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Last active August 29, 2015 14:04
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new astrometry effects
Raw
gistfile1.txt
{
"metadata": {
"name": "",
"signature": "sha256:6ec126d84dfa1e4aa4b7113d948809c39615de6e070db65eb710081944407a39"
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"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Effect of new astrometry on one tile"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"On 25 July 2014 Erin Sheldon sent this messsage to des-wl-test:\n",
"\n",
"> Eli Rykoff has been re-solving the astronomy on some of the coadd tiles and\n",
"> seeing huge improvements. It would be interesting to see if we detect a\n",
">difference in our shapes.\n",
"\n",
"Over the weekend I ran im3shape on the two tiles he generated. These are the results of comparing the im3shape v6 catalog version of that tile with the new astronomy one.\n",
"\n",
"For reasons I have not yet looked into one of the tiles did not have many objects in it in the re-run version compared to the old one, so this comparison looks at the other tile, DES0453-4748.\n",
"\n",
"A significant issue noted in our shape catalogs is a mean e_1 value > 0 which persists with high S/N. At least on this one tile, that effect appears to go away with the new astrometry.\n",
"\n",
"Cell 6 is the money cell."
]
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Import dependencies and load and sort tables"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import astropy.table\n",
"import scipy.stats\n",
"tile='DES0453-4748'\n",
"old_table=astropy.table.Table.read(\"old/{0}-r.fits\".format(tile))\n",
"new_table=astropy.table.Table.read(\"new/{0}-r.fits\".format(tile))\n",
"old_table.sort(\"identifier\")\n",
"new_table.sort(\"identifier\")"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Find objects in both old and new cats and cut down both to this set"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There's some objects that are not in both arrays. I'm not entirely sure why this is - the process I used was the same. Possibly some failures do not occur in one analysis or the other"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"in_both = set(old_table['identifier']).intersection(new_table['identifier'])\n",
"in_old = np.array([x in in_both for x in old_table['identifier']])\n",
"in_new = np.array([x in in_both for x in new_table['identifier']])\n",
"old_table=old_table[in_old]\n",
"new_table=new_table[in_new]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Find objects which have all flags == 0 in both catalogs"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The same story - the flags are somewhat different. This is partly due to a problem doing the post-processing on the new astrometry files - I could not extract the coadd objects IDs for them, presumably because the metadata is different from what's found in the nornal files. I haven't investigated thie s properly yet, though."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"okay=(old_table['info_flag']==0)&(old_table['error_flag']==0)&(new_table['info_flag']==0)&(new_table['error_flag']==0)\n",
"old_table=old_table[okay]\n",
"new_table=new_table[okay]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Plot old e1 versus new e1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The black line is y=x. The red line is a least-squares fit (not entirely appropriate since the errors on the shapes are not consistent across e, but not bad) of new to old.\n",
"\n",
"There's a shift of about 1% in the slope, which is important enough, but there is also a 6e-3 shift in the mean value (see below)."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"figure(figsize=(8,8))\n",
"plot(old_table['e1'], new_table['e1'], ',')\n",
"x=linspace(-0.8, 0.8, 2)\n",
"fit=scipy.stats.linregress(old_table['e1'], new_table['e1'])\n",
"slope = fit[0]\n",
"intercept = fit[1]\n",
"plot(x, x, 'k-')\n",
"plot(x, slope*x+intercept, 'r-')\n",
"xlim(-0.8, 0.8)\n",
"ylim(-0.8, 0.8)\n",
"grid()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
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pp5xC8+a+R+GddNJJbNq0yclbupbJvRqTawPVl83snocd+u+gBQxhNPP5P/Zy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\n4GaMMcYUhAc3Y4wxpiD/A4KEHLSWIlEXAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x1099cbc50>"
]
}
],
"prompt_number": 4
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Linear fit of old e1 to new e1"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print \"slope-1, intercept = \", slope-1, intercept"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"slope-1, intercept = 0.0116507314932 -0.00666677161393\n"
]
}
],
"prompt_number": 5
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Mean e1 and e2 in old and new catalogs - MAIN RESULT OF THIS NOTEBOOK!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A few summary statistics - this is the fun bit! "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print \"Old mean e_1 = (%.3f \u00b1 %.3f) * 10^-3\" % (old_table['e1'].mean()*1e3, old_table['e1'].std()/sqrt(len(old_table))*1e3)\n",
"print \"New mean e_1 = (%.3f \u00b1 %.3f) * 10^-3\" % (new_table['e1'].mean()*1e3, new_table['e1'].std()/sqrt(len(new_table))*1e3)\n",
"\n",
"print \"Old mean e_2 = (%.3f \u00b1 %.3f) * 10^-3\" % (old_table['e2'].mean()*1e3, old_table['e2'].std()/sqrt(len(old_table))*1e3)\n",
"print \"New mean e_2 = (%.3f \u00b1 %.3f) * 10^-3\" % (new_table['e2'].mean()*1e3, new_table['e2'].std()/sqrt(len(new_table))*1e3)\n",
"de1 = new_table['e1'] - old_table['e1']\n",
"de2 = new_table['e2'] - old_table['e2']\n",
"print\n",
"print \"Change in e_1 = (%.3f \u00b1 %.3f) * 10^-3\" % ( de1.mean()*1e3, de1.std()/sqrt(len(old_table))*1e3)\n",
"print \"Change in e_2 = (%.3f \u00b1 %.3f) * 10^-3\" % ( de2.mean()*1e3, de2.std()/sqrt(len(old_table))*1e3)\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Old mean e_1 = (6.502 \u00b1 2.277) * 10^-3\n",
"New mean e_1 = (-0.089 \u00b1 2.313) * 10^-3\n",
"Old mean e_2 = (-2.851 \u00b1 2.303) * 10^-3\n",
"New mean e_2 = (-1.562 \u00b1 2.340) * 10^-3\n",
"\n",
"Change in e_1 = (-6.591 \u00b1 0.211) * 10^-3\n",
"Change in e_2 = (1.289 \u00b1 0.206) * 10^-3\n"
]
}
],
"prompt_number": 6
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"The mean e_1 value which was different from zero by ~ 3 sigma reduces to being very close to zero. Note that sigma here includes the shape noise."
]
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"\n",
"\n",
"The e_2 was slightly but not significantly discrepant from zero before, and it too is now closer to zero."
]
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Histogram of old and new, and plot of the different between them."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"_=hist(old_table['e1'], bins=50, histtype='step', label='old')\n",
"_=hist(new_table['e1'], bins=50, histtype='step', label='new')\n",
"legend()\n",
"figure(figsize=(8,8))\n",
"plot(old_table['e1'], new_table['e1']-old_table['e1'],',')\n",
"xlabel(\"Old e1\")\n",
"ylabel(\"Difference new e1 - old e1\")\n",
"grid()\n",
"_=axis('equal')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
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tvq4x4Xryf0EIITycFL0QQng42XQjhHCqcxWFPDFne63x5ydGEfuAjwqJWh4p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\nrfLz8532fsXFxej1egD0en2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"text": [
"<matplotlib.figure.Figure at 0x10b6f5c90>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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N391/y3hS+z/xeNW/2tX9a76T0y39n2jT/vnU/lOnW5af2j51/KnxSVX/2nj9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\n",
"text": [
"<matplotlib.figure.Figure at 0x10b6f5b50>"
]
}
],
"prompt_number": 7
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"The mean e1 effect was constant with S/N. Here we plot mean e1 in log10(S/N) bins"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This function is a handy one for binning one quantity in another with equal counts per bin"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def mean_in_bins(x, y, n):\n",
" xs=np.sort(x)\n",
" N=len(xs)\n",
" dn=N*1.0/n\n",
" dx=xs[-1]-xs[0]\n",
" bounds = [xs[0]-dx*0.0001]\n",
" for i in xrange(n-1):\n",
" si=int((i+1)*dn)\n",
" bounds.append(xs[si])\n",
" bounds.append(xs[-1]+dx*0.0001)\n",
" b=np.digitize(x, bounds)\n",
" Y = []\n",
" X = []\n",
" sY = []\n",
" for i in xrange(n):\n",
" yi = y[b==i+1]\n",
" ni = len(yi)\n",
" Y.append(yi.mean())\n",
" sY.append(yi.std()/sqrt(ni))\n",
" X.append(x[b==i+1].mean())\n",
" return np.array(X), np.array(Y), np.array(sY)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 8
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"First with 21 bins in SNR"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Anyone have any idea what's happening between log10(snr)=1.8 and log10(snr)=2.0 (snr in 60 - 100)? I plotted these objects in RA and Dec and there was no obvious group of objects (e.g. from a sattelite/plane trail)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"figure(figsize=(8,6))\n",
"X, Y, sY = mean_in_bins(log10(old_table['snr']), old_table['e1'], 21)\n",
"_=errorbar(X-0.005, Y, sY, fmt='.', label=\"Old\")\n",
"X, Y, sY = mean_in_bins(log10(new_table['snr']), new_table['e1'], 21)\n",
"_=errorbar(X+0.005, Y, sY, fmt='.', label=\"New\")\n",
"xl=xlim()\n",
"plot(xl, [0,0],'k-')\n",
"grid()\n",
"xlabel(\"Log10(SNR)\")\n",
"ylabel(\"Mean e1\")\n",
"_=xlim(*xl)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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wAg1gsI1uMOkZR+v+F6bVNyqyjyPKnF/m7ADzy55fryG37Ndffz22bt2K8+fP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\nBDYEREREBDYEREREBOD/A194gEl9RJ03AAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x10d2dbf50>"
]
}
],
"prompt_number": 9
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"And now with just 4 bins:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"figure(figsize=(8,6))\n",
"X, Y, sY = mean_in_bins(log10(old_table['snr']), old_table['e1'], 4)\n",
"_=errorbar(X-0.005, Y, sY, fmt='.', label=\"Old\")\n",
"X, Y, sY = mean_in_bins(log10(new_table['snr']), new_table['e1'], 4)\n",
"_=errorbar(X+0.005, Y, sY, fmt='.', label=\"New\")\n",
"xl=xlim()\n",
"plot(xl, [0,0],'k-')\n",
"grid()\n",
"xlabel(\"Log10(SNR)\")\n",
"ylabel(\"Mean e1\")\n",
"_=xlim(*xl)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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UVNRhn5aWFi1btkwVFRU6deqUSktL9f7773d7vMvlUlVVlaqqqrRx48Z+e04A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\n",
"text": [
"<matplotlib.figure.Figure at 0x10bb82e90>"
]
}
],
"prompt_number": 10
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"And just to check for any visible effect with PSF e1, though there's not really enough data here to see:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"figure(figsize=(8,6))\n",
"X, Y, sY = mean_in_bins(old_table['mean_psf_e1_sky'], old_table['e1'], 21)\n",
"_=errorbar(X-0.00005, Y, sY, fmt='.', label=\"Old\")\n",
"X, Y, sY = mean_in_bins(new_table['mean_psf_e1_sky'], new_table['e1'], 21)\n",
"_=errorbar(X+0.00005, Y, sY, fmt='.', label=\"New\")\n",
"xl=xlim()\n",
"plot(xl, [0,0],'k-')\n",
"grid()\n",
"xlabel(\"Mean PSF e1\")\n",
"ylabel(\"Mean e1\")\n",
"_=xlim(*xl)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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rpURs4NLXemFLxsuVjajHwZSw6+Gjjz4yeuC9e/equLhYRUVFkqSVK1dq586d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\nAAAAEiJRAAAACZEoAACAhP4/wBrpY5WEXTUAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x10bb15890>"
]
}
],
"prompt_number": 11
}
],
"metadata": {}
}
]
}
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