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Merge pull request #166 from LSSTDESC/tqz/add_blending_notebook
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add blending notebook
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@ztq1996
ztq1996 authored Nov 9, 2024
2 parents 9b14421 + 9cd409a commit 75f52e8
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283 changes: 283 additions & 0 deletions examples/creation_examples/blending_degrader_demo.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"id": "2610a0f0-0c71-4401-896f-734442bcd66d",
"metadata": {},
"source": [
"## Blending Degrader demo\n",
"\n",
"author: Shuang Liang\n",
"\n",
"This notebook demonstrate the use of `rail.creation.degradation.unrec_bl_model`, which uses Friends of Friends to finds sources close to each other and merge them into unrecognized blends"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f7a6adc3-68e8-4a1d-842f-bfb0960a1c4a",
"metadata": {},
"outputs": [],
"source": [
"from rail.creation.degraders.unrec_bl_model import UnrecBlModel\n",
"\n",
"from rail.core.data import PqHandle\n",
"from rail.core.stage import RailStage\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd, numpy as np"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6912a740-31ea-4987-b06d-81ff17cd895a",
"metadata": {},
"outputs": [],
"source": [
"DS = RailStage.data_store\n",
"DS.__class__.allow_overwrite = True\n"
]
},
{
"cell_type": "markdown",
"id": "a282c2ed-141b-4507-8254-dc8fbc9864dc",
"metadata": {},
"source": [
"### Create a random catalog with ugrizy+YJHF bands as the the true input"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1078bc2a-fc54-41c3-bd30-6c447bb971d4",
"metadata": {},
"outputs": [],
"source": [
"data = np.random.normal(23, 3, size = (1000,12))\n",
"data[:, 0] = np.random.uniform(low=0, high=0.03, size=1000)\n",
"data[:, 1] = np.random.uniform(low=0, high=0.03, size=1000)\n",
"\n",
"data_df = pd.DataFrame(data=data, # values\n",
" columns=['ra', 'dec', 'u', 'g', 'r', 'i', 'z', 'y', 'Y', 'J', 'H', 'F'])\n",
"\n",
"data_truth_handle = DS.add_data('input', data_df, PqHandle)\n",
"data_truth = data_truth_handle.data"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "33c99a4d-8375-4003-9a9a-70fa85a3eb82",
"metadata": {},
"outputs": [],
"source": [
"#data_df.to_parquet('bl_test.pq')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a5636721-a734-4746-bd93-8101bc558b6e",
"metadata": {},
"outputs": [],
"source": [
"plt.scatter(data_truth['ra'], data_truth['dec'], s=5)\n",
"plt.xlabel(\"Ra [Deg]\", fontsize=14)\n",
"plt.ylabel(\"Dec [Deg]\", fontsize=14)\n",
"plt.show()\n"
]
},
{
"cell_type": "markdown",
"id": "1da27deb-d167-4f38-8c59-f270184d6ab3",
"metadata": {},
"source": [
"### The blending model"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a07f72a0-e24c-4844-90f0-d5a49ac4362b",
"metadata": {},
"outputs": [],
"source": [
"## model configuration; linking length is in arcsecs\n",
"\n",
"blModel = UnrecBlModel.make_stage(name='unrec_bl_model', ra_label='ra', dec_label='dec', linking_lengths=1.0, \\\n",
" bands='ugrizy')\n",
"blModel.get_config_dict()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e5f4862a-0621-46d4-8901-7e84b461c424",
"metadata": {},
"outputs": [],
"source": [
"# run the model\n",
"\n",
"outputs = blModel(data_truth)\n",
"\n",
"samples_w_bl = outputs['output'].data\n",
"component_ind = outputs['compInd'].data\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bc5158dd-f474-4731-b847-b4a7358656b9",
"metadata": {},
"outputs": [],
"source": [
"fig, ax = plt.subplots(figsize=(6, 5), dpi=100)\n",
"\n",
"ax.scatter(data_truth['ra'], data_truth['dec'], s=10, facecolors='none', edgecolors='b', label='Original')\n",
"ax.scatter(samples_w_bl['ra'], samples_w_bl['dec'], s=5, c='r', label='w. Unrec-BL')\n",
"\n",
"ax.legend(loc=2, fontsize=12)\n",
"ax.set_xlabel(\"Ra [Deg]\", fontsize=14)\n",
"ax.set_ylabel(\"Dec [Deg]\", fontsize=14)\n",
"\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "268b3d37-b7fd-4ac1-8457-2104a87c9e6d",
"metadata": {},
"outputs": [],
"source": [
"b = 'r'\n",
"plt.hist(data_truth[b], bins=np.linspace(10, 30, 20), label='Original')\n",
"plt.hist(samples_w_bl[b], bins=np.linspace(10, 30, 20), fill=False, label='w. Unrec-BL')\n",
"\n",
"plt.xlabel(fr'Magnitude ${b}$', fontsize=14)\n",
"plt.legend(fontsize=12)\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a1d51c15-1e04-4b22-9abb-9b267965dbeb",
"metadata": {},
"outputs": [],
"source": [
"flux = 10**(-(data_truth[b]-28.10)/2.5) # r band zp for lsst is 28.10\n",
"flux_bl = 10**(-(samples_w_bl[b]-28.10)/2.5)\n",
"\n",
"plt.hist(flux, bins=np.linspace(0, 10000, 40), label='Original')\n",
"plt.hist(flux_bl, bins=np.linspace(0, 10000, 40), fill=False, label='w. Unrec-BL')\n",
"\n",
"plt.xlabel(fr'Flux ${b}$', fontsize=14)\n",
"plt.yscale('log')\n",
"plt.legend(fontsize=12)\n",
"plt.show()\n"
]
},
{
"cell_type": "markdown",
"id": "f3ba003e-da62-4bfc-b70e-c07c1112efc0",
"metadata": {},
"source": [
"### Study one BL case"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4d3fbd87-b227-43bf-b712-e8a069b51a54",
"metadata": {},
"outputs": [],
"source": [
"## find a source with more than 1 truth component\n",
"\n",
"group_size = 1\n",
"while group_size==1:\n",
"\n",
" rand_ind = np.random.randint(len(samples_w_bl))\n",
" this_bl = samples_w_bl.iloc[rand_ind]\n",
" group_id = this_bl['group_id']\n",
" \n",
" FoF_group = component_ind.query(f\"group_id == {group_id}\")\n",
" group_size = len(FoF_group)\n",
"\n",
"truth_comp = data_truth.iloc[FoF_group.index]\n",
"\n",
"print('Truth RA / Blended RA:')\n",
"print(truth_comp['ra'].to_numpy(), '/', this_bl['ra'])\n",
"print(\"\")\n",
"\n",
"print('Truth DEC / Blended DEC:')\n",
"print(truth_comp['dec'].to_numpy(), '/', this_bl['dec'])\n",
"print(\"\")\n",
"\n",
"for b in 'ugrizy':\n",
" print(f'Truth mag {b} / Blended mag {b}:')\n",
" print(truth_comp[b].to_numpy(), '/', this_bl[b])\n",
" print(\"\")\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8dacb910-dd26-404f-ba61-4278094b6355",
"metadata": {},
"outputs": [],
"source": [
"\n",
"fig, ax = plt.subplots(figsize=(6, 5), dpi=100)\n",
"\n",
"ax.scatter(this_bl['ra']*3600, this_bl['dec']*3600, s=1e4, c='r')\n",
"ax.scatter(truth_comp['ra']*3600, truth_comp['dec']*3600, s=1e4, facecolors='none', edgecolors='b')\n",
"\n",
"ax.scatter([], [], s=1e2, facecolors='none', edgecolors='b', label='Truth Components')\n",
"ax.scatter([], [], s=1e2, c='r', label='Merged Source')\n",
"\n",
"fig_size = 1 ## in arcsecs\n",
"ax.set_xlim(this_bl['ra']*3600-fig_size, this_bl['ra']*3600+fig_size)\n",
"ax.set_ylim(this_bl['dec']*3600-fig_size, this_bl['dec']*3600+fig_size)\n",
"\n",
"ax.legend(fontsize=12)\n",
"ax.set_xlabel(\"Ra [arcsecs]\", fontsize=14)\n",
"ax.set_ylabel(\"Dec [arcsecs]\", fontsize=14)\n",
"\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5fc4b38b-55d1-43ff-9039-ee9c49c54f4d",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.12.3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

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