source": []
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+ "cell_type": "code",
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+ "id": "a4584502",
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+ "source": []
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+ "toc_visible": true
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+ "display_name": "Python 3 (ipykernel)",
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+ "language": "python",
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+ "name": "python3"
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+ "mimetype": "text/x-python",
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+ "nbconvert_exporter": "python",
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