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{
"cells": [
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Calibrating the velocity field against observations "
]
},
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{
"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Copyright (C) 2024 Richard Stiskalek\n",
"# This program is free software; you can redistribute it and/or modify it\n",
"# under the terms of the GNU General Public License as published by the\n",
"# Free Software Foundation; either version 3 of the License, or (at your\n",
"# option) any later version.\n",
"#\n",
"# This program is distributed in the hope that it will be useful, but\n",
"# WITHOUT ANY WARRANTY; without even the implied warranty of\n",
"# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General\n",
"# Public License for more details.\n",
"#\n",
"# You should have received a copy of the GNU General Public License along\n",
"# with this program; if not, write to the Free Software Foundation, Inc.,\n",
"# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.\n",
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"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import jax\n",
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"from jax import numpy as jnp\n",
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"from numpyro.infer import MCMC, NUTS, init_to_median\n",
"import corner\n",
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"from getdist import plots\n",
"from scipy.stats import multivariate_normal\n",
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"\n",
"import csiborgtools\n",
"\n",
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"from flow_calibration import *\n",
"\n",
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"%load_ext autoreload\n",
"%autoreload 2\n",
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"%matplotlib inline\n",
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"\n",
"paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## LOS density & radial velocity plots "
]
},
{
"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"2024-06-28 10:18:55.688429: reading the catalogue,\n",
"2024-06-28 10:18:55.695821: reading the interpolated field,\n",
"2024-06-28 10:18:55.702331: calculating the radial velocity.\n",
"2024-06-28 10:18:55.711839: reading the catalogue,\n",
"2024-06-28 10:18:55.716579: reading the interpolated field,\n",
"2024-06-28 10:18:55.722066: calculating the radial velocity.\n",
"2024-06-28 10:18:55.731522: reading the catalogue,\n",
"2024-06-28 10:18:55.736014: reading the interpolated field,\n",
"2024-06-28 10:18:55.741692: calculating the radial velocity.\n"
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]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
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"/mnt/users/rstiskalek/csiborgtools/csiborgtools/flow/flow_model.py:91: UserWarning: The number of radial steps is even. Skipping the first step at 0.0 because Simpson's rule requires an odd number of steps.\n",
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" warn(f\"The number of radial steps is even. Skipping the first \"\n"
]
}
],
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"source": [
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"fpath = \"/mnt/extraspace/rstiskalek/catalogs/PV_compilation.hdf5\"\n",
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"\n",
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"loader_carrick = csiborgtools.flow.DataLoader(\"Carrick2015\", [0], \"LOSS\", fpath, paths, ksmooth=0, )\n",
"# loaders_csiborg2X = [csiborgtools.flow.DataLoader(\"csiborg2X\", i, \"LOSS\", fpath, paths, ksmooth=1, verbose=False) for i in range(20)]\n",
"# loaders_csiborg2 = [csiborgtools.flow.DataLoader(\"csiborg2_main\", i, \"LOSS\", fpath, paths, ksmooth=1, verbose=False) for i in range(20)]\n",
"\n",
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"loader_CF4 = csiborgtools.flow.DataLoader(\"CF4gp\", [0], \"LOSS\", fpath, paths, ksmooth=0, )\n",
"loader_lilow = csiborgtools.flow.DataLoader(\"Lilow2024\", [0], \"LOSS\", fpath, paths, ksmooth=0, )"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
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" nan, nan, nan, nan], dtype=float32)"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"loader_lilow.los_density[0, 0]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
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]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"loader_lilow.rdist"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[[ 35.210815, 35.04605 , 34.74849 , ..., nan,\n",
" nan, nan],\n",
" [-441.1905 , -422.74704 , -404.55774 , ..., -31.251247,\n",
" -26.673668, -22.087921],\n",
" [-136.28223 , -143.65129 , -151.05142 , ..., nan,\n",
" nan, nan],\n",
" ...,\n",
" [-648.0296 , -644.74 , -641.37463 , ..., nan,\n",
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" [ 326.15454 , 337.55508 , 349.01004 , ..., nan,\n",
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" nan, nan]]], dtype=float32)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"loader_lilow.los_radial_velocity"
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]
},
{
"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_411260/1158227546.py:5: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). Consider using `matplotlib.pyplot.close()`.\n",
" fig, axs = plt.subplots(2, 1, figsize=(7, 7), sharex=True)\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 700x700 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 700x700 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 700x700 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 700x700 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 700x700 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 700x700 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 700x700 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 700x700 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 700x700 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 700x700 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 700x700 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
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{
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"data": {
2024-06-28 10:55:42 +00:00
"image/png": "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
2024-06-27 14:02:33 +00:00
"text/plain": [
"<Figure size 700x700 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
2024-06-28 10:55:42 +00:00
"image/png": "iVBORw0KGgoAAAANSUhEUgAAArIAAAKxCAYAAACv7U8uAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/H5lhTAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd1iTV/vA8W8WG8LeW0QUB4h771VHra3d07ZvWzvtHm/H293+umv3sLu21rbaoa17742ATJG9CZuQ5PfHARQ3kDDP57q4Ag/JeQ4KyZ3z3Oe+FSaTyYQkSZIkSZIkdTLK9p6AJEmSJEmSJLWEDGQlSZIkSZKkTkkGspIkSZIkSVKnJANZSZIkSZIkqVOSgawkSZIkSZLUKclAVpIkSZIkSeqUZCArSZIkSZIkdUrq9p5AZ2Y0GsnKysLR0RGFQtHe05EkSZIkSepQTCYTZWVl+Pr6olSaf/1UBrKtkJWVRUBAQHtPQ5IkSZIkqUM7ceIE/v7+Zh9XBrKt4OjoCIj/HCcnp3aejSRJkiRJUsei0+kICAhojJnMTQayrdCQTuDk5CQDWUmSJEmSpHOwVAqm3OwlSZIkSZIkdUoykJUkSZIkSZI6JRnISpIkSZIkSZ2SzJGVJEmSJAmDwYBer2/vaUidjEajQaVStdv5ZSArSZIkSd2YyWQiJyeHkpKS9p6K1Ek5Ozvj7e3dLjX1ZSArSZIkSd1YQxDr6emJnZ2dbPAjXTSTyURlZSV5eXkA+Pj4tPkcZCArSZIkSd2UwWBoDGLd3NzaezpSJ2RrawtAXl4enp6ebZ5mIDd7SZIkSVI31ZATa2dn184zkTqzht+f9sixloGsJEmSJHVzMp1Aao32/P2RgawkSZIkSZLUKckcWUmSzstkNJJ6cC+H1/5DbkoSFSXF2Dk7Exo1iJFXXY+dk7a9pyhJkiR1UzKQlSTpnEpyc1j94dtkxB1pcry8sIBDa1dxbNc2rn/5bZw8PNtphpIkSW0jLS2NkJAQ9u/fT1RU1AXvf9NNN1FSUsJvv/1m8bl1ZzK1QJKks8qIj+WbR+8lI+4IGmsbYi6Zw1XPvcat733OvMefw9nbh+oyHfHbNrX3VCVJ6qZycnK45557CA0NxdramoCAAGbNmsXatWvNfq6AgACys7Pp27ev2ce+EL1ez6OPPkq/fv2wt7fH19eXG264gaysrCb3Kyoq4tprr8XJyQlnZ2cWLFhAeXl54/erq6u56aab6NevH2q1mksvvfSMc23YsAGFQnHGR05OjqV/zBaRgWwLLF68mD59+jB48OD2nookWUTG0SP88uLT1FZV4hMewY3/9z7jbrgNv4g+aD29CI6KIWrKJQBkxse282wlSeqO0tLSiImJYd26dbz++uscPnyYVatWMX78eBYuXNiiMQ0GA0aj8YzjtbW1qFQqvL29UastczH72Wef5aabbjrr9yorK9m3bx///e9/2bdvH8uXLychIYHZs2c3ud+1115LbGws//77L3/88QebNm3i9ttvb/y+wWDA1taWe++9l0mTJp13PgkJCWRnZzd+eHp2zCtvMpBtgYULF3L06FF2797d3lORJLMrzcvh9zdfoq62huCoGK546gW0nt7im7t3w/33wyuv4Gcjyq1kJhzFdJYnfkmSOimTCSoq2v7DZGrWNO+66y4UCgW7du1i3rx5hIeHExkZyaJFi9ixYwcAb775ZuMqZkBAAHfddVeTFcolS5bg7OzMihUr6NOnD9bW1qSnpxMcHMzzzz/PDTfcgJOTE7fffjtpaWkoFAoOHDjQ+PjY2FhmzpyJk5MTjo6OjB49muTk5LPOd/fu3Xh4ePDqq682+79Eq9Xy77//Mn/+fHr16sWwYcN4//332bt3L+np6QDExcWxatUqPvvsM4YOHcqoUaN47733+PHHHxtXbu3t7fnwww+57bbb8Pb2Pu85PT098fb2bvxQKjtmyChzZCVJamQyGlnxxstUl+nwCu3J7AefQGNlLb75118wbx5UVwPgaWuL5tIp1FRUUJCRjkdgcPtNXJIk86msBAeHtj9veTnY21/UXYuKili1ahUvvvgi9md5jLOzMwBKpZJ3332XkJAQUlJSuOuuu3jkkUf44IMPGu9bWVnJq6++ymeffYabm1vjyuP//d//8fTTT/PMM8+cdQ6ZmZmMGTOGcePGsW7dOpycnNi6dSt1dXVn3HfdunVcdtllvPbaa01WSFujtLQUhULR+LNu374dZ2dnBg0a1HifSZMmoVQq2blzJ3Pnzm3W+FFRUdTU1NC3b1+effZZRo4caZZ5m5sMZCVJapR2aD95aclY29kz56EnTwaxO3fCpZeCXg/jxoHBgHLzZnwys0n39iAzLlYGspIktZmkpCRMJhMRERHnvd/999/f+HlwcDAvvPACd9xxR5NAVq/X88EHHzBgwIAmj50wYQIPPvhg49dpaWlNvr948WK0Wi0//vgjGo0GgPDw8DPm8Ouvv3LDDTfw2WefceWVV17sj3he1dXVPProo1x99dU4OTkBIl/49Mv/arUaV1fXZuW3+vj48NFHHzFo0CBqamr47LPPGDduHDt37mTgwIFmmb85yUBWkqRGB//9G4DIsRNxdHMXBwsLYf58EcTOng3LloFSCffcg//Gf0Uge2AvUVMvaceZS5JkNnZ2YnW0Pc57kUwXmYawZs0aXn75ZeLj49HpdNTV1VFdXU1lZWVjNyorKyv69+9/xmNPXdk8mwMHDjB69OjGIPZsdu7cyR9//MGyZcvO2Fi1efNmpk+f3vh1bW0tJpOJZcuWNR77+OOPufbaa5s8Tq/XM3/+fEwmEx9++OF559gSvXr1olevXo1fjxgxguTkZN566y2++eYbs5+vtWQgK0kSAGWFBaTs3QVA/0knn1y55x5IT4ewMPj6a2h40n77bfwmjAUgY+8uTCaT7A4kSV2BQnHRl/jbS8+ePVEoFMTHx5/zPmlpacycOZM777yTF198EVdXV7Zs2cKCBQuora1tDGRtbW3P+tx1tpSFU9na2l5wnj169MDNzY0vvviCSy65pEnQO2jQoCb5tu+++y6ZmZlNcmi9vLyajNcQxB4/frwxnaGBt7c3eXl5Te5fV1dHUVHRBfNhL2TIkCFs2bKlVWNYSsfM3JUkqc3FbdmAyWTEv3df3PwDxMGNG+GHH8QK7I8/gvaU5gdWVvi88x5Ko5FyBehiD7fPxCVJ6nZcXV2ZOnUqixcvpqKi4ozvl5SUsHfvXoxGI2+88QbDhg0jPDz8jHJVrdG/f382b96MXq8/533c3d1Zt24dSUlJzJ8/v8l9bW1tCQsLa/xwdXXF0dGxyTFHR8fG+zcEsYmJiaxZswY3N7cm5xo+fHjjz91g3bp1GI1Ghg4d2qqf9cCBA/j4+LRqDEuRgawkSQBkHYsDIGzwMHGgrg7uvlt8fscdEBNzxmM0MYPw0ouKBZnffNUm85QkSQKRo2owGBgyZAi//PILiYmJxMXF8e677zJ8+HDCwsLQ6/W89957pKSk8M033/DRRx+Z7fx33303Op2Oq666ij179pCYmMg333xDQkJCk/t5enqybt064uPjufrqq8+6GexC9Ho9l19+OXv27OG7777DYDCQk5NDTk4OtbW1APTu3Ztp06Zx2223sWvXLrZu3crdd9/NVVddha+vb+NYR48e5cCBAxQVFVFaWsqBAwearAy//fbb/P777yQlJXHkyBHuv/9+1q1b1+KSZpYmA1lJkjCZTGQniidf77D63KgPPoAjR8DNDZ5//pyP9QsXmy0y9+5qdvkcSZKklgoNDWXfvn2MHz+eBx98kL59+zJ58mTWrl3Lhx9+yIABA3jzzTd59dVX6du3L9999x0vv/yy2c7v5ubGunXrKC8vZ+zYscTExPDpp5+eNWfW29ubdevWcfjwYa699loMBkOzzpWZmcmKFSvIyMggKioKHx+fxo9t27Y13u+7774jIiKCiRMnMmPGDEaNGsUnn3zSZKwZM2YQHR3NypUr2bBhA9HR0URHRzd+v7a2lgcffJB+/foxduxYDh48yJo1a5g4cWIz/4XahsJ
2024-06-27 14:02:33 +00:00
"text/plain": [
"<Figure size 700x700 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
2024-06-28 10:55:42 +00:00
"image/png": "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
2024-06-27 14:02:33 +00:00
"text/plain": [
"<Figure size 700x700 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
2024-06-28 10:55:42 +00:00
"image/png": "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
2024-06-27 14:02:33 +00:00
"text/plain": [
"<Figure size 700x700 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
2024-06-28 10:55:42 +00:00
"image/png": "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
2024-06-27 14:02:33 +00:00
"text/plain": [
"<Figure size 700x700 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
2024-06-28 10:55:42 +00:00
"image/png": "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
2024-06-27 14:02:33 +00:00
"text/plain": [
"<Figure size 700x700 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
2024-06-26 09:43:26 +00:00
},
{
"data": {
2024-06-28 10:55:42 +00:00
"image/png": "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
2024-06-27 14:02:33 +00:00
"text/plain": [
"<Figure size 700x700 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
2024-06-28 10:55:42 +00:00
"image/png": "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
2024-06-27 14:02:33 +00:00
"text/plain": [
"<Figure size 700x700 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
2024-06-28 10:55:42 +00:00
"image/png": "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
2024-06-27 14:02:33 +00:00
"text/plain": [
"<Figure size 700x700 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
2024-06-28 10:55:42 +00:00
"image/png": "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
2024-06-27 14:02:33 +00:00
"text/plain": [
"<Figure size 700x700 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
2024-06-28 10:55:42 +00:00
"image/png": "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
2024-06-27 14:02:33 +00:00
"text/plain": [
"<Figure size 700x700 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
2024-06-28 10:55:42 +00:00
"image/png": "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
2024-06-27 14:02:33 +00:00
"text/plain": [
"<Figure size 700x700 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
2024-06-28 10:55:42 +00:00
"image/png": "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
2024-06-27 14:02:33 +00:00
"text/plain": [
"<Figure size 700x700 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
2024-06-28 10:55:42 +00:00
"image/png": "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
2024-06-27 14:02:33 +00:00
"text/plain": [
"<Figure size 700x700 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
2024-06-28 10:55:42 +00:00
"image/png": "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
"text/plain": [
"<Figure size 700x700 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 700x700 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 700x700 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 700x700 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 700x700 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 700x700 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAArIAAAKxCAYAAACv7U8uAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/H5lhTAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOydd3yV5fn/32dk771DEhIyGGGGPRUUBBQHzjqrtaJWxVbt19patbXtz1Er1daFdaEyFFBBBdkQICRASAjZe+89Ts7vjzvnhJhBEs7IuN+v13k9J+cZ9xUSTj7nej7XdSm0Wq0WiUQikUgkEolkmKE0dwASiUQikUgkEslgkEJWIpFIJBKJRDIskUJWIpFIJBKJRDIskUJWIpFIJBKJRDIskUJWIpFIJBKJRDIskUJWIpFIJBKJRDIskUJWIpFIJBKJRDIsUZs7gOFMe3s7BQUFODg4oFAozB2ORCKRSCQSyZBCq9VSW1uLr68vSqXh86dSyF4GBQUFBAQEmDsMiUQikUgkkiFNbm4u/v7+Br+uFLKDYMOGDWzYsIG2tjZA/HAcHR3NHJVEIpFIJBLJ0KKmpoaAgAAcHByMcn2FHFE7eGpqanBycqK6uloKWYlEIpFIJJKfYWytJIu9JBKJRCKRSCTDEilkJRKJRCKRSCTDEilkJRKJRCKRSCTDElnsJZFIJBKJBI1GQ2trq7nDkAwzLCwsUKlUZltfClmJRCKRSEYxWq2WoqIiqqqqzB2KZJji7OyMt7e3WXrqSyErkUgkEskoRidiPT09sbW1lQN+JP1Gq9XS0NBASUkJAD4+PiaPQQpZiUQikUhGKRqNRi9i3dzczB2OZBhiY2MDQElJCZ6enia3GchiL4lEIpFIRik6T6ytra2ZI5EMZ3S/P+bwWEshK5FIJBLJKEfaCSSXgzl/f6SQlUgkEolEIpEMS6SQHYqUlMC994KnJ1x5JXzxhbkjkkgkEolEIhlySCE71CgvhylT4IMPoLQU9uyBm2+Gp5+G9nZzRyeRSCQSyagkKysLhUJBQkJCv46/++67ue6664wak0QK2aHH738PBQUwdizs3AlPPile/9vf4I03zBubRCKRSCRDiKKiIh555BFCQkKwsrIiICCAVatWsWfPHoOvFRAQQGFhIRMmTDD4tS9Fa2srTz31FBMnTsTOzg5fX1/uvPNOCgoKuhxXUVHB7bffjqOjI87Oztx3333U1dXp9zc1NXH33XczceJE1Gp1j0J73759KBSKbo+ioiJjf5uDQgrZocSJE/DOO+L5Bx/ANdfAP/4Br78uXnvuOSgsNFt4EolEIpEMFbKyspg2bRp79+7lH//4B2fPnmXXrl0sXryYdevWDeqaGo2G9h7ufra0tKBSqfD29katNk7n0j/96U/cfffdPe5raGjg1KlT/OEPf+DUqVNs3bqVlJQUVq9e3eW422+/nXPnzvHDDz+wc+dODhw4wAMPPKDfr9FosLGx4dFHH+XKK6/sM56UlBQKCwv1D09Pz8v+Ho2BFLJDiZdeAq0Wbr8d5s/vfP2RRyAmBmpr4ZlnzBefRCKRSEY2Wi3U15vnodUOKNSHHnoIhULB8ePHueGGGxg3bhzjx4/niSee4NixYwC8+uqr+ixmQEAADz30UJcM5caNG3F2dmb79u1ERUVhZWVFTk4OQUFBvPDCC9x55504OjrywAMP9GgtOHfuHCtXrsTR0REHBwfmz59Penp6j/GeOHECDw8P/va3vw34x+Lk5MQPP/zA2rVrCQ8PZ9asWbz55pvExcWRk5MDQHJyMrt27eLdd99l5syZzJs3j3/9619s2rRJn7m1s7Pjrbfe4v7778fb27vPNT09PfH29tY/lMqhKRmHZlSjkZwc2LFDPP+//+u6T6nszMp+/jk0NZk0NIlEIpGMEhoawN7ePI+Ghn6HWVFRwa5du1i3bh12dnbd9js7OwOgVCp54403OHfuHB9++CF79+7ld7/73c++5Qb+9re/8e6773Lu3Dl95vH//b//R3R0NPHx8fzhD3/otkZ+fj4LFizAysqKvXv3EhcXx7333ktbW1u3Y/fu3cvSpUt56aWXeOqpp/r9ffZFdXU1CoVC/70ePXoUZ2dnpk+frj/myiuvRKlUEhsbO+DrT548GR8fH5YuXcrhw4cNErMxkJO9hgr/+Y8o5lq8GCIju++fNQt8fYV/9vBhuOIK08cokUgkEskQIC0tDa1WS0RERJ/HPfbYY/rnQUFBvPjiizz44IP8+9//1r/e2trKv//9b6Kjo7ucu2TJEtavX6//Oisrq8v+DRs24OTkxKZNm7CwsABg3Lhx3WLYtm0bd955J++++y4333xzf7/FPmlqauKpp57i1ltvxdHRERB+4Z/f/ler1bi6ug7I3+rj48Pbb7/N9OnTaW5u5t1332XRokXExsYydepUg8RvSKSQHQq0tcG774rnvfl6FArRiut//4Mff5RCViKRSCSGx9YWLrr1bvK1+4m2nzaEH3/8kb/+9a+cP3+empoa2traaGpqoqGhQT+NytLSkkmTJnU79+LMZk8kJCQwf/58vYjtidjYWHbu3MnmzZu7FVYdPHiQ5cuX679uaWlBq9WyefNm/Wv/+c9/uP3227uc19raytq1a9Fqtbz11lt9xjgYwsPDCQ8P1389Z84c0tPTee211/joo48Mvt7lIoXsUODgQdE71s0Nfmbc7sLSpULI/vAD/PWvpotPIpFIJKMDhQJ6uFU/1AgLC0OhUHD+/Plej8nKymLlypX8+te/5qWXXsLV1ZVDhw5x33330dLSoheyNjY2PU6m6smycDE2NjaXjHPs2LG4ubnx/vvvc80113QRvdOnT+/it33jjTfIz8/v4qH18vLqcj2diM3Ozmbv3r36bCyAt7c3JSUlXY5va2ujoqLikn7YSxETE8OhQ4cu6xrGQnpkhwJffSW2q1dDH5/s0FUYnjol+s1KJBKJRDIKcXV15aqrrmLDhg3U19d3219VVUVcXBzt7e288sorzJo1i3HjxnVrV3U5TJo0iYMHD9La2trrMe7u7uzdu5e0tDTWrl3b5VgbGxtCQ0P1D1dXVxwcHLq85uDgoD9eJ2JTU1P58ccfcXNz67LW7Nmz9d+3jr1799Le3s7MmTMv63tNSEjAx8fnsq5hLKSQNTdabaeQvVTjZG9vmDBBnPP998aOTCKRSCSSIcuGDRvQaDTExMSwZcsWUlNTSU5O5o033mD27NmEhobS2trKv/71LzIyMvjoo494++23Dbb+ww8/TE1NDbfccgsnT54kNTWVjz76iJSUlC7HeXp6snfvXs6fP8+tt97aYzHYpWhtbeXGG2/k5MmTfPLJJ2g0GoqKiigqKqKlpQWAyMhIrr76au6//36OHz/O4cOHefjhh7nlllvw9fXVXyspKYmEhAQqKiqorq4mISGhS2b49ddf5+uvvyYtLY3ExEQee+wx9u7dO+iWZsZGCllzc+qU6FhgayusA5di1Sqx3bbNuHFJJBKJRDKECQkJ4dSpUyxevJj169czYcIEli5dyp49e3jrrbeIjo7m1Vdf5W9/+xsTJkzgk08+4a8GtOW5ubmxd+9e6urqWLhwIdOmTeOdd97p0TPr7e3N3r17OXv2LLfffjsajWZAa+Xn57N9+3by8vL03QR0jyNHjuiP++STT4iIiOCKK65gxYoVzJs3j//+979drrVixQqmTJnCjh072LdvH1OmTGHKlCn6/S0tLaxfv56JEyeycOFCTp8+zY8//sgVQ7Q2R6Htr2Na0o2amhqcnJyorq7u4lMZEM88Ay+/DNdfD1u2XPr4kydhxgzhYSothX54dCQSiUQi6YmmpiYyMzMJDg7G2tra3OFIhil9/R4ZRCv1gczImhONBnQVgLfe2r9zpk2DwEDRPFraCyQSiUQikYxipJA1Jz/9BPn54OLSaRm4FAqFyN4CbN1qvNgkEolEIpFIhjhSyJqTDz8U21tuASur/p+nE7Lbt0Mf1ZISicQIVFdDR3GFRCKRSMyLFLLmorm5s2DrF78Y2Llz5oCnJ1RViayuRCIxDZs2gZcXuLv
"text/plain": [
"<Figure size 700x700 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAArIAAAKyCAYAAAApeT2AAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/H5lhTAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOydd3iUZdaH7ynJpPfeE0oChCT03sG6iIpiWQu71hXws+2qu25x1XVde8Guay9rBUVReu8klCSE9N57z2Rmvj+ezIRAgADTkjz3dc31TmbeciaEmd+c55zfURgMBgMSiUQikUgkEkk/Q2nrACQSiUQikUgkkvNBClmJRCKRSCQSSb9EClmJRCKRSCQSSb9EClmJRCKRSCQSSb9EClmJRCKRSCQSSb9EClmJRCKRSCQSSb9EClmJRCKRSCQSSb9EClmJRCKRSCQSSb9EbesA+jN6vZ6SkhLc3d1RKBS2DkcikUgkEonErjAYDDQ2NhISEoJSaf78qRSyF0BJSQnh4eG2DkMikUgkEonEriksLCQsLMzs55VC9gJwd3cHxD+Oh4eHjaORSCQSiUQisS8aGhoIDw83aSZzI4XsBWAsJ/Dw8JBCViKRSCQSieQ0WKoEUzZ7SSQSiUQikUj6JVLISiQSiUQikUj6JVLISiQSiUQikUj6JbJGViKRSCQSCTqdDq1Wa+swJP0MBwcHVCqVza4vhaxEIpFIJIMYg8FAWVkZdXV1tg5F0k/x8vIiKCjIJp76UshKJBKJRDKIMYrYgIAAXFxc5IAfSZ8xGAy0tLRQUVEBQHBwsNVjkEJWIpFIJJJBik6nM4lYX19fW4cj6Yc4OzsDUFFRQUBAgNXLDGSzl0QikUgkgxRjTayLi4uNI5H0Z4x/P7aosZZCViKRSCSSQY4sJ5BcCLb8+5FCViKRSCQSiQmDwWDrECSSPiOFrEQikUgkEgBaGxuoyMuhprQYXWenrcOxK/Ly8lAoFKSkpPRp/6VLl3LllVdaNCaJFLISiUQikUiA5ro66ivKMej1dLS0UF1UgLatzdZhnZGysjJWrFhBTEwMGo2G8PBwFi5cyIYNG8x+rfDwcEpLS4mPjzf7uc+GVqvl4YcfZvTo0bi6uhISEsItt9xCSUlJj/1qamr47W9/i4eHB15eXtx22200NTWZnm9ra2Pp0qWMHj0atVrdq9DevHkzCoXilFtZWZmlX+Z5IYWsRCKRSCSDnI62VhqrKwFw8fBE7eiIXqejprSI9pZmG0fXO3l5eYwbN46NGzfy7LPPcuTIEdauXcucOXNYtmzZeZ1Tp9Oh1+tPebyjowOVSkVQUBBqtWUMn/7xj3+wdOnSXp9raWnh4MGD/PWvf+XgwYN8++23ZGRkcMUVV/TY77e//S2pqamsW7eOH3/8ka1bt3LnnXeantfpdDg7O3Pvvfcyf/78M8aTkZFBaWmp6RYQEHDBr9ESSCErkZgbrRZaW0HWmUkkkn6AXq+nqboaAGd3Dzz8A/AJDUPj4oJBb6CurNQuxew999yDQqFg7969LF68mOHDhzNq1CgeeOABdu/eDcALL7xgymKGh4dzzz339MhQfvDBB3h5ebF69WpGjhyJRqOhoKCAqKgonnjiCW655RY8PDy48847ey0tSE1N5Te/+Q0eHh64u7szY8YMsrOze4133759+Pv788wzz5zza/X09GTdunUsWbKE2NhYJk+ezGuvvcaBAwcoKCgAID09nbVr1/Luu+8yadIkpk+fzquvvsoXX3xhyty6urryxhtvcMcddxAUFHTGawYEBBAUFGS6KZX2KRntMyqJpD+i18NTT4GHB7i4QHQ0fP+9raOSSCSS02IwGGhrbECn60Tt6Ii7rx80N6NsbcPL3RMnhQKam6nLyUZbXQ3NzZa7ncOX/5qaGtauXcuyZctwdXU95XkvLy8AlEolr7zyCqmpqXz44Yds3LiRP/3pTz32bWlp4ZlnnuHdd98lNTXVlHl87rnnSExMJDk5mb/+9a+nXKO4uJiZM2ei0WjYuHEjBw4c4Pe//z2dvdQWb9y4kQULFvDUU0/x8MMP9/l1non6+noUCoXpte7atQsvLy/Gjx9v2mf+/PkolUr27NlzzudPSkoiODiYBQsWsGPHDrPEbAnkQASJxBx0dsL118M333Q/lp8PV10FV14Jr74KYWE2C08ikUh6I23LBjpd3FEoFHgGBKFsawM3NwAUgJc1g2lqgl5EaW9kZWVhMBiIi4s743733Xef6X5UVBRPPvkkd999N6+//rrpca1Wy+uvv05iYmKPY+fOncuDDz5o+jkvL6/H8ytXrsTT05MvvvgCBwcHAIYPH35KDN999x233HIL7777Ltddd12fXt/ZaGtr4+GHH+aGG27Aw8MDEPXCJy//q9VqfHx8zqm+NTg4mDfffJPx48fT3t7Ou+++y+zZs9mzZw9jx441S/zmRGZkJZILxWCAP/xBiFiNBt57D6qr4dFHQa0WWdmRI4WY7aX2SiKRSGxBWXYm+378DgBXL28cNBobR9R3+moRtn79eubNm0doaCju7u7cfPPNVFdX09LSYtrH0dGRhISEU449MbPZGykpKcyYMcMkYntjz549XHvttXz88ceniNht27bh5uZmuv3rX//i008/7fHYp59+eso5tVotS5YswWAw8MYbb5ztV3DOxMbGctdddzFu3DimTp3K+++/z9SpU3nxxRfNfi1zIDOyEsmF8sQT8O67oFTCF1+IDCzAv/4FN9wAd90Fu3bBvfdCejqsXAnSfFwikdiQjrZW1rz8Hww6HWqNBic3d/GEi4vIjJ5Ep1ZLTUkhBp0eF08vUYJgbs5hutiwYcNQKBQcO3bstPvk5eXxm9/8hj/84Q889dRT+Pj4sH37dm677TY6OjpM06icnZ17NfTvrWThRIyjWc/EkCFD8PX15f333+fyyy/vIXrHjx/fo972lVdeobi4uEcNbWBgYI/zGUVsfn4+GzduNGVjAYKCgqioqOixf2dnJzU1NWethz0bEydOZPv27Rd0DkshM7ISyYXw3nvw97+L+6+91i1ijYweDdu3wyuvCPH6xhvwwgtWD1MikUhOZPc3X1BXXoqLtzfObu7dQk6hEMv7J93UXl54REZjcHGhWduBVq3udb8Lup3DF3wfHx8uvvhiVq5cSXPzqY1odXV1HDhwAL1ez/PPP8/kyZMZPnz4KXZVF0JCQgLbtm0741hWPz8/Nm7cSFZWFkuWLOmxr7OzM0OHDjXdfHx8cHd37/GYu7u7aX+jiM3MzGT9+vX4+vr2uNaUKVNMr9vIxo0b0ev1TJo06YJea0pKCsHBwRd0DkshhaxEcr789BPVDz3A0agwMlfcQ8GMaZRlZ1KRl0N1UQENVRVoO9pFpnbFCnj+eXHcI49AZqZtY5dIJIOW6uJCDqxZBcCUxdej6GM3upOrG05d9bPNtTUWi6+vrFy5Ep1Ox8SJE/nmm2/IzMwkPT2dV155hSlTpjB06FC0Wi2vvvoqOTk5fPzxx7z55ptmu/7y5ctpaGjg+uuvZ//+/WRmZvLxxx+TkZHRY7+AgAA2btzIsWPHuOGGG3ptBjsbWq2Wa665hv379/Ppp5+i0+koKyujrKyMjo4OAEaMGMEll1zCHXfcwd69e9mxYwfLly/n+uuvJyQkxHSutLQ0UlJSqKmpob6+npSUlB6Z4ZdeeolVq1aRlZXF0aNHue+++9i4ceN5W5pZGllaIJGcB4YjR9j76EPsnD8NvVIJ5fnwxJ9P2U+hUDJ8ynSmX3czXvfdB+vWwc8/w8MPw7ffWj9wiUQyqDEYDGz64G30uk5ixk4gYlQiubm5fT7e1cuHtqYm2pqb0La327SuNiYmhoMHD/LUU0/x4IMPUlpair+/P+PGjeONN94gMTGRF154gWeeeYZHH32UmTNn8vTTT3PLLbeY5fq+vr5s3LiRP/7xj8yaNQuVSkVSUhLTpk07Zd+goCA2btzI7Nmz+e1vf8tnn32GSqXq87WKi4tZvXo1INwETmTTpk3Mnj0bgE8//ZTly5czb948lEolixcv5pVXXumx/2WXXUZ+fr7
"text/plain": [
"<Figure size 700x700 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 700x700 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 700x700 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 700x700 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 700x700 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 700x700 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 700x700 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 700x700 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 700x700 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 700x700 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 700x700 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 700x700 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 700x700 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 700x700 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 700x700 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 700x700 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 700x700 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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"text/plain": [
"<Figure size 700x700 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
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"source": [
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"ks = [i for i in range(50)]\n",
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"# ks = [0, 1, 2, 3, 4, 5, 6, ]\n",
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"\n",
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"for k in ks:\n",
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" fig, axs = plt.subplots(2, 1, figsize=(7, 7), sharex=True)\n",
" fig.subplots_adjust(wspace=0)\n",
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" cols = plt.rcParams['axes.prop_cycle'].by_key()['color']\n",
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"\n",
" # # CSiBORG2\n",
" # x = loaders_csiborg2X[0].rdist\n",
" # y = np.asarray([loaders_csiborg2[i].los_density[k, :] for i in range(len(loaders_csiborg2X))])\n",
" # ylow, ymed, yhigh = np.percentile(y, [16, 50, 84], axis=0)\n",
" # axs[0].fill_between(x, ylow, yhigh, color=cols[0], alpha=0.25)\n",
" # axs[0].plot(x, ymed, color=cols[0], label=\"CSiBORG2\")\n",
"\n",
" # y = np.asarray([loaders_csiborg2[i].los_radial_velocity[k, :] for i in range(len(loaders_csiborg2X))])\n",
" # ylow, ymed, yhigh = np.percentile(y, [16, 50, 84], axis=0)\n",
" # axs[1].fill_between(x, ylow, yhigh, color=cols[0], alpha=0.25)\n",
" # axs[1].plot(x, ymed, color=cols[0], label=\"CSiBORG2\")\n",
"\n",
" # # CSiBORG2X\n",
" # x = loaders_csiborg2X[0].rdist\n",
" # y = np.asarray([loaders_csiborg2X[i].los_density[k, :] for i in range(len(loaders_csiborg2X))])\n",
" # ylow, ymed, yhigh = np.percentile(y, [16, 50, 84], axis=0)\n",
" # axs[0].fill_between(x, ylow, yhigh, color=cols[1], alpha=0.25)\n",
" # axs[0].plot(x, ymed, color=cols[1], label=\"CSiBORG2X\")\n",
"\n",
" # y = np.asarray([loaders_csiborg2X[i].los_radial_velocity[k, :] for i in range(len(loaders_csiborg2X))])\n",
" # ylow, ymed, yhigh = np.percentile(y, [16, 50, 84], axis=0)\n",
" # axs[1].fill_between(x, ylow, yhigh, color=cols[1], alpha=0.25)\n",
" # axs[1].plot(x, ymed, color=cols[1], label=\"CSiBORG2X\")\n",
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"\n",
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" # Plot Carrick+2015\n",
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" axs[0].plot(loader_carrick.rdist, loader_carrick.los_density[0, k, :], color=\"red\", label=\"Carrick+2015\")\n",
" axs[1].plot(loader_carrick.rdist, loader_carrick.los_radial_velocity[0, k, :] * 0.43, color=\"red\")\n",
"\n",
" # Plot CF4\n",
" c = cols[4]\n",
" axs[0].plot(loader_CF4.rdist, loader_CF4.los_density[0, k, :], color=c, label=\"CF4\")\n",
" axs[1].plot(loader_CF4.rdist, loader_CF4.los_radial_velocity[0, k, :], color=c)\n",
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"\n",
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" # Plot Lilow2024\n",
" c = cols[5]\n",
" axs[0].plot(loader_lilow.rdist, loader_lilow.los_density[0, k, :], color=c, label=\"Lilow+2024\")\n",
" axs[1].plot(loader_lilow.rdist, loader_lilow.los_radial_velocity[0, k, :], color=c)\n",
"\n",
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"\n",
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" axs[1].set_xlabel(r\"$r ~ [\\mathrm{Mpc} / h]$\")\n",
" axs[0].set_ylabel(r\"$\\rho_{\\rm LOS} / \\langle \\rho_{\\rm matter} \\rangle$\")\n",
" axs[1].set_ylabel(r\"$v_{\\rm LOS} ~ [\\mathrm{km/s}]$\")\n",
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" axs[0].set_yscale(\"log\")\n",
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"\n",
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" axs[0].legend(loc=\"upper right\")\n",
" axs[0].set_xlim(0, 200)\n",
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"\n",
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" fig.tight_layout(w_pad=0, h_pad=0)\n",
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" fig.savefig(f\"../../plots/LOSS_los_{k}.png\", dpi=500, bbox_inches=\"tight\")\n",
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"\n",
" fig.show()"
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]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Test running a model"
]
},
{
"cell_type": "code",
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"execution_count": 19,
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"metadata": {},
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"2024-06-27 13:05:36.632317: reading the catalogue,\n",
"2024-06-27 13:05:36.639136: reading the interpolated field,\n",
"2024-06-27 13:05:36.646839: calculating the radial velocity.\n"
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]
}
],
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"source": [
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"fpath_data = \"/mnt/extraspace/rstiskalek/catalogs/PV_compilation.hdf5\"\n",
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"# fpath_data = \"/mnt/extraspace/rstiskalek/catalogs/A2.h5\"\n",
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"# fpath_data = \"/mnt/extraspace/rstiskalek/catalogs/PV_mock_CB2_17417_large.hdf5\"\n",
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"\n",
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"simname = \"CF4\"\n",
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"catalogue = \"LOSS\"\n",
"loader = csiborgtools.flow.DataLoader(simname, [0], catalogue, fpath_data, paths, ksmooth=0, )\n",
"\n",
"SN_hyperparams = {\"e_mu_mean\": 0.1, \"e_mu_std\": 0.05, \n",
" \"mag_cal_mean\": -18.25, \"mag_cal_std\": 0.5,\n",
" \"alpha_cal_mean\": 0.148, \"alpha_cal_std\": 0.05,\n",
" \"beta_cal_mean\": 3.112, \"beta_cal_std\": 1.0,\n",
" }\n",
"calibration_hyperparams = {\"Vext_std\": 250,\n",
" \"alpha_mean\": 1.0, \"alpha_std\": 0.5,\n",
" \"beta_mean\": 1.0, \"beta_std\": 0.5,\n",
" \"sigma_v_mean\": 150., \"sigma_v_std\": 100.,\n",
" \"sample_alpha\": True, \"sample_beta\": True,\n",
" }\n",
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"get_model_kwargs = {\"zcmb_max\": 0.05}"
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]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
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"#### Running HMC"
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]
},
{
"cell_type": "code",
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"execution_count": 20,
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"metadata": {},
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"Selected 50/50 galaxies.\n"
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]
}
],
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"source": [
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"model = csiborgtools.flow.get_model(loader, **get_model_kwargs)\n",
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"model_kwargs = {\"distmod_hyperparams\": SN_hyperparams, \"calibration_hyperparams\": calibration_hyperparams,}"
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]
},
{
"cell_type": "code",
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"execution_count": 21,
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"metadata": {},
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"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
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"warmup: 2%|▏ | 24/1000 [00:15<10:42, 1.52it/s, 1023 steps of size 1.33e-02. acc. prob=0.70]\n"
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]
},
{
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"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[21], line 5\u001b[0m\n\u001b[1;32m 2\u001b[0m mcmc \u001b[38;5;241m=\u001b[39m MCMC(kernel, num_warmup\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m500\u001b[39m, num_samples\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m500\u001b[39m)\n\u001b[1;32m 4\u001b[0m rng_key \u001b[38;5;241m=\u001b[39m jax\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39mPRNGKey(\u001b[38;5;241m5\u001b[39m)\n\u001b[0;32m----> 5\u001b[0m \u001b[43mmcmc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrng_key\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mextra_fields\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mpotential_energy\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m)\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[43mmodel_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 6\u001b[0m mcmc\u001b[38;5;241m.\u001b[39mprint_summary()\n\u001b[1;32m 7\u001b[0m samples \u001b[38;5;241m=\u001b[39m mcmc\u001b[38;5;241m.\u001b[39mget_samples()\n",
"File \u001b[0;32m~/csiborgtools/venv_csiborg/lib/python3.11/site-packages/numpyro/infer/mcmc.py:644\u001b[0m, in \u001b[0;36mMCMC.run\u001b[0;34m(self, rng_key, extra_fields, init_params, *args, **kwargs)\u001b[0m\n\u001b[1;32m 642\u001b[0m map_args \u001b[38;5;241m=\u001b[39m (rng_key, init_state, init_params)\n\u001b[1;32m 643\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnum_chains \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m--> 644\u001b[0m states_flat, last_state \u001b[38;5;241m=\u001b[39m \u001b[43mpartial_map_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmap_args\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 645\u001b[0m states \u001b[38;5;241m=\u001b[39m tree_map(\u001b[38;5;28;01mlambda\u001b[39;00m x: x[jnp\u001b[38;5;241m.\u001b[39mnewaxis, \u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m], states_flat)\n\u001b[1;32m 646\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
"File \u001b[0;32m~/csiborgtools/venv_csiborg/lib/python3.11/site-packages/numpyro/infer/mcmc.py:450\u001b[0m, in \u001b[0;36mMCMC._single_chain_mcmc\u001b[0;34m(self, init, args, kwargs, collect_fields)\u001b[0m\n\u001b[1;32m 444\u001b[0m collection_size \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_collection_params[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcollection_size\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 445\u001b[0m collection_size \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 446\u001b[0m collection_size\n\u001b[1;32m 447\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m collection_size \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 448\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m collection_size \u001b[38;5;241m/\u001b[39m\u001b[38;5;241m/\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mthinning\n\u001b[1;32m 449\u001b[0m )\n\u001b[0;32m--> 450\u001b[0m collect_vals \u001b[38;5;241m=\u001b[39m \u001b[43mfori_collect\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 451\u001b[0m \u001b[43m \u001b[49m\u001b[43mlower_idx\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 452\u001b[0m \u001b[43m \u001b[49m\u001b[43mupper_idx\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 453\u001b[0m \u001b[43m \u001b[49m\u001b[43msample_fn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 454\u001b[0m \u001b[43m \u001b[49m\u001b[43minit_val\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 455\u001b[0m \u001b[43m \u001b[49m\u001b[43mtransform\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_collect_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcollect_fields\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 456\u001b[0m \u001b[43m \u001b[49m\u001b[43mprogbar\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprogress_bar\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 457\u001b[0m \u001b[43m \u001b[49m\u001b[43mreturn_last_val\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 458\u001b[0m \u001b[43m \u001b[49m\u001b[43mthinning\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mthinning\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 459\u001b[0m \u001b[43m \u001b[49m\u001b[43mcollection_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcollection_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 460\u001b[0m \u001b[43m \u001b[49m\u001b[43mprogbar_desc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpartial\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_get_progbar_desc_str\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlower_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mphase\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 461\u001b[0m \u001b[43m \u001b[49m\u001b[43mdiagnostics_fn\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdiagnostics\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 462\u001b[0m \u001b[43m \u001b[49m\u001b[43mnum_chains\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnum_chains\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mchain_method\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mparallel\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 463\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 464\u001b[0m states, last_val \u001b[38;5;241m=\u001b[39m collect_vals\n\u001b[1;32m 465\u001b[0m \u001b[38;5;66;0
"File \u001b[0;32m~/csiborgtools/venv_csiborg/lib/python3.11/site-packages/numpyro/util.py:367\u001b[0m, in \u001b[0;36mfori_collect\u001b[0;34m(lower, upper, body_fun, init_val, transform, progbar, return_last_val, collection_size, thinning, **progbar_opts)\u001b[0m\n\u001b[1;32m 365\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m tqdm\u001b[38;5;241m.\u001b[39mtrange(upper) \u001b[38;5;28;01mas\u001b[39;00m t:\n\u001b[1;32m 366\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m t:\n\u001b[0;32m--> 367\u001b[0m vals \u001b[38;5;241m=\u001b[39m \u001b[43mjit\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_body_fn\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43mi\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvals\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 368\u001b[0m t\u001b[38;5;241m.\u001b[39mset_description(progbar_desc(i), refresh\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m 369\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m diagnostics_fn:\n",
"File \u001b[0;32m<string>:1\u001b[0m, in \u001b[0;36m<lambda>\u001b[0;34m(_cls, step_size, inverse_mass_matrix, mass_matrix_sqrt, mass_matrix_sqrt_inv, ss_state, mm_state, window_idx, rng_key)\u001b[0m\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
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]
}
],
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"source": [
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"kernel = NUTS(model, init_strategy=init_to_median(num_samples=100))\n",
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"mcmc = MCMC(kernel, num_warmup=500, num_samples=500)\n",
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"\n",
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"rng_key = jax.random.PRNGKey(5)\n",
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"mcmc.run(rng_key, extra_fields=(\"potential_energy\",), **model_kwargs)\n",
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"mcmc.print_summary()\n",
"samples = mcmc.get_samples()"
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]
},
{
2024-03-21 15:50:37 +00:00
"cell_type": "code",
2024-06-26 09:43:26 +00:00
"execution_count": 25,
2024-03-16 17:02:00 +00:00
"metadata": {},
2024-06-20 13:33:00 +00:00
"outputs": [],
2024-03-16 17:02:00 +00:00
"source": [
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"from numpyro.infer import log_likelihood"
2024-03-16 17:02:00 +00:00
]
},
{
"cell_type": "code",
2024-06-26 09:43:26 +00:00
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"ll_single = log_likelihood(model, samples, **model_kwargs)[\"ll\"]\n",
"ll_mult = log_likelihood(model_mult, samples, **model_kwargs, )[\"ll\"]"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
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" 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
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" 0., 0., 0., 0., 0., 0., 0.], dtype=float32)"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ll_single - ll_mult"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
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" -375.5034 , -374.6402 , -376.51093, -377.72595, -380.00995,\n",
" -379.5835 , -377.2257 , -379.3488 , -379.24078, -377.37265,\n",
" -381.14996, -376.83673, -376.86017, -376.98822, -376.59003,\n",
" -377.82474, -378.2244 , -376.83997, -375.96432, -374.3552 ,\n",
" -372.27533, -380.0143 , -374.36603, -373.62616, -373.85562,\n",
" -373.1689 , -374.1674 , -373.0071 , -373.59494, -376.51263,\n",
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" -376.40887, -376.2823 , -381.99615, -379.41455, -377.62433,\n",
" -376.36798, -378.97614, -375.00592, -377.91043, -377.10544,\n",
" -375.4801 , -376.89624, -374.7605 , -375.33405, -374.52933,\n",
" -375.02234, -375.1839 , -376.97693, -376.73566, -376.3647 ,\n",
" -377.85864, -376.54773, -377.09457, -379.05438, -378.02417,\n",
" -375.28592, -375.15805, -374.34442, -380.4338 , -376.1878 ,\n",
" -373.36633, -376.19815, -376.89926, -376.19022, -377.86075,\n",
" -377.2689 , -377.83676, -375.71466, -377.34888, -375.27258,\n",
" -376.75476, -377.30792, -377.51782, -379.70868, -376.17413,\n",
" -375.9779 , -377.96228, -374.87674, -373.77216, -375.6394 ,\n",
" -375.8968 , -373.49118, -373.32666, -374.5882 , -375.28967,\n",
" -375.78192, -377.6034 , -377.48282, -377.46722, -376.3749 ,\n",
" -376.20535, -376.85107, -376.78308, -376.67972, -376.1621 ,\n",
" -378.71033, -374.8225 , -377.3939 , -373.82043, -375.69586,\n",
" -377.2488 , -374.63022, -374.32742, -374.76212, -379.32126,\n",
" -379.09344, -375.42993, -378.1632 , -375.69092, -375.98285,\n",
" -377.10352, -376.98743, -374.9332 , -375.23105, -374.81265,\n",
" -374.3098 , -372.58917, -373.61246, -376.50525, -376.5714 ,\n",
" -374.68048, -375. , -375.02234, -377.66193, -379.3037 ,\n",
" -378.351 , -375.5417 , -378.686 , -376.46448, -377.3991 ,\n",
" -376.28412, -379.40198, -374.2234 , -372.92682, -376.41956], dtype=float32)"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 10,
2024-03-16 17:02:00 +00:00
"metadata": {},
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"outputs": [
{
"data": {
"text/plain": [
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"Array([391.9341 , 392.08212, 391.02206, 389.15616, 398.34534, 398.74045,\n",
" 394.00363, 394.9679 , 392.753 , 392.53403, 390.58237, 389.0428 ,\n",
" 390.0239 , 393.4534 , 392.26123, 390.88834, 389.4346 , 388.27728,\n",
" 388.42407, 390.7624 , 393.82834, 396.3672 , 393.64532, 392.78622,\n",
" 394.62543, 392.79187, 394.0692 , 394.19113, 393.68393, 393.9558 ,\n",
" 394.51782, 394.80338, 394.38104, 392.66638, 392.84058, 393.24478,\n",
" 394.0837 , 394.68433, 390.162 , 392.5648 , 390.56827, 394.7597 ,\n",
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" 389.4813 , 392.70554, 391.0798 , 391.58707, 391.3387 , 392.6452 ,\n",
" 391.1168 , 391.28464, 389.49527, 391.098 , 390.07748, 391.25574,\n",
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" 392.20618, 388.82373, 390.24686, 391.1798 , 390.0974 , 391.47324,\n",
" 396.02036, 393.31866, 394.73904, 393.04593, 396.20193, 392.5486 ,\n",
" 392.00214, 396.90887, 394.09827, 391.98636, 391.64633, 392.54788,\n",
" 390.28278, 391.5285 , 391.0113 , 390.20108, 391.2806 , 391.9383 ,\n",
" 394.31247, 392.9718 , 395.74432, 395.6111 , 396.32532, 396.84842,\n",
" 394.43015, 396.00247, 402.50272, 392.72025, 391.83334, 392.79337,\n",
" 396.3226 , 401.59814, 397.6963 , 395.19052, 394.43784, 392.80142,\n",
" 390.1531 , 389.4234 , 389.03558, 389.0597 , 392.2272 , 390.56104,\n",
" 394.64798, 391.2173 , 392.151 , 388.85483, 393.61578, 395.56732,\n",
" 396.60648, 393.86707, 392.8924 , 392.639 , 393.36166, 394.42212,\n",
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" 395.2757 , 394.46317, 396.52585, 393.2268 , 391.0806 , 390.68488,\n",
" 390.83286, 393.68234, 391.00925, 389.52612, 392.32867, 391.63733,\n",
" 392.44458, 397.25595, 392.09634, 394.46027, 392.6832 , 393.11966,\n",
" 392.70135, 392.94568, 390.5427 , 391.2181 , 390.14978, 393.05313,\n",
" 392.1329 , 395.2773 , 390.55872, 389.75958, 392.8294 , 390.34613,\n",
" 390.57422, 393.61807, 393.4588 , 392.6946 , 393.0386 , 390.5007 ,\n",
" 391.86188, 392.18567, 393.70074, 391.2875 , 389.60342, 390.3574 ,\n",
" 392.36462, 396.8469 , 395.1043 , 393.78467, 392.90707, 394.79532,\n",
" 392.64337, 392.04562, 391.07858, 393.60815, 399.3589 , 395.53397,\n",
" 392.84903, 392.6835 , 391.1862 , 391.29715, 393.18088, 396.14114,\n",
" 396.67612, 394.8662 , 395.70645, 393.47675, 396.14685, 395.2865 ,\n",
" 391.64557, 392.49634, 393.31058, 395.3479 , 390.1821 , 392.41928,\n",
" 391.62433, 394.0529 , 392.24847, 391.0419 , 391.29785, 392.43967,\n",
" 391.05963, 392.9988 , 390.8053 , 391.57315, 392.09854, 396.18353,\n",
" 393.75366, 393.8414 , 391.85266, 396.80035, 394.54794, 399.54883,\n",
" 393.41476, 390.52176, 392.8705 , 390.99615, 391.3205 , 392.70792,\n",
" 392.3243 , 389.38223, 392.36218, 392.93887, 393.47202, 391.486 ,\n",
" 390.32257, 390.8464 , 389.24442, 393.06158, 390.1328 , 394.20773,\n",
" 392.92813, 391.93857, 393.84244, 393.34424, 392.4594 , 392.7215 ,\n",
" 389.22064, 390.21176, 390.24298, 390.88086, 388.82172, 390.49158,\n",
" 390.16858, 392.40662, 393.3886 , 394.6822 , 393.8078 , 396.0063 ,\n",
" 393.62537, 392.98868, 395.08075, 394.6271 , 389.055 , 390.7642 ,\n",
" 390.36136, 394.7984 , 389.74716, 392.25842, 392.42697, 393.6167 ,\n",
" 390.31824, 391.90237, 399.44983, 393.19128, 393.52548, 392.53802,\n",
" 390.68805, 391.63797, 396.24985, 394.57785, 392.13458, 390.36664,\n",
" 390.1804 , 390.89145, 395.8922 , 396.92868, 390.8619 , 391.1419 ,\n",
" 392.4267 , 393.0737 , 397.35675, 398.8355 , 396.29037, 391.05008,\n",
" 389.4605 , 391.93326, 391.39075, 390.61874, 394.65973, 394.07922,\n",
" 394.02365, 397.24677, 397.92264, 392.71225, 393.26868, 392.57773,\n",
" 389.75055, 391.72717, 392.82794, 390.9369 , 392.5645 , 391.68805,\n",
" 393.5547 , 392.33005, 389.79407, 396.49945, 393.72034, 393.51117,\n",
" 392.1612 , 390.8545 , 390.19092, 389.54028, 390.081 , 393.38885,\n",
" 389.43814, 391.40814, 392.77875, 390.07477, 389.34094, 390.62073,\n",
" 391.42688, 396.5186 , 397.25574, 388.39703, 391.31366, 398.72787,\n",
" 391.05505, 392.2018 , 391.27768, 393.10678, 395.05096, 393.76315,\n",
" 392.66342, 392.51956, 390.97275, 390.50626, 393.93262, 391.706 ,\n",
" 390.8292 , 394.87894, 393.27704, 390.10495, 389.4763 , 388.9353 ,\n",
" 388.51953, 388.58496, 389.6656 , 394.77295, 391.35245, 391.1977 ,\n",
" 391.07584, 391.1299 , 390.0027 , 392.0007 , 392.77393, 392.88647,\n",
" 388.6409 , 392.9662 , 390.44272, 391.4916 , 389.46524, 392.48782,\n",
" 391.1928 , 391.6972 , 392.49786, 397.5695 , 391.7954 , 391.18668,\n",
" 394.03104, 391.3363 , 393.5302 , 391.74683, 389.46597, 389.0724 ,\n",
" 395.04776, 395.0067 , 394.68988, 392.55582, 391.55222, 391.8968 ,\n",
" 392.51648, 389.999 , 390.82315, 391.85864, 397.0075 , 393.12473,\n",
" 394.1504 , 388.86996, 390.43127, 393.28723, 394.8042 , 394.32358,\n",
" 394.70056, 394.62448, 393.04788, 391.87747, 392.00067, 390.53983,\n",
" 390.52933, 390.52118, 391.3734 , 389.67596, 391.80765, 391.9735 ,\n",
" 388.86786, 389.31982, 389.36093, 390.76718, 393.16095, 392.73416,\n",
" 392.00232, 391.61856, 394.8812 , 398.4053 , 398.8124 , 393.909 ,\n",
" 399.0349 , 393.04034, 390.39853, 390.08875, 389.0157 , 388.95517,\n",
" 392.23053, 390.5207 ], dtype=float32)"
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]
},
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"execution_count": 10,
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"metadata": {},
"output_type": "execute_result"
}
],
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"source": [
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"mcmc.get_extra_fields()[\"potential_energy\"]"
2024-03-26 09:42:53 +00:00
]
},
{
"cell_type": "code",
2024-06-20 13:33:00 +00:00
"execution_count": null,
2024-04-23 11:02:09 +00:00
"metadata": {},
"outputs": [],
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"source": []
},
{
"cell_type": "code",
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"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(774.734619140625, 755.6143798828125)"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"samples = mcmc.get_samples()\n",
"csiborgtools.numpyro_gof(model, mcmc)"
]
},
{
"cell_type": "code",
"execution_count": 117,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"\n",
"Reading LOSS fitted to Carrick2015 with ksmooth = 0.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"BIC = 773.225037 +- 0.000000\n",
"AIC = 754.104797 +- 0.000000\n",
"logZ = -356.240234 +- 0.000000\n",
"chi2 = 1.207006 +- 0.228673\n"
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]
}
],
2024-03-08 10:44:19 +00:00
"source": [
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"data, names, __ = read_samples(\"LOSS\", \"Carrick2015\", 0, return_MCsamples=False)"
]
},
{
"cell_type": "code",
"execution_count": 118,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"key = \"beta\"\n",
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"\n",
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"plt.figure()\n",
"plt.hist(data[:, names.index(key)], bins=\"auto\", density=1, histtype=\"step\")\n",
"plt.hist(samples[key], bins=\"auto\", density=1, histtype=\"step\")\n",
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"\n",
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"plt.show()"
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]
},
{
"cell_type": "code",
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"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {},
"outputs": [],
"source": [
"samples = mcmc.get_samples()"
]
},
{
"cell_type": "code",
"execution_count": 120,
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"metadata": {},
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"outputs": [
{
"data": {
"text/plain": [
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"Array([-369.51086, -369.97043, -370.17526, ..., -372.54755, -376.03503,\n",
" -374.88458], dtype=float32)"
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]
},
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"execution_count": 120,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
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"samples[\"ll_values\"]"
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]
},
{
"cell_type": "code",
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"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 121,
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"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
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"11"
]
},
"execution_count": 121,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nparam = 0\n",
"for val in samples.values():\n",
" if val.ndim == 1:\n",
" nparam += 1\n",
" elif val.ndim == 2:\n",
" nparam += val.shape[-1]\n",
" else:\n",
" raise ValueError(\"Invalid dimensionality of samples to count the number of parameters.\")\n",
" \n",
"\n",
"nparam\n",
" \n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 122,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"|V| = 197.8179931640625 +- 99.38513946533203\n",
"l = 213.2463176948003 +- 116.2995226818662\n",
"b = -5.31730133782022 +- 27.004291397137365\n",
"beta = 0.4450029134750366 +- 0.10768470168113708\n"
]
}
],
"source": [
"Vmag = np.sqrt(samples[\"Vext\"][:, 0]**2 + samples[\"Vext\"][:, 1]**2 + samples[\"Vext\"][:, 2]**2)\n",
"\n",
"V = np.vstack([samples[\"Vext\"][:, 0], samples[\"Vext\"][:, 1], samples[\"Vext\"][:, 2]]).T\n",
"V = csiborgtools.cartesian_to_radec(V)\n",
"\n",
"l, b = csiborgtools.radec_to_galactic(V[:, 1], V[:, 2])\n",
"\n",
"print(f\"|V| = {np.mean(Vmag)} +- {np.std(Vmag)}\")\n",
"print(f\"l = {np.mean(l)} +- {np.std(l)}\")\n",
"print(f\"b = {np.mean(b)} +- {np.std(b)}\")\n",
"if \"beta\" in samples:\n",
" print(f\"beta = {np.mean(samples['beta'])} +- {np.std(samples['beta'])}\")"
]
},
{
"cell_type": "code",
"execution_count": 123,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1390x1390 with 36 Axes>"
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]
},
"metadata": {},
"output_type": "display_data"
}
],
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"source": [
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"data = [l, b, Vmag]\n",
"labels = [r\"$l$\", r\"$b$\", r\"$|\\bf{V}_{\\rm ext}|$\"]\n",
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"if \"alpha\" in samples:\n",
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" data.append(samples[\"alpha\"])\n",
" labels.append(r\"$\\alpha$\")\n",
"\n",
"if \"beta\" in samples:\n",
" data.append(samples[\"beta\"])\n",
" labels.append(r\"$\\beta$\")\n",
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"\n",
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"if \"h\" in samples:\n",
" data.append(samples[\"h\"])\n",
" labels.append(r\"$h$\")\n",
"\n",
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"if \"sigma_v\" in samples:\n",
" data.append(samples[\"sigma_v\"])\n",
" labels.append(r\"$\\sigma_v$\")\n",
"\n",
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"data = np.vstack(data).T\n",
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"fig = corner.corner(data, labels=labels, show_titles=True, title_fmt=\".3f\", title_kwargs={\"fontsize\": 12}, smooth=1)\n",
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"fig.savefig(f\"../../plots/mock_{simname}_{catalogue}.png\", dpi=500, bbox_inches=\"tight\")"
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]
},
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{
"cell_type": "code",
"execution_count": 125,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Reading Pantheon+ fitted to csiborg2_main with ksmooth = 0.\n",
"BIC = 10055.604150 +- 27.237237\n",
"AIC = 10010.412744 +- 27.237237\n",
"logZ = -5000.136133 +- 23.062465\n",
"chi2 = 0.985968 +- 0.117400\n",
"Removed no burn in\n"
]
},
{
"data": {
"text/plain": [
"<getdist.mcsamples.MCSamples at 0x7f34904c5b90>"
]
},
"execution_count": 125,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"read_samples(\"Pantheon+\", \"csiborg2_main\", 0, return_MCsamples=True)"
]
},
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{
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"cell_type": "markdown",
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"metadata": {},
"source": [
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"## Vizualize the results"
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]
},
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{
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"data, names, gof = read_samples(\"Pantheon+_groups\", \"Carrick2015\", 0)\n",
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"\n",
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"fig = corner.corner(data, labels=names_to_latex(names, True), show_titles=True,\n",
" title_fmt=\".3f\", title_kwargs={\"fontsize\": 12}, smooth=1)"
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]
},
{
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"cell_type": "markdown",
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"metadata": {},
"source": [
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"### $\\texttt{LOSS}$ comparison"
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]
},
{
"cell_type": "code",
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"execution_count": 126,
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"metadata": {},
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Reading LOSS fitted to Carrick2015 with ksmooth = 0.\n",
"BIC = 773.225037 +- 0.000000\n",
"AIC = 754.104797 +- 0.000000\n",
"logZ = -356.240234 +- 0.000000\n",
"chi2 = 1.207006 +- 0.228673\n",
"Removed no burn in\n"
]
}
],
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"source": [
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"LOSS_Carrick_0 = read_samples(\"LOSS\", \"Carrick2015\", 0, return_MCsamples=True)\n",
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"# LOSS_Carrick_1 = read_samples(\"LOSS\", \"Carrick2015\", 1, return_MCsamples=True)\n",
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"\n",
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"# LOSS_CB1_0 = read_samples(\"LOSS\", \"csiborg1\", 0, return_MCsamples=True)\n",
"# LOSS_CB1_1 = read_samples(\"LOSS\", \"csiborg1\", 1, return_MCsamples=True)\n",
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"\n",
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"# LOSS_CB2_0 = read_samples(\"LOSS\", \"csiborg2_main\", 0, return_MCsamples=True)\n",
"# LOSS_CB2_1 = read_samples(\"LOSS\", \"csiborg2_main\", 1, return_MCsamples=True)"
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]
},
{
"cell_type": "code",
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"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X = [\n",
" LOSS_Carrick_0,\n",
" # LOSS_Carrick_1,\n",
" # LOSS_CB1_0,\n",
" LOSS_CB1_1,\n",
" LOSS_CB2_0,\n",
" LOSS_CB2_1,\n",
" ]\n",
"\n",
"# params = [\"l\", \"b\", \"Vmag\", \"beta\"]\n",
"params = None\n",
"\n",
"g = plots.get_subplot_plotter()\n",
"g.settings.figure_legend_frame = False\n",
"g.settings.alpha_filled_add = 0.75\n",
"# g.settings.title_limit_fontsize = 14\n",
"g.triangle_plot(X, params=params, filled=True, legend_loc='upper right', )\n",
"g.export(f\"../plots/LOSS_comparison.png\", dpi=500,)\n"
]
},
{
"cell_type": "markdown",
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"metadata": {},
"source": [
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"### $\\texttt{Foundation}$ comparison"
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]
},
{
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
"outputs": [],
"source": [
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"FOUNDATION_Carrick_0 = read_samples(\"Foundation\", \"Carrick2015\", 0, return_MCsamples=True)\n",
"FOUNDATION_Carrick_1 = read_samples(\"Foundation\", \"Carrick2015\", 1, return_MCsamples=True)\n",
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"\n",
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"FOUNDATION_CB1_0 = read_samples(\"Foundation\", \"csiborg1\", 0, return_MCsamples=True)\n",
"FOUNDATION_CB1_1 = read_samples(\"Foundation\", \"csiborg1\", 1, return_MCsamples=True)\n",
"\n",
"FOUNDATION_CB2_0 = read_samples(\"Foundation\", \"csiborg2_main\", 0, return_MCsamples=True)\n",
"FOUNDATION_CB2_1 = read_samples(\"Foundation\", \"csiborg2_main\", 1, return_MCsamples=True)"
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]
},
{
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"X = [\n",
" FOUNDATION_Carrick_0,\n",
" # FOUNDATION_Carrick_1,\n",
" # FOUNDATION_CB1_0,\n",
" FOUNDATION_CB1_1,\n",
" FOUNDATION_CB2_0,\n",
" FOUNDATION_CB2_1,\n",
" ]\n",
"\n",
"g = plots.get_subplot_plotter()\n",
"g.settings.figure_legend_frame = False\n",
"g.settings.alpha_filled_add = 0.75\n",
"# g.settings.title_limit_fontsize = 14\n",
"g.triangle_plot(X, filled=True, legend_loc='upper right')\n",
"g.export(f\"../plots/FOUNDATION_comparison.png\", dpi=500,)"
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]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
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"### $\\texttt{Pantheon+}$ comparison"
]
},
{
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"PANTHEONP_Carrick_0 = read_samples(\"Pantheon+\", \"Carrick2015\", 0, return_MCsamples=True)\n",
"PANTHEONP_Carrick_1 = read_samples(\"Pantheon+\", \"Carrick2015\", 1, return_MCsamples=True)\n",
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"\n",
"# PANTHEONP_CB1_0 = read_samples(\"Pantheon+\", \"csiborg1\", 0, return_MCsamples=True)\n",
"# PANTHEONP_CB1_1 = read_samples(\"Pantheon+\", \"csiborg1\", 1, return_MCsamples=True)\n",
"\n",
"PANTHEONP_CB2_0 = read_samples(\"Pantheon+\", \"csiborg2_main\", 0, return_MCsamples=True)\n",
"PANTHEONP_CB2_1 = read_samples(\"Pantheon+\", \"csiborg2_main\", 1, return_MCsamples=True)\n"
]
},
{
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
"X = [\n",
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" PANTHEONP_Carrick_0,\n",
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" # PANTHEONP_Carrick_1,\n",
" # PANTHEONP_CB1_0,\n",
" # PANTHEONP_CB1_1,\n",
" PANTHEONP_CB2_0,\n",
" PANTHEONP_CB2_1,\n",
" ]\n",
"\n",
"g = plots.get_subplot_plotter()\n",
"g.settings.figure_legend_frame = False\n",
"g.settings.alpha_filled_add = 0.75\n",
"# g.settings.title_limit_fontsize = 14\n",
"g.triangle_plot(X, filled=True, legend_loc='upper right')\n",
"# g.export(f\"../plots/PANTHEONP_comparison.png\", dpi=500,)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### $\\texttt{Pantheon+}$ groups"
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]
},
{
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"LG = -1\n",
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"\n",
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"PANTHEONP_Carrick = read_samples(\"Pantheon+\", \"Carrick2015\", 0, return_MCsamples=True, subtract_LG_velocity=LG, )\n",
"PANTHEONP_Carrick_Groups = read_samples(\"Pantheon+_groups\", \"Carrick2015\", 0, return_MCsamples=True, subtract_LG_velocity=LG)\n",
"PANTHEONP_Carrick_Groups_zSN = read_samples(\"Pantheon+_groups_zSN\", \"Carrick2015\", 0, return_MCsamples=True, subtract_LG_velocity=LG)\n",
"PANTHEONP_Carrick_zSN = read_samples(\"Pantheon+_zSN\", \"Carrick2015\", 0, return_MCsamples=True, subtract_LG_velocity=LG)\n",
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"\n",
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"# ksmooth = 1\n",
"# PANTHEONP_CB2 = read_samples(\"Pantheon+\", \"csiborg2_main\", ksmooth, return_MCsamples=True, subtract_LG_velocity=LG)\n",
"# PANTHEONP_CB2_Groups = read_samples(\"Pantheon+_groups\", \"csiborg2_main\", ksmooth, return_MCsamples=True, subtract_LG_velocity=LG)\n",
"# PANTHEONP_CB2_Groups_zSN = read_samples(\"Pantheon+_groups_zSN\", \"csiborg2_main\", ksmooth, return_MCsamples=True, subtract_LG_velocity=LG)\n",
"\n",
"\n"
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]
},
{
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"params = [\"Vmag\", \"l\", \"b\"]\n",
"CMB = MCSamples(samples=multivariate_normal([627, 276, 30], [22, 3, 3]).rvs(size=20000),\n",
" names=params, labels=names_to_latex(params, True), label=\"CMB\")\n",
"\n",
"\n",
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"X = [\n",
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" PANTHEONP_Carrick,\n",
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" # PANTHEONP_Carrick_Groups,\n",
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" # PANTHEONP_Carrick_Groups_zSN,\n",
" PANTHEONP_Carrick_zSN,\n",
" # PANTHEONP_CB2,\n",
" # PANTHEONP_CB2_Groups,\n",
" # PANTHEONP_CB2_Groups_zSN,\n",
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" # CMB,\n",
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" ]\n",
"\n",
"g = plots.get_subplot_plotter()\n",
"g.settings.figure_legend_frame = False\n",
"g.settings.alpha_filled_add = 0.75\n",
"# g.settings.title_limit_fontsize = 14\n",
"g.triangle_plot(X, filled=True, legend_loc='upper right')\n",
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"g.export(f\"../../plots/PANTHEON_GROUPS_Carrick_comparison_LG.png\", dpi=500,)"
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]
},
{
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"cell_type": "markdown",
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"metadata": {},
"source": [
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"### $\\texttt{2MTF}$ comparison"
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]
},
{
"cell_type": "code",
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"execution_count": 24,
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"metadata": {},
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Reading 2MTF fitted to Carrick2015 with ksmooth = 0.\n",
"BIC = 19517.031250 +- 0.000000\n",
"AIC = 19470.867188 +- 0.000000\n",
"logZ = -9731.227539 +- 0.000000\n",
"chi2 = 0.000000 +- 0.000000\n",
"Removed no burn in\n",
"\n",
"Reading 2MTF fitted to Carrick2015 with ksmooth = 1.\n",
"BIC = 19632.685547 +- 0.000000\n",
"AIC = 19586.521484 +- 0.000000\n",
"logZ = -9788.892578 +- 0.000000\n",
"chi2 = 0.000000 +- 0.000000\n",
"Removed no burn in\n",
"\n",
"Reading 2MTF fitted to csiborg1 with ksmooth = 0.\n",
"BIC = 19922.607596 +- 33.988735\n",
"AIC = 19876.443533 +- 33.988735\n",
"logZ = -9934.180538 +- 17.010780\n",
"chi2 = 0.000000 +- 0.000000\n",
"Removed no burn in\n",
"\n",
"Reading 2MTF fitted to csiborg1 with ksmooth = 1.\n",
"BIC = 19840.144473 +- 31.749545\n",
"AIC = 19793.980411 +- 31.749545\n",
"logZ = -9891.951984 +- 16.078607\n",
"chi2 = 0.000000 +- 0.000000\n",
"Removed no burn in\n",
"\n",
"Reading 2MTF fitted to csiborg2_main with ksmooth = 0.\n",
"BIC = 19248.799609 +- 38.583873\n",
"AIC = 19202.635547 +- 38.583873\n",
"logZ = -9598.394336 +- 19.251815\n",
"chi2 = 0.000000 +- 0.000000\n",
"Removed no burn in\n",
"\n",
"Reading 2MTF fitted to csiborg2_main with ksmooth = 1.\n",
"BIC = 19167.596582 +- 20.190445\n",
"AIC = 19121.432520 +- 20.190445\n",
"logZ = -9555.558252 +- 9.820362\n",
"chi2 = 0.000000 +- 0.000000\n",
"Removed no burn in\n"
]
}
],
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"source": [
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"TWOMTF_Carrick_0 = read_samples(\"2MTF\", \"Carrick2015\", 0, return_MCsamples=True)\n",
"TWOMTF_Carrick_1 = read_samples(\"2MTF\", \"Carrick2015\", 1, return_MCsamples=True)\n",
"\n",
"TWOMTF_CB1_0 = read_samples(\"2MTF\", \"csiborg1\", 0, return_MCsamples=True)\n",
"TWOMTF_CB1_1 = read_samples(\"2MTF\", \"csiborg1\", 1, return_MCsamples=True)\n",
"\n",
"TWOMTF_CB2_0 = read_samples(\"2MTF\", \"csiborg2_main\", 0, return_MCsamples=True)\n",
"TWOMTF_CB2_1 = read_samples(\"2MTF\", \"csiborg2_main\", 1, return_MCsamples=True)\n"
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]
},
{
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"X = [\n",
" TWOMTF_Carrick_0,\n",
" # TWOMTF_Carrick_1,\n",
" # TWOMTF_CB1_0,\n",
" TWOMTF_CB1_1,\n",
" TWOMTF_CB2_0,\n",
" TWOMTF_CB2_1,\n",
" ]\n",
"\n",
"g = plots.get_subplot_plotter()\n",
"g.settings.figure_legend_frame = False\n",
"g.settings.alpha_filled_add = 0.75\n",
"# g.settings.title_limit_fontsize = 14\n",
"g.triangle_plot(X, filled=True, legend_loc='upper right')\n",
"g.export(f\"../plots/2MTF_comparison.png\", dpi=500,)"
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]
},
{
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"cell_type": "markdown",
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"metadata": {},
"source": [
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"### $\\texttt{SFI++ galaxies}$ comparison"
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]
},
{
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"SFIGAL_Carrick_0 = read_samples(\"SFI_gals\", \"Carrick2015\", 0, return_MCsamples=True)\n",
"SFIGAL_Carrick_1 = read_samples(\"SFI_gals\", \"Carrick2015\", 1, return_MCsamples=True)\n",
"\n",
"# SFIGAL_CB1_0 = read_samples(\"SFI_gals\", \"csiborg1\", 0, return_MCsamples=True)\n",
"# SFIGAL_CB1_1 = read_samples(\"SFI_gals\", \"csiborg1\", 1, return_MCsamples=True)\n",
"\n",
"SFIGAL_CB2_0 = read_samples(\"SFI_gals\", \"csiborg2_main\", 0, return_MCsamples=True)\n",
"SFIGAL_CB2_1 = read_samples(\"SFI_gals\", \"csiborg2_main\", 1, return_MCsamples=True)"
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]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
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"X = [\n",
" SFIGAL_Carrick_0,\n",
" # SFIGAL_Carrick_1,\n",
" # SFIGAL_CB1_0,\n",
" # SFIGAL_CB1_1,\n",
" # SFIGAL_CB2_0,\n",
" SFIGAL_CB2_1,\n",
" ]\n",
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"\n",
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"g = plots.get_subplot_plotter()\n",
"g.settings.figure_legend_frame = False\n",
"g.settings.alpha_filled_add = 0.75\n",
"# g.settings.title_limit_fontsize = 14\n",
"g.triangle_plot(X, filled=True, legend_loc='upper right')\n",
"g.export(f\"../plots/SFI_gals_comparison.png\", dpi=500,)"
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]
},
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{
"cell_type": "markdown",
"metadata": {},
"source": [
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"### $\\texttt{SFI++ groups}$ comparison"
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]
},
{
"cell_type": "code",
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"execution_count": null,
2024-03-26 14:11:04 +00:00
"metadata": {},
2024-04-23 11:02:09 +00:00
"outputs": [],
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"source": [
"SFIGROUP_Carrick_0 = read_samples(\"SFI_groups\", \"Carrick2015\", 0, return_MCsamples=True)\n",
"SFIGROUP_Carrick_1 = read_samples(\"SFI_groups\", \"Carrick2015\", 1, return_MCsamples=True)\n",
"\n",
"SFIGROUP_CB2_0 = read_samples(\"SFI_groups\", \"csiborg2_main\", 0, return_MCsamples=True)\n",
"SFIGROUP_CB2_1 = read_samples(\"SFI_groups\", \"csiborg2_main\", 1, return_MCsamples=True)"
]
},
{
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
2024-04-23 11:02:09 +00:00
"outputs": [],
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"source": [
"X = [\n",
" SFIGROUP_Carrick_0,\n",
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" SFIGAL_Carrick_0,\n",
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" # SFIGROUP_Carrick_1,\n",
" # SFIGROUP_CB2_0,\n",
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" # SFIGROUP_CB2_1,\n",
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" ]\n",
"\n",
"g = plots.get_subplot_plotter()\n",
"g.settings.figure_legend_frame = False\n",
"g.settings.alpha_filled_add = 0.75\n",
"# g.settings.title_limit_fontsize = 14\n",
"g.triangle_plot(X, filled=True, legend_loc='upper right')\n",
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"g.export(f\"../plots/SFI_gals_vs_groups_comparison.png\", dpi=500,)"
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]
},
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{
"cell_type": "markdown",
"metadata": {},
"source": [
2024-04-23 11:02:09 +00:00
"### SN to TF comparison"
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]
},
{
"cell_type": "code",
2024-06-20 13:33:00 +00:00
"execution_count": null,
2024-04-01 22:19:14 +00:00
"metadata": {},
2024-06-20 13:33:00 +00:00
"outputs": [],
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"source": [
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"LG = 0\n",
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"\n",
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"# PANTHEONP_Carrick = read_samples(\"Pantheon+\", \"Carrick2015\", 0, return_MCsamples=True, subtract_LG_velocity=LG, )\n",
"# PANTHEONP_Groups_Carrick = read_samples(\"Pantheon+_groups\", \"Carrick2015\", 0, return_MCsamples=True, subtract_LG_velocity=LG, )\n",
"# TWOMTF_Carrick = read_samples(\"2MTF\", \"Carrick2015\", 0, return_MCsamples=True, subtract_LG_velocity=LG)\n",
"# SFIGAL_Carrick = read_samples(\"SFI_gals\", \"Carrick2015\", 0, return_MCsamples=True, subtract_LG_velocity=LG)\n",
"\n",
"k = 1\n",
"PANTHEONP_CB2 = read_samples(\"Pantheon+\", \"csiborg2_main\", k, return_MCsamples=True, subtract_LG_velocity=LG, )\n",
"PANTHEONP_Groups_CB2 = read_samples(\"Pantheon+_groups\", \"csiborg2_main\", k, return_MCsamples=True, subtract_LG_velocity=LG, )\n",
"TWOMTF_CB2 = read_samples(\"2MTF\", \"csiborg2_main\", k, return_MCsamples=True, subtract_LG_velocity=LG)\n",
"SFIGAL_CB2 = read_samples(\"SFI_gals\", \"csiborg2_main\", k, return_MCsamples=True, subtract_LG_velocity=LG)"
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]
},
{
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
"\n",
"params = [\"Vmag\", \"l\", \"b\"]\n",
"CMB = MCSamples(samples=multivariate_normal([627, 276, 30], [22, 3, 3]).rvs(size=20000),\n",
" names=params, labels=names_to_latex(params, True), label=\"CMB\")\n",
"\n",
"\n",
"X = [\n",
" # PANTHEONP_Carrick,\n",
" # PANTHEONP_Groups_Carrick,\n",
" # TWOMTF_Carrick,\n",
" # SFIGAL_Carrick,\n",
" PANTHEONP_CB2,\n",
" PANTHEONP_Groups_CB2,\n",
" TWOMTF_CB2,\n",
" SFIGAL_CB2,\n",
" CMB,\n",
" ]\n",
"\n",
"g = plots.get_subplot_plotter()\n",
"g.settings.figure_legend_frame = False\n",
"g.settings.alpha_filled_add = 0.75\n",
"# g.settings.title_limit_fontsize = 14\n",
"g.triangle_plot(X, filled=True, legend_loc='upper right')\n",
"# g.export(f\"../../plots/SN_TF_CB2_consistency.png\", dpi=500,)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Mock $\\texttt{CB2}$ comparison"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"SMALLMOCK_CB2_0 = read_samples(\"CB2_small\", \"csiborg2_main\", 0, return_MCsamples=True)\n",
"SMALLMOCK_CB2_1 = read_samples(\"CB2_small\", \"csiborg2_main\", 1, return_MCsamples=True)\n",
"\n",
"LARGEMOCK_CB2_0 = read_samples(\"CB2_large\", \"csiborg2_main\", 0, return_MCsamples=True)\n",
"LARGEMOCK_CB2_1 = read_samples(\"CB2_large\", \"csiborg2_main\", 1, return_MCsamples=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
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"source": [
"X = [\n",
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" # SMALLMOCK_CB2_0,\n",
" # SMALLMOCK_CB2_1,\n",
" LARGEMOCK_CB2_0,\n",
" LARGEMOCK_CB2_1,\n",
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" ]\n",
"\n",
"g = plots.get_subplot_plotter()\n",
"g.settings.figure_legend_frame = False\n",
"g.settings.alpha_filled_add = 0.75\n",
"# g.settings.title_limit_fontsize = 14\n",
"g.triangle_plot(X, filled=True, legend_loc='upper right')\n",
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"g.export(f\"../plots/CB2_mocks_large.png\", dpi=500,)"
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]
},
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{
"cell_type": "markdown",
"metadata": {},
"source": [
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"## External flow consistency"
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]
},
{
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"cell_type": "markdown",
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"metadata": {},
"source": [
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"#### Carrick2015"
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]
},
{
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
"outputs": [],
"source": [
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"X = [\n",
" # LOSS_Carrick_0,\n",
" # FOUNDATION_Carrick_0,\n",
" PANTHEONP_Carrick_0,\n",
" TWOMTF_Carrick_0,\n",
" SFIGAL_Carrick_0,\n",
" ]\n",
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"\n",
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"params = [\"Vmag\", \"l\", \"b\", \"beta\"]\n",
"g = plots.get_subplot_plotter()\n",
"g.settings.figure_legend_frame = False\n",
"g.settings.alpha_filled_add = 0.75\n",
"# g.settings.title_limit_fontsize = 14\n",
"g.triangle_plot(X, params=params, filled=True, legend_loc='upper right',)\n",
"g.export(f\"../plots/Carrick2015_external_flow.png\", dpi=500,)"
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]
},
{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### CSiBORG1"
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]
},
{
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"X = [\n",
" # LOSS_CB1_1,\n",
" # FOUNDATION_CB1_1,\n",
" PANTHEONP_CB1_1,\n",
" TWOMTF_CB1_1,\n",
" # SFIGAL_CB1_1,\n",
" ]\n",
"\n",
"params = [\"Vmag\", \"l\", \"b\", \"beta\"]\n",
"g = plots.get_subplot_plotter()\n",
"g.settings.figure_legend_frame = False\n",
"g.settings.alpha_filled_add = 0.75\n",
"# g.settings.title_limit_fontsize = 14\n",
"g.triangle_plot(X, params=params, filled=True, legend_loc='upper right',)\n",
"g.export(f\"../plots/CB1_external_flow.png\", dpi=500,)"
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]
},
{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### CSiBORG2"
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]
},
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{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
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"source": [
"X = [\n",
" # LOSS_CB2_1,\n",
" # FOUNDATION_CB2_1,\n",
" PANTHEONP_CB2_1,\n",
" TWOMTF_CB2_1,\n",
" SFIGAL_CB2_1,\n",
" ]\n",
"\n",
"params = [\"Vmag\", \"l\", \"b\", \"beta\"]\n",
"g = plots.get_subplot_plotter()\n",
"g.settings.figure_legend_frame = False\n",
"g.settings.alpha_filled_add = 0.75\n",
"# g.settings.title_limit_fontsize = 14\n",
"g.triangle_plot(X, params=params, filled=True, legend_loc='upper right',)\n",
"g.export(f\"../plots/CB2_external_flow.png\", dpi=500,)"
]
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},
{
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"k = 1\n",
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"LG = 0\n",
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"\n",
"# Carrick\n",
"# LOSS_Carrick_LG = read_samples(\"LOSS\", \"Carrick2015\", k, return_MCsamples=True, subtract_LG_velocity=LG)\n",
"# FOUNDATION_Carrick_LG = read_samples(\"Foundation\", \"Carrick2015\", k, return_MCsamples=True, subtract_LG_velocity=LG)\n",
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"# PANTHEON_Carrick_LG = read_samples(\"Pantheon+\", \"Carrick2015\", k, return_MCsamples=True, subtract_LG_velocity=LG)\n",
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"# TWOMTF_Carrick_LG = read_samples(\"2MTF\", \"Carrick2015\", k, return_MCsamples=True, subtract_LG_velocity=LG)\n",
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"SFIGAL_Carrick_LG = read_samples(\"SFI_gals\", \"Carrick2015\", k, return_MCsamples=True, subtract_LG_velocity=LG)\n",
"SFIGROUPS_Carrick_LG = read_samples(\"SFI_groups\", \"Carrick2015\", k, return_MCsamples=True, subtract_LG_velocity=LG)\n",
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"\n",
"\n",
"# # CSiBORG2\n",
"# LOSS_CB2_LG = read_samples(\"LOSS\", \"csiborg2_main\", k, return_MCsamples=True,subtract_LG_velocity=LG)\n",
"# FOUNDATION_CB2_LG = read_samples(\"Foundation\", \"csiborg2_main\", k, return_MCsamples=True, subtract_LG_velocity=LG)\n",
"# PANTHEON_CB2_LG = read_samples(\"Pantheon+\", \"csiborg2_main\", k, return_MCsamples=True, subtract_LG_velocity=LG)\n",
"# TWOMTF_CB2_LG = read_samples(\"2MTF\", \"csiborg2_main\", k, return_MCsamples=True, subtract_LG_velocity=LG)\n",
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"SFIGAL_CB2_LG = read_samples(\"SFI_gals\", \"csiborg2_main\", k, return_MCsamples=True, subtract_LG_velocity=LG)\n",
"SFIGROUP_CB2_LG = read_samples(\"SFI_groups\", \"csiborg2_main\", k, return_MCsamples=True, subtract_LG_velocity=LG)\n",
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"\n",
"# # CSiBORG1\n",
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"# LOSS_CB1_LG = read_samples(\"LOSS\", \"csiborg1\", k, return_MCsamples=True, subtract_LG_velocity=LG)\n",
"# FOUNDATION_CB1_LG = read_samples(\"Foundation\", \"csiborg1\", k, return_MCsamples=True, subtract_LG_velocity=LG)\n",
"# PANTHEON_CB1_LG = read_samples(\"Pantheon+\", \"csiborg1\", k, return_MCsamples=True, subtract_LG_velocity=LG)\n",
"# TWOMTF_CB1_LG = read_samples(\"2MTF\", \"csiborg1\", k, return_MCsamples=True, subtract_LG_velocity=LG)\n",
"# SFIGAL_CB1_LG = read_samples(\"SFI_gals\", \"csiborg1\", k, return_MCsamples=True, subtract_LG_velocity=LG)"
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]
},
{
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"params = [\"Vmag\", \"l\", \"b\"]\n",
"CMB = MCSamples(samples=multivariate_normal([627, 276, 30], [22, 3, 3]).rvs(size=20000),\n",
" names=params, labels=names_to_latex(params, True), label=\"CMB\")\n",
"\n",
"X = [\n",
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" # LOSS_Carrick_LG,\n",
" # FOUNDATION_Carrick_LG,\n",
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" # PANTHEON_Carrick_LG,\n",
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" # TWOMTF_Carrick_LG,\n",
" # SFIGAL_Carrick_LG,\n",
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" # SFIGROUPS_Carrick_LG,\n",
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" # LOSS_CB1_LG,\n",
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" # FOUNDATION_CB1_LG,\n",
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" # PANTHEON_CB1_LG,\n",
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" # TWOMTF_CB1_LG,\n",
" # SFIGAL_CB1_LG,\n",
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" # LOSS_CB2_LG,\n",
" # FOUNDATION_CB2_LG,\n",
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" # PANTHEON_CB2_LG,\n",
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" # TWOMTF_CB2_LG,\n",
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" SFIGAL_CB2_LG,\n",
" SFIGROUP_CB2_LG,\n",
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" CMB,\n",
" ]\n",
"\n",
"g = plots.get_subplot_plotter()\n",
"g.settings.figure_legend_frame = False\n",
"g.settings.alpha_filled_add = 0.75\n",
"# g.settings.title_limit_fontsize = 14\n",
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"g.triangle_plot(X, params=params, filled=True, legend_loc='upper right', )\n",
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"# g.export(f\"../plots/ALL_dipole.png\", dpi=500,)"
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]
},
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{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "venv_csiborg",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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"version": "3.11.4"
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}
},
"nbformat": 4,
"nbformat_minor": 2
}
