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@rbiswas4
Created January 31, 2019 17:31
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{
"cells": [
{
"metadata": {},
"cell_type": "markdown",
"source": "If I use two points in the same band to estimate a rise time, then possibly the simplest estimator of the rise time is \nx = Delta m / Delta T. \n\nHow far apart should the two observations be taken. ie. What should Delta T be ?\n\nFor fast transients, we would like Delta T to be small, so that the transient is not past the peak so,\n\nDelta T <~ Rise Time\n\nBut we also want this to be sufficiently large so that the slopes are not mis-classified due to noise. Here,\nwe work out the probability distribution of inferred differences given two points of observation"
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n%matplotlib inline",
"execution_count": 1,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "import seaborn as sns\nsns.set_style('whitegrid')\nsns.set_context('talk')",
"execution_count": 2,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "def sigmaF(m5):\n \"\"\" We will do a very simple formula here\n \"\"\"\n m5 = np.asarray(m5)\n F5 = 10.0**(- 0.4 *m5)\n N = F5/ 5\n return N",
"execution_count": 3,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "def single_sample(m, m5, rng=np.random.RandomState(0), numSamples=1000000):\n \"\"\"\n \"\"\"\n F = 10.0**(-0.4 * m)\n N = sigmaF((m5))\n samp = rng.normal(F, N, size=numSamples)\n return -2.5 * np.log10(samp)",
"execution_count": 4,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "def mdiffs(m1, m2, m5s, rng=np.random.RandomState(0), numSamples=1000000):\n \"\"\"\n Difference between two magnitudes (assumed sample band) measured with\n two m5 values. Background domination assumed\n \n Parameters\n ----------\n m1 : Magnitude at first epoch\n m2 : magnitude at second epoch\n m5s : array of 2 m5 values correponding to m1 and m2 measurements\n rng : instance of `np.random.RandomState(0)`\n numSamples : number of samples used to calculate the distribution\n \n Returns\n --------\n A tuple of (df, fig), where df is a dataframe with samples of m1\n and m2 and the difference between them\n \"\"\"\n sample_m1 = single_sample(m1, m5s[0], rng, numSamples)\n sample_m2 = single_sample(m2, m5s[1], rng, numSamples)\n diffs = sample_m2 - sample_m1\n fig, ax = plt.subplots()\n sns.distplot(diffs, ax=ax, hist=False)\n ax.axvline(m2-m1)\n return pd.DataFrame(dict(m1=sample_m1, m2=sample_m2, diffs=diffs)), fig",
"execution_count": 5,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "# Consider a transient at 22 in some band rising at 1 mag per day. If we take the measurements an hour apart, the mag is smaller by 1/24. \n# If the two observations are with m5 of 24.3 ad 24.2, the distributions of differences in mags measured are:",
"execution_count": 6,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "df, fig = mdiffs(22, 22 -2/24., (24.3, 24.2))",
"execution_count": 7,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "<Figure size 432x288 with 1 Axes>",
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "fig.savefig('test_mags=22+m5s_(24.3, 24.2).pdf')",
"execution_count": 15,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "# What is the probability that the difference will be what we expect for a transient rising at 1./3. mag per day?",
"execution_count": 8,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "df.query('diffs > -2./24/3.').m1.size / np.float(len(df))",
"execution_count": 9,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 9,
"data": {
"text/plain": "0.067701"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "# What if the transient was 19th mag instead?",
"execution_count": 10,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "df, fig = mdiffs(19, 19 -2/24., (24.3, 24.2))",
"execution_count": 11,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "<Figure size 432x288 with 1 Axes>",
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "df.query('diffs > -2./24/3.').m1.size / np.float(len(df))",
"execution_count": 12,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 12,
"data": {
"text/plain": "0.0"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "# What if the transient was 23 th mag instead?",
"execution_count": 13,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "df, fig = mdiffs(23, 23 -2/24., (24.3, 24.2))",
"execution_count": 14,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "<Figure size 432x288 with 1 Axes>",
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "df.query('diffs > -2./24/3.').m1.size / np.float(len(df))",
"execution_count": 18,
"outputs": [
{
"data": {
"text/plain": "0.276352"
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "# Scratch"
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "",
"execution_count": null,
"outputs": []
}
],
"metadata": {
"kernelspec": {
"name": "python3",
"display_name": "Python 3",
"language": "python"
},
"language_info": {
"name": "python",
"version": "3.6.7",
"mimetype": "text/x-python",
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"pygments_lexer": "ipython3",
"nbconvert_exporter": "python",
"file_extension": ".py"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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