diff --git a/dmu0/dmu0_DES/readme.md b/dmu0/dmu0_DES/readme.md index 985b5f2f..23b025bd 100644 --- a/dmu0/dmu0_DES/readme.md +++ b/dmu0/dmu0_DES/readme.md @@ -1,11 +1,13 @@ Dark Energy Survey - Public Data Release 1 (DR1) ==================================================================================== -Downloaded from https://des.ncsa.illinois.edu/easyweb/db-access +Downloaded from https://des.ncsa.illinois.edu/easyweb/db-access using SQL given below. HELP coverage then limited using filter_and_tag.sh. ## SQL QUERIES -1. First test sql +Job 1a49d93e-9aec-4706-a6c2-aa72182825b3 submitted + +1. SQL used to download raw unfiltered data on HELP coverage. ```sql -- Insert Query -- diff --git a/dmu1/dmu1_ml_Lockman-SWIRE/4_Selection_function.ipynb b/dmu1/dmu1_ml_Lockman-SWIRE/4_Selection_function.ipynb index 2e8cba6c..db140b91 100644 --- a/dmu1/dmu1_ml_Lockman-SWIRE/4_Selection_function.ipynb +++ b/dmu1/dmu1_ml_Lockman-SWIRE/4_Selection_function.ipynb @@ -14,8 +14,10 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 1, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "%matplotlib inline\n", @@ -46,9 +48,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Diagnostics done using: master_catalogue_lockman-swire_20171201.fits\n" + ] + } + ], "source": [ "TMP_DIR = os.environ.get('TMP_DIR', \"./data_tmp\")\n", "OUT_DIR = os.environ.get('OUT_DIR', \"./data\")\n", @@ -75,11 +85,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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nHw1P6ZatHdrWzYSglWqPhhQLB2ixgqcqCVZDklZ37p8ubbtsZvZxM9tvZvsn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\nAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "sns.distplot(master_catalogue['m_wfc_g'][(master_catalogue['m_wfc_g'] > 0) & (master_catalogue['m_wfc_g'] < 90.)])" ] @@ -93,9 +124,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "sns.distplot(master_catalogue['f_wfc_g'][(master_catalogue['f_wfc_g'] < 10.) & (master_catalogue['f_wfc_g'] > 0)] )" ] @@ -109,9 +161,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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AP7wMTR2pNLFpLwAAXvEyNK1UmrbY0ySxaS8AAL7xMjR1ptKUp9IEAIBH/AxNHeppasSJ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\nvnMbue6SCV08Wdb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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "sns.distplot(master_catalogue['ferr_wfc_g'][(master_catalogue['ferr_wfc_g'] > 0.) &(master_catalogue['ferr_wfc_g'] < 3.)])" ] @@ -125,9 +198,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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QDEIJABAMQgkAEAxCCQAQjLRd0SFZZlYh6R9xXu4s6aNWLCfdaG/2ilJbpWi1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\nYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "ax = sns.jointplot((np.log10(master_catalogue['m_wfc_g'])),\n", " (master_catalogue['merr_wfc_g'] ))\n", @@ -144,7 +228,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": { "collapsed": true }, @@ -161,7 +245,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": { "collapsed": true }, @@ -174,9 +258,74 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "<Table length=10>\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
idxhp_idx_O_10f_wfc_g_p90
01547095nan
11547099nan
21547100nan
31547101nan
41547102nan
51547103nan
61547121nan
71547123nan
81547124nan
91547125nan
\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "f_wfc_g_p90 = group[\"hp_idx_O_{}\".format(str(ORDER)), 'f_wfc_g'].groups.aggregate(lambda x: np.nanpercentile(x, 10.))\n", "f_wfc_g_p90['f_wfc_g'].name = 'f_wfc_g_p90'\n", @@ -185,9 +334,74 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "<Table length=10>\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
idxhp_idx_O_10ferr_wfc_g_mean
01547095nan
11547099nan
21547100nan
31547101nan
41547102nan
51547103nan
61547121nan
71547123nan
81547124nan
91547125nan
\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "ferr_wfc_g_mean = group[\"hp_idx_O_{}\".format(str(ORDER)), 'ferr_wfc_g'].groups.aggregate(np.nanmean)\n", "ferr_wfc_g_mean['ferr_wfc_g'].name = 'ferr_wfc_g_mean'\n", @@ -196,7 +410,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": { "collapsed": true }, @@ -216,7 +430,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": { "collapsed": true }, @@ -228,37 +442,168 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "<Table length=10>\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
idxhp_idx_O_13
0122683392
1122683393
2122683394
3122683395
4122683396
5122683397
6122683398
7122683399
8122683400
9122683401
\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "depths[:10].show_in_notebook()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ - "depths.add_column(hp.pixelfunc.ang2pix(2**ORDER,\n", + "depths.add_column(Column(hp.pixelfunc.ang2pix(2**ORDER,\n", " hp.pixelfunc.pix2ang(2**13, depths['hp_idx_O_13'], nest=True)[0],\n", " hp.pixelfunc.pix2ang(2**13, depths['hp_idx_O_13'], nest=True)[1],\n", " nest = True),\n", " name=\"hp_idx_O_{}\".format(str(ORDER))\n", + " )\n", " )\n", " " ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "<Table length=10>\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
idxhp_idx_O_13hp_idx_O_10
01226833921916928
11226833931916928
21226833941916928
31226833951916928
41226833961916928
51226833971916928
61226833981916928
71226833991916928
81226834001916928
91226834011916928
\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "depths[:10].show_in_notebook()" ] @@ -267,7 +612,72 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "<Table length=10>\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
idxhp_idx_O_13hp_idx_O_10ferr_wfc_g_mean
0990141371547095nan
1990141341547095nan
2990141331547095nan
3990141321547095nan
4990141311547095nan
5990141291547095nan
6990141281547095nan
7990141271547095nan
8990141261547095nan
9990141251547095nan
\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "join(depths, ferr_wfc_g_mean)[:10].show_in_notebook()" ] @@ -275,7 +685,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "for col in master_catalogue.colnames:\n",