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{
"cells": [
{
"cell_type": "markdown",
"id": "c9b77d49-e51e-4ce0-9291-ad5caa0a53dc",
"metadata": {},
"source": [
"## Imports"
]
},
{
"cell_type": "code",
"execution_count": 125,
"id": "168c6241-63b1-415d-a4ce-ea92b161fbcc",
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"from datasets import ClassLabel, Dataset, DatasetDict, Features, Image, load_dataset\n",
"from energyflow.utils import (\n",
" center_ptyphims,\n",
" phi_fix,\n",
" pixelate,\n",
" ptyphims_from_p4s,\n",
" reflect_ptyphims,\n",
" rotate_ptyphims,\n",
")\n",
"from tqdm.auto import tqdm"
]
},
{
"cell_type": "markdown",
"id": "1d39bd4a-569c-43a1-a34d-978505182aa6",
"metadata": {},
"source": [
"## Load data"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "da622147-c81b-457a-8444-5781a4d02001",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using custom data configuration dl4phys--top_landscape-961457c8730ac19c\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Downloading and preparing dataset parquet/dl4phys--top_landscape to /Users/lewtun/.cache/huggingface/datasets/parquet/dl4phys--top_landscape-961457c8730ac19c/0.0.0/0b6d5799bb726b24ad7fc7be720c170d8e497f575d02d47537de9a5bac074901...\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8002bb9640d74c5a81fd1fc5fefebbe3",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Downloading data files: 0%| | 0/3 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e90f3005ccac49c99211384679840244",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Downloading data: 0%| | 0.00/1.28G [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a49088067d3f44b582eed1b82948f091",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Downloading data: 0%| | 0.00/509M [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b6e236a13c544dd498ca936b34c03dac",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Downloading data: 0%| | 0.00/508M [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "25c6b13a82834c15bc02b7669cff9c8b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Extracting data files: 0%| | 0/3 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dataset parquet downloaded and prepared to /Users/lewtun/.cache/huggingface/datasets/parquet/dl4phys--top_landscape-961457c8730ac19c/0.0.0/0b6d5799bb726b24ad7fc7be720c170d8e497f575d02d47537de9a5bac074901. Subsequent calls will reuse this data.\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8a82090c8c074af6833fbdee83558a15",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/3 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ds = load_dataset(\"dl4phys/top_landscape\")"
]
},
{
"cell_type": "code",
"execution_count": 75,
"id": "c4c6d156-7545-46d8-862e-353f8ea4995b",
"metadata": {},
"outputs": [],
"source": [
"def convert_to_numpy(split):\n",
" df = ds[split].to_pandas()\n",
" return (df.to_numpy()[:, :800]).reshape(-1, 200, 4), df[\"is_signal_new\"].values"
]
},
{
"cell_type": "code",
"execution_count": 76,
"id": "9b975524-3aa7-44ca-90a1-feef78d848b0",
"metadata": {},
"outputs": [],
"source": [
"events, labels = convert_to_numpy(\"test\")"
]
},
{
"cell_type": "markdown",
"id": "4d32e789-897f-4285-afa5-18e83eb66bf5",
"metadata": {},
"source": [
"## Rotate basis"
]
},
{
"cell_type": "code",
"execution_count": 66,
"id": "6a317845-4672-4947-b8ed-2fb26d37da45",
"metadata": {},
"outputs": [],
"source": [
"def rotate_basis(events):\n",
" ptyphims = []\n",
"\n",
" for event in tqdm(events, desc=\"Rotating to (pT,eta,phi,m)\"):\n",
" ptyphims.append(ptyphims_from_p4s(event, phi_ref=\"hardest\", mass=True))\n",
"\n",
" return np.array(ptyphims)"
]
},
{
"cell_type": "markdown",
"id": "7bc97994-3805-4e77-bb8f-30126dc05ae8",
"metadata": {},
"source": [
"## Centre image"
]
},
{
"cell_type": "code",
"execution_count": 67,
"id": "6c9a636d-cc35-4ce9-bf28-34909e1be3aa",
"metadata": {},
"outputs": [],
"source": [
"def centre_images(ptyphims):\n",
"\n",
" ptyphims_center = []\n",
"\n",
" for event in tqdm(ptyphims, desc=\"Centring images\"):\n",
" ptyphims_center.append(center_ptyphims(event))\n",
" return np.array(ptyphims_center)"
]
},
{
"cell_type": "markdown",
"id": "b778e7d2-590a-4e0c-a595-d84c15618762",
"metadata": {},
"source": [
"## Reflect and rotate"
]
},
{
"cell_type": "code",
"execution_count": 68,
"id": "1e8fc265-d10c-4393-9c98-3778a967b98d",
"metadata": {},
"outputs": [],
"source": [
"def reflect_and_rotate(ptyphims_center):\n",
"\n",
" ptyphims_center_rr = []\n",
" for event in tqdm(ptyphims_center, desc=\"Reflect and rotate\"):\n",
" ptyphims_center_rr.append(reflect_ptyphims(rotate_ptyphims(event)))\n",
" return np.array(ptyphims_center_rr)"
]
},
{
"cell_type": "markdown",
"id": "9b021d17-3b7f-4e0b-8e14-478f802b3f04",
"metadata": {},
"source": [
"## Pixelate"
]
},
{
"cell_type": "code",
"execution_count": 112,
"id": "15d8cd5a-8596-4c3f-a445-d3688b59f677",
"metadata": {},
"outputs": [],
"source": [
"def create_images(events):\n",
" ptyphims = rotate_basis(events)\n",
" ptyphims_center = centre_images(ptyphims)\n",
" ptyphims_center_rr = reflect_and_rotate(ptyphims_center)\n",
"\n",
" images = []\n",
" for event in tqdm(ptyphims_center_rr, \"Pixelate\"):\n",
" images.append(\n",
" pixelate(\n",
" event,\n",
" npix=40,\n",
" img_width=0.8,\n",
" nb_chan=1,\n",
" norm=False,\n",
" charged_counts_only=False,\n",
" )\n",
" )\n",
" return np.array(images).reshape(len(images), 40, 40)"
]
},
{
"cell_type": "code",
"execution_count": 130,
"id": "26037400-a45a-4a89-9e06-1e8b30a32818",
"metadata": {},
"outputs": [],
"source": [
"def create_dataset():\n",
" dsets = DatasetDict()\n",
" features = Features({\"image\": Image(), \"label\": ClassLabel(names=[\"qcd\", \"top\"])})\n",
"\n",
" for split in [\"train\", \"validation\", \"test\"]:\n",
" events, labels = convert_to_numpy(split)\n",
" images = create_images(events)\n",
" dsets[split] = Dataset.from_dict(\n",
" {\"image\": images, \"label\": labels}, features=features\n",
" )\n",
"\n",
" return dsets"
]
},
{
"cell_type": "code",
"execution_count": 131,
"id": "ff5ebefa-ec76-449a-ad96-939046675681",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e39fb375772b46f89673ff2739acdb91",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Rotating to (pT,eta,phi,m): 0%| | 0/1211000 [00:00<?, ?it/s]"
]
},
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"output_type": "display_data"
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{
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"text/plain": [
"Centring images: 0%| | 0/1211000 [00:00<?, ?it/s]"
]
},
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},
{
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"text/plain": [
"Reflect and rotate: 0%| | 0/1211000 [00:00<?, ?it/s]"
]
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{
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"model_id": "ff9d6606093f47aeb3569d106edf19f6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Pixelate: 0%| | 0/1211000 [00:00<?, ?it/s]"
]
},
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},
{
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"Rotating to (pT,eta,phi,m): 0%| | 0/403000 [00:00<?, ?it/s]"
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{
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"model_id": "9134b103a01642fdae44a25a04528d16",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Centring images: 0%| | 0/403000 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
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"Reflect and rotate: 0%| | 0/403000 [00:00<?, ?it/s]"
]
},
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},
{
"data": {
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"model_id": "98588c31e21c4b289a8614293d9bff73",
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"version_minor": 0
},
"text/plain": [
"Pixelate: 0%| | 0/403000 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "45cf7dc169344653a86de6cedfce5796",
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"version_minor": 0
},
"text/plain": [
"Rotating to (pT,eta,phi,m): 0%| | 0/404000 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6eb133792e4543129423a155e537daed",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Centring images: 0%| | 0/404000 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e5e1f07da3b44bf19422cf09d975d635",
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"text/plain": [
"Reflect and rotate: 0%| | 0/404000 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2c11a5af58b3497e950e146ee6021aab",
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},
"text/plain": [
"Pixelate: 0%| | 0/404000 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"dsets = create_dataset()"
]
},
{
"cell_type": "code",
"execution_count": 132,
"id": "3ab0fa0c-f8b1-45b2-a6ab-847e135ee0ae",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"DatasetDict({\n",
" train: Dataset({\n",
" features: ['image', 'label'],\n",
" num_rows: 1211000\n",
" })\n",
" validation: Dataset({\n",
" features: ['image', 'label'],\n",
" num_rows: 403000\n",
" })\n",
" test: Dataset({\n",
" features: ['image', 'label'],\n",
" num_rows: 404000\n",
" })\n",
"})"
]
},
"execution_count": 132,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dsets"
]
},
{
"cell_type": "code",
"execution_count": 133,
"id": "d8a7a571-e339-42b3-b3c0-b924399fef77",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Pushing split train to the Hub.\n"
]
},
{
"data": {
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"model_id": "868b7073ee0248adb5727faefceb8456",
"version_major": 2,
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},
"text/plain": [
"Pushing dataset shards to the dataset hub: 0%| | 0/1 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
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" 0%| | 0/1211 [00:00<?, ?ba/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Pushing split validation to the Hub.\n"
]
},
{
"data": {
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"model_id": "aa4d2782f68c4b7fa7f1e2d89363413f",
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"Pushing dataset shards to the dataset hub: 0%| | 0/1 [00:00<?, ?it/s]"
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},
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},
{
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"version_minor": 0
},
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]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Pushing split test to the Hub.\n"
]
},
{
"data": {
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"model_id": "9b94c38a09e24a29ade3032dcd004317",
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"Pushing dataset shards to the dataset hub: 0%| | 0/1 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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" 0%| | 0/404 [00:00<?, ?ba/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"dsets.push_to_hub(\"dl4phys/top_landscape_images\")"
]
},
{
"cell_type": "code",
"execution_count": 135,
"id": "a3897885-c44a-4fbb-b344-6e99671356b7",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<PIL.PngImagePlugin.PngImageFile image mode=L size=40x40>,\n",
" <PIL.PngImagePlugin.PngImageFile image mode=L size=40x40>,\n",
" <PIL.PngImagePlugin.PngImageFile image mode=L size=40x40>]"
]
},
"execution_count": 135,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dsets[\"train\"][\"image\"][:3]"
]
},
{
"cell_type": "code",
"execution_count": 136,
"id": "b49d6a92-cac5-4f4c-993a-6ca2fb0bbdf6",
"metadata": {},
"outputs": [],
"source": [
"dsets.set_format(\"numpy\")"
]
},
{
"cell_type": "code",
"execution_count": 141,
"id": "15674b90-2059-46f1-84a6-8a5fe1bb7f8a",
"metadata": {},
"outputs": [],
"source": [
"sample = dsets[\"train\"].shuffle(seed=42).select(range(10_000))"
]
},
{
"cell_type": "code",
"execution_count": 142,
"id": "a350535c-3bad-4f5e-a1ea-d25a5deca99c",
"metadata": {},
"outputs": [],
"source": [
"sample_labels = np.array(sample[\"label\"][:])"
]
},
{
"cell_type": "code",
"execution_count": 156,
"id": "8982fc85-ec72-4a54-ad80-c7edaa4e3630",
"metadata": {},
"outputs": [],
"source": [
"sample_images = np.asarray([np.array(img) for img in sample[:][\"image\"]])"
]
},
{
"cell_type": "code",
"execution_count": 157,
"id": "1435a954-9dfd-427d-957b-0b952cfe5128",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(10000, 40, 40)"
]
},
"execution_count": 157,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sample_images.shape"
]
},
{
"cell_type": "code",
"execution_count": 150,
"id": "16c60c6d-557c-4c3f-a484-664e8be094e2",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(10,)"
]
},
"execution_count": 150,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sample_images.shape"
]
},
{
"cell_type": "code",
"execution_count": 140,
"id": "4b674182-6d10-4ac4-8ec9-758f11dfa942",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[0, 0, 0, ..., 0, 0, 0],\n",
" [0, 0, 0, ..., 0, 0, 0],\n",
" [0, 0, 0, ..., 0, 0, 0],\n",
" ...,\n",
" [0, 0, 0, ..., 0, 0, 0],\n",
" [0, 0, 0, ..., 0, 0, 0],\n",
" [0, 0, 0, ..., 0, 0, 0]], dtype=uint8)"
]
},
"execution_count": 140,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.array(dsets[\"train\"][0][\"image\"])"
]
},
{
"cell_type": "code",
"execution_count": 113,
"id": "20ce1145-09b7-4c30-b6fc-c0b0093cbb81",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b1d2e843521c40a1b8a45af085a6fc62",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Rotating to (pT,eta,phi,m): 0%| | 0/100 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "501071866f634923abfad021c4763b4b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Centring images: 0%| | 0/100 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "45b7513400604428946e434dc9fae8d2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Reflect and rotate: 0%| | 0/100 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f4381efba8c84b1b87a56f4c473bc996",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Pixelate: 0%| | 0/100 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"images = create_images(events)"
]
},
{
"cell_type": "code",
"execution_count": 158,
"id": "2a16e0c9-7918-49bb-ac99-9a53ad884543",
"metadata": {},
"outputs": [],
"source": [
"top_images = sample_images[sample_labels.flatten() == 1]\n",
"qcd_images = sample_images[sample_labels.flatten() == 0]"
]
},
{
"cell_type": "code",
"execution_count": 159,
"id": "6976667b-022a-4fe1-ac6b-6a7df597de83",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 576x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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rj9oylOfX6rJoQ1NVe4G93fbBJLcA54w6MUnTxVojaRBLuoYmyQXAhcD13dLLk9yY5D1Jzhh2cpKmk7VG0lL13dAkORX4CPDKqvoh8E7gscAW5v5X9fYFHrctyY4kOw5zaPCMJU00a42k5eiroUkyw1yBeV9VfRSgqvZV1ZGqOgq8C7io12Or6sqq2lpVW2dYN6y8JU0ga42k5epnyinAu4Fbquod89Y3z7vbC4Fdw09P0rSw1kgaRD9TTk8Hfhf4epKd3dofAS9JsgUoYDfweyPIT9L0sNZMoBNNMw3Lmg2n91x3mmm69DPl9HkgPXZ9avjpSJpW1hpJg/CTgiVJUvNsaCRJUvNsaCRJUvNsaCRJUvP6mXJSA0465ZSe60cfeGD0wdPrOk6gavSxJU29IwfuW+kUtAp4hkaSJDXPhkaSJDXPhkaSJDXPhkaSJDXPhkaSJDXPhkaSJDXPse0JMZbx7IU4ni1Jx8nMyT3X6/CDY85keniGRpIkNc+GRpIkNc+GRpIkNc+GRpIkNc+GRpIkNc8pJ43MSaed1nP96MGDY85EksbLaabx8wyNJElqng2NJElqng2NJElqng2NJElq3qINTZL1Sb6U5IYkNyV5U7f+6CTXJ7k1yYeS9P6cZ0nqg7VG0iD6mXI6BFxcVfcnmQE+n+RvgT8ErqiqDyb5C+BlwDtHmKtWqdvev6Xn+uNfs7/n+oMXPb7n+tprvzKslNQma42kZVv0DE3Nub/7daa7FXAx8OFu/Wrg0lEkKGk6WGskDaKva2iSrEmyE9gPbAe+DRyoqtnuLncC54wkQ0lTw1ojabn6amiq6khVbQHOBS4CnthvgCTbkuxIsuMwh5aXpaSpYK2RtFxLmnKqqgPAdcBTgQ1Jjl2Dcy6wZ4HHXFlVW6tq6wzrBslV0pSw1khaqn6mnM5OsqHbfhhwCXALc8XmRd3dLgM+PqIcJU0Ba42kQfQz5bQZuDrJGuYaoGuq6pNJbgY+mOTNwNeAd48wT61ij/mdnT3XZ3uuwto9dy05xpFfeUrP9TXXfXXJz6VVy1ojadkWbWiq6kbgwh7rtzH3HrckDcxaI2kQflKwJElqng2NJElqng2NJElqng2NJElqXj9TTpoiax+5acF9s3fvG0qMNRtO77l+5MB9Cz/GaSZJ0gl4hkaSJDXPhkaSJDXPhkaSJDXPhkaSJDXPhkaSJDXPhkaSJDXPsW0dZ1ij2Sd09pm9108wti1J0ol4hkaSJDXPhkaSJDXPhkaSJDXPhkaSJDXPhkaSJDXPKacptebMjT3Xj3z/3pHHPvKt20YeQ5LGYe0F5/dcn9393TFnIs/QSJKk5tnQSJKk5tnQSJKk5tnQSJKk5i3a0CRZn+RLSW5IclOSN3XrVyX5TpKd3W3LyLOVNLGsNZIG0c+U0yHg4qq6P8kM8Pkkf9vte01VfXh06WlUxjHNJC2RtUbNcZpp9Vi0oamqAu7vfp3pbjXKpCRNH2uNpEH0dQ1NkjVJdgL7ge1VdX236y1JbkxyRZJ1o0pS0nSw1kharr4amqo6UlVbgHOBi5L8PPA64InALwIbgdf2emySbUl2JNlxmEPDyVrSRLLWSFquJU05VdUB4DrgOVW1t+YcAt4LXLTAY66sqq1VtXUG/2MlaXHWGklL1c+U09lJNnTbDwMuAb6RZHO3FuBSYNfo0pQ06aw1kgbRz5TTZuDqJGuYa4CuqapPJvn7JGcDAXYC/2V0aUpLlPReL68xXcWsNVpxazY9ouf6kX37x5yJlqqfKacbgQt7rF88kowkTSVrjaRB+EnBkiSpeTY0kiSpeTY0kiSpeTY0kiSpef1MOUntcZpJ0gLWXnD+gvuW+t1MJ/3CE3uuH73xGyOPreN5hkaSJDXPhkaSJDXPhkaSJDXPhkaSJDXPhkaSJDXPhkaSJDXPsW1J0lQZ5nj0QuPZ44it43mGRpIkNc+GRpIkNc+GRpIkNc+GRpIkNc+GRpIkNc8pp8Zk3bqe63Xo0JgzkSRp9fAMjSRJap4NjSRJap4NjSRJap4NjSRJap4NjSRJal6qanzBku8Bt3e/ngXcM7bgxzO2sY09fD9TVWePMd6CrDXGNvbExl6wzoy1oTkucLKjqrYa29jGnrzYq8m0/g2MbexpiD2fbzlJkqTm2dBIkqTmrWRDc6WxjW3siY29mkzr38DYxp6G2D+xYtfQSJIkDYtvOUmSpOatSEOT5DlJvpnk1iSXjzn27iRfT7IzyY4Rx3pPkv1Jds1b25hke5JvdT/PGGPsNybZ0x37ziTPG0Hc85Jcl+TmJDcleUW3PvLjPkHscRz3+iRfSnJDF/tN3fqjk1zfvdY/lOTkMca+Ksl35h33lmHHXs2sM5NbZ7o41hprzfGqaqw3YA3wbeAxwMnADcCTxhh/N3DWmGL9MvAUYNe8tf8FXN5tXw78yRhjvxF49YiPeTPwlG77NOCfgCeN47hPEHscxx3g1G57Brge+CXgGuDF3fpfAP91jLGvAl40yuNerTfrzGTXmS6OtcZac9xtJc7QXATcWlW3VdWDwAeBF6xAHiNXVZ8D7n3I8guAq7vtq4FLxxh75Kpqb1V9tds+CNwCnMMYjvsEsUeu5tzf/TrT3Qq4GPhwtz6q414o9jSzzkxwneliW2usNcdZiYbmHOCOeb/fyZheCJ0CPpPkK0m2jTHuMZuqam+3fTewaczxX57kxu5U8UhOQx+T5ALgQua6+LEe90NiwxiOO8maJDuB/cB25s4QHKiq2e4uI3utPzR2VR077rd0x31FknWjiL1KWWempM6AtQZrDTCdFwU/o6qeAjwX+P0kv7xSidTcebtxdrfvBB4LbAH2Am8fVaAkpwIfAV5ZVT+cv2/Ux90j9liOu6qOVNUW4FzmzhA8cRRx+omd5OeB13U5/CKwEXjtuPKRdYYx1Bmw1mCt+YmVaGj2AOfN+/3cbm0sqmpP93M/8DHmXgzjtC/JZoDu5/5xBa6qfd2L8SjwLkZ07ElmmPtH/r6q+mi3PJbj7hV7XMd9TFUdAK4DngpsSLK22zXy1/q82M/pTotXVR0C3sv4X+sryToz4XUGrDXWmuOtREPzZeBx3RXZJwMvBj4xjsBJHp7ktGPbwLOBXSd+1NB9Aris274M+Pi4Ah/7R955ISM49iQB3g3cUlXvmLdr5Me9UOwxHffZSTZ02w8DLmHuffXrgBd1dxvVcfeK/Y15RT3MvZ8+7tf6SrLOTHCd6eJYa6w1xxvW1cVLuQHPY+6q8G8DfzzGuI9hbtrhBuCmUccGPsDcacfDzL2n+TLgTOBa4FvAZ4GNY4z9l8DXgRuZ+0e/eQRxn8HcKd4bgZ3d7XnjOO4TxB7Hcf8C8LUuxi7gv897zX0JuBX4a2DdGGP/fXfcu4C/optOmJabdWZy60wX21pjrTnu5icFS5Kk5k3jRcGSJGnC2NBIkqTm2dBIkqTm2dBIkqTm2dBIkqTm2dBIkqTm2dBIkqTm2dBIkqTm/X9Vhy3o9sK6ywAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 576x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAjQAAAEnCAYAAAC+IdmrAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAXx0lEQVR4nO3df7DldX3f8eeL3WVXZRUQJCugiBp/NNbFbrb+mkyKomjGiq1tpdMMtnTWduKMRk3FJJNoJk5NG2XadGqKo8IkRiX+GH/ERFdC6jhScNUFFtCAuJQfK1tUIsiwcHff/eN+15zFc/aee37d+7nn+Zg5c7/n8z3nfN7f4e6b1/2e7+ecVBWSJEktO2alC5AkSRqXgUaSJDXPQCNJkppnoJEkSc0z0EiSpOYZaCRJUvMMNJIkqXkGGknSqpVkb5KXrnQdWv0MNFqWJK9Pcn2SB5J8P8n/TPK4nv0/n+TPk9yT5O+SXJfkLUnWJTkjSSW5v7vdneTzSc5ZyWOSJLXPQKOhJXkr8AfAbwCPA54PnAF8KcmGJE8FrgZuB55TVY8D/gWwDdjc81LHV9VxwHOBncCnk7x+VschqQ1J/gR4EvC57o+g/5Tknya5Icm9Sf4mybN6Hr83yTuS3JjkR0k+nGTTyh2BZil+9YGGkeSxwF3Av6uqy3vGjwO+B7wNOAc4oap+ZcBrnNE9dkNVLfSMv43FkLSlqg5N7SAkNSfJXuDfV9WXk/w88C3gPOBvgF8HdgDPrqqHusfeD7wC+AnwOeDKqvrt2VeuWfMMjYb1QmAT8Knewaq6H/gC8DLgpcAnRnjtTwFPAJ4xZo2S1rZ/BfxFVe2sqoeBPwQexWJ/Oux/VNXtVfVD4N3A+StQp1aAgUbDOgm4p/fMSo99wMnA47vt5bqr+3niiLVJmg9PBG47fKc7o3s7cGrPY27v2b6te47mgIFGw7oHOCnJ+j77tnT7f9BtL9fhZvTDEWuTtHb1XhdxF/Dkw3eSBDgduLPnMaf3bD+Jv/+DSWucgUbDugo4APyz3sHuGppXsPh+9peBfz7Ca78G2A98Z7wSJa1BdwNndtuXA7+S5CVJNgBvZbEvfa3n8b+W5LQkJwK/BXx8ptVqxRhoNJSq+jvgXcAfJTm3W9V0BosN5h7gI8DvAi9M8l+T/BxAkqcl+dMkxz/yNZOckuSN3fPe4QXBkvr4z8BvJ7kXeBXwb4A/YrHvvAp4VVU91PP4PwO+BNwKfBf4/ZlWqxXjKictS5ILWVxZ8DRgI/C/gX9dVXd1+5/BYgM5G1gP7AU+zGIDOp3FVU4/AdL93AX896r6q5keiKQ1p3dF1ErXotkz0GhkSf4t8HvAi6rq/650PZLmm4FmvvW7wFMaSlV9OMkCi0smDTSSpBXjGRpJktQ8LwqWJEnNGyvQdKtdvpPkliQXTaooSeplr5G0lJHfckqyDvhbFr+/5w7g68D5VXXjoOccm421iceMNJ+k1e0+fnRPVZ086de110g67EF+wkN1IP32jXNR8Hbglqq6FSDJx4BXAwObzCYewz/OS8aYUtJq9eX6xG1LP2ok9hq1J33/nwtetzqWq+uKgfvGecvpVI78zow7OPL7NCRpEuw1kpY09WXbSXaw+PXubOLR055O0pyy10jzbZwzNHdy5JeAncaRXxAGQFVdUlXbqmrbBjaOMZ2kOWWvkbSkcQLN14GnJ3lKkmOB1wGfnUxZkvRT9hpJSxr5LaeqWui+WPCLwDrgQ1V1w8Qq04pY9/QzB+47ePOtM6xEWmSv0WHrnvX0vuMHb7p5xpUMwYt/Z26sa2iq6gvAFyZUiyT1Za+RtBQ/KViSJDXPQCNJkppnoJEkSc0z0EiSpOZN/YP1NBvrt/xc3/GFfd9f1uus2pVMfoy4NPdW5WomrRqeoZEkSc0z0EiSpOYZaCRJUvMMNJIkqXkGGkmS1DwDjSRJap7LtteIOu7RK13Cz5jUUnLA5dnSHDlm06a+44cefHDGlaglnqGRJEnNM9BIkqTmGWgkSVLzDDSSJKl5BhpJktQ8Vzk15pjNm/uOL/dLJXPWP+g7Xt+6Ydk1DTLSaiZJc29Sq5mOeXT/1Z+HHnhgIq+v1cUzNJIkqXkGGkmS1DwDjSRJap6BRpIkNW+si4KT7AXuAw4CC1W1bRJFSVIve42kpUxildM/qap7JvA6GsKh++5b1uPXnfKEvuMHJ7iaabmycWPf8TpwYMaVqDH2Gi2Lq5nmi285SZKk5o0baAr4UpJvJNkxiYIkqQ97jaSjGvctpxdX1Z1JngDsTPLtqvpK7wO65rMDYBP9P+RIkpZgr5F0VGOdoamqO7uf+4FPA9v7POaSqtpWVds20P/aCUk6GnuNpKWMHGiSPCbJ5sPbwMuAPZMqTJLAXiNpOOO85XQK8Okkh1/nz6rqryZSlSbm4N37V7qEn+FqJi2TvUYr7rbfe0Hf8Sf/zlUzrkSDjBxoqupW4LkTrEWSfoa9RtIwXLYtSZKaZ6CRJEnNM9BIkqTmGWgkSVLzJvFdTlpDjtm8eeC+5X6PlCStRutPP23gvoXb7+g7fuZ/+07f8YMDXuehl/f//tRjv7jrqLVpdJ6hkSRJzTPQSJKk5hloJElS8ww0kiSpeQYaSZLUPAONJElqnsu2dQSXZkta6+qxj1n2cw7+4IfLerzLs2fPMzSSJKl5BhpJktQ8A40kSWqegUaSJDXPQCNJkprnKqfGrD/zjL7jC7funWkdktSqgzf0/6JJgGMe/ei+44ceeGBa5WhCPEMjSZKaZ6CRJEnNM9BIkqTmGWgkSVLzlgw0ST6UZH+SPT1jJybZmeTm7ucJ0y1T0lpnr5E0jmHO0FwKnPuIsYuAK6rq6cAV3X3NwMKte/vepDXgUuw1WmGHHnig702r35KBpqq+AjzyW7leDVzWbV8GnDfZsiTNG3uNpHGMeg3NKVW1r9v+PnDKhOqRpF72GklDGfui4KoqoAbtT7Ijya4kux7mwLjTSZpT9hpJRzNqoLk7yRaA7uf+QQ+sqkuqaltVbdvAxhGnkzSn7DWShjJqoPkscEG3fQHwmcmUI0lHsNdIGsowy7Y/ClwFPCPJHUkuBN4DnJPkZuCl3X1JGpm9RtI4lvxyyqo6f8Cul0y4FklzzF4jaRx+UrAkSWqegUaSJDXPQCNJkppnoJEkSc1b8qJgSctzzObNfccP3XffjCuRpPnhGRpJktQ8A40kSWqegUaSJDXPQCNJkppnoJEkSc0z0EiSpOa5bFszt9aXNa+V45CklniGRpIkNc9AI0mSmmegkSRJzTPQSJKk5hloJElS81zl1Jqk/3jVbOsYg6uAJEmT5hkaSZLUPAONJElqnoFGkiQ1z0AjSZKat2SgSfKhJPuT7OkZe2eSO5Ps7m6vnG6ZktY6e42kcQxzhuZS4Nw+4xdX1dbu9oXJlqWBqvrfpPZdir1G0oiWDDRV9RXghzOoRdIcs9dIGsc419C8Mcl13WniEyZWkSQdyV4jaUmjBpr3A08FtgL7gPcOemCSHUl2Jdn1MAdGnE7SnLLXSBrKSIGmqu6uqoNVdQj4ALD9KI+9pKq2VdW2DWwctU5Jc8heI2lYIwWaJFt67r4G2DPosZI0KnuNpGEt+V1OST4K/DJwUpI7gN8FfjnJVqCAvcAbpleiemV9//9ktbAw40qkybLXrC7HbNrUd/zQgw/OuBJpOEsGmqo6v8/wB6dQi6Q5Zq+RNA4/KViSJDXPQCNJkppnoJEkSc0z0EiSpOYteVGwVhdXM0k65rnP6jt+6NqbJjaHq5mmY9BKVbC/j8szNJIkqXkGGkmS1DwDjSRJap6BRpIkNc9AI0mSmmegkSRJzXPZtiQ1ZpLLszVbLs2eHs/QSJKk5hloJElS8ww0kiSpeQYaSZLUPAONJElqnqucNLQHX7W97/imz10z40r+XjZu7DteBw7MuBJJzUgG76uaXR2aKM/QSJKk5hloJElS8ww0kiSpeQYaSZLUvCUDTZLTk1yZ5MYkNyR5Uzd+YpKdSW7ufp4w/XIlrVX2GknjGGaV0wLw1qr6ZpLNwDeS7AReD1xRVe9JchFwEfD26ZWqlTap1UzrTz+t7/hfXP35gc95+RO39h2vhx7qO77ulCf0HT949/6jF6eVZK/RbLiSaU1a8gxNVe2rqm922/cBNwGnAq8GLusedhlw3pRqlDQH7DWSxrGsa2iSnAGcBVwNnFJV+7pd3wdOmWxpkuaVvUbScg0daJIcB3wSeHNV/bh3X1UV0PccXpIdSXYl2fUwftiZpKOz10gaxVCBJskGFhvMR6rqU93w3Um2dPu3AH0vTqiqS6pqW1Vt20D/T3WVJLDXSBrdMKucAnwQuKmq3tez67PABd32BcBnJl+epHlhr5E0jmFWOb0I+FXg+iS7u7HfBN4DXJ7kQuA24F9OpUK1a/tz+g4vXHN93/FBK5kAfvT6F/QdP+HSq/qOu5qpSfaaGVv32McO3Hfwxz8euE9ajZYMNFX1VWDQN3m9ZLLlSJpX9hpJ4/CTgiVJUvMMNJIkqXkGGkmS1DwDjSRJat4wq5yk0QxYzTSKQauZBll30uP7jh+85weTKEdaE1zJpLXEMzSSJKl5BhpJktQ8A40kSWqegUaSJDXPQCNJkppnoJEkSc1z2bbWpJVcnj3oC/9cIqvVpl743IH78rVr+4+v7/+/jVpYmEhN0qg8QyNJkppnoJEkSc0z0EiSpOYZaCRJUvMMNJIkqXmucppTrlSYHlczqRWDVjIBfPGu3X3HX/7ErdMppof9SaPwDI0kSWqegUaSJDXPQCNJkppnoJEkSc1bMtAkOT3JlUluTHJDkjd14+9McmeS3d3tldMvV9JaZa+RNI5hVjktAG+tqm8m2Qx8I8nObt/FVfWH0ytP0+JqgVUkGbyvanZ1rDx7zSoyi9VMg9ifNIolA01V7QP2ddv3JbkJOHXahUmaL/YaSeNY1jU0Sc4AzgKu7obemOS6JB9KcsKki5M0n+w1kpZr6ECT5Djgk8Cbq+rHwPuBpwJbWfyr6r0Dnrcjya4kux7mwPgVS1rT7DWSRjFUoEmygcUG85Gq+hRAVd1dVQer6hDwAWB7v+dW1SVVta2qtm1g46TqlrQG2WskjWqYVU4BPgjcVFXv6xnf0vOw1wB7Jl+epHlhr5E0jmFWOb0I+FXg+iS7u7HfBM5PshUoYC/whinUJ61987WS6WjsNZJGNswqp68C/daVfmHy5UiaV/YaSePwk4IlSVLzDDSSJKl5BhpJktQ8A40kSWqegUaSJDXPQCNJkppnoJEkSc0z0EiSpOYZaCRJUvMMNJIkqXkGGkmS1DwDjSRJap6BRpIkNc9AI0mSmmegkSRJzTPQSJKk5hloJElS89avdAHSLGV9/1/5WliYcSXSFCT9x6tmW4dGsu8tL+w7vuV9X5txJW3yDI0kSWqegUaSJDXPQCNJkppnoJEkSc1bMtAk2ZTkmiTXJrkhybu68ackuTrJLUk+nuTY6Zcraa2y10gaxzCrnA4AZ1fV/Uk2AF9N8pfAW4CLq+pjSf4YuBB4/xRrlcbmaqZVzV4zLlczrR7bn9N//JrrBz7F1UzjWfIMTS26v7u7obsVcDbwiW78MuC8aRQoaT7YaySNY6hraJKsS7Ib2A/sBL4L3FtVh//cvQM4dSoVSpob9hpJoxoq0FTVwaraCpwGbAeeOewESXYk2ZVk18McGK1KSXPBXiNpVMta5VRV9wJXAi8Ajk9y+Bqc04A7BzznkqraVlXbNrBxnFolzQl7jaTlGmaV08lJju+2HwWcA9zEYrN5bfewC4DPTKlGSXPAXiNpHMOsctoCXJZkHYsB6PKq+nySG4GPJfl94FvAB6dYp1axbOz/13Ad8LS/lmX6vcbvOtKE3XLx8/uOP+3X/8+MK9GSgaaqrgPO6jN+K4vvcUvS2Ow1ksbhJwVLkqTmGWgkSVLzDDSSJKl5BhpJktS8YVY5SUflaiY1w9VMmjBXM60enqGRJEnNM9BIkqTmGWgkSVLzDDSSJKl5BhpJktQ8A40kSWqegUaSJDXPQCNJkppnoJEkSc0z0EiSpOYZaCRJUvMMNJIkqXkGGkmS1DwDjSRJap6BRpIkNc9AI0mSmmegkSRJzVsy0CTZlOSaJNcmuSHJu7rxS5N8L8nu7rZ16tVKWrPsNZLGsX6IxxwAzq6q+5NsAL6a5C+7fb9RVZ+YXnmS5oi9RtLIlgw0VVXA/d3dDd2tplmUpPljr5E0jqGuoUmyLsluYD+ws6qu7na9O8l1SS5OsnFaRUqaD/YaSaMaKtBU1cGq2gqcBmxP8gvAO4BnAr8InAi8vd9zk+xIsivJroc5MJmqJa1J9hpJo1rWKqequhe4Eji3qvbVogPAh4HtA55zSVVtq6ptG/APK0lLs9dIWq5hVjmdnOT4bvtRwDnAt5Ns6cYCnAfsmV6ZktY6e42kcQyzymkLcFmSdSwGoMur6vNJ/jrJyUCA3cB/mF6Z82XhJf9o4L71V3xjeS+W9B+v/tda3vOGFwx8qZP+11XLm1taHnuNpJENs8rpOuCsPuNnT6UiSXPJXiNpHH5SsCRJap6BRpIkNc9AI0mSmmegkSRJzRtmlZNmbNkrmY5mwGqmQWaxkinr+//a1cLC1OeWJK1NnqGRJEnNM9BIkqTmGWgkSVLzDDSSJKl5BhpJktQ8A40kSWqey7Ybs9wlz6txibTLsyVJk+YZGkmS1DwDjSRJap6BRpIkNc9AI0mSmmegkSRJzXOVU2OWu0Jo3ZNO67/j4f6vs3D7HcstSZKkFecZGkmS1DwDjSRJap6BRpIkNc9AI0mSmmegkSRJzUtVzW6y5P8Bt3V3TwLumdnkR3Ju53buyXtyVZ08w/kGstc4t3Ov2bkH9pmZBpojJk52VdU253Zu5157c68m8/rfwLmdex7m7uVbTpIkqXkGGkmS1LyVDDSXOLdzO/eanXs1mdf/Bs7t3PMw90+t2DU0kiRJk+JbTpIkqXkrEmiSnJvkO0luSXLRjOfem+T6JLuT7JryXB9Ksj/Jnp6xE5PsTHJz9/OEGc79ziR3dse+O8krpzDv6UmuTHJjkhuSvKkbn/pxH2XuWRz3piTXJLm2m/td3fhTklzd/a5/PMmxM5z70iTf6znurZOeezWzz6zdPtPNY6+x1xypqmZ6A9YB3wXOBI4FrgWePcP59wInzWiuXwKeB+zpGfsvwEXd9kXAH8xw7ncCb5vyMW8Bntdtbwb+Fnj2LI77KHPP4rgDHNdtbwCuBp4PXA68rhv/Y+A/znDuS4HXTvO4V+vNPrO2+0w3j73GXnPEbSXO0GwHbqmqW6vqIeBjwKtXoI6pq6qvAD98xPCrgcu67cuA82Y499RV1b6q+ma3fR9wE3AqMzjuo8w9dbXo/u7uhu5WwNnAJ7rxaR33oLnnmX1mDfeZbm57jb3mCCsRaE4Fbu+5fwcz+kXoFPClJN9IsmOG8x52SlXt67a/D5wy4/nfmOS67lTxVE5DH5bkDOAsFlP8TI/7EXPDDI47yboku4H9wE4WzxDcW1UL3UOm9rv+yLmr6vBxv7s77ouTbJzG3KuUfWZO+gzYa7DXAPN5UfCLq+p5wCuAX0vySytVSC2et5tlun0/8FRgK7APeO+0JkpyHPBJ4M1V9ePefdM+7j5zz+S4q+pgVW0FTmPxDMEzpzHPMHMn+QXgHV0NvwicCLx9VvXIPsMM+gzYa7DX/NRKBJo7gdN77p/Wjc1EVd3Z/dwPfJrFX4ZZujvJFoDu5/5ZTVxVd3e/jIeADzClY0+ygcV/5B+pqk91wzM57n5zz+q4D6uqe4ErgRcAxydZ3+2a+u96z9zndqfFq6oOAB9m9r/rK8k+s8b7DNhr7DVHWolA83Xg6d0V2ccCrwM+O4uJkzwmyebD28DLgD1Hf9bEfRa4oNu+APjMrCY+/I+88xqmcOxJAnwQuKmq3teza+rHPWjuGR33yUmO77YfBZzD4vvqVwKv7R42rePuN/e3e5p6WHw/fda/6yvJPrOG+0w3j73GXnOkSV1dvJwb8EoWrwr/LvBbM5z3TBZXO1wL3DDtuYGPsnja8WEW39O8EHg8cAVwM/Bl4MQZzv0nwPXAdSz+o98yhXlfzOIp3uuA3d3tlbM47qPMPYvj/ofAt7o59gC/0/M7dw1wC/DnwMYZzv3X3XHvAf6UbnXCvNzsM2u3z3Rz22vsNUfc/KRgSZLUvHm8KFiSJK0xBhpJktQ8A40kSWqegUaSJDXPQCNJkppnoJEkSc0z0EiSpOYZaCRJUvP+P9rhJaCXVj+MAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 576x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"for i in range(5):\n",
" plt.figure(figsize=(8, 4))\n",
" plt.subplot(1, 2, 1)\n",
" plt.title(\"QCD\")\n",
" plt.imshow(qcd_images[i])\n",
" plt.subplot(1, 2, 2)\n",
" plt.title(\"top\")\n",
" plt.imshow(top_images[i])\n",
" plt.tight_layout()\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 160,
"id": "76f95308-03f5-4322-ad06-4ada5a32e018",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(8, 4))\n",
"plt.subplot(1, 2, 1)\n",
"plt.imshow(np.sum(qcd_images, axis=0))\n",
"plt.title(\"Average QCD\")\n",
"\n",
"plt.subplot(1, 2, 2)\n",
"plt.imshow(np.sum(top_images, axis=0))\n",
"plt.title(\"Average Top\")\n",
"\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 120,
"id": "8defd2e1-ea2e-4e0f-8e41-a7e03375e2b7",
"metadata": {},
"outputs": [],
"source": [
"features = Features({\"image\": Image(), \"label\": ClassLabel(names=[\"qcd\", \"top\"])})"
]
},
{
"cell_type": "code",
"execution_count": 121,
"id": "eb612755-bf67-4cb5-93b4-54abc0bccc24",
"metadata": {},
"outputs": [],
"source": [
"image_ds = Dataset.from_dict(\n",
" {\"image\": images[:10], \"label\": labels[:10]}, features=features\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 122,
"id": "f80849ed-51f2-449c-b28e-5668c7ad8f8f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'top'"
]
},
"execution_count": 122,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"image_ds.features[\"label\"].int2str(1)"
]
},
{
"cell_type": "code",
"execution_count": 123,
"id": "e27cfaf2-ec35-4f7f-acb5-290b53bab405",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAACgAAAAoCAAAAACpleexAAAAMUlEQVR4nGNgGFaAcaAdMAqIB2cG0G6WgTPMA1OICYt5Zpu/4TGE9gmdmeY2jIKRAwBzSQMHEvdnSQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<PIL.PngImagePlugin.PngImageFile image mode=L size=40x40>"
]
},
"execution_count": 123,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"image_ds[0][\"image\"]"
]
},
{
"cell_type": "code",
"execution_count": 124,
"id": "cdb8d9d7-1651-4253-8e71-204057fcca52",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Dataset({\n",
" features: ['image', 'label'],\n",
" num_rows: 10\n",
"})"
]
},
"execution_count": 124,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"image_ds"
]
},
{
"cell_type": "code",
"execution_count": 110,
"id": "9d878580-8ef7-428c-89e8-93049f9ed0d4",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4ec63edd3a57415e9fc578aa099fdc3f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Pushing dataset shards to the dataset hub: 0%| | 0/1 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b5174ae5bbc94ff6824b2ab272f95a4e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/1 [00:00<?, ?ba/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"image_ds.push_to_hub(\"dl4phys/top_landscape_images\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4f2e222f-9d9c-4ebf-9afb-34ff678c14de",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "dl4phys",
"language": "python",
"name": "dl4phys"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.13"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|