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Simplifying detection efficiency validation notebook. #1035

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Simplifying detection efficiency validation notebook. #1035

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Simplifying detection efficiency validation notebook.
astronomerritt Oct 16, 2024
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astronomerritt Oct 16, 2024
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202 changes: 87 additions & 115 deletions docs/notebooks/demo_DetectionEfficiencyValidation.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -8,7 +8,13 @@
"source": [
"# Detection Efficiency Filter Demo\n",
"\n",
"This notebook demonstrates the detection efficiency/fading function filter."
"This notebook demonstrates the detection efficiency/fading function filter. The equation for the fading function is taken from Veres & Chesley (2017):\n",
"\n",
"$$\n",
"\\epsilon(m) = \\frac{F}{1 + e^\\frac{m-m_{5\\sigma}}{w}}\n",
"$$\n",
"\n",
"where $\\epsilon(m)$ is the probability of detection, $F$ is the peak detection efficiency, $m$ and $m_{5\\sigma}$ are respectively the observed magnitude and $5\\sigma$ limiting magnitude of the pointing, and $w$ is the width of the fading function."
]
},
{
Expand All @@ -17,8 +23,7 @@
"metadata": {},
"outputs": [],
"source": [
"from sorcha.modules import PPFootprintFilter as fp\n",
"from sorcha.modules import PPFadingFunctionFilter as ff\n",
"from sorcha.modules.PPFadingFunctionFilter import PPFadingFunctionFilter\n",
"from sorcha.modules.PPModuleRNG import PerModuleRNG"
]
},
Expand All @@ -29,34 +34,22 @@
"outputs": [],
"source": [
"import pandas as pd\n",
"import sqlite3 as sql\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import os"
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": null,
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create a dataframe of synthetic observations"
]
},
{
"cell_type": "markdown",
"metadata": {},
"outputs": [],
"source": [
"def getSqlData(database,rows_start,nrows):\n",
" \"\"\"Import dataset from local SQL database\n",
" \n",
" Parameters:\n",
" -----------\n",
" database ... path to database\n",
" rows_start ... number of row to start\n",
" rows_max ... number of rows to select\n",
" \n",
" Returns:\n",
" --------\n",
" observations ... pandas dataframe containing observations from JPL database\n",
" \"\"\"\n",
" con = sql.connect(database)\n",
" observations = pd.read_sql(\"\"\"SELECT *, observationStartMJD as observationStartMJD_TAI FROM observations LIMIT \"\"\"+str(rows_start)+','+str(nrows), con)\n",
" return observations"
"Only the apparent magnitude and the five-sigma limiting depth are needed for this. For simplicity, we will set the five-sigma limiting depth to be constant for all observations."
]
},
{
Expand All @@ -65,64 +58,34 @@
"metadata": {},
"outputs": [],
"source": [
"def randobs(ronsky=3,ra=180,dec=45, n=100000):\n",
"\n",
" \"\"\"Create random observations centered on RA, Dec with radius r.\n",
" Random observations are generated correctly only for declinations < dec+r.\n",
" \n",
" Parameters:\n",
" ------------\n",
" ronksy ... on sky radius [deg]\n",
" ra ... Right Ascension of center [deg]\n",
" dec ... Declination of center\n",
" \n",
" Returns:\n",
" --------\n",
" raout ... Right Ascension of fake observations\n",
" decout ... Declination of fake observations\n",
" \n",
" \"\"\"\n",
" # radius of the circle\n",
" # center of the circle (x, y)\n",
" # random angle\n",
" \n",
" rnd1=np.random.rand(n)\n",
" rnd2=np.random.rand(n)\n",
" \n",
" alpha = 2 * np.pi * rnd1\n",
" # random radius\n",
" r = ronsky * np.sqrt(rnd2)\n",
" # calculating coordinates\n",
" raout = r * np.cos(alpha) + ra\n",
" decout = r * np.sin(alpha) + dec\n",
" \n",
" return np.mod(raout+360,360),decout"
"nobs_per_field = 1000000"
]
},
{
"cell_type": "markdown",
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"## Import LSST Opsim database \n",
"This database contains LSST pointings and environmental information such as seeing.\n"
"mags = np.random.uniform(23.0, 26.0, nobs_per_field)\n",
"five_sigma = np.zeros(nobs_per_field) + 24.5\n",
"random_obs = pd.DataFrame({\"PSFMag\":mags, \"fiveSigmaDepth_mag\":five_sigma})"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"metadata": {},
"outputs": [],
"source": [
"db_path=\"oneline_v2.0.db\""
"random_obs"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Select the first exposure of the LSST survey and initialise number of simulated detections."
"We can apply the fading function filter implementation in Sorcha to the randomised observations."
]
},
{
Expand All @@ -131,9 +94,17 @@
"metadata": {},
"outputs": [],
"source": [
"LSSTdf=getSqlData(db_path,0,1)\n",
"peak_efficiency = 1.0\n",
"width = 0.1\n",
"rng=PerModuleRNG(2021)\n",
"nobs_per_field=1000000"
"reduced_obs = PPFadingFunctionFilter(random_obs, peak_efficiency, width, rng)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we calculate the probabilty of each observation to survive Sorcha's fading function filter, binned by magnitude."
]
},
{
Expand All @@ -142,14 +113,21 @@
"metadata": {},
"outputs": [],
"source": [
"LSSTdf"
"bin_width = 0.04\n",
"magbins = np.arange(23.0, 26.0, bin_width)\n",
"\n",
"magcounts, _ = np.histogram(random_obs['PSFMag'].values, bins=magbins)\n",
"\n",
"redmagcounts, _ = np.histogram(reduced_obs['PSFMag'].values, bins=magbins)\n",
"\n",
"sorcha_probability = redmagcounts/magcounts"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Some random measurements are then created."
"And we can compare this to the calculated detection probability of each magnitude bin, using Equation One."
]
},
{
Expand All @@ -158,16 +136,7 @@
"metadata": {},
"outputs": [],
"source": [
"magbins=[]\n",
"i=22.0\n",
"while(i<=26.0):\n",
" magbins.append(i)\n",
" i=i+0.05\n",
"\n",
"rao,deco=randobs(ronsky=2.5,ra=LSSTdf['fieldRA'][0],dec=LSSTdf['fieldDec'][0],n=nobs_per_field)\n",
"dfobs0=pd.DataFrame(np.array([rao,deco]).T,columns=['RA_deg','Dec_deg'])\n",
"dfobs0['FieldMJD_TAI']=LSSTdf['observationStartMJD_TAI'][0]\n",
"dfobs0['FieldID']=LSSTdf['observationId'][0]"
"calculated_probability = peak_efficiency / (1.0 + np.exp((magbins - 24.5) / width))"
]
},
{
Expand All @@ -176,29 +145,28 @@
"metadata": {},
"outputs": [],
"source": [
"dfobs0"
"fig, ax = plt.subplots(1, figsize=(8, 6))\n",
"ax.bar(magbins[:-1], sorcha_probability, align=\"edge\", width=bin_width, color=\"mediumaquamarine\", label=\"Sorcha detection probability\")\n",
"ax.set_ylim((0, 1.1))\n",
"ax.set_xlim((23, 26)) \n",
"ax.set_xlabel(\"magnitude\")\n",
"ax.set_ylabel(\"detection probability\")\n",
"ax.plot(magbins, calculated_probability, color=\"darkslateblue\", label=\"calculated detection probability\")\n",
"ax.legend()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create magnitudes"
"Sorcha's detection probability clearly follows the expected curve."
]
},
{
"cell_type": "code",
"execution_count": null,
"cell_type": "markdown",
"metadata": {},
"outputs": [],
"source": [
"mags=np.arange(nobs_per_field)\n",
"mags=np.random.uniform(22.0,26.0,len(dfobs0))\n",
"dfobs = pd.merge(dfobs0, LSSTdf, left_on=\"FieldID\", right_on=\"observationId\", how=\"left\")\n",
"\n",
"# Match keywords\n",
"dfobs['PSFMag'] = mags\n",
"dfobs['fiveSigmaDepth_mag']=dfobs['fiveSigmaDepth']\n"
"We can also take a look at how the fading function/detection efficiency changes with parameters."
]
},
{
Expand All @@ -207,7 +175,8 @@
"metadata": {},
"outputs": [],
"source": [
"mags"
"def deteff(mags, fivesig, peak, width):\n",
" return peak / (1.0 + np.exp((mags - fivesig) / width))"
]
},
{
Expand All @@ -216,7 +185,8 @@
"metadata": {},
"outputs": [],
"source": [
"dfobs['PSFMag']"
"colors = plt.get_cmap('gnuplot', 3)\n",
"x = colors(np.arange(0,3,1))"
]
},
{
Expand All @@ -225,7 +195,9 @@
"metadata": {},
"outputs": [],
"source": [
"dfobs"
"widths = [0.05, 0.1, 0.15]\n",
"colors = [\"tomato\", \"darkslateblue\", \"mediumturquoise\"]\n",
"styles = [\"solid\", \"dotted\", \"dashed\"]"
]
},
{
Expand All @@ -234,32 +206,32 @@
"metadata": {},
"outputs": [],
"source": [
"magcounts,magpins=np.histogram(dfobs['PSFMag'],bins=magbins)\n",
"fig, ax = plt.subplots(1, figsize=(8, 6))\n",
"\n",
"reduced_dfobs=ff.PPFadingFunctionFilter(dfobs, 1.0, 0.1, rng)\n",
"ax.axvline(24.5, 0, 1, linestyle=\"-\", alpha=0.5, color=\"grey\", linewidth=0.8)\n",
"ax.axhline(0.5, 0, 1, linestyle=\"-\", alpha=0.5, color=\"grey\", linewidth=0.8)\n",
"\n",
"redmagcounts,redmagbins=np.histogram(reduced_dfobs['PSFMag'],bins=magbins)\n",
"for i, width in enumerate(widths):\n",
" y = deteff(magbins, 24.5, 1., width)\n",
" ax.plot(magbins, y, color=colors[i], linestyle=styles[i], label=f\"$F = $ 1.0, $w = {width}$\")\n",
"\n",
"res=redmagcounts/magcounts\n",
"y = deteff(magbins, 24.5, .9, 0.1)\n",
"ax.plot(magbins, y, color=\"mediumpurple\", linestyle=\"dashdot\", label=f\"$F = $ 0.9, $w = {width}$\")\n",
"\n",
"fiveSigma=dfobs.fiveSigmaDepth_mag.values[0]\n",
"ax.set_xlim((23, 26))\n",
"ax.legend()\n",
"ax.set_xlabel(\"magnitude\")\n",
"ax.set_ylabel(\"detection probability\")\n",
"\n",
"restheor=[]\n",
"i=0\n",
"while(i<len(magbins)):\n",
" a=1./(1.+np.exp((magbins[i]-fiveSigma)/.1))\n",
" restheor.append(a)\n",
" i=i+1\n",
"\n",
"plt.clf()\n",
"plt.scatter(magpins[:-1]+0.025,res)\n",
"plt.plot(magbins,restheor, 'r-')\n",
"plt.xlabel('Magnitude')\n",
"plt.ylabel('Observed fraction')\n",
"#plt.savefig('deteff_.jpg', bbox_inches='tight')\n",
"plt.show()\n",
"\n"
"fig.tight_layout()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
Expand All @@ -282,7 +254,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.13"
"version": "3.10.14"
}
},
"nbformat": 4,
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