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checkpoint
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@delucchi-cmu
delucchi-cmu committed Jun 5, 2024
1 parent 93ebf5c commit 2234508
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239 changes: 239 additions & 0 deletions incubator/mask/cone_extrema.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [],
"source": [
"import hipscat\n",
"import healpy as hp\n",
"import pandas as pd\n",
"from tqdm import tqdm\n",
"from hipscat.inspection import plot_pixel_list\n",
"from hipscat.pixel_math import HealpixPixel\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"# plot_pixel_list([HealpixPixel(0,11), HealpixPixel(4, 78)])"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [],
"source": [
"step = 2"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [],
"source": [
"## i know this is dumb. i don't care.\n",
"\n",
"def min_max_sep(bounds_a, bounds_b):\n",
" min_sep = float(\"inf\")\n",
" max_sep = 0.0\n",
"\n",
" for i in range(4*step):\n",
" for j in range (4*step):\n",
" sep_sq = (bounds_a[0][i]-bounds_b[0][j])**2 + (bounds_a[1][i]-bounds_b[1][j])**2\n",
" min_sep = min(min_sep, sep_sq)\n",
" max_sep = max(max_sep, sep_sq)\n",
" return (min_sep, max_sep)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3933 partitions\n",
"7732278 iterations\n"
]
}
],
"source": [
"\n",
"\n",
"gaia_full_partition_frame = pd.read_csv(\"gaia_partition_info.csv\")\n",
"gaia_full_partition_list = [\n",
" HealpixPixel(order, pixel)\n",
" for order, pixel in zip(\n",
" gaia_full_partition_frame[\"Norder\"],\n",
" gaia_full_partition_frame[\"Npix\"],\n",
" )\n",
" ]\n",
"num_partitions = len(gaia_full_partition_list)\n",
"print(num_partitions, \"partitions\")\n",
"print(int(.5 * num_partitions * (num_partitions - 1)), \"iterations\")"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 3933/3933 [09:51<00:00, 6.64it/s] \n"
]
}
],
"source": [
"\n",
"all_seps = []\n",
"\n",
"for a in tqdm(range(0, num_partitions)):\n",
" for b in range(a, num_partitions):\n",
"\n",
" bounds_a = hp.vec2dir(hp.boundaries(2**gaia_full_partition_list[a].order, gaia_full_partition_list[a].pixel, step=step, nest=True), lonlat=True)\n",
" bounds_b = hp.vec2dir(hp.boundaries(2**gaia_full_partition_list[b].order, gaia_full_partition_list[b].pixel, step=step, nest=True), lonlat=True)\n",
" all_seps.append(min_max_sep(bounds_a, bounds_b))"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"7736211"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(all_seps)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[(0.0, 506.24999999999966),\n",
" (0.0, 1236.6206396835553),\n",
" (6299.120639683556, 15411.620639683555),\n",
" (4972.652051530026, 13072.652051530025),\n",
" (8516.402051530025, 18641.402051530025),\n",
" (11289.449049432578, 23391.34255485142),\n",
" (7239.44904943258, 16351.949049432578),\n",
" (10791.34255485142, 24620.924450944753),\n",
" (6066.342554851419, 15516.34255485142),\n",
" (9433.42445094475, 26107.389504989897)]"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"all_seps[:10]"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [],
"source": [
"min_map_arrays = np.array(all_seps).T\n",
"min_map_arrays[0:10]\n",
"mins = min_map_arrays[0]\n",
"maxs = min_map_arrays[1]"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 3933/3933 [00:01<00:00, 2135.54it/s]\n"
]
}
],
"source": [
"pix_a_order = []\n",
"pix_a_pixel = []\n",
"pix_b_order = []\n",
"pix_b_pixel = []\n",
"\n",
"for a in tqdm(range(0, num_partitions)):\n",
" for b in range(a, num_partitions):\n",
" pix_a_order.append(gaia_full_partition_list[a].order)\n",
" pix_a_pixel.append(gaia_full_partition_list[a].pixel)\n",
" pix_b_order.append(gaia_full_partition_list[b].order)\n",
" pix_b_pixel.append(gaia_full_partition_list[b].pixel)\n",
"\n",
"\n",
"big_beautiful_frame = pd.DataFrame({\"Norder_a\": pix_a_order,\n",
" \"Npix_a\": pix_a_pixel,\n",
" \"Norder_b\": pix_b_order,\n",
" \"Npix_b\": pix_b_pixel,\n",
" \"min_sep\": mins,\n",
" \"max_sep\": maxs,\n",
" })\n",
"big_beautiful_frame.to_csv(\"bbf.csv\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "hipscatenv",
"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": "3.10.14"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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