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@mwcraig
Last active December 5, 2018 23:11
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"%matplotlib inline \n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Cases\n",
"\n",
"The idea is that we want to identify stars that do not change \"significantly\" in magnitude over the the course of a night. Stars that don't change much are useful as comparisons.\n",
"\n",
"One approach (the easiest) would be to compute the chi-squared for a line with slope zero. Not sure where to set the boundary."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"time = np.linspace(0, 0.15, num=200)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## slope 0, noisy data\n",
"\n",
"We want to be able to identify these."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"mag_flat = np.random.randn(len(time))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x11a16d748>]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(time, mag_flat, '.')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## sine, noisy data, average is zero\n",
"\n",
"These look like they are constant but have a noisy sine wave in them. Ideally this case does not get identified as \"not varying\"."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x120ff34e0>]"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sine_like = np.sin(2 * np.pi * time / 0.08) + np.random.randn(len(time)) \n",
"sine_like -= sine_like.mean()\n",
"plt.plot(time, sine_like, '.')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## linear\n",
"\n",
"Ideally this case does not get identified as \"not varying\"."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x11b3b0780>]"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"line = 2 * time + np.random.randn(len(time)) / 10\n",
"line -= line.mean()\n",
"plt.plot(time, line, '.') "
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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