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@rbiswas4
Created November 28, 2018 07:18
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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": 12,
"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": "# 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": 13,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 13,
"data": {
"text/plain": "0.06755"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "# What if the transient was 19th mag instead?",
"execution_count": null,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "df, fig = mdiffs(19, 19 -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": 15,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 15,
"data": {
"text/plain": "0.0"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "# What if the transient was 23 th mag instead?",
"execution_count": 16,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "df, fig = mdiffs(23, 23 -2/24., (24.3, 24.2))",
"execution_count": 17,
"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": [
{
"output_type": "execute_result",
"execution_count": 18,
"data": {
"text/plain": "0.276352"
},
"metadata": {}
}
]
},
{
"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": "3.6.5",
"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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