mirror of
https://github.com/Richard-Sti/csiborgtools.git
synced 2024-12-23 03:18:03 +00:00
224 lines
425 KiB
Text
224 lines
425 KiB
Text
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{
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"cells": [
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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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"# Tests of velocities of haloes in CSiBORG"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import csiborgtools\n",
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"\n",
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"%matplotlib inline\n",
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"%load_ext autoreload\n",
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"%autoreload 2"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## FoF vs SPH velocity"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"kind = \"main\"\n",
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"nsnap = 99\n",
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"nsim = 17417\n",
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"\n",
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"field_reader = csiborgtools.read.CSiBORG2Field(nsim, kind)\n",
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"catalogue = csiborgtools.read.CSiBORG2Catalogue(nsim, nsnap, kind)\n",
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"boxsize = csiborgtools.simname2boxsize(\"csiborg2_main\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"velocity_field = field_reader.velocity_field(\"SPH\", 1024)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"pos = catalogue[\"cartesian_pos\"] / boxsize\n",
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"vel = catalogue[\"cartesian_vel\"]\n",
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"mass = catalogue[\"totmass\"]\n",
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"\n",
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"spherical_pos = catalogue[\"spherical_pos\"]\n",
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"RA = np.deg2rad(spherical_pos[:, 1])\n",
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"dec = np.deg2rad(spherical_pos[:, 2])\n",
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"\n",
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"def project_radial(vx, vy, vz, RA, dec):\n",
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" vr = vx * np.cos(dec) * np.cos(RA) + vy * np.cos(dec) * np.sin(RA) + vz * np.sin(dec)\n",
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" return vr"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 36,
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"metadata": {},
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"outputs": [],
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"source": [
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"vx, vy, vz = csiborgtools.field.evaluate_cartesian_cic(velocity_field[0], velocity_field[1], velocity_field[2], pos=pos)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 43,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAABdEAAAGGCAYAAACUkchWAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/H5lhTAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdeZxP1f/A8de597PMvmEwdhUhtEuLhCxt2n9KKyVCiUpalDZRKUpJfYUirVQqElkqJCUhsu9jxuzLZ733/P64M5+ZO5+xG2M5z8djfr+Z8zn3fu7n+nbu/bzvOe+3kFJKFEVRFEVRFEVRFEVRFEVRFEUJo1X2ASiKoiiKoiiKoiiKoiiKoijK8UoF0RVFURRFURRFURRFURRFURRlH1QQXVEURVEURVEURVEURVEURVH2QQXRFUVRFEVRFEVRFEVRFEVRFGUfVBBdURRFURRFURRFURRFURRFUfZBBdEVRVEURVEURVEURVEURVEUZR9UEF1RFEVRFEVRFEVRFEVRFEVR9kEF0RVFURRFURRFURRFURRFURRlHxyVfQAnM9M02bVrF7GxsQghKvtwFEU5wUkpycvLIyUlBU1Tz0CPJTWeK4pyNKnxvPKo8VxRlKNFjeWVS43niqIcLQc7nqsgegXatWsXderUqezDUBTlJLN9+3Zq165d2YdxSlHjuaIoFUGN58eeGs8VRTna1FheOdR4rijK0Xag8VwF0StQbGwsYP0jxMXFVfLRKIpyzCxaBNdcA7fcAuPGgePoDLW5ubnUqVMnNLYox44azxXlBOHzwRVXwOrV0LkzTJsGx+HsNDWeVx41nivKCUBK+PRTuOEGcLsr+2j2SY3llUuN54pyEnvxRXj1VSuu8vHHFX4/f7DjuQqiV6DiJUVxcXFqUFeUU0VWFvTubf0eFwdJSUf9LdRyxWNPjeeKcoJ48kkrgF61KkycCPHxlX1E+6XG82NPjeeKcgKYOhUeeADeew+WLj1qE1IqihrLK4cazxXlJDZyJJx/Plx55TG9nz/QeK4SdymKohwtUlo3/Dt2wOmnw5tvVvYRHdeGDx/OBRdcQGxsLMnJyVx//fWsW7fO1sfr9dK3b1+qVKlCTEwMN910E3v27LH12bZtG1dffTVRUVEkJyfz2GOPEQwGbX3mz5/Pueeei9vt5vTTT2fixIkV/fEURTnWDAP+/NP6fdw4qF69co9HURRFOXRpafDQQ9bvN9xw3AfQFUVRlApy662QmFjZR2GjguiKoihHy+TJ8Pnn1s3+1KkQE1PZR3RcW7BgAX379mXJkiXMmTOHQCBAx44dKSgoCPV55JFH+Pbbb/n8889ZsGABu3bt4sYbbwy9bhgGV199NX6/n99++41JkyYxceJEhg4dGuqzefNmrr76aq644gpWrFjBgAEDuO+++5g9e/Yx/byKolQwXYfvv4fZs+Gmmyr7aBRFUZTD0b8/ZGRAy5YweHBlH42iKIpyrKxda6XETU+v7CPZJyGllJV9ECer3Nxc4uPjycnJUcuLFOVkt3EjnH025OfDSy9ZKQWOspN9TElPTyc5OZkFCxbQpk0bcnJyqFatGlOnTuXmm28GYO3atTRp0oTFixdz0UUX8cMPP3DNNdewa9cuqhfNOh03bhyDBw8mPT0dl8vF4MGD+e6771i1alXovbp160Z2djazZs06qGM72c+9oijHlhpTKo8694pyHJs+HW680Xoo+vvvcO65lX1E+6XGk8qlzr+inES8XmjVClauhNtusyYlHkMHO56omeiKoihHSkq4804rgH7ZZWrWzGHKyckBIKkoj/zy5csJBAJ06NAh1OfMM8+kbt26LF68GIDFixfTvHnzUAAdoFOnTuTm5rJ69epQn9L7KO5TvA9FUU5ws2dDr16Ql1fZR6IoiqIcrsxMePBB6/fBg4/7ALqiKIpyFD36qBVAr1YNXn+9so9mn1SCMUVRlCMlBDz3HAwYAB99ZM2eUQ6JaZoMGDCASy65hLPOOguA1NRUXC4XCQkJtr7Vq1cnNTU11Kd6mbzHxX8fqE9ubi4ej4fIyMiw4/H5fPh8vtDfubm5R/YBFUWpGFlZ0KMH7NoFNWvCsGGVfUSKoijK4XjiCUhNhTPPhGeeqeyjURRFUY6V6dNh7Fjr98mTrXv645Saia4oinI0dOwIq1ZBvXqVfSQnpL59+7Jq1SqmTZtW2YcCWEVP4+PjQz916tSp7ENSFKU8/fpZAfRGjdQqIEVRlBPZE09Ahw4wYQJERFT20SiKoijHwtat1oQYgMceg86dK/d4DkAF0RVFUQ5XTg5s3lzyt6aG1MPRr18/Zs6cyc8//0zt2rVD7TVq1MDv95OdnW3rv2fPHmrUqBHqs2fPnrDXi1/bX5+4uLhyZ6EDDBkyhJycnNDP9u3bj+gzKopSAT77zMqXqOvWKqCoqMo+IkVRFOVwNWwIc+ZA69aVfSSKoijKsRAMwu23Q3Y2XHghvPhiZR/RAamIj6IoykFYs3gdsyf+jGmaJY39+kHLltbyI+WQSSnp168f06dPZ968eTRo0MD2+nnnnYfT6WTu3LmhtnXr1rFt2zZaF33Bat26Nf/88w9paWmhPnPmzCEuLo6mTZuG+pTeR3Gf1vv5kuZ2u4mLi7P9KIpyHNm9G/r0sX5/8knrxrsM0zSZM3kBq35de4wPTlEURTlo69cfsEtBbiFfvjGT9B0ZoTZPvoev3vyOPVvTK/LoFEVRlIqycyfs2QNxcfDJJ+ByHfIu0rbv5cs3ZlKQW1gBBxhO5URXFEXZD78vwORnP+XTV78GCbMn/swTk/uT/Msc+Phja/Z5mXzbysHp27cvU6dO5euvvyY2NjaUwzw+Pp7IyEji4+Pp2bMnAwcOJCkpibi4OPr370/r1q256KKLAOjYsSNNmzblzjvvZOTIkaSmpvL000/Tt29f3G43AL179+btt9/m8ccfp0ePHsybN4/PPvuM7777rtI+u6IoR0BKuO8+qwjduefC00+HdUnbvpeRd7/N3/NXg4BbBl7LPS/ehsvtrIQDVhRFUco1b56VwqVPH3j7bavOUBl/L1jNK3eMYe/OTCY99ykDxj1Act2qDO8+mrRte/nwmU94aOz9dLizDaKc7RVFUZTjVL168Oef8M8/1mqkQyClZN7UXxjdZzyefC9fvDGTIR8/RIs2TSvoYC1CSikr9B1OYbm5ucTHx5OTk6NmMSrKCcg0TR5q/ST//bGJ4qFSd2jUcvp4X5uHlp8Hzz5rFRU9Bk62MWVfX3Q+/PBD7rnnHgC8Xi+DBg3ik08+wefz0alTJ955551QqhaArVu30qdPH+bPn090dDR33303r7zyCg5HyXPi+fPn88gjj7BmzRpq167NM888E3qPg3GynXtFOaFt2QIXXAB5ebB8OTRrZnt5x3+7ePD8wfi9foygtXpIaILTWtbn7d+Hox8HxZ/VmFJ51LlXlONEQQE0b26lRuzdG959N6zLnI8WMPKet9GEwDQlQojQPbmma5iGCQKQcOOAq+kz6p5j+hHUeFK51PlXlBOUlOU+ND0U7z/xMZ+N/Dp0DdB0DdM0GTypPx3uaHPI+zvY8UTNRFcURdkHX6GPdcs22tpkwGBA4Bc08qycjeXMgFQOzsE8w42IiGDs2LGMLa7WXY569erx/fff73c/bdu25a+//jrkY1QU5ThUv75VyHnp0rAAOsB/yzfhyffa2qQp2fDXZgpzPcQmxhyjA1UURVH26emnrQB6nTowYkS5XVb8vApNKwqWY793LG6jqOmPWStgVEUesKIoinLETBNuuslahfTgg4cdTF82q+i7fdE1wDRMNF1jxbxVhxVEP1gqJ7qiKMoh6MZampNBMCLKSufiUM8iFUVRjrnq1eG66yr7KBRFUZTD8dtvMHq09fv48VY+3H1QCVoURVFOIqNGwYwZ8Oij1urSo+hYZPRSQXRFUZSDdLrM4i7WALDq9v4HzNu18e8tePI9x+LQFEVRTn7vvgtffFHZR6EoiqIcCa+X4F33WMv577kHOncGICM1m9Rte21dfYW+g1q5qCiKohzftq5PpfDnhTBkiNU
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"text/plain": [
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"<Figure size 1500x400 with 4 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"fig, axs = plt.subplots(1, 4, figsize=(15, 4))\n",
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"\n",
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"axs[0].hexbin(vel[:, 0], vx, gridsize=50, bins=\"log\", mincnt=1)\n",
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"axs[0].set_xlabel(r\"FoF $v_x$\")\n",
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"axs[0].set_ylabel(r\"SPH $v_x$\")\n",
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"\n",
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"axs[1].hexbin(vel[:, 1], vy, gridsize=50, bins=\"log\", mincnt=1)\n",
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"axs[1].set_xlabel(r\"FoF $v_y$\")\n",
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"axs[1].set_ylabel(r\"SPH $v_y$\")\n",
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"\n",
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"axs[2].hexbin(vel[:, 2], vz, gridsize=50, bins=\"log\", mincnt=1)\n",
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"axs[2].set_xlabel(r\"FoF $v_z$\")\n",
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"axs[2].set_ylabel(r\"SPH $v_z$\")\n",
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"\n",
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"\n",
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"vr_fof = project_radial(vel[:, 0], vel[:, 1], vel[:, 2], RA, dec)\n",
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"vr_sph = project_radial(vx, vy, vz, RA, dec)\n",
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"axs[3].hexbin(vr_fof, vr_sph, gridsize=50, bins=\"log\", mincnt=1)\n",
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"axs[3].set_xlabel(r\"FoF $v_r$\")\n",
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"axs[3].set_ylabel(r\"SPH $v_r$\")\n",
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"\n",
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"for i in range(4):\n",
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" axs[i].axline([0, 0], [1, 1], color=\"red\", ls=\"--\")\n",
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"\n",
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"fig.tight_layout()\n",
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"fig.savefig(\"../plots/velocity_comparison.png\")\n",
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"fig.show()"
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]
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},
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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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"## Correlation between the peculiar velocity and total mass"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 27,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"SignificanceResult(statistic=-0.011239061725954754, pvalue=1.637324987020833e-17)\n"
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]
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}
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],
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"source": [
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"from scipy.stats import spearmanr\n",
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"\n",
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"kind = \"main\"\n",
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"nsnap = 99\n",
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"nsim = 17417\n",
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"\n",
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"catalogue = csiborgtools.read.CSiBORG2Catalogue(nsim, nsnap, kind)\n",
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"\n",
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"vel = catalogue[\"cartesian_vel\"]\n",
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"mass = catalogue[\"totmass\"]\n",
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"velmag = np.linalg.norm(vel, axis=1)\n",
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"\n",
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"print(spearmanr(mass, velmag))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 28,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"mask = mass > 1e12\n",
|
||
|
|
"plt.figure()\n",
|
||
|
|
"plt.hexbin(np.log10(mass), velmag, mincnt=1, bins=\"log\")\n",
|
||
|
|
"\n",
|
||
|
|
"plt.xlabel(r\"$\\log M_{\\rm FoF} ~ [M_\\odot / h]$\")\n",
|
||
|
|
"plt.ylabel(r\"$|\\mathbf{V}| ~ [\\mathrm{km} / \\mathrm{s}]$\")\n",
|
||
|
|
"\n",
|
||
|
|
"plt.savefig(\"../plots/velocity_mass.png\", dpi=300, bbox_inches=\"tight\")\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"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",
|
||
|
|
"version": "3.11.7"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 2
|
||
|
|
}
|