From 8b8378fe7206a7ae92b56d63167b5eafd08b94ea Mon Sep 17 00:00:00 2001 From: fjaviersanchez Date: Fri, 4 May 2018 04:29:02 -0700 Subject: [PATCH 1/3] Added a couple of notebooks with some rought analysis --- Validation/DC2_matching_and_depth.ipynb | 1587 ++++++++++++++++++++++ Validation/DC2_rough_angular_power.ipynb | 544 ++++++++ 2 files changed, 2131 insertions(+) create mode 100644 Validation/DC2_matching_and_depth.ipynb create mode 100644 Validation/DC2_rough_angular_power.ipynb diff --git a/Validation/DC2_matching_and_depth.ipynb b/Validation/DC2_matching_and_depth.ipynb new file mode 100644 index 0000000..f73ed40 --- /dev/null +++ b/Validation/DC2_matching_and_depth.ipynb @@ -0,0 +1,1587 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Populating the interactive namespace from numpy and matplotlib\n" + ] + } + ], + "source": [ + "%pylab inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import glob" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import lsst.daf.persistence" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import GCRCatalogs" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import healpy as hp" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.neighbors import KDTree\n", + "from lsst.sims.utils import cartesianFromSpherical, sphericalFromCartesian, rotationMatrixFromVectors" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def rotate(ra0,dec0,ra1,dec1,ra,dec):\n", + " #Code from GCR_simsinterface\n", + " xyz = cartesianFromSpherical(np.radians(ra0), np.radians(dec0))\n", + " xyz1 = cartesianFromSpherical(np.radians(ra1), np.radians(dec1))\n", + " if np.abs(1.0-np.dot(xyz, xyz1))<1.0e-10:\n", + " transformation = np.identity(3, dtype=float)\n", + " \n", + " first_rotation = rotationMatrixFromVectors(xyz, xyz1)\n", + " xx = np.dot(first_rotation, xyz)\n", + " rng = np.random.RandomState(99)\n", + " mag = np.NaN\n", + " while np.abs(mag)<1.0e-20 or np.isnan(mag):\n", + " random_vec = rng.random_sample(3)\n", + " comp = np.dot(random_vec, xx)\n", + " yy = random_vec - comp*xx\n", + " mag = np.sqrt((yy**2).sum())\n", + " yy /= mag\n", + " zz = np.cross(xx, yy)\n", + " to_self_bases = np.array([xx,\n", + " yy,\n", + " zz])\n", + "\n", + " out_of_self_bases =to_self_bases.transpose()\n", + " d_dec = 0.1\n", + " north = cartesianFromSpherical(np.radians(ra0),\n", + " np.radians(dec0+d_dec))\n", + " north = np.dot(first_rotation, north)\n", + " north_true = cartesianFromSpherical(np.radians(ra1),\n", + " np.radians(dec1+d_dec))\n", + "\n", + " north = np.dot(to_self_bases, north)\n", + " north_true = np.dot(to_self_bases, north_true)\n", + " north = np.array([north[1], north[2]])\n", + " north /= np.sqrt((north**2).sum())\n", + " north_true = np.array([north_true[1], north_true[2]])\n", + " north_true /= np.sqrt((north_true**2).sum())\n", + "\n", + " c = north_true[0]*north[0]+north_true[1]*north[1]\n", + " s = north[0]*north_true[1]-north[1]*north_true[0]\n", + " norm = np.sqrt(c*c+s*s)\n", + " c = c/norm\n", + " s = s/norm\n", + "\n", + " nprime = np.array([c*north[0]-s*north[1],\n", + " s*north[0]+c*north[1]])\n", + "\n", + " yz_rotation = np.array([[1.0, 0.0, 0.0],\n", + " [0.0, c, -s],\n", + " [0.0, s, c]])\n", + "\n", + " second_rotation = np.dot(out_of_self_bases,\n", + " np.dot(yz_rotation,\n", + " to_self_bases))\n", + "\n", + " transformation = np.dot(second_rotation,\n", + " first_rotation)\n", + " xyz = cartesianFromSpherical(np.radians(ra), np.radians(dec)).transpose()\n", + " xyz = np.dot(transformation, xyz).transpose()\n", + " ra_out, dec_out = sphericalFromCartesian(xyz)\n", + " return np.degrees(ra_out), np.degrees(dec_out)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Path of the processed 1.1p tests at NERSC" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "data_path = '/global/projecta/projectdirs/lsst/global/in2p3/Run1.1-test2/output'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's call the Butler" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "butler = lsst.daf.persistence.Butler(data_path)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's check some numbers" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "datarefs = butler.subset('calexp')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have ~10,000 single visits" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "truth_path = '/global/projecta/projectdirs/lsst/groups/CS/descqa/catalog/ANL_AlphaQ_v2.1.2.hdf5'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We use protoDC2 to match inputs and outputs" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "gc = GCRCatalogs.load_catalog('proto-dc2_v2.1.2')" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "data = gc.get_quantities(['mag_true_u_lsst', 'mag_true_g_lsst','mag_true_r_lsst', 'mag_true_i_lsst', 'mag_true_z_lsst', 'mag_true_Y_lsst', 'redshift', 'ra', 'dec','galaxy_id'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For protoDC2_v2 the field wasn't rotated yet!" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "ra_new, dec_new = rotate(0.,0.,55.064,-29.783,data['ra'],data['dec'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We build a KDTree to make spatial matching" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "X = np.zeros((len(data['ra']),2))\n", + "X[:,0]=ra_new\n", + "X[:,1]=dec_new\n", + "tree = KDTree(X)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's select one visit (or several)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1199 {'visit': 181898, 'filter': 'r', 'raft': '2,0', 'sensor': '0,0'}\n", + "1200 {'visit': 181898, 'filter': 'r', 'raft': '2,1', 'sensor': '0,0'}\n", + "1201 {'visit': 181898, 'filter': 'r', 'raft': '2,2', 'sensor': '0,0'}\n", + "1202 {'visit': 181898, 'filter': 'r', 'raft': '2,3', 'sensor': '0,0'}\n", + "1203 {'visit': 181898, 'filter': 'r', 'raft': '2,4', 'sensor': '0,0'}\n", + "1204 {'visit': 181898, 'filter': 'r', 'raft': '3,0', 'sensor': '0,0'}\n", + "1205 {'visit': 181898, 'filter': 'r', 'raft': '3,1', 'sensor': '0,0'}\n", + "1206 {'visit': 181898, 'filter': 'r', 'raft': '3,2', 'sensor': '0,0'}\n", + "1207 {'visit': 181898, 'filter': 'r', 'raft': '3,3', 'sensor': '0,0'}\n", + "1208 {'visit': 181898, 'filter': 'r', 'raft': '3,4', 'sensor': '0,0'}\n", + "1209 {'visit': 181898, 'filter': 'r', 'raft': '4,1', 'sensor': '0,0'}\n", + "1210 {'visit': 181898, 'filter': 'r', 'raft': '4,2', 'sensor': '0,0'}\n", + "1211 {'visit': 181898, 'filter': 'r', 'raft': '4,3', 'sensor': '0,0'}\n", + "1212 {'visit': 181898, 'filter': 'r', 'raft': '2,0', 'sensor': '0,1'}\n", + "1213 {'visit': 181898, 'filter': 'r', 'raft': '2,1', 'sensor': '0,1'}\n", + "1214 {'visit': 181898, 'filter': 'r', 'raft': '2,2', 'sensor': '0,1'}\n" + ] + } + ], + "source": [ + "ra_aux = []\n", + "dec_aux = []\n", + "nchild = []\n", + "mag = []\n", + "mag_err = []\n", + "for i, visitId in enumerate(datarefs.cache[:1215]):\n", + " if visitId['filter']=='r':\n", + " print(i,visitId)\n", + " src_cat = butler.get('src',visitId)\n", + " calexp = butler.get('calexp',visitId)\n", + " calib = calexp.getCalib()\n", + " calib.setThrowOnNegativeFlux(False)\n", + " nchild.append(src_cat.get('deblend_nChild'))\n", + " ra_aux.append(np.degrees(src_cat.get('coord_ra')))\n", + " dec_aux.append(np.degrees(src_cat.get('coord_dec')))\n", + " mag.append(calib.getMagnitude(src_cat.get('ext_photometryKron_KronFlux_flux')))\n", + " mag_err.append(calib.getMagnitude(src_cat.get('ext_photometryKron_KronFlux_fluxSigma')))" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Schema(\n", + " (Field['L'](name=\"id\", doc=\"unique ID\"), Key(offset=0, nElements=1)),\n", + " (Field['Angle'](name=\"coord_ra\", doc=\"position in ra/dec\"), Key(offset=8, nElements=1)),\n", + " (Field['Angle'](name=\"coord_dec\", doc=\"position in ra/dec\"), Key(offset=16, nElements=1)),\n", + " (Field['L'](name=\"parent\", doc=\"unique ID of parent source\"), Key(offset=24, nElements=1)),\n", + " (Field['Flag'](name=\"calib_detected\", doc=\"Source was detected as an icSource\"), Key['Flag'](offset=32, bit=0)),\n", + " (Field['Flag'](name=\"calib_psfCandidate\", doc=\"Flag set if the source was a candidate for PSF determination, as determined by the star selector.\"), Key['Flag'](offset=32, bit=1)),\n", + " (Field['Flag'](name=\"calib_psfUsed\", doc=\"Flag set if the source was actually used for PSF determination, as determined by the\"), Key['Flag'](offset=32, bit=2)),\n", + " (Field['Flag'](name=\"calib_psf_reserved\", doc=\"set if source was reserved from PSF determination\"), Key['Flag'](offset=32, bit=3)),\n", + " (Field['I'](name=\"deblend_nChild\", doc=\"Number of children this object has (defaults to 0)\"), Key(offset=40, nElements=1)),\n", + " (Field['Flag'](name=\"deblend_deblendedAsPsf\", doc=\"Deblender thought this source looked like a PSF\"), Key['Flag'](offset=32, bit=4)),\n", + " (Field['D'](name=\"deblend_psfCenter_x\", doc=\"If deblended-as-psf, the PSF centroid\", units=\"pixel\"), Key(offset=48, nElements=1)),\n", + " (Field['D'](name=\"deblend_psfCenter_y\", doc=\"If deblended-as-psf, the PSF centroid\", units=\"pixel\"), Key(offset=56, nElements=1)),\n", + " (Field['D'](name=\"deblend_psfFlux\", doc=\"If deblended-as-psf, the PSF flux\"), Key(offset=64, nElements=1)),\n", + " (Field['Flag'](name=\"deblend_tooManyPeaks\", doc=\"Source had too many peaks; only the brightest were included\"), Key['Flag'](offset=32, bit=5)),\n", + " (Field['Flag'](name=\"deblend_parentTooBig\", doc=\"Parent footprint covered too many pixels\"), Key['Flag'](offset=32, bit=6)),\n", + " (Field['Flag'](name=\"deblend_masked\", doc=\"Parent footprint was predominantly masked\"), Key['Flag'](offset=32, bit=7)),\n", + " (Field['Flag'](name=\"deblend_skipped\", doc=\"Deblender skipped this source\"), Key['Flag'](offset=32, bit=8)),\n", + " (Field['Flag'](name=\"deblend_rampedTemplate\", doc=\"This source was near an image edge and the deblender used \"ramp\" edge-handling.\"), Key['Flag'](offset=32, bit=9)),\n", + " (Field['Flag'](name=\"deblend_patchedTemplate\", doc=\"This source was near an image edge and the deblender used \"patched\" edge-handling.\"), Key['Flag'](offset=32, bit=10)),\n", + " (Field['Flag'](name=\"deblend_hasStrayFlux\", doc=\"This source was assigned some stray flux\"), Key['Flag'](offset=32, bit=11)),\n", + " (Field['D'](name=\"base_NaiveCentroid_x\", doc=\"centroid from Naive Centroid algorithm\", units=\"pixel\"), Key(offset=72, nElements=1)),\n", + " (Field['D'](name=\"base_NaiveCentroid_y\", doc=\"centroid 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(Field['D'](name=\"base_Blendedness_abs_flux_child\", doc=\"flux of the child, measured with a Gaussian weight matched to the child\", units=\"count\"), Key(offset=152, nElements=1)),\n", + " (Field['D'](name=\"base_Blendedness_abs_flux_parent\", doc=\"flux of the parent, measured with a Gaussian weight matched to the child\", units=\"count\"), Key(offset=160, nElements=1)),\n", + " (Field['D'](name=\"base_Blendedness_raw_child_xx\", doc=\"shape of the child, measured with a Gaussian weight matched to the child\", units=\"pixel^2\"), Key(offset=168, nElements=1)),\n", + " (Field['D'](name=\"base_Blendedness_raw_child_yy\", doc=\"shape of the child, measured with a Gaussian weight matched to the child\", units=\"pixel^2\"), Key(offset=176, nElements=1)),\n", + " (Field['D'](name=\"base_Blendedness_raw_child_xy\", doc=\"shape of the child, measured with a Gaussian weight matched to the child\", units=\"pixel^2\"), Key(offset=184, nElements=1)),\n", + " (Field['D'](name=\"base_Blendedness_raw_parent_xx\", doc=\"shape of the parent, measured with a Gaussian weight matched to the child\", units=\"pixel^2\"), Key(offset=192, nElements=1)),\n", + " (Field['D'](name=\"base_Blendedness_raw_parent_yy\", doc=\"shape of the parent, measured with a Gaussian weight matched to the child\", units=\"pixel^2\"), Key(offset=200, nElements=1)),\n", + " (Field['D'](name=\"base_Blendedness_raw_parent_xy\", doc=\"shape of the parent, measured with a Gaussian weight matched to the child\", units=\"pixel^2\"), Key(offset=208, nElements=1)),\n", + " (Field['D'](name=\"base_Blendedness_abs_child_xx\", doc=\"shape of the child, measured with a Gaussian weight matched to the child\", units=\"pixel^2\"), Key(offset=216, nElements=1)),\n", + " (Field['D'](name=\"base_Blendedness_abs_child_yy\", doc=\"shape of the child, measured with a Gaussian weight matched to the child\", units=\"pixel^2\"), Key(offset=224, nElements=1)),\n", + " (Field['D'](name=\"base_Blendedness_abs_child_xy\", doc=\"shape of the child, measured with a Gaussian weight matched to the child\", units=\"pixel^2\"), Key(offset=232, nElements=1)),\n", + " (Field['D'](name=\"base_Blendedness_abs_parent_xx\", doc=\"shape of the parent, measured with a Gaussian weight matched to the child\", units=\"pixel^2\"), Key(offset=240, nElements=1)),\n", + " (Field['D'](name=\"base_Blendedness_abs_parent_yy\", doc=\"shape of the parent, measured with a Gaussian weight matched to the child\", units=\"pixel^2\"), Key(offset=248, nElements=1)),\n", + " (Field['D'](name=\"base_Blendedness_abs_parent_xy\", doc=\"shape of the parent, measured with a Gaussian weight matched to the child\", units=\"pixel^2\"), Key(offset=256, nElements=1)),\n", + " (Field['Flag'](name=\"base_Blendedness_flag\", doc=\"General Failure Flag\"), Key['Flag'](offset=32, bit=22)),\n", + " (Field['Flag'](name=\"base_Blendedness_flag_noCentroid\", doc=\"Object has no centroid\"), Key['Flag'](offset=32, bit=23)),\n", + " (Field['Flag'](name=\"base_Blendedness_flag_noShape\", doc=\"Object has no shape\"), Key['Flag'](offset=32, bit=24)),\n", + " (Field['D'](name=\"base_SdssShape_xx\", doc=\"elliptical Gaussian adaptive moments\", units=\"pixel^2\"), Key(offset=264, nElements=1)),\n", + " (Field['D'](name=\"base_SdssShape_yy\", doc=\"elliptical Gaussian adaptive moments\", units=\"pixel^2\"), Key(offset=272, nElements=1)),\n", + " (Field['D'](name=\"base_SdssShape_xy\", doc=\"elliptical Gaussian adaptive moments\", units=\"pixel^2\"), Key(offset=280, nElements=1)),\n", + " (Field['F'](name=\"base_SdssShape_xxSigma\", doc=\"1-sigma uncertainty on xx moment\", units=\"pixel^2\"), Key(offset=288, nElements=1)),\n", + " (Field['F'](name=\"base_SdssShape_yySigma\", doc=\"1-sigma uncertainty on yy moment\", units=\"pixel^2\"), Key(offset=292, nElements=1)),\n", + " (Field['F'](name=\"base_SdssShape_xySigma\", doc=\"1-sigma uncertainty on xy moment\", units=\"pixel^2\"), 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doc=\"adaptive moments of the PSF model at the object position\", units=\"pixel^2\"), Key(offset=352, nElements=1)),\n", + " (Field['F'](name=\"base_SdssShape_flux_xx_Cov\", doc=\"uncertainty covariance between base_SdssShape_flux and base_SdssShape_xx\", units=\"count*pixel^2\"), Key(offset=360, nElements=1)),\n", + " (Field['F'](name=\"base_SdssShape_flux_yy_Cov\", doc=\"uncertainty covariance between base_SdssShape_flux and base_SdssShape_yy\", units=\"count*pixel^2\"), Key(offset=364, nElements=1)),\n", + " (Field['F'](name=\"base_SdssShape_flux_xy_Cov\", doc=\"uncertainty covariance between base_SdssShape_flux and base_SdssShape_xy\", units=\"count*pixel^2\"), Key(offset=368, nElements=1)),\n", + " (Field['Flag'](name=\"base_SdssShape_flag\", doc=\"General Failure Flag\"), Key['Flag'](offset=32, bit=25)),\n", + " (Field['Flag'](name=\"base_SdssShape_flag_unweightedBad\", doc=\"Both weighted and unweighted moments were invalid\"), Key['Flag'](offset=32, bit=26)),\n", + " (Field['Flag'](name=\"base_SdssShape_flag_unweighted\", doc=\"Weighted moments converged to an invalid value; using unweighted moments\"), Key['Flag'](offset=32, bit=27)),\n", + " (Field['Flag'](name=\"base_SdssShape_flag_shift\", doc=\"centroid shifted by more than the maximum allowed amount\"), Key['Flag'](offset=32, bit=28)),\n", + " (Field['Flag'](name=\"base_SdssShape_flag_maxIter\", doc=\"Too many iterations in adaptive moments\"), Key['Flag'](offset=32, bit=29)),\n", + " (Field['Flag'](name=\"base_SdssShape_flag_psf\", doc=\"Failure in measuring PSF model shape\"), Key['Flag'](offset=32, bit=30)),\n", + " (Field['D'](name=\"ext_shapeHSM_HsmPsfMoments_x\", doc=\"HSM Centroid\", units=\"pixel\"), Key(offset=376, nElements=1)),\n", + " (Field['D'](name=\"ext_shapeHSM_HsmPsfMoments_y\", doc=\"HSM Centroid\", units=\"pixel\"), Key(offset=384, nElements=1)),\n", + " (Field['D'](name=\"ext_shapeHSM_HsmPsfMoments_xx\", doc=\"HSM moments\", units=\"pixel^2\"), Key(offset=392, 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box\"), Key['Flag'](offset=32, bit=37)),\n", + " (Field['Flag'](name=\"ext_shapeHSM_HsmShapeRegauss_flag_parent_source\", doc=\"parent source, ignored\"), Key['Flag'](offset=32, bit=38)),\n", + " (Field['Flag'](name=\"ext_shapeHSM_HsmShapeRegauss_flag_galsim\", doc=\"GalSim failure\"), Key['Flag'](offset=32, bit=39)),\n", + " (Field['D'](name=\"ext_shapeHSM_HsmSourceMoments_x\", doc=\"HSM Centroid\", units=\"pixel\"), Key(offset=448, nElements=1)),\n", + " (Field['D'](name=\"ext_shapeHSM_HsmSourceMoments_y\", doc=\"HSM Centroid\", units=\"pixel\"), Key(offset=456, nElements=1)),\n", + " (Field['D'](name=\"ext_shapeHSM_HsmSourceMoments_xx\", doc=\"HSM moments\", units=\"pixel^2\"), Key(offset=464, nElements=1)),\n", + " (Field['D'](name=\"ext_shapeHSM_HsmSourceMoments_yy\", doc=\"HSM moments\", units=\"pixel^2\"), Key(offset=472, nElements=1)),\n", + " (Field['D'](name=\"ext_shapeHSM_HsmSourceMoments_xy\", doc=\"HSM moments\", units=\"pixel^2\"), Key(offset=480, nElements=1)),\n", + " (Field['Flag'](name=\"ext_shapeHSM_HsmSourceMoments_flag\", doc=\"general failure flag, set if anything went wrong\"), Key['Flag'](offset=32, bit=40)),\n", + " (Field['Flag'](name=\"ext_shapeHSM_HsmSourceMoments_flag_no_pixels\", doc=\"no pixels to measure\"), Key['Flag'](offset=32, bit=41)),\n", + " (Field['Flag'](name=\"ext_shapeHSM_HsmSourceMoments_flag_not_contained\", doc=\"center not contained in footprint bounding box\"), Key['Flag'](offset=32, bit=42)),\n", + " (Field['Flag'](name=\"ext_shapeHSM_HsmSourceMoments_flag_parent_source\", doc=\"parent source, ignored\"), Key['Flag'](offset=32, bit=43)),\n", + " (Field['D'](name=\"ext_shapeHSM_HsmSourceMomentsRound_x\", doc=\"HSM Centroid\", units=\"pixel\"), Key(offset=488, nElements=1)),\n", + " (Field['D'](name=\"ext_shapeHSM_HsmSourceMomentsRound_y\", doc=\"HSM Centroid\", units=\"pixel\"), Key(offset=496, nElements=1)),\n", + " (Field['D'](name=\"ext_shapeHSM_HsmSourceMomentsRound_xx\", doc=\"HSM moments\", units=\"pixel^2\"), Key(offset=504, nElements=1)),\n", + " (Field['D'](name=\"ext_shapeHSM_HsmSourceMomentsRound_yy\", doc=\"HSM moments\", units=\"pixel^2\"), Key(offset=512, nElements=1)),\n", + " (Field['D'](name=\"ext_shapeHSM_HsmSourceMomentsRound_xy\", doc=\"HSM moments\", units=\"pixel^2\"), Key(offset=520, nElements=1)),\n", + " (Field['Flag'](name=\"ext_shapeHSM_HsmSourceMomentsRound_flag\", doc=\"general failure flag, set if anything went wrong\"), Key['Flag'](offset=32, bit=44)),\n", + " (Field['Flag'](name=\"ext_shapeHSM_HsmSourceMomentsRound_flag_no_pixels\", doc=\"no pixels to measure\"), Key['Flag'](offset=32, bit=45)),\n", + " (Field['Flag'](name=\"ext_shapeHSM_HsmSourceMomentsRound_flag_not_contained\", doc=\"center not contained in footprint bounding box\"), Key['Flag'](offset=32, bit=46)),\n", + " (Field['Flag'](name=\"ext_shapeHSM_HsmSourceMomentsRound_flag_parent_source\", doc=\"parent source, ignored\"), Key['Flag'](offset=32, bit=47)),\n", + " (Field['F'](name=\"ext_shapeHSM_HsmSourceMomentsRound_Flux\", doc=\"HSM flux\"), Key(offset=528, nElements=1)),\n", + " (Field['D'](name=\"base_CircularApertureFlux_3_0_flux\", doc=\"flux within 3.000000-pixel aperture\", units=\"count\"), Key(offset=536, nElements=1)),\n", + " (Field['D'](name=\"base_CircularApertureFlux_3_0_fluxSigma\", doc=\"1-sigma flux uncertainty\", units=\"count\"), Key(offset=544, nElements=1)),\n", + " (Field['Flag'](name=\"base_CircularApertureFlux_3_0_flag\", doc=\"General Failure Flag\"), Key['Flag'](offset=32, bit=48)),\n", + " (Field['Flag'](name=\"base_CircularApertureFlux_3_0_flag_apertureTruncated\", doc=\"aperture did not fit within measurement image\"), Key['Flag'](offset=32, bit=49)),\n", + " (Field['Flag'](name=\"base_CircularApertureFlux_3_0_flag_sincCoeffsTruncated\", doc=\"full sinc coefficient image did not fit within measurement image\"), Key['Flag'](offset=32, bit=50)),\n", + " (Field['D'](name=\"base_CircularApertureFlux_4_5_flux\", doc=\"flux within 4.500000-pixel aperture\", units=\"count\"), Key(offset=552, nElements=1)),\n", + " (Field['D'](name=\"base_CircularApertureFlux_4_5_fluxSigma\", doc=\"1-sigma flux uncertainty\", units=\"count\"), Key(offset=560, nElements=1)),\n", + " (Field['Flag'](name=\"base_CircularApertureFlux_4_5_flag\", doc=\"General Failure Flag\"), Key['Flag'](offset=32, bit=51)),\n", + " (Field['Flag'](name=\"base_CircularApertureFlux_4_5_flag_apertureTruncated\", doc=\"aperture did not fit within measurement image\"), Key['Flag'](offset=32, bit=52)),\n", + " (Field['Flag'](name=\"base_CircularApertureFlux_4_5_flag_sincCoeffsTruncated\", doc=\"full sinc coefficient image did not fit within measurement image\"), Key['Flag'](offset=32, bit=53)),\n", + " (Field['D'](name=\"base_CircularApertureFlux_6_0_flux\", doc=\"flux within 6.000000-pixel aperture\", units=\"count\"), Key(offset=568, nElements=1)),\n", + " (Field['D'](name=\"base_CircularApertureFlux_6_0_fluxSigma\", doc=\"1-sigma flux 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bit=57)),\n", + " (Field['Flag'](name=\"base_CircularApertureFlux_9_0_flag_apertureTruncated\", doc=\"aperture did not fit within measurement image\"), Key['Flag'](offset=32, bit=58)),\n", + " (Field['Flag'](name=\"base_CircularApertureFlux_9_0_flag_sincCoeffsTruncated\", doc=\"full sinc coefficient image did not fit within measurement image\"), Key['Flag'](offset=32, bit=59)),\n", + " (Field['D'](name=\"base_CircularApertureFlux_12_0_flux\", doc=\"flux within 12.000000-pixel aperture\", units=\"count\"), Key(offset=600, nElements=1)),\n", + " (Field['D'](name=\"base_CircularApertureFlux_12_0_fluxSigma\", doc=\"1-sigma flux uncertainty\", units=\"count\"), Key(offset=608, nElements=1)),\n", + " (Field['Flag'](name=\"base_CircularApertureFlux_12_0_flag\", doc=\"General Failure Flag\"), Key['Flag'](offset=32, bit=60)),\n", + " (Field['Flag'](name=\"base_CircularApertureFlux_12_0_flag_apertureTruncated\", doc=\"aperture did not fit within measurement image\"), Key['Flag'](offset=32, bit=61)),\n", + " (Field['Flag'](name=\"base_CircularApertureFlux_12_0_flag_sincCoeffsTruncated\", doc=\"full sinc coefficient image did not fit within measurement image\"), Key['Flag'](offset=32, bit=62)),\n", + " (Field['D'](name=\"base_CircularApertureFlux_17_0_flux\", doc=\"flux within 17.000000-pixel aperture\", units=\"count\"), Key(offset=616, nElements=1)),\n", + " (Field['D'](name=\"base_CircularApertureFlux_17_0_fluxSigma\", doc=\"1-sigma flux uncertainty\", units=\"count\"), Key(offset=624, nElements=1)),\n", + " (Field['Flag'](name=\"base_CircularApertureFlux_17_0_flag\", doc=\"General Failure Flag\"), Key['Flag'](offset=32, bit=63)),\n", + " (Field['Flag'](name=\"base_CircularApertureFlux_17_0_flag_apertureTruncated\", doc=\"aperture did not fit within measurement image\"), Key['Flag'](offset=632, bit=0)),\n", + " (Field['D'](name=\"base_CircularApertureFlux_25_0_flux\", doc=\"flux within 25.000000-pixel aperture\", units=\"count\"), Key(offset=640, nElements=1)),\n", + " (Field['D'](name=\"base_CircularApertureFlux_25_0_fluxSigma\", doc=\"1-sigma flux uncertainty\", units=\"count\"), Key(offset=648, nElements=1)),\n", + " (Field['Flag'](name=\"base_CircularApertureFlux_25_0_flag\", doc=\"General Failure Flag\"), Key['Flag'](offset=632, bit=1)),\n", + " (Field['Flag'](name=\"base_CircularApertureFlux_25_0_flag_apertureTruncated\", doc=\"aperture did not fit within measurement image\"), Key['Flag'](offset=632, bit=2)),\n", + " (Field['D'](name=\"base_CircularApertureFlux_35_0_flux\", doc=\"flux within 35.000000-pixel aperture\", units=\"count\"), Key(offset=656, nElements=1)),\n", + " (Field['D'](name=\"base_CircularApertureFlux_35_0_fluxSigma\", doc=\"1-sigma flux uncertainty\", units=\"count\"), Key(offset=664, nElements=1)),\n", + " (Field['Flag'](name=\"base_CircularApertureFlux_35_0_flag\", doc=\"General Failure Flag\"), Key['Flag'](offset=632, bit=3)),\n", + " (Field['Flag'](name=\"base_CircularApertureFlux_35_0_flag_apertureTruncated\", doc=\"aperture did not fit within measurement image\"), Key['Flag'](offset=632, bit=4)),\n", + " (Field['D'](name=\"base_CircularApertureFlux_50_0_flux\", doc=\"flux within 50.000000-pixel aperture\", units=\"count\"), Key(offset=672, nElements=1)),\n", + " (Field['D'](name=\"base_CircularApertureFlux_50_0_fluxSigma\", doc=\"1-sigma flux uncertainty\", units=\"count\"), Key(offset=680, nElements=1)),\n", + " (Field['Flag'](name=\"base_CircularApertureFlux_50_0_flag\", doc=\"General Failure Flag\"), Key['Flag'](offset=632, bit=5)),\n", + " (Field['Flag'](name=\"base_CircularApertureFlux_50_0_flag_apertureTruncated\", doc=\"aperture did not fit within measurement image\"), Key['Flag'](offset=632, bit=6)),\n", + " (Field['D'](name=\"base_CircularApertureFlux_70_0_flux\", doc=\"flux within 70.000000-pixel aperture\", units=\"count\"), Key(offset=688, nElements=1)),\n", + " (Field['D'](name=\"base_CircularApertureFlux_70_0_fluxSigma\", doc=\"1-sigma flux uncertainty\", units=\"count\"), Key(offset=696, nElements=1)),\n", + " (Field['Flag'](name=\"base_CircularApertureFlux_70_0_flag\", doc=\"General Failure Flag\"), Key['Flag'](offset=632, bit=7)),\n", + " (Field['Flag'](name=\"base_CircularApertureFlux_70_0_flag_apertureTruncated\", doc=\"aperture did not fit within measurement image\"), Key['Flag'](offset=632, bit=8)),\n", + " (Field['D'](name=\"base_GaussianFlux_flux\", doc=\"flux from Gaussian Flux algorithm\", units=\"count\"), Key(offset=704, nElements=1)),\n", + " (Field['D'](name=\"base_GaussianFlux_fluxSigma\", doc=\"1-sigma flux uncertainty\", units=\"count\"), Key(offset=712, nElements=1)),\n", + " (Field['Flag'](name=\"base_GaussianFlux_flag\", doc=\"General Failure Flag\"), Key['Flag'](offset=632, bit=9)),\n", + " (Field['D'](name=\"base_LocalBackground_flux\", doc=\"background in annulus around source\", units=\"count\"), Key(offset=720, nElements=1)),\n", + " (Field['D'](name=\"base_LocalBackground_fluxSigma\", doc=\"1-sigma flux uncertainty\", units=\"count\"), Key(offset=728, nElements=1)),\n", + " (Field['Flag'](name=\"base_LocalBackground_flag\", doc=\"General Failure Flag\"), Key['Flag'](offset=632, bit=10)),\n", + " (Field['Flag'](name=\"base_LocalBackground_flag_noGoodPixels\", doc=\"no good pixels in the annulus\"), Key['Flag'](offset=632, bit=11)),\n", + " (Field['Flag'](name=\"base_LocalBackground_flag_noPsf\", doc=\"no PSF provided\"), Key['Flag'](offset=632, bit=12)),\n", + " (Field['Flag'](name=\"base_PixelFlags_flag\", doc=\"general failure flag, set if anything went wring\"), Key['Flag'](offset=632, bit=13)),\n", + " (Field['Flag'](name=\"base_PixelFlags_flag_offimage\", doc=\"Source center is off image\"), Key['Flag'](offset=632, bit=14)),\n", + " (Field['Flag'](name=\"base_PixelFlags_flag_edge\", doc=\"Source is outside usable exposure region (masked EDGE or NO_DATA)\"), Key['Flag'](offset=632, bit=15)),\n", + " (Field['Flag'](name=\"base_PixelFlags_flag_interpolated\", doc=\"Interpolated pixel in the Source footprint\"), Key['Flag'](offset=632, bit=16)),\n", + " (Field['Flag'](name=\"base_PixelFlags_flag_saturated\", doc=\"Saturated pixel in the Source footprint\"), Key['Flag'](offset=632, bit=17)),\n", + " (Field['Flag'](name=\"base_PixelFlags_flag_cr\", doc=\"Cosmic ray in the Source footprint\"), Key['Flag'](offset=632, bit=18)),\n", + " (Field['Flag'](name=\"base_PixelFlags_flag_bad\", doc=\"Bad pixel in the Source footprint\"), Key['Flag'](offset=632, bit=19)),\n", + " (Field['Flag'](name=\"base_PixelFlags_flag_suspect\", doc=\"Source''s footprint includes suspect pixels\"), Key['Flag'](offset=632, bit=20)),\n", + " (Field['Flag'](name=\"base_PixelFlags_flag_interpolatedCenter\", doc=\"Interpolated pixel in the Source center\"), Key['Flag'](offset=632, bit=21)),\n", + " (Field['Flag'](name=\"base_PixelFlags_flag_saturatedCenter\", doc=\"Saturated pixel in the Source center\"), Key['Flag'](offset=632, bit=22)),\n", + " (Field['Flag'](name=\"base_PixelFlags_flag_crCenter\", doc=\"Cosmic ray in the Source center\"), Key['Flag'](offset=632, bit=23)),\n", + " (Field['Flag'](name=\"base_PixelFlags_flag_suspectCenter\", doc=\"Source''s center is close to suspect pixels\"), Key['Flag'](offset=632, bit=24)),\n", + " (Field['D'](name=\"base_PsfFlux_flux\", doc=\"flux derived from linear least-squares fit of PSF model\", units=\"count\"), Key(offset=736, nElements=1)),\n", + " (Field['D'](name=\"base_PsfFlux_fluxSigma\", doc=\"1-sigma flux uncertainty\", units=\"count\"), Key(offset=744, nElements=1)),\n", + " (Field['F'](name=\"base_PsfFlux_area\", doc=\"effective area of PSF\", units=\"pixel\"), Key(offset=752, nElements=1)),\n", + " (Field['Flag'](name=\"base_PsfFlux_flag\", doc=\"General Failure Flag\"), Key['Flag'](offset=632, bit=25)),\n", + " (Field['Flag'](name=\"base_PsfFlux_flag_noGoodPixels\", doc=\"not enough non-rejected pixels in data to attempt the fit\"), Key['Flag'](offset=632, bit=26)),\n", + " (Field['Flag'](name=\"base_PsfFlux_flag_edge\", doc=\"object was too close to the edge of the image to use the full PSF model\"), Key['Flag'](offset=632, bit=27)),\n", + " (Field['Flag'](name=\"base_Variance_flag\", doc=\"Set for any fatal failure\"), Key['Flag'](offset=632, bit=28)),\n", + " (Field['D'](name=\"base_Variance_value\", doc=\"Variance at object position\"), Key(offset=760, nElements=1)),\n", + " (Field['Flag'](name=\"base_Variance_flag_emptyFootprint\", doc=\"Set to True when the footprint has no usable pixels\"), Key['Flag'](offset=632, bit=29)),\n", + " (Field['D'](name=\"ext_photometryKron_KronFlux_flux\", doc=\"flux from Kron Flux algorithm\", units=\"count\"), Key(offset=768, nElements=1)),\n", + " (Field['D'](name=\"ext_photometryKron_KronFlux_fluxSigma\", doc=\"1-sigma flux uncertainty\", units=\"count\"), Key(offset=776, nElements=1)),\n", + " (Field['F'](name=\"ext_photometryKron_KronFlux_radius\", doc=\"Kron radius (sqrt(a*b))\"), Key(offset=784, nElements=1)),\n", + " (Field['F'](name=\"ext_photometryKron_KronFlux_radius_for_radius\", doc=\"radius used to estimate (sqrt(a*b))\"), Key(offset=788, nElements=1)),\n", + " (Field['F'](name=\"ext_photometryKron_KronFlux_psf_radius\", doc=\"Radius of PSF\"), Key(offset=792, nElements=1)),\n", + " (Field['Flag'](name=\"ext_photometryKron_KronFlux_flag\", doc=\"general failure flag, set if anything went wrong\"), Key['Flag'](offset=632, bit=30)),\n", + " (Field['Flag'](name=\"ext_photometryKron_KronFlux_flag_edge\", doc=\"bad measurement due to image edge\"), Key['Flag'](offset=632, bit=31)),\n", + " (Field['Flag'](name=\"ext_photometryKron_KronFlux_flag_bad_shape_no_psf\", doc=\"bad shape and no PSF\"), Key['Flag'](offset=632, bit=32)),\n", + " (Field['Flag'](name=\"ext_photometryKron_KronFlux_flag_no_minimum_radius\", doc=\"minimum radius could not enforced: no minimum value or PSF\"), Key['Flag'](offset=632, bit=33)),\n", + " (Field['Flag'](name=\"ext_photometryKron_KronFlux_flag_no_fallback_radius\", doc=\"no minimum radius and no PSF provided\"), Key['Flag'](offset=632, bit=34)),\n", + " (Field['Flag'](name=\"ext_photometryKron_KronFlux_flag_bad_radius\", doc=\"bad Kron radius\"), Key['Flag'](offset=632, bit=35)),\n", + " (Field['Flag'](name=\"ext_photometryKron_KronFlux_flag_used_minimum_radius\", doc=\"used the minimum radius for the Kron aperture\"), Key['Flag'](offset=632, bit=36)),\n", + " (Field['Flag'](name=\"ext_photometryKron_KronFlux_flag_used_psf_radius\", doc=\"used the PSF Kron radius for the Kron aperture\"), Key['Flag'](offset=632, bit=37)),\n", + " (Field['Flag'](name=\"ext_photometryKron_KronFlux_flag_small_radius\", doc=\"measured Kron radius was smaller than that of the PSF\"), Key['Flag'](offset=632, bit=38)),\n", + " (Field['Flag'](name=\"ext_photometryKron_KronFlux_flag_bad_shape\", doc=\"shape for measuring Kron radius is bad; used PSF shape\"), Key['Flag'](offset=632, bit=39)),\n", + " (Field['D'](name=\"base_GaussianFlux_apCorr\", doc=\"aperture correction applied to base_GaussianFlux\"), Key(offset=800, nElements=1)),\n", + " (Field['D'](name=\"base_GaussianFlux_apCorrSigma\", doc=\"aperture correction applied to base_GaussianFlux\"), Key(offset=808, nElements=1)),\n", + " (Field['Flag'](name=\"base_GaussianFlux_flag_apCorr\", doc=\"set if unable to aperture correct base_GaussianFlux\"), Key['Flag'](offset=632, bit=40)),\n", + " (Field['D'](name=\"base_PsfFlux_apCorr\", doc=\"aperture correction applied to base_PsfFlux\"), Key(offset=816, nElements=1)),\n", + " (Field['D'](name=\"base_PsfFlux_apCorrSigma\", doc=\"aperture correction applied to base_PsfFlux\"), Key(offset=824, nElements=1)),\n", + " (Field['Flag'](name=\"base_PsfFlux_flag_apCorr\", doc=\"set if unable to aperture correct base_PsfFlux\"), Key['Flag'](offset=632, bit=41)),\n", + " (Field['D'](name=\"ext_photometryKron_KronFlux_apCorr\", doc=\"aperture correction applied to ext_photometryKron_KronFlux\"), Key(offset=832, nElements=1)),\n", + " (Field['D'](name=\"ext_photometryKron_KronFlux_apCorrSigma\", doc=\"aperture correction applied to ext_photometryKron_KronFlux\"), Key(offset=840, nElements=1)),\n", + " (Field['Flag'](name=\"ext_photometryKron_KronFlux_flag_apCorr\", doc=\"set if unable to aperture correct ext_photometryKron_KronFlux\"), Key['Flag'](offset=632, bit=42)),\n", + " (Field['D'](name=\"base_ClassificationExtendedness_value\", doc=\"Set to 1 for extended sources, 0 for point sources.\"), Key(offset=848, nElements=1)),\n", + " (Field['Flag'](name=\"base_ClassificationExtendedness_flag\", doc=\"Set to 1 for any fatal failure.\"), Key['Flag'](offset=632, bit=43)),\n", + " (Field['I'](name=\"base_FootprintArea_value\", doc=\"Number of pixels in the source''s detection footprint.\", units=\"pixel\"), Key(offset=856, nElements=1)),\n", + " (Field['Flag'](name=\"calib_astrometryUsed\", doc=\"set if source was used in astrometric calibration\"), Key['Flag'](offset=632, bit=44)),\n", + " (Field['Flag'](name=\"calib_photometry_used\", doc=\"set if source was used in photometric calibration\"), Key['Flag'](offset=632, bit=45)),\n", + " (Field['Flag'](name=\"calib_photometry_reserved\", doc=\"set if source was reserved from photometric calibration\"), Key['Flag'](offset=632, bit=46)),\n", + " 'base_CircularApertureFlux_flag_badCentroid'->'base_SdssCentroid_flag'\n", + " 'base_GaussianFlux_flag_badCentroid'->'base_SdssCentroid_flag'\n", + " 'base_GaussianFlux_flag_badShape'->'ext_shapeHSM_HsmSourceMoments_flag'\n", + " 'base_LocalBackground_flag_badCentroid'->'base_SdssCentroid_flag'\n", + " 'base_NaiveCentroid_flag_badInitialCentroid'->'base_SdssCentroid_flag'\n", + " 'base_PsfFlux_flag_badCentroid'->'base_SdssCentroid_flag'\n", + " 'base_SdssShape_flag_badCentroid'->'base_SdssCentroid_flag'\n", + " 'base_Variance_flag_badCentroid'->'base_SdssCentroid_flag'\n", + " 'ext_photometryKron_KronFlux_flag_badInitialCentroid'->'base_SdssCentroid_flag'\n", + " 'ext_shapeHSM_HsmPsfMoments_flag_badCentroid'->'base_SdssCentroid_flag'\n", + " 'ext_shapeHSM_HsmShapeRegauss_flag_badCentroid'->'base_SdssCentroid_flag'\n", + " 'ext_shapeHSM_HsmSourceMomentsRound_flag_badCentroid'->'base_SdssCentroid_flag'\n", + " 'ext_shapeHSM_HsmSourceMoments_flag_badCentroid'->'base_SdssCentroid_flag'\n", + " 'slot_ApFlux'->'base_CircularApertureFlux_12_0'\n", + " 'slot_CalibFlux'->'base_CircularApertureFlux_12_0'\n", + " 'slot_Centroid'->'base_SdssCentroid'\n", + " 'slot_InstFlux'->'base_GaussianFlux'\n", + " 'slot_ModelFlux'->'base_GaussianFlux'\n", + " 'slot_PsfFlux'->'base_PsfFlux'\n", + " 'slot_PsfShape'->'ext_shapeHSM_HsmPsfMoments'\n", + " 'slot_Shape'->'ext_shapeHSM_HsmSourceMoments'\n", + ")" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "src_cat.getSchema()" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "flat_list = [item for sublist in mag_err for item in sublist]" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "mag_err = flat_list" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "nchild = np.array(nchild)\n", + "mag = np.array(mag)\n", + "ra_aux = np.array(ra_aux)\n", + "dec_aux = np.array(dec_aux)\n", + "mag_err = np.array(mag_err)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "X2 = np.zeros((np.count_nonzero(nchild==0),2))\n", + "X2[:,0] = ra_aux[nchild==0]\n", + "X2[:,1] = dec_aux[nchild==0]\n", + "dist, ind = tree.query(X2)\n", + "ind = ind.flatten()" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(-1, 1)" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(data['mag_true_r_lsst'][ind],mag[nchild==0]-data['mag_true_r_lsst'][ind],s=0.2)\n", + "plt.xlim(20,24)\n", + "plt.ylim(-1,1)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(20, 24)" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(data['mag_true_r_lsst'][ind],ra_aux[nchild==0]-ra_new[ind],s=0.2)\n", + "plt.xlim(20,24)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "patches_paths = glob.glob('/global/projecta/projectdirs/lsst/global/in2p3/Run1.1-test2/output/deepCoadd-results/r/4848/*')" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "tracts = [4430,4432,4638,4640,4849,4851,5062,5064,5066,4431,4637,4639,4848,4850,4852,5063,5065]" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [], + "source": [ + "test = butler.get('deepCoadd_meas',filter='r',tract=tract,patch=patches[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Schema(\n", + " (Field['L'](name=\"id\", doc=\"unique ID\"), Key(offset=0, nElements=1)),\n", + " (Field['Angle'](name=\"coord_ra\", doc=\"position in ra/dec\"), Key(offset=8, nElements=1)),\n", + " (Field['Angle'](name=\"coord_dec\", doc=\"position in ra/dec\"), Key(offset=16, nElements=1)),\n", + " (Field['L'](name=\"parent\", doc=\"unique ID of parent source\"), Key(offset=24, nElements=1)),\n", + " (Field['Flag'](name=\"merge_footprint_i\", doc=\"Detection footprint overlapped with a detection from filter i\"), Key['Flag'](offset=32, bit=0)),\n", + " (Field['Flag'](name=\"merge_footprint_r\", doc=\"Detection footprint overlapped with a detection from filter r\"), Key['Flag'](offset=32, bit=1)),\n", + " (Field['Flag'](name=\"merge_footprint_z\", doc=\"Detection footprint overlapped with a detection from filter z\"), Key['Flag'](offset=32, bit=2)),\n", + " (Field['Flag'](name=\"merge_footprint_g\", doc=\"Detection footprint overlapped with a detection from filter g\"), Key['Flag'](offset=32, bit=3)),\n", + " (Field['Flag'](name=\"merge_footprint_y\", doc=\"Detection footprint overlapped with a detection from filter y\"), Key['Flag'](offset=32, bit=4)),\n", + " (Field['Flag'](name=\"merge_footprint_u\", doc=\"Detection footprint overlapped with a detection from filter u\"), Key['Flag'](offset=32, bit=5)),\n", + " (Field['Flag'](name=\"merge_footprint_sky\", doc=\"Detection footprint overlapped with a detection from filter sky\"), Key['Flag'](offset=32, bit=6)),\n", + " (Field['Flag'](name=\"merge_peak_i\", doc=\"Peak detected in filter i\"), Key['Flag'](offset=32, bit=7)),\n", + " (Field['Flag'](name=\"merge_peak_r\", doc=\"Peak detected in filter r\"), Key['Flag'](offset=32, bit=8)),\n", + " (Field['Flag'](name=\"merge_peak_z\", doc=\"Peak detected in filter z\"), Key['Flag'](offset=32, bit=9)),\n", + " (Field['Flag'](name=\"merge_peak_g\", doc=\"Peak detected in filter g\"), Key['Flag'](offset=32, bit=10)),\n", + " (Field['Flag'](name=\"merge_peak_y\", doc=\"Peak detected in filter y\"), Key['Flag'](offset=32, bit=11)),\n", + " (Field['Flag'](name=\"merge_peak_u\", doc=\"Peak detected in filter u\"), Key['Flag'](offset=32, bit=12)),\n", + " (Field['Flag'](name=\"merge_peak_sky\", doc=\"Peak detected in filter sky\"), Key['Flag'](offset=32, bit=13)),\n", + " (Field['I'](name=\"deblend_nChild\", doc=\"Number of children this object has (defaults to 0)\"), Key(offset=40, nElements=1)),\n", + " (Field['Flag'](name=\"deblend_deblendedAsPsf\", doc=\"Deblender thought this source looked like a PSF\"), Key['Flag'](offset=32, bit=14)),\n", + " (Field['D'](name=\"deblend_psfCenter_x\", doc=\"If deblended-as-psf, the PSF centroid\", units=\"pixel\"), Key(offset=48, nElements=1)),\n", + " (Field['D'](name=\"deblend_psfCenter_y\", doc=\"If deblended-as-psf, the PSF centroid\", units=\"pixel\"), Key(offset=56, nElements=1)),\n", + " (Field['D'](name=\"deblend_psfFlux\", doc=\"If deblended-as-psf, the PSF flux\"), Key(offset=64, nElements=1)),\n", + " (Field['Flag'](name=\"deblend_tooManyPeaks\", doc=\"Source had too many peaks; only the brightest were included\"), Key['Flag'](offset=32, bit=15)),\n", + " (Field['Flag'](name=\"deblend_parentTooBig\", doc=\"Parent footprint covered too many pixels\"), Key['Flag'](offset=32, bit=16)),\n", + " (Field['Flag'](name=\"deblend_masked\", doc=\"Parent footprint was predominantly masked\"), Key['Flag'](offset=32, bit=17)),\n", + " (Field['Flag'](name=\"deblend_skipped\", doc=\"Deblender skipped this source\"), Key['Flag'](offset=32, bit=18)),\n", + " (Field['Flag'](name=\"deblend_rampedTemplate\", doc=\"This source was near an image edge and the deblender used \"ramp\" edge-handling.\"), Key['Flag'](offset=32, bit=19)),\n", + " (Field['Flag'](name=\"deblend_patchedTemplate\", doc=\"This source was near an image edge and the deblender used \"patched\" edge-handling.\"), Key['Flag'](offset=32, bit=20)),\n", + " (Field['Flag'](name=\"deblend_hasStrayFlux\", doc=\"This source was assigned some stray flux\"), Key['Flag'](offset=32, bit=21)),\n", + " (Field['D'](name=\"base_NaiveCentroid_x\", doc=\"centroid from Naive Centroid algorithm\", units=\"pixel\"), Key(offset=72, nElements=1)),\n", + " (Field['D'](name=\"base_NaiveCentroid_y\", doc=\"centroid from Naive Centroid algorithm\", units=\"pixel\"), Key(offset=80, nElements=1)),\n", + " (Field['Flag'](name=\"base_NaiveCentroid_flag\", doc=\"General Failure Flag\"), Key['Flag'](offset=32, bit=22)),\n", + " (Field['Flag'](name=\"base_NaiveCentroid_flag_noCounts\", doc=\"Object to be centroided has no counts\"), Key['Flag'](offset=32, bit=23)),\n", + " (Field['Flag'](name=\"base_NaiveCentroid_flag_edge\", doc=\"Object too close to edge\"), Key['Flag'](offset=32, bit=24)),\n", + " (Field['Flag'](name=\"base_NaiveCentroid_flag_resetToPeak\", doc=\"set if CentroidChecker reset the centroid\"), Key['Flag'](offset=32, bit=25)),\n", + " (Field['D'](name=\"base_SdssCentroid_x\", doc=\"centroid from Sdss Centroid algorithm\", units=\"pixel\"), Key(offset=88, nElements=1)),\n", + " (Field['D'](name=\"base_SdssCentroid_y\", doc=\"centroid from Sdss Centroid algorithm\", units=\"pixel\"), Key(offset=96, nElements=1)),\n", + " (Field['F'](name=\"base_SdssCentroid_xSigma\", doc=\"1-sigma uncertainty on x position\", units=\"pixel\"), Key(offset=104, nElements=1)),\n", + " (Field['F'](name=\"base_SdssCentroid_ySigma\", doc=\"1-sigma uncertainty on y position\", units=\"pixel\"), Key(offset=108, nElements=1)),\n", + " (Field['Flag'](name=\"base_SdssCentroid_flag\", doc=\"General Failure Flag\"), Key['Flag'](offset=32, bit=26)),\n", + " (Field['Flag'](name=\"base_SdssCentroid_flag_edge\", doc=\"Object too close to edge\"), Key['Flag'](offset=32, bit=27)),\n", + " (Field['Flag'](name=\"base_SdssCentroid_flag_noSecondDerivative\", doc=\"Vanishing second derivative\"), Key['Flag'](offset=32, bit=28)),\n", + " (Field['Flag'](name=\"base_SdssCentroid_flag_almostNoSecondDerivative\", doc=\"Almost vanishing second derivative\"), Key['Flag'](offset=32, bit=29)),\n", + " (Field['Flag'](name=\"base_SdssCentroid_flag_notAtMaximum\", doc=\"Object is not at a maximum\"), Key['Flag'](offset=32, bit=30)),\n", + " (Field['Flag'](name=\"base_SdssCentroid_flag_resetToPeak\", doc=\"set if CentroidChecker reset the centroid\"), Key['Flag'](offset=32, bit=31)),\n", + " (Field['D'](name=\"base_Blendedness_old\", doc=\"blendedness from dot products: (child.dot(parent)/child.dot(child) - 1)\"), Key(offset=112, nElements=1)),\n", + " (Field['D'](name=\"base_Blendedness_raw_flux\", doc=\"measure of how flux is affected by neighbors: (1 - flux.child/flux.parent)\"), Key(offset=120, nElements=1)),\n", + " (Field['D'](name=\"base_Blendedness_raw_flux_child\", doc=\"flux of the child, measured with a Gaussian weight matched to the child\", units=\"count\"), Key(offset=128, nElements=1)),\n", + " (Field['D'](name=\"base_Blendedness_raw_flux_parent\", doc=\"flux of the parent, measured with a Gaussian weight matched to 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nElements=1)),\n", + " (Field['Flag'](name=\"ext_convolved_ConvolvedFlux_2_6_0_flag_apCorr\", doc=\"set if unable to aperture correct ext_convolved_ConvolvedFlux_2_6_0\"), Key['Flag'](offset=1696, bit=10)),\n", + " (Field['D'](name=\"modelfit_CModel_initial_apCorr\", doc=\"aperture correction applied to modelfit_CModel_initial\"), Key(offset=1800, nElements=1)),\n", + " (Field['D'](name=\"modelfit_CModel_initial_apCorrSigma\", doc=\"aperture correction applied to modelfit_CModel_initial\"), Key(offset=1808, nElements=1)),\n", + " (Field['Flag'](name=\"modelfit_CModel_initial_flag_apCorr\", doc=\"set if unable to aperture correct modelfit_CModel_initial\"), Key['Flag'](offset=1696, bit=11)),\n", + " (Field['D'](name=\"ext_convolved_ConvolvedFlux_1_3_3_apCorr\", doc=\"aperture correction applied to ext_convolved_ConvolvedFlux_1_3_3\"), Key(offset=1816, nElements=1)),\n", + " (Field['D'](name=\"ext_convolved_ConvolvedFlux_1_3_3_apCorrSigma\", doc=\"aperture correction applied to 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" (Field['D'](name=\"ext_convolved_ConvolvedFlux_0_kron_apCorrSigma\", doc=\"aperture correction applied to ext_convolved_ConvolvedFlux_0_kron\"), Key(offset=1856, nElements=1)),\n", + " (Field['Flag'](name=\"ext_convolved_ConvolvedFlux_0_kron_flag_apCorr\", doc=\"set if unable to aperture correct ext_convolved_ConvolvedFlux_0_kron\"), Key['Flag'](offset=1696, bit=14)),\n", + " (Field['D'](name=\"ext_convolved_ConvolvedFlux_0_4_5_apCorr\", doc=\"aperture correction applied to ext_convolved_ConvolvedFlux_0_4_5\"), Key(offset=1864, nElements=1)),\n", + " (Field['D'](name=\"ext_convolved_ConvolvedFlux_0_4_5_apCorrSigma\", doc=\"aperture correction applied to ext_convolved_ConvolvedFlux_0_4_5\"), Key(offset=1872, nElements=1)),\n", + " (Field['Flag'](name=\"ext_convolved_ConvolvedFlux_0_4_5_flag_apCorr\", doc=\"set if unable to aperture correct ext_convolved_ConvolvedFlux_0_4_5\"), Key['Flag'](offset=1696, bit=15)),\n", + " (Field['D'](name=\"ext_convolved_ConvolvedFlux_1_kron_apCorr\", doc=\"aperture correction applied to ext_convolved_ConvolvedFlux_1_kron\"), Key(offset=1880, nElements=1)),\n", + " (Field['D'](name=\"ext_convolved_ConvolvedFlux_1_kron_apCorrSigma\", doc=\"aperture correction applied to ext_convolved_ConvolvedFlux_1_kron\"), Key(offset=1888, nElements=1)),\n", + " (Field['Flag'](name=\"ext_convolved_ConvolvedFlux_1_kron_flag_apCorr\", doc=\"set if unable to aperture correct ext_convolved_ConvolvedFlux_1_kron\"), Key['Flag'](offset=1696, bit=16)),\n", + " (Field['D'](name=\"modelfit_CModel_exp_apCorr\", doc=\"aperture correction applied to modelfit_CModel_exp\"), Key(offset=1896, nElements=1)),\n", + " (Field['D'](name=\"modelfit_CModel_exp_apCorrSigma\", doc=\"aperture correction applied to modelfit_CModel_exp\"), Key(offset=1904, nElements=1)),\n", + " (Field['Flag'](name=\"modelfit_CModel_exp_flag_apCorr\", doc=\"set if unable to aperture correct modelfit_CModel_exp\"), Key['Flag'](offset=1696, bit=17)),\n", + " (Field['D'](name=\"ext_convolved_ConvolvedFlux_3_6_0_apCorr\", doc=\"aperture correction applied to ext_convolved_ConvolvedFlux_3_6_0\"), Key(offset=1912, nElements=1)),\n", + " (Field['D'](name=\"ext_convolved_ConvolvedFlux_3_6_0_apCorrSigma\", doc=\"aperture correction applied to ext_convolved_ConvolvedFlux_3_6_0\"), Key(offset=1920, nElements=1)),\n", + " (Field['Flag'](name=\"ext_convolved_ConvolvedFlux_3_6_0_flag_apCorr\", doc=\"set if unable to aperture correct ext_convolved_ConvolvedFlux_3_6_0\"), Key['Flag'](offset=1696, bit=18)),\n", + " (Field['D'](name=\"ext_convolved_ConvolvedFlux_2_kron_apCorr\", doc=\"aperture correction applied to ext_convolved_ConvolvedFlux_2_kron\"), Key(offset=1928, nElements=1)),\n", + " (Field['D'](name=\"ext_convolved_ConvolvedFlux_2_kron_apCorrSigma\", doc=\"aperture correction applied to ext_convolved_ConvolvedFlux_2_kron\"), Key(offset=1936, nElements=1)),\n", + " (Field['Flag'](name=\"ext_convolved_ConvolvedFlux_2_kron_flag_apCorr\", doc=\"set if unable to aperture correct ext_convolved_ConvolvedFlux_2_kron\"), Key['Flag'](offset=1696, bit=19)),\n", + " (Field['D'](name=\"ext_convolved_ConvolvedFlux_1_6_0_apCorr\", doc=\"aperture correction applied to ext_convolved_ConvolvedFlux_1_6_0\"), Key(offset=1944, nElements=1)),\n", + " (Field['D'](name=\"ext_convolved_ConvolvedFlux_1_6_0_apCorrSigma\", doc=\"aperture correction applied to ext_convolved_ConvolvedFlux_1_6_0\"), Key(offset=1952, nElements=1)),\n", + " (Field['Flag'](name=\"ext_convolved_ConvolvedFlux_1_6_0_flag_apCorr\", doc=\"set if unable to aperture correct ext_convolved_ConvolvedFlux_1_6_0\"), Key['Flag'](offset=1696, bit=20)),\n", + " (Field['D'](name=\"ext_convolved_ConvolvedFlux_3_4_5_apCorr\", doc=\"aperture correction applied to ext_convolved_ConvolvedFlux_3_4_5\"), Key(offset=1960, nElements=1)),\n", + " (Field['D'](name=\"ext_convolved_ConvolvedFlux_3_4_5_apCorrSigma\", doc=\"aperture correction applied to ext_convolved_ConvolvedFlux_3_4_5\"), Key(offset=1968, nElements=1)),\n", + " (Field['Flag'](name=\"ext_convolved_ConvolvedFlux_3_4_5_flag_apCorr\", doc=\"set if unable to aperture correct ext_convolved_ConvolvedFlux_3_4_5\"), Key['Flag'](offset=1696, bit=21)),\n", + " (Field['D'](name=\"ext_convolved_ConvolvedFlux_2_3_3_apCorr\", doc=\"aperture correction applied to ext_convolved_ConvolvedFlux_2_3_3\"), Key(offset=1976, nElements=1)),\n", + " (Field['D'](name=\"ext_convolved_ConvolvedFlux_2_3_3_apCorrSigma\", doc=\"aperture correction applied to ext_convolved_ConvolvedFlux_2_3_3\"), Key(offset=1984, nElements=1)),\n", + " (Field['Flag'](name=\"ext_convolved_ConvolvedFlux_2_3_3_flag_apCorr\", doc=\"set if unable to aperture correct ext_convolved_ConvolvedFlux_2_3_3\"), Key['Flag'](offset=1696, bit=22)),\n", + " (Field['D'](name=\"modelfit_CModel_apCorr\", doc=\"aperture correction applied to modelfit_CModel\"), Key(offset=1992, nElements=1)),\n", + " (Field['D'](name=\"modelfit_CModel_apCorrSigma\", doc=\"aperture correction applied to modelfit_CModel\"), Key(offset=2000, nElements=1)),\n", + " (Field['Flag'](name=\"modelfit_CModel_flag_apCorr\", doc=\"set if unable to aperture correct modelfit_CModel\"), Key['Flag'](offset=1696, bit=23)),\n", + " (Field['D'](name=\"ext_convolved_ConvolvedFlux_2_4_5_apCorr\", doc=\"aperture correction applied to ext_convolved_ConvolvedFlux_2_4_5\"), Key(offset=2008, nElements=1)),\n", + " (Field['D'](name=\"ext_convolved_ConvolvedFlux_2_4_5_apCorrSigma\", doc=\"aperture correction applied to ext_convolved_ConvolvedFlux_2_4_5\"), Key(offset=2016, nElements=1)),\n", + " (Field['Flag'](name=\"ext_convolved_ConvolvedFlux_2_4_5_flag_apCorr\", doc=\"set if unable to aperture correct ext_convolved_ConvolvedFlux_2_4_5\"), Key['Flag'](offset=1696, bit=24)),\n", + " (Field['D'](name=\"ext_convolved_ConvolvedFlux_1_4_5_apCorr\", doc=\"aperture correction applied to ext_convolved_ConvolvedFlux_1_4_5\"), Key(offset=2024, nElements=1)),\n", + " (Field['D'](name=\"ext_convolved_ConvolvedFlux_1_4_5_apCorrSigma\", doc=\"aperture correction applied to ext_convolved_ConvolvedFlux_1_4_5\"), Key(offset=2032, nElements=1)),\n", + " (Field['Flag'](name=\"ext_convolved_ConvolvedFlux_1_4_5_flag_apCorr\", doc=\"set if unable to aperture correct ext_convolved_ConvolvedFlux_1_4_5\"), Key['Flag'](offset=1696, bit=25)),\n", + " (Field['D'](name=\"modelfit_CModel_dev_apCorr\", doc=\"aperture correction applied to modelfit_CModel_dev\"), Key(offset=2040, nElements=1)),\n", + " (Field['D'](name=\"modelfit_CModel_dev_apCorrSigma\", doc=\"aperture correction applied to modelfit_CModel_dev\"), Key(offset=2048, nElements=1)),\n", + " (Field['Flag'](name=\"modelfit_CModel_dev_flag_apCorr\", doc=\"set if unable to aperture correct modelfit_CModel_dev\"), Key['Flag'](offset=1696, bit=26)),\n", + " (Field['D'](name=\"base_ClassificationExtendedness_value\", doc=\"Set to 1 for extended sources, 0 for point sources.\"), Key(offset=2056, nElements=1)),\n", + " (Field['Flag'](name=\"base_ClassificationExtendedness_flag\", doc=\"Set to 1 for any fatal failure.\"), Key['Flag'](offset=1696, bit=27)),\n", + " (Field['I'](name=\"base_FootprintArea_value\", doc=\"Number of pixels in the source''s detection footprint.\", units=\"pixel\"), Key(offset=2064, nElements=1)),\n", + " 'base_CircularApertureFlux_flag_badCentroid'->'base_SdssCentroid_flag'\n", + " 'base_GaussianFlux_flag_badCentroid'->'base_SdssCentroid_flag'\n", + " 'base_GaussianFlux_flag_badShape'->'ext_shapeHSM_HsmSourceMoments_flag'\n", + " 'base_InputCount_flag_badCentroid'->'base_SdssCentroid_flag'\n", + " 'base_LocalBackground_flag_badCentroid'->'base_SdssCentroid_flag'\n", + " 'base_NaiveCentroid_flag_badInitialCentroid'->'base_SdssCentroid_flag'\n", + " 'base_PsfFlux_flag_badCentroid'->'base_SdssCentroid_flag'\n", + " 'base_SdssShape_flag_badCentroid'->'base_SdssCentroid_flag'\n", + " 'base_Variance_flag_badCentroid'->'base_SdssCentroid_flag'\n", + " 'ext_convolved_ConvolvedFlux_0_flag_badCentroid'->'base_SdssCentroid_flag'\n", + " 'ext_convolved_ConvolvedFlux_1_flag_badCentroid'->'base_SdssCentroid_flag'\n", + " 'ext_convolved_ConvolvedFlux_2_flag_badCentroid'->'base_SdssCentroid_flag'\n", + " 'ext_convolved_ConvolvedFlux_3_flag_badCentroid'->'base_SdssCentroid_flag'\n", + " 'ext_convolved_ConvolvedFlux_flag_badCentroid'->'base_SdssCentroid_flag'\n", + " 'ext_shapeHSM_HsmPsfMoments_flag_badCentroid'->'base_SdssCentroid_flag'\n", + " 'ext_shapeHSM_HsmShapeBj_flag_badCentroid'->'base_SdssCentroid_flag'\n", + " 'ext_shapeHSM_HsmShapeKsb_flag_badCentroid'->'base_SdssCentroid_flag'\n", + " 'ext_shapeHSM_HsmShapeLinear_flag_badCentroid'->'base_SdssCentroid_flag'\n", + " 'ext_shapeHSM_HsmShapeRegauss_flag_badCentroid'->'base_SdssCentroid_flag'\n", + " 'ext_shapeHSM_HsmSourceMoments_flag_badCentroid'->'base_SdssCentroid_flag'\n", + " 'modelfit_DoubleShapeletPsfApprox_flag_badCentroid'->'base_SdssCentroid_flag'\n", + " 'slot_ApFlux'->'base_CircularApertureFlux_12_0'\n", + " 'slot_CalibFlux'->'base_CircularApertureFlux_12_0'\n", + " 'slot_Centroid'->'base_SdssCentroid'\n", + " 'slot_InstFlux'->'base_GaussianFlux'\n", + " 'slot_ModelFlux'->'modelfit_CModel'\n", + " 'slot_PsfFlux'->'base_PsfFlux'\n", + " 'slot_PsfShape'->'base_SdssShape_psf'\n", + " 'slot_Shape'->'ext_shapeHSM_HsmSourceMoments'\n", + ")" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test.getSchema()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "magmodel=np.zeros(0)\n", + "ra_coadd=np.zeros(0)\n", + "dec_coadd=np.zeros(0)\n", + "isprimary=np.zeros(0,dtype=bool)\n", + "nchild_coadd=np.zeros(0,dtype=int)\n", + "psfmag=np.zeros(0)\n", + "magmodel_err=np.zeros(0)\n", + "psfmag_err=np.zeros(0)\n", + "blendedness=np.zeros(0)\n", + "modelflux=np.zeros(0)\n", + "modelflux_err=np.zeros(0)\n", + "psfflux=np.zeros(0)\n", + "psfflux_err=np.zeros(0)\n", + "extendedness=np.zeros(0)\n", + "for tract in tracts:\n", + " patches_paths = glob.glob('/global/projecta/projectdirs/lsst/global/in2p3/Run1.1-test2/output/deepCoadd-results/r/'+str(tract)+'/*')\n", + " patches = [pp[-3:] for pp in patches_paths]\n", + " for patch in patches:\n", + " try:\n", + " cat = butler.get('deepCoadd_meas',filter='r',tract=tract,patch=patch)\n", + " calib = lsst.afw.image.Calib(butler.get('deepCoadd_md',filter='r',tract=tract,patch=patch))\n", + " calib.setThrowOnNegativeFlux(False)\n", + " ra_coadd = np.concatenate([ra_coadd,cat.get('coord_ra')]).ravel()\n", + " dec_coadd = np.concatenate([dec_coadd,cat.get('coord_dec')]).ravel()\n", + " magmodel= np.concatenate([magmodel,calib.getMagnitude(cat.get('modelfit_CModel_flux'))])\n", + " magmodel_err=np.concatenate([magmodel_err,calib.getMagnitude(cat.get('modelfit_CModel_fluxSigma'))])\n", + " psfmag=np.concatenate([psfmag,calib.getMagnitude(cat.get('base_PsfFlux_flux'))])\n", + " psfmag_err=np.concatenate([psfmag_err,calib.getMagnitude(cat.get('base_PsfFlux_fluxSigma'))])\n", + " isprimary=np.concatenate([isprimary,cat.get('detect_isPrimary')])\n", + " blendedness=np.concatenate([blendedness,cat.get('base_Blendedness_old')])\n", + " nchild_coadd=np.concatenate([nchild_coadd,cat.get('deblend_nChild')])\n", + " modelflux=np.concatenate([modelflux,cat.get('modelfit_CModel_flux')])\n", + " modelflux_err=np.concatenate([modelflux_err,cat.get('modelfit_CModel_fluxSigma')])\n", + " psfflux=np.concatenate([psfflux,cat.get('base_PsfFlux_flux')])\n", + " psfflux_err=np.concatenate([psfflux_err,cat.get('base_PsfFlux_fluxSigma')])\n", + " extendedness=np.concatenate([extendedness,cat.get('base_ClassificationExtendedness_value')])\n", + " except:\n", + " continue" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "X2 = np.zeros((np.count_nonzero(isprimary),2))\n", + "X2[:,0] = np.degrees(ra_coadd[isprimary])\n", + "X2[:,1] = np.degrees(dec_coadd[isprimary])\n", + "dist, ind = tree.query(X2)\n", + "ind = ind.flatten()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/global/common/software/lsst/common/miniconda/py3-4.3.21/lib/python3.6/site-packages/numpy/lib/function_base.py:973: RuntimeWarning: invalid value encountered in greater_equal\n", + " not_smaller_than_edge = (sample[:, i] >= edges[i][-1])\n" + ] + }, + { + "data": { + "image/png": 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CsQwAQhjjINZV+QZFAhSWXEit+ZJgbQA3RylZJMXXHai4a9Pl30sl2lFJh9As7/v8FF+I7w34F5ttlLdr13jAuBdijtZtm16mdUtYKC3vzfDj2BR6BQvwx3QgAIgB316dBFYctMS51BUnjTKIrhCtQGl9KoBnp3zcpeaokg6xcya3bjIi30kYY4yheJIwxhhD8SRhjDGGsjU1iTiFt6G0zWTnQ6ktKJQ2wgKitdu8zZSoI3pFTvHob9nk67f92fJ2OrGQeH5d5WEhzdh6+rCN0Fo65Rtc7PCx2D69SOtqJKodKweAbS2uO/zdS+fTuv5M+XlRX+SDMeCWDOqHUD4JBUtwVBfJqCq4hQAASfSKWBadVxoc0StCBP4bdCroC2d70qGIuD4iHouIgxFxS0n990bEAxHxYET8dUS8daTuyaL8/og4MIn+GGOMmQxj30lERB3AJwBcB+AQgPsiYl9mPjLysb8H8O2Z+XJE3ABgL4C3j9S/OzNfHLcvxhhjJssk7iSuAXAwM5/IzA6ATwHYPfqBzPzrzHy5ePt5AJdOYL/GGGPeZCYxSVwC4OmR94eKMsYPAPjjkfcJ4J6I+EJE7GGNImJPRByIiAPdrLh4aowxZk2sq3AdEe/GcJJ450jxOzPzcETsAHB3RDyamZ9b3TYz92K4TIWF2nYRbYwITlUC60EI1CKDWyiRSvQjpqbKK1RAPmXAE33k21PZzsq3p9qIRG3ozIsAcOQwpjiMKhsbmuWnjDLMKWZq5aauFeEQ3NYsDwoIAOfO8LqTC+XnxeBVIVyrsWCZ5ISZrv0qr6v1K2S6aygHpshmyExuFQJTAtBZ61gflJmuwvY2O5O4kzgM4LKR95cWZf+IiPhmAL8GYHdmHn2tPDMPF/8/D+BODJevjDHGbAImMUncB2BXRFwRES0ANwHYN/qBiHgLgM8A+EBmfmWkfDYi5l97DeC9AB6aQJ+MMcZMgLGXmzKzFxEfBnAXho8/356ZD0fEB4v62wD8FIDzAPxKsRTTy8yrAVwI4M6irAHgtzPzT8btkzHGmMkwEU0iM/cD2L+q7LaR1z8I4AdL2j0B4K2ry09FINasFWSXB16rTRMtAHyNUSU5qap/gBl4amKBWSQ4GpAAf8wUBwCDFu97b6b8O/dEUECVdEgF6+tsKy/vT6uENLyu1iw/jisrfCx2Th+jdW9pHy0t/4eV82ibrhBULprli/4vL5aLBSea3EipEkGxGIRMBwL0cWT7GojzAkvraC5T1yoznIoAf6nMebTR5jbMKRyWwxhjDMWThDHGGIonCWOMMRRPEsYYYyhbNApscKMYEaJCRUsVIl/UiRtMtWkqoVnUEYGNZpgDkG0R0bVdLpT2p4QJS4iNvanyupWFakamzjbejmVJ680JQZEY5gAgT5SP08zFwsTW4+fMi7350vJL2i+XlgPAkc45tG6eZLoDgEadPDxRUQtlfr9oClOkEGuZqF3rVBOn1TkNIijLKLAClskyRYbBEDFsk0Vy3sL4TsIYYwzFk4QxxhiKJwljjDGUralJAGvXHpQBpqWioVWgrTQEXjeYJ4HcWsowJ/QFEnivO8+3t3iBMh6R/Yi17O4s31xfJdVjcdz6wtTVEIHhZsrXmNtNvvbcEWnw+iR13oCl1AOwvXGS1j23ssDbkeB/Ly3wYHL5sghASYap1uPjVy+PZ1jsjEUMFE1ENkNqKgUQzBSrDKxd7tpM0ncVpHPQ5+cMz4AptIpNbrTznYQxxhiKJwljjDEUTxLGGGMoW1OTqAVCBOUrI4TXQK5nEr1CJfvpz4nsLWJa7s+U91FpCN05sW5OvnKvLfwJs2v3Log8O+hPiefr22ItdkACrylLxhRfow8S/K/T4+N3sQjwN19fLi2fqfHn9fvi4E+LRf9z24ul5XWiswBAf5oflDrxLzANCwB64jjWuqydOPbi+klxPSa5HqMjokWqJEYswB/fmoQlJEqh98iESZtAr/CdhDHGGIonCWOMMRRPEsYYYyieJIwxxlC2pnAdwYPoMaFZGdymhJmOBBIcTPGh66nMbyLDV2ehXLBbWRCZ34Qe3ycCdW9GtFFCM/lafSFAK4ObNFsxEbqnGq090GCnx4/joUUekO9dC4+Wlr/S54O7zAYQwOVT5ZnuAKBHMtoNhLFQxJ9EjzzzQbR4AEAKL1i/RXYmDr3wHNLAlACAFdJQZGiE+l4k86SCBQUEgOwJAZ022nhxWjGRO4mIuD4iHouIgxFxS0l9RMQvFfUPRMTbTretMcaYjWPsSSIi6gA+AeAGAFcCeH9EXLnqYzcA2FX82wPgk2toa4wxZoOYxJ3ENQAOZuYTmdkB8CkAu1d9ZjeA38whnwdwTkTsPM22xhhjNohJaBKXAHh65P0hAG8/jc9ccpptAQARsQfDuxBMNRaQC3PlvWmQ9VtiVAOAvlgDHZC63jRv0xEaAl2/BbC0gwQtFPHEVrbz9UymFWRd6A4zYvG5SeqUTqD+DGmIfREzncj3Aog1+vp0+b5m2tzE1qjx/j2xsqO0/BumnqFtmn0e7bBb419str6ttPz8847TNi8/y6MnBlmGJ9LHsI1YNq93yXkmfl1SGPeiK3QCtn5PkhEBAMTYRqv8dyGXeDKqKjqGxGa6yZCZezPz6sy8utUQyqsxxpiJMYk7icMALht5f2lRdjqfaZ5GW2OMMRvEJO4k7gOwKyKuiIgWgJsA7Fv1mX0Avq94yukdAI5l5pHTbGuMMWaDGPtOIjN7EfFhAHdhuGJ8e2Y+HBEfLOpvA7AfwI0ADgJYBPD9qu0pdxqBJM9FJ/EvDESSk75I3MPqetMiGJoIoCcelefbE4l71LrvoF2+TpuzYk21qx5g51UUElgPANAT+yJ6RSyr4G98XbrXKW/XZ9oHgG1Nvi59YbM8+N8LPZ48aCr4M/Qv9YnGBqBJRIQdsydom6Ot82hdkiRRymai6vpkezWRjKp5XJwXxJsE8OB/IQIGKtNI9ojgJ/rAgvgBQLJLa5PrDoqJmOkycz+GE8Fo2W0jrxPAD51uW2OMMZuDLSNcG2OMWX88SRhjjKF4kjDGGEPZmgH+MhHM0MKyZDW54FlTWaM6RECtEEwOOIWJiHRDZX7rLglxsFX+nbtz/G+DmjDuMQOhMloNRIA/Zd4CESJTCOHREYInEcKXVrjJ8qkT22ndoenyuktbL9E2UzUuXG9vcBH6WL88JeBUnW9vsE0Emnu53GinHoJgwSIBoN4hZjoh8CozavMVZYwj5cpMV4VBxe0F6aCKkLjJ8Z2EMcYYiicJY4wxFE8SxhhjKFtXk1gha65kbbIhgoZlQ6xlt8vdbwOy3g8AjUWRJEhoEs2TJDihSFSkAgb2psrr+iTpDAD0Zvj2Wq+QNuVL5gCAEGa17oLQF0jQQNVGGvdI8L+aaNMXmXEWSbanOvja8+HuubTunPoiresS8WbHFNcxmtNck4geCf6nhlZIHFRbErJdfZlfjwORBKy+QtopM50gGsSUO+kgflsY30kYY4yheJIwxhhD8SRhjDGGsjU1iUECnfJFUu6fEAG7xK5yqTwpTUzz5+trIlE6hCYxWCSahNA/lF7BPBndOb49pmOouvpyNc+I0iuYHFATCY56s3xRvUeCHQ5EHxQ1soB/QeNV2uay5lFa93ed8iRGAPB17edKy59ZPoe2OW/bSVp3dLY8YqQaW2LVKNqVlysrUW+Wn4NKrwDTD1UAvYHSqsi+1PbE9R0o3x4N/LcF8J2EMcYYiicJY4wxFE8SxhhjKJ4kjDHGULamcJ2J7BJ3DxO9lNgks1CV18XiMm0DkjUPALLFjUI1lm1PiGgsEx8ADEhQQxXQsCky+LHAgMrQx7KWAcBA1LFsfCHipNXFIRmcKB+LlRl+PF5a5GrtoeVyY9zxGd7mleQpBi9qlGe6A4DZ2kpp+YPNy0rLAaBZ4wM1aJUffxW0Uo07M9OphyoaS9UC3tFrQQnNItAgqwuIDIjkoZkzFd9JGGOMoYw1SUTE9oi4OyIeL/5/w59XEXFZRPxZRDwSEQ9HxI+M1P10RByOiPuLfzeO0x9jjDGTZdw7iVsA3JuZuwDcW7xfTQ/Aj2XmlQDeAeCHIuLKkfpfyMyrin/OdW2MMZuIcTWJ3QDeVby+A8CfA/iJ0Q9k5hEAR4rXxyPiywAuAfBI5b0OBsgT5WahoNoDX0dMaX5j5h0xv66UryGfql20iUFvmgRkAxBqfXS+PJJfiAQtgzY/JWKw9gCEnXmR7OlFro2s9FnSIdoEnW1qTb18XwMS+A8Aun3e99lG+TFuBs/atF2IJs/2ttG6VpTrbOc2eFDAHTPHad2hdrlxTyW3El9L6hUMFeiyocxvLKeP0h0qkD3xhSeN0lNSjMU6Me6dxIXFJAAAzwK4UH04Ii4H8C0A/mak+Icj4oGIuL1suWqk7Z6IOBARBzopFEpjjDET45STRETcExEPlfzbPfq5zEyIYMMRMQfg9wH8aGa+FrvgkwC+FsBVGN5t/Bxrn5l7M/PqzLy6FSLWtTHGmIlxyuWmzLyW1UXEcxGxMzOPRMROAM+TzzUxnCB+KzM/M7Lt50Y+86sA/nAtnTfGGPPmMu5y0z4ANxevbwbw2dUfiIgA8OsAvpyZP7+qbufI2/cBeGjM/hhjjJkg4wrXtwL4dET8AICnAHwPAETExQB+LTNvBPCvAHwAwIMRcX/R7qPFk0wfi4irMFymehLAfzqtvWbS6I00o5QSp1UWKmamq2reEf3IFRJxVvVPmPPq3XLxLYUQXlvhgl30y4X1/jQ/jZpiLLqz/G+UxmL5ymVvmm+vLp4XqHVIlr6TvO8d8b1e6syUlh/tzdE2J2t83JlhDgBeHZQvrzZZ+FUAPfKQAQBgW/nDDr1X+PFQEWKZB089ZDBoVxSamUiuzLLiWh2whzgq/l5kh0WVFYOR1YyF68VYk0RmHgXwnpLyZwDcWLz+K5Bo3Jn5gXH2b4wx5s3FjmtjjDEUTxLGGGMoWzLAXwJItpbI1veUOUatFzKYyQ44xXomX3+MRvk6vDIKSTMdMefJ4ISzPEBd/WS5ZqLMQP0WH6fmohgLsqDNgskBwEAEGmwsldf15sVauwh499ziQmn5V6Z3lpYDwDvnvkLrTg64XvGNrfLMdAPxN97Ts9tp3SOtcjtTrc8fLe+Jp87rZGyD6EqnQmVirHXZdS/2pfQKch0n0fMA6Ex37Ldkk+sOCt9JGGOMoXiSMMYYQ/EkYYwxhrIlNQlkcj8EW/uTugN/7pkFDEy1Bir0Dx6AkMc0CaFJZEME5CO+C4g2EBpHJGknxqJ1jPQBQGcbCWgIHpAPA6HPCDsJlRfUYezwY/Vqp1xDmKmJ7ysElXqFKHlqexe1XqV1s9PlfVwkmhjAdQcAYFLG4ARtIoNCDoSOhfK4nlqTEEnF6LWgNAkB/b3oWZMwxhhzBuJJwhhjDMWThDHGGIonCWOMMZStKVwD62dOYfsRIqkSyVOJ5AMWbEzM5UJgy3a5uKrE81jmwms2ytvVloTI1xBB45gxCkB9hRi0hD7ZOsYru3Pl22se5/3r0RRYQI9krTvW52bEp7vn0bpvbD9D67rEWDgV/CGDFfaQAYCpVnm741N8/FiARABgcQZVgD/1wIDwMFZDidrM3KrEbhXAUwXjZGyC7HMK30kYY4yheJIwxhhD8SRhjDGGsoU1ibWu41XTMJLlEFFB/CZMKoObSDrE9BSW3AgAYq48mQ4Aun6rkiJlXZjfKizFTr3Mj+PKNpHEiJiwOuVx+gAAg5N8bPvbyr/X8yvztM03zzzNtycW8I8Oyo/JRY1jtI0y9X3Nwsul5c/O86CALZGQiJkYhSwizwvFgGhctarr+kzv61T8vWAmUJl0qIKOsY74TsIYYwzFk4QxxhjKWJNERGyPiLsj4vHi/9KHBiPiyYh4MCLuj4gDa21vjDFmYxhXk7gFwL2ZeWtE3FK8/wny2Xdn5otjtB8PtWYpkuawtUS69ghABgyECPBHpmwZFFAlMSJB46ItDntP9Z3Q5NtLMbYjnaKXAAAPGklEQVSN43zdfNAo93gMmiJIXocfkyCBARvLItDcEv8bammlPDjhsQ7PzqMSC10gJK5zakuknI/fN01z/eOpZeLXEAYFpS/0SZzGxiJvMxDbi34FfaGqRsh+F4RPIsQ5TXuufF3q92cTeCjGXW7aDeCO4vUdAL57ndsbY4x5Exl3krgwM48Ur58FUJ4XcTjB3hMRX4iIPRXaIyL2RMSBiDjQxcqY3TbGGHM6nHK5KSLuAXBRSdVPjr7JzIygDzW+MzMPR8QOAHdHxKOZ+bk1tEdm7gWwFwAWYvvG34MZY8xZwCknicy8ltVFxHMRsTMzj0TETgDPk20cLv5/PiLuBHANgM8BOK32xhhjNoZxhet9AG4GcGvx/2dXfyAiZgHUMvN48fq9AH7mdNuvC1IcqpLpbrLkMl9eq2SmE9+XBhkEACa6iyCD0RWie0sEGiTiZU2I0zmzduOe0H5lELp+r/z4L/Z4tr2uyCTXEmn1mEC9LLY3X1vm2yOKcmOBD0bvFb6v5vHycWdBFQGgLsZ90Kxwbamge7Id+V6DakvaLItkDsR3GpzZZrpbAVwXEY8DuLZ4j4i4OCL2F5+5EMBfRcSXAPwtgD/KzD9R7Y0xxmwOxrqTyMyjAN5TUv4MgBuL108AeOta2htjjNkc2HFtjDGGsnUD/DGYMaWqmY4hzTEqmJdoR9YtQ5jVJNLwx9rw/iULhqY0jq4I/jfN9RQWAK7frrbOTQ1awkA2mOFjUSPtVvr8WB3pnEPrvm3mcVr3ZG9babnSHVrC0DlDBmphnrvfTvR4MiVmpmu/Is4LYZirVTDTpTBZBkmWBQBYrpB0SAW0ZNfcWWymM8YYcwbjScIYYwzFk4QxxhjKmadJVFnDm7ReoXYldIKoEV+DWAOtlPxIJWsX3zdWypMf5TQPXKcIMe71FTIWDd6/zhwfC+aHqKmhWFFB3sr7vtzjl9QLnTlatzjg+szF9eOl5bPkfAGAJ3t8XyzB0XybizqvzIljRYIk9qfEtfMq316/zce9Rnw3ZIiGCB8PCwyogvgNlAZXxSeh9IpNgO8kjDHGUDxJGGOMoXiSMMYYQ/EkYYwxhnLmCdebHCZsFZUT3RcTvAPCxFZBlNMGvGrCPxO1Vaa7eleIq93ydqE0zSW+rx4RUHt9Lp6/3JmhdYd7PHPvBe1nSsvnxfkyG1yEvrT1Umn5RbOv0jb/UKepXmQgRNpEXAcibiGiS7ItqsCUyozaIQ9jqIc7zjJ8J2GMMYbiScIYYwzFk4QxxhiKNYk3A2GOUaYaZqZjgf+GjdRaLNEelHlQBQXsEY1DBENTGkL0VN/LF6ZrPd6/QUusc5OqvvABNohJDAB6i+WXTv18/p3mGzyRzckBT1Z0eaNcy6gLTWKmz4P1zdeXSst3tLkjLeeEeEMS9xDPHgBgIH55pF7RKN9otvgG4zg/JsGCViodQwX4oxWb2zCn8J2EMcYYiicJY4wxFE8SxhhjKGNpEhGxHcD/BnA5gCcBfE9mvrzqM99QfOY1vhbAT2Xm/4qInwbwHwG8UNR9NDP3YzNRJWDghIMCqiTvytdQI/1I8mw4AMSMOCWI9kCTEQGIJZUJiAsCUSFhUk34JEg8PjTKl+cBAF11ddTKN7jU4R6U4z3+fQ91zqN1RwdPlZZ3xLE/R/z5d1HjWHl5i/skZhZ4gqN+s1xPUdpCZVhOMXHN5RTXe7BcrhNlfx01hE2QWEgx7p3ELQDuzcxdAO4t3v8jMvOxzLwqM68C8K0AFgHcOfKRX3itftNNEMYYc5Yz7iSxG8Adxes7AHz3KT7/HgB/l5nlfxoZY4zZVIw7SVyYmUeK188CEN59AMBNAH5nVdkPR8QDEXF7RPDYBMYYY9adU04SEXFPRDxU8m/36OdyuDhOF9ciogXguwD87kjxJzHUKK4CcATAz4n2eyLiQEQc6II/b26MMWZynFK4zsxrWV1EPBcROzPzSETsBPC82NQNAL6Ymc+NbPurryPiVwH8oejHXgB7AWAhtm+80lNVnK5gtGNZ0AAghOkne+UGqGhxcVVmreuWC94qaGHOTPHtVTD1KUF70BQZzYgJr9YRGciUhn+y3EB28iT/voPz+L62N07QOiZQzxMTGwBsq03TupcG5Wr9sT5vc8H8SVp3uL1QWt7kOvgpjHZ8nPrt8u/cUIEklaGTPXShhHBhHuVBOqtlg9wMova4y037ANxcvL4ZwGfFZ9+PVUtNxcTyGu8D8NCY/THGGDNBxp0kbgVwXUQ8DuDa4j0i4uKI+OqTShExC+A6AJ9Z1f5jEfFgRDwA4N0APjJmf4wxxkyQsXwSmXkUwyeWVpc/A+DGkfcnAbzhQfDM/MA4+zfGGPPm4gB/640IykbX9pWOITSEqIvsLQyiYwBAkr5L45uokwH+CLUOb1NfEQH5yFq2SlTUEEmHOueUl8/OctPZ84vztG7xXG60myJr1odEzL2Z5tqT5mxvcN1hrsUfFmH6ggqemOKXJ/rCIEqSDg1IQEgAqKmERFVQ29vCgfwYDsthjDGG4knCGGMMxZOEMcYYiicJY4wxFAvXp4IZXZTJRZpjlLBVQWiuQHa54hktETGzSlRPMU4qcmetWy68Dqb4GMntER03hPjLstkBAOrl3+vkcW6mu3CeG+a+fHInrXtu7uHS8q8RprMaC5cK4GLS9211ns3un8y/QOseab+ltDzFgxMDYX6TRjtimGwt8mjD6sEKGkVZPfShzHS0E6LNJhe7fSdhjDGG4knCGGMMxZOEMcYYijWJUzHpAFtibTJZULuKGb6Y0a6q7hBszV9kppPGIwUxVAUJ1AdwoxXAjYB9EX9QxFVE43j59rozfC17sVsta92z/bnS8ovrx2mb5/pcX5ghh/GiZnnGOgB4dIlrJkxKSyWxKdlOBesj12N/hp/TtVf5WATLtjjprHqbXHdQ+E7CGGMMxZOEMcYYiicJY4wxFGsSmwimPTCtAqiYkEjpBMJDkY3y7UVPBJNr8IXpEP3IhkiMxLZXQT5qcusCejO8TnooCCeWue7Q2sbH/SjRJPrgWX3OVx4Fsq4/EzyI35Uzz9C6/7Ptn5VXvMiTGCkvBM9vCfSnyhu2XxTnrUjMFcvkO6uglQJ1rW5VfCdhjDGG4knCGGMMxZOEMcYYiicJY4wxFAvXVZFB/JR4tfYAf1XNdFSgVgHK1L5YFjyxvVjp0rpsCXG6QtY6JTbWiAmvMy/Mg0KPr5GvFSe5YNzawTfYZhEIAbzSny0t31YTTkBBs1bex0saXAh/orOD1k3PlAfX6zR5/5RhToradHsqgJ64HquYZcUDF/Thk7UnCtw0jHUnERH/LiIejohBRFwtPnd9RDwWEQcj4paR8u0RcXdEPF78f+44/THGGDNZxl1uegjAvwHwOfaBiKgD+ASAGwBcCeD9EXFlUX0LgHszcxeAe4v3xhhjNgljTRKZ+eXMfOwUH7sGwMHMfCIzOwA+BWB3UbcbwB3F6zsAfPc4/THGGDNZ1kOTuATA0yPvDwF4e/H6wsw8Urx+FsCFbCMRsQfAnuLtyj35ew9NuqNroqpnpqpcwTkfwIuVWpZxcmJb2ggmOxYT5ilR90VRdzsp/5De3YTH4rCo+4vJ7ebNYVOfF+vMN6y1wSkniYi4B8BFJVU/mZmfXesOGZmZIezDmbkXwN6iTwcyk2ogZxMei9fxWLyOx+J1PBavExEH1trmlJNEZl5brTtf5TCAy0beX4rX/yx5LiJ2ZuaRiNgJ4Pkx92WMMWaCrIdP4j4AuyLiiohoAbgJwL6ibh+Am4vXNwOY2J2JMcaY8Rn3Edj3RcQhAP8CwB9FxF1F+cURsR8AMrMH4MMA7gLwZQCfzszXMrvfCuC6iHgcwLXF+9Nh7zj9PsPwWLyOx+J1PBav47F4nTWPReSkM68ZY4w5Y3BYDmOMMRRPEsYYYyibepKIiMsi4s8i4pEi/MePFOVnXTgPMRYfj4hHI+KBiLgzIs7Z6L6+2bCxGKn/sYjIiDh/o/q4XqixiIgfLs6NhyPiYxvZz/VAXCNXRcTnI+L+iDgQEddsdF/fbCJiKiL+NiK+VIzF/yjK1/7bmZmb9h+AnQDeVryeB/AVDEN7fAzALUX5LQB+dqP7uoFj8V4AjaL8Z8/msSjeX4bhQxJPATh/o/u6gefFuwHcA6Bd1O3Y6L5u4Fj8KYAbivIbAfz5Rvd1HcYiAMwVr5sA/gbAO6r8dm7qO4nMPJKZXyxeH8fw6ahLcBaG82BjkZl/msMnyADg8xj6UM5oxHkBAL8A4L+iuid+SyHG4kMAbs3MlaLujPcgibFIAAvFx7YB4LlYzxByyGvJeZvFv0SF385NPUmMEhGXA/gWDGfE0w7ncSayaixG+Q8A/ni9+7ORjI5FROwGcDgzv7ShndogVp0XXw/g2yLibyLiLyLin29k39abVWPxowA+HhFPA/ifAP7bxvVs/YiIekTcj6FJ+e7MrPTbuSUmiYiYA/D7AH40M/9R0Psc3jedFX81AnwsIuInAfQA/NZG9W29GR0LDL/7RwH81IZ2aoMoOS8aALZjuMTw4wA+HaGSoJw5lIzFhwB8JDMvA/ARAL++kf1bLzKzn5lXYbi6cE1EfNOq+tP67dz0k0RENDE84L+VmZ8pip8rwnjgbArnQcYCEfHvAXwHgO8tDvwZT8lYfB2AKwB8KSKexPDC+GJElMUdO6Mg58UhAJ8plh3+FsPwkWeDkF82FjcDeO3172IYmfqsITNfAfBnAK5Hhd/OTT1JFH/5/DqAL2fmz49UnXXhPNhYRMT1GK7Bf1dmLm5U/9aTsrHIzAczc0dmXp6Zl2P4I/m2zHx2A7v6piOukT/AULxGRHw9gBbO8EioYiyeAfDtxet/DeDx9e7behMRF7z2pGNETAO4DsCjqPDbuakd1xHxTgB/CeBBvB5I+6MYrjN+GsBbMHyK5Xsy86UN6eQ6IcbilwC0ARwtyj6fmR9c/x6uH2wsMnP/yGeeBHB1Zp7pP4zsvLgHwyjjVwHoAPgvmfl/N6ST64QYi1cB/CKGS3DLAP5zZn5hQzq5TkTEN2MoTNcxvBn4dGb+TESchzX+dm7qScIYY8zGsqmXm4wxxmwsniSMMcZQPEkYY4yheJIwxhhD8SRhjDGG4knCGGMMxZOEMcYYyv8Hnfeog/5bN7YAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist2d(data['mag_true_r_lsst'][ind],magmodel[isprimary]-data['mag_true_r_lsst'][ind],range=[(20,30),(-1,1)],bins=50);" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": {}, + "outputs": [], + "source": [ + "def depth_map_snr(ra,dec,mags,snr,nside=2048):\n", + " good = np.logical_or(np.logical_not(np.isnan(ra)),np.logical_not(np.isnan(dec)))\n", + " pix_nums = hp.ang2pix(nside,np.pi/2.-dec[good]*np.pi/180,ra[good]*np.pi/180)\n", + " map_out = np.zeros(12*nside**2)\n", + " #Binned statistic 2d is awfully slow (because it doesn't use the fact that all bins are equal width\n", + " #median_snr, xed, _, _ = binned_statistic_2d(mags,pix_nums,snr,statistic='median',bins=(50,12*nside**2),range=[(20,30),(0,12*nside**2)])\n", + " #bin_centers = 0.5*xed[1:]+0.5*xed[:-1]\n", + " #depth = bin_centers[np.argmin(np.fabs(median_snr-5),axis=0)]\n", + " map_out = np.zeros(12*nside**2)\n", + " bin_centers = np.linspace(22+6/30.,28-6/30.,30.)\n", + " for px in np.unique(pix_nums):\n", + " mask = px==pix_nums\n", + " if np.count_nonzero(mask)>0:\n", + " median_snr = binned_statistic(mags[mask],snr[mask],np.nanmedian,nbins=30,range=(22,28))\n", + " mask2 = np.isnan(median_snr)==False\n", + " if np.count_nonzero(mask2)>0:\n", + " depth = bin_centers[mask2][np.argmin(np.fabs(median_snr[mask2]-5.))]\n", + " map_out[px]=depth\n", + " else:\n", + " map_out[px]=0\n", + " else:\n", + " map_out[px]=0.\n", + " return map_out\n" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [], + "source": [ + "def binned_statistic(x, values, func, nbins, range):\n", + " '''The usage is approximately the same as the scipy one\n", + " from https://stackoverflow.com/questions/26783719/effic\n", + " iently-get-indices-of-histogram-bins-in-python'''\n", + " from scipy.sparse import csr_matrix\n", + " r0, r1 = range\n", + " mask = (x > r0) & (x < r1)\n", + " x = x[mask]\n", + " values = values[mask]\n", + " N = len(values)\n", + " digitized = (float(nbins) / (r1-r0) * (x-r0)).astype(int)\n", + " S = csr_matrix((values, [digitized, np.arange(N)]), shape=(nbins, N))\n", + " return np.array([func(group) for group in np.split(S.data, S.indptr[1:-1])])" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/global/common/software/lsst/common/miniconda/py3-4.3.21/lib/python3.6/site-packages/ipykernel/__main__.py:10: DeprecationWarning: object of type cannot be safely interpreted as an integer.\n", + "/global/common/software/lsst/common/miniconda/py3-4.3.21/lib/python3.6/site-packages/numpy/lib/nanfunctions.py:990: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "/global/common/software/lsst/common/miniconda/py3-4.3.21/lib/python3.6/site-packages/ipykernel/__main__.py:7: RuntimeWarning: invalid value encountered in greater\n", + "/global/common/software/lsst/common/miniconda/py3-4.3.21/lib/python3.6/site-packages/ipykernel/__main__.py:7: RuntimeWarning: invalid value encountered in less\n" + ] + } + ], + "source": [ + "m5map = depth_map_snr(np.degrees(ra_coadd[isprimary]),np.degrees(dec_coadd[isprimary]),\n", + " magmodel[isprimary],modelflux[isprimary]/modelflux_err[isprimary])" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 141, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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NDAxJUhMDQ5LUxMCQJDUxMCRJTQwMSVITA0OS1GTQ8TCkI93qTZ9a6BKksTHoHkaSU5LsSrI7yaYplifJxf3yW5KcMGn5siQ3JfnkkHVKkmY3WGAkWQZcApwKrAPOSrJuUrNTgbX9ayNw6aTlbwB2DlWjJKndkHsYJwK7q+r2qnoQuArYMKnNBuCK6lwHHJvkOIAkK4HTgcsGrFGS1GjIwFgB7BmZ3tvPa23zR8CbgYdm+pAkG5NMJJnYt2/f4VUsSZrWWF4lleTlwD1VdcNsbatqS1Wtr6r1y5cvn4fqJGlpGjIw7gRWjUyv7Oe1tHkR8Mokd9AdyvrnST4wXKmSpNkMGRjbgbVJ1iQ5GjgT2DqpzVbgtf3VUicB362qu6rqLVW1sqpW9+t9tqrOHrBWSdIsBrsPo6r2JzkfuAZYBlxeVTuSnNsv3wxsA04DdgP3A+cMVY8k6fAMeuNeVW2jC4XReZtH3hdw3izb+Dzw+QHKkyQdhLE86S1JGj8GhiSpiYEhSWpiYEiSmhgYkqQmBoYkqYnjYUgaawc7JskdF50+UCVyD0OS1MTAkCQ1MTAkSU0MDElSEwNDktTEwJAkNTEwJElNDAxJUpNBAyPJKUl2JdmdZNMUy5Pk4n75LUlO6OevSvK5JLcm2ZHkDUPWKUma3WCBkWQZcAlwKrAOOCvJuknNTgXW9q+NwKX9/P3AG6tqHXAScN4U60qS5tGQexgnArur6vaqehC4Ctgwqc0G4IrqXAccm+S4flzvGwGq6nvATmDFgLVKkmYxZGCsAPaMTO/l4T/6s7ZJshp4HnD9nFcoSWo21ie9kzwW+AhwYVXdO02bjUkmkkzs27dvfguUpCVkyKfV3gmsGple2c9rapPkEXRhcWVVfXS6D6mqLcAWgPXr19fhl63F7GCffCrpHwy5h7EdWJtkTZKjgTOBrZPabAVe218tdRLw3aq6K0mA9wI7q+pdA9YoSWo02B5GVe1Pcj5wDbAMuLyqdiQ5t1++GdgGnAbsBu4HzulXfxHwa8BXktzcz3trVW0bql5J0swGHUCp/4HfNmne5pH3BZw3xXrXAhmyNh35PLwkza+xPuktSRofDtGqseEegzTe3MOQJDUxMCRJTQwMSVITA0OS1MTAkCQ1MTAkSU28rFaD8TJZaXFxD0OS1MTAkCQ1MTAkSU0MDElSEwNDktTEwJAkNTEwJElNBg2MJKck2ZVkd5JNUyxPkov75bckOaF1XUnS/Brsxr0ky4BLgJcBe4HtSbZW1a0jzU4F1vavFwCXAi9oXFfzzBvxpKVtyDu9TwR2V9XtAEmuAjYAoz/6G4Ar+qFar0tybJLjgNUN6+owGQCSDsaQgbEC2DMyvZduL2K2Nisa19UIf/ylzsH+t3DHRacPVMnic8Q/SyrJRmBjP/n9JLsOcVNPAv52bqo6YtjnxW+p9RcOss95+4CVzJ/D+Z5/qrXhkIFxJ7BqZHplP6+lzSMa1gWgqrYAWw632CQTVbX+cLdzJLHPi99S6y/Y5yENeZXUdmBtkjVJjgbOBLZOarMVeG1/tdRJwHer6q7GdSVJ82iwPYyq2p/kfOAaYBlweVXtSHJuv3wzsA04DdgN3A+cM9O6Q9UqSZrdoOcwqmobXSiMzts88r6A81rXHdhhH9Y6AtnnxW+p9Rfs82DS/WZLkjQzHw0iSWqy5AIjyaokn0tya5IdSd7Qz39nktv6R5R8LMmxC13rXJmhz7/X9/fmJJ9J8rSFrnWuTNfnkeVvTFJJnrRQNc61Gb7n/5rkzv57vjnJaQtd61yZ6XtO8vr+v+kdSd6xkHXOpRm+5w+OfMd3JLl5zj97qR2S6u8kP66qbkzyOOAG4Ay6S3c/259wfztAVf3WApY6Z2bo896qurdvcwGwrqrOXcBS58x0fa6qW5OsAi4DjgeeX1WL4j6FGb7nXwG+X1V/sKAFDmCGPj8FeBtwelU9kOTJVXXPQtY6V2b6tz3S5g/prjr93bn87CW3h1FVd1XVjf377wE7gRVV9Zmq2t83u44uQBaFGfp870izxwCL5q+H6frcL/4fwJtZRP2FWfu8KM3Q5/8AXFRVD/TLFkVYwOzfc5LQ/ZHwp3P92UsuMEYlWQ08D7h+0qLfAP5svuuZD5P7nOT3k+wBXgP89sJVNpzRPifZANxZVV9e0KIGNsW/7df3hx8vT/KEBStsQJP6/Azg55Ncn+QLSX52IWsbyjS/YT8PfLOq/nquP2/JBkaSxwIfAS4c/Us7yduA/cCVC1XbUKbqc1W9rapW0fX3/IWsbwijfab7Xt/KIg3GA6b4ni8F/inwXOAu4A8XsLxBTNHno4AnAicB/wn4UP+X96Ix3W8YcBYD7F3AEg2MJI+g+z/6yqr66Mj8XwdeDrymFtnJnen6POJK4F/Nb1XDmqLPPw2sAb6c5A66w443JnnqwlU5t6b6nqvqm1X1o6p6CHgP3ZOkF41p/m3vBT5anS8BD9E9b2lRmOE37CjgVcAHh/jcJRcY/V8Z7wV2VtW7RuafQndc+5VVdf9C1TeEGfq8dqTZBuC2+a5tKFP1uaq+UlVPrqrVVbWa7kflhKq6ewFLnTMzfM/HjTT7l8BX57u2oUzXZ+DjwEv6Ns8AjmaRPIRxhj4DvBS4rar2DvLZi+wP6VkleTHwl8BX6P7qgO4wxcXATwLf6uddt4iuGJquz/8GeGY/7+vAuVU15UMejzTT9bl/gsCBNncA6xfRVVLTfc9n0R2OKuAO4N/3z2w74s3Q5/8DXE7X7weBN1XVZxekyDk207/tJO+n++3aPN36h/XZSy0wJEmHZskdkpIkHRoDQ5LUxMCQJDUxMCRJTQwMSVITA0OS1MTAkCQ1MTC0aCVZnWSQu5r7MSbeNM2yO5J8pR+XYGI+PrNh3WOTvG5kerD/b7R4GRhaNNIZl3/TL6mq51bV+oUupHcs8LpZW0kzGJf/uKRD0v+lvCvJFXTPSFo1qclRSa5MsjPJ1UkePbLux5Pc0I9atnFkezuTvKef/5kkj+qXvS3J/0tyLd0jVQ639uck+Yt+5LSH0o0A+LuT2kz5mUnOTvKlfi/mfyZZNlL/bVP0+SLgp/v27+w3s2yqfkrTqipfvo7YF7Ca7nk6J02zrIAX9dOX0z1T6MDyJ/b/+yi6sPkn/Tr7gef2yz4EnA08n+7ZPY8GjgF2j25r0ud+DbiZbiS0jdO0eSTdwx5P7Kd/D3gn/eN6+nlTfibwz4BPAI/o2/0J8NqZ+tzP/+qk/28e1s+F/j59jffLPQwtBl+vquumWbanqv6qf/8B4MUjyy5I8mW6ERZXAQee3vu1qjowHvINdD+uPw98rKrur27sga0z1PPiqnoucCpwXpJfmKLNS4Ebq3v0NsAtdAE2+nC36T7zZLow2Z5u3OaT6ca7aOnzqKn6KU3rqIUuQJoD9x14k+Q84N/1k6/j4cOwVt/uF+l+tF9YVfcn+TzdX/0AD4y0/xHdHkiz6p/4W1X3JPkYcGKSnxmp6zTgWXR7DwecANzY+BEB/ldVvWW6EmaZPuCw+qmlxz0MLSpVdUl1J5ufC3wDeHqSF/aLfxW4tn//eODv+rA4nm5ktpn8BXBGkkcleRzwiqkaJXlMv5wkjwF+ie5Q0I/rqqpv0D1G/9l9u2fQDXpzVeNn/l/g1Ume3K//xCQ/NbLeVH3+HvC4WfoozcjA0GK3i+6w0E7gCXTDlQJ8mu6E+E66E8LTHdICoKpupBvF7Mt0471vn6bpU4Br+0NdXwI+VVWfnqLdnwKP7S9t3QKcVVXfGm0w3WdW1a3AfwY+k+QW4M+B0UGSHtbnftt/leSrIye9pYPieBjSIpJkNfDJqnrWApeiRcg9DElSE/cwJElN3MOQJDUxMCRJTQwMSVITA0OS1MTAkCQ1MTAkSU0MDElSEwNDktTk/wP9Seei6csOlAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(m5map[m5map!=0],weights=np.ones(np.count_nonzero(m5map[m5map!=0]))/np.count_nonzero(m5map[m5map!=0]),bins=25,range=(22,27))\n", + "plt.xlabel(r'r-band 5-$\\sigma$ depth')\n", + "plt.ylabel('Area fraction')" + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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wqrHvhHDoZQUxn66jXjpM6CtlKaFMtg0hAj8ZhBBCSA6V4lXgmYfux9hi9lqeXM/fUc0DCZRPs1ldu2reytDr1Co+aOzTuJfP0wsmL9z7l/bWgsn3TKwVSiu15tZmDdBTXF4ft06drSk+OW7Q2p915Jma6NX6OHHuQHF9WpHqcwDl9e7UumcKq4xPnp2qjVsSFAClCpt6Qa9xXVKvEev1Qy/8QygSEeQJA0ZRDxfR50itOepKGl7CB9lnP6NNa+OyXb4DUWXf8aqHCDZfLCEWTopXECng2+nFWro2iSvcmLlUZu7IY8jOz3SKyVKX3WmK0dI/Nl6pJjthnlqYqiWh1ugfVjsJpZxxUhOfjrMbZB5NTUhtJiO9TyYEmVRunTrrOgilrsty69TZLbUfhBffaOMO9T3Tps+UCVbQE2Aqi45skzaeA40ehy0PpseVcqYRQq+DicR+QWf/kaT1IU+lp82nks4QGFGv8z5oPiUN8JNBCCGE5DAk4wpgCwlrxxnPnBNNImaNNpV6GUGAauFXnQXEPqWnHGguro7XzGKDFN1WTIieivTCO9qSUp1iAgWq2XNS50ntt/v0tXjHpbal7q1WjJ4jj1Wn3nuWeo81dvy6L+0kYz9XWg16pljbTmesSTmE6fNIOMWIk4PVhmnokAubAF9v098/hmUwJENDpUgIIYTkcE3xCmAX/vW6hy19U3GaUU/BnjONVYiCdqCQJ+jOzHrtyd9TEVphFBlbBoRMCDpQXP+9FVLhDpamNUvrmKMVlTgKtR2Hp/K8xAX2mr01xqe+fCuAshqHbq+RAHwbXqHxwkA0bRMXNK096s+G54QjrPbGiv3ymdPtrLLsdPu1/rS69ZTocrdqMel0+5XcqwCK7wrQKSqPSN5fXTVGYOkp0gSVIiGEEJLDNcUh8/Qj9yY93axX3PpMv6YKvXUbwA9sFtqs17RdbxK8dSwJ9ve8V3X7lPISZQSUqkTv96ppCKkwgDZp0prWNtuEi3iq2rbR5/TW63ROU7vNWz/U7b11xtSY7Xul1wqtetMetV79Si/Y31tntOiQjNRxnte0fN5Dr1Osu9uQjX6vUyvSrZHvm6cQjzz42cbjbnS4plhC8+kVQMynRQaaCZ1Zo+poo/M06iKyEorRV0VpU/lNPYq4s4T5VPAmQC9/piQGHzSR6H4tOgF2anKxfZxd7dYmEulH4zmzbNfEeznXqSfwNn3oMAw7+d91aMFNqK7Hac9jH1x0NhpB7qd+r1PmUx3jmpoMPdrEKeqE4IVTjXoYlO+KniRjHt6kHxrLcmz1ybBNrmFy80DzKSGEEJJDpThkXvSux4qQDKlqAZRPtut58LGYWDd6ncJpQLuvi/NAqDkW1J+WdUkgD/tEr/Notg2LaKuwPKcU2e6FJaQyyLRxetEllTw1qDPlpMbsVbGwfXjXkqoeImjTpBeM743bjrdtFY9UiMXB6QutTOja4cq+ZynT6p6JtVr1Dc+6UYRr9Lo1a4ZnRu30RhBliaGFtURnudHWGiBTjilzK7n5oFIkhBBCcqgUrwA2JMOtf5g7AOjUbbruYfHE3C3XS+SpW9YeNVoNCJ4LPJA9qbcNvG9ymNlq4V+9Nqf3pZRZmzAJvX+rlSs0KSVmx3F2tVtLw+aN3yv8q/HSu8n2jSf2AwC++YFnkuNIXZ8tGn16aW+tALKHpzYFbx1RK1Lb/2pvrJbyTf9tledEdx2rMAH9s+V+b01RO+TofQCwPl3VAZ1eYB5UUoGT4hVAvEk3HG9RmSgL86gqc+OZhrSpSfDySno/dk2mMu0skTJXbqfEU1My77YZXwZNZCmHGWvqGzQh6r48b08gu1deTGIq44yXRF326X5tH5Xx3F1/77z7lrofNibx4PSFLTkNecWJvf41dpI7OH0Bp1E3uQK+qXQQXvyuzQSlvVVTycIlN/GL3vXYlsdBbhxoPiWEEEJyGKd4BXjqg69o3CeL/GI+1aYc/RSccne3T9gT3fWkA0WbvJhNlSKaQibamiUHVZOQsdmiunqfHrenwqw61QqmTQ5RXcFDzqWLE3vX6qnHNuElWw0X0eo3ldHGKlzP2Ui38/q3nw8vt6om1V6QWEmgVHTyOW+KffRCQmyYhuDFKW7MXKpt07lPbZjG5ELA899/c+VDZZxiCZUiIYQQksM1xctgq2sQ8rSqHW3k9Ub+t17/sNUBNLoShn3C1muEl6MY21THSAXN6wBzu5aonVO8tTl9nGS8sdfZpE5tOIe+zjahE/o65NjFiXQIhOdEY9XmoLU/6yDkOd40KT7pXzvd2Pa6XVP/9nqAtCpMOes0fa6sg41XgFjYSpYbAPDkTuh1MLZkMkc5ekDarBy+rgxRZIfhpLgD2NJQQDX5N1AWEBaKDDWmlM3e4x1cOJq1kaTFGzP1VFjej4VXMFZPCGK6kh8ePRl5E2TKfOeZ3bYSS+dlaWnyJrU/uoO8Ve1YU9ehKUpMTTVfp+cMpLfryS5lUrYTRiq5uIc28Tbt1/16YxmUEF7QJaGsV/Mg72ZrbtWTnpg+i3PNVEudSRvZb5OF6z50lhvrTNPpjaB3uG+21dO96ThicvNC8ykhhBCSQ6W4A2iFKMiTaKfXKbLWiFl0fH6seEIV9SjK8cLR0nFAwjSgYhJTSZdTiZVXe2ONuVI91Qb45rk2ibKbFB9QVUGpwrra5Ch9NIV3eMfrPjy8ElJewnHvmnTRYttuq0V9PdpmC0qVh/KUsA1R0U4sgmc21Y4uqbhGqwq1ovP6lc+plxVHl0UTC0enW89pWrTPFeDYUigUn3y3Rhd31UIwUiWjJhfoc3IzQ6VICCGE5FAp7gDV9UPkr2N9f/732ux66RhgqmTo7BtSJHXl6Holu00TKacEryKCVkHeGpddIxoUZpAKktfrdd6xdhxeH22qQuj8qYIXNJ/iyP4zNdWoc6G2yXZz8uxUEVbSNk9p27AWYSvJFbz3MxUsrx1otPJro4SljacO90ys1frQFg7P2mEtHDq8QipiCL3DZek1nQjDK/Qtf+vwDCCz1lxgIP9NC5UiIYQQkkOluANohShPm0JntfRqk32yxqgRhahDMlaOlk/LXmFW64lqPff0cV5hW+u+r2laP0ypk1SYg1VLM+MrtTXCQbk7vXyu9jwz4yutlJyHt1YoyBi1ivTQa5ptPGP1uFKeoltd37W47afr7fR6X6qAs27fFNCvFaGnMO37eXppr+thavObBvV9k6d67dndt5VkeiO1NUXPuqMTZ6QKg5MbG06KO4j+IpWhFuUXr69+12zl8DA/Ue7LzT86BsvmN+33OkCDJXD3TK9SdsqSitWzbXQ7/YPfNmdmU3iBnjC8cXgThPx46iw2tsTTiXMHiv1fOnUYgB8nmMIbm8YbW5HxBs1OQF4+Vy+Zt9e/jekclGvWXos2n2ra5MMVtEkzFeOaynqj88rah5uD0xcKpxpZOtDmUKGPen7TFP3uZlEyKhV2ob+/ErP4jZ+6HwBuugw3NzN8HCKEEEJymPv0MpCMNpq6maYsTaMDhyXcQkIxivCLAWgzqlWPqSd0vT+lYlImSn1sG1Ofzs/ZFs9MmGqbcuqxjjmDTJT6eOto47WXTDvHXvDstnPAtsmss2diLanq9XFtrABCU3FpOd4mB2gK9h/0udN4uUw9hx+d5cbmSO33Ou42IPs+yXdJO+RIiJSgk2bYJY9+N5YhVavl9lvfd+OqReY+LaFSJIQQQnKoFFsilS5Cr+O6dWf/h+IJ1Esj5QX5awcb+dvmSA29Tulmnq89jh87X3Nf99zZ9dN9m8D7FIMcQKw68Zx72qQw07RVbXqfnENCIjRtwyNSeLUW21yPF76iVZbtQytFUfCi7DR6XXUrSlGPQ/A+Lzahgj7u1qmzyXRzHvZzqite6PRuQFoVNmHXG/V3yirG8SVgzXE40vsBYOJM+T2e+vBnk+e/HqFSLKGjTUtuf/vnAQDPPHR/sa2Tf/m87BjWZGqxsYs6PjHO5jYb82MAlBNlbMgWAlRNVPrHcas/mG0qu3uZXnR2F9t+q+ZU7UzTZqwz4yuYObS1OEVbfmqQF6xFJz7XWOcYr8+UyVFPmDIZDsrYkzJve9du+/CSlrdNDi7IBJ7KumRfA/5kp53GZP/o4q7KAyNQfp+0F2rx3VPfQZnkJOn3xmo5F4iptD9RvpZ2E2eAi7dmbbf2CECuN2g+JYQQQnKoFLfIbe8tF9ulOoaYZA5+qY/Td8mTa6kiS5NNPTbKKkZAZfx3cpV6+R9Po+oco5/cB4VbWLSjTcrpxZpBT56dKh6h24ZwtInV0/2msKEZuv9ULtZBarlNH7Xxololw+Yc1WrZy3pj2+ttbZV2G4eoxvsNP/NMU65Z62ijHWnsvs3FMUzOXgRQKkVxFFuZ31OxmABVRxvZp78/UcUiApmKlHAKqYyh+7zQzfrSzjXFksdqueRRvs7OtWsFGM3HwTCNGxsqRUIIISSHSvEyOPJgtuB+8t3Zk+PCa4FOr1ofMcutWN1m92tit++qQVtwuNPtF9tEUWploWslNuEFYKfq46X6SwVxD8Jb49SKy6pArW5sVh69zcPLyGOrbuisNUVQvloztIpJO7h4eWJT2X9SFT+0yrKVQrzEBWdXu7X1UY/U/dbvWWpt0I6xCfkMbTyxHwAweee5xrYjM+vFZ1q0nQ6r2OiWyk+2iyrUQfmiEAXtHCfImqEXzK8rbchxlyaBvV/r56+pJW5k+O4SQgghOVSKO8AL3/gMAOBrH7+teEp93uey541zx0rvtr4jYPbNZf8v3Vlu0+ndgGyN0aa62lwcq62/aGXhrRU1PdXrtZ8UWlm0WdvS4RGeEvJUjbTTqteqHq8Woq5m4a0v2v71cVYNzoyv1BRoqv6ivrf6fjelQmu7Pqn/9jxwvftilWeqconXzqMpaF/2DUoWAQC4uwwlkVRuNp9vmJ9AYRtR3qSiCrVqFEtLL/9IyhphYxIMoxS1QizCp/JMizpgv/QHKNuvHsi2nX5H5lNw8NEbL0TjZoaT4g5w/PPflL1QX7Tn7qmXphG0KWd5thoepBOC60nPuqt7QUXaSUZ+qLRzhzUryo/W6aW9rc2f9gdQTxaeU8dWQjCajrPj1ueRySI1EbY9p56kPROpnMeeo+katxK7mCy3hWpCcjnOG2Nt8lZOSqkcsxWHKfnbid/zJlHrTJMqYgyUzmISalGEVaAeRwgn/ALwE3oD2cTpfd/sNpvFJutLvr9BLW9k7XatbKJ3gIa1mwG+y4QQQkgOleIOIE+3XukZnVtR77eVM/ST68axzARkS+bUzmu26xI8XuYTwZr1mvKc2varvbFav1tVZp6i04rI9jcokN4rSdU0pqbA/dQ1eFUn2oSq6NeeYmyTK1WrU1uCq+k47/5ZUg5UnqOQ55DjoS0PgP+5urg6Xlse8KrLlH9v1jI8aTVYfs/KTFIbM9UsUV4mKd1eXovTDgBMP5GHeOSOc6MrmxjNHWw28ls88xVlZyU3DFSKhBBCSA6V4mVQVskoFds3/W72VDr/unpqKaCuGAX95GrDL/q9TqEKizAM9VqCob1gb0Hn1kylefPWiuR/b51P99UmNVpKlZ04dyCZRs6eu4mm9cWmlGe2vVabepv8n0oUIOicoJ5i9EIsUgrUVv7YSho6wUseYMev30+vSoZdZ/YC9MefzK/jgXp4j07tJp/vDaMcAW05Kb9H2tHGUqrBkVqwv7cWKapz8skxTOTObqt51M3er/WLsIuN/FJ6B0eL16Mr2Xf12b+abXj+pxqHRa5DOCleBp5ZdP511Vva6Y0UCYdlcux36wv/+kst/W7MlPtr1cd7nVpmEIknOzh9oTH7iG4v5q1TC1PFpCt9eOWKvIlvqwm+hSYnEikMLKQcblKTBpB5AwOld7Ce7HScYJNTjR2n3adJTThtigHb6wGqHrXeeduYYLXjzeJEcx+Cl9EmlT/V8z4Nd2exiJ5X6kR3Hcs9/6FGLxOszWavK3GKyuGmydEGqOc87Xc3i23i7b0xmX0HRldiMdn54GhXAAAgAElEQVQJvQMj6J4pv4/CaH4bbsSE4KSE5lNCCCEkh0rxMpCYKG2ekSdXMaN+9U0jxcL/+kzdjOM56YgpVdThaG+keEq2sV1AvXTU8mIXF2eqzhT6ad+auzrdfq3w6+mlvYVLvldpQ0gpxJRJ1StztLg26Y7XqipPFco2fdzeB56t7POywOgMOKnyTPo8nmqzGYTamjdFGXv30TrXANWwhzaxpSlztVf2y1N3XskrQStKayKd6K4Xr21JKL1tEF5FDPmeeSZSXSkDACaPlybb7pmsffcrZTzj+W/OAhT3fi077rmX7cLGZNbfjV4yitShUiSEEEJyWGR4G4iDTXche5q0uRY1Y4udWm1FvQ4i64faFd0LVpYnYrv214RdZ0zltGwKsk6x1aLBbRVUm2oXnsJpc65BuUFTTi96bc5zdkk5wrQJ6/BCQ1IJCVJ1Ib1j2t4zD33fvXCOpmogGh2ob7Mz2WLDGi9vqQ5zEuQ7NrbYqdRFBLK1fFk33HMy+55JVhpxmgHKNcPVA6HYvjGZtbvRK2KwyHAJzafb4EXvegyA9j6tbtecePi+suSN2t5cfDgUZiX945EyNcmPiWdatYVcAdQKEOusJ14RWy8OT7alPFJTFem1GVL/+Nttdr8dT9vSTqnt3ths/9spQJyaaGxquZRzj2e+vOvQQm3i85yG9HGeibfJeensard4eLrYrXska2wycR2vaD+32pnGe7CzMYlAPXm+PIwC5UQ2vqTMp105riwkPGpui570xEQqE6XmRp8MSR2aTwkhhJAcKsXLwFOGFp3QuOIcIPFSx7JH2KJkjnpw1zGJUh5KO8TYIq0aa5JaxVjtydxTDDYHpm2XMpO2UVApE6UXptGUDNueM5W4WzuzWMcWrWZTCcQHmX+9Y60abEoQLjSVyBqUdceOVR8reDGS3rFzX78FQPY5E4cjz/TqqWCv1JQsAdjwIf1a2ne6ffS7VWWpLSqTC5mS0yEURexgfkkrh2MRdrE+XW9nwy8mztRDMja6wPPfT6eamxUqRUIIISSHSnHIjM+PqWw1ZVBxoRolm0fePnb7FQcbiy67I+s1q6iuG05017E2ty8752zmdaArZ6SKx9ocmE1YpwqtRIrsL4fKdUNPwaWSA6RUp6egdHurqvS1WKXbNumAzkDTpoCvh17T85SWddLxEgwMqqbRNDZPbet+xTIg6vDk2anaOb1x69AQu3490V3HCsowIaAhJKNbb6+D9iUnqVV0AGprhfvmyrXBfXOybugfC1Sdarh+SAAqRUIIIaSASnFIeHlRNbbyhfw9eXysCPHQ3qc2zylQriXaJ/TlxW6hELUatOov5Vav1xm9/KZeuIMNNk95anop12Q74Ic2eMH+li+dOlwoMltr0fPO1OPQ4SVt07vJuLxQD1sDUf/dJqjeu9dt60ba97MpcYFgPxte/U3twaq3CbpKC5B99mwoUb/XKT+3joq0ntqd3khREFhXsRAKT9M8/GJtumyjvUlv++gpAMClQ5kFZfElE+W4WSSYKBinOGROPHwfYj5BVRxnEngxiTYziHausSEZepLUsYhezCLgJ3XWP4qXE9fWNCk2xc2lYvq2GlKRmjjaToB2kvZiAfXEmnqo8Mycuo0NbdCkHkK8ostbTRzeJsF703veJk7RCw2yWW68xPex2y9Mqd6kKOZTzzwqE2XHqfAkx9FkmsE4xRKaTwkhhJAcmk93GDGbalfyMJ89so7kirHf6xTONJKZQ/Ko2sLBQmEGzZ+I9ZO3KESdEcSqxonueqPzjN7e1sFmq9liLF7g+KCSUCnaOJtobCC77cP2lTLZAmUowz3f/Jetx6D7PbvaxV2HFtxjvTHOjK9su4xUWxNsarx6bE3v22pvrHV+U6FvHM9Cr1ME8o8t5daS0vJZc7TR+21mGwDYPZ8ZmWgyJU1QKRJCCCE5VIo7wDMP3Q+gWuli9hMbAKr1FUUxBpX7VIL34TxR6/VAby1R9q/M7wEAdFRRYtsGqIZzAIPXrlJB3t52CYzX6eGaAtKnJnrJgsJeqjX791bzqGp1JarWU5jeGuEgFexV1kjVabRjTIV6ePfRw1un9Y5LJSloi60sApSONpXPlbFs6M+mrBXG2bLShTiXbTqpDleOZq91mNPofLZPHGy89UO9jQqRDIKT4jY48fB9AMqFf/mCji12ynyL+e9Opxcqyb4BYO/xDpbv9n+ENhfHCkcbXYzVZq3Rzjc2wfLumV4tW8hqb6xmUtU/XtY5wnMU0XiOGZ5J0pLypPQq3gP1GEdvIpMJ+a5DC4XDytmJepYbey0nzh2o5BOVbd6kaa9de5V6nrdN1950nRavRFaqnbdt0CS61cTuOtOR4GW+sRQZlnrdYumgSIy/WH6OrTOadr4ptnVj8d1bzW+NnvikmLdGJ/4mJAXNp4QQQkgOQzK2iKhEoCwQLIv3y7OhZr5Zn461LP+dXigcawR5eo7dvutsY93XtVnUqkIPXR5qUA5Oiw7TuBwnDY1XJQOohxKk9gHtQ0OArcUHbiWcoqlkk6eEB7VpOm5QqSvAr0oyyNTsxSIC2XttQ0Q85yidg9d+voD6Z1OHBonZX9BKccRZCtBFhuW755lLpeqFlH3SMATDhyEZJVSKhBBCSA6V4gDmP/ZSAChziao1Qsnav6ay8YtqPH+s3GaVoq4VFxyHAo9UoHNKRQqeUhSaHG6aau01IWEJntOJkKp4odVQmzUuT0VqNeipJHv+VA3Cpj68cXiqN5WHtI3ya7suuRPqPfVe66B8TxU2WRy0E46mqahwp9uvrSnazE9A5qBTrOerQsJANYifirE9VIolVIqEEEJIDr1PBzD75i8D0LlMR9y6bkCmEme+ki1yLM9mO/vdWCjEvgrFELw6c0IRyNzrFOEWQlOQv2Cf6IHyyV1vs6RCMrwK8FolWC/E1Npfk7oSUp6ssoYnHqe6fVN+UyBTUvac3hiP7D8z0OPTklLAbdmKwvVCLLyAfq9+ZSrURhS6XifV74HNb+p9luSz4e3T6lGvowPVNIhiOZnorhdrj0X6w16oBeiX3qWhsNxI7tM9J2MlDyohKTgptqS7kH2Be4f72MgLoepMGZbn/1FmIv3GK3fVQjKAclKz8VveZKdNq9rxwCZW1iYtMVFpU2nKEUfa6R9H7WAhNMUW6swm0pdXLkrwigw3YX+wFyeyScArV+Udl2rjtV9cm6wl8faQ8bc1hwqLa5O10IZUEWDvPnm5SRfXJt2Qk6Zxe+fyCi5Lm1MLU9hzOPtM6AnPy3Vq92mHG3l9y9EzRb9NLC92iwLbwvpMv8gEJSZSmfRGe3VT6ka3bPfcPdmD6fMbz0hudmg+JYQQQnKoFFsy/VT2tLo+XZaykaw133hllpljeTZg9UD2eCpPpv1uGZLhOQ2kcppaR5pBpFzhbd+aPRNrhSrURYZtSENbtEk1ZRK0WWI8BaMdfppyg+ptM+MrhVm1TQFhnbNV99U2f6sdT9uwC0+FN+GFkjSV2/IqZ1i2WnVE0FYMXR7KVljR779XwcWaVb3cvZL9CTOXapUzOr2RInvU+FL1u+FVy1ieLU2nYvEhpAkqRUIIISSHSrEluz/6WPbi3jJ4XxSiML4EHP7QEwCAuX94R7FdHG2iCkgW9SfrgV6QsijL3TO9SiFWIL0+2OQib+spypO9Vpj6WC+Vm5DKWyrt9HqWp8Zse+9cOkdqm7CHE+cO1K7fU32p2oX2/JZUzcKU08tWz+ONP5XLdFDe2tQ+LyTDrhEfe8GzxTa9fm3XFLViFBUoqn2ud0vt3GLB0J9zzylN2FDqcWOhOu7RXr3wsEYsOIQ0wTjFbXDy3VkCcPnSaUca+TLvPZ5NbOfvWMe+J7Mv/YWjpfnJZuyQL3mYn3DNrTb3qcYzX6UcHJomR9unZw5rcgLxfmi9DCvej3rK23KQGdOL7WtTwkomxbsOLdSShOvJ3PPiTI2trfk0lWg8Za7WeVpTpmlLU8HnpnNdXB0v3jsv36mH9Xg+vbS3FhvrFbsWU6wuqSaf/aAeFnXxbTGv7pvLz628S73SUTYfqkyOUx9mgnCAcYoamk8JIYSQHCrFbXD2bZkJVZ5O9eK+OOHorDVCcBxm9NMvUK2SoR0bdO5Iu80+cR+cvlBxhJA2TZlsTi/trZljPYcITRuHju1gM9m0zdyixyHtPIcbL3fnVpxems7ZtsSUvo5B7QbdWxti8qVThyv3rW3/3jm0+ddro02r1uLgWSe0erQZbTyHMq0YrXrUpaOsUuxPlFmldJhGqkoGy0lRKWqoFAkhhJAcOtpsA7sOIdluugudYj3QXQtxMttIu03nKXlCubFLVg9Zq+x/x7mivVWF9jXgrxvKcaNzXeDOqhJtUompagsppxRP7aUcVoRKoPtUtc+m7DVyLhtm4hU29sIdBjnJpBIFeMelstGkCgMPCoWxSk6v/abG4Tnm2PHo91OPY7uWAb1uKM5l1mlM1w8VRhd3uVYX+Q4tz2bfB1kzHO2hlr1mdCVWVCNQWnf2nLz+LGVkuHBS3AFk0lufHqltA1ArqtrpjRRfdF0OR9qIE46O8VoRL1U1GTbhmUM9CieIO9cbYxiB9I+n54yjfzjtZJjKEOM55ui+9Q+2kJpwPKcgLwuMHbd3bFu8LD5ixtX3yCuc3FR4+Oxqt5XTk7S1/UqfWzXV2gTv3uSok4TLZ04/UMm+8Sez9itH6+Wh5IFvtOcbruQ7IllsdNm19Rl5le3bPR+x0c0mQJ0QvO51KkWKA77xU5njHJOEE4DmU0IIIaSASnEHKB1idrlZazyz6ejirso2HYbh5UG16tFzwhG0o41uY7OLCNpNXitGmw/VU1LaLGvVo1YdbcIemtRZymTrKdFUP9YhZlDi7zYllfR2bbaUMdnk5k1m4jY8ezzr//aXnnTHmlLC3rjlPlgVPjXRq32uvOLLF1fHG0uSVZJ/351bOFTWGlsmSptJ5fuRlWrLtkkWGx2SIcWGhdUDoTCJisl04kysOcWJctTmVgm1uvV9VIw3M1SKhBBCSA5DMnaAc7+bVRQ+8+Tzavu8gsKx28f4fPaULE+/OgxDnqC90AxPRabCKbxCrzaUQ7fz9mlXe+nXC8nwlFCqWPB2nTbahiqkgutlnc8LggeaQyuaHFC8++GFlzSNK7WeqdcU2+Rz1ejrTb0HbUJJmhx/bBkpLxhfSqQtL3bd0CRLR60veuvvViFqJCRD0GpQlKLkMu4dqOuCxXsu4fa3f37gGG8kGJJRQqVICCGE5HBNcQcZWwpF8L4Qep3ammLodcr1EdmXt+8Dtfpx+okbKshf1hlTnqNtaigCqK0L6aBsry+r1nT9xRRamVjlpL1P26jIpsD+puLFep+MVSsjvc7YpJjOPn4Lph54pnFseptU9UjVNPTWU711TLsG2lYlS/um2pNyLv0e2PaeQtTesJ5CFMTaIevZbVQiUFWH9piUSlybXcf4Uu4NK96navgSknFpMvveeZ6pkpaR3JxwUrwMJD7x9onM6aHXA/oTeWaNPONGpzdSy2Uaep3itXXL8eIbNeIkI27sgD/xedlFmljtjdXa6W2pzDbyg7m82MWtR/Kkz7kr/8HpC0lzn528TsLPsdk0gWinmlRybo+Us0tqIv72N3zFzTnapqp9qsSTd31CUyyjF1/Z9EDgtfeKEtuHEn1ObcYVTi1MFZ/J4rOjktx72WpsXtNU/K42o44tlVlrBPmeyb59T44Vk6COV7QZbWTf3q/1ceGF1YLFoyuxlrWK4Ro3DzSfEkIIITl0tNkG8x97KQDg4C9lT97zr8sE99hSQO9w1SwKVIP29d8aCUwWs6pFjhWz0u6ZXs1sqnNK6qKusq+pKsaeiTXXWaJNvlVBq6U2eI4oTUg7q1y36qDzFydegPtechyAH7TfxpnFy1uqnW9krGtz+3Dgjudaj7NtPtSttk+FqjQlSwCya7KfiUFFrO0+bfYXU//ux8vPyPk7sm26jQ3TiN1+EZ6hleL+OZPfVClBMZHqkAxRjxuTpUIEMjPqrpXN4rXl4q1lHzdyjlQ62pRQKRJCCCE5VIpb5KkPvqK2rtddyFXedCycALwai56buaBDNOxaYkcF9NuKAbIfqD61e0/yTfUUdd07L4RDAqUP3PFcba1SrzdaZaG5nKoaVlGm1vIGqZ8UnlL0nIHaVNXQ6rEp/6velhqvTgnnOSVtF+9+eGud2qnGy7MreJYL2aYVoKhGm+7Nq5IxMrNepIgTBagdYzact9TmOR1diYVCtM43oz0USlGHZ0h7YXQlYnk223bbe2+89UUqxRI62myR7335F/EfvvAyAMDsJ2VrNimdundEOdiooqdSCdxxJBAkn6PeLmajjRnU0M46gjZbyUSZ8kyVH7uJ7rr7Y1fkrTx2HkB18hT0cakk5G2qzx/Zf6ZW7mnu67e4uTe9v4Hsh7tp4vXKROnJy0uabietPRNrriOMjfNLOfwsrk3Wxug5wsg+z9S7nQnRy1drJ8FUQWEvp+7yYrcSMwtUP0N94zWts9FAsjmpBz67jBDmJyqTG5BNYuIcYx1oRnvVbDXCRn4Lrbk1mxxLT9Sivfloja5UH3TJjQvNp4QQQkgOleIW+cpf3cC+n8oU1Kl7syfH2/9pFrd2afI2LN6ZtZOsGov3XEoWFy7NpuU+aS8m1kpGG9Tbwbi966w4YpoaVDUj5VQjHJy+UFNTohy1a75HysypVZVVQKISgXpFjqbYPk/xyXFeXlbPPNsmD2vKDDr39VsaHYK80lOeetTYfU1jbDIRH9l/xi2wbO+pVyBYY7d5Dl8p+r0Ogl1OcKpjyL59c8Bor6rQegdGGosGb3RLNajLQkn70YTVXtp0z9SV6OqBgCMP3nhmU1KHSpEQQgjJoVLcBhLIe+LhLMD33CtfmP1/LADInizPZ+lQ3bU/2Q7Uc5+OLu5CnF0FUHU8EOUnqx6x6zvkCKIQNTYkQ//tOeE05bT0aFKJNnQjFYYxKCeoPcZzNvEy8Xjoftu013g5TO16HYBamIaMdVDIhZeP1KpCHdLiKWe7VthUPURnMALShaqBdCiGt62jMjABWehRJ/t4F0H4eh1evg9STHu0F1s502i8wsGyXihri16R4WJN8Uy5X2ozsnLGzQOVIiGEEJJDpXgZHHkwC+Z96oOvAACMz9fXDju9kWLNxAva1wqxaJM/VUsl8jjbr7UH4KbQ0ts1et3HKkZvTUgrHq0imtaP9HqdKMpjL3i2cY2rKWQhta5mK8FrT9OUp6vus00AuxfqMaiCve1Xq0/JgepV40ilhRsUrrFVD1RvXbcplZ8XptO0Lm3Xo3USCftZ7HcjOqtV738dxiThTUIqVRuQro9YeJg6XqXS/uKtoebdCpQq9kYMvyBpGKe4gzzz0P2FaUgy24wtdmpZamK37yQ5LjPW2B8Snfxb0NlC5Idk7Y76D2ybeDKvvVd4uN/rJM2kqTjFFNacq7cB9fhE3d4zBTclFde5O7XJsU0S7LZxlW3iML3yU0f2n3FzqrY5V6rsk1fKyjOBWhOyDr/Q93Z50X/IAfyHtNqk2OsUD4C143sB40vZa29CEza6zebMs2+7r5ahRk+sejIUrAn2ZsxzyjjFEppPCSGEkByaT3cQbWo5+e77AWRmmI5xOd9wzJviXBMWx4qwi1SZHb1v5eh6cSyQKVEbvO8FVOv8qFZRNjlNiFKQY3VWnFS1Dk+J2ILJuoixl6fVhlpoc6goIm1SFeV6sVuOwZocz652i3NZxxU5B5AuEGzHNIimCh3e+WWM3jFeOalUH7LNU8n23ur3UptP5X3XpaC8jDRAc3Hsfte3koiVRaOz0RRhFfWIlgoSTpGikhVnkpUwSAmVIiGEEJJDpTgkZM3j6UfudYsMy9P16Fz2xL2OUuVZhRh1aizBcdrRpIL1Bzni2OO1w4VVoFqRpEIbrLPGwekLFQUnbbwKHja1Wdscpl51B8FTVFpBNZ1Dp2Pz1us85xerMrXySylL3adXMNkrXrzV8BIhFZLjtSs+Qyovr6DVpE0wAdQtJ5ML9UoX3lpiKgyjOK5bD9RfPRDcgsPS5uCjVIikhJPikHnRux7D6Xdk8YzLs2VsosRtSc7TykTlONroSuSCjVPUfzeViQLax5gJ+sfRTjR6skvlT20qOaXRDjo6fs6aN2Xi0cm5hUHmSy+2z+vXTsCDnF9S1+XlNPUceew5dZ9eQWFBj7HJU1SbpvVxcg5Jbr+cmzaX4TwUdOsm0tDrFLGzMtmtmiLCmonuOlZnzbYnsge+Jk9Tmdy0c4x4fN/+9s9X+hrt+UnC7bYbObk3uTxoPiWEEEJyqBSvIFIYdaM7gsV7qqWjbGkooHzyjjOXqnGMqKpCW05q35NjuDhTVYhe4WHr6KL36ZhE65ijSRWgHYTnfOM52lhnHRtaoPc1lTxqwov38ypteNUjtGr0nIBS6tIr0dQm96n+2zM1Nyl+nbfWy2TkOct4ZvZauJBqX3w2HZOpoGMX5VxFGSflVCNkf9fzllqFKI5tADD9VNa/V0mjeyYb48FHH6uNjRCASpEQQggpYPD+kJH1RAC1QqcA8Nw9VYeZTm+kWD8UdRhnVyu5I4FsLbIpZCN2+64qTFWxSDnapConpNYivXUsXcjXq9rQlJ/V26aVUkoNpuoHppxqNG0qV2i8TDlee1kb/NKpw5h985cr7Y9/5OUA/GQI3rV790/wkjEA1ewzgK8OveorgpfXV2Nzn47MrNesImIREYcbzehKLBSiXhfUTjRN+4S1aa4dDoLB+yU0nw6ZjclQmG+0B5xMkJKNRmIN9U9M4VyzOFb8cBTFV9WEaMtDLS92a5lH9IToxS5aM6jOFuNNfG1TgNksN3Luud4tyR97z8TXJjG5/vH34vIsnlnUm4hTxYA1qSLAXmadk/DjCgHg6Fu/AAAIvz9bm7h1e/0e2Pvn3SvvgUcmLz1xeuZ1L7OSPTaV9WZzcawsOGy8si8cLROBa8TBRqdj0yZXAJg4U7bV5Z4ATohka9B8SgghhORQKQ6Z57//M/jGT4kTQGk2FdUoeRp7h7Mn5LHFDtZm62pQVKMQeh1Mzl50z+mpQk0qPCKVoUb3ZVVkk/nSKpZBxY4FLwzE/j3IfGpVk44P7P2nLLn4nu98tpLAHKgWCLb9NuUXTcUd2jhFnYlHlOrURA9NawKeebepRFah0maq2/u9DiTKImUq9T4vEiM7fux8zflKv17uVfvtO+FDo4u7kDK4FgpQTKaT9YTdG5PNcYd7TsZCIbLcE9kOVIqEEEJIDpXiVaIsYpo9l+w9nj3drhyOrcIvRmbWG9cN9bqaVnJeIL+Qyn2ZcrTxjvfW5LaSF1XjlZ86+3im8jaO9ZJB6t71Sl/d73y22CbtZZ8XvuA5yXhhFd65vO1yrA4zmcV8pV3haAO/6siphUxlasVnHayETrdfbNMZZ2wOW33PRPmJlWJDhVN4KlPwVKRQsXiYzDax28fEmbxajHJKK74rTgko+/fBRz/bOC5C2kClSAghhORQKQ6Z5bfcW7yWtZFdK5voHag+j5RrJKEocNrJn7L73ZGiioYES3tefx6p4sJNbv2Wpjyodp+XD7VNCIenGFMJAKbufrZxn1cj0KvNqFO7eaEhTXlWmypWeGnbmoode3hqWSterzC0F2LTlJhBq3xvTdG+71pF2u2aTrdfC/Xw1KqsS3Z6oUxtWBQXztqMz4/h/DFZWZX/g6qOUa4p2vCmqQ9TIZKdgZPikNFlbMrcjiNu7BVQVvzWdHoB66osFFD9QbPlnJpCLdqYT1OTo95uJ7emAsFebJwldU4vv2lq3J6ZWB9v84p6hYdXe2M4O+FPYIMy1ejzNJlXdR96rPtNH/vfNAcAOP2Rl7uTXSozkRcmkTJ52jAdXSasEk+Ymz+1M411sNET5+Tx6vu+Ph2L/g5+KWt/6t7yAVGWDHTS8Hp8bxm7yMmQ7DQ0nxJCCCE5VIpD5vnv/wzOvi3LalM61zS3X5/pF1lrJFAf0HlQyxyV1unBC8rXzhip4Hc5RqsmG3jvFQbWx1lF9+zxA7Vgb41tv2dizXUesaZaHcxvFagXvC80hTFYPEcbQSvXVFYcL2erNlvbhAQpFa+zEYkq0++/bidY02fHcdaCUoOCVn3WDLrhONA0FRIW1qczdacVoATon7q3GoTS78bis9/vliEXNvxitEeFSIYHlSIhhBCSQ6U4ZJ5+5N4i3EIH7F+a9J9HDn8SuPDC7PW6CsA+cMdzAKoqr0m1afTTfpPi8tDB+1rFNOVP9foamVmvqbZUEP/ppb1u/5I3c8/Reh5Vrx8bwN603inYNHC6zqC3PmmvQTvVeLUQreLWfWrHGcvTj2ROWrtnLtaL+8J//2vKz0m51lHqXYdn6L50sH9xnCoorNtZi8WmWv+WHxhRgJ3VLOwIAMaWquk2+xPZfqB0vlmfjhjNHXKe//4sGF8sL4QMA06KQ2bv8U4lATiAyoRYZLTJvVE3uiO4cDT7cdFONTUHl277Ek1ynLT3zKx2EtAmRN2HxYth1DGSnkcqkP0I7zlcPaf+UdeTo5hgnz1+oPK3bl95MDA/5ilPWZ0txys75bWTfnR+U1u82HM2SjkgeXlgo3JcsaZSfZ1yD5Z7zc40ers+t500C8ecXtf9nIh5VbxJMXPJNZsCmTepmEHL/93hAQB2z8ei+K94pgIovg/Pz/+m6ZQME5pPCSGEkByWjhoyL/njUXzq/7p7YLulO0tHBCmhI0/IgF8E1pq+hNHFXUVcozZfNpkudUWEVHkpaav70H9bldQU6yZ4qqqpf6BqlgN89TOo/9Q4NPoaAD+frFZ3nmnamku1qvWcgcTJSNCmb7l2yXfrlYJKFQj2VPKggsL2M9FkmZB2kolJqzyJSdRZmqQyjCDOOJMLAWvT+TblZCam141j2TikegjZOajaR6IAACAASURBVFg6qoRKkRBCCMnhmuJVQivD6v/l0/LzPpdte+6eTXfdRp7cxcVeFFS/u1k87Wi1ZNVJZS0vXyvSakCUjg6TsIoslU1n90yvsWjws8cPYLlbVWH63N5apagkUVyVvJrOWpVdF/TWUE8v7cXK/B4AwC1HzxTbivqP3eo6rFaH+ppSTjSFGpypXpucS/q1ak1bAMQ5Rl+LDa6H44Tj3Vubj9Tbtnum534mvONEBUpe0776PMo+IXb7WJ/OPp375mRrJlLkcw+U6hAonW9uo0IkVwAqRUIIISSHSnHI6PVEUYf6KVgog5vLfVJx3KPf61TqLWpGZtaLp/VTvUzleWt7Wi15+0XFDFpnBDI10aamofQ5OXuxpkC8dS+7HSjV8sHpC0V/3nqd9UzV63B6DVBCPWRbv9cBpqvnlr5OY2+j4vbuySBSnqkri5mC1ffK1i7UeCET3n3Ux1p1KmuXnserlzAg9DqFQqykgwPQR6keRTFqj9Qy7WEdWVN80bsea2xDyDDgpDhkdLmbTq8uzEunhPyHpFv6EI0viXv6CDZM4dbQ69QcTmSf9+OlJwQv1EJ+4MVU2tbhRpvp2sTjaecQ+wN/6PBZN7dqU9iCjtnUZkIvNk/6tOPeM13Plepduy6xJO31JOo5qqTyw3qhJHYC3q2cagT9XqccYGRy0xmFbKxjv9epnROOE5N+j8XULMRuH+PzWb/aOUb2FX3kD33ASPG6mMBz82hnlYWBydWH5lNCCCEkh0pxyOgiqRJqsXI41lSjzu4hLurieLBvDli8J3/9ZPZUfuFov+ZgoR1urOOEV1VBKyJRP4OcXiyHDp+tHG9f2z4EHXwu+7Ty80IyvDCJlInSXkvb0BPPFKz7KJxwEiZeb1yDQkMKFWZMn54S1deu71VTmI70o+l0+8U5OybkxwvXWF7s1j5rQBl2MfO5zEQqywS2HaAVY3mccPvbP18bMyFXGipFQgghJIfB+1cAWyUDAM4f89uOLYVCKeog6PGl/Lg7ynp3gqwfCSMz626geyrY2yq+JhWUCt4XvCK2ti+dTEAjY5Lx6DbeWqWb5s2M1fbd1N4jlWDAY1B/Fj1+q2K9fYLnVDPo2lP1FL2cplblRVU5Q1ib21dztJl+InvWXrpz011HF448yHRt1woM3i+h+XTILL/l3mIynPlK5lHQu2UMy7PVEjmFWWmpbpYaXdyFFSmbU2QGKR1trElrc3GscLCQyXF0fgyr+UTsxamlMrhoGidWJxfr5uJYEVsoeHGTldi73OnGM6V65kc7eWqHInu89rrUTj6eydjLRarH34QXX2nHocfoTVS2gr02heqxpiY5ew+We91Gr1z9unjAUvs8Z50iiw3qXqfywBe7fUgvsm9j5hLNpOSahuZTQgghJIdKccj0DowUYRmdldx0+Jfr6B3YBwBFVYB9cyP53+WxOl+koM1RtRgzpwistol4CtH2s9Xcp3umy1CO4lhTvaHyWpVzsrk+0a2ro0EOMZZ+r1MplKvxlNXm4phrAq7ELKpx6ALBWnlZE6aX+1RUppddRhf1LcMjynFLu5Rzj4f3HtgQHqD8rMXcFDrzuV1YvOdSrZ2owlGJRVSfTcvk8TH0DmfnoqmUXC9QKRJCCCE5VIpD5tAnn8WZe28BAFz85sz9/dLkCLpnsidseeJevDNr3+/2y4wg6ilcsuBIgPT0EyNYvKfqHKHXEXXoBpCtT9oclRrrwt/kJNOUzUW391SeVXxNWXQ89ZeqzqCzvgDZ2pkNVUmpq+Vup1hHW0GZY7VYU5wuxwtU1xq9wr/6XDpnrN1n89b2Aayi2l/lPpoIi9VeXeHqkBNb+LfvWRF6neIzs6EKWlukjzA/oTIvVZNOeKxPR2akIdcdnBSHTP+p49h3KDOVLr4kK8y692sb6B3Mbv1GPZys+MGRlG/9biwmQ5kcl2ehJrnsOIkTy8xe1SThoddpNHVtLo7V4tR0CjWhMnHOONvyl57jiuf8Yp02PMcPzwxpz2eJxpSpzYbWhLh7pudWq19GdWLX6eE8L1Fx+JmayLbNff2WZMxgqlSTHpsc73mY2oxAOom3560qhYHloUinA5TJWSZJiYvVfYXupnpgi0VfktFm93y27eCjNJWS6xeaTwkhhJAcKsUrwMKrs6f3530xe0K/8MLRwnxqn0vGFjuV/KdAZka18V7758o2545lT+obk+V+iWfURKNOdByamBBFMZ5e2luLfxztjdRMr9o8Z815qcK2nnOPl3FmebFbmDA9c6EN6/CUn6fYCscVjNVUmFZmnlnZmn13z/QKFevFM6bO35mpKz89NktTXlfZZxWfvqbdx84DADZ0SSp5/5z+XNNrt1ry7JY/2AUg+yxOfZgKkVz/UCkSQgghOVSKQ+bEw/fhyINZ5v+Rvdka3dKP34le/jwiivFQ7o+w8NpqweHs/44K8s/+P3csVNQi4KvDpvJSQJl55NxrVjE6l6kHWZ/cXByrBFwDwEa3zLdac6bpDi6lBFTVh7euZrd5lTP0uqCosaLkUbfMxCJ5PXXweaqyRNu1P0GXw7Lrnl6GH2+NUBhU9aIpnMaOUV5Je3FECoCb59Q65Og+a+uSvRHMPJG9lrJmG93INURyQ0GlSAghhOQw9+kVRJTi5gU/hRiQ5UktwzPq3qK6QLFNETdiPEgtXjIAIFMA3rnsGiRQV1peAHkqX6heU0zlT9U0hWRMdNfdgHQZoygj7zpS+UK9flOB+jqPqyjGQRU/rNenHqe9x5uLY+41CHod0cth2tTeQ4f1yDl1KI89liEXNwbMfVpC8+kVJDUZClMf/izOPXR//lcZryiToXbCsbFiG4kfOwCIs1nu1Wh+kGeeAJ59TXP2F12wtqkCfFMOTzvR6XZeeaZaoV3l1GJ/4Jd7dTOkPtaGmejCzB5ynasz9YlJm1ZTmWQ886lnxhWK+ErVv53MA6plwQDz/jjlnuyk21FOUvoe2H61c5Xs2zeX/b08O1bP1UvIDQbNp4QQQkgOzafXME8/cm9tmzafSkC/4GXC0cH7olRsEHfodYp+pTKHbu8pLWueqzh7qLCKVCiGpcmUaftImQvt+XVfYX6iuOZU6aim3KTeOIFM2Xkq0CpbL/REY++pVnFW0Xlm1063Xwuj0W1SatOOZ3x+rPJZKMZoPmNjix3c9t7PuOck1w80n5ZQKRJCCCE5XFO8DojdvnKSKQsQa9Wo98kxgFIDKs2XoJXD7vns+WhttjzeBvRDuenbunue8tFOKZ7DipCqhKED0mFUpnaqqZB33aYYsJcAYKuMzKzX1KPuy6rjie56EZifCrXoLmT7eoeh1pTLMBlJr7Y226y+PccZIcxPFOvMco91jl1bJzE7f3Ut0VorCLneofn0OuDpR+5NVjD3kB+vlGOJLixrzXLa3DbiZF0RpE13oYOVo81ONYI3GWmsJ6o2TcpErCd8z4PVK+0EZF6UYj5NZdHx0I4/nmeqV+ne4sUhpiZFeRAC6pORZw4F0p6lKaSPyePZ2Nan40Dv52xcZTt6ol6/0HxaQvMpIYQQkkOleJ1w4uH7APiu8FZFeo42QL0SgqdStDqxTinaicNTllKuSjLraGcdDy/8IlVc2KqxzcWxWv9eJhk9Rs+U6eU3bTKHbi6OFWEUqSognjNNyslIt/UcblJKXmOdqcQ8qq0B3vvpWSJsntNBYRjS/+1v/3yyHbn2oFIsoVIkhBBCcuhoc52gXeCzv8u1HPtEr6mopF41yN91688TAvS7sdi/qVWM5GW1yQR6I0VBY13guHDWMWojdvu1wP/V3ljldXHtDeEcWiV6GWdsu36vU+trZX5P4cCTWlMs1h1nLyYLMuv1y2Del7GlvJjx0XVXNVqHIu0k5YVaWFXY726W1ycONOJc1d2s9a/VppflyAvTSbHd9UxCriU4KV4nyKS1nqiQridHz9Ql21IOGrqY8WTu/bhyONb66Jvc2foHVsyno4u7diTzScrsKFRMpTaWL+Hws9ytJ+Lu98qk4mIq1aWkpKCwTuVmJ0rvusURad+TY8UDhH4PUqZmm5y9krBbxZsWk1x+XGgwpUt7wcuYJJ8FnR6wjSlV4mvpeEOuR2g+JYQQQnLoaHOd4WW5SaEVhjVveeEaEvvW78aiTJBOUO6ZQQFfdeyej1i8J1MxojbGljLzrA7fGKQEmxKHr/bGKnlZgWrJK3EyScUheg4u3jGDHGGEVF7RlOLynIA8tDnac4TRcYb63FpZakcbOXZyIX9fDpdxsLbYtR6vF7sopciW7ixVJNXi9QEdbUqoFAkhhJAcrileZ8iT9zMP3V9zgLAqASif4kcXdxXKyTL+ZBfr05kq0H0+d0+2v1Be3T6slvHyYsr5d8+PFMpTtq04GVBSKgxAkaFG8Eoq9dX6YXGGXFWt5NljZH0QqK5BejlhJeOM4AX7e+ErXnku68wSu31sGFWYJRao3gOhei/y69X7lRNWyilGtoV8jB117MphG9bjG2Oswu30RoptWiEScr1CpUgIIYTkcE3xOsYG9HtKUWM9GD01UYRc9EKtn73HO4VnqeApRVmfWpsu24mC0celiti2qToBpBMLpNKqeet3dn3So9/r1NTszOd2YWMyu2btVaqVZBMSAqPvtfc+2VR3e493ivur1WHKw9Q/f73qhR2PbavRitiOe3RxV3F9rKRxbcM1xRKaT69jxGklD/drNHlZJL+lOLt4P5xrs+tJU6Bl7/EONvJxiLNGv7tZy6mpHWysWTH0OqWptsV16InNc3CxYRKVczr96MkwNUHaZOjnjwGTC9VxZNeSPxyYBwI9We6el3tVD6PRDwsyAY8vlmEvhTlUJe62U5me9FKTVicxebadDG3IT7+7iQ0mDCfXGTSfEkIIITlDNZ+GEL4jxvj/Odt/KMb4q9vtl+bTKqm8qJqmUlNa0WnVobPbCGIaFTOqZK9ZORxr7vobM5dqatML4UihQw9SFT+02dVW0xAz6sr8nsLZZmV+T3GsNU12eiO1vK8aG4qhx7j3eLZNzKht0edMmV31vbD31kvaMCgkwob4DArO9/prMuP3u7H2GTry4GeT4yFXB5pPS4atFP99COF/DyHsAoAQwlQI4dcBPDTk8xJCCCFbZthrii8D8C8BPB5CeATAzwD4XQAvH/J5byrk6Vue2LWTTFmotl9bcxy0Vih96DUlWS/U+U2FQWtP+jxe8LnXvhLonm8Ttbe82HWdaayiLOofzpR1D3Ubqyw3dAWKRBFlb4xyf8YWO7X75yk6vc+uscp715+oO77oFHqeMheF1hb73mlLQbHNj+hRarDart+tr3M//ci9DOgn1zRDnRRjjAshhO8D8BiARwF8KMb4d4Z5TlL9AZX4w7HFTsVcqjn8SeDSZPajeO5Y3WS6by77v3tmE6fuzdrJZKizmFhTmZf7VCaefU+M4PyxdiWJBJkklrtljKFX3Nd6pOpYRpl4Ro3TiSb0OqUXZh6v2FGxjJ7HqJ2gds/HIk9tm+vTE+U3/W72//zrmjPV6HMJ4/Njxb3/87/9AQDAX3/vywae1xu/PVeyj9VqX/KZa4K5Ucm1zFDNpyGElwF4HMAJAN8L4PUhhF8JIUwN87yEEELIdhi2+fQ/A3gwxvghAAghfALAPwPwBIAXDvncNx1FxhoVs9dU1QLQqmCzUIi6Soa46Z8/lj35L92ZtfXIlE7dPGuViJhdNyYBG3jhOZv0u5vFsev5M5xkyVmbrTvVaGwx5X1PjhUOQtLaU7Pa9CmUYRib2DcnmXvq90HGv3RnB8/7XNbuuXualaJWh199U9a+d7D+tfRM2bYP/R4f+WhmkHkRmtXY8lvurZk+NTpmVY9Bc/Ld9xevJUSoGPN0bG0FIORaYdiT4t0xxhPyR4xxGcCPhxD+6yGflxBCCNkyw15TPNGw/beGed6blaNv/QKALC+q91Rva+bJU/zCa4GxxayNzV+qt3ku/6N5Xd4mRWC3SxaWzmoZ3nHhaLqPQgUWqjMbv870UpxPOcR4zjG2coXNQQoAG8d66KjCvUBVoZ0/5g4TgJ/7VNZd5bh+d7NQkVKB5Ktv0iEz2fUdeizr49S9dacXoEwKYPPLtmX3Rx/D/He9AkA9kUL1vS5z31pufd9n8MxDmVqUtUW9pjgoyxIh1xo7PimGEL4SY3xJ/vpraEhOEmP8pp0+NyGEEHI57Hjwfgjh1THGP8xfv6apXYzxD7Z7DgbvD+apD1YVgLdOJuh9Wk2mQibs2ta+Ob9KggSz69RvlmpqtLpHqs2RqvECxgXPs9QquZGZdbcuokUrHquYvHVJb+1PmP3ERrF+mGov9SxlvVfOnzqm6X7o9V2v31TtRH3t3jqjd++b+tLrxocOnwUA7H/TXONx5MrA4P0SJgS/wRH396hi77ysMnYymn5ipDbJ6dg1b4K1jiWx268ULdb96xARL3OOl7lFkB/Vmc/tKsbo/TDbicqbAPU2PWHKPdJOPfacgyZzMe3u/4PMFJsqraTvh5fZxnuYsJPiIKeWVHt7/3SRYc902ybPrj5OOwqNHzsPAJh985cH9kGuDJwUS4YdkjEWQvjHIYSnQwjL+f8/G0KYGOZ5CSGEkO0wbO/TXwTwYgD/I4BnANwG4D0AZgH82JDPTeCrQs9MaMtJaWeSVEC33ifmuNLcVg8l8Bx55NlsciFUSi8BmVoqHXKqZtTFey5Vcq8KTblSdfC+DteQYP842y9GI+rRFu1dPRCU40x57dZUOz4/hn5vIm8Hc1/6rrJsIlNc9fapslOWrGJFXYE2OcJ0ep0ixEIcZ3SmJGtG1X14fcp7cYQB++QaZ9iT4vcBOBpjPJv//WchhMcAzIGTIiGEkGuMYU+K3wAwCeCs2tYF8PUhn5fk3P72zwOoVkPwVIqnHlNP/LLWpt3wrYrQDjzWQccL71g5HFW+z2bnDX2cqEdZz3z2NZeSDjPR5DIdXdxVKERhc3GsMQ3c+nTEytG6A48g5/admqpquXpNsXauolLH/JhSZKVCk3ujrQFeTlU7tsr1NCj/zNEq2yaKsT9Rr7QibTVaXUueWq4fkuuFYU+KHwbwf+fJwE8iy2LzkwD+TQjh9dIoxvj7Qx7HTY+ehLx8pPYHM8skU+0jm+Sy116GlabSVLZfe5w2A0rBZOmr0wu1MkzaPCtIvN8gM7E3nmjaBdTNyV7i7lQibu2lqosFS5/l9ZWTv3Uo8hyQPJNpm4cA7SGbKjWlz6knQ9mW8kiVc4k5+mj+QEbI9cSwJ0VJ/v0es/2d+T8gi2M8MuRxEEIIIQNhSMZNhC0o6zEoA0mTGvRc+FMOJZ5KGTQOu61t+Sl7XqBUbWIG1tcCoOZk0jbcIaXCUvdWh6+k2D9XjlHUcSpMRth7vOMWPrbvp3aW8UJmUveBVS+uXxiSUTLsIsOEEELIdcOwzafkGmKQUsn2lW19peAXKu53y+w1kt9U77djqKgyZ5uMUa932nF7VTj0tXjrfELoNTvyjC0F9A77qkuHeXjZgnSowsZMdRzeOmzpQNNpvLdazYk61AkGdI7XpgD983esuzlSm9SlVvn+uMvXUuSakBsBmk9vQmwKOE3KBCbHAekJR34w9Y+lmG7lB/62936mtq8p607TD30qzZnen8rcoidWrw8P26/OuuM52gjaQcczJ1vHmtS91YWHbSyljMlus31oUl6r+jhOgDcmNJ+W0HxKCCGE5AxVKYYQdgO4HcBeABcAPJXXVLwsqBR3hhMP3wcgUwVtnCROPHxfkV1GZ5CRPiyDlIXn+JNyWGmT33SQ4vJUoaeOvHFIX6LcJKayd7jvmqabVJenLO11Wbx74N3zNqWaBqnBpn10pLlxoVIsGcqaYghhClmKtzcDWANwHsB+ALtCCB8F8JMqyw0hhBByTTAsR5tfBtAD8BJdaDiEcBTAP873v3lI5yYtERUnRWIHcfer/hx/9Ce3A0grCqHt2pwNlLd9NOXz3Jjxs9ekMqz46qieLaae6aV8kBaFWOYEHXHzfkqJpsU7q/dKj+NQLr5O3Zte50spUZ3TtEltetmFPDyHpdsZhE9uIoY1Kb4RwKEY44reGGM8HkJ4B7L0b4QQQsg1xbAmxTMAXg7g086+lwFYHNJ5yTYYvfNcq3aPf/pbgIZUcUB9PWvQGpQoVe3VKnj5PG3aufH5sUroBgDA8ey0hYUtblhGIklB/fhNlbM127b3eAfn8uoYGzN171Bh4bVyPq8CRXN1ku5CpwgbKcNLmlV1UxhO07150bsea21BIORGYliT4nsA/F4I4bcA/CmAcwD2Afg2AN+DMsUbuQZom6z56R/+RRz56N9x92kT4lbxJotUgV09+Xp5UPWY9PHaCUfTJgRC8DLsACO1zDfrM/WwCG/C8s9Z3eYVWl6fjgNDUvQ5PbOol0Bco8NmCLlZGMqkGGP8lRDCnwL4mwBeBWAPgIsAvgzg/hjjnw3jvIQQQsjlwOB9UuH4R14OAAjzE7Vwija5U4Fq2EAqJCOVREDwsr+kQjK8cWizpO1jO4H63nildJUUWtbB+7a9Ns16qtRTj6lQi7Z5Xy36WhhucXPDkIySoaV5CyEcAPAWAC9FGaf4ZQAfjTGeGdZ5CSGEkO0yFKUYQvhrAP49gC8hW1OUOMVvA3AHgLfEGD+x3f6pFIeHVopCuv5eXlA4kS90EF4+UaG70Kmt13kp4Lwx2n2HP1k6tgg6VEEruJSSbFMRQ6uwNipPO8lYpejlVj3y4GcL5T4ijjxK3du0emuz6zWnmu5CB7e+j+uGhEpRMyyl+AiAH48xfszuCCG8GcAHALxkSOcml8HRt34BQPajWkx4TiYZi0xcl4MuhKv7tXGMKccVfbydHE/fFQA0T9xtJkJdIss7p6ALCbd1QGrysm26721MnnLs+PxY8ZqmUkKaGVbu09sA/E7Dvt/J9xNCCCHXFMNSio8B+LkQws/oXKd5LtT35vvJNU6ptJodYarhDoOz3Hh4sXK6r5QjzlbPJaZXXdDXmi2BZhNsp9dRzjqo9aFVrVWIWjlK+9lPbAAAvvqm5ufT7HzV/tsi7Y8wKw0hrRjWpPijAH4VwOkQwgmUcYpHAHwRwA8O6byEEELIthl2lYzbAXwrVJxijPHpy+2XjjZXBqmi0SZEQDuDaJrymzYFsrcJlfBCEFJhCSlnFr1epwP7vcK6qXGl6hdeLk0OTvbaN2YuFXlKxdGG64ekDXS0KRlaSAYAxBifAvDUMM9BCCGE7BTDjFP8AWTZbJ4E8K9ijJfUvg/EGP/7YZ2b7AzWvV9jlVS/W087limz/AHUWTesq666ItKIGhM92umN1JTQMw/dX1sDlSoZNlVa6pq8wP/UuLaqENusvw7y+rV96OujQiRkewwrTvGnAfwPAP4DgAcABABvijF+Pd9/Psa4b7v903x6dXjmofsbwwt0thjPpGp/4L3ivtokqAv4WhPsoFJGTYmsdWHelAl2q8V7d2JS9EykHt5EmcoaREgbaD4tGZZS/AkA35mbTxFCeAjAH4YQXh9jfAbZJEkIIYRcUwxrUnwegDn5I8b43hDCcwA+FUJ4IwAqvesIyVE6Pt/sUJIpv2ybqDzAd8jJ2vtOMoJOBrAVJZQlHfCddcYWO0UYhVa1TWPM+oiVfZ6DC3ojyULJHm1UoecgpJUqTaSE7DzDmhSfAXAXsvALAECM8Z+HEFYAfBLA+JDOSwghhGybYU2K/xrAG6AmRQCIMf5yCGENwM8O6bxkCMga3lMffEVjxYdK5Yo8bWpntd6XtybnqaZ9uZ1h6c6tjfVF73qssZqHPrd+bR1ZtHrUAf3CVtO3tcG7B55TEtUhIcOFpaPIlhBT6uTxLCOMl/N0bKlcMh7tZf9fOFo1TXoTijZb7kSBWxmr0GTabFseyraXCbOzilrS8hRNTjUpD1c605BhQkebkmHlPgUAhBB+2NkWQgjvHuZ5CSGEkO0w7Iw2TwP4EwDvjDEuhRCOAPgwgM0Y4wPb7ZdK8eojYQ/a9CgKUVTT2FIoXntZcaxCG1vs7IhCtAwqjuyFYqTCM6yi8ypndHohmfvU69Oei6ZScqWgUiwZqlIE8DJktRS/FEL4WQCPA/iPAF4z5PMSQgghW2bYad6WQwjvAXAvgH+AzAHn5+P1uJBJKnhZYmqqUK03ipLSRW+RK65hKyLpv0kx2hALrfLa1JL09qXyxPq5TAOOPEhlSMjVZqiTYgjhbwD4JQC/AeBvAngUWazi22KM/2WY5ybDRTxSxYxaNQkONkAMykozLLxJrvR0rRf59SZH7UgEDC6wbM2lmbk1e/3Kb89SA5951dIWroIQMiyGOikC+BcAfiTG+HEACCG8Gpli/DyAA0M+NyGEELIlhj0p3hVjLB6BY4ybAH42hPA7Qz4vuUKIGRUoQx6qhYczVTUMB5qtsvd4B+fvyMarQy5SsZBtzKce3YUOeof9OEZ9L85sqVdCyLAZ9qS4HkJ4OYC9AC4AeCrGuBxj/JMhn5cQQgjZMsOqkjEF4BcBvBnAGjIP1P0AdgH4KICfjDGe3W7/DMm49tDZbiQ/6tVaN0wxKDwDyBTl2nR1myjFTi8UmXokc4/dD1Sz4oiavhbvByEAQzI0wwrJ+GUAmwBeEmPcF2O8Nca4F8BL1X5CCCHkmmJYSvECgEMxxhVn324A38gnyW1BpXhtcvwjLwcAHH3rF67ySAbjKUZRduNLqCnF8Xxl/MLRfi3Nm7fe2O9uMvieXDdQKZYMa03xDICXA/i0s+9lABaHdF5yFbkeJkNBT2xl4ePs/zXUM9Ss5SVAtQONlMoqk4aXfXBCJOT6ZFiT4nsA/F4I4bcA/CmAcwD2Afg2AN8D4J1DOi8hhBCybYYyKcYYfyWE8KfIAvZfBWAPgIsAvgzg/hjjnw3jvIS0RapOnHj4vmKbzd2qt2mnGimiLNv63Vj0t/Zddw9tzISQgL/GAQAAErVJREFU4TO0kIwY45eRBeoTQggh1wVDmxRDCAcAvAWZx6nEKX4ZwEdjjIxZJtcERx78bKEWde5WWyC5XGMs1w8lCF877Yz/3uPDGywhZOgMJSQjhPDXAMwBeCuyX5GF/Fw/DODpEMLrhnFeQi6HTi8UycDXp2PlXwo61RBy4zAspfgIgB+PMX7M7gghvBnABwC8ZEjnJoQQQrbFsOIUlwHMxBjXnH3jAJZijJPb7Z9ximQYaDOoxCxaroUcroTsNIxTLBlWRpvHAPxcHqhfkP/9s/l+Qggh5JpiWObTHwXwqwBOhxBOoIxTPALgiwB+cEjnJWTbyNqg1IjUUCEScnMwrDjFZwDcH0K4HcC3QsUpxhifHsY5CSGEkMtlqKWjYoxPAXhqmOcgZKehKiTk5mVYa4oIIfxACOGfhhD+dghhl9n3gWGdlxBCCNkuw4pT/GkAD+d//gSAz4UQXqCa/PAwzksIIYRcDsMyn/4EgO/MzacIITwE4A9DCK/P1xvp/ksIIeSaY1iT4vOQZbQBAMQY3xtCeA7Ap0IIbwTAOENCCCHXHMOaFJ8BcBey8AsAQIzxn4cQVgB8EsD4kM5LCCGEbJthOdr8awBvsBtjjL8M4KcBzA/pvIQQQsi2GVac4i8k9n0EwEeGcV5CCCHkchhaSAYhhBByvcFJkRBCCMnhpEgIIYTkcFIkhBBCcjgpEkIIITmcFAkhhJAcToqEEEJIDidFQgghJIeTIiGEEJLDSZEQQgjJ4aRICCGE5HBSJIQQQnKu+KQYQvhHIYTXX+nzEkIIIYO4GkrxdQAeDSH84VU4NyGEENLIsIoMNxJjfB0AhBBmr/S5CSGEkBRDVYohhAMN24/GGFlomBBCyDXFsM2nT4YQvktvCCH8BIDHhnxeQgghZMsMe1L8MQAfDCF8IIRwLITwewDeCYCONoQQQq45hjopxhh/D8CdAF4N4C8AnAFwd4zxS8M8LyGEELIdhr2muAfALwDYD+CfAHgTgL81zHMSQggh22XY5tMvAdgF4K4Y408jM5u+K4TwH4d8XkIIIWTLDHtS/PsxxrfFGM8BQIzxiwDuBvDUkM9LCCGEbJmhxinGGP+ds20VwN8d5nkJIYSQ7bDjk2II4dEY4zvy1/+mqV2M8b/b6XMTQgghl8MwlOJ/Ua+PD6F/QgghZCjs+KQYY3yfev3QTvdPCCGEDIuh5z7NK2L8EIDDABYA/FqM8T8P+7yEEELIVhl2nOL/AuDXACwC+B1kwfu/km8nhBBCrimGrRT/LoDXxxiflA0hhA8D+DiA/2PI5yaEEEK2xJWopzhn/j4BIF6B8xJCCCFbYtiT4s8A+FAI4UUhhG4I4XYAjwJ4bwhhRP4NeQyEEEJIK0KMwxNtIYRN9WcEEJy/Y4yxs5V+3zjy/VSahBCyQ3x88zfC4FY3B8NeU/wrQ+6fEEII2TGGMimGEP4t8P+3d+dRc9X1Hcffn5knIQiiIGBD2AoEKmBlSYQWFalsaktBNimlYMopYalK21NULAfxSIFWC5wjSNlCWyR4QKDKJihRpMVjgBgIIGGJrA1bkM0kJM+3f/y+k+c+k2cm8zzJ5CHM53XO5N772+/knuc7v3vv3OEm4JaIeLkbfZiZma1q3bqe90Pgk8CDku6SdKqknbvUl5mZ2SrRlaAYEdMj4ihgPOVrGWsBF0t6WtIlkj6Tv7VoZmb2ttHVOz+j+EVEnBYRk4BdgZ9TnnDzuKTjutm/mZnZcHT9MW9VETEfmAZMk1QHNlid/ZuZmbXTrRttLie/oB8RU4YqExFLgRe60b+ZmdlIdGumOKNL7ZqZmXVNV4JiRFzRjXbNzMy6yY9YMzMzSw6KZmZmyUHRzMwsOSiamZmlrgVFSZtLOih/Lqo574hu9WtmZjZSXQmKkvYHHqD8nuIsSRfkl/UbLupGv2ZmZiujWzPFM4EjIuJDlJ+PmgjcIGls5vu3u8zM7G2nW0Fx64i4EZY92u2TwOvATZLe1aU+zczMVkq3guICSZs1NiJiCeUh4E8CtwP1VhXNzMxGS7eC4u3A56oJ+YsZU4DZwLgu9WtmZjZi3Xr26Qmt2o6IqZLO7FK/ZmZmI9atZ58uBha3yX+yG/2amZmtDH9538zMLDkompmZJQdFMzOz5KBoZmaWHBTNzMySImK0x2BmZva24JmimZlZclA0MzNLDopmZmbJQdHMzCw5KJqZmSUHRVsjSVpb0k8l1SUtlTQrX/9dKTNN0hOVvJ1atHW0pLn5OrqSLknfkPSIpIckfb6p3mRJSyQd0sF495F0j6T7c/knlbzDJc2WNEfS2S3qH1nZj1mS+hv7I+mIbHe2pFskbZjpUzN9lqSfS9o+0zeSdMuKxmzWi/yVDFsjSToR6IuI8yS9HhHrDlFmGvDDiLimTTsbADOBSUAA9wC7RsQCSZ8D9gKOiYh+SRtHxPNZrw7cBiwELmvXR5bfGZgfEc9K2hG4NSImSHofcF/2+YKkK4D/iIgft2nrg8D1EbG1pD7gWWD7iHhR0jnAmxFxuqT1IuLVrHMAcEJE7J/blwOXRMRd7cZt1ms8U7Q11ZHADaugnf2A2yLi5YhYQAl0+2fe8cAZEdEP0AiI6W+Ba4FqWksRcV9EPJubc4C1Ja0FbAXMjYgXMu924OAVNHcEMD3Xla91JAlYjxIkaQTEtA4l6DdcT3kPzazCQdHWOJLGAltFxLxMGifpXkl3Szqwqfg/52nFf8sg1GwC8FRl++lMA9gaOFzSTEk3S5qY/U8ADgIuHOEuHAzcGxGLgEeB7SRtmbO+A4HNVlD/cOAqgIh4ixK87ydnjMCljYKSTpT0GHAOUD39OxP46AjHb/aO5aBoa6INgVcq21tExC7AXwDnSto6078MbAtMBjYAThlmP2sBCyNiEnAxcFmmnwuc0phBDoekHYCzgeMAcnZ6PHA1cCcwD1japv5ulNOjD+T2mKy/M7AJMJuy32T7346IrSn7/tVKU89neTOrcFC0NdHvgHGNjYh4JpePAzMoAYKIeC6KRcDlwIeHaOsZBs/MNs00KLPG7+f6dcAf5vokYLqkecAhwAXNM1RJB1VuipmUaZtmO38VEY9Vxv+DiNgtIv4I+DXwSJt9/yw5S0w7ZRuPRblB4HvAHw9RbzplFtowjvI+mlmFg6KtcXJ2VZc0TtL6jdOiedflHsCDuT0+l6IEhAeGaO5WYN9sZ31g30yDct1tr1zfkwxWEfH7EbFlRGwJXEO5geX67OvHkiZExHURsVO+Zkp6L3Aj8KXmm1skbZzL9YETgEuG2m9JNeAwBq4nQgng20vaKLf3AR7K8hMr5T4NzK1sb9vi/TDraX2jPQCzEfoR8BHgTeAiSf2UD3lnRcSDWebKDBYCZgFTAXLmNjUijo2IlyV9Hfhl1jkjIl7O9bOyjZOB14Fj2w0og9Y2wMtDZJ+UeadJOi3T9s2bd86T9KFK/49kewcAkyKiUf5jwFM5IwYg72b9GvAzSW8BvwGOafQpaW/gLWABsOzrJpRgf2O7/THrRf5Khq2RJO0CnBwRR432WBryqxZTIuLvRnssKyLpZ8Cf56zbzJKDoq2xJE0BroiIljem2PJy9rxH45SvmQ1wUDQzM0u+0cbMzCw5KJqZmSUHRTMzs+SgaGZmlhwUzczMkoOimZlZclA0MzNLDopmZmbJQdHMzCw5KJqZmSUHRTMzs+SgaGZmlhwUzczMkoOimZlZclA0M3sbkTRPUkiaNsrjmJbjmLc6666slX3/HBTNrCdIOj3/WA716hvt8Vnnuhl0fSD0qA01PhazCCQEVP4BNS0HpTVto4G05dopi6i2U22jUjeailTbHFRfg5cxRPlO8gbabt1v23aWG2ObdpZrN9rkLWtx2baa8pR5g9+W5rSo/JcNzhOBGs0pKm/pEHUybWC72l4M3qbR1kAdNadnvcHjqIyn2odAqCkvj9dc3jN70a0RsT/D8yLwWFPaavu1dUljI2Lx6urPhsdBsUctZhG79+0LqqF6HWoqSwmatxtpEtRrg7drNaiVMlHXsrSQoD44PSSoiahBNOrUBLkd5S8mUWOgnBplm9brTdtZdmA928ntoco02hyUN1SZ6pLBdQfyYlBZarEsUJa0KOdlqnm1DEKNPAJlO6rk1WpRAlCtBI9arT//K/pLeq7XCOpZtl7rp6agrrKsKejLMn2Z15d5fbWl1ChlxuR6X20p9cZSQZ+WUqeUH6Ol1NXPGC2llss6/cvSawRjtIS6clnJW7beKENUtvupE9QEYwjqgjGIusQYatQQY1SnhqirRo0a9fFzNxzB4X9jRBwz3EqSPgx8A9gJaO53YkQ8OkSdecAWwH8BzwNHA08BO3fWpf4JOBF4N3ADcHxE/DYz/wH4S2DzzH8FuBP4UkQ80tT/fwJPAH8DjANuzLZey3LvAS4EDgBeB77N8h/tWg2yo7qSBJyQY9gWWAz8NMf74BDj/U2WXbe675UyAFtIyz5l7hURM5q6/ApwErB28z634qBoZr3mYEmHU4LIvcBXI+K+dhUk7QjMAC4BvghsDHwXeBI4n+Vnns0Oy+UjlMDR0TiBJcB84P3AEZS/2Y22Pg5sk2N4BvgAcBAwWdLEiFhYaeuzwELKLPn3gCMpQefUzL8YODTXnwX+ns7jQ6d1z6cEKICHgA2APwM+JmmXiHi8UvawHO98yntd3ff7gHUoH0wW5zbAq039HUp5/14Exg+xz0PyNUUz6yVLgf8D5lGCw6eA/5W0opnbecDNEfH5iJgTEXcA04ANIuLKiOjk9OvkiPggJZh1YhGwbURsl/0DHCJpq1w/BVg/IrbPdhunkTcF9mhqayElaG4D3JNpnwDI9hpB7dyI2JYyk1u0ogF2WlfSlpQZL8BxEbE9ZbY3B3gP8OWmpt9ote8RcRBl1gfwXETsnq97m9pY0mqf23FQNLNe8V1g44iYGBEfYCCIrAWcKGkXSXdXXwCSNgT2BC5oau8NOr8WeUdEzAaIiKWt+moyIyKez/Wrcylgh1zfHLhD0quS+oHbKnU3aWrrJxHxTET0Aw9n2vtzuWOl3PdyjM9TZsYr0mndyQycUr0oT3kurOzL7k3lV7TvnWi3zy359KmZ9YTGdbbK9q2SXgLeRwkw6wG7DVF1V6AO/KopfRLwyw67n9+03aqvjuQM7XpgLPAaZSbUR7neCWW8Va9U1pc0mhlp/yvpV5SAWPVsF/oZ0T57pmhmPUHSyZI2qWzvQwmIAPMiYkZEqPrKvEaAWbtSdxtgP8oNIZ0YNKNs01fVnpI2yvVDK+lzKDfqjM3t/SJiMnB2h2NpNqeyfihA9vvxVVh3JgPvwVWVU567U06r/mtT+Xb7DvBmLt+VN/CsMp4p9qjXWDDntiVXl09rb43yYMxGbtwwyn4B+KakpyinPv8g098Azm1T7xeUP8LnSDqDMqs8H5geEbcMf8gdGwfMlTSfcp0O4JqIeFzSWMr10Tpwi6QnKddIhy0iHpN0LeXGnpMlfZpymnGF722ndSPiCUnfAY4HzpJ0AmUmtxmwPvA14H862fdcb5wO3Qh4WNICyt2nvxvm7i/HQbF3LYyISaM9CLOVIWnmMIqfSZl17ABsRbkT8S7g6xHx61aVIuIlSYcC36Kc+nsGuBQ4a6Tj7tC1wKOUmdSbwA+AqTmmhyVNAU6n3Fn5InAyg68rDsexlI/HB1DuCr0QmAActQrrnkS56/Svge0owfApYDplX6ta7nu6DPgYsDcDQbP5lPGIqLObpuydRtJMB0Vb0/k4fmepfAfxipF8l3RV8DVFMzOz5KDYu/59tAdgtgr4OLZVyqdPzczMkmeKZmZmyUHRzMwsOSj2AEmbSbpD0oOS5kj6Qqb/i6SHJc2WdJ2k9472WM3aHK9XS5qVr3mSZnVa16xTvqbYAySNB8ZHxL2S3k15JNSBlAcH/yQilkg6GyAiThnFoZq1PF4bPy+UZb4J/DYizhhuXbN2PFPsARHxXOMJ8vlbYg8BEyLiRxHReCbg3ZQgaTaqWh2vjfx8rNdhwFXDrWu2Ig6KPSZ/wmVnyqOrqqYAN6/u8Zi10+J4/SgwPyLmjqCuWVsOij1E0rqUxyd9MSJeraSfSnmK/JWjNTazZq2OV8oPzi43S+ywrllbfvZpj5A0hvJH4sqI+H4l/RjgT4FPdPhDqWZd1+Z47QM+Q/k5p2HVNeuEg2IPyGswlwIPRcS3Kun7A/8I7BkRb7aqb7Y6tTpe097AwxHx9Ajqmq2Q7z7tAZI+AtwJ3A/0Z/JXKD9/sxbwUqbdHRFTl2/BbPVpdbxGxE2SplGO0+9Uym8CXBIRn2pXd7XtgK3RHBTNzMySb7QxMzNLDopmZmbJQdHMzCw5KJqZmSUHRTMzs+SgaGZmlhwUzczM0v8D8BnJ89R9s/QAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hp.gnomview(m5map,rot=(55.064,-29.783), title='Depth', reso=2.0,unit='5-$\\sigma$ r-band depth',min=22)" + ] + }, + { + "cell_type": "code", + "execution_count": 142, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: AstropyDeprecationWarning: \"clobber\" was deprecated in version 2.0 and will be removed in a future version. Use argument \"overwrite\" instead. [astropy.utils.decorators]\n" + ] + } + ], + "source": [ + "m5map=hp.write_map('/global/projecta/projectdirs/lsst/groups/LSS/DC2_R1.1p/depth_coadd_r.fits.gz',m5map)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "import astropy.table" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "tab_data = astropy.table.Table([ra_coadd,dec_coadd,magmodel,magmodel_err,psfmag,psfmag_err,\n", + " modelflux,modelflux_err,psfflux,psfflux_err,blendedness,isprimary,nchild_coadd,extendedness],\n", + " names=('ra','dec','magmodel','magmodel_err','psfmag','psfmag_err','modelflux','modelflux_err',\n", + " 'psfflux','psfflux_err','blendedness','isprimary','nchild','extendedness'))" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "tab_data.write('/global/projecta/projectdirs/lsst/groups/LSS/DC2_R1.1p/catalog_r1p1.fits.gz',overwrite=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 154, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([ 28230., 221749., 289348., 445706., 576747., 766347.,\n", + " 899674., 933696., 1081204., 872464.]),\n", + " array([ 0.00430875, 0.10528425, 0.20625974, 0.30723524, 0.40821074,\n", + " 0.50918623, 0.61016173, 0.71113723, 0.81211272, 0.91308822,\n", + " 1.01406371]),\n", + " )" + ] + }, + "execution_count": 154, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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t7NeA24A9SXYArwDXAlTV80n2AC8wuEPspqp6r7W7EbgPOBd4rD1gEFgPJJkF5hjcJUZVzSW5FXim1bulquZGmIskaUSLDpSq+m9ATrB74wnafAn40vuUzwAff5/yPwF+6QR93Qvcu9DxSpJOL796RZLUhYEiSerCQJEkdWGgSJK6MFAkSV0YKJKkLgwUSVIXBookqQsDRZLUhYEiSerCQJEkdWGgSJK6MFAkSV0YKJKkLgwUSVIXBookqQsDRZLUhYEiSerCQJEkdWGgSJK6MFAkSV0YKJKkLgwUSVIXBookqQsDRZLUhYEiSerCQJEkdbFi0gNYKqZ2PjrpIUjSh5orFElSF0s6UJJsSfJSktkkOyc9Hkk6ky3ZQElyFvBvgM8AlwF/J8llkx2VJJ25lmygABuA2ar6TlX9KfAgsHXCY5KkM9ZSDpS1wKtDrw+2MknSBCz7u7yS3ADc0F7+nyQvLaKbC4E/6jeqDz3nu/ydaXM+o+eb20fu7y8upNJSDpRDwCVDry9uZcepqnuAe0Y5UJKZqpoepY+lxPkuf2fanJ3veCzlU17PAOuTrEtyNrAN2DvhMUnSGWvJrlCq6miSfwA8DpwF3FtVz094WJJ0xlqygQJQVb8F/NYYDjXSKbMlyPkuf2fanJ3vGKSqJnFcSdIys5SvoUiSPkQMlOZUX+OSgTvb/j9I8slJjLOnBcz577W5Ppfkd5N8YhLj7GWhX9WT5K8lOZrkmnGOr7eFzDfJp5J8K8nzSf7LuMfY2wL+nz4/yX9M8vttzp+fxDh7SHJvkjeTfPsE+8f/N6uqzvgHg4v6/wv4S8DZwO8Dl82r81ngMSDAVcBTkx73GOb8s8DKtv2ZpTznhcx3qN4TDK7NXTPpcZ/mf9+PAS8AP9Fe//lJj3sMc/414Pa2vRqYA86e9NgXOd9fAD4JfPsE+8f+N8sVysBCvsZlK3B/DTwJfCzJReMeaEennHNV/W5VHWkvn2TwWZ+laqFf1fOrwG8Cb45zcKfBQub7d4GvVdX3AKrqTJhzAX8uSYCPMgiUo+MdZh9V9Q0G4z+Rsf/NMlAGFvI1Lsvtq14+6Hx2MHi3s1Sdcr5J1gJ/G7h7jOM6XRby7/uXgZVJvp7k2STXjW10p8dC5vyvgb8C/CHwHPCFqvrBeIY3dmP/m7WkbxvWeCT5GwwC5ecnPZbT7NeBL1bVDwZvYJe9FcAVwEbgXOC/J3myqv7nZId1Wm0GvgV8GvhJYF+S/1pV70x2WMuDgTKwkK9xWdBXvSwhC5pPkr8KfBn4TFW9NaaxnQ4Lme808GALkwuBzyY5WlX/YTxD7Goh8z0IvFVV3we+n+QbwCeApRooC5nz54HbanCRYTbJy8BPA0+PZ4hjNfa/WZ7yGljI17jsBa5rd05cBbxdVa+Ne6AdnXLOSX4C+BrwK8vgXesp51tV66pqqqqmgIeBG5domMDC/p9+BPj5JCuSfAS4EnhxzOPsaSFz/h6DFRlJ1gA/BXxnrKMcn7H/zXKFwom/xiXJ32/7/y2Du34+C8wC/5fBO50la4Fz/mfABcBd7V370VqiX7C3wPkuGwuZb1W9mOS3gT8AfgB8uare9xbUpWCB/8a3AvcleY7B3U9frKol+S3ESb4KfAq4MMlBYBfwYzC5v1l+Ul6S1IWnvCRJXRgokqQuDBRJUhcGiiSpCwNFktSFgSJJ6sJAkSR1YaBIkrr4/+IvCylTHUjkAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(data['redshift'][ind])" + ] + }, + { + "cell_type": "code", + "execution_count": 158, + "metadata": {}, + "outputs": [], + "source": [ + "nz, ze = np.histogram(data['redshift'][ind])" + ] + }, + { + "cell_type": "code", + "execution_count": 160, + "metadata": {}, + "outputs": [], + "source": [ + "tab_z = astropy.table.Table([0.5*(ze[1:]+ze[:-1]),nz],names=('z','Nz'))\n", + "tab_z.write('/global/projecta/projectdirs/lsst/groups/LSS/DC2_R1.1p/nz_matched_r1p1p.fits.gz')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "lsst2", + "language": "python", + "name": "lsst2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Validation/DC2_rough_angular_power.ipynb b/Validation/DC2_rough_angular_power.ipynb new file mode 100644 index 0000000..e7b283f --- /dev/null +++ b/Validation/DC2_rough_angular_power.ipynb @@ -0,0 +1,544 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import fitsio\n", + "import matplotlib.pyplot as plt\n", + "import astropy.table\n", + "import healpy as hp" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [], + "source": [ + "import flatmaps as fm\n", + "import pymaster as nmt" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib\n", + "matplotlib.rcParams.update({'font.size': 14})" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 142, + "metadata": {}, + "outputs": [], + "source": [ + "data = fitsio.read('/global/projecta/projectdirs/lsst/groups/LSS/DC2_R1.1p/catalog_r1p1.fits.gz')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NSIDE = 2048\n", + "ORDERING = RING in fits file\n", + "INDXSCHM = IMPLICIT\n" + ] + } + ], + "source": [ + "mask = hp.read_map('/global/projecta/projectdirs/lsst/groups/LSS/DC2_R1.1p/depth_coadd_r.fits.gz')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "nz = fitsio.read('/global/projecta/projectdirs/lsst/groups/LSS/DC2_R1.1p/nz_matched_r1p1p.fits.gz')" + ] + }, + { + "cell_type": "code", + "execution_count": 158, + "metadata": {}, + "outputs": [], + "source": [ + "mi = fm.FlatMapInfo((np.min(np.degrees(data['ra'])),np.max(np.degrees(data['ra']))),(np.min(np.degrees(data['dec'])),np.max(np.degrees(data['dec']))),nx=364,ny=261)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "def binned_statistic(x, values, func, nbins, range):\n", + " '''The usage is approximately the same as the scipy one\n", + " from https://stackoverflow.com/questions/26783719/effic\n", + " iently-get-indices-of-histogram-bins-in-python'''\n", + " from scipy.sparse import csr_matrix\n", + " r0, r1 = range\n", + " mask = (x > r0) & (x < r1)\n", + " x = x[mask]\n", + " values = values[mask]\n", + " N = len(values)\n", + " digitized = (float(nbins) / (r1-r0) * (x-r0)).astype(int)\n", + " S = csr_matrix((values, [digitized, np.arange(N)]), shape=(nbins, N))\n", + " return np.array([func(group) for group in np.split(S.data, S.indptr[1:-1])])" + ] + }, + { + "cell_type": "code", + "execution_count": 159, + "metadata": {}, + "outputs": [], + "source": [ + "def depth_map_snr_nonHP(ra, dec, mags, snr, snrthreshold, flatSkyGrid):\n", + " # not based on healpix, original version modified to use flatmaps\n", + " # also added the functionality to add snr_threshold\n", + " good = np.logical_or(np.logical_not(np.isnan(ra)),np.logical_not(np.isnan(dec)))\n", + " pix_nums = np.array(flatSkyGrid.pos2pix(ra, dec))\n", + "\n", + " map_out = np.zeros(flatSkyGrid.get_size())\n", + " map_var_out = np.zeros(flatSkyGrid.get_size())\n", + " mask_nans=np.zeros(flatSkyGrid.get_size());\n", + "\n", + " #Binned statistic 2d is awfully slow (because it doesn't use the fact that all bins are equal width\n", + " #median_snr, xed, _, _ = binned_statistic_2d(mags,pix_nums,snr,statistic='median',bins=(50,12*nside**2),\n", + " # range=[(20,30),(0,12*nside**2)])\n", + " #bin_centers = 0.5*xed[1:]+0.5*xed[:-1]\n", + " #depth = bin_centers[np.argmin(np.fabs(median_snr-5),axis=0)]\n", + "\n", + " bin_centers = np.linspace(22+6/30.,28-6/30.,30.)\n", + " for px in np.unique(pix_nums):\n", + " mask_nans[px]=1\n", + " mask = px==pix_nums\n", + " if np.count_nonzero(mask)>0:\n", + " median_snr = binned_statistic(mags[mask],snr[mask],np.nanmedian, nbins=30, range=(22,28))\n", + " std_snr = binned_statistic(mags[mask],snr[mask],np.nanstd, nbins=30, range=(22,28))\n", + "\n", + " mask2 = np.isnan(median_snr)==False\n", + " if np.count_nonzero(mask2)>0:\n", + " depth = bin_centers[mask2][np.argmin(np.fabs(median_snr[mask2]-snrthreshold))]\n", + " std = std_snr[np.argmin(np.fabs(median_snr[mask2]-snrthreshold))]\n", + " map_out[px]=depth\n", + " map_var_out[px]= std\n", + " else:\n", + " map_out[px]=0\n", + " map_var_out[px]= 0\n", + " else:\n", + " map_out[px]=0.\n", + " map_var_out[px]= 0\n", + "\n", + " map_out[mask_nans<1]=0\n", + " map_var_out[mask_nans<1]=0\n", + "\n", + " return map_out, map_var_out\n", + "\n", + "def desc_method(ra, dec, band, mags, snr, flatSkyGrid, SNRthreshold= 5):\n", + " # make a histograms of the S/N in bins of magnitude for all objects in a given pixel\n", + " # define the 5 sigma depth as the magnitude of the histogram whose median S/N is ~5.\n", + " # SNRthreshold= 5 => 5sigma depth. can tweak it.\n", + "\n", + " depth, depth_std= depth_map_snr_nonHP(ra, dec,\n", + " mags= mags,\n", + " snr= snr,\n", + " snrthreshold= SNRthreshold,\n", + " flatSkyGrid= flatSkyGrid)\n", + "\n", + " return depth, depth_std" + ] + }, + { + "cell_type": "code", + "execution_count": 160, + "metadata": {}, + "outputs": [], + "source": [ + "sel = (data['isprimary'])" + ] + }, + { + "cell_type": "code", + "execution_count": 161, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/global/common/cori/software/python/2.7-anaconda-4.4/lib/python2.7/site-packages/ipykernel/__main__.py:17: DeprecationWarning: object of type cannot be safely interpreted as an integer.\n", + "/global/common/cori/software/python/2.7-anaconda-4.4/lib/python2.7/site-packages/ipykernel/__main__.py:7: RuntimeWarning: invalid value encountered in greater\n", + "/global/common/cori/software/python/2.7-anaconda-4.4/lib/python2.7/site-packages/ipykernel/__main__.py:7: RuntimeWarning: invalid value encountered in less\n" + ] + } + ], + "source": [ + "# This is slow so I saved one map\n", + "#depth_map, depth_std = desc_method(np.degrees(data['ra'][sel]),np.degrees(data['dec'][sel]),'r',data['magmodel'][sel],data['modelflux'][sel]/data['modelflux_err'][sel],mi)" + ] + }, + { + "cell_type": "code", + "execution_count": 163, + "metadata": {}, + "outputs": [], + "source": [ + "mi.write_flat_map('/global/projecta/projectdirs/lsst/groups/LSS/DC2_R1.1p/flatmap_depth_r1p1',depth_map)" + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "metadata": {}, + "outputs": [], + "source": [ + "mp, depth_map = fm.read_flat_map('/global/projecta/projectdirs/lsst/groups/LSS/DC2_R1.1p/flatmap_depth_r1p1.npz')" + ] + }, + { + "cell_type": "code", + "execution_count": 164, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/global/common/cori/software/python/2.7-anaconda-4.4/lib/python2.7/site-packages/ipykernel/__main__.py:2: RuntimeWarning: invalid value encountered in less\n", + " from ipykernel import kernelapp as app\n" + ] + } + ], + "source": [ + "mask_flat = depth_map > 25.0\n", + "sel = (data['isprimary']) & (data['magmodel']<25.0) & (data['extendedness']==1)" + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "metadata": {}, + "outputs": [], + "source": [ + "def compute_cls(ra,dec,mask,show_plots=True,ncov=0):\n", + " ipix = mi.pos2pix(ra,dec)\n", + " good_ipix = np.in1d(ipix,np.where(mask)[0])\n", + " ipix=ipix[good_ipix]\n", + " mp = np.bincount(ipix,minlength=mi.get_size())\n", + " dmap = np.zeros(len(mp))\n", + " dmap[mask] = mp[mask]/np.mean(mp[mask])-1.\n", + " #Check how these maps look like\n", + " if show_plots:\n", + " plt.figure()\n", + " mi.view_map(dmap*mp)\n", + " plt.figure()\n", + " mi.view_map(mask)\n", + " #Compute Cls\n", + " cl,lbpw,wsp=mi.compute_power_spectrum(dmap*mask,mask)\n", + " ells = np.mean(lbpw,axis=0)\n", + " cl_list = []\n", + " return ells, cl, np.sum(mp[mask])" + ] + }, + { + "cell_type": "code", + "execution_count": 165, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1685815" + ] + }, + "execution_count": 165, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.count_nonzero(sel)" + ] + }, + { + "cell_type": "code", + "execution_count": 166, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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KxzVoD157yLI+IowMYdBhR+tFHFn/pLcmHl57HyC+53plH8PxYUAUTS1cYhgukqWbErMw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\n8xiQuBgM7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SXd2gYtcP3JBB7zZ0nY0Tq982kN0HsRhGbJxNx+vLiAplSKXGMCWqrKcltyio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\nMAwjyv8HB2LZw5BH//cAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ells_data_tot, cl_data_tot, total_parts = compute_cls(np.degrees(data['ra'][sel]),np.degrees(data['dec'][sel]),mask_flat)" + ] + }, + { + "cell_type": "code", + "execution_count": 167, + "metadata": {}, + "outputs": [], + "source": [ + "total_area = 4*np.pi*np.count_nonzero(mask>25)/len(mask)" + ] + }, + { + "cell_type": "code", + "execution_count": 168, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2.998252734197768e-09" + ] + }, + "execution_count": 168, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "total_area/total_parts" + ] + }, + { + "cell_type": "code", + "execution_count": 169, + "metadata": {}, + "outputs": [], + "source": [ + "errors = 1./np.sqrt(total_area/(4.*np.pi)*(ells_data_tot[1]-ells_data_tot[0]))*cl_data_tot*np.sqrt(2/(2*ells_data_tot+1))+total_area/total_parts" + ] + }, + { + "cell_type": "code", + "execution_count": 170, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 170, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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8sfXOMCHvLQ6czCQjI91ad2j3cSYt2MzY/rrci14DKoVeA1JKueLyF5Zz4EQm\nBmjV0LomtOd4OsO7NydxzQ5OOfzxJY8cvAn09aFpgwAigv0LXz93dN1Z6qE814A0AZVCE5BSylWJ\nySlMnLeJnDyDn7cXDYJ8OJGRQ06eYWDor7TN2MiM3GvIxQcBin7z1qWh2ToIoZJ0EIJSqrzi46LZ\n+ezVTL85Dm8v4ejpbHLyrDSzLL0FH/jEc5XXWnzIpb6vo9hrYxrWY2rSDo+7iVUTkBM6CEEpVVHx\ncdH8a1gnANpFBRPs742I4OXrx1IuIhcv0nIcNPRKR4DmYYEs23GU+Auaetzs2pqAlFKqiiUs20Wz\nsEBEBBFrCp8XbrqATIc3Xgh5ePGJz4MYDI3rB5CbZ3hj5R5r/SEPoglIKaWq2K6jaUwY0JY9x9PJ\nzXMQ6OvF9oOnAXAg1POB9/IGECsp5KRswsdLOJNrdcu1/+fnHtMVpwlIKaWqWGxkMI3rB9AsLJCs\nXAeZOQ6e/2IHIiCAePvycl48/mSzOSeaHMcf14Qyc/IY92EyPZ5dat8bqCGagJRSqoqN7R/LpAWb\n8fX2IsjPm/Mah1A/0AcfL8EAp7PyAGGraYUDLwSDD8UHJhw9nUXck3V7dm1NQE7oKDilVGUUXeAu\nLSsPhzE8Pvh8dj57NXv/dQ3Tb45DgPz2EA3lNHd7L8aHXAACfKy9p7NyeGrxtjqbhPQ+oFLofUBK\nqerS+h+fsuOZq+j8RBKZOXm09DpKrkNIIYJAX2+MgaxcB9FhAQT5+dSaiUt1OQallHJzBQvfNW0Q\nwC/H0tnniERw0JTjnMwNJdcrAH8fL1JOnEHE7mirh3bBKaWUDYpeJ2oUYk3LYxBOE0hvNpObl8eZ\nXAfeXkJkiH8ZR6udNAEppZQNil4nOno6C28BEE5Tj69MF66R1QRILg5jOPJ7Fpe/sNzmiKueJiCl\nlLJJfFw0nZvV56JW4ex+7hraNKoHCA68+MT0ws+cwdfkYIBfjqXXucEIOgihFDoIQSlV02Ie+pRg\nf29M7hmy8oRcvPInL/XCx1u4tUcLnorvaHeY56STkSqlVC3VNiqYkb1iyMjzxV8MjTnBTV7LAcO1\nnZrwwdr9daYlpAlIKaXcyNj+scxY8QsGyMCXHO8gVpgLGOX9KZ8l7yMnz9SZSUs1ATmhN6IqpewS\nHxdNnsNY3W4GUvMCMT7+3OnzJdl440NunZm0VBOQE7ocg1LKTn+KDKZJ/QAAfL2FEyaEflkvYhBy\n8QZMnRjdpMTwAAAdaklEQVQVpwlIKaXczNj+sXh7Cw0CfcnOM2TlGhx4UXQd1d3H0unx7FKGv7ba\nvkArSROQUkq5mYJ7hLJyHSX2eNGcozzo9QFCHkdPn+F4WpYtMVYFTUBKKeWGCu4RKiBYX9gHiGKG\nGYrJ//refSydgS9+UytHxmkCUkopNxbo6w1YnW8Fj3SCkMLuOENaZi5Tk3bUuiSkCUgppdzU3NE9\n+dewTvnT9PxxBSjPUNgC8iGPtNMnmDKsc60bnq0JSCml3Fh8XDS3XdzynPv7ySZ+NwHM+fDDWjc8\nWxOQUkq5uafiO+bPE1eS8JXpAsCXp5sT5JVbs4FVkiYgpZSqBSKC/SlYFsi32De3tTUXbwIcGSSu\nqD3zV3rMgnQicj/wV6z/raXAOFOJmVgdDgcHDhwgPT29qkJUSjnh6+tLZGQkoaGhdodiOy+xrv/k\nlBydnT9d6VDvb0lIyiC+SwsIjrQhwvLxiNmwRaQR8D1wPpADrAAmGmNKvYOrtNmwjx49SlZWFtHR\n0Xh5aUNSqepgjCEzM5OUlBSioqI8PgkNf201mw6c5Ex+BpL8xx/5yPo+fzhgASsb38mqfRkE+fvw\n4xNX1liMOhu2cz5AAOCb/zhamYOdPHmSqKgoTT5KVSMRISgoiOjoaI4erdRHtk6YO7onPz19VeFz\ng5V8ChIRCAFkM+dMX9qlLEA4q6nkVmz/9hSRviKySERSRMSIyEgnZcaIyB4ROSMiG0SkT3nOYYw5\nBkwF9gMHgaXGmN2ViTsvLw9fX9/KHEIp5aLAwEBycnLsDsNtBPp641swNjtfQV/WGfzp7PULK3I7\n8jfv/wFWy8kdp+yxPQEBwcAWYByQWXKniAwHpgOTgQuBVcBnItKiSJlkEdni5NE0f38YcC0QA0QD\nvUSkb2UDF5GyCymlKk0/a8X9a1gnvIrUSckLKZ86LmKniWacz/8YiPslngK2JyBjzBJjzMPGmPng\ntL04AZhljJlpjNlujPkbcAi4t8gx4owxHZ08DuYXuQLYZYxJNcZkAp8CF1fzW1NKqWoRHxdNs7DA\ns7Z78UdXnEFofeZdNmU1QU7tLyzjTq0h2xNQaUTED+gKJJXYlQT0KsehfsVq9QSIiDdwKbDjHOe8\nW0TWi8j6Y8eOVSBqpZSqfhHB/gCFQ7MLBiP80Rqy9uw2TVmfGsDBY6k1G6AL3DoBARGAN3CkxPYj\nQGNXD2KM+R5YAvwAbAZ2A4vOUfZ1Y0w3Y0y3Ro0aVShopZSqbnNH9yTQ1xsD+Hid3Q0H4MALg9CA\nNFLSHAycurSmwyyVuyegKmOMecQY094Yc74x5v9KuwdIV0RVStUGTRtYi9bllTrYTWgth3AgHDn+\nG9sOuc/3mrsnoONAHhBVYnsUcLi6TurJK6Jeeuml3HfffXaHUSmuvocTJ04QFRXF7t2uDYi88cYb\neeGFFyobXp2mdVqzIoL98fUWp62fotaa9gCcoh498pKrPzAXuXUCMsZkAxuAASV2DcAaDafKQURK\nfYwcOdLuEGvU5MmTufrqq2nTpo1L5R977DGeffZZKtMyXrFiBUOGDCE6OhoRYdasWWW+5rnnnqN7\n9+6EhobSqFEjBg8ezJYtW4qViYmJcfp/es0115x1vHvuuYfx48cXPr/88ssLy/v4+BAVFcW1117L\n0qXl766xo049XZcWYWcNyXbOSlM7cqLwO1mpu1CqjO0JSESCRSROROLy42mR/7xgmPU0YKSI/FVE\n2ovIdKApMKMaY6qTXXCHDh0qfMycOfOsbdOnT7cttuzs7Bo9X0ZGBm+88QZ/+ctfXH5Np06daN26\nNe+++26Fz5uWlkbHjh2ZPn06gYFnj2JyZvny5YwZM4ZVq1bx9ddf4+PjwxVXXEFq6h8XldetW1fs\n/3Ljxo2ICDfddFOxYxljWLRoEUOHDi3ctnHjRp588kkOHTrErl27mDdvHuHh4QwYMID333/f5fdm\nV50qaBEe5EIpa0nvXLxZeaIBA1/4yvb1g2xPQEA3rMEBPwCBwJP5/34KwBgzFxgPPAokA5cAVxtj\n9lVXQHW1C65x48aFjwYNGpy1reD9OhwOHn74YSIiIoiMjGTixIk4HEUm+zCG559/njZt2hAYGEin\nTp3O+gLJyspi/PjxREVFERAQwMUXX8y3335buP/SSy/l3nvvZeLEiTRq1IjevXvzzjvv0LBhQ7Ky\nii8xfNtttzFkyJByvdfc3FzGjRtHWFgYYWFhPPDAA8Xew5IlSxARevfuDVhf8s5aEJdeemmx4w4Z\nMoQPPvigXLEUdfXVVzN58mRuuOEGl2fR+OKLL7jrrrvo2LEjnTp1Ys6cORw7dozvvvuusEyjRo2K\n/V8uWbKE0NDQsxLQunXryMrK4pJLLgFg9+7dnDx5kr59+9K4cWNiYmLo27cv77zzDkOGDOEf//iH\ny++tZJ0WeP75553W7WOPPQZUvk492dzRPZk7umfhiLiyeeFLHoLhCf/3mfqFvYvY2T4ZqTFmOX+M\nJDxXmVeAV2okoIr67CE4/GPNnrNxJ7jqX1V+2Pfee49x48axatUqkpOTufXWW+natSu33HILAI8+\n+ijz588nISGBdu3asXr1akaNGkVYWFhhl8+DDz7IRx99xFtvvUXr1q2ZNm0agwYNYufOnTRp0gSA\nd999l7vvvpuVK1dijCEmJoZx48aRmJhY+MV56tQpPv7443J/Qb333nuMHDmS1atXs3nzZkaNGkWT\nJk2YMGECACtXrqRr166FNzj26tWLQ4cOFb4+JSWFK6644qwE1KNHD5555hkyMzMJDAxk8uTJTJ48\nudRYPvvsM/r0KdfkHaU6ffo0DoeDsLAwp/uNMbz55pvcfvvtZ7WyFi5cyDXXXIOPj/XR37BhAyJC\nly5dzjrOoEGDWLRoEampqYSHh5cZV8k6LXDvvfdy5513Fj6fOnUq7733XuG2knWqKsaajrRsKUQS\n7nOGXsfnM6VtG55Y5k18XHR1h+eU7QnIHYnIYGBwbGys3aHYokOHDjz11FMAtG3blpkzZ/LVV19x\nyy23kJ6ezrRp00hKSir8Um3VqhVr164lISGBa665hvT0dF599VXeeOONwoQ0Y8YMvv76axISEnjm\nmWcKX1fyAvRtt93GW2+9VZiA3n//fUJDQ51eyyhNkyZNeOmllxARzjvvPH7++WemTZtWmID27dtH\n06ZNC8v7+fnRuLE1sj8zM5Nrr72W/v378/jjjxc7btOmTcnJyeHgwYO0adOGe+6556xWRknR0VX7\n4R43bhxxcXH07NnT6f4vv/ySPXv2MGrUqLP2JSYm8vTTTxc+37BhA23atHE6yaefnx+Ay1NOlazT\nAiEhIYSEhAAwZcoUPvjgA5YvX07B56tknaqKCQ7wISfPUThR6bk4gAZhDeH8MXRfPZVd2Z1rJkAn\nNAE5YYxZDCzu1q3b2Z/gc6mGlohdOncu/gvZtGnTwokgt23bxpkzZxg0aFCxv3RzcnKIiYkBrG6d\nnJycYl0x3t7e9OzZk23bthVu69q161nnHjVqFF26dOHAgQM0a9aMt956ixEjRhT+xe6qiy++uFh8\nPXv25J///Ce///47oaGhZGZmEhVVcnCl1XoYOXIkeXl5zJkz56y/5gv+Qs/MtGaNCg8Pd6l1UFUm\nTJjAt99+y7fffou3t7fTMjNnzqR79+5ccMEFxbbv2rWLX375hSuv/GNm5I0bNzr9fwDYsWMHTZs2\nLUweBQoGTpQctHKuOi3w3HPPkZCQwLJly2jbtm3h9pJ1qirO19uLrBwHhtJbRPtTM0iMvJdGkceJ\nTTkIh7dA4441GKlFE5A6S8m/eEWk8PpJwc/FixfTokWLUl/nTNEv9Hr1zl7h8YILLqBLly7MmjWL\noUOHsn79+mq5QB0REcGJEyfO2v7UU0+xYsUK1q1b5zS+ggv/BTcp12QX3P3338+HH37IsmXLaN26\ntdMyR48eJTExkYSEhLP2LVy4kMsvv7zY+9q4cSMPPfTQWWVzcnKYN28ew4YNczm+c9UpwDPPPMOM\nGTOKtXwKlKxTVTleAg5TeneccRj+uWg7vl438Fi9WTD3Fbh7OQQ2qJkg82kCcsLTu+BK06FDB/z9\n/dm3bx+XXXaZ0zJt2rTBz8+P7777rrBLJS8vj9WrV3PrrbeWeY5Ro0bx/PPPc/z4cXr37k27du3K\nHeeaNWswxhQmvO+//56mTZsWdjVdeOGFZw2Bnj9/Ps8//zzLli2jWbNmTo+7ZcsWoqOjC//Sr6ku\nuHHjxjF37lyWLVvGeeedd85ys2bNwt/fv/B6XVGJiYmMGDGi8PmePXtITU09qwVkjGH8+PGcOnWK\nSZMmuRyjszoFK6m/8cYbfPPNN0672ErWqSq/uaN7Mvy11Ww79Dvwx8J155JrICM7j4hgP+JvHw+z\nrobEsTD8XajBiV81ATlRoS44DxESEsLEiROZOHEixhj69u1LWloa33//PV5eXtx9993Uq1ePe++9\nl0mTJhEREUGrVq148cUXOXLkCGPGjCnzHLfccgsTJkzg1VdfZcaMio22P3jwIOPHj2fMmDH8+OOP\n/Pvf/+bRRx8t3H/llVcyadIkfvvtNxo2bMiWLVsYMWIEkydPpkWLFhw+bN3n7OfnV6yLbeXKlcW6\nsMrbBZeWlsauXbsAqzW5f/9+kpOTCQ8PL2xRvvzyy7z88sv89NNPAIwdO5Y5c+awcOFCwsLCCmML\nDg4mODi48NjGGN544w1uvvnmYtsBjh07xvfff8/8+fMLt23YsAGwulgPHz7M6dOnSU5O5uWXX2br\n1q0sXLiwMHlmZ2fTo0cP4I8Wy3/+8x8A1q5di5+f31l1ClbL56WXXmLRokXUq1evMPYGDRoQEBDg\ntE5Vzch1GI6ezoIWV8AVT0LSI/D9q9Cz7M9olTHG6OMcj65du5pz2bZt2zn31Qbz5s0z1n9/cf36\n9TNjx44ttm3EiBHmmmuuKXzucDjMSy+9ZNq3b2/8/PxMRESEueKKK0xSUlJhmTNnzphx48aZyMhI\n4+fnZy666CKzcuXKUs9T1F133WVCQkJMWlpase1vv/22AcyePXvO+dp+/fqZ0aNHm7Fjx5r69eub\nBg0amAkTJpjc3Nxi5S6++GLz8ssvFztuyUe/fv0Ky2dmZprQ0FCzevXqc567LMuWLXN6nhEjRhSW\nefzxx4v93zgrD5jHH3+82LG//vprA5g1a9acdd4333zT9OzZs9i2hx56qPBY3t7eJjw83Fx88cXm\nscceM4cPHz7ne3j77bfN22+/7XRf0Tp1OBwmNDTUaexLly41xpSvTmv7Z64m3DRjlWn90Cem9UOf\nmJhJn5iWZTwuevZL64UOhzHv32LMk+HG7F9bqRiA9cbF71iPWJK7okpbknv79u20b9++hiPyHFdd\ndRXNmjUrvGG2wOOPP878+fPZtGlTuQcmlPT5558zbtw4tm3bds4L+kUlJCSQmJhIUlLJydndX3x8\nPL179+bBBx+s9LHONQgBqrdO9TNXtuGvrWb9XquFGuTvw+kzuaWWb1w/gO//cbn1JPMEvNYPjANG\nr4Cgig2u0SW5K6muzoRQG5w4cYJFixaRlJTEuHHjztq/ZMkSEhISKp18wLrPZezYsRw4cMCl8r6+\nvvz3v/+t9Hnt0Lt3b6fXhaqaJ9WpO5o7uifdYsIJ8rc+H+e6muMlEBXix9Hfz/yxMTAMbpwFaUfg\n49HgqP7lvLUFVAptAdW8mJgYUlNTeeSRR8p1AVzVffqZc03RwQgdmoSy53i6da2nCH8fL0b1aUXS\ntiMk3d+v+AHWzoQ938B1r4OfK1P8FFeeFpAOQlBuZe/evXaHoFSd0iqi3lkJqGE9PxI3HWTiQCcj\nTLv/1XrUwGg4TUBKKVXHdGhSfGYL7xLDsk9k5PCvYZ2cT8FTg8Ow9RqQUkrVIQUTlJ6Lt0DnZvVt\nm/+tKE1ATuggBKWUqn6agJwwdXQ5BqWUZwry98GlNetqmCYgpZSqg8rqinMHmoCUUkrZQhOQzYa/\ntprhr622OwyllIcI8vdxm5aRJiCllFK20ATkhI6CU0qp6qcJyIm6PApu5MiRiAgigq+vL5GRkfTv\n35+EhARycnJcPs7y5csREY4fP16N0Sql6jJNQDZKTE5h84FTrNmTysAXvyExOaVGznvFFVdw6NAh\n9u7dS1JSEoMHD+bxxx+nT58+pKen10gMSimlCcgmickpTE3aQcuGQXSPCeOJIeczNWlHjSQhf39/\nGjduTHR0NHFxcUyYMIHly5ezceNGnn/+eQDeffddunfvTkhICJGRkdx4442kpFix7d27l/79+wPW\nMsoiUjg1/+eff06fPn0ICwsjPDycK6+8ku3bt1f7e1JK1T6agGySsGwXU4Z1pn6gL14i9GoTwZRh\nnUlYtsuWeDp27MigQYNYsGABYK2A+eSTT7Jp0yY++eQTjh8/Xjidf/PmzQvLbd26lUOHDjF9+nQA\n0tPTGT9+PGvXrmX58uXUr1+fwYMHk52dbcv7Ukq5L52M1Ca7jqbRPab4gk/dY8LZdTTNpoigQ4cO\nLF26FIA///nPhdtbt27Nq6++Svv27Tlw4ADNmjUrXIY6MjKSiIiIwrLDhg0rdsy3336b0NBQ1q5d\nyyWXXFID70IpVdTc0T2LLVTnTrQFZJPYyGDWlfiFWLc3ldjIYJsispZnl/yZcDdu3Eh8fDwtW7Yk\nJCSEbt2s5T32799f6jF2797NrbfeSps2bQgNDSUqKgqHw1Hm65RSnkcTkE3G9o9l0oLNnMrMwWEM\nq3YfZ9KCzYztH2tbTNu2baN169akp6dz5ZVXEhQUxJw5c1i3bh2ff/45QJldaddeey3Hjh3jtdde\nY82aNfzwww/4+PhoF5xSNgvy9yEkwOespRrspF1wNimYCv2hBT+SmZPHE4u2MnFgO9umSN+yZQuf\nf/45jz76KD/99BPHjx9n8uTJtGrVCoD//e9/xcr7+fkBkJeXV7jtt99+46effuKVV14pHKSwceNG\ncnNLX5deKeWZNAE5ISKDgcGxsdXbGomPi+b9NVbXVE1OjZGVlcXhw4dxOBwcO3aMr776ismTJ9O1\na1cmTpxIRkYG/v7+vPzyy4wdO5bt27fzz3/+s9gxWrZsiYjw6aefMnjwYAIDAwkLCyMiIoKZM2fS\nvHlzUlJSeOCBB/Dx0V8zpdTZtAvOibp8IyrA0qVLadKkCS1atODyyy9n0aJFPPHEE6xYsYJ69erR\nqFEjZs+ezcKFC+nQoQNPPvkk06ZNK3aM6OhonnzySR555BGioqK477778PLyYu7cuWzevJmOHTsy\nduxYnn76afz9/W16p0opsP7AdaeutwJijCm7lIfq1q2bWb9+vdN927dvp3379pU+R8FEpO4yOaBS\n7qqqPnOerCa+b0RkgzGmmytltW/EZpp4lFKeSrvglFJK2UITkFJKKVtoF1wlFL1xUylVffRaddVw\nty5/bQFVkLe3d7mWL1BKVVxmZia+vr52h6GqmCagCmrQoAFHjhzB4XDYHYpSdZYxhoyMDFJSUoiM\njLQ7HFXFtAuugiIiIjhw4AA7duywOxSl6jRfX1+ioqIIDXW/+1hU5WgCqiAvLy9atGhhdxhKKVVr\neUwXnIhMFJGtIrJFRG63Ox6llPJ0HtECEpFOwK1AV0CAZSLyiTHmpL2RKaWU5/KUFlB7YLUx5owx\nJhPYBAyyOSallPJoticgEekrIotEJEVEjIiMdFJmjIjsEZEzIrJBRPqU8zRbgEtFpIGIhAGXAvas\ne6CUUgpwjy64YKwE8U7+oxgRGQ5MB8YA3+b//ExEOhhj9ueXScb5exlojDlojNkmIi8BXwOngO+B\nPCfllVJK1RC3mg1bRNKA+4wxs4psWwNsNsaMKrJtJzDfGPOPCp7nDeBjY8ynTvbdDdyd/7QdUDDO\nuj5W8iqq6LaS+yOA4xWJrwzO4qiq15RW7lz7yqqXsp5XVz2dK7aqeE1ZZVypE1e31URdVaSeXH2d\nHb9T4F515Wmfv5bGmEYulTTGuM0DSANGFnnuB+QCN5YolwB8U85jR+b/bAdsBnzK+frXS9tWcj+w\nvprq6Kw4quo1pZU7176y6qWs59VVT9VZV2WVcaVOKlJ37vQ7VRV1VV2/U+5WV576+XPl4Q5dcKWJ\nALyBIyW2HwGuKOexEkWkPpAO3GWMKe860YvL2OZsf3WoyHlcfU1p5c61r6x6ceV5damuuiqrjCt1\n4uq2mqirip6jsnWlv1Oul6uNdVUmt+6CE5GmQArQzxizoki5x4DbjDHtbAnUBSKy3ri4KJMn03py\nndaV67SuXGN3Pdk+Cq4Mx7EGC0SV2B4FHK75cMrldbsDqCW0nlyndeU6rSvX2FpPbt0Cyt+2Bthk\njLm7yLafgQWmgoMQlFJK2c/2a0AiEgzE5j/1AlqISByQaqxh1tOAOSKyFvgOuAdoCsywI16llFJV\nw/YWkIhcCixzsmu2MWZkfpkxwINAE6x7hu4vek1IKaVU7WN7AlJKKeWZ3H0QglJKqTpKE1ANE5Fr\nRWSHiOwUkb/aHY87E5GPReSEiMy3OxZ3JSLNRWS5iGwTkc0icqPdMbmr/Lkg14tIcv6yLKPKfpXn\nEpEgEdknIlOr7RzaBVdzRMQH2Ab0B34HNgIXG2N+szUwN5V/fTAEGGGMucHmcNySiDQBoowxySLS\nGNgAtDXGpNscmtsREW/A3xiTISL1sK4nd9PPn3Mi8izWALFfjTETq+Mc2gKqWT2ArcaYFGPMaWAJ\nMNDmmNyWMWY5cNruONyZMeaQMSY5/9+Hse6dC7c3KvdkjMkzxmTkP/XHWhtMbAzJbYnIn4DzgM+q\n8zyagMqhCpaOKJjZocAB6uiyEDW0zEatV5X1JCJdAW9jzK/VHbcdqqKu8rvhNmF99v5tjKmuiXBt\nU0W/U1OBar/PUhNQ+RQsHTEOyCy5s8jSEZOBC4FVWEtHtKjJIN2E1pVrqqSeRCQcazmTu0seow6p\ndF0ZY04aYy4AWgG3ikjJWVbqgkrVk4jEAz8bY36u9kjtnAm1Nj8oMXN3/rY1wMwS23YCz+X/uxfW\nMhAF+/4D3Gr3e3HHuiqy7VKspTdsfx/uWk9Y3UkrgDvsfg/uXlcl9r0C3GD3e3G3egKeA34F9mJ1\n6Z4CHquO+LQFVEVExA/oCiSV2JWElXgA1gIdRSQ6fwaIq4Avai5K9+BiXXk8V+pJRASYBXxtjJlT\nowG6ERfrKkpEQvL/XR/oyx/rfXkEV+rJGPMPY0xzY0wMMBErWT1VHfFoAqo6pS0d0RjAWEtA/B1r\n5odk4AXjmSNwyqwrABFZCswDrhaRAyLSs+ZCdAuu1FNvYDgwNH94cbKIdKrBGN2FK3XVEliZfw1o\nJfBfY8yPNReiW3Dps1dTbJ8LztMYYxYBi+yOozYwxpR3zSePY4z5Fv1D0iXGmLVAnN1x1CamyMTQ\n1UF/catObV46oqZpXblG68l1Wleucat60gRURYwx2Vg3AQ4osWsA1igTlU/ryjVaT67TunKNu9WT\ndsGVgy4d4TqtK9doPblO68o1taqe7B4mWJseWEOCjZPHrCJlxmANX8zC+kujr91xa12570PrSevK\nk+tJ54JTSillC70GpJRSyhaagJRSStlCE5BSSilbaAJSSillC01ASimlbKEJSCmllC00ASmllLKF\nJiCllFK20ASkVC0kIo+JyI8icpPdsShVUZqAlKplRORqrKW3vwMG2hyOUhWmCUip2mcUMBMIAQ7a\nHItSFaYJSKlaRET8sVo9i7CWUNalBlStpQlIqdrlYiAXyAbCgW/sDUepitMEpFTt0hvYCNwOfGCM\nybQ5HqUqTBekU6p2OR/YA/wZ6GtzLEpViiYgpWqXpkAMkGSM2WFzLEpVinbBKVW7NAAaAf+wOxCl\nKksTkFK1ixcw3RhzwO5AlKosTUBK1RIiMgLoBMSIiLeIvCQif7I7LqUqShOQUrWAiAQBNwLXAucB\nW4C9xpidtgamVCWIMcbuGJRSSnkgbQEppZSyhSYgpZRSttAEpJRSyhaagJRSStlCE5BSSilbaAJS\nSillC01ASimlbKEJSCmllC3+HwN2LEeByIE/AAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.errorbar(ells_data_tot,cl_data_tot-total_area/total_parts,errors,fmt='o',fillstyle='none',label='Data')\n", + "plt.loglog(np.arange(12001),cls_th,label='Theory, b(z)=1.27/$D_{+}(z)$')\n", + "plt.xlabel('$\\ell$',fontsize=16)\n", + "plt.ylabel('$C_{\\ell}$',fontsize=16)\n", + "plt.legend(loc='best')" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [], + "source": [ + "import h5py\n", + "f = h5py.File('/global/projecta/projectdirs/lsst/groups/CS/descqa/catalog/ANL_AlphaQ_v2.1.hdf5')" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[u'H_0',\n", + " u'NP',\n", + " u'N_s',\n", + " u'Omega_DE',\n", + " u'Omega_Nu',\n", + " u'Omega_b',\n", + " u'Omega_matter',\n", + " u'boxSize',\n", + " u'haloMassDefinition',\n", + " u'particleMass',\n", + " u'sigma_8',\n", + " u'w_de']" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "f['metaData/simulationParameters'].keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(0.21999999999999997, 0.0448, 0.80000000000000004, -1.0, 0.96299999999999997, 0.70999999999999996)\n" + ] + } + ], + "source": [ + "h0 = f['metaData/simulationParameters/H_0'].value/100.\n", + "ns = f['metaData/simulationParameters/N_s'].value\n", + "w0 = f['metaData/simulationParameters/w_de'].value\n", + "sigma8 = f['metaData/simulationParameters/sigma_8'].value\n", + "Omega_b = f['metaData/simulationParameters/Omega_b'].value\n", + "Omega_c = f['metaData/simulationParameters/Omega_matter'].value-Omega_b\n", + "print(Omega_c,Omega_b,sigma8,w0,ns,h0)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "import pyccl as ccl" + ] + }, + { + "cell_type": "code", + "execution_count": 138, + "metadata": {}, + "outputs": [], + "source": [ + "cosmo = ccl.Cosmology(Omega_c=Omega_c,Omega_b=Omega_b,n_s=ns,sigma8=sigma8,h=h0,w0=w0)" + ] + }, + { + "cell_type": "code", + "execution_count": 139, + "metadata": {}, + "outputs": [], + "source": [ + "b = 1.27/ccl.growth_factor(cosmo,1/(1+nz['z']))" + ] + }, + { + "cell_type": "code", + "execution_count": 140, + "metadata": {}, + "outputs": [], + "source": [ + "tracer = ccl.ClTracerNumberCounts(cosmo,True,False,bias=(nz['z'],b),n=(nz['z'],nz['Nz']))" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "metadata": {}, + "outputs": [], + "source": [ + "cls_th = ccl.angular_cl(cosmo,tracer,tracer,np.arange(12001))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "nmt", + "language": "python", + "name": "nmt" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 3e5901d437ddd88e0d1b7fab2e69225aa8b1a6b4 Mon Sep 17 00:00:00 2001 From: fjaviersanchez Date: Thu, 17 May 2018 03:50:54 -0700 Subject: [PATCH 2/3] Requirements added --- Validation/DC2_matching_and_depth.ipynb | 64 ++++- Validation/DC2_rough_angular_power.ipynb | 31 ++- Validation/Requirements_and_install.md | 26 ++ Validation/flatmaps.py | 294 +++++++++++++++++++++++ 4 files changed, 408 insertions(+), 7 deletions(-) create mode 100644 Validation/Requirements_and_install.md create mode 100644 Validation/flatmaps.py diff --git a/Validation/DC2_matching_and_depth.ipynb b/Validation/DC2_matching_and_depth.ipynb index f73ed40..b72fd0a 100644 --- a/Validation/DC2_matching_and_depth.ipynb +++ b/Validation/DC2_matching_and_depth.ipynb @@ -1,5 +1,19 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Example of access to DC2 Run 1.1p data and some analysis" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "DC2 will contain a large amount of data and the resources at NERSC offer a unique opportunity to analyze these data." + ] + }, { "cell_type": "code", "execution_count": 1, @@ -44,6 +58,13 @@ "import GCRCatalogs" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`GCRCatalogs` contains information about the input galaxies for DC2 so we are going to use it as truth table. Note that this is just an approximation!" + ] + }, { "cell_type": "code", "execution_count": 5, @@ -63,6 +84,13 @@ "from lsst.sims.utils import cartesianFromSpherical, sphericalFromCartesian, rotationMatrixFromVectors" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "protoDC2_v2 and DC2 Run 1.1p are not centered on the same coordinates so we are going to rotate the field using the code from GCR_simsinterface" + ] + }, { "cell_type": "code", "execution_count": 7, @@ -265,7 +293,9 @@ { "cell_type": "code", "execution_count": 23, - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stdout", @@ -621,6 +651,13 @@ "ind = ind.flatten()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Some plots showing the difference between output and matched magnitudes" + ] + }, { "cell_type": "code", "execution_count": 42, @@ -708,7 +745,14 @@ "metadata": {}, "outputs": [], "source": [ - "test = butler.get('deepCoadd_meas',filter='r',tract=tract,patch=patches[0])" + "test = butler.get('deepCoadd_meas',filter='r',tract=tract,patch=patches[0]) # We check the r-band coadds" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What columns does the catalog contain?" ] }, { @@ -1466,7 +1510,7 @@ } ], "source": [ - "m5map=hp.write_map('/global/projecta/projectdirs/lsst/groups/LSS/DC2_R1.1p/depth_coadd_r.fits.gz',m5map)" + "#m5map=hp.write_map('/global/projecta/projectdirs/lsst/groups/LSS/DC2_R1.1p/depth_coadd_r.fits.gz',m5map)" ] }, { @@ -1490,13 +1534,21 @@ " 'psfflux','psfflux_err','blendedness','isprimary','nchild','extendedness'))" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can save an load the results (please do not overwrite/erase the files)" + ] + }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ - "tab_data.write('/global/projecta/projectdirs/lsst/groups/LSS/DC2_R1.1p/catalog_r1p1.fits.gz',overwrite=True)" + "#tab_data.write('catalog_r1p1.fits.gz',overwrite=True)\n", + "tab_data = astropy.table.Table.read('/global/projecta/projectdirs/lsst/groups/LSS/DC2_R1.1p/catalog_r1p1.fits.gz')" ] }, { @@ -1551,8 +1603,8 @@ "metadata": {}, "outputs": [], "source": [ - "tab_z = astropy.table.Table([0.5*(ze[1:]+ze[:-1]),nz],names=('z','Nz'))\n", - "tab_z.write('/global/projecta/projectdirs/lsst/groups/LSS/DC2_R1.1p/nz_matched_r1p1p.fits.gz')" + "#tab_z = astropy.table.Table([0.5*(ze[1:]+ze[:-1]),nz],names=('z','Nz'))\n", + "#tab_z.write('/global/projecta/projectdirs/lsst/groups/LSS/DC2_R1.1p/nz_matched_r1p1p.fits.gz')" ] }, { diff --git a/Validation/DC2_rough_angular_power.ipynb b/Validation/DC2_rough_angular_power.ipynb index e7b283f..b7faee0 100644 --- a/Validation/DC2_rough_angular_power.ipynb +++ b/Validation/DC2_rough_angular_power.ipynb @@ -42,6 +42,13 @@ "%matplotlib inline" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are going to read the data catalog that we created in `DC2_matching_and_depth`" + ] + }, { "cell_type": "code", "execution_count": 142, @@ -88,6 +95,13 @@ "mi = fm.FlatMapInfo((np.min(np.degrees(data['ra'])),np.max(np.degrees(data['ra']))),(np.min(np.degrees(data['dec'])),np.max(np.degrees(data['dec']))),nx=364,ny=261)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have to create the depth map for this pixelation scheme" + ] + }, { "cell_type": "code", "execution_count": 16, @@ -115,6 +129,7 @@ "metadata": {}, "outputs": [], "source": [ + "# Code from: https://github.com/LSSTDESC/HyperSupremeStructure-HSC-LSS\n", "def depth_map_snr_nonHP(ra, dec, mags, snr, snrthreshold, flatSkyGrid):\n", " # not based on healpix, original version modified to use flatmaps\n", " # also added the functionality to add snr_threshold\n", @@ -206,7 +221,14 @@ "metadata": {}, "outputs": [], "source": [ - "mi.write_flat_map('/global/projecta/projectdirs/lsst/groups/LSS/DC2_R1.1p/flatmap_depth_r1p1',depth_map)" + "#mi.write_flat_map('/global/projecta/projectdirs/lsst/groups/LSS/DC2_R1.1p/flatmap_depth_r1p1',depth_map)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have already one depth map made so, let's read it and use it." ] }, { @@ -218,6 +240,13 @@ "mp, depth_map = fm.read_flat_map('/global/projecta/projectdirs/lsst/groups/LSS/DC2_R1.1p/flatmap_depth_r1p1.npz')" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We select objects with good signal to noise and that have `extendedness==1` as galaxies." + ] + }, { "cell_type": "code", "execution_count": 164, diff --git a/Validation/Requirements_and_install.md b/Validation/Requirements_and_install.md new file mode 100644 index 0000000..4616f60 --- /dev/null +++ b/Validation/Requirements_and_install.md @@ -0,0 +1,26 @@ +# Requirements and install instructions + +## DC2_matching_and_depth + +In order to use this notebook you will need: + +* The DM stack (in particular `lsst.daf.persistence` you can check [here](pipelines.lsst.io). +* `scikit-learn` (`pip install scikit-learn` or `conda install scikit-learn`). +* `glob` (`pip install glob`). +* The LSST sims stack (in particular `lsst.sims.utils` you can check [here](pipelines.lsst.io)). +* `astropy` (`pip install astropy` or `conda install astropy`). +* `healpy` (`pip install healpy`). +* `GCRCatalogs` (check [here](https://github.com/LSSTDESC/gcr-catalogs) for instructions). + +## DC2_rough_angular_power + +In order to use this notebook you will need: + +* `healpy` (`pip install healpy`) +* `astropy` (`pip install astropy` or `conda install astropy`) +* `flatmaps` (included in this directory, no install needed) +* `pymaster` (follow instructions [here](https://github.com/damonge/NaMaster/NERSC_installation.md)) +* `h5py` (`pip install h5py`) +* `CCL` (follow instructions [here](https://github.com/LSSTDESC/CCL)) + +For questions please ping `@fjaviersanchez` on Slack or open an issue in this repo. diff --git a/Validation/flatmaps.py b/Validation/flatmaps.py new file mode 100644 index 0000000..036fda5 --- /dev/null +++ b/Validation/flatmaps.py @@ -0,0 +1,294 @@ +import numpy as np +import matplotlib.pyplot as plt +from matplotlib import cm +import pymaster as nmt + +class FlatMapInfo(object) : + def __init__(self,x_range,y_range,nx=None,ny=None,dx=None,dy=None) : + """ + Creates a flat map + x_range : [x_i,x_f] range in the x axis covered by the map + y_range : [y_i,y_f] range in the y axis covered by the map + nx,ny : Number of pixels in the x/y axes. If None, dx/dy must be provided + dx,dy : Resolution in the x/y axes. If None, nx/ny must be provided + """ + self.x0=x_range[0] + self.xf=x_range[1] + self.lx=self.xf-self.x0 + self.y0=y_range[0] + self.yf=y_range[1] + self.ly=self.yf-self.y0 + + if nx is None and dx is None : + raise ValueError("Must provide either nx or dx") + + if ny is None and dy is None : + raise ValueError("Must provide either ny or dy") + + if nx is None : + self.nx=int(self.lx/dx)+1 + else : + self.nx=nx + self.dx=self.lx/self.nx + + if ny is None : + self.ny=int(self.ly/dy)+1 + else : + self.ny=ny + self.dy=self.ly/self.ny + + self.npix=self.nx*self.ny + + def get_dims(self) : + """ + Returns map size + """ + return [self.ny,self.nx] + + def get_size(self) : + """ + Returns map size + """ + return self.npix + + def pos2pix(self,x,y) : + """ + Returns pixel indices for arrays of x and y coordinates. + Will return -1 if (x,y) lies outside the map + """ + x=np.asarray(x) + scalar_input=False + if x.ndim==0 : + x=x[None] + scalar_input=True + + y=np.asarray(y) + if y.ndim==0 : + y=y[None] + + if len(x)!=len(y) : + raise ValueError("x and y must have the same size!") + + ix=np.floor((x-self.x0)/self.dx).astype(int) + ix_out=np.where(np.logical_or(ix<0,ix>=self.nx))[0] + + iy=np.floor((y-self.y0)/self.dy).astype(int) + iy_out=np.where(np.logical_or(iy<0,iy>=self.ny))[0] + + ipix=ix+self.nx*iy + ipix[ix_out]=-1 + ipix[iy_out]=-1 + + if scalar_input : + return np.squeeze(ipix) + return ipix + + def pix2pos(self,ipix) : + """ + Returns x,y coordinates of pixel centres for a set of pixel indices. + """ + ipix=np.asarray(ipix) + scalar_input=False + if ipix.ndim==0 : + ipix=ipix[None] + scalar_input=True + + i_out=np.where(np.logical_or(ipix<0,ipix>=self.npix))[0] + if len(i_out)>0 : + raise ValueError("Pixels outside of range") + + ix=ipix%self.nx + ioff=np.array(ipix-ix) + iy=ioff.astype(int)/(int(self.nx)) + + x=self.x0+(ix+0.5)*self.dx + y=self.y0+(iy+0.5)*self.dy + + if scalar_input : + return np.squeeze(x),np.squeeze(y) + return x,y + + def get_empty_map(self) : + """ + Returns a map full of zeros + """ + return np.zeros(self.npix,dtype=float) + + def view_map(self,map_in,ax=None, xlabel='x', ylabel='y', + title=None, addColorbar=True,posColorbar= False, cmap = cm.magma, + colorMax= None, colorMin= None): + """ + Plots a 2D map (passed as a flattened array) + """ + if len(map_in)!=self.npix : + raise ValueError("Input map doesn't have the correct size") + + # set up the colorbar if min, max not given. + if colorMax is None or colorMin is None: + if posColorbar: + ind= np.where(map_in>0)[0] + colorMin= np.percentile(map_in[ind], 15) + colorMax= np.percentile(map_in[ind], 95) + else: + colorMin= np.percentile(map_in, 15) + colorMax= np.percentile(map_in, 95) + + if ax is None : + plt.figure() + ax=plt.gca() + if title is not None : + ax.set_title(title,fontsize=15) + image= ax.imshow(map_in.reshape([self.ny,self.nx]), + origin='lower', interpolation='nearest', + aspect='equal', extent=[self.x0,self.xf,self.y0,self.yf], + vmin= colorMin, vmax= colorMax, cmap= cmap) + #if addColorbar : + # plt.colorbar(image) + ax.set_xlabel(xlabel,fontsize=15) + ax.set_ylabel(ylabel,fontsize=15) + + def write_flat_map(self,filename,maps) : + """ + Saves a set of maps in npz format. + We'll try to implement other more standard formats with proper WCS coordinates etc. ASAP. + """ + if maps.ndim<1 : + raise ValueError("Must supply at least one map") + if maps.ndim==1 : + maps=np.array([maps]) + if len(maps[0])!=self.npix : + raise ValueError("Map doesn't conform to this pixelization") + + np.savez(filename,x_range=[self.x0,self.xf],y_range=[self.y0,self.yf],nx=self.nx,ny=self.ny, + maps=maps) + + def compute_power_spectrum(self,map1,mask1,map2=None,mask2=None,l_bpw=None,return_bpw=False,wsp=None,return_wsp=False) : + """ + Computes power spectrum between two maps. + map1 : first map to correlate + mask1 : mask for the first map + map2 : second map to correlate. If None map2==map1. + mask2 : mask for the second map. If None mask2==mask1. + l_bpw : bandpowers on which to calculate the power spectrum. Should be an [2,N_ell] array, where + the first and second columns list the edges of each bandpower. If None, the function will + create bandpowers of its own taylored to the properties of your map. + return_bpw : if True, the bandpowers will also be returned + wsp : NmtWorkspaceFlat object to accelerate the calculation. If None, this will be precomputed. + return_wsp : if True, the workspace will also be returned + """ + same_map=False + if map2 is None : + map2=map1 + same_map=True + + same_mask=False + if mask2 is None : + mask2=mask1 + same_mask=False + + if len(map1)!=self.npix : + raise ValueError("Input map has the wrong size") + if (len(map1)!=len(map2)) or (len(map1)!=len(mask1)) or (len(map1)!=len(mask2)) : + raise ValueError("Sizes of all maps and masks don't match") + + lx_rad=self.lx*np.pi/180 + ly_rad=self.ly*np.pi/180 + + if l_bpw is None : + ell_min=max(2*np.pi/lx_rad,2*np.pi/ly_rad) + ell_max=min(self.nx*np.pi/lx_rad,self.ny*np.pi/ly_rad) + d_ell=2*ell_min + n_ell=int((ell_max-ell_min)/d_ell)-1 + l_bpw=np.zeros([2,n_ell]) + l_bpw[0,:]=ell_min+np.arange(n_ell)*d_ell + l_bpw[1,:]=l_bpw[0,:]+d_ell + return_bpw=True + + #Generate binning scheme + b=nmt.NmtBinFlat(l_bpw[0,:],l_bpw[1,:]) + + #Generate fields + f1=nmt.NmtFieldFlat(lx_rad,ly_rad,mask1.reshape([self.ny,self.nx]),[map1.reshape([self.ny,self.nx])]) + if same_map and same_mask : + f2=f1 + else : + f2=nmt.NmtFieldFlat(lx_rad,ly_rad,mask2.reshape([self.ny,self.nx]),[map2.reshape([self.ny,self.nx])]) + + #Compute workspace if needed + if wsp is None : + wsp=nmt.NmtWorkspaceFlat(); + wsp.compute_coupling_matrix(f1,f2,b) + return_wsp=True + + #Compute power spectrum + cl_coupled=nmt.compute_coupled_cell_flat(f1,f2) + cl_uncoupled=wsp.decouple_cell(cl_coupled)[0] + + #Return + if return_bpw and return_wsp : + return cl_uncoupled,l_bpw,wsp + else : + if return_bpw : + return cl_uncoupled,l_bpw + elif return_wsp : + return cl_uncoupled,wsp + else : + return cl_uncoupled + + def u_grade(self,mp,x_fac,y_fac=None) : + """ + Up-grades the resolution of a map and returns the associated FlatSkyInfo object. + mp : input map + x_fac : the new map will be sub-divided into x_fac*nx pixels in the x direction + y_fac : the new map will be sub-divided into y_fac*ny pixels in the y direction + if y_fac=None, then y_fac=x_fac + """ + if y_fac is None : + y_fac=x_fac + if len(mp)!=self.npix : + raise ValueError("Input map has a wrong size") + + fm_ug=FlatMapInfo([self.x0,self.xf],[self.y0,self.yf],nx=x_fac*self.nx,ny=y_fac*self.ny) + mp_ug=np.repeat(np.repeat(mp.reshape([self.ny,self.nx]),y_fac,axis=0),x_fac,axis=1).flatten() + + return fm_ug,mp_ug + + def d_grade(self,mp,x_fac,y_fac=None) : + """ + Down-grades the resolution of a map and returns the associated FlatSkyInfo object. + mp : input map + x_fac : the new map will be sub-divided into floor(nx/x_fac) pixels in the x direction + y_fac : the new map will be sub-divided into floor(ny/y_fac) pixels in the y direction + if y_fac=None, then y_fac=x_fac. + Note that if nx/ny is not a multiple of x_fac/y_fac, the remainder pixels will be lost. + """ + if y_fac is None : + y_fac=x_fac + if len(mp)!=self.npix : + raise ValueError("Input map has a wrong size") + + nx_new=self.nx/int(x_fac) + ny_new=self.ny/int(y_fac) + xf_new=self.x0+self.dx*x_fac*nx_new + yf_new=self.y0+self.dy*y_fac*ny_new + + ix_max=nx_new*int(x_fac) + iy_max=ny_new*int(y_fac) + mp2d=mp.reshape([self.ny,self.nx])[:iy_max,:ix_max] + fm_dg=FlatMapInfo([self.x0,xf_new],[self.y0,yf_new],nx=nx_new,ny=ny_new) + mp_dg=np.mean(np.mean(np.reshape(mp2d.flatten(),[ny_new,int(y_fac),nx_new,int(x_fac)]),axis=-1),axis=-2).flatten() + + return fm_dg,mp_dg + +def read_flat_map(filename,i_map=0) : + """ + Reads a flat-sky map and the details of its pixelization scheme. + The latter are returned as a FlatMapInfo object. + i_map : map to read. If -1, all maps will be read. + """ + data=np.load(filename) + + fmi=FlatMapInfo(data['x_range'],data['y_range'],nx=data['nx'],ny=data['ny']) + if i_map==-1 : + i_map=np.arange(len(data['maps'])) + return fmi,data['maps'][i_map] From dfa51413fa12de23b222dfab41055a09fe248c56 Mon Sep 17 00:00:00 2001 From: fjaviersanchez Date: Thu, 24 May 2018 08:55:23 -0700 Subject: [PATCH 3/3] Fixed bugs + some speed ups --- Validation/DC2_matching_and_depth.ipynb | 690 ++---------------------- 1 file changed, 58 insertions(+), 632 deletions(-) diff --git a/Validation/DC2_matching_and_depth.ipynb b/Validation/DC2_matching_and_depth.ipynb index b72fd0a..87f4116 100644 --- a/Validation/DC2_matching_and_depth.ipynb +++ b/Validation/DC2_matching_and_depth.ipynb @@ -214,15 +214,6 @@ "We have ~10,000 single visits" ] }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "truth_path = '/global/projecta/projectdirs/lsst/groups/CS/descqa/catalog/ANL_AlphaQ_v2.1.2.hdf5'" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -232,7 +223,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -241,7 +232,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -257,7 +248,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -273,7 +264,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -292,7 +283,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 15, "metadata": { "scrolled": true }, @@ -342,10 +333,8 @@ }, { "cell_type": "code", - "execution_count": 26, - "metadata": { - "collapsed": true - }, + "execution_count": 16, + "metadata": {}, "outputs": [ { "data": { @@ -596,7 +585,7 @@ ")" ] }, - "execution_count": 26, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -607,38 +596,20 @@ }, { "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "flat_list = [item for sublist in mag_err for item in sublist]" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [], - "source": [ - "mag_err = flat_list" - ] - }, - { - "cell_type": "code", - "execution_count": 40, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ - "nchild = np.array(nchild)\n", - "mag = np.array(mag)\n", - "ra_aux = np.array(ra_aux)\n", - "dec_aux = np.array(dec_aux)\n", - "mag_err = np.array(mag_err)" + "nchild = np.concatenate(np.array(nchild)).ravel()\n", + "mag = np.concatenate(np.array(mag)).ravel()\n", + "ra_aux = np.concatenate(np.array(ra_aux)).ravel()\n", + "dec_aux = np.concatenate(np.array(dec_aux)).ravel()\n", + "mag_err = np.concatenate(np.array(mag_err)).ravel()" ] }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 18, "metadata": { "scrolled": true }, @@ -660,7 +631,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -669,15 +640,15 @@ "(-1, 1)" ] }, - "execution_count": 42, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -692,7 +663,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -701,15 +672,15 @@ "(20, 24)" ] }, - "execution_count": 46, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -723,7 +694,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -732,20 +703,11 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "tracts = [4430,4432,4638,4640,4849,4851,5062,5064,5066,4431,4637,4639,4848,4850,4852,5063,5065]" - ] - }, - { - "cell_type": "code", - "execution_count": 69, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ - "test = butler.get('deepCoadd_meas',filter='r',tract=tract,patch=patches[0]) # We check the r-band coadds" + "tracts = [4430,4432]#,4638,4640,4849,4851,5062,5064,5066,4431,4637,4639,4848,4850,4852,5063,5065]" ] }, { @@ -757,535 +719,7 @@ }, { "cell_type": "code", - "execution_count": 71, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Schema(\n", - " (Field['L'](name=\"id\", doc=\"unique ID\"), Key(offset=0, nElements=1)),\n", - " (Field['Angle'](name=\"coord_ra\", doc=\"position in ra/dec\"), Key(offset=8, nElements=1)),\n", - " (Field['Angle'](name=\"coord_dec\", doc=\"position in ra/dec\"), Key(offset=16, nElements=1)),\n", - " (Field['L'](name=\"parent\", doc=\"unique ID of parent source\"), Key(offset=24, nElements=1)),\n", - " (Field['Flag'](name=\"merge_footprint_i\", doc=\"Detection footprint overlapped with a detection from filter i\"), Key['Flag'](offset=32, bit=0)),\n", - " (Field['Flag'](name=\"merge_footprint_r\", doc=\"Detection footprint overlapped with a detection from filter r\"), Key['Flag'](offset=32, bit=1)),\n", - " (Field['Flag'](name=\"merge_footprint_z\", doc=\"Detection footprint overlapped with a detection from filter z\"), Key['Flag'](offset=32, bit=2)),\n", - " (Field['Flag'](name=\"merge_footprint_g\", doc=\"Detection footprint overlapped with a detection from filter g\"), Key['Flag'](offset=32, bit=3)),\n", - " (Field['Flag'](name=\"merge_footprint_y\", doc=\"Detection footprint overlapped with a detection from filter y\"), Key['Flag'](offset=32, bit=4)),\n", - " (Field['Flag'](name=\"merge_footprint_u\", doc=\"Detection footprint overlapped with a detection from filter u\"), Key['Flag'](offset=32, bit=5)),\n", - " (Field['Flag'](name=\"merge_footprint_sky\", doc=\"Detection footprint overlapped with a detection from filter sky\"), Key['Flag'](offset=32, bit=6)),\n", - " (Field['Flag'](name=\"merge_peak_i\", doc=\"Peak detected in filter i\"), Key['Flag'](offset=32, bit=7)),\n", - " (Field['Flag'](name=\"merge_peak_r\", doc=\"Peak detected in filter r\"), Key['Flag'](offset=32, bit=8)),\n", - " (Field['Flag'](name=\"merge_peak_z\", doc=\"Peak detected in filter z\"), Key['Flag'](offset=32, bit=9)),\n", - " (Field['Flag'](name=\"merge_peak_g\", doc=\"Peak detected in filter g\"), Key['Flag'](offset=32, bit=10)),\n", - " (Field['Flag'](name=\"merge_peak_y\", doc=\"Peak detected in filter y\"), Key['Flag'](offset=32, bit=11)),\n", - " (Field['Flag'](name=\"merge_peak_u\", doc=\"Peak detected in filter u\"), Key['Flag'](offset=32, bit=12)),\n", - " (Field['Flag'](name=\"merge_peak_sky\", doc=\"Peak detected in filter sky\"), Key['Flag'](offset=32, bit=13)),\n", - " (Field['I'](name=\"deblend_nChild\", doc=\"Number of children this object has (defaults to 0)\"), Key(offset=40, nElements=1)),\n", - " (Field['Flag'](name=\"deblend_deblendedAsPsf\", doc=\"Deblender thought this source looked like a PSF\"), Key['Flag'](offset=32, bit=14)),\n", - " (Field['D'](name=\"deblend_psfCenter_x\", doc=\"If deblended-as-psf, the PSF centroid\", units=\"pixel\"), Key(offset=48, nElements=1)),\n", - " (Field['D'](name=\"deblend_psfCenter_y\", doc=\"If deblended-as-psf, the PSF centroid\", units=\"pixel\"), Key(offset=56, nElements=1)),\n", - " (Field['D'](name=\"deblend_psfFlux\", doc=\"If deblended-as-psf, the PSF flux\"), Key(offset=64, nElements=1)),\n", - " (Field['Flag'](name=\"deblend_tooManyPeaks\", doc=\"Source had too many 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(Field['D'](name=\"base_NaiveCentroid_x\", doc=\"centroid from Naive Centroid algorithm\", units=\"pixel\"), Key(offset=72, nElements=1)),\n", - " (Field['D'](name=\"base_NaiveCentroid_y\", doc=\"centroid from Naive Centroid algorithm\", units=\"pixel\"), Key(offset=80, nElements=1)),\n", - " (Field['Flag'](name=\"base_NaiveCentroid_flag\", doc=\"General Failure Flag\"), Key['Flag'](offset=32, bit=22)),\n", - " (Field['Flag'](name=\"base_NaiveCentroid_flag_noCounts\", doc=\"Object to be centroided has no counts\"), Key['Flag'](offset=32, bit=23)),\n", - " (Field['Flag'](name=\"base_NaiveCentroid_flag_edge\", doc=\"Object too close to edge\"), Key['Flag'](offset=32, bit=24)),\n", - " (Field['Flag'](name=\"base_NaiveCentroid_flag_resetToPeak\", doc=\"set if CentroidChecker reset the centroid\"), Key['Flag'](offset=32, bit=25)),\n", - " (Field['D'](name=\"base_SdssCentroid_x\", doc=\"centroid from Sdss Centroid algorithm\", units=\"pixel\"), Key(offset=88, nElements=1)),\n", - " (Field['D'](name=\"base_SdssCentroid_y\", doc=\"centroid from Sdss Centroid algorithm\", units=\"pixel\"), Key(offset=96, nElements=1)),\n", - " (Field['F'](name=\"base_SdssCentroid_xSigma\", doc=\"1-sigma uncertainty on x position\", units=\"pixel\"), Key(offset=104, nElements=1)),\n", - " (Field['F'](name=\"base_SdssCentroid_ySigma\", doc=\"1-sigma uncertainty on y position\", units=\"pixel\"), Key(offset=108, nElements=1)),\n", - " (Field['Flag'](name=\"base_SdssCentroid_flag\", doc=\"General Failure Flag\"), Key['Flag'](offset=32, bit=26)),\n", - " (Field['Flag'](name=\"base_SdssCentroid_flag_edge\", doc=\"Object too close to edge\"), Key['Flag'](offset=32, bit=27)),\n", - " (Field['Flag'](name=\"base_SdssCentroid_flag_noSecondDerivative\", doc=\"Vanishing second derivative\"), Key['Flag'](offset=32, bit=28)),\n", - " (Field['Flag'](name=\"base_SdssCentroid_flag_almostNoSecondDerivative\", doc=\"Almost vanishing second derivative\"), Key['Flag'](offset=32, bit=29)),\n", - " (Field['Flag'](name=\"base_SdssCentroid_flag_notAtMaximum\", doc=\"Object is not at a maximum\"), Key['Flag'](offset=32, bit=30)),\n", - " (Field['Flag'](name=\"base_SdssCentroid_flag_resetToPeak\", doc=\"set if CentroidChecker reset the centroid\"), Key['Flag'](offset=32, bit=31)),\n", - " (Field['D'](name=\"base_Blendedness_old\", doc=\"blendedness from dot products: (child.dot(parent)/child.dot(child) - 1)\"), Key(offset=112, nElements=1)),\n", - " (Field['D'](name=\"base_Blendedness_raw_flux\", doc=\"measure of how flux is affected by neighbors: (1 - flux.child/flux.parent)\"), Key(offset=120, nElements=1)),\n", - " (Field['D'](name=\"base_Blendedness_raw_flux_child\", doc=\"flux of the child, measured with a Gaussian weight matched to the child\", units=\"count\"), Key(offset=128, nElements=1)),\n", - " (Field['D'](name=\"base_Blendedness_raw_flux_parent\", doc=\"flux of the parent, measured with a Gaussian weight matched to the child\", units=\"count\"), Key(offset=136, 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(Field['D'](name=\"base_Blendedness_raw_child_xy\", doc=\"shape of the child, measured with a Gaussian weight matched to the child\", units=\"pixel^2\"), Key(offset=184, nElements=1)),\n", - " (Field['D'](name=\"base_Blendedness_raw_parent_xx\", doc=\"shape of the parent, measured with a Gaussian weight matched to the child\", units=\"pixel^2\"), Key(offset=192, nElements=1)),\n", - " (Field['D'](name=\"base_Blendedness_raw_parent_yy\", doc=\"shape of the parent, measured with a Gaussian weight matched to the child\", units=\"pixel^2\"), Key(offset=200, nElements=1)),\n", - " (Field['D'](name=\"base_Blendedness_raw_parent_xy\", doc=\"shape of the parent, measured with a Gaussian weight matched to the child\", units=\"pixel^2\"), Key(offset=208, nElements=1)),\n", - " (Field['D'](name=\"base_Blendedness_abs_child_xx\", doc=\"shape of the child, measured with a Gaussian weight matched to the child\", units=\"pixel^2\"), Key(offset=216, nElements=1)),\n", - " 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(Field['Flag'](name=\"base_Blendedness_flag\", doc=\"General Failure Flag\"), Key['Flag'](offset=32, bit=32)),\n", - " (Field['Flag'](name=\"base_Blendedness_flag_noCentroid\", doc=\"Object has no centroid\"), Key['Flag'](offset=32, bit=33)),\n", - " (Field['Flag'](name=\"base_Blendedness_flag_noShape\", doc=\"Object has no shape\"), Key['Flag'](offset=32, bit=34)),\n", - " (Field['Flag'](name=\"base_InputCount_flag\", doc=\"Set for any fatal failure\"), Key['Flag'](offset=32, bit=35)),\n", - " (Field['I'](name=\"base_InputCount_value\", doc=\"Number of images contributing at center, not including anyclipping\"), Key(offset=264, nElements=1)),\n", - " (Field['Flag'](name=\"base_InputCount_flag_noInputs\", doc=\"No coadd inputs available\"), Key['Flag'](offset=32, bit=36)),\n", - " (Field['D'](name=\"base_SdssShape_xx\", doc=\"elliptical Gaussian adaptive moments\", units=\"pixel^2\"), Key(offset=272, nElements=1)),\n", - " (Field['D'](name=\"base_SdssShape_yy\", doc=\"elliptical 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(Field['D'](name=\"ext_shapeHSM_HsmShapeLinear_resolution\", doc=\"resolution factor (0=unresolved, 1=resolved)\"), Key(offset=512, nElements=1)),\n", - " (Field['Flag'](name=\"ext_shapeHSM_HsmShapeLinear_flag\", doc=\"general failure flag, set if anything went wrong\"), Key['Flag'](offset=32, bit=57)),\n", - " (Field['Flag'](name=\"ext_shapeHSM_HsmShapeLinear_flag_no_pixels\", doc=\"no pixels to measure\"), Key['Flag'](offset=32, bit=58)),\n", - " (Field['Flag'](name=\"ext_shapeHSM_HsmShapeLinear_flag_not_contained\", doc=\"center not contained in footprint bounding box\"), Key['Flag'](offset=32, bit=59)),\n", - " (Field['Flag'](name=\"ext_shapeHSM_HsmShapeLinear_flag_parent_source\", doc=\"parent source, ignored\"), Key['Flag'](offset=32, bit=60)),\n", - " (Field['Flag'](name=\"ext_shapeHSM_HsmShapeLinear_flag_galsim\", doc=\"GalSim failure\"), Key['Flag'](offset=32, bit=61)),\n", - " (Field['D'](name=\"ext_shapeHSM_HsmShapeRegauss_e1\", doc=\"PSF-corrected shear using Hirata & 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config.pseudoFilterList)\"), Key['Flag'](offset=1696, bit=4)),\n", - " (Field['D'](name=\"ext_convolved_ConvolvedFlux_3_3_3_apCorr\", doc=\"aperture correction applied to ext_convolved_ConvolvedFlux_3_3_3\"), Key(offset=1704, nElements=1)),\n", - " (Field['D'](name=\"ext_convolved_ConvolvedFlux_3_3_3_apCorrSigma\", doc=\"aperture correction applied to ext_convolved_ConvolvedFlux_3_3_3\"), Key(offset=1712, nElements=1)),\n", - " (Field['Flag'](name=\"ext_convolved_ConvolvedFlux_3_3_3_flag_apCorr\", doc=\"set if unable to aperture correct ext_convolved_ConvolvedFlux_3_3_3\"), Key['Flag'](offset=1696, bit=5)),\n", - " (Field['D'](name=\"base_PsfFlux_apCorr\", doc=\"aperture correction applied to base_PsfFlux\"), Key(offset=1720, nElements=1)),\n", - " (Field['D'](name=\"base_PsfFlux_apCorrSigma\", doc=\"aperture correction applied to base_PsfFlux\"), Key(offset=1728, nElements=1)),\n", - " (Field['Flag'](name=\"base_PsfFlux_flag_apCorr\", doc=\"set if unable to aperture correct base_PsfFlux\"), Key['Flag'](offset=1696, bit=6)),\n", - " (Field['D'](name=\"ext_convolved_ConvolvedFlux_0_3_3_apCorr\", doc=\"aperture correction applied to ext_convolved_ConvolvedFlux_0_3_3\"), Key(offset=1736, nElements=1)),\n", - " (Field['D'](name=\"ext_convolved_ConvolvedFlux_0_3_3_apCorrSigma\", doc=\"aperture correction applied to ext_convolved_ConvolvedFlux_0_3_3\"), Key(offset=1744, nElements=1)),\n", - " (Field['Flag'](name=\"ext_convolved_ConvolvedFlux_0_3_3_flag_apCorr\", doc=\"set if unable to aperture correct ext_convolved_ConvolvedFlux_0_3_3\"), Key['Flag'](offset=1696, bit=7)),\n", - " (Field['D'](name=\"ext_convolved_ConvolvedFlux_3_kron_apCorr\", doc=\"aperture correction applied to ext_convolved_ConvolvedFlux_3_kron\"), Key(offset=1752, nElements=1)),\n", - " (Field['D'](name=\"ext_convolved_ConvolvedFlux_3_kron_apCorrSigma\", doc=\"aperture correction applied to ext_convolved_ConvolvedFlux_3_kron\"), Key(offset=1760, nElements=1)),\n", - " (Field['Flag'](name=\"ext_convolved_ConvolvedFlux_3_kron_flag_apCorr\", doc=\"set if unable to aperture correct ext_convolved_ConvolvedFlux_3_kron\"), Key['Flag'](offset=1696, bit=8)),\n", - " (Field['D'](name=\"base_GaussianFlux_apCorr\", doc=\"aperture correction applied to base_GaussianFlux\"), Key(offset=1768, nElements=1)),\n", - " (Field['D'](name=\"base_GaussianFlux_apCorrSigma\", doc=\"aperture correction applied to base_GaussianFlux\"), Key(offset=1776, nElements=1)),\n", - " (Field['Flag'](name=\"base_GaussianFlux_flag_apCorr\", doc=\"set if unable to aperture correct base_GaussianFlux\"), Key['Flag'](offset=1696, bit=9)),\n", - " (Field['D'](name=\"ext_convolved_ConvolvedFlux_2_6_0_apCorr\", doc=\"aperture correction applied to ext_convolved_ConvolvedFlux_2_6_0\"), Key(offset=1784, nElements=1)),\n", - " (Field['D'](name=\"ext_convolved_ConvolvedFlux_2_6_0_apCorrSigma\", doc=\"aperture correction applied to ext_convolved_ConvolvedFlux_2_6_0\"), Key(offset=1792, nElements=1)),\n", - " (Field['Flag'](name=\"ext_convolved_ConvolvedFlux_2_6_0_flag_apCorr\", doc=\"set if unable to aperture correct ext_convolved_ConvolvedFlux_2_6_0\"), Key['Flag'](offset=1696, bit=10)),\n", - " (Field['D'](name=\"modelfit_CModel_initial_apCorr\", doc=\"aperture correction applied to modelfit_CModel_initial\"), Key(offset=1800, nElements=1)),\n", - " (Field['D'](name=\"modelfit_CModel_initial_apCorrSigma\", doc=\"aperture correction applied to modelfit_CModel_initial\"), Key(offset=1808, nElements=1)),\n", - " (Field['Flag'](name=\"modelfit_CModel_initial_flag_apCorr\", doc=\"set if unable to aperture correct modelfit_CModel_initial\"), Key['Flag'](offset=1696, bit=11)),\n", - " (Field['D'](name=\"ext_convolved_ConvolvedFlux_1_3_3_apCorr\", doc=\"aperture correction applied to ext_convolved_ConvolvedFlux_1_3_3\"), Key(offset=1816, nElements=1)),\n", - " (Field['D'](name=\"ext_convolved_ConvolvedFlux_1_3_3_apCorrSigma\", doc=\"aperture correction applied to ext_convolved_ConvolvedFlux_1_3_3\"), Key(offset=1824, nElements=1)),\n", - " (Field['Flag'](name=\"ext_convolved_ConvolvedFlux_1_3_3_flag_apCorr\", doc=\"set if unable to aperture correct ext_convolved_ConvolvedFlux_1_3_3\"), Key['Flag'](offset=1696, bit=12)),\n", - " (Field['D'](name=\"ext_convolved_ConvolvedFlux_0_6_0_apCorr\", doc=\"aperture correction applied to ext_convolved_ConvolvedFlux_0_6_0\"), Key(offset=1832, nElements=1)),\n", - " (Field['D'](name=\"ext_convolved_ConvolvedFlux_0_6_0_apCorrSigma\", doc=\"aperture correction applied to ext_convolved_ConvolvedFlux_0_6_0\"), Key(offset=1840, nElements=1)),\n", - " (Field['Flag'](name=\"ext_convolved_ConvolvedFlux_0_6_0_flag_apCorr\", doc=\"set if unable to aperture correct ext_convolved_ConvolvedFlux_0_6_0\"), Key['Flag'](offset=1696, bit=13)),\n", - " (Field['D'](name=\"ext_convolved_ConvolvedFlux_0_kron_apCorr\", doc=\"aperture correction applied to ext_convolved_ConvolvedFlux_0_kron\"), Key(offset=1848, nElements=1)),\n", - " (Field['D'](name=\"ext_convolved_ConvolvedFlux_0_kron_apCorrSigma\", doc=\"aperture correction applied to ext_convolved_ConvolvedFlux_0_kron\"), Key(offset=1856, nElements=1)),\n", - " (Field['Flag'](name=\"ext_convolved_ConvolvedFlux_0_kron_flag_apCorr\", doc=\"set if unable to aperture correct ext_convolved_ConvolvedFlux_0_kron\"), Key['Flag'](offset=1696, bit=14)),\n", - " (Field['D'](name=\"ext_convolved_ConvolvedFlux_0_4_5_apCorr\", doc=\"aperture correction applied to ext_convolved_ConvolvedFlux_0_4_5\"), Key(offset=1864, nElements=1)),\n", - " (Field['D'](name=\"ext_convolved_ConvolvedFlux_0_4_5_apCorrSigma\", doc=\"aperture correction applied to ext_convolved_ConvolvedFlux_0_4_5\"), Key(offset=1872, nElements=1)),\n", - " (Field['Flag'](name=\"ext_convolved_ConvolvedFlux_0_4_5_flag_apCorr\", doc=\"set if unable to aperture correct ext_convolved_ConvolvedFlux_0_4_5\"), Key['Flag'](offset=1696, bit=15)),\n", - " (Field['D'](name=\"ext_convolved_ConvolvedFlux_1_kron_apCorr\", doc=\"aperture correction applied to ext_convolved_ConvolvedFlux_1_kron\"), Key(offset=1880, nElements=1)),\n", - " (Field['D'](name=\"ext_convolved_ConvolvedFlux_1_kron_apCorrSigma\", doc=\"aperture correction applied to ext_convolved_ConvolvedFlux_1_kron\"), Key(offset=1888, nElements=1)),\n", - " (Field['Flag'](name=\"ext_convolved_ConvolvedFlux_1_kron_flag_apCorr\", doc=\"set if unable to aperture correct ext_convolved_ConvolvedFlux_1_kron\"), Key['Flag'](offset=1696, bit=16)),\n", - " (Field['D'](name=\"modelfit_CModel_exp_apCorr\", doc=\"aperture correction applied to modelfit_CModel_exp\"), Key(offset=1896, nElements=1)),\n", - " (Field['D'](name=\"modelfit_CModel_exp_apCorrSigma\", doc=\"aperture correction applied to modelfit_CModel_exp\"), Key(offset=1904, nElements=1)),\n", - " (Field['Flag'](name=\"modelfit_CModel_exp_flag_apCorr\", doc=\"set if unable to aperture correct modelfit_CModel_exp\"), Key['Flag'](offset=1696, bit=17)),\n", - " (Field['D'](name=\"ext_convolved_ConvolvedFlux_3_6_0_apCorr\", doc=\"aperture correction applied to ext_convolved_ConvolvedFlux_3_6_0\"), Key(offset=1912, nElements=1)),\n", - " (Field['D'](name=\"ext_convolved_ConvolvedFlux_3_6_0_apCorrSigma\", doc=\"aperture correction applied to ext_convolved_ConvolvedFlux_3_6_0\"), Key(offset=1920, nElements=1)),\n", - " (Field['Flag'](name=\"ext_convolved_ConvolvedFlux_3_6_0_flag_apCorr\", doc=\"set if unable to aperture correct ext_convolved_ConvolvedFlux_3_6_0\"), Key['Flag'](offset=1696, bit=18)),\n", - " (Field['D'](name=\"ext_convolved_ConvolvedFlux_2_kron_apCorr\", doc=\"aperture correction applied to ext_convolved_ConvolvedFlux_2_kron\"), Key(offset=1928, nElements=1)),\n", - " (Field['D'](name=\"ext_convolved_ConvolvedFlux_2_kron_apCorrSigma\", doc=\"aperture correction applied to ext_convolved_ConvolvedFlux_2_kron\"), Key(offset=1936, nElements=1)),\n", - " (Field['Flag'](name=\"ext_convolved_ConvolvedFlux_2_kron_flag_apCorr\", doc=\"set if unable to aperture correct ext_convolved_ConvolvedFlux_2_kron\"), Key['Flag'](offset=1696, bit=19)),\n", - " (Field['D'](name=\"ext_convolved_ConvolvedFlux_1_6_0_apCorr\", doc=\"aperture correction applied to ext_convolved_ConvolvedFlux_1_6_0\"), Key(offset=1944, nElements=1)),\n", - " (Field['D'](name=\"ext_convolved_ConvolvedFlux_1_6_0_apCorrSigma\", doc=\"aperture correction applied to ext_convolved_ConvolvedFlux_1_6_0\"), Key(offset=1952, nElements=1)),\n", - " (Field['Flag'](name=\"ext_convolved_ConvolvedFlux_1_6_0_flag_apCorr\", doc=\"set if unable to aperture correct ext_convolved_ConvolvedFlux_1_6_0\"), Key['Flag'](offset=1696, bit=20)),\n", - " (Field['D'](name=\"ext_convolved_ConvolvedFlux_3_4_5_apCorr\", doc=\"aperture correction applied to ext_convolved_ConvolvedFlux_3_4_5\"), Key(offset=1960, nElements=1)),\n", - " (Field['D'](name=\"ext_convolved_ConvolvedFlux_3_4_5_apCorrSigma\", doc=\"aperture correction applied to ext_convolved_ConvolvedFlux_3_4_5\"), Key(offset=1968, nElements=1)),\n", - " (Field['Flag'](name=\"ext_convolved_ConvolvedFlux_3_4_5_flag_apCorr\", doc=\"set if unable to aperture correct ext_convolved_ConvolvedFlux_3_4_5\"), Key['Flag'](offset=1696, bit=21)),\n", - " (Field['D'](name=\"ext_convolved_ConvolvedFlux_2_3_3_apCorr\", doc=\"aperture correction applied to ext_convolved_ConvolvedFlux_2_3_3\"), Key(offset=1976, nElements=1)),\n", - " (Field['D'](name=\"ext_convolved_ConvolvedFlux_2_3_3_apCorrSigma\", doc=\"aperture correction applied to ext_convolved_ConvolvedFlux_2_3_3\"), Key(offset=1984, nElements=1)),\n", - " (Field['Flag'](name=\"ext_convolved_ConvolvedFlux_2_3_3_flag_apCorr\", doc=\"set if unable to aperture correct ext_convolved_ConvolvedFlux_2_3_3\"), Key['Flag'](offset=1696, bit=22)),\n", - " (Field['D'](name=\"modelfit_CModel_apCorr\", doc=\"aperture correction applied to modelfit_CModel\"), Key(offset=1992, nElements=1)),\n", - " (Field['D'](name=\"modelfit_CModel_apCorrSigma\", doc=\"aperture correction applied to modelfit_CModel\"), Key(offset=2000, nElements=1)),\n", - " (Field['Flag'](name=\"modelfit_CModel_flag_apCorr\", doc=\"set if unable to aperture correct modelfit_CModel\"), Key['Flag'](offset=1696, bit=23)),\n", - " (Field['D'](name=\"ext_convolved_ConvolvedFlux_2_4_5_apCorr\", doc=\"aperture correction applied to ext_convolved_ConvolvedFlux_2_4_5\"), Key(offset=2008, nElements=1)),\n", - " (Field['D'](name=\"ext_convolved_ConvolvedFlux_2_4_5_apCorrSigma\", doc=\"aperture correction applied to ext_convolved_ConvolvedFlux_2_4_5\"), Key(offset=2016, nElements=1)),\n", - " (Field['Flag'](name=\"ext_convolved_ConvolvedFlux_2_4_5_flag_apCorr\", doc=\"set if unable to aperture correct ext_convolved_ConvolvedFlux_2_4_5\"), Key['Flag'](offset=1696, bit=24)),\n", - " (Field['D'](name=\"ext_convolved_ConvolvedFlux_1_4_5_apCorr\", doc=\"aperture correction applied to ext_convolved_ConvolvedFlux_1_4_5\"), Key(offset=2024, nElements=1)),\n", - " (Field['D'](name=\"ext_convolved_ConvolvedFlux_1_4_5_apCorrSigma\", doc=\"aperture correction applied to ext_convolved_ConvolvedFlux_1_4_5\"), Key(offset=2032, nElements=1)),\n", - " (Field['Flag'](name=\"ext_convolved_ConvolvedFlux_1_4_5_flag_apCorr\", doc=\"set if unable to aperture correct ext_convolved_ConvolvedFlux_1_4_5\"), Key['Flag'](offset=1696, bit=25)),\n", - " (Field['D'](name=\"modelfit_CModel_dev_apCorr\", doc=\"aperture correction applied to modelfit_CModel_dev\"), Key(offset=2040, nElements=1)),\n", - " (Field['D'](name=\"modelfit_CModel_dev_apCorrSigma\", doc=\"aperture correction applied to modelfit_CModel_dev\"), Key(offset=2048, nElements=1)),\n", - " (Field['Flag'](name=\"modelfit_CModel_dev_flag_apCorr\", doc=\"set if unable to aperture correct modelfit_CModel_dev\"), Key['Flag'](offset=1696, bit=26)),\n", - " (Field['D'](name=\"base_ClassificationExtendedness_value\", doc=\"Set to 1 for extended sources, 0 for point sources.\"), Key(offset=2056, nElements=1)),\n", - " (Field['Flag'](name=\"base_ClassificationExtendedness_flag\", doc=\"Set to 1 for any fatal failure.\"), Key['Flag'](offset=1696, bit=27)),\n", - " (Field['I'](name=\"base_FootprintArea_value\", doc=\"Number of pixels in the source''s detection footprint.\", units=\"pixel\"), Key(offset=2064, nElements=1)),\n", - " 'base_CircularApertureFlux_flag_badCentroid'->'base_SdssCentroid_flag'\n", - " 'base_GaussianFlux_flag_badCentroid'->'base_SdssCentroid_flag'\n", - " 'base_GaussianFlux_flag_badShape'->'ext_shapeHSM_HsmSourceMoments_flag'\n", - " 'base_InputCount_flag_badCentroid'->'base_SdssCentroid_flag'\n", - " 'base_LocalBackground_flag_badCentroid'->'base_SdssCentroid_flag'\n", - " 'base_NaiveCentroid_flag_badInitialCentroid'->'base_SdssCentroid_flag'\n", - " 'base_PsfFlux_flag_badCentroid'->'base_SdssCentroid_flag'\n", - " 'base_SdssShape_flag_badCentroid'->'base_SdssCentroid_flag'\n", - " 'base_Variance_flag_badCentroid'->'base_SdssCentroid_flag'\n", - " 'ext_convolved_ConvolvedFlux_0_flag_badCentroid'->'base_SdssCentroid_flag'\n", - " 'ext_convolved_ConvolvedFlux_1_flag_badCentroid'->'base_SdssCentroid_flag'\n", - " 'ext_convolved_ConvolvedFlux_2_flag_badCentroid'->'base_SdssCentroid_flag'\n", - " 'ext_convolved_ConvolvedFlux_3_flag_badCentroid'->'base_SdssCentroid_flag'\n", - " 'ext_convolved_ConvolvedFlux_flag_badCentroid'->'base_SdssCentroid_flag'\n", - " 'ext_shapeHSM_HsmPsfMoments_flag_badCentroid'->'base_SdssCentroid_flag'\n", - " 'ext_shapeHSM_HsmShapeBj_flag_badCentroid'->'base_SdssCentroid_flag'\n", - " 'ext_shapeHSM_HsmShapeKsb_flag_badCentroid'->'base_SdssCentroid_flag'\n", - " 'ext_shapeHSM_HsmShapeLinear_flag_badCentroid'->'base_SdssCentroid_flag'\n", - " 'ext_shapeHSM_HsmShapeRegauss_flag_badCentroid'->'base_SdssCentroid_flag'\n", - " 'ext_shapeHSM_HsmSourceMoments_flag_badCentroid'->'base_SdssCentroid_flag'\n", - " 'modelfit_DoubleShapeletPsfApprox_flag_badCentroid'->'base_SdssCentroid_flag'\n", - " 'slot_ApFlux'->'base_CircularApertureFlux_12_0'\n", - " 'slot_CalibFlux'->'base_CircularApertureFlux_12_0'\n", - " 'slot_Centroid'->'base_SdssCentroid'\n", - " 'slot_InstFlux'->'base_GaussianFlux'\n", - " 'slot_ModelFlux'->'modelfit_CModel'\n", - " 'slot_PsfFlux'->'base_PsfFlux'\n", - " 'slot_PsfShape'->'base_SdssShape_psf'\n", - " 'slot_Shape'->'ext_shapeHSM_HsmSourceMoments'\n", - ")" - ] - }, - "execution_count": 71, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "test.getSchema()" - ] - }, - { - "cell_type": "code", - "execution_count": 18, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -1331,7 +765,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -1344,7 +778,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -1357,9 +791,9 @@ }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1372,7 +806,7 @@ }, { "cell_type": "code", - "execution_count": 115, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -1403,7 +837,7 @@ }, { "cell_type": "code", - "execution_count": 100, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -1424,7 +858,7 @@ }, { "cell_type": "code", - "execution_count": 128, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -1446,24 +880,24 @@ }, { "cell_type": "code", - "execution_count": 141, + "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 141, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1478,14 +912,14 @@ }, { "cell_type": "code", - "execution_count": 132, + "execution_count": 30, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1498,24 +932,16 @@ }, { "cell_type": "code", - "execution_count": 142, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: AstropyDeprecationWarning: \"clobber\" was deprecated in version 2.0 and will be removed in a future version. Use argument \"overwrite\" instead. [astropy.utils.decorators]\n" - ] - } - ], + "outputs": [], "source": [ "#m5map=hp.write_map('/global/projecta/projectdirs/lsst/groups/LSS/DC2_R1.1p/depth_coadd_r.fits.gz',m5map)" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -1524,7 +950,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -1543,7 +969,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1553,7 +979,7 @@ }, { "cell_type": "code", - "execution_count": 154, + "execution_count": 33, "metadata": { "scrolled": false }, @@ -1561,23 +987,23 @@ { "data": { "text/plain": [ - "(array([ 28230., 221749., 289348., 445706., 576747., 766347.,\n", - " 899674., 933696., 1081204., 872464.]),\n", - " array([ 0.00430875, 0.10528425, 0.20625974, 0.30723524, 0.40821074,\n", - " 0.50918623, 0.61016173, 0.71113723, 0.81211272, 0.91308822,\n", - " 1.01406371]),\n", + "(array([ 301., 2742., 3208., 4033., 7110., 4370., 6314.,\n", + " 8054., 10432., 7532.]),\n", + " array([ 0.01647048, 0.11584118, 0.21521189, 0.31458259, 0.4139533 ,\n", + " 0.513324 , 0.61269471, 0.71206541, 0.81143612, 0.91080682,\n", + " 1.01017753]),\n", " )" ] }, - "execution_count": 154, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1590,7 +1016,7 @@ }, { "cell_type": "code", - "execution_count": 158, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -1599,7 +1025,7 @@ }, { "cell_type": "code", - "execution_count": 160, + "execution_count": null, "metadata": {}, "outputs": [], "source": [