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csiborgtools/notebooks/matching.ipynb

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Gravitational potential calculation (#60) * Rename file * add velocity field * Add velcoity field * Move smoothening * Add verbosity flags * remove blank * Simplify paths * Add potential calculation * Update paths * Add potential field * Add potential calculation * Move away sky matching * Move interpolation functions * Update nbs
2023-05-12 12:07:58 +00:00
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "5a38ed25",
"metadata": {
"ExecuteTime": {
"end_time": "2023-04-12T14:25:46.519408Z",
"start_time": "2023-04-12T14:25:03.003304Z"
},
"scrolled": true
},
"outputs": [],
"source": [
"import sys\n",
"import numpy as np\n",
"import h5py\n",
"import matplotlib.pyplot as plt\n",
"import scienceplots\n",
"from glob import glob\n",
"\n",
"sys.path.append(\"../\")\n",
"import csiborgtools\n",
"\n",
"\n",
"%matplotlib widget\n",
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
Add Quijote (#61) * Rename paths object * Remove redshift calculation * Explicit keywrod arg * Rename box units * Basic renaming * Little docs * Rename paths * add imports * Sort imports * Add Quijote cat * Split boxes * add Quijote path * Add origin argument * Update nbs
2023-05-13 16:37:34 +00:00
"execution_count": 2,
Gravitational potential calculation (#60) * Rename file * add velocity field * Add velcoity field * Move smoothening * Add verbosity flags * remove blank * Simplify paths * Add potential calculation * Update paths * Add potential field * Add potential calculation * Move away sky matching * Move interpolation functions * Update nbs
2023-05-12 12:07:58 +00:00
"id": "3648b42e",
"metadata": {},
"outputs": [],
"source": [
Add Quijote (#61) * Rename paths object * Remove redshift calculation * Explicit keywrod arg * Rename box units * Basic renaming * Little docs * Rename paths * add imports * Sort imports * Add Quijote cat * Split boxes * add Quijote path * Add origin argument * Update nbs
2023-05-13 16:37:34 +00:00
"paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)\n",
Gravitational potential calculation (#60) * Rename file * add velocity field * Add velcoity field * Move smoothening * Add verbosity flags * remove blank * Simplify paths * Add potential calculation * Update paths * Add potential field * Add potential calculation * Move away sky matching * Move interpolation functions * Update nbs
2023-05-12 12:07:58 +00:00
"files = glob(\"/mnt/extraspace/rstiskalek/CSiBORG/overlap/overlap_0*\")\n",
"\n",
"nsim0 = 7444\n",
"files = [f for f in files if str(nsim0) in f]\n",
"\n",
"nsimxs = np.sort([int(f.split(\"_\")[-1].split(\".\")[0][1:]) for f in files])"
]
},
{
"cell_type": "code",
Add Quijote (#61) * Rename paths object * Remove redshift calculation * Explicit keywrod arg * Rename box units * Basic renaming * Little docs * Rename paths * add imports * Sort imports * Add Quijote cat * Split boxes * add Quijote path * Add origin argument * Update nbs
2023-05-13 16:37:34 +00:00
"execution_count": 6,
Gravitational potential calculation (#60) * Rename file * add velocity field * Add velcoity field * Move smoothening * Add verbosity flags * remove blank * Simplify paths * Add potential calculation * Update paths * Add potential field * Add potential calculation * Move away sky matching * Move interpolation functions * Update nbs
2023-05-12 12:07:58 +00:00
"id": "5d73697d",
"metadata": {},
"outputs": [],
"source": [
"cat0 = csiborgtools.read.HaloCatalogue(nsim0, paths, minmass=(\"totpartmass\", 1e12))\n",
"catxs = [csiborgtools.read.HaloCatalogue(nsimx, paths, minmass=(\"totpartmass\", 1e12)) for nsimx in nsimxs]"
]
},
{
"cell_type": "code",
Add Quijote (#61) * Rename paths object * Remove redshift calculation * Explicit keywrod arg * Rename box units * Basic renaming * Little docs * Rename paths * add imports * Sort imports * Add Quijote cat * Split boxes * add Quijote path * Add origin argument * Update nbs
2023-05-13 16:37:34 +00:00
"execution_count": 7,
Gravitational potential calculation (#60) * Rename file * add velocity field * Add velcoity field * Move smoothening * Add verbosity flags * remove blank * Simplify paths * Add potential calculation * Update paths * Add potential field * Add potential calculation * Move away sky matching * Move interpolation functions * Update nbs
2023-05-12 12:07:58 +00:00
"id": "a8c63faa",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
Add Quijote (#61) * Rename paths object * Remove redshift calculation * Explicit keywrod arg * Rename box units * Basic renaming * Little docs * Rename paths * add imports * Sort imports * Add Quijote cat * Split boxes * add Quijote path * Add origin argument * Update nbs
2023-05-13 16:37:34 +00:00
"100%|██████████| 100/100 [02:52<00:00, 1.73s/it]\n"
Gravitational potential calculation (#60) * Rename file * add velocity field * Add velcoity field * Move smoothening * Add verbosity flags * remove blank * Simplify paths * Add potential calculation * Update paths * Add potential field * Add potential calculation * Move away sky matching * Move interpolation functions * Update nbs
2023-05-12 12:07:58 +00:00
]
}
],
"source": [
"reader = csiborgtools.read.NPairsOverlap(cat0, catxs, paths)"
]
},
Add Quijote (#61) * Rename paths object * Remove redshift calculation * Explicit keywrod arg * Rename box units * Basic renaming * Little docs * Rename paths * add imports * Sort imports * Add Quijote cat * Split boxes * add Quijote path * Add origin argument * Update nbs
2023-05-13 16:37:34 +00:00
{
"cell_type": "code",
"execution_count": 8,
"id": "99aa9bce",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 100/100 [00:32<00:00, 3.07it/s]\n"
]
}
],
"source": [
"x = reader.cat0(\"totpartmass\")\n",
"y1 = reader.summed_overlap(True)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 100/100 [01:01<00:00, 1.63it/s]\n"
]
}
],
"source": [
"y2 = reader.prob_nomatch(True)"
]
},
{
"cell_type": "code",
"execution_count": 45,
"id": "f4b8934e",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_26899/1670519415.py:1: 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",
" plt.figure()\n"
]
},
{
"data": {
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"model_id": "5ce0c4068b4248f89c7b7d94392ab53a",
"version_major": 2,
"version_minor": 0
},
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"text/html": [
"\n",
" <div style=\"display: inline-block;\">\n",
" <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
" Figure\n",
" </div>\n",
" <img src='data:image/png;base64,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
" </div>\n",
" "
],
"text/plain": [
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"plt.scatter(1 - np.mean(y1, axis=1), np.mean(y2, axis=1), c=np.log10(x), s=2)\n",
"plt.colorbar(label=r\"$\\log_{10} M_{\\rm halo} / M_\\odot$\")\n",
"\n",
"t = np.linspace(0.3, 1, 100)\n",
"plt.plot(t, t, color=\"red\", linestyle=\"--\")\n",
"\n",
"plt.xlabel(r\"$1 - \\langle \\mathcal{O}_a^{\\mathcal{A} \\mathcal{B}} \\rangle_{\\mathcal{B}}$\")\n",
"plt.ylabel(r\"$\\langle \\eta_a^{\\mathcal{A} \\mathcal{B}} \\rangle_{\\mathcal{B}}$\")\n",
"plt.tight_layout()\n",
"plt.savefig(\"../plots/prob_nomatch_vs_overlap.png\", dpi=450)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "887da7fa",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "20e95e79",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 18,
"id": "56b70f96",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8eabaceb0b6447efb4d690d6fe5eb507",
"version_major": 2,
"version_minor": 0
},
"image/png": "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
"text/html": [
"\n",
" <div style=\"display: inline-block;\">\n",
" <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
" Figure\n",
" </div>\n",
" <img src='data:image/png;base64,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
" </div>\n",
" "
],
"text/plain": [
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
]
},
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"output_type": "display_data"
},
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"\n",
" <div style=\"display: inline-block;\">\n",
" <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
" Figure\n",
" </div>\n",
" <img src='data:image/png;base64,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
" </div>\n",
" "
],
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"\n",
" <div style=\"display: inline-block;\">\n",
" <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
" Figure\n",
" </div>\n",
" <img src='data:image/png;base64,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
" </div>\n",
" "
],
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"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
]
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"\n",
" <div style=\"display: inline-block;\">\n",
" <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
" Figure\n",
" </div>\n",
" <img src='data:image/png;base64,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
" </div>\n",
" "
],
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" <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
" Figure\n",
" </div>\n",
" <img src='data:image/png;base64,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
" </div>\n",
" "
],
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"\n",
" <div style=\"display: inline-block;\">\n",
" <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
" Figure\n",
" </div>\n",
" <img src='data:image/png;base64,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
" </div>\n",
" "
],
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"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
]
},
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"output_type": "display_data"
},
{
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"\n",
" <div style=\"display: inline-block;\">\n",
" <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
" Figure\n",
" </div>\n",
" <img src='data:image/png;base64,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
" </div>\n",
" "
],
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"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
]
},
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"output_type": "display_data"
},
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"\n",
" <div style=\"display: inline-block;\">\n",
" <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
" Figure\n",
" </div>\n",
" <img src='data:image/png;base64,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
" </div>\n",
" "
],
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]
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"\n",
" <div style=\"display: inline-block;\">\n",
" <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
" Figure\n",
" </div>\n",
" <img src='data:image/png;base64,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
" </div>\n",
" "
],
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"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
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"\n",
" <div style=\"display: inline-block;\">\n",
" <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
" Figure\n",
" </div>\n",
" <img src='data:image/png;base64,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
" </div>\n",
" "
],
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"\n",
" <div style=\"display: inline-block;\">\n",
" <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
" Figure\n",
" </div>\n",
" <img src='data:image/png;base64,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
" </div>\n",
" "
],
"text/plain": [
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.close(\"all\")\n",
"ks = [1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100]\n",
"for k in ks:\n",
" plt.figure()\n",
" plt.title(r\"Crossing with ${}$ simulations.\".format(k))\n",
" plt.hexbin(x, np.mean(y1[:, :k], axis=1), mincnt=1, xscale=\"log\", bins=\"log\", gridsize=50)\n",
" plt.colorbar()\n",
" plt.xlabel(r\"$M_{\\rm tot} / M_\\odot$\")\n",
" plt.ylabel(\"Mean overlap\")\n",
" plt.tight_layout()\n",
" plt.ylim(0, 1)\n",
" # plt.savefig(\"../plots/overlap_std_{}.png\".format(k), dpi=300, bbox_inches=\"tight\")\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "62e81b80",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6031ccb85fdc4e818919c1219e66084c",
"version_major": 2,
"version_minor": 0
},
"image/png": "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
"text/html": [
"\n",
" <div style=\"display: inline-block;\">\n",
" <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
" Figure\n",
" </div>\n",
" <img src='data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAoAAAAHgCAYAAAA10dzkAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAC0a0lEQVR4nO29fXRU553n+b31olJZbavAxhApWLiZhjbGEoxswO2E0DnYTFoNps/Mtse7jN0+s56zbBJnh2HOQGOso7YJ7m4mx+PEg6d94ulss3G7k9MDeJUYe3FzWKfNkpENmJdAmsayRwoYW5RwyqVSve0ft36Pnnvr3qpbqiqpVPX9nKMj6da9z33uS9X91u/VyGazWRBCCCGEkIbBN90TIIQQQgghUwsFICGEEEJIg0EBSAghhBDSYFAAEkIIIYQ0GBSAhBBCCCENBgUgIYQQQkiDQQFICCGEENJgUAASQgghhDQYFICEEEIIIQ0GBSAhhBBCSINBAUgIIYQQ0mBQABJCCCGENBgUgIQQQgghDQYFICGEEEJIg0EBSAghhBDSYFAAEkIIIYQ0GBSAhBBCCCENBgUgIYQQQkiDQQFICCGEENJgUAASQgghhDQYFICEEEIIIQ0GBSAhhBBCSINBAUgIIYQQ0mBQABJCCCGENBgUgIQQQgghDQYFICGEEEJIg0EBSAghhBDSYFAAEkIIIYQ0GBSAhBBCCCENBgUgIYQQQkiDQQFICCGEENJgUAASQgghhDQYFICEEEIIIQ0GBSAhhBBCSINBAUgIIYQQ0mBQABJCCCGENBgUgIQQQgghDQYFICGEEEJIg0EBSAghhBDSYFAAEkIIIYQ0GBSAhBBCCCENBgUgIYQQQkiDQQFICCGEENJgUAASQgghhDQYFICEEEIIIQ0GBSAhhBBCSINBAUgIIYQQ0mBQABJCCCGENBgUgIQQQgghDQYFICGEEEJIg0EBSAghhBDSYFAAEkIIIYQ0GHUlAF944QUsWLAAzc3NWLlyJY4fP15w/eeeew6LFy9GOBzG/Pnz8W//7b/F2NjYFM2WEEIIIWR6qBsB+Oqrr2LLli3o7e3Fu+++i66uLqxbtw4ff/yx4/o//OEPsW3bNvT29uLcuXP4/ve/j1dffRV//Md/PMUzJ4QQQgiZWoxsNpud7klUgpUrV+Kee+7B9773PQBAJpPB/Pnz8c1vfhPbtm3LW/8b3/gGzp07h8OHD6tl/+7f/Tv8f//f/4e33357yuZNCCGEEDLV1IUFcHx8HAMDA1i7dq1a5vP5sHbtWrzzzjuO2/zO7/wOBgYGlJv4H//xH/GTn/wEv/d7vzclcyaEEEIImS4C0z2BSvDJJ58gnU5j7ty5luVz587FL37xC8dt/uf/+X/GJ598gi996UvIZrNIpVL43/63/62gCziRSCCRSKj/M5kMRkZGcPPNN8MwjMocDCGEEEKqSjabxWeffYa2tjb4fHVhCyuZuhCAk+HIkSP49re/jf/8n/8zVq5ciX/4h3/At771LTz99NPYuXOn4za7d+9GX1/fFM+UEEIIIdXgo48+whe/+MXpnsa0UBcxgOPj47jhhhvw4x//GBs3blTLH330UUSjURw4cCBvmy9/+ctYtWoV/vzP/1wt27dvH/7Nv/k3+PWvf+34jcBuARwdHcVtt92Gjz76CDfddFNlD4oQQgghVeH69euYP38+otEoWltbp3s600JdWACbmprQ3d2Nw4cPKwGYyWRw+PBhfOMb33Dc5vPPP88TeX6/H4BpGnYiFAohFArlLb/pppsoAAkhhJAZRiOHb9WFAASALVu24NFHH8Xdd9+NFStW4LnnnkMsFsNjjz0GAHjkkUfQ3t6O3bt3AwDWr1+P73znO1i+fLlyAe/cuRPr169XQpAQQgghpB6pGwH40EMP4erVq3jqqadw+fJlLFu2DK+//rpKDPnwww8tFr8nn3wShmHgySefxNDQEObMmYP169dj165d03UIhBBCCCFTQl3EAE4X169fR2trK0ZHR+kCJoQQQmYIfH7XSR1AQgghhBDiHQpAQgghhJAGgwKQEEIIIaTBoAAkhBBCCGkwKAAJIYQQQhoMCkBCCCGEkAaDApAQQgghpMGgACSEEEIIaTAoAAkhhBBCGgwKQEIIIWQGsO/YIO579i3sOzY43VMhdQAFICGEEDID2HvkIoaicew9cnG6p0LqAApAQgghZAawec1CtEfC2Lxm4XRPhdQBRjabzU73JGYqbCZNCCGEzDz4/KYFkBBCCCGk4aAAJIQQQghpMCgACSGEEEIaDApAQgghhJAGgwKQEEIIIaTBoAAkhBBCCGkwKAAJIYQQQhoMCkBCCCGkRpjOdm9sNddYUAASQgghNcJ0tntjq7nGggKQEEJIzdMo1qnpbPfGVnONBVvBlQFbyRBCyNRw37NvYSgaR3skjJ9t++p0T4fMcPj8pgWQEELIDIDWKUIqCy2AZcBvEIQQQsjMg89vWgAJIYQQQhoOCkBCCCFVpVESOAiZSVAAEkIIqSosL0JI7UEBSAghpKowgYOQ2oNJIGXAIFJCCCFk5sHnNy2AhBBCCADGKpLGggKQEEJIw6KLPsYqkkairgTgCy+8gAULFqC5uRkrV67E8ePHXddds2YNDMPI++np6ZnCGRNCCJlOdNHHWEXSSNSNAHz11VexZcsW9Pb24t1330VXVxfWrVuHjz/+2HH9v/3bv8WvfvUr9XP69Gn4/X78T//T/zTFMyeEkMpCV6Z3dNG3aVUHfrbtq9i0qmO6p0VI1ambJJCVK1finnvuwfe+9z0AQCaTwfz58/HNb34T27ZtK7r9c889h6eeegq/+tWv0NLS4mmfDCIlhNQi7JtLSGH4/K4TC+D4+DgGBgawdu1atczn82Ht2rV45513PI3x/e9/H//yX/5Lz+KPEEJqFboyCSHFCEz3BCrBJ598gnQ6jblz51qWz507F7/4xS+Kbn/8+HGcPn0a3//+9wuul0gkkEgk1P/Xr1+f3IQJIaSKbFrVQTcmIaQgdWEBLJfvf//7uOuuu7BixYqC6+3evRutra3qZ/78+VM0Q0IIIYSQylEXAvCWW26B3+/HlStXLMuvXLmCefPmFdw2Fovhr//6r/Gv//W/Lrqf7du3Y3R0VP189NFHZc2bEEJIaTDBhZDKUBcCsKmpCd3d3Th8+LBalslkcPjwYdx7770Ft/3Rj36ERCKBTZs2Fd1PKBTCTTfdZPkhhBAydbBWHyGVoS4EIABs2bIFL730En7wgx/g3Llz2Lx5M2KxGB577DEAwCOPPILt27fnbff9738fGzduxM033zzVUyaEEFIiTHAhpDLURRIIADz00EO4evUqnnrqKVy+fBnLli3D66+/rhJDPvzwQ/h8Vr17/vx5vP3223jjjTemY8qEEEJKhAkuhFSGuqkDOB2wjhAhhJBSkbZzUnyaTD18fteRC5gQQgiZCTCOkdQCFICEEEI8wyzc8mEcI6kF6AIuA5qQCSGNBtvMkXqAz29aAAkhhJQArVeE1Ae0AJYBv0EQQsjUweQJUin4/KYFkBBCFIxvq22YPEFI5aAAJISQHBQYtQ3dz4RUjropBE0IIeWyec1C5WIktQeLQBNSOWgBJISQHJtWdeBn275atsigK5nY4T1Bag0KQEJIQ1ONBzNdycQO7wlSa1AAEkIammo8mBmrVr9M9gsD7wlSa7AMTBkwjZyQmQ9Li5RGo58vFsKuD/j8pgWQEFJHTMY6U6m4v0ahkVyZTveTF0se4/3ITIACkBBSNzSSOJkuGsmV6XQ/efnCUO59SAFJpgIKQEJI3dBI4mS6aCSL6WTvp3LvQ36RIVMBYwDLgDEEhBBSO0xlfGI199XocZZTAZ/ftAASQgiZJLXmqpxKy1k199VIVlYyfVAAEkLIDKNU4VUtoVZrrsqpDAFguAGZ6dAFXAY0IRNCpoNSS5FUq3QJXZVkpsLnNy2AhBAy4yjV+lQtaxVdlYTMXGgBLAN+gyCENBK0+JF6gc9vWgAJIYR4pNZi/gghk4cCkBB
" </div>\n",
" "
],
"text/plain": [
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"plt.scatter(x, np.mean(y1, axis=1), s=1)\n",
"plt.xscale(\"log\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "02aad13e",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "22478552",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "a89b63fe",
"metadata": {},
"outputs": [],
"source": []
},
Gravitational potential calculation (#60) * Rename file * add velocity field * Add velcoity field * Move smoothening * Add verbosity flags * remove blank * Simplify paths * Add potential calculation * Update paths * Add potential field * Add potential calculation * Move away sky matching * Move interpolation functions * Update nbs
2023-05-12 12:07:58 +00:00
{
"cell_type": "code",
"execution_count": 136,
"id": "619ec149",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 40/40 [01:18<00:00, 1.96s/it]\n",
"100%|██████████| 40/40 [00:21<00:00, 1.82it/s]\n"
]
}
],
"source": [
"x = reader.cat0(\"totpartmass\")\n",
"mu, std = reader.counterpart_mass(True, 0.1, return_full=False)"
]
},
{
"cell_type": "code",
"execution_count": 137,
"id": "5612b4bd",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "062f1cbcc50b4bb2a0df793fe2075e39",
"version_major": 2,
"version_minor": 0
},
"image/png": "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
"text/html": [
"\n",
" <div style=\"display: inline-block;\">\n",
" <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
" Figure\n",
" </div>\n",
" <img src='data:image/png;base64,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
" </div>\n",
" "
],
"text/plain": [
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"plt.scatter(x, mu, s=1)\n",
"# plt.hexbin(x, np.mean(y, axis=1), mincnt=1, xscale=\"log\", bins=\"log\", gridsize=50)\n",
"t = np.linspace(1e12, 1e15, 100)\n",
"plt.plot(t, t, c=\"red\", ls=\"--\")\n",
"plt.xscale(\"log\")\n",
"plt.yscale(\"log\")\n",
"plt.xlabel(r\"Reference $M_{\\rm tot} / M_\\odot$\")\n",
"plt.ylabel(r\"Cross $M_{\\rm tot} / M_\\odot$\")\n",
"plt.tight_layout()\n",
"# plt.savefig(\"../plots/prob_nomatch_vs_mass.png\", dpi=300, bbox_inches=\"tight\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 126,
"id": "dc7d0e3a",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3dfb752fa43248149ccc4befff0085b3",
"version_major": 2,
"version_minor": 0
},
"image/png": "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
"text/html": [
"\n",
" <div style=\"display: inline-block;\">\n",
" <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
" Figure\n",
" </div>\n",
" <img src='data:image/png;base64,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
" </div>\n",
" "
],
"text/plain": [
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"plt.hexbin(x, np.mean(y, axis=1), mincnt=1, xscale=\"log\", bins=\"log\", gridsize=50)\n",
"plt.xlabel(r\"$M_{\\rm tot} / M_\\odot$\")\n",
"plt.ylabel(\"Mean probability of no match\")\n",
"plt.tight_layout()\n",
"plt.savefig(\"../plots/prob_nomatch_vs_mass.png\", dpi=300, bbox_inches=\"tight\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4eeb5e5f",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 84,
"id": "1cbd19d5",
"metadata": {},
"outputs": [],
Add Quijote (#61) * Rename paths object * Remove redshift calculation * Explicit keywrod arg * Rename box units * Basic renaming * Little docs * Rename paths * add imports * Sort imports * Add Quijote cat * Split boxes * add Quijote path * Add origin argument * Update nbs
2023-05-13 16:37:34 +00:00
"source": []
Gravitational potential calculation (#60) * Rename file * add velocity field * Add velcoity field * Move smoothening * Add verbosity flags * remove blank * Simplify paths * Add potential calculation * Update paths * Add potential field * Add potential calculation * Move away sky matching * Move interpolation functions * Update nbs
2023-05-12 12:07:58 +00:00
},
{
"cell_type": "code",
"execution_count": 118,
"id": "fb82e729",
"metadata": {},
"outputs": [],
Add Quijote (#61) * Rename paths object * Remove redshift calculation * Explicit keywrod arg * Rename box units * Basic renaming * Little docs * Rename paths * add imports * Sort imports * Add Quijote cat * Split boxes * add Quijote path * Add origin argument * Update nbs
2023-05-13 16:37:34 +00:00
"source": []
Gravitational potential calculation (#60) * Rename file * add velocity field * Add velcoity field * Move smoothening * Add verbosity flags * remove blank * Simplify paths * Add potential calculation * Update paths * Add potential field * Add potential calculation * Move away sky matching * Move interpolation functions * Update nbs
2023-05-12 12:07:58 +00:00
},
{
"cell_type": "code",
"execution_count": 117,
"id": "a2fb7aa8",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_28288/2049075485.py:5: DeprecationWarning: Starting with ImageIO v3 the behavior of this function will switch to that of iio.v3.imread. To keep the current behavior (and make this warning disappear) use `import imageio.v2 as imageio` or call `imageio.v2.imread` directly.\n",
" images.append(imageio.imread(filename))\n"
]
}
],
"source": [
"import imageio\n",
"images = []\n",
"for k in ks:\n",
" filename = \"../plots/overlap_{}.png\".format(k)\n",
" images.append(imageio.imread(filename))\n",
"imageio.mimsave(\"../plots/overlap_movie.gif\", images, duration=0.5)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "322910d7",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "98f3b679",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 21,
"id": "312c96c9",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 22,
"id": "174206f5",
"metadata": {},
"outputs": [],
"source": [
"clumpmap0 = csiborgtools.read.read_h5(paths.particles_path(nsim0))[\"clumpmap\"]\n",
"parts0 = csiborgtools.read.read_h5(paths.initmatch_path(nsim0, \"particles\"))[\"particles\"]\n",
"clid2map0 = {clid: i for i, clid in enumerate(clumpmap0[:, 0])}\n",
"\n",
"# # clumpmapx = csiborgtools.read.read_h5(paths.particles_path(nsimx))[\"clumpmap\"]\n",
"# # partsx = csiborgtools.read.read_h5(paths.initmatch_path(nsimx, \"particles\"))[\"particles\"]\n",
"# # clid2mapx = {clid: i for i, clid in enumerate(clumpmapx[:, 0])}\n",
"\n",
"cat0 = csiborgtools.read.HaloCatalogue(nsim0, paths, load_initial=True, minmass=None, with_lagpatch=True, load_clumps_cat=True)\n",
"catx = csiborgtools.read.HaloCatalogue(nsimx, paths, load_initial=True, minmass=None, with_lagpatch=True, load_clumps_cat=True)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "0666813a",
"metadata": {},
"outputs": [],
"source": [
"f = np.load(paths.velocity_field_path(\"PCS\", 7444))"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6374f3a71c834f3f96f4c0212e27b398",
"version_major": 2,
"version_minor": 0
},
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"text/html": [
"\n",
" <div style=\"display: inline-block;\">\n",
" <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
" Figure\n",
" </div>\n",
" <img src='data:image/png;base64,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
" </div>\n",
" "
],
"text/plain": [
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"plt.imshow(np.sum(f[0, ...], axis=0))\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 76,
"id": "f453a77e",
"metadata": {},
"outputs": [],
"source": [
"x = pair.dist(in_initial=True, norm_kind=\"sum_patch\")\n",
"y = pair.overlap(False)\n",
"\n",
"x = np.concatenate(x)\n",
"y = np.concatenate(y)"
]
},
{
"cell_type": "code",
"execution_count": 80,
"id": "c761a313",
"metadata": {},
"outputs": [],
"source": [
"from scipy.special import comb\n",
"from itertools import combinations"
]
},
{
"cell_type": "code",
"execution_count": 81,
"id": "baba038a",
"metadata": {},
"outputs": [],
"source": [
"ics = paths.get_ics()"
]
},
{
"cell_type": "code",
"execution_count": 88,
"id": "b7885003",
"metadata": {},
"outputs": [],
"source": [
"import random\n",
"\n",
"combs = list(combinations(ics, 2))\n",
"random.shuffle(combs)"
]
},
{
"cell_type": "code",
"execution_count": 91,
"id": "12d2c301",
"metadata": {},
"outputs": [],
"source": [
"random.Random(32).shuffle(combs)"
]
},
{
"cell_type": "code",
"execution_count": 92,
"id": "d2021330",
"metadata": {},
"outputs": [
{
"data": {
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" ...]"
]
},
"execution_count": 92,
"metadata": {},
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}
],
"source": [
"combs"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "df1e935d",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 77,
"id": "8f2ca472",
"metadata": {},
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"image/png": "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
"text/html": [
"\n",
" <div style=\"display: inline-block;\">\n",
" <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
" Figure\n",
" </div>\n",
" <img src='data:image/png;base64,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
" </div>\n",
" "
],
"text/plain": [
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"plt.scatter(x, y, s=0.1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cee886fd",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 53,
"id": "8eb7c2ee",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
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},
{
"data": {
"text/plain": [
"array([[4.3213072e-01, 3.3612296e-01, 4.9542564e-01, 1.1641532e-10],\n",
" [4.2634511e-01, 3.3506793e-01, 4.9537191e-01, 1.1641532e-10],\n",
" [4.2104098e-01, 3.3217159e-01, 4.9831006e-01, 1.1641532e-10],\n",
" ...,\n",
" [4.3010166e-01, 3.2942852e-01, 4.9635866e-01, 1.1641532e-10],\n",
" [4.2487869e-01, 3.3458143e-01, 4.9207175e-01, 1.1641532e-10],\n",
" [4.2629939e-01, 3.2695419e-01, 4.9106851e-01, 1.1641532e-10]],\n",
" dtype=float32)"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"for i in list(clid2map0.keys())[50000:]:\n",
" print(i)\n",
" k = clid2map0[i]\n",
"\n",
" X = csiborgtools.read.load_parent_particles(k, parts0, clumpmap0, clid2map0, cat0.clumps_cat)\n",
" if X is not None:\n",
" break\n",
"X"
]
},
{
"cell_type": "code",
"execution_count": 54,
"id": "a3eea061",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "be2ab0860ad24dc8a4a1087ae569f08a",
"version_major": 2,
"version_minor": 0
},
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" </div>\n",
" "
],
"text/plain": [
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"plt.scatter(X[:, 0], X[:, 1], s=1)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1df6fd0f",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 5,
"id": "de571afb",
"metadata": {},
"outputs": [],
"source": [
"parts = csiborgtools.read.read_h5(paths.particles_path(nsim0))[\"particles\"]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "a90765b8",
"metadata": {},
"outputs": [],
"source": [
Add Quijote (#61) * Rename paths object * Remove redshift calculation * Explicit keywrod arg * Rename box units * Basic renaming * Little docs * Rename paths * add imports * Sort imports * Add Quijote cat * Split boxes * add Quijote path * Add origin argument * Update nbs
2023-05-13 16:37:34 +00:00
"box = csiborgtools.read.CSiBORGBox(nsnap0, nsim0, paths)\n",
Gravitational potential calculation (#60) * Rename file * add velocity field * Add velcoity field * Move smoothening * Add verbosity flags * remove blank * Simplify paths * Add potential calculation * Update paths * Add potential field * Add potential calculation * Move away sky matching * Move interpolation functions * Update nbs
2023-05-12 12:07:58 +00:00
"\n",
"field = csiborgtools.field.DensityField(box, \"CIC\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "e97f1d15",
"metadata": {},
"outputs": [],
"source": [
"rho = np.load(paths.density_field_path(\"PCS\", 7444, False))\n",
"rho_rsp = np.load(paths.density_field_path(\"PCS\", 7444, True))"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "6368ca0f",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2823edefcb6e4838a49ac881e78c2ab1",
"version_major": 2,
"version_minor": 0
},
"image/png": "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"\n",
" <div style=\"display: inline-block;\">\n",
" <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
" Figure\n",
" </div>\n",
" <img src='data:image/png;base64,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
" </div>\n",
" "
],
"text/plain": [
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"plt.imshow(np.sum(rho, axis=1))\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "b5db51bc",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "03895ddc21014d9d97c20a6c50671295",
"version_major": 2,
"version_minor": 0
},
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" <img src='data:image/png;base64,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
" </div>\n",
" "
],
"text/plain": [
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"plt.imshow(np.sum(rho_rsp, axis=1))\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 25,
"id": "b79fddeb",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.0018435717"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.abs(x - 1).max()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fb4398be",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 94,
"id": "81ce3e39",
"metadata": {},
"outputs": [],
"source": [
"k0 = clumpmap0[clid2map0[0], 2] + 1\n",
"# parts.shape[0]"
]
},
{
"cell_type": "code",
"execution_count": 96,
"id": "5384bc24",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"182510395"
]
},
"execution_count": 96,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"parts.shape[0] - k0"
]
},
{
"cell_type": "code",
"execution_count": 44,
"id": "32f9ca63",
"metadata": {},
"outputs": [],
"source": [
"clumps_cat = csiborgtools.read.ClumpsCatalogue(nsim0, paths, load_fitted=True, minmass=None, maxdist=155 / 0.705)"
]
},
{
"cell_type": "code",
"execution_count": 119,
"id": "6a1bf230",
"metadata": {},
"outputs": [],
"source": [
"cat = csiborgtools.read.HaloCatalogue(nsim0, paths, minmass=None)"
]
},
{
"cell_type": "code",
"execution_count": 124,
"id": "85eb7844",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1, 3, 5, ..., 21809643, 21810694, 21818034],\n",
" dtype=int32)"
]
},
"execution_count": 124,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cat['ID']"
]
},
{
"cell_type": "code",
"execution_count": 114,
"id": "b3fea4ab",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"153575"
]
},
"execution_count": 114,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(cat)"
]
},
{
"cell_type": "code",
"execution_count": 118,
"id": "146b4c6e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1355"
]
},
"execution_count": 118,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"numpy.isnan(cat[\"lagpatch\"]).sum()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "08e2b141",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 88,
"id": "b8c91301",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([-42.401093 , -32.8627 , -45.08371 , ..., 9.587616 ,\n",
" 5.0563965, 12.45813 ], dtype=float32)"
]
},
"execution_count": 88,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cat['x0']"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9cf882e0",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 46,
"id": "49a14648",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"265246"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(clumps_cat)"
]
},
{
"cell_type": "code",
"execution_count": 50,
"id": "ba45cf01",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0119bb1def8d463ba494175ee5416a8c",
"version_major": 2,
"version_minor": 0
},
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAoAAAAHgCAYAAAA10dzkAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAx0ElEQVR4nO3dfXSU9Z3//1e4yXDnTOQmmeQQIIICURCMGsYqlZISICpU7C5KASvCQoO7gGJIv4hidw2L7ireosctsaegwi5oJQWMoYGtDKApWSBKjrBgQJhAoZkBhASS6/eHv1zLcJtAkpnJ5/k4Z46Z63pfM5/3uTLOi891kyjLsiwBAADAGC1CPQAAAAA0LQIgAACAYQiAAAAAhiEAAgAAGIYACAAAYBgCIAAAgGEIgAAAAIYhAAIAABiGAAgAAGAYAiAAAIBhCIAAAACGIQACAAAYhgAIAABgGAIgAACAYQiAAAAAhiEAAgAAGIYACAAAYBgCIAAAgGEIgAAAAIYhAAIAABiGAAgAAGAYAiAAAIBhCIAAAACGIQACAAAYhgAIAABgGAIgAACAYQiAAAAAhiEAAgAAGIYACAAAYBgCIAAAgGEIgAAAAIYhAAIAABiGAAgAAGAYAiAAAIBhCIAAAACGIQACAAAYhgAIAABgGAIgAACAYQiAAAAAhiEAAgAAGIYACAAAYBgCIAAAgGEIgAAAAIYhAAIAABiGAAgAAGAYAiAAAIBhCIAAAACGIQACAAAYhgAIAABgGAIgAACAYQiAAAAAhiEAAgAAGIYACAAAYJiwC4BvvfWW+vfvL6fTKafTKY/HozVr1tjr7733XkVFRQU9pk6dGvQaZWVlysjIULt27RQbG6vZs2fr7NmzQTWFhYW67bbb5HA41KtXL+Xm5jZFewAAACHXKtQDOF/Xrl21YMEC3XjjjbIsS++9955GjRqlbdu26eabb5YkTZ48Wc8//7y9Tbt27eyfq6urlZGRIbfbrU2bNunQoUOaMGGCWrdurRdeeEGStHfvXmVkZGjq1KlaunSpCgoK9Pjjjys+Pl7p6elN2zAAAEATi7Isywr1IK6kY8eOevHFFzVp0iTde++9GjBggF555ZWL1q5Zs0b33XefDh48qLi4OEnS4sWLlZWVpSNHjig6OlpZWVnKy8vTzp077e3Gjh2riooKrV27tilaAgAACJmwmwE8V3V1tVasWKGTJ0/K4/HYy5cuXarf//73crvduv/++/XMM8/Ys4Ber1f9+vWzw58kpaena9q0aSopKdHAgQPl9XqVlpYW9F7p6emaMWPGZcdTWVmpyspK+3lNTY2OHTumTp06KSoqqgE6BgAAjc2yLB0/flwJCQlq0SLszoZrEmEZAHfs2CGPx6PTp0+rQ4cOWrVqlZKTkyVJjzzyiLp3766EhARt375dWVlZKi0t1cqVKyVJPp8vKPxJsp/7fL7L1gQCAZ06dUpt27a96LhycnI0f/78Bu0VAACExv79+9W1a9dQDyMkwjIA9u7dW8XFxfL7/frP//xPTZw4URs2bFBycrKmTJli1/Xr10/x8fEaOnSo9uzZo549ezbquLKzszVr1iz7ud/vV7du3bR//345nc5GfW8AANAwAoGAEhMTdd1114V6KCETlgEwOjpavXr1kiSlpKToiy++0KJFi/T2229fUJuamipJ2r17t3r27Cm3262tW7cG1ZSXl0uS3G63/d/aZefWOJ3OS87+SZLD4ZDD4bhgee0VywAAIHKYfPpWRBz4rqmpCTr37lzFxcWSpPj4eEmSx+PRjh07dPjwYbsmPz9fTqfTPozs8XhUUFAQ9Dr5+flB5xkCAAA0V2E3A5idna0RI0aoW7duOn78uJYtW6bCwkKtW7dOe/bs0bJlyzRy5Eh16tRJ27dv18yZMzV48GD1799fkjRs2DAlJydr/PjxWrhwoXw+n+bOnavMzEx79m7q1Kl6/fXX9fTTT+uxxx7T+vXrtXz5cuXl5YWydQAAgCYRdgHw8OHDmjBhgg4dOiSXy6X+/ftr3bp1+ulPf6r9+/frs88+0yuvvKKTJ08qMTFRY8aM0dy5c+3tW7ZsqdWrV2vatGnyeDxq3769Jk6cGHTfwKSkJOXl5WnmzJlatGiRunbtqnfffZd7AAIAACNExH0Aw1UgEJDL5ZLf7+ccQAAAIgTf3xFyDiAAAAAaDgEQAADAMARAAAAAwxAAAQAADEMABAAAMAwBEAAAwDAEQAAAAMMQAAEAAAxDAAQAADAMARAAAMAwBEAAqIcec/LUY05eqIcBANeEAAgAV4kwCCBSEQAB4CIIdwCaMwIgAFxGXUIgQRFApCEAAgAAGIYACAAAYBgCIAAAgGEIgAAAAIYhAALAebioA0BzRwAEgDq6XDDktjEAIkmrUA8AAMIdwQ5Ac8MMIAAAgGEIgABwFZgVBBDJCIAA0IAIhgAiAQEQAADAMARAAAAAwxAAAQAADEMABABx7h4AsxAAAQAADMONoAHg/8csIABTMAMIAABgGAIgAACAYQiAAAAAhiEAAkAD6zEnj/MJAYQ1AiAAAIBhCIAAAACGIQACAAAYhgAIAABgGG4EDcBoXKwBwETMAAJAI+FqYADhigAIAABgmLALgG+99Zb69+8vp9Mpp9Mpj8ejNWvW2OtPnz6tzMxMderUSR06dNCYMWNUXl4e9BplZWXKyMhQu3btFBsbq9mzZ+vs2bNBNYWFhbrtttvkcDjUq1cv5ebmNkV7AAAAIRd2AbBr165asGCBioqK9OWXX+onP/mJRo0apZKSEknSzJkz9cknn2jFihXasGGDDh48qAcffNDevrq6WhkZGaqqqtKmTZv03nvvKTc3V/PmzbNr9u7dq4yMDA0ZMkTFxcWaMWOGHn/8ca1bt67J+wUAAGhqUZZlWaEexJV07NhRL774oh566CF16dJFy5Yt00MPPSRJ2rVrl/r27Suv16tBgwZpzZo1uu+++3Tw4EHFxcVJkhYvXqysrCwdOXJE0dHRysrKUl5ennbu3Gm/x9ixY1VRUaG1a9fWeVyBQEAul0t+v19Op7NhmwbQJJriHL19CzIa/T0A1B3f32E4A3iu6upqffDBBzp58qQ8Ho+Kiop05swZpaWl2TV9+vRRt27d5PV6JUler1f9+vWzw58kpaenKxAI2LOIXq836DVqa2pfA4AZuEADgKnC8jYwO3bskMfj0enTp9WhQwetWrVKycnJKi4uVnR0tGJiYoLq4+Li5PP5JEk+ny8o/NWur113uZpAIKBTp06pbdu2Fx1XZWWlKisr7eeBQOCa+gQAAAiFsJwB7N27t4qLi7VlyxZNmzZNEydO1FdffRXqYSknJ0cul8t+JCYmhnpIAAAA9RaWATA6Olq9evVSSkqKcnJydOutt2rRokVyu92qqqpSRUVFUH15ebncbrckye12X3BVcO3zK9U4nc5Lzv5JUnZ2tvx+v/3Yv3//tbYKAADQ5MIyAJ6vpqZGlZWVSklJUevWrVVQUGCvKy0tVVlZmTwejyTJ4/Fox44dOnz4sF2Tn58vp9Op5ORku+bc16itqX2NS3E4HPbtaWofAAAAkSbszgHMzs7WiBEj1K1bNx0/flzLli1TYWGh1q1bJ5fLpUmTJmnWrFnq2LGjnE6nnnjiCXk8Hg0aNEiSNGzYMCUnJ2v8+PFauHChfD6f5s6dq8zMTDkcDknS1KlT9frrr+vpp5/WY489pvXr12v58uXKy+OEcAAA0PyFXQA8fPiwJkyYoEOHDsnlcql///5at26dfvrTn0qSXn75ZbVo0UJjxoxRZWWl0tPT9eabb9rbt2zZUqtXr9a0adPk8XjUvn17TZw4Uc8//7xdk5SUpLy8PM2cOVOLFi1S165d9e677yo9Pb3J+wUAAGhqEXEfwHDFfYSAyNZUt4HhPoBAeOH7O0LOAQQAAEDDIQACQBPhxtMAwgUBEAAAwDBhdxEIADQmZuEAgBlAAAAA4xAAAQAADEMABAAAMAznAAJAI+O8QwDhhhlAAAAAwxAAAQAADEMABAAAMAwBEAAAwDAEQAAAAMNwFTAAI3AlLgD8H2YAAQA
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" <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
" Figure\n",
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" <img src='data:image/png;base64,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
" </div>\n",
" "
],
"text/plain": [
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"m = numpy.isfinite(clumps_cat[\"lambda200c\"])\n",
"\n",
"plt.figure()\n",
"plt.hist(np.log10(clumps_cat[\"lambda200c\"][m]), bins=\"auto\")\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "38aa3685",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "86f807ab",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 5,
"id": "629f3c3e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1, 2, 3, 5, 6, 7, 9, 11, 12, 13], dtype=int32)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"\n",
"clumps_cat[\"index\"][:10]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "0b1b12e0",
"metadata": {},
"outputs": [],
"source": [
"X = csiborgtools.read.load_parent_particles(3, parts, clumpmap0, clid2map0, clumps_cat)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "b9ae282b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"clump = csiborgtools.fits.Clump(X, clumps_cat[2], box)\n",
"clumps_cat[2][\"index\"]"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "b75f9eb6",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(0.0008713664483347365, 1.8111896e-06)"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"clump.spherical_overdensity_mass(200)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "b5f4b080",
"metadata": {},
"outputs": [],
"source": [
"def f(mass):\n",
" return numpy.cumsum(mass)\n",
"\n",
"@jit(nopython=True)\n",
"def g(mass):\n",
" return numpy.cumsum(mass)\n",
"\n",
"x = clump['M']"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "0163c51a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"73.4 µs ± 401 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n"
]
}
],
"source": [
"%timeit f(x)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "51ff9cf3",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"31.8 µs ± 385 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n"
]
}
],
"source": [
"%timeit g(x)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8886e9e8",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 19,
"id": "13f72d14",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3.79 ms ± 47.2 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
]
}
],
"source": [
"%timeit clump.spherical_overdensity_mass(200)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "9baf6db9",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ea062d858e094536ac06bd22ee092002",
"version_major": 2,
"version_minor": 0
},
"image/png": "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" <div style=\"display: inline-block;\">\n",
" <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
" Figure\n",
" </div>\n",
" <img src='data:image/png;base64,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
" </div>\n",
" "
],
"text/plain": [
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"plt.plot(rs, y)\n",
"plt.yscale(\"log\")\n",
"plt.xscale(\"log\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "bd137d39",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"224 µs ± 4.17 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
]
}
],
"source": [
"%timeit clump.r()"
]
},
{
"cell_type": "code",
"execution_count": 87,
"id": "0e0f4a71",
"metadata": {},
"outputs": [],
"source": [
"x, y, z = clump['x'], clump['y'], clump['z']\n",
"x0, y0, z0 = clumps_cat[3]['x'], clumps_cat[3]['y'], clumps_cat[3]['z']\n"
]
},
{
"cell_type": "code",
"execution_count": 93,
"id": "80a18b34",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"145 µs ± 539 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n"
]
}
],
"source": [
"%timeit ((x - x0)**2 + (y - y0)**2 + (z - z0)**2)**0.5"
]
},
{
"cell_type": "code",
"execution_count": 92,
"id": "f929a601",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"242 µs ± 6.44 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
]
}
],
"source": [
"%timeit clump.r()"
]
},
{
"cell_type": "code",
"execution_count": 267,
"id": "cfd1af49",
"metadata": {},
"outputs": [],
"source": [
"delta_bckg *= 2"
]
},
{
"cell_type": "code",
"execution_count": 260,
"id": "28ac7a6d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.23 ms ± 6.98 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
]
}
],
"source": [
"%timeit delta1, delta2, cellmins, nonzero = overlapper.make_deltas(pos, pos2, mass, mass)\n",
"# __ = overlapper.make_deltas(pos, pos2, mass, mass)"
]
},
{
"cell_type": "code",
"execution_count": 271,
"id": "9d157f6e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.75 ms ± 11.8 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
]
}
],
"source": [
"%timeit overlapper(pos, pos2, mass, mass, delta_bckg)"
]
},
{
"cell_type": "code",
"execution_count": 259,
"id": "32f13411",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3072a8519f3e4da4a108df78ed44e6a9",
"version_major": 2,
"version_minor": 0
},
"image/png": "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
"text/html": [
"\n",
" <div style=\"display: inline-block;\">\n",
" <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
" Figure\n",
" </div>\n",
" <img src='data:image/png;base64,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
" </div>\n",
" "
],
"text/plain": [
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, axs = plt.subplots(ncols=2, figsize=(10, 5))\n",
"axs[0].imshow(np.sum(delta1, axis=0))\n",
"axs[1].imshow(np.sum(delta2, axis=0))\n",
"fig.tight_layout()"
]
},
{
"cell_type": "code",
"execution_count": 93,
"id": "6df1d619",
"metadata": {},
"outputs": [],
"source": [
"delta = overlapper.make_bckg_delta(pos, mass / mass)"
]
},
{
"cell_type": "code",
"execution_count": 282,
"id": "0d5525e5",
"metadata": {},
"outputs": [],
"source": [
"from functools import cache \n",
"from functools import lru_cache"
]
},
{
"cell_type": "code",
"execution_count": 283,
"id": "f2918a51",
"metadata": {},
"outputs": [],
"source": [
"from time import sleep\n",
"\n",
"@lru_cache(maxsize=1024)\n",
"def f(x):\n",
" sleep(0.5)\n",
" return x * x"
]
},
{
"cell_type": "code",
"execution_count": 286,
"id": "4d37bc4f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"79 ns ± 0.372 ns per loop (mean ± std. dev. of 7 runs, 10,000,000 loops each)\n"
]
}
],
"source": [
"%timeit f(2)"
]
},
{
"cell_type": "code",
"execution_count": 77,
"id": "a46d6f48",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0, 0, 277961071],\n",
" [ 1, 277961072, 277980356],\n",
" [ 2, 277980357, 277985437],\n",
" ...,\n",
" [ 21836472, 460470781, 460470782],\n",
" [ 21836487, 460470783, 460471462],\n",
" [ 21836785, 460471463, 460471466]], dtype=int32)"
]
},
"execution_count": 77,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"clumpmap0[:]"
]
},
{
"cell_type": "code",
"execution_count": 72,
"id": "4a900d77",
"metadata": {},
"outputs": [],
"source": [
"dens = overlapper.make_delta(pos, mass, subbox=True)"
]
},
{
"cell_type": "code",
"execution_count": 75,
"id": "81b5381f",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7c3c84eac2ab4bc99d8af24f3aeb5b04",
"version_major": 2,
"version_minor": 0
},
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"\n",
" <div style=\"display: inline-block;\">\n",
" <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
" Figure\n",
" </div>\n",
" <img src='data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAoAAAAHgCAYAAAA10dzkAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAApH0lEQVR4nO3df3CV5Z03/k8C5ABCAggkRAKLolhF2GfZSrNtWSpUoPM4tPqdqbUzxV1HRxedVdpty05rq7v9xtqZrW2X0p1pR9qZIq0d0anfp7oVS3zaBbtSWdS2rPBQweWXpSXBYA4hub9/+DRtVuC+rZBDcr1eM/cM55xPrvPJfZLD+1zn5LqqsizLAgCAZFRXugEAAPqXAAgAkBgBEAAgMQIgAEBiBEAAgMQIgAAAiREAAQASIwACACRGAAQASIwACACQGAEQACAxAiAAQGIEQACAxAiAAACJEQABABIjAAIAJEYABABIjAAIAJAYARAAIDECIABAYgRAAIDECIAAAIkRAAEAEiMAAgAkRgAEAEiMAAgAkBgBEAAgMQIgAEBiBEAAgMQIgAAAiREAAQASIwACACRGAAQASIwACACQGAEQACAxAiAAQGIEQACAxAiAAACJEQABABIjAAIAJEYABABIjAAIAJAYARAAIDECIABAYgRAAIDECIAAAIkRAAEAEiMAAgAkRgAEAEiMAAgAkBgBEAAgMQIgAEBiBEAAgMQIgAAAiREAAQASIwACACRmaKUbYGDq6emJvXv3xujRo6OqqqrS7QDwJmVZFkeOHInGxsaorjYflBoBMHGrVq2KL3zhC7F///6YPXt2fOUrX4nLL7889+v27t0bTU1N/dAhAGfSnj17YvLkyZVug34mACbsO9/5TqxYsSK+9rWvxdy5c+O+++6LRYsWxfbt22PixImn/NrRo0dHRMS74n0xNIb1R7sAnEbHoyt+HP+r9/mctFRlWZZVugkqY+7cufH2t789/vmf/zkiXn9bt6mpKW677bb45Cc/ecqvbW9vj7q6upgfS2NolQAIMNAcz7piYzwSbW1tUVtbW+l26Gfe9E/UsWPHYsuWLbFw4cLe66qrq2PhwoWxadOmN9SXy+Vob2/vcwAAA5MAmKhf//rX0d3dHfX19X2ur6+vj/3797+hvqWlJerq6noPn/8DgIFLAKSQlStXRltbW++xZ8+eSrcEAPyR/BFIosaPHx9DhgyJAwcO9Ln+wIED0dDQ8Ib6UqkUpVKpv9oDAM4gM4CJqqmpiTlz5sSGDRt6r+vp6YkNGzZEc3NzBTsDAM40M4AJW7FiRSxbtiz+/M//PC6//PK47777oqOjI/7qr/6q0q0BAGeQAJiwD37wg/HKK6/EnXfeGfv3748//dM/jccee+wNfxgCAAwu1gHkj2IdQICBzTqAafMZQACAxAiAAACJEQABABIjAAIAJEYABABIjAAIAJAYARAAIDECIABAYgRAAIDECIAAAIkRAAEAEiMAAgAkRgAEAEiMAAgAkBgBEAAgMQIgAEBiBEAAgMQIgAAAiREAAQASIwACACRGAAQASIwACACQGAEQACAxAiAAQGIEQACAxAiAAACJEQABABIjAAIAJEYABABIjAAIAJAYARAAIDECIABAYgRAAIDECIAAAIkRAAEAEjO00g3A2ax65Mjcmqqp5522++v+xYunbSwAOBkzgIn67Gc/G1VVVX2Oiy++uNJtAQD9wAxgwi699NJ44oknei8PHerHAQBS4H/8hA0dOjQaGhoq3QYA0M+8BZywF198MRobG+P888+PD3/4w7F79+5KtwQA9AMzgImaO3durFmzJmbMmBH79u2Lu+66K9797nfH888/H6NHj35DfblcjnK53Hu5vb29P9sFAE4jATBRS5Ys6f33rFmzYu7cuTF16tT47ne/GzfccMMb6ltaWuKuu+7qzxYBgDPEW8BERMSYMWPioosuih07dpzw9pUrV0ZbW1vvsWfPnn7uEAA4XQRAIiLi1VdfjZ07d8akSZNOeHupVIra2to+BwAwMAmAifrYxz4Wra2t8atf/Sr+7d/+LT7wgQ/EkCFD4kMf+lClWwMAzjCfAUzUyy+/HB/60Ifi0KFDMWHChHjXu94VmzdvjgkTJlS6tbNK9YRzc2u6xp1TaKxhLx/KrRnytgsLjZW99F+5NT1HjxYaC4D0CICJWrduXaVbAAAqxFvAAACJEQABABIjAAIAJEYABABIjAAIAJAYARAAIDECIABAYqwDSJKKLrjc2Zi/5V3HpGGFxqqN/EWlixoa5+XWHC/Qe0REadvu3Jqso6PQWP29+HT1yJH9en8RFtgGBgczgAAAiREAAQASIwACACRGAAQASIwACACQGAEQACAxAiAAQGIEQACAxAiAAACJsRMISSoX3CWjyC4fQzuzQmO1TxueW1M63FNorOqjXbk1x0cMKTTWkIvydxUpatjLh3Jrjr+0p9BYRXb5OJ27chTdVaS/+wI4E8wAAgAkRgAEAEiMAAgAkBgBEAAgMQIgAEBiBEAAgMQIgAAAiREAAQASYyFoklQeW+xHv1yX/xqpXPdWu/m9oxOLvSbrHj4qt6b02+PF7nNSKbfmtxcWW1S64af5daWR+QtiR0R0/+LFQnWni8WbgZSYAQQASIwACACQGAEQACAxAiAAQGIEQACAxAiAAACJEQABABIjAAIAJEYABABIjJ1ASFLtC78pVNc+ZUJuTU1bVmis7uFVuTVj/7PY7h3tU/N/dTsL7GJSVOfEnkJ1HZOG5dYM39FZaKwhb7swt6a/dws53apHjsytsUMJcCaYARyEnnrqqbjqqquisbExqqqq4uGHH+5ze5Zlceedd8akSZNixIgRsXDhwnjxxYH9HykAUJwAOAh1dHTE7NmzY9WqVSe8/d57740vf/nL8bWvfS2efvrpOOecc2LRokXR2VlsZgYAGNi8BTwILVmyJJYsWXLC27Isi/vuuy8+9alPxdKlSyMi4lvf+lbU19fHww8/HNdee21/tgoAVIAZwMTs2rUr9u/fHwsXLuy9rq6uLubOnRubNm066deVy+Vob2/vcwAAA5MAmJj9+/dHRER9fX2f6+vr63tvO5GWlpaoq6vrPZqams5onwDAmSMAUsjKlSujra2t99izZ0+lWwIA/kgCYGIaGhoiIuLAgQN9rj9w4EDvbSdSKpWitra2zwEADEwCYGKmTZsWDQ0NsWHDht7r2tvb4+mnn47m5uYKdgYA9Bd/BTwIvfrqq7Fjx47ey7t27YqtW7fGuHHjYsqUKXH77bfHP/7jP8aFF14Y06ZNi09/+tPR2NgY73//+yvXdD/rGV5TqK7IwsxFFniOiDhezq8rOtaxAhOwQ0rFxuouFakqttj18QL32Tl9YqGxhnR259YU+w7PXhZ5BipFAByEnnnmmXjPe97Te3nFihUREbFs2bJYs2ZNfPzjH4+Ojo646aab4vDhw/Gud70rHnvssRg+fHilWgYA+pEAOAjNnz8/suzkMzZVVVVx9913x913392PXQEAZwufAQQASIwACACQGAEQACAxAiAAQGIEQACAxAiAAACJEQABABJjHUA4hVHP7cut2fs/mwqNNaQzfzeNoxNP32uyI9PzdzEpqrq2q1Bdx+L8+xz6yDmFxhramf/0VDthQqGxul95pVAdQCrMAAIAJEYABABIjAAIAJAYARAAIDECIABAYgRAAIDECIAAAIkRAAEAEmMhaJJU3XmsUF33uNrcmnG/LBca6zcXl3JrhhQbKrry2yqsyCLPQ14eXmisYQercmvO2VfsmyztbS9UB8CbZwYQACAxAiAAQGIEQACAxAiAAACJEQABABIjAAIAJEYABABIjAAIAJAYARAAIDF2AoFTKLJjyLDfFBurffqw3JrhB4u9JqsusJnG6B3Ffr27avPrOiceLzTWsLb8sY6PGFJorCHjzsmtqR5eU2is6o6O3Jqeo0cLjQUwGJgBBABIjAAIAJAYARAAIDECIABAYgRAAIDECIAAAIkRAAEAEiMAAgAkxkLQJKm
" </div>\n",
" "
],
"text/plain": [
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "8aa1881c",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "3a0e02f7",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 55,
"id": "7b83c9ba",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"((134842, 7), (134842, 4))"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x.shape, x0.shape"
]
},
{
"cell_type": "code",
"execution_count": 56,
"id": "fad76b87",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a102651d5dfa4a76970dbb8c3493c534",
"version_major": 2,
"version_minor": 0
},
"image/png": "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
"text/html": [
"\n",
" <div style=\"display: inline-block;\">\n",
" <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
" Figure\n",
" </div>\n",
" <img src='data:image/png;base64,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
" </div>\n",
" "
],
"text/plain": [
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"# plt.scatter(x[:, 0], x[:, 1], s=0.1)\n",
"plt.scatter(x0[:, 0], x0[:, 1], s=0.1)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d0423f44",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "6276d479",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "50a4a6d6",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "7883167e",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "179d520e",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "209c181f",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "b3253603",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 11,
"id": "c6430d5c",
"metadata": {},
"outputs": [],
"source": [
"unique_clump_ids = numpy.unique(clump_ids)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "ec9e90ab",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0, 1, 2, 3, 5, 6, 7, 9, 11, 12, 13, 14, 15,\n",
" 17, 19, 21, 22, 23, 29, 32, 34, 39, 43, 134, 137, 138,\n",
" 139, 149, 151, 156, 157, 158, 159, 161, 163, 165, 167, 168, 169,\n",
" 172, 173, 175, 178, 179, 181, 183, 186, 187, 188, 189, 191, 193,\n",
" 194, 196, 197, 198, 204, 207, 209, 211, 212, 213, 214, 215, 216,\n",
" 217, 218, 221, 223, 225, 227, 228, 231, 232, 237, 238, 240, 243,\n",
" 245, 247, 248, 249, 252, 257, 258, 259, 260, 262, 263, 264, 267,\n",
" 268, 269, 270, 272, 273, 274, 275, 276, 278], dtype=int32)"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_clump_ids[:100]"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "273df2d6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"131 ns ± 0.529 ns per loop (mean ± std. dev. of 7 runs, 10,000,000 loops each)\n"
]
}
],
"source": [
"%timeit numpy.nan == numpy.nan"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "6da0af29",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"791 ns ± 5.14 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)\n"
]
}
],
"source": [
"%timeit numpy.isnan(numpy.nan)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "bd84b541",
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "Object dtype dtype('O') has no native HDF5 equivalent",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[29], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m test \u001b[39m=\u001b[39m {\u001b[39m\"\u001b[39m\u001b[39masd\u001b[39m\u001b[39m\"\u001b[39m: \u001b[39m1\u001b[39m}\n\u001b[1;32m 2\u001b[0m \u001b[39mwith\u001b[39;00m h5py\u001b[39m.\u001b[39mFile(\u001b[39m\"\u001b[39m\u001b[39mtest.hp\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mw\u001b[39m\u001b[39m'\u001b[39m) \u001b[39mas\u001b[39;00m f:\n\u001b[0;32m----> 3\u001b[0m f\u001b[39m.\u001b[39;49mcreate_dataset(\u001b[39m\"\u001b[39;49m\u001b[39mtest\u001b[39;49m\u001b[39m\"\u001b[39;49m, data\u001b[39m=\u001b[39;49mtest)\n",
"File \u001b[0;32m~/csiborgtools/venv_csiborg/lib/python3.9/site-packages/h5py/_hl/group.py:183\u001b[0m, in \u001b[0;36mGroup.create_dataset\u001b[0;34m(self, name, shape, dtype, data, **kwds)\u001b[0m\n\u001b[1;32m 180\u001b[0m parent_path, name \u001b[39m=\u001b[39m name\u001b[39m.\u001b[39mrsplit(\u001b[39mb\u001b[39m\u001b[39m'\u001b[39m\u001b[39m/\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m1\u001b[39m)\n\u001b[1;32m 181\u001b[0m group \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mrequire_group(parent_path)\n\u001b[0;32m--> 183\u001b[0m dsid \u001b[39m=\u001b[39m dataset\u001b[39m.\u001b[39;49mmake_new_dset(group, shape, dtype, data, name, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwds)\n\u001b[1;32m 184\u001b[0m dset \u001b[39m=\u001b[39m dataset\u001b[39m.\u001b[39mDataset(dsid)\n\u001b[1;32m 185\u001b[0m \u001b[39mreturn\u001b[39;00m dset\n",
"File \u001b[0;32m~/csiborgtools/venv_csiborg/lib/python3.9/site-packages/h5py/_hl/dataset.py:88\u001b[0m, in \u001b[0;36mmake_new_dset\u001b[0;34m(parent, shape, dtype, data, name, chunks, compression, shuffle, fletcher32, maxshape, compression_opts, fillvalue, scaleoffset, track_times, external, track_order, dcpl, dapl, efile_prefix, virtual_prefix, allow_unknown_filter, rdcc_nslots, rdcc_nbytes, rdcc_w0)\u001b[0m\n\u001b[1;32m 86\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 87\u001b[0m dtype \u001b[39m=\u001b[39m numpy\u001b[39m.\u001b[39mdtype(dtype)\n\u001b[0;32m---> 88\u001b[0m tid \u001b[39m=\u001b[39m h5t\u001b[39m.\u001b[39;49mpy_create(dtype, logical\u001b[39m=\u001b[39;49m\u001b[39m1\u001b[39;49m)\n\u001b[1;32m 90\u001b[0m \u001b[39m# Legacy\u001b[39;00m\n\u001b[1;32m 91\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39many\u001b[39m((compression, shuffle, fletcher32, maxshape, scaleoffset)) \u001b[39mand\u001b[39;00m chunks \u001b[39mis\u001b[39;00m \u001b[39mFalse\u001b[39;00m:\n",
"File \u001b[0;32mh5py/h5t.pyx:1664\u001b[0m, in \u001b[0;36mh5py.h5t.py_create\u001b[0;34m()\u001b[0m\n",
"File \u001b[0;32mh5py/h5t.pyx:1688\u001b[0m, in \u001b[0;36mh5py.h5t.py_create\u001b[0;34m()\u001b[0m\n",
"File \u001b[0;32mh5py/h5t.pyx:1748\u001b[0m, in \u001b[0;36mh5py.h5t.py_create\u001b[0;34m()\u001b[0m\n",
"\u001b[0;31mTypeError\u001b[0m: Object dtype dtype('O') has no native HDF5 equivalent"
]
}
],
"source": [
"test = {\"asd\": 1}\n",
"with h5py.File(\"test.hp\", 'w') as f:\n",
" f.create_dataset(\"test\", data=test)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "29e5a4ed",
"metadata": {},
"outputs": [],
"source": [
"f"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "317aef53",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2360"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"k0, kf = minmax_clump(259, clump_ids)\n",
"\n",
"kf - k0 + 1"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "8fe50188",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([259, 259, 259, ..., 259, 259, 259], dtype=int32)"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"clump_ids[k0:kf+1]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d0508ab0",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 3,
"id": "011a9fea",
"metadata": {},
"outputs": [],
"source": [
"import numpy\n",
"\n",
"nsim = nsim0\n",
"nsnap = nsnap0\n",
"\n",
"\n",
"part0 = h5py.File(paths.initmatch_path(nsim, \"particles\"), 'r')['particles']\n",
"cmap = h5py.File(paths.particles_path(nsim, \"clumpmap\"), 'r')\n",
"partf = h5py.File(paths.particles_path(nsim), 'r')[\"particles\"]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "8f8c9116",
"metadata": {},
"outputs": [],
"source": [
"part0[cmap[str(558655)], :]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "1d74f0e7",
"metadata": {},
"outputs": [],
"source": [
"cat = csiborgtools.read.ClumpsCatalogue(nsim, paths, rawdata=True)"
]
},
{
"cell_type": "code",
"execution_count": 44,
"id": "b595aa4b",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"10000it [00:51, 193.86it/s]\n"
]
}
],
"source": [
"from tqdm import tqdm\n",
"y = np.zeros((10000, 2), dtype=np.int32)\n",
"\n",
"for i, hid in tqdm(enumerate(cat[\"index\"][cat.ismain][:10000])):\n",
" n = np.sum(cat[\"parent\"] == hid)\n",
" y[i, :] = hid, n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "2bf99732",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([558655, 558659, 558673, 558675, 558677, 558692, 558705, 558717,\n",
" 558724, 558726, 558739, 558746, 558857, 559232, 559256, 559292,\n",
" 559325, 559343, 559349, 559384, 559388, 559406, 559415, 559453,\n",
" 559467, 559473, 559492, 559496, 559541, 559544, 559548, 559570,\n",
" 559571, 559604, 559612, 559637, 559643, 559648, 559655, 559724,\n",
" 559735, 559779, 559780, 559792, 559802, 559804, 559817, 559910,\n",
" 559918, 559926, 559960, 559977, 559982, 559997, 560012, 560031,\n",
" 560122, 560213, 560325, 560375, 560426, 560517, 560798, 562156,\n",
" 563235, 563254, 563586, 563651, 563671, 563892, 564050, 564068,\n",
" 564138, 564153, 564358, 564525, 564734, 564745, 564779, 564831,\n",
" 565008, 565180, 565272, 565276, 565521, 565620, 565701, 565891,\n",
" 566046, 566604, 566705, 567047, 567336, 567720, 568897, 569232,\n",
" 569376, 569773, 573130, 575459, 575880, 576414, 579319, 579445,\n",
" 579722, 579770, 580117, 580388, 580441, 580507, 580518, 581594,\n",
" 581767, 582047, 582713, 583256, 586403, 587027, 587239, 588764,\n",
" 593803, 595742, 596219, 599285, 600189, 603135, 605484],\n",
" dtype=int32)"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cat[\"index\"][cat[\"parent\"] == 558655]"
]
},
{
"cell_type": "code",
"execution_count": 45,
"id": "64aa63a2",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([558655, 127], dtype=int32)"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y[np.argmax(y[:, 1]), :]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "4a5c0091",
"metadata": {},
"outputs": [],
"source": [
"keys = list(cmap.keys())"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "d6befe8b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[558655 558659 558673 558675 558677 558692 558705 558717 558724 558726\n",
" 558739 558746 558857 559232 559256 559292 559325 559343 559349 559384\n",
" 559388 559406 559415 559453 559467 559473 559492 559496 559541 559544\n",
" 559548 559570 559571 559604 559612 559637 559643 559648 559655 559724\n",
" 559735 559779 559780 559792 559802 559804 559817 559910 559918 559926\n",
" 559960 559977 559982 559997 560012 560031 560122 560213 560325 560375\n",
" 560426 560517 560798 562156 563235 563254 563586 563651 563671 563892\n",
" 564050 564068 564138 564153 564358 564525 564734 564745 564779 564831\n",
" 565008 565180 565272 565276 565521 565620 565701 565891 566046 566604\n",
" 566705 567047 567336 567720 568897 569232 569376 569773 573130 575459\n",
" 575880 576414 579319 579445 579722 579770 580117 580388 580441 580507\n",
" 580518 581594 581767 582047 582713 583256 586403 587027 587239 588764\n",
" 593803 595742 596219 599285 600189 603135 605484]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
" 0%| | 0/127 [00:00<?, ?it/s]"
]
}
],
"source": [
"\n",
"csiborgtools.fits.load_parent_particles(558655, part0, cmap, cat)\n",
"# Xf = csiborgtools.fits.load_parent_particles(378, partf, cmap, cat)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "ee1a8686",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f9cee980a8fb4354aa78f811a8915e85",
"version_major": 2,
"version_minor": 0
},
"image/png": "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
"text/html": [
"\n",
" <div style=\"display: inline-block;\">\n",
" <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
" Figure\n",
" </div>\n",
" <img src='data:image/png;base64,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
" </div>\n",
" "
],
"text/plain": [
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"\n",
"plt.scatter(X[:, 0], X[:, 1], s=0.1)\n",
"# plt.scatter(Xf[:, 0], Xf[:, 1], s=0.1)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1bbda411",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 3,
"id": "6e9d36ca",
"metadata": {},
"outputs": [],
"source": [
"pid_final = np.load(\"pid_final.npy\")\n",
"pid_init = np.load(\"pid_initial.npy\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "ac9c5c58",
"metadata": {},
"outputs": [],
"source": [
"pid_order = np.argsort(pid_final).astype(np.int32)\n",
"\n",
"pid_order_2 = np.argsort(pid_order).astype(np.int32)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "bcf3be0a",
"metadata": {},
"outputs": [],
"source": [
"# Sort them\n",
"\n",
"pid_init = pid_init[np.argsort(pid_init)]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "1218ba7f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.alltrue(pid_init[pid_order_2] == pid_final)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "0a12563b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 6, 7, 8, ..., 429538984, 456581538,\n",
" 456482476], dtype=int32)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "95d9f0f7",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "c7998177",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "1b1ef66c",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 25,
"id": "5b9362d2",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 6, 7, 8, ..., 429538984, 456581538,\n",
" 456482476], dtype=int32)"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"\n",
"\n",
"pid_final[pid_order][pid_order_2]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a333cacf",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "cf7a4005",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "884cfa0f",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "ebcc11ae",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 18,
"id": "3f155db8",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1, 2, 3, ..., 460357796, 460357797,\n",
" 460357798], dtype=int32)"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"part0['ID']"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "88feaaff",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 8,
"id": "20d1a6a0",
"metadata": {},
"outputs": [],
"source": [
"indx1 = numpy.argsort(part0['ID'])\n",
"\n",
"indx2 = numpy.argsort(pid)\n",
"\n",
"indx1_inv = numpy.argsort(indx1)\n",
"part0 = part0[indx2][indx1_inv]\n",
"# pid = pid[indx2]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([(0.6463347 , 0.5915494 , 0.3729033 , 1.1641532e-10, 285313656),\n",
" (0.22073722, 0.49019635, 0.9627326 , 5.9604645e-08, 58672855),\n",
" (0.7210556 , 0.18949312, 0.37298316, 5.9604645e-08, 459417395),\n",
" ...,\n",
" (0.46851808, 0.2966002 , 0.44987014, 1.1641532e-10, 2684309),\n",
" (0.7481397 , 0.02954267, 0.8927611 , 5.9604645e-08, 402473389),\n",
" (0.9159686 , 0.8301242 , 0.67392564, 5.9604645e-08, 287235917)],\n",
" dtype=[('x', '<f4'), ('y', '<f4'), ('z', '<f4'), ('M', '<f4'), ('ID', '<i4')])"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"part0"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "53d0f130",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "78041a0a",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "b6059fb3",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "aafbd8d9",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 10,
"id": "4d989091",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 6, 7, 8, ..., 429538984, 456581538,\n",
" 456482476], dtype=int32)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pid"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "c97c3f51",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1, 2, 3, ..., 460471465, 460471466,\n",
" 460471467], dtype=int32)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pid"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1, 2, 3, 5, 6, 7, 9, 11, 12, 13, 15, 17, 19,\n",
" 21, 22, 23, 29, 32, 34, 39, 134, 137, 138, 139, 149, 151,\n",
" 156, 157, 158, 159, 161, 163, 167, 168, 169, 172, 173, 175, 178,\n",
" 181, 186, 188, 189, 191, 196, 197, 198, 204, 209, 211], dtype=int32)"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cat = csiborgtools.read.ClumpsCatalogue(nsim, paths, load_fitted=False, rawdata=True)\n",
"parent_ids = cat[\"index\"][cat.ismain]\n",
"\n",
"parent_ids[:50]"
]
},
{
"cell_type": "code",
"execution_count": 54,
"id": "af4fa7e0",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"11031538"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"i = cat[\"index\"][np.argsort(cat[\"mass_cl\"])[::-3][213]]\n",
"i"
]
},
{
"cell_type": "code",
"execution_count": 55,
"id": "c57a56ac",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[11031538 11032027 11033906 11034471 11034477 11034692 11035306 11049411\n",
" 11057461 11058959]\n",
"[24440, 256, 133, 0, 0, 0, 113, 0, 0, 0]\n"
]
}
],
"source": [
"# clid = 10170336\n",
"# clid = 10170336 + 12\n",
"clid = i\n",
"\n",
"\n",
"mmain_indxs = cat[\"index\"][cat[\"parent\"] == clid]\n",
"# mask = clump_ids == clid\n",
"# print(mask.sum())\n",
"print(mmain_indxs)\n",
"\n",
"print([numpy.sum(clump_ids == j) for j in mmain_indxs])\n"
]
},
{
"cell_type": "code",
"execution_count": 56,
"id": "4ad427f1",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"24942"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mask = numpy.isin(clump_ids, mmain_indxs)\n",
"mask.sum()"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "410c3b97efc14cd8b1b34d7a3666483f",
"version_major": 2,
"version_minor": 0
},
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"\n",
" <div style=\"display: inline-block;\">\n",
" <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
" Figure\n",
" </div>\n",
" <img src='data:image/png;base64,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
" </div>\n",
" "
],
"text/plain": [
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# coords = part0[mask, :]\n",
"plt.figure()\n",
"plt.scatter(part0['x'][mask], part0['y'][mask], s=1)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "72a723d2",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "33d088f2",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "e54f3005",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 193,
"id": "b097637b",
"metadata": {},
"outputs": [],
"source": [
"particles0 = h5py.File(paths.initmatch_path(nsim0, \"particles\"), 'r')['particles']\n",
"particlesx = h5py.File(paths.initmatch_path(nsimx, \"particles\") , 'r')['particles']"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "c3cf4c91",
"metadata": {},
"outputs": [],
"source": [
"# delta_bckg = overlapper.make_bckg_delta(particles0, verbose=True)\n",
"# delta_bckg = overlapper.make_bckg_delta(particlesx, delta=delta_bckg, verbose=True)\n",
"# np.save(\"./bckg_{}_{}.npy\".format(nsim0, nsimx), delta_bckg)\n",
"# delta_bckg = np.load(\"./bckg_{}_{}.npy\".format(nsim0, nsimx))"
]
},
{
"cell_type": "code",
"execution_count": 194,
"id": "f7a602a5",
"metadata": {},
"outputs": [],
"source": [
"map0 = h5py.File(paths.initmatch_path(nsim0, \"halomap\"), 'r')\n",
"mapx = h5py.File(paths.initmatch_path(nsimx, \"halomap\"), 'r')"
]
},
{
"cell_type": "code",
"execution_count": 174,
"id": "79ddf8f5",
"metadata": {},
"outputs": [],
"source": [
"keys = np.array(sorted([int(x) for x in mapx.keys()]))"
]
},
{
"cell_type": "code",
"execution_count": 175,
"id": "326b4ae8",
"metadata": {},
"outputs": [],
"source": [
Add Quijote (#61) * Rename paths object * Remove redshift calculation * Explicit keywrod arg * Rename box units * Basic renaming * Little docs * Rename paths * add imports * Sort imports * Add Quijote cat * Split boxes * add Quijote path * Add origin argument * Update nbs
2023-05-13 16:37:34 +00:00
"paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)\n",
Gravitational potential calculation (#60) * Rename file * add velocity field * Add velcoity field * Move smoothening * Add verbosity flags * remove blank * Simplify paths * Add potential calculation * Update paths * Add potential field * Add potential calculation * Move away sky matching * Move interpolation functions * Update nbs
2023-05-12 12:07:58 +00:00
"\n",
"halocat = csiborgtools.read.HaloCatalogue(nsimx, paths, rawdata=True)\n",
"clumpcat = csiborgtools.read.ClumpsCatalogue(nsimx, paths, rawdata=True)"
]
},
{
"cell_type": "code",
"execution_count": 176,
"id": "6d5a86b2",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"10"
]
},
"execution_count": 176,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"clid = 2\n",
"\n",
"\n",
"np.sum(clumpcat[\"parent\"] == clid)\n",
"\n",
"# mmain_indxs = clumpcat[\"index\"][clumpcat[\"parent\"] == clid]\n",
"\n",
"# mmain_indxs\n",
"# mmain_mask = numpy.isin(clump_ids, mmain_indxs, assume_unique=True)"
]
},
{
"cell_type": "code",
"execution_count": 195,
"id": "099d3357",
"metadata": {},
"outputs": [],
"source": [
"fs = list(map0.keys())"
]
},
{
"cell_type": "code",
"execution_count": 196,
"id": "21aa49af",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 173643/173643 [01:29<00:00, 1947.49it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"362439022\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"from tqdm import tqdm\n",
"tot = 0\n",
"for f in tqdm(fs):\n",
" tot += map0[f].shape[0]\n",
"\n",
"print(tot)"
]
},
{
"cell_type": "code",
"execution_count": 197,
"id": "278de818",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<HDF5 dataset \"particles\": shape (182510395, 4), type \"<f4\">"
]
},
"execution_count": 197,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"particles0"
]
},
{
"cell_type": "code",
"execution_count": 198,
"id": "e55b65c4",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1.9858541317605498"
]
},
"execution_count": 198,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tot / 182510395"
]
},
{
"cell_type": "code",
"execution_count": 172,
"id": "373423b2",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<HDF5 dataset \"particles\": shape (182036133, 4), type \"<f4\">"
]
},
"execution_count": 172,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"particlesx"
]
},
{
"cell_type": "code",
"execution_count": 177,
"id": "d5b65fcb",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"86031"
]
},
"execution_count": 177,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"i = 10170336\n",
"\n",
"\n",
"k = np.where(cat[\"ID\"] == i)[0][0]\n",
"k"
]
},
{
"cell_type": "code",
"execution_count": 178,
"id": "709cb935",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10170336\n"
]
},
{
"data": {
"text/plain": [
"<HDF5 dataset \"10170336\": shape (2085,), type \"<i8\">"
]
},
"execution_count": 178,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# i = np.random.choice(keys[keys<100000])\n",
"\n",
"# # i = 10040155 #+ 11\n",
"\n",
"print(i)\n",
"# i = 32\n",
"\n",
"\n",
"mask = map0[str(i)]\n",
"mask"
]
},
{
"cell_type": "code",
"execution_count": 183,
"id": "7f4a40a5",
"metadata": {},
"outputs": [],
"source": [
"halo = particlesx[mask, :]"
]
},
{
"cell_type": "code",
"execution_count": 184,
"id": "e83806b1",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2085.000032720428"
]
},
"execution_count": 184,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.sum(halo[:, 3]) / 1.1641532e-10"
]
},
{
"cell_type": "code",
"execution_count": 186,
"id": "7fe3494d",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "92b87db2ae5440c19f4b4694e9e98aaf",
"version_major": 2,
"version_minor": 0
},
"image/png": "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
"text/html": [
"\n",
" <div style=\"display: inline-block;\">\n",
" <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
" Figure\n",
" </div>\n",
" <img src='data:image/png;base64,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
" </div>\n",
" "
],
"text/plain": [
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"plt.scatter(halo[:, 0], halo[:, 1], s=2)\n",
"# plt.scatter(cat['x'][k], cat['y'][k], s=10, c='r')\n",
"\n",
"plt.xlabel(r\"x\")\n",
"plt.ylabel(r\"y\")\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c0c2d4fe",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "2f2ead3e",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "d48adc68",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "276b5823",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "13f4cf79",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "11a909af",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "105862df",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "a2f25a4e",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "93fbe29d",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "64608a92",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 21,
"id": "35442e33",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_7442/2249068321.py:2: RuntimeWarning: divide by zero encountered in log10\n",
" plt.imshow(np.log10(np.sum(delta_bckg, axis=0)))\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "bb36c88a57034c6ea702eb545cc6e57b",
"version_major": 2,
"version_minor": 0
},
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" </div>\n",
" "
],
"text/plain": [
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"plt.imshow(np.log10(np.sum(delta_bckg, axis=0)))\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "1d8ad8fe",
"metadata": {},
"outputs": [],
"source": [
"f = h5py.File(\"../data/particles_7444.h5\", \"r\")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "020ac8e4",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 4.42346358e-01, 7.09757663e-03, 4.64053304e-01, -1.96926287e-03,\n",
" -2.67177823e-03, -6.45721859e-04, 1.16415322e-10])"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f[\"particles\"][0, :]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1eb74f46",
"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": "3.9.7"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
Reference in a new issue Copy permalink