2023-04-18 16:49:21 +00:00
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "5a38ed25",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-04-12T14:25:46.519408Z",
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"start_time": "2023-04-12T14:25:03.003304Z"
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},
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"import sys\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import scienceplots\n",
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"import astroquery\n",
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"from tqdm import trange, tqdm\n",
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"\n",
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"sys.path.append(\"../\")\n",
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"import csiborgtools\n",
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"\n",
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"%matplotlib widget \n",
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"%load_ext autoreload\n",
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"%autoreload 2"
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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": 38,
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"id": "79919270",
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"metadata": {},
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"outputs": [],
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"source": [
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"# # Norma\n",
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"cluster = {\"RA\": (16 + 15 / 60 + 32.8 / 60**2) * 15,\n",
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" \"DEC\": -60 + 54 / 60 + 30 / 60**2,\n",
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" \"DIST\": 67.8}\n",
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"\n",
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"Xclust = np.array([cluster[\"DIST\"], cluster[\"RA\"], cluster[\"DEC\"]]).reshape(1, -1)"
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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": 39,
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"id": "96740b90",
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"metadata": {},
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"outputs": [],
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"source": [
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2023-05-13 16:37:34 +00:00
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"paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)\n",
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2023-04-18 16:49:21 +00:00
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"nsims = paths.get_ics(False)"
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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": 29,
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"id": "90033fb1",
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"metadata": {},
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"outputs": [],
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"source": [
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"Xclust = np.array([cluster[\"DIST\"], cluster[\"RA\"], cluster[\"DEC\"]]).reshape(1, -1)"
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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": 33,
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"id": "194baa83",
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"metadata": {},
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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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"100%|██████████| 101/101 [00:44<00:00, 2.25it/s]\n"
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]
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}
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],
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"source": [
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"matches = np.full(len(nsims), np.nan)\n",
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"\n",
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"for ii in trange(101):\n",
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2023-07-24 12:10:21 +00:00
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" cat = csiborgtools.read.CSiBORGHaloCatalogue(nsims[ii], paths, minmass=('M', 1e13))\n",
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2023-04-18 16:49:21 +00:00
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" dist, ind = cat.angular_neighbours(Xclust, ang_radius=5, rad_tolerance=10)\n",
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" dist = dist[0]\n",
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" ind = ind[0]\n",
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"\n",
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" if ind.size > 0:\n",
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" matches[ii] = np.max(cat['M'][ind])\n"
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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": 37,
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"id": "392f6eee",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "6ff7d5b9dc2f4b3fa6f563ee91f59655",
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"version_major": 2,
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"version_minor": 0
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},
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"text/html": [
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"\n",
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" <div style=\"display: inline-block;\">\n",
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" <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
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" Figure\n",
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" </div>\n",
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" <img src='data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAoAAAAHgCAYAAAA10dzkAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAflklEQVR4nO3df3TV9X348Vf4kcCEBBMlISMBWltjtbAOK6a6ddXMyOH4o6Q/7HGWUk532hOpkLNV6dZST6fB7kxoK2D1ODw7p8zWP7CjntrDsonHs4AYxo6uk1ZrD1hMqG4kiCMw8vn+0a+p0UT5kZub3Pfjcc49x3zuzcfXy+Anz3NzbyjKsiwLAACSMS7fAwAAMLIEIABAYgQgAEBiBCAAQGIEIABAYgQgAEBiBCAAQGIEIABAYgQgAEBiBCAAQGIEIABAYgQgAEBiBCAAQGIEIABAYgQgAEBiBCAAQGIEIABAYgQgAEBiBCAAQGIEIABAYgQgAEBiBCAAQGIEIABAYgQgAEBiBCAAQGIEIABAYgQgAEBiBCAAQGIEIABAYgQgAEBiBCAAQGIEIABAYgQgAEBiBCAAQGIEIABAYgQgAEBiBCAAQGIEIABAYgQgAEBiBCAAQGIEIABAYgQgAEBiBCAAQGIEIABAYgQgAEBiBCAAQGIEIABAYgQgAEBiBCAAQGIEIABAYgQgAEBiBCAAQGIEIABAYgQgAEBiBCAAQGIEIABAYibke4CxrK+vLw4cOBBTp06NoqKifI8DAJyELMvi8OHDUV1dHePGpflcmAA8AwcOHIiampp8jwEAnIb9+/fHzJkz8z1GXgjAMzB16tSI+O0foNLS0jxPAwCcjJ6enqipqen/Pp4iAXgG3vixb2lpqQAEgDEm5ZdvpfmDbwCAhAlAAIDECEAAgMQIQACAxAhAAIDECEAAgMQIQACAxAhAAIDECEAAgMQIQACAxAhAAIDECEAAgMQIQACAxAhAAIDETMj3AABA7s2+7dF8j3DKfrVmUb5HKFieAQQASIwABABIjAAEAEiMAAQASIwABABIjAAEAEiMAAQASIwABABIjAAEAEiMAAQASIwABABIjAAEAEiMAAQASIwABABIjAAEAEiMAAQASIwABABIjAAEAEiMAAQASIwABABIjAAEAEiMAAQASIwABABIjAAEAEiMAAQASIwABABIjAAEAEhMwQbgN77xjSgqKhpwq6ur67//6NGj0dzcHBUVFTFlypRoamqKrq6uPE4MADAyCjYAIyIuvPDCePnll/tvTz75ZP99K1eujK1bt8bDDz8c27dvjwMHDsTixYvzOC0AwMiYkO8BcmnChAlRVVX1tuPd3d3xwAMPxObNm+OKK66IiIhNmzbFBRdcEDt27IhLL710pEcFABgxBf0M4C9+8Yuorq6O97znPXHjjTfGvn37IiKio6Mjjh8/Hg0NDf2Prauri9ra2mhvbx/yfL29vdHT0zPgBgAw1hRsAC5YsCAefPDBeOyxx2Ljxo3x4osvxh/90R/F4cOHo7OzM4qLi2PatGkDPqeysjI6OzuHPGdra2uUlZX132pqanK8BQDA8CvYHwEvXLiw/5/nzp0bCxYsiFmzZsUPf/jDmDx58mmdc9WqVdHS0tL/cU9PjwgEAMacgn0G8K2mTZsW73//++P555+PqqqqOHbsWBw6dGjAY7q6ugZ9zeAbSkpKorS0dMANAGCsSSYAX3vttXjhhRdixowZMX/+/Jg4cWK0tbX13793797Yt29f1NfX53FKAIDcK9gfAf/FX/xFXHPNNTFr1qw4cOBArF69OsaPHx+f+cxnoqysLJYtWxYtLS1RXl4epaWlsXz58qivr/cOYACg4BVsAL700kvxmc98Jl599dU499xz4/LLL48dO3bEueeeGxERa9eujXHjxkVTU1P09vZGY2NjbNiwIc9TAwDkXlGWZVm+hxirenp6oqysLLq7u70eEIBRbfZtj+Z7hFP2qzWLcnJe378Teg0gAAC/JQABABIjAAEAEiMAAQASIwABABIjAAEAEiMAAQASIwABABIjAAEAEiMAAQASIwABABIjAAEAEiMAAQASIwABABIjAAEAEiMAAQASIwABABIjAAEAEiMAAQASIwABABIjAAEAEiMAAQASIwABABIjAAEAEiMAAQASIwABABIjAAEAEiMAAQASIwABABIjAAEAEiMAAQASIwABABIjAAEAEiMAAQASIwABABIjAAEAEiMAAQASIwABABIjAAEAEiMAAQASIwABABIjAAEAEiMAAQASIwABABIjAAEAEiMAAQASIwABABIjAAEAEiMAAQASIwABABIjAAEAEiMAAQASIwABABKTRACuWbMmioqKYsWKFf3Hjh49Gs3NzVFRURFTpkyJpqam6Orqyt+QAAAjpOADcNeuXfG9730v5s6dO+D4ypUrY+vWrfHwww/H9u3b48CBA7F48eI8TQkAMHIKOgBfe+21uPHGG+P++++Ps88+u/94d3d3PPDAA3H33XfHFVdcEfPnz49NmzbFv/3bv8WOHTvyODEAQO4VdAA2NzfHokWLoqGhYcDxjo6OOH78+IDjdXV1UVtbG+3t7UOer7e3N3p6egbcAADGmgn5HiBXHnroodi9e3fs2rXrbfd1dnZGcXFxTJs2bcDxysrK6OzsHPKcra2tcfvttw/3qAAAI6ognwHcv39/3HLLLfH9738/Jk2aNGznXbVqVXR3d/ff9u/fP2znBgAYKQUZgB0dHXHw4MH4wz/8w5gwYUJMmDAhtm/fHt/5zndiwoQJUVlZGceOHYtDhw4N+Lyurq6oqqoa8rwlJSVRWlo64AYAMNYU5I+Ar7zyynjmmWcGHFu6dGnU1dXFrbfeGjU1NTFx4sRoa2uLpqamiIjYu3dv7Nu3L+rr6/MxMgDAiCnIAJw6dWpcdNFFA46dddZZUVFR0X982bJl0dLSEuXl5VFaWhrLly+P+vr6uPTSS/MxMgDAiCnIADwZa9eujXHjxkVTU1P09vZGY2NjbNiwId9jAQDkXFGWZVm+hxirenp6oqysLLq7u70eEIBRbfZtj+Z7hFP2qzWLcnJe378L9E0gAAAMTQACACRGAAIAJEYAAgAkRgACACRGAAIAJEYAAgAkRgACACRGAAIAJEYAAgAkRgACACRGAAIAJEYAAgAkRgACACRGAAIAJEYAAgAkRgACACRGAAIAJEYAAgAkRgACACRGAAIAJEYAAgAkRgACACRGAAIAJEYAAgAkRgACACRGAAIAJEYAAgAkRgACACRGAAIAJEYAAgAkRgACACRGAAIAJEYAAgAkRgACACRGAAIAJEYAAgAkRgACACRGAAIAJEYAAgAkRgACACRGAAIAJEYAAgAkRgACACRGAAIAJEYAAgAkRgACACRGAAIAJEYAAgAkRgACACRGAAIAJEYAAgAkRgACACSmYANw48aNMXfu3CgtLY3S0tKor6+Pn/zkJ/33Hz16NJqbm6OioiKmTJkSTU1N0dXVlceJAQBGRsEG4MyZM2PNmjXR0dERTz/9dFxxxRVx3XXXxX/+539GRMTKlStj69at8fDDD8f27dvjwIEDsXjx4jxPDQCQe0VZlmX5HmKklJeXx9/+7d/GJz7xiTj33HNj8+bN8YlPfCIiIp577rm44IILor29PS699NKTOl9PT0+UlZVFd3d3lJaW5nJ0ADgjs297NN8jnLJfrVmUk/P6/l3AzwC+2YkTJ+Khhx6KI0eORH19fXR0dMTx48ejoaGh/zF1dXVRW1sb7e3tQ56nt7c3enp6BtwAAMaagg7AZ555JqZMmRIlJSXxxS9+MbZs2RIf+MAHorOzM4qLi2PatGkDHl9ZWRmdnZ1Dnq+1tTXKysr6bzU1NTneAABg+BV0AJ5//vmxZ8+e2LlzZ3zpS1+KJUuWxM9+9rPTPt+qVauiu7u7/7Z///5hnBYAYGRMyPcAuVRcXBznnXdeRETMnz8/du3aFd/+9rfj05/+dBw7diwOHTo04FnArq6uqKqqGvJ8JSUlUVJSkuu
|
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" </div>\n",
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" "
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|
],
|
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"text/plain": [
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"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
|
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|
|
"x = np.log10(matches[~np.isnan(matches)])\n",
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"\n",
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"\n",
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"plt.figure()\n",
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"plt.hist(x, bins=10)\n",
|
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"plt.show()"
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]
|
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|
},
|
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|
|
{
|
|
|
|
"cell_type": "code",
|
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|
|
"execution_count": null,
|
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|
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"id": "9cc660b0",
|
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|
|
"metadata": {},
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"outputs": [],
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"source": []
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}
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],
|
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|
"metadata": {
|
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|
"kernelspec": {
|
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|
|
"display_name": "venv_galomatch",
|
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|
|
"language": "python",
|
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|
|
"name": "python3"
|
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|
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},
|
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|
|
"language_info": {
|
|
|
|
"codemirror_mode": {
|
|
|
|
"name": "ipython",
|
|
|
|
"version": 3
|
|
|
|
},
|
|
|
|
"file_extension": ".py",
|
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|
|
"mimetype": "text/x-python",
|
|
|
|
"name": "python",
|
|
|
|
"nbconvert_exporter": "python",
|
|
|
|
"pygments_lexer": "ipython3",
|
|
|
|
"version": "3.8.0"
|
|
|
|
}
|
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|
|
},
|
|
|
|
"nbformat": 4,
|
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|
"nbformat_minor": 5
|
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|
|
}
|