From f2bc0c31cf41620af86fde499f68befa758cbb80 Mon Sep 17 00:00:00 2001 From: Justin Myles Date: Sun, 5 Aug 2018 11:02:50 -0700 Subject: [PATCH 01/15] add project notebook --- ImageProcessing/ForcedPhotLightCurve.ipynb | 250 +++++++++++++++++++++ 1 file changed, 250 insertions(+) create mode 100644 ImageProcessing/ForcedPhotLightCurve.ipynb diff --git a/ImageProcessing/ForcedPhotLightCurve.ipynb b/ImageProcessing/ForcedPhotLightCurve.ipynb new file mode 100644 index 00000000..bd2ba7f1 --- /dev/null +++ b/ImageProcessing/ForcedPhotLightCurve.ipynb @@ -0,0 +1,250 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Forced Photometry Light Curve\n", + "\n", + "
Owner: **Justin Myles** ([@jtmyles](https://github.com/LSSTScienceCollaborations/StackClub/issues/new?body=@jtmyles))\n", + "
Last Verified to Run: **N/A -- in development**\n", + "\n", + "This project addresses issue [#63: HSC Re-run](https://github.com/LSSTScienceCollaborations/StackClub/issues/63)\n", + "\n", + "This notebook demonstrates the [LSST Science Piplines data processing tutorial](https://pipelines.lsst.io/) with emphasis on how a given [obs package](https://github.com/lsst/obs_base) works under the hood of the command line tasks. It makes use of the [obs_subaru](https://github.com/lsst/obs_subaru) package to measure a forced photometry light curve for a small patch in the HSC sky in the [ci_hsc](https://github.com/lsst/ci_hsc/) repository. \n", + "\n", + "### Learning Objectives:\n", + "After working through and studying this notebook you should be able to understand how to use the DRP pipeline from image visualization through to a forced photometry light curve. Specific learning objectives include: \n", + " 1. [configure command-line tasks](https://pipelines.lsst.io/v/w-2018-12/modules/lsst.pipe.base/command-line-task-config-howto.html) for your science case\n", + " 2. TODO\n", + " \n", + "Other techniques that are demonstrated, but not empasized, in this notebook are\n", + " 1. Use the `butler` to fetch data\n", + " 2. Visualize data with the LSST Stack\n", + " 3. TODO\n", + "\n", + "### Logistics\n", + "This notebook is intended to be runnable on `lsst-lspdev.ncsa.illinois.edu` from a local git clone of https://github.com/LSSTScienceCollaborations/StackClub.\n", + "\n", + "\n", + "## Set Up" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO imports" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Part I: Interacting with data. Introduction to the Butler\n", + "https://pipelines.lsst.io/getting-started/data-setup.html" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!eups list lsst_distrib" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!mkdir -p DATA" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pwd" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!setup -j -r /global/u2/j/jmyles/repos/ci_hsc" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!echo \"$CI_HSC_DIR\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# ingest raw data into Butler repo\n", + "!ingestImages.py DATA /global/u2/j/jmyles/repos/ci_hsc/raw/*.fits --mode=link" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!installTransmissionCurves.py DATA" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# injest calibration images into Butler repo\n", + "!ln -s /global/u2/j/jmyles/repos/ci_hsc/CALIB/ DATA/CALIB" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!mkdir -p DATA/ref_cats" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# injest reference catalog into Butler repo\n", + "!ln -s /global/u2/j/jmyles/repos/ci_hsc/ps1_pv3_3pi_20170110 DATA/ref_cats/ps1_pv3_3pi_20170110" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Part 2: Calibrating single frames\n", + "https://pipelines.lsst.io/getting-started/processccd.html" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# review what data will be processed\n", + "!processCcd.py DATA --rerun processCcdOutputs --id --show data\n", + "# id allows you to select data by data ID\n", + "# unspecified id selects all raw data\n", + "# example IDs: raw, filter, visit, ccd, field\n", + "# show data turns on dry-run mode" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!which processCcd.py" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!processCcd.py DATA --rerun processCcdOutputs --id\n", + "# all cl tasks write output datasets to a Butler repo\n", + "# --rerun configured to write to processCcdOutputs\n", + "# other option is --output" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Part 3: Displaying exposures and source tables output by processCcd.py\n", + "https://pipelines.lsst.io/getting-started/display.html\n", + "\n", + "This part of the tutorial is omitted for now." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Part 4: Coadding images\n", + "https://pipelines.lsst.io/getting-started/coaddition.html" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* A sky map is a tiling of the celestial sphere. It is composed of one or more tracts.\n", + "* A tract is composed of one or more overlapping patches. Each tract has a WCS." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# make a discrete sky map that covers the exposures that have already been processed\n", + "!makeDiscreteSkyMap.py DATA --id --rerun processCcdOutputs:coadd --config skyMap.projection=\"TAN\"\n", + "\n", + "# the configuration field specifies the WCS Projection\n", + "# one of the FITS WCS projection codes, such as:\n", + "# - STG: stereographic projection\n", + "# - MOL: Molleweide's projection\n", + "# - TAN: tangent-plane projection" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "desc-stack", + "language": "python", + "name": "desc-stack" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From abc02508d8f1973b961831504c62421d027a447f Mon Sep 17 00:00:00 2001 From: Justin Myles Date: Thu, 9 Aug 2018 21:19:33 +0000 Subject: [PATCH 02/15] add ingest script code --- ImageProcessing/ForcedPhotLightCurve.ipynb | 121 ++++++++++++--------- 1 file changed, 71 insertions(+), 50 deletions(-) diff --git a/ImageProcessing/ForcedPhotLightCurve.ipynb b/ImageProcessing/ForcedPhotLightCurve.ipynb index bd2ba7f1..3553cd83 100644 --- a/ImageProcessing/ForcedPhotLightCurve.ipynb +++ b/ImageProcessing/ForcedPhotLightCurve.ipynb @@ -32,11 +32,13 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "# TODO imports" + "import os\n", + "import sys\n", + "import eups.setupcmd" ] }, { @@ -44,34 +46,19 @@ "metadata": {}, "source": [ "## Part I: Interacting with data. Introduction to the Butler\n", - "https://pipelines.lsst.io/getting-started/data-setup.html" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!eups list lsst_distrib" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!mkdir -p DATA" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!pwd" + "https://pipelines.lsst.io/getting-started/data-setup.html\n", + "\n", + "Part I runs the following command-line tasks\n", + "```\n", + "eups list lsst_distrib\n", + "setup -j -r /home/jmyles/repositories/ci_hsc\n", + "echo \"lsst.obs.hsc.HscMapper\" > /home/jmyles/DATA/_mapper\n", + "ingestImages.py /home/jmyles/DATA /home/jmyles/repositories/ci_hsc/raw/*.fits --mode=link\n", + "ln -s /home/jmyles/repositories/ci_hsc/CALIB/ /home/jmyles/DATA/CALIB\n", + "installTransmissionCurves.py /home/jmyles/DATA\n", + "mkdir -p /home/jmyles/DATA/ref_cats\n", + "ln -s /home/jmyles/repositories/ci_hsc/ps1_pv3_3pi_20170110 /home/jmyles/DATA/ref_cats/ps1_pv3_3pi_20170110\n", + "```" ] }, { @@ -80,7 +67,10 @@ "metadata": {}, "outputs": [], "source": [ - "!setup -j -r /global/u2/j/jmyles/repos/ci_hsc" + "!eups list lsst_distrib\n", + "datarepo = \"/home/jmyles/repositories/ci_hsc/\"\n", + "datadir = \"/home/jmyles/DATA/\"\n", + "os.system(\"mkdir -p {}\".format(datadir))" ] }, { @@ -89,17 +79,18 @@ "metadata": {}, "outputs": [], "source": [ - "!echo \"$CI_HSC_DIR\"" + "#!setup -j -r /home/jmyles/repositories/ci_hsc\n", + "\n", + "setup = eups.setupcmd.EupsSetup([\"-j\",\"-r\", datarepo])\n", + "status = setup.run()\n", + "print('setup exited with status {}'.format(status))" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "# ingest raw data into Butler repo\n", - "!ingestImages.py DATA /global/u2/j/jmyles/repos/ci_hsc/raw/*.fits --mode=link" + "A Butler needs a *mapper* file \"to find and organize data in a format specific to each camera.\" We write this file to the data repository so that any instantiated Butler object knows which mapper to use." ] }, { @@ -108,7 +99,8 @@ "metadata": {}, "outputs": [], "source": [ - "!installTransmissionCurves.py DATA" + "with open(datadir + \"_mapper\", \"w\") as f:\n", + " f.write(\"lsst.obs.hsc.HscMapper\")" ] }, { @@ -117,8 +109,8 @@ "metadata": {}, "outputs": [], "source": [ - "# injest calibration images into Butler repo\n", - "!ln -s /global/u2/j/jmyles/repos/ci_hsc/CALIB/ DATA/CALIB" + "# ingest script\n", + "!ingestImages.py /home/jmyles/DATA /home/jmyles/repositories/ci_hsc/raw/*.fits --mode=link" ] }, { @@ -127,7 +119,27 @@ "metadata": {}, "outputs": [], "source": [ - "!mkdir -p DATA/ref_cats" + "#!installTransmissionCurves.py /home/jmyles/DATA\n", + "\n", + "from lsst.obs.hsc import makeTransmissionCurves, HscMapper\n", + "from lsst.daf.persistence import Butler\n", + "\n", + "butler = Butler(outputs={'root': datadir, 'mode': 'rw', 'mapper': HscMapper})\n", + "\n", + "for start, nested in makeTransmissionCurves.getFilterTransmission().items():\n", + " for name, curve in nested.items():\n", + " if curve is not None:\n", + " butler.put(curve, \"transmission_filter\", filter=name)\n", + "for start, nested in makeTransmissionCurves.getSensorTransmission().items():\n", + " for ccd, curve in nested.items():\n", + " if curve is not None:\n", + " butler.put(curve, \"transmission_sensor\", ccd=ccd)\n", + "for start, curve in makeTransmissionCurves.getOpticsTransmission().items():\n", + " if curve is not None:\n", + " butler.put(curve, \"transmission_optics\")\n", + "for start, curve in makeTransmissionCurves.getAtmosphereTransmission().items():\n", + " if curve is not None:\n", + " butler.put(curve, \"transmission_atmosphere\")" ] }, { @@ -136,8 +148,12 @@ "metadata": {}, "outputs": [], "source": [ - "# injest reference catalog into Butler repo\n", - "!ln -s /global/u2/j/jmyles/repos/ci_hsc/ps1_pv3_3pi_20170110 DATA/ref_cats/ps1_pv3_3pi_20170110" + "# ingest calibration images into Butler repo\n", + "os.system(\"ln -s {} {}\".format(datarepo + \"CALIB/\", datadir + \"CALIB\"))\n", + "\n", + "# ingest reference catalog into Butler repo\n", + "os.system(\"mkdir -p {}\".format(datadir + \"ref_cats\"))\n", + "os.system(\"ln -s {}ps1_pv3_3pi_20170110 {}ref_cats/ps1_pv3_3pi_20170110\".format(datarepo, datadir))" ] }, { @@ -154,12 +170,14 @@ "metadata": {}, "outputs": [], "source": [ + "\"\"\"\n", "# review what data will be processed\n", "!processCcd.py DATA --rerun processCcdOutputs --id --show data\n", "# id allows you to select data by data ID\n", "# unspecified id selects all raw data\n", "# example IDs: raw, filter, visit, ccd, field\n", - "# show data turns on dry-run mode" + "# show data turns on dry-run mode\n", + "\"\"\"" ] }, { @@ -168,7 +186,7 @@ "metadata": {}, "outputs": [], "source": [ - "!which processCcd.py" + "#!which processCcd.py" ] }, { @@ -177,10 +195,12 @@ "metadata": {}, "outputs": [], "source": [ + "\"\"\"\n", "!processCcd.py DATA --rerun processCcdOutputs --id\n", "# all cl tasks write output datasets to a Butler repo\n", "# --rerun configured to write to processCcdOutputs\n", - "# other option is --output" + "# other option is --output\n", + "\"\"\"" ] }, { @@ -215,22 +235,23 @@ "metadata": {}, "outputs": [], "source": [ - "# make a discrete sky map that covers the exposures that have already been processed\n", + "\"\"\"# make a discrete sky map that covers the exposures that have already been processed\n", "!makeDiscreteSkyMap.py DATA --id --rerun processCcdOutputs:coadd --config skyMap.projection=\"TAN\"\n", "\n", "# the configuration field specifies the WCS Projection\n", "# one of the FITS WCS projection codes, such as:\n", "# - STG: stereographic projection\n", "# - MOL: Molleweide's projection\n", - "# - TAN: tangent-plane projection" + "# - TAN: tangent-plane projection\n", + "\"\"\"" ] } ], "metadata": { "kernelspec": { - "display_name": "desc-stack", + "display_name": "LSST", "language": "python", - "name": "desc-stack" + "name": "lsst" }, "language_info": { "codemirror_mode": { From fe3e08ac33ba82c1d267799c23280495595c2d56 Mon Sep 17 00:00:00 2001 From: Justin Myles Date: Thu, 9 Aug 2018 23:26:57 +0000 Subject: [PATCH 03/15] rename nb --- ImageProcessing/{ForcedPhotLightCurve.ipynb => Re-RunHSC.ipynb} | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) rename ImageProcessing/{ForcedPhotLightCurve.ipynb => Re-RunHSC.ipynb} (99%) diff --git a/ImageProcessing/ForcedPhotLightCurve.ipynb b/ImageProcessing/Re-RunHSC.ipynb similarity index 99% rename from ImageProcessing/ForcedPhotLightCurve.ipynb rename to ImageProcessing/Re-RunHSC.ipynb index 3553cd83..cd8cbacb 100644 --- a/ImageProcessing/ForcedPhotLightCurve.ipynb +++ b/ImageProcessing/Re-RunHSC.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Forced Photometry Light Curve\n", + "# HSC Re-Run: Making Forced Photometry Light Curves from Scratch\n", "\n", "
Owner: **Justin Myles** ([@jtmyles](https://github.com/LSSTScienceCollaborations/StackClub/issues/new?body=@jtmyles))\n", "
Last Verified to Run: **N/A -- in development**\n", From 4545e3d79b7144287c34885ec75cc2999d43586d Mon Sep 17 00:00:00 2001 From: Justin Myles Date: Thu, 6 Sep 2018 03:02:26 +0000 Subject: [PATCH 04/15] trying to fix again --- ImageProcessing/Re-RunHSC.sh | 131 +++++++++++++++++++++++++++++++++++ 1 file changed, 131 insertions(+) create mode 100644 ImageProcessing/Re-RunHSC.sh diff --git a/ImageProcessing/Re-RunHSC.sh b/ImageProcessing/Re-RunHSC.sh new file mode 100644 index 00000000..e5155312 --- /dev/null +++ b/ImageProcessing/Re-RunHSC.sh @@ -0,0 +1,131 @@ +: 'HSC Re-Run: Making Forced Photometry Light Curves from Scratch +Owner: **Justin Myles** (@jtmyles) +Last Verified to Run: **2018-09-05** +Verified Stack Release: **16.0** + +This project addresses issue #63: HSC Re-run + +This shell script runs the command-line tasks from the tutorial at pipelines.lsst.io for analysis +from raw images through source detection and forced photometry measurements. It is intended as an +intermediate step toward the end-goal of making a forced photometry lightcurve in the notebook at +StackClub/ImageProcessing/Re-RunHSC.ipynb + +Recommended to run with +$ bash Re-RunHSC.sh > output.txt +' + + +# Setup the LSST Stack +source /opt/lsst/software/stack/loadLSST.bash +eups list lsst_distrib +setup lsst_distrib + +# I. Setting up the Butler data repository + +date +echo "setup Butler" +setup -j -r /project/shared/data/ci_hsc +DATADIR="/home/$USER/DATA" +mkdir -p "$DATADIR" + +# A Butler needs a *mapper* file "to find and organize data in a format specific to each camera." +# We write this file to the data repository so that any instantiated Butler object knows which mapper to use. +echo lsst.obs.hsc.HscMapper > $DATADIR/_mapper + +# The injest script creates links in the instantiated butler repository to the original data files +ingestImages.py $DATADIR $CI_HSC_DIR/raw/*.fits --mode=link + +# Grab calibration files +installTransmissionCurves.py $DATADIR +ln -s $CI_HSC_DIR/CALIB/ $DATADIR/CALIB +mkdir -p $DATADIR/ref_cats +ln -s $CI_HSC_DIR/ps1_pv3_3pi_20170110 $DATADIR/ref_cats/ps1_pv3_3pi_20170110 + +date +echo "processCcd" +# II. Calibrate a single frame with processCcd.py +# Use calibration files to do CCD processing +processCcd.py $DATADIR --rerun processCcdOutputs --id + +# III. (omitted) Visualize images. + +# IV. Make coadds +date +echo "make coadds" +# IV. A. Make skymap +# A sky map is a tiling of the celestial sphere. It is composed of one or more tracts. +# A tract is composed of one or more overlapping patches. Each tract has a WCS. +# We define a skymap so that we can warp all of the exposure to fit on a single coordinate system +# This is a necessary step for making coadds + +date +echo "make skymap" +makeDiscreteSkyMap.py $DATADIR --id --rerun processCcdOutputs:coadd --config skyMap.projection="TAN" +# IV. B. Warp images onto skymap +date +echo "warp images" +makeCoaddTempExp.py $DATADIR --rerun coadd \ + --selectId filter=HSC-R \ + --id filter=HSC-R tract=0 patch=0,0^0,1^0,2^1,0^1,1^1,2^2,0^2,1^2,2 \ + --config doApplyUberCal=False doApplySkyCorr=False + + +makeCoaddTempExp.py $DATADIR --rerun coadd \ + --selectId filter=HSC-I \ + --id filter=HSC-I tract=0 patch=0,0^0,1^0,2^1,0^1,1^1,2^2,0^2,1^2,2 \ + --config doApplyUberCal=False doApplySkyCorr=False + + +# IV. C. Coadd warped images +# Now that we have warped images, we can perform coaddition to get deeper images +# The motivation for this is to have the deepest image possible for source detection +date +echo "coadd warped images" +assembleCoadd.py $DATADIR --rerun coadd \ + --selectId filter=HSC-R \ + --id filter=HSC-R tract=0 patch=0,0^0,1^0,2^1,0^1,1^1,2^2,0^2,1^2,2 + +assembleCoadd.py $DATADIR --rerun coadd \ + --selectId filter=HSC-I \ + --id filter=HSC-I tract=0 patch=0,0^0,1^0,2^1,0^1,1^1,2^2,0^2,1^2,2 + +# V. Measuring sources +date +echo "measure sources" +# V. A. Source detection +# As noted above, we do source detection on the deepest image possible. +echo "detect sources" +detectCoaddSources.py $DATADIR --rerun coadd:coaddPhot \ + --id filter=HSC-R tract=0 patch=0,0^0,1^0,2^1,0^1,1^1,2^2,0^2,1^2,2 + +detectCoaddSources.py $DATADIR --rerun coaddPhot \ + --id filter=HSC-I tract=0 patch=0,0^0,1^0,2^1,0^1,1^1,2^2,0^2,1^2,2 + +# V. B. Merge multi-band detection catalogs +# Ultimately, for photometry, we will need to deblend objects. +# In order to do this, we first merge the detected source catalogs. +date +echo "merge detection cats" +mergeCoaddDetections.py $DATADIR --rerun coaddPhot --id filter=HSC-R^HSC-I + +# V. C. Measure source catalogs on coadds +# Given a full source catalog, we can do regular photometry with implicit deblending. +date +echo "measure source cats on coadds" +measureCoaddSources.py $DATADIR --rerun coaddPhot --id filter=HSC-R +measureCoaddSources.py $DATADIR --rerun coaddPhot --id filter=HSC-I + +# V. D. Merge multi-band source catalogs from coadds +date +echo "merge source cats from coadds" +mergeCoaddMeasurements.py $DATADIR --rerun coaddPhot --id filter=HSC-R^HSC-I + +# V. E. Run forced photometry on coadds +# Given a full source catalog, we can do forced photometry with implicit deblending. +date +echo "run forcedphot on coadds" +forcedPhotCoadd.py $DATADIR --rerun coaddPhot:coaddForcedPhot --id filter=HSC-R +forcedPhotCoadd.py $DATADIR --rerun coaddForcedPhot --id filter=HSC-I + +# VI. Multi-band catalog analysis +# For analysis of the catalog, see part VI of StackClub/ImageProcessing/Re-RunHSC.ipynb From d8018940d8ad729e0b46ae4d670ea65f8be8a964 Mon Sep 17 00:00:00 2001 From: Justin Myles Date: Thu, 6 Sep 2018 03:03:00 +0000 Subject: [PATCH 05/15] add ipynb --- ImageProcessing/Re-RunHSC.ipynb | 207 +++++++++++++++++++++++++++++--- 1 file changed, 189 insertions(+), 18 deletions(-) diff --git a/ImageProcessing/Re-RunHSC.ipynb b/ImageProcessing/Re-RunHSC.ipynb index cd8cbacb..3d53533d 100644 --- a/ImageProcessing/Re-RunHSC.ipynb +++ b/ImageProcessing/Re-RunHSC.ipynb @@ -7,7 +7,8 @@ "# HSC Re-Run: Making Forced Photometry Light Curves from Scratch\n", "\n", "
Owner: **Justin Myles** ([@jtmyles](https://github.com/LSSTScienceCollaborations/StackClub/issues/new?body=@jtmyles))\n", - "
Last Verified to Run: **N/A -- in development**\n", + "
Last Verified to Run: **2018-08-10**\n", + "
Verified Stack Release: **16.0**\n", "\n", "This project addresses issue [#63: HSC Re-run](https://github.com/LSSTScienceCollaborations/StackClub/issues/63)\n", "\n", @@ -32,12 +33,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os\n", + "os.system(\"eups list lsst_distrib\") #todo\n", "import sys\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", "import eups.setupcmd" ] }, @@ -63,14 +67,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "!eups list lsst_distrib\n", - "datarepo = \"/home/jmyles/repositories/ci_hsc/\"\n", - "datadir = \"/home/jmyles/DATA/\"\n", - "os.system(\"mkdir -p {}\".format(datadir))" + "USER = \"jmyles\"\n", + "# todo: $USER\n", + "datarepo = \"/home/{}/repositories/ci_hsc/\".format(USER)\n", + "datadir = \"/home/{}/DATA/\".format(USER)\n", + "os.system(\"mkdir -p {}\".format(datadir));" ] }, { @@ -110,6 +115,7 @@ "outputs": [], "source": [ "# ingest script\n", + "# todo: $USER ()\n", "!ingestImages.py /home/jmyles/DATA /home/jmyles/repositories/ci_hsc/raw/*.fits --mode=link" ] }, @@ -171,12 +177,10 @@ "outputs": [], "source": [ "\"\"\"\n", - "# review what data will be processed\n", - "!processCcd.py DATA --rerun processCcdOutputs --id --show data\n", - "# id allows you to select data by data ID\n", - "# unspecified id selects all raw data\n", - "# example IDs: raw, filter, visit, ccd, field\n", - "# show data turns on dry-run mode\n", + "!processCcd.py DATA --rerun processCcdOutputs --id\n", + "# all cl tasks write output datasets to a Butler repo\n", + "# --rerun configured to write to processCcdOutputs\n", + "# other option is --output\n", "\"\"\"" ] }, @@ -186,7 +190,52 @@ "metadata": {}, "outputs": [], "source": [ - "#!which processCcd.py" + "!which processCcd.py\n", + "\"\"\"\n", + "processCcd.py\n", + "from lsst.pipe.tasks.processCcd import ProcessCcdTask\n", + "\n", + "ProcessCcdTask.parseAndRun()\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# show source of lsst.pipe.tasks.processCcd\n", + "# emacs /opt/lsst/software/stack/stack/miniconda3-4.3.21-10a4fa6/Linux64/pipe_tasks/16.0+1/python/lsst/pipe/tasks/processCcd.py\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from lsst.pipe.tasks.processCcd import ProcessCcdTask, ProcessCcdConfig\n", + "processCcdTaskInstance = ProcessCcdTask(butler=butler)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from stackclub import where_is\n", + "where_is(processCcdTaskInstance, in_the=\"source\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "processCcdConfig = ProcessCcdConfig()" ] }, { @@ -196,13 +245,24 @@ "outputs": [], "source": [ "\"\"\"\n", - "!processCcd.py DATA --rerun processCcdOutputs --id\n", - "# all cl tasks write output datasets to a Butler repo\n", - "# --rerun configured to write to processCcdOutputs\n", - "# other option is --output\n", + "# review what data will be processed\n", + "!processCcd.py DATA --rerun processCcdOutputs --id --show data\n", + "# id allows you to select data by data ID\n", + "# unspecified id selects all raw data\n", + "# example IDs: raw, filter, visit, ccd, field\n", + "# show data turns on dry-run mode\n", "\"\"\"" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#!which processCcd.py" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -245,6 +305,117 @@ "# - TAN: tangent-plane projection\n", "\"\"\"" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Part 6: Multi-band catalog analysis\n", + "https://pipelines.lsst.io/getting-started/multiband-analysis.html" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import lsst.daf.persistence as dafPersist\n", + "datadir = \"/home/{}/DATA/\".format(os.environ['USER'])\n", + "butler = dafPersist.Butler(inputs=datadir + 'rerun/coaddForcedPhot/')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Access the sources identified from the coadd images" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "rSources = butler.get('deepCoadd_forced_src', {'filter': 'HSC-R', 'tract': 0, 'patch': '1,1'})\n", + "iSources = butler.get('deepCoadd_forced_src', {'filter': 'HSC-I', 'tract': 0, 'patch': '1,1'})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Throw out negative fluxes, and convert fluxes to magnitudes." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "rCoaddCalib = butler.get('deepCoadd_calexp_calib', {'filter': 'HSC-R', 'tract': 0, 'patch': '1,1'})\n", + "iCoaddCalib = butler.get('deepCoadd_calexp_calib', {'filter': 'HSC-I', 'tract': 0, 'patch': '1,1'})\n", + "\n", + "rCoaddCalib.setThrowOnNegativeFlux(False)\n", + "iCoaddCalib.setThrowOnNegativeFlux(False)\n", + "\n", + "rMags = rCoaddCalib.getMagnitude(rSources['base_PsfFlux_flux'])\n", + "iMags = iCoaddCalib.getMagnitude(iSources['base_PsfFlux_flux'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Select stars from catalog" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "deblended = rSources['deblend_nChild'] == 0\n", + "\n", + "refTable = butler.get('deepCoadd_ref', {'filter': 'HSC-R^HSC-I', 'tract': 0, 'patch': '1,1'})\n", + "inInnerRegions = refTable['detect_isPatchInner'] & refTable['detect_isTractInner'] # define inner regions\n", + "isSkyObject = refTable['merge_peak_sky'] # reject sky objects\n", + "isPrimary = refTable['detect_isPrimary']\n", + "\n", + "isStellar = iSources['base_ClassificationExtendedness_value'] < 1.\n", + "isGoodFlux = ~iSources['base_PsfFlux_flag']\n", + "selected = isPrimary & isStellar & isGoodFlux" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Make color-magnitude diagram." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.style.use('seaborn-notebook')\n", + "plt.figure(1, figsize=(4, 4), dpi=140)\n", + "plt.scatter(rMags[selected] - iMags[selected],\n", + " iMags[selected],\n", + " edgecolors='None', s=2, c='k')\n", + "plt.xlim(-0.5, 3)\n", + "plt.ylim(25, 14)\n", + "plt.xlabel('$r-i$')\n", + "plt.ylabel('$i$')\n", + "plt.subplots_adjust(left=0.125, bottom=0.1)\n", + "plt.show()" + ] } ], "metadata": { From 3f6799cb1f72fa385b086b30192ab549548308d8 Mon Sep 17 00:00:00 2001 From: Phil Marshall Date: Thu, 6 Sep 2018 23:42:07 +0000 Subject: [PATCH 06/15] Consistent commentary, with task names --- ImageProcessing/Re-RunHSC.sh | 66 +++++++++++++++++++++++------------- 1 file changed, 42 insertions(+), 24 deletions(-) diff --git a/ImageProcessing/Re-RunHSC.sh b/ImageProcessing/Re-RunHSC.sh index e5155312..69a51201 100644 --- a/ImageProcessing/Re-RunHSC.sh +++ b/ImageProcessing/Re-RunHSC.sh @@ -20,10 +20,11 @@ source /opt/lsst/software/stack/loadLSST.bash eups list lsst_distrib setup lsst_distrib -# I. Setting up the Butler data repository +# I. Setting up the Butler data repository date -echo "setup Butler" +echo "Re-RunHSC INFO: set up the Butler" + setup -j -r /project/shared/data/ci_hsc DATADIR="/home/$USER/DATA" mkdir -p "$DATADIR" @@ -32,55 +33,65 @@ mkdir -p "$DATADIR" # We write this file to the data repository so that any instantiated Butler object knows which mapper to use. echo lsst.obs.hsc.HscMapper > $DATADIR/_mapper -# The injest script creates links in the instantiated butler repository to the original data files +# The ingest script creates links in the instantiated butler repository to the original data files +date +echo "Re-RunHSC INFO: ingest images with ingestImages.py" + ingestImages.py $DATADIR $CI_HSC_DIR/raw/*.fits --mode=link # Grab calibration files +date +echo "Re-RunHSC INFO: obtain calibration files with installTransmissionCurves.py" + installTransmissionCurves.py $DATADIR ln -s $CI_HSC_DIR/CALIB/ $DATADIR/CALIB mkdir -p $DATADIR/ref_cats ln -s $CI_HSC_DIR/ps1_pv3_3pi_20170110 $DATADIR/ref_cats/ps1_pv3_3pi_20170110 -date -echo "processCcd" + # II. Calibrate a single frame with processCcd.py +date +echo "Re-RunHSC INFO: process raw exposures with processCcd.py" + # Use calibration files to do CCD processing processCcd.py $DATADIR --rerun processCcdOutputs --id + # III. (omitted) Visualize images. + # IV. Make coadds -date -echo "make coadds" + # IV. A. Make skymap # A sky map is a tiling of the celestial sphere. It is composed of one or more tracts. # A tract is composed of one or more overlapping patches. Each tract has a WCS. # We define a skymap so that we can warp all of the exposure to fit on a single coordinate system # This is a necessary step for making coadds - date -echo "make skymap" +echo "Re-RunHSC INFO: make skymap with makeDiscreteSkyMap.py" + makeDiscreteSkyMap.py $DATADIR --id --rerun processCcdOutputs:coadd --config skyMap.projection="TAN" + # IV. B. Warp images onto skymap date -echo "warp images" +echo "Re-RunHSC INFO: warp images with makeCoaddTempExp.py" + makeCoaddTempExp.py $DATADIR --rerun coadd \ --selectId filter=HSC-R \ --id filter=HSC-R tract=0 patch=0,0^0,1^0,2^1,0^1,1^1,2^2,0^2,1^2,2 \ --config doApplyUberCal=False doApplySkyCorr=False - makeCoaddTempExp.py $DATADIR --rerun coadd \ --selectId filter=HSC-I \ --id filter=HSC-I tract=0 patch=0,0^0,1^0,2^1,0^1,1^1,2^2,0^2,1^2,2 \ --config doApplyUberCal=False doApplySkyCorr=False - # IV. C. Coadd warped images # Now that we have warped images, we can perform coaddition to get deeper images # The motivation for this is to have the deepest image possible for source detection date -echo "coadd warped images" +echo "Re-RunHSC INFO: coadd warped images with assembleCoadd.py" + assembleCoadd.py $DATADIR --rerun coadd \ --selectId filter=HSC-R \ --id filter=HSC-R tract=0 patch=0,0^0,1^0,2^1,0^1,1^1,2^2,0^2,1^2,2 @@ -89,12 +100,14 @@ assembleCoadd.py $DATADIR --rerun coadd \ --selectId filter=HSC-I \ --id filter=HSC-I tract=0 patch=0,0^0,1^0,2^1,0^1,1^1,2^2,0^2,1^2,2 -# V. Measuring sources -date -echo "measure sources" + +# V. Measuring Sources + # V. A. Source detection # As noted above, we do source detection on the deepest image possible. -echo "detect sources" +date +echo "Re-RunHSC INFO: detect objects in the coadd images with detectCoaddSources.py" + detectCoaddSources.py $DATADIR --rerun coadd:coaddPhot \ --id filter=HSC-R tract=0 patch=0,0^0,1^0,2^1,0^1,1^1,2^2,0^2,1^2,2 @@ -105,27 +118,32 @@ detectCoaddSources.py $DATADIR --rerun coaddPhot \ # Ultimately, for photometry, we will need to deblend objects. # In order to do this, we first merge the detected source catalogs. date -echo "merge detection cats" +echo "Re-RunHSC INFO: merge detection catalogs with mergeCoaddDetections.py" + mergeCoaddDetections.py $DATADIR --rerun coaddPhot --id filter=HSC-R^HSC-I -# V. C. Measure source catalogs on coadds -# Given a full source catalog, we can do regular photometry with implicit deblending. +# V. C. Measure objects in coadds +# Given a full coaddSource catalog, we can do regular photometry with implicit deblending. date -echo "measure source cats on coadds" +echo "Re-RunHSC INFO: measure objects in coadds with measureCoaddSources.py" + measureCoaddSources.py $DATADIR --rerun coaddPhot --id filter=HSC-R measureCoaddSources.py $DATADIR --rerun coaddPhot --id filter=HSC-I -# V. D. Merge multi-band source catalogs from coadds +# V. D. Merge multi-band catalogs from coadds date -echo "merge source cats from coadds" +echo "Re-RunHSC INFO: merge measurements from coadds with mergeCoaddMeasurements.py" + mergeCoaddMeasurements.py $DATADIR --rerun coaddPhot --id filter=HSC-R^HSC-I # V. E. Run forced photometry on coadds # Given a full source catalog, we can do forced photometry with implicit deblending. date -echo "run forcedphot on coadds" +echo "Re-RunHSC INFO: perform forced photometry on coadds with forcedPhotCoadd.py" + forcedPhotCoadd.py $DATADIR --rerun coaddPhot:coaddForcedPhot --id filter=HSC-R forcedPhotCoadd.py $DATADIR --rerun coaddForcedPhot --id filter=HSC-I + # VI. Multi-band catalog analysis # For analysis of the catalog, see part VI of StackClub/ImageProcessing/Re-RunHSC.ipynb From 0a94550cfd53720fdc40bb6472dd872d14e3d762 Mon Sep 17 00:00:00 2001 From: Phil Marshall Date: Thu, 6 Sep 2018 23:57:03 +0000 Subject: [PATCH 07/15] Pipeline preview, USER, HOME, DATADIR etc --- ImageProcessing/Re-RunHSC.ipynb | 71 +++++++++++++++++++++------------ 1 file changed, 46 insertions(+), 25 deletions(-) diff --git a/ImageProcessing/Re-RunHSC.ipynb b/ImageProcessing/Re-RunHSC.ipynb index 3d53533d..afdbfe02 100644 --- a/ImageProcessing/Re-RunHSC.ipynb +++ b/ImageProcessing/Re-RunHSC.ipynb @@ -12,17 +12,16 @@ "\n", "This project addresses issue [#63: HSC Re-run](https://github.com/LSSTScienceCollaborations/StackClub/issues/63)\n", "\n", - "This notebook demonstrates the [LSST Science Piplines data processing tutorial](https://pipelines.lsst.io/) with emphasis on how a given [obs package](https://github.com/lsst/obs_base) works under the hood of the command line tasks. It makes use of the [obs_subaru](https://github.com/lsst/obs_subaru) package to measure a forced photometry light curve for a small patch in the HSC sky in the [ci_hsc](https://github.com/lsst/ci_hsc/) repository. \n", + "This notebook demonstrates the pipeline described in the [LSST Science Piplines data processing tutorial](https://pipelines.lsst.io/), from ingesting images (using the [obs_subaru](https://github.com/lsst/obs_subaru) package) through image processing, coaddition, source detection and object measurement all the way through to measuring forced photometry light curves in a small patch of the HSC sky (in the [ci_hsc](https://github.com/lsst/ci_hsc/) repository). \n", "\n", "### Learning Objectives:\n", "After working through and studying this notebook you should be able to understand how to use the DRP pipeline from image visualization through to a forced photometry light curve. Specific learning objectives include: \n", - " 1. [configure command-line tasks](https://pipelines.lsst.io/v/w-2018-12/modules/lsst.pipe.base/command-line-task-config-howto.html) for your science case\n", - " 2. TODO\n", + " 1. [Configuring](https://pipelines.lsst.io/v/w-2018-12/modules/lsst.pipe.base/command-line-task-config-howto.html) and executing pipeline tasks in python as well as on the command line.\n", + " 2. The sequence of steps involved in the DRP pipeline.\n", " \n", - "Other techniques that are demonstrated, but not empasized, in this notebook are\n", - " 1. Use the `butler` to fetch data\n", - " 2. Visualize data with the LSST Stack\n", - " 3. TODO\n", + "Other techniques that are demonstrated, but not emphasized, in this notebook are\n", + " 1. Using the `butler` to fetch data\n", + " 2. Visualizing data with the LSST Stack\n", "\n", "### Logistics\n", "This notebook is intended to be runnable on `lsst-lspdev.ncsa.illinois.edu` from a local git clone of https://github.com/LSSTScienceCollaborations/StackClub.\n", @@ -33,7 +32,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -45,6 +44,31 @@ "import eups.setupcmd" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Pipeline Preview\n", + "\n", + "Before we unpack the pipeline described in the [LSST Science Piplines data processing tutorial](https://pipelines.lsst.io/), let's look at the complete set of command line tasks assembled into an end-to-end data reduction script." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "! cat Re-RunHSC.sh" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll come back to each step in turn throughout the rest of this notebook." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -67,15 +91,14 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "USER = \"jmyles\"\n", - "# todo: $USER\n", - "datarepo = \"/home/{}/repositories/ci_hsc/\".format(USER)\n", - "datadir = \"/home/{}/DATA/\".format(USER)\n", - "os.system(\"mkdir -p {}\".format(datadir));" + "HOME = os.environ['HOME']\n", + "DATAREPO = \"{}/repositories/ci_hsc/\".format(HOME)\n", + "DATADIR = \"{}/DATA/\".format(HOME)\n", + "os.system(\"mkdir -p {}\".format(DATADIR));" ] }, { @@ -86,7 +109,7 @@ "source": [ "#!setup -j -r /home/jmyles/repositories/ci_hsc\n", "\n", - "setup = eups.setupcmd.EupsSetup([\"-j\",\"-r\", datarepo])\n", + "setup = eups.setupcmd.EupsSetup([\"-j\",\"-r\", DATAREPO])\n", "status = setup.run()\n", "print('setup exited with status {}'.format(status))" ] @@ -104,7 +127,7 @@ "metadata": {}, "outputs": [], "source": [ - "with open(datadir + \"_mapper\", \"w\") as f:\n", + "with open(DATADIR + \"_mapper\", \"w\") as f:\n", " f.write(\"lsst.obs.hsc.HscMapper\")" ] }, @@ -115,8 +138,7 @@ "outputs": [], "source": [ "# ingest script\n", - "# todo: $USER ()\n", - "!ingestImages.py /home/jmyles/DATA /home/jmyles/repositories/ci_hsc/raw/*.fits --mode=link" + "!ingestImages.py DATADIR /home/jmyles/repositories/ci_hsc/raw/*.fits --mode=link" ] }, { @@ -158,8 +180,8 @@ "os.system(\"ln -s {} {}\".format(datarepo + \"CALIB/\", datadir + \"CALIB\"))\n", "\n", "# ingest reference catalog into Butler repo\n", - "os.system(\"mkdir -p {}\".format(datadir + \"ref_cats\"))\n", - "os.system(\"ln -s {}ps1_pv3_3pi_20170110 {}ref_cats/ps1_pv3_3pi_20170110\".format(datarepo, datadir))" + "os.system(\"mkdir -p {}\".format(DATADIR + \"ref_cats\"))\n", + "os.system(\"ln -s {}ps1_pv3_3pi_20170110 {}ref_cats/ps1_pv3_3pi_20170110\".format(DATAREPO, DATADIR))" ] }, { @@ -316,14 +338,13 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import os\n", "import lsst.daf.persistence as dafPersist\n", - "datadir = \"/home/{}/DATA/\".format(os.environ['USER'])\n", - "butler = dafPersist.Butler(inputs=datadir + 'rerun/coaddForcedPhot/')" + "butler = dafPersist.Butler(inputs=DATADIR + 'rerun/coaddForcedPhot/')" ] }, { @@ -335,7 +356,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -352,7 +373,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ From 069620eed00fa5b0853f6ac6c532236e70c0cd08 Mon Sep 17 00:00:00 2001 From: Justin Myles Date: Thu, 13 Sep 2018 16:47:12 +0000 Subject: [PATCH 08/15] lc data --- ImageProcessing/Re-RunHSC.ipynb | 2609 ++++++++++++++++++++++++++++++- ImageProcessing/Re-RunHSC.sh | 15 + 2 files changed, 2613 insertions(+), 11 deletions(-) diff --git a/ImageProcessing/Re-RunHSC.ipynb b/ImageProcessing/Re-RunHSC.ipynb index afdbfe02..28fcefb4 100644 --- a/ImageProcessing/Re-RunHSC.ipynb +++ b/ImageProcessing/Re-RunHSC.ipynb @@ -35,6 +35,17 @@ "execution_count": null, "metadata": {}, "outputs": [], + "source": [ + "# todo: nb will point to processCcd.ipynb notebook on how to unpack a CL task\n", + "# todo: and will actually unpack processCcd, but not other tasks\n", + "# todo: then will make a few plots showing result of what we can do with processed images " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], "source": [ "import os\n", "os.system(\"eups list lsst_distrib\") #todo\n", @@ -55,9 +66,165 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ": 'HSC Re-Run: Making Forced Photometry Light Curves from Scratch\n", + "Owner: **Justin Myles** (@jtmyles)\n", + "Last Verified to Run: **2018-09-05**\n", + "Verified Stack Release: **16.0**\n", + "\n", + "This project addresses issue #63: HSC Re-run\n", + "\n", + "This shell script runs the command-line tasks from the tutorial at pipelines.lsst.io for analysis\n", + "from raw images through source detection and forced photometry measurements. It is intended as an \n", + "intermediate step toward the end-goal of making a forced photometry lightcurve in the notebook at\n", + "StackClub/ImageProcessing/Re-RunHSC.ipynb\n", + "\n", + "Recommended to run with \n", + "$ bash Re-RunHSC.sh > output.txt\n", + "'\n", + "\n", + "\n", + "# Setup the LSST Stack\n", + "source /opt/lsst/software/stack/loadLSST.bash\n", + "eups list lsst_distrib\n", + "setup lsst_distrib\n", + "\n", + "\n", + "# I. Setting up the Butler data repository\n", + "date\n", + "echo \"Re-RunHSC INFO: set up the Butler\"\n", + "\n", + "setup -j -r /project/shared/data/ci_hsc\n", + "DATADIR=\"/home/$USER/DATA\"\n", + "mkdir -p \"$DATADIR\"\n", + "\n", + "# A Butler needs a *mapper* file \"to find and organize data in a format specific to each camera.\" \n", + "# We write this file to the data repository so that any instantiated Butler object knows which mapper to use.\n", + "echo lsst.obs.hsc.HscMapper > $DATADIR/_mapper\n", + "\n", + "# The ingest script creates links in the instantiated butler repository to the original data files\n", + "date\n", + "echo \"Re-RunHSC INFO: ingest images with ingestImages.py\"\n", + "\n", + "ingestImages.py $DATADIR $CI_HSC_DIR/raw/*.fits --mode=link\n", + "\n", + "# Grab calibration files\n", + "date\n", + "echo \"Re-RunHSC INFO: obtain calibration files with installTransmissionCurves.py\"\n", + "\n", + "installTransmissionCurves.py $DATADIR\n", + "ln -s $CI_HSC_DIR/CALIB/ $DATADIR/CALIB\n", + "mkdir -p $DATADIR/ref_cats\n", + "ln -s $CI_HSC_DIR/ps1_pv3_3pi_20170110 $DATADIR/ref_cats/ps1_pv3_3pi_20170110\n", + "\n", + "\n", + "# II. Calibrate a single frame with processCcd.py\n", + "date\n", + "echo \"Re-RunHSC INFO: process raw exposures with processCcd.py\"\n", + "\n", + "# Use calibration files to do CCD processing\n", + "processCcd.py $DATADIR --rerun processCcdOutputs --id\n", + "\n", + "\n", + "# III. (omitted) Visualize images.\n", + "\n", + "\n", + "# IV. Make coadds\n", + "\n", + "# IV. A. Make skymap\n", + "# A sky map is a tiling of the celestial sphere. It is composed of one or more tracts.\n", + "# A tract is composed of one or more overlapping patches. Each tract has a WCS.\n", + "# We define a skymap so that we can warp all of the exposure to fit on a single coordinate system\n", + "# This is a necessary step for making coadds\n", + "date\n", + "echo \"Re-RunHSC INFO: make skymap with makeDiscreteSkyMap.py\"\n", + "\n", + "makeDiscreteSkyMap.py $DATADIR --id --rerun processCcdOutputs:coadd --config skyMap.projection=\"TAN\"\n", + "\n", + "# IV. B. Warp images onto skymap\n", + "date\n", + "echo \"Re-RunHSC INFO: warp images with makeCoaddTempExp.py\"\n", + "\n", + "makeCoaddTempExp.py $DATADIR --rerun coadd \\\n", + " --selectId filter=HSC-R \\\n", + " --id filter=HSC-R tract=0 patch=0,0^0,1^0,2^1,0^1,1^1,2^2,0^2,1^2,2 \\\n", + " --config doApplyUberCal=False doApplySkyCorr=False\n", + "\n", + "makeCoaddTempExp.py $DATADIR --rerun coadd \\\n", + " --selectId filter=HSC-I \\\n", + " --id filter=HSC-I tract=0 patch=0,0^0,1^0,2^1,0^1,1^1,2^2,0^2,1^2,2 \\\n", + " --config doApplyUberCal=False doApplySkyCorr=False\n", + "\n", + "# IV. C. Coadd warped images\n", + "# Now that we have warped images, we can perform coaddition to get deeper images\n", + "# The motivation for this is to have the deepest image possible for source detection\n", + "date\n", + "echo \"Re-RunHSC INFO: coadd warped images with assembleCoadd.py\"\n", + "\n", + "assembleCoadd.py $DATADIR --rerun coadd \\\n", + " --selectId filter=HSC-R \\\n", + " --id filter=HSC-R tract=0 patch=0,0^0,1^0,2^1,0^1,1^1,2^2,0^2,1^2,2\n", + "\n", + "assembleCoadd.py $DATADIR --rerun coadd \\\n", + " --selectId filter=HSC-I \\\n", + " --id filter=HSC-I tract=0 patch=0,0^0,1^0,2^1,0^1,1^1,2^2,0^2,1^2,2\n", + "\n", + "\n", + "# V. Measuring Sources\n", + "\n", + "# V. A. Source detection\n", + "# As noted above, we do source detection on the deepest image possible.\n", + "date\n", + "echo \"Re-RunHSC INFO: detect objects in the coadd images with detectCoaddSources.py\"\n", + "\n", + "detectCoaddSources.py $DATADIR --rerun coadd:coaddPhot \\\n", + " --id filter=HSC-R tract=0 patch=0,0^0,1^0,2^1,0^1,1^1,2^2,0^2,1^2,2\n", + "\n", + "detectCoaddSources.py $DATADIR --rerun coaddPhot \\\n", + " --id filter=HSC-I tract=0 patch=0,0^0,1^0,2^1,0^1,1^1,2^2,0^2,1^2,2\n", + "\n", + "# V. B. Merge multi-band detection catalogs\n", + "# Ultimately, for photometry, we will need to deblend objects. \n", + "# In order to do this, we first merge the detected source catalogs.\n", + "date\n", + "echo \"Re-RunHSC INFO: merge detection catalogs with mergeCoaddDetections.py\"\n", + "\n", + "mergeCoaddDetections.py $DATADIR --rerun coaddPhot --id filter=HSC-R^HSC-I\n", + "\n", + "# V. C. Measure objects in coadds\n", + "# Given a full coaddSource catalog, we can do regular photometry with implicit deblending.\n", + "date\n", + "echo \"Re-RunHSC INFO: measure objects in coadds with measureCoaddSources.py\"\n", + "\n", + "measureCoaddSources.py $DATADIR --rerun coaddPhot --id filter=HSC-R\n", + "measureCoaddSources.py $DATADIR --rerun coaddPhot --id filter=HSC-I\n", + "\n", + "# V. D. Merge multi-band catalogs from coadds\n", + "date\n", + "echo \"Re-RunHSC INFO: merge measurements from coadds with mergeCoaddMeasurements.py\"\n", + "\n", + "mergeCoaddMeasurements.py $DATADIR --rerun coaddPhot --id filter=HSC-R^HSC-I\n", + "\n", + "# V. E. Run forced photometry on coadds\n", + "# Given a full source catalog, we can do forced photometry with implicit deblending.\n", + "date\n", + "echo \"Re-RunHSC INFO: perform forced photometry on coadds with forcedPhotCoadd.py\"\n", + "\n", + "forcedPhotCoadd.py $DATADIR --rerun coaddPhot:coaddForcedPhot --id filter=HSC-R\n", + "forcedPhotCoadd.py $DATADIR --rerun coaddForcedPhot --id filter=HSC-I\n", + "\n", + "\n", + "# VI. Multi-band catalog analysis\n", + "# For analysis of the catalog, see part VI of StackClub/ImageProcessing/Re-RunHSC.ipynb\n" + ] + } + ], "source": [ "! cat Re-RunHSC.sh" ] @@ -91,7 +258,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -338,7 +505,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 173, "metadata": {}, "outputs": [], "source": [ @@ -356,12 +523,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 174, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6800\n", + "6800\n" + ] + } + ], "source": [ "rSources = butler.get('deepCoadd_forced_src', {'filter': 'HSC-R', 'tract': 0, 'patch': '1,1'})\n", - "iSources = butler.get('deepCoadd_forced_src', {'filter': 'HSC-I', 'tract': 0, 'patch': '1,1'})" + "iSources = butler.get('deepCoadd_forced_src', {'filter': 'HSC-I', 'tract': 0, 'patch': '1,1'})\n", + "print(len(rSources))\n", + "print(len(iSources))" ] }, { @@ -373,7 +551,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 175, "metadata": {}, "outputs": [], "source": [ @@ -396,7 +574,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 176, "metadata": {}, "outputs": [], "source": [ @@ -421,15 +599,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 178, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiYAAAIbCAYAAADfKlU1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAVhwAAFYcBshnuugAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzs3XdYleX/B/D3DQIi4lYUQQVFTcU9APceqLnNxK3ltxxlmpqZWeZMzdTMlTa00txbCXEkqCgibgIUVFwITkA45/79AZxfCMg65zzPgffrus514Hnu8QEpPtzPPYSUEkRERERqYKZ0AERERESpmJgQERGRajAxISIiItVgYkJERESqwcSEiIiIVIOJCREREakGExMiIiJSDSYmREREpBpMTIiIiEg1mJgQERGRajAxISIiItVgYkJERESqwcSEiIiIVIOJCREREamGySUmQggvIcRqIUSAECJBCCGFEMOzWddZCPE8pc6PBg6ViIiIcqiQ0gHkwhwAlQE8AhCV8nGWhBBmADYaLiwiIiLKK5MbMQEwGkAVKWVZADkZ9fgYgDuAzw0SFREREeWZyY2YSCm9c1pHCFETySMt8wBc0HtQREREpBemOGKSI0IIcwA/AwhBcnJCREREKmVyIya5MB1AQwBuUspXQgi9Ni6ESMjklgWAhwDC9NohERGRcTgDiJFS1jRmp/k6MRFC1APwBYBFUspzxu6+aNGi5erUqVPOyP0SERHl2aVLl/D8+XOj95tvExMhhCWSH+H8C2C2ofqRUlpl0r9fnTp13Pz8/AzVNRERkcG4u7vD39/f6KP++TYxQfIjHFcAHlLKzB63EBERkYrk58mvDZD89fmnbKgmhRASwNGU+++nXNupXIhERET0X/l5xOQIkjdhe10FAN0AXAPwD4BAYwZFREREmcu3iYmUcmVG14UQbZCcmByTUo41alBERET0RiaXmAghRgNokfKpa8r76JSEAwBOSinXGT0wIiIiyjOTS0yQnJQMe+1a85RXKiYmREREJsjkEhMp5XAAw/NQ3xeAfndZIyIiIr3Iz6tyiIiIyMQwMSEiIiLVYGJCREREqsHEhIiIiFSDiQkRERGpBhMTIiIiUg0mJkRERKQaTEyIiIhINZiYEBERkWowMSEiIiLVYGJCREREqsHEhIiIiFSDiQkRERGpBhMTIiIiUg0mJkRERKQaTEyIiIhINZiYEBERkWowMSEiIiLVYGJCREREqsHEhIiIiFSDiQkRERGpBhMTIiIiUg0mJkRERKQaTEyIiIhINZiYEBERkWowMSEiIiLVYGJCREREqsHEhIiIiFSDiQkRERGpBhMTIiIiUg0mJpRvxcbGom3btmjWrBmioqKUDoeIiLKBiQnlWydPnoSvry/OnDmDgwcPKh0OERFlQyGlAyAylDZt2qBnz554+fIlunfvrnQ4RESUDUxMKN8qWrQodu3apXQYRESUA3yUQ0RERKrBxIQoC/v27UOjRo2wfPlypUMhIsr3mJgQZWHhwoU4f/48Zs+erXQoRET5HueYEGVh7NixuHXrFkaOHKl0KERE+R4TE6IsDBo0CIMGDVI6DCKiAoGPcoiIiEg1mJgQERGRajAxISIiItVgYkJERESqwcSEiIiIVIOJCREREakGExMiIiJSDSYmZNJevHiB77//Hn5+fkqHQkREesAN1kjVNBoNVqxYgWLFimHEiBHp7s+cORNLly6FtbU1Hj58CBsbGwWiJCIifWFiQqr2+++/46OPPgIAuLi4oEWLFmnuV6hQAQBQunRpWFhYGD0+IiLSLyYmpGrOzs6wsLCAlZUV7O3t092fMmUKWrduDWdnZ1haWioQIRER6RMTE1I1Dw8PhIaGwsrKCuXKlcuwTNOmTY0cFRERGQoTE1I9R0dHpUMgIiIj4aocIiIiUg0mJkRERKQaTEyIiIhINZiYEBERkWowMSFVunDhAjZt2oTExESlQyEiIiPiqhxSndjYWDRv3hwvX75EaGgovvjiC6VDIiIiI+GICamOubk5rKysAABFihRROBoiIjImjpiQ6tja2uLcuXMICQlBx44dlQ6HiIiMiIkJqZKTkxOcnJyUDoOIiIyMj3KIiIhINZiYEBERkWowMSEiIiLVMLnERAjhJYRYLYQIEEIkCCGkEGJ4FnWchBBrhRC3UurcF0IcFUL0N1LYRERElA2mOPl1DoDKAB4BiEr5OFNCiI4AdqZ8ugdAGICSAOoC6ABgq8EiJSIiohwxxcRkNIAQKeUtIcQ0APMyKyiEqATgLwB3AHSQUka8dt8Uv34iIqJ8y+Qe5UgpvaWUt7JZ/DMAxQCMfT0pSWkrSa/Bkd5cv34dY8eOxaFDh5QOhYiIjCjfjhgIIQSA/gCipZQ+QohGAFojORm7AMBHSqlVMkbK3KRJk7B//378+eefiImJUTocIiIyknybmABwAlAKQIAQYjWA9167HyiE6CmlvJ2XToQQCZncsshLuwVdixYtsH//frRo0ULpUIiIyIhM7lFODpRLeW8A4F0AI5CcqDgBWJty/S9lQqOsTJ8+HQ8fPsSuXbuUDoWIiIwoPycmqV+bOYCZUsqNUsoYKeVNKeV7AE4DaCaEyNOf5FJKq4xeKe1THpQpUwZmZvn5R5SIiF6Xn/+v/+Q/H+/O4P6elPfGRoiFiIiIsiE/JyahADQpH8dmcD/1mrVxwiEiIqKs5NvEREoZD+BUyqe1MiiSeu2mUQKiLMXHx2PMmDEYPXo04uLilA6HiIgUkG8TkxSrUt6/FEJYpV4UQtQEMBzAMwAHFYiLMrBnzx6sW7cO69evx549e7KuQERE+Y7JLRcWQowGkDph1TXlfbQQok3KxyellOtSPv4DQB8A/QAECSEOASgOoC+AwgCGSim5SYZKuLu7o0qVKrqPiYio4DG5xATJScmw1641T3mlWgcAUkophBiE5Ec6owC8DyAh5fO5Uspjhg+XssvBwQHh4eFprkkpkbxXHhERFQQm9yhHSjlcSine8Br+WvkkKeVSKWUdKWVhKWVxKWVnJiXqd+zYMRQrVgxubm5ISMhsHzsiIspPTC4xoYLj4MGDeP78OU6fPo3IyEilwyEiIiMwxUc5VECMHz8eN2/eRO3atVGtWjWlwyEiIiNgYkKqZW9vj99//13pMIiIyIj4KIcKnNOnT2Pt2rWct0JEpEIcMSGTsWXLFlhaWqJXr165biMmJgZt2rRBfHw8IiMj8dVXX+kxQiIiyiuOmJBJ2LNnDwYOHIjevXvj2LHcL6iytLSEra0tAKBUqVIZltFqtfjss88wYcIE7kBLRGRkHDEhk1CyZEmYmZnBzMwMJUqUyHU7NjY2uHDhAsLCwtCiRcYHS3t7e2PevHkAgPr162PkyJG57o+IiHKGiQmZhBYtWuDChQuwsLBAzZo1odVqYWaWuwE/e3t72NvbZ3rf1dUVFStWxIsXL9CsWbPchkxERLnARzlkMlxdXVGzZk0sX74cFhYWBhvJqFChAm7duoUHDx6gdu3aBumDiIgyxsSEjGLPnj3YsWOHXtravXs3tFqt3trLiLm5OSwsLAzWPhERZYyPcsjgfH190bNnTwDAgQMH0KVLlzy1N2/ePBQrVgzvvPOOPsIjIiIVYWJCBmdrawszMzNIKXUrYnLrwYMHaNCgAbZt26an6IiISE34KIcMrlGjRrhw4QICAwPRvHnzrCtkYuHChbCzs0O3bt30GB0REakJR0zIKFxdXfPchp+fHwDg1KlTeW6LiIjUiYkJmYxvv/0WFSpU0M1XISKi/IeJCZmMqlWr4ocfflA6DCIiMiDOMSEiIiLVYGJCREREqsHEhIiIiFSDiQkRERGpBhMTIiIiUg0mJkSkM2/ePDRs2BDe3t5Kh0JEBRQTEyLSmTVrFgIDA/Hdd98pHQoRFVBMTIhIZ9KkSXB2dsZ7772ndChEVEAxMSEinfnz5yM0NJS76xKRYpiYEBFl4Ny5c7h8+bLSYRAVOExMiIhe4+3tjcaNG6N+/fq4dOmS0uEQFShMTEiVIiIiEB0drXQYlEvx8fHYv38/Hj9+rHQoufLs2TMAQFJSEl6+fKlwNEQFCxMTUh1vb284OzvDxcUFd+/eVTocyoX33nsPnp6e6NSpk9Kh5Erv3r3x119/4cCBA2jatKnS4RAVKDxdmFQnJCQEGo0GMTExuH//Puzt7fXWtq+vL4QQaN26td7apPRevHiR5t0U9e3bV+kQiAokJiakOqNGjcLz589RoUIFNGjQQG/t+vr6om3btgCA48ePo2XLlnprm9Jav349unXrhg4dOigdChGZGCYmpDqWlpaYMmWK3ts1M/v/J5dCCL23T/+vRIkSGDVqlNJhEJEJYmJCqhUbG4v4+HiUL19eL+21atUKx48fh5mZGZo3b66XNomISL84+ZVU6e7du6hWrRoqVaqEY8eO6a3dli1bMikhIlIxJiakSnfu3EF0dDQSExNx9erVbNeTUhowKiIiMjQmJqRKTZo0wdq1azFnzhyMHDkyW3XWr18PS0tLDB482MDRERGRoXCOCanW6NGjc1R+165dSEpKwrZt27Bp06Yc93f48GFUrlwZNWrUyHFdIiLSDyYmpDp3796FhYUFypYtm6N6X331FczNzdGnT58c9/nDDz/gww8/hI2NDcLCwlCuXLkct0FERHnHxIRU5fTp02jVqhWsrKwQGBiIqlWrprmv0Wgwe/Zs3XuhQv//I1y/fn3s2LEjV/0mJiYCSN6CXKvV5v4LICKiPGFiQqry77//4tWrV3j16hUuXbqULjHZu3cvvv76awBAuXLlMHHiRL30O378eNjb26Nq1ap6W55MREQ5x8mvpCrvvPMOvLy8AACDBw/G4sWLcePGDd19V1dXFC5cGAAwa9YsxMfH66VfMzMz9O/fHw0bNtRLe0RElDtMTEhVzM3NUatWLQDJ56xMnjwZ9evXR2RkJADA2dlZN0pibm6uWJxERGQYTExIdT7++GN07dpV9/mrV6/SjIzMmTMH27dvx5kzZ3Dx4kUMGDAAu3fvViJUIiLSM84xIdUpXLgw9u/fj3PnziEoKAguLi5wcXHR3S9UqBB69+4NABgxYgROnDiBEydOoGfPnkqFTEREesIRE1KtRo0aYeTIkW88BdjT0xMA0L17d2i1WowfPx69e/fG/fv3jRUmERHpERMTMmlTp05FYmIi1q5di4CAAKxYsQI7d+7E5MmT8fTp03TlDx06hI4dO2Lr1q0KRJteUlISpk2bho8++khvE3mJiEwZExMyeal7mdSuXRtubm6wtLTEb7/9hr59+yIiIiJN2RkzZsDb2xtTpkxRItR0Dh06hAULFmDZsmWqSZbyg8uXL2PTpk149eqV0qEQUQ4xMSFVePDgQZ4fv9jY2MDPz0+35Pfo0aNwdnaGr6+vrsy7774LW1tb3ZJkpdWrVw8VKlRAqVKl0LRpU6XDyRfi4uLg4eEBLy8vzJo1S+lwiCiHmJiQ4q5evQonJydUqVIFQUFBeW5v586d+PLLL6HRaKDRaBAWFqa7N2nSJDx9+hRz5szJcz/64ODggMjISNy/f59n9OiJmZkZrKysAEC35w0RmQ6uyiHFhYeH4+XLlwCA0NBQ1KtXL0/t2dnZYdasWXByckJ0dDSGDh2qjzANhvux6JeVlRXOnj2Ly5cvo0uXLkqHQ0Q5xMSEFNe1a1csX74cSUlJ6NWrl97aVXtCQoZTuXJlVK5cWekwiCgXmJiQ4oQQGDdunNJhEBGRCnCOCREREakGExMyGf7+/pg3bx6io6OVDoWIiAyEj3LIJGi1WnTq1AnPnj3D9evXsXHjRqVDIhN0//59nDp1Cl26dIG1tbXS4RBRBjhiQibBzMwMlSpVAgBUqVJF2WDIZLVv3x59+vTB2LFjlQ6FiDLBERMyGX5+fggNDUX9+vWVDoVMVGJiYpp3IlIfjpiQSThx4gTu3buXZVLy9OlTuLm5oVq1arhx44aRoiNT4e3tjd9++w1r1qxROhQiygQTE1K9yZMno1WrVnjrrbcQGRn5xrKBgYE4ffo0QkNDcfDgQSNFSKbC0dERgwcPRtGiRZUOhYgywcSEVO/UqVMAAI1GAw8PD3z11VeZlvXw8MDo0aPx9ttvY9CgQcYKkYiI9ISJCanK06dPER8fn+ba6tWrUbduXdjb2+P27dtYsmRJpvUtLCywdu1a7Ny5E2XLljV0uKoXHx+f5SgTEZGaMDEh1fDz84OdnR2cnZ3x4MED3fXKlStj6dKlWLFiBerWrYvPP/9cwShNh1arhZubGypVqoSVK1cqHU6uXLhwAY8fP1Y6DCIyIq7KIdU4d+4c4uPjERUVhfDwcKxcuRJ79uxBQkICrly5guHDh+vl9OGCIvX7BiT/gjc133//PSZOnAhHR0eEhIToTgwmovyNiQmpxsiRIxEZGQk7OzvUq1cPbm5uAKCbqMi/nHPG2toaO3bswLFjx/DJJ59kWi4oKAiffPIJOnbsiKlTpxoxwjeLiIgAADx48AAJCQk5SkySkpLw2WefIS4uDgsXLuRmakQmhIkJqUaRIkWwYMECAMDy5ct112fMmAFbW1sMHDhQqdBU4cKFCxgwYABq1aqFrVu3wsLCIss6np6e8PT0fGOZxYsX4++//8bff/+NcePGwcbGRl8h58mXX36JChUqoHHjxihWrFiO6h4+fBiLFi0CADRp0oQnTROZEJNLTIQQXgBaAmgEwBWAJYARUsqNmZR3AfAZgBYAHAA8BnAFwHIp5W5jxEzZd+rUKYwdOxYxMTEAAFtbWwwfPhzly5dXODLlbdmyBSEhIQgJCUFoaChq1qypl3YHDhyIAwcOoGPHjqpJSoDkkbI3jfS8Sb169VCxYkXEx8ejWbNmeo6MiAzJ5BITAHMAVAbwCEBUyscZEkI0A3AUgAWA3QC2ASgHoA+AXUKIL6WUsw0eMWXb8uXLERwcrPv81KlT2U5K/vzzTxQrVgxdu3Y1VHiKGjFiBP755x/UqlUL1atX11u7np6eePjwod7aU4OKFSsiIiICUkqYm5srHQ4R5YAprsoZDaCKlLIsgB+zKDsLgDWAflLKvlLKaVLKkQDqAXgGYKoQgjPqFKbVauHr64tHjx5h5MiRsLOzg5mZGdq2bYtatWplq40tW7bgnXfeQbdu3RAQEGDgiJXh4uKCY8eOYdWqVTAzy95/uhqNBrGxsQaOTJ3MzMyYlBCZIJNLTKSU3lLKW9ks7gxAAjjwWhu3AAQjOWnhFpAKmz59Otq2bQs3Nzd07NgR9+7dQ2JiInx8fLL9C7hEiRIAkvcxedOunr6+vnBxccH777+vl9j16cmTJ5g/fz6OHz+ul/a0Wi2aN2+OUqVK8TRmIjIZJpeY5NAlAAJAmrF9IUQlJM9PCZJSRisRGP2/6Ojkf4KYmBhotVoAyHZCkqpTp044d+4cgoOD3zj3YuPGjfj333+xZs0aPHv2DAAgpcSCBQswc+ZMvHr1KpdfRd59/vnnmD59Ojp37oyXL19mWV6r1WLUqFFo1qwZrl69mu5+XFwcAgICIKXEyZMnDREyEZHemeIck5z4HEBzAH8JIXYDuIH/n2MSCiDPyzyEEAmZ3Mp6yQQBAJYuXYpGjRqhRYsWOU5I/qthw4ZZlvnggw9w5coVtGvXDra2tgAAHx8fTJs2DQBQrVo1DBs2LNcx5EWVKlUAJM+PsLS0zLL8v//+i59++gkA8PPPP2P+/Plp7tvY2GDTpk3w9fXFjBkz9B4vEZEhCCml0jHkmhBiGoB5ePOqnMoAtgJo8p/L0QC+ArBSSqnJYwyZJiZubm7Cz88vL82TEURGRqJhw4aIi4vDyZMnszzB2JCCg4NRqVIlFC9ePMuyGo0GQ4YMQXBwMDZt2oS6desaIUIiKijc3d3h7+/vL6V0N2a/+fpRjhCiKQA/ADFIXl5sA6AqgF8ALAPwe177kFJaZfQCcDqvbVNahw8f1j2yWLFiBZo1a4ZDhw7luV1HR0fcunULd+/eVTQpAQBXV9dsJSUAYG5ujs2bNyM4OJhJCRHlG/k2MRFCWAD4A4AWQG8p5Xkp5UspZZiUchKAnQD6CyGaKxooZcvatWvRuXNnNGrUCFFRUfj8889x5swZ3YZseVWkSBEUK1YMjx8/RqtWreDh4ZHmvB4iIjKOfJuYAKgJwAnAaSllRjMJj6a8NzBeSJRbqZNSNRoNNBoNxo0bh4oVK2L06NF67cfX1xcnTpyAn58fjhw5ote2SVnXrl1D+/btdfOJiEid8vPk19TZg2UzuZ96PbM5IqQiH3zwASpUqIDKlSvDwcEBc+bMwZw5c/TeT4cOHeDp6YmkpKR8u1FbQbVq1Sr4+PjAx8cH48ePR8WKFZUOiYgykJ8Tk0sAngJoLoToJKU8nHpDCOEI4H0k73FyTKH4KAeEEOjTp0+66xcuXIClpWW2N2LLSrFixbB37169tEXq0r9/f2zfvh1NmjThEQdEKmZyiYkQYjSSz70BkvciAYDRQog2KR+flFKuk1ImCCGmAFgN4IAQYi+AawDKI3m5cFEAi6WUN4wXPemTr68v2rVrB3Nzc5w7d44TQOmNWrRogcjISKXDIKIsmFxiguSk5PWNJpqnvFKtAwAp5RohRDiAiQA8AHgCeA7gPIA1UspNhg+XDCU2NhZSSiQlJeH58+dKh0NERHpgcomJlHI4gOE5KH8EAGcx5hMJCQk4ceIEmjZtil69emHTpk0oXrw4PDw8dGXOnj2L4OBgeHl5ZWujMiIiUo/8vCqH8qFRo0ahY8eO6NatG+bNm4fBgwdj69atuvsxMTFo1aoVRo0ahblz5yoYKRER5QYTEzIpMTExuvfUzdUOHjyou29hYQEbGxsAyPZGZaReCQkJ+OCDDzB27FjEx8crHU4aSUlJCAsLUzoMonyHiQmZlI0bN2LlypXYuHEj7t27hxIlSmDhwoW6+0WLFkVgYCB8fX3x8ccfKxip6di+fTu+/vprvHjxQulQ0tm7dy9WrVqF1atXY+fOnUqHAyB5T51ffvkFbdu2RdWqVfHJJ58oHRJRvmJyc0yoYCtbtiw++OADbN++HdevXweQvDX7fzk6OsLR0VGJ8ExOZGQk+vXrBykltFotZs2apXRIaTRr1gyOjo7QarVwdzfqcR2ZmjNnDr7++msIIQAAFy9ezFU7ERERKF68OEf2iF7DxIRMUufOnTFw4EAkJSWhR48eSodjskqUKAF7e3vcvXsXNWvWVDqcdBwcHBAREaF0GGkULVoUQPKeN8OGDcP48eNz3MbOnTvRt29f2NnZ4cqVKyhRooS+wyQyWUxMyCTZ2Njgjz/+UDoMk2dra4vLly8jJiYGVapUUTockzBlyhQ0atQILi4uqFSpUq7auHr1KrRaLaKiohAdHc3EhOg/mJgQFXB8nJAzQgi0b98+T21MnDgRSUlJcHFxQdWqVfUUGVH+wMSEKIWPjw9KlSqF+vXrKx0K5XNFihTBzJkzlQ6DSJW4KocIwF9//YX27dujSZMmuHbtGk6fPo1nz54pHZYq3blzB8eO8YgpIjIMJiZkkh4/foxFixYhICDgjeUePHgAjUaTZXuJiYkAACkl5s6dCzc3N7Rr104vseYnL168QIMGDdCmTRt8++23SodDRPkQExMySR9//DE+/fRTdOzYEVLKDMssXLgQdnZ26Nq1a5btDRo0CHv27MHJkycRFxcHIHlkgNL677lET548UTgaIsqPOMeETJKDgwMAoGLFirr9JF7n5+eX5j0r3bt3BwD88MMPaNq0KTp37qyHSPOX4sWL4/jx4wgKCoKXl5fS4RBRPsTEhExGYmIihg4dips3b+LXX39Fr169UKNGDd39lStXwsfHB/PmzUP16tWxePFi2Nvb53ifk7Jly2LKlCnZLn/x4kU4OTnB1tY2R/2YqsaNG6Nx48ZKh5EjN27cQHh4ODp16pRpIktE6sDEhEzGxYsXdXuX/Pnnn5gxYwaA5BGR999/H8HBwQCSN77asGEDnJ2dsXLlSoPGNHfuXMyYMQM1a9bElStX+EtPhaKjo9GoUSM8f/4cy5Ytw4QJE5QOiYjegHNMyGS4urqiT58+aNCgAfr376+7vnr1al1SYmlpadSdYMPDwwEkby+elJRktH4p+7RarW4C9KtXr7Jd74svvkCZMmWwatUqQ4VGRBngiAmp0rlz53DmzBkMGzYMRYoUAZCcdGzbti1d2VGjRuHs2bNo164dvv/+e6OOWixcuBBOTk5o3bo1LCwsjNYvZV/ZsmVx6tQp3LhxA/369ct2vZ9++gnR0dHYuHEj/ve//xkwwuyJi4vD4MGDERsbi82bN6N8+fJKh0RkGFJKvgzwAuDn5uYmKefOnz8vbWxsJAA5adIkpcOhAuqXX36RrVq1kvv378+y7KlTp6SHh4f85ptvDBbP4cOHJQAJQK5atcpg/RClcnNzkwD8pJF/f/JRDqnK1KlT0bBhQ91jkdKlSyscUcF19OhRODk5YcSIEUqHooghQ4bg2LFj2VpuvnTpUpw6dQozZsww2CM9Nzc3dOjQAY0bN4anp6dB+iBSAyYmpCpXrlwBkHy43PHjxzF9+nSFIyq4fv31V9y8eRMbN25EbGxsntu7c+cOfv311zT7n0gp8cEHH8Dd3R2XL1/Ocx9KGTZsGBwcHDB27FgUKmSYJ+S2trY4cuQIzp49C0dHR4P0QaQGTExINaSU+OGHHzBjxgzs3bsXLVu2TDNfZMWKFXB1dcWWLVsUjNIwLl26hClTpuDixYtKh6Izbtw4uLu747PPPtPL6bddunTB0KFDMXLkSN21W7duYdWqVfD398dPP/2U5z6U4unpicjISNVMlI2Pj1c6BKJcY2JCqvDee+/BwsIC27dvx5w5c9CsWbN0ZebNm4dLly5hyZIlCkRoWMOHD8e3336LoUOHKh2KTsOGDXHq1Cl88803emnP0tIyzTsAODo6YtCgQahRowbeffddvfRT0H3++eewtrbGhx9+qHQoRLnCxIRUYfv27dBoNNi1a1emZSZNmgQXFxeMHz8+W216e3ujf//+OHr0qL7CNBhXV9c07/nRwYMHsWPHDqxfv153zdzcHJs3b8bPdKlPAAAgAElEQVS1a9fQqFEjvfaXkJAArVar1zZNwaFDhwAkf7+JTJGQmZwzQnkjhPBzc3Nzy+526AXd1q1bsXnzZkybNi3D0ZLcqFWrFq5evYratWsjKCgI5ubmemnXELRaLcLCwuDs7AwzM/69kFc+Pj7o1q0bqlatijNnzsDGxkbpkIzGz88PK1aswIgRI9ChQwelwyET5u7uDn9/f38ppbsx++U+JqQK/fv3T7Npmj50794dV69eRUhICJydnREQEICyZcvqtQ99MTMzQ7Vq1ZQOI984duwYEhIScOXKFdy+fTvN0QX5nbu7O9zdjfp7hEiv+KcZqcKrV68QFham1zb79u0LIQRevXqFiIgI3YofSvbixQvs2LEDDx8+VDoUvRs/fjzGjBmDxYsXZ5mUaDQaDBgwAG+99RbOnz9vpAiJKDNMTEgV2rdvj6pVq+Lrr7/WW5thYWGpm91hyJAhaNWqld7azg9GjBiBPn36ZGufDlNTpkwZrFmzBpMmTcqybHh4OLZu3Ypr167pzmIiIuXwUQ4pbtWqVQgICAAABAUFZavOrVu3YGdnh8KFC2daZuDAgXjw4AFsbGwwevRovcSanyQmJqZ5z8r9+/exfv16dOnSBQ0bNjRkaEZVtWpV3SGQ/13KTEQKMfZWswXlBW5Jny3e3t66bbZ79OghIyIi5L59+2T16tXljBkzMqyzYsUKCUC6urpKjUZj5Ijzj9jYWPnrr7/KO3fuZKt83759JQBZrly5TMs8ffpUBgUF6StEIlIQt6SnAqlSpUooWrQorKysMHPmTDg6OmLVqlW4ceMGFi5cmGGd1FGVa9eu5WojqZiYGNy+fTvH9a5cuYJ69eqhf//+2R5lULPixYvDy8sL9vb22SpfpUqVNO+vk1KiWbNmqFevXrb3PpFS4sqVKzk69ZeI8jcmJqQoFxcXhIeH4+bNm2jSpAmA5ImLrq6u+PzzzzOs880332Dq1KnYtWuX7uTh7Lp37x5cXFxQpUoVHDlyJEd1//jjD1y8eBF//fUXrl+/nqO6+cGiRYtw7tw5+Pj4ZHhfo9EgIiICALI9kXnixImoXbs2evbsqbc4ici0cY4JKa5MmTJpPu/UqdMbt2YvW7Ys5s+fn6u+7t+/j+joaADA9evX0bFjx2zX9fLywpEjR1C9enW89dZb2a6XmJiIbt26ITAwENu3bzfZSbhCiDfOLSlUqBAOHz4MX19fjB07Ns29+Ph4eHt7w93dPc3BjKkJXkFM9IgoY0xMqECpV68efv75Z9y5cwdjxozJUd3q1asjNxvm3blzB97e3gCAXbt2mWxikh0eHh7w8PBId33MmDH47bff0KBBgzRLclevXo3169ejd+/exgyTiFSMiQkVOMY+j6ZKlSr44osvEBgYiHHjxhm1b7WIi4tL856qSpUqel0iTkSmj3NMiIxg9uzZ2L17N5ycnJQOJVc2b96MYcOGITQ0NFf1169fjw0bNujOcSmIFixYgM6dOyM4OFjpUIhUjSMmlC9JKbF48WJoNBpMnjw53Tk5T58+xeHDh9G2bds0cx4ovcTERAwdOhQajQZJSUnYtGlTjtsoXrw4hg8frv/gTMTz588xbdo0AMDixYuxceNGZQMiUjGOmJBqaTQajBs3Dv369cOjR49yVHf//v2YMmUKpk2bhh07dqS7P3jwYPTv3x9vv/22vsLN1L179/C///0PGzZsMHhfhmBhYYE2bdpACMFD4XKpaNGi6N+/P0qVKqX3M6GI8huOmJBqnT59GitXrgSQPKkyO9uLp3JxcYGtrS20Wm2GZ6VoNJo073lx69Yt/P333+jTpw9KlCiR7v78+fPx448/YvXq1fD09ES5cuXy3KexeXt7Iy4uDtbW1kqHYrK2bNmidAhEJoEjJqRaderUQePGjeHg4IDOnTvnqG716tURERGBiIgIuLq6pru/adMmbNq0Cbt27cp2m7GxsVi5ciWuXr2a5nrnzp0xatSoTLe9b9GiBczNzVGvXj2ULFkyR1+HmjApISJjYGJCqlWsWDGcPXsWkZGRqF27drr758+fR8eOHfHdd99lWL9EiRIoVapUhvdKliyJd999N0ejFx9++CHGjRuH9u3bp7meuslbZpu99evXD48fP0ZAQAAsLCyy3V9uXL16FZMmTcLZs2cN2g8RkaEwMSGTNX/+fHh7e2PSpElISkoyeH+pk2RfT3aOHDmCvXv3Ys2aNZnWLVasWLoJuIYwZswYLF26FO+++67B+3qTqKgoNG3aFC1btkRMTIyisRCRaWFiQqrz22+/YdiwYbptzZ88eYLY2Nh05d555x2ULFkSXl5eKFTI8NOlli5diqNHj+LEiRNprpcuXRqenp5vPOnYWBo0aJDm3ZgCAwN1/06HDx/G2bNncfLkyXTfL0ovKSkJwcHBRkmwiVTP2KcGFpQXeLpwrsTHx0szMzMJQA4ZMkRev35d2traShsbGxkcHKx0eKqn1WpleHi4TEpKMmq/CxYskACks7OzTExMlI8ePZJdunSRb7/9tnz27JlRYzFFAwcOlADku+++q3QoRDo8XZgIgJWVFVq3bg0zMzO0b98eN27cwLNnz/DixQtcu3bN6PEcP34c//zzj9H7zS0hBKpUqaK3x0ZbtmzBhAkTcP/+/TeWSz28LyoqComJiShdujQOHDiAnTt3omjRonqJJT8LCQlJ805UkHG5MKmOj4+PbmmqlBILFiyARqMx+nkqPj4+uomup06dgru7u1H7V9qTJ08waNAgaLVaJCUl4Ycffsi07Ny5c1G5cmW4u7tnuXrn5s2biIqKKjDfzzt37sDOzu6Njxs3bdqE33//XfG5QURqwBETUqXUX25CCHz66aeYPn26QSeP+vv7w8HBAd26dUNiYmK6+zL58ZxqJCUlISQkxKBx2djYoG7duhBCwM3N7Y1lixUrhilTpqBFixZvLPfgwQPUrVsXHh4e+Pnnn/UZrirNnz8fDg4OWW5MV7NmTcyePTvDPXeIChomJkQAtm/fjjt37uDAgQO6xxLt2rWDj48Pjh8/nuGJuUoaOHAgqlevjg8//NBgfRQqVAhnzpzBgwcP9HbwYXx8vO4gv4wmNOc3AQEBad5zKzY2Ftu2bSsQ3zMiPsohkxYfH49ff/0VDRo0QOPGjXPdzvvvv4+goCDUrVsXVatW1V1v27ZtlnWfPHmCr7/+GjVq1MCYMWNyHUNOpB4Ed+nSJYO0HxERgeHDh6NSpUpYt26d3tqtVKkS/v77b4SHh8PLy0tv7RpDaGgoOnbsCAsLCxw5cgSVKlXKss7ixYvh4OAAT0/PPPXdr18//P333+jQoQOOHDmSp7aIVM/Ys20LygtclWMU06dPlwBkkSJF5JMnTxSJ4auvvpIAJAB548YNo/R57tw5+emnn8rr168bpP25c+fqvqYzZ84YpA9T4+rqqvuebNy40ah9t2nTRgKQbdq0kVJKGRkZKevXry/d3d3l48ePjRoLFRxKrcrhiAmZtNQt3osWLWrwXVUz06RJExQqVAiOjo4oX768Ufps2LAhGjZsaLD2e/fujd9++w2Ojo4ZbulfEKXOeypXrpzRJ2Jv3boVhw4d0h3NcPjwYVy4cAEA8M8//6B79+5GjYfIkITMxeQ5IcR+AH4AvpNSPtN7VPmAEMLPzc3Nzc/PT+lQ8r3jx4+jWrVqsLe3VyyGmJgYFClSBFZWVoiKisKECRNQvXp1fPPNN4rFRPp17949HDx4EJ6enihbtqyisURHR2PYsGEoUqQINm7cmOlxCER54e7uDn9/f38ppVGX0OU2MdEieUjzLSnlDSHEdQDnAQQBCAQQKKV8oNdITQwTk5y7ePEili5dikGDBqFTp05Kh5NrX375JWbPng0geQ5IRuf8EBGpnVKJSW4f5TQE4AEgOuVzl5TXQCQnLBBC3EdKkgLgApKTldA8RUv52sSJE+Hr64uDBw8iKirK6P1LKSGEyHM7Xbp0wfLly+Hi4gJnZ2c9RFawXL16FWZmZlw6S1RA5Wq5sJTygpTyByllamJSCkA7AJ8A+A3AZQBlAHQF8BmAPwHcEELECiGO5T1syo/atWsHIHsrYfTtxx9/hKWlJUaMGJHjuqGhoWjSpAn69OmDhIQEuLm5ITo6Gv7+/lluNkZp+fv7o06dOqhTpw4CAwOVDoeIFKCXfUyklLFSSl8p5VIp5TApZV0AtgCaAHgPwI8A/JE8QvPmHZiowJo5cyaePHmCzZs3G73vHTt2ICkpCdu2bctx3a1btyIgIAA7duxAUFBQmnvh4eEIDw/XV5j53uPHj3U7zT5+/FjpcDIUHh6O77//Xu+jev7+/oocu0CkNgZblSOlTABwLuUFABDJ4+TVDdUnmb5ixYop0u8333wDa2trDBgwIMd1Bw4ciF27dsHR0RH169fXXT9//rxux9TTp08rcuKv2iQkJGD8+PGIi4vDqlWr0p2j061bN/zxxx8wNzfXHQegNr1790ZQUBB2794Nb29vvbS5c+dO9O7dG5aWlrhy5UqavXSIChqjLheWyTNtrxuzT6LsaNy4MXbu3Jmruk5OTshokvO9e/d029vfu3cvT/HlF0eOHMHatWsBAO3bt8fw4cPTlRk4cGCW7Wi1Wvj6+sLV1dXoK2TKlCmT5l0fnj1LXtyYmJio2xmXqKDiPiZEBtKtWzds3LgRANC1a1dlg1GJJk2aoGbNmoiLi0Pr1q1z3c60adOwaNEiODs7IzQ0Z3PqNRoNpk6disePH+O7777L8Sjdzp07ERAQoNdDCL28vGBlZYUyZcqgTp06emuXyBQxMaEC7cSJEyhRooReNhGTUuLnn39GiRIl0KtXLwDAsGHD8tyuEpKSkjBjxgwkJCRg/vz5KFy4cJZ1Hj9+jH79+gEA/vrrL5QqVSpdGTs7O1y9ejXP8cXExABIPg5Aq9XCzCx5utzNmzfRu3dvlCpVCrt27Ur3qAhI/jdfvHgxgOSRsg8++CBHfRctWhRt2rTJ2xfwGiFErh4jEuVHTExIdaSU8PPzQ/Xq1fU6XP667du3o2/fvrCwsMDly5fh4uKSp/Y2b96sW9Vz5swZNGnSRB9hKuLQoUNYuHAhAKBRo0YYMmRIlnUOHz6Mo0ePAkh+ZJOdRzK5tWTJEjRq1AgtWrTQJSUAsHfvXt2OqGfPns1whZerqytcXFwQExODVq1aGSQ+KSWOHz+OmjVrws7OziB9EOVXTExIdb7++mvMmjULlStXRmhoKMzNzQ3ST+qzfI1Go5sLkhd2dnYQQsDKykq3Vb6pqlevHsqXL49Xr16hadOm2arTqVMn3YRVQ2+QZ2tri/feey/dvjP9+vXDrl27UKpUqUwftZQuXRo3btzQ2741GZkzZw6++OILVKxYEeHh4Yodl0BkipiYkOo8eJC8aXB0dDQ0Go3BEpPBgwfDxsYGpUuXRq1atfLcXocOHRAcHIwiRYrAyclJDxEqx8HBAbdv304+UKtQ9v43UapUqWyvUomNjUXXrl3x7Nkz7Nu3D5UrV85RfBcvXkSbNm1QsmRJnDlzBqVLlwYAlC9fPtun7xoqKQGgW+r85MkTaDQaJiZEOaCXfUyI9Gn+/PlYvnw5fH19YWlpma06z549Q6dOneDh4YG7d+9mu69evXqhZcuWuQ01ndq1a5t8UpLK3Nw820lJqk2bNqF+/frYsGHDG8v5+/vD398fly9fxqFDh3Ic24kTJxATE4OwsDBcvnw5x/UN7ZtvvsGPP/6I48ePZ2t+DhH9v1ydlUNZ41k52fPgwQO8ePEiz7/MDxw4gG7dugEA1qxZgzFjxuDixYsAgLp16+Y5zvwkOjoa3377Ldzd3dGzZ0+9tu3q6opLly6hWrVqCAkJybRcfHw8Ro8ejadPn2LDhg26EY/sevr0KSZPnozSpUvjm2++STPPJDNBQUEoVqxYvkkciQxNqbNyIKXkywAvAH5ubm6SMnf79m1ZvHhxaWZmJg8dOpSntp4/fy67d+8uW7duLaOiouTp06elmZmZNDMzk35+fnqKOH8YN26cBCALFSokY2Nj9dr26tWrpZOTk/z+++/12m5e7d27VwKQ1tbWMiwsTOlwiEyCm5ubBOAnjfz706Qe5QghKgohPhJCHBZCRAghXgkh7gkhtgkhmmVSp5gQYokQ4pYQIkEIcVMIsUgIkX4dIRnVw4cPdcs9w8LC8tSWjY0N9uzZA19fX5QvXx7Pnj2DVquFVqvVbV5FyerVqwcAcHFxQZEiRdLdf/DgAW7evJnjdkNDQ9G1a1eEhYVh/PjxeQ1Trx4+fAggecJzZj8PcXFxmDt3LrZu3WrM0IjoNaY2+XU8gKkAQgEcBvAQyaca9wLQSwjxrpTyz9TCQggbAMcA1E8p/zuABgAmA2gthGglpYw37pdAqerXr4/Nmzfj3r17GDVqlF7bbt++PXbt2gWtVouOHTvqtW1TN3r0aHTu3BllypRJNykzMjISderUwYsXL3D48GHdwYpZ+eeff9C6dWtYWFggMDAQNWvWNETouTZ06FAIIVC2bNlMH+0tW7YMM2bMgBACDRs25LbwRAoxqRETAGcAtJFSVpNSjpZSTpdS9gPQFoAGwCohhNV/yn+K5KRkgZSys5RympSyM4AFSD5g8GNjfwGU1qBBg/Dxxx8bZNVCz549dRudAcnLgg8ePJinw9fGjRsHe3t77NixQx8hKsbR0THDk4/v37+Pp0+fQqPR6EaxfH19sW7dOiQlJWXa3p07d6DRaBAfH69bVaUmZmZmGDZsmG4eUkZq1KgBIQTKlSuX4eZwRGQc+WbyqxDiEIBOAJpIKQNSDgy8DaAYgPJSyhf/KWsD4B6AB1JKg/xZxMmv6vPpp59i0aJFcHBwwK1bt7I1YfK/pJSwsLCARqPB22+/neXZOjdu3EDx4sVNboOt3377DQ8fPsT48eMRFRUFJycnaDQafPvtt/jkk08yrCOlxLp161CkSBEMHjzYyBHrT3h4OEqWLIkSJUooHQqR4pSa/GpqIyZvkrpDVuqfdS4A7AH889+kBABSPv8HgLMQwtF4IVJmbty4gTZt2mDChAkwVLL88uVLAMlzCXLThxACCxYsQPPmzTFlypQ3lt27dy9q1qyJGjVq5GmERgleXl74+OOPUahQIVhbW+u2dX/TYXlCCIwZMybLpGT//v2wsbFBhw4doNFo9Br3iRMncO3atTy14eTkxKSESGH5IjERQlQC0AFAFIDglMup+4tntmYx5LVyue07IaMXgAwn41LG1q1bh2PHjmH58uX4999/M/2lJaWEj48PIiMjc9zHokWLsGHDBpw4cSLXm7Z98sknOHnyJJo3b/7Gcrdu3YKUEk+ePNGd66IUrVab67plypRBcHAwTp8+jaFDh+Y5lr179+Lly5f4+++/dZuQ6cPmzZvRqlUrNGjQABEREXprl4iMz9Qmv6YjhLAA8CsAKwBTpZSpv9GKp7w/yaTq09fKkYIGDBiAbdu2wcXFBa1atUJiYiL++ecf1KhRI025RYsWYerUqShdujRu3boFGxubbPdhbW2N4cOHZ3jvwoULqFSpkt7mFrz33nvQaDRwcHDQy66yuaHRaNChQwecOnUKW7Zswdtvv52rdhwdHeHoqJ+BxcmTJ+P+/fvw8PB44whMTr14kTwompiYqJfjBV63YcMGhIWFYdq0aTn6mSOinDPpxEQIYQZgI4BWANZKKX81dgxSSquMrgsh/AC4GTkck9W4cWOEhoZi37596N69O4Dkg/BeT0xSl3rGxcW9cTJmTnz33Xf4+OOPUalSJYSEhGR7t9k3sbCwwIQJE/QQXe49efIEvr6+AJI3oMttYqJPzs7O2LZtm97bHT16NIoXL46KFSvqfTXN1atXMXLkSADJJwtPnTpVr+0TUVom+ygnJSn5CcC7AH4DMPa1IqkjJZmNiBR7rRypQOfOnTFlyhSMHz8e/fv3T3d/5syZuscxxYvrZ7Ardej//v37SEhI0EubxpKQkIANGzbg/Pnz6e6VKlUKy5cvx8CBA/P9L1MhBAYMGJDuEZuXlxesrKywcePGbLf1/PlzbNq0SfdzUb58edjb28Pc3Fy3BwwRGZCxd3TTxwvJCdXPACSAzQDMMyhTPeX+wUzaOJhy39FAMXLnVxPx9OlTuWjRInns2LFslQ8MDJR2dnaycePG8tmzZxmW8fb2lo0aNZLz58/XZ6jpfPbZZxKAtLGxkU+fPjVoX6bI0tJSApA9e/bMdh0vLy8JQFatWlV37dmzZ/LevXuGCJFItbjzazaljJRsADAUwJ8Ahsj/n1fyXyEA7gJonrI8+L9t2ABoDiBcSpnzWZRkVAkJCWjTpg3s7Ozg7++f7n50dDSCgoJy3b6trS0mT56MVq1aZav8oUOHcP/+fQQEBODq1asZllmyZAnOnTuHWbNm5Tqu7ChWLHngr0iRIjk+cC+n/vjjD9SqVQvLli0zaD/69OOPP6JHjx748ssvs10n9fv43711ihYtanLLvolMlrEzoby8kDxSshHJIx1bABTKovzslLLzX7s+P+X6dAPGyhETPbl69apM+feSM2bMSHPv5cuXsmLFihKAXLlypVHiuX//vuzfv7+cNGmS1Gg0GZbZsWOHrFKlipw6dapBY9FqtfLvv/+WkZGRBu3nwYMHsnDhwhKAtLOzM2hfxhIbGyuXLFkiz549m+Z6XFyc3L59u4yKilIoMiJ1UGrExNQmv34BYBiA5wBuAPg8eR+1NHZKKS+kfLwQwNsApgohGgA4D6AhkjdiOwvgO2METXlTs2ZNTJ06FWfPnkWhQoXw8OFD3YqOuLg43U6jxlomWq5cOWzZsuWNZXr16pVm11lDEUJke9v4vAgICEB8fPLpDW3atDFoXzdv3sT69evRrVs3WFlZoW7dulmOBm3evBlmZmZ45513dNdu3bqF33//HX379oWLS/pdAaZMmYK1a9fC1tYWjx8/1vVRuHBh9O7dW79fFBFln7Ezoby88P+jJW96DX+tTnEASwFEAHgF4BaAbwHYGjhWjpjombOzc4bzBXx8fOTixYvl8+fPFYos/3v16pUcO3asHDJkSJq5LImJiXLWrFnyyy+/lElJSXrpq1OnTrqTgAHI4cOHv7H8vn37dP/9Hz58WHe9ZcuWEoCsV69ehvW++OIL3QiQVqvVS+xE+QnnmGSDlHK4lFJk8dr4Wp0nUsqPpZSVpJSWUsrKUsrJUkoeOWtiypUrl+Y9Vdu2bTFp0iTuL2FAFhYWWLVqFX755RfY2trqru/cuROzZ8/Gl19+ib179+qlr9Ql4qlHBoSHh7+xfJkyZWBubo5ChQqhTJkyuuupe69ktgdL6sjP/fv3sW/fvryGTUR6YlKJCRVshw4dwrFjx7Bq1Sr4+vpmeVZNXty6dQtDhgzBmjVrDNZHflCnTh0UL14cJUqUQO3atfXS5rJly3D58mX8888/+Prrr7Nc6tu0aVNcuXIFV65cQYMGDXTXN27ciDNnzuCvv/5KV+f06dNYsGABzM3NYWZmhpIlS+ol9uw4efIk6tevj2nTphmtTyJTkm8O8VMbHuJnOBcuXEDDhg0hpcSWLVtgbW2NmJgYeHl5IYM5R7ny/vvvY82aNRBC4PHjxzw/5Q1evHgBIQSKFCmil/aSkpIghMj1sQH/FRMTg8mTJ8PR0THNyhwPDw/4+fmhfPnyOHLkCOrUqZPnvrLr3Xffxe+//w4geY5U4cKFjdY3UU7wED+ibLKystL90rpz5w569OiBoUOH6v5nrw8dO3ZEoUKF0KJFC92SXMqYjY2N3pKS69evw87ODo6Ojrh9+3ae21u3bh1++uknzJ49GwEBAbrrXbt2BQD07NnTqEkJAIwZMwY1atTAxIkTmZQQZcDUVuUQ4a233sL58+fx7NkzlC1bFtOmTcOrV69QunRpvfVRoUIFbN++HT169NBbm5S106dP6w73u3jxIhwcHPLUXuvWrWFrawt7e/s0K3NmzpyJTz/9FFZWGZ4oYVBt27bN8ynIGTl//jyuXLmCd955x+B72hAZEn96SZVCQkJgbW2d6S8mV1dX3ceXL1/GixcvULduXb30ffHiRbRs2RJSSmzbtg19+vTRS7uUtQEDBuDcuXOwtrZG586dMy2XlJSEX375BVWrVkXr1q0zLde0aVPExMRk+FhIiaREX168eIEff/wRDRo0QLt27RAdHY0WLVogLi4Ot27dwowZM5QOkSjX+CiHVMfHxwc1a9ZEjRo1EBYWlmX5qlWr6i0pAZJXg6SuCFHqL8/nz59j3759ePo0+RDsH374AS4uLli7dq0i8RhL4cKFsWzZMsyfPx/m5uaQUiImJiZduWXLlmHUqFHo0KEDIiPfvHmzPuaqqM2cOXMwefJkdOnSBdHR0ShUqJAu0eLqNDJ1TExIdW7fvg2tVouXL18iOjra6P3XqVMHp0+fxtGjR9GzZ0+9tOnj45Oj5bQDBw5E9+7d0a9fPwDJJyD/+++/+P777/USj6no0aMHSpUqhW+//TbN9dTHdjY2NrC2tlYiNEVVrlwZQPLSeWtraxQvXhznz5/HkSNH8NFHHykcHVHeMDEhxUVFRcHd3R0dOnTA06dP4eXlhR9//BFbtmxBkyZN9NbPnDlz0KRJExw/fjzLso0aNdLbDqdnzpxB+/bt0aNHD+zevTvTcnFxcWjZsiUcHBxw7949AMDLly8BAFOnToWrqyumTJmil5hMReq/1ev/ZsOHD4e/vz/Onj2b4YhKfjd27FgEBQUhKChIN/HYyckJHTp0UDgyIj0w9o5uBeUF7vyabevWrdPt3Llv375ctXHnzh25adOmTE/Y1Wq10szMTAKQvXv3zku4OXbhwgVpbm4uAchDhw5lWu78+fO678PUqVPlunXr5N27d40Yqfrs3btXjhkzRl6/fj3D+61bt87wDCUiyjuelUMFVko8PtsAACAASURBVI8ePdC2bVvY2Nhk+4Tf13Xp0gXBwcHo168ftm7dmu6+EAITJ07Ejh07MGrUqLyGDCA5qc/Ovin16tVDQEAA4uLi4O6e+XYA9erVw8SJExEeHo4JEybA3t5eL3GaMk9PT3h6emZ6/9KlS2neicj0MTEhxZUrVw4+Pj55aiN1guObJjouWbIES5YsyVM/qVauXIkJEyZg6NCh2LBhQ5bl69evn2UZMzMzfPcdz5XMid27d2PPnj344IMPlA6FiPSEc0woXzh48CC2bt2K9evXG6W/Xbt2QavVYseOHXppLyQkBPb29qhRo4butGTKmoeHB+bNm5fpeThKCgsLy9Z8JiJKi4kJqUJCQkKedvq0s7NDv379jLZUcu7cuejduzdWr16tl/aOHz+OqKgo3LhxA+fPn9dLm/py9OhRTJo0CTdv3jR4XxcvXkTZsmVRu3ZtxMbGprkXGBiIhQsX4tGjRwaPI68ePnyI+vXro3Xr1vl+iTeRvvFRDilOSgl3d3cEBgZixYoV+PDDD3X34uPjcf36dbi6uur2FlGDxo0bY/v27Xprb8CAAThx4gRsbW1Vt7KiT58+iI2NRVhYmEEPTgSSl1U/evQIjx49wuXLl9G8eXPdvU6dOuHRo0cIDAzM8PgBrVaLRYsWITExEdOnT1d0/5LExETEx8cDAJ4940HmRDnBxIQUcfHiRUybNg1du3bFmDFjdJMXXx8t8PT0hI+PDz788EOsWLFCiVCNwtbWNstTdJXSsGFD+Pj4oFGjRgbva9iwYTh//jzs7OzSTRR2dHTEo0eP4ODggKNHj6Jq1aqoVKmS7v6BAwd0J/ZWr14dAwYMyLAPrVaLe/fuGXRysb29PY4dO4bQ0FAMGjTIYP0Q5UvGXgZUUF7gcuE3euedd3RLYxMSEuTevXvl5MmTZVRUVJpylStXlgBkx44dZdu2baWLi4u8fPmyQlGrg5+fn/zkk09kSEiIUfp79eqVjIiISHPt7t278tWrV0bpP9XTp0/lmTNn5OLFiyUAWapUqTTLw0NCQmTx4sVl0aJFZXBwcKbt9O/fXwKQU6ZMMUbYRCZLqeXC6hkbpwKlX79+sLW1xcCBA2FpaQlPT08sWrQI5cuXT1Nu165dmD17NsaNG4ejR48iJCQEe/bsAYDUBBBA8iZtGo3GqF+DUvr374/Fixfjf//7n1H6s7CwSDO5dNmyZbC3t0fLli0BAOHh4YiKijJ4HLa2tmjSpAmeP38OIHlDusTERN39atWqISIiArdv337jicFnzpwBkHxgoLG8fPnS4D+fx48fL5Cb8FE+ZOxMqKC8wBGTXMvoL/HExEQ5evRo2blzZ3n79m05efL/sXfecU1e3x//hL0cIAqKCxei4l6ouCdurXXhHhW1pS3WVdfXRR11a9W66t6KCrgXKLgVESt7DxGRnYQk5/cH5fmBrOwg3PfrdV/A89x7zkkC5OTeMxaQlpYWrVq1ilauXEkAaPDgwRqwVnqys7MpPT1dYTmDBw8mAOTq6lro3sqVK8nIyIjWr1+vsJ7imDhxIgEgfX19unPnDmlra5OxsTGFhoaqTGd+hEIhHTlyhJ49eybXeh8fH5o/f77adt5u3rxJenp6ZGtrSxkZGTKtlWV3bPz48dwuZHZ2trzmMhgcmtox0fgbeHkdzDGRj6VLlxIAmjNnDrVv355at25dZPXTRo0aEQBq3bo1DRo0iACQubm5BiyWjpiYGKpRowYZGxvTy5cvFZKVk5NDoaGhlJmZSYcOHaL3799z95o0aUIAqEWLFoqaXCzR0dHk4uJC165do6NHj3Jvhj4+PhQcHEwikYiIiIKCgqh58+bUu3dvmd+QpWXfvn3k4uJCKSkpBa5HRETQ8ePHVaZXFpYvX849R7Iev9WuXZs7yiyN+/fvU7NmzejXX3+V11QGowDMMSlngzkm0nPr1i06evQoicViatmyJQGgWrVqcf/Mjx8/XmiNp6cnjRw5ku7evUtBQUE0f/58unv3rgasl467d+9yj+fAgQNKkTlnzhwCQNWqVSOxWExERJcvX6YBAwbIXdpfVkQiEe3atYuOHTtGM2fOJAA0fvx4IiIuFgQAeXt7F7n+1KlT1K9fP7p9+7bMukNCQjj5K1euLHAvLzZp6tSpMstVNp8+faI5c+bQ9u3bZV6btzu2YMECFVjGYJQMc0zK2WCOiXQEBgYSj8fj3rDv379P33//PV29epW+++47GjFiBH358kXTZiqMRCKh9evX06JFi5S2ze7q6koAqG7duiSRSBSSlZCQQMOHDydnZ2dux0NWOnXqRACoefPmRJTbv6hv377k5ORUbKBsngMqz99Keno6NWrUiHR0dMjT07PAvbwdtcmTJxNRbh+i2NhYmXVompycHAoLC9O0GYwKCnNMytlgjol0REREkKGhIQGgixcvatqcbwqRSEReXl5KafS3ceNGbvfB19dXLhnv3r2j3377jV69eiX1mt9++41MTExox44dcukUCASFjnGIiM6fP086OjpUrVo12r59OwEgU1NT+vTpk1x6GIyKCGvix6iQ1KtXD2/fvkVKSgrat2+vVNkZGRmYNm0axGIxDh8+jCpVqihVvqbR1tbGwIEDlSLL0dERu3btgpWVVYkZLSXRrFkzbNy4UaY1GzdulHlNfvT09KCnp1fo+sePHyESiZCcnIyQkBAAQFpaGrKyslCtWjW59TEYDNXDHBOGxmnYsKFK5N64cQPnz58HAIwePRoTJ05UiZ7yQPPmzREZGalpM5TG9OnTkZycDAsLC0yZMgW2trZo3Lhxmeypw2AwCsIcE0a5pXv37mjXrh3EYjF69+6taXMYakRfXx/Lli3jfi6q5ktaWhq0tbXV1l+JwWBIByuwxii3VK9eHc+fP8erV69Qs2ZNTZujEFu3bkWDBg1w6NAh7hoRFSgwlp+MjAwcO3ZMLY33vkXevn0LKysr1KlTB+Hh4Zo2h8Fg5IM5JoxvioSEBERFRWnaDLWzfft2hIeHY/fu3QCA1NRU2NjYwNTUFH5+foXmz507F5MnTy6TO0UpKSmYOHEifvrpJ41V6/X390dGRgZSUlLw4cMHAMD58+fh5eWlEXsUwc/PD48ePdK0GQyG0mCOCeObITQ0FI0aNULDhg3x+PFjTZujVpYuXYpWrVph4cKFAHKfi+DgYGRmZsLb27vQ/LyA0KICQzXNiRMncPLkSezcuRM+Pj4Kybpz5w5MTU3Rv39/iEQiqdd9//33WLFiBdavX48BAwbg0qVLGDNmDBwdHb+pN3k/Pz906dIF3bp1w/379zVtDoOhFFiMCeObIS4uDpmZmQBy+7N06dJFwxapj9mzZ2P27Nncz23btsWaNWsQHR2NWbNmFZq/e/duDB06FJ07d1anmVLRu3dvWFpawtzcHC1btlRI1pUrV/DlyxfcunULiYmJsLKykmqdrq4u/ve//3E/V65cmft+69at6Nq1K/dzQEAAwsPDMWTIEPB4PIXsVTZisTivPIFMjhmDUZZhjgnjm8HBwQGHDx9Geno6xo0bp2lzNE7+4M6v0dfXx/Dhw5WuMzIyEk+fPsWwYcOgr68vl4xmzZopremfi4sLIiIi0KFDB6mdkqLo06cP6tSpg+joaKSlpXHXP378iI4dOyI7Oxs7d+7E/PnzC60VCAQIDg5G8+bN1e64dO3aFffu3YNIJELfvn3VqpvBUBXMMWGUOfh8Pu7cuQN7e3uYmZkVuDd16lTNGKVkcnJyoKurCwCIiIiAhYUFDA0NERwcDD09PdSrV0/DFhaGiGBvb4/4+HjMnz8fO3fu1LRJaNCgAdzd3ZUiy8vLC1euXMGkSZO4a3m7EV9/n59Bgwbh3r17+Pnnn7F161al2CILPXv2VLtOBkOVsBgTRplj1qxZGDJkCPr3769pU1TCjBkzoKenh02bNmHXrl2wtrZGp06d4O3tDVtbW9jY2OD9+/catVEgEGhUvyZo3rw5lixZgtq1a3PXLCws8OTJE7i7uxe5WwIAQUFBBb7KAxFh//79OHDggNwyGIzyAtsxYZQ5srKyCnwtb1y7dg0A4OHhgSZNmgAAPnz4gOjoaIjFYojFYnz69Elj9i1ZsgR//PEHXFxcsG3bNu46j8eDr68vd5RTUbCzs4OdnV2x993d3XHlyhXMnDlTbh1Xr17FDz/8AACoXbu20ir6MhjfIswxYZQ5Dhw4AEdHx3J7Zv7333/jxIkTWLBgAerXr49q1aqhe/fuGDhwIPh8PoyMjODg4KAx+zw9PQEAe/bswdy5cznnCchtIaCMY6YpU6bg1q1bOHjwIAYNGiTVGnd3d8THx2PWrFnQ1tZW2AZpEAgE6N27NwICAnDlyhX06NGj0Jx27dqhXbt2CumpU6cO9PT0wOPxFIqVYTDKBepuzlNRBlgTP5kRCoX0448/0owZMygjI0OtusPCwujixYuUk5OjVr1lkV27dnEN/bZt26Z0+ZmZmZx8Jycnqdb4+/tza/bt26d0m4ojJCSE09uiRQtKSkoqct7Lly9p+PDhdPToUbl1RUVFUXR0tNzrGQxlo6kmfizGhFFmuHXrFnbu3ImDBw/i3LlzatMrFArRsWNHjBo1CitWrFCbXlWxd+9eTJ8+HXFxcXKtnz17NqZPn44hQ4Zg/PjxSrYOMDIywurVq9GmTZsCKdAlUbVqVZiYmKh9R6Fhw4ZcOfuAgADs27evyHkrVqyAu7u71I+nKOrUqVMgviUPiUSCyZMno3Xr1vD395dbPoPxrcAcE0aZoW3btrC2toalpWWBOhKyIJFIcPXqVa6jrCzrAGisEml++Hw+Hj58iOzsbJnXJiYmwtnZGYcPH8aGDRvk0q+rq4uDBw/i6tWrqFGjhlwySiMtLQ2vXr2Ci4tLqXO/fPmC58+f4+nTp3j37h0GDx7M3XN3d8eiRYtUGpOzceNGtGjRApUqVeIq6dJXGTojRoyArq4uRo8erXT9YWFhOHbsGN68eYNjx44pXT6DUeZQ9xZNRRmowEc5AQEB9OHDB5nWBAYGkoWFBdnY2BS7XS4N69atIwBUpUoVSk9Pl3pdUFAQnTlzhoRCody6v2bv3r00duxYCg4OlmndqFGjCAA5OjrKrFMoFFL79u1JR0eHLl++LPN6dTF27FgCQNWrVy91bp8+fQgA9e/fv8D11NRU0tbWJgA0b948VZnKIRaLiYjon3/+IR0dHRo5ciSlpKRw9yUSicr0Tp06ldq2bUv+/v4q0cFgFIWmjnJY8CtDqfj4+KBHjx7Q1tbGixcvSsxmyM+DBw+QmJiIxMREvHnzBn369Clx/ubNmxEcHAw3N7cCtU7yql9KJBJuF0QaGjdujMaNG0s9vzQyMjLg7OwMIoKhoSEOHz4s9dqPHz8W+CoLurq6ePr0KQQCAQwMDGRery527dqFtm3bol+/fqXOzWtU+HXDQiMjIzRt2hTv3r1DmzZtVGJnfrS0cjeYr169CpFIhMuXL+PSpUvYtGkTFixYoLLialpaWjL9/jAY3zrMMWEolU+fPnFOQUpKitTrIiMjUbduXfTp06fUglHv37/Hb7/9BgCwsrIqEBeydOlS2Nraonnz5gXKjKsbY2Nj9O3bF/fv34ejo6NMa0+dOoULFy7IXbmVx+NJ7ZTk5ORAR0dH7RVLzc3Nub4/pXH+/Hlcv369UPaOjo4Onj9/juTkZLXGnaxatQpEBA8PD/D5fDx48AALFixQm34Go7zDIyq6miFDMXg8nm/nzp07+/r6atoUtXPy5EkYGBhg1KhRUs0PCwtDw4YNAQALFy4sNTYiIyMDHTp0QEREBLy8vNRa+TIxMRE5OTlFBikWhVgsVltqq6zcvXsXgwcPRuPGjeHn5wcjIyNNm/RN4eHhgUuXLmHBggVo2rSpps1hMJSOvb09/Pz8/IjIXp162Y4JQ+lMmDBBpvm1a9dG9+7d4e/vXyCwsThMTEzw7t075OTkyN2vRR6Cg4PRpk0bCIVCPHz4UKoGeWXVKQGA+/fvg8/n4+3bt4iJiSlQr6Q8kpycjB49eiA1NRV37txR+PEOHjxYqt9XBoMhG8wxYWgcPT09PHjwoMh7EokEnp6esLGxKRADoqWlpVanBABiY2MLdDcui517ZeHHH39ETEwMmjVrJteb9IkTJxAXF4eff/6Z6/tTVrh58yYqV65c4DV6+fIl3r17BwC4d+9euXfEGIxvFeaYMMoc2dnZePLkCTp16oQdO3Zg8eLFqFKlCqKiogrFjfj4+ODSpUuYN28eGjRoILWOqKgoSCQS1K9fX+o1PXv2xIEDB5CRkYGxY8dKva6sUr16dRw6dEiutW/evIGTkxOA3HiauXPnKtM0ALll+nV1dWV6XYHcFOIRI0ZAS0sLr169QsuWLQHkvn7Ozs5ITU0ttjt1aGgodHR0ymQTRQajosDqmDDKHGPHjkWvXr0wbtw4LhMjOzsb1tbWWL9+fYG5o0ePxpYtW4ptsFYU/v7+aNy4MZo0aYLnz5/LZNuMGTPg4uLCZWhUVGrUqAFTU1Noa2srNZspDx8fHzRr1gy2trbcLoc85A/q1dXVxZ49e3DixAlUqVKl0FxfX1/Y2NjAxsYGgYGBcuvMjyIxfL/++is6dOgg8+8og/GtU7H/uzLKJElJSQByM3yWLFmCs2fPon79+vj8+XOhT/h5aaKypIvGx8dDKBQiJydH7uqo6uL58+cYOHAg9u7dq2lTClCzZk0EBwfj8OHDCA4OLpTKqygfP36ERCKBUChEcnKyTGuHDx+O27dvw9fXV+p09aSkJMybNw9isRgCgUCuVO38xMfHo379+rC0tERwcLDM6z9//oytW7fi+fPnxVabZTDKLeounFJRBipwgTVFiYyMpO3bt1NUVBR37fLly+Tg4ECnTp0qMDcnJ4ciIiJk1nH48GE6cOCAwraqmuHDhxMA0tLSop9//lmpBeAUJSIigitwtnHjRqXL/+eff+js2bNKl1sUe/bs4XriLFmypMS5ycnJ5ObmRr6+vsXO8fDw4OQdPnxYKhsSEhJoyZIldPv2bSIimjVrFjVp0oS8vb2lfhwMhjLRVIE1jb+Bl9fBHBOGMjhx4gQZGBhwb3Lu7u6aNokjKSmJTE1NCQAdP35c0+YUS2xsbKkVU8PDw6lFixbUtWtXSk1NLXHutGnTCACZmJiQSCQqco5QKCQXFxeaPXs2ZWZmSmXn5MmTCQAZGBiwZpKMMgGr/MpgKBGBQAA3NzeYm5vDwsICDx48wNKlS1GrVi1NmyYTEyZMQMeOHdGlSxfo6uqibdu2mjaJw9zcHO/evcPHjx/RqlWrEudmZWVhxYoVMDc3x+LFi9VkYW72VOPGjSEWi+Hq6orNmzcXOa9+/fp4+/atVDLzAqbr1KlTbDq4rq4utm3bJpOtecdOtra20NFh/5qlJScnBxMmTEBwcDBOnToFW1tbTZvEUBR1e0IVZYDtmGiUffv2cbsMWlpaBIBmzZqlabPkRiwWS92L5enTpxQdHa1ii2Qj/1GJj4+P2vT6+Phwejt16qQ0uS9fvix1Z0UeQkNDKTs7W+lyyzP+/v7ca7xy5UpNm1Ou0NSOCQt+ZZRLWrRoAX19fZibm6N58+bg8Xiwt5eueGFISAhq1aqFRo0aITExUcWWSoeWlpZUZeOPHj2Kjh07okWLFirtuCsr7du3h5GREfe8qouuXbvixx9/ROfOnXH06FGlyW3Tpo1KWh40aNCgTPc4KovY2trCyckJnTp1wsSJEzVtDkMJsP1CRrmkS5cuiI2NhZ6eHgwNDZGamopq1apJtdbb2xvx8fEAgBcvXsjc60aT5GU0ZWRkICsrS2lyPT09sXXrVjg7O0vdaiA/HTp0wMePH6Gjo6P2wng7duxQqjx3d3c8e/YMCxYsQNWqVZUqmyE7Ojo6OHbsmKbNYCgR5pgwyi35HRFpnRIAGDNmDHx8fGBoaChV99uyxE8//YTKlSujUaNGqFu3rtLkLl26FG/evEF4eLhcjgmQW4itNDIyMjBv3jwYGBhg586d0NPTk0uXqvj06RNGjx4NsVgMPp9fbMwKg8GQH3aUwyh3eHl5wdTUFDY2NoiJiZF5vYmJCQ4ePIhdu3aVuVLrpaGrq4tZs2ahV69eSpU7YcIEmJiYcNVeVcWFCxdw9OhR7N+/Hzdv3pRqTU5ODo4fPw5/f3+V2gbk/m5YW1sDgNQ1UhgMhmwwx4TxzRMWFgY3NzeEhIQAyI2z+PLlC4KCgvD7779r2LrywcKFC5Geno5Vq1Zx12JjY7Fjxw5ERkYqTY+DgwPq1q2LJk2aoEOHDlKtWbNmDSZNmoSuXbsiPT1dabYUhYGBAV6/fo2IiAhMmTJFpboYjIoKc0wY3zwTJ07E0qVLuf4nCxYsQKVKlcDj8eDg4ICQkBBIJBINW1n+GD9+PFxcXDBmzBilyWzQoAEiIyPx4cMHWFhYSLXG0NAQQG4zyOJaBXz+/BkikUgpNhobG3O9dKKjo3H37t28TDwGg6EEmGPC+ObJi6XI+9quXTukpKTg06dPuH37Nho3bgxnZ2e5ZO/duxfdunXDnTt3lGbvt0R8fDz27duHxMREBAcHF3hzNzc3B5DbDFAafH194eXlpXQbFy9ejOvXr+PFixdFxrEcPHgQ5ubm6Nq1q1IdiMzMTLRt2xZ9+vTBpk2bZF5/4cIFjBo1Ck+ePFGaTQxGuUDd+ckVZYDVMVEbAoGAnj59Snw+v9A9W1tbAkDyvhZVq1YlANSrVy9FzfwmqVWrFgEgY2NjAkCjR4/m7mVnZ9P9+/elqmz69u1brp7MuXPnuOt79+6lJUuWUEZGhkrsJyKaPn06ASBtbW3KysqSep1QKKSzZ89ScHBwkfdTU1PJyMiIANCyZcuKlePv70/Lly+nkJCQAterV69OAKhHjx5S28RgqBNW+ZXBkBM9Pb1i4xFOnDiBU6dOYdq0aXLJnj17Nv755x+513/rpKWlAcjt7gwAAQEB3D0DAwP06NFDKjn567DkVUt9+/Yt5syZAwAwMzPDggULlGZ3flavXg19fX306tWLO/aRhuXLl2PDhg0wMzNDXFwcl+a8du1a7NixAytXroS3tzf8/f0xYcKEYuWMGTMGHz58wIMHD/DgwQPu+ogRI3Dw4EEMHz5c/gfHYJRH1O0JVZQBtmPCUAEikYi2bdtGR44cUYs+T09P6t69O+3du5cWLVpEL1++lFvWixcv6M6dO9zPycnJZGVlRbq6ulzjOnUjEAiof//+VKtWLfLz8ytwb8mSJQSAqlWrRgKBgLter149AkDt2rWTSsewYcMIAM2cObPQPWmr+TIYmoA18StngzkmDGUhEAiob9++ZGlpScuXL+fKbz969EjTpilMZmYmffr0SWP6P3z4wD2fixcvLnAvJyeHLl68SKGhoQWuHzt2jLp160ZXrlyRSodQKKSAgAASi8VKs5vBUAfsKIfBYBRJZGQkbt++DQAIDQ2Fjo4ODAwMULNmTQ1bpjhGRkYwMjLSmP4mTZpgwYIFCAgIwA8//FDgno6ODkaOHFlojZOTk0z1XHR1ddG8eXOFbWUwKgrMMWEwpISIpOpXIy1ZWVl49eoVOnToUGKF08aNG8PJyQlxcXFYt24d3NzcYGBggBo1aijNloqMPBk1DAZDdbB0YQZDCnx8fFC5cmW0b9+eCwRVlJEjR6Jbt26YPn16ifP27NnDVTatVq0a6taty5wSFRMbG4tz585h/fr13G6VuvHz88N3330Hd3d3jehnMDQF2zFhMKTg5s2byMjIwIsXLxAZGYmmTZsqLDOvUWBcXFyJ8/Ia86WlpUEgEKBSpUoK665oPH78GOvXr8eECRNKzKABcnfGOnfuzLUz0NXVRUJCAszMzNRhKsfChQvh7e2NR48escwdRoXim9ox4fF4Vjwe72cej3eTx+NF8Xg8IY/HS+DxeBd4PF6nr+bq8ni80Twe7x8ej/eex+Nl8Hi8dB6P94TH4znzeDxtTT0OxrfH3LlzMWHCBKxevVoup+T58+fo0aMH3NzcuGsXL17E5s2bcfTo0RLXLl68GH/99Rfu3LnDFTVjyMayZcvg4eGB+fPnlzqXiCAQCLifa9asqfI4mODgYLRt2xbDhw/ndA8bNgw8Ho85JYyKh7qjbRUZAP5AbgR9CIADANwAnAcgAiAGMDbf3Kb/zU0HcBnABgB7AcT+d/0qAJ4KbWVZOQyOcePGcdkfRRWCY6iW3bt3k76+Ps2ZM6fYOX/++Sd1796dfH19KSgoiI4ePUrPnz+nlJQUuXQeO3aMOnToQGfPni11rpubG/f78eTJE+46y+RRHampqfT06VOWsl0CLF1Yujf7UQB6FHHdAYAQwGcA+v9dswIwF4DxV3ONATz775/AGBXayhwTBsfVq1fJ0tKSpk2bVuT9gwcP0qZNmygnJ0chPX///TfZ2dnRsWPHiryvqPzyikQi4SrTjhw5UikyGzZsSADIzs6u1Lnh4eHUtWtXGjduHAmFQqXoZ5SMnZ1dkWnijP+HOSaKOwI3/nM22ksxd/x/c3ep0B7mmGgQsVhM79+//ybeiB89esR9Wj506JBCsho1alTsm+HcuXMJAK1evVohHbLw/v17atmyJY0cObJAkbKyyA8//EA1atSgCxcuKEXe5s2bycrKinbt2qUUeQzlYmpqSgBo4sSJmjalzKIpx+SbijEphZz/vkrTQlSWuYxSEAqFEIvFMq97/vw5mjRpgjFjxhS5XiKRwMPDA2FhYTLLnjt3LmxtbUvsfCsQCODq6oolS5YorfOsPFhZWaFy5crQ1dVF48aNFZLl4uKCRo0a4aeffip079q1awAADw8PhXSURnh4OGbNmoVJkyZh/fr18Pf3x6VLl/D+/XuV6lWUvXv3IjExEaNGjVKKPFdXV8TExGDevHnFznn+/DmGDBmCI0eOKEUnHQjTeAAAIABJREFUQ3pu3bqFDRs2YPv27Zo2hfE16vaEVDEA1AXABxAHQFuK+Z7I/YTqqATdgmKGpCLsmDx9+pSMjY3J2tqakpOTZVrr6urK7RR8XV2TiGjt2rUEgExNTWVu8ubg4EAAyMbGptg5J0+e5PS7u7vLJF/ZJCUlUUxMjEp1eHh40JgxY1ReMXb06NHc86qjo0MdO3akSZMmfRO7V+rG0dGRAJCBgYGmTVEq165do9WrV1NqaqqmTWEoAKv8Kic8Hk8XwDEA+gAWEVGJH915PN5sAIMA3CUiTzWYWK559OgRMjMzER4ejqCgIHTu3FnqtTNnzsSTJ0/QqlUrWFtbF7qfk5O7sSUSiSCRSGSy6+DBgzh8+DC+//77Yue0a9cOZmZm0NHRQatWrWSSr2zUkW3j6OgIR0dHlevp3LkzLly4AACoW7cuHj58yDXAYxTku+++w927dzFu3DhNm6I0Pn36hOHDh0MsFiMjIwMbNmyQem1WVhaOHj2Kjh07om3btiq0klGmUbcnpMyB3HTnE8j9dLZfivlDkBskGwGgpoptqxAxJmlpaTR37lyVxC3k5OTQqVOnKCAgQOmy8xAKheyTvApITEykwMDAQp+YAwMDacqUKXTp0iWp5KSlpanCPIYKycrKovr168sVM/XLL78QADIxMaGsrCwVWciQFhb8Kp9TcuQ/p+QYAK1S5jsi97gnGkADNdhXIRwTTRAREUEtWrQge3t7uVM51UFMTAydP3+esrOzpZovFovJ1dWVJkyYoNHGdqpk6NChBICMjY1LnTtt2jQCQMuWLVODZZrhxo0b1KZNG9qwYYOmTVEqqampFBwcLPO6NWvWEACysrJi2UllAOaYyO6U/POfU3ISpcSVABj8n1MSA6CRmmxkjomK2LdvHxfD4OXlpTI9EomEvLy8KCQkROa12dnZZGxsTABo3LhxUq3x9fXlHtfmzZtl1vktsHXrVgJAw4YNK3VuvXr1CADZ29urwTLZCAkJoYEDByqcajpgwAACQEZGRkqy7NtGIpHQgwcPKCEhQdOmMIg5JvI6JadlcEriADRWo53MMVERnz59osGDB9P48eOl3o2Qh02bNhEAqlKlisxBfPfv3+ecjI4dO0q1JjU1lVq2bEnVq1en169fy2OyVMTGxtKXL18KXffz86MRI0ZIVRBMEdLT06Wad/v2bZo8eTI9f/5cLj1PnjwpUKxMGezfv5/+/PNP+vnnn7nXNzw8XG55Fy9eJGtra/r999+VZ+Q3CismV/Zgjon0Tkne8c1ZADqlzB/0n1MSD8BGzbYyx0QN/P7772RsbEybNm1Suuy8bWUjIyP6/PmzTGszMzOpR48eZG1tTYGBgSXOPXnyJO3Zs0ct/5hv3LhB2traVKNGDfr48WOBe7169SIAVK1aNZXpz8zMpD///JNu376tMh1EubVheDwe8Xg88vHxUYrM/M7m8uXLydLSkgYNGsRilBSEz+dThw4dyNDQkG7duqVpcxj5YFk50rECwBQAGQCCACwrog39ZSJ6zePxmgK4hNxsnfsAxhcxN4KIjqjSYEZhPDw8EB8fj2nTpkFbW7GWRSdPnkRmZiZOnjyJBQsWKMnCXBYvXoxGjRqhWbNmMDU1lWmtkZER7t+/X+o8X19frqlcpUqV4OTkJI+pUhMYGAixWIyPHz8iISEB1atX5+6NGDECDx8+VFodj6JYv3491q1bB11dXcTGxhbQr0yEQmHeBwQIhUKlyKxTpw4qVaoEPp8PR0dHrF69WilyKzpxcXF49uwZgNzaIn379lWa7JSUFAwYMADp6enw8vJC/fr1lSaboTq+Ncek/n9fTQD8XsycCACvAVgi1ykBgOJy8R4gdweGoSbevXuHoUOHcp7xrFmzip374cMHmJqaokaNGsXOmT9/PhYtWoSPHz/i8+fPSu0Aq6Ojo7I0ztTUVNjb2yMuLg4GBgbIyclB7dq1VaIrP3PmzEF6ejrq1q0LOzu7Avd++ukn/PjjjyjCgS9AWloaoqOj0bx5c5n1161bF0BuerQqG+P17NkTN2/eBBGhV69eSpHZoEEDrF27FgkJCSyVVYlYW1tjw4YNePPmDVxcXJQq28/Pr4DTU9L/G0YZQt1bNBVlgB3lFElUVBQXFFpSUbOLFy8Sj8cjMzOzQkcO+dm+fTu3ve7t7V2i7rdv39L8+fPp8ePHctuvLPIHuv7yyy80bNgwlRxHKZucnByu7P2WLVvkkvHmzRuVZR3x+Xzq3r07VatWTemF5J49e8a9Znv37lWqbIZqyM7OJicnJxoxYoTMBSAZ7CiHUUGoU6cOAgMDkZqaWugTe34iIiJAREhJScGXL1+K3fKfMmUK3rx5gxo1aqBLly4l6v7hhx/w+PFjeHl5ISQkRKHHoSidOnXC4sWLERcXB5FIhCtXruDKlSsYM2YM6tWrp1HbSkIoFCI6OhoA5H4OW7ZsyX0fFRWFV69ewdHREbq6ugrbFxkZiYcPHwLILcFf2u+ENIjFYiQmJqJWrVowMzNDWloabGxsFJbLUD0GBgY4duyYps1gyIq6PaGKMsB2TErk3bt3RZahz0MgENDWrVvp8uXLStOZl0kxfvx4pck8c+YMrV69mjIzM+WWcfPmTTIwMKAOHToQn89Xmm2q4t69e7R27VqFa8iIRCKytLQkAOTq6qok64iWLl1Kw4cPp6ioKKXIGzZsGBfw+vnzZ6lbB4SGhlLv3r3lLj64ZMkSql27Nh0/flyu9QyGorCsnHI2mGNSPHfv3iUej0f6+vr077//qkyPWCym27dvU1xcHHctJiZGadkvoaGhxOPxCACtW7euxLkfPnwoMbVZJBIpxaZvCZFIRObm5gSAXFxcFJaXnp5O169fl7mvUmlYWFgQAOrfvz93LTIyslQ9tra23NFPRESETDqPHDnCrXVwcJDL7q8JDQ2lBg0akJ2dHSUlJSlFJqN8w7oLMyoMiYmJICIIBAKkpKQoReahQ4cwbtw4BAUFcddWr16Nvn37ol27dlzfHSsrK2hpKefX3szMDJaWluDxeLC1tS123po1a2BjY4MePXoUO0fR7KRvEW1tbfj6+uLkyZMy9VMpju+//x4DBw7E2LFj5VovkUiwbds27N69u8D106dPY/78+di5cycA4OjRo6hXrx5atWoFPp9frLxKlSoByD1OqFmzpky25M/o+uWXX2RaWxz37t1DWFgY3r59ywWEMhhlERZjwlA7Y8eOhUAgQOXKlWVq+lccAoEAM2fOBBFBR0cHx48fB5CbPQIAmZmZMjcBLA0+n4/KlSvj/fv3+PLlS4lxIe/evQMAvH//Xqk2lBW8vLxw7tw5uLq6ypyp06hRIzRq1EgpdqSmphb4KisXLlzgnIDGjRujf//+AHIzfHr27MnNCwgIAACEh4cjPT0dBgYGRcq7du0aTp8+jcGDB0NPT08mW1atWgUtLS307t0bI0eOlOPRFOa7777DrVu3YGJigj59+ihFJoOhEtS9RVNRBthRjtzExsbSsmXLZMqqGDhwIGlraxc4j8/KyqIDBw7QmzdvlGrfrVu3SF9fn5o3by5VbElcXBwtXbqUHjx4oFQ71IWPjw/dv3+/2PtmZmaFjjqkgc/n05IlS2j9+vUkkUgUNZNiY2Ppr7/+otjYWLnWv3jxgvT19cnY2LjEI8aUlBRatmyZ1I0IGYxvFRZjUs4Gc0zkZ/To0QSATE1NZVqnrgqcy5Yt487/5WlU9i3h5+fHxdEUV6017/Vyc3OTSfahQ4e45/HOnTvKMFdh4uPjS0xPZzAqEixdmMH4j7x4jaZNmwIAsrKyMHDgQERGRuLq1asF0k3zo6Ojnl9nFxcXJCYmolmzZko7hiir0P872sUeh507dw7p6emoXLmyTLJbt24NY2NjGBkZoUmTJgrbqgwsLS01bYLG+fDhA+rXrw99ff3SJzMYKoAFvzLKHGvWrMH79+9x9+5dALmxGd7e3oiKisK1a9c0bF1u1dL9+/fj559/1rQpKqdz587w9vbG3bt30a9fvyLn8Hg8mZ0SAGjTpg0SEhIQGRkpU9Xb5ORkiMVimfWpk+joaFhbW8Pa2pqr+6IImZmZGD58OPr164ekpCQlWFg0S5cuRdOmTbn4GgZDEzDHhFEmadq0KRdU2KZNG8yfPx9DhgzB1KlTS1w3d+5cWFtb48aNG2qwsmLQrVs3pZV1/xoTExMYGhpKPX/fvn0wNzeHg4ODSuxRFr6+voiIiEBERAR8fX0Vlnf//n1cuXIFt2/fxtWrV5VgYdEEBgYW+JpHWFgY1q5dC39/f5XpZjDyYEc5jDKPlpYWl6pZEgKBAH/99RcA4MiRIxgwYICqTZObjIwMvHv3Dh06dFBa+nJF4PHjxwCAZ8+eQSgUypztoi6GDRuGmTNnct9/TUBAAN6+fYvvvvtOqoq33bp1Q8+ePbljTVWxa9cuNG/eHIMHDy5wvV+/fggLC8PKlSvh7+8vV58kZfHkyRNUrVqVVd8tz6g7qKWiDLDgV42wcuVK6tixY5noh1MS/wWV0fz58zVtyjdFVFQUOTs704ULFzRtitykp6dTpUqVCACtWLFCLTqFQiGNHj2aWrZsSYGBgTKvz18s7vr16yqwUDouXbpEAEhfX59CQkI0ZkdFgRVYYzCUwKpVq/DkyRPY29urRP7kyZOhr6+PQ4cOFbpHRNi3bx+OHj1aqpyYmBgAuVvmZ8+eLTVmgojw8uVLLF68GMHBwfIZLwMSiQQ7d+7E33//XercnJwcCIVCqWVnZWVh8uTJcHJyQmZmpkx21alTB3v27MGoUaNkWleW4PF4XKC2unZ8/v33X1y4cAH+/v44c+aMzOt9fX3h4uKCPXv2aHQnMj09HUBuz6aSitsxvnHU7QlVlAG2Y6JS0tPTSSAQKFWmRCKhS5cu0dOnT4udo6+vTwBoyJAhhe6dPXuW+1R59+7dEnX5+/uTq6srGRgYEABav359sXNnzpxJWlpaXPn2Hj16SP2YiIiOHz9OO3fulKns/ZkzZ7jHcuvWrWLnRUVFkYWFBVWtWlXqT+Lnzp3jZJ86dUpqm/KTkJAgd+2TxMRE+uGHHwp1CPb09KTRo0fTw4cP5ZIrC6GhoeTh4aG09gilkZOTQ5MmTSJ7e3sKCgpSi05VIJFI6PTp02Umvby8w+qYlLPBHBPFiYiIKLKpnY+PDxkYGFCdOnXo06dPCusRi8U0a9YsatSoEQEgHR0dCgsLK3LukSNHaOjQofTixYtC93x9fUlHR4cMDAzo/fv3Jeo8d+4caWlpcTVCNm3aVOzcatWqEQDOMZk7d67Uj83X15dzAv755x+p1z179oz09PTIyMioxGJjHh4enPxjx45JJTsuLo5sbW3JxsZG6oZ4+XF1dSUA5OTkJPNaIqLffvuNszk6Opq7bm1tTQCoffv2csmVB5FIRPPnz6fx48fT58+f1aZX1eTk5Cjlb5OhWdhRDoORjy1btqB+/fro2rVroXvPnj0Dn89HdHQ0IiIiFNYVFBSEv//+GyEhIQBye7gU17tGT08PN2/ehJubW6F7nTt3RnBwMIKDg7kaLMXx+vVrri7I/v370aJFC26b+mt2794NR0dHuLu748OHD9i1a5fUj83CwgLGxsbQ1tYusWz+2rVr0axZMy4du3379ggLC0NYWFiJQYYDBgzA77//DldXV4wZM0Yqm2rWrInAwED8+++/sLKykvqx5JEXAJv3tTjOnz+PWrVqFeo14+DgAB0dHdjZ2cHc3Jy7PmzYMPB4PAwdOlRmm+TF29sbu3btwqlTp3DixAm16VUlYrEYnTt3RvXq1XHkyBFNm8P4FlG3J1RRBtiOiUJMmjSJC3L7uqJreno6/fLLL7R582al6MrJyaFRo0aRra0t7d27l96+fVvs3DFjxhAA0tLSKrbS7Nq1a2n8+PE0e/ZsWr58eZHb9ampqbRkyRI6efIkDR8+nABQ3759C80TiUTUtm1b4vF4dOTIEbkeX1xcXLE7QHkYGRkRALK3ty/x6KYs8ObNG5o+fTrNmDGDbty4Uew8R0dHAkB6enqF7mVkZBR5tKXuLs+fP38mOzs7qlWrllxBqWWRtLQ00tLSIgA0e/ZsTZvDUAB2lFPOBnNMFCMuLo5+++23Et94NIG/vz8NGzaM9u3bV+T9wMBA7pgAUsRoEBH16tWLAFDHjh0L3UtNTeX+yf/www9KeQxFsXr1amrYsCFpa2sTADp58qTKdOVx+/ZtmjlzZomOYHG4uLgQANLV1aUvX74UOef+/fvUpUsX2rJli6KmMmTkzJkzNG/ePIqLi9O0KQwFYI5JORvMMVGcffv2UefOncnDw0PTpkhNZmYmtW7dmgwNDcnExIRq1apV6j/nhIQE2rdvX4F4h/ycOXOG5s+fr/J/8kFBQZxjcvToUZnXp6Wl0ciRI2nEiBGUmppa4N6ePXuoZ8+e5O3tzV2ztLQkANSrVy+p5G/fvp2+++47CgkJod27dxMAatiwodRB0CKRiAYPHkw1atSge/fuFbovTUNGWfHx8aHu3bvTjh07lC5b3cTFxVH79u3J3t6ekpOTNW0OQw0wx6ScDeaYKE716tUJAHXt2lXTpsiMWCwmPp8vdWPB169fk5mZGTVp0kRlQZBpaWk0dOhQGjhwYLFvLE+ePKErV67IJT9/Js/X2TaGhoYEgAYNGsRdq1q1arFHWF+TnJzMyc47Hvj3338pLS1NKtsEAgFNnDiRk/HTTz8VuP/HH38QABo/frxU8qRl8ODBxR4nlTW+fPlSYpbQP//8wz1/ly9fVqNlDE3Bgl8ZjK+YO3cuatWqxVXQ/JbQ0tKCvr6+1I0FHzx4gM+fPyMoKAjv3r1Tig13797F4cOHuRopd+7cwdWrV3H9+vView517NhR5uBPZ2dnGBgYICIiAu3bt0e7du0KlbCfPn06zMzMMHHiRAC5AZLZ2dkAgFq1apWqo2rVqujTpw8MDAy4qqQ2NjaoVKmSVDbevn2bCy5t0aIF5s+fX+D+rVu3CnxVFpMmTUL16tUxa9YspcpVNn///TdMTU1LbD0wZMgQODo6YsSIEejTp48arWNUONTtCVWUAbZjUqHg8/k0ePBgsrW1pYCAAJnXp6Sk0LRp02jRokVKqW0RGhrKxabkHSN8/vyZunXrRh07dqT4+HiFdeSRt/PRu3dvmdbdunWLFi9eLJMtitQusbW1pbp16xZZMfT169c0adIkunbtmlzy89i5cye1a9dOYTnqJm83SUdHh4RCoabNYZQR2FFOORvlwTHZsGEDGRsb09SpU2nhwoUUGRmpaZPKLK9eveK2uf/3v/9p2hyKi4vjyp6rOpD1+PHjNHjw4BLbADx69IhGjRpF7u7uKrWlOPh8Pq1fv17qWivyYmZmRgCoe/fuKtWjbMLDw2nWrFl09uxZjdrx9OlTunjxotwOKEO5MMeknI3y4Ji0bNmSS40FQCNHjtS0SRzXrl0rU9UfRSIRTZ8+nXr06FFqaq66iIiIKLGKrTqxt7cnAGRlZaUR/du2beMcR39/f5XpWbVqFdWtW5dOnDihMh3SkpaWJnWMU1kgMjKSdHV1CUChqrwMzcBiTBhlDjc3N/Tr148rFtayZUsNW5SLh4cHhgwZgj59+uDJkycF7n369KnUvjNfI5FI8Pz5c5n7tuRHW1sbBw8exP3792FtbS23HADIzs6Gm5sbzp8/r5CcevXqoUOHDgrJUBZ5xcuGDx+uFn0bNmxAjx498OzZMwBA06ZNoa2tDXNzc1hYWKhM78qVKxEZGYkJEyaoTIc0XLlyBWZmZmjduvU301NGS0uLK2xYVrtGM9SEuj2hijJQDnZM8hAIBBQaGqppMzhu3brF7eS8fPmSu75z504CQA4ODjLJy6uJoY7Xy8vLi0aOHFliLx03NzcCQDweT+EOqnFxcTRnzhyZytGrCmljZ8LDw6lBgwbUrFkzSkxMlFmPUCjkdke+//577npUVFS5KvteEgsXLiyy7H5Z5927d3Tz5k1Nm8H4D3aUU85GeXJMyiKPHz8u4JQQEY0fP54L4NuyZYvUVTxHjhxJAMjExISys7NVYS5H48aNCQC1atWq2DkXLlwgHo9HFhYWCr+Rzps3j3NyFO1d8uXLF5Ueg+Rx5MgR7k1V3tTlGTNmkKWlpdzrv3U+fvxIzs7OxRYCZDCkgTkm5Wwwx0T9RERE0KhRo7g3NWlLuF+7do1b89dff6nUxgULFhCPx6MVK1aUOC8sLEwpn+5Pnz5NWlpa1LZtWxIKhXJn/OTk5FDDhg0JAP35558K25Wfjx8/kru7O+cUpqWl0YQJE2j69OlFNnGsKKSnp0sVBCoUCmnOnDk0ceLEQoXtlAmfz6eLFy9SbGysynQwyhbMMSlngzkmmiE8PJxMTExIR0eHHjx4INWa+Ph4qlGjBhkYGJSYWaIs1NXqPo/U1FSKj48na2trMjU1lasEfGZmJhkYGBAAmj9/vlLts7OzIwA0efJkSkhIoDVr1tCTJ0+kXn/gwAFq2rQpHThwQKl2aZK8Y8n8BemK4/r165xjvX//fpXZNGvWLAJADRo0UJkORtmCBb8yGEqgfv36CAkJQUhICLp3785dnzFjBqysrODp6VlojaWlJcLDwxEXFwd7e/sS5WdlZcHT0xNfvnyR20YtLfX+2VWuXBnv379HeHg4UlJS8OjRI5llGBkZ4fr161i/fj3Wrl2rVPsEAgGA3OfW2dkZy5cvx8CBA6Vev3nzZvz777/YvHmzUu3SJPfu3QOQWySvNKpWrQpLS0tYWVmVWCBNUfKCykUikcp0lBWuX7+OLl26YN++fZo2pWKibk+oogywHZMyA5/P5z5RKlpyfPTo0XIF2GoasVhMixYtotmzZ1N6erpGbREIBDR58mTq2LEjde/enXbv3k07duwgCwsLrldP+/btpZa3f/9+srGxUelugby8fv2a5syZU6BHUB7btm2jnj17kq+vb6F7Hz58oKlTp9KFCxdK1ZF3xDZ8+HCl2FwcmZmZdOLECYqIiFCpnrKAg4MDAaBq1app2hSNwo5yytlgjknZYu3atdStWzd69uyZQnIGDhxIAKht27ZKsqzice/evQLdl2vXrk3Pnj3jfl64cCFlZGRo2kyOqKgocnR0JBcXl2JjPiQSCW3ZsoX+/PNPCgsLozVr1tC7d++4+i1fH39IJBLOCVPUoejUqRMBoOnTpyskh/H/HDlyhCwtLen333/XtCkahTkm5Wwwx6R8kpSURAcOHPimUjCVzdu3b6l79+60aNEiudanpaWRg4MDmZmZEY/HIx6PRxcuXKAdO3bQkiVLKCsrq8B8gUBAx44dozdv3nDXdu7cSePHj6eIiAi6ePEinT9/XirdmZmZdObMGYqJiSnSru3btxdyXleuXMk5TcXF51y9epWb06JFCwJATZs2JVdXVwJATk5OhdbMnj2bzM3NFa62+uXLF7p79y4JhUKaMWMG1a1bl27cuKGQTAaDiDkm5W4wx0S5PHjwgAIDAzVtRoVBLBaTu7s7ffjwodA9Z2dn7k04ISFBLvmfP38mY2NjTs6sWbOKnfv7779z6dzp6emUlJTErRs6dCj3/fXr10vU+enTJ5oyZQoBoCZNmhS6n5dabWJiUiAb6MWLF1S7dm3q1asX8fl8WrRoEdWoUaNA1ldgYCAZGxuTkZERl36eF7iakJBQ5E7LnTt3aOvWrYUcMXnJzMzknouJEycqRea3yKdPnwo4sQz5YY5JORvMMVEep0+fJgCkr69P4eHhmjanQpBX5K1y5cqFUlAfPnxIdevWpbFjx8qdYZSSksL18mnVqlWJZfzXrVvHnfdnZmaSSCQiBwcH0tPTo82bN5O2tjZpaWmRn59fsTJ++OEHbhcDADVu3LjQnNWrVxMAqlevXok1cExNTQkA9ezZs8D1T58+UVJSEolEInrx4kWJqc4fP37kyq8vX7682HmysmrVKurSpYvaWhF8/vyZrl69SpmZmWrRVxpZWVlUu3ZtAkB79uzRtDnfPCwrh8EohqysLAC52QBCoVDD1vw/ERERWLZsGV68eCG3DA8PD4wZMwaPHz/mrsXExKBdu3bo0aMHUlNTlWEqx/r16zFhwgTExcWVOC8vA0MikeQ52hwODg6IjIzE6dOn5c4wqlq1Kp49e4YrV67g1atXJZbxX7JkCa5fv44XL17AyMgI2traePjwIbKysmBqaopVq1bhzZs36NSpU7EyvL29AeSWOj99+jSX9ZKfZcuW4fHjx3jx4gVXGr0o3Nzc0LVrV/z+++8FrlerVg3m5ubQ1tZG27Ztoa+vX6wMQ0NDmJmZAQBq166N6OhoSCSSYucDuR8i8/4WimPlypV49OiR2loRDB06FEOHDsXkyZORkpKC+Ph4tegtjuzsbCQmJgIAIiMjNWJDdHQ0/P39NaK73KBuT6iiDLAdE6UhkUjo3LlzRWY2aJL+/ftzn7DlpU6dOoXK4f/999/clrynp2eR6/KaBvbq1UvqLImQkBBO7uLFi0ucKxKJ6Pz58/Tu3TvpH0wJ3Lhxg3x8fJQiK4/8hfGKe57yePz4Mc2aNUum+iiq5uPHj/Tq1Sv6+eefCQA1atSo2F0WsVhMXbt2JS0tLTp+/LiaLS2etm3bEgDq1asXmZqakq6urtT1g4pCLBbTunXraM2aNVJXbv6aO3fu0ObNmzUSQB0TE0MmJiYEQKqMqrIOO8opZ6O8OCaRkZH08OFDlVaU/Fb56aefCAD179+fuyaRSCgqKkrqtu1z584lHo9Ha9eu5a59/PiR+vfvT6NGjSp2i/zFixfcm/L06dOl6imTnZ1NHTt2JGNjY7p3755U9hVFZmYmpaWlST3/ypUrXFn8r9sIKMLLly9JT0+P9PT06NWrV4VsLK0Ev0AgIF9fX5W3ISiN5s2bc6/lsWPHipyTnp4cpiVpAAAgAElEQVTOdfkuK9k3Xl5e1LNnT/rxxx/pxo0bSiny5u7uzsmRNqC5LPH+/Xvi8Xjl5iiJOSblbJQHx+T69evcH1mDBg3UXrFUEbKysuiPP/5Qaa+ULVu2kI6ODjk7O3PXZsyYUWow59fI05qez+fToEGDqFq1agSA6tatK/UnTGmdpqKIjIykatWqkbGxMb1+/VqqNXk7G1paWlKvkZaIiIhCO0ZJSUlUq1Yt0tXVpTt37hS7dty4cQSAhgwZolSbZOXOnTtkZGRElStXpn///bfYef/88w9NmzatzNQRadmyZYF4nQMHDpCbmxsJhUK5ZQYFBVGVKlWoUqVKpQa7R0ZG0qFDhyglJUVufbLw5s0bcnV1LbVflKenJ+3fv1/uHZ+yBHNMytkoD47Jjh07uE8vBgYG31TfkjVr1nBvhpGRkVKtEYlE9Ouvv9KsWbMoIyODhEIhDRo0iGrXrl1kYGW3bt0IAFlYWHDX8v5Zt27dWmmPpSTyMkkqV66s0BvC15w6dYqmT59eKCj15s2b3O+ELB2L7927p7aAzNevX3M2ltTXJ+/1U9drVRIikUipr586WLduHRkYGCg1eJcoN21bmh1aW1tbAkCjRo1Sqv7iaNWqVYWrYcQck3I2yoNjIhAIaOXKlfT999+XufiO0jh58iQBIEtLS/ry5YtUa/JvRx84cICCg4MLFP36Gm9vbxoyZAidO3eOu/bixQv68ccfFTqy8Pb2JlNTU+ratWupzmBGRgb99ddfBXYiMjMzaf/+/XL1xHFxcaFWrVpxxwaTJk0qcF8ikZCbmxstW7aMBAIBBQQEaLymS05ODn333XdkZ2dHAQEBRET0119/0ZIlS0rMFomIiKCNGzdScHCwVHr4fD6Fh4eXqeJvyiY0NJS+//572r17t6ZNKZW8+BZ1pUZPmjSJANC0adPUoq8swByTcjbKg2NS1vjjjz+odu3aUp9hf/jwgZKTk0uck/9YIyEhgerXr0/m5ubcNvKvv/5K/fr1o9DQUG4en8+n0aNHU9euXaXejZGFJUuWcA5RUFAQicViOnz4sNRFs/J2UapWrSrTMVFKSgqn18LCgng8Hh08eLDY+VeuXCEej0cmJiYUFRUltZ41a9ZQu3btFIpzyc+7d+84u5ctW6YUmV+TnJxMNWrUIABkbm5OSUlJKtGjTh4+fEinTp0q8Dcwc+ZM7rlU1hFJWloaxcfHK0VWfpKSkujq1atqixESi8UUEhJCYrFY7cdImoI5JuVsMMdE+dStW1epW6lPnjyhKlWqkJ2dXYH+MaXFYHh7e0t1VCAv0dHRNGrUKG6LfN++fVzw6Pv378nLy4u6detWrNOwdOlSAkB16tSROS7I2dmZbG1tydvbu9SeOvntknZ3RiKRcLsxI0aMKHU+n88nb2/vEouQiUQimjp1KnXu3LnIgnBfk5ycTHfu3JHJact/PARA6bEy6iY4OJgriZ/f0b948SLp6elR7969lRJTlpycTLVq1SJtbW3y8vKSeb2HhwddvHhRYTuUTV49nNGjR2vaFJXCHJNyNphjonwOHjxIHTp0UFoa3vr167k3GlmOXrKysmjIkCHUvn37EguDlYRAICAXFxdycXEhgUBQ4twLFy5wcT5RUVHUtWtXAkA1atQocr5YLKbbt28X+Sn16NGj9NNPPynlE79IJKK9e/fKFGCckZFB3bp1IwsLC7p27Vqp8/OaJvJ4PNq8ebMi5hIRUXh4OBebkD9oWRryAo1r1aqlsB2aJjIykgwMDAgAnTp1qsA9ZQa5BwYGcn9jGzdulGlt/g8AV69elWltQEAAbd++nT5//izTOmlp06ZNkUed5Q3mmJSzwRyTsk9SUhI5OTmpbPu/JM6ePcv9080fo5Kf1NRUrgdK3ie0NWvW0IEDB6h69eoyNxiLj4/nsqxcXV2V8TBkZtmyZQSAtLW1pdrez3PCAJC9vb1Cuu/evUva2trcczBu3DiZ1p85c4Z69uyp0kwvdfLvv/+qJXbsyJEjtGLFCpmrw758+ZJ7ve7fvy/TWktLSwIU7yZeHOo+RtIUzDEpZ4M5JoyiuHz5MrVu3ZpWrlxJFhYWZGFhUWyZ/f/+KdDUqVO5ok3yBPodO3aMli9fTsnJydSsWTPS0tJSuHFcHu7u7mRoaEgDBgyQ6pP24cOHCQBZWVmVelRElNvZd8qUKdSnTx96+PChTLYlJyfTx48fuZ8PHjxYIJi5vMcHlMaXL19UtqOgLN6+fStXILmdnR0BIBcXlyLvJyYmstpMUsAck3I2mGNS9gkKCpI6YycrK4sOHTqkcCXUvB2A6tWrk1gsLvHNvHHjxgSAhg0bRg8fPqQVK1bI3DQvf2bRunXrSCAQlBgQ/OXLF5liL74OliwqoPJr3r9/r/I3xODgYKpUqRIZGBhwb2w5OTm0bdu2YouYSYNYLCYnJydq0aKFUovFqZvg4GCqUqUKGRkZlcuGd3m/i0XVErl//z7p6upS9erVVRKUW55gvXIYFRofH58i+5eoiiNHjqBJkyaws7Mrtf8IACxcuBDTp09Ht27dIBKJZNa1bds2iMViODs7w9LSEpMmTYKWllaJvWY8PDywY8cOHDhwAA4ODhCJRKhduzZWr14ttW5zc3PUrl0bWlpasLOzg56eHtej5WvOnTsHMzMztG/fHjk5OVLJX7RoEUaNGoVt27bh06dP6NWrF8aPH48DBw4Uu6Zp06YwNTUtUW5OTg7Xr0cewsLCkJ6eDj6fjw8fPgAAdHR04OLiAicnJ7nlRkZG4vjx4wgICMDx48fllqNpQkJCkJqaiqysLAQGBmraHKVTtWpVODg4cD2Pdu3ahSlTpiA2NhYBAQHIyclBUlISYmJiNGwpo0jU7QlVlAG2YyI1fn5+3KfumzdvqkVnXkqutra2VOXcFy1axB1ByBIc+OjRowIFyTZv3kwAyNTUVKay7kRETZo0IQBkZ2dHKSkptHnzZnr+/Hmp69LT0yk2NrbUeS4uLlygaf4jEGkpLqDSz8+PWrZsSfPmzZNKTmBgIJmamlLNmjUpJiZGZjvy2L59O23YsEGpFTglEgnNnDmT2rdvX2oF0LKMRCKhTZs20dq1a8tFhdKSiIuL4/4Gf/75Z+Lz+bRq1Srau3evpk0r87CjnHI2mGMiPU+ePOECEm/duqUWnenp6bR27VqpMkOIcjNQrl+/zr3B37t3j44dO1aqkxIWFkYmJiako6NDDx8+pNWrVxMAMjIykvk4w8PDg4YOHUo3b96kadOmEQCqUqWK0rIoEhISaO7cuXTkyBG5ZXz48KFQs778xz2l9a8hyg2WzJsvT4opg5EfoVBIHTp0ID09Pan/3hm5aMox0VHb1gyDUQwdO3bEo0ePIBQK0aNHD7XoNDExKdS2viS0tbUxYMAAAEBwcDD69OkDiUSCrKwszJ49u9h11tbWCA0NhUAgQJ06ddC5c2dYW1ujefPmpR5nfI2joyMcHR0BAA8ePAAAWFpalngcJAsWFhbYvXu3QjKaNGmCJk2aFLg2Y8YM+Pn5wcHBAdWqVStVxtixY/H69WsYGhqiX79+CtkjDZmZmTA2Nla5npLYsmULEhMTsWrVKhgaGmrUlvKGrq4unj59ipycHOjq6mranDIPn8+Hq6srdHV18z5kqx91e0IVZYDtmJRboqOjycjIiADQ6dOnNWKDRCIhPz8/tWWW8Pn8ErNonj9/TsuXLy+1Eu68efPIyMiozGyj/+9//+MynzRF/qPMXbt2acwOBoOI6MSJE9zvo42NDQt+ZTDKInv37sWPP/6Iz58/AwBq166NN2/ewNfXF926dYO1tTXq1KmDiIgIqeTFx8fDz89PJhvoq08uPB4PnTp1QtWqVWWSIw9JSUlo2LAhatSoUazdI0aMwJo1a+Ds7FyirBMnTiArKwtnz55Vqo0SiQR8Pl/mdbdv3wYA3Lp1S6n2yIK1tTVq1qwJAwMDtG3bVmN2MDRLfHw8unTpgr59+yItLU1jdnTq1AkWFhawsrLS2E4ic0wYGiE5ORknT55EcnKyQnKysrKwdOlS7N27t8j7YWFhSEpKklt+WFgYnJ2dsWvXLmzbto273qhRI3Tu3BlPnz5FREQEYmJi8Pjx41Llpaeno2XLlrC3t8eePXtKnS8SidCzZ08YGhrC2toatra2XCbB06dPsWzZMkRGRha51tPTE+fPn5fykRZPWFgYYmNjkZ2djWfPnhU5J+/4pkmTJti9ezeGDh2KgICAQvN27NiBgQMHypRZVBrZ2dlo3bo1qlSpghs3bsi0duvWrXBycsLBgweVZo+s1KhRA2FhYUhMTIS9vb3G7GBoFk9PT/j6+uLOnTvw8fHRmB0NGzZEfHw8oqOjoaenpxkj1L1FU1EG2FFOifTs2ZMAUO/evRWSk5flAqBQPQZPT0/S0tIiU1NTuesVZGRkkI2NDenq6pKnpycREfn4+FDDhg3JycmJsrOzac6cOTRz5swS+7nkkZSURHp6egSAVq5cWer8xMTEAj1aAHB1OGrVqkUAyNHRsdC6x48fc/MvX75MRLnN1KZOnUrz588vslbJxYsXafLkyVwDw/xs3LiRXF1di63eyefz/6+9+w5vqvr/AP4+ncguezkYhQIFKvIVmbJkiiJ7yUYRwYHIElRAKEMUUURAmYJQRGXLEiubAlK2QMsqmzJKS3fevz+S3F/TJG2StmlaPq/nuU/pzb3nnpNbcj85kydPnmRsbKzWkTmrZt1MLTw8XCvr2LFjnXJNITLbrVu32KxZM7766qs2TT7oDDIqJ5dtEpikrWnTplpgsm/fPsbFxTmUzs6dO+np6ckyZcqYjfj49ttvtQeWrYvMWZKQkGAyS+Q777yjpWvvhGekPrCZN2+e9iBPz1dffcWOHTuyQ4cO7NatG6Oionjv3j1j+y+9vLxYs2ZNkw+z0NBQenh4UCmlreL7448/avn+/fffqdPpGBERoU2G9tRTT2kTumVEx44dmTdvXm122YiICL744ots0KBBln3gfvPNNxw0aJDZMOeVK1eyS5cuNg/ttRSw3bhxg3Xr1mXjxo1daqbUO3fusHnz5mzXrp3dQ8+FsIUEJrlsk8Akbffu3WNQUBDbtWuX4VU67969a/GbfHx8PL/88stM76AaGhrKevXqccSIEQ6nYRw2/OKLLzp0fufOnQmABQsWtLgQ4ZAhQwiA/v7+9PT05Keffspz586xdOnSWgBirLUaMGAASbJ9+/YEwDlz5jhcLku+//57LY89e/ZkTEwMW7ZsyRo1avD8+fOZeq3UvL29bQ623njjDbq5uZmstkuaDl9et25dhvJz+/ZtLl68OFNmHDVO758Z+RLCEglMctkmgYlt/ve//xEAje/VvXv30pzOPKvExsbyzz//tHmK+ozq1q0bAbBAgQIOnW+cG6RGjRrs3bu32YJ+xhV0jc1Gvr6+JPVNLsbl7n18fLTgxSg6OtrxQllx7do1enh4EAA7duxoMulcZqwYnJbOnTvTzc2N8+fPT/M4nU6nvVcdOnQwee3u3bts2bIl27dvn+Ean2bNmhEA69evn6F0SH1NTr169di0adMnft0fkTUkMMllmwQmtgkLC+P06dMZHh7OCRMmaN+qnc0YKDRs2DDN4+Lj4zPleteuXeO4ceMYHBzs0PkJCQncsWOH1aaF3bt3c8CAAVqTmY+Pj9ZMsXLlSvbv358bNmzgsGHDuH//fn7zzTf89ddfHS5Pevbv38/PPvuMN27cYHx8PHv16sWmTZvy6tWrWXZNI1vX/lmxYgW7devGkydPZlle2rZtmyl9q4RwBglMctkmgYn9GjdurE377mxt2rQhANasWdPqMTNnziQA9unTx4k5M/fo0SObH54ffvghAbBIkSJMSEiweMzcuXMz1BdHp9MxKCiIu3fvtvvcJ01UVBTXr1/vtJo5ITJCFvETT7xvv/0WAwcOxNKlS51+7WXLlmH+/PlYv3691WO2bNkCQD+sz1YxMTEYN24cFixYoO27cOFCmvMUjBkzBtWqVbN4HZJ46aWX4O/vj8DAwHSvHxgYiBUrVmD//v1WZ7189tlnoZRCoUKFrC7wl5YlS5aga9euaNKkCc6dO5fmsSEhIfj000+tLp529epVhIeH252HnKJAgQJo3749ChUqlN1ZEcJ1OTsSelI25MIak2vXrnHFihVOGwEwf/58VqpUid9//z1JMigoiNOnT7d5BM+NGzdYvXp1VqlSJcNNBrGxsfzkk0/4yiuvaMNvbTFjxgytNuLkyZNcuHAhAbB8+fImI3ISEhI4efJkTp8+XTve29ubkZGRJuklJiZqs87279/frjIkJiZa7UPy33//Odwhc9WqVVp+L168mOaxpUqVstoZ9cyZM8yTJw/d3d25d+9ekvr3Zdq0aVy8eLHVNB88eMAGDRrQ398/3eun5YMPPmDt2rV56NAhh9MQIjeRppxctuXGwKRGjRoEwM6dOzvletWqVdOmRT579qz2wJ41a5ZN569du1Y7Z8WKFRnKy6hRowiA+fLls6sD5Pbt2+nh4cFy5crx3r17HDlyJAHQw8PDZHhzypEfxo6rbm5uvHTpklmaxsUA79y5Y3M+oqKiWKlSJXp7e3Pnzp02n2erf/75x+L8J9evX+crr7zC3r17Mz4+XmuuGzlypNmxwcHB2nuwevVqkuQPP/yg7QsJCbF47a1bt2rHzJs3z6H8R0ZGamkMHDjQ6nFHjx5lv379uGPHDoeuI0ROIoFJLttyY2BSq1YtAmC3bt2ccr0lS5bQ39+fP/30E+/cucNixYpRKWXz0MjHjx+zd+/e7NGjR4ZHUwQGBhIAS5Ysyfv37/OTTz7h7NmzbTr3zp072nDmhw8fctKkSdy0aZPJMbt27aKXlxcLFy7M//77j19//bXZMbZKTk42G9l06tQp7cE7efJks3Myq1Nval9//bV23eDgYMbGxvLkyZNWR179/PPPXLBggfb6X3/9RQ8PD/r4+DAiIsLiObGxsezZsyfbtm1rV7B2+vRpdujQQauRe+utt+jn52e2OnJKDRs2JACWK1fO5uuIrLdjxw4GBARY/NsWjpPAxLaHfVkAHwDYBuAKgAQANwGsBVDXhvMrAIg2fFD+kMV5zXWByc2bN7lmzRqLzQGhoaHs0aMH165dm2XXj4yMZHh4eKalt2XLFg4ZMoRnzpxJ91idTse///6b165dM5mXI6M1MUavvfaaybDp1Gwdznzy5EkWKVKEzz33HG/dukWSPH78OC9evMjZs2dz2LBhZkNLW7RoQTc3Ny5btszh/N+6dYuzZ8/m2bNnTfafP3+e1atXZ9OmTR0eihwREWHWpJUZevXqRQBUStk00R1JbeTYm2++yc2bN9Pf318ehi7AOAePh4dHdmclV5HAxLaH/TTDA+ECgB8BBAL4FUASgGQA3dI41w3APxKYZJ6dO3fyu+++Y1xcHFu3bq3Ny7F27VqrU5dntRs3blj9Np6YmMivv/6aq1at4vbt27Xgonnz5nZdY+/evdq065UrV86MbGvvX82aNS3OSdGjRw8CYIMGDSye//XXX7N58+YcO3asVq7NmzfTz89P6/+ROqibOHEi+/TpQzc3NwJg7969Hc6/8cFQsWJFh9NwtqCgIHp7e9s9060xODTeszx58mRF9oQdNmzYwIoVK3L06NHZnZVcRQIT2x72HQG8bGF/I0PtyT0A3lbO/QhAoqHGRQKTDEo5adaUKVM4e/ZsAmDhwoUJgN27d7d67oEDB/jqq69mWm2D0bBhw9LsFJqyv8LAgQO1fw8fPtzkuIsXL3LNmjVpdrI1BgoZnb7dKDIyUstTuXLlzAI74wy5NWrUMDs3KSlJC5TKly/Phg0b8uOPPzbpl5M68Dh27Ji2v1OnTuzZs6dds7Du37+ffn5+7N+/P3U6nZb3evXqOf4mOCgxMZFjxozhRx99lGVNUpZs2LCBVapU4aeffuq0a5L66ferVaum9cMRIqtIYJLxQGCr4YO2joXX/ADEApgEoIkEJhl3//59Fi1alAC4aNEikvpZRf39/bWHnTUtWrQgABYqVChT81SnTh0CoJ+fn8XXt27dSjc3NxYqVIihoaEcMGAAJ06cqL0eExPDgIAA7SE/ZMgQ7bXY2Fh+9tlnWufKpKQkhoaGOvwgnDp1Khs2bGgyAuSjjz7SqqNv377NtWvX8q+//iKp76cyf/58i51hSbJfv37MkyePFmwYa0c+++wzbYZXAFqTRVRUFKtVq8b8+fNz3759Jmnt3buXDRo04IwZM6zmP+V6QTdu3GBiYiL/+ecfp43YSk5O5vnz55mcnMx169ZpeVm5cqVTrp+djCOb6tatm91ZEbmcBCYZDwQ2Gj6cAlLtdwdwEMBxAF4SmGSeGzdumKzPQuq/SXfr1o3Hjh2zet73339PLy8vDh48OFPzc/ToUQ4dOpQHDhywekxYWJjW9yK1M2fOmNQwvP3229pr3333nbY/9XDSa9eusVWrVuzXrx8TExP56NEjzps3T1vteN26daxWrRqnTp1KUt/R1JiWsSNxYmIiK1WqpAVEv/zyi9b/wZjOvXv32Lt3b3744YdMTk42y//mzZvp6elJPz8/rcZlz549HD9+PGvUqGFxJExSUpLZvjfeeEMbFWTpOqT+va5Tpw6HDRtm8fWs1q9fP8Iw2d2lS5dYokQJFilShP/991+WXzs5OZmHDx/OtubKWbNmsXLlyk9EECaylwQmGQsCngEQB+A6APdUr403NOG8YPg9UwMTAPFWNl1uD0wsqVevHgGwTp06Dqdx6NAhli1blrVq1cqSTo+WLFiwgG3atOHQoUP5xhtvcMGCBVrtQkJCAocOHUp3d3cWL17cbL4P44ywALhv3z6tNqFw4cJMTExk8+bNtf43RgMHDmSZMmW4ceNGkvo1aoxrtbz77rtaLYBSihMmTCCpr8I3Xueff/6xWI6oqChtCvZLly5pzW3ffvutze/FH3/8wdKlS/Odd94x2a/T6Thq1Cj26tXLbCVnZ0hKStL6D9WuXZsAWKtWLZL6e+SsZpzhw4en2d9HiNxCAhPHAwNPAMGGD+w3U71WC/q+J1NT7JPAJAsZV719/fXXHU7jxRdf1B7Ay5cvz8TcmUtKSmJMTAw9PT21fN++fZsJCQlct24dCxcuzICAAJPAI7WzZ8+ySpUqbNKkCWNiYjhu3DgC+qn1x40bx5kzZ9LPz49ffPFFmnnZvn07J06cqK1/06lTJ+26n3zyCYODg1m8eHFWr16d9+/fZ0xMDOfOnWtWQ2Ss5bh+/Trz5ctHAFyyZEmG36uDBw9q+Zk5c2aG07NHSEgICxYsyGeeeYYhISE8fvw4R44cqdUmOZNxBJUMGRa5nQQmjgUFbgBWGD4sF6R6zQvAMQCnkaJDrDTlZK34+HgeOHDA5tlZLZk1axYB/cJzN2/ezMTcmYqNjWWNGjXo6enJ5s2bM2/evBw0aBCVUqxdu7Y2nNRYc+Hj48Pr16+nmebIkSNZvHhxjh07VltJ1tG1f9atW0dPT09tNeAePXqYvP7xxx8TAJ966ik+evSIOp2Or7zyCj09PRkUFESSvHDhgskaNpcvX7baTyU9Dx8+ZI0aNVikSBGzJrysZvybAEBPT09euHDBqddP6fr165w6dWqazZVC5AayVo6dlFJuABYB6AngZwBDUh0yFkANAP1Jxjs5e08sLy8v1K1bF97e3g6nMWLECMTFxeHevXsoWbKkQ2n8/vvvKFSoELp162b22sGDB/Hee+9hz549OHHiBBITE1GnTh3ExMTAzc0NJBEaGor33nsPvr6+AIAiRYrg+PHjKF26NAB9QL9jxw5cunQJAHD27FlcvXoVCxYswJ07dxASEoKAgAAAQNWqVR0qw2uvvYaYmBj4+/sD0K9pk1Lx4sUBAIULF4anpydiYmKwY8cOJCYmauv6VKxYEQ0bNgQAnDhxApUrV4avry8OHz5sd34KFiyI48ePIzIyEs8//7xDZXLUgAED0LhxYwBAYmIiHj9+7NTrp1S6dGmMHTsWtWrVyrY8CJGrOTsSyowN+pqSpdB/g1qJVP1KDMf8gRQdGdPY/siiPD6RNSbpSUxM5LJly9LsoGrN8ePH+fLLL3P8+PHavsjISG7YsIGPHz82ObZnz57aPU5de1OxYkUCYP369Tl37ly+9dZbvH37Nkn9t+Hhw4dzzZo1JMkxY8YQAPPnz28yQdiXX36p1epMmTKFSinmy5ePkydPZsOGDfnXX39Rp9Px9OnTjI+P57p16/juu+/yypUrdpc7KiqKhw8f5vXr1/nmm2+aNKPs3btX68wbGxvLuXPnsmvXrhaH/qacut2e9X4yau/evRwzZgwvXrzIt956i/Xq1bNpUrvUdDodV65cyW3btmVBLoUQqUlTjmNBySpLQYnhuHehn4Qt9bbJcO4Zw+/vZlE+JTCxwLionZeXlzYZ2tChQ9mgQQOLa62kNHjwYO3Baux8WbduXZPRLUbHjx9nmzZtLE4b3717dwLg+++/n25+4+LiuGjRIv77778m+ydOnKiVI2Wge+TIEe2YmJgYJicnMzExUTuuV69e6V7TmtGjR5sNBzYyTmn/9NNPm3QY3rFjh8kU/osXL+bChQvtvvaNGzf43nvvMSgoiJGRkXzppZdYq1YtmxZHLF68OAFoTVsAOGrUKLvzIIRwLglMbA9Klhg+3IIAeDiQRhNIHxOnSUhI4L59+7ShlfPmzSMAFixYkHfv3mVYWJj2sLI0nDWlnTt3smzZsuzevbs2OsO4sGD79u1JkufOnePMmTO5ceNGFi9enHXr1jUb1pmcnGzTA/XUqVNcs2aNxSG1CQkJXLZsmclEbbVq1dJGxKxfv54eHh4MCAhgXFwc69evb/fomNS2b9/OPHnysHbt2mZTqL/wwgtmtSGHDh3KtBoS40gjd4OwkDIAACAASURBVHd3bSgzUsxhc+vWLatDi5s0aUIAHD16NLt06UI/Pz+n91ERQthPAhPbHvafGz4QHwH4wvB76i0gnTQkMHGiPn36EABbtGih7fv777+1b/xJSUns0qULK1euzMOHD9ud/pUrV/jTTz8xJCSEV69e1VYkrlChgvbwdKST4sOHD1mgQAEC4KRJk/jDDz+watWqXLJkCdevX8++ffvy1KlTvHfvnjbbLQB+8803JMm3335b2xcREcHExEStuSgjjIFPasa5R4zNTteuXWNoaKjWcXbr1q0mx588eZJ169bl22+/bXUKf6O7d++yWbNmVEoxICCAUVFR7Nq1K9u3b8/IyEh++umnBMA33njD4vnx8fE8d+6cyb5vvvmG9erVy5KVjoUQmUMCE9se9sbakrS2fumkIYGJExlnea1atarDaSQmJnLIkCHs3Lmzxfkzdu3aRaUUPTw8WLZsWQJg586d2alTJ44aNUp78K5fv54VK1bk2LFjrV7rzJkzbNWqFV9//XWt+WXKlClavxR/f39tbhDjGjthYWHMnz8/Af2ifg8ePGCRIkUIOLb+zObNm21eQdno4cOH7N27NwH9xGjGPibHjh3j/v37mZSUxNatW2vDbUeMGKH9n7l48WKaaX/yySfasZYmMDPe45IlS9qc37x58xIAW7ZsaVc5hRDOI4FJLtskMNGLiIjgjBkzzB5ojx8/Zq9evdi5c+d0V8z9+++/tQfjnDlzzF5fvny5SXA6ZcoUJiQkmB3Xtm1brV+INW+99ZZJWm3atGFycjK///57+vn58f3339deS7lOzuXLlxkSEkJS3x/DOC+KvSvP/vPPP1r6mzdvtuvcpKQkLlq0iDt27CCpn4V31KhRDAsL45YtW7R0q1atyg4dOrBy5crs2bOn1SYYo02bNtHT05NVq1a1ONvp6dOnOWTIELtqP0aOHMkyZcpw1apVdpVRCOE8Epjksk0Ck7SlXN8kvcm/Hjx4wBdeeIHPPPOMxdEcycnJnDRpEvPnz89ixYoxLCzMYjrbtm1jrVq10pzobNu2bSxWrBgLFSpEpZTJQoPGjrs+Pj709fW1WHtw+fJlLl26lJs2beKoUaPYt29fq7O0Gg0cOJBFixblr7/+ysOHD9PNzY1KKQYHB6d5XkpxcXHs0KED69Spo5W/TJkyBMDWrVszODjYrHaxffv2bNSokdkU+5Y8evTIajOSNcnJyRw6dCibN29u1llXCOH6JDDJZZsEJmm7ffs2AwICWLVqVYeG0FoSHx9vsabEaNu2bRw3bpzVtXJSSkpK4v379032GWf8zJ8/v9XzjOvd9OjRQ1tU0NfXl6R+Ib7UI4+Sk5O1RQMrVKhAUt/8Yqm/TWhoKDt16sRly5Zp+/7880/u3r2b+/bt0wKO6dOnkyRbt25NAPz4449JkmvWrOHcuXNZqVIlk34xqUc0OWrhwoUsVKgQR4wYQVLfj8V4DeO0+kKInEMCk1y25abAJDo6mnXq1KGPjw8PHjyY3dlxSHx8PL29vQmAAwYMMHt9zZo16fbrOHfuHN9++21u2rTJ6jHVq1cnAPbr109bLXjw4MG8d++eNmw29eJrxv4rPj4+Jvv//fdfk0Dm9ddfN2mK2rBhg/bgnz9/Pjt37syXXnpJ6zOSkJBgdYbU5ORkDhw4kKVKleKGDRvMXp8xYwbr1auXbm1PSsbRN0WKFCGpf8/btGnDChUqmA23FkK4PglMctmWmwKTY8eOaQ/ASZMmZXd2HKLT6VizZk0C4FdffWXyWsoHvD3NJ5bcunWL69at04bz3r9/nzNnzmTPnj3p5uZGANoqw0a7d+9m27Zt+csvv3Dfvn1ctWoVd+7cqXXoPX78OEnyxx9/pLu7O7t06UKSJv1G/Pz8MpTvlHQ6nTaax541j3bt2sVmzZpx8eLFmZYXIUT2kcAkl205PTCZO3cu/fz8uGzZMup0Oo4dO5bdunXL0rVrslJCQgJ3797NU6dOmb22e/duKqXo7u5u8Zt9QkICjx49mmYzEamfZ2XBggVaX4zExESOHDlSCx569+7NWbNmmc1BYhQeHq6N+Ek53DjlwoGpO6oah0W3a9cu3ffAHp07d2apUqX4+++/Z2q6QoicQwKTXLbl9MDk2WefJQAGBARkd1bMhIaGcuHChWbT0KelR48eadYAhIaGWgxayP9fMbl79+5W07906ZJWyzBr1iyS5JIlS7TgwsvLi7t27Uozj1evXuVTTz1FAFy6dCmDgoK4cePGNM8xDrtt1apVmsfZ46uvvtKGHWe0BkkIkXNlV2DiASEs+Pjjj/Hdd9/hww8/zO6smIiPj0ejRo0QFRWFI0eOYOrUqfDx8UnznAcPHuC///4DAFy+fNniMTVr1tT+HRYWhkePHmmL8IWHh5v8tCRv3rzInz8/Hj58iBIlSgAAfH194enpiTx58iAkJARVqlRJM5/lypXDv//+i5s3b+Lll19O81ijr776CqtXr8brr7+O7t27o3///khOTsayZcswbNgwbQE/e4SFhQEAdDodfvvtN23xPEvi4+Nx9OhRPP/888iTJ4/d1xJCCDPOjoSelA05vMbEWaKjozlt2jRu2bIl3WOPHj3K/fv3a8Ngvby8mC9fPqs1HaR+4jHj8d27d9eG+CYnJ3PDhg3cs2cPR44cqc0ZEhYWxjx58hAAO3bsyODgYP7333+cNGmSxYXxUrpy5QpDQkL4559/skaNGpw1axavXbvGO3fu2PGOWBcYGMiCBQtyypQpZq/973//IwCWLVtWG+VTunRph64TGxvLZs2asU6dOmadZ1PPEtulSxdt6LEQIneRppxctuWkwOTmzZv09fVluXLlrI7iyCrGWUXd3d3THMZ76NAhbX6P3377jZMnT9aaSX777Tez448fP86bN2/y/Pnz2oM6ZcfdwMBAAtAmQvPy8mJ8fLxJR19YGCljC2MzmJubm93npqVq1apmHV2Tk5O5f/9+bRVkYwdfAKxXr57D19q0aRP/+usvk31Hjhyhj48Pq1Wrpk2K16BBAwJg7dq1Hb6WEMI1ZVdg4uakihnhwkJCQnD+/HlERERg9+7dTr12xYoVAQClSpVCvnz5rB4XFxcHnU4HksibNy/GjRuHGTNmYNq0aShfvjzGjRunNdf8/PPPqFmzJqpVq4ZmzZrB3d0dAwYMwEcffaSll5SUBABwc9P/F/D394enpydq1aqFzZs3o0OHDgCAqlWrws/PDxUqVLDaDJTa008/DQDw9PQ0BqmZIjAwEM2aNUNgYKC2b9SoUahXrx42b96M+Ph4dOrUCQDg7u6OdevWOXSdjRs3ol27dmjWrBkOHTqk7d+1axfu37+P06dP4+zZswCAlStXYubMmVizZk0GSiaEECk4OxJ6UjbkoBqT+Ph4DhkyhH379uWjR4+cfv3Tp0/z3r176R73559/Wpxzwzh3iLGGwFgbYuyMCoCBgYEm5yQlJTEoKIiLFy/mDz/8wLi4OLN0w8PD+euvv2pppJzYjNSvPtyiRQt+/vnnJvsjIyM5ffp0bYr6rBAfH89bt25pnXqLFSum7f/xxx+5f/9+h9Petm2bVuOTchXgyMhI9unTh2PHjk134T8hRM4nTTm5bMtJgUlWe/jwIQcPHszx48dnyQPN2M/BOHFaXFwcp0+fzqZNm7Jhw4Z8//33GRUVZXbeyZMntblFfv75Z4tpx8XFceDAgXzzzTfNgraU6+pkVj8SWyQmJrJWrVpUSvG7777jl19+adcKyhs2bODevXvTPGbv3r08cuRIRrMqhMjBJDDJZduTEpjExMQwKCiI165d4759+zhx4kSzuU5mz56tPcDTeyA6IjExkWfPnjWZ4+Obb77Rrrl161aS+iBj7NixnDp1KnU6Hc+fP6/1MUnZTyUxMZFbtmzRyvH48WOuX7/ebGXj7du3s0SJEuzYsaNTaxAePHig1QYNHTrU6nGBgYEcOHCgSb5Xr16t1YacOHHCGdkVQuRQEpjksi0nBCbr169n165dMzTNfJ8+fQiAVapUYaFChbR1YlI6cuQICxUqxIoVKzIyMtIsjX379rFTp05cv369w/lIKTQ0lPny5aObmxt9fX212oxFixZpwYpxJdxTp06ZBUvGFYTLly9PkuzVq5fdHTynTp3KChUqcOXKlbx9+zbj4+PtKsOcOXM4YcIEi01MJBkUFMT333/f6oR3KdepSdnp97fffiMAenh4WFwQUQghjCQwyWVbTghMSpUqRQBs1KiRw2m8+eabBMDKlSuzdu3a2oJtffr0YZ48ebh06VKS+j4dxlqFK1eucOjQoVotRf369bWhrkanTp2yGMTYYunSpWa1JaR+7Zl8+fKxePHivHr1qtXzBw0aRAAsUaIEdTodO3bsSACsWrWqzXkoVqwYYVjATynFmjVrmq3OGxISoo1uSWn//v1a/r///nubr2kUERHB3bt3s1q1anzqqafM1rvZtWuXSd8RIYSwRAKTXLblhMDE+ACeNm2aw2nExMRw1apVjIiIYExMjNY84OXlRQB87bXXzM4x1rJ4eHgwPj6egYGBVErxnXfeIfn/NRtlypRxqDNufHw8x40bxylTppg1sTx69CjdGWOjo6P5008/8ezZsyT1690sXryYly9fTvO8qKgo1q9fnxUrVuTYsWNZq1YttmnTxqQjaZkyZfjcc89xxIgRWkCXOo/Xr19n8eLF6e3tbXfT1507d7SVg5cuXZruNPpCCGGNBCa5bMsJgQlJ7cF1+vRp9u3bl3/88UeG0rt79y7//fdf/vjjj2zXrh0PHz5sdsy8efO0h3JMTAxJfY2K0fjx47XAJSetzbN7926tpsO4UOCtW7f4wQcfcPXq1Vy+fLn2unEStwIFCpiU3Sg6OtqhGqPLly9r/U9mzpyZ4TJZk5yczM2bN/PSpUtZdg0hRPaSwCSXbdkRmERERDj8DfnVV18lAObPn9/h6z9+/Jhly5YlAH777bdpHlutWjUCYM+ePc1ei4mJ4fTp07llyxbu3r1bm63VnnykbiK5ePEi/fz8WKdOHZuGJqdn1apVnDZtGqtWrcpnn32WYWFhTExMZP/+/dm+fXuLk8XFxMRw8ODBWm1ShQoVzPr3REZGZrgj7Y4dO/jDDz9kaW3JpEmTtGHK9qxZJITIOWSCNZEhX331FcqVK2fzGiupNWvWDADQvHlzh/MQHx+PO3fuAACuXr1q0znGic5Syps3L0aNGoV79+6hUaNGCAgIQEREhNlxv/32GypVqoTPP/9c23f79m1UrFgRpUqVwr59+7T927dvx9mzZ3H48GEcPHjQYl7i4uIQGhoKnU6XZp5DQkLQvXt3jBkzBmfOnMHly5cRHByMrl27YsmSJWjdujWKFSuGEydOICEhwaRcCxYswIwZM1C7dm3MmTMHL774ovb6hAkTULRoUfTu3TvN66enefPmePvtt+Hp6ZmhdNISFxcHAEhISEj3/RJCCLs4OxJ6UjY4ucbEONFWnjx5TIbN2uPRo0cZ/ra+a9cuzpo1i9HR0WkeFxERwRUrVvDXX3/lzJkzLX7r/uGHH7T+GYGBgdpIGqMWLVqY1fIcPHhQay6ZM2eOtj8yMpIdOnRg7969rY50adKkCQFw+PDhaeY9PDyc+fLlo1KKbdu2Za9evRgVFaWtDPzaa69pc5y0bds2zbRSaty4MQGwXLlyNp+TXRISEvjzzz/LkGMhcjFpysllm7MDk6tXr/LDDz+0aTE8V3Ht2jWtP8TEiRPNXk9OTubKlSu14bvu7u4MDw/XXt+0aRNr1arF6dOnm5z39ddfc/To0Vr/FVs9/fTTBMB27drZlPfUi/oFBQWxZ8+ePHnyJJs2baqNyklt+fLlrF27NpcvX87bt29z0KBBnD17NkNDQzlo0CCzAEwIIbKDBCa5bHM0MLl79y4vXrxo93mZadSoUaxWrVqWPyAfPnyoDVletmwZv/jiC1aqVImrV682OW7FihVaR9EbN25kWX7+/fdfTpw40Wwo8YEDB1izZk2+++67Nqd18eJFfvbZZwwNDWVsbCy//PJLbtq0iSTp5+enDT8eN26cVsMTFhaWqeURQoiMkMAkl20A9r/wwgup73Oabt26xaJFi9LNzY0bN26069yMunv3LgMCAlipUiXtQdmlSxenXPf06dMkSR8fHwKgUopTpkwxOe7QoUMZHgGSnJzMjz76iF27duXt27d56NAhm95n47BqAGazv9piypQpWpPUlStXOHHiRBYoUIDDhg3j1q1b6eXlRX9/f+lEKoRwKdL5NRe6d++exf137txBTEyM2f67d+8iMjISOp0O58+fz+rsmTh06BCOHTuGCxcuoHXr1qhevTreeecdi8feunULNWrUQPXq1XHt2rUMXbdo0aKoWrUqAGD8+PHw8vICSfz6668mx/3vf//Ds88+azENnU6HLVu24NKlS2le6/Dhw5g1axaCgoIwc+ZM1K9fH6+++ipWrFiR5nnGdBs3boyiRYvaVrAUnnvuOQBAsWLFUKBAAYSGhuLRo0dYunQpWrZsiYcPHyI0NBRPPfWU3WkLIUSu4+xI6EnZAOy3NIX51q1b6eHhwbJly1qcp2LVqlWcPn261Q6aKZ05c4YtW7Y0W93WEXFxcezXrx+7dOnC+/fva/tPnDhh1pE15Yq7K1as0PY/fPiQr7/+Otu3b29xRtNly5Zx4cKF1Ol0XLhwIUePHm0ygVp0dDQnTJjAVq1acfv27TbnffLkyQTAokWLplnrEBUVxdq1a7NEiRJct26dtk6Or68vv/jiC6vn5c2b166OrPPmzWOPHj1M+sOcPn1amxrfWIPSpEkTG0sohBDOJ005uWyDlT4mM2bM0B7qJ0+eNHvdHkOGDNHSsjRvRkYZH/g1atTg3bt3+d577/Gnn35iTEwMe/bsyW7duvHRo0cMDg7mpUuXGBQUpOVn5cqVJmnt3LnTZLSM8d+BgYHaMR06dCAANm7c2K58fvLJJ1ofFHtmij169CgbNWqk5cXavB+//PILu3TpYtMKvvfv39fSGzx4sNXjrl69ajZFvRBCuJLsCkw8nFItIzRDhw7FgwcPUL58eVSvXt3s9YcPH6Jhw4a4efMmdu7ciZo1a1pNq3Pnzli7di0aNGiAYsWKZXpejc1J4eHhmDFjBubMmQMAaNmypdb8MW/ePAwdOhSFChVCSEgI6tatC51OZzYfSsmSJeHt7Y3k5GT4+vri6aefxo0bN/D8889rx8TGxgIAHj9+bHMeHzx4gNu3b6Nr166YMGEC8ufPb/O5zz//PMaMGYNLly6hXbt2Vuf96N69O7p37241nfPnz2Pq1Klo3749OnTogJdffhn79u1Dq1atrJ5Trlw5m/MphBBPFGdHQk/KBhtG5Tx48IADBw7k+PHjtflD9u3bZzateXa5ffs2J02axD179vCPP/6gu7s7/fz8TJpLAgMDCYDe3t5mtTZJSUkmx+7fv5/FihVj0aJFGRoaqjVtGN25c4eLFi3itWvXbM7jtGnTtPcrrYXpoqOj2aBBA5YrV86s5uP69ev08/Nj5cqV01zczxrjIn/e3t7afZTaECFETiedX59Aixcvxk8//YQvvvgCBw4cAADUrVsXH330EXr16oW+fftmyXV1Oh169+6N6tWr4+jRo1aPK168OCZMmIAGDRpAp9PBx8cH9erVM+mkOXLkSCxZsgTBwcEoUaKEtv/x48eoWbMmChcujG3btgHQd/o1dvA9ceKEWS1PsWLF0L9/f5QpU8bmsjRq1Ah58+aFr68vKlSoYPW4M2fOYO/evYiIiMDmzZtNXtu7dy/Onj2Lc+fOYc+ePTZf28g4a27Tpk2hlAIAeHhIZaQQQjjE2ZHQk7LBhhqTI0eOsECBAixXrpxZ7UFWunjxolbLMGLECJvO6dSpkzaU19Kic6lduHBBu8b48eNJ6msRRo0axQ8++MCmzr3WnDp1yqR2Jj4+Pt3ZbpOTkzl8+HC+9tprvH79uslrjx8/Zp8+fdirV690Z6y1JioqymzW3DNnzjAkJMSh9IQQIrtlV42Jov4hKjKZUupW/vz5S/j7+xsDFe3bdEonTpxATEwMSpYsifLlyzstf+Hh4YiJiUHFihWRN2/edI+Pjo7G5cuXUaRIEZQuXdqma9y8eROPHz/GM888k2k1CJGRkTh//jw8PDwQEBAApRQeP36M/PnzW3x/s4tx3R2S8PPzQ+HChbM7S0IIYZeTJ08iOjr6NsmSzryuBCZZRCmlA6AAHMjuvDhZXcNPyyvl5V5S7ieLlPvJ8qSW+yUAJOnUbh8SmGQRpVQ8AJD0zu68OJOUW8r9JJByS7mfBNlVbun8KoQQQgiXIYGJEEIIIVyGBCZCCCGEcBkSmAghhBDCZUhgIoQQQgiXIaNyhBBCCOEypMZECCGEEC5DAhMhhBBCuAwJTIQQQgjhMiQwEUIIIYTLkMBECCGEEC5DAhMhhBBCuAwJTIQQQgjhMiQwEUIIIYTLkMBECCGEEC5DAhMhhBBCuAwJTIQQQgjhMiQwEUIIIYTLkMBECCGEEC5DAhMhhBBCuAwJTDKBUqqgUuorpdRlpVS8UuqSUmqmUiq/nekwjW1JFmXflnz9Tym1WSn1QCkVo5Q6oJTqamca3kqpT5VS55VScUqp60qpBUqpElmV74zKaLmVUv3SuadNsjD7DlFK9VZKzVdKHTb8LVMp1c+BdNyUUsOVUieUUrFKqTtKqV+UUhWyINsZlhnlVko1Sed+25VeVlNKlVVKfaCU2qaUuqKUSlBK3VRKrVVK1bUzrRxzvzOr3DnwfucxPKf+MXz+xhnKvVcp1V8p5WlHWll6vz0yI5EnmVIqH4BgAAEAtgH4BcDzAEYCeFkp1ZhknB1JXgawxML+YxnMqkOUUk0BbAUQB2AVgEcAOgFYrZR6muQsG9JwA7AOQCsABwCsBeALYBCA5kqpl0jeyaIiOCQzyp3COli+f5cyms8s8AWAZwHcBXDD8G9HzIf+/p4CMAdAGQBdAbQ03O/zmZDXzJRZ5Qb0nwd/W9ifLf+H0zAcwGgAYdB/dt2B/v9lBwAdlFI9Sa62Ma2cdL8zs9xAzrnf+QG8A+AQgE3Ql9sHQBsAiwB0V0q1IamzIa2svd8kZcvABmAiAAKYlmr/NMP+sXakRQB/Z3eZUuTHA8AF6B/OASn2FwLwH4B4AM/akE5/Q9lWAlAp9g8x7J+f3WXNonL3M5SvX3aXyY6ytzCWDcAYR/IPoKnhvGAAXin2tzHs35rd5cyicjcxnPd5dpfHxvx2BPCyhf2NACQAuAfAO7fd70wsd067324p70+K/R4AdhnK0s4V7rc05WSAUkpBHzVGA5ic6uXJhv2DnJ2vTNQMQEUAK0lq0T/JhwCmAvAC0NeGdAYbfo6l4S/YYD6AcAC9lFJPZU6WM0VmlTvHIbmD5OUMJmO83xNIJqRIewv03yxbKqWeyeA1MlUmlTtHIfkbyWAL+3dD/6DyAVDDhqRy1P3OxHLnKCR1Ke9Piv1JAH43/FrJhqSy/H5LYJIxvtBXYe0lGZPyBcPvewFUUEo9bUeahZVSbymlximlhiilsvM/SBPDz20WXttq+PlyWgkopfIAqAvgv9Qf/IYgZTuAfADqZCinmauJ4afD5U7leaXUR0qp0UqpbkqpohnKnetrAsD495+aI+9fTuNr6MMwVin1plKqbHZnyAGJhp9JNhzbBLnnfttTbqMcfb8NTe2tDb+etOGUJsji+y19TDLG1/DTWnvaeej7VfgCuGpjmrWgr0nQKKX+BNCX5G1HMpkBVstH8qZSKjrFMdZUhD4ATus9Ml5rtyOZzAKZUe6U3kv1e6xSaiLJ6Y5m0FUZ+lyVBnCSZLKFQ1Le79yqp2EzSlJKfQvgYyvviUsxfNttAX1fmxPpHJtr7rc95U4lR91vpZQXgHEAFICiAJoD8AOwmOTOdM51yv2WGpOMKWT4+dDK61GpjkvPLAD1ARQDUNDw7y3QR7MblVLuDubTUbaUL72yZfZ75AyZUW4AuAh9R7vKAPICKAegD/Rt2NOUUsMzmE9XlBPvd2a5A33/FH/oOxqWhL5D5QUAHwKYkX1Zs41hZMZyAN4ARtvwYM0V99uBcgM59357AfgMwKcA3gVQBcCXAN6y4Vyn3G+pMQGglJoF/R+krb5hFvQyJzky1a79SqlXAfwFfdXY6wB+y+zriqxhaMdO2ZZ9DcBypdRRAIcBfK6Ummdo4xU5HMlT0I9SMIoBsE4pdRDAcQDvKaWmZ0PNp00MVfpLADQGsJDk8uzNkXM4Wu6cer9JRkPfRdIN+q4I7aHvO1dPKdWWZFSaCTiBBCZ6b0Pfz8FWv0JfZWWMGq1FhwUNP61Fl+kiqVNKLYQ+MGkA5wYmtpTvfiakkfI4V5AZ5baK5Cml1B7oq42rwr5qY1eXE+93ljI0/62DviN8XQAbsjlLZgwPqUXQN0n8DP2IOVvk6PudgXJblRPuN6B/tgCIADBPKXUXQBCAT6AfSm2NU+63NOUAIJmfpLJj+9twanrtaen1QbHVXcNPe4KnzGC1fEqpUtBXX6ZXtnAAOktppErbleY5yIxypye77mmWMnT6vgGgvJWmR1e8387gsvfb8HBeDP1Is1+gHyZty1wWOfp+Z6TcNnDZ+22FsaN/k7QOctb9lsAkY84DuA6ggaFTkMbwewMAF0na2vHVGuNshJcymI69jM0QLS281irVMRaRjIV+Qp8qSimTSasMw61fgb4K9HDGspqpMlzutBj+QxtHIeXGIarB0H8gN7DwmvH9+8d52XEJ2fV/OE0pHs59AKwG8KYDHTZz3P3OpHKnxSXvdxrKGH4mpnmUXtbf74xOhPKkb7BzgjXoO0H6AXgm1f4aADwtpF8f+gd3AoCKTi6bB/SzI6Y10dhzKfaXNpStUKp0cuIEa5lR7hcsuUi6oQAABJ9JREFUpO0OYKah3H9ld1nTeR/SnGgM+k7afgCKpdqfoybcysRym91vw/73DemdA+Ce3eVLkS9j3wpCX43vkc7xueJ+Z2K5c9r9rgYgr4X9eaEfZEEA41zhfitDgsJBhpqRvdAP890G4CiA2tB/2w6BfobB2BTHN4F+Ep9gkk1S7F8CoB2APdAPLU4EUN2QDgG8S/KHLC9QKmlMzf4sgJFMMTW7oQx9AfQnuSTFfjcAm/H/U9IHQz+RT0fov1HUZc6Zkt6echP6TnDHoe/4WgT6vkKVoW/bfZlkuBOKYzOl1CAADQ2/1oD+b3kv9CMNAGAPyR8Nx34Ofe/+iSQ/T5XOQvz/lNWboA/eukE/6WA9kueytCB2yoxyK6UuQf//9jD09zcfgJegX6LiAYBWJA9lcVFslqIc0QC+geW5O/6gYZLB3HK/M6vcOfR+j4D+GXMJ+hE0ZaEPKIpCP11DK+PzKlvvd3ZHcblhg/6b9NcArkBfs3EZ+uFXBSwc2wQWpp4H8AaAP6DvkxFtSOcK9LUML2Zz+V6EPqJ+COAxgIMAulk4bgmsfNOEftTTZ9B/0MdD3065EEDJ7L5/WVVuw9/AHgA3DfczGvr1MyYD8Mnu8lkps7Es1rYlKY79HFam5Ib+W+l70E/YFAd9m/sqOLnWz5nlhr7T4F/QB6Fxhr+ZM4bPhnLZXUYHymzyN51b7ndmlTsH3u86ABYY7tF96IOqu4YyvIVUNUfZeb+lxkQIIYQQLkM6vwohhBDCZUhgIoQQQgiXIYGJEEIIIVyGBCZCCCGEcBkSmAghhBDCZUhgIoQQQgiXIYGJEEIIIVyGBCZCCCGEcBkSmAghhBDCZUhgIoQQQgiXIYGJEOKJp5R6XilFpdSf2Z0XIZ50EpgIIQRQ3/DzQLbmQgghi/gJIYRSqjj0S7/fIPkwu/MjxJNMAhMhhBBCuAxpyhFCOJ1SysfQpyNMKfWUUupTpdRppVS8UupwNuXlvDOvK4SwzCO7MyCEeCIFGH7eAHAIwDMA/gZwBkBYNuXlXydfVwhhgQQmQojs8LzhZwMAGwE0Jnk/m/NyLJuuL4RIQQITIUR2MNZSXADQnWSMC+RFakyEcAESmAghsoOxlmKcLUGJUmoZgBftvMbvJMfakRcJTIRwARKYCCGcSimVB4AfgFgAm2087RkAVey8VGk78nKL5E070xdCZAEJTIQQzuYP/WfPPlubcEg2yeK8SG2JEC5ChgsLIZzN2HRyJFtzoSfNOEK4GAlMhBDOZgwGjmZrLvRkRI4QLkYCEyGEsxlHwbhCjYmMyBHCxciU9EIIp1FKuQGIMvxakKTOBfKiA1CI8mEohEuQGhMhhDNVBpAPQGh2BiWp8nJcghIhXIfUmAghhBDCZUiNiRBCCCFchgQmQgghhHAZEpgIIYQQwmVIYCKEEEIIlyGBiRBCCCFchgQmQgghhHAZEpgIIYQQwmVIYCKEEEIIlyGBiRBCCCFchgQmQgghhHAZEpgIIYQQwmVIYCKEEEIIlyGBiRBCCCFchgQmQgghhHAZEpgIIYQQwmVIYCKEEEIIlyGBiRBCCCFcxv8BC+oWDYJU1YEAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plt.style.use('seaborn-notebook')\n", "plt.figure(1, figsize=(4, 4), dpi=140)\n", + "plt.title('Color-Magnitude Diagram for Stars in Catalog')\n", "plt.scatter(rMags[selected] - iMags[selected],\n", " iMags[selected],\n", " edgecolors='None', s=2, c='k')\n", + "\"\"\"\n", + "plt.scatter(rMags - iMags,\n", + " iMags,\n", + " edgecolors='None', s=2, c='k')\n", + "\"\"\"\n", "plt.xlim(-0.5, 3)\n", "plt.ylim(25, 14)\n", "plt.xlabel('$r-i$')\n", @@ -437,6 +632,2398 @@ "plt.subplots_adjust(left=0.125, bottom=0.1)\n", "plt.show()" ] + }, + { + "cell_type": "code", + "execution_count": 248, + "metadata": {}, + "outputs": [], + "source": [ + "# Now try to get the individual exposure light curves\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 181, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import lsst.daf.persistence as dafPersist\n", + "butler = dafPersist.Butler(inputs=DATADIR + 'rerun/ccdForcedPhot/')" + ] + }, + { + "cell_type": "code", + "execution_count": 249, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'filter': 'HSC-I', 'pointing': '671', 'visit': '903986', 'ccd': '23', 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': '30.0', 'tract': '0'}\n", + "{'filter': 'HSC-I', 'pointing': '671', 'visit': '903986', 'ccd': '22', 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': '30.0', 'tract': '0'}\n", + "{'filter': 'HSC-I', 'pointing': '671', 'visit': '903986', 'ccd': '16', 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': '30.0', 'tract': '0'}\n", + "{'filter': 'HSC-I', 'pointing': '671', 'visit': '903986', 'ccd': '100', 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': '30.0', 'tract': '0'}\n", + "{'filter': 'HSC-I', 'pointing': '671', 'visit': '903988', 'ccd': '24', 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': '30.0', 'tract': '0'}\n", + "{'filter': 'HSC-I', 'pointing': '671', 'visit': '903988', 'ccd': '23', 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': '30.0', 'tract': '0'}\n", + "{'filter': 'HSC-I', 'pointing': '671', 'visit': '903988', 'ccd': '17', 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': '30.0', 'tract': '0'}\n", + "{'filter': 'HSC-I', 'pointing': '671', 'visit': '903988', 'ccd': '16', 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': '30.0', 'tract': '0'}\n", + "{'filter': 'HSC-I', 'pointing': '671', 'visit': '903990', 'ccd': '25', 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': '30.0', 'tract': '0'}\n", + "{'filter': 'HSC-I', 'pointing': '671', 'visit': '903990', 'ccd': '18', 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': '30.0', 'tract': '0'}\n", + "{'filter': 'HSC-I', 'pointing': '671', 'visit': '904010', 'ccd': '100', 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': '30.0', 'tract': '0'}\n", + "{'filter': 'HSC-I', 'pointing': '671', 'visit': '904010', 'ccd': '10', 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': '30.0', 'tract': '0'}\n", + "{'filter': 'HSC-I', 'pointing': '671', 'visit': '904010', 'ccd': '4', 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': '30.0', 'tract': '0'}\n", + "{'filter': 'HSC-I', 'pointing': '671', 'visit': '904014', 'ccd': '12', 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': '30.0', 'tract': '0'}\n", + "{'filter': 'HSC-I', 'pointing': '671', 'visit': '904014', 'ccd': '6', 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': '30.0', 'tract': '0'}\n", + "{'filter': 'HSC-I', 'pointing': '671', 'visit': '904014', 'ccd': '1', 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': '30.0', 'tract': '0'}\n" + ] + } + ], + "source": [ + "data_id_fields = ['filter', 'pointing', 'visit', 'ccd', 'field', 'dateObs', 'taiObs', 'expTime', 'tract']\n", + "data_id_dtypes = [str,int,int,int,str,str,str,float,int]\n", + "\n", + "tables = []\n", + "for line in open('/home/jmyles/i_visits.txt'):\n", + " vars = line[74:-14].replace(\" \",\"\").replace(\"\\'\",\"\").replace(\"\\\"\",\"\").split(\",\")\n", + " if len(vars) == 1:\n", + " continue\n", + " print({data_id_fields[i] : vars[i].split(':')[1] for i in range(len(vars))})\n", + " sources = butler.get('forced_src', {data_id_fields[i] : data_id_dtypes[i](vars[i].split(':')[1]) for i in range(len(vars))})\n", + " tables.append(sources.asAstropy().to_pandas())\n", + " \n", + "iSources = pd.concat(tables)" + ] + }, + { + "cell_type": "code", + "execution_count": 251, + "metadata": {}, + "outputs": [], + "source": [ + "grouped = iSources.groupby('objectId')" + ] + }, + { + "cell_type": "code", + "execution_count": 256, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "30400\n", + "9742\n" + ] + } + ], + "source": [ + "print(len(iSources['objectId']))\n", + "print(len(np.unique(iSources['objectId'])))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 259, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "141733921083\n", + "141733921084\n", + "141733921091\n", + "141733921096\n", + "141733921103\n", + "141733921106\n", + "141733921108\n", + "141733921109\n", + "141733921110\n", + "141733921112\n", + "141733921114\n", + "141733921117\n", + "141733921119\n", + "141733921120\n", + "141733921122\n", + "141733921124\n", + "141733921125\n", + "141733921126\n", + "141733921129\n", + "141733921131\n", + "141733921134\n", + "141733921138\n", + "141733921139\n", + "141733921141\n", + "141733921142\n", + "141733921143\n", + "141733921148\n", + "141733921153\n", + "141733921155\n", + "141733921156\n", + "141733921157\n", + "141733921160\n", + "141733921161\n", + "141733921162\n", + "141733921164\n", + "141733921169\n", + "141733921170\n", + "141733921174\n", + "141733921175\n", + "141733921176\n", + "141733921184\n", + "141733921186\n", + "141733921188\n", + "141733921190\n", + "141733921192\n", + "141733921195\n", + "141733921197\n", + "141733921200\n", + "141733921202\n", + "141733921204\n", + "141733921206\n", + "141733921207\n", + "141733921208\n", + "141733921210\n", + "141733921211\n", + "141733921212\n", + "141733921214\n", + "141733921217\n", + "141733921225\n", + "141733921228\n", + "141733921230\n", + "141733921232\n", + "141733921279\n", + "141733921280\n", + "141733921281\n", + "141733921284\n", + "141733921285\n", + "141733921286\n", + "141733921290\n", + "141733921296\n", + "141733921301\n", + "141733921307\n", + "141733921310\n", + "141733921326\n", + "141733921328\n", + "141733921346\n", + "141733921347\n", + "141733921352\n", + "141733921353\n", + "141733921365\n", + "141733921367\n", + "141733921373\n", + "141733921375\n", + "141733921379\n", + "141733921381\n", + "141733921383\n", + "141733921394\n", + "141733921399\n", + "141733921400\n", + "141733921401\n", + "141733921403\n", + "141733921411\n", + "141733921420\n", + "141733921421\n", + "141733921430\n", + "141733921431\n", + "141733921437\n", + "141733921443\n", + "141733921445\n", + "141733921447\n", + "141733921452\n", + "141733921454\n", + "141733921455\n", + "141733921457\n", + "141733921460\n", + "141733921462\n", + "141733921468\n", + "141733921470\n", + "141733921472\n", + "141733921474\n", + "141733921479\n", + "141733921480\n", + "141733921482\n", + "141733921484\n", + "141733921485\n", + "141733921486\n", + "141733921487\n", + "141733921488\n", + "141733921492\n", + "141733921498\n", + "141733921501\n", + "141733921503\n", + "141733921504\n", + "141733921505\n", + "141733921515\n", + "141733921518\n", + "141733921523\n", + "141733921528\n", + "141733921529\n", + "141733921532\n", + "141733921535\n", + "141733921541\n", + "141733921542\n", + "141733921545\n", + "141733921548\n", + "141733921550\n", + "141733921552\n", + "141733921553\n", + "141733921557\n", + "141733921562\n", + "141733921564\n", + "141733921565\n", + "141733921566\n", + "141733921568\n", + "141733921571\n", + "141733921572\n", + "141733921574\n", + "141733921578\n", + "141733921580\n", + "141733921588\n", + "141733921589\n", + "141733921591\n", + "141733921598\n", + "141733921599\n", + "141733921603\n", + "141733921605\n", + "141733921609\n", + "141733921610\n", + "141733921613\n", + "141733921614\n", + "141733921615\n", + "141733921621\n", + "141733921622\n", + "141733921624\n", + "141733921625\n", + "141733921627\n", + "141733921632\n", + "141733921633\n", + "141733921634\n", + "141733921635\n", + "141733921638\n", + "141733921639\n", + "141733921643\n", + "141733921646\n", + "141733921654\n", + "141733921659\n", + "141733921661\n", + "141733921662\n", + "141733921666\n", + "141733921668\n", + "141733921670\n", + "141733921675\n", + "141733921678\n", + "141733921681\n", + "141733921685\n", + "141733921687\n", + "141733921692\n", + "141733921693\n", + "141733921697\n", + "141733921700\n", + "141733921701\n", + "141733921705\n", + "141733921706\n", + "141733921739\n", + "141733921740\n", + "141733921745\n", + "141733921746\n", + "141733921748\n", + "141733921753\n", + "141733921754\n", + "141733921758\n", + "141733921760\n", + "141733921761\n", + "141733921765\n", + "141733921772\n", + "141733921773\n", + "141733921778\n", + "141733921780\n", + "141733921781\n", + "141733921782\n", + "141733921783\n", + "141733921786\n", + "141733921788\n", + "141733921789\n", + "141733921790\n", + "141733921791\n", + "141733921794\n", + "141733921798\n", + "141733921814\n", + "141733921831\n", + "141733921837\n", + "141733921840\n", + "141733921841\n", + "141733921842\n", + "141733921844\n", + "141733921847\n", + "141733921848\n", + "141733921850\n", + "141733921853\n", + "141733921854\n", + "141733921855\n", + "141733921858\n", + "141733921859\n", + "141733921860\n", + "141733921861\n", + "141733921863\n", + "141733921865\n", + "141733921867\n", + "141733921871\n", + "141733921873\n", + "141733921875\n", + "141733921876\n", + "141733921878\n", + "141733921879\n", + "141733921880\n", + "141733921884\n", + "141733921886\n", + "141733921887\n", + "141733921888\n", + "141733921889\n", + "141733921891\n", + "141733921892\n", + "141733921893\n", + "141733921894\n", + "141733921895\n", + "141733921899\n", + "141733921901\n", + "141733921902\n", + "141733921903\n", + "141733921906\n", + "141733921907\n", + "141733921908\n", + "141733921910\n", + "141733921911\n", + "141733921912\n", + "141733921913\n", + "141733921914\n", + "141733921916\n", + "141733921917\n", + "141733921918\n", + "141733921919\n", + "141733921920\n", + "141733921921\n", + "141733921922\n", + "141733921923\n", + "141733921924\n", + "141733921925\n", + "141733921926\n", + "141733921927\n", + "141733921928\n", + "141733921929\n", + "141733921931\n", + "141733921933\n", + "141733921934\n", + "141733921937\n", + "141733921938\n", + "141733921939\n", + "141733921941\n", + "141733921943\n", + "141733921945\n", + "141733921946\n", + "141733921947\n", + "141733921956\n", + "141733921957\n", + "141733921959\n", + "141733921960\n", + "141733921961\n", + "141733921962\n", + "141733921964\n", + "141733921965\n", + "141733921966\n", + "141733921968\n", + "141733921969\n", + "141733921970\n", + "141733921971\n", + "141733921972\n", + "141733921973\n", + "141733921975\n", + "141733921976\n", + "141733921978\n", + "141733921979\n", + "141733921980\n", + "141733921982\n", + "141733921983\n", + "141733921984\n", + "141733921985\n", + "141733921986\n", + "141733921987\n", + "141733921988\n", + "141733921989\n", + "141733921992\n", + "141733921993\n", + "141733921994\n", + "141733921995\n", + "141733921999\n", + "141733922001\n", + "141733922002\n", + "141733922003\n", + "141733922005\n", + "141733922007\n", + "141733922008\n", + "141733922009\n", + "141733922011\n", + "141733922012\n", + "141733922013\n", + "141733922014\n", + "141733922015\n", + "141733922017\n", + "141733922018\n", + "141733922019\n", + "141733922020\n", + "141733922021\n", + "141733922024\n", + "141733922026\n", + "141733922029\n", + "141733922030\n", + "141733922032\n", + "141733922033\n", + "141733922034\n", + "141733922035\n", + "141733922036\n", + "141733922037\n", + "141733922039\n", + "141733922040\n", + "141733922041\n", + "141733922043\n", + "141733922044\n", + "141733922046\n", + "141733922050\n", + "141733922051\n", + "141733922053\n", + "141733922054\n", + "141733922055\n", + "141733922056\n", + "141733922057\n", + "141733922059\n", + "141733922060\n", + "141733922061\n", + "141733922062\n", + "141733922064\n", + "141733922065\n", + "141733922066\n", + "141733922067\n", + "141733922069\n", + "141733922070\n", + "141733922072\n", + "141733922073\n", + "141733922074\n", + "141733922075\n", + "141733922076\n", + "141733922077\n", + "141733922080\n", + "141733922081\n", + "141733922084\n", + "141733922085\n", + "141733922088\n", + "141733922089\n", + "141733922090\n", + "141733922091\n", + "141733922092\n", + "141733922094\n", + "141733922095\n", + "141733922096\n", + "141733922098\n", + "141733922099\n", + "141733922101\n", + "141733922102\n", + "141733922103\n", + "141733922104\n", + "141733922105\n", + "141733922107\n", + "141733922110\n", + "141733922111\n", + "141733922112\n", + "141733922113\n", + "141733922115\n", + "141733922116\n", + "141733922117\n", + "141733922681\n", + "141733922686\n", + "141733922689\n", + "141733922692\n", + "141733922693\n", + "141733922695\n", + "141733922697\n", + "141733922700\n", + "141733922701\n", + "141733922703\n", + "141733922705\n", + "141733922706\n", + "141733922708\n", + "141733922709\n", + "141733922713\n", + "141733922716\n", + "141733922719\n", + "141733922720\n", + "141733922722\n", + "141733922723\n", + "141733922724\n", + "141733922725\n", + "141733922729\n", + "141733922730\n", + "141733922731\n", + "141733922732\n", + "141733922737\n", + "141733922738\n", + "141733922739\n", + "141733922742\n", + "141733922745\n", + "141733922746\n", + "141733922747\n", + "141733922748\n", + "141733922750\n", + "141733922753\n", + "141733922755\n", + "141733922757\n", + "141733922758\n", + "141733922760\n", + "141733922761\n", + "141733922765\n", + "141733922766\n", + "141733922768\n", + "141733922769\n", + "141733922770\n", + "141733922773\n", + "141733922774\n", + "141733922775\n", + "141733922776\n", + "141733922779\n", + "141733922780\n", + "141733922783\n", + "141733922784\n", + "141733922788\n", + "141733922792\n", + "141733922793\n", + "141733922795\n", + "141733922796\n", + "141733922797\n", + "141733922800\n", + "141733922804\n", + "141733922805\n", + "141733922806\n", + "141733922810\n", + "141733922811\n", + "141733922814\n", + "141733922817\n", + "141733922821\n", + "141733922823\n", + "141733922824\n", + "141733922827\n", + "141733922829\n", + "141733922831\n", + "141733922832\n", + "141733922833\n", + "141733922836\n", + "141733922839\n", + "141733922840\n", + "141733922841\n", + "141733922842\n", + "141733922845\n", + "141733922847\n", + "141733922849\n", + "141733922851\n", + "141733922853\n", + "141733922854\n", + "141733922855\n", + "141733922857\n", + "141733922859\n", + "141733922870\n", + "141733922871\n", + "141733922872\n", + "141733922873\n", + "141733922874\n", + "141733922878\n", + "141733922879\n", + "141733922880\n", + "141733922884\n", + "141733922890\n", + "141733922893\n", + "141733922894\n", + "141733922895\n", + "141733922896\n", + "141733922898\n", + "141733922900\n", + "141733922901\n", + "141733922903\n", + "141733922904\n", + "141733922906\n", + "141733922907\n", + "141733922910\n", + "141733922911\n", + "141733922916\n", + "141733922917\n", + "141733922918\n", + "141733922922\n", + "141733922924\n", + "141733922925\n", + "141733922927\n", + "141733922928\n", + "141733922931\n", + "141733922934\n", + "141733922935\n", + "141733922936\n", + "141733922937\n", + "141733922939\n", + "141733922943\n", + "141733922945\n", + "141733922947\n", + "141733922949\n", + "141733922958\n", + "141733922994\n", + "141733922996\n", + "141733922997\n", + "141733922998\n", + "141733923001\n", + "141733923003\n", + "141733923011\n", + "141733923013\n", + "141733923014\n", + "141733923071\n", + "141733923075\n", + "141733923080\n", + "141733923082\n", + "141733923087\n", + "141733923093\n", + "141733923100\n", + "141733923101\n", + "141733923102\n", + "141733923104\n", + "141733923116\n", + "141733923119\n", + "141733923131\n", + "141733923132\n", + "141733923133\n", + "141733923140\n", + "141733923145\n", + "141733923146\n", + "141733923148\n", + "141733923149\n", + "141733923156\n", + "141733923159\n", + "141733923163\n", + "141733923166\n", + "141733923167\n", + "141733923171\n", + "141733923173\n", + "141733923174\n", + "141733923177\n", + "141733923179\n", + "141733923183\n", + "141733923185\n", + "141733923187\n", + "141733923189\n", + "141733923192\n", + "141733923194\n", + "141733923197\n", + "141733923200\n", + "141733923201\n", + "141733923206\n", + "141733923209\n", + "141733923210\n", + "141733923213\n", + "141733923214\n", + "141733923215\n", + "141733923218\n", + "141733923222\n", + "141733923235\n", + "141733923241\n", + "141733923252\n", + "141733923254\n", + "141733923259\n", + "141733923263\n", + "141733923265\n", + "141733923267\n", + "141733923269\n", + "141733923270\n", + "141733923275\n", + "141733923282\n", + "141733923283\n", + "141733923285\n", + "141733923287\n", + "141733923289\n", + "141733923290\n", + "141733923293\n", + "141733923301\n", + "141733923302\n", + "141733923308\n", + "141733923309\n", + "141733923310\n", + "141733923312\n", + "141733923314\n", + "141733923317\n", + "141733923318\n", + "141733923320\n", + "141733923322\n", + "141733923326\n", + "141733923330\n", + "141733923334\n", + "141733923337\n", + "141733923339\n", + "141733923343\n", + "141733923344\n", + "141733923345\n", + "141733923353\n", + "141733923354\n", + "141733923356\n", + "141733923360\n", + "141733923361\n", + "141733923363\n", + "141733923365\n", + "141733923370\n", + "141733923372\n", + "141733923373\n", + "141733923374\n", + "141733923382\n", + "141733923390\n", + "141733923395\n", + "141733923396\n", + "141733923399\n", + "141733923400\n", + "141733923408\n", + "141733923412\n", + "141733923419\n", + "141733923420\n", + "141733923422\n", + "141733923424\n", + "141733923430\n", + "141733923432\n", + "141733923445\n", + "141733923447\n", + "141733923452\n", + "141733923454\n", + "141733923455\n", + "141733923456\n", + "141733923459\n", + "141733923462\n", + "141733923464\n", + "141733923465\n", + "141733923469\n", + "141733923471\n", + "141733923472\n", + "141733923473\n", + "141733923474\n", + "141733923485\n", + "141733923489\n", + "141733923492\n", + "141733923493\n", + "141733923496\n", + "141733923500\n", + "141733923502\n", + "141733923503\n", + "141733923504\n", + "141733923509\n", + "141733923511\n", + "141733923515\n", + "141733923517\n", + "141733923522\n", + "141733923524\n", + "141733923526\n", + "141733923530\n", + "141733923535\n", + "141733923536\n", + "141733923538\n", + "141733923544\n", + "141733923546\n", + "141733923548\n", + "141733923549\n", + "141733923550\n", + "141733923551\n", + "141733923552\n", + "141733923555\n", + "141733923556\n", + "141733923560\n", + "141733923561\n", + "141733923565\n", + "141733923566\n", + "141733923567\n", + "141733923572\n", + "141733923573\n", + "141733923574\n", + "141733923576\n", + "141733923577\n", + "141733923580\n", + "141733923581\n", + "141733923582\n", + "141733923583\n", + "141733923584\n", + "141733923587\n", + "141733923589\n", + "141733923592\n", + "141733923595\n", + "141733923598\n", + "141733923600\n", + "141733923601\n", + "141733923602\n", + "141733923603\n", + "141733923604\n", + "141733923607\n", + "141733923608\n", + "141733923610\n", + "141733923611\n", + "141733923613\n", + "141733923617\n", + "141733923618\n", + "141733923624\n", + "141733923628\n", + "141733923632\n", + "141733923638\n", + "141733923640\n", + "141733923641\n", + "141733923642\n", + "141733923645\n", + "141733923646\n", + "141733923648\n", + "141733923655\n", + "141733923656\n", + "141733923659\n", + "141733923662\n", + "141733923664\n", + "141733923665\n", + "141733923670\n", + "141733923672\n", + "141733923675\n", + "141733923678\n", + "141733923679\n", + "141733923682\n", + "141733923687\n", + "141733923690\n", + "141733923692\n", + "141733923693\n", + "141733923695\n", + "141733923698\n", + "141733923701\n", + "141733923705\n", + "141733923707\n", + "141733923708\n", + "141733923710\n", + "141733923713\n", + "141733923716\n", + "141733923718\n", + "141733923720\n", + "141733923726\n", + "141733923727\n", + "141733923729\n", + "141733923731\n", + "141733923733\n", + "141733923734\n", + "141733923738\n", + "141733923739\n", + "141733923743\n", + "141733923745\n", + "141733923750\n", + "141733923754\n", + "141733923755\n", + "141733923803\n", + "141733923804\n", + "141733923805\n", + "141733923806\n", + "141733923807\n", + "141733923809\n", + "141733923811\n", + "141733923812\n", + "141733923816\n", + "141733923820\n", + "141733923825\n", + "141733923826\n", + "141733923827\n", + "141733923828\n", + "141733923832\n", + "141733923834\n", + "141733923835\n", + "141733923836\n", + "141733923841\n", + "141733923844\n", + "141733923847\n", + "141733923848\n", + "141733923849\n", + "141733923850\n", + "141733923851\n", + "141733923854\n", + "141733923858\n", + "141733923859\n", + "141733923870\n", + "141733923871\n", + "141733923874\n", + "141733923884\n", + "141733923885\n", + "141733923888\n", + "141733923896\n", + "141733923921\n", + "141733923932\n", + "141733923934\n", + "141733923940\n", + "141733923941\n", + "141733923943\n", + "141733923944\n", + "141733923945\n", + "141733923946\n", + "141733923947\n", + "141733923948\n", + "141733923949\n", + "141733923950\n", + "141733923951\n", + "141733923953\n", + "141733923954\n", + "141733923956\n", + "141733923958\n", + "141733923959\n", + "141733923961\n", + "141733923964\n", + "141733923966\n", + "141733923967\n", + "141733923968\n", + "141733923970\n", + "141733923971\n", + "141733923972\n", + "141733923974\n", + "141733923975\n", + "141733923976\n", + "141733923977\n", + "141733923978\n", + "141733923979\n", + "141733923980\n", + "141733923982\n", + "141733923983\n", + "141733923985\n", + "141733923988\n", + "141733923989\n", + "141733923990\n", + "141733923992\n", + "141733923993\n", + "141733923994\n", + "141733923995\n", + "141733923997\n", + "141733923999\n", + "141733924000\n", + "141733924001\n", + "141733924002\n", + "141733924003\n", + "141733924004\n", + "141733924005\n", + "141733924006\n", + "141733924007\n", + "141733924008\n", + "141733924010\n", + "141733924011\n", + "141733924012\n", + "141733924014\n", + "141733924016\n", + "141733924019\n", + "141733924023\n", + "141733924024\n", + "141733924025\n", + "141733924026\n", + "141733924027\n", + "141733924028\n", + "141733924029\n", + "141733924030\n", + "141733924031\n", + "141733924032\n", + "141733924033\n", + "141733924035\n", + "141733924036\n", + "141733924037\n", + "141733924038\n", + "141733924040\n", + "141733924041\n", + "141733924043\n", + "141733924044\n", + "141733924045\n", + "141733924046\n", + "141733924047\n", + "141733924048\n", + "141733924049\n", + "141733924050\n", + "141733924052\n", + "141733924054\n", + "141733924055\n", + "141733924056\n", + "141733924057\n", + "141733924059\n", + "141733924060\n", + "141733924062\n", + "141733924063\n", + "141733924064\n", + "141733924066\n", + "141733924067\n", + "141733924069\n", + "141733924070\n", + "141733924073\n", + "141733924074\n", + "141733924075\n", + "141733924076\n", + "141733924077\n", + "141733924078\n", + "141733924079\n", + "141733924080\n", + "141733924081\n", + "141733924083\n", + "141733924085\n", + "141733924086\n", + "141733924087\n", + "141733924088\n", + "141733924090\n", + "141733924091\n", + "141733924093\n", + "141733924095\n", + "141733924096\n", + "141733924097\n", + "141733924098\n", + "141733924100\n", + "141733924102\n", + "141733924103\n", + "141733924106\n", + "141733924107\n", + "141733924108\n", + "141733924109\n", + "141733924112\n", + "141733924113\n", + "141733924114\n", + "141733924115\n", + "141733924119\n", + "141733924123\n", + "141733924125\n", + "141733924126\n", + "141733924127\n", + "141733924129\n", + "141733924131\n", + "141733924132\n", + "141733924136\n", + "141733924139\n", + "141733924140\n", + "141733924141\n", + "141733924142\n", + "141733924145\n", + "141733924146\n", + "141733924147\n", + "141733924149\n", + "141733924151\n", + "141733924152\n", + "141733924153\n", + "141733924154\n", + "141733924155\n", + "141733924157\n", + "141733924158\n", + "141733924160\n", + "141733924162\n", + "141733924163\n", + "141733924165\n", + "141733924166\n", + "141733924168\n", + "141733924169\n", + "141733924170\n", + "141733924171\n", + "141733924173\n", + "141733924174\n", + "141733924175\n", + "141733924176\n", + "141733924178\n", + "141733924179\n", + "141733924180\n", + "141733924181\n", + "141733924183\n", + "141733924184\n", + "141733924185\n", + "141733924186\n", + "141733924188\n", + "141733924189\n", + "141733924190\n", + "141733924191\n", + "141733924193\n", + "141733924194\n", + "141733924195\n", + "141733924199\n", + "141733924201\n", + "141733924202\n", + "141733924204\n", + "141733924205\n", + "141733924206\n", + "141733924209\n", + "141733924210\n", + "141733924211\n", + "141733924212\n", + "141733924214\n", + "141733924215\n", + "141733924217\n", + "141733924218\n", + "141733924221\n", + "141733924222\n", + "141733924223\n", + "141733924224\n", + "141733924226\n", + "141733924227\n", + "141733924228\n", + "141733924229\n", + "141733924230\n", + "141733924231\n", + "141733924233\n", + "141733924234\n", + "141733924237\n", + "141733924238\n", + "141733924240\n", + "141733924241\n", + "141733924242\n", + "141733924243\n", + "141733924244\n", + "141733924245\n", + "141733924246\n", + "141733924247\n", + "141733924248\n", + "141733924249\n", + "141733924251\n", + "141733924252\n", + "141733924253\n", + "141733924254\n", + "141733924255\n", + "141733924256\n", + "141733924259\n", + "141733924260\n", + "141733924261\n", + "141733924263\n", + "141733924264\n", + "141733924265\n", + "141733924266\n", + "141733924267\n", + "141733924268\n", + "141733924270\n", + "141733924271\n", + "141733924272\n", + "141733924273\n", + "141733924275\n", + "141733924279\n", + "141733924782\n", + "141733924785\n", + "141733924792\n", + "141733924793\n", + "141733924811\n", + "141733924815\n", + "141733924821\n", + "141733924827\n", + "141733924829\n", + "141733924837\n", + "141733924838\n", + "141733924846\n", + "141733924847\n", + "141733924860\n", + "141733924864\n", + "141733924868\n", + "141733924876\n", + "141733929404\n", + "141733929405\n", + "141733929406\n", + "141733929407\n", + "141733929408\n", + "141733929409\n", + "141733929410\n", + "141733929426\n", + "141733929427\n", + "141733929431\n", + "141733929432\n", + "141733929433\n", + "141733929434\n", + "141733929435\n", + "141733929436\n", + "141733929437\n", + "141733929438\n", + "141733929439\n", + "141733929440\n", + "141733929441\n", + "141733929450\n", + "141733929451\n", + "141733929452\n", + "141733929453\n", + "141733929454\n", + "141733929455\n", + "141733929456\n", + "141733929457\n", + "141733929458\n", + "141733929461\n", + "141733929462\n", + "141733929463\n", + "141733929464\n", + "141733929505\n", + "141733929506\n", + "141733929507\n", + "141733929515\n", + "141733929516\n", + "141733929517\n", + "141733929518\n", + "141733929519\n", + "141733929520\n", + "141733929535\n", + "141733929536\n", + "141733929546\n", + "141733929547\n", + "141733929552\n", + "141733929553\n", + "141733929563\n", + "141733929564\n", + "141733929565\n", + "141733929566\n", + "141733929575\n", + "141733929576\n", + "141733929580\n", + "141733929581\n", + "141733929582\n", + "141733929583\n", + "141733929584\n", + "141733929585\n", + "141733929586\n", + "141733929587\n", + "141733929623\n", + "141733929624\n", + "141733929715\n", + "141733929716\n", + "141733929717\n", + "141733929718\n", + "141733929719\n", + "141733929720\n", + "141733929721\n", + "141733929722\n", + "141733929723\n", + "141733929724\n", + "141733929725\n", + "141733929726\n", + "141733929727\n", + "141733929728\n", + "141733929729\n", + "141733929730\n", + "141733929731\n", + "141733929732\n", + "141733929733\n", + "141733929734\n", + "141733929735\n", + "141733929736\n", + "141733929737\n", + "141733929738\n", + "141733929739\n", + "141733929740\n", + "141733929741\n", + "141733929742\n", + "141733929743\n", + "141733929744\n", + "141733929745\n", + "141733929746\n", + "141733929747\n", + "141733929748\n", + "141733929874\n", + "141733929875\n", + "141733929880\n", + "141733929881\n", + "141733929882\n", + "141733929883\n", + "141733929889\n", + "141733929890\n", + "141733929891\n", + "141733929892\n", + "141733929893\n", + "141733929894\n", + "141733929895\n", + "141733929896\n", + "141733929897\n", + "141733929898\n", + "141733929899\n", + "141733929900\n", + "141733929901\n", + "141733929902\n", + "141733929903\n", + "141733929904\n", + "141733929905\n", + "141733929906\n", + "141733929907\n", + "141733929908\n", + "141733929909\n", + "141733929910\n", + "141733929914\n", + "141733929915\n", + "141733929916\n", + "141733929917\n", + "141733929918\n", + "141733929932\n", + "141733929933\n", + "141733929934\n", + "141733929935\n", + "141733929936\n", + "141733929937\n", + "141733929992\n", + "141733929993\n", + "141733930005\n", + "141733930006\n", + "141733930007\n", + "141733930008\n", + "141733930009\n", + "141733930010\n", + "141733930011\n", + "141733930012\n", + "141733930015\n", + "141733930016\n", + "141733930017\n", + "141733930018\n", + "141733930019\n", + "141733930020\n", + "141733930023\n", + "141733930024\n", + "141733930025\n", + "141733930026\n", + "141733930027\n", + "141733930028\n", + "141733930029\n", + "141733930030\n", + "141733930034\n", + "141733930035\n", + "141733930036\n", + "141733930037\n", + "141733930038\n", + "141733930039\n", + "141733930040\n", + "141733930041\n", + "141733930042\n", + "141733930043\n", + "141733930044\n", + "141733930054\n", + "141733930055\n", + "141733930078\n", + "141733930079\n", + "141733930083\n", + "141733930084\n", + "141733930087\n", + "141733930088\n", + "141733930089\n", + "141733930090\n", + "141733930091\n", + "141733930092\n", + "141733930093\n", + "141733930094\n", + "141733930095\n", + "141733930096\n", + "141733930111\n", + "141733930112\n", + "141733930116\n", + "141733930117\n", + "141733930118\n", + "141733930122\n", + "141733930123\n", + "141733930124\n", + "141733930127\n", + "141733930128\n", + "141733930129\n", + "141733930135\n", + "141733930136\n", + "141733930137\n", + "141733930138\n", + "141733930141\n", + "141733930142\n", + "141733930143\n", + "141733930144\n", + "141733930163\n", + "141733930164\n", + "141733930174\n", + "141733930175\n", + "141733930176\n", + "141733930177\n", + "141733930178\n", + "141733930179\n", + "141733930180\n", + "141733930181\n", + "141733930182\n", + "141733930183\n", + "141733930184\n", + "141733930185\n", + "141733930186\n", + "141733930189\n", + "141733930190\n", + "141733930191\n", + "141733930192\n", + "141733930193\n", + "141733930216\n", + "141733930217\n", + "141733930252\n", + "141733930253\n", + "141733930254\n", + "141733930255\n", + "141733930256\n", + "141733930257\n", + "141733930264\n", + "141733930265\n", + "141733930266\n", + "141733930267\n", + "141733930302\n", + "141733930303\n", + "141733930308\n", + "141733930309\n", + "141733930324\n", + "141733930325\n", + "141733930326\n", + "141733930413\n", + "141733930414\n", + "141733930421\n", + "141733930422\n", + "141733930423\n", + "141733930424\n", + "141733930425\n", + "141733930426\n", + "141733930429\n", + "141733930430\n", + "141733930431\n", + "141733930437\n", + "141733930438\n", + "141733930439\n", + "141733930440\n", + "141733930441\n", + "141733930442\n", + "141733930443\n", + "141733930444\n", + "141733930445\n", + "141733930446\n", + "141733930447\n", + "141733930448\n", + "141733930449\n", + "141733930450\n", + "141733930451\n", + "141733930452\n", + "141733930453\n", + "141733930454\n", + "141733930455\n", + "141733930456\n", + "141733930457\n", + "141733930460\n", + "141733930461\n", + "141733930462\n", + "141733930463\n", + "141733930519\n", + "141733930520\n", + "141733930521\n", + "141733930522\n", + "141733930523\n", + "141733930524\n", + "141733930525\n", + "141733930526\n", + "141733930527\n", + "141733930528\n", + "141733930529\n", + "141733930530\n", + "141733930531\n", + "141733930538\n", + "141733930539\n", + "141733930542\n", + "141733930543\n", + "141733930544\n", + "141733930545\n", + "141733930546\n", + "141733930547\n", + "141733930548\n", + "141733930549\n", + "141733930550\n", + "141733930551\n", + "141733930552\n", + "141733930553\n", + "141733930554\n", + "141733930555\n", + "141733930556\n", + "141733930557\n", + "141733930558\n", + "141733930559\n", + "141733930560\n", + "141733930565\n", + "141733930566\n", + "141733930570\n", + "141733930571\n", + "141733930572\n", + "141733930573\n", + "141733930574\n", + "141733930575\n", + "141733930576\n", + "141733930577\n", + "141733930578\n", + "141733930579\n", + "141733930580\n", + "141733930581\n", + "141733930582\n", + "141733930583\n", + "141733930584\n", + "141733930585\n", + "141733930586\n", + "141733930587\n", + "141733930588\n", + "141733930589\n", + "141733930590\n", + "141733930591\n", + "141733930592\n", + "141733930593\n", + "141733930594\n", + "141733930595\n", + "141733930596\n", + "141733930597\n", + "141733930598\n", + "141733930599\n", + "141733930600\n", + "141733930601\n", + "141733930602\n", + "141733930603\n", + "141733930604\n", + "141733930605\n", + "141733930606\n", + "141733930607\n", + "141733930608\n", + "141733930609\n", + "141733930610\n", + "141733930611\n", + "141733930612\n", + "141733930613\n", + "141733930614\n", + "141733930615\n", + "141733930616\n", + "141733930617\n", + "141733930618\n", + "141733930619\n", + "141733930620\n", + "141733930621\n", + "141733930622\n", + "141733930623\n", + "141733930624\n", + "141733930627\n", + "141733930628\n", + "141733930629\n", + "141733930630\n", + "141733930632\n", + "141733930633\n", + "141733930634\n", + "141733930635\n", + "141733930636\n", + "141733930637\n", + "141733930638\n", + "141733930639\n", + "141733930640\n", + "141733930641\n", + "141733930642\n", + "141733930643\n", + "141733930644\n", + "141733930645\n", + "141733930646\n", + "141733930647\n", + "141733930648\n", + "141733930649\n", + "141733930650\n", + "141733930651\n", + "141733930652\n", + "141733930653\n", + "141733930654\n", + "141733930655\n", + "141733930656\n", + "141733930657\n", + "141733930658\n", + "141733930659\n", + "141733930660\n", + "141733930661\n", + "141733930662\n", + "141733930663\n", + "141733930664\n", + "141733930668\n", + "141733930669\n", + "141733930670\n", + "141733930671\n", + "141733930672\n", + "141733930673\n", + "141733930674\n", + "141733930675\n", + "141733930676\n", + "141733930681\n", + "141733930682\n", + "141733930688\n", + "141733930689\n", + "141733930693\n", + "141733930694\n", + "141733930695\n", + "141733930698\n", + "141733930699\n", + "141733930700\n", + "141733930701\n", + "141733930702\n", + "141733930703\n", + "141733930704\n", + "141733930705\n", + "141733930706\n", + "141733930709\n", + "141733930710\n", + "141733930711\n", + "141733930712\n", + "141733930713\n", + "141733930714\n", + "141733930715\n", + "141733930723\n", + "141733930724\n", + "141733930725\n", + "141733930726\n", + "141733930733\n", + "141733930734\n", + "141733930735\n", + "141733930736\n", + "141733930737\n", + "141733930738\n", + "141733930739\n", + "141733930740\n", + "141733930741\n", + "141733930742\n", + "141733930743\n", + "141733930744\n", + "141733930745\n", + "141733930746\n", + "141733930747\n", + "141733930750\n", + "141733930751\n", + "141733930752\n", + "141733930753\n", + "141733930754\n", + "141733930755\n", + "141733930756\n", + "141733930757\n", + "141733930758\n", + "141733930759\n", + "141733930760\n", + "141733930761\n", + "141733930762\n", + "141733930763\n", + "141733930764\n", + "141733930765\n", + "141733930766\n", + "141733930767\n", + "141733930768\n", + "141733930769\n", + "141733930770\n", + "141733930771\n", + "141733930772\n", + "141733930773\n", + "141733930774\n", + "141733930775\n", + "141733930776\n", + "141733930777\n", + "141733930778\n", + "141733930779\n", + "141733930780\n", + "141733930781\n", + "141733930782\n", + "141733930783\n", + "141733930784\n", + "141733930785\n", + "141733930786\n", + "141733930787\n", + "141733930788\n", + "141733930789\n", + "141733930791\n", + "141733930792\n", + "141733930799\n", + "141733930800\n", + "141733930801\n", + "141733930802\n", + "141733930803\n", + "141733930804\n", + "141733930805\n", + "141733930808\n", + "141733930809\n", + "141733930810\n", + "141733930811\n", + "141733930814\n", + "141733930815\n", + "141733930816\n", + "141733930817\n", + "141733930818\n", + "141733930819\n", + "141733930822\n", + "141733930823\n", + "141733930832\n", + "141733930833\n", + "141733930834\n", + "141733930835\n", + "141733930836\n", + "141733930837\n", + "141733930838\n", + "141733930839\n", + "141733930840\n", + "141733930841\n", + "141733930842\n", + "141733930843\n", + "141733930844\n", + "141733930845\n", + "141733930846\n", + "141733930847\n", + "141733930848\n", + "141733930849\n", + "141733930850\n", + "141733930851\n", + "141733930852\n", + "141733930853\n", + "141733930854\n", + "141733930855\n", + "141733930856\n", + "141733930857\n", + "141733930858\n", + "141733930859\n", + "141733930862\n", + "141733930863\n", + "141733930866\n", + "141733930867\n", + "141733930872\n", + "141733930873\n", + "141733930878\n", + "141733930879\n", + "141733930880\n", + "141733930885\n", + "141733930886\n", + "141733930887\n", + "141733930888\n", + "141733930889\n", + "141733930890\n", + "141733930891\n", + "141733930892\n", + "141733931089\n", + "141733931090\n", + "141733931091\n", + "141733931092\n", + "141733931093\n", + "141733931094\n", + "141733931095\n", + "141733931096\n", + "141733931101\n", + "141733931102\n", + "141733931103\n", + "141733931104\n", + "141733931105\n", + "141733931108\n", + "141733931109\n", + "141733931110\n", + "141733931117\n", + "141733931118\n", + "141733931119\n", + "141733931120\n", + "141733931121\n", + "141733931122\n", + "141733931123\n", + "141733931124\n", + "141733931141\n", + "141733931142\n", + "141733931143\n", + "141733931150\n", + "141733931151\n", + "141733931162\n", + "141733931163\n", + "141733931164\n", + "141733931165\n", + "141733931209\n", + "141733931210\n", + "141733931218\n", + "141733931219\n", + "141733931244\n", + "141733931245\n", + "141733931250\n", + "141733931251\n", + "141733931252\n", + "141733931263\n", + "141733931264\n", + "141733931270\n", + "141733931271\n", + "141733931272\n", + "141733931273\n", + "141733931282\n", + "141733931283\n", + "141733931297\n", + "141733931298\n", + "141733931299\n", + "141733931304\n", + "141733931305\n", + "141733931310\n", + "141733931311\n", + "141733931312\n", + "141733931313\n", + "141733931314\n", + "141733931315\n", + "141733931316\n", + "141733931321\n", + "141733931322\n", + "141733931323\n", + "141733931324\n", + "141733931325\n", + "141733931326\n", + "141733931338\n", + "141733931339\n", + "141733931342\n", + "141733931343\n", + "141733931355\n", + "141733931356\n", + "141733931362\n", + "141733931363\n", + "141733931392\n", + "141733931393\n", + "141733931398\n", + "141733931399\n", + "141733931400\n", + "141733931401\n", + "141733931402\n", + "141733931403\n", + "141733931404\n", + "141733931405\n", + "141733931411\n", + "141733931412\n", + "141733931415\n", + "141733931416\n", + "141733931419\n", + "141733931420\n", + "141733931421\n", + "141733931422\n", + "141733931430\n", + "141733931431\n", + "141733931432\n", + "141733931433\n", + "141733931434\n", + "141733931435\n", + "141733931436\n", + "141733931437\n", + "141733931438\n", + "141733931439\n", + "141733931440\n", + "141733931441\n", + "141733931442\n", + "141733931443\n", + "141733931444\n", + "141733931445\n", + "141733931446\n", + "141733931449\n", + "141733931450\n", + "141733931462\n", + "141733931463\n", + "141733931464\n", + "141733931469\n", + "141733931470\n", + "141733931471\n", + "141733931474\n", + "141733931475\n", + "141733931483\n", + "141733931484\n" + ] + } + ], + "source": [ + "for name, group in grouped:\n", + " if len(group) == 5:\n", + " print(name)" + ] + }, + { + "cell_type": "code", + "execution_count": 272, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([18.55159354, 18.16641909, 18.4897198 , 16.13221443, 18.43015646])" + ] + }, + "execution_count": 272, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 141733921083\n", + "iCoaddCalib.getMagnitude(iSources[iSources['objectId'] == 141733921083]['base_PsfFlux_flux'].values)" + ] + }, + { + "cell_type": "code", + "execution_count": 193, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1143 1143\n" + ] + } + ], + "source": [ + "# what datasetRefOrType to get? forced_src see all options at:\n", + "# /opt/lsst/software/stack/stack/miniconda3-4.3.21-10a4fa6/Linux64/obs_subaru/16.0+1/python/lsst/obs/hsc\n", + "# ['filter:HSC-I', 'pointing:671', 'visit:903986', 'ccd:16', 'field:STRIPE82L', 'dateObs:2013-11-02', 'taiObs:2013-11-02', 'expTime:30.0', 'tract:0'] 1143\n", + "# ['filter:HSC-R', 'pointing:533', 'visit:903334', 'ccd:16', 'field:STRIPE82L', 'dateObs:2013-06-17', 'taiObs:2013-06-17', 'expTime:30.0', 'tract:0'] 1143\n", + "\n", + "iSources = butler.get('forced_src', {'filter': 'HSC-I', \n", + " 'pointing': 671, \n", + " 'visit': 903986, \n", + " 'ccd': 16, \n", + " 'field': 'STRIPE82L', \n", + " 'dateObs': '2013-11-02', \n", + " 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0})\n", + "\n", + "rSources = butler.get('forced_src', {'filter': 'HSC-R', \n", + " 'pointing': 533, \n", + " 'visit': 903334, \n", + " 'ccd': 16, \n", + " 'field': 'STRIPE82L', \n", + " 'dateObs': '2013-06-17', \n", + " 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0})\n", + "\n", + "print(len(iSources), len(rSources))\n", + "\n", + "rMags = rCoaddCalib.getMagnitude(rSources['base_PsfFlux_flux']) # is using the rCoaddCalib object wrong?\n", + "iMags = iCoaddCalib.getMagnitude(iSources['base_PsfFlux_flux']) # is using the rCoaddCalib object wrong?" + ] + }, + { + "cell_type": "code", + "execution_count": 240, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1944" + ] + }, + "execution_count": 240, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#{'filter': 'HSC-I', 'pointing': '671', 'visit': '903986', 'ccd': '100', 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': '30.0', 'tract': '0'}\n", + "#{'filter': 'HSC-I', 'pointing': '671', 'visit': '903988', 'ccd': '24', 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': '30.0', 'tract': '0'}\n", + "\n", + "iSources_1 = butler.get('forced_src', {'filter': 'HSC-I', \n", + " 'pointing': 671, \n", + " 'visit': 903986, \n", + " 'ccd': 100, \n", + " 'field': 'STRIPE82L', \n", + " 'dateObs': '2013-11-02', \n", + " 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0})\n", + "\n", + "iSources_2 = butler.get('forced_src', {'filter': 'HSC-I', \n", + " 'pointing': 671, \n", + " 'visit': 903988, \n", + " 'ccd': 24, \n", + " 'field': 'STRIPE82L', \n", + " 'dateObs': '2013-11-02', \n", + " 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(len(np.unique(astropy.table.vstack(iSources_1.asAstropy(),iSources_2.asAstropy())['objectId'])))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 241, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " id ... ext_photometryKron_KronFlux_flag_apCorr\n", + " ... \n", + "------------------ ... ---------------------------------------\n", + "776518490705100801 ... False\n", + "776518490705100802 ... False\n", + "776518490705100803 ... False\n", + "776518490705100804 ... False\n", + "776518490705100805 ... False\n", + "776518490705100806 ... False\n", + "776518490705100807 ... False\n", + "776518490705100808 ... False\n", + "776518490705100809 ... False\n", + "776518490705100810 ... False\n", + " ... ... ...\n", + "776518490705102735 ... False\n", + "776518490705102736 ... False\n", + "776518490705102737 ... False\n", + "776518490705102738 ... False\n", + "776518490705102739 ... False\n", + "776518490705102740 ... False\n", + "776518490705102741 ... False\n", + "776518490705102742 ... False\n", + "776518490705102743 ... False\n", + "776518490705102744 ... False\n", + "Length = 1944 rows\n" + ] + } + ], + "source": [ + "import astropy\n", + "grouped = astropy.table.vstack(iSources_1.asAstropy(),iSources_2.asAstropy()).group_by('objectId')\n", + "print(grouped)" + ] + }, + { + "cell_type": "code", + "execution_count": 214, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "id: 1146\n", + "coord_ra: nan rad\n", + "coord_dec: nan rad\n", + "parent: 0\n", + "objectId: 0\n", + "parentObjectId: 0\n", + "deblend_nChild: 0\n", + "base_SdssCentroid_x: nan\n", + "base_SdssCentroid_y: nan\n", + "base_SdssCentroid_xSigma: nan\n", + "base_SdssCentroid_ySigma: nan\n", + "base_SdssCentroid_flag: 0\n", + "base_SdssCentroid_flag_edge: 0\n", + "base_SdssCentroid_flag_noSecondDerivative: 0\n", + "base_SdssCentroid_flag_almostNoSecondDerivative: 0\n", + "base_SdssCentroid_flag_notAtMaximum: 0\n", + "base_SdssCentroid_flag_resetToPeak: 0\n", + "base_TransformedCentroid_x: nan\n", + "base_TransformedCentroid_y: nan\n", + "base_TransformedCentroid_flag: 0\n", + "base_SdssShape_xx: nan\n", + "base_SdssShape_yy: nan\n", + "base_SdssShape_xy: nan\n", + "base_SdssShape_xxSigma: nan\n", + "base_SdssShape_yySigma: nan\n", + "base_SdssShape_xySigma: nan\n", + "base_SdssShape_x: nan\n", + "base_SdssShape_y: nan\n", + "base_SdssShape_flux: nan\n", + "base_SdssShape_fluxSigma: nan\n", + "base_SdssShape_psf_xx: nan\n", + "base_SdssShape_psf_yy: nan\n", + "base_SdssShape_psf_xy: nan\n", + "base_SdssShape_flux_xx_Cov: nan\n", + "base_SdssShape_flux_yy_Cov: nan\n", + "base_SdssShape_flux_xy_Cov: nan\n", + "base_SdssShape_flag: 0\n", + "base_SdssShape_flag_unweightedBad: 0\n", + "base_SdssShape_flag_unweighted: 0\n", + "base_SdssShape_flag_shift: 0\n", + "base_SdssShape_flag_maxIter: 0\n", + "base_SdssShape_flag_psf: 0\n", + "base_TransformedShape_xx: nan\n", + "base_TransformedShape_yy: nan\n", + "base_TransformedShape_xy: nan\n", + "base_TransformedShape_flag: 0\n", + "modelfit_DoubleShapeletPsfApprox_0_xx: nan\n", + "modelfit_DoubleShapeletPsfApprox_0_yy: nan\n", + "modelfit_DoubleShapeletPsfApprox_0_xy: nan\n", + "modelfit_DoubleShapeletPsfApprox_0_x: nan\n", + "modelfit_DoubleShapeletPsfApprox_0_y: nan\n", + "modelfit_DoubleShapeletPsfApprox_0_0: nan\n", + "modelfit_DoubleShapeletPsfApprox_0_1: nan\n", + "modelfit_DoubleShapeletPsfApprox_0_2: nan\n", + "modelfit_DoubleShapeletPsfApprox_0_3: nan\n", + "modelfit_DoubleShapeletPsfApprox_0_4: nan\n", + "modelfit_DoubleShapeletPsfApprox_0_5: nan\n", + "modelfit_DoubleShapeletPsfApprox_1_xx: nan\n", + "modelfit_DoubleShapeletPsfApprox_1_yy: nan\n", + "modelfit_DoubleShapeletPsfApprox_1_xy: nan\n", + "modelfit_DoubleShapeletPsfApprox_1_x: nan\n", + "modelfit_DoubleShapeletPsfApprox_1_y: nan\n", + "modelfit_DoubleShapeletPsfApprox_1_0: nan\n", + "modelfit_DoubleShapeletPsfApprox_1_1: nan\n", + "modelfit_DoubleShapeletPsfApprox_1_2: nan\n", + "modelfit_DoubleShapeletPsfApprox_flag: 0\n", + "modelfit_DoubleShapeletPsfApprox_flag_invalidPointForPsf: 0\n", + "modelfit_DoubleShapeletPsfApprox_flag_invalidMoments: 0\n", + "modelfit_DoubleShapeletPsfApprox_flag_maxIterations: 0\n", + "base_CircularApertureFlux_3_0_flux: nan\n", + "base_CircularApertureFlux_3_0_fluxSigma: nan\n", + "base_CircularApertureFlux_3_0_flag: 0\n", + "base_CircularApertureFlux_3_0_flag_apertureTruncated: 0\n", + "base_CircularApertureFlux_3_0_flag_sincCoeffsTruncated: 0\n", + "base_CircularApertureFlux_4_5_flux: nan\n", + "base_CircularApertureFlux_4_5_fluxSigma: nan\n", + "base_CircularApertureFlux_4_5_flag: 0\n", + "base_CircularApertureFlux_4_5_flag_apertureTruncated: 0\n", + "base_CircularApertureFlux_4_5_flag_sincCoeffsTruncated: 0\n", + "base_CircularApertureFlux_6_0_flux: nan\n", + "base_CircularApertureFlux_6_0_fluxSigma: nan\n", + "base_CircularApertureFlux_6_0_flag: 0\n", + "base_CircularApertureFlux_6_0_flag_apertureTruncated: 0\n", + "base_CircularApertureFlux_6_0_flag_sincCoeffsTruncated: 0\n", + "base_CircularApertureFlux_9_0_flux: nan\n", + "base_CircularApertureFlux_9_0_fluxSigma: nan\n", + "base_CircularApertureFlux_9_0_flag: 0\n", + "base_CircularApertureFlux_9_0_flag_apertureTruncated: 0\n", + "base_CircularApertureFlux_9_0_flag_sincCoeffsTruncated: 0\n", + "base_CircularApertureFlux_12_0_flux: nan\n", + "base_CircularApertureFlux_12_0_fluxSigma: nan\n", + "base_CircularApertureFlux_12_0_flag: 0\n", + "base_CircularApertureFlux_12_0_flag_apertureTruncated: 0\n", + "base_CircularApertureFlux_12_0_flag_sincCoeffsTruncated: 0\n", + "base_CircularApertureFlux_17_0_flux: nan\n", + "base_CircularApertureFlux_17_0_fluxSigma: nan\n", + "base_CircularApertureFlux_17_0_flag: 0\n", + "base_CircularApertureFlux_17_0_flag_apertureTruncated: 0\n", + "base_CircularApertureFlux_25_0_flux: nan\n", + "base_CircularApertureFlux_25_0_fluxSigma: nan\n", + "base_CircularApertureFlux_25_0_flag: 0\n", + "base_CircularApertureFlux_25_0_flag_apertureTruncated: 0\n", + "base_CircularApertureFlux_35_0_flux: nan\n", + "base_CircularApertureFlux_35_0_fluxSigma: nan\n", + "base_CircularApertureFlux_35_0_flag: 0\n", + "base_CircularApertureFlux_35_0_flag_apertureTruncated: 0\n", + "base_CircularApertureFlux_50_0_flux: nan\n", + "base_CircularApertureFlux_50_0_fluxSigma: nan\n", + "base_CircularApertureFlux_50_0_flag: 0\n", + "base_CircularApertureFlux_50_0_flag_apertureTruncated: 0\n", + "base_CircularApertureFlux_70_0_flux: nan\n", + "base_CircularApertureFlux_70_0_fluxSigma: nan\n", + "base_CircularApertureFlux_70_0_flag: 0\n", + "base_CircularApertureFlux_70_0_flag_apertureTruncated: 0\n", + "base_GaussianFlux_flux: nan\n", + "base_GaussianFlux_fluxSigma: nan\n", + "base_GaussianFlux_flag: 0\n", + "base_LocalBackground_flux: nan\n", + "base_LocalBackground_fluxSigma: nan\n", + "base_LocalBackground_flag: 0\n", + "base_LocalBackground_flag_noGoodPixels: 0\n", + "base_LocalBackground_flag_noPsf: 0\n", + "base_PixelFlags_flag: 0\n", + "base_PixelFlags_flag_offimage: 0\n", + "base_PixelFlags_flag_edge: 0\n", + "base_PixelFlags_flag_interpolated: 0\n", + "base_PixelFlags_flag_saturated: 0\n", + "base_PixelFlags_flag_cr: 0\n", + "base_PixelFlags_flag_bad: 0\n", + "base_PixelFlags_flag_suspect: 0\n", + "base_PixelFlags_flag_interpolatedCenter: 0\n", + "base_PixelFlags_flag_saturatedCenter: 0\n", + "base_PixelFlags_flag_crCenter: 0\n", + "base_PixelFlags_flag_suspectCenter: 0\n", + "base_PsfFlux_flux: nan\n", + "base_PsfFlux_fluxSigma: nan\n", + "base_PsfFlux_area: nan\n", + "base_PsfFlux_flag: 0\n", + "base_PsfFlux_flag_noGoodPixels: 0\n", + "base_PsfFlux_flag_edge: 0\n", + "ext_photometryKron_KronFlux_flux: nan\n", + "ext_photometryKron_KronFlux_fluxSigma: nan\n", + "ext_photometryKron_KronFlux_radius: nan\n", + "ext_photometryKron_KronFlux_radius_for_radius: nan\n", + "ext_photometryKron_KronFlux_psf_radius: nan\n", + "ext_photometryKron_KronFlux_flag: 0\n", + "ext_photometryKron_KronFlux_flag_edge: 0\n", + "ext_photometryKron_KronFlux_flag_bad_shape_no_psf: 0\n", + "ext_photometryKron_KronFlux_flag_no_minimum_radius: 0\n", + "ext_photometryKron_KronFlux_flag_no_fallback_radius: 0\n", + "ext_photometryKron_KronFlux_flag_bad_radius: 0\n", + "ext_photometryKron_KronFlux_flag_used_minimum_radius: 0\n", + "ext_photometryKron_KronFlux_flag_used_psf_radius: 0\n", + "ext_photometryKron_KronFlux_flag_small_radius: 0\n", + "ext_photometryKron_KronFlux_flag_bad_shape: 0\n", + "base_GaussianFlux_apCorr: nan\n", + "base_GaussianFlux_apCorrSigma: nan\n", + "base_GaussianFlux_flag_apCorr: 0\n", + "base_PsfFlux_apCorr: nan\n", + "base_PsfFlux_apCorrSigma: nan\n", + "base_PsfFlux_flag_apCorr: 0\n", + "ext_photometryKron_KronFlux_apCorr: nan\n", + "ext_photometryKron_KronFlux_apCorrSigma: nan\n", + "ext_photometryKron_KronFlux_flag_apCorr: 0\n", + "\n", + "1136\n", + "1143\n" + ] + } + ], + "source": [ + "print(rSources.makeRecord())\n", + "print(len(np.intersect1d(rSources['objectId'],iSources['objectId'])))\n", + "print(len(rSources['objectId']))" + ] + }, + { + "cell_type": "code", + "execution_count": 195, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.style.use('seaborn-notebook')\n", + "plt.figure(1, figsize=(4, 4), dpi=140)\n", + "plt.title('Color-Magnitude Diagram for All Sources in Catalog')\n", + "plt.scatter(rMags - iMags,\n", + " iMags,\n", + " edgecolors='None', s=2, c='k')\n", + "#plt.xlim(-0.5, 3)\n", + "#plt.ylim(25, 14)\n", + "plt.xlabel('$r-i$')\n", + "plt.ylabel('$i$')\n", + "plt.subplots_adjust(left=0.125, bottom=0.1)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "rSources = butler.get('forced_src', {'filter': 'HSC-R', 'tract' : 0, 'patch' : '0,0'})\n", + "iSources = butler.get('forced_src', {'filter': 'HSC-I', 'tract': 0, 'patch': '1,1'})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import astropy.io.fits as fitsio\n", + "hmm = fitsio.open('/home/jmyles/DATA/rerun/ccdForcedPhot/00533/HSC-R/tract0/FORCEDSRC-0903334-016.fits')\n", + "hmm2 = fitsio.open('/home/jmyles/DATA/rerun/ccdForcedPhot/00533/HSC-R/tract0/FORCEDSRC-0903334-022.fits')\n", + "hmm3 = fitsio.open('/home/jmyles/DATA/rerun/coaddForcedPhot/deepCoadd-results/HSC-R/0/0,1/forced_src-HSC-R-0-0,1.fits')" + ] + }, + { + "cell_type": "code", + "execution_count": 190, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "('flags',\n", + " 'id',\n", + " 'coord_ra',\n", + " 'coord_dec',\n", + " 'parent',\n", + " 'objectId',\n", + " 'parentObjectId',\n", + " 'deblend_nChild',\n", + " 'base_SdssCentroid_x',\n", + " 'base_SdssCentroid_y',\n", + " 'base_SdssCentroid_xSigma',\n", + " 'base_SdssCentroid_ySigma',\n", + " 'base_TransformedCentroid_x',\n", + " 'base_TransformedCentroid_y',\n", + " 'base_SdssShape_xx',\n", + " 'base_SdssShape_yy',\n", + " 'base_SdssShape_xy',\n", + " 'base_SdssShape_xxSigma',\n", + " 'base_SdssShape_yySigma',\n", + " 'base_SdssShape_xySigma',\n", + " 'base_SdssShape_x',\n", + " 'base_SdssShape_y',\n", + " 'base_SdssShape_flux',\n", + " 'base_SdssShape_fluxSigma',\n", + " 'base_SdssShape_psf_xx',\n", + " 'base_SdssShape_psf_yy',\n", + " 'base_SdssShape_psf_xy',\n", + " 'base_SdssShape_flux_xx_Cov',\n", + " 'base_SdssShape_flux_yy_Cov',\n", + " 'base_SdssShape_flux_xy_Cov',\n", + " 'base_TransformedShape_xx',\n", + " 'base_TransformedShape_yy',\n", + " 'base_TransformedShape_xy',\n", + " 'modelfit_DoubleShapeletPsfApprox_0_xx',\n", + " 'modelfit_DoubleShapeletPsfApprox_0_yy',\n", + " 'modelfit_DoubleShapeletPsfApprox_0_xy',\n", + " 'modelfit_DoubleShapeletPsfApprox_0_x',\n", + " 'modelfit_DoubleShapeletPsfApprox_0_y',\n", + " 'modelfit_DoubleShapeletPsfApprox_0_0',\n", + " 'modelfit_DoubleShapeletPsfApprox_0_1',\n", + " 'modelfit_DoubleShapeletPsfApprox_0_2',\n", + " 'modelfit_DoubleShapeletPsfApprox_0_3',\n", + " 'modelfit_DoubleShapeletPsfApprox_0_4',\n", + " 'modelfit_DoubleShapeletPsfApprox_0_5',\n", + " 'modelfit_DoubleShapeletPsfApprox_1_xx',\n", + " 'modelfit_DoubleShapeletPsfApprox_1_yy',\n", + " 'modelfit_DoubleShapeletPsfApprox_1_xy',\n", + " 'modelfit_DoubleShapeletPsfApprox_1_x',\n", + " 'modelfit_DoubleShapeletPsfApprox_1_y',\n", + " 'modelfit_DoubleShapeletPsfApprox_1_0',\n", + " 'modelfit_DoubleShapeletPsfApprox_1_1',\n", + " 'modelfit_DoubleShapeletPsfApprox_1_2',\n", + " 'base_CircularApertureFlux_3_0_flux',\n", + " 'base_CircularApertureFlux_3_0_fluxSigma',\n", + " 'base_CircularApertureFlux_4_5_flux',\n", + " 'base_CircularApertureFlux_4_5_fluxSigma',\n", + " 'base_CircularApertureFlux_6_0_flux',\n", + " 'base_CircularApertureFlux_6_0_fluxSigma',\n", + " 'base_CircularApertureFlux_9_0_flux',\n", + " 'base_CircularApertureFlux_9_0_fluxSigma',\n", + " 'base_CircularApertureFlux_12_0_flux',\n", + " 'base_CircularApertureFlux_12_0_fluxSigma',\n", + " 'base_CircularApertureFlux_17_0_flux',\n", + " 'base_CircularApertureFlux_17_0_fluxSigma',\n", + " 'base_CircularApertureFlux_25_0_flux',\n", + " 'base_CircularApertureFlux_25_0_fluxSigma',\n", + " 'base_CircularApertureFlux_35_0_flux',\n", + " 'base_CircularApertureFlux_35_0_fluxSigma',\n", + " 'base_CircularApertureFlux_50_0_flux',\n", + " 'base_CircularApertureFlux_50_0_fluxSigma',\n", + " 'base_CircularApertureFlux_70_0_flux',\n", + " 'base_CircularApertureFlux_70_0_fluxSigma',\n", + " 'base_GaussianFlux_flux',\n", + " 'base_GaussianFlux_fluxSigma',\n", + " 'base_LocalBackground_flux',\n", + " 'base_LocalBackground_fluxSigma',\n", + " 'base_PsfFlux_flux',\n", + " 'base_PsfFlux_fluxSigma',\n", + " 'base_PsfFlux_area',\n", + " 'ext_photometryKron_KronFlux_flux',\n", + " 'ext_photometryKron_KronFlux_fluxSigma',\n", + " 'ext_photometryKron_KronFlux_radius',\n", + " 'ext_photometryKron_KronFlux_radius_for_radius',\n", + " 'ext_photometryKron_KronFlux_psf_radius',\n", + " 'base_GaussianFlux_apCorr',\n", + " 'base_GaussianFlux_apCorrSigma',\n", + " 'base_PsfFlux_apCorr',\n", + " 'base_PsfFlux_apCorrSigma',\n", + " 'ext_photometryKron_KronFlux_apCorr',\n", + " 'ext_photometryKron_KronFlux_apCorrSigma')" + ] + }, + "execution_count": 190, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hmm[1].data.dtype.names" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/ImageProcessing/Re-RunHSC.sh b/ImageProcessing/Re-RunHSC.sh index 69a51201..fb981f24 100644 --- a/ImageProcessing/Re-RunHSC.sh +++ b/ImageProcessing/Re-RunHSC.sh @@ -54,6 +54,8 @@ date echo "Re-RunHSC INFO: process raw exposures with processCcd.py" # Use calibration files to do CCD processing +# Does calibration happen here? What is the end result of the calibration process? +# What specifically does this task do? processCcd.py $DATADIR --rerun processCcdOutputs --id @@ -144,6 +146,19 @@ echo "Re-RunHSC INFO: perform forced photometry on coadds with forcedPhotCoadd.p forcedPhotCoadd.py $DATADIR --rerun coaddPhot:coaddForcedPhot --id filter=HSC-R forcedPhotCoadd.py $DATADIR --rerun coaddForcedPhot --id filter=HSC-I +# V. F. Run forced photometry on individual exposures +# Given a full source catalog, we can do forced photometry on the individual exposures. +# Note that as of 2018_08_23, the forcedPhotCcd.py task doesn't do deblending, +# which could lead to bad photometry for blended sources. +# This tasks requires a coadd tract stored in the Butler to grab the appropriate +# coadd catalogs to use as references for forced photometry. + +date +echo "Re-RunHSC INFO: perform forced photometry on individual exposures with forcedPhotCcd.py" + +forcedPhotCcd.py $DATADIR --rerun coaddPhot:ccdForcedPhot --id filter=HSC-R --clobber-config --configfile=/project/shared/data/ci_hsc/forcedPhotCcdConfig.py &> ccd_r.txt +forcedPhotCcd.py $DATADIR --rerun ccdForcedPhot --id filter=HSC-I --clobber-config --configfile=/project/shared/data/ci_hsc/forcedPhotCcdConfig.py&> ccd_i.txt # VI. Multi-band catalog analysis # For analysis of the catalog, see part VI of StackClub/ImageProcessing/Re-RunHSC.ipynb + From 5f201db1a83a72e225b3e4722f7e9aa013573a89 Mon Sep 17 00:00:00 2001 From: Justin Myles Date: Fri, 14 Sep 2018 01:42:52 +0000 Subject: [PATCH 09/15] sbs cmd --- ImageProcessing/Re-RunHSC.ipynb | 2788 ++++++------------------------- ImageProcessing/Re-RunHSC.sh | 12 +- 2 files changed, 481 insertions(+), 2319 deletions(-) diff --git a/ImageProcessing/Re-RunHSC.ipynb b/ImageProcessing/Re-RunHSC.ipynb index 28fcefb4..65234c54 100644 --- a/ImageProcessing/Re-RunHSC.ipynb +++ b/ImageProcessing/Re-RunHSC.ipynb @@ -43,16 +43,26 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os\n", - "os.system(\"eups list lsst_distrib\") #todo\n", + "import re\n", "import sys\n", + "import glob\n", + "import numpy as np\n", + "import pandas as pd\n", + "import astropy.io.fits as fitsio\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\", category=UserWarning)\n", + "\n", "import matplotlib.pyplot as plt\n", + "from matplotlib.ticker import ScalarFormatter, FormatStrFormatter\n", "%matplotlib inline\n", - "import eups.setupcmd" + "\n", + "import eups.setupcmd\n", + "import lsst.daf.persistence as dafPersist" ] }, { @@ -66,7 +76,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -129,6 +139,8 @@ "echo \"Re-RunHSC INFO: process raw exposures with processCcd.py\"\n", "\n", "# Use calibration files to do CCD processing\n", + "# Does calibration happen here? What is the end result of the calibration process?\n", + "# What specifically does this task do?\n", "processCcd.py $DATADIR --rerun processCcdOutputs --id\n", "\n", "\n", @@ -219,9 +231,30 @@ "forcedPhotCoadd.py $DATADIR --rerun coaddPhot:coaddForcedPhot --id filter=HSC-R\n", "forcedPhotCoadd.py $DATADIR --rerun coaddForcedPhot --id filter=HSC-I\n", "\n", + "# V. F. Run forced photometry on individual exposures\n", + "# Given a full source catalog, we can do forced photometry on the individual exposures.\n", + "# Note that as of 2018_08_23, the forcedPhotCcd.py task doesn't do deblending,\n", + "# which could lead to bad photometry for blended sources.\n", + "# This tasks requires a coadd tract stored in the Butler to grab the appropriate \n", + "# coadd catalogs to use as references for forced photometry.\n", + "\n", + "date\n", + "echo \"Re-RunHSC INFO: perform forced photometry on individual exposures with forcedPhotCcd.py\"\n", + "\n", + "forcedPhotCcd.py $DATADIR --rerun coaddPhot:ccdForcedPhot --id filter=HSC-R --clobber-config --configfile=/project/shared/data/ci_hsc/forcedPhotCcdConfig.py &> ccd_r.txt\n", + "forcedPhotCcd.py $DATADIR --rerun ccdForcedPhot --id filter=HSC-I --clobber-config --configfile=/project/shared/data/ci_hsc/forcedPhotCcdConfig.py &> ccd_i.txt\n", + "\n", "\n", "# VI. Multi-band catalog analysis\n", - "# For analysis of the catalog, see part VI of StackClub/ImageProcessing/Re-RunHSC.ipynb\n" + "# For analysis of the catalog, see part VI of StackClub/ImageProcessing/Re-RunHSC.ipynb\n", + "date\n", + "echo \"Re-RunHSC INFO: parse output of forcedPhotCcd.py\"\n", + "grep 'forcedPhotCcd INFO: Performing forced measurement on DataId' ccd_r.txt ccd_i.txt > data_ids.txt\n", + "# The following sed commands clean up the output log file used to determine which DataIds have measured forced photometry.\n", + "sed -i 's/ccd_[i,r].txt:forcedPhotCcd INFO: Performing forced measurement on DataId(initialdata={//g' data_ids.txt\n", + "sed -i 's/}, tag=set())//g' data_ids.txt\n", + "sed -i 's/'\"'\"'//g' data_ids.txt\n", + "sed -i 's/ //g' data_ids.txt\n" ] } ], @@ -258,13 +291,14 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "HOME = os.environ['HOME']\n", "DATAREPO = \"{}/repositories/ci_hsc/\".format(HOME)\n", "DATADIR = \"{}/DATA/\".format(HOME)\n", + "CI_HSC = \"/project/shared/data/ci_hsc/\"\n", "os.system(\"mkdir -p {}\".format(DATADIR));" ] }, @@ -500,63 +534,61 @@ "metadata": {}, "source": [ "# Part 6: Multi-band catalog analysis\n", - "https://pipelines.lsst.io/getting-started/multiband-analysis.html" + "https://pipelines.lsst.io/getting-started/multiband-analysis.html\n", + "\n", + "Access the sources identified from the coadd images " ] }, { "cell_type": "code", - "execution_count": 173, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ - "import os\n", - "import lsst.daf.persistence as dafPersist\n", - "butler = dafPersist.Butler(inputs=DATADIR + 'rerun/coaddForcedPhot/')" + "butler_coadd = dafPersist.Butler(inputs=DATADIR + 'rerun/coaddForcedPhot/')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Access the sources identified from the coadd images" + "Grab their measured forced photometry." ] }, { "cell_type": "code", - "execution_count": 174, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "6800\n", - "6800\n" + "6800 sources with forced photometry measured from coadds\n" ] } ], "source": [ - "rSources = butler.get('deepCoadd_forced_src', {'filter': 'HSC-R', 'tract': 0, 'patch': '1,1'})\n", - "iSources = butler.get('deepCoadd_forced_src', {'filter': 'HSC-I', 'tract': 0, 'patch': '1,1'})\n", - "print(len(rSources))\n", - "print(len(iSources))" + "rSources = butler_coadd.get('deepCoadd_forced_src', {'filter': 'HSC-R', 'tract': 0, 'patch': '1,1'})\n", + "iSources = butler_coadd.get('deepCoadd_forced_src', {'filter': 'HSC-I', 'tract': 0, 'patch': '1,1'})\n", + "print('{} sources with forced photometry measured from coadds'.format(len(rSources)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Throw out negative fluxes, and convert fluxes to magnitudes." + "Discard sources with negative fluxes and convert fluxes to magnitudes." ] }, { "cell_type": "code", - "execution_count": 175, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ - "rCoaddCalib = butler.get('deepCoadd_calexp_calib', {'filter': 'HSC-R', 'tract': 0, 'patch': '1,1'})\n", - "iCoaddCalib = butler.get('deepCoadd_calexp_calib', {'filter': 'HSC-I', 'tract': 0, 'patch': '1,1'})\n", + "rCoaddCalib = butler_coadd.get('deepCoadd_calexp_calib', {'filter': 'HSC-R', 'tract': 0, 'patch': '1,1'})\n", + "iCoaddCalib = butler_coadd.get('deepCoadd_calexp_calib', {'filter': 'HSC-I', 'tract': 0, 'patch': '1,1'})\n", "\n", "rCoaddCalib.setThrowOnNegativeFlux(False)\n", "iCoaddCalib.setThrowOnNegativeFlux(False)\n", @@ -569,18 +601,18 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Select stars from catalog" + "Make selection from catalog for stars only." ] }, { "cell_type": "code", - "execution_count": 176, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "deblended = rSources['deblend_nChild'] == 0\n", "\n", - "refTable = butler.get('deepCoadd_ref', {'filter': 'HSC-R^HSC-I', 'tract': 0, 'patch': '1,1'})\n", + "refTable = butler_coadd.get('deepCoadd_ref', {'filter': 'HSC-R^HSC-I', 'tract': 0, 'patch': '1,1'})\n", "inInnerRegions = refTable['detect_isPatchInner'] & refTable['detect_isTractInner'] # define inner regions\n", "isSkyObject = refTable['merge_peak_sky'] # reject sky objects\n", "isPrimary = refTable['detect_isPrimary']\n", @@ -599,12 +631,12 @@ }, { "cell_type": "code", - "execution_count": 178, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -616,15 +648,11 @@ "source": [ "plt.style.use('seaborn-notebook')\n", "plt.figure(1, figsize=(4, 4), dpi=140)\n", - "plt.title('Color-Magnitude Diagram for Stars in Catalog')\n", + "plt.title('Coadd Forced Photometry (Stars)')\n", "plt.scatter(rMags[selected] - iMags[selected],\n", - " iMags[selected],\n", - " edgecolors='None', s=2, c='k')\n", - "\"\"\"\n", - "plt.scatter(rMags - iMags,\n", - " iMags,\n", - " edgecolors='None', s=2, c='k')\n", - "\"\"\"\n", + " iMags[selected],\n", + " edgecolors='None', s=2, c='k')\n", + "\n", "plt.xlim(-0.5, 3)\n", "plt.ylim(25, 14)\n", "plt.xlabel('$r-i$')\n", @@ -634,2236 +662,402 @@ ] }, { - "cell_type": "code", - "execution_count": 248, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "# Now try to get the individual exposure light curves\n", - "import pandas as pd" + "Instantiate Butler with forced photometry measured for individual exposures" ] }, { "cell_type": "code", - "execution_count": 181, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ - "import os\n", - "import lsst.daf.persistence as dafPersist\n", - "butler = dafPersist.Butler(inputs=DATADIR + 'rerun/ccdForcedPhot/')" + "butler_ccd = dafPersist.Butler(inputs=DATADIR + 'rerun/ccdForcedPhot/')" ] }, { "cell_type": "code", - "execution_count": 249, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'filter': 'HSC-I', 'pointing': '671', 'visit': '903986', 'ccd': '23', 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': '30.0', 'tract': '0'}\n", - "{'filter': 'HSC-I', 'pointing': '671', 'visit': '903986', 'ccd': '22', 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': '30.0', 'tract': '0'}\n", - "{'filter': 'HSC-I', 'pointing': '671', 'visit': '903986', 'ccd': '16', 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': '30.0', 'tract': '0'}\n", - "{'filter': 'HSC-I', 'pointing': '671', 'visit': '903986', 'ccd': '100', 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': '30.0', 'tract': '0'}\n", - "{'filter': 'HSC-I', 'pointing': '671', 'visit': '903988', 'ccd': '24', 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': '30.0', 'tract': '0'}\n", - "{'filter': 'HSC-I', 'pointing': '671', 'visit': '903988', 'ccd': '23', 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': '30.0', 'tract': '0'}\n", - "{'filter': 'HSC-I', 'pointing': '671', 'visit': '903988', 'ccd': '17', 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': '30.0', 'tract': '0'}\n", - "{'filter': 'HSC-I', 'pointing': '671', 'visit': '903988', 'ccd': '16', 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': '30.0', 'tract': '0'}\n", - "{'filter': 'HSC-I', 'pointing': '671', 'visit': '903990', 'ccd': '25', 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': '30.0', 'tract': '0'}\n", - "{'filter': 'HSC-I', 'pointing': '671', 'visit': '903990', 'ccd': '18', 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': '30.0', 'tract': '0'}\n", - "{'filter': 'HSC-I', 'pointing': '671', 'visit': '904010', 'ccd': '100', 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': '30.0', 'tract': '0'}\n", - "{'filter': 'HSC-I', 'pointing': '671', 'visit': '904010', 'ccd': '10', 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': '30.0', 'tract': '0'}\n", - "{'filter': 'HSC-I', 'pointing': '671', 'visit': '904010', 'ccd': '4', 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': '30.0', 'tract': '0'}\n", - "{'filter': 'HSC-I', 'pointing': '671', 'visit': '904014', 'ccd': '12', 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': '30.0', 'tract': '0'}\n", - "{'filter': 'HSC-I', 'pointing': '671', 'visit': '904014', 'ccd': '6', 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': '30.0', 'tract': '0'}\n", - "{'filter': 'HSC-I', 'pointing': '671', 'visit': '904014', 'ccd': '1', 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': '30.0', 'tract': '0'}\n" - ] - } - ], - "source": [ - "data_id_fields = ['filter', 'pointing', 'visit', 'ccd', 'field', 'dateObs', 'taiObs', 'expTime', 'tract']\n", - "data_id_dtypes = [str,int,int,int,str,str,str,float,int]\n", - "\n", - "tables = []\n", - "for line in open('/home/jmyles/i_visits.txt'):\n", - " vars = line[74:-14].replace(\" \",\"\").replace(\"\\'\",\"\").replace(\"\\\"\",\"\").split(\",\")\n", - " if len(vars) == 1:\n", - " continue\n", - " print({data_id_fields[i] : vars[i].split(':')[1] for i in range(len(vars))})\n", - " sources = butler.get('forced_src', {data_id_fields[i] : data_id_dtypes[i](vars[i].split(':')[1]) for i in range(len(vars))})\n", - " tables.append(sources.asAstropy().to_pandas())\n", - " \n", - "iSources = pd.concat(tables)" - ] - }, - { - "cell_type": "code", - "execution_count": 251, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ - "grouped = iSources.groupby('objectId')" + "# associate each visit ID with an MJD\n", + "# store in lookup hashtable\n", + "visit_to_mjd = {} \n", + "\n", + "raw_files = glob.glob(CI_HSC + 'raw/HSCA*.fits')\n", + "\n", + "for infile in raw_files:\n", + " visit_id = infile[len(CI_HSC) + len(\"raw/HSCA\"):-7]\n", + " hdulist = fitsio.open(infile)\n", + " try:\n", + " mjd = hdulist[1].header['MJD']\n", + " except:\n", + " mjd = hdulist[0].header['MJD']\n", + " visit_to_mjd[visit_id] = mjd\n" ] }, { "cell_type": "code", - "execution_count": 256, + "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "30400\n", - "9742\n" + "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903334, 'ccd': 23, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", + "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903334, 'ccd': 22, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", + "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903334, 'ccd': 16, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", + "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903334, 'ccd': 100, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", + "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903336, 'ccd': 24, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", + "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903336, 'ccd': 17, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", + "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903338, 'ccd': 25, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", + "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903338, 'ccd': 18, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", + "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903342, 'ccd': 100, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", + "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903342, 'ccd': 10, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", + "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903342, 'ccd': 4, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", + "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903344, 'ccd': 11, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", + "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903344, 'ccd': 5, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", + "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903344, 'ccd': 0, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", + "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903346, 'ccd': 12, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", + "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903346, 'ccd': 6, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", + "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903346, 'ccd': 1, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", + "{'filter': 'HSC-I', 'pointing': 671, 'visit': 903986, 'ccd': 23, 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0}\n", + "{'filter': 'HSC-I', 'pointing': 671, 'visit': 903986, 'ccd': 22, 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0}\n", + "{'filter': 'HSC-I', 'pointing': 671, 'visit': 903986, 'ccd': 16, 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0}\n", + "{'filter': 'HSC-I', 'pointing': 671, 'visit': 903986, 'ccd': 100, 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0}\n", + "{'filter': 'HSC-I', 'pointing': 671, 'visit': 903988, 'ccd': 24, 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0}\n", + "{'filter': 'HSC-I', 'pointing': 671, 'visit': 903988, 'ccd': 23, 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0}\n", + "{'filter': 'HSC-I', 'pointing': 671, 'visit': 903988, 'ccd': 17, 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0}\n", + "{'filter': 'HSC-I', 'pointing': 671, 'visit': 903988, 'ccd': 16, 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0}\n", + "{'filter': 'HSC-I', 'pointing': 671, 'visit': 903990, 'ccd': 25, 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0}\n", + "{'filter': 'HSC-I', 'pointing': 671, 'visit': 903990, 'ccd': 18, 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0}\n", + "{'filter': 'HSC-I', 'pointing': 671, 'visit': 904010, 'ccd': 100, 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0}\n", + "{'filter': 'HSC-I', 'pointing': 671, 'visit': 904010, 'ccd': 10, 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0}\n", + "{'filter': 'HSC-I', 'pointing': 671, 'visit': 904010, 'ccd': 4, 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0}\n", + "{'filter': 'HSC-I', 'pointing': 671, 'visit': 904014, 'ccd': 12, 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0}\n", + "{'filter': 'HSC-I', 'pointing': 671, 'visit': 904014, 'ccd': 6, 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0}\n", + "{'filter': 'HSC-I', 'pointing': 671, 'visit': 904014, 'ccd': 1, 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0}\n" ] } ], "source": [ - "print(len(iSources['objectId']))\n", - "print(len(np.unique(iSources['objectId'])))" + "data_id_fields = [('filter', str), ('pointing', int), ('visit', int), \n", + " ('ccd', int), ('field', str), ('dateObs', str), \n", + " ('taiObs', str), ('expTime', float), ('tract', int)]\n", + "\n", + "i_tables = []\n", + "r_tables = []\n", + "\n", + "for line in open(DATADIR + 'data_ids.txt'):\n", + " fields = line.split(\",\")\n", + " \n", + " data_id_dict = {data_id_fields[i][0] : data_id_fields[i][1](fields[i].split(':')[1]) for i in range(len(fields))}\n", + " print(data_id_dict)\n", + " \n", + " sources = butler_ccd.get('forced_src', data_id_dict)\n", + " source_table = sources.asAstropy().to_pandas()\n", + " source_table['visit'] = fields[2].split(':')[1]\n", + " source_table['mjd'] = [visit_to_mjd[key] if key in visit_to_mjd else 56598.2 for key in source_table['visit'] ]\n", + "\n", + " if fields[0] == 'filter:HSC-R':\n", + " r_tables.append(source_table)\n", + " elif fields[0] == 'filter:HSC-I':\n", + " i_tables.append(source_table)\n", + " else:\n", + " print('Failed to read filter')" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 259, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "141733921083\n", - "141733921084\n", - "141733921091\n", - "141733921096\n", - "141733921103\n", - "141733921106\n", - "141733921108\n", - "141733921109\n", - "141733921110\n", - "141733921112\n", - "141733921114\n", - "141733921117\n", - "141733921119\n", - "141733921120\n", - "141733921122\n", - "141733921124\n", - "141733921125\n", - "141733921126\n", - "141733921129\n", - "141733921131\n", - "141733921134\n", - "141733921138\n", - "141733921139\n", - "141733921141\n", - "141733921142\n", - "141733921143\n", - "141733921148\n", - "141733921153\n", - "141733921155\n", - "141733921156\n", - "141733921157\n", - "141733921160\n", - "141733921161\n", - "141733921162\n", - "141733921164\n", - "141733921169\n", - "141733921170\n", - "141733921174\n", - "141733921175\n", - "141733921176\n", - "141733921184\n", - "141733921186\n", - "141733921188\n", - "141733921190\n", - "141733921192\n", - "141733921195\n", - "141733921197\n", - "141733921200\n", - "141733921202\n", - "141733921204\n", - "141733921206\n", - "141733921207\n", - "141733921208\n", - "141733921210\n", - "141733921211\n", - "141733921212\n", - "141733921214\n", - "141733921217\n", - "141733921225\n", - "141733921228\n", - "141733921230\n", - "141733921232\n", - "141733921279\n", - "141733921280\n", - "141733921281\n", - "141733921284\n", - "141733921285\n", - "141733921286\n", - "141733921290\n", - "141733921296\n", - "141733921301\n", - "141733921307\n", - "141733921310\n", - "141733921326\n", - "141733921328\n", - "141733921346\n", - "141733921347\n", - "141733921352\n", - "141733921353\n", - "141733921365\n", - "141733921367\n", - "141733921373\n", - "141733921375\n", - "141733921379\n", - "141733921381\n", - "141733921383\n", - "141733921394\n", - "141733921399\n", - "141733921400\n", - "141733921401\n", - "141733921403\n", - "141733921411\n", - "141733921420\n", - "141733921421\n", - "141733921430\n", - "141733921431\n", - "141733921437\n", - "141733921443\n", - "141733921445\n", - "141733921447\n", - "141733921452\n", - "141733921454\n", - "141733921455\n", - "141733921457\n", - "141733921460\n", - "141733921462\n", - "141733921468\n", - "141733921470\n", - "141733921472\n", - "141733921474\n", - "141733921479\n", - "141733921480\n", - "141733921482\n", - "141733921484\n", - "141733921485\n", - "141733921486\n", - "141733921487\n", - "141733921488\n", - "141733921492\n", - "141733921498\n", - "141733921501\n", - "141733921503\n", - "141733921504\n", - "141733921505\n", - "141733921515\n", - "141733921518\n", - "141733921523\n", - "141733921528\n", - "141733921529\n", - "141733921532\n", - "141733921535\n", - "141733921541\n", - "141733921542\n", - "141733921545\n", - "141733921548\n", - "141733921550\n", - "141733921552\n", - "141733921553\n", - "141733921557\n", - "141733921562\n", - "141733921564\n", - "141733921565\n", - "141733921566\n", - "141733921568\n", - "141733921571\n", - "141733921572\n", - "141733921574\n", - "141733921578\n", - "141733921580\n", - "141733921588\n", - "141733921589\n", - "141733921591\n", - "141733921598\n", - "141733921599\n", - "141733921603\n", - "141733921605\n", - "141733921609\n", - "141733921610\n", - "141733921613\n", - "141733921614\n", - "141733921615\n", - "141733921621\n", - "141733921622\n", - "141733921624\n", - "141733921625\n", - "141733921627\n", - "141733921632\n", - "141733921633\n", - "141733921634\n", - "141733921635\n", - "141733921638\n", - "141733921639\n", - "141733921643\n", - "141733921646\n", - "141733921654\n", - "141733921659\n", - "141733921661\n", - "141733921662\n", - "141733921666\n", - "141733921668\n", - "141733921670\n", - "141733921675\n", - "141733921678\n", - "141733921681\n", - "141733921685\n", - "141733921687\n", - "141733921692\n", - "141733921693\n", - "141733921697\n", - "141733921700\n", - "141733921701\n", - "141733921705\n", - "141733921706\n", - "141733921739\n", - "141733921740\n", - "141733921745\n", - "141733921746\n", - "141733921748\n", - "141733921753\n", - "141733921754\n", - "141733921758\n", - "141733921760\n", - "141733921761\n", - "141733921765\n", - "141733921772\n", - "141733921773\n", - "141733921778\n", - "141733921780\n", - "141733921781\n", - "141733921782\n", - "141733921783\n", - "141733921786\n", - "141733921788\n", - "141733921789\n", - "141733921790\n", - "141733921791\n", - "141733921794\n", - "141733921798\n", - "141733921814\n", - "141733921831\n", - "141733921837\n", - "141733921840\n", - "141733921841\n", - "141733921842\n", - "141733921844\n", - "141733921847\n", - "141733921848\n", - "141733921850\n", - "141733921853\n", - "141733921854\n", - "141733921855\n", - "141733921858\n", - "141733921859\n", - "141733921860\n", - "141733921861\n", - "141733921863\n", - "141733921865\n", - "141733921867\n", - "141733921871\n", - "141733921873\n", - "141733921875\n", - "141733921876\n", - "141733921878\n", - "141733921879\n", - "141733921880\n", - "141733921884\n", - "141733921886\n", - "141733921887\n", - "141733921888\n", - "141733921889\n", - "141733921891\n", - "141733921892\n", - "141733921893\n", - "141733921894\n", - "141733921895\n", - "141733921899\n", - "141733921901\n", - "141733921902\n", - "141733921903\n", - "141733921906\n", - "141733921907\n", - "141733921908\n", - "141733921910\n", - "141733921911\n", - "141733921912\n", - "141733921913\n", - "141733921914\n", - "141733921916\n", - "141733921917\n", - "141733921918\n", - "141733921919\n", - "141733921920\n", - "141733921921\n", - "141733921922\n", - "141733921923\n", - "141733921924\n", - "141733921925\n", - "141733921926\n", - "141733921927\n", - "141733921928\n", - "141733921929\n", - "141733921931\n", - "141733921933\n", - "141733921934\n", - "141733921937\n", - "141733921938\n", - "141733921939\n", - "141733921941\n", - "141733921943\n", - "141733921945\n", - "141733921946\n", - "141733921947\n", - "141733921956\n", - "141733921957\n", - "141733921959\n", - "141733921960\n", - "141733921961\n", - "141733921962\n", - "141733921964\n", - "141733921965\n", - "141733921966\n", - "141733921968\n", - "141733921969\n", - "141733921970\n", - "141733921971\n", - "141733921972\n", - "141733921973\n", - "141733921975\n", - "141733921976\n", - "141733921978\n", - "141733921979\n", - "141733921980\n", - "141733921982\n", - "141733921983\n", - "141733921984\n", - "141733921985\n", - "141733921986\n", - "141733921987\n", - "141733921988\n", - "141733921989\n", - "141733921992\n", - "141733921993\n", - "141733921994\n", - "141733921995\n", - "141733921999\n", - "141733922001\n", - "141733922002\n", - "141733922003\n", - "141733922005\n", - "141733922007\n", - "141733922008\n", - "141733922009\n", - "141733922011\n", - "141733922012\n", - "141733922013\n", - "141733922014\n", - "141733922015\n", - "141733922017\n", - "141733922018\n", - "141733922019\n", - "141733922020\n", - "141733922021\n", - "141733922024\n", - "141733922026\n", - "141733922029\n", - "141733922030\n", - "141733922032\n", - "141733922033\n", - "141733922034\n", - "141733922035\n", - "141733922036\n", - "141733922037\n", - "141733922039\n", - "141733922040\n", - "141733922041\n", - "141733922043\n", - "141733922044\n", - "141733922046\n", - "141733922050\n", - "141733922051\n", - "141733922053\n", - "141733922054\n", - "141733922055\n", - "141733922056\n", - "141733922057\n", - "141733922059\n", - "141733922060\n", - "141733922061\n", - "141733922062\n", - "141733922064\n", - "141733922065\n", - "141733922066\n", - "141733922067\n", - "141733922069\n", - "141733922070\n", - "141733922072\n", - "141733922073\n", - "141733922074\n", - "141733922075\n", - "141733922076\n", - "141733922077\n", - "141733922080\n", - "141733922081\n", - "141733922084\n", - "141733922085\n", - "141733922088\n", - "141733922089\n", - "141733922090\n", - "141733922091\n", - "141733922092\n", - "141733922094\n", - "141733922095\n", - "141733922096\n", - "141733922098\n", - "141733922099\n", - "141733922101\n", - "141733922102\n", - "141733922103\n", - "141733922104\n", - "141733922105\n", - "141733922107\n", - "141733922110\n", - "141733922111\n", - "141733922112\n", - "141733922113\n", - "141733922115\n", - "141733922116\n", - "141733922117\n", - "141733922681\n", - "141733922686\n", - "141733922689\n", - "141733922692\n", - "141733922693\n", - "141733922695\n", - "141733922697\n", - "141733922700\n", - "141733922701\n", - "141733922703\n", - "141733922705\n", - "141733922706\n", - "141733922708\n", - "141733922709\n", - "141733922713\n", - "141733922716\n", - "141733922719\n", - "141733922720\n", - "141733922722\n", - "141733922723\n", - "141733922724\n", - "141733922725\n", - "141733922729\n", - "141733922730\n", - "141733922731\n", - "141733922732\n", - "141733922737\n", - "141733922738\n", - "141733922739\n", - "141733922742\n", - "141733922745\n", - "141733922746\n", - "141733922747\n", - "141733922748\n", - "141733922750\n", - "141733922753\n", - "141733922755\n", - "141733922757\n", - "141733922758\n", - "141733922760\n", - "141733922761\n", - "141733922765\n", - "141733922766\n", - "141733922768\n", - "141733922769\n", - "141733922770\n", - "141733922773\n", - "141733922774\n", - "141733922775\n", - "141733922776\n", - "141733922779\n", - "141733922780\n", - "141733922783\n", - "141733922784\n", - "141733922788\n", - "141733922792\n", - "141733922793\n", - "141733922795\n", - "141733922796\n", - "141733922797\n", - "141733922800\n", - "141733922804\n", - "141733922805\n", - "141733922806\n", - "141733922810\n", - "141733922811\n", - "141733922814\n", - "141733922817\n", - "141733922821\n", - "141733922823\n", - "141733922824\n", - "141733922827\n", - "141733922829\n", - "141733922831\n", - "141733922832\n", - "141733922833\n", - "141733922836\n", - "141733922839\n", - "141733922840\n", - "141733922841\n", - "141733922842\n", - "141733922845\n", - "141733922847\n", - "141733922849\n", - "141733922851\n", - "141733922853\n", - "141733922854\n", - "141733922855\n", - "141733922857\n", - "141733922859\n", - "141733922870\n", - "141733922871\n", - "141733922872\n", - "141733922873\n", - "141733922874\n", - "141733922878\n", - "141733922879\n", - "141733922880\n", - "141733922884\n", - "141733922890\n", - "141733922893\n", - "141733922894\n", - "141733922895\n", - "141733922896\n", - "141733922898\n", - "141733922900\n", - "141733922901\n", - "141733922903\n", - "141733922904\n", - "141733922906\n", - "141733922907\n", - "141733922910\n", - "141733922911\n", - "141733922916\n", - "141733922917\n", - "141733922918\n", - "141733922922\n", - "141733922924\n", - "141733922925\n", - "141733922927\n", - "141733922928\n", - "141733922931\n", - "141733922934\n", - "141733922935\n", - "141733922936\n", - "141733922937\n", - "141733922939\n", - "141733922943\n", - "141733922945\n", - "141733922947\n", - "141733922949\n", - "141733922958\n", - "141733922994\n", - "141733922996\n", - "141733922997\n", - "141733922998\n", - "141733923001\n", - "141733923003\n", - "141733923011\n", - "141733923013\n", - "141733923014\n", - "141733923071\n", - "141733923075\n", - "141733923080\n", - "141733923082\n", - "141733923087\n", - "141733923093\n", - "141733923100\n", - "141733923101\n", - "141733923102\n", - "141733923104\n", - "141733923116\n", - "141733923119\n", - "141733923131\n", - "141733923132\n", - "141733923133\n", - "141733923140\n", - "141733923145\n", - "141733923146\n", - "141733923148\n", - "141733923149\n", - "141733923156\n", - "141733923159\n", - "141733923163\n", - "141733923166\n", - "141733923167\n", - "141733923171\n", - "141733923173\n", - "141733923174\n", - "141733923177\n", - "141733923179\n", - "141733923183\n", - "141733923185\n", - "141733923187\n", - "141733923189\n", - "141733923192\n", - "141733923194\n", - "141733923197\n", - "141733923200\n", - "141733923201\n", - "141733923206\n", - "141733923209\n", - "141733923210\n", - "141733923213\n", - "141733923214\n", - "141733923215\n", - "141733923218\n", - "141733923222\n", - "141733923235\n", - "141733923241\n", - "141733923252\n", - "141733923254\n", - "141733923259\n", - "141733923263\n", - "141733923265\n", - "141733923267\n", - "141733923269\n", - "141733923270\n", - "141733923275\n", - "141733923282\n", - "141733923283\n", - "141733923285\n", - "141733923287\n", - "141733923289\n", - "141733923290\n", - "141733923293\n", - "141733923301\n", - "141733923302\n", - "141733923308\n", - "141733923309\n", - "141733923310\n", - "141733923312\n", - "141733923314\n", - "141733923317\n", - "141733923318\n", - "141733923320\n", - "141733923322\n", - "141733923326\n", - "141733923330\n", - "141733923334\n", - "141733923337\n", - "141733923339\n", - "141733923343\n", - "141733923344\n", - "141733923345\n", - "141733923353\n", - "141733923354\n", - "141733923356\n", - "141733923360\n", - "141733923361\n", - "141733923363\n", - "141733923365\n", - "141733923370\n", - "141733923372\n", - "141733923373\n", - "141733923374\n", - "141733923382\n", - "141733923390\n", - "141733923395\n", - "141733923396\n", - "141733923399\n", - "141733923400\n", - "141733923408\n", - "141733923412\n", - "141733923419\n", - "141733923420\n", - "141733923422\n", - "141733923424\n", - "141733923430\n", - "141733923432\n", - "141733923445\n", - "141733923447\n", - "141733923452\n", - "141733923454\n", - "141733923455\n", - "141733923456\n", - "141733923459\n", - "141733923462\n", - "141733923464\n", - "141733923465\n", - "141733923469\n", - "141733923471\n", - "141733923472\n", - "141733923473\n", - "141733923474\n", - "141733923485\n", - "141733923489\n", - "141733923492\n", - "141733923493\n", - "141733923496\n", - "141733923500\n", - "141733923502\n", - "141733923503\n", - "141733923504\n", - "141733923509\n", - "141733923511\n", - "141733923515\n", - "141733923517\n", - "141733923522\n", - "141733923524\n", - "141733923526\n", - "141733923530\n", - "141733923535\n", - "141733923536\n", - "141733923538\n", - "141733923544\n", - "141733923546\n", - "141733923548\n", - "141733923549\n", - "141733923550\n", - "141733923551\n", - "141733923552\n", - "141733923555\n", - "141733923556\n", - "141733923560\n", - "141733923561\n", - "141733923565\n", - "141733923566\n", - "141733923567\n", - "141733923572\n", - "141733923573\n", - "141733923574\n", - "141733923576\n", - "141733923577\n", - "141733923580\n", - "141733923581\n", - "141733923582\n", - "141733923583\n", - "141733923584\n", - "141733923587\n", - "141733923589\n", - "141733923592\n", - "141733923595\n", - "141733923598\n", - "141733923600\n", - "141733923601\n", - "141733923602\n", - "141733923603\n", - "141733923604\n", - "141733923607\n", - "141733923608\n", - "141733923610\n", - "141733923611\n", - "141733923613\n", - "141733923617\n", - "141733923618\n", - "141733923624\n", - "141733923628\n", - "141733923632\n", - "141733923638\n", - "141733923640\n", - "141733923641\n", - "141733923642\n", - "141733923645\n", - "141733923646\n", - "141733923648\n", - "141733923655\n", - "141733923656\n", - "141733923659\n", - "141733923662\n", - "141733923664\n", - "141733923665\n", - "141733923670\n", - "141733923672\n", - "141733923675\n", - "141733923678\n", - "141733923679\n", - "141733923682\n", - "141733923687\n", - "141733923690\n", - "141733923692\n", - "141733923693\n", - "141733923695\n", - "141733923698\n", - "141733923701\n", - "141733923705\n", - "141733923707\n", - "141733923708\n", - "141733923710\n", - "141733923713\n", - "141733923716\n", - "141733923718\n", - "141733923720\n", - "141733923726\n", - "141733923727\n", - "141733923729\n", - "141733923731\n", - "141733923733\n", - "141733923734\n", - "141733923738\n", - "141733923739\n", - "141733923743\n", - "141733923745\n", - "141733923750\n", - "141733923754\n", - "141733923755\n", - "141733923803\n", - "141733923804\n", - "141733923805\n", - "141733923806\n", - "141733923807\n", - "141733923809\n", - "141733923811\n", - "141733923812\n", - "141733923816\n", - "141733923820\n", - "141733923825\n", - "141733923826\n", - "141733923827\n", - "141733923828\n", - "141733923832\n", - "141733923834\n", - "141733923835\n", - "141733923836\n", - "141733923841\n", - "141733923844\n", - "141733923847\n", - "141733923848\n", - "141733923849\n", - "141733923850\n", - "141733923851\n", - "141733923854\n", - "141733923858\n", - "141733923859\n", - "141733923870\n", - "141733923871\n", - "141733923874\n", - "141733923884\n", - "141733923885\n", - "141733923888\n", - "141733923896\n", - "141733923921\n", - "141733923932\n", - "141733923934\n", - "141733923940\n", - "141733923941\n", - "141733923943\n", - "141733923944\n", - "141733923945\n", - "141733923946\n", - "141733923947\n", - "141733923948\n", - "141733923949\n", - "141733923950\n", - "141733923951\n", - "141733923953\n", - "141733923954\n", - "141733923956\n", - "141733923958\n", - "141733923959\n", - "141733923961\n", - "141733923964\n", - "141733923966\n", - "141733923967\n", - "141733923968\n", - "141733923970\n", - "141733923971\n", - "141733923972\n", - "141733923974\n", - "141733923975\n", - "141733923976\n", - "141733923977\n", - "141733923978\n", - "141733923979\n", - "141733923980\n", - "141733923982\n", - "141733923983\n", - "141733923985\n", - "141733923988\n", - "141733923989\n", - "141733923990\n", - "141733923992\n", - "141733923993\n", - "141733923994\n", - "141733923995\n", - "141733923997\n", - "141733923999\n", - "141733924000\n", - "141733924001\n", - "141733924002\n", - "141733924003\n", - "141733924004\n", - "141733924005\n", - "141733924006\n", - "141733924007\n", - "141733924008\n", - "141733924010\n", - "141733924011\n", - "141733924012\n", - "141733924014\n", - "141733924016\n", - "141733924019\n", - "141733924023\n", - "141733924024\n", - "141733924025\n", - "141733924026\n", - "141733924027\n", - "141733924028\n", - "141733924029\n", - "141733924030\n", - "141733924031\n", - "141733924032\n", - "141733924033\n", - "141733924035\n", - "141733924036\n", - "141733924037\n", - "141733924038\n", - "141733924040\n", - "141733924041\n", - "141733924043\n", - "141733924044\n", - "141733924045\n", - "141733924046\n", - "141733924047\n", - "141733924048\n", - "141733924049\n", - "141733924050\n", - "141733924052\n", - "141733924054\n", - "141733924055\n", - "141733924056\n", - "141733924057\n", - "141733924059\n", - "141733924060\n", - "141733924062\n", - "141733924063\n", - "141733924064\n", - "141733924066\n", - "141733924067\n", - "141733924069\n", - "141733924070\n", - "141733924073\n", - "141733924074\n", - "141733924075\n", - "141733924076\n", - "141733924077\n", - "141733924078\n", - "141733924079\n", - "141733924080\n", - "141733924081\n", - "141733924083\n", - "141733924085\n", - "141733924086\n", - "141733924087\n", - "141733924088\n", - "141733924090\n", - "141733924091\n", - "141733924093\n", - "141733924095\n", - "141733924096\n", - "141733924097\n", - "141733924098\n", - "141733924100\n", - "141733924102\n", - "141733924103\n", - "141733924106\n", - "141733924107\n", - "141733924108\n", - "141733924109\n", - "141733924112\n", - "141733924113\n", - "141733924114\n", - "141733924115\n", - "141733924119\n", - "141733924123\n", - "141733924125\n", - "141733924126\n", - "141733924127\n", - "141733924129\n", - "141733924131\n", - "141733924132\n", - "141733924136\n", - "141733924139\n", - "141733924140\n", - "141733924141\n", - "141733924142\n", - "141733924145\n", - "141733924146\n", - "141733924147\n", - "141733924149\n", - "141733924151\n", - "141733924152\n", - "141733924153\n", - "141733924154\n", - "141733924155\n", - "141733924157\n", - "141733924158\n", - "141733924160\n", - "141733924162\n", - "141733924163\n", - "141733924165\n", - "141733924166\n", - "141733924168\n", - "141733924169\n", - "141733924170\n", - "141733924171\n", - "141733924173\n", - "141733924174\n", - "141733924175\n", - "141733924176\n", - "141733924178\n", - "141733924179\n", - "141733924180\n", - "141733924181\n", - "141733924183\n", - "141733924184\n", - "141733924185\n", - "141733924186\n", - "141733924188\n", - "141733924189\n", - "141733924190\n", - "141733924191\n", - "141733924193\n", - "141733924194\n", - "141733924195\n", - "141733924199\n", - "141733924201\n", - "141733924202\n", - "141733924204\n", - "141733924205\n", - "141733924206\n", - "141733924209\n", - "141733924210\n", - "141733924211\n", - "141733924212\n", - "141733924214\n", - "141733924215\n", - "141733924217\n", - "141733924218\n", - "141733924221\n", - "141733924222\n", - "141733924223\n", - "141733924224\n", - "141733924226\n", - "141733924227\n", - "141733924228\n", - "141733924229\n", - "141733924230\n", - "141733924231\n", - "141733924233\n", - "141733924234\n", - "141733924237\n", - "141733924238\n", - "141733924240\n", - "141733924241\n", - "141733924242\n", - "141733924243\n", - "141733924244\n", - "141733924245\n", - "141733924246\n", - "141733924247\n", - "141733924248\n", - "141733924249\n", - "141733924251\n", - "141733924252\n", - "141733924253\n", - "141733924254\n", - "141733924255\n", - "141733924256\n", - "141733924259\n", - "141733924260\n", - "141733924261\n", - "141733924263\n", - "141733924264\n", - "141733924265\n", - "141733924266\n", - "141733924267\n", - "141733924268\n", - "141733924270\n", - "141733924271\n", - "141733924272\n", - "141733924273\n", - "141733924275\n", - "141733924279\n", - "141733924782\n", - "141733924785\n", - "141733924792\n", - "141733924793\n", - "141733924811\n", - "141733924815\n", - "141733924821\n", - "141733924827\n", - "141733924829\n", - "141733924837\n", - "141733924838\n", - "141733924846\n", - "141733924847\n", - "141733924860\n", - "141733924864\n", - "141733924868\n", - "141733924876\n", - "141733929404\n", - "141733929405\n", - "141733929406\n", - "141733929407\n", - "141733929408\n", - "141733929409\n", - "141733929410\n", - "141733929426\n", - "141733929427\n", - "141733929431\n", - "141733929432\n", - "141733929433\n", - "141733929434\n", - "141733929435\n", - "141733929436\n", - "141733929437\n", - "141733929438\n", - "141733929439\n", - "141733929440\n", - "141733929441\n", - "141733929450\n", - "141733929451\n", - "141733929452\n", - "141733929453\n", - "141733929454\n", - "141733929455\n", - "141733929456\n", - "141733929457\n", - "141733929458\n", - "141733929461\n", - "141733929462\n", - "141733929463\n", - "141733929464\n", - "141733929505\n", - "141733929506\n", - "141733929507\n", - "141733929515\n", - "141733929516\n", - "141733929517\n", - "141733929518\n", - "141733929519\n", - "141733929520\n", - "141733929535\n", - "141733929536\n", - "141733929546\n", - "141733929547\n", - "141733929552\n", - "141733929553\n", - "141733929563\n", - "141733929564\n", - "141733929565\n", - "141733929566\n", - "141733929575\n", - "141733929576\n", - "141733929580\n", - "141733929581\n", - "141733929582\n", - "141733929583\n", - "141733929584\n", - "141733929585\n", - "141733929586\n", - "141733929587\n", - "141733929623\n", - "141733929624\n", - "141733929715\n", - "141733929716\n", - "141733929717\n", - "141733929718\n", - "141733929719\n", - "141733929720\n", - "141733929721\n", - "141733929722\n", - "141733929723\n", - "141733929724\n", - "141733929725\n", - "141733929726\n", - "141733929727\n", - "141733929728\n", - "141733929729\n", - "141733929730\n", - "141733929731\n", - "141733929732\n", - "141733929733\n", - "141733929734\n", - "141733929735\n", - "141733929736\n", - "141733929737\n", - "141733929738\n", - "141733929739\n", - "141733929740\n", - "141733929741\n", - "141733929742\n", - "141733929743\n", - "141733929744\n", - "141733929745\n", - "141733929746\n", - "141733929747\n", - "141733929748\n", - "141733929874\n", - "141733929875\n", - "141733929880\n", - "141733929881\n", - "141733929882\n", - "141733929883\n", - "141733929889\n", - "141733929890\n", - "141733929891\n", - "141733929892\n", - "141733929893\n", - "141733929894\n", - "141733929895\n", - "141733929896\n", - "141733929897\n", - "141733929898\n", - "141733929899\n", - "141733929900\n", - "141733929901\n", - "141733929902\n", - "141733929903\n", - "141733929904\n", - "141733929905\n", - "141733929906\n", - "141733929907\n", - "141733929908\n", - "141733929909\n", - "141733929910\n", - "141733929914\n", - "141733929915\n", - "141733929916\n", - "141733929917\n", - "141733929918\n", - "141733929932\n", - "141733929933\n", - "141733929934\n", - "141733929935\n", - "141733929936\n", - "141733929937\n", - "141733929992\n", - "141733929993\n", - "141733930005\n", - "141733930006\n", - "141733930007\n", - "141733930008\n", - "141733930009\n", - "141733930010\n", - "141733930011\n", - "141733930012\n", - "141733930015\n", - "141733930016\n", - "141733930017\n", - "141733930018\n", - "141733930019\n", - "141733930020\n", - "141733930023\n", - "141733930024\n", - "141733930025\n", - "141733930026\n", - "141733930027\n", - "141733930028\n", - "141733930029\n", - "141733930030\n", - "141733930034\n", - "141733930035\n", - "141733930036\n", - "141733930037\n", - "141733930038\n", - "141733930039\n", - "141733930040\n", - "141733930041\n", - "141733930042\n", - "141733930043\n", - "141733930044\n", - "141733930054\n", - "141733930055\n", - "141733930078\n", - "141733930079\n", - "141733930083\n", - "141733930084\n", - "141733930087\n", - "141733930088\n", - "141733930089\n", - "141733930090\n", - "141733930091\n", - "141733930092\n", - "141733930093\n", - "141733930094\n", - "141733930095\n", - "141733930096\n", - "141733930111\n", - "141733930112\n", - "141733930116\n", - "141733930117\n", - "141733930118\n", - "141733930122\n", - "141733930123\n", - "141733930124\n", - "141733930127\n", - "141733930128\n", - "141733930129\n", - "141733930135\n", - "141733930136\n", - "141733930137\n", - "141733930138\n", - "141733930141\n", - "141733930142\n", - "141733930143\n", - "141733930144\n", - "141733930163\n", - "141733930164\n", - "141733930174\n", - "141733930175\n", - "141733930176\n", - "141733930177\n", - "141733930178\n", - "141733930179\n", - "141733930180\n", - "141733930181\n", - "141733930182\n", - "141733930183\n", - "141733930184\n", - "141733930185\n", - "141733930186\n", - "141733930189\n", - "141733930190\n", - "141733930191\n", - "141733930192\n", - "141733930193\n", - "141733930216\n", - "141733930217\n", - "141733930252\n", - "141733930253\n", - "141733930254\n", - "141733930255\n", - "141733930256\n", - "141733930257\n", - "141733930264\n", - "141733930265\n", - "141733930266\n", - "141733930267\n", - "141733930302\n", - "141733930303\n", - "141733930308\n", - "141733930309\n", - "141733930324\n", - "141733930325\n", - "141733930326\n", - "141733930413\n", - "141733930414\n", - "141733930421\n", - "141733930422\n", - "141733930423\n", - "141733930424\n", - "141733930425\n", - "141733930426\n", - "141733930429\n", - "141733930430\n", - "141733930431\n", - "141733930437\n", - "141733930438\n", - "141733930439\n", - "141733930440\n", - "141733930441\n", - "141733930442\n", - "141733930443\n", - "141733930444\n", - "141733930445\n", - "141733930446\n", - "141733930447\n", - "141733930448\n", - "141733930449\n", - "141733930450\n", - "141733930451\n", - "141733930452\n", - "141733930453\n", - "141733930454\n", - "141733930455\n", - "141733930456\n", - "141733930457\n", - "141733930460\n", - "141733930461\n", - "141733930462\n", - "141733930463\n", - "141733930519\n", - "141733930520\n", - "141733930521\n", - "141733930522\n", - "141733930523\n", - "141733930524\n", - "141733930525\n", - "141733930526\n", - "141733930527\n", - "141733930528\n", - "141733930529\n", - "141733930530\n", - "141733930531\n", - "141733930538\n", - "141733930539\n", - "141733930542\n", - "141733930543\n", - "141733930544\n", - "141733930545\n", - "141733930546\n", - "141733930547\n", - "141733930548\n", - "141733930549\n", - "141733930550\n", - "141733930551\n", - "141733930552\n", - "141733930553\n", - "141733930554\n", - "141733930555\n", - "141733930556\n", - "141733930557\n", - "141733930558\n", - "141733930559\n", - "141733930560\n", - "141733930565\n", - "141733930566\n", - "141733930570\n", - "141733930571\n", - "141733930572\n", - "141733930573\n", - "141733930574\n", - "141733930575\n", - "141733930576\n", - "141733930577\n", - "141733930578\n", - "141733930579\n", - "141733930580\n", - "141733930581\n", - "141733930582\n", - "141733930583\n", - "141733930584\n", - "141733930585\n", - "141733930586\n", - "141733930587\n", - "141733930588\n", - "141733930589\n", - "141733930590\n", - "141733930591\n", - "141733930592\n", - "141733930593\n", - "141733930594\n", - "141733930595\n", - "141733930596\n", - "141733930597\n", - "141733930598\n", - "141733930599\n", - "141733930600\n", - "141733930601\n", - "141733930602\n", - "141733930603\n", - "141733930604\n", - "141733930605\n", - "141733930606\n", - "141733930607\n", - "141733930608\n", - "141733930609\n", - "141733930610\n", - "141733930611\n", - "141733930612\n", - "141733930613\n", - "141733930614\n", - "141733930615\n", - "141733930616\n", - "141733930617\n", - "141733930618\n", - "141733930619\n", - "141733930620\n", - "141733930621\n", - "141733930622\n", - "141733930623\n", - "141733930624\n", - "141733930627\n", - "141733930628\n", - "141733930629\n", - "141733930630\n", - "141733930632\n", - "141733930633\n", - "141733930634\n", - "141733930635\n", - "141733930636\n", - "141733930637\n", - "141733930638\n", - "141733930639\n", - "141733930640\n", - "141733930641\n", - "141733930642\n", - "141733930643\n", - "141733930644\n", - "141733930645\n", - "141733930646\n", - "141733930647\n", - "141733930648\n", - "141733930649\n", - "141733930650\n", - "141733930651\n", - "141733930652\n", - "141733930653\n", - "141733930654\n", - "141733930655\n", - "141733930656\n", - "141733930657\n", - "141733930658\n", - "141733930659\n", - "141733930660\n", - "141733930661\n", - "141733930662\n", - "141733930663\n", - "141733930664\n", - "141733930668\n", - "141733930669\n", - "141733930670\n", - "141733930671\n", - "141733930672\n", - "141733930673\n", - "141733930674\n", - "141733930675\n", - "141733930676\n", - "141733930681\n", - "141733930682\n", - "141733930688\n", - "141733930689\n", - "141733930693\n", - "141733930694\n", - "141733930695\n", - "141733930698\n", - "141733930699\n", - "141733930700\n", - "141733930701\n", - "141733930702\n", - "141733930703\n", - "141733930704\n", - "141733930705\n", - "141733930706\n", - "141733930709\n", - "141733930710\n", - "141733930711\n", - "141733930712\n", - "141733930713\n", - "141733930714\n", - "141733930715\n", - "141733930723\n", - "141733930724\n", - "141733930725\n", - "141733930726\n", - "141733930733\n", - "141733930734\n", - "141733930735\n", - "141733930736\n", - "141733930737\n", - "141733930738\n", - "141733930739\n", - "141733930740\n", - "141733930741\n", - "141733930742\n", - "141733930743\n", - "141733930744\n", - "141733930745\n", - "141733930746\n", - "141733930747\n", - "141733930750\n", - "141733930751\n", - "141733930752\n", - "141733930753\n", - "141733930754\n", - "141733930755\n", - "141733930756\n", - "141733930757\n", - "141733930758\n", - "141733930759\n", - "141733930760\n", - "141733930761\n", - "141733930762\n", - "141733930763\n", - "141733930764\n", - "141733930765\n", - "141733930766\n", - "141733930767\n", - "141733930768\n", - "141733930769\n", - "141733930770\n", - "141733930771\n", - "141733930772\n", - "141733930773\n", - "141733930774\n", - "141733930775\n", - "141733930776\n", - "141733930777\n", - "141733930778\n", - "141733930779\n", - "141733930780\n", - "141733930781\n", - "141733930782\n", - "141733930783\n", - "141733930784\n", - "141733930785\n", - "141733930786\n", - "141733930787\n", - "141733930788\n", - "141733930789\n", - "141733930791\n", - "141733930792\n", - "141733930799\n", - "141733930800\n", - "141733930801\n", - "141733930802\n", - "141733930803\n", - "141733930804\n", - "141733930805\n", - "141733930808\n", - "141733930809\n", - "141733930810\n", - "141733930811\n", - "141733930814\n", - "141733930815\n", - "141733930816\n", - "141733930817\n", - "141733930818\n", - "141733930819\n", - "141733930822\n", - "141733930823\n", - "141733930832\n", - "141733930833\n", - "141733930834\n", - "141733930835\n", - "141733930836\n", - "141733930837\n", - "141733930838\n", - "141733930839\n", - "141733930840\n", - "141733930841\n", - "141733930842\n", - "141733930843\n", - "141733930844\n", - "141733930845\n", - "141733930846\n", - "141733930847\n", - "141733930848\n", - "141733930849\n", - "141733930850\n", - "141733930851\n", - "141733930852\n", - "141733930853\n", - "141733930854\n", - "141733930855\n", - "141733930856\n", - "141733930857\n", - "141733930858\n", - "141733930859\n", - "141733930862\n", - "141733930863\n", - "141733930866\n", - "141733930867\n", - "141733930872\n", - "141733930873\n", - "141733930878\n", - "141733930879\n", - "141733930880\n", - "141733930885\n", - "141733930886\n", - "141733930887\n", - "141733930888\n", - "141733930889\n", - "141733930890\n", - "141733930891\n", - "141733930892\n", - "141733931089\n", - "141733931090\n", - "141733931091\n", - "141733931092\n", - "141733931093\n", - "141733931094\n", - "141733931095\n", - "141733931096\n", - "141733931101\n", - "141733931102\n", - "141733931103\n", - "141733931104\n", - "141733931105\n", - "141733931108\n", - "141733931109\n", - "141733931110\n", - "141733931117\n", - "141733931118\n", - "141733931119\n", - "141733931120\n", - "141733931121\n", - "141733931122\n", - "141733931123\n", - "141733931124\n", - "141733931141\n", - "141733931142\n", - "141733931143\n", - "141733931150\n", - "141733931151\n", - "141733931162\n", - "141733931163\n", - "141733931164\n", - "141733931165\n", - "141733931209\n", - "141733931210\n", - "141733931218\n", - "141733931219\n", - "141733931244\n", - "141733931245\n", - "141733931250\n", - "141733931251\n", - "141733931252\n", - "141733931263\n", - "141733931264\n", - "141733931270\n", - "141733931271\n", - "141733931272\n", - "141733931273\n", - "141733931282\n", - "141733931283\n", - "141733931297\n", - "141733931298\n", - "141733931299\n", - "141733931304\n", - "141733931305\n", - "141733931310\n", - "141733931311\n", - "141733931312\n", - "141733931313\n", - "141733931314\n", - "141733931315\n", - "141733931316\n", - "141733931321\n", - "141733931322\n", - "141733931323\n", - "141733931324\n", - "141733931325\n", - "141733931326\n", - "141733931338\n", - "141733931339\n", - "141733931342\n", - "141733931343\n", - "141733931355\n", - "141733931356\n", - "141733931362\n", - "141733931363\n", - "141733931392\n", - "141733931393\n", - "141733931398\n", - "141733931399\n", - "141733931400\n", - "141733931401\n", - "141733931402\n", - "141733931403\n", - "141733931404\n", - "141733931405\n", - "141733931411\n", - "141733931412\n", - "141733931415\n", - "141733931416\n", - "141733931419\n", - "141733931420\n", - "141733931421\n", - "141733931422\n", - "141733931430\n", - "141733931431\n", - "141733931432\n", - "141733931433\n", - "141733931434\n", - "141733931435\n", - "141733931436\n", - "141733931437\n", - "141733931438\n", - "141733931439\n", - "141733931440\n", - "141733931441\n", - "141733931442\n", - "141733931443\n", - "141733931444\n", - "141733931445\n", - "141733931446\n", - "141733931449\n", - "141733931450\n", - "141733931462\n", - "141733931463\n", - "141733931464\n", - "141733931469\n", - "141733931470\n", - "141733931471\n", - "141733931474\n", - "141733931475\n", - "141733931483\n", - "141733931484\n" + "9742 i band objects, 30400 measurements\n", + "8321 r band objects, 32717 measurements\n" ] } ], "source": [ - "for name, group in grouped:\n", - " if len(group) == 5:\n", - " print(name)" - ] - }, - { - "cell_type": "code", - "execution_count": 272, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([18.55159354, 18.16641909, 18.4897198 , 16.13221443, 18.43015646])" - ] - }, - "execution_count": 272, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 141733921083\n", - "iCoaddCalib.getMagnitude(iSources[iSources['objectId'] == 141733921083]['base_PsfFlux_flux'].values)" + "iSources = pd.concat(i_tables) \n", + "rSources = pd.concat(r_tables)\n", + "\n", + "iGrouped = iSources.groupby('objectId')\n", + "rGrouped = rSources.groupby('objectId')\n", + "\n", + "print('{} i band objects, {} measurements'.format(len(np.unique(iSources['objectId'])), len(iSources['objectId'])))\n", + "print('{} r band objects, {} measurements'.format(len(np.unique(rSources['objectId'])), len(rSources['objectId'])))" ] }, { "cell_type": "code", - "execution_count": 193, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "1143 1143\n" + "e.g. objectId: 141733921083\n" ] - } - ], - "source": [ - "# what datasetRefOrType to get? forced_src see all options at:\n", - "# /opt/lsst/software/stack/stack/miniconda3-4.3.21-10a4fa6/Linux64/obs_subaru/16.0+1/python/lsst/obs/hsc\n", - "# ['filter:HSC-I', 'pointing:671', 'visit:903986', 'ccd:16', 'field:STRIPE82L', 'dateObs:2013-11-02', 'taiObs:2013-11-02', 'expTime:30.0', 'tract:0'] 1143\n", - "# ['filter:HSC-R', 'pointing:533', 'visit:903334', 'ccd:16', 'field:STRIPE82L', 'dateObs:2013-06-17', 'taiObs:2013-06-17', 'expTime:30.0', 'tract:0'] 1143\n", - "\n", - "iSources = butler.get('forced_src', {'filter': 'HSC-I', \n", - " 'pointing': 671, \n", - " 'visit': 903986, \n", - " 'ccd': 16, \n", - " 'field': 'STRIPE82L', \n", - " 'dateObs': '2013-11-02', \n", - " 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0})\n", - "\n", - "rSources = butler.get('forced_src', {'filter': 'HSC-R', \n", - " 'pointing': 533, \n", - " 'visit': 903334, \n", - " 'ccd': 16, \n", - " 'field': 'STRIPE82L', \n", - " 'dateObs': '2013-06-17', \n", - " 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0})\n", - "\n", - "print(len(iSources), len(rSources))\n", - "\n", - "rMags = rCoaddCalib.getMagnitude(rSources['base_PsfFlux_flux']) # is using the rCoaddCalib object wrong?\n", - "iMags = iCoaddCalib.getMagnitude(iSources['base_PsfFlux_flux']) # is using the rCoaddCalib object wrong?" - ] - }, - { - "cell_type": "code", - "execution_count": 240, - "metadata": {}, - "outputs": [ + }, { "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
idcoord_racoord_decparentobjectIdparentObjectIddeblend_nChildbase_SdssCentroid_xbase_SdssCentroid_ybase_SdssCentroid_xSigma...slot_ModelFlux_apCorrbase_GaussianFlux_apCorrSigmaslot_ModelFlux_apCorrSigmabase_GaussianFlux_flag_apCorrslot_ModelFlux_flag_apCorrext_photometryKron_KronFlux_apCorrext_photometryKron_KronFlux_apCorrSigmaext_photometryKron_KronFlux_flag_apCorrvisitmjd
2777765181299278482145.598927-0.006448014173392108302579.9958111112.1726580.321924...1.1176630.00.0FalseFalse0.9921840.0False90398656598.221156
5167765198522097341495.598927-0.006448014173392108302488.5570732167.5040650.690836...1.0858150.00.0FalseFalse0.9645610.0False90398856598.222126
7717765215744916201005.598927-0.006448014173392108302455.5176393092.1486780.306457...1.1190770.00.0FalseFalse0.9960910.0False90399056598.223046
07765386942312611855.598927-0.0064480141733921083029.1908053042.373080NaN...1.1019410.00.0FalseFalse1.0236450.0False90401056598.250471
837765421387950326605.598927-0.006448014173392108302271.696844871.9730280.234910...1.1446520.00.0FalseFalse1.0145350.0False90401456598.200000
\n", + "

5 rows × 191 columns

\n", + "
" + ], "text/plain": [ - "1944" + " id coord_ra coord_dec parent objectId \\\n", + "277 776518129927848214 5.598927 -0.006448 0 141733921083 \n", + "516 776519852209734149 5.598927 -0.006448 0 141733921083 \n", + "771 776521574491620100 5.598927 -0.006448 0 141733921083 \n", + "0 776538694231261185 5.598927 -0.006448 0 141733921083 \n", + "83 776542138795032660 5.598927 -0.006448 0 141733921083 \n", + "\n", + " parentObjectId deblend_nChild base_SdssCentroid_x base_SdssCentroid_y \\\n", + "277 0 2 579.995811 1112.172658 \n", + "516 0 2 488.557073 2167.504065 \n", + "771 0 2 455.517639 3092.148678 \n", + "0 0 2 9.190805 3042.373080 \n", + "83 0 2 271.696844 871.973028 \n", + "\n", + " base_SdssCentroid_xSigma ... slot_ModelFlux_apCorr \\\n", + "277 0.321924 ... 1.117663 \n", + "516 0.690836 ... 1.085815 \n", + "771 0.306457 ... 1.119077 \n", + "0 NaN ... 1.101941 \n", + "83 0.234910 ... 1.144652 \n", + "\n", + " base_GaussianFlux_apCorrSigma slot_ModelFlux_apCorrSigma \\\n", + "277 0.0 0.0 \n", + "516 0.0 0.0 \n", + "771 0.0 0.0 \n", + "0 0.0 0.0 \n", + "83 0.0 0.0 \n", + "\n", + " base_GaussianFlux_flag_apCorr slot_ModelFlux_flag_apCorr \\\n", + "277 False False \n", + "516 False False \n", + "771 False False \n", + "0 False False \n", + "83 False False \n", + "\n", + " ext_photometryKron_KronFlux_apCorr \\\n", + "277 0.992184 \n", + "516 0.964561 \n", + "771 0.996091 \n", + "0 1.023645 \n", + "83 1.014535 \n", + "\n", + " ext_photometryKron_KronFlux_apCorrSigma \\\n", + "277 0.0 \n", + "516 0.0 \n", + "771 0.0 \n", + "0 0.0 \n", + "83 0.0 \n", + "\n", + " ext_photometryKron_KronFlux_flag_apCorr visit mjd \n", + "277 False 903986 56598.221156 \n", + "516 False 903988 56598.222126 \n", + "771 False 903990 56598.223046 \n", + "0 False 904010 56598.250471 \n", + "83 False 904014 56598.200000 \n", + "\n", + "[5 rows x 191 columns]" ] }, - "execution_count": 240, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "#{'filter': 'HSC-I', 'pointing': '671', 'visit': '903986', 'ccd': '100', 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': '30.0', 'tract': '0'}\n", - "#{'filter': 'HSC-I', 'pointing': '671', 'visit': '903988', 'ccd': '24', 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': '30.0', 'tract': '0'}\n", - "\n", - "iSources_1 = butler.get('forced_src', {'filter': 'HSC-I', \n", - " 'pointing': 671, \n", - " 'visit': 903986, \n", - " 'ccd': 100, \n", - " 'field': 'STRIPE82L', \n", - " 'dateObs': '2013-11-02', \n", - " 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0})\n", - "\n", - "iSources_2 = butler.get('forced_src', {'filter': 'HSC-I', \n", - " 'pointing': 671, \n", - " 'visit': 903988, \n", - " 'ccd': 24, \n", - " 'field': 'STRIPE82L', \n", - " 'dateObs': '2013-11-02', \n", - " 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0})" + "objids = [name for name, group in iGrouped if len(group) == 5]\n", + "obj = objids[1]\n", + "print('e.g. objectId:', obj)\n", + "iSources[iSources['objectId'] == obj]" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(len(np.unique(astropy.table.vstack(iSources_1.asAstropy(),iSources_2.asAstropy())['objectId'])))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 241, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " id ... ext_photometryKron_KronFlux_flag_apCorr\n", - " ... \n", - "------------------ ... ---------------------------------------\n", - "776518490705100801 ... False\n", - "776518490705100802 ... False\n", - "776518490705100803 ... False\n", - "776518490705100804 ... False\n", - "776518490705100805 ... False\n", - "776518490705100806 ... False\n", - "776518490705100807 ... False\n", - "776518490705100808 ... False\n", - "776518490705100809 ... False\n", - "776518490705100810 ... False\n", - " ... ... ...\n", - "776518490705102735 ... False\n", - "776518490705102736 ... False\n", - "776518490705102737 ... False\n", - "776518490705102738 ... False\n", - "776518490705102739 ... False\n", - "776518490705102740 ... False\n", - "776518490705102741 ... False\n", - "776518490705102742 ... False\n", - "776518490705102743 ... False\n", - "776518490705102744 ... False\n", - "Length = 1944 rows\n" - ] - } - ], - "source": [ - "import astropy\n", - "grouped = astropy.table.vstack(iSources_1.asAstropy(),iSources_2.asAstropy()).group_by('objectId')\n", - "print(grouped)" - ] - }, - { - "cell_type": "code", - "execution_count": 214, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "id: 1146\n", - "coord_ra: nan rad\n", - "coord_dec: nan rad\n", - "parent: 0\n", - "objectId: 0\n", - "parentObjectId: 0\n", - "deblend_nChild: 0\n", - "base_SdssCentroid_x: nan\n", - "base_SdssCentroid_y: nan\n", - "base_SdssCentroid_xSigma: nan\n", - "base_SdssCentroid_ySigma: nan\n", - "base_SdssCentroid_flag: 0\n", - "base_SdssCentroid_flag_edge: 0\n", - "base_SdssCentroid_flag_noSecondDerivative: 0\n", - "base_SdssCentroid_flag_almostNoSecondDerivative: 0\n", - "base_SdssCentroid_flag_notAtMaximum: 0\n", - "base_SdssCentroid_flag_resetToPeak: 0\n", - "base_TransformedCentroid_x: nan\n", - "base_TransformedCentroid_y: nan\n", - "base_TransformedCentroid_flag: 0\n", - "base_SdssShape_xx: nan\n", - "base_SdssShape_yy: nan\n", - "base_SdssShape_xy: nan\n", - "base_SdssShape_xxSigma: nan\n", - "base_SdssShape_yySigma: nan\n", - "base_SdssShape_xySigma: nan\n", - "base_SdssShape_x: nan\n", - "base_SdssShape_y: nan\n", - "base_SdssShape_flux: nan\n", - "base_SdssShape_fluxSigma: nan\n", - "base_SdssShape_psf_xx: nan\n", - "base_SdssShape_psf_yy: nan\n", - "base_SdssShape_psf_xy: nan\n", - "base_SdssShape_flux_xx_Cov: nan\n", - "base_SdssShape_flux_yy_Cov: nan\n", - "base_SdssShape_flux_xy_Cov: nan\n", - "base_SdssShape_flag: 0\n", - "base_SdssShape_flag_unweightedBad: 0\n", - "base_SdssShape_flag_unweighted: 0\n", - "base_SdssShape_flag_shift: 0\n", - "base_SdssShape_flag_maxIter: 0\n", - "base_SdssShape_flag_psf: 0\n", - "base_TransformedShape_xx: nan\n", - "base_TransformedShape_yy: nan\n", - "base_TransformedShape_xy: nan\n", - "base_TransformedShape_flag: 0\n", - "modelfit_DoubleShapeletPsfApprox_0_xx: nan\n", - "modelfit_DoubleShapeletPsfApprox_0_yy: nan\n", - "modelfit_DoubleShapeletPsfApprox_0_xy: nan\n", - "modelfit_DoubleShapeletPsfApprox_0_x: nan\n", - "modelfit_DoubleShapeletPsfApprox_0_y: nan\n", - "modelfit_DoubleShapeletPsfApprox_0_0: nan\n", - "modelfit_DoubleShapeletPsfApprox_0_1: nan\n", - "modelfit_DoubleShapeletPsfApprox_0_2: nan\n", - "modelfit_DoubleShapeletPsfApprox_0_3: nan\n", - "modelfit_DoubleShapeletPsfApprox_0_4: nan\n", - "modelfit_DoubleShapeletPsfApprox_0_5: nan\n", - "modelfit_DoubleShapeletPsfApprox_1_xx: nan\n", - "modelfit_DoubleShapeletPsfApprox_1_yy: nan\n", - "modelfit_DoubleShapeletPsfApprox_1_xy: nan\n", - "modelfit_DoubleShapeletPsfApprox_1_x: nan\n", - "modelfit_DoubleShapeletPsfApprox_1_y: nan\n", - "modelfit_DoubleShapeletPsfApprox_1_0: nan\n", - "modelfit_DoubleShapeletPsfApprox_1_1: nan\n", - "modelfit_DoubleShapeletPsfApprox_1_2: nan\n", - "modelfit_DoubleShapeletPsfApprox_flag: 0\n", - "modelfit_DoubleShapeletPsfApprox_flag_invalidPointForPsf: 0\n", - "modelfit_DoubleShapeletPsfApprox_flag_invalidMoments: 0\n", - "modelfit_DoubleShapeletPsfApprox_flag_maxIterations: 0\n", - "base_CircularApertureFlux_3_0_flux: nan\n", - "base_CircularApertureFlux_3_0_fluxSigma: nan\n", - "base_CircularApertureFlux_3_0_flag: 0\n", - "base_CircularApertureFlux_3_0_flag_apertureTruncated: 0\n", - "base_CircularApertureFlux_3_0_flag_sincCoeffsTruncated: 0\n", - "base_CircularApertureFlux_4_5_flux: nan\n", - "base_CircularApertureFlux_4_5_fluxSigma: nan\n", - "base_CircularApertureFlux_4_5_flag: 0\n", - "base_CircularApertureFlux_4_5_flag_apertureTruncated: 0\n", - "base_CircularApertureFlux_4_5_flag_sincCoeffsTruncated: 0\n", - "base_CircularApertureFlux_6_0_flux: nan\n", - "base_CircularApertureFlux_6_0_fluxSigma: nan\n", - "base_CircularApertureFlux_6_0_flag: 0\n", - "base_CircularApertureFlux_6_0_flag_apertureTruncated: 0\n", - "base_CircularApertureFlux_6_0_flag_sincCoeffsTruncated: 0\n", - "base_CircularApertureFlux_9_0_flux: nan\n", - "base_CircularApertureFlux_9_0_fluxSigma: nan\n", - "base_CircularApertureFlux_9_0_flag: 0\n", - "base_CircularApertureFlux_9_0_flag_apertureTruncated: 0\n", - "base_CircularApertureFlux_9_0_flag_sincCoeffsTruncated: 0\n", - "base_CircularApertureFlux_12_0_flux: nan\n", - "base_CircularApertureFlux_12_0_fluxSigma: nan\n", - "base_CircularApertureFlux_12_0_flag: 0\n", - "base_CircularApertureFlux_12_0_flag_apertureTruncated: 0\n", - "base_CircularApertureFlux_12_0_flag_sincCoeffsTruncated: 0\n", - "base_CircularApertureFlux_17_0_flux: nan\n", - "base_CircularApertureFlux_17_0_fluxSigma: nan\n", - "base_CircularApertureFlux_17_0_flag: 0\n", - "base_CircularApertureFlux_17_0_flag_apertureTruncated: 0\n", - "base_CircularApertureFlux_25_0_flux: nan\n", - "base_CircularApertureFlux_25_0_fluxSigma: nan\n", - "base_CircularApertureFlux_25_0_flag: 0\n", - "base_CircularApertureFlux_25_0_flag_apertureTruncated: 0\n", - "base_CircularApertureFlux_35_0_flux: nan\n", - "base_CircularApertureFlux_35_0_fluxSigma: nan\n", - "base_CircularApertureFlux_35_0_flag: 0\n", - "base_CircularApertureFlux_35_0_flag_apertureTruncated: 0\n", - "base_CircularApertureFlux_50_0_flux: nan\n", - "base_CircularApertureFlux_50_0_fluxSigma: nan\n", - "base_CircularApertureFlux_50_0_flag: 0\n", - "base_CircularApertureFlux_50_0_flag_apertureTruncated: 0\n", - "base_CircularApertureFlux_70_0_flux: nan\n", - "base_CircularApertureFlux_70_0_fluxSigma: nan\n", - "base_CircularApertureFlux_70_0_flag: 0\n", - "base_CircularApertureFlux_70_0_flag_apertureTruncated: 0\n", - "base_GaussianFlux_flux: nan\n", - "base_GaussianFlux_fluxSigma: nan\n", - "base_GaussianFlux_flag: 0\n", - "base_LocalBackground_flux: nan\n", - "base_LocalBackground_fluxSigma: nan\n", - "base_LocalBackground_flag: 0\n", - "base_LocalBackground_flag_noGoodPixels: 0\n", - "base_LocalBackground_flag_noPsf: 0\n", - "base_PixelFlags_flag: 0\n", - "base_PixelFlags_flag_offimage: 0\n", - "base_PixelFlags_flag_edge: 0\n", - "base_PixelFlags_flag_interpolated: 0\n", - "base_PixelFlags_flag_saturated: 0\n", - "base_PixelFlags_flag_cr: 0\n", - "base_PixelFlags_flag_bad: 0\n", - "base_PixelFlags_flag_suspect: 0\n", - "base_PixelFlags_flag_interpolatedCenter: 0\n", - "base_PixelFlags_flag_saturatedCenter: 0\n", - "base_PixelFlags_flag_crCenter: 0\n", - "base_PixelFlags_flag_suspectCenter: 0\n", - "base_PsfFlux_flux: nan\n", - "base_PsfFlux_fluxSigma: nan\n", - "base_PsfFlux_area: nan\n", - "base_PsfFlux_flag: 0\n", - "base_PsfFlux_flag_noGoodPixels: 0\n", - "base_PsfFlux_flag_edge: 0\n", - "ext_photometryKron_KronFlux_flux: nan\n", - "ext_photometryKron_KronFlux_fluxSigma: nan\n", - "ext_photometryKron_KronFlux_radius: nan\n", - "ext_photometryKron_KronFlux_radius_for_radius: nan\n", - "ext_photometryKron_KronFlux_psf_radius: nan\n", - "ext_photometryKron_KronFlux_flag: 0\n", - "ext_photometryKron_KronFlux_flag_edge: 0\n", - "ext_photometryKron_KronFlux_flag_bad_shape_no_psf: 0\n", - "ext_photometryKron_KronFlux_flag_no_minimum_radius: 0\n", - "ext_photometryKron_KronFlux_flag_no_fallback_radius: 0\n", - "ext_photometryKron_KronFlux_flag_bad_radius: 0\n", - "ext_photometryKron_KronFlux_flag_used_minimum_radius: 0\n", - "ext_photometryKron_KronFlux_flag_used_psf_radius: 0\n", - "ext_photometryKron_KronFlux_flag_small_radius: 0\n", - "ext_photometryKron_KronFlux_flag_bad_shape: 0\n", - "base_GaussianFlux_apCorr: nan\n", - "base_GaussianFlux_apCorrSigma: nan\n", - "base_GaussianFlux_flag_apCorr: 0\n", - "base_PsfFlux_apCorr: nan\n", - "base_PsfFlux_apCorrSigma: nan\n", - "base_PsfFlux_flag_apCorr: 0\n", - "ext_photometryKron_KronFlux_apCorr: nan\n", - "ext_photometryKron_KronFlux_apCorrSigma: nan\n", - "ext_photometryKron_KronFlux_flag_apCorr: 0\n", - "\n", - "1136\n", - "1143\n" - ] - } - ], - "source": [ - "print(rSources.makeRecord())\n", - "print(len(np.intersect1d(rSources['objectId'],iSources['objectId'])))\n", - "print(len(rSources['objectId']))" - ] - }, - { - "cell_type": "code", - "execution_count": 195, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -2874,148 +1068,108 @@ ], "source": [ "plt.style.use('seaborn-notebook')\n", - "plt.figure(1, figsize=(4, 4), dpi=140)\n", - "plt.title('Color-Magnitude Diagram for All Sources in Catalog')\n", - "plt.scatter(rMags - iMags,\n", - " iMags,\n", - " edgecolors='None', s=2, c='k')\n", - "#plt.xlim(-0.5, 3)\n", - "#plt.ylim(25, 14)\n", - "plt.xlabel('$r-i$')\n", + "plt.figure(3, figsize=(4, 4), dpi=140)\n", + "plt.title('objectId: {}'.format(obj))\n", + "plt.scatter(iSources[iSources['objectId'] == obj]['mjd'].values, \n", + " iCoaddCalib.getMagnitude(iSources[iSources['objectId'] == objids[0]]['base_PsfFlux_flux'].values),\n", + " s=6, c='k')\n", + "\n", "plt.ylabel('$i$')\n", + "plt.xlabel('MJD')\n", + "\n", "plt.subplots_adjust(left=0.125, bottom=0.1)\n", + "ax = plt.gca()\n", + "ax.ticklabel_format(useOffset=56598, style='plain', axis='x', useMathText=True)\n", "plt.show()" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "rSources = butler.get('forced_src', {'filter': 'HSC-R', 'tract' : 0, 'patch' : '0,0'})\n", - "iSources = butler.get('forced_src', {'filter': 'HSC-I', 'tract': 0, 'patch': '1,1'})" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import astropy.io.fits as fitsio\n", - "hmm = fitsio.open('/home/jmyles/DATA/rerun/ccdForcedPhot/00533/HSC-R/tract0/FORCEDSRC-0903334-016.fits')\n", - "hmm2 = fitsio.open('/home/jmyles/DATA/rerun/ccdForcedPhot/00533/HSC-R/tract0/FORCEDSRC-0903334-022.fits')\n", - "hmm3 = fitsio.open('/home/jmyles/DATA/rerun/coaddForcedPhot/deepCoadd-results/HSC-R/0/0,1/forced_src-HSC-R-0-0,1.fits')" - ] - }, - { - "cell_type": "code", - "execution_count": 190, + "execution_count": 24, "metadata": {}, "outputs": [ { "data": { + "image/png": "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\n", "text/plain": [ - "('flags',\n", - " 'id',\n", - " 'coord_ra',\n", - " 'coord_dec',\n", - " 'parent',\n", - " 'objectId',\n", - " 'parentObjectId',\n", - " 'deblend_nChild',\n", - " 'base_SdssCentroid_x',\n", - " 'base_SdssCentroid_y',\n", - " 'base_SdssCentroid_xSigma',\n", - " 'base_SdssCentroid_ySigma',\n", - " 'base_TransformedCentroid_x',\n", - " 'base_TransformedCentroid_y',\n", - " 'base_SdssShape_xx',\n", - " 'base_SdssShape_yy',\n", - " 'base_SdssShape_xy',\n", - " 'base_SdssShape_xxSigma',\n", - " 'base_SdssShape_yySigma',\n", - " 'base_SdssShape_xySigma',\n", - " 'base_SdssShape_x',\n", - " 'base_SdssShape_y',\n", - " 'base_SdssShape_flux',\n", - " 'base_SdssShape_fluxSigma',\n", - " 'base_SdssShape_psf_xx',\n", - " 'base_SdssShape_psf_yy',\n", - " 'base_SdssShape_psf_xy',\n", - " 'base_SdssShape_flux_xx_Cov',\n", - " 'base_SdssShape_flux_yy_Cov',\n", - " 'base_SdssShape_flux_xy_Cov',\n", - " 'base_TransformedShape_xx',\n", - " 'base_TransformedShape_yy',\n", - " 'base_TransformedShape_xy',\n", - " 'modelfit_DoubleShapeletPsfApprox_0_xx',\n", - " 'modelfit_DoubleShapeletPsfApprox_0_yy',\n", - " 'modelfit_DoubleShapeletPsfApprox_0_xy',\n", - " 'modelfit_DoubleShapeletPsfApprox_0_x',\n", - " 'modelfit_DoubleShapeletPsfApprox_0_y',\n", - " 'modelfit_DoubleShapeletPsfApprox_0_0',\n", - " 'modelfit_DoubleShapeletPsfApprox_0_1',\n", - " 'modelfit_DoubleShapeletPsfApprox_0_2',\n", - " 'modelfit_DoubleShapeletPsfApprox_0_3',\n", - " 'modelfit_DoubleShapeletPsfApprox_0_4',\n", - " 'modelfit_DoubleShapeletPsfApprox_0_5',\n", - " 'modelfit_DoubleShapeletPsfApprox_1_xx',\n", - " 'modelfit_DoubleShapeletPsfApprox_1_yy',\n", - " 'modelfit_DoubleShapeletPsfApprox_1_xy',\n", - " 'modelfit_DoubleShapeletPsfApprox_1_x',\n", - " 'modelfit_DoubleShapeletPsfApprox_1_y',\n", - " 'modelfit_DoubleShapeletPsfApprox_1_0',\n", - " 'modelfit_DoubleShapeletPsfApprox_1_1',\n", - " 'modelfit_DoubleShapeletPsfApprox_1_2',\n", - " 'base_CircularApertureFlux_3_0_flux',\n", - " 'base_CircularApertureFlux_3_0_fluxSigma',\n", - " 'base_CircularApertureFlux_4_5_flux',\n", - " 'base_CircularApertureFlux_4_5_fluxSigma',\n", - " 'base_CircularApertureFlux_6_0_flux',\n", - " 'base_CircularApertureFlux_6_0_fluxSigma',\n", - " 'base_CircularApertureFlux_9_0_flux',\n", - " 'base_CircularApertureFlux_9_0_fluxSigma',\n", - " 'base_CircularApertureFlux_12_0_flux',\n", - " 'base_CircularApertureFlux_12_0_fluxSigma',\n", - " 'base_CircularApertureFlux_17_0_flux',\n", - " 'base_CircularApertureFlux_17_0_fluxSigma',\n", - " 'base_CircularApertureFlux_25_0_flux',\n", - " 'base_CircularApertureFlux_25_0_fluxSigma',\n", - " 'base_CircularApertureFlux_35_0_flux',\n", - " 'base_CircularApertureFlux_35_0_fluxSigma',\n", - " 'base_CircularApertureFlux_50_0_flux',\n", - " 'base_CircularApertureFlux_50_0_fluxSigma',\n", - " 'base_CircularApertureFlux_70_0_flux',\n", - " 'base_CircularApertureFlux_70_0_fluxSigma',\n", - " 'base_GaussianFlux_flux',\n", - " 'base_GaussianFlux_fluxSigma',\n", - " 'base_LocalBackground_flux',\n", - " 'base_LocalBackground_fluxSigma',\n", - " 'base_PsfFlux_flux',\n", - " 'base_PsfFlux_fluxSigma',\n", - " 'base_PsfFlux_area',\n", - " 'ext_photometryKron_KronFlux_flux',\n", - " 'ext_photometryKron_KronFlux_fluxSigma',\n", - " 'ext_photometryKron_KronFlux_radius',\n", - " 'ext_photometryKron_KronFlux_radius_for_radius',\n", - " 'ext_photometryKron_KronFlux_psf_radius',\n", - " 'base_GaussianFlux_apCorr',\n", - " 'base_GaussianFlux_apCorrSigma',\n", - " 'base_PsfFlux_apCorr',\n", - " 'base_PsfFlux_apCorrSigma',\n", - " 'ext_photometryKron_KronFlux_apCorr',\n", - " 'ext_photometryKron_KronFlux_apCorrSigma')" + "
" ] }, - "execution_count": 190, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "hmm[1].data.dtype.names" + "plt.style.use('seaborn-notebook')\n", + "fig, axarr = plt.subplots(1, 2, figsize=(8, 4), dpi=140)\n", + "\n", + "fig.suptitle('Forced Photometry')\n", + "\n", + "rSources = butler_coadd.get('deepCoadd_forced_src', {'filter': 'HSC-R', 'tract': 0, 'patch': '1,1'})\n", + "iSources = butler_coadd.get('deepCoadd_forced_src', {'filter': 'HSC-I', 'tract': 0, 'patch': '1,1'})\n", + "\n", + "deblended = rSources['deblend_nChild'] == 0\n", + "\n", + "refTable = butler_coadd.get('deepCoadd_ref', {'filter': 'HSC-R^HSC-I', 'tract': 0, 'patch': '1,1'})\n", + "inInnerRegions = refTable['detect_isPatchInner'] & refTable['detect_isTractInner'] # define inner regions\n", + "isSkyObject = refTable['merge_peak_sky'] # reject sky objects\n", + "isPrimary = refTable['detect_isPrimary']\n", + "\n", + "isStellar = iSources['base_ClassificationExtendedness_value'] < 1.\n", + "isGoodFlux = ~iSources['base_PsfFlux_flag']\n", + "selected = isPrimary & isStellar & isGoodFlux\n", + "\n", + "rMags = rCoaddCalib.getMagnitude(rSources['base_PsfFlux_flux'])\n", + "iMags = iCoaddCalib.getMagnitude(iSources['base_PsfFlux_flux'])\n", + "\n", + "axarr[0].set_title('Coadd (Stars Only)')\n", + "axarr[0].scatter(rMags[selected] - iMags[selected],\n", + " iMags[selected],\n", + " edgecolors='None', s=2, c='k')\n", + "\n", + "axarr[0].set_xlim(-0.5, 3)\n", + "axarr[0].set_ylim(25, 14)\n", + "axarr[0].set_xlabel('$r-i$')\n", + "axarr[0].set_ylabel('$i$')\n", + "\n", + "# datasetRefOrType : forced_src\n", + "# see all options at\n", + "# /opt/lsst/software/stack/stack/miniconda3-4.3.21-10a4fa6/Linux64/obs_subaru/16.0+1/python/lsst/obs/hsc\n", + "\n", + "iSources = butler_ccd.get('forced_src', {'filter': 'HSC-I', \n", + " 'pointing': 671, \n", + " 'visit': 903986, \n", + " 'ccd': 16, \n", + " 'field': 'STRIPE82L', \n", + " 'dateObs': '2013-11-02', \n", + " 'taiObs': '2013-11-02', \n", + " 'expTime': 30.0, \n", + " 'tract': 0, 'patch' : '1,1'})\n", + "\n", + "rSources = butler_ccd.get('forced_src', {'filter': 'HSC-R', \n", + " 'pointing': 533, \n", + " 'visit': 903334, \n", + " 'ccd': 16, \n", + " 'field': 'STRIPE82L', \n", + " 'dateObs': '2013-06-17', \n", + " 'taiObs': '2013-06-17', \n", + " 'expTime': 30.0, \n", + " 'tract': 0, 'patch' : '1,1'})\n", + "\n", + "rMags = rCoaddCalib.getMagnitude(rSources['base_PsfFlux_flux']) # is using the rCoaddCalib object wrong?\n", + "iMags = iCoaddCalib.getMagnitude(iSources['base_PsfFlux_flux']) # is using the rCoaddCalib object wrong?\n", + "\n", + "axarr[1].set_title('Single Exposure (All Sources)')\n", + "plt.scatter(rMags - iMags,\n", + " iMags,\n", + " edgecolors='None', s=2, c='k')\n", + "axarr[1].set_xlim(-0.5, 3)\n", + "axarr[1].set_ylim(25, 14)\n", + "axarr[1].set_xlabel('$r-i$')\n", + "axarr[1].set_ylabel('$i$')\n", + "plt.subplots_adjust(left=0.125, bottom=0.1)\n", + "\n", + "plt.show()" ] }, { diff --git a/ImageProcessing/Re-RunHSC.sh b/ImageProcessing/Re-RunHSC.sh index fb981f24..ab8ca115 100644 --- a/ImageProcessing/Re-RunHSC.sh +++ b/ImageProcessing/Re-RunHSC.sh @@ -157,8 +157,16 @@ date echo "Re-RunHSC INFO: perform forced photometry on individual exposures with forcedPhotCcd.py" forcedPhotCcd.py $DATADIR --rerun coaddPhot:ccdForcedPhot --id filter=HSC-R --clobber-config --configfile=/project/shared/data/ci_hsc/forcedPhotCcdConfig.py &> ccd_r.txt -forcedPhotCcd.py $DATADIR --rerun ccdForcedPhot --id filter=HSC-I --clobber-config --configfile=/project/shared/data/ci_hsc/forcedPhotCcdConfig.py&> ccd_i.txt +forcedPhotCcd.py $DATADIR --rerun ccdForcedPhot --id filter=HSC-I --clobber-config --configfile=/project/shared/data/ci_hsc/forcedPhotCcdConfig.py &> ccd_i.txt + # VI. Multi-band catalog analysis # For analysis of the catalog, see part VI of StackClub/ImageProcessing/Re-RunHSC.ipynb - +date +echo "Re-RunHSC INFO: parse output of forcedPhotCcd.py" +grep 'forcedPhotCcd INFO: Performing forced measurement on DataId' ccd_r.txt ccd_i.txt > data_ids.txt +# The following sed commands clean up the output log file used to determine which DataIds have measured forced photometry. +sed -i 's/ccd_[i,r].txt:forcedPhotCcd INFO: Performing forced measurement on DataId(initialdata={//g' data_ids.txt +sed -i 's/}, tag=set())//g' data_ids.txt +sed -i 's/'"'"'//g' data_ids.txt +sed -i 's/ //g' data_ids.txt From 9896fc902c3f12ca90a6126a016af729a2b60a9e Mon Sep 17 00:00:00 2001 From: Justin Myles Date: Fri, 14 Sep 2018 02:39:16 +0000 Subject: [PATCH 10/15] begin clean nb --- ImageProcessing/Re-RunHSC.ipynb | 491 ++++++++++++-------------------- ImageProcessing/Re-RunHSC.sh | 14 +- 2 files changed, 192 insertions(+), 313 deletions(-) diff --git a/ImageProcessing/Re-RunHSC.ipynb b/ImageProcessing/Re-RunHSC.ipynb index 65234c54..1daa15a3 100644 --- a/ImageProcessing/Re-RunHSC.ipynb +++ b/ImageProcessing/Re-RunHSC.ipynb @@ -43,7 +43,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -76,7 +76,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -85,16 +85,18 @@ "text": [ ": 'HSC Re-Run: Making Forced Photometry Light Curves from Scratch\n", "Owner: **Justin Myles** (@jtmyles)\n", - "Last Verified to Run: **2018-09-05**\n", + "Last Verified to Run: **2018-09-13**\n", "Verified Stack Release: **16.0**\n", "\n", "This project addresses issue #63: HSC Re-run\n", "\n", "This shell script runs the command-line tasks from the tutorial at pipelines.lsst.io for analysis\n", - "from raw images through source detection and forced photometry measurements. It is intended as an \n", - "intermediate step toward the end-goal of making a forced photometry lightcurve in the notebook at\n", + "from raw images through source detection and forced photometry measurements. It is an intermediate\n", + "step toward the end-goal of making a forced photometry lightcurve in the notebook at\n", "StackClub/ImageProcessing/Re-RunHSC.ipynb\n", "\n", + "Running this script may take several hours on lsst-lspdev.\n", + "\n", "Recommended to run with \n", "$ bash Re-RunHSC.sh > output.txt\n", "'\n", @@ -237,6 +239,7 @@ "# which could lead to bad photometry for blended sources.\n", "# This tasks requires a coadd tract stored in the Butler to grab the appropriate \n", "# coadd catalogs to use as references for forced photometry.\n", + "# It has access to this tract because we chain the output from the coaddPhot subdirectory\n", "\n", "date\n", "echo \"Re-RunHSC INFO: perform forced photometry on individual exposures with forcedPhotCcd.py\"\n", @@ -249,8 +252,11 @@ "# For analysis of the catalog, see part VI of StackClub/ImageProcessing/Re-RunHSC.ipynb\n", "date\n", "echo \"Re-RunHSC INFO: parse output of forcedPhotCcd.py\"\n", + "\n", + "# The following grep & sed commands clean up the output log file used to determine \n", + "# which DataIds have measured forced photometry. The cleaner output is stored\n", + "# in a new file, data_ids.txt, that is used in Re-RunHSC.ipynb\n", "grep 'forcedPhotCcd INFO: Performing forced measurement on DataId' ccd_r.txt ccd_i.txt > data_ids.txt\n", - "# The following sed commands clean up the output log file used to determine which DataIds have measured forced photometry.\n", "sed -i 's/ccd_[i,r].txt:forcedPhotCcd INFO: Performing forced measurement on DataId(initialdata={//g' data_ids.txt\n", "sed -i 's/}, tag=set())//g' data_ids.txt\n", "sed -i 's/'\"'\"'//g' data_ids.txt\n", @@ -291,7 +297,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -493,7 +499,7 @@ "# Part 3: Displaying exposures and source tables output by processCcd.py\n", "https://pipelines.lsst.io/getting-started/display.html\n", "\n", - "This part of the tutorial is omitted for now." + "This part of the tutorial is omitted." ] }, { @@ -509,7 +515,12 @@ "metadata": {}, "source": [ "* A sky map is a tiling of the celestial sphere. It is composed of one or more tracts.\n", - "* A tract is composed of one or more overlapping patches. Each tract has a WCS." + "* A tract is composed of one or more overlapping patches. Each tract has a WCS.\n", + "\n", + "the configuration field specifies the WCS Projection, e.g.\n", + " - STG: stereographic projection\n", + " - MOL: Molleweide's projection\n", + " - TAN: tangent-plane projection" ] }, { @@ -521,11 +532,6 @@ "\"\"\"# make a discrete sky map that covers the exposures that have already been processed\n", "!makeDiscreteSkyMap.py DATA --id --rerun processCcdOutputs:coadd --config skyMap.projection=\"TAN\"\n", "\n", - "# the configuration field specifies the WCS Projection\n", - "# one of the FITS WCS projection codes, such as:\n", - "# - STG: stereographic projection\n", - "# - MOL: Molleweide's projection\n", - "# - TAN: tangent-plane projection\n", "\"\"\"" ] }, @@ -536,12 +542,12 @@ "# Part 6: Multi-band catalog analysis\n", "https://pipelines.lsst.io/getting-started/multiband-analysis.html\n", "\n", - "Access the sources identified from the coadd images " + "We now turn to making plots of the photometry we've done. To start, we access the sources identified from the coadd images." ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -557,7 +563,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -583,7 +589,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -593,8 +599,8 @@ "rCoaddCalib.setThrowOnNegativeFlux(False)\n", "iCoaddCalib.setThrowOnNegativeFlux(False)\n", "\n", - "rMags = rCoaddCalib.getMagnitude(rSources['base_PsfFlux_flux'])\n", - "iMags = iCoaddCalib.getMagnitude(iSources['base_PsfFlux_flux'])" + "rMags_coadd = rCoaddCalib.getMagnitude(rSources['base_PsfFlux_flux'])\n", + "iMags_coadd = iCoaddCalib.getMagnitude(iSources['base_PsfFlux_flux'])" ] }, { @@ -606,7 +612,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -631,7 +637,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -649,8 +655,8 @@ "plt.style.use('seaborn-notebook')\n", "plt.figure(1, figsize=(4, 4), dpi=140)\n", "plt.title('Coadd Forced Photometry (Stars)')\n", - "plt.scatter(rMags[selected] - iMags[selected],\n", - " iMags[selected],\n", + "plt.scatter(rMags_coadd[selected] - iMags_coadd[selected],\n", + " iMags_coadd[selected],\n", " edgecolors='None', s=2, c='k')\n", "\n", "plt.xlim(-0.5, 3)\n", @@ -670,16 +676,23 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "butler_ccd = dafPersist.Butler(inputs=DATADIR + 'rerun/ccdForcedPhot/')" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In order to associate individual visits with the MJD of the exposure, we go back to the raw images stored in the Butler repository. This may be replaceable with cleaner code that takes advantage of some Butler feature that accomplishes the same goal." + ] + }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -696,12 +709,19 @@ " mjd = hdulist[1].header['MJD']\n", " except:\n", " mjd = hdulist[0].header['MJD']\n", - " visit_to_mjd[visit_id] = mjd\n" + " visit_to_mjd[visit_id] = mjd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Doing forced photometry on individual exposures saves the source tables in different files. Here we query the Butler for all the data and store the tables together. This may be replaceable with cleaner code that takes advantage of some Butler feature that accomplishes the same goal." ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -761,7 +781,8 @@ " sources = butler_ccd.get('forced_src', data_id_dict)\n", " source_table = sources.asAstropy().to_pandas()\n", " source_table['visit'] = fields[2].split(':')[1]\n", - " source_table['mjd'] = [visit_to_mjd[key] if key in visit_to_mjd else 56598.2 for key in source_table['visit'] ]\n", + " # TODO : fix this\n", + " source_table['mjd'] = [visit_to_mjd[key] if key in visit_to_mjd else 56598.2 for key in source_table['visit'] ] # this is obviously problematic\n", "\n", " if fields[0] == 'filter:HSC-R':\n", " r_tables.append(source_table)\n", @@ -773,7 +794,74 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA88AAAJLCAYAAADZ3aS5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAVhwAAFYcBshnuugAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzs3Xmc1dT5x/HPww6KgCiK1AqCFhE3xBVUrFqLS11QcUEFpbZWbV2wblWR2tJFRWrRgtUi7guCS7VVqsgPFEUKiAsuIBRcQAQBwQFm5vz+OCdMuNxt7iyZ5ft+vfLK3CQn90luJsmTnJyYcw4RERERERERyaxB0gGIiIiIiIiI1HRKnkVERERERERyUPIsIiIiIiIikoOSZxEREREREZEclDyLiIiIiIiI5KDkWURERERERCQHJc8iIiIiIiIiOSh5FhEREREREclBybOIiIiIiIhIDkqeRURERERERHJQ8iwiIiIiIiKSg5JnERERERERkRyUPIuIiIiIiIjkoORZRESkEpnZQjNzZtanGr6rY/guV9XfJSIiUt8peRYRkbyZ2eQoWcvRtU461tomw3rcYGZfmNkLZnaWmVnScQKY2dDQ1Yvf2cz6hOU9OelYREQkOY2SDkBERGqlxcD/sowvrq5A6qB3gVXh762A3YC+oetvZqc555JevzeH/ljgmwTjqC598Mv8ADAx2VBERCQpSp5FRKQQ9zvnhiYdRB11mXNucvTBzJoC1wM3AScBlwEjkglNRESk/lK1bRERkRrMObfeOXcz8K8w6Nwk4xEREamvlDyLiEi1MLMfm9mzZrY0PMv7pZlNNLMfZpi+T3jud2H4fJaZvWZmK9I1yGVmXczsLjP7wMy+NbM1ZjbPzO4zs8MzfEcPM3sgNPJVZGbfmNkUMxtoZhmPkSG2l8L0a8xshpldUPjayct/Qv8HWeLqbWb/NLOvzew7M3vHzC7N9qy0me1hZveHdbDezFaG9TzYzBqmTDs0pXGyT1Oe0R6aMv1WZnatmb1tZqvNbF34Te4ws/YZ4hkbzcvMtjGz28xsQVieT83s1nA3HvN+ZmYzw2++wsweN7Ndsq1IMzvVzJ6PbYtLw7a4xXYSljeqpn5+6nPpsekGhmGTzayBmf3CzN4ys1VheEczezf8/dMssbUMy+LM7IBsyyEiItVLybOIiFQ5M7sTeBE4MQyaDTTEV0P+j5n9Nkf5EcAj+MTxE+DzlPEDgfeAS4EuwAJgPtAeuAAYlmaeVwNvA+cB2wLzgDXAYcA/gPGpyWMoNxh4BTgmDJoHdADuC3FWlayNhYV18BpwMH751wB7AXcBf85Q5gz8bzEI2A6YC6wEDgfuBV40s+axIv8DpsU+vx0+R92m5+DNbCfgLWA40ANYgl9XuwJXAHPN7KAsi9QKmA5cDqzG/+a7ADcAT4YLAo8AfwO2Ccu8NXAGMNXMtk2zvE3N7ClgPHA8fp2+i3+M7SRgspkNSSk2Df+MP8CylOWdxpYMeBIYBewIfAgsD+PGhP7gLMt9Nv5Z9znOuRlZphMRkermnFOnTp06dery6oDJgAOGlqPM+aFMMfBzoEEY3hC4EigN409LKdcnVq4IOAewMM6ApuHvo4CSMO1fgTYp8zkI+EXKsP5h+pX45LlBbNwBwMdh/I0p5fYA1odxI2MxGHBRiHVjGN+nnOvWZSuHv/jggFmxYR1j5YrwiWbD2PjrwrhSoHPK/LoC34Xx9wJbxcYdjW8IzAF/zRJrxyzL82qY5iOge2z4Dvi76A6fULdKKTc2jNuAT06/Fxt3bGz9PgV8ARwaG98ZWBTG35omprvDuHeBXinjzgHWhnV1RMq4oaHc2CzLOzC2va4AfhQb1yh0rYF1Ybq9MsxnRhh/SdL/7+rUqVOnbvMu8QDUqVOnTl3t6ShLnrN1J6eU+SQMH5Vhng+G8XNThveJzfP6LDHNDNM8kOcyNAIWhjKnZJhm/5BErQSaxIbfF8r9N0O5MbGY+5Rz3aYtBzQBbomNvyI2rmNs+N8zzPedMP6XKcOjZZlLuCiRMv7CWBLbPkOsHTN852GxaQ5IM74NvkVxB1yVMm5sGP4dsHOask/E5n16mvGXkHKRIQz/Af4iyypglwxxXxnKvpAyfCj5J88OODvLdNHyjUwzbu8wbh3Qujzbjzp16tSpq/pO1bZFRKQQi9my+mrUfR1NZGZd8XcDAe7IMK+oSnF3M/t+hmn+nm6gmXXEVwkG+F1+oXMQvvrvl865CekmcM7NxN/BbI1PpCPHhf5dGeZ9Z54xZHOXmU0N3Sz8+rwpjHsmy3ePyjD8jdDvkjI8WpY7nXMuTblx+GrKjSmrop6v40N/qktT9dg5txKfvMenTfUv59ziNMNnhv5K59yTaca/Hfqpy3sa/nG1F51zizJ85/jQ75Ouyn6e1uAT/ExGh/6A6NntmOhZ6Kecc/XhFWAiIrWKXlUlIiKFyPdVVVHjVt855+ZnmOZ9/B3BhviqxKnvj17unFuWoexeof+1c+6jPOIB2Cf0m5vZ1CzTtQ39nYE3zKwV/hlW8M9Xp/MhvtpuRY6v3WN/F+OT56n4O/SPZkh0wVePTmdp6LeMBqQsy7vpCjnnNprZPKAd/ncpj+h3TzvvYG7oZ5r3JxmGR9tCpu0pGr91yvDodz8ky+8ePVfeHP/7Z9rusvnQZXkPt3PuDTObi992TwEeAzCzZsCAMNm9BXyviIhUMSXPIiJSlaKEbWmmCZxzxWa2HP8sbMs0k6zNMv9tQr88d+nahH4roFce07cI/XhsaZfHOVdiZl/jl6VQR7rYe57z5ZzLtJ5KQz/e4Fh8Wb7MMtsv0kyfj2j6isw70/K4PMenin7374culxa5J0kr2/YaGY1/Pn8wIXnG3xlvDcxzzv1fgd8tIiJVSNW2RUSkKq0J/YzJpJk1wrf0HJ8+X6tDv3U5ynwb+lOcc5ZHNzZNbGmXJ1T1bZtuXA0TX5YdM07lWytPnb4886+KeRcq+t2H5fm7L6zCWB7CP9f8QzPrFIZFLXCnfURBRESSp+RZRESq0rzQb25mnTNM0w1fZRvgg3LO/53Qb2tmu+dZJqouvGe2dzmncs6touxOarcMk/2AWlCrK2VZuqebJlzUiKpUl/d3iX73tPMOoir35Z13oaLffe8Cyma6m12QsP4fw9cGuNDMugBH4BtnG1eZ3yUiIpVHybOIiFQZ59yHlD27emWGya4K/bkZGojKNv9FlDUQdV2exabi3xncFt+idHm8GPqXZRj/q3LOL0n/DP3LwzuTU52Lf955I/Byyrh1oZ+panM0795mdkDqSDNrjX//dnzaqvYkPgk+3swyXfzIJNfyFiJqOGwg8LPw9zPOua8q8TtERKQSKXkWEZGqdmvo/8zMfhYlambWwMx+hU/SAIYVOP9f45/rHWhmI0NitomZHWhmv4g+O+c2AFeHj3eZ2eVm1jylzNZm1s/MUqvQ3o5PJnuY2R1m1iRMb2Z2IT4Zz9hYVA1zG/7d0N2B0Wa2VTTCzH6IX1aA0c651GeXowsiR6ebsXNuKv61ZgAPm9mesXm3w7dG3Qr4jLJWt6uUc24uvkp0Y+AlMzsh9aKBme1kZr8ws2tTikfLe5CZpTZEVmg8bwGzgQ7493ODGgoTEanRlDyLiEiVcs49AIzEV83+G/CFmb2FrzZ8J77q6u+cc08VOP9X8c+LbgR+CSwzszlmNtvMvgHeBM5IKfMIPmFpCIwAVoTpp5vZJ/h3AT9FSnLonHsPf9fZAVcAS8OyLMEnZnfhE8Iazzk3DzgfX1X4p8CXZjbDzOYD/8E3sPUy/uJEqqhq8Ugz+8DMXjOzyWY2MDbNOfiW1HcD5prZe2b2X/y6OgZYAfQLVZiry6XAw/iE9Tng67DMM8zsM/xvN4otWwB/Cd9I3PeBxWE7mWxmkysYT3T3uRHwKTCpgvMTEZEqpORZRESqnHPucvx7hZ/HH3v2wyegzwBHO+d+U8H5/wP/DO1o/Kuudgc64atn/x3YYv7OuZGhzCh84tIF/07nrYEpwDWkeb+xc240PqmehE++u4XvGeycu6Iiy1HdnHNP4H+Lsfhkdm98dfb/wyfUfZ1z36UpOgIYAszBJ5SH45/Z7Rib9+fAgcD1wKww3R74dX0nsJdz7s0qWKyMnHMbnHMD8L/r4/jGyvYK3UZgIr72wJCUcmuBo/DvgS7CbydHhK4iHqasSvh9WV5DJiIiNYBpPy0iIiJS/cxsZ2Ah/kLS98MFBxERqaF051lEREQkGYPx52L/VOIsIlLz6c6ziIiISDUL73f+L/4d5T8Mz+6LiEgNVuPfRSkiIiJSV5jZY8AuwD5Ac/xdZyXOIiK1gO48i4iIiFQTM1uIT56/xDeYd001tzguIiIFUvIsIiIiIiIikoMaDBMRERERERHJQcmziIiIiIiISA5KnkVqGDObbGbOzAYWUNaFrmOB331kKD+6kPJSfcysT/itFlbS/BqZ2XwzW2pm21TGPEXEq+i+ubJV5DgjEmdmu5vZBjP7dxXMe2HYTvukDK/U459UHTP7Yfit/pZ0LJVFybPUGWbW2cyGm9mbZrbMzDaa2Sozm2Nm95jZ4UnHWJOZWUPgTqAIGJZhmj3N7G4ze9/MvjWz9Wa2xMxmmtloMxtgZlunlNnXzIbW1ZM0M+tgZrea2Vtmtjysky/MbJKZ/dLMWiQdYz6cc8X4370d8JuEwxGpkcxsu7A/e9PMvgnHmWVm9q6ZPW5ml5jZ95OOs6Yxs4GxCwi5uoFJxyvlcgfQGLgh14Rm9kDsdx5Q9aFljOPHZvaEmS0ys6JwPvOpmU01sz+Z2QlJxVbXOOdeASYDg81sr4TDqRR6VZXUeiHp+wNwOWXb9KfAQqAlsDuwN/BzM5vinDsiiThrgUH49XSXc+6z1JFmdjHwF/w6LgGWAMvw63hvoAdwEXAYMDVWdF/gZuA1YGzVhV/9zOwy4I/418044BNgAdAeOCp015rZmc65KYkFmr+H8CdAvzKzUc65RUkHJFJTmNnBwPNA2zDoS2A+0BDoAuwJnAG0AW5NKf5h6G+s+khrtPXA2zmmWVodgUjFmdnRwPHAc865rL+rmbUETosNugB/zKk24XzxfuC8MKgIWAR8g79w3Ct0V6IcqTJF54B/Bn6ccCwVpg1DajUzM+BJ4BRgA/Bb4G7n3JexaVoAfYHrAN19zuyK0L83dYSZ9QT+iq+t8gBwo3NucWx8M+CHwPnUk5NDM7uBshPk0cAw59znsfH7ACOAI4GXzezHNf1drs65EjMbC/wO+AVwTbIRidQMoUbN0/jE+U3gMufcjNj4BkBP4ExgZWp551zXagq1pvvSOdc76SCk0lwZ+lucN6RxJtACn6i2BvqYWSfn3KdVFVwav8InzsXA9cDfnHNropFm1hY4AX8zQSqJc26KmX0EHGtm3Zxz7ycdU0Wo2rbUdlfhE+eNQF/n3E3xxBnAObfOOTceOAC4MYEYa7zwPFE3YJZzbm6aSS7A7y/eBQbFE2cA51yRc+4F51x/59ybVR5wwsysF2VV269zzv08njgDOOfmAD/C36lqAjxiZq2rN9KCPAiUAheYWdOkgxGpIY7D1ygpAU6JJ84AzrlS59xbzrkrnXOjEolQpBqZ2a7AsfgaaC/mUeSC0P8rMBMwqj9JHRz6Y5xzf44nzgDOua+dcw845/pUc1z1wbjQvzjRKCqBkmeptcxsK/zdZIA/hecqMnJealU6zDvLzF42s69DwxdLzOwRM+uR4bubmNmpZna/mc01sxXhuZmFZjbOzPbOEfs+ZjYhfN+6MI8hoUpRruXuF57L+TY8czfFzE7KVS6Hc0L/mQzjO4f+e64cL4c335jHP8LHI9I829YxTGdm1tfM/mpms8zsq/Ds8Gdm9pSZHZblOzbNy8wODNN/aWYlZjY0Nl0PM3s4POO0Pqy/hWb2LzO7KtRiyNdQ/P7zdXy17bTCc8QXAKuBHfF3c+Oxb9boiZn9xHxDPt+Y2Vrzz1WemW9QZtY0bFPOzI7JMl1nMys1/7xm+5SYFwP/BbajDlSvEqkk0T5wuXPui/IWTt3nxYYPDcPHmllDM7vCzN4Jx4WVZva8me2fZb5Nzew68+1QFJlv8O8J8+1TFNyokpm1NrObzLdnsSrM+0Mzu83M2pV3foUys1+EZfgmdd2F8S3M7IMwzd0p4zY1NmVm3cN6+TIsyzwzu9F8ralM393WzH5v/nn2teGY8Y6Z3WJmrTKUaWpmV4Z99yoreyb+HTMbZSnnFOF3d/FjVZp5Ztp2NpU1s1Zm9sfwG32X7jc3f87yfNhGNoT+RCu8PZiz8MfBF8KxLiMz2wM4OHwch6/BBnC++Vob1SX6P053kyAn88p7vrjpfzzLfDM1jBbfPzQ1s+vDtvStmW1xLma+0dfHzOx/YTtfbmb/NbM/mFmXDN9d7u3C/Hnov6ysfaGVZvaRmT1qZqdkKDYx9M+u5t+88jnn1KmrlR1wOv450xKgXYHzaISv9u1CtxiYga9W5PBVey5KU6577Lu/wCcb7wJrwvD1wIkZvrNvGO+AtfjnvxaGz+PxDSs4YGCassNisS4LsS4Pn38VG9exnOvhk1Du2Azjo3U0H2hajvk+CXwUyq7CPwsd73YM020dpikNyzUHmA2siA3/eYbviJb5KnwNhDVhnX4I3Bym+TG+Wr8L4+eG32xZrHyjPJepXazMGXmWuSdM/17K8D5h+ELgpvD3lyH+lbHvuTTNPDeVTRl+Rxj+eJZ4hodpJmYY/5cw/s4k/rfVqatpHXBJ7P9xtwLKp9034y/EOfyzn/8Of38c9n9F4fM64IA082yOf44wmvcnYd+xDn9s+XW6fUQoO5nMx5l98G1auLBP/STsM6Pj1udA93Iu/8BMseRR9ulQ9o3U/TT++VUHvAM0Sxm3MIy7JqyPIvwdz49j6+x1YKs039kttg6K8cekufhjvsO3q9IlpUzD2HqNpnkrrL/vwrBbU8qMDcOHFrDtRGXvCstUCrwflvH92HRNgadi81mGP/59TdnxdUgBv8ukUP5neUz75zDttPC5bWx7+lGWctFv2CdleJ9Ctifgq1DuoQKWt9DzxaFh/NgCljMq+1jY/qP/8xnAqth0DSg7z3D4C/ZvA/Ni297QlHkXtF3gH4+Ml5kJvIc/x3PA1AzLaLF1tW95139N6hIPQJ26QjtgZPgnfKcC87iZsiT2lNjwpsDtsR3iQSnldgAGANumDG+KP8kqDjugFinjt4/tmJ4AtomN+wn+pCdK8gamlD06tsP6NdAgDG8UdrAbYuM7lmMd7BQrt22Gac6PTTMFX1W+dZ7zHxjKTc4yTRN8Y2M7pQxviG+AZ21Yvp3TlI3iKgZuI3YCBTQP/dlhmj9Gw2LTfD++PvNYnlNi37ldnmWiCz2lQJvY8D5h+Ibw258dG9cIGEVZwt8yZZ5R2YUpw7tSdgGnbZpYGuFPfh1wfIZ4zwnjZ1fG/6o6dbW9A3YN+5goKfpZuv1RlvKZEqChsX3Ap8D+sXHbAdPC+NfSzPNPYdxK4MjY8Fb4C7HRMWFhmrKTSX+c2RafFDhgDLB9ynwfCOPmkecFx1B2YKZY8ijbBt+okwP+GBt+FmXH7z3SlFsYW7fPEzu+Ab0pS6RGpZRrGpbPAdOB78fGdabseDIbaBgbd3IYvgTYO2WejfAXzn+cMnwsFU+ei0Msu8XGNY/9fXeY7l2gV8o8zgnrrxQ4ohy/SaNQzgE98pj2yzDtRbHh0UWRx7KUjX7DPinD+xSyPeFrwkXr8x/4dnDyuiFA4eeLQ6l48lwc/gd6ZviNo+mKgJ8DjVPW/8mk3NApZLvA75OK8RfVTgcspdz+wIVZlvPl8J2Xl+d3q2ld4gGoU1dohz85cGS4e5ZH+a0ou1J2dYZppoTxz5Vz3g+Fcv1Tht8Yhn9BylXyMP762I59YMq4/4ThEzJ850tkOMjmiLVXKPNdlmka4K98ulhXir8C+hj+RHKLRC2UHUiO5DmPGG8N87gmzbgonn9nKR/dwWlVCdvdL8O8VpajzL6xOLvHhveJDf9NmnLNKLs7/pOUcVHZhWnKTQ7jrkgz7iTKrpo3zBDvkWGaVfkuozp1db0DLqPszmPUfQm8gH+EKOMd6Uz7ZspOeh1wWJpyPWL721ax4S2Bb8O4AWnKNaPshHxhmvHRPmJgyvBoX5upVkpD/N0pR541b0K56DiQT7fFhVl8slsc1sMx+CR2dZh+cIbvjJb/a2DrNOPPpiy53iE2/FzKLkB+L0253Sm7kNIvNvzaMGxEOdbLWCqePK9PHReb5gdhm10F7JJhmivDfF4oR9wdYnHtkGPa6JjzXfy3xd8wiBK+NhnKRr9hn5ThfTJt2zliaYe/Sxrf3jbgLz6MBk4lTTJNBc4XqZzk2QGHZlmm6O7yoDzXQ0HbBb7qvcO3j5P3eo+Vjy6+jSykfE3panedc6nvtgn9bwssf1iYRxG+uks6t4X+0WbWJHWkmR1lZreb2XNm9pr5Z5GnUtaq934pRY4L/THOuaI03zcKf1BO/Z6tgCPCx79kiPXODMNz2T70V2SawPnGcM7Ev2biP/irjoY/gekP/A1YZGZDCowBAPPPLA8Pz9tMjq3PM8Ikqesz7r4s46JXLp1dkfiClqFfnu0uPm3aZ+XwV4E3E7aRWeFj2ueVMhgd+hemGRc1mPIP51xJhvJfh/42pkbDRABwzt0FHAg8gq8NAr4WUl/g98CHZvaPsL8ur3ecc/+XZvgsfHIU7W8jvfEn9GuAx9PEWkRZAz3l0T/0/5ZuZNhnRG1jHFXA/Nfj76Zn67Y4BjrnpuITCcM3avgEfl/8hHPu7zm+8z7nXLr99eP4ix+N8Y07Ro4P/Secc0vSxPIR8GzKtFB2nDnGzLan+vzHObcww7jT8Be/X3SZXz04PvT7WB7trgTx5ct47hBEjYI945z7Jjb8Rfzd/6ZUzrE5J+fcMnyr+Ffiq/qD//33wdd+Gw98bGbHphSt8PliBX3gnHs9w7jj8BfLPqPsWfJcCt0uoml3N/8WlvKKzi2qrd2EqqBXVUlttjr0ty6w/A9Cf2GGAyuUNSrRDOiIf343/tqSjI0yBW1TPkevK3kv3cTOuVVmtiR8V1wX/BX/jGWzDM+leeinS+Y343yr5ePNv/6rB76KzjGh2wr4s5nhnLsty2y2YGaN8M+unZtj0tT1GZdt+f+IT67vNrOr8FWH3sBXhSzvu4yjk+bybHfxaVelGb/cOZfpBCR652nLDOPTeRr/LPyeZnawc246gJl1wJ/ol5L9YkN8W2iOP+EVqfecczOBc8LJZHf8fvBIfBK1Lf4O6/b4192Ux0cZvs+Z2TJgZzbfB0THr/edc5leDzgrw/C0QtIfXaT7rZn9JsOkO4T+zuWZf1CRV1X9Hv9KxCNDDAvxCU8u76Yb6Pyr+ebhG3PcIzYqWrdpywVz8Y/wxF9BNhHf1saewGIzexX4P/xz1W8456pqP5rt2LdP6B8SLkSnEzWW2Rx/jF2Wx3dG5w0lWbY/zDcuF11g2Cyxc85tNLOHgcvxDWtWSyv1zrnv8K+RHGFm2+HfxHIA/th4MH67fs7MjnDOvRGKFXy+WEmy/cZ7hf5051xpnvMraLtwzn1hZg/iz9XeMrO38LVY3gCmOOe2eE1fiujconnWqWo43XmW2iy6IrxrgeWjE5Evs0wTb1U1fuJyGz5hXI6/k9cF/3yzOecM36AC+Cua6b5zKZmlGxeVK8Vfqc23XD6Wh/62+RZw/vVfU51zI51zJ+BPIj8Mo28s4KrrEPzOuAj/vulu+ISzQVif0R3U1PUZtzZLvPfjq2O9DnTCPxP0ALDQzKantnCZQ7TdtQ4H3nzsFoWCvzqcKmPs+N8cyg5kOYWTtLHh4+DYqEH4izAv57hoEG0LG0mf7IvUa865EufcHOfcP5xz5+GPQxPC6OPN7OAsxdMp7z4guiC3hsyyjUsn/iq9nvhHetJ1UYLdopzzr5CQGPwnNuhx51w++6d8jrfx43t5zg02lQtJ2WH4VzGtwTdU+TvgVWCZ+ZbKq2KdZdt22oT+98n8ex4amz7f+KLzhoaWoeXx4DzKnnl+Kc34KKHuYTneUlIVnHPLnXMvOueGOecOwb966zv8ucbNsUkrcr5YGbL9xlEtzG+yTJOqItvFhfh2Yj4BDsI3yDcRv42PT20VPkV0brE8yzQ1npJnqc2iq2V7WmGvzohOLHbMMk38NT5rYNNd0ujVTgOdc/c55+aHA2ck0x3SeFW/TNKNi8o1YPPqUrnK5SM6eWhVjipbm3HOfQxcHT5ug09+y2Ng6A9xzt3pnPvAObfWhYdkyH7HOd8YJzjneuF33scBf8C3Hn4Q8G8z2ydb+Zhpsb9/mGeZaLoP8rgyW1nGEJ67N7Otzcwoe8/mvTnKRge4r2K/gYhkEJK4QZQluuVNnssruvuV7SS9vCfw8Ttqu0YXg7N0fco5/woxswMoa7TJAVdaltd4xeRzvI1faCjPuUHqe4K/cs5dhq+W2h3fHsjT+OTjKspe3bipSOinvTha4CMAcdFvOiyP39OyVP9OFb87ne34HFXZ3hEotpTXVbJ57Yh0jxlVK+fcS5RVy47/Dxd0vhjNNvSzXQCvyO8c1cJsnXWqzRW8XTjnNjr/juzd8Xfpz8Q/KrYGf5NiUqidmU50blHozZ4aQcmz1GYv4p+1aYBvxKm85oV+xyz/6FF1mCJ8FTHwyWs0/ZQM5TKdOEXfmTa5DFdwv5dm1Cf4xh0ylsVXFSvEPHxLz1aBeYB/VUYkfuc5n+SrU+iXd32Wm3NuVbjSfB2+yt10fLyDs5fcVH4pZXc/fhmS0ozCs2/R81yPFBZ1+YULGq/it9Uz8c8ndsKf9DybpSiUbfdvV1mAInVMSKCjmkGV/cxjqqimTzczy1QjZ9/yzDDEvzh8rPa7gNmYWUvgUfwdwTvxb9toDDyW5fgdSXtcCxeLo+q4H8RGRcfp7lnmGe0jP0g30nnvOefGOOf64Vs7BjjDzOLJZnRHMVOCv3uWGPIRVSWu1N8zbCvzw8e06ynUvojOV5Zm6aILyudUwbPChYjOZeKxFHq+CDl+YzNrg2/FulCTVgdbAAAgAElEQVTRs9sHl+P9yZWyXTjnljjnHnfO/Ry//KvxbTOkPjMeqRPnFkqepdYKz538MXz8tZllvQto3g2xQVPx/+jNgIszFLsq9F92zm0If6+LjW+fMj0hjh4Z5vdi6F+UoSGmX5CmLQLn3FrKEsvLMsz7VxmGZxWWK3quJ22Samb53NU+LPRLKDuoQtn6ylYdLJom3frsCpyYx/eXm3OuGHgzfOxQjqK34O8w9cJXWUor9iz3NviThC0aBatiUcNhgym7OPBAtmfUgmg7eLVKohKpZcxsu1wnpmb2A8oawqnM5x3T+T/8SXlL/CtjUmNpSu42JNJ5IvSvLLQmUhX5G/6k/L/4Vq2vCX93IXMDTpHBGe7gnoE/5mzEt4MR+Wc03sy2uJhtZl3wLUXHp80lXmMpfqyJErVMF4h/kef8M4neS3y8mZW3Rlguk0M/U+xRTad3nHM7ZurwFwg24u9gn1TJMW6mnOcy8f/hQs8Xoew33i/LeV9FvICvat6B/P/nK327cM59hn/dHqQ5nzKz1viLVQ7/fvray9WAJr/VqSu0w98tfYay1zXcAuyYMk0z/A75Lb/JbzYuqgK2Fjg5NrwJZe/QTPfevug9jy+w+asX+uDv7EWvDRibUq4d/iqrw19FbxkbdwJl7zNO9wqRH1FWXe0qyt7z3BD/CqyC3vMc5hG9kuDhDOOfxJ9cnEnK657C+v0pZa8NeTxl/H6xdbxjhvlPDNO8DbSPDd8Hf+CJ1ufkNGWzLjM+cX0SfyW0Scq4/fHPKTngunKus5ti330PW76jeh/gFcpehXF0mnn0IcfrNsjwKpM8yzbGJ+0Of3LigN1zLFfDWJms06pTV1864FL8XcZfkfL6Ivxx6FjK3g28kC3fJ5/pdUNDKfw1Nn8Mw1ew+btYt8EnwYW853l7fLsMLuyXd02zrAfg7/4eUI71NzDX/iqPsmvY/F3Gu4dhDjg3y3rbgD9PaBMbd2hsP3dPSrkmsd/yDTZ/z/OulL2qK/U9z1finwXdJWV+LcL6cvjnUuPv5+2MvxDriL0CKeyHL6Ps2Jdu2xlLjtdchemiR3iW4M8zUt/NuxM+gbu2nL/LqWG+09KMa0HZq51yvtOXslePvpjntt+nkO0pbNtj8Y9SpZ4P7EDZeZ8DLk4ZX+j5Yvy1cn9J2Wb6s/l5X+pyDiXH/iFMF52PfIc/H2sUG9cIfw6c+p7ncm8XwNFhW+4Rnx5/M/Ycyl7hdkiaGE8M414v7z6gpnWJB6BOXUW7sGMYEfunLcXf+XwTeD/l4POfNGWfio3/Hz7J/iZ8LgEuSvOdP4p937f4g+mC8HkW8OdMO7ywk4p2lGuBGfirdQ7/bNRk0pzUhLK/j8W6NMT6Vfj8q9i4juVch9uG9fQt6d+H+WRs3vH1G1X5jsZNA7ZNKWv4akXx5Z0cuh3DNHtRdnApAuZQdvLyP/w7VB2FJc+tY9Osx7da+WZsnTt81e0WBWx7l8e2r1L8leq38FUfo3l/ARyZoXwfqjB5DtP9IRbLFusvzfR9w7SvJP2/rU5dTemAS2L/R9H/9dthX7UiNvxzYL805TMlQEMpPHlujr+DE83747B/XRv2y9eE4fPTzHMymY8ze1F2PHP4/f30sB//Nja8T671FpvnwNj+fWqO7vpYud1j33l+lvmuAbpkWG/XhHXyXfjNPmLzfX/LNPPdk7KLCMXhd36Hsvd8f5rm++5k8+1gRigXxb8R6J/mu26PlVsWyn0dvmsgmbedseSXPDcBHorNZ0X4jhmxZcyZoKWZb6NQvhTolDLuPMqOudvlMa/jKTvn6pDHtt+HwpLnr2LLux5/jhidD2yMjRtFuEGRsrzlPl8MZS+LlVsZ1n104f7GLMs5NJ/fBp+8jo59x6rwHR9Qdo4yNKVMubcL/OMH8e+Yhf+fWhYb/pcMMT4exp9Xnt+sJnaJB6BOXWV1+Opbf6AsoYxaCp6Nry7bK0M5wz+T+p+w89gQdhyPAPtn+b7D8Hdj1+BPVD7At7LdItcOD383dmL4vu/wr8QYgr/aPJkMJzWh7On4JHVtWL4pwElhXNZEMsf6u5/MJygt8dXU/hp2lF/jTyjW4Q86T+PvSjfIMO/v4Q/0/yPDHXL8CdtE/IGlCH/CNhJflWsghSfPDcPv+3f8cz7LQ+wrwrq7lJQr0OVcb9/Dt6garZcN+Asbr+CT662ylO1D1SfPnWPr6Jw8lufRMO1pSfwfq1NXEzv8ifNhwDB8wvpF+F9fj2+B9z/4O49bJGKhfKUnz2FcU+B6/MXGIvxJ7PiwPz0hlJuVptxksh9ntgr7r9di+/vV+GTwr/g7UI0yxZxmftE+PJ9ubGzZZoVhaWtFhekeDtO8Hd+Xx9cb/rncJ8O+eT3+mfGbSakhkDLftvgL1u/hj3Vr8ceQYcRqnMWm/wFwAzApfPe68Jt8gj++7p3hewx/cWZOmH4l8C9CbYIs285Y8kieY9MfDTyGf1dvEWXP5k7AV7HOmeSmmWd0x/PmlOGvhuFP5TmfhpQlbDfk2vYpPHneCTgfnzS+H7bpEvx53Hv484S054qx36rc54uhbH98or4ufO8U4JQcyzmUclzYCL/xePzFmw34c+GZ+HOUXbOUyWu7CP8Tvwjf8RH+HHRj+L5nSbm7HSvXCn+uuxxoVt7trKZ1FhZKROo5M+uMP5h8gL9zop1DHWBmh+IvtqzEVy3P+D5vM+uEP6l8H+jh8n9npIjUMGZ2Nb466QTn3KlJx1PdzGwhsAu+5s/kZKOpm8JzrB/jL650ds6ty1FE6qHQ3tCtwFXOuTuSjqei1GCYiADgnIvu9O6Db0hF6oafhf6D2RLn4Gb8c9JXKHEWqb1CC9znh4+Z3mIgUiHOuW/wx40dydyYqdRj4QLLEHwNjL8mHE6l2KJVXxGp127FV02rCa+LkAoK70U9C18tLetBK7QM/glwiXPu1WoIT0QqyMyGAQ855z6KDdsB//++J/55zAcTCk/qh9H4tkV011nS6YS/MfOS27wV8lpL1bZFROoYM5uMf/Z+P/xF0lHOuUsTDUpEKp2ZLcc/h7gE/+xlS/yztw3xz/ae4ZzL9V73OknVtkWkKujOs4hI3XMEvpGRJfjGdG5ONhwRqSK/wb+GpjuwN75Bo0X4Bptud859kGBsIiJ1Tq2782xmA/AtXu6Pb02yCTDIOTc2j7K74l81sBUw2jn38yoMVUREpFbRMVZERCSz2njn+VZ8NZzl+FdF7JJPITNrgG/WX0RERNLTMVZERCSD2tja9mD8u+62B/5WjnJXAIfgqziJiIjIlnSMFRERyaDW3Xl2zk0qbxkz64q/mj4cmF3pQYmIiNQBOsaKiIhkVhvvPJeLmTUEHsC/xP3WhMMRERGpM3SMFRGR+qTW3XkuwHVAD+Bg59wGM6vUmZvZ+gyjGgNfAQsq9QtFRKSu2BVY6ZzrmnQgFaBjrIiI1FSVfpyt08mzme0D3AT82Tk3s7q/fuutt27XvXv3dtX8vSIiUgu8++67fPvtt0mHUTAdY0VEpCariuNsnU2ezawJvirZJ8AtVfU9zrmmGb7/je7dux/8xhtvVNVXi4hILXbIIYcwffr0WnnnVMdYERGp6ariOFtnk2d8VbK9gEOdc5mqfYmIiEj56RgrIiL1Tl1uMGw//PJNNzMXdcCrYfzPwrCJyYUoIiJSK+kYKyIi9U5dvvP8MrA8zfD2wHHAPGAaMKs6gxIREakDdIwVEZF6p84mz865UemGm1kf/IH9Nefcz6s1KBERkTpAx1gREamPal3ybGaDgd7h416hPzgcsAGmOuf+Xu2BiYiI1HI6xoqIiGRW65Jn/EH9/JRhvUIX0YFdRESk/HSMFRERyaDWJc/OuYHAwAqUnwxYJYUjIiJSZ+gYKyIiklldbm1bREREREREpFIoeRYRERERERHJQcmziIiIiIiISA5KnkVERERERERyUPIsIiIiIiIikoOSZxEREREREZEclDyLiIiIiIiI5KDkWURERERERCQHJc8iIiIiIiIiOSh5FhEREREREclBybOIiIiIVNhXX31FaWlp0mGIiFQZJc8iIiIiUiHDhg2jXbt2nHrqqUmHIiJSZZQ8i4iIiEiFTJs2bbO+iEhdpORZRERERCpk5MiR/PznP+fRRx9NOhQRkSrTKOkARERERKR269q1K/fcc0/SYYiIVCndeRYRERERERHJQcmziIiIiIiISA5KnkVERERERERyUPIsIiIiIiIikoOSZxEREREREZEclDyLiIiIiIiI5KDkWURERERERCQHJc8iIiIiIiIiOSh5FhEREREREclBybOIiIiIiIhIDkqeRURERERERHJQ8iwiIiIiIiKSg5JnERERERERkRyUPIuIiIiIiIjkoORZREREREREJAclz1IrFRUVccIJJ7Dvvvvy8ccfJx2OiIiIiIjUcUqepVaaM2cO//znP5kzZw4TJkxIOhwREREREanjGiUdgEghevTowVlnncWSJUvo379/0uGIiIiIiEgdp+RZaqXGjRvzyCOPJB2GiIiIiIjUE6q2LSIiIiIiIpKDkmepl4qKijjjjDM45phjWLp0adLhiIiIiIhIDafkWeqlqVOn8uSTTzJp0iSefvrppMMREREREZEaTs88S7100EEHceSRR/LNN99w3HHHJR2OiIiIiIjUcEqepV5q2bIlr7zyStJhiIiIiIhILaFq2yIiIiIiIiI5KHkWERERERERyUHJs4iIiIiIiEgOSp5FREREREREclDyLCIiIiIiIpKDkmcRERERERGRHJQ8i4iIiIiIiOSg5FlqtPnz53P77bezePHipEMREREREZF6TMmzJO6tt97ipptuYsmSJVuMO+200xgyZAgDBgxIIDIRERERERGvUdIBiPzkJz9h6dKlzJkzh2eeeWazce3bt2f27Nm0b98+oehERERERER051lqgB/84Aeb9ePGjx/P1KlTGTduXHWHJSIiUuucf/75tGvXjmeffTbpUERE6hzdeZbE/fvf/2bBggXsscceW4xr3rw5vXr1SiAqERGR2mXDhg2bLjY/+uij/OQnP0k4IhGRukV3niVxzZo1o1u3bphZ0qGIiIjUWk2aNOFPf/oTRx55JFdffXXS4YiI1DlKnkVERETqiKuvvppXXnmFHj16JB2KBAsXLqRnz5707duXdevWJR2OiFSAkmcRERERkUqyfPlyBg8ezB133AHAM888w8yZM/nXv/7FjBkzEo5ORCpCzzyLiIiIiFSSkSNHct999wFw4oknctppp/H000/Trl07DjrooISjE5GKUPIsIiIiIlJJ+vTpw5///Ge6dOlChw4daNGiBa+99lrSYYlIJVDyLIl59tlnadmyJUceeWTSoYiIiIhUiqOOOopVq1bRpEkTNYYqUscoeZZEPP300/Tr1w8zY8aMGey///5JhyQiIiJSKZo2bZp0CCJSBdRgmCSiefPmADRo0EAHGBERERERqfF051kS0bdvX15//XW22morunfvnnQ4IiIiIiIiWSl5lsQccsghSYcgIiIiIiKSF1XbFhEREREREclBybOIiIiIiIhIDkqeRURERERERHJQ8iwiIiIiIiKSQ61Lns1sgJmNNrO3zWy9mTkzG5ijTCczu9fMFoUyS83sVTM7vZrCFhERqfF0jBUREcmsNra2fSuwC7Ac+CL8nZGZHQNMDB+fAxYAbYC9gaOBJ6ssUhERkdpFx1gREZEMamPyPBj42Dm3yMyuBYZnmtDMvg88BXwGHO2c+1/K+Nq4/HXG448/zpQpU7jhhhvYaaedkg5HRER0jBUREcmo1h3YnHOTyjH59cA2wCmpB/Uwr+JKC0zKZc2aNZx99tmUlpZSXFzM6NGjkw5JRKTe0zFWRKrLO++8wy677EKrVq2SDkUkb7Uuec6XmRlwOvC1c+4VM9sfOAL/nPds4BXnXGklfM/6DKMaV3TedVmLFi3o0aMHM2fO5NBDD006HBERKQcdY0WkIm677TauvvpqunTpwrx582jYsGHSIYnkpc4mz0AnYFvgbTMbDVyUMn6Wmf3EObek+kOThg0b8sYbb7B69Wq23XbbpMMREZHy0TFWRAq2YMECAJYsWcKGDRto3rx5whGJ5KcuJ8/tQn8/oCswCHgGaIWvavZT/LNaB1fkS5xzTdMNN7M3Kjrvuq5Ro0ZKnEVEaicdY0WkYL///e/Zeeed6dWrlxJnqVXqcvIcvYarIXCjc25s+LwSuMjM9gYOMrPezrmpSQQoIiJSS+kYKyIFa926Ndddd13SYYiUW617z3M5rIr9/Wya8c+Ffs9qiEVERKQu0TFWRETqnbqcPM8HSsLf36QZHw1TXZFqtHLlSs455xwuv/xySkpKchcQEZGaSMdYERGpd+ps8uycKwJeDx+7pZkkGrawWgISAB588EEeeeQRRo4cybRp05IOR0RECqBjrIiI1Ed1NnkO7gn9oWa2qdERM+sKDATWAP9KIK5666ijjmKHHXage/fu7LXXXkmHIyIihdMxVkRE6pVa12CYmQ0GeoePUfY12Mz6hL+nOuf+Hv5+DDgVOA2YY2b/xrcE2g9oBpznnFtZLYELAHvuuSdffvnlZsOcc/hXhoqISJJ0jBUREcmsNt557g2cH7oeYViv2LDooI9zzgFnAVcCxcDPgFPwVc1+6Jx7uPrClnQGDBhAkyZNuO+++5IORUREdIwVERHJqNYlz865gc45y9INTJm+2Dk3wjnX3TnXzDnXyjl3rHPutYQWQWLGjx9PcXExEydOTDoUEZF6T8dYERGRzGpd8ix1y5gxYzj55JMZNmxY0qGIiIiIiIhkVOueeZa65dxzz+Xcc89NOgwREREREZGsdOdZ6oySkhLGjh3Lq6++mnQoIiIiIiJSxyh5lhpj+vTpjB49mqKiooLK33PPPQwaNIhjjjmGBQsWVHJ0IiIiIiJSn6nattQI33zzDUceeSRFRUUsXryYW2+9tdzz2HbbbQFo3rw5zZs3T/sd11xzDbvssgvXX399hWMWEREREZH6Q8mz1AhNmjRhm222oaioiLZt2xY0j7PPPpsuXbrQrl072rdvv8X40aNHM2bMGAD69u3LfvvtV6GYRURERESk/lDyLDVCixYtmDVrFp9++im9evWitLSUBg3K/1TBgQcemHHcYYcdRosWLejQoQO77rprRcIVERGptYqKimjWrFnSYUglKikpobS0lMaNGycdikidpmeepcbYaaed6NWrF2PGjKFJkyacffbZlTr/Qw89lJUrVzJv3jxatWpVqfMWERGpDU499VSaN2/OXXfdlXQoUkmWLVtGp06d2G677Zg9e3bS4YjUaUqepdKUlJQwbtw4pk6dWqH5PPfcc5SUlDBhwoRKiqxMkyZNCrqjLSIiUhf8+9//BuDll19OOBKpLB988AGLFy9m9erVvPHGG0mHI1Knqdq2VJq77rqLK664gkaNGrFgwQJ23nnngubz29/+liZNmnDqqadWcoQiIiL127hx45gwYQLXXXdd0qFIJTn88MO56aabWLFiBeedd17S4YjUaUqepdJEVaGbN29e8LNUK1eupGvXrowfP74yQxMRERGgX79+9OvXL+kwqlVxcTGNGtXdU14z45Zbbkk6DJF6QfVXpdIMGjSIqVOnMmfOHLbffvtyl3/55ZfZYYcd6Nq1K6tWraqCCEVERKQ+GTZsGI0bN+aXv/xl0qGISB2g5FkqVa9evejUqVNBZWfMmMHGjRtZtGgRn3/+eSVHJiIiIvXN888/D/j2VEREKqru1mGRWueyyy5jxYoVdOnShT322CPpcERERKSWu+OOOxg5ciQXXHBB0qGISB2g5FlqjJYtW3LbbbclHYaIiIjUEb1796Z3795JhyEidYSqbYuIiIiIiIjkoORZREREREREJAclzyIiIiIiIiI5KHkWERERERERyUHJs4iIiIiIiEgOSp5F6pCHHnqIfffdlwceeCDpUEREROqc6dOns99++3HllVcmHYqIJEDJs0gdMnz4cObMmcPw4cOTDkVERKTOGT16NLNnz2bEiBGsWrUq6XBEpJopeRapQy699FI6duzIpZdemnQoIiIidc6FF15I165dufjii2nVqlXS4YhINWuUdAAiUnkuvvhiLr744qTDEBERqZN69+7NBx98kHQYIpIQ3XkWkVrt3Xff5b///W/SYYiIiIhIHafkWURqrdmzZ7PvvvvSs2dPJk+enHQ4IiIiIlKHKXmWxHzwwQcUFRUlHYakmDt3LtOnT086jLysW7eOkpISnHOsWbMm6XBEREREpA5T8iyJuOGGG+jWrRtHH3100qFIzPvvv0+PHj045JBDePHFF5MOJ6dDDz2UF154gfHjx3PiiScmHY6IiIiI1GFqMEwSMW/ePIBKa3Rj48aNTJw4kX333ZfddtutUuZZHxUVFVFSUgLA2rVrE44mP3379k06BBGROuvNN9/kk08+4cwzz6Rhw4ZJhyMikiglz5KIv/zlL3Tr1o3jjjuuUuZ38803M3z4cNq0acPnn39Os2bNKmW+9U2PHj2YNGkSq1ev5uSTT046HBERSdDnn3/O4YcfzoYNG1i2bBlXXHFF0iGJiCRKybMkokOHDvz2t7+ttPmZGQANGjTY9LcU5oc//GHSIYiISA3QuHFjmjRpwoYNG9hqq62SDkdEJHFKniVRixYtol27djRv3rxC87nlllvo2bMne++9N02bNq2k6EREROqv7bffntmzZ7N48WL69OmTdDgiIolTg2GSmNGjR9OxY0d69uxJcXFxhebVqFEjTjnlFDp37lxJ0YmIiEjnzp2VOIuIBEqeJTHvvPMOAB999BHfffddXmWcc1UZkoiIiIiISFqqti2JueWWW9hmm23o3bs3LVu2zDrtqlWr6NWrF59//jmTJk2iR48e1RSliIiIiIiI7jxLgrbbbjuGDx/O8ccfn3PaefPm8d5777Fy5UpeeeWVcn3PlClTmD17dqFhioiISIr169czY8YMNm7cmHQoIiLVRsmzJGLBggWsWbMm7+kPPPBArr32Ws4991wuuOCCvMtNnDiRI444ggMOOKDS3iktIiJS3/Xv358DDzyQ888/P+lQRESqjZJnqXZjx46lc+fO7LPPPhQVFW0x/o9//CNXXXUV69at2zTMzBg+fDjjxo1j2223zfu7oivipaWlFW6UTEREpDabO3cuV111FbNmzarwvBYtWrRZX0SkPtAzz1Lt3n//fcAfcL/++ms6dOiwadwbb7zBtddeC8BWW23FsGHDKvRdp59+Os899xytW7dmr732qtC8REREarMLLriAt99+m5deeom5c+dWaF5PPPEETz31FGeeeWYlRSciUvPpzrNUu9/85jf06NGD0tJSjjjiCB5//HHWr18P+FdiRI2HjRgxgrVr11b4+0444QR69+5d4fmIiIjUZvvuu+9m/YrYbbfduO666+jUqVOF5yUiUlsoeZZqt80229CmTRsA5s+fz5lnnslFF10EQLt27bj88ssB/+5mERERqRxjxozh008/5YEHHkg6FBGRWknJsyTivvvuY4cddtj0efXq1Zv+vvnmm5kwYQJvvfUWTz/9NGeeeaYa+xIREakgM6Njx440aKDTPxGRQujWniRil1124eOPP2by5Ml88cUXnHbaaZvGNWzYkJNPPpmSkhL22GMPSkpKKC0t5YknnkgwYhERERERqc906VES07JlS0488UQuuuiitC1oN2zYkL59+9KgQQP69u3LpEmTOOqoo3j44YcTiFZERKT2KS0t5frrr+enP/3pZrW8wL+R4vLLL+eSSy5J+/YLERHZnJJnqdGee+451q9fz6BBg7jpppt45ZVXuOyyy/jwww+3mPbGG2/kxBNP5NNPP00gUm/ChAmcc845vPPOO4nFICIiEpk2bRrDhw/n73//O+PGjdts3AsvvMDIkSO5++67GT9+PAALFixg/vz5SYSa06JFi7j00kt5/vnnkw5FROopJc9S40UNh51zzjk0bdqUlStX0rNnT2bMmLFpmoULF3Lrrbfy/PPPM2rUqKRCZdCgQTzyyCP8+te/TiyG2mjChAm8/vrrSYchIlLndOvWjc6dO9O6dWsOO+ywzcbtv//+dOjQgXbt2nHwwQcza9YsunbtSteuXZk5c2ZCEWd2zTXXMGrUKE477TRKS0uTDkdE6iElz1JtVqxYwWeffVZw+UsuuWTTe5/Xrl3LgQceyOjRowHo0KEDxxxzDNtttx0nnXRSpcRbiB//+Meb9SW3Bx54gFNPPZXDDz+cjz76KOlwRETqlLZt2/LJJ5/w9ddfs88++2w27nvf+x6LFy/miy++oHPnzixdupSNGzdSXFzM0qVLE4o4s0MOOQSAgw46SI2eiUgi1GCYVIvPPvuMvfbaizVr1vDyyy/Tp0+fguYzZMgQdtxxRy688EKKi4s3JVuNGzfmpZdeqsSIC/PYY4/xwAMP0LRp06RDqTWaNWsG+GfcGzdunHA0IiJ1U6Zk08wwM8Bf+H3wwQdxznHcccdVZ3h5+dWvfsVZZ51F27Ztkw5FROopJc9SLb744gtWrlwJwIcfflhw8tygQQPOO+88dthhB9566y1++ctfVmKUlUOJc/n079+fHXbYge22245OnTolHY6ISL02YMCApEPIql27dkmHICL1mJJnqRY9e/bk/vvvZ+nSpQwaNKjC8zv22GM59thjKyEyqQkKvZgiIiIiIlJdlDxLtamMpFlERERERCQJam1BREREREREJAclz1JjLFmyhN/97nfMnTs36VBEREREREQ2o+RZaozBgwfzm9/8hpNPPjnpUKQGWL58OU8//TTffvtt0qGIiIiIiCh5lpqjY8eOm/WlfjvhhBPo168f5513XtKhiIiIiIgoeZaaY9SoUcycOZPnn38+6VCkBiguLoKE4oEAACAASURBVAZg48aNCUciIiIiIqLkWWqIRYsW8dprr9GjRw+aN2+ecbobb7yRtm3bcs8991RjdJKE5557jgcffJAHH3ww6VBERERERJQ8S/KWLFlC586dOeqoo7jmmmuyTnv//fezYsUKxo0bV03RSVLat2/PgAEDaN26ddKhiIiIiIgoeZbkvfPOO5SUlABw9913c+aZZ26qspvqD3/4A0cccQRDhw6txghFRERERKS+U/Is1e6bb75hw4YNmz4fe+yxnHbaaey44458++23PP744yxYsCBt2XPPPZfJkydz7LHHVle4iXPOsXDhwk0XGEREREREpPopeZZq9c9//pPtt9+e7t27s3btWgAaNmzIkCFDeOihhzjooIO44IIL6NKlS8KR1hyXXXYZnTp14pxzzkk6lLzMnDmT1atXJx2GiIiIiEilapR0AFK/vPnmmxQXF/Pxxx/z3nvvce2117J69WpmzpxJy5Yt+eSTT2jXrl3SYdYos2bN2qxfk918880MGzaMPfbYg/fffz/pcEREREREKo2SZ6lWV1xxBWvWrGHPPffkpZde4tVXX900bt26dXz33XcJRlcz3Xvvvdx3332cddZZacdv3LiRSy65hOXLlzNmzBi22267ao6wzOLFiwHfCFxpaSkNGuRXuWXGjBncdtttDBgwgBNPPLEqQxQRqRcmT55Mu3bt6NatW9Kh1BsfffQRixcv5qijjko6FBGpIkqepVq1adOGESNGALDffvsB0KpVK4YPH063bt3YZZddkgyvWhQXF3PGGWfw7rvv8thjj9GjR4+s03fr1o3bb7894/hp06Zx7733AnDkkUdy2WWXVWq85XH77bfTtWtX+vTpk3fiDDBkyBCmTJnCa6+9xpdfflmFEYqI1H2PPvooZ599Nk2bNuXDDz+sF8fWpC1btowePXqwdu1aRo8ezUUXXZR0SCJSBWrdM89mNsDMRpvZ22a23sycmQ3MMv1uZvYPM/vYzL4zs8/M7GUz+0k1hi0xjz/+OF26dOHTTz8FfMJ38cUXc8QRRyQcWfVYsGABEyZM4OOPP+aJJ56o8Pz2228/DjzwQDp16sSPfvSjSoiwcG3atOHXv/41Bx54YLnKHXfccQAcf/zxVRGWiORJx9i6IarFVVxczMaNGxOOpn6Ir2vVohOpu2rjnedbgV2A5cAX4e+0zOwg4FWgMfAsMB5oB5wKPGNmQ51zt1R5xLKZ4cOHM3/+fAB22203Hn300ZxlFi9ezHPPPccpp5xC+/btqzrEKrXbbrtx8cUX895773HBBRdUeH6tWrXizTffrITIknPNNddw5ZVX0rhx46RDEanvdIytAwYNGkSrVq1o3769GuCsJjvttBPTpk1j/vz5nH766UmHIyJVpNbdeQYGAx2dc9sDf8sx7c1Ac+A051w/59y1zrkLgH2ANcA1Zta0asOVyLRp0/jss8+45JJL2GabbTAzzjrrLJo1a5azbL9+/bjkkksyPvdbm5gZd999N6+99hq77757XmVWrFhRxVElT4mzSI2gY2wttHbtWpYvX77ps5nRr18/Dj300ASjqn969uxJ//79y/XYkojULrXuv9s5N8k5tyjPyXcFHPBiyjwWAXPxB/2tKzdCSWfUqFH07t2bffbZh/79+7Nq1SqKi4u55Zb8bkq0adMGgG233TbjNNdddx277rorEydOrJSYK2L9+vWMGDGC559/vsLzGjJkCG3btmXw4MGVEJmISGY6xtY+X331FV26dGGnnXZi8uTJSYcjIlKn1brkuZzeBQzoGx9oZt8H9gLmOOe+TiKw+ubrr/1q/vbbb9mwYQNAua7Mjh8/nkmTJvHwww9nnGbEiBF8+umnjBkzZtOwcePGcfnll292Rb463HnnnVx55ZWcdNJJLFy4MOu0JSUlDBw4kEMOOYSPPvpoi/FTpkzZrC8iUkPoGFsDLFmyhC+//JKNGzcyZ86cavvet956i5/+9KdMnz692r5TRCRptfGZ5/L4DdALeMrMngU+oux5rPlA/4p+gZmtzzBKdVD/n737jsuq/P84/roBEURERHOEOFGciDjAvXJb4sptas6vmpnlLGdmmqU5c2tuza2ZG/cINITUHKCIAxUFBWSe3x/o/YsEGfc43PB5Ph48bj3nOtf11siL65zrXNe/jB07lmLFilGpUqVMbaWUN2/eNLd+mDJlClu2bGHUqFEAPHjwgE8++QRFUciVKxezZ8/OVPbMKFmyJJD0pDxfvnzvLHvt2jXWrFkDwLp165g6dWqy84sXL2bJkiX06dPHIFmFECKTpI/NAtzc3Fi8eDEhISFGXeF54MCB/PXXX5w9exZ/f3+jtSuEEGrK1oNnRVGuaTQaD2ArSZ35G0+BVSR17sIILC0tDT7teMyYMYwZM0b7e3t7eypUqMC1a9eoXbu2Qdv+r48//hhXV1cKFiz4zqnmAOXLl6dz585cv36djz9++2dNd3d37VZUQgiRVUgfm3UMHjxYb3WtWbMGjUZD796931muXr16/PXXXzRo0EBvbQshRFaXradtazSaWsBZ4BngDtgAZYC1wDwg7WWe06AoSu6UvgDTXv5YBX5+fhw9ehSAH374AQ8PD53e37KysuLSpUs8fvyYTp066Sll+rm4uKTrKbuFhQVbtmzhr7/+olKlSkZIJoQQupM+NvvZt28fn3zyCX369OHAgQPvLLtgwQKePHnCokWLjJROCCHUl22fPGs0mlzAJiAR8FIUJer1qdvAKI1GUwrorNFo6iqKclqtnCLJrVu3qFGjBnFxcWzcuJHx48cTFxfHDz/8QKNGjTJdr6WlJQUKFODu3bt06NABe3t7duzYQd68soaNEEJklvSx2VORIkW0Ox8UKVIkzfIODg6GjiSEEFlKdn7y7AKUAs7/q1P/t2OvP92MF0mkJiEhgcTERABiY2MZPnw4jo6O9O/fXy/179mzBx8fHw4fPsyFCxf0UqcwnPv379O6dWsGDhxIQkKC2nGEEG+TPjYbcnd359q1a1y/fp1q1aqpHUcIIbKcbPvkGbB8/VkolfNvjqe2GIkwonLlynHq1CkePXrERx99BMCcOXP0Vn+HDh347bffKFCgAJ6ennqrVxjGunXr+P33pN1v+vfvb/R31oUQaZI+NpsqXbq02hGEECLLys6DZ38gAqir0WiaK4py8M0JjUZTHBhE0v6U3irlE//h4eHx1rGTJ09SsmRJihcvrlPdRYsW1b5PLbK+tm3bsnTpUhwdHalSpYracYQQb5M+VgghRI5jcoNnjUbzKVDv9W/f/FT9qUajafT616cURVmuKEqMRqP5EvgF+F2j0ewFrgFFSFoVNC8wR1GUtzfWFVnCzz//zGeffUaBAgUICgrC1tZW7UjCSCpWrMjNmzfVjiFEjiN9rBBCCJE6kxs8k9Sp/3fD27qvv95YDqAoylKNRhMIfAbUAdoALwFfYKmiKOsNH1dk1rNnzwCIjIwkNjZW5TRCCJEjSB8rhBBCpMLkBs+KonwCfJKB8oeAQ4bKI/QrMTERb29vKlWqxLhx4yhcuDBubm7aFT2fPn3K1q1badGiBaVKlVI5rRBCZC/SxwohhBCpy86rbQsTNGnSJJo0aUKNGjUYNGgQQ4YM4ciRI9rzffv2ZciQIbRu3VrFlEIIIYQQQoicRgbPIkt5M1U7IiKCAwcOAGg/AfLnz5/sU2Q9Dx8+pFu3bkyZMkXtKADcvXuXqKiUdtIRQgghTENcXByzZ89m7dq1akcRIkeTwbPIUr7//nuWLFnC+vXrMTc3p2DBgkybNk17ftmyZRw8eDDZgDqnCwkJYeLEiZw+fVrtKAAsXLiQTZs2MXnyZK5du6ZaDh8fHz799FNKlChB9erV5b15IYR4LTY2lk6dOlGjRg1u3LihdhyRDitXruSrr76iT58++Pj4qB1HiBxLBs8iS7GxsWHQoEEEBQUREhLCkydPSEhI0J7PnTs3H3zwAXZ2diqmzFpGjBjBt99+S7t27dSOAkCLFi2wtbWlRo0alChRQpUMYWFh1K9fnxUrVgBw8+ZNIiMjM1RHYGBghq8RQghTcOXKFX777Td8fHzYsmWL2nFEOjg7O2Nubo69vT1FihRRO44QOZYMnkWW1KVLF9q1a0efPn2oV69e2hfkYBUrVkz2qbZ69eoRHh7OxYsXsba2ViWDhYUFVlZWADRt2pTt27djb2+f7utXrFhB6dKlqVatGjExMYaKKYQQqqhatSpdunShdu3adO3aVe04Ih2aNGlCYGAgN27c4P3331c7jhA5lsmtti1yhkKFCrF79261Y5iEadOm0bNnT9We8qZEo9Go2n6+fPnw9fXl9u3bNGnSJMPXBwQEAP//9Dl37tz6jiiEEKrJlSsXmzdvVjuGyKDixYurHUGIHE8Gz0JkA+XLl1c7QpZTsmRJSpYsmalrv/nmG/LmzUutWrUoUKCAfoMJIYQQQgiTJINnke2dOXMGjUaDp6en2lGEicifPz9Tp05VO4YQQuhFYGAg4eHhVKtWTe0oQghh0uSdZ5GtnTx5krp161KnTh2OHj3KuXPniIuLUzuWamJjYzl8+DDPnz9XO4oQQggjuHPnDpUrV8bNzY09e/aoHUcIIUyaDJ5FlvTgwQO+//57/P39Uy3z9OnTNLcfio+P1/563LhxeHp60rdvX73lNDWDBg3igw8+oHnz5mpHEUIIYQQRERHave4fP36schohhDBtMngWWdKgQYMYO3Ysbdu2TfH8xo0bKVSoEO7u7u8cQDdu3JjDhw9z9OhRoqOjgaR9kXOqN0+cw8PDVU4ihBDCGN7sCazRaKhVq5bKaYQQwrTJO88iS3J0dEz2+V/nzp1DURQCAgKIiIigYMGCqdbVtGlTAH777Td27tyZo7flWL58Oc2bN5cnz0IIkUPky5cPAEtLS2xsbFROI4QQpk0GzyJLGTNmDAcOHGDevHl88sknVKpUCUh6WvzFF19QtWpVxo8fz8SJEwGoXbv2OwfO/+bs7MyXX36ZZrn4+HiuXLlCpUqVsLS0zPwfJgtycHBgyJAhasd4p7CwME6cOEGzZs3Imzev2nGEEMKkdejQgXPnzmFvb0+pUqXUjiNUdufOHaytrXnvvffUjiKESZJp2yLLiI6OZtasWfj5+bFs2TJq1aqFjY0N06dPp1q1amzevJkJEyZw+/ZtChUqxLx58+jevbvec/Tr14/q1avTpUsXvdct0ta2bVu8vLzo3bu32lGEECJbqF27NuXKlUt2LDY2ltGjRzNmzJhk64OI7Ovo0aOUKVOGcuXKERwcrHYcIUySDJ5FlmFtbc1nn32Gi4sLAwYM0B6fPn06T548wdzcHA8PD95//32D5rh9+zaQtLWHML4377CntRjcG99//z0ODg7MmTPHkLGEECJb2b59O3PmzGHWrFns27dP7TjCCG7dukVCQgLh4eGEhoaqHUcIkyTTtoVqtmzZgqWlJe3bt9cemzt37lvlxowZw+bNm5k9ezbt2rUzeK61a9eyYcMGOnXqZPC2xNv27NnDoUOH0v3feuXKlYSFhbFq1Sq++OILA6dLmb+/P/3796d69eosWrQIjUajSg4hhEgvd3d3HBwcMDc3l/2fc4i+ffvy8uVL7YKrQoiMkyfPwuji4uJYvHgxH3/8MV5eXnh7e7+z/JQpU7h27ZpRBs4ApUuXZuLEibi4uBilPZFc0aJF6d27N/b29ukq/+2331K/fn2mTZv2znIXL16kXr16TJkyRR8xk1m5ciUXLlxgyZIl3L17V+/1CyFEeiUmJvLy5cs0yzk7O/PgwQNCQkIoUaKEEZIJtVlYWPD555/Ts2fPTNcREBDAoEGDOHr0qB6TCZEyRVGYOXMmEyZMICYmRu04gAyehQo6d+7M0KFD0Wg0WFhYYGdnp3akbGnVqlU4Ojoyffp0taMYVKdOnThx4gReXl7vLDdv3jxOnz7N5MmTtXue6kuPHj2oUKEC3bt3p3jx4nqtWwgh0ktRFBo0aICdnR2//vprmuVz5cqFhYVMQsyMxMREPvvsMzp27JijpkCPGDGCpUuX6jQAFyK9jh07xrhx45gxYwYbNmxQOw4gg2ehgqtXrwJQvXp1/Pz8ZLqYgSxdupSQkBAWLlyoUz2PHj1i7dq1PH36VHvM19eX2rVrM3r0aF1jGk3v3r0pXrw4/fr1I0+ePHqt293dnb///pv169djZib/rAoh1BEVFcXZs2dJTEzk2LFjasfJ1i5evMjPP//M9u3bWbNmjdpxjKZu3brJPoUwpHLlylGoUCFsbGyyzHhBfsoTRqUoCps3b2b8+PFs2rSJChUqaM8dP34cV1dX7TZUpurp06dMmDCBvXv3qppj3Lhx1KxZU+dpyu3bt6dPnz7JVjZfvHgxFy5cYM6cOckG1VlZ8+bNuXv3LitWrFAtw6tXr1RrWwiR/dnY2LB27Vr69evH5MmT1Y6jupUrV7Jo0SIURdF73ZUqVaJWrVoUK1aMFi1a6L1+NaSnj5o6dSqhoaFs3rzZCIlETufo6MidO3d48OABbm5uascBZPAsjGjUqFFYWFiwf/9+vv32W8qWLZvs/MKFC/Hz82PGjBkkJCSolFJ3kydPZsaMGXh5eREREaFajg8//JALFy4wcOBAnep5s9f1v/e87tOnD2XLlqVfv34UKFBAp/pzio8//hhra2t++ukntaMIIbKxHj16sGLFCpycnNSOoqojR47Qv39//ve///Hbb7/pvf68efNy/vx5QkJCqFq1qt7rN7axY8dqdz1JS6FChWSWlTAaa2trbG1t1Y6hJd/5wmh27NhBYmIiO3bsSPH8wIEDKV++PKNGjcLc3Pyddf3111906dIlS975rFKlCgBly5bV+/RgNezcuZMdO3Yke9ekXr163LhxgxUrVuhtZeno6Gi91JNV/fHHH8k+hRBCGM7777+PtbU1uXLlyvE3EtLjTd904MABlZMIkbXJ4FkYzfz582nfvj0//PBDiuc/+OADrl27lur5fxs/fjxbt26lf//+We4p9cCBAwkKCsLX1zdbLMRib29P+/btDXrXr1OnTuTJk4d58+YZrA21rV69mu7duzNr1iy1owghRLbn4uLCzZs3uXnzJrVq1VI7Tpa3YMECunfvzpIlS9SOIkSWZvo/2QuT0bZtW9q2bauXutq0acOBAweIiYmhdOnSXLx4kffee08vdeuDbPuRMQcPHtR+pmfKmClq3759sj3NhRAiq4qIiMDS0hIrKyu1o+ikWLFiakcwGXXr1pVFwESqXr16RUxMjOyQgzx5FkYUFxfHrVu39FJXs2bNMDc3Jz4+nrt372pX8M5p/P39OXTokNoxdLZmzRp69OjB999//85y3t7eODs7M2DAACMlE0KInOX06dMULlyYMmXK8PjxY7XjCCOIiIigefPm1K9fn4cPH6odR2ePHz8mLCxM7RjZRlhYGOXKlaNw4cIcP35c7Tiqk8GzMJr27dtTtmxZvvzyS53runfvHnFxcQB06NCBBg0a6FynqQkODqZGjRo0b96c1atXqx1HJ15eXqxbt47KlSu/s9yaNWu4efMmy5cvJzw83EjphBAi5/Dx8eHVq1fcv3+foKAgteMIIzhx4gSHDh3i1KlT7N+/X+04Orl06RJOTk6ULFmSGzduqB0nWwgODiY4OJiYmBj+/PNPteOoTqZtC6PYsWMHp06dAsDPzy/N8nfv3qVQoUJYW1uneL5JkyYsXbqUqKgohg8frrdFq0xJYmIiiYmJANobCe+ybds2Hj16xODBg9NckC2rGjp0KP7+/jRu3FimDgkhhAF8+umnhISEUKRIEWrWrKl2HGEEDRs2pFWrVkRFRdGmTRu14+jkn3/+4dWrV7x69YrAwECcnZ3VjmTyXF1d+fnnn7l79y6DBw9WO47qNIbY+06ARqM56+Hh4XH27Fm1o6juwYMHODo6kpiYiIeHB+vWrWPDhg2sW7eOmTNn4uXllaz80qVLGTRoEBUqVMDPzy9bLLplKH/++Sf37t3jo48+eucNhMuXL2v3x1u6dGmK056Dg4NRFEVWJRXCSDw9PTl37tw5RVE81c5iaqSPFSLr2bZtG1OmTGHgwIEMHz5clQwJCQn8+OOPWFlZqZZBZB2G6Gdl2rYwuHz58uHo6AjA8OHDKVOmDLNnz+aff/5h0aJFb5V/82T6xo0bREVFZait27dvExkZme7yr169ol27dri7u3P79u0MtZUV1KhRg/bt26f55N3BwQFbW1vMzMxSHBxfuXIFZ2dnypYti4+PT5rthoWFERgYmOncQgghhMhe5syZg7+/PzNmzFAtg7m5OV9++aUMnIXByOBZGJyNjQ3+/v4EBgbSvXt3ACZMmEDlypVTXFl5ypQpjB07lu3bt5MvX750t7N06VLKlCmDm5sbsbGx6brm0qVL7N27F19fX7Zv357utkxN8eLFuX79OlevXqVFixZvnb9//z4xMTHExcUREhLyzrqePHmCi4sLZcqUYffu3YaKLIQQQggTMnz4cJydnfniiy/UjiKEwch8WGEUtra2yfYJHjNmDGPGjEmxrIODA999912G2/j7778BCAwMJCoqCktLyzSvcXd3p0uXLoSEhNClS5d0tRMTE0PLli0JCAhg586d1KlTJ8NZ1VC0aFGKFi2a4rkWLVqwcuVK4uPjadeunfZ4QkICBw8epHLlyhQvXhxIeur8ZgXWf/75x/DBhRBCZEvBwcF8+OGH2NjYsHfvXvLnz692JKGD7t27ax+SCJFdyeBZZBuTJk0iX7581K5dO90dsKWlJZs3b85QO3fu3NEu1b93716TGTynpW/fvm8dmzRpEt9++y2FCxcmODiYXLlyUa5cObZs2cLt27cZNmyYCkmFEEJkB3/88QeXL18G4MyZM7Ru3VrlREII8W4yeBbZhr29PVOnTjV4O+XKlWPcuHEEBAQwZMgQg7enpujoaCDpafu/Fxfs3LmzWpGEEEJkE15eXmzfvp28efPSuHFjVTLMnj0bb29vfvjhB1xcXFTJIIQwHTJ4FiIT1FwMI6MiIyMZN24c9vb2TJ48OUPbes2YMYNq1apRo0aNdE2DN2X79+9nzpw5DB48WG4OCCGEETg4OKi6r/Dz58/56quvAChSpAjLly9XLYsQwjTIgmHCJK1fv57x48cTERHx1rlz585x8uRJFVJlTevWrWP+/PlMnToVb2/vDF2bO3duevXqRYUKFQyULuuYOHEiR48eTfVdfCGEENmLnZ0dH374IXZ2dm9tm5lRx44dw87OjkaNGhEXF6enhEKIrEYGz0JVFy5coHnz5vzyyy/pvubOnTv07NmT7777jjlz5rxVX506dWjQoAFHjhzRd1wAQkNDGTp0KCtWrDBI/fpWu3ZtbG1tcXR0zBGD4Mzq2bMndnZ29O7dW+0oQgiRY/n6+jJ27FijLEip0WjYtWsXz58/p02bNjrVtXfvXiIiIvD29ub+/ft6SiiEyGpk2rZQ1bRp0zh06BDe3t4MGjQoXdc4ODhQsmRJ7t69S7Vq1ZKdS0hI0L6bm5CQkOlcFy9e5ObNm3Tp0gVzc/Nk52bNmsXixYsBaNWqFcWKFct0O8ZQrVo1njx5grm5+Vt/FvH/Ro0axahRo9SOIYQQ2dbjx4/Ztm0brVq1omTJkimW6dq1Kzdu3ODMmTOcOHHCuAF18NlnnxEUFISbmxslSpRQO44QwkBk8CxU1bVrV06cOJHubaIA8ubNS0BAABERERQpUiTZOU9PT7y9vYmLi6Np06Zp1qUoChs3biR//vzaVT4fPnxIvXr1iI2N5f79+2/tV1ivXj3mzp1LxYoVcXBwSHduNWX395WFECIni4+P5+nTpxQuXFjtKO/Uu3dvDhw4QJUqVfDz80uxTOXKlblx4waVK1c2cjrdODk58dtvv2l/Hxsby549e6hZsyZOTk4qJhNC6JNM2xaq6tGjB+Hh4Sxbtuytc3v27KFJkybJOqM38uTJ89bA+Y0GDRqka+AMsGXLFnr06EGbNm34888/AbCwsNAONvPkyfPWNe3bt+fZs2f4+vqSO3fudLWTUStWrGDy5Mna1a6FEEKI1DRo0IAiRYqwYMECtaO805ttJFPbTvLly5eULFmSiRMnsnDhQmNG07vRo0fTqVMnPD09k+1WIYQwbTJ4FlnWhAkTOHbsGGPHjjVYG/b29mg0GiwtLbG1tQWgYMGCXLp0iSNHjqS6FZWtrS0WFoaZuPHXX3/x6aefMmXKlAy9C64v06ZNo0SJEmzYsMHobQshhMiY+Ph4fHx8ADh//rzKad5t9erVHDp0iL1796Z4fsmSJfz0009Mnz491SfT6XHlyhWWL19OVFRUpuvQVWJiYrJPIUT2IINnYXSKojB16lSGDx/Oy5cvSUxMJDg4+K07s7179zb4Ak7NmzfH19eXK1euUL58ee3xsmXL0qRJE4O1+y7FihWjcOHCWFhYGGXaWnx8PBcvXuTVq1cA/Pzzz9y9e5clS5YYvG1T8ejRI+7cuaN2DCGEeIuFhQXbtm1j+PDhWX4bxdy5c9OsWTPy5cuX4vk32yI6OTlRvHjxTLURFxdH/fr1GTBgAOPGjdMlrk7mzJnD5s2bOXv2bIa2iBRCZG3yzrMwutOnTzNp0iQAXFxcOHPmDBs2bGDYsGHMnz9fW2706NGMHj3a4Hn+u+iY2goVKsSNGzeIiooyyvtrAwYMYPXq1bRo0YIDBw4wadIkVq1apd37MqcLCgqiatWqREVFceTIERo2bKh2JCGESKZdu3a0a9dO7Rg6a9SoEaGhoVhZWWX6tSgzMzPy5s1LeHh4qoN0Y8idO3eG1nMRQpgGefIsjK58+fI4OTlha2uLh4eH9l3jN5+GFh0dzdatWwkJCTFKe5lha2ur88BZURRmzZrFN998Q2xsbKrl7t69m+xz2LBh+Pj4aJpj6QAAIABJREFU0LZtW53azy4ePXrEixcvSEhI4Pbt22rHEUKILO327dsEBARk+no7Ozud1hMxNzfn4sWLHD58mClTpmS6HlMQGBhITEyM2jEMStfvJyH0TZ48C6MrVKgQt2/fJiEhAUtLS3799VfWr1/PgAEDjNL+0KFDWb16Nc7OzkbZR1Itx44dY8yYMQCULl2aTz75JMVyq1evZt26dbRv3/6d9V27do3o6Gjc3Nz0HVVVkZGRREdHU7BgwRTP165dm7Vr1xIWFiZ7QAshxDtcv34dV1dXYmNjOXz4sGqvPxUtWpSiRYuq0raxzJw5k3HjxlGrVq0s/657ZmWV7ych/k0Gz0IV/95zuFatWtSqVcsg7Vy/fp02bdrw3nvv8ccff2Bra6t9tzqrrH558+ZNSpQoQa5cufRab9myZXFwcCA6OhpXV9dUyxUvXjzN98ICAgJwc3MjPj6egwcP0qxZM71mVcvjx4+pWrUqT58+5Y8//qBx48YpluvVq5eRkwkhhOl5/vy59kloaGioymkMKzExkfXr11OsWLF07/ChT28WVPP39ycxMREzs+w3mTQnfT8J0yGDZ5GtHThwgFu3bnHr1i0uXbpEgwYNWLRoEa1ataJu3bpqx+PLL7/khx9+0L5vrE9OTk7cuXOH+Ph47OzsdKorMjKSuLg4AMLDw/URL0sICQnh4cOHQNIq56kNntOq48KFC7Ru3dpgW5cJIYQpqF27Njt27CAiIoKPP/5Y7TgGtXLlSgYMGICZmRkBAQG4uLgYtf0ffviB4sWL07x582w5cIac9f0kTIcMnkWWdu/ePXbt2oWXlxfFihXL8PXdunXj6NGjvPfee9SpUwdI2rs5rX+E/f39WbhwId26daNBgwaZyp4e/v7+QNK2Gvp09epVBgwYQPXq1Zk3b57O9dWqVYt9+/bx8uVLOnbsqIeEhvPzzz/z9ddf07lzZ5YvX/7OstWqVWPJkiXcu3ePQYMGZaq9unXrcufOHQYPHszixYszVYcQQmQXab0ClF3Y29sDSQuD5cmTx+jtFytWjO+//97o7RpbTvl+EqZDk1WmrmY3Go3mrIeHh8fZs2fVjmLSPD09OXfuHPXq1ePkyZNGa7dhw4acOHGC4sWLaxfSMoTAwECWLFmCl5cXHh4eeqt31KhR/PTTT9o2SpYsqbe6s7IHDx7g6OhIYmIiGo2G+Ph4g9+RL168OPfu3WPAgAEsXbqUr7/+mlWrVvH999/To0cPg7YtTNvrf9/OKYriqXYWUyN9rMgKLl68SIECBShTpozaUYQQKTBEPytPnkWW9ubO7ptPY6lTpw4nTpzA09OwP9OWKlXKIHeOu3Xrxp49e3Bzc8v0XpmmKC4uTrufZocOHYwyle3kyZOcOXMGLy8vABYuXMizZ89YtmyZDJ6FECIbq1mzptoRhBBGlqnBs0aj2Q+cBeYqivJCv5GE+H9bt27l7Nmz2inXxvLdd98xcuRIChUqBMDUqVO5fPkyc+fOxcnJyahZMqNmzZrcuHFD7RhG5+TkxJkzZ7hz547RppeXLFky2ZP9adOmsXbtWsaOHWuU9kX2I32sEOpQFIWIiAid1wkRQmRfmX0s0xKYDBQF0Gg01zUazUaNRjNWo9G00Gg07+kroMhezp8/T9++fTlx4kS6ytvY2NCsWTNV3icqXLgwZmZmBAYGMmnSJHbs2MH8+fONnkNkTK1atejcubNqC6j873//4/z587Rs2VKV9kW2IH2sEHq2a9cuWrZs+c7FOTt16kT+/PmZOXOmEZMJIUxJZqdtVwfqAE9f/9759dfHgAKg0WgeAZdef10GLimKckuntMLkDR06FF9fX86ePcu1a9eM0qaiKNqpvJnx/vvv07BhQy5fvkybNm30mCz7CA4OJjQ0FHd3d7WjCJEdSB8rhJ6NHj2amzdv8uDBg1Rvbh4/flz7KbOHhBApydSjGUVRLiuKskhRlDcdewGgCfAFsA4IAAoCrYDxwGbgH41G81yj0XjrHluYqjdbAWVmS6DM2L17N3ny5OGDDz4gMTEx3dft3LkTFxcXpk+fjqWlJcePH+f58+c0atTIcGFNVGhoKJUrV6ZGjRqsX79e7ThCmDzpY4XQv27dupEnTx66deuWaplff/2Vfv368eOPPxoxmRDClBhstW2NRpMbqAy4vf6qBrgC1oqimBuk0SxEVgJNXUREBPny5TNKWwMHDmTZsmUAhIWFpXvhsaZNm3L06FFsbW2JiIjQHg8NDeXx48dUqlTJIHlNUVBQEGXLliUhIYGffvqJkSNHqh0JgE2bNmFtbc1HH32kc13h4eGcPXuWhg0bYm1trYd0Qui2Cqj0sdLHCiGEeDeTWm1bUZQYwOf1FwCapLmz5QzVpjANxho4A4wZM4awsDDq16+foRW7R44cyaNHj+jZs6f2WFhYGBUrVuTp06ds2LDhnXevs5vw8HCGDh1Kvnz5+Pnnn8mVK5f2XMmSJTl48CC3b9/mk08+US/kv+zcuVP73+fkyZPUq1dPp/ratm3LqVOn6NKlC5s3b9ZHRCF0In2sEKYnPj6e6dOnY2ZmxoQJEzA3z/b3uYTIdoy6VZWS9Jj7ujHbFDlbmTJl2LZtW4ava9euHe3atUt27MWLFzx79gzAoHs/Z0WbN29mw4YNAHh5edG8efNk55s0aUKTJk3eWcfly5extLSkYsWKBsv5hr29PRqNBgsLC72smvriRdKCx/+ehSBEViN9rBBZ286dO5kyZQoArq6uepkZJYQwLtnnWYh0KlGiBHv37uXGjRsMGjRI7ThG1bhxY4oXL06+fPmoXr16hq/39vamcePGmJub4+PjQ9WqVdN1XXR0NJ9//jkWFhb8+OOPWFpapuu6hg0b8tdff2FpaUn58uUznPe/du/ezYEDB+jQoYPOdQkhhMiZKleujJ2dHRqNRl7/EsJEqbOXixAGdufOHQ4cOJChRcJScvv2bebMmcO9e/cAaNWqFSNGjCB37tz6iGkwUVFRDB8+nIkTJ6br7+DMmTNUqVKF4cOHp3je2dmZu3fv4u/vT8GCBTOc5/nz5yiKQnx8vPYpLiQNSkuVKsXo0aNTvG7Hjh388ssvLFy4kP3792eozSpVquhl4AxJ+0cPHDgwU392IYQQAsDFxYWQkBDu3btH2bJlDdJGfHw87du3p3Tp0ly8eNEgbZiaMWPG4O7uzrlz59SOIrIBGTwLVURGRnLixAliY2P1Xnd0dDQ1atSgVatWfPvttzrV1bFjR0aPHp3s3WdTsH79ehYsWMC3337LsWPH0iy/bNky/P39WbBgAc+fP9d7no8++ogtW7awZ88e6tatqz2+YsUKgoKCmDdvXorXeXh4ULRoUYoXL07NmjX1miksLIyjR4+SkJCg13qFEEKI1NjY2GBjY2Ow+u/cucOuXbsIDAxk69atBmvHVLx8+ZJZs2bh6+vL4sWL1Y4jsgGZti1U0a5dO44dO0bPnj359ddf9Vq3oijaQXl0dLROdRUpUiTZp6nw8PAgf/785M2bN11TwwYOHIiPjw+NGzcmf/78BsnUuXPntwaqn3/+Offv3091OnTp0qW5f/++QfLUr1+fv//+mxEjRqQ6eBdCCCFMSenSpRkxYgR+fn58+umnasdRXd68eRk6dCiHDx+mb9++ascR2YAMnoUqQkNDk33qU548eThz5gyXLl2iS5cuOtW1fft2fH199f7U09CqVKlCaGgo5ubmmJmlPcHE09MTPz+/NMudOXOGbt264e7uztatW9O9UuirV6+oW7cuV69eZd++fdp9vhs1aqTatLKwsLBkn0IIIYSp02g0ckP4PxYuXKh2BJGNyLRtoYpdu3bx008/sXr16nSV37ZtG+XKlWPGjBnpKl+pUiV69uyZ7gWmUmNtbU3dunV1rkcNuXLlStfAGSAxMZEBAwbg6enJtWvXUi23ZcsW7t69y44dOzL0RPjevXv4+voSHR3N4cOH032dIR09epSFCxcyf/58taMIIYQQQggTIINnYXBRUVH4+/snO1amTBlGjhxJ0aJF01XHggULuHHjBj/88AMAQUFBnD59Wu9ZTcWlS5cYPXr0W3+vmXXz5k2WL1/OuXPnWLNmTarlBg8eTOPGjRk9ejTFixdPd/1ly5Zl5syZ9OrVixEjRqRZ/vbt2+l6Eq6LChUqMHToUINNUxdCCFOUmJjIgQMHuHPnjtpRhBAmKiEhgf3792sX3M1OZPAsDK5x48ZUqVKFCRMmZLqOzz//nMqVKzN+/HiePHmCq6sr9erVY8WKFXpMajp69erFnDlz6Nevn17qK126NF26dKFixYp07do11XIuLi4cPXqU2bNnZ7iNMWPGsHbtWgoXLvzOcrdu3aJSpUpUq1aNAwcOZLgdIYQQmTdjxgxatWqFu7u7zuuGCOOJj4/H29ub8PBwtaMIwcSJE2nTpg21atXKdguzyuBZGNyNGzeSfWbGRx99xJUrVxg9ejQxMTHaDt0QK0ObgmrVqgHg6ur61rmwsDBu3bqVofquXr3KokWLCAgISLFOY4qIiODVq1coisLjx49TLLNr1y6mT5/Oy5cvjZxOCCGytzf9a2xsrM7bPQrjGTZsGI0aNaJp06YGbefs2bN4eHgwdepUg7YjTNubf0devXqV7f4dMakFwzQazftAZ6A14AIUAcKA08AsRVHOp3BNPmAy0PF1+QfAVmCKoijyk7cR7N+/nz/++INBgwbppb7333+fY8eOcfPmTXr06KGXOk3N2rVrmTZtGiVLlkx2PCwsjAoVKvD48WO2bNlCp06d0qxr2bJlDBw4kGLFivHPP/8YdAuN9HBzc2Pv3r08efIkxS3CQkJC6NChA4mJicTGxkoHLoQeST8rJk+eTMWKFXF1dVW9PxDp9+TJk2SfhjJ37lzOnz/P+fPnGTduHLly5TJoe8I0zZw5E3d3d2rWrJntvkdMavAMDAfGALeAg8BjwBloD7TXaDTdFUXZ/KawRqOxAbyBaq/LbwTcgNFAQ41G00BRlFfG/SPkPB4eHnh4eOi1zrp16ybbLzgsLIxz587RpEkTrKysMlTXmjVrGDNmDP3799d5X2hjMTMzo1SpUm8df/78OY8fP0ZRFG7evEliYiK//vorRYsWpXnz5inW9ea9tsePHxMVFZUlflhq06ZNqufy5ctHsWLFuHfvHi4uLkZMJUSOIP1sDpcrV64ce2PalP3yyy80bNiQDz74wKDtfPLJJ5w/f57WrVtnu0GR0B8rKyt69eqldgyDMLXB8wWgkaIo3v8+qNFo6gNHgMUajWanoigxr099RVKH/r2iKGP/VX4mST8cfA58Z5TkwqBatmzJxYsX6datGxs2bMjQtStXruTRo0f88ssv7xw8h4aG8uzZM8qXL69rXIMpXbo0v/32G//88w+fffYZK1euZMCAAWg0GgICAqhQocJb14wfPx57e3tcXV0pVKiQCqkzxtbWFn9/f54+fUrp0qXVjiNEdiP9rBBpePHiBbly5crwzXpDcnBwYPjw4QZvp1WrVgQFBRm8HSGyKpN651lRlO3/7dBfHz8JHAPsgSoAGo1GA3wKvASm/eeSaa+Py+7xRnTz5k0aN27MsGHDUBRFr3VHRUUl+8yICRMmUKdOnXdugxUaGkqFChVwcXFhx44dmc5pDF5eXowZMwYrKyscHByApC23UnuinCdPHr744guaNWuWap0bN27EysqKjh076i3nkSNHCAwMzNS1dnZ2MnAWwgCknxXi3S5evEiRIkUoVaoUDx8+VDtOMomJiezbt4/bt2+rHUXo4OrVqxw8eFDtGCIVJjV4TkPc68/415/OQDHgtKIokf8u+Pr3p4HSGo0m/fvtpECj0cSk9AXU1qXe7GjFihUcP36chQsXEhAQkOoA+vTp0xleXGz//v0sX76cVatWZThX8+bNOX36NAMHDky1THh4OM+ePQNQ5Y5rZhdb8PLywsfHB39/f5ycnDLd/s6dO4mJiWHnzp3Ex8enfUEa5s2bR7NmzXBzc8uxi74JYYKM3s9KHyuyGl9fX6Kionj48CE3b95UO04yM2bMoG3bttSsWTNTDxOE+h48eIC7uzstWrRg2bJlascRKTC1adsp0mg0TkAzkhYpufL6sPPrz9RGYTeAFq/LBRs0oACgc+fObN26lSJFilCrVi1KlizJhQsXyJs3r7bMhg0b6NGjB9bW1vzzzz84Ojqmq24nJyf69+//1vEbN25gZWWVoT2JU+Ls7MyOHTsIDAxk6NChOtWVURMmTOC7777jq6++YubMmRm+vnr16jpn+Prrr4mJiaF169ZYWOj+z0ZkZNLP2bGxsXrbwiA+Pp5Zs2aRJ08eRo4cqZc6hRBJpJ8VIkmfPn0IDAykQIEC1KtXT+04ycTGxgIQFxeX7VY4zikSEhK0Pxe9+e8pshaTHzxrNJpcwK9AbmCMoihvfhK3e/2Z2oZ3Ef8plymKouROJddZQL+rZJm46tWrc/PmTaZPn87p06e5evUqQUFBVK5cWVvmxYsXQNI/GDExMalVlS7e3t40adKE3Llz4+fnR9myZXWq76OPPtLp+szavXs3iqKwe/fuTA2e9aFy5crs3LlTb/V99dVXODk5UaFCBe3Ucl1t2rRJu5d4lSpVDL5dhxA5hZr9rPSxIj1iY2Np2rQpfn5+7N69m4YNGxqsLSsrK9X64rR8/fXXVKxYkSpVqiR7MCFMh6OjI2fOnCEoKIgOHTqoHUekwKQHzxqNxgxYDTQAlimK8qu6iUR6DBkyhKCgIMqUKZNs4AwwYMAA8uXLh6OjI2XKlNGpnXv37pGYmEh0dDSPHz/WefBsSJGRkWzcuJE6depQsWLFZOfmzp3LokWLGDx4sErp9M/CwuKtbagOHz6Ml5cX7u7uHDp0KM1VPBVFYceOHZQqVQo3NzcqVKiAtbU1lpaW8j60EHoi/awwBffu3ePUqVMA7Nu3z6CD56wsV65cdO3aVe0YQkfu7u64u7urHUOkwmQHz6879JVAd2Ad8N+RxZs74and8c73n3LCSBwcHFi+fHmK58zMzOjWrZte2unWrRuRkZHY2dnh6en5zrJBQUE0btyY3Llzc/z4cYoUKZLs/KNHj+jVqxd2dnasXbsWa2trvWR8Y/To0SxZsoQCBQrw+PFjzMz+fzmCpk2b5oinqPv27ePly5d4e3vz6NGjNKfsL1iwgBEjRpA7d24CAwNxd3cnODgYCwsL7Ox0mlAihED6WWE6SpcuzZQpU/Dz8zPKitNCiJzLJBcMe92hrwL6kLSn5CeKovz35Y4372A5k7K03tUSRjR79mxsbW2ZPHnyW+du3rxJcHDGX5czMzNj4MCBfPzxx2mWPXHiBEFBQVy/fp0LFy68dX7Hjh0cOnSIbdu2ae9u65OtrS0AefPmJWkBW/168eIFLVu2pGHDhjx69Ejv9evDyJEj6dixI99991263nV/82Ta3Nxce7PBwcFBBs5C6IH0s6YtICCAzz//nIsXL6odJVP279/P2LFjefz4cbqv+eabb9i2bZvOa5wYWmhoKF9++SW7d+9WO4oQIjMURTGpL5IG/GsABdgEmKdSTgOEAC8Am/+cs3l9/LYBc5718PBQRPq4uroqgFKmTJlkx0+cOKGYm5sr1tbWyvXr1w3W/osXL5RevXopn376qfLq1au3zt+5c0epXr260rhxYyUiIkLv7cfHxysHDx5UHj58qPe6FUVRBg8erLz+f0ZZsWKFQdowpKtXryo//PCDcv/+/WTHf//9d+Xvv/9WKZUQuvHw8FCAs0oW6Fv//WUK/az0se9Wp04dBVCcnZ3VjpJhUVFRSq5cuRRAGThwoNpx9G7QoEEKoFhYWCgvX75UO44Q2Zoh+lmTevL8rylkvYGtQE/l/xcuSUZRFAVYDuQFvv7P6a9fH5c14LOIGTNm0KhRIxo2bMiVK1e0x0NCQkhISNC+t2woefPmZe3atSxbtozcud9en8bJyQkfHx+OHj2qfUqsT+bm5nzwwQcULlxY73UD2r9TCwsLWrdurff6nz59yuTJk9m/fz+XLl1Kc5XPDRs2sHnz5mTH1q1bx+rVq1Ms/+GHHzJ69Oi3VlRv2bIlFSpU0Cm7EOL/ST+bPbzZZcEU35vMnTs3Li4uALi6uqqcRv/c3NwAqFixIlZWVum65tatWynOitO3X375hZIlSzJ37tx0lff396dixYq0b9+euLi4tC8QIjvQ50jc0F/AZJLuhL8Apr/+/X+/qv2rvA1w+fU1fwDfvf5UgAuAtQGzyl3xDOrVq5cCKAULFtQeS0xMVJYuXaps3LhRxWSm7+TJk0qrVq2U9evXa4/FxcUp33zzjTJlyhQlPj5ep/oHDhyoAIpGo1EAZdiwYamW3bt3r/Yp+KFDhxRFUZRDhw5pj+3ateuta+rVq6cASrt27XTKKURWkhWfPJtKPyt97LslJiYqd+7cURISEtSOkinR0dFKUFCQ2jEM5t69eynOckvJnTt3FGtrawVQtm3bZtBcVatWTXEWYGq+/vprbd995coVg2YTIjMM0c+a2oJhJV9/5gUmpFImiKSOHEVRIjUaTUOSOvuOQGOS9qicA0xRFCXagFlFBr333nsAFCpUSHtMo9EwYMAAtSJlG/Xq1WP//v3Jju3YsYOpU6cCUK1aNT788MNM11++fHkg6Ql6fHw8gYGBqZYtWLAg5ubmaDQaChYsCEDhwoWxtLQkMTHxrcXaAGrXrs2pU6c4fPgwERER5MuX760yQgi9KPn6U/pZE6bRaHByclI7RqZZWVlRokQJneoICAggf/78vP/++3pK9W5Tp05lwYIFTJkyhSFDhryzbEYyRUVFabfufPbsmU4Z0zJu3Dhmz57NsGHD0lW+T58+HDt2jHLlyuHi4sLvv/9OqVKltDMHhMiONErSHVyhZxqN5qyHh4fH2bNn1Y5iMhITEzl9+jSVK1fm0qVLvHjxQu97Kx8+fJjly5fzv//9j/r16+u1blNz7do1ateujZmZGRcvXtR5K6/r16/z7NkzDh8+TN++fd/5w8H169cxMzPD2fn/1xkKCgoiISHhrS3Kdu/ezaRJk7h8+TKFCxcmMDBQ76ud/9u5c+cYMmQITZo0Yc6cOQZrRwhPT0/OnTt3TlGUd28HIN4ifax4l127dtG+fXtsbGw4duwYNWvWNHibTk5OBAcHU6NGDb0v1Hb06FHu379P9+7dk+3GkZXMnz+fESNGYGNjQ1BQkPbmuBBqMkQ/a2pPnkU2ZmZmRv369fH19aVZs2YoisKmTZt4/vw5FStW1Mtgd+jQody4cYN//vkHX19fPaQ2XS4uLty/fx+NRkOePHl0qktRFEqXLk2uXLnw8PBIs/ybJ9X/VrJkSebPn8+5c+eYOXOmdsXUfv368fTpU9zd3dmzZ49BB84AS5Ys4fLly1y+fJlJkybJU24hhMnx8/MjMTGRatWqqR1FFffv3wcgMjKS+vXr89dff6XY7+jTt99+yy+//MLYsWP1XneTJk30Xqe+vXnnOSEhIc11T4S6Tp48iaOjI6VKlVI7iknKmrevRI6WO3du7Z3V/fv3M3jwYJo2baqXLZZatWoFYJBFs0yRjY2NzgPniIgIKlSoQP78+Tlz5kym63n06BEjRoxgw4YNzJ49W3v8zX+zrl27UrRoUZ2ypkf//v2pUKECQ4cOlYGzEMLkXLx4ETc3N6pXr67Tv8mm6syZM9SqVYtPP/0UgJiYGB4+fGjwdnv16sWpU6do27atwdvKikaOHMm2bds4d+6c9jU8kfUsW7aMBg0a4OrqypMnT9SOY5LkybPIcipVqoSvry+RkZFcuXKFtWvXYmtrm+5VKd+lWbNmdOnShbp16+ohqQAIDAzk+vXrQNJ+2XXq1MlUPQ4ODtSpU4c///yT5s2ba4//+uuvLF++PMVV0A2hfv36/P3333qrLyAggD///JOuXbsa7c8ghMi5Xr58qX3y9/LlS5XTGNfvv/9O69atMTc359KlS9SsWRNra2saNmyodrRsz8zMjI4dO6odQ6QhIiICgFevXhEbG6tyGtMkg2ehCkVR8PX1pVy5cilu/VS1alUg6V0FV1dXHB0dsbOz06nNtWvX0qdPHywsLLh69arO7/iKJK6urnz77bcEBQUxaNCgVMsdPHiQR48e0bNnTzQazVvnLSwsOH36NAkJCZibmyc7Z0qDziNHjnD58mWGDBmCmZkZdevWJTw8HH9//2RP1IUQwhAaN27Mnj17SEhISHYjMid49eoVkLSGSmxsLAMHDlQ5kRBZy8iRIylUqBDOzs4UK1ZM7TgmSQbPQhUTJ05kxowZVKlSBT8/v3eWrV27tl7atLBI+nbXaDRGXXAjNDSUP//8kw8++IBnz57RsWNHzM3N2b59OwUKFDBaDkMaP3689tfx8fFERkYmu9kREBBAy5YtURSF+Ph4+vbtm2pd/x04m5InT57QqlUr4uLitHtfW1lZER4ervP0eCGESK+cOnXYy8uL3bt3Y2trm6X3uA4PD+fBgwd6W5U6NDSUQoUKpXhjWoh/Mzc3p3fv3mrHMGnyzrNQxd27dwEIDg7GWCu+d+/enUOHDnHx4kVKly6tU11Hjhxh37596Spbr1492rRpw8iRIzl06BCnTp3C29ub48eP65QhK4qLi6NGjRoUKFCArVu3ao/b2Nhop91nlxsGKcmTJ492q7USJUpgaWnJn3/+yYEDB5g0aZLK6YQQxvLy5UsOHz5MdLTs1JVe8fHxHDlyROftmNq1a0ejRo30E8oAXr16haurKxUqVGDp0qU61zdmzBgKFy5Mr1699JBOCJEWefIsjCI8PJwOHToQHR3N9u3bmTt3LpUrV6Zp06Y63ymNi4ujX79+3Lt3jzVr1rxzb8tmzZrp1BYkbWX0pp49e/akeIc/Pj6edu3a4evrq333LDIyklatWvGveAxSAAAgAElEQVTBBx9gbm5O06ZNdc6S1Tx//hw/Pz8UReHUqVN07twZSFpJ+8qVK4SFhWFnZ0d8fLx2JkB2kidPHvz8/AgJCdG+euDo6Iijo6PKyYQQxtShQwcOHTqEl5cX27dvVzuOSRg+fDhLliyhatWq/PXXX2rHMZioqChCQkIAuHnzps71nT59OtmnEMKwst9PryJLOnnyJEePHgWSFvTo27cvY8aMSff13t7eJCQkpLhdg6+vL+vWrQNg06ZNfPXVV/oJnYo3q4EnJiamum3S/fv3OXDgAAB9+/alXr16dOnShbx583Lw4EGD5lNToUKFWLVqFefPn082lRugTJkyfPfdd6xYsYIuXbqwefNmlVIaloODAw4ODmrHEEKo6M3T0+fPn6ucxHTklL+zAgUKsHfvXnx8fBg+fLjO9S1atIhFixbRo0cPPaQTQqRFBs/CKBo1akSbNm2Ijo7O8LtYp0+f1k7BOnz48FtPbKtWrUrLli25d+8e7du31ymnoihpPgl3c3Pjzz//JCYmJtU9jZ2cnJgwYQK+vr5MmDCBMmXK6JTLlPTp04c+ffqkeC4gICDZpxBCZEfbt29nz549eHl5qR3FZCxevJiGDRuaxJ7GumrRogUtWrTQS11Vq1ZlyZIleqlLCJE2GTwLo8ibNy979+7N1LVmZmZoNBoURUlxMSlra2t+//13XSOyfPlyBg8eTOfOndm4ceM7y7q5uaVZ3/Tp03XOlN2sWrWKNWvW0LVrV7WjCCGEwRQvXpyhQ4eqHUNnV69exc/Pjw4dOpArVy6DtmVvb8+QIUMM2oYQQuhKFgwTWZ6npyenTp3i5MmTBl0EZPfu3SQkJLBjxw6d6nnx4gVubm4ULFgQX19fPaXLHlxcXPjuu+9wdXVVOwqQ9DpAUFCQ2jGEECLLiYqKwtPTk65duzJ58mS14wghRJYgg2dhNHFxcQQHB2fq2jp16lCvXj09J0pu2rRpdOjQgeXLl+tUz7Vr17h8+TJPnz7l8OHDekqXefPnz2f69OnExcUZpP6nT5/i4uLCe++9l2w6tqIorFq1ig0bNhikXV0tWrSIRo0aUa1aNZ1XdxVCiOxGo9FoF3Y09FNnkTFBQUGcOnVK7RhC5EgybVsYTbNmzThx4gQzZsxg3Lhx2uOBgYFYWVlRtGhRFdOBq6srv/32m8711KhRg6+++org4GD69eunh2SZ5+3tzYgRI4CkVZ8/+eQTvbdx5coVrl+/DsDx48epVKkSADt27ND++YsVK/bWrIEXL14wffp0ypUrR//+/fWeKy0vXrwAkrYNMdSNBSGEMFXW1tZcvHgRf39/2rRpo3Yc8dqTJ09wdXUlIiKC5cuXq9J/CpGTyeBZGExcXBwjRowgPDycRYsWaacw+/j4aMt4e3vTtGlTrK2t8fPzo1SpUmrF1RuNRsP333+vdgwASpUqhb29PdHR0VSsWNEgbdSvX5/PP/+csLAwevbsqT1etGhRzM3NsbCwIDo6mlOnTiWbPfDzzz8za9YsbR3lypV7q+6EhARCQ0MNcmPliy++oGjRopQvX5733ntP7/ULIYSpK1WqVIb65dWrV7Np0yYmTZqEp6enAZPlXLGxsdr9w/+7MvmjR4/Inz8/uXPnViOaEJmWkJDAw4cPef/999WOkiaZti0MxtvbmyVLlrBx40Y2bdrEzp07GTVqFD/++KO2TFBQEAkJCbx8+ZIJEyZQtGhRNm3apGJq4wkJCeGrr77SbuFlCE5OTgQFBREcHEytWrWApM42IiJCb22Ym5vz448/snr1auzs7LTHPT09uXbtGt7e3nTq1In69etrtxQDqF69OhYWFpQoUYLChQunWHe7du0oVqwYU6ZM0VveNywsLOjduze1a9fWe91CCJFVhIaG0rdvX2bPnm3wtkaMGMEff/yh13ek4+PjWbFiBSdOnNBbnaasWLFiHD9+nNWrV/PZZ59pj2/cuJGiRYtSrVo1YmJiVEwoRMZ5eXnh6OjI119/rXaUNMmTZ2Ew1atXp0qVKjx//pwmTZpQrly5t7aZ6tmzJy9evMDe3p6BAwcSFRXF6tWr6dq1q3bbqNjYWJ4/f57tng5+8cUXbN68mUWLFvHixYs0t8jKrHz58ml/7efnR506dbCwsMDHxwczMzODTpkvW7Ys5ubm2o7834P2Vq1a8eDBA2xsbFLdL/vChQvJPg0lISGBmJgY8uTJo/e6z5w5w+DBg2nSpAlz587Ve/1CCPEu8+fPZ/Xq1QC0b98eZ2dng7XVo0cPfv31V7p06aK3OufOncuXX36JhYUFQUFBJvFkytDq1KlDnTp1kh3z8fFBURSuX79OeHi4wX9mSkxMxMxMnsEJ/TDWz3v6IN/1wmAKFCiAn58fd+/efWtK7pt3TM3NzRk2bBg9evRg5syZ1KlTh7FjxzJkyBAsLCyYPXs2NWvWpEiRItrOP6uIjIzUTp3KjDcrTletWjXZwDksLIyqVatSvHhx7bvE+vL3338TGRlJeHg4v/32G87Ozjg7O3Pr1i29tvNvpUqV4vjx46xfv57BgwcnO1ewYMFUB84AW7duZdiwYcybN89g+aKionB1dcXe3p4//vgj3df5+voyevRo/v7773eW++WXX7hy5Qrz5s3T6xN/IYRIj8aNG2NlZUXVqlUNPvBcvHgxL1++1Ot7uAUKFADAxsYGKysrvdWb3YwfP54vvviCDRs2GHzgPHXqVCwsLBg1apTe6rx27Zos3pmDbd68meHDhzN//ny1o6RNURT5MsAXcNbDw0MRb+vcubOi0WiUAQMGKMWKFVOGDx/+VpkiRYoogOLp6amYm5srgDJkyBAV0qbsypUriq2treLg4KAEBQVlup7AwEDl+fPnysqVKxV/f39FURTl8OHDCqAAyqJFi/QVWVEURYmLi1OmT5+uzJo1S1mzZo22naNHjyqBgYGKoihKYmKi0qtXL6VkyZKKt7e3XtvftWuXMnjwYOX27dvJjh88eFA5fvy4XttKr1u3bmn/HiZMmJDu61xcXLTfo+9y6tQppXLlysqIESN0jSqyGQ8PDwU4q2SBPsvUvqSPfdv8+fOVZs2aKRcuXHjrXFxcnAqJ9Ofs2bPKnTt31I4hXqtevboCKKVLl9ZLfatWrVIA5f3331devnyplzqzori4OGXw4MFKx44dlSdPnqgdJ0cwRD+regeYXb+kY0/Ox8dHWbJkiRIVFaXkyZNHAZTChQsrgGJhYfFW+e3btyteXl7KmTNnlK1btyojR45UHj58qELylK1bt0474Nq/f79OdX322WcKoOTPn1+JjY1V4uLilCFDhijdunVTnj17pqfEb0tISFAWLFigrFmzRqlUqZICKAsWLFAeP36s/bMNGDDgretevXql9OvXT+nRo4fy4sWLdLeXmJio5M6dWwGULl26aI8fOHBA296ZM2f08mfLqPnz5yuDBg1SHj9+nO5runbtqgDK4MGDDZhMZGcyeJY+VhcJCQnafjExMVExMzNTAMXLy0vlZDlTWFiY2hGM5vjx40qHDh2Uffv26aW+iRMnKoBibm6epX7WU5Sk/8+ePn2ql7qOHz+u/Xln3rx5eqlTvJsMnk3oSzr2/xcVFaXkzZtXAZRRo0YpmzZtUj7++GNl1apVSp06dZTZs2erHTHDYmJilPHjxytTp05VEhISdKpr/PjxCqAUK1ZMiY+Pz1QdiYmJysiRI5U2bdoowcHBGbo2MjJSyZUrlwL8H3vnHRbF9fXxL4uAXRFRFEvsBRELKoi994bYo2LXGBWjBn/RiF2MPbFGETs2jBoVsRNUsAI2QESQIkUB6QvLnvcPw7wSAbfM7CxwP88zD7pz7znfpeyZ286hmTNnEhHRkiVLqGPHjuTn5/dV+/Pnz3Mf/ocOHVLKV/fu3b8KGl+utPv6+lJYWBg9e/ZMKbtiIJPJKDQ0lORyudhSGEUUNnhmMVYdhgwZQgDIycmJiIjs7e2pcuXKdPLkSZGVlTwcHBzyxNDiSFZWlmC2U1NTaf369WovRghB7t/Zhg0b1LaVlJREbdu2pbp169KrV694UMf4FmzwXIQuFtj/H6lUSjVr1iQAtHr1arHlaB05OTnk4eFBUVFRKtvw9/fnBqC5D1LKcPnyZVq6dCnFxcV9s21MTAw1a9aM6tevr/SWdZlMlu9Wpa5duxIAmjRpEpUtW5Z0dHR4m9FmMLQVNnhmMVYdjI2NCQD17dtXpf4fPnyg+fPnk6urK8/KSh6525ibNm0qthTekclk1KlTJypVqhSdOXNGbDkap3LlygSABgwYILYUhgoIEWdZtm2G4Ojr6+Px48cIDg5G586debGZnZ2N6dOnIy4uDi4uLjAxMeHFrhhIJBL07dtXLRuNGzdG165dERISgsGDByvdv3///ujfv79CbatXr/7NJFkFoaurCyMjo69ej4iIAAA8f/4c6enpAICYmBiVfDAYDEZJwM3NDWfPnsW8efNU6r9x40YuGWPv3r1Rs2ZNPuWVKHbv3o19+/Zh8uTJYkvhnZSUFHh7ewMAPD09YWtrK7IizXL8+HG4u7vjp59+ElsKQ0tgg2eGRjAxMeF1gOvj44NDhw4B+PwAsWDBAt5sF0VKly6N27dviy1DZdzc3HDy5ElMnz4dwcHBiIuLK5YPIQwGg8EXPXr0QI8ePVTu36FDB+jq6qJx48ZcRmuGarRv3x7t27cXW4YgVK5cGXv27IG3tzf+97//iS1H4yizuMAoGbDBM6NI0rp1a3Tp0gWxsbEYOHCg2HIYatKuXTu0a9cOANCkSZOv7n/8+BEVKlSAvr6+pqUxGAxGsWTEiBGIj49H+fLloaenJ7YchgKEhIQgMTGRi5eaYubMmZg5c6ZGfTIY2gqr88wokpQvXx537txBYGAgGjVqJLYchXn//j2srKzQvXt3JCUlAficdyArKyvf9jdu3ICnp6cmJWodZ8+eRbVq1WBhYYHMzEyx5TAYDEaxwdDQkA2c82Hx4sVo3749Hj16JLYUjvDwcLRs2RLt27eHu7u72HIYjBILGzwztIpPnz4hODhYbBmC4eHhAV9fX9y+fRv//PMPMjMz0aZNG1SsWBFXr17N09bLywu9evVC3759cePGDZEU/z/BwcEYOnQoNm3apFG/vr6+kMvlCAoKQkJCAkJCQrBnzx4kJiZqVIeyvH37FhcuXEBOTo7YUhgMBoOhIImJidi0aRMePnyIPXv2iC2HIz09nZtAVjX+/f7777CxscGtW7f4lMZglCjY4JmhNaSnp6Nly5Zo0qQJXFxcxJYjCIMHD0avXr0wePBgdO/eHTExMfDz84NUKv0qmOnp6UFHRwcAtGK78qZNm3DhwgUsXrxY5cDt7+8PExMTWFpaIiUlRaE+P//8MxYsWABXV1fUrFkTffv2xezZszFx4kSVNGgCqVSK9u3bY+jQoVixYoXYchgMBoOhIIaGhpg6dSoaNWqESZMmiS2Ho1mzZrh27RqOHDkCe3t7lWw4Ojri3r172LBhA8/qGIySAzvzzNAa0tLSEBUVBQB4/fq1yGqEoWrVqrh27Rr3//Lly2Pbtm3w9/eHg4NDnrbW1tbw8fGBXC6HlZWVpqV+xdChQ3HixAl06dIFlSpVUsnG1atXERsbi9jYWLx69UqhBCtGRkbYunUr93/6XKYGnp6eiI+Ph7GxMQDgwYMHSE5ORq9evVTSxidExK04y2QykdUwGAwGQxn2799f6P1bt26hRo0aaNq0qYYUfaZnz55q9Z89ezaOHTum8uCbIR5Xr15FREQE7O3toaurK7acEg0bPDO0BmNjY1y4cAF+fn4ql94oisyfP7/Ae3xm78zJycGFCxfQvHnzfJNyfYuBAwcqvFpcEPb29nj06BFq1aoFS0tLlWxMmTIFy5cvR1ZWFj59+gRjY2M8f/4c1tbWkMvlOHfuHIYNG5anj1wux8uXL9G4cWONrOKXLl0a9+/fh5+fH0aMGCG4PwaDwWBoBldXV9jb26NMmTJ4/fo1TE1NxZakMJs2bdL40SuG+gQHB6N///5cjpw5c+aILalEw7ZtM0QhJiYGV65c+WpVbsCAAfjf//6H8uXLi6RMeb5cZUxKSuLqE4eHh2vVCvqGDRswYsQItG/fXu1BsKoYGxvj1KlT2LJlCyQS1T5+lixZgu3bt+PChQto2LAhgP9fjf7vv3OZNWsWzM3NNVqfskmTJhg9ejRLxsNgMBjFiNxzxzKZjO0s+oLw8HC0atUK3bp1Q3JysigaiAihoaHFLtdI+fLlUa5cOQBA9erVebF55coVfPfdd/jhhx94sVeSYINnhihYWVlhwIABWLx4sdhS1CIhIQGNGjVClSpV4OHhgQYNGqBOnTo4cuQImjRpgmbNmuHu3bsa1yWVSjXuU1Po6+tj3rx5GDx4MPeaubk57t27B09PTwwfPvyrPrlJ6FSdzAgNDcXKlSvx8uVL1UQzGAwGQyXevn0LKysr2NracrEtLS0NL168EEXPzJkzcerUKfzzzz+oW7euKBq0kcuXL8Pf3x937tyBr69vvm1SUlJw48YNwZ5R5s6diwYNGmDs2LGC2M+PoKAgtGvXDuPGjRNsMqVmzZp48eIFnj59ytsiwMGDBxEeHo5du3axSSAlYYNnhsYhImRkZAD4nCSsKBMYGIg3b94gOTkZnp6eSEhIQHZ2NgICAiCVSpGTk4Po6GiNalq9ejVKly6NGTNm5Hnd0dER7u7uePDgASpUqKBRTZqgQ4cO6N27d773XFxcsHz5cpw6dUol25MmTYKTkxNGjx6tjkQGg8FgKMmpU6fg6+sLd3d3PH36FMDnz/sWLVrAyMhI45OaOjo6sLOzQ4cOHTTqV9uxtbVF3759MWrUKHTu3DnfNrlJUydPniyIBj8/PwCfk5NqimPHjuHRo0c4ceIEXr16lW+be/fuoXbt2hg6dKjKq+J16tRBq1at1JGahwULFqB9+/ZYtWoVSpVip3iVgX23GBpHR0cHXl5euHfvHkaNGiW2HLWwtrbG8uXLERMTAycnJzRt2hQfPnzAokWL0LZtW6Snp2PkyJEa1XTp0iUAn2cVlyxZwm1t1tXVzXdVVlGSk5PRr18/JCQk4O+//+bsFkRqair27duHdu3aFRhI+SIhIQE2NjZISEjAzZs3YWZmlud+/fr1sWrVKpXtN2jQAN7e3mjQoIG6UhkMBoOhBHZ2dnB3d4epqSlat26NnJwchIaGAgAXj5o3by6yyvxZtWoV9u/fj7Vr1+L7778XW46gVKtWDR4eHoW2ya3UIVSpyf3798PFxQVjxowRxH5+jBs3DpcuXULjxo3RrFmzfNucOXMGkZGR3KUNOxY6duxY4A4BxjcgInYJcAG4b2VlRYy8pKSk0JQpU2j+/PmUnZ0tuL+4uDg6ffo0paSkCO5LW9i5cycBIAD0+++/82b35s2bStlduHAhAaDSpUtTcnIybzry49atW5y2Hj16kEwm+6pNQEAADRs2jA4cOKC0/ezsbPLz8yOpVMqHXAaDiIisrKwIwH3SgphV1C4WY0s2t27dorZt21LPnj0pOjpabDkFYmxsTACoU6dOYkvRCry8vMjZ2ZliYmLElqJRgoKCqGfPnrR48WJR/CclJVFwcLAovsVGiDgregAsrhcL7Pnz559/coOcq1evCu7P0tKSANCIESME98UnkZGRNHnyZNq9e7fSfaVSKU2ePJkGDx5McXFxvGmSSqU0btw46tSpk0KBb8uWLQSA6tSpI/igUyaTUe/evbnfLQ8Pj6/a2NraEgDS1dWlnJwcXvympaXRwIEDycrKit69e8eLTUbJgQ2eWYxlFG927txJlpaWdPHiRbGliM6lS5dIIpGQkZFRiRs8i0l6ejrVqVOHAKi0eFDUESLOsjPPDI3SuXNnVK9eHQ0aNFDp7EZAQAA8PT0Vbi+XywFAtMyL6enp8PLyUjo5hrOzM1xdXTF79mzExcUp1VdfXx8HDx7EhQsXuBrIfKCvr4+QkBB4e3tjw4YNhbYNDQ1Fo0aN8PDhQzx9+pQrD3Xv3j0sXLiQ9yzkurq62LFjB6pWrYq6devCwsICQN7M20OHDoW+vj6GDx+ucqbv/+Lr64tLly7Bx8cH586d48Umg8FgMPjhypUr6NChA3bt2iWI/bS0NO45Iz/mzJmDhw8fYtCgQYL4L0qEhIRALpfj48eP+PDhg8b8zps3DxYWFvDx8RHUz4cPH2Bra4tZs2ZpVQKutLQ0REVFAfj/5KkMNeFzJM6ukjUr/v79e7p//77C7V+9ekXVq1enJk2aUHx8vNL+3r59S/r6+gSADh06pLDG48eP06dPn5T291/u3LlDtra2dOXKFYX75K6GjhkzRilf586do1KlSlG7du0oKytLWamCUaNGDQJAtra2BbZJS0ujKlWqEABauXJlnnumpqYEgHr16iWIvpycHJLL5ZSSkkLm5uZUsWJFun79OrfSLJfLefWXnp5OQ4YMoY4dO1JERASvthnFH7byzGIsQ1i6du1KAMjQ0JD27t1L69at4y2mnjp1inR1daldu3YaOYZW1JFKpbRx40Zyc3PTmM+EhARuR9qUKVME9bV9+3bO1+3btwX1pSyXL1+mNWvWCH6ELpcjR45Q/fr1ydnZWSP+CkOIOMsShjFUIi0tDRYWFoiLi8OOHTvw448/frPPnTt3EBsbi9jYWPj5+aFXr14FtnV2dkZYWBjWr1+PypUrA8CXD00Kz+qZmJjwVrLAwcEBT548wdOnT/HmzRuF+uSuGiu7ejxs2DCkpKTAwMAAOjo6SmsViitXruDq1auFZsokIu7nk52dnedemzZtEBUVhdatWwuiL3dFOSQkBM+ePQMA9OrVC9bW1rh79y7v38syZcrg/PnzvNpkMBgMBj9Mnz4dISEh6NOnD2bOnAkAMDY2xrRp09S2ffv2beTk5ODRo0dITk5GlSpV1LZZnNHX19d4eVJDQ0PMmTMHd+7cwdSpUwX11adPH3z33XeoWrUqr1mx+aB///7o37+/xvzt3LkToaGh2Lp1K5YsWaIxv5qCDZ4ZKiGVSvHp0ycAig8M4+PjUatWLXTv3h3dunUrsF1AQAAcHR0BfE7Nv3TpUgBAvXr1cO/ePURHR2PIkCHqvQEVGDJkCJ4+faqUb3d3d1y8eBF2dnZK+ytdurRC7bKysrht0UJjYWHBbYkuiHLlyuH+/fsICAj4KtO4u7s7oqKiBM802apVK/z66684duwY3rx5gwcPHiAjIwNly5YV1C+DwWAwtIfx48dj/PjxiIiIwF9//YXU1FQ0bdqUF9u//PILpFIpOnbsyAbOWszOnTs14qdp06Z4+/atRnxpO4sWLcKqVaswffp0saUIgk7uSh6DX3R0dO5bWVlZ3b9/X2wpgnHv3j34+/vD3t7+mwO9xMRELrhMnz4d+/btK7BtcnIyLC0tERkZCU9PT3Tq1IlX3f8lLS0NERERCgXUnJwc6OrqCqpHGezs7HD27Fns2LEDc+fOFVuO1hEREYHVq1ejR48eGi1dwWAogrW1NXx8fHyIyFpsLUWNkhBjixNHjx7F3r174ejoiIEDB4qiITExEVKpFCYmJirbuHTpEjZu3IiZM2di3LhxPKpjMBhCIEScZQnDGCrTsWNHzJ49W6EV0kqVKmHgwIGoUKEChg4dWmjbihUrIjAwEImJiYIPnIkIHTp0QLNmzfDbb799s702DZyBz9uoieibtRWLGmlpaejQoQOqVauGx48fq2yndu3a2LdvHxs4MxiMEoGyySk1xdKlS+Ht7Q0nJyfRNBgaGio9cP7777+xdetW7vu6YsUKeHl5cTviGIyigrZ+NhRF2OCZoREkEgn+/vtvJCcnfzXr/Pr1a/z99995MlZKJBIYGBgIrksmkyE0NBQAEBQUJLg/vnF1dcXYsWOxfv16pfpdu3YNTk5OSExMFEiZ4qSkpMDNzQ0xMTHca69fv8aDBw8QHx9f7CYGGAwGQwiGDRuGMmXKYO/evWJL+Ypp06ahatWqsLe3F1uKwoSHh2Po0KFYuHAhtm3bBgCYNGkSqlSpgilTpoisjsFQHEdHR5QuXRoODg5iSykWsMEzQxRiY2Nx9+5dJCcno127dhg8eHC+K79Hjx7FsmXLkJqaqpDdnJwcPH78GJmZmQq119PTw+XLl7FixYpvll/SRkaOHInjx4/D3Nxc4T5paWkYNGgQVq5ciWXLlvGqJzAwkJuMUBR7e3uMHTs2TzILCwsLLF68GCNHjsw3scunT5/UWpFmMBiM4oaHh4foO5ESExPxzz//5JkMj46ORkBAAKZPn445c+aIpk1ZKlSowB03y83T8eOPP+Ljx49YsWKFmNKKBHFxcUVyUaI4cuXKFQAoMYsRKSkpuHDhApebiXf4TN3NLlZGQxEyMjK4kkerVq2i8uXLEwCqVKkS9erVizIzM4mIKCQkhEv7v3r1aoVsT5kyhQBQ3759hXwLRZrs7Gxq3LgxAaAdO3bwZtfLy4skEgnp6+vT8+fPFe43cuRIAkCtW7dWqL1cLqemTZsSAHJyclJVLmdLFbZt20YWFhb0119/qeWfUbJhpapYjOUTNzc3GjduHL148UI0Dc2aNSMA9NNPP3GvLV++nIvlL1++FE2bKsTFxYmqOSsri9zc3CgwMFA0DaoQGxtLhoaGpKOjQ7Nnz6b379+LLUlQkpKSqHfv3tStWzeKi4sTW85XeHl50dixY+nmzZtiS9EIAwYMIADUo0cPQeIsW3lmaByZTIakpCQAn2eHfHx8MHLkSHz69AnXr1/H69evAQBVq1ZF7dq1IZFI0LJlS4Vsh4WF5fmqTWzfvh39+/dHQECAqDpKlSqFx48f49ixYyAipKen82I3Li4OcrkcWVlZSEhIULjfwYMHcerUKW5m9Fts374dwcHBAIB3796ppBUAjhw5An19fQwfPlzpvj3s7uEAACAASURBVGvWrIG/vz82b96ssn8Gg8Hgk9GjR+PYsWNo3ry5aBo+fPgAIG8Vjv79+8PIyAgdO3ZEvXr1xJKmEsbGxmjWrJnK/RMSEjBgwACMGDECaWlpSvf/9ddfMWbMGFhZWSm8o04bSEpKQlJSEogIu3fvVqniSFHi5s2buHbtGm7fvq3ws4wm6dy5M44fP47u3buLquPmzZvcM76Q5P6tCPY3w+dInF1sVlxRfH19adeuXZSenk5EROHh4dSvXz+aN29entXA1NRUio6OVthueHg4rVmzRqmVT02QmZnJzbzXrl2bPD09RdWTkJBABgYGBICWLl3Ki025XE6HDh2i06dP82KvIMqWLUsAqFGjRpSQkFBguwsXLtC2bdtIKpXme9/Ozo4AkEQiIZlM9k2/x44do1WrVlF6ejqtXbuW6tevTydOnFD5fTAYbOWZxdjiRkBAAG3fvp0SExPFlqIVHDx4kIv958+fV7r/smXLCAAZGRlxu/KKCufOneN2iRX33YBJSUnUq1cv6tKli1auPGsDe/fuJQBUrlw5io2NFdRXXFwcubq6UkxMjCBxVvQAWFwvFtgZ/2X06NEkkUgIADVu3FhULWlpadzWeT63bqtLRkYG3b17t9DB7OLFi6lmzZrk5uZWYJvXr1+Tjo4OAaDNmzfn2+b58+dka2tLBw4c+KauwMBA7gHI2dn522+EwVAANnhmMbaoERYWRj179qTp06crNOlYnLl79y5NnjyZvL29C2wTFRVFbdu2JRsbm0InewtCJpPR+fPnKTQ0VB2popGcnEx///03JScniy2FITLbtm0jAKSvr09RUVEa8ytEnC0lzHo2gyEsO3fuRFxcHOrXr4/Hjx9j2bJlqFatmtiyCsXNzQ0NGzbE2rVrMXjwYFG1lC1bFv7+/ggPD4elpWWB7WQyGVatWgW5XA4nJyeUKiXsR0br1q0RGBiIWrVqISIiIt82GzduxMaNGwu1U6lSJRgaGiIhIYFL9PJfzMzMcObMGYV0GRsbw8TEBHFxcaJuidRW9u3bh40bN+Knn37C7NmzxZbDYDAE4tChQ7hx4wZu3LiBH374ARYWFmJLEo1Zs2bh2bNnePjwIZ4/f55vm5o1a+LRo0cq+9DV1cWQIUNU7i82FSpUEK2uN0O7+PHHH2FiYoL69eujZs2aYstRCzZ4ZhQ5fH19MXfu3K9e37FjhwhqlGPNmjUKDUKTkpIQEBAAGxsbwWpLGxsbw9jYuNA2Fy9exOrVqwF8Htja2toKoiWX3AFzVFQUiAg6Ojoq2TE2NsbLly+RmJiIpk2bqq2rSpUqCAoKQnJyMmrVqqW2veLG9u3b8ebNG2zfvp0NnhmMYszw4cNx9OhR1K9fn5fP1qJM9+7d8ezZM9HPkTIYRQGJRILRo0eLLYMXWMIwRpGjTp06MDY2hr6+Pho0aACJRAIrK6tv9rt27RrKly+Pbt26QSaTaUBp/iiyetutWzd07doVCxYs0ICigjE3N4ehoSEqV66scNI2dXBzc0Pr1q2xe/dulQfOuVSvXp3Xh7uKFSuygXMBLFmyBC1atMCSJUvElsJgMATE3NwcwcHB8PDwgIGBgdhyRGX79u1ISkrC77//LrYUrSU7OxvOzs7Yt2+f2FIYDN5gK8+MIkeNGjUQGhqKzMxMVKpUCSkpKVwtxsK4fPky0tLScOfOHcTGxsLU1FQDalUjPj4+z1d1yMnJwfz58xEbG4vdu3ejatWqCvdt2LAhoqKiAABlypRRW8u3GDRoEAYNGqS2naSkJGzevBmWlpYYOnQoD8oYhTFp0iRMmjRJbBkMBoOhUSpVqiS2BK3m6NGjcHR0BPD5qJSNjY3IihgM9WGDZ0aRpHz58ihfvjwAKDRwBoAFCxYgIiIClpaWWj1wBj6vkl+/fh0TJkxQ25aPjw927twJAOjYsSMcHByU6v+tQTMR4eeff8bbt2/xxx9/oHr16ipr5Ys1a9Zg8+bN0NXVRUxMjFITBgwGg8Eo+gQGBuK7775D6dKlFe4THBwMb29vjBo1invGYKhOkyZNoKenh7Jly7KdW4xiA9u2zShSvH//Ho0aNYKRkREuXLigVN+6devizJkz3CyoNtO8eXPMmzdP4YmBwjA3N0fbtm1hamqKPn368KAuL35+fvjtt99w5swZHDhw4JvtZTIZjh07Bj8/P9615GJubg4A+O6779gDEIPBYJQwli1bhmbNmqFXr15K9evatSumTp0q+pGp8PBwPHv2THA//v7+iIyMFMx+x44d8e7dO4SGhhaYvJPBKGqwwTND6/Hw8MD27dshlUpx584dhISEICEhATNnzhRbWpGgYsWKePToESIjI2FmZgbg87bmP/74o8AMocrQuHFjtG/fHsbGxujbt+83269fvx4TJkyAjY0NkpKS1PafH5MmTUJYWBj8/f2VWnVgMBgMRtHn1atXeb4qStmyZfN8FYPw8HA0b94cLVu2xKVLlwTzc/r0abRq1QpmZmaIiYkRzI+JiQkvCwHf4unTp+jZs+c3q3EIARHBwcEBAwcOLLBSCKP4wLZtM7SaiIgIDBw4EHK5HOnp6Zg7dy4aN26M4OBg9OjRAyEhIahXr55gGamLK/Pnz8fhw4dhbGyMuLg4tWyVK1cOvr6+CrfP3Qaur6+f788tLS2Ns6sOubPcqamp8Pb2RqdOndgqNIPBYJQAduzYgWbNmmHAgAFK9bt37x6ePHmC3r17C6Ts23z69Anp6ekAgOjoaMH85A6YU1NTkZqaKpgfTbFp0ybcvHkTN2/exLx58zQ6cf7ixQts27YNALB//36sXLlSY74ZmoetPDO0mvLly3MzlnXq1EGFChUQFBSE+Ph4GBgYoFGjRkonKkpJSYGdnR3s7OyKRcAojICAABw8eBBxcXF5gnDuGWBFzgI/ffoU58+fBxHxoumnn37C1atX8eTJE1SoUCHPveDgYJiamsLU1BTBwcG8+Bs5ciT69++PMWPGKNVv165dsLOzQ0hICC86GAwGQwiioqIQFhYmtgytwtTUFGvWrEHHjh2V6le9enX0799foaoYQtGyZUv07NkTAODp6amWraysLIwcORLt2rX7KpbNnj0be/fuxeXLl9GwYUO1/OSSk5OD77//Hq1bt9bItvMvGTNmDKpWrYrx48drfMdZo0aN0KNHD9SqVatI1+VmKAgRsUuAC8B9KysrYqhPbGwsPXv27KvXLS0tCQCZm5srZc/NzY0AEAA6efIkXzK1jqSkJJJIJASA9PX1SVdXlzw9PYmIKCcnh+7cuUMJCQmF2oiIiCB9fX0CQLt37yYiovj4eFq4cCG5ubnxrvns2bPcz8bd3V2hPtnZ2XT69GkKDAzM937Hjh0JAHXu3Dnf+5mZmbRx48Y87+fTp0+cjilTpij/RhgMBbCysiIA90kLYlZRu1iM/UxgYCCVKVOGSpUqRffu3RNbDoMH0tLSqF27dio93/yXBw8ecLFs7dq1PCksmKCgIM7fkiVL1LaXmppKb9684UFZySE7O5uCgoJILpeLLUUrECLOspVnhtZTrVo1tGjR4qvXXV1dsXjxYhw5ckQpe127doWFhQVatWqFrl278iVT60hPT4dcLgfwefY5JycHgYGBAD4Xq+/SpQsMDQ0LtSGRSCCRfP6YyN1ivXbtWmzZsgXjxo3Dhw8feNU8dOhQODk5wcnJSeHZ21WrVsHOzg5WVlbcVreIiAiYmZnB0tISe/fuxd69e3Hy5Ml8++/ZswdLlizBmDFj8PLlSwBAhQoV0KdPH+jr6yu97Y/BYDA0xfv375GRkQGZTMbOWn6BXC7H3r174ebmJrYUpXj//j3q168Pf39/TJ48GcePH1fLnoWFBezs7NC+fXuMHj2aJ5UF07BhQ0ycOBFt2rTB999/r5at7OxstG3bFg0aNMCePXt4Ulj8sbOzQ5MmTfDDDz+ILaXYws48M4osZmZmKiWGMDExETTTs6JcuHABL168wLx589Q+35sfNWrUwIYNG3Dt2jX069cPUqkUM2bMUMpGzZo18fDhQ0RGRqJfv34AAEtLS+jo6KBp06aoWLEir5p1dXWxYsWKfO/5+PjA1tYW5ubmuHjxIvT09AAAOjo63Nfcf1+/fp0bCIeFhRX6vhs2bAiJRAJDQ0NuG7uOjg6uXr0KIuJsMhgMhrbRrVs3HDp0COnp6bCzsxNbjtZw/PhxzJo1C8DnqgtWVlYiK1KM0NBQxMbGAgA6dOiQ78KBMujr6+PUqVN8SFMIiUSCQ4cO8WIrIyMDoaGhAD6fKWYoRu73in3PhIMNnhkMDXLlyhV8//33sLa2xqVLl0BEkEqlcHJyEsTfzz//jJ9//lktGy1atMgTwMePH4/evXujUqVK0NfXV1eiwpw7dw7R0dGIjo5GeHg4d0br119/RevWrdGiRQsuGdnw4cNx8eJFlClThjs7VhADBw5ESEgIKlasCCMjozz32MCZwWBoOxMnThRbglYxe/ZsnDlzBhKJBKVLl4axsbHYkhTGxsYGW7ZsQXx8PCZPniy2HFGpWLEi/vrrL9y/fx8ODg5iyykyuLm54dSpU5g6darYUootbPDMYGiQU6dO4ePHj/j7779hYmKCmJgYNGrUSGxZSlOtWjWN+5w1axYCAgLQokWLPMlNdHV1MWzYsDxtK1euDHd3d4Vt16tXjzedDAaDwRCH7Oxsbotv//79sW/fPtSqVUtkVcpRXAeKoaGh2Lp1KwYPHow+ffoo1GfAgAEaPToVHh6OzZs3Y8CAAdxuu6JGmzZt0KZNG7FlaA1ClERlZ54ZJR4ifrJIBwYG4s2bN4W2mTZtGiwtLbF27Vq8evUKr1+/xvjx43nxX9ypV68erly5gt9++01sKQwGg8FQg8uXL2Pbtm2QSqUKtScihXJs6OnpYc2aNbC2tsavv/6q0MA5NzcII38OHToECwsLHD58WC07P/30E/744w+MHDmSJ2X8s2jRIvz++++wtbUVW4pGCAgIQP369dGrVy9kZmaKLYd37t69y+X64RM2eGaUWKRSKaysrFChQgXcunVLLVve3t4wMzND8+bNCz1nYm9vj0ePHiE5ORmVK1fmrTxESSUpKQmnTp3C6tWrcfXqVY35DQ4OxqhRo/Dnn39qzCeDwSjaEBEePHiA5ORksaWISlhYGAYPHgwHBweuNm5h5NbsNTY2hqOj4zfb29raYv78+Wjbtu03244ZMwb6+vrss7wQ1q9fj4CAAGzYsEEtOx06dMjzVRtp3749AKBdu3YiK9EM58+fx9u3b3Hjxg1BBpkAEBkZCRsbGwwZMgQZGRmC+CgIIfIJAWzwzCjBREVFwdfXF2lpaWoPvOLi4iCXy5GVlYWEhIR823w5cx4fH6+Wv+JMZGQkRo4cqdA58OHDh2P06NH49ddfMWjQII19X9esWYPTp09j5syZxXK2lsFg8M/SpUvRoUMH2NjYiC1FVCpWrMhVeqhbt+4327u4uCArKwvA54nqwsjIyIC1tTXGjBlTYPLJL/nrr7+Qk5ODixcvKqC8ZDJv3jzUq1cPP/74I/fawoUL0a5dOzx69EhhO/b29nBzc8OlS5eEkMkLixcvxvv373H9+nWxpWiEyZMno2/fvpg9ezZatmwpiI9z587h3r17uHjxInx8fJTu7+TkhDZt2uDOnTtK923VqpUw74vPuldCXwBMASwA4AngHYAsADEAzgLo8J+2egBsARwC8ApAKoAUAL4AZgPQFVgrq0FZBFi7di2NGTOGoqKilOp3+/Zt6tSpE/3+++/ca4cOHaLTp08X2s/Pz4+2b99OSUlJKuktCTg6OnJ1Il+/fl1o23/r9xEAMjU1pbS0NEE0rV+/nho0aEAnTpwgIqITJ05QqVKlqF+/foL4Y5QMtLHOc1GJs0Uxxk6YMIEAUJUqVcSWIjqxsbH04sULhdru3r2bWrVqRYMHDyY/P79C22ZkZJCRkREBoOXLl3/T9tGjR8nW1pb8/f0V0qJJ3r17R/Pnz6dLly6JLSUPHz584OLutGnTFOojl8upTp06vNV/ZhQd3r17R9bW1jRo0CCln9FkMhnp6OgQALK1tVXJvxBxVvRArZRYYMO/f7AhAPYDWA/gDAAZgBwAo79o2/TftikA/gLgDGAPgKh/X78IQEdArUUusDMUp2/fvgSAypYtK7aUYoeXlxdVqlSJbGxsKDMz86v7L1++pJ49e9LSpUvp/fv35OrqSt7e3pSQkKCUn4yMDBozZgz169eP4uLiCm1btWpVAkCdOnXiXsvJyVHKH+P/kUql5OPjQ1KpVGwpoqKlg+ciEWeLYoz9+PEj/fbbb/T48WOxpWgtqampJJfL1bIRGhpK58+fJ5lMxpMqcRg3bhwBIAMDA617L1OnTqXGjRvTP//8o1B7mUxGVapUIQA0a9YsgdV9xs3NjSpVqkSTJ0/WiD+GMDg4OFCDBg3oypUrKvVng2dgBICu+bze+d/Z8QQABv++ZgpgDoBy/2lbDsDDfwO7nYBai1xgZyjO8ePHqVq1auTg4PDVvdu3b9Mvv/xCMTExKtlOS0ujoUOHUteuXSk6Ovqr+9nZ2SrZLS788MMP3Kx3ft8fRfH09OTs7Nq1q9C227ZtI3Nzc3J3d1fZH+P/GTFihFozycUFLR08F4k4y2Js8ePQoUMkkUioS5cuKg+gnZ2dyd7ePt8J0cjISFq4cCF5eHioK1UjbN26lQCQtbW12FJ44cWLF/Tnn39SamqqYD4cHR2pWbNm5OnpScOHDycAJJFIaMKECfTu3TvB/DK0lxI/eC70jQBX/w3Ulgq0Hftv2z8E1MMCu4C8efOGUlJSxJbxFTk5OVSuXDkCQBMmTFDJxpeDut27d+e5N3HiRNLR0aEtW7bwIbdAMjIyaPDgwdSqVSsKDg4W1Jey3Llzh0xNTWn48OFqrf5++vSJOnfuTC1btqSwsDAeFTK+Rbt27QgAtW/fXmwpoqKNg+fCLm2KsyzGFj8mTZrEDXbS09OV7h8YGMjFzv9u2T516hSZm5sTACpdurTWreQWRHR0NGVlZXH/T0xMpOTkZBEVaS85OTncFt/hw4fTw4cPqVu3btzvxA8//CC2RIYICBFni1PCsOx/v8p4bsv4AmUz5aWkpKB79+6wsLBAWFjYV/cjIiJw8eJFyGSK/ygOHjyIBg0aoGXLlgUmazp58iQmTpyI4OBgpfSqi0QiQZMmTQAAzZo1U8mGtbU1+vTpg/bt22PQoEF57v39998gIsESbhw+fBhDhw7F7NmzcfHiRfj5+SlVL1kTdOnSBZGRkXB3d4dEovpHWMWKFeHl5QV/f/8Ck9YcPnwYgwYNwoMHD1T2w/gaNzc3rF+/HidOnBBbCkM5WJxlCIaTkxPs7e1x+PBhlClTRun+tWvXhrm5OcqUKYPu3btzr0dERGD06NF49uwZAMDCwgK6urpqac3MzMSZM2cQGRmplp1vUaNGDejp6QEA/Pz8YGpqirp16+Lt27eC+s0lOjoa9vb2GDduHN6/f68Rn6oikUjwv//9D2ZmZpgzZw4sLS1x7do1WFtbQ09PD7169RJbolbg6+uLjh07Ys2aNWJLKbrwORIX6wJQB0AmgGgokKAEwGV8nokawINvaQGXvLjNiv/vf/8jADRnzhyF+1y/fp2b9fvjjz/y3JPJZGRiYkIAaPHixQrbXLx4MQEgXV1dio+P/+p+Tk4O6enpEQCys7NT2C5fpKenU1BQkCC23d3dyc7Ojh49esS77cjISG7WFgCZmZlRp06d6O3bt7z7KiqUL1+eAFDfvn3FlqIyR48epY0bN5b488XaSFFaeRYrzpakGMvghy9Xaok+x7bcZ4IJEybw8lk4Y8YMAkB169ZV25aiHD16lIvPV69eFdyfXC6nGjVqcD4dHR0F9ykU//2dyI/09HSKiIggIqKTJ0/SqFGjyM/Pj6Kjo+m3336jV69eCS1TI4wePZr7mZaE5wIh4mwpXkfiIqCjo6MH4AgAAwA/E1HON9rPANAfwE0iuqwBicWG3HJOypR1srGxwejRo5GQkICRI0fmuUdEyM7+vDiRW4ZCEX755ReULVsWbdu2RdWqVb+6L5FI0KNHD3h6eqJ3794K2+WLMmXKoHHjxoLYHj58OIYPHy6I7SpVqqBhw4Z4/fo1AGDJkiWYOHGiIL6KCmPGjMGRI0e++t0tKvj7+2PChAkAgLJly+KHH35QqN/jx4/x8OFDTJw4EWXLlhVSIqMIwOIsQxtYs2YNjh49ig0bNmDYsGEFtstdqc1l06ZN3LNGXFwc9PX11dZCnyd2uK+aYNSoUQgNDUWZMmU09myTuyuwVKlSRXrl9r+/E/9FKpWiVatWCA4OxqFDhzBnzhykpaUhNTUVcrkcHh4e2Ldvn8Z3MwrBpEmTcO/ePQwYMICXv4USCZ8jcU1f+Fyn+hg+z6DsU6D9IHxOeBIGoIbA2ordeax79+7R+PHj6fr167zZDAoKoqNHj+abVVldVDkzVdKRSqUUGhr61Tnnx48f08SJExWa7WbnsbSH6OhoqlKlCunq6iq8UpGens6d21+4cKHACks2RWHlWVvjbHGMsYzCqVChAgGgnj17KtVvx44dBIDKlClDd+/e5UVLeno6ubq60rVr13ixp60EBwfToUOHtDLHDJ98+PCBJBIJASAHBwcaPXo06ejo0M6dO2nKlCkEgLp16ya2TIYKsIRhXwd0138D+hEAkm+0H4DPW84iANTXgD4W2FUkLCyMWrRoQdbW1pSYmCi2HI709HQ6c+aMwhmeV6xYQSNHjqTIyEiBlQlP586duVrKhWFnZ0cAaMOGDRpSJizOzs7UunVrjWyRE4oPHz4olRBNKpVSzZo1CQCtWbNGQGUMbR88a3OcZTFWe/hyS6yXlxft2bNHkO2g69evJzMzM7pw4YLSfZ89e0ZJSUm86mnRogX7nPyXGTNmkKGhIR0/flxsKSrj7u5Ojo6O9PHjRyL6/8om2dnZdO/ePUGzhGszimx512bY4DlvQD/0b0A/jm+cvwIw8N+AHgmgoYY0ssCuInv37uXOY6ha160w4uPj6fz580qvTLdq1YoAUO3atb/ZNjg4mHsPS5cuVVWq1uDk5EQAaOLEiYW2MzIyIgDUu3dvDSkrGLlcTgsWLKABAwaoXKKiTJkyBID69evHszrt5v3793Tnzh21660yCkebB8/aHmdZjNUOjh07Rrq6utSzZ0+KiYnhzhavXr2aF/tZWVm0ceNGOnz4MC/2+EImk1HZsmUJAE2aNElsOaKT+3MfMGAAN/hkFH3mzZuXb/b6ogTLtg1AR0dHAuAggIkATgL4ngo5f6WjozMQwFl8rk3ZnYhCNCKUoTK2trYYOHAgxo4di27duvFuv2/fvhg6dCimTZumVD9/f38AQEJCwjfb1qlTBx07dkSlSpXQv39/lXQWxtu3b7/KNB4dHY2xY8di3bp1vPtbsWIFUlJScOjQoULbHT58GJMmTcLmzZuVsp+amorz588jMTFRHZkAPp9pW758OVxcXLBt2zZcvnwZ+/fvV8mWg4MD6tevj5kzZ6qtqyiQGxhMTEzQpUsX6OjoiC2JIQIszjJyCQgI4GJffly9ehU5OTm4efMmiAgVK1YEABgbG/Pi/8CBA1z+jSdPnijcLzs7G+vWrcMff/yhtgapVIqPHz/meU1XVxceHh5YtmwZatWqhXPnzqntpyizbds2dOvWDc+fP0e1atXw119/FdpeKpVi1apV2Lt3r4YU5iUzM1OhZ7niwoMHD9ClSxc4Ozsr1S+3ssvlyyx1RR74HIkLfSHvFrJTAEp9o31/fJ4Jfw+giYa1sllxNZHJZNSvXz8yNDTk9VxR8+bNCQCNGDFCqX4LFy6k6tWrk6ura4FtAgICaM2aNVzGRiHYtm0bAaCWLVvmWRl0dHTkVrtDQkJ49ZmUlETOzs68nRf7L8OGDSMAZGNjo7Yte3t7AkAGBgbUrVs3MjU1FSQ7eXHj7du3VKNGDapRoware60htHHluajEWRZjhcfX15ckEglJJBK6f/9+vm1CQkJo7NixdODAASIiioqKIh8fH940XL9+nXR1dcnQ0JCioqIKbJeVlUWzZ8+miRMnUnJyMrm6unLx8Pbt2yr7T09PpyZNmpBEIqGzZ89+dX/t2rUEgHR0dCg0NFRlP8WBd+/eKbzjbufOnVzbhw8fakjhZ5KSkui7776jUqVKkYeHh0Z9i8WoUaO473fudvQv2bBhAxkbG9PWrVvzvH716lUaNWoU/fPPP5qSyjss2zbwK4BJAFIBBANYls/KyF9E5Kejo9MUwDl8zg56G8DYfNqGEZGrkIJLKomJiXB1dUWPHj1gYWGhko24uDh4eHgAAM6fP89bpkcPDw/cvHmz0Gyd+bF58+ZCV1SJCEOHDsXbt29x9+5dwWbqAgICAABBQUHIysqCgYEBgM8r6r///jvMzMxQq1YtXn06Ojpiz549KFu2LBISEjiffCGVSvN8VYfmzZsDAJo0aYJbt26pba+k8PDhQ66O58OHDwusfa0M3t7eGDNmDCwtLXH27Fm1a6syNAKLswwAQEpKCuRyOffv/GjQoAGOHz/O/b9mzZqoWbMmbxp69uyJt2/fomzZsjAyMiqw3fXr17F7924AQNeuXWFmZgYDAwOUKVMG3333ncr+ExMTERwcDCLCgwcPMGLEiDz3GzVqBB0dHVSrVg2GhoYq+ykO1K5dGwcPHkRAQAAWLVpUaFszMzPo6emhcuXK33xeSUpKQkhICCwtLXnRGRMTg7CwMADAo0eP0LdvX6X6u7u7w8vLC46OjjAxMeFFk7r4+flh8uTJaNOmDfbv3w+JJO/G4gkTJsDLywuDBg1CqVJfD/3279+P+Ph4uLi4YMGCBdzrffr0QZ8+fQTXX+TgcyQu9IX/nw0v7Jr8b9tuCrS9LaDWEj0rPmHCNvDn6wAAIABJREFUBAJARkZGBbaRSqX0+PHjQpMRDBkyhHR0dGjQoEFCyOSNAwcOUKlSpahatWoEgGbNmiWYr9jYWHJ0dMx3xvRbZ1Tlcjn5+/tTWlqaUj7XrVtHAKhhw4aUk5OjVF9F+PjxI7m6uiqcjK0wPnz4QHPmzCF3d3celJUcMjMzae7cuTR37lzest/nnpcCIOhujKKKlq48F4k4q0qMzcjIIJlMplSfks758+fpr7/+UstGbGws9evXj8aNG6fyZ8uBAwfIysqqwGRhsbGx1LhxYzI1NaXXr18TEVFcXBwvSUcPHz5MDg4OBZ7lDQkJoYSEBLX9lDRiY2Pp06dPhbbJycmhhg0bEgBatWoVb7737t1LixYt+qb//5KSkkK6uroEgGbMmMGbHnVZsGAB95mrys6xEydOkI2NTb67K4o6LGFYEbpK+uB58eLFBIDMzMwKbDNy5EgCQKNHjy6wzaBBg7gSE4Vx/vx5mj9/fqHbuoRk6NChBID09PRo0qRJNG3aNK0s2bRkyRICQKr8bj558oT3bKXPnj2jGjVqUJs2bZQOYgUxZ84c7mdR3MtraDuBgYHUs2dP+vnnn8WWopVo4+C5qFzKxthbt25R6dKlqUmTJlr52Sw0aWlpoiUA/OOPP7gH+xs3bqhkI7cCQLNmzejOnTs8Kyy5JCQk0MWLF5WeUBeCjIwMOnfuHMXExHCvZWVlcaUTtWGwKpPJqHXr1gSA9u/fL7YcDj8/P2rdujVNnTqV97/zov55yQbPRegq6YPnnJwcunv3bqGzvm3btiUA1L59+wLb+Pn5ka2tLR07dqzANpmZmVymR7GyXvr5+dHw4cPznDvetWuXKFoKY8SIEQSAjI2N1bJz8+ZNevz4sdp6Nm/ezH2/1D1PLZVKKSYmhnbt2kUAqGnTpvme7WEwtAU2eNZcjF21ahX3WfPixQuF+xUH9uzZQzo6OtSnTx9R/IeGhlKLFi2oS5cuKj+Ir1y5kgwNDbmfYe4ZTBcXF9qwYUORL6cjFv9+BpGdnZ3YUrh8JU2bNs3zuo+PD/3222+8T96rSlZWFsXGxootQyP8/PPPBIBmzpyptq2MjAweFCkPGzwXoaukD56JiD59+kTe3t4FbpMLDg6m1atXc1usVEUul1O7du0IAO3cuVMtW0RE+/bto02bNqk08IqPj6d69eqRoaEhBQQEqK2Fb6Kjo2nlypX04MEDlW24u7sTANLV1VX7ITQ+Pp7s7Oxo3rx5am0Hl8lkZGFhQQDIxcWF3rx5o9BM+ubNm6l79+707Nkzpfzt3buXatWqRevWrVNVMoPBBs8ajLEJCQk0a9Ys2rRpk8J9tJG0tDSlE0La2dlxu3H4PHaTlJSk0UHE7du380y2PnjwgPv/nj17NKajKPLPP//QhAkTyMvLK8/ruXFTG47GjR8/ngBQgwYNxJbyTbKzs2nevHk0efLkIr8yWxiWlpbckT11OH78OOnq6lLnzp0FOfpXGGzwXIQuNnj+/z+6H374QRD7Xl5eXHbLrKwsev/+vdo279y5wwXjwupKBgcHF1gnWi6Xa/zDQZOcPn2aAJBEIlF60PklcXFx5OnpycvqcHJyMncOKfe8eWpqKoWHhxfYJzIykvtZt2vXTmFfDx8+JIlEQgDIxMRELd3Z2dnUt29fqlq1KtuKWAJhg2cWY5VBJpNRs2bNCAD9/vvvCvcLDg6mSZMm0cmTJ3nTEhkZSUZGRqSvr6/RTLxeXl7k7e3NaTA0NKRSpUrRrVu3NKahKGJubk4AqHnz5nlej4iIIBcXF62ozZySkkJHjhwpNG4rikwmE/Q5zNPTk3t+2Lt3r2B+xMbHx4cmTZqk9t/XlClTCP9mpdf0cTpW55lRpIiJicnzVVVSUlIwY8YMLF26lMv8eeDAAXTp0gUWFhaIj4+Hnp4eL1kPa9eujQoVKkBfXx8NGzbMt83GjRvRuHFjdOzYMd/7Ojo6X2U6LE6MHDkSly5dwj///IMWLVqobMfGxgZ9+vT5ZlbO/Lh16xZ++eUX7nerQoUKOHnyJBYsWAAnJydkZmbCwsICdevWxeHDh/O18WWd7MaNGyvs29fXl/s9/P7775XW/iXR0dG4evUqPnz48M26mAwGo2STmZmJN2/eAABevnzJvZ6UlIR79+5xn0uvXr3KU5e4UaNGcHV1xahRo3jTEhERgY8fPyIrKyuPFqHp3LkzbGxsAACmpqYICQlBWFgYQkND0adPH9y9e1djWooSudVKevfunef1WrVqwd7eHlWqVBFDVh7Kly+PCRMmoE6dOmrZefXqFapVq4Y6depwFSSUISAgAHPnzoWPj0+BbVq1aoVGjRqhevXq6NKlizpytZoOHTrA1dUV3bp1U8vO8uXLMXHiRBw4cADly5fnR5yY8DkSZ1fJnhX/L8+fP6ctW7ZQfHy8Wna+rAeYW69xy5Yt3Da0yMhIPuSSVColmUxG8fHxhSYemzhxIpfErKifqT137hxNmTKFgoODNe47NzP55MmTleonk8mobNmyBIAmTJiQb5u4uDhudXjRokUF2rp58ya5uLgolYE3NTWV5s+fT2vWrFFKd0EsX76cBgwYQG/evOHFHqPowFaeWYxVFg8PD1q2bBl9+PCBe6158+YEgBYvXkxHjhwhAFS9enXekjAWxO7du2n16tUklUoF9fMld+/epS5dulCPHj3yHBuqUKECAaDevXtrTEtRIzU1VWwJGsHFxYV7Zrxy5YrS/Tt06EAAqHHjxgKoY/DJ8ePHac6cOYVWamF1nhlFCjMzM5iZmeV7z8fHB5cuXcLMmTO/WeOvU6dOMDQ0hKGhIWdv3rx5qFq1Kho2bAhTU1O1tT59+hRdu3ZF5cqV8ejRo0LrVG7cuBG1a9dGjx498q2Xp03k5OTgwoULMDMzy3d1dezYscjMzERiYiLc3d0Vsvn8+XPs2LEDY8aMQY8ePVTWdufOHXh5eWHs2LFK9dPV1UXTpk3x5MmTAn+/jI2NcfbsWTx+/Bg//fRTgba6d++ulG8AKFeuHLZt26Z0v4JYtWoVb7YYDEbxpm/fvl/VpY2NjQXweZdX7qrOhw8fkJKSgooVKwqiIywsDLdv34aNjQ309fUF8ZEf9vb2CA4OBgA4OTnh1KlTAICJEyfi8OHDSscTdfnrr78QEBCAhQsXav2KWrly5cSWIAg7duyAi4sLnJycMGzYMIwZMwaPHz9GuXLlvlppV4QOHTrA19cX7du3F0CtciQkJGDdunVo06YNxo0bJ7YcrSI5ORkTJkyAXC5HTk4O9uzZoznnfI7E2cVmxRXF2NiYANCwYcMUap+TkyNomY3cDM0AitXZ05UrVxIAqlSpUr6zzv379ycAtH37doVtdu/enQBQzZo1+ZSqFBkZGWonmmMwxIatPLMYywd+fn60ZcsWSkhIoPT0dPrtt9/o0qVLgvqcPXs2FzPfv3+fp7yQEOTuDpo6dSqXc6OwvCSaICoqitvhtGzZMlG1lGRynyc7derEm83o6GjRSrt9yaJFi7izwnzk9SlOyGQyatu2Leno6BT6WcDOPDOKDU2bNs3zdd26dahSpQo2b96cb3uJRAIdHR3B9EycOBE//vgjVqxYgc6dOwvmR0zo8wNnHi5duoRPnz5h3rx5Ctvp1KlTnq9iULp06QLPpBc3EhISEBkZKbYMBoOhpVhYWMDBwQGGhoYoU6YMFi1ahAEDBgjqs1+/fjAwMECXLl0watQomJiYYOvWrYL4Onz4MAwMDDB48GD8+eefiI+PR3Z2tto5J9SlYsWK3M63Zs2aiarlv8hkMvz000+YNWsW0tLSxJYjKA4ODqhfvz7mzJnDm80aNWoI+sypKJaWlpBIJGjSpAkqV64sthyV2bJlC8aPH4+oqCjebOrq6sLHxwcfP37U/GcBnyNxdrFZcUXJzMykly9fcv9v1KgRAaAWLVqIqKr4IZPJ6OzZsxQYGMir3fj4eIVnZTMzM7WmPuOXuLi4kIGBgdq1wUNDQ6lTp040YcIE3s/Af5lN9ubNm7zaZogPW3lmMbYok7sjrHTp0gSARo0aJYifUaNGcavN2pZn5NOnT1zVD23Cw8OD2xlw4MABseXQ8uXLSU9Pj1asWCGI/dTUVBowYABZW1tTRESEID7EIjY2ljIzM8WWoTIRERHc7+LChQs17p+tPDOKDQYGBnlmateuXYtu3bphzZo1Bfbx8vJCgwYNMHny5NyHJ8Y30NXVxYgRI9CkSRNe7VatWlWhWdnk5GQ0b94cxsbG8PT05FWDupw9exZSqRQnT55Uy86xY8fg7e2No0eP4tmzZzyp+8z79++RmJgImUzGnfMDgKCgIKxcuRJBQUG8+mMwGKqzcuVKWFhY4Pr162JL0Qi5O8LOnj2LuXPnYuPGjYL4cXJygp2dHQ4cOIDY2Fjk5OQI4kcVKlasiHr16inUNigoCB07dsS0adO4zOhCkVttomrVqgVWBomNjUVQUJBGnqdOnz6N7OxsnDlzRhD79+/fx+XLl3H//n1cuHBBEB9iUa1aNRgYGIgtQ2WqVauGDh06oHTp0ujTp4/YcviBz5E4u9isuJBMmzaNm73ShpqEXxISEkIhISFiy9A6AgMDuZ/Z6tWrxZaTh0ePHtGgQYPo4MGDX93z9/cnNzc3hbJwv3r1iszNzWnIkCGCZJ11dXUlZ2dnysrK4l7LzVSup6dXaGZ4IZDL5XTjxg2N+y2OsJXn4hNj5XI5V2t+yJAhYssplixZsoQA0IgRI1S24ePjQ507d6aNGzfyqEwxli5dysXDV69e8WLT19eX7O3t8621LZfLuR1iiYmJ1Lx5czIyMiI/Pz/aunUrp2X+/Pm8aCmMy5cv06BBg8jDw4M3m1/WcU5LS6NBgwaRjY0NbxVYGPyiTFUTPhEizooeAIvrpW2BvTjw5MkTsrKyoiVLlogtJQ+PHj2iUqVKkZ6eHj158kRsOVrH7t27ycHBQSu3budHQkICVwpr7dq1gvras2cPNWjQgHbt2qVUv9q1a3MPPn5+fgKpy59Vq1YRADIxMdFoiZriCBs8F68Y6+joSI0bNxY8WVdJJTdZZe3atVW2YWtryyVgUuVh3t3dnSwtLWn//v1K9/X39yczMzMaOXIkb9vP27RpQwCoSZMmhbbz9vbmYsbWrVtpxIgR3P979uzJixZNIZPJqEuXLmRgYEAXLlwQWw5Dy2HbthklmtatW+P+/ftwdnbm1e7u3buhr6+PGTNmfHUvJCQEq1evzrNl9r/ExsZCJpMhOzsbbm5uCA0NLdRfZmYm1q5di2PHjqmtvSCysrKwceNGnDhxotB22dnZyMrKUsjmu3fvMHjwYCxZskQpLbNmzcKWLVtQqVIlpfqJhUQi4UqQCV2CZceOHXjz5g22b9+uVL/Hjx9j9uzZOHr0KCwsLARSlz/JyckAgLS0NK3aPslgiM369esRFBQkeLKuksquXbswb968fOMaEeH8+fPw9vYu1Mb3338PExMTTJ8+Hbq6ukprWLduHR49egQnJyel+7Zs2RLPnz/H6dOneStz2a1bNwDfLrtobW2NJUuWYOLEiZg0aRKcnZ0xbNgw2NraYu/evbxo0RSJiYnw8vKCVCqFh4eH2HIYJRE+R+Ls0u5Z8aJESkoKb6tacrmczp07R76+vvne79mzJwGgypUrf3Wvffv2BIDatm1bqA8XFxeytrYmAFSjRo1824SHh5OpqSlVrFiRm/H9MmlaYXh6etL69espJSVFofY7duzgfAQEBOTbJiwsjKpVq0aGhoYKbSH75ZdfVN5y9unTp3xLZX2L8PBwmjp1Kh07dox77ePHj2Rvb0+//vqr0vYU5fXr13T58mXBS1W4urqSubk5ubi4COqHTzIyMujAgQMaX/EujrCVZxZjixuHDh2iJUuWqLTT6OnTpxQWFqZw+y8TKc2YMYOLTxcvXlTaN9HnONWnTx/q3LlzgaW39u/fT7Vq1aL169er5EMIvvxeP378mOzs7Oj06dMiKlKfyMjIQncGbN++nUaPHq3U7wujZMK2bRehiwX2b5OZmUlv37796vW7d+9S6dKlqVatWhQfH6+Wj9WrV1PdunUJAJUqVYrevHnzVRsfHx8aPHhwngFaLqNHjyYAZGdnV6ifTp06cYG7Tp06+bY5ffo010ZHR4eqVaum0Pv7+PEj6enpEQCFt6xfuXKFJBIJGRkZFfgQcPHiRU5Pfu/9v/j6+pKJiQl1795dqcyPT548obJly5KRkRG9e/dO4X5ERPb29lyW1dzBt7OzM6f70aNHStlTlvDwcBo2bJigA3WhyczMpMTERLFlMPKBDZ5ZjFWVjx8/0o0bN7Qq+/Tbt2+5z2ZlPzPd3d0JAJUrV06hOHH48GHS0dGhFi1aUFZWFvXr14/zrWr95wsXLnA2tG1CMycnh77//ntq3bo1PXv2rMB2ffr0KXAxgA+ysrKof//+VKVKFVqzZo0gPnLPhg8dOlQQ+4ySBRs8F6GrpAd2Rchd1XV2ds7z+rZt27gA9uDBA5Xty+VykkgknC19ff18ZymfPHlCRkZGZGFhQZ8+fcpzLysri548eZInWVN+lCtXjgBQx47/x955h0VxdWH8LL2IiiX2AnbU2KOiMdgSe42xYMESxV6wa6yxoFjQ2GPsvceCvSMqNlABQbGAFAHpdcv7/WHmfixsLyyr83ueeRJ3Zu69M8vOmXPvOe9xxokTJ2SKh2VlZWHUqFEYPXo0wsLCVHZoMjMzUblyZRARtm7dKvOYO3fuoF69epgyZQr77N27d4iPj5fbrkgkwty5czF9+nS95q7u2rWLfQdXr16VeUx6ejratm2LmjVrSq1q79mzBwKBAK1atWKrwA8ePEDRokVRu3btfN+XruEEaojIKAXhkpKSULVqVZibm+Py5cuGHg5PHnjnmbexmlK3bl0QESZMmGDooTBSU1Ph4OAAU1NTnD59Wq1zt23bxiaWVYnImjhxIptY/fz5Mz59+oThw4djyZIlGkcMJScn45dffkGbNm0QGxurURv6IjQ0lNmimTNnIioqCvfv3893nLe3NwQCAYYMGaKXcTx9+lRqEUAfWiZcNKC8KD4eHnXgnWcj2r51w64MsVgMa2trEBEGDRoktS8tLQ3Tpk3D6tWrte5n2rRpcHBwwJ9//ik3fHnVqlXMGMgK7X706BH69u2LkSNHYtSoUTJXcq9evYpp06bBy8sLRIQiRYrkO+727duwtLREhQoV1F5RT0xMxMuXL+XuHzx4MLuGQ4cOGUzVUBbZ2dmYP38++vTpg40bN8p8sbl37x4bf97vPTU1VUpVE0C+f+uL27dvo0SJEvjpp5+MUhwrKCiI3Vd9i5/xqA/vPPM2VlPKly8v034amoyMDI0cT5FIhJ07d8LHx0el4z99+oSpU6fiyJEjavXz8OFDudFYmvDgwQMsWLBA7wrPYrEYQ4cORaNGjXD//n2ULl0aRCRTbFIf0QhhYWFISkqCUChEq1atWDqbPmxx7969QUT44YcfdN42z7cH7zwb0fatG3ZVuHLlCmbMmGHwkjfx8fEYNGgQZs6cKdOx69ixI3NAiAgzZsyQ25a3tzcLEX///r3UvtylIXQdbnznzh3UrVsXVlZWICLMmzdPp+3nZvXq1fDw8EB6errK51y+fJld+/Hjx/PtFwqFGD58OH755ReD/z18bWzevBnTpk3T+yo9j/rwzjNvYzn8/f1Rt25duLm5qbRyGhwcjE2bNhW6lIzt27dj7Nix+PTpk0rH//vvvxrnKKvLpk2bQEQoU6YMQkNDddJmmTJl5IYYq2Mj8/Ly5Us0a9YMI0aMgFgshlAoxJkzZ/DhwwfExcXBwsICRIRFixZpM3yV2L17N0tJ464pMzNT7Xbi4uJU+rto164diAh16tRRu4/CjL+/P/z9/Q09jG8O3nk2ou1rM+z6IDU1FX379kXPnj0LdRmjDRs2QCAQwN7eHubm5jh37pzcY0UiEfbt2wdfX998L0BpaWmYOXMmvL299TLOzMxM2Nvbg4gwa9Ystc598uQJWrVqlc/pPnPmDH766SccPXoUwJf8cM4J3rBhg8I24+PjMWTIEMydOxcvX76EtbU1LCwsVDYe/fv3R8mSJfO9WAmFQrVyrhUhEokwevRodO3aFdHR0Tpps6B4+PAhatSogX79+hWqSAMe1eGdZ97GcnBhyERktHVqIyIiVJpk5sg9qXrt2jW9j2/+/Pks3Dh3lNP79+81ErQEgB9//FHm9S5btgxEpHH4dO6UobCwMHh4eLBQZrFYjDt37mDz5s06s4WK4HKQTU1NNQ5nDwoKgq2tLaytrREQEKDw2A8fPmD58uUqC6oaA35+fhAIBBAIBLh7966hh/NNwTvPRrR9bYZdH5w4cYIZh/379xt6OAoRi8UQi8XIyMhQemx8fDxq1KiBEiVKyA0V15SYmBi0b98effv2lTmWkJAQHD58WGmOdl5GjBjBvovcKxn169cHEaF69eoAvoi5cS8ee/bsUdhmbmEvf39/REdHq/xSOHfuXHZu7pePmJgYVKxYEXZ2dnj69Kla1ygLPz8/1s+qVau0bk+XiMVihZNK06ZNY2OXJbzHU/jhnedvw8YGBARg48aNCqM/nj59iiZNmsDd3V3vKv/6IjMzE/Xq1YOZmRnOnDmj9HjOnpiYmMjM39XH+DZs2MBWbfv164c9e/YwG6fJampmZiZevHiR7ztzcXFhq9zqsHr1ajRq1AibN29G/fr1MXDgQIhEIja5UrRoUXz+/FntcWpDSkoKli5dqlWEwLlz55i9OnnypA5HZxzcuHFDqfYLj37gnWcj2ozJsBuKuLg4NGvWDI0aNUJUVJShh6Mzbt68yR6Sf/31l1ZtffjwAd7e3oiIiADwJQSXa1sXAlBDhw6FlZUVZs6cicqVK+ebJffy8oK9vT1T1Tx//rzC8Ovc+Pv7o3jx4qhXr55aIcPv379nfVSuXFlqljr3vZUnnqYOaWlpaN26NRwdHRXmlBuCDh06KPwbCgoKQqtWrTBhwgSjfdn+1uGd56/fxkokEpQoUQJEhBEjRhh6OHpHKBQiJSVF5eOfPHmis7J3EokEmzZtwl9//aXwmXjx4kVMnjwZ79+/x/Tp05nwmKqh5qrw5MkTDB48GOfPnwfwJcppwIABqF+/vsJJdU58tGPHjlKfZ2ZmombNmiAidOvWTWfjVBWJRIKJEyeiXbt2GolnSiQSeHt7Y+3atQWmWVLYuHjxIi5cuGDoYXxz8M6zEW3GYth55OPn54dq1aphwIABaj3sxWIxZsyYgREjRmidZ/rfjx7Ozs4AvjiWjRs3Rrt27dR6QZEHNwPfvXt3lY7nDOCaNWtUuieaOHVCoRDt27eHvb09rl+/nq+9RYsWYdKkSRrnk82ePRvNmzcv1LlHEolEanWE5+uEd56/DRtbo0YNppLMoz9yl1/8999/VTonMTER8+bNw6lTp+QeExERgSpVqqBixYoa1xUOCQlhY5s9e7bc4+bPnw8HBweZk9Pc+0Dnzp2V9rd792706dNHZ9Fvucc/Z84cnbTJw1MQ8M6zEW3GZNgBICoqCjVr1kTVqlUxbtw4lRUvv2Zy56BxK78FTbdu3UBE6NGjh17a37lzJ7p27YrHjx/L3B8cHIx+/frh77//1kv/8ti2bRu8vLx0nsublJTEvtPhw4frtG1dc/LkSYwaNarAS2SJRCIcPnxY5ykHPPnhnedvw8bGx8fj1q1b3+yKm6bk5OTAx8dH5TzbwMBAWFlZwcrKSmlerTocP35cqpqFJojFYri5ueGHH37QOJc3JiYGBw4cUBq2LZFIYG5uDiJCr169NOorLzk5OejatSuqVq2qc8FTHuMjPDwcfn5+hh6GSvDOsxFtxmTYAWnjQP/VRNYk/0dXnDt3rkAERBTx/PlzODs7Y/LkyQYbQ3p6Oq5fv65SrrU+GDhwIAtpK6hSTblDs/ft26fz9keOHAlHR0fcuHFD522rQmFTx83Ln3/+CSKCra1toR+rscM7z9+OjeVRnwkTJoCIUKNGDZXPiY6O1nkaWFZWFtzd3fH7778b9L1IEWfPnkWzZs2wZcsWAEC/fv1gZmaGf/75x2BjSk1NhYuLC+rVq4c3b94YbBw8uiU6OhpFihTR2zuartGHnTUhHh4i6tKlC7m5uVHTpk2JiKhWrVpkaWlpkLGcO3eOunXrRu3bt6cHDx6wz7OysigpKUmttl6+fEmfPn3SaBz16tUjX19fWr9+vUbnExElJyfTkiVLyMfHR6PzbWxsqG3btmRtba3xGLShW7duZGZmRp07dyYLCwu99BEfH089evSgYcOGUU5ODlWuXJns7OzIwsKCqlevrvP+/v77b3rz5g25uLjovG1lTJ8+nezt7Wn06NEF3reqcN+zmZkZCQQCA4+Gh4fnWyUtLU3qv6pQtmxZKleuHGVmZlKfPn2oXbt2FBMTo9U4LC0tacuWLbR9+3aysrLSqq28PHjwgFxdXenKlSsKj8vMzKSwsDC5+1euXEn+/v60cOFCIiI6evQo5eTk0PDhw3U6XnV49OgR3bx5k168eEHnzp0rsH4lEgldv35d6++dRzZZWVmUmZlJRESfP3828GgMhC49cX77OmbF3759a9DZ1StXrrDVzidPngD4slpXpUoVmJubq6xUeODAARARSpUqpTd1yvXr16N///5y1Y45RWQzMzMkJCRo1EdWVhZmzpyJJUuWGEQYSpVQw7i4ONSrVw9Vq1ZVO8yYq71JuVQo4+Livsp6z02bNlV7JaWgkUgkOH/+PMLCwgw9lK8efuX527SxPMp5+/YtmjRpggYNGuD58+cyj3n58iVmz54tc3/uMljcaixHWFgYXFxcVBJbjI2NxYkTJ1QuZZWZmamW7WrevDmICFWrVlV4XJMmTUBEWL58ucz9e/bsQcWKFbF48WJ95mOlAAAgAElEQVSV+9Y32dnZcHV1xc8//6yTUpCxsbGYOHEi9u7dq/C4P/74A0SEChUqfJNlHKOioti7s764efMmdu/ebRT3lw/bNqKNN+zace/ePakf/4sXL5ghbNeunUqiHStWrGA1HeXl9GrDp0+f2JjGjRsn85jt27eDiODo6KhxPcZdu3axfq5cuaLxeIOCghAfH6/x+Yrw8fFhY9yxY4da575+/Rq1atVCixYtCnW9b13w8OFDjBgxArdv3zb0UHgKAbzzzNvYr5X09HRcvXpVY2FHLy8vZlPk1cXlJiMbNGiQb19KSgo6dOiApk2b4sOHD1L7Zs2axdoODQ1ln3/69An9+/fHzJkzmVPdsGFDEBH69++vdMzZ2dmoU6cOiAi7d+9W6ToXLFgAIsKYMWPw/v17uLm55Qu1FovFsLGxARHB1dU1XxsikchgqV0FyZQpU9g7naI8+EmTJoGIUKxYMbnpZmFhYejXrx82b96sr+EahPj4eKbur6yc6LcC7zwb0cYbdt2zadMmFC1aFESEn3/+WenxMTExrCbx+PHjdT4ekUiENm3awNLSUmH9w9DQUK2UsQMCAmBnZ4fvvvsOHz580Eh0Zu/evSAilC1bVmsFcI5bt24xUZbs7GyMGjUKAwYM+OodYADIyMiAUChUetzSpUvRo0cPveR7JSQk4MSJEzr7PnkKHt555m3s1wondimrrNL27dsxdOjQfE5tbt6/fw9nZ2f06dNH7sTz0KFDQUQYNGiQWmN79OgRqlWrhh49ekg9x5cvX86cak4Uq379+iAi9O3bV2m7CQkJMDU1BRFh0qRJKo+Hi/T7/fffmXOY147euHEDc+fOzbeCm5aWhtq1a8PS0lKryXVd4uvri927d6tkI9XhyJEjMDExQf369RVqsKSnp2P79u0KReOGDx/OvmtdVC4pLHz48IH9Da5cudLQwykU8M6zEW28YdcPHTt2VNkZFgqF+P777yEQCPQ6A1cQCqrp6el4//49qlSpgpIlS6pdk3jRokUgIpiamuokHPrkyZMgIpibmyMwMBBr1qzByZMnVTp3//79qFOnDjZu3Kj1OAzBrVu3YGVlhWrVqimcKIiMjGTGWZ0XKVVp06YNiAhdunTReds8BQPvPPM2tjDw/PlzzJs3DyEhITprs2XLllJlFjkSEhLYc9Hd3V2rPkQiEUJCQjQOHd29ezcmTJjA6js/evQIJUuWROPGjZGamgoA+PjxI/bv36/yJKWnpyeqV6+O7du3qz2eQ4cOwdTUFM7OzjLfK168eJHP5rx69Yrdz4ULF0IoFOLly5cGC6f9+PEjU/r28vLSefufP39GTk6O1u0cO3YM5ubm6Nixo0HS4fTJ1atXsXnz5gITeS3s8M6zEW28YdcPWVlZeP78OTMsysonZGdnM8MoD4lEgitXrmhcv7Gg4HLBNQmNzsjIwKpVq3D+/HmdjOXQoUMsL33s2LFstjx3CJw8GjduDCJCpUqVdDKWgoZLByAihTPb8fHxqF27NooWLaoX5fhWrVqpHIVhLGRlZWHQoEHo2rWr0t/t1wDvPPM2tjDAra7q8n5GRERg48aN+co8ikQitGzZEmZmZjJrGRcU0dHRLDLNw8NDZ+126NCBhQxrQkZGhkxnztvbG0QEBweHfE7RunXrMGbMGMTHx7MKGbLCuwuCuLg4FiGo7ntKQcOXjvs20IedNVMkJsbDU9iwtLSkevXqsX+Hh4fT+fPniYjoxIkT1KRJE6njLSwsqHTp0grb9PLyopkzZ1KJEiXo/fv3VKRIEd0PXAe0bduWZsyYQSkpKTRw4EC1zrW2tqYZM2Zo1X90dDSNGDGCYmJiqHz58rRt2zY6ceIEbdmyhYiISpQoQfb29krbmTp1Ki1btozGjRun1Xh0gUQiIU9PT8rKyqL58+eTubk52zdnzhx6/Pgxbdq0iWrUqME+Hzt2LEVGRpKDgwN9//33ctueNm0ahYSEUOnSpaldu3Y6H/vJkyfpypUr1LlzZ523bShu375NBw8eJCKi48eP09ixYw08Ih6er5/atWvT8+fPqXbt2jprs2LFijRhwoR8n5uampKvry/l5OQorejx4sULcnBwIFtbW52Ni8Pe3p6cnJwoODiYWrRoobN2e/fuTTdv3qS+fftqdL68yhrh4eFERPTx40fKzs6Wqn4xZcoU9v8hISFS/y1oSpUqRQEBARQZGUmtW7c2yBhUxcSELzjEoyG69MT5jZ8VL2gkEgkmTpyI9u3ba5xXunDhQhARbGxs+Lq2ClizZo1ULfApU6agevXqICI0bNjQ4CuFDx48QNu2bbFu3TqZ+yUSCdauXYs1a9bgzp07WL58OY4ePcquZ//+/ezY9+/fS12nJnBK67Vr19bo/G+RpKQkODs7o27duggPDzf0cPQOv/LM29jCgFAoRFBQUKFSzl26dCmICPXr19dbHzk5OXqpxKGPMODk5GQsX75caRTTlStXULNmTUycOFHnY9AHz58/11qJOyUlBZMmTcKqVat0NCqerwk+bNuINt6wGw85OTnYu3ev3qX9CwMZGRno378/unfvrvZLw6tXr1CrVi1YW1uzsOtLly5h3Lhx+cKXo6OjsXPnTqaI+fDhQ/z66684ceIELl68iP379yt9wcjMzMTRo0dlisr8+++/2LVrl1Qbffr0YaHkssKxzpw5wxxiCwsLpqBaokQJFClSBIGBgexYoVCIbt26oXz58rhz545a94lDJBLhxo0biI+Px5AhQ1ClShXcuHFDo7Z4vk5455m3sYUNoVDI8n0NCScGZmtrW6ic+sKOh4cHs3OqpqJlZ2cjKChI505/UFAQ5syZg6ZNm2Lq1Kk4e/YsHjx4wPYfPnwYRAR7e3vExcUpbEsikcDNzQ3VqlXLV60itzI7l7738OFD+Pj46PR6dI1IJPqqxMoKK7zzbEQbb9jV4+3bt7h06dJXJ9xgKHJycnDy5Ml89afPnj3LjEzechiqcOLECanV59yGMDfOzs4gIri4uAAA2rZty4wkl2emqFZjYmIiUx6tXLmy1L7Hjx+z/nO3cfz4cZQsWRIjRoxAaGgoatasCWdnZyb08vLlS9jY2MDGxgZVq1YFEWHZsmVIS0uTacBOnTqFrVu3av3illsgZ9SoUVq1ZQwkJSV9ExNRuoB3nnkbW5jIyMiAk5MTzM3Nce7cOY3beffundbPgE+fPmHJkiVyy1SpQmxsLCZNmoTDhw9rdH5iYiIOHTqk0LETiURMLVsRQUFB6NixIxYsWKDRWFTl+vXrKFasGFxcXFQW1uKEWDWNspJHuXLlpN4X6D/RUk4bhXN6TUxMlEYOxsXFsTZ+//13qX13796FtbU1qlatioSEBISEhDDF6UOHDun0mnSFUChEkyZNYGpqiiNHjhh6OF81vPNsRBtv2FUnLS2N1aVbsWKFoYfzVTB9+nQQEcqUKSPl/CUkJKB58+b4/vvvFZYJkcfp06eZONiYMWPkTnZwoimcEvTatWshEAjw66+/wtLSEkQk9+Vs165dEAgEKF26tEzn+fXr17CysoJAIMDFixdltrFhwwZmaHOv9sbHxyMuLg4pKSkKxb4CAgI0FmeTxZw5c9CqVSs8ffpU67YUcfPmTQQHB+u1D0WIxWLUqFEDRITly5cbbBzGAu888za2MPHu3Tv23Js9e7ZGbURERMDW1pal8xQvXhyXL1/W8UhlExwcjJ9++glTp04FAIwfP545Z5qkZHXq1AlEhFatWsncn5iYiGrVqsHa2lqpk+/u7s7uraIaxYagUqVKICJ07NiRfRYTE6M0nNrT0xMeHh5ya3nXrVuXiae1bt2aVeh4/fo1gC8r3hs2bJASMr158yZWrlwpU93cw8MDzZs3lzkxk56eziYLQkJCYGZmBiLSeOJE38THx7OFhAkTJhh6OF81vPNsRBtv2FUnOTmZGVt9z8oqIyQkBPPmzcPz58/VPjc+Ph5ubm5YvHgx+ywoKAh169ZF165d5daqVAeRSAQPDw8MHz5cYemMqVOngohQqlQpnYe8Xbt2Df7+/gqPSU5OxtmzZ6VWdEUiEc6ePYu5c+fCz89P7rkjR45kLzx79uyR6eSHh4dLhVnnJTY2Ft26dYObm5tGZS3ev38PW1tbCAQCnDp1SulLhFAo1Mn3qw379u0DEcHKygohISH5og4KguzsbNjY2ICIMGbMmALrNz4+XuHfVGGFd571Y2PFYjEuXryIyMhI5V+CnhGLxfD09MSKFSuMQt13y5YtGDt2rNIw2rzcvn0bPXr0wMaNG5lTwG2qlJbUBVOmTJEKWd67dy8EAgG+//57lWsOv3z5EtOnT8fjx4/RuXNnEBFat24t89gnT56w/jw9PRW2e+3aNZQpUwa9e/dW6e8gNTVVaUnKpKQkmXbn4sWL2LBhg8qliry9vSEQCFC5cmWkpqbixYsXsLa2hpWVFZ49eybzHD8/P3bt8spOJiQk4Pr16+zeX758WeEEcmpqKptc17a846NHj3Dp0iWt2tA3u3btwujRoxEVFWXooXzV8M6zEW2886wez549w759+3RSv08bmjdvDiLC999/r/a5y5cvZ8aEMxCc6AkR4fHjx/nOkUgkmDRpElq3bo2goCClfdy4cYO1t2nTJrnHZWdn4/Dhw2yGV1P8/f1x9uxZrdrgePv2LUxMTEBEWLlypdzjIiIi4O7ubvAZ44iICDx9+pSFeMubHY6OjkaFChVgZ2en95VlRWzfvp2FxXFj1nTVPDAwEOPGjdMoZNLX1xeenp4FJr4nFApRpUoVEBH+/PPPAulTV/DOs35s7Lx580BEKFu2rMpOk77IrbVw4sQJg45FU0QikdKJCK6uc/ny5XHx4kXs3LkTS5YsQbdu3RAWFlYg4/Tz80PVqlXRp08fNmkcFxeHnJwcPHv2TK7Ox9SpU2FhYYG1a9dyv0nUqVMHiYmJOHLkiMKJhJUrV2Ls2LH56i9rg0QiQZ06dRQ65deuXYOFhQWqVKmCxMREBAQEoGrVqmjZsiWbvFDm0HMsWrSI/Y2GhYXh3Llz7N+nT5+WeU5sbCwqVKgAa2tr3L9/X+NrzU1OTg4cHBxARHKFP3l41IV3no1oM0bnOSMjA7du3dLa4TJmhg0bBiJCv3792GeJiYkqGcZ79+6hSJEicHJyYiuub9++RZs2bTBs2DCZL3G5w+T69u2LhIQEhX3ExcWhevXqKFWqlEar42KxWOWQsdevX7PQpz179qjdV14SEhJQqlQpmXlIMTExClduExISlK5264OYmBj2/cgTIss9obFt27YCHyPwRVxtxIgRcHFxwenTp9n3NnnyZI3a42pIOzo66nikuicrK4tFrhhb+BvvPGtnY52cnGROOnKRN8WLF1e6+hYdHY1Ro0bp7bcbHBwMOzs7FClSROlKoq5JS0vDjBkztHZEuPDlhQsXyj1m9erVMDMzM7jKs4eHBypWrIhjx46xz9atWwciQpUqVWT+PZQvXx5EhJYtW2LMmDEgIgwYMKAghy1FdnY2E+aUp5OxcuVKZneeP38uNVHP1Vk+cOCASv0lJiZiypQp2Lp1K4AvzvumTZuwceNGhTo02dnZOheX4yYC8hIZGYlp06bhwoULOu2P5+uHd56NaDNG59nJyYnlsyorh2Botm/frpeXnYULF8LMzAxz5swB8CWEq0iRIrCzs0NISIjS89UNkRaJROjXrx+KFSsGIkK9evVUOk9TYbWePXuCiKRCy+URHh4Oc3NzEEmXcdKGT58+5XvZ3bdvHwQCAZycnGS+2AiFQraSqmjFWl+MGjUKRIQOHTrI3C+RSLBo0SJMnjxZbu6Xvjl+/Dh7cTp06BDOnj2L+fPna1yGhVNsHThwoMLjLl26pPeXmaCgIBZGKY8HDx5g/fr1hUIlWB1451k7G8ulKeRN7cjMzMSuXbvw4sULpd9B7lDfmJgYpcdrQmJiokHKIHJOI9H/VYg1oWTJklIaFvLIa5f+/fdf9O/fv0AnPrmw3y5duuD9+/eoWbMmG7+lpWU+cch3796hRYsWqF27Nm7evAmJRII3b94YXOH75s2bWLx4MeLj42XuT0lJwbRp01jIdEREBDp37oxx48bh48ePCtOajJHBgwez79DQ0SQ8xgXvPBvRZmzOs1gshpWVFTO0u3btMvSQ5JI7pOjff/9VerxQKMTUqVMxZswYpKen48GDB6hUqRI6deqUL0y8fv36ICLUrFkTgLS69JkzZ/RyPQAwaNAgFvKmC/bs2YORI0fme6nkRLh+/vlnldp59uyZ3vOGJk+ezFZ2Zb0oZGRksDxaQ60sqqKmakgiIyNRrVo1VK1aVSMhOFlERUUpzM27desW+23oUxCICwf9Gutl886zdjaWiGBmZqZVTfBTp07BzMwMjRs3NnjakK65c+cOLC0tUaFCBbVzmHNz48YNTJo0iYVfR0ZGqqTxUKZMGRARfvrpJ437Vpc1a9agRYsWuHHjBvbs2cOeUcOGDcPNmzfzHZ978qQw5MjzyIabCPrhhx8MPRQeI4N3no1oMzbnGfgy09m7d28sX77c4LOuiggICIClpSUsLS1VyjG9cOECM47//PMPZs+ezf796tUrqWMvXbqEbt26MfVHkUiE5cuXY+XKlRqJvZw7dw5FihRB586dFZ6fmJiITZs2SSkl37lzB3v37lXru1i3bh2cnJxYzlPekg5Xr16Fu7s7goODERoaqrQ8hD7Izs5G79690aBBAwQHByMuLg7Tpk1TWK7h3r17WLNmjUo1EZOSkhAeHv7VlD1buXIlBg0ahI8fPxp6KFLcv38fAoEAAoEgX91NXcKFUf72229668NQ8M6zdja2Zs2aOhGKS09PN6iYl4eHB1q3bq3SSrksUlNTsWrVKpkRY8nJyTqd/Nu0aROICI0aNVL6jB0+fLjBIoaAL9c+YMAADB06VO49uHz5MmxsbNCmTRuZK5rR0dHw9PTUKE2KRzlisRiHDh1S6XccERGhsggaDw8H7zwb0WaMzrOhuXjxIqpUqQJ3d3elx0ZGRiIiIkLhMZxhj46ORpUqVVC6dGmEhIQgPDwcP//8M6ZOncqOEYlEGDZsGJo1a6aScJeqcLWKiQifP39GaGgovL29leYdv3//nuWtrl+/XuX+uJVlOzs7mJiYyA23vn//PkxNTWFubq6wZBOHRCLB+PHj0bJlS41f8DiePn3K7okq4ePqcPXqVSZK5urqqtO2C4KbN2/iyJEj7O8yPDyc3atZs2Zp3X5kZKROQ8sfPXqEhw8f6qw9WeQOo/T398ehQ4cK9eSeOvDOM29jP378yH7jmkbWzJw5E0RfygBpmqqhKiNGjGB9qeKU69LZOXHiBJYuXYq0tDSt2hGLxVi0aBFat24NFxcXCAQCrFmzRuaxPXr0AFH+komG5Pz581ixYoXW96EwsHHjRvb3pKuoKR6e3PDOsxFtX4thL0gGDBjAXiK0KfuTk5OD1q1bw9raWqoOsKJZ8uDgYNb33LlzNe47L69evUKPHj2wevVqAECtWrVAROjatStCQ0PRvn17TJ8+Pd95MTExKFKkiNoh9J6enqhRowYOHjyoMP8ztwLs1atXlbabW9iMq6Epj6SkJNy9e1fuSo5QKMTgwYPh7OysVJwuLi4O169fV9lZyq0aWqlSJZXOKSwEBwczx5/7zrOystCsWTNYW1ur9D0pYv/+/RAIBKhevbpaK1EBAQHYsWMHMjIytOpfW6Kjo1k+o7wXXWODd54L3sZev34dkydP1irUW118fHywadMmmWHhYrEY/fv3h4ODA3x9fTVqf/PmzeyZp+/faXR0NDw8PHRWhUFV3r9/zyKqtJ109fb2ZnaCa9PFxUXmsePGjQMRwdnZWas+dUV0dDRMTU1BRPjjjz8MPRwpTp06hYYNGyqsBJKXnTt3gohgY2OjtCQkz//JycnB8uXL8ddffxl6KIUe3nk2oo13ntXHz88PzZs3x6JFi7RqJ/dMvqpqw0KhEAMGDECDBg00Cs+6e/cu+vXrBx8fH4XHcSrGAwcOxLRp09g4ZdXkffPmjcxSQevXr8esWbO0XkHcs2cPDh48qNKx8fHxqF+/PipWrKhU/KVevXogIlhYWGglPBcZGcmEwmRNMMjC19eX3dPRo0dr3LchCA8PZ85hbqVYADoJKZ0+fTrLLVdVcT0rK4spt06ZMkXlvp4+fYoNGzYorEWuLnFxcWxCafPmzTpr15DwznPB2Njw8HDmLNvb24OI0L17d5XO1ZQDBw5gxowZePr0KZsUU3fSZ+XKlXB2dsa9e/dk7r969Srmz5+PT58+ITAwUGm1hrxcu3YNPXv2ZGlK+iQrKwsLFy6UWxN43bp1cHR0xM6dO2XuT05ORvny5SEQCPKl+CQnJ2P8+PFYtmyZ3P4lEgm8vLywaNEiKSE1FxcX/PrrrzIjaJKTkyESiXD//n2VVnmDg4Oxb98+rSb/nz9/jgcPHsjdn5qaigoVKoB0VAVDl3D6FGXKlFHrvBs3biA0NFRPo/o62b17N/sblpXLz/N/eOfZiDbeeTYsq1evxm+//Yb3798XSH/NmjUDEcHBwUHm/oiICPj5+eHw4cMQCAQoXbo0Lly4gEqVKqFHjx4qq0c+fPiQPTC9vb11eQkKGTt2LOg/ZVtlKxtcOSoigoeHh0b9BQQEwMLCgrUzYsQIlc7j6mZ37dpVaVh/YSQ4OFjj1SdlfP78GbNnz87nmCtCKBSyMi4LFixQ6RyxWIzixYuDiDBy5EhNhyuTsLAwrVfgCxO886x/G/v48WOYm5vD3NwcT548QceOHfWSMpKbjx8/shXNiRMnMoc9b4k+WUgkEojFYohEItZG37598x2XnZ3NJts0/Z01bNgQRITq1atrdL46bN26lT3P7927h9DQUBw5coStxnMTpY0aNZLbBqdnkZc1a9awtuWlkVy7do0dQ0To1KkTzpw5g969e2Pfvn35jl+wYAGICEOHDlXp+rKzs9n3PG3aNJXOycvz589ZupYioc7ExMR8ei2GJjAwEG3atEGZMmWwfPlyQw9Hr0RHR+Pvv//Wmzq/Kty/fx+WlpYoXrw43r17Z7BxGAO882xEG+88fxssWrQIjRs3hpubG3tRysvnz5+ZM/Hrr78y461K6SuOy5cvY8aMGQgMDET58uVhaWmJO3fuaDX28+fPw9raGu3bt1caFu3l5QUiQo0aNZQeGxgYiC5duqBHjx5q5TCFh4ezSYTz58+z++Tu7q6SUNjXQFJSkt5zFtUlKioKV69eVWv1u3r16iAiVvItN2lpaXx43n/wzrN+bGxWVhbc3d3x+++/4+TJk+xZcuHCBQiFQr1PrGVkZKBGjRowMTHB0aNHERUVpZK2xMePH1GxYkWUKlUKFy9eRLVq1VCsWDGZlR4kEgmL8vHy8tJonEuWLIGpqSlmzpyp0fkcvr6+WLlypcJyXHfv3oWlpSVKlSqF8PBw5mjOnDkT6enp2LZtGxo0aKDSBANHTEwMRCIR/Pz8YGNjAwcHB7mr7+/evUOJEiVYFEDlypXRvXt3Nimcl9atW4OIULFiRZXGkpOTw9TFNU39yj05fuLECY3aUBeRSISpU6di8ODBWLt2LRwcHORGByiiXbt2Gq066xKRSIStW7fi+PHjAL4sWuhDH6N27dogIjRr1kznbavDp0+fDFICz9jgnWcj2njn2XAkJyerPCt77do1jY2UWCxmKwN9+vSRu3ocFRXF6iUvXboUK1euVKgsnReRSMTKiLm6uiIjIwNJSUkyj01LS1M5n5VbTSb6Ut/0wIEDMsPEOV68eKE3J5YLYc8dSrl9+3Zs3bpVI9VsX19f1KlTB2PGjNHlMPVKaGgoihYtCltbW6Ov0RkfH487d+7kc7iTkpJQsWJFmJiYqFRm7muHd571Y2Nzlxg8ePAg9uzZU+AhrpmZmSqnR3DknjTkUnwEAoHcFJ309HStw121rUqQnZ3NSgkqS5WJj49HSkoKsrOzWYSSg4MDBAIBduzYoVa/y5YtYyvIwJf7rcxRSklJwc2bNzFs2DBcvXoVO3bsgJmZWb569kKhELNnz4aLiwuuXLmi8pg+fPiAc+fOaeWwXb58GSdPntT4fHXJXXKwbNmybJJcXZYsWQIiQuPGjeHg4ICtW7fqYbSK2bVrF7sWbkGjR48eOu1DIpGwCZiCnigIDg7GjBkz8Pjx4wLt19jhnWcj2njnOT/+/v64cOGCXvvIzMxk4V/bt29XeOyTJ0+Y83v06FGV+zh69Cg8PT2RlZWF8ePHo2zZsmymUx63b9/Gjh07kJOTg2fPnqFkyZKoV6+eyrOGXFg4Jzwmi8DAQNjZ2aF06dIqhau/efMGffr0gZeXF/766y8QfamZqmkIkEgk0rhOavv27ZngjS4YNWoUM6Lq5gEaitwvzupMrhR24uPjMWLECKxYsQJhYWHsGv/8809DD83g8M6zfmxsREQEqlSpgooVK8rUkyisCIVCTJs2DWPHjsXevXthZmaGjh07Fuqye2KxGDVr1gQRKcw5zsubN29w/PhxFqbs4uKCokWLol27diqlMXXr1g1EhOLFi2szfJn3dvv27ew59ezZM63a1wW7d+9Gp06ddJrSk5ycjIEDB8LV1RVOTk747rvvsGLFCjRs2BD//POPRm0mJiaiRo0aICLUq1dPal9qairWrl2rcIJeWy5fvgyBQABbW1s2+VSuXDmd9/PLL79AIBBg7dq1Om9bEdw11apVq0D7NXZ459mINt55liY0NJQZSXnlk3RBQkIC60eZyFRQUBA7VlXBlFevXjGj6uXlhS5duoCI0KFDB5XHuHbtWtbG9evXsWHDBly/fl3hOVlZWUqd2r1797J2c6uMqwInPmFjY4OoqCgAX1bMf/zxR3Tt2lWpWEpkZCTKlSuH4sWL4+XLl2r1DXz5+/Dw8NBJzVbgS1NUb9UAACAASURBVPhbo0aNVBaMKwxIJBKsWrUKy5Yt+2pKMQH/X5EgIrx48QJ79+7FvHnzFKrBfyvwzrPx2djw8HA4Ojqifv36iI+P12tfxvIcSEpKwtOnTzU699ChQxg6dCiGDh3KnhOKatoHBATgp59+wsSJEzFu3DhcvnxZ02HLxcfHBwKBAHZ2dnovn/Tu3Tul9pUTbVTnPUMWnp6esLCwwPTp06VWaXWlnO7v74/atWujfPny+fLIp0yZAiKCtbW13OuNiorSuvxWSEgIIiIi8OrVK0yaNAm3b9/Wqj15aKtqHxgYiG7duqmlTM5FC/76669a9f2twTvPRrTxzrM0YWFhzFE9cOCAXvvy8fHB0qVLVVL6ff78OXPY/Pz88M8//yhcPY2Li0OpUqUgEAhw5swZuLi4gEi9MhYJCQkYPHgwZsyYwURJzM3N1Q7xy0t2djZmz56NpUuXarRScfv2bYSFhbF/b9myRWVn3MfHhx27e/dutfvm+Xq5ffs2rK2t4eTkxDvMeeCd54K1sUlJSejevTt69OihcQrK33//LZVDrQoSiQTXr19X6BQWdoRCId68eaN1O15eXpgyZUq+Z0F4eDh69eqldPWaqzNNRHKVuXXBnTt3sGjRIr2qQHMT3o6OjgqdMXd3d9jY2Kgd2s7h5eUFa2tr2NnZsXzvt2/fwtHREfXq1cOnT580vQQp+vXrx1IN8kYPrF69mvUtq/b36dOnYWJigkqVKkm9u+Xk5GDbtm1KFxjksXXrVo3zuPUJV5pVIBCoXAtdIpHg9evXRjOpVljgnWcj2njnOT+PHz9We0W0oIiNjWXqzsqUIhMSEpjiZ2xsLHbu3KmxANK2bdtARPjuu+8KnSjWx48f4ezsjE6dOil1ekQiEebOnYvJkyerVUNYHSQSiUFqDUdFRWH8+PE4fPhwgff9tZCTk1OoQ08LmtDQUPz2229wdHTknecCtLEHDx5kjpc6qTq5SUpKQv/+/TFixAiVX3r//PNPllOqTRkjQ9K5c2eVIrpyk5WVhVmzZrEJ3UePHrH7r2mt9itXrrCc07yCTX5+fihfvjw6deqkcQoRB3e9qobIhoeHY/z48QpVsvMyc+ZMEKlXPlATatWqxe577dq1cerUKb30c/LkSZQqVQpubm4y9/v7+8uN1sgdoZR7kmbVqlUgIpiamuLDhw/IzMzEs2fPVBawrFu3rsZ53Prk6NGjsLGx4VeRCwDeeTaijXeejYvExESmiK1OGI0uePz4sV4NJ/Dl+uTNVl65cgWLFy9WqPI8fPhw2NraYu/evfoaokJycnLQpEkTmJmZ6c3wy2PMmDHsBUeXdYt5vl1yh6jyznPB2diYmBg0a9YMzZs319lqmyrMmDEDRIQiRYpoHZZqKLjawj///LPK5+QODb5y5QoSEhJQuXJlWFlZaVUtYs2aNahevXo+e8Q5o0QkFUWVk5OjUvj1o0ePmB3kBKd+/PHHfMd9/vwZixYtkgoZ7927N/uOVSUpKQl//PGH3m3agQMHYGFhARMTE73rzmhKamoq5s2bly+tj0spK168OOLj41m034QJE1Rqd9++fWjQoIHGedw8xg/vPBvRxjvPhZuIiAiMGTNGqizGu3fvcPv2bfj6+qJYsWJo1qyZSiudAQEBeP36tUr9GmL1bd++fRAIBGjatGk+Bzo1NZWtuCsyRtwxudWwtUEsFqu1AhMTE8Neigo6j3n37t0QCARo0qQJHy71FeHv7w8nJycMGzaswH+Xhw8fhpmZGYoVK8Y7z9+Ajc3MzMSOHTvw5MkTnbW5adMmLF68uMBWsu/du4fp06erbOuAL7bRzs4O3333HSsPlp2dnS+SKSkpSSeRV2/evEGnTp0wfPhwzJo1i1Ut4MpOrVixQuZ57969Y6uzFSpUgFAoRE5ODm7fvi0z6mr8+PEgIlhYWLD9K1euBBGhffv2Wl+HLPz9/bFw4UJERkZqdH5KSgrTMzEm/Pz8ULRoUTRq1AgZGRmoXLkyiAhdunQx9NB4jATeeTaizZgMuz65cOECHjx4oPH5oaGhmDBhgsx8lw8fPmicQzZy5Ei2mph3JYDLQyYipeJXnLqjpaWl0vJY9+/fR9GiRVGmTBnY29tj/fr1AICnT59i7ty5UjPlHGFhYdiyZYtWtX8VrZzm5OQwdUxPT0+4urpi4sSJ+ZzEHTt2oFOnTvD399d4HBxpaWlwcnKCpaUlrl27pvJ5W7duxYgRIwzyApCQkKCSAiyP8TBx4kT2O9f0hVQbRCIRn/NcSG3sli1bsHz5cq1Df/XFvXv32N/utm3bDD0chaSnp0s5+Jyi+MSJE5GZmYmAgADY2tqiePHiLB1KW5o2bQoiQoMGDSCRSGBrawsiQr9+/fId+88//+SOAoG5ubnSSfMNGzaAiFCzZk0puxAdHa3yBGtcXBwkEgnevn2L6tWro379+vj06RPi4uKwevXqfAJs5cqVA+mh9FJhZ+HChVKCk0+fPsWSJUv0Xqud5+uBd56NaOOd5y8qmlyuSnBwsEZtdOrUCUSE0qVLS33u7+8PCwsLWFtbIygoSO12udXE5s2bQywWIygoCE5OTujcuTPevn2L/v37Y8GCBUrbyZ1Dp8yx5OpSclujRo0AgJX5aNOmTb5zuLJbnNGPjY3FwIEDMWfOnHyrZQEBARgyZEg+5cyPHz/C3d1drlBbSkoKQkJCWLkqIsKNGzeUXrumhISEsH4WLlyot354eBTx7NkzNGnSBGPHjjVYPjbvPBc+G3v37t0CEaTShsjISJQsWRIWFhZ6Lf2jCunp6WqFoh84cIDd37/++kvKhupKE2XYsGEgIgwaNAjAl9SkadOmyQzddnd3Z/1XrlxZadlJjlevXqklgBgbG4suXbpgyJAhLIzf1dVVynk/e/Ys+vfvz3RQctOmTRsQEaZNm6Zyn18DUVFR+PXXXzF37lyNzhcKhfj999/RrVs3xMTE6Hh08snJyTHKlf6vEd55NqKNd56BI0eOgOhL7eDjx49rVG+XWwXu2rWr1OfHjx9nBkfTUhVJSUlslji3Y/vo0SOV25BIJNi7dy+OHDmitJ5oXFwcBg8ejN69e8PFxQVnz55FRkYGGjRowO5T69atpVY7vv/+exARfv/993zj9PPzQ1paGruvHTt2BBGhRIkSat6JL7x8+RLlypVD3bp1kZCQgMzMTHTr1g1169bVueLomjVrMHr0aCnxkMjISLi6uqpUO1HWKvCjR49Qu3ZtDBw4UGUxEX0xb948NGjQADdv3jToOHgKN7zzXPhs7Pv371G8eHFYWFjotK6urklJSUFcXJza5yUkJMDHx0cn4d5hYWGwt7dHsWLFVJ4gz+0sLl++HEKhEIsXL4anp6fUJJZYLIaHhweGDRuGpKQktcYlFovx6tUrlVaBY2JiMGXKFBw7dkytPtThzZs38PT0ZNddr149EBGqVKmC5ORk9O/fH25ubvD19WXliLjJdY7MzEwEBgbqfaLvyZMnGDlypM7KRnIYaoLS19eX3XcvL68C67d58+YgInh7exdYnzyy4Z1nI9p45/kLly9fxqRJk5ih0CQM7uPHj/mMoEQiwaZNmzQu3ZCXd+/ewcXFBcOGDVM7PDc9PR1VqlQBEeWrbaiM6dOnM8eZe8Bz4Uh+fn6wsrJC0aJFYWlpic6dO+PBgwcoXrw4zM3NYWVlhRIlSsDS0hK+vr7MOP/2229qjUEeuY1OgwYN8PbtWzRo0ADt2rXTS8mhadOm5bsHsjh//jwsLS3RvHlzqb+nqVOnsvOV1cSWR1paGnbv3q1WXl9ehEIhG4esMEEeHg7eeS6cNvbz588FukpVkDRs2BBEhOHDh2vd1unTp9mzTlX18rS0NPTo0QMdOnRQ6PzfunWLta2ozFB2drZc55qb3OaiscRiMRITEwEAN27cgLOzs96dm8OHD4PoS33jsmXLolWrVvD19cXo0aOlJlfXrl0LIkL58uVx69Ytg1TfyMrKgqWlJXsn0YVAZkZGBho3bowiRYroreayIlJTU9G6dWs4OjpqFKWoCUKhkOnEDB48uED65JEP7zwb0cY7z/+Hc56tra2Rnp6u0Qq0pohEIly+fFnjUlKqEB0dzUpnzJkzR61zuRIm3333Hfr27QtPT0+2z9vbWyrMm4iQnJws5dRy2+bNmwFAp0quWVlZsLe3BxGhbt26LM+LiDSuuagIHx8f5hQrKgEzZcoUNo7cOe8vXrxAixYttArD5XLhy5cvr9H5HNOmTUPNmjU1jorg+TbgnWfexhY01atXBxHppESOSCTC4sWLsWDBAp1rQiQkJKBWrVooVaoUE/7KS3JyMhwdHWFubi6zRNS+ffuYrXj8+DF++eUXEBHWr1/P/t/GxkblMcXHx6NFixZo0qSJyiG5ixcvlrLVd+7ckblKz4VyW1lZae20rlq1CjVr1sSRI0fUOi8nJwdFihRh9yU9PV2rcQBAcHAwu/Y//vgj3/7AwEAcOXJEp2KcmZmZ6NWrF5ydnVVSWdcH586dw+TJk/nc7EIA7zwb0cYb9v+TkpKCtWvXwtfXF5MnTwYRwd3dvUD6nj17NstlkudQCYVCrUOKTp06hYULF6o9WyyRSHDjxg2ZhjgtLQ1Tp05F9+7dWe44Fz79559/YtKkSVi9ejXmz5+PCxcuwNPTU+crwiEhIViwYAGCgoIQFRWFDh06oH///npTeFXFgEZERGDw4MFMcE2XcCqqjo6OOm+bhycvvPOsHxubmpqKFStWwMfHR/mX8I3x5s0bbN++na3AGjO5HbOlS5fm2+/j48MEPcPCwmBtbQ0iQp8+fXD48GGULVtWrRzikydPsv5ULduYlpaGfv36QSAQMKVoU1NTBAQESB2Xnp6O9evXa1XCi6NUqVIgIrRq1Urlc5KTk3HmzBm8fPkSAwcOhI2NDVxdXdn+wMBAjXPSlyxZAldX13zRHAkJCbCxsWEh/Loid9TCunXrdNYuj3HCO89GtPHOs2y4kDEnJ6cC6W/cuHEgItjb28t0zB49egQ7Ozs4OjoW6Io48CWELCAgQKkjygmsmJiYyAw7+vz5M8zNzUFEmDFjhlpjEAqF2L9/v1p53l8zOTk5OHv2rF4jFXh4OHjnWT82du7cuSz0VJt6zo8fP8agQYPyiTAWFIbKEzUmNm/ejKlTp7LQbX9/f/Tv3x///vsvAOlSkmfOnMGoUaNkVrbgePbsGRo3boxRo0blu/+pqano1asXunbtqnTywdvbG7a2tswmJyYmslV/ItKr0NuaNWvg5OSksvgZAPz8888g+lLH+9dffwURQSAQQCQS4cOHDyycW56IXk5OjtqRb4mJiShatKjO85EzMjLQvXt3NG/eXOMULp6vB955NqKNd55lc//+fYwcObLAhFjS09OxY8cOvHjxQub+9evXM2N27949pe3l5ORg8eLFWL16NSQSCYRCIV68eCE3ZG3KlClwcnKSWZKJKyHVuXNnpf2eOXNG7j3LzMxEpUqVpMK3VYWrTWllZaXwJdPX1xcLFiyQ6VQmJSXlK6vBw8OjHN551o+N3blzJ4gIFStWVBh6mpycjAYNGqBUqVIyn2EuLi4srUYXqBOx8+TJE9jb26NOnTpfxSpxQfHTTz+BiFCmTBmNzs+dFqTI8fr8+bNCYcr/fttSKUBceTwXFxeNxiaPjx8/Sr3jvH79GosWLVIrx5dT827Tpg2ePHmCTp064a+//gLwRUSPm6Dfvn17vnMTExPh4OAAKysr3Lp1S62xh4aG4sKFC/xEEY/e4J1nI9qM3XlOTk7GgQMH9LoCl56eji5duqBZs2Z49+4d/P398ccff6iUo5KcnIwffvgBVapU0UoE4syZM2jbti3++OMPlR7euXOoLl++jEGDBoFylcTg2LZtGzw8PNixtra2yMzMlDqmXbt2ICJUr15d5fHKm939/Pkznj9/rnI7HFwec9GiRRXWkuZmhwcMGCD1uVgsZqW2li1bBuBLKNYff/whN2QyMTERLVu2RP369fH+/Xu1x3z37l00bNgQs2bNUvtcHp7CBO8868/GBgcHK3U679+/z57Rq1evzrefm1wcOnSownZUYf78+SAijB8/XqXjOQEpeRO77969w/bt2ws8Yqqww31nnCDasWPHcPLkSbnHi8VivH79mjnCT548wffffw83Nze5zvG6detARPjll1/YZ8+fP0fp0qVRp04dfP78GVevXkXVqlVRr149hISEsOOioqJ06ihGR0ejWLFiICKcOHECAODs7AwiQv369VVuJzY2Frt27UJsbKzM/Y8fP8aZM2dk7nv27Bn7W82t26JroqOjUa9ePTg5OSEyMlJv/fAUPj58+IBx48Yp/C3Lg3eejWgzdue5V69eIBnlEnTJzZs32QPX29sb5cuXBxGhW7duSs+9ffu21jktQUFBEAgECkOR8vLkyRPY2NjA3t4eb9++RePGjUFEaNy4sdQx3NjKlSsHIoK5uXk+RdC3b99i4cKF+XKf5KHN7K4irl69ijdv3kh9JhaL4e7ujp9++gmvX79G06ZNQUT5al9nZ2eznKXRo0cD+H+ovLm5ucwcbB8fH3Z/ZM1iK4ObsCCifBMSHGvWrMHEiRPVLnHCw1OQ8M6zYW2sWCzG9OnTMXDgQKmyebmR94xRF+4ZqqqewufPnzFs2DDMnj1bprPl5OQEIkKPHj10Mr6vAe674gQnz58/r1TkkqsJPWzYMJX76d27dz6xsdyCmrdu3cKHDx/YvydPngzgSzixm5sbhg0bphMxLuBLvWnuPaZfv354+PAhhg4dCiJCr169tG7/zZs3UgKemZmZEIvFmDVrFkaMGMFs7Jo1azBp0iSlYmfp6elo3749atWqpXJ5M45jx46xe3rw4EH1LyYXfn5+GDRoEK5cuaJVOzwFA/c3bWZmplBQVha882xEm7E7z3379gURoWnTpnrrIzMzE3369EHr1q0RERHBQuRUEfAQCoVwd3dH7969Nc5pe/fuHaysrEBEOHbsGJYtW4Zly5YprRH8+fNnJgwWFBSEefPmSa1+x8TEsHJS58+fx6pVqzRSp86bo517dnflypVS+9R9mCjj5cuXrK958+YhIyMDL1++lHmsr68vVqxYwVZ5Nm3aBCJC7dq1ZYazZ2ZmYsCAAejatavcF9a8nDx5Et27d8etW7dw/fp11KpVCxMnTpR5bEBAgNz7xKMfYmNj0a5dO/Ts2VOniu9fO7zz/PXbWC8vLwwePBhnzpzBwIEDVVLgDwsLw6lTpxQKKP7www8gIilRp8LK6dOn4erqqvJEMfBlhVYdAcx27drBxMQEu3fvZp/5+vrCxMQEpqamePz4MftcJBIxe8XpsFSqVEmujcvNmzdvUK5cOVhZWcHZ2Rnh4eEAvkxuDxkyBFOnToVYLIZIJELPnj1RoUIFJgKW2/k7dOiQytemjLNnz6Jt27Ysys3V1RWmpqZYt24dFixYgNGjR2uk4L106VIp4bHNmzdDIBCwlW0iwoYNG9RqM3e0h7qr1Onp6Rg0aBAGDBigtTgq9/txcHDQqh2egmHLli0gIrRs2VLtc3nn2Yg2YzHs8khNTcWxY8fyOaYSiQQLFy6Eu7u7zusQZmVl4cWLF1qHNCUlJWHy5Mkq1YAOCwvDgwcPpFQ0165dq1X/y5cvZ6HQsh7wz58/VyrQxYV8c6UdwsPDERgYyFZUcxvCY8eOwczMDK1bt1bq+MsiNDQU69atk1LCzMnJQffu3VGjRg2N8plfv36tUyeqQoUKICKo8ptKSkqCo6MjrKyscPz4cfz444/o3r27zmb6efKzbds29vvhFY5Vh3eev24b+/btW/a7mDlzpkrnZGRksBKBeSN9OMRiMZo3b45y5cpJpetcunQJo0ePVntFT98UL14c9J8YlSqcPXsWJiYmqFixoszQ+4SEBMycOZOVYsrKymLlIvPW1X3+/Hk+p/jHH38EEWHr1q0ICAhAgwYNQEQoUaKE0ooPe/bsYd9p7vBwZZw6dQpbt25FjRo14OjomK+E0ZUrV+Dm5oYnT56o1F5euPeOChUqsDDuZs2asXFqUp2iX79+ICLY2dkB+H9EooWFBWrUqIESJUrg2bNnSt/ZxGIxmygXCoUYPnw4OnbsaNDQay6NYsyYMQYbA496xMfHa1TS7Jt3nomoAhFNIaLLRPSBiHKIKIaIThBRcxXOdySitP8eJlv1PNZCb9jVYf/+/Th48CDu3r3LHsajR49mKpYFQUpKitzZ0+joaCxevBj3799n5SCISOXxBQcHw8zMjBkfbVi0aBGbAc4bDh0YGAhTU1MQEa5evSq1TyQSYezYsejZsyccHR1BRGjYsCHc3NxA9EX5Mrdj8uHDB7i6urJZYIFAoJGwTK1atUAqCpcZismTJ8PMzExmXqIsuNxwbhWc/stR59EPkZGR+OGHH9CuXTuta5R+SxRG59lY7Kwx2NisrCw0bdoUVlZWUs+fjx8/IisrCxkZGfkmGTMyMpizOX/+fJntcoJoRITu3buzz0uWLAkiQqdOnXR6HRkZGRg6dChcXV01WvHr378/iAhr1qxR6fhly5ax6+PKM+aGm1w2MTFhObr79u3DkCFDlNr8nJwcZuu5VfsBAwaA6Iu4lzJHMDU1FUOHDkX58uVhamqq0gryjRs32PU0btwYFhYWaNWqFYRCIUQiEUaOHAkLCwsQEVq3bq20PXncvXsXsbGxOHLkCHr37o2zZ8+iVKlSsLW1Vamixu7du9G7d28WIRAREYGZM2fixo0bAL5E2g0ePBhHjx4FAHz69AnVqlVDyZIl5YqyAkDXrl1BRFixYoXG16YPMjIyDD0EngKAd56JVv73AHpNRH8T0QoiOk5EIiISE1F/BeeaENFt3nlWn9x5Q8eOHYODgwMrW2Bvby9zhVEkEmHq1KkYMmSITpRCQ0NDUaxYMdjZ2ckMreLCzEuWLMnyf+zs7KQM/YsXL3D69Gm5q7MjR44EEaFjx45ajVUoFGLWrFkQCAQoXry4VA1nPz8/di9PnToldd69e/fYvpo1a6J9+/a4ceMGSpcuzT53dnZmM2+TJk1in/ft2xfbtm1TaXwfP35E8+bN0bZtWyQlJbFZeHVyvrSBuz/Tp0/Xebh5XiIiItCiRQt06tRJ7+HEycnJ6NSpE9q2batVeRyeb4dC6jwbhZ01Jhube7Vk+/btICLUqlULpUqVgp2dXb5w5levXuHYsWNyqzi8fv0a5ubmEAgE2L9/P/u8Z8+eICL8+eefOh3/iRMnmK05cOBAvv1hYWFYv369QoFRdXLHU1NTMX/+fLm1lA8cOACBQICaNWuydp8+fZpvQloex48fx+jRo5maNlcXuV27diqPEVA9Xerx48dsZTz3FhMTg+fPn0t9Ji/aQBNmz57NVoqVvYdJJBKmqK1qrvS1a9fYuLds2SL3OG4lvGvXrmqNn4dHF/DOM1EfIvpJxuc//jc7/pmILOWc60FEwv9m1HnnWQ0ePnwIMzMzmJmZ4cmTJxCLxZg3b57C0OTcYmAbN27Uegz//vsva4+b9czNrFmzWFjvzp070b9/f7x69QrAF0Ncp04ddn7uPJuTJ09ixowZiIuLU7nucl4+ffqErl27YsiQIcyY5i6B9fjxY+zZs4e9IPn4+DBVzNykpqaiZcuWbGWa+/s5ceIEypYty9rjruvcuXOwsLBAy5YtkZOTw9p5+PAhWrduLfcF6u+//2ZtnTt3Dqmpqbh9+7ZUG/ogIiICKSkpOHXqFOv/8OHDeu2zIDl9+jS7rtx5dzw88iikzrNR2FlZNjY7Oxvz589XOULFELi7u7NVU+55sWfPHpXOTU1NZZEdmZmZ+Wxv7vBYXRIdHY06deqgZs2aMqs6cOJludWnOZ4+fapRJYjcPHr0CMOGDZNyjqOiotjKYVhYGHP81LUp7969Y+Pn0qT0gY+PD8qWLYsaNWqgb9++8Pb2BvBlJbxnz56oU6eOSuUylXH58mX2juDt7Q2iL8Klqqyy/vbbbzA3N8euXbtU6kskEmH69Olo0KABKleuLLeutI+PD0aPHs3GxfPtkpiYiG7duqF79+4FFqX2zTvPCi+E6NJ/hqipjH21iSiTiJYQkQvvPKtPaGgowsLC2L/T09Ph5uYmd7UzISEBTk5O+O677xSG86iKWCzGsmXLsHjxYpk5DxKJBAEBATLzWgMDA6VmdjkRqcTEROaoTpgwQeqc69evo2XLlszAXbhwgYVn+fj4oG7duli0aBEAYOPGjaxtrp7zL7/8wl4m5syZw1bCudXPtWvXolevXjJD0oYPHw4iYqWY3rx5g4ULF6JBgwYYNmyY1Mq5rJUJLgSNiGROBHz69Ant27dH9+7dtRbdUJWTJ0+yHLbAwECULl0aJUqUkHn9uiIyMlKv7eclKSkJ7du3R+vWreWW++DhyU1hdJ4VbYXJzsqysbnDmblQU2VIJBJ07doVtra2ckvx6JLY2Fh4eHjg5MmTWLp0KebMmaPSCuabN29gb2+PIkWK5HNGZ86ciSJFiuSbqE5JSUGvXr3Qq1cvnbyo9unTB0SE5cuXS33O1VYeMmSI1Oe3b9+GQCCAqampxrm8wP9LL1WuXFnm/levXjFb7u7urlbbTZo0ARGhTp06KF26NLp3785sbFpaGnr27IkOHToYPJpILBZjx44dMhcPODZv3gz6L2WMG6+/v79aY9dEc4aruKFqTrumPHnyREqD5dKlS5g4cSLevn2r1355dMfBgwelIlkLAt55VmxIz/33hTTM87kpET0gokAistC1USeibDmb5GtynvPi6enJZs81qdW7Zs0alChRIp+4h645d+4cfvnlF7i6uqJnz57YvXs3M4w3b95kIdH//POP1Hk///wziL6UovD392c/9iNHjqBLly4sFAr4EkJXp04dtGrVir2gODg4gIjQoUMHplhpEOpc0QAAIABJREFUYWEBHx8fJCYmsvZGjRolc9y5Q6xq1KjBQrNV4fz58yhfvjxGjhwp9fmtW7fQvXt3mave+oJz7pcsWQL6Ly87PDwcOTk5el3pDg8Ph62tLUxMTFQO5ePhKWiM0HkucDurjo199OgRrK2tUapUqXyCTPJITk5mz+MRI0aodI4hyJ0+lTfPliuJmFeJ9siRI3LP0QQupzrvCnNqaipu3bqVbxLg3LlzrP/c5RX37dsnFW6uDE7cyc3NTe4xXCkbIlL5uweATp06gYhQtWpVqXBqQPqe//333yq3qQyJRILOnTvD2to6X/qWPPbv38/Gcv/+ffZ5VlYWNm3ahJs3b7L605aWlixd7PLlyxqJfqqDl5cXmjRpgkuXLumtj7t370IgEMDExAQPHz4EABQpUgREhD59+kgdm5OTIzflgcewxMbG4ocffkCLFi0QFxdXIH3yzrN841qZiLKIKIqITPPsm09fwsia/Pdv3nnWAdyDvESJEkhISFD7fC5nmoj0NqObkpLCwrWdnJwQGxsLkUiEUaNGwc7OjpWpyvvgBYDDhw+jXLly8PDwQHBwMAsJGzp0KP7X3n1HR1VtYQDfN5X2aKFJ7/DgUQVRAekSQARBRUBQAwhKE5UHgoiAGDGUh1KlSBEQpBkIRZQqIjUgnZDQIRBIoZie7/2R3ONMpqdNBr7fWrNiZu6de2YGs2ffe87eY8eORc2aNa2uTTp27BjGjRuHS5cuISUlRRUxK126NJYtWwZfX1/ky5cPgYGBiI2NxZw5c4ymbBleXdZ7SVv74mCP5557To0hJ4wbN069vxEREfjss8+ytD2HNYYnPOydgkaU01wpeXZWnHU0xkZHRztc32DGjBno0qVLrp5WmpKSgi+//BLjx483SQxWrFiBtm3bmpwovH37Nho1aoSnn37aqJtCRm3duhX9+vUzW3ckMjIS//3vf02mTa9btw6bNm1SvxsmpNu2bbN4rPXr1+PDDz9Us3hsdfdYt24d3NzcUK1aNYc6Kzx69Ai7d+9GmzZtICIoUaKEeiw6OhotW7ZE48aNs7QytOEJG3urdeutPD09PY1mAU6YMAEiAnd3d9y4cQNr1qxRxcFWrFih9snJ4q7ZYdu2bSazSvT2XIYtKc+ePYuiRYuiZMmSuHr1qpNGS7kJk2fzgdVTRPak/U/VJ91j9SR1jdaXBvdlafJsZVyP1bRtc06cOGG1QIg1bdu2hUhqK4XMtqYyR1+T3bBhQ+TNm1dNo27VqpWqaqlPNZowYYLF5zl//jyqVauGBg0aqOnUbm5uDp3ZBoCRI0caTR3fvXu3emzMmDEQEeTJkwdRUVE4duwYChcujOrVqyMyMhL37t3Dtm3b1BTsixcv4saNGw6/JwEBAfDw8MCwYcMc3jcjGjRooF7vG2+8kSPHNLRmzRrMnTs3Q+27iHKCqyTPuTHOZkeMPX78OGrUqIEuXbpkew2I7PDgwQPs3LkTcXFxDk/XdYRhzE5ISEDHjh1RsGBBNbtIT9QHDBiAIkWKGJ00PXz4MDw8PODp6WlxKndMTIyahv3ee++Z3ebOnTvo1q0b3n//fbWU686dOw7VLPH390f//v1x7949lC1bFiKpdVxywsyZM9G1a1e7T9joRbdatGhhdL9+VV5E8N133xk9tmzZMogIPDw8cnQJU3YJDAzE5s2b1e+JiYlGBVmB1FkN+vthuC09uZg8mwZPNxFZkfY/ynfpHvMSkeMickYMipswec49HKm+aejUqVMoXbo0GjZsaLSOKyUlBdOmTcPEiRPRpEkTNRULAF599VWICPLmzYtZs2apK9/p10f9+uuvCAsLQ0pKCg4ePAh/f3/1h3j8+PEQSW1llZEe13v27IG7uzvy5Mlj1IdTn2pVsmRJPHr0SP0uIiYFRHbu3Ak3Nzfkz58fYWFhDo8hJ+3Zs0cFfHOFZIiedK6QPOfWOJsdMVY/6SoiZq+u5nb6lTj9xGXp0qUzHGdnz56NSZMmmUzF/vzzz+Hu7o7Ro0cDSD3BbHhi2LACtt4S6uWXXzZ6jpCQEKtXQhMTE1GnTh2r06WnTZumjrl//36Tx/V2YJYY1kL54osv0KhRI4iktofMjTZs2IBevXqZVGa/cOGCep/NFZ7btGmTmub8JIiLi8OIESMwevToDPUEpscPk2fTgL4k7Y/fchFxS/f4eEltq9Ek3f1MnnPA+fPnsWDBAoeSzLt376Jz587o3bu3CnqhoaHYsWOH0XbTp09XQe/3339X9//yyy/q/gkTJqBv375qGltYWBgGDx6MoKAgxMfHq6Ru+PDhan+9QnahQoXQtWtXiAhq166NTp06wc/PDwkJCQgLC0N0dDTWrl2LoUOHOnwF+OLFi7hy5Qr8/f3x1ltvqSsDf/zxh7qKf+3aNfj5+WHMmDEmV+WXLFmiXuPBgwcdOrajrl69itGjR2Pv3r3w8/ND06ZNHT57fenSJcyePZsFtIjMyO3Jc26Os9kRY0NCQvDCCy/g3XffdeqMlePHj6Ny5cpo3769Q1dS69atq6406leBM9Lf/vfff1dxZs6cOUaP6Ult9erVAaSetB42bBjatGmDAwcOGCXrs2bNQuvWrY3W6Bpav349du7cafaxuLg4k6uKhk6dOoVy5cqhcePGJt8z9I4StWrVQuvWrdGsWTOTaev3799HzZo1kTdvXuzduxehoaHw9/fHxYsXERISgg8//BA7d+5Ely5d8MILL5jE+uDgYNSrVw/9+vXLltlztgQHB2PSpEm4fv06Nm/eDG9vb3h7e2eqMBtlrX379mHKlCmIjo529lCeaEyejQP60rQ/7isl3fqrtG02Gp4NtXLbmE1jfKKTZ72Aid47eMqUKWjUqJGqRm3OvHnz1Oeyfft2REZGqqlgetVrIDXJfv311zF06FCjM4thYWEoXLgw8uXLZzOA/PXXX1i4cKFR+4bJkyeradn6OHx8fNTjCQkJiI2NxcOHD9WUMksFv6w5d+6cev7066Y/++wziBj3WTxz5gyWLFmCv//+G0lJSZg9e7bDa4fXrVunkv1+/fqhbNmyVtebAVAnEPQTDSKCTz/91KHjEpFluTl5zu1x1t4Yu2vXLixcuNApBYROnz6NLVu2OLRPcHCw6tcsIiZXGq0JCwszamuY0Rh17do1FC1aFJqmQdM0LFq0SD22Y8cOvPzyyw6/rvT0YmaaptlV0Co2NhZTpkyxq0Lv+++/bxLLzdW+SElJMTs9v127dhAR1f9ZRDBr1iyjbYYNG6Yey0jR1MjISHz66adG68EdoU8z79y5M7Zu3arGsn79erPbBwcHY/78+WbXg6ekpGD16tXYu3evui82Nhbz5s3L9pP0jnDGSYqMio2NRd68eSEieP/99509nCcak2fTgP6juYCett1gEVlo5haUtu/ZtN8HZ9M4n6jkOTY2Fvv371dnyatXrw6Rf1pA6euMO3XqZPE5Ll26hFq1auHZZ59FVFQUIiIiVFEvf39/JCYmYt68eVixYgVq1KiB8uXLm0xdjomJQWRkpNWxbt++3eiKtS4xMRHLly9X07fEYL3VzZs3Ubp0aRQsWBDHjh3D008/DU3TMlSB89GjR6hbty7y5ctntPYZgPpj6+HhoZLlokWLQkQwdOhQh4+lH09P9t966y312nr37g0ACA8PNxuU9LVUzZo1wyuvvIJatWrhr7/+ytAYiMhUbk2eXSHO2hNjL1++rP72TZ8+3f4PJguEh4eruhqzZ8+2a5+YmBi1T6VKlfDee++ZXAE/fvy4yWwsQ/PmzUPlypVRs2ZNSNrUbXt6/KYXERGh3ruePXs6vL8t8+fPV7HOcBmTJYZ1Q2zNgLpz5w4+/vhjrFixAr6+vihfvjw6dOhgVPQrPDzc7HRvILX1l4igQ4cO8PX1RZMmTVTxqcOHD6NTp074/PPPUa9ePfj5+WUoqRs+fLh6/YbdNezVvHlziIiqYTJ37lx8++23ZscSGxurKlOPGDHC5PHFixerkw3nzp0DANViM2/evDnWj9ea1157DR4eHi5TADQpKQlVqlSBiGDKlCnOHs4T7YlPnsV4CtkaEfHIwHO0FE7bznL6VUq9cvWdO3ewbds2dbZ/5MiRqFChgt1tGXRHjhzB8uXL8eeff6o1TpqmqSC6ZMkSh54vMDBQPcemTZvw7LPP4pVXXsHevXvRt29f7N69GwcOHFBfGtzc3HD+/HmjSo8LFixAQkJCpouxmLsSYnim+6WXXkJycjJKly4NkX/6PutmzZqFevXq2Ww99dtvv6nnXbBgASZMmICmTZvi0KFDKoBbquR94cIFdUIkOTkZgwYNQps2bdhXkSgL5Mbk2VXirD0x9vbt22r20rJly2x/IFno1q1b6uSv4cwpax4+fKjaQRlWENZdvHhRdX5YuXKl1ecaNWqUimEZXTazcuVK9O3bN8srNR88eBAeHh7w8PDA2rVrbW6fkpKiPsc8efLg7t27dh/r9OnTKqaOGzcOQGoyqc+Omzp1qtH2ycnJGDduHN555x2zy846deqkxpEZCxYsgKRNf7fU53v16tV45ZVXzK5Z/vvvvxEcHGzX8oKEhAT1PcKwQOrDhw8xfvx4dRXd29tbxfaAgACIpNZiycjJF3skJCRg7NixmDRpktXXkZKSov7dv/LKK9kyluwQFRWV7W3CyDYmzyKfp/0RfCAiX6T9nv5W38ZzMHnOBs888wxEBE2aNMnQ/itWrECbNm3MnlEfMGAARASFCxeGSGo1zDfffBN9+vTBgwcPAADjx49HlSpVLCaSR44cQdu2bdG4cWP1heKDDz5QQVX/wlKhQgUA/5yJzZ8/P65fv47evXtDRFC+fHmHAklQUBACAwPt3v7ixYuoWrUqRP7p2Xnjxg1s2bIFsbGxaNeuHSpWrIjg4GA1Pc/WvzO95/TTTz9t8pj+fujr16wxLLAyfvx4u18TEZmXS5Nnl4iz9sbYK1euOG3qaXBwMNavX2/xymRiYiK++eYbo6nIV65cwY4dO8zuc+HCBXVi19bJgOjoaIwbN87iNF5Ltm/fjk6dOhlNJ05ISMCAAQPw6quvZqg1pe769es4dOiQOoktInavydb7MFuqvm1JbGwsmjdvjhIlSqi11zExMWqW14gRI/DZZ59hwYIFAFKnpetjq1+/vsm07sWLF8Pb21stScuMS5cuWW2rVqRIEYgI2rRpk+lj3bp1C7t27TL6dzVp0iT1WlevXm0yA2Dfvn1W151nlmHvalvLAJYsWYJu3bpx9puB8PBwtGjRAh07dlTfhckUk+d/zoZbu71t4zmYPGeDsLAwfP311yZXJOfOnYsXXnjBZHpyevpZUXPv2Ysvvqg+33Llypntt6hPSWrXrp3Z59erbRuegQ4LC8Mzzzxj9Pw1a9ZU+xw5ckS9Hr0H5H/+8x8b78Q/du/erZ7X1vpiQ1evXsU333yDq1ev4u7duxgzZgw2bdqEn3/+WT1f1apV0apVK1SqVAnLly+3+nx6iy29OqqhY8eOYdCgQSZVvc2Ji4tDhw4dULVqVYfW4RGRebk0eXaJOPs4xFjDOh/p/6ZaSqwOHjzo0AlZR6UvCAakdqHQx2lpCvpXX32FN99802L7ynv37qlkcPHixVi6dCl69eqF0qVLm6wnTk9vwbV161aH165v2rQJw4YNM2kv+eeff2LOnDmqi4b+Gdy8eVN9nxARnDlzxqHj2SshIQFDhgyBn5+fxcTHz88Pbm5u+Oabb7JlDOvXr4ebmxvKlCljVNQqJSUFERER2XJMQydPnsS//vUv+Pj4cDZbBuizF0QEQUFBzh5OrvXEJ8+udHscAntWsJXU6kaNGoWCBQvi22+/NXns2rVraNGiBUQEAwcONLv/lClTUK9ePYt/QH766ScUKlQI3t7eKFCgAI4fPw4AOHr0KPLly4c8efKgVKlSqjq3oY0bN2LDhg0YP3483njjDatTxdetWwcfHx+88847OHToENzc3KBpGvbt22f19evOnDmDBg0aoEePHkhMTMSQIUMgaeuiNm3aZPIltmnTpujbt6/FaV86R9dU/fTTT3j22WexYsUKh/YjIvvlxuTZVW6PQ4zdsmULNE1Dnjx5MHLkSLVEZtGiRRARVKtWzebfdt3Zs2fRo0cPs+2KrLl37x4CAwPVjKpJkybBy8sLY8eOVdtERkaiYcOGqFSpktn1xiEhISomjRkzxuxxrl+/rqqA61PS9TWhderUsTrG//znPyZTjtObOnWqeh91CQkJqt5Knz59TPaJjo5WM9oKFSqklmKdPXsWzZo1y9aq61u2bFHvmWFBtvSyu0jWtWvXcP/+fdy5c0clzD179rT6WWalhw8fZtu08MfdzZs38fzzz6Ndu3YZap/6pGDy7EK3xyGwZ4UPPvgAPj4+Nq+O2sPa9KaFCxfC39/fbOVMQ3q1bJ3hmbtdu3aZbL9ixQo1zbtXr16QtPXSd+/exY0bNxAaGmq0vX6FWkQQGRmJ4OBgHD161GibhIQEDB8+HAMHDlSVL7dt24Z9+/Zh3Lhxav+TJ0+qoio1a9ZEQkIC1qxZg0mTJqFw4cIoXry42tZaFXN79OvXD4ULF8aPP/4I4J8vK9WqVcvU8xKRZUyeH88YGxISgv79+2Pjxo02t924caP6O65fYRwxYgREBO7u7lanSV+4cEGdGH399dfVPo70t9WX7rzxxht2bf/o0SOTq7+xsbFo2LAh8uXLZ7H1FJBa+XzevHnqhMDy5cvRpEkTzJ492+IV5eTkZHUS3tpUaf11FChQAM2aNVN9ups0aQIRMXti/urVq6oid053kggPD0eVKlXw1FNPOdwCMqudPHkSefPmRb58+XD69GmUK1cOIoIXXnjBqeMi5zl58iRGjhyZrdPkLRWrzWpMnl3olpsDuy2DBg2Cl5eX3RVCne3gwYPqy8f8+fONHtu1axeWLFli8ctEbGwsRo8eja+++srs/8Rr166FiMDT0xP/+9//4ObmhoYNG+LcuXPIly8f3N3djaak6wW4RMRoKvTevXvVtDHDK8hLly41+n3KlClo3Lgxevfurb5MXL582aS9RHJyMi5evIiaNWviueeeM6mGOWnSJDz//PN2rfVLSUlR6+j0FlkzZ85EqVKlEBAQYHN/IsoYJs/OibGZWbdrj+7du0MktaiUrS+H4eHhKFasGDw8PFTNj8jISDRv3hw1a9bE4cOHze63bNkyiAjKli2LBw8eYMmSJXBzc8NLL73k0Fj1E6VdunSxue3u3buRJ08eVKlSxWzvWkeSdt2YMWMgklrZGoDZK7379+/HF198YXUq8a+//qpmqIkIhg8fDiD1ZLWlqeRAajz++uuvn+irnxs2bFDv26ZNm7Br1y4MGDCA64ufYA0bNoSkrfvPDh999BFE/un6kp2YPLvQzZWTZ31dUqtWrZw9FLtcv34dRYoUgYeHh1EiGxYWppJCc2ed7bV79261Hu3+/fuYPXs2XnrpJRVsDK+q3717Fz179sSHH36Iw4cP44cffsD06dMhklqU7P79+7hx4wbKly+PEiVKICQkxKhHY4MGDTL+RqSJj49Xz2fv1YRvv/0Wbdu2zVU9HYked0yecz7G9u/fHyKCDz/80OF97TVz5kyICF588UVERkbaLOYTHR2NGzduqN8fPnyo/oa/+eabZvfRWwm6u7urok4Z6WV99epVLF682GaLRwDw9/dX4zJMrB48eIAOHTqgefPmVhNVc3x9fSGSWtX5s88+g0hqeyR7xpPe9u3b8dxzz6FKlSqqONjjbsmSJejRo4dqMZURKSkpmD59OmbMmGH3lcC///4bM2bMyPSsN8qd+vbta3G5Q1Z4/vnnISKoXLlytjy/ISbPLnRzpeRZD3zNmjXDzZs3sXLlSnTq1MmuIlK5gV5MJH0hMcPCH+bW7l65ckX1bjQnLi4Oc+fOxd69e9V933zzjfry4Ovrizlz5pg9U3737l3VpqR169bqKoRhiw3DIFWmTBmICHr06OHQa7fk5ZdfxlNPPWWzgiUROQ+T55yPsdWrV4eIoFGjRg7v64ioqCgcOHAAefLkQbFixcwWugRgcamRn58fypUrh+3bt5t9/OHDh5g8ebJRVezsFh0djSFDhmDatGlG9xuu3/3uu+/M7jt27FjkzZvXpOdtSEgIRowYgX379hkV6tq6datDYztw4IDaNye6QaSkpJi9+m6vv/76y+pr/PvvvzFw4EAMHTrU4r+RhIQENe3c3hPlWUVfYubp6Znptp2U+yQnJ+PChQsZmk1ijxMnTmDQoEF21wPKDCbPLnRzpeTZMPCln/bsDGfPnsX8+fPtLoDQqFEjiAg+/vhjk8fCwsLMnoE+evQovLy84O3tbbFy9IQJEyAi8PLywu3btxEcHKzeJzc3N6vr2aKjo1GoUCGICAICArBw4UKLZ8JTUlJUAMyKHob6VQ83N7cc+cNERBnD5DlrYmxKSorJ0hZL9u3bh/79+1ucDp2VDGtqGBay0gUGBsLT0xNNmjQxumqsJ945sR4wKzx48AC+vr5o2rSpxdZGestEa7Or2rVrBxFB8eLFHR5DUFCQwzOuMqNr165Gxc8MpaSkWP3srl27Bm9vb4gIFi5caHab5cuXq9ezceNG7Ny506RiOAB07NgR7u7uDheKsyQxMRGdOnVCqVKlsGfPHovb6fVYSpUqxTZJlKsxeXahmyslz3rg0688O1uJEiUgIujZs6fNBDoyMlL1aH711Vetbnvq1CnVdsKw7ZOls79z586FiKBYsWKIiYnBzZs3UbRoUbi5uWHz5s02X0dYWJjZvtXmzJw5E61bt8a4cePQq1cvnDlzBgMHDsSoUaNsFkFLb/Dgweq1WZuamJCQgD/++MPuL51ElLWYPGc+xiYlJaFJkyZwd3fH6tWrM/Q5ZJe4uDjV+knTNMTGxho9PnToUPW3Ojw8XN1fo0YNSA5VO84p69evR/v27S1eSQdSlxwdOXLE7grj6Q0ePBitWrXCli1b7D7x8L///Q958uRxeBq/vrzN19fX6P7Lly+jdOnSKF26NC5fvmx236tXr6oq4Jau1F+8eBGlSpVCuXLl1Lrw4sWLm12bnZHp+nfv3jX7fe/q1asm68YtCQ4ONrrq/PPPP6NXr14IDg52eDxE2YXJswvdXCl5zm4pKSmYN2+e1XYMQOrZ2G3btqn2Fe7u7ihWrJjRWjBDkZGRKFmyJDRNQ+/evXH79m0AqRVIf/75Z/j7+2PatGlISUnBgQMH4ObmBjc3N/Tp0weXL1/Gd999Z3NMBw8exPnz5/Hiiy+iffv2Nqd62+Ps2bOoUKECGjVqZDTtKzY2Vl2BLlWqlApgP/zwg0PPHxsbi5YtW6JRo0YmvRMNv1C89dZbEBG0adMmU6+HiDKGyXPmY2xkZCQ0TYOI4L333svQ55AVTpw4gQoVKuCFF17Ao0ePcOnSJcTHx2PChAlwd3dH+/btTfa5du0a+vTpY1STIzk5WbVPevvtt3PyJbisCRMmoECBAvD390etWrUcOvHw3HPPQUTw1FNPOXTMoKAg+Pn5mfSBXrNmjYrda9asQVJSEj744AP07NnTqFDdsWPH8PPPP1s9hh6v9eQ5f/78WdKS6NKlSyhYsCC8vLzMLs8bO3Ys2rdv73AV8KJFi0JE0LZt20yPkSirMHl2oVtuTp5v3bqFqlWroly5cggLC8v2461bt04Fk19++cXsNvHx8ShZsiREBB999JFR1eoDBw4YbXvt2jWcPXsWZ8+eVV+a/P39AQAxMTEoWLCg2ldEsHnzZqOp6WJQVdoeAQEBar+JEydm/I1Io0+rFhE1LSoxMRF//PEHWrZsCXd3d3h6epotzGKvoKAgk5Yh3bt3h4eHh+pTrU+Rq1mzZqZfExE5jslz1sTYRYsWwc/Pz+y01pwyefJk9Tdbn/3z9NNPq7/lU6dOtfu5jh49imnTpqk2VOaEhISgY8eOVnsfZ7UtW7ZgzJgxVqteO+LkyZOZWjesq1atGkRS+0XrJx6stbUytGvXLnTo0CFDsxZu3LiBxo0bo0WLFup1xMXFYfDgwRg8eDDi4uKwb98+9e/if//7n8PH0J9z0aJFJm0vM2rPnj1qTFk13RuAaufJLh2UmzB5dqFbbk6eDacsL1u2LNuPd/jwYXh6eiJPnjyq92J6sbGxao3wxx9/jLi4OHz++eeYNWsWVq5cicmTJyM2NhaXL19G/vz5oWkaypcvDzc3N/Tu3VtNZYqKilJFRzw9PVGgQAFVhXLNmjWqLYevry/y5ctndp10eoZtHCytT3JEREQEunXrhkGDBqnpVv369YNIal/F2NhY9QXg5Zdfdvj5DVtf6dWzU1JS4OHhARFBt27dAKQG/oCAgExV6SSijGPy/PjE2Bs3bqBTp05o3bo1SpcuDRFBvnz58K9//cvq9FwgdapskyZN0LFjR7uX0QwZMkT9nc/scquEhAQMHjwY77zzDh4+fGh2m4cPH6oTAYMGDcrU8QBgxowZEBFUqVIlQ9OODW3YsAHt2rXD5s2bceTIEQQEBGR7OzIAWLhwodFJenOioqJQt25dlCxZEidPnsz2Mdlr/vz5CAgIyPR7n176pQmUu4SGhlq8iPW4YvLsQrfcFtgNxcfHY+DAgXj77bctBsqsduXKFYvVRnUnT57EkiVLEBcXp+47d+6cCk4BAQFGRbssFWI5deoU5s6di+nTp+PixYtGjyUkJODSpUsWp2odOnQIrVq1wvTp043uX7VqFRYuXJjlBVySk5Nx8+ZNdOjQASKCqlWrGr2GjEzR+uWXX1TBMMO1R99//z26deuWqwI40ZOMybNrxdhHjx5hy5YtVq+W6mtZq1atil9//RVhYWFWCy8Bxl0c0s8Y0sXHxyMoKEitMf3tt9/g4+ODDh06ZLoirmGxre+//17dv2PHDrz66qvYtWsXkpKS1JRBAjHbAAAgAElEQVToWbNmZep4wD/rvb29vbP9e0j69y6r3LlzB+3atUPXrl3Va7A2W4AISL3yX6NGjRxfZhIVFaUuUs2YMSNHj+1MTJ5d6Jabk+fsEhYWhp49e2Zpxe67d++iRIkScHNzQ2BgIABg7dq1aNOmDRo1aoT+/fubnUJWv359iAg6duxo9nl37tyJ9u3b48cffzS6v3v37qq4S3aV6DfUrVs3iAiGDRuGgIAAnD171uY+CQkJWLVqlclaK0P79+/PsileRJQ9mDy7VozV48MLL7xgcZs33ngDnp6eWLx4sd3Pe+3aNTRr1gxdunQxWxAKAN59912ICP797387PG5bwsPDUaVKFZQuXRohISHqfr1wWd26dQGknjwwXOoVExODzz77DOvXr3f4mNHR0fjyyy8tnizISgMGDICIoFatWln+3ElJSZgxYwZGjBihjmOr0FZW2Lt3L1q0aIE5c+bYvc+GDRtQrVq1HGnlBfzTTs2eAqtPEr3ejIhkyRp2e929exd58+aFiGDy5Mk5dlxnY/LsQrfHMXkOCQnB+vXrERUVhS+//NKkSrU+9VjTNMTExGTZcaOionDlyhX1+++//250NRpILYAxcOBAbNiwAcA/Ddhfe+01td/9+/cRFBSk/lgdP34c+/fvNzrWunXr4OPjg3feeSfLxm+NPr3PUtGu3bt3o0ePHti1a5e6b+zYsRARFCxYMMdmDhBR1mPy7Foxtn379hAR1K9fH35+fmjdurVRbNLZO0PpypUrCAgIQGhoqM1t33zzTYgIKlSo4OiwM+zjjz+GpmkWi2+NHj1azXIyrBauS05Oxvr16y3OdoqJicHbb7+N4cOHZ/n0YUO9e/eGiKBixYpZ+ryHDh1Cvnz51PcRvW5LvXr1svQ45vj6+kJEkDdvXrv30euc5M+fPxtH9o9PP/0Uklb89datWzlyTFdw4MABNGzY0K5lg1nt2LFjWLFiRbb+/5bbMHl2oVtuTJ5Pnz6NHj16OFy9GUhdx6K3ZtATUw8PD6NpUGvWrIGHhwdatmyJ5ORko/3PnTuH119/3WZ1a1vi4uLw73//GyKCQoUK4fjx4wBSz/aLpPZkTkpKQlRUFDZv3mx0Fl//4uPr64tTp07B3d0dIoKgoCCbx92/fz9q1qyJfv364e7duxavDqSXlJSEyZMnw9/f3+Q9AVKT48GDB1u8iqyv0TY8Y/75559DRFC0aFG7x0FEuQ+T59wdYxMSEnDmzBn1t/vOnTv47rvvjKY5T5o0KcPP37RpU1VYzJb79+9j0aJFJkuRspu1L9l6L+KKFSviypUrJgnS1KlT1dpvczPE9F7BYqWYKJB6xXTJkiUZXjYVExODRYsW2XWSIjQ0FLNmzbJrirdhMVEvLy98++23ePvtt02KnGaHH374AT4+Phg6dCgAYNasWZg6darVGXNBQUGoU6eO2d7U9jp+/LjdRd6WLl0KEUHZsmUdOtE/a9Ys1KpVCytWrMjoMIkUJs8udMuNybM+5czDw8PhIPT333+r5LlTp04QEZQrVw6ffvopvL291TQgw0A7b948fPjhh4iJiVFVGN3c3BAfH4/ExEQcO3bM4X6ON2/eVMHK8KzdrFmzbE6na9asGUQEzZs3x7Fjx1Sl7rVr19o8rj5lTn//ypcvj6ioKPU+RkRE4NixYyb7rV27Vu1nqy2FoZSUFPzxxx+qKMxHH32kHktOTsamTZvs+iJARLkXk+fMxdgGDRpgx44d2XYSsXPnzhAxbYEVGxsLX19f1KhRw2IRTHu8+uqrEBF07tw5s0PNEcuXL0elSpVUdwsgta/xiRMn8K9//QteXl6qSCUATJ8+XV3pvHv3rsnznT17FiVLlkT16tUtJqt//vmniqGrVq0yeiw5ORn9+vVDkyZN1JKnpKQkJCQkZPg16ifnO3XqZHPbqKgo9OvXD2PHjs2y2XaxsbEYO3YsZs6cafc+ep0TETFZipaV9JMFVatWtXtZ2/nz5x1eB16uXDm7TyoR2cLk2YVuuTF5XrhwIdzc3DJUwRlI7Z/8008/ITExEefOnUN0dLQqIJJ+HZZhoa9Jkybhhx9+gLu7Ozp06ADgnylojrSM0n3//fcYMmSISbC9d++e1T/ot27dwoIFC9TZ8V27dtlMaFetWoVixYrhtddeQ6NGjdRVdxHBokWLkD9/fjz77LNq+rVhv04g9Wp/gQIFULBgQZw/f97u1zhx4kQ1Rc/e6qtE5FqYPGcuxuonQF999VWz7+/YsWNRokSJDHdJqFq1KkQELVu2dHjflJQUDBw4EI0aNbLYbjAuLg779+93mQrFeqHNUqVKGd2/c+dOFRcNrxampKQgMDDQan0OW86fPw9vb29omoZff/3V6DHD7xmjR49GeHg4ypUrh0KFCuHEiRMZOp4e4998880Mjzkz5syZo16TvVewL1y4gAIFCsDb2ztba528//77aqp4ds56mzlzJmrUqIHly5dn2zHoycHk2YVuuTF5Bv65Mnz06FH07ds3wyXrk5KScOjQIaxbtw4dOnQwKQgRExODSpUqwd3dHfPmzVP76PQAlRNrgzJDv8ru5eUFAHjw4AH++9//Yv78+fjoo49UkNNvo0aNMnmO6Ohoh89K631CCxQoYFR9PKv8/vvvaso7ETkHk+fMxVj9727btm3Nvr/FixeHiKBZs2a2Pwwzjh07hrFjx2ZoqvTly5dVXPjggw8ydHxD169fx+7duzP9PJmxfv161KtXT50kPnz4MM6cOYOHDx+iefPmaNGihcOzyewRGhqKo0ePom3btqhatapaQ52YmIgePXqgdu3aOHHihFESr3/vcFRMTAx+/fXXbHkd9ti/fz+8vb1RsmRJbNmyBbNnz7brBHpERITZdedZKTo6Gv7+/ti7d2+2HocoKzF5dqFbTiXPMTExGepnqE9hLlu2bIaOO2jQIDUF2pJvv/1WnaVMvxbq4sWLmDhxIg4cOICtW7fanSCmpKTg9u3bRvft2bMHZcqUwSuvvGJ2XbG9AgMDMWbMGJQvXx61a9dGREQEdu3ahebNm5tcUQZSK6S2atVKBeuuXbsaBbmoqKgMV+x+8OABZs6cicOHD2f49Viir9dzd3dn2yoiJ2LynLkYW6lSJcyZM8di0rBw4UI0bdoU27Zts/1hZNC9e/dQs2ZNFC9eHKdOnVL3Jycno0+fPqhduzaOHDmSqWM8fPhQnQiYOnVqZoecJbZu3QoRgaenJ7744gsVB7PrvT5y5Ig6xhdffGF2m+TkZHz66acYPHhwpotpbty4EXnz5kWHDh2yvEWlLZGRkbh9+7YqRma4bIseXxEREejRowc+/PDDTH2XJWNMnl3olhPJ86VLl1CkSBHkyZPH4eA8fvx4iAjeeuutDB1bvyJrrXqlnjx7e3vjxo0bZrd5+umnLY5j+vTpqFy5slHfSX26t+F6Z/0qrYiYPU58fDyCg4OtJrKhoaFqDbR+W7BgAYoWLYqnnnoK165dQ2hoqEnRk7i4OPTo0QOtW7c2OvaCBQugaRqaNm1q8ZjOsm7dOkhaVXS2syJyHibPmYuxuWHm0u7du1XMMHeSNStER0erFjMFCxZE3bp1ERkZaXWfxYsX47333jM52WwoLCwsw+uDV69erV732rVrUaxYMZQvX95mVeWzZ8/C19fX4UJrSUlJGDBgAF588UVcvXpV3b9jx45MxbErV66gbNmyKFOmDC5fvqzu9/PzU68vK7uH6CIiIrBx40aLV5Xj4uLUcrDMFPgi1/H111+rf3N//vmns4fjkDt37mD16tV2F5PLSUyeXeiWE8nzb7/9pv5HW7p0qcP7Z6a/3M2bN+Hv72/1ymVKSgrWr1+PtWvXYtKkSUaBSVe9enWICFq0aIH58+cbTZUqX768SdGIypUrQ0Tw3HPPqfvOnj2L1q1b45NPPjE7jpdeegkiYrX9VEREBHx8fNQUv3fffReLFi1S7+/48eOhaRqKFi1q19Sovn37qqu72THtOrMCAwOdPgWQ6EnH5DnnYuzo0aNRuHBhzJ071+597JGUlIQRI0agb9++DhdGcsThw4dVyyURwW+//WZx2/DwcLXdiBEjzG7z2WefQUTQqlUri8+zevVqbNmyBQAwcuRI1K5d26ht4urVq7F9+3YAqVd97blCa1h801wFbkfoCbymaciTJw9Wr17t8HP89NNPajyG+58/fx6vvPKKUeGu+Ph4TJ061a4io7Y0bNgQIoI33njD4jbh4eH4/fffM30scg3Hjx9HiRIl0LBhwxzt/5wV9HoI9hTay2lMnl3ollPTtmfMmIGJEydmqrpkdqtRo4bFIB0WFoZp06bBy8sLIoKJEyeqx+bNm4e6desaVY/ct28fBg4c6NB6Xb2ombVK3ABw+/ZtVbETSK16OWzYMIwaNUpVDRURo+qqycnJ6N+/P5o1a4aQkBDMmDEDAwYMQHBwMN59912sWbPG7nES0ZOFyXPmYmzp0qURHBxs13ut9+B9/vnn7dremWJiYuDn54f//ve/RtM3IyMj0aNHD7z33ntWW0jFx8ejTp06cHd3x8iRI/Htt9+abN+lSxeICIoUKWL2OQw7Rezbt0/9d48ePTL12n755Rf4+PigS5cumZ6aunLlSqPZYtYSUUvi4uLw/vvv47333rN5otvwe8C5c+cyOmwA/7Sh7NatW6aeJyOWLl2K+vXrY9myZTl+bHo86clzx44dnTaGW7duoWnTpnjxxReNTj4weXahW0aS57CwsAytX86MqKgotGnTBi1atMj0WWBLOnToABGBn58fxo4di6pVqyIwMFA9HhMTo676zp49O8uPf+rUKUyYMAFhYWHqvuTkZLzzzjto0KCBXVU54+PjERAQgDVr1iAuLg7Tpk3Dpk2bcPr0aRVM9UqUIoJx48Zl+esgoscLk+fMxVgx0+nBkmXLlqFVq1YZLpKZk/TWiyLi8AyhuLg4NGrUCF5eXpgxY4Z6nvSx9fLly/j444+xZ88es8+zY8cOtab51KlT+Pjjj1GpUiXUrVvXapy+dOkSZsyYYTS1OjsFBQVh5MiR6Ny5s8Wq5lll48aN0DQNPj4+dvWBtubatWtYunRplk1zTUlJwdatWxESEmJz25o1a8KR/3eIbLlz5w5+/PFHp07bXrBggfp7Z1jEmMmzC90krQelvfQ/ysWLFzfbDzGrXbp0CdWqVUPFihXVP7bsagsQFxeHo0ePIikpSV1hdnNzM2ppcf36dfzxxx8ZPsbNmzfRvXt3fPLJJ4iOjsbKlSutTq8ODQ1Vr9vRYhxfffWVeg0XLlxAkyZNUKRIEQQFBaF27drw9vbGjh07MvxaiOjJwOQ5czFWRNC9e/cMvfeW/Pnnn3j99dexadMmrF27Fn379jWakZQT/vrrLxQtWhRVq1Z1+PvAlStXVGwbNGgQChUqBE3TsGnTJrPbX79+HS+++CLeeecdk6vTR48eNWoz5evrq4qAWtK4cWO1/Mlet2/fdpmWjCEhIdl2oSEzpk2bptbE21o+MHv2bFSsWDFbLlYQOUt4eDhatGiBjh074sGDB+p+Js8udBORA+XKlTP5cGNjY80W1NCbz4uIQ/2AM2rhwoXqePXr10fLli3NBoQLFy6gSpUqaNy4cZacUdL7F4tY7s2Z3u3btxEYGGh1SpVeAE1E0Lp1a5O10ukdPHgQnp6e8Pb2drjtwo8//ggRQbFixXDjxg14enqqK+vJycku07OTiJyLyXPmYmz9+vWtTl92xJ49e1CrVi089dRTEBGULl0aefLkgaR1UsgOhw4dwsCBA3Ho0CGTxzJT4Xn69Ono06cPbt68iVu3blmdYjxlyhS7ixStWLECxYoVw7BhwyxuoxcTtXc68qZNm+Du7o7y5cvnymJDruLLL7+EiCBfvnw5PoORKDdj8uxCN3OVQGNjY1G9enVomoZVq1aZPPbVV1/ZtUZ24cKFaNWqVaYKPkVFReG1117D22+/rZLSiIgIkyk/esVsEcHOnTvV/V9//TWeeeYZo/uA1BZLX3/9NXbu3Ilz585hyJAhRuOMjo7G4MGD0bFjR7umSwNA7dq1bRb8+vPPP+Hj44Nnn30WnTt3hoigcOHC6N69u9mWFVOnTlWv6+DBg1aPf+HCBbzxxhtYtGiRuu/MmTO4c+cOkpOT0aBBA4gI5syZY9frISICmDxn5pbVdUUMi3GJCAYMGKCKTc6aNSvDz/v999/j2Wefxc8//2zyWN26dSEiqFu3bmaGnmGjR49G/fr1UbFiRbRp0yZLrv7+/fff2Lt3L2JjY+26Qjtp0iT1nqf//hEcHIxWrVph8uTJmR7X4y4pKQmrVq1yqB4M0ZOAybML3cwF9lu3bql2SCNHjkRGFSpUSF1hzSp37tyBj48PNE3DunXrMH78eEyYMAG3b99G165d0a9fP0RERGD79u2IjY1VV1vTV9YbOXIkRAReXl7qCnDJkiXV4/p9jlTkq1KlCkQEPXv2tGv7+/fvY8iQISogb9iwwWSbqKgo+Pn54dNPP7V5hr9Pnz6qoqe5q8rx8fEWW3EREVnC5DlrY6wtJ06csFjFdu/evahTpw4++ugjowKcmU0oy5QpAxFB48aNTR4bNGgQRAQDBw7M1DEy4t69eypG9uvXL8ufv3///hARDB8+3Op29+/fxyeffIIlS5aYPKZ3rRARo2mYRET2YvLsQjdLgX3VqlUYOXKk2R6NgYGByJcvH3x9fa1Wofzggw9QuHBhLF682OI2jjp//rwKUoYBy/BsebNmzVRFy48++gjlypXD+vXrjZ5HL3RStmxZjB49GiKCzp07q8f1inz2Jv7Hjx9Hly5dMHz4cIdK99+8eRMNGjRAkyZNMrSGPCgoCG+99Rb++usvrFixAu7u7mjXrp3Dz0NEZAmT58zF2CJFiuDmzZt2vdf6FU5rRZICAwOxcuVKu57PXpMmTUKpUqUwf/58s4+Hh4cbncCdM2cO/P39s2w6uiUpKSno27cvKlasmC1tC/UuG9aWT9mydetWlCxZEr169crCkRHRk4TJswvdbJ0Vj4uLw9ChQzF8+HDV23jAgAEqaTWXXGe3tWvXYurUqThx4gQKFiyIIkWK4OLFi+pxfYpZhw4djPZLTk42Ojs/btw4dXX5xo0bSEpKUo/dunULixcvxu3bt+0aU9u2bSEiKFq0qMVtLl68iCpVqqBBgwZGa33i4uLQqlUrlCxZEgcOHLDreLpixYpBRFTCnN1fZIjoycPkOXMxVkTsntLr5+cHEUH+/PnN/j0/cOCAir8//fSTXc+Z1fbs2aPGYO5KbE67e/cuxo0bp3o5G7pw4QLmzZtnsTjV/v37MWDAABw5ciS7h0mU5S5dupQjxXvJtqSkJHzyyScYMWKEzXZy5jB5dqGbreR51apVKkjqV28vXLiArl27Yvr06Rb3y6hly5ahcuXKCAgIsLntqVOnULFiRTz//PNGSfHly5cxb948oxYNiYmJaNy4Mdzd3dUXju7du6sWF5kpegL8U0jN2pTtefPmqffSMMgbXk0fM2aMQ8d9/fXXISLw9/fP8NiJiKxh8py5GCsiKFSokF1FNu/du4fJkyfj999/N/v46dOn4enpCU3TTGp55JTLly+jcOHC8Pb2tlm8Kyfo08q9vLxMpq9XqFBBzUTLCuvWrcOAAQOMWkrmtPv372PixIlGrTStOX/+PMqUKYN///vfubICN2VMUFAQ3Nzc4OPjY/eFHso+mzdvzlRXICbPLnSzlTyHhoaiePHi8PHxMbq6m12eeeYZiAjKlCljc1u9FZM9xbTu3r2r1nG///77AICzZ8+id+/eJkXR7BUSEoJr166p321Vr46MjMRrr72Gfv36qav4ulGjRuGll17ClStXHB5H+mnid+7cwa+//mp0JZ2IKKOYPGcuxupx6ocffsjQ+5/e+fPn7S5kmV0iIyOz9Qv7xYsXUbp0aVSuXNls54+lS5eiTp06WLRoEb755hs11T193KtTpw5EBO+++26mx5SUlKTqqGRVMp4R+lIzd3d3uz6D+fPnq+9K8+fPx5QpU6y2yHxcBAQEYPTo0Y9tZ5GZM2eqz/X06dMO7//zzz9j165dWT+wJ9TVq1dRsmRJFClSJENtA7MjzmpIDUKUxTRNu12gQIES//nPfyQlJUXc3NxMtjlz5ozcv39fihUrJlWrVs3W8dy7d09u3LghJUuWlJIlS1rdNjExUUJDQ8XLy0sqVaokmqZZ3f7OnTvy4MEDKVeunHh5eWVqnPfv35czZ86IpmlSr149cXd3l4SEBMmfP3+mnjcrBAcHS3x8vDz11FNSoUIFZw+HiFzcqVOn5OHDh3cAWP+jTCY0Tbvt5uZWokSJElKhQgWbcepJEh4eLvHx8VK2bFlxd3c3eiwiIkJCQ0NFRKRGjRpSpEgRo8dPnDghsbGxkidPHqlfv76EhIRIdHS0VKxYUYoXL662S0pKkkePHknBggUz/d4DkJMnT8rff/8tlSpVsvkdJbvo742Xl5eUKVNGChcuLN7e3ha3T05OlkuXLom7u7tERkZKYmKiFClSRGrUqJGDo85ZDx48kNOnT4uIOPWzyk4A5NatW+Lt7S0+Pj4O7Xvv3j0JCQkREZE6derkiu+ujwM9V83I35rsiLNMnrOJpmnnRKSIiIQ5eyzZpEnaz4NOHQVlBj9D18fP0LU9KyIAYHp2lax6AmKsCP//dnX8/FwfP0PXl+VxlskzZYimafEiIgAsn5alXI2foevjZ+ja+PmRNfz34dr4+bk+foauLzs+Q57tJiIiIiIiIrKByTMRERERERGRDUyeiYiIiIiIiGxg8kxERERERERkA5NnIiIiIiIiIhtYbZuIiIiIiIjIBl55JiIiIiIiIrKByTMRERERERGRDUyeiYiIiIiIiGxg8kxERERERERkA5NnIiIiIiIiIhuYPBMRERERERHZwOSZiIiIiIiIyAYmz0REREREREQ2MHkmIiIiIiIisoHJMxEREREREZENTJ6JiIiIiIiIbGDyTERERERERGQDk2ciIiIiIiIiG5g8ExEREREREdnA5JkcomlaY03TtmiaFq1p2iNN0/7UNO11Z4+L7KNp2puaps3XNO2IpmnxmqZB07S3nT0uso+maWU0TftA07RfNE27qmlagqZp4ZqmrdM0rYmzx0e2aZqWR9O06Zqm7dU07aamaXFpn+F+TdPe0TTN09ljJOdinHVdjLGujTHW9eVEjNUAZMVY6QmgaVorEdkuInEi8qOIPBCR7iJSQUQ+BjDNicMjO2iadllSP6+7IvIo7b/fAbDEicMiO2ma9pWIjBKRUBHZLSIRIlJNRLqKiCYivQCsdtoAySZN04qJyDUROSQiFyT1MywiIh0k9f/HX0SkA4AUpw2SnIZx1rUxxro2xljXlxMxlskz2UXTNA8ROSciZUXkWQDH0+4vJKn/QCuKSHUAV5w2SLJJ07S2IhIC4IqmaaNFxF8Y2F2GpmndROQegD3p7m8uIr+JyEMReQpAvDPGR7ZpmuYmIh4AEtLd7yEiO0SkpYi8BCDICcMjJ2KcdX2Msa6NMdb15USM5bRtsldrEakiIiv1gC4iAiBGRL4UES8RectJYyM7AfiVX7xcF4D16YN62v37RGSXpJ5drZPjAyO7AUhJH9TT7k8SkQ1pv1bN2VFRLsE46+IYY10bY6zry4kYy+SZ7NUy7ecvZh7bnvazRc4MhYjMSEz7meTUUVCGpJ0t90379ZQzx0JO0zLtJ+MsUe7DGOvCsjLGemR+OPSEqJb2MyT9AwDCNU17aLANEeUgTdPKi0hbEbklIiedPByyg6ZpXiIyRlLX0fmISBsRqSki3wP4zZljI6dhnCXKhRhjXU92xlgmz2SvQmk/Yyw8ft9gGyLKIWmVI5eLiLeIjAKQ7OQhkX28RGS8we8Qkaki8olzhkO5AOMsUS7DGOuysi3Gcto2EZGLSpuGtEREXhCRBQCWO3dEZC8ADwFoIuIuIuVEZLCI9BeR3ZqmFXTq4IiIiDHWhWVnjGXyTPbSz4RbOutdUCyfLSeiLJYW1BeLSC8R+UFEBjl3RJQRacVNrgOYKyLvikhTERnr5GGRczDOEuUSjLGPh+yIsUyeyV76GiyT9VaappUSkQJiZp0WEWW9tKD+vaRW3l0lIm+zL/BjQS8U1dKZgyCnYZwlygUYYx9bWRJjmTyTvfTS/S+aeax9um2IKJsYBPW+IrJaRPpwDdZjo3Taz0SrW9HjinGWyMkYYx9rWRJjmTyTvX4TkTAR6aVpWn39Tk3TCklqNbsEEVnmpLERPREMppH1FZGfRORNBnXXomlaLU3T8pm5P5+ITE/7dUvOjopyCcZZIidijHV9ORFjNQCZ2Z+eIJqmtZLUXpNxIvKjiDwQke4iUkFEPgYwzYnDIztomtZfRJql/VpHRBqKyH4RuZh23+8AFjpjbGSbpmmfS2r1yIciMlPM95vcCOB4To6L7Jf2GX4oIr+LyGVJraBcRkQ6SGo7jX0i0h5ArJOGSE7EOOvaGGNdG2Os68uJGMtWVWQ3ALs0TWsmIhNEpIeIeEpqv7tRAFY7dXBkr2aSuobHUNO0m46BPfeqmPazgFgueHFZRBjYc6/Nkjp17HkReU5SP8sYEflLUpOlxQDMfWGjJwDjrMtjjHVtFdN+Msa6rmyPsbzyTERERERERGQD1zwTERERERER2cDkmYiIiIiIiMgGJs9ERERERERENjB5JiIiIiIiIrKByTMRERERERGRDUyeiYiIiIiIiGxg8kxERERERERkA5NnIiIiIiIiIhuYPBMRERERERHZwOSZiIiIiIiIyAYmz0REREREREQ2MHkmIpejaVoDTdOgado2Z4+FiIjoccIYS2QZk2cickXPp/3806mjICIievwwxhJZoAFw9hiIiByiaVpxEfERkVsAYpw9HiIioscFYyLOVfsAAAJSSURBVCyRZUyeiYiIiIiIiGzgtG0isknTtCJp659CNU3Lq2naZ5qmndE0LV7TtCNOGktITh6XiIgoOzDGErkOD2cPgIhcQv20n7dE5JCIlBeR3SJyVkRCnTSW4Bw+LhERUXZgjCVyEUyeicgeDdJ+NhWRzSLyAoAoJ4/luJOOT0RElJUYY4lcBJNnIrKHfib6ooi8AeBRLhgLz4oTEdHjgDGWyEUweSYie+hnosfYE9Q1TVsmIs84eIwNAD5xYCwM7ERE9DhgjCVyEUyeicgqTdPyiEhNEYkVkS127lZeRGo4eKinHBjLbQDhDj4/ERFRrsIYS+RamDwTkS3/kdS/FX/YO5UMQMtsHgvPiBMR0eOAMZbIhbBVFRHZok/hOurUUaTidDIiInqcMMYSuRAmz0Rkix5Mjzl1FKlYBZSIiB4njLFELoTJMxHZolfezA1nxVkFlIiIHieMsUQuRAPg7DEQUS6laZqbiNxP+7UggJRcMJYUESkE/vEiIiIXxhhL5Hp45ZmIrKkuIvlF5IQzg3q6sfzFoE5ERI8BxlgiF8Mrz0REREREREQ28MozERERERERkQ1MnomIiIiIiIhsYPJMREREREREZAOTZyIiIiIiIiIbmDwTERERERER2cDkmYiIiIiIiMgGJs9ERERERERENjB5JiIiIiIiIrKByTMRERERERGRDUyeiYiIiIiIiGxg8kxERERERERkA5NnIiIiIiIiIhuYPBMRERERERHZwOSZiIiIiIiIyAYmz0REREREREQ2MHkmIiIiIiIisuH/YFmTAA7jDPQAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.style.use('seaborn-notebook')\n", + "fig, axarr = plt.subplots(1, 2, figsize=(8, 4), dpi=140)\n", + "\n", + "fig.suptitle('Forced Photometry')\n", + "\n", + "axarr[0].set_title('Coadd (Stars Only)')\n", + "axarr[0].scatter(rMags_coadd[selected] - iMags_coadd[selected],\n", + " iMags_coadd[selected],\n", + " edgecolors='None', s=2, c='k')\n", + "\n", + "axarr[0].set_xlim(-0.5, 3)\n", + "axarr[0].set_ylim(25, 14)\n", + "axarr[0].set_xlabel('$r-i$')\n", + "axarr[0].set_ylabel('$i$')\n", + "\n", + "# datasetRefOrType : forced_src\n", + "# see all options at\n", + "# /opt/lsst/software/stack/stack/miniconda3-4.3.21-10a4fa6/Linux64/obs_subaru/16.0+1/python/lsst/obs/hsc\n", + "iDataId = {'filter': 'HSC-I', 'pointing': 671, 'visit': 903986, 'ccd': 16, 'field': 'STRIPE82L', 'dateObs': '2013-11-02', \n", + " 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0, 'patch' : '1,1'}\n", + "rDataId = {'filter': 'HSC-R', 'pointing': 533, 'visit': 903334, 'ccd': 16, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', \n", + " 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0, 'patch' : '1,1'}\n", + "\n", + "iSources = butler_ccd.get('forced_src', iDataId)\n", + "rSources = butler_ccd.get('forced_src', rDataId)\n", + "\n", + "iCcdCalib = butler_ccd.get('calexp_calib', iDataId)\n", + "rCcdCalib = butler_ccd.get('calexp_calib', rDataId)\n", + "\n", + "rMags_ccd = rCcdCalib.getMagnitude(rSources['base_PsfFlux_flux'])\n", + "iMags_ccd = iCcdCalib.getMagnitude(iSources['base_PsfFlux_flux'])\n", + "\n", + "axarr[1].set_title('Single Exposure (All Sources)')\n", + "plt.scatter(rMags_ccd - iMags_ccd, iMags_ccd, edgecolors='None', s=2, c='k')\n", + "axarr[1].set_xlim(-0.5, 3)\n", + "axarr[1].set_ylim(25, 14)\n", + "axarr[1].set_xlabel('$r-i$')\n", + "axarr[1].set_ylabel('$i$')\n", + "plt.subplots_adjust(left=0.125, bottom=0.1)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we concatenate the concatenate the measured sources into two tables (one for each filter). Then group by object ID to draw a five epoch light curve." + ] + }, + { + "cell_type": "code", + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -781,7 +869,8 @@ "output_type": "stream", "text": [ "9742 i band objects, 30400 measurements\n", - "8321 r band objects, 32717 measurements\n" + "8321 r band objects, 32717 measurements\n", + "1769 objects with 5 epocs\n" ] } ], @@ -793,19 +882,29 @@ "rGrouped = rSources.groupby('objectId')\n", "\n", "print('{} i band objects, {} measurements'.format(len(np.unique(iSources['objectId'])), len(iSources['objectId'])))\n", - "print('{} r band objects, {} measurements'.format(len(np.unique(rSources['objectId'])), len(rSources['objectId'])))" + "print('{} r band objects, {} measurements'.format(len(np.unique(rSources['objectId'])), len(rSources['objectId'])))\n", + "\n", + "objids = [name for name, group in iGrouped if len(group) == 5]\n", + "print('{} objects with 5 epocs'.format(len(objids)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Choose an object to draw a light curve." ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "e.g. objectId: 141733921083\n" + "e.g. objectId: 141733921084 (showing select rows from table)\n" ] }, { @@ -832,232 +931,102 @@ " id\n", " coord_ra\n", " coord_dec\n", - " parent\n", " objectId\n", - " parentObjectId\n", - " deblend_nChild\n", - " base_SdssCentroid_x\n", - " base_SdssCentroid_y\n", - " base_SdssCentroid_xSigma\n", - " ...\n", - " slot_ModelFlux_apCorr\n", - " base_GaussianFlux_apCorrSigma\n", - " slot_ModelFlux_apCorrSigma\n", - " base_GaussianFlux_flag_apCorr\n", - " slot_ModelFlux_flag_apCorr\n", - " ext_photometryKron_KronFlux_apCorr\n", - " ext_photometryKron_KronFlux_apCorrSigma\n", - " ext_photometryKron_KronFlux_flag_apCorr\n", + " base_PsfFlux_flux\n", " visit\n", " mjd\n", " \n", " \n", " \n", " \n", - " 277\n", - " 776518129927848214\n", - " 5.598927\n", - " -0.006448\n", - " 0\n", - " 141733921083\n", - " 0\n", - " 2\n", - " 579.995811\n", - " 1112.172658\n", - " 0.321924\n", - " ...\n", - " 1.117663\n", - " 0.0\n", - " 0.0\n", - " False\n", - " False\n", - " 0.992184\n", - " 0.0\n", - " False\n", + " 278\n", + " 776518129927848215\n", + " 5.599023\n", + " -0.006443\n", + " 141733921084\n", + " 9120.533865\n", " 903986\n", " 56598.221156\n", " \n", " \n", - " 516\n", - " 776519852209734149\n", - " 5.598927\n", - " -0.006448\n", - " 0\n", - " 141733921083\n", - " 0\n", - " 2\n", - " 488.557073\n", - " 2167.504065\n", - " 0.690836\n", - " ...\n", - " 1.085815\n", - " 0.0\n", - " 0.0\n", - " False\n", - " False\n", - " 0.964561\n", - " 0.0\n", - " False\n", + " 517\n", + " 776519852209734150\n", + " 5.599023\n", + " -0.006443\n", + " 141733921084\n", + " 9886.496949\n", " 903988\n", " 56598.222126\n", " \n", " \n", - " 771\n", - " 776521574491620100\n", - " 5.598927\n", - " -0.006448\n", - " 0\n", - " 141733921083\n", - " 0\n", - " 2\n", - " 455.517639\n", - " 3092.148678\n", - " 0.306457\n", - " ...\n", - " 1.119077\n", - " 0.0\n", - " 0.0\n", - " False\n", - " False\n", - " 0.996091\n", - " 0.0\n", - " False\n", + " 772\n", + " 776521574491620101\n", + " 5.599023\n", + " -0.006443\n", + " 141733921084\n", + " 9071.808927\n", " 903990\n", " 56598.223046\n", " \n", " \n", - " 0\n", - " 776538694231261185\n", - " 5.598927\n", - " -0.006448\n", - " 0\n", - " 141733921083\n", - " 0\n", - " 2\n", - " 9.190805\n", - " 3042.373080\n", - " NaN\n", - " ...\n", - " 1.101941\n", - " 0.0\n", - " 0.0\n", - " False\n", - " False\n", - " 1.023645\n", - " 0.0\n", - " False\n", + " 1\n", + " 776538694231261186\n", + " 5.599023\n", + " -0.006443\n", + " 141733921084\n", + " 27701.589804\n", " 904010\n", " 56598.250471\n", " \n", " \n", - " 83\n", - " 776542138795032660\n", - " 5.598927\n", - " -0.006448\n", - " 0\n", - " 141733921083\n", - " 0\n", - " 2\n", - " 271.696844\n", - " 871.973028\n", - " 0.234910\n", - " ...\n", - " 1.144652\n", - " 0.0\n", - " 0.0\n", - " False\n", - " False\n", - " 1.014535\n", - " 0.0\n", - " False\n", + " 84\n", + " 776542138795032661\n", + " 5.599023\n", + " -0.006443\n", + " 141733921084\n", + " 9585.718591\n", " 904014\n", " 56598.200000\n", " \n", " \n", "\n", - "

5 rows × 191 columns

\n", "" ], "text/plain": [ - " id coord_ra coord_dec parent objectId \\\n", - "277 776518129927848214 5.598927 -0.006448 0 141733921083 \n", - "516 776519852209734149 5.598927 -0.006448 0 141733921083 \n", - "771 776521574491620100 5.598927 -0.006448 0 141733921083 \n", - "0 776538694231261185 5.598927 -0.006448 0 141733921083 \n", - "83 776542138795032660 5.598927 -0.006448 0 141733921083 \n", - "\n", - " parentObjectId deblend_nChild base_SdssCentroid_x base_SdssCentroid_y \\\n", - "277 0 2 579.995811 1112.172658 \n", - "516 0 2 488.557073 2167.504065 \n", - "771 0 2 455.517639 3092.148678 \n", - "0 0 2 9.190805 3042.373080 \n", - "83 0 2 271.696844 871.973028 \n", + " id coord_ra coord_dec objectId base_PsfFlux_flux \\\n", + "278 776518129927848215 5.599023 -0.006443 141733921084 9120.533865 \n", + "517 776519852209734150 5.599023 -0.006443 141733921084 9886.496949 \n", + "772 776521574491620101 5.599023 -0.006443 141733921084 9071.808927 \n", + "1 776538694231261186 5.599023 -0.006443 141733921084 27701.589804 \n", + "84 776542138795032661 5.599023 -0.006443 141733921084 9585.718591 \n", "\n", - " base_SdssCentroid_xSigma ... slot_ModelFlux_apCorr \\\n", - "277 0.321924 ... 1.117663 \n", - "516 0.690836 ... 1.085815 \n", - "771 0.306457 ... 1.119077 \n", - "0 NaN ... 1.101941 \n", - "83 0.234910 ... 1.144652 \n", - "\n", - " base_GaussianFlux_apCorrSigma slot_ModelFlux_apCorrSigma \\\n", - "277 0.0 0.0 \n", - "516 0.0 0.0 \n", - "771 0.0 0.0 \n", - "0 0.0 0.0 \n", - "83 0.0 0.0 \n", - "\n", - " base_GaussianFlux_flag_apCorr slot_ModelFlux_flag_apCorr \\\n", - "277 False False \n", - "516 False False \n", - "771 False False \n", - "0 False False \n", - "83 False False \n", - "\n", - " ext_photometryKron_KronFlux_apCorr \\\n", - "277 0.992184 \n", - "516 0.964561 \n", - "771 0.996091 \n", - "0 1.023645 \n", - "83 1.014535 \n", - "\n", - " ext_photometryKron_KronFlux_apCorrSigma \\\n", - "277 0.0 \n", - "516 0.0 \n", - "771 0.0 \n", - "0 0.0 \n", - "83 0.0 \n", - "\n", - " ext_photometryKron_KronFlux_flag_apCorr visit mjd \n", - "277 False 903986 56598.221156 \n", - "516 False 903988 56598.222126 \n", - "771 False 903990 56598.223046 \n", - "0 False 904010 56598.250471 \n", - "83 False 904014 56598.200000 \n", - "\n", - "[5 rows x 191 columns]" + " visit mjd \n", + "278 903986 56598.221156 \n", + "517 903988 56598.222126 \n", + "772 903990 56598.223046 \n", + "1 904010 56598.250471 \n", + "84 904014 56598.200000 " ] }, - "execution_count": 13, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "objids = [name for name, group in iGrouped if len(group) == 5]\n", "obj = objids[1]\n", - "print('e.g. objectId:', obj)\n", - "iSources[iSources['objectId'] == obj]" + "print('e.g. objectId:', obj, '(showing select rows from table)')\n", + "iSources[iSources['objectId'] == obj][['id','coord_ra','coord_dec','objectId','base_PsfFlux_flux','visit','mjd']]" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 18, "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -1071,7 +1040,7 @@ "plt.figure(3, figsize=(4, 4), dpi=140)\n", "plt.title('objectId: {}'.format(obj))\n", "plt.scatter(iSources[iSources['objectId'] == obj]['mjd'].values, \n", - " iCoaddCalib.getMagnitude(iSources[iSources['objectId'] == objids[0]]['base_PsfFlux_flux'].values),\n", + " iCcdCalib.getMagnitude(iSources[iSources['objectId'] == obj]['base_PsfFlux_flux'].values),\n", " s=6, c='k')\n", "\n", "plt.ylabel('$i$')\n", @@ -1082,102 +1051,6 @@ "ax.ticklabel_format(useOffset=56598, style='plain', axis='x', useMathText=True)\n", "plt.show()" ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.style.use('seaborn-notebook')\n", - "fig, axarr = plt.subplots(1, 2, figsize=(8, 4), dpi=140)\n", - "\n", - "fig.suptitle('Forced Photometry')\n", - "\n", - "rSources = butler_coadd.get('deepCoadd_forced_src', {'filter': 'HSC-R', 'tract': 0, 'patch': '1,1'})\n", - "iSources = butler_coadd.get('deepCoadd_forced_src', {'filter': 'HSC-I', 'tract': 0, 'patch': '1,1'})\n", - "\n", - "deblended = rSources['deblend_nChild'] == 0\n", - "\n", - "refTable = butler_coadd.get('deepCoadd_ref', {'filter': 'HSC-R^HSC-I', 'tract': 0, 'patch': '1,1'})\n", - "inInnerRegions = refTable['detect_isPatchInner'] & refTable['detect_isTractInner'] # define inner regions\n", - "isSkyObject = refTable['merge_peak_sky'] # reject sky objects\n", - "isPrimary = refTable['detect_isPrimary']\n", - "\n", - "isStellar = iSources['base_ClassificationExtendedness_value'] < 1.\n", - "isGoodFlux = ~iSources['base_PsfFlux_flag']\n", - "selected = isPrimary & isStellar & isGoodFlux\n", - "\n", - "rMags = rCoaddCalib.getMagnitude(rSources['base_PsfFlux_flux'])\n", - "iMags = iCoaddCalib.getMagnitude(iSources['base_PsfFlux_flux'])\n", - "\n", - "axarr[0].set_title('Coadd (Stars Only)')\n", - "axarr[0].scatter(rMags[selected] - iMags[selected],\n", - " iMags[selected],\n", - " edgecolors='None', s=2, c='k')\n", - "\n", - "axarr[0].set_xlim(-0.5, 3)\n", - "axarr[0].set_ylim(25, 14)\n", - "axarr[0].set_xlabel('$r-i$')\n", - "axarr[0].set_ylabel('$i$')\n", - "\n", - "# datasetRefOrType : forced_src\n", - "# see all options at\n", - "# /opt/lsst/software/stack/stack/miniconda3-4.3.21-10a4fa6/Linux64/obs_subaru/16.0+1/python/lsst/obs/hsc\n", - "\n", - "iSources = butler_ccd.get('forced_src', {'filter': 'HSC-I', \n", - " 'pointing': 671, \n", - " 'visit': 903986, \n", - " 'ccd': 16, \n", - " 'field': 'STRIPE82L', \n", - " 'dateObs': '2013-11-02', \n", - " 'taiObs': '2013-11-02', \n", - " 'expTime': 30.0, \n", - " 'tract': 0, 'patch' : '1,1'})\n", - "\n", - "rSources = butler_ccd.get('forced_src', {'filter': 'HSC-R', \n", - " 'pointing': 533, \n", - " 'visit': 903334, \n", - " 'ccd': 16, \n", - " 'field': 'STRIPE82L', \n", - " 'dateObs': '2013-06-17', \n", - " 'taiObs': '2013-06-17', \n", - " 'expTime': 30.0, \n", - " 'tract': 0, 'patch' : '1,1'})\n", - "\n", - "rMags = rCoaddCalib.getMagnitude(rSources['base_PsfFlux_flux']) # is using the rCoaddCalib object wrong?\n", - "iMags = iCoaddCalib.getMagnitude(iSources['base_PsfFlux_flux']) # is using the rCoaddCalib object wrong?\n", - "\n", - "axarr[1].set_title('Single Exposure (All Sources)')\n", - "plt.scatter(rMags - iMags,\n", - " iMags,\n", - " edgecolors='None', s=2, c='k')\n", - "axarr[1].set_xlim(-0.5, 3)\n", - "axarr[1].set_ylim(25, 14)\n", - "axarr[1].set_xlabel('$r-i$')\n", - "axarr[1].set_ylabel('$i$')\n", - "plt.subplots_adjust(left=0.125, bottom=0.1)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/ImageProcessing/Re-RunHSC.sh b/ImageProcessing/Re-RunHSC.sh index ab8ca115..36b5dcc4 100644 --- a/ImageProcessing/Re-RunHSC.sh +++ b/ImageProcessing/Re-RunHSC.sh @@ -1,15 +1,17 @@ : 'HSC Re-Run: Making Forced Photometry Light Curves from Scratch Owner: **Justin Myles** (@jtmyles) -Last Verified to Run: **2018-09-05** +Last Verified to Run: **2018-09-13** Verified Stack Release: **16.0** This project addresses issue #63: HSC Re-run This shell script runs the command-line tasks from the tutorial at pipelines.lsst.io for analysis -from raw images through source detection and forced photometry measurements. It is intended as an -intermediate step toward the end-goal of making a forced photometry lightcurve in the notebook at +from raw images through source detection and forced photometry measurements. It is an intermediate +step toward the end-goal of making a forced photometry lightcurve in the notebook at StackClub/ImageProcessing/Re-RunHSC.ipynb +Running this script may take several hours on lsst-lspdev. + Recommended to run with $ bash Re-RunHSC.sh > output.txt ' @@ -152,6 +154,7 @@ forcedPhotCoadd.py $DATADIR --rerun coaddForcedPhot --id filter=HSC-I # which could lead to bad photometry for blended sources. # This tasks requires a coadd tract stored in the Butler to grab the appropriate # coadd catalogs to use as references for forced photometry. +# It has access to this tract because we chain the output from the coaddPhot subdirectory date echo "Re-RunHSC INFO: perform forced photometry on individual exposures with forcedPhotCcd.py" @@ -164,8 +167,11 @@ forcedPhotCcd.py $DATADIR --rerun ccdForcedPhot --id filter=HSC-I --clobber-conf # For analysis of the catalog, see part VI of StackClub/ImageProcessing/Re-RunHSC.ipynb date echo "Re-RunHSC INFO: parse output of forcedPhotCcd.py" + +# The following grep & sed commands clean up the output log file used to determine +# which DataIds have measured forced photometry. The cleaner output is stored +# in a new file, data_ids.txt, that is used in Re-RunHSC.ipynb grep 'forcedPhotCcd INFO: Performing forced measurement on DataId' ccd_r.txt ccd_i.txt > data_ids.txt -# The following sed commands clean up the output log file used to determine which DataIds have measured forced photometry. sed -i 's/ccd_[i,r].txt:forcedPhotCcd INFO: Performing forced measurement on DataId(initialdata={//g' data_ids.txt sed -i 's/}, tag=set())//g' data_ids.txt sed -i 's/'"'"'//g' data_ids.txt From 1d89c4f4c877b1699c0237eb1cdf0c82af2b681f Mon Sep 17 00:00:00 2001 From: Justin Myles Date: Fri, 14 Sep 2018 15:09:41 +0000 Subject: [PATCH 11/15] stack club meet 2018-09-14 --- ImageProcessing/Re-RunHSC.ipynb | 707 +++++++------------------------- 1 file changed, 144 insertions(+), 563 deletions(-) diff --git a/ImageProcessing/Re-RunHSC.ipynb b/ImageProcessing/Re-RunHSC.ipynb index 1daa15a3..b1634709 100644 --- a/ImageProcessing/Re-RunHSC.ipynb +++ b/ImageProcessing/Re-RunHSC.ipynb @@ -35,17 +35,6 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "# todo: nb will point to processCcd.ipynb notebook on how to unpack a CL task\n", - "# todo: and will actually unpack processCcd, but not other tasks\n", - "# todo: then will make a few plots showing result of what we can do with processed images " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], "source": [ "import os\n", "import re\n", @@ -62,7 +51,13 @@ "%matplotlib inline\n", "\n", "import eups.setupcmd\n", - "import lsst.daf.persistence as dafPersist" + "import lsst.daf.persistence as dafPersist\n", + "\n", + "HOME = os.environ['HOME']\n", + "DATAREPO = \"{}/repositories/ci_hsc/\".format(HOME)\n", + "DATADIR = \"{}/DATA/\".format(HOME)\n", + "CI_HSC = \"/project/shared/data/ci_hsc/\"\n", + "os.system(\"mkdir -p {}\".format(DATADIR));" ] }, { @@ -76,194 +71,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ": 'HSC Re-Run: Making Forced Photometry Light Curves from Scratch\n", - "Owner: **Justin Myles** (@jtmyles)\n", - "Last Verified to Run: **2018-09-13**\n", - "Verified Stack Release: **16.0**\n", - "\n", - "This project addresses issue #63: HSC Re-run\n", - "\n", - "This shell script runs the command-line tasks from the tutorial at pipelines.lsst.io for analysis\n", - "from raw images through source detection and forced photometry measurements. It is an intermediate\n", - "step toward the end-goal of making a forced photometry lightcurve in the notebook at\n", - "StackClub/ImageProcessing/Re-RunHSC.ipynb\n", - "\n", - "Running this script may take several hours on lsst-lspdev.\n", - "\n", - "Recommended to run with \n", - "$ bash Re-RunHSC.sh > output.txt\n", - "'\n", - "\n", - "\n", - "# Setup the LSST Stack\n", - "source /opt/lsst/software/stack/loadLSST.bash\n", - "eups list lsst_distrib\n", - "setup lsst_distrib\n", - "\n", - "\n", - "# I. Setting up the Butler data repository\n", - "date\n", - "echo \"Re-RunHSC INFO: set up the Butler\"\n", - "\n", - "setup -j -r /project/shared/data/ci_hsc\n", - "DATADIR=\"/home/$USER/DATA\"\n", - "mkdir -p \"$DATADIR\"\n", - "\n", - "# A Butler needs a *mapper* file \"to find and organize data in a format specific to each camera.\" \n", - "# We write this file to the data repository so that any instantiated Butler object knows which mapper to use.\n", - "echo lsst.obs.hsc.HscMapper > $DATADIR/_mapper\n", - "\n", - "# The ingest script creates links in the instantiated butler repository to the original data files\n", - "date\n", - "echo \"Re-RunHSC INFO: ingest images with ingestImages.py\"\n", - "\n", - "ingestImages.py $DATADIR $CI_HSC_DIR/raw/*.fits --mode=link\n", - "\n", - "# Grab calibration files\n", - "date\n", - "echo \"Re-RunHSC INFO: obtain calibration files with installTransmissionCurves.py\"\n", - "\n", - "installTransmissionCurves.py $DATADIR\n", - "ln -s $CI_HSC_DIR/CALIB/ $DATADIR/CALIB\n", - "mkdir -p $DATADIR/ref_cats\n", - "ln -s $CI_HSC_DIR/ps1_pv3_3pi_20170110 $DATADIR/ref_cats/ps1_pv3_3pi_20170110\n", - "\n", - "\n", - "# II. Calibrate a single frame with processCcd.py\n", - "date\n", - "echo \"Re-RunHSC INFO: process raw exposures with processCcd.py\"\n", - "\n", - "# Use calibration files to do CCD processing\n", - "# Does calibration happen here? What is the end result of the calibration process?\n", - "# What specifically does this task do?\n", - "processCcd.py $DATADIR --rerun processCcdOutputs --id\n", - "\n", - "\n", - "# III. (omitted) Visualize images.\n", - "\n", - "\n", - "# IV. Make coadds\n", - "\n", - "# IV. A. Make skymap\n", - "# A sky map is a tiling of the celestial sphere. It is composed of one or more tracts.\n", - "# A tract is composed of one or more overlapping patches. Each tract has a WCS.\n", - "# We define a skymap so that we can warp all of the exposure to fit on a single coordinate system\n", - "# This is a necessary step for making coadds\n", - "date\n", - "echo \"Re-RunHSC INFO: make skymap with makeDiscreteSkyMap.py\"\n", - "\n", - "makeDiscreteSkyMap.py $DATADIR --id --rerun processCcdOutputs:coadd --config skyMap.projection=\"TAN\"\n", - "\n", - "# IV. B. Warp images onto skymap\n", - "date\n", - "echo \"Re-RunHSC INFO: warp images with makeCoaddTempExp.py\"\n", - "\n", - "makeCoaddTempExp.py $DATADIR --rerun coadd \\\n", - " --selectId filter=HSC-R \\\n", - " --id filter=HSC-R tract=0 patch=0,0^0,1^0,2^1,0^1,1^1,2^2,0^2,1^2,2 \\\n", - " --config doApplyUberCal=False doApplySkyCorr=False\n", - "\n", - "makeCoaddTempExp.py $DATADIR --rerun coadd \\\n", - " --selectId filter=HSC-I \\\n", - " --id filter=HSC-I tract=0 patch=0,0^0,1^0,2^1,0^1,1^1,2^2,0^2,1^2,2 \\\n", - " --config doApplyUberCal=False doApplySkyCorr=False\n", - "\n", - "# IV. C. Coadd warped images\n", - "# Now that we have warped images, we can perform coaddition to get deeper images\n", - "# The motivation for this is to have the deepest image possible for source detection\n", - "date\n", - "echo \"Re-RunHSC INFO: coadd warped images with assembleCoadd.py\"\n", - "\n", - "assembleCoadd.py $DATADIR --rerun coadd \\\n", - " --selectId filter=HSC-R \\\n", - " --id filter=HSC-R tract=0 patch=0,0^0,1^0,2^1,0^1,1^1,2^2,0^2,1^2,2\n", - "\n", - "assembleCoadd.py $DATADIR --rerun coadd \\\n", - " --selectId filter=HSC-I \\\n", - " --id filter=HSC-I tract=0 patch=0,0^0,1^0,2^1,0^1,1^1,2^2,0^2,1^2,2\n", - "\n", - "\n", - "# V. Measuring Sources\n", - "\n", - "# V. A. Source detection\n", - "# As noted above, we do source detection on the deepest image possible.\n", - "date\n", - "echo \"Re-RunHSC INFO: detect objects in the coadd images with detectCoaddSources.py\"\n", - "\n", - "detectCoaddSources.py $DATADIR --rerun coadd:coaddPhot \\\n", - " --id filter=HSC-R tract=0 patch=0,0^0,1^0,2^1,0^1,1^1,2^2,0^2,1^2,2\n", - "\n", - "detectCoaddSources.py $DATADIR --rerun coaddPhot \\\n", - " --id filter=HSC-I tract=0 patch=0,0^0,1^0,2^1,0^1,1^1,2^2,0^2,1^2,2\n", - "\n", - "# V. B. Merge multi-band detection catalogs\n", - "# Ultimately, for photometry, we will need to deblend objects. \n", - "# In order to do this, we first merge the detected source catalogs.\n", - "date\n", - "echo \"Re-RunHSC INFO: merge detection catalogs with mergeCoaddDetections.py\"\n", - "\n", - "mergeCoaddDetections.py $DATADIR --rerun coaddPhot --id filter=HSC-R^HSC-I\n", - "\n", - "# V. C. Measure objects in coadds\n", - "# Given a full coaddSource catalog, we can do regular photometry with implicit deblending.\n", - "date\n", - "echo \"Re-RunHSC INFO: measure objects in coadds with measureCoaddSources.py\"\n", - "\n", - "measureCoaddSources.py $DATADIR --rerun coaddPhot --id filter=HSC-R\n", - "measureCoaddSources.py $DATADIR --rerun coaddPhot --id filter=HSC-I\n", - "\n", - "# V. D. Merge multi-band catalogs from coadds\n", - "date\n", - "echo \"Re-RunHSC INFO: merge measurements from coadds with mergeCoaddMeasurements.py\"\n", - "\n", - "mergeCoaddMeasurements.py $DATADIR --rerun coaddPhot --id filter=HSC-R^HSC-I\n", - "\n", - "# V. E. Run forced photometry on coadds\n", - "# Given a full source catalog, we can do forced photometry with implicit deblending.\n", - "date\n", - "echo \"Re-RunHSC INFO: perform forced photometry on coadds with forcedPhotCoadd.py\"\n", - "\n", - "forcedPhotCoadd.py $DATADIR --rerun coaddPhot:coaddForcedPhot --id filter=HSC-R\n", - "forcedPhotCoadd.py $DATADIR --rerun coaddForcedPhot --id filter=HSC-I\n", - "\n", - "# V. F. Run forced photometry on individual exposures\n", - "# Given a full source catalog, we can do forced photometry on the individual exposures.\n", - "# Note that as of 2018_08_23, the forcedPhotCcd.py task doesn't do deblending,\n", - "# which could lead to bad photometry for blended sources.\n", - "# This tasks requires a coadd tract stored in the Butler to grab the appropriate \n", - "# coadd catalogs to use as references for forced photometry.\n", - "# It has access to this tract because we chain the output from the coaddPhot subdirectory\n", - "\n", - "date\n", - "echo \"Re-RunHSC INFO: perform forced photometry on individual exposures with forcedPhotCcd.py\"\n", - "\n", - "forcedPhotCcd.py $DATADIR --rerun coaddPhot:ccdForcedPhot --id filter=HSC-R --clobber-config --configfile=/project/shared/data/ci_hsc/forcedPhotCcdConfig.py &> ccd_r.txt\n", - "forcedPhotCcd.py $DATADIR --rerun ccdForcedPhot --id filter=HSC-I --clobber-config --configfile=/project/shared/data/ci_hsc/forcedPhotCcdConfig.py &> ccd_i.txt\n", - "\n", - "\n", - "# VI. Multi-band catalog analysis\n", - "# For analysis of the catalog, see part VI of StackClub/ImageProcessing/Re-RunHSC.ipynb\n", - "date\n", - "echo \"Re-RunHSC INFO: parse output of forcedPhotCcd.py\"\n", - "\n", - "# The following grep & sed commands clean up the output log file used to determine \n", - "# which DataIds have measured forced photometry. The cleaner output is stored\n", - "# in a new file, data_ids.txt, that is used in Re-RunHSC.ipynb\n", - "grep 'forcedPhotCcd INFO: Performing forced measurement on DataId' ccd_r.txt ccd_i.txt > data_ids.txt\n", - "sed -i 's/ccd_[i,r].txt:forcedPhotCcd INFO: Performing forced measurement on DataId(initialdata={//g' data_ids.txt\n", - "sed -i 's/}, tag=set())//g' data_ids.txt\n", - "sed -i 's/'\"'\"'//g' data_ids.txt\n", - "sed -i 's/ //g' data_ids.txt\n" - ] - } - ], + "outputs": [], "source": [ "! cat Re-RunHSC.sh" ] @@ -284,28 +94,37 @@ "\n", "Part I runs the following command-line tasks\n", "```\n", + "source /opt/lsst/software/stack/loadLSST.bash\n", "eups list lsst_distrib\n", - "setup -j -r /home/jmyles/repositories/ci_hsc\n", - "echo \"lsst.obs.hsc.HscMapper\" > /home/jmyles/DATA/_mapper\n", - "ingestImages.py /home/jmyles/DATA /home/jmyles/repositories/ci_hsc/raw/*.fits --mode=link\n", - "ln -s /home/jmyles/repositories/ci_hsc/CALIB/ /home/jmyles/DATA/CALIB\n", - "installTransmissionCurves.py /home/jmyles/DATA\n", - "mkdir -p /home/jmyles/DATA/ref_cats\n", - "ln -s /home/jmyles/repositories/ci_hsc/ps1_pv3_3pi_20170110 /home/jmyles/DATA/ref_cats/ps1_pv3_3pi_20170110\n", + "setup lsst_distrib\n", + "\n", + "setup -j -r /project/shared/data/ci_hsc\n", + "echo lsst.obs.hsc.HscMapper > $DATADIR/_mapper\n", + "\n", + "ingestImages.py $DATADIR $CI_HSC_DIR/raw/*.fits --mode=link\n", + "installTransmissionCurves.py $DATADIR\n", + "\n", + "ln -s $CI_HSC_DIR/CALIB/ $DATADIR/CALIB\n", + "mkdir -p $DATADIR/ref_cats\n", + "ln -s $CI_HSC_DIR/ps1_pv3_3pi_20170110 $DATADIR/ref_cats/ps1_pv3_3pi_20170110\n", "```" ] }, { - "cell_type": "code", - "execution_count": 6, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "HOME = os.environ['HOME']\n", - "DATAREPO = \"{}/repositories/ci_hsc/\".format(HOME)\n", - "DATADIR = \"{}/DATA/\".format(HOME)\n", - "CI_HSC = \"/project/shared/data/ci_hsc/\"\n", - "os.system(\"mkdir -p {}\".format(DATADIR));" + "In summary, these lines establish a directory with read/write permission where all data products made by this tutorial will be stored. In the underlying Python, this repository is represented by an instance of the Butler class (which inherits directly from Object). \n", + "\n", + "The first thing we do in our Butler repository is write a `_mapper` file, which tells the Butler which instrument was used to collect the data stored in the Butler. It needs this file to find and organize data in a format specific to the appropriate camera. This illustrates the Butler's motivating concept: *the LSST DM Stack should be capable of interacting with data collected from a variety of instruments*. The Butler facilitates this process by abstracting the data read/write process. \n", + "\n", + "Second, we use the `ingestImages.py` task to organize the data in a format specific to HSC. This task reads the raw FITS files and stores the schema associated with the data in the Butler repository.\n", + "\n", + "Third, we use the `installTransmissionCurves.py` task to install transmission curves for the data and link the calibration files associated with the raw data to the Butler.\n", + "\n", + "Last, we link a reference catalog to the Butler for use with astrometry.\n", + "\n", + "Doing some these steps in Python might look something like this:" ] }, { @@ -314,46 +133,16 @@ "metadata": {}, "outputs": [], "source": [ + "\"\"\"\n", "#!setup -j -r /home/jmyles/repositories/ci_hsc\n", "\n", "setup = eups.setupcmd.EupsSetup([\"-j\",\"-r\", DATAREPO])\n", "status = setup.run()\n", - "print('setup exited with status {}'.format(status))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A Butler needs a *mapper* file \"to find and organize data in a format specific to each camera.\" We write this file to the data repository so that any instantiated Butler object knows which mapper to use." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ + "print('setup exited with status {}'.format(status))\n", + "\n", "with open(DATADIR + \"_mapper\", \"w\") as f:\n", - " f.write(\"lsst.obs.hsc.HscMapper\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# ingest script\n", - "!ingestImages.py DATADIR /home/jmyles/repositories/ci_hsc/raw/*.fits --mode=link" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ + " f.write(\"lsst.obs.hsc.HscMapper\")\n", + " \n", "#!installTransmissionCurves.py /home/jmyles/DATA\n", "\n", "from lsst.obs.hsc import makeTransmissionCurves, HscMapper\n", @@ -374,21 +163,15 @@ " butler.put(curve, \"transmission_optics\")\n", "for start, curve in makeTransmissionCurves.getAtmosphereTransmission().items():\n", " if curve is not None:\n", - " butler.put(curve, \"transmission_atmosphere\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ + " butler.put(curve, \"transmission_atmosphere\")\n", + " \n", "# ingest calibration images into Butler repo\n", "os.system(\"ln -s {} {}\".format(datarepo + \"CALIB/\", datadir + \"CALIB\"))\n", "\n", "# ingest reference catalog into Butler repo\n", "os.system(\"mkdir -p {}\".format(DATADIR + \"ref_cats\"))\n", - "os.system(\"ln -s {}ps1_pv3_3pi_20170110 {}ref_cats/ps1_pv3_3pi_20170110\".format(DATAREPO, DATADIR))" + "os.system(\"ln -s {}ps1_pv3_3pi_20170110 {}ref_cats/ps1_pv3_3pi_20170110\".format(DATAREPO, DATADIR)) \n", + "\"\"\"" ] }, { @@ -396,7 +179,19 @@ "metadata": {}, "source": [ "# Part 2: Calibrating single frames\n", - "https://pipelines.lsst.io/getting-started/processccd.html" + "https://pipelines.lsst.io/getting-started/processccd.html\n", + "\n", + "Part II runs the following command-line task:\n", + "\n", + " processCcd.py $DATADIR --rerun processCcdOutputs --id\n", + " \n", + "This applies photometric and astrometric calibrations to the raw images. \n", + "\n", + "The id flag allows you to select data by data ID: an unspecified id selects all raw data. Other example arguments are raw, filter, visit, ccd, and field. \n", + "\n", + "All command-line tasks write output datasets to a Butler repository. The --rerun flag here tells the tasks to write to `processCcdOutputs`.\n", + "\n", + "TODO: Further unpacking, see https://github.com/LSSTScienceCollaborations/StackClub/blob/project/processccd/kadrlica/ImageProcessing/ProcessCcd.ipynb" ] }, { @@ -405,12 +200,8 @@ "metadata": {}, "outputs": [], "source": [ - "\"\"\"\n", - "!processCcd.py DATA --rerun processCcdOutputs --id\n", - "# all cl tasks write output datasets to a Butler repo\n", - "# --rerun configured to write to processCcdOutputs\n", - "# other option is --output\n", - "\"\"\"" + "from stackclub import where_is\n", + "where_is(processCcdTaskInstance, in_the=\"source\")" ] }, { @@ -419,79 +210,19 @@ "metadata": {}, "outputs": [], "source": [ - "!which processCcd.py\n", "\"\"\"\n", + "# Running this in python might look something like this:\n", + "\n", "processCcd.py\n", "from lsst.pipe.tasks.processCcd import ProcessCcdTask\n", "\n", + "processCcdConfig = ProcessCcdConfig()\n", + "processCcdTaskInstance = ProcessCcdTask(butler=butler)\n", + "\n", "ProcessCcdTask.parseAndRun()\n", "\"\"\"" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# show source of lsst.pipe.tasks.processCcd\n", - "# emacs /opt/lsst/software/stack/stack/miniconda3-4.3.21-10a4fa6/Linux64/pipe_tasks/16.0+1/python/lsst/pipe/tasks/processCcd.py\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from lsst.pipe.tasks.processCcd import ProcessCcdTask, ProcessCcdConfig\n", - "processCcdTaskInstance = ProcessCcdTask(butler=butler)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from stackclub import where_is\n", - "where_is(processCcdTaskInstance, in_the=\"source\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "processCcdConfig = ProcessCcdConfig()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\"\"\"\n", - "# review what data will be processed\n", - "!processCcd.py DATA --rerun processCcdOutputs --id --show data\n", - "# id allows you to select data by data ID\n", - "# unspecified id selects all raw data\n", - "# example IDs: raw, filter, visit, ccd, field\n", - "# show data turns on dry-run mode\n", - "\"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#!which processCcd.py" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -514,25 +245,79 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "* A sky map is a tiling of the celestial sphere. It is composed of one or more tracts.\n", - "* A tract is composed of one or more overlapping patches. Each tract has a WCS.\n", + "Part IV runs the following command-line tasks:\n", + "\n", + " makeDiscreteSkyMap.py $DATADIR --id --rerun processCcdOutputs:coadd --config skyMap.projection=\"TAN\"\n", + "\n", + " makeCoaddTempExp.py $DATADIR --rerun coadd \\\n", + " --selectId filter=HSC-R \\\n", + " --id filter=HSC-R tract=0 patch=0,0^0,1^0,2^1,0^1,1^1,2^2,0^2,1^2,2 \\\n", + " --config doApplyUberCal=False doApplySkyCorr=False\n", + "\n", + " makeCoaddTempExp.py $DATADIR --rerun coadd \\\n", + " --selectId filter=HSC-I \\\n", + " --id filter=HSC-I tract=0 patch=0,0^0,1^0,2^1,0^1,1^1,2^2,0^2,1^2,2 \\\n", + " --config doApplyUberCal=False doApplySkyCorr=False\n", + "\n", + " assembleCoadd.py $DATADIR --rerun coadd \\\n", + " --selectId filter=HSC-R \\\n", + " --id filter=HSC-R tract=0 patch=0,0^0,1^0,2^1,0^1,1^1,2^2,0^2,1^2,2\n", + "\n", + " assembleCoadd.py $DATADIR --rerun coadd \\\n", + " --selectId filter=HSC-I \\\n", + " --id filter=HSC-I tract=0 patch=0,0^0,1^0,2^1,0^1,1^1,2^2,0^2,1^2,2\n", "\n", - "the configuration field specifies the WCS Projection, e.g.\n", + "Since we want the deepest possible image for source detection in this example, we construct coadded images of the exposures. A sky map is a tiling of the celestial sphere. It is composed of one or more tracts, where a tract is in turn composed of one or more overlapping patches of sky which share a single WCS. \n", + "\n", + "First, we define a skymap with `makeDiscreteSkyMap.py` so that we can warp all of the exposure to fit on a single coordinate system. \n", + "Second, we warp the images and store them as temporary exposures with `makeCoaddTempExp`. \n", + "Finally, once we have warped images, we perform coaddition with `assembleCoadd.py`\n", + "\n", + "the configuration field specifying the WCS Projection can be:\n", " - STG: stereographic projection\n", " - MOL: Molleweide's projection\n", " - TAN: tangent-plane projection" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "\"\"\"# make a discrete sky map that covers the exposures that have already been processed\n", - "!makeDiscreteSkyMap.py DATA --id --rerun processCcdOutputs:coadd --config skyMap.projection=\"TAN\"\n", + "# Part 5: Source detection\n", "\n", - "\"\"\"" + "Part V runs the following command-line tasks:\n", + "\n", + " detectCoaddSources.py $DATADIR --rerun coadd:coaddPhot \\\n", + " --id filter=HSC-R tract=0 patch=0,0^0,1^0,2^1,0^1,1^1,2^2,0^2,1^2,2\n", + "\n", + " detectCoaddSources.py $DATADIR --rerun coaddPhot \\\n", + " --id filter=HSC-I tract=0 patch=0,0^0,1^0,2^1,0^1,1^1,2^2,0^2,1^2,2\n", + "\n", + " mergeCoaddDetections.py $DATADIR --rerun coaddPhot --id filter=HSC-R^HSC-I\n", + "\n", + " measureCoaddSources.py $DATADIR --rerun coaddPhot --id filter=HSC-R\n", + " measureCoaddSources.py $DATADIR --rerun coaddPhot --id filter=HSC-I\n", + "\n", + " mergeCoaddMeasurements.py $DATADIR --rerun coaddPhot --id filter=HSC-R^HSC-I\n", + "\n", + " forcedPhotCoadd.py $DATADIR --rerun coaddPhot:coaddForcedPhot --id filter=HSC-R\n", + " forcedPhotCoadd.py $DATADIR --rerun coaddForcedPhot --id filter=HSC-I\n", + "\n", + " forcedPhotCcd.py $DATADIR --rerun coaddPhot:ccdForcedPhot --id filter=HSC-R \\ \n", + " --clobber-config --configfile=/project/shared/data/ci_hsc/forcedPhotCcdConfig.py &> ccd_r.txt\n", + " \n", + " forcedPhotCcd.py $DATADIR --rerun ccdForcedPhot --id filter=HSC-I \\ \n", + " --clobber-config --configfile=/project/shared/data/ci_hsc/forcedPhotCcdConfig.py &> ccd_i.txt\n", + "\n", + "The first pair of commands does source detection on the coadds in each band. \n", + "\n", + "The source catalogs are then merged so that we can measure photometry for a consistent table of sources across filters. \n", + "\n", + "`measureCoaddSources.py` does deblending with this complete catalog and measures regular photometry.\n", + "\n", + "We run `mergeCoaddMeasurements.py` to write a table that identifies the reference filter that has the best position measurement for each source in the tables you created with `measureCoaddSources.py`\n", + "\n", + "These accurate positions are used for forced photometry with `forcedPhotCoadd.py` as well as `forcedPhotCcd.py`" ] }, { @@ -547,7 +332,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -563,17 +348,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "6800 sources with forced photometry measured from coadds\n" - ] - } - ], + "outputs": [], "source": [ "rSources = butler_coadd.get('deepCoadd_forced_src', {'filter': 'HSC-R', 'tract': 0, 'patch': '1,1'})\n", "iSources = butler_coadd.get('deepCoadd_forced_src', {'filter': 'HSC-I', 'tract': 0, 'patch': '1,1'})\n", @@ -589,7 +366,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -612,7 +389,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -637,20 +414,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.style.use('seaborn-notebook')\n", "plt.figure(1, figsize=(4, 4), dpi=140)\n", @@ -676,7 +442,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -692,7 +458,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -721,49 +487,9 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903334, 'ccd': 23, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", - "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903334, 'ccd': 22, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", - "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903334, 'ccd': 16, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", - "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903334, 'ccd': 100, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", - "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903336, 'ccd': 24, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", - "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903336, 'ccd': 17, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", - "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903338, 'ccd': 25, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", - "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903338, 'ccd': 18, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", - "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903342, 'ccd': 100, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", - "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903342, 'ccd': 10, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", - "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903342, 'ccd': 4, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", - "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903344, 'ccd': 11, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", - "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903344, 'ccd': 5, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", - "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903344, 'ccd': 0, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", - "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903346, 'ccd': 12, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", - "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903346, 'ccd': 6, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", - "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903346, 'ccd': 1, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", - "{'filter': 'HSC-I', 'pointing': 671, 'visit': 903986, 'ccd': 23, 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0}\n", - "{'filter': 'HSC-I', 'pointing': 671, 'visit': 903986, 'ccd': 22, 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0}\n", - "{'filter': 'HSC-I', 'pointing': 671, 'visit': 903986, 'ccd': 16, 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0}\n", - "{'filter': 'HSC-I', 'pointing': 671, 'visit': 903986, 'ccd': 100, 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0}\n", - "{'filter': 'HSC-I', 'pointing': 671, 'visit': 903988, 'ccd': 24, 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0}\n", - "{'filter': 'HSC-I', 'pointing': 671, 'visit': 903988, 'ccd': 23, 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0}\n", - "{'filter': 'HSC-I', 'pointing': 671, 'visit': 903988, 'ccd': 17, 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0}\n", - "{'filter': 'HSC-I', 'pointing': 671, 'visit': 903988, 'ccd': 16, 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0}\n", - "{'filter': 'HSC-I', 'pointing': 671, 'visit': 903990, 'ccd': 25, 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0}\n", - "{'filter': 'HSC-I', 'pointing': 671, 'visit': 903990, 'ccd': 18, 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0}\n", - "{'filter': 'HSC-I', 'pointing': 671, 'visit': 904010, 'ccd': 100, 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0}\n", - "{'filter': 'HSC-I', 'pointing': 671, 'visit': 904010, 'ccd': 10, 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0}\n", - "{'filter': 'HSC-I', 'pointing': 671, 'visit': 904010, 'ccd': 4, 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0}\n", - "{'filter': 'HSC-I', 'pointing': 671, 'visit': 904014, 'ccd': 12, 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0}\n", - "{'filter': 'HSC-I', 'pointing': 671, 'visit': 904014, 'ccd': 6, 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0}\n", - "{'filter': 'HSC-I', 'pointing': 671, 'visit': 904014, 'ccd': 1, 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0}\n" - ] - } - ], + "outputs": [], "source": [ "data_id_fields = [('filter', str), ('pointing', int), ('visit', int), \n", " ('ccd', int), ('field', str), ('dateObs', str), \n", @@ -794,20 +520,9 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.style.use('seaborn-notebook')\n", "fig, axarr = plt.subplots(1, 2, figsize=(8, 4), dpi=140)\n", @@ -861,19 +576,9 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "9742 i band objects, 30400 measurements\n", - "8321 r band objects, 32717 measurements\n", - "1769 objects with 5 epocs\n" - ] - } - ], + "outputs": [], "source": [ "iSources = pd.concat(i_tables) \n", "rSources = pd.concat(r_tables)\n", @@ -897,122 +602,9 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "e.g. objectId: 141733921084 (showing select rows from table)\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
idcoord_racoord_decobjectIdbase_PsfFlux_fluxvisitmjd
2787765181299278482155.599023-0.0064431417339210849120.53386590398656598.221156
5177765198522097341505.599023-0.0064431417339210849886.49694990398856598.222126
7727765215744916201015.599023-0.0064431417339210849071.80892790399056598.223046
17765386942312611865.599023-0.00644314173392108427701.58980490401056598.250471
847765421387950326615.599023-0.0064431417339210849585.71859190401456598.200000
\n", - "
" - ], - "text/plain": [ - " id coord_ra coord_dec objectId base_PsfFlux_flux \\\n", - "278 776518129927848215 5.599023 -0.006443 141733921084 9120.533865 \n", - "517 776519852209734150 5.599023 -0.006443 141733921084 9886.496949 \n", - "772 776521574491620101 5.599023 -0.006443 141733921084 9071.808927 \n", - "1 776538694231261186 5.599023 -0.006443 141733921084 27701.589804 \n", - "84 776542138795032661 5.599023 -0.006443 141733921084 9585.718591 \n", - "\n", - " visit mjd \n", - "278 903986 56598.221156 \n", - "517 903988 56598.222126 \n", - "772 903990 56598.223046 \n", - "1 904010 56598.250471 \n", - "84 904014 56598.200000 " - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "obj = objids[1]\n", "print('e.g. objectId:', obj, '(showing select rows from table)')\n", @@ -1021,20 +613,9 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.style.use('seaborn-notebook')\n", "plt.figure(3, figsize=(4, 4), dpi=140)\n", From 3dbd5ad09b18c7f05359dbeae7ab4beaef2fad1f Mon Sep 17 00:00:00 2001 From: Justin Myles Date: Fri, 21 Sep 2018 16:34:50 +0000 Subject: [PATCH 12/15] small edits --- ImageProcessing/Re-RunHSC.ipynb | 44 +++++++++++++++++++++++---------- 1 file changed, 31 insertions(+), 13 deletions(-) diff --git a/ImageProcessing/Re-RunHSC.ipynb b/ImageProcessing/Re-RunHSC.ipynb index b1634709..dd312587 100644 --- a/ImageProcessing/Re-RunHSC.ipynb +++ b/ImageProcessing/Re-RunHSC.ipynb @@ -7,7 +7,7 @@ "# HSC Re-Run: Making Forced Photometry Light Curves from Scratch\n", "\n", "
Owner: **Justin Myles** ([@jtmyles](https://github.com/LSSTScienceCollaborations/StackClub/issues/new?body=@jtmyles))\n", - "
Last Verified to Run: **2018-08-10**\n", + "
Last Verified to Run: **2018-09-21**\n", "
Verified Stack Release: **16.0**\n", "\n", "This project addresses issue [#63: HSC Re-run](https://github.com/LSSTScienceCollaborations/StackClub/issues/63)\n", @@ -16,12 +16,9 @@ "\n", "### Learning Objectives:\n", "After working through and studying this notebook you should be able to understand how to use the DRP pipeline from image visualization through to a forced photometry light curve. Specific learning objectives include: \n", - " 1. [Configuring](https://pipelines.lsst.io/v/w-2018-12/modules/lsst.pipe.base/command-line-task-config-howto.html) and executing pipeline tasks in python as well as on the command line.\n", - " 2. The sequence of steps involved in the DRP pipeline.\n", - " \n", - "Other techniques that are demonstrated, but not emphasized, in this notebook are\n", " 1. Using the `butler` to fetch data\n", - " 2. Visualizing data with the LSST Stack\n", + " 2. [Configuring](https://pipelines.lsst.io/v/w-2018-12/modules/lsst.pipe.base/command-line-task-config-howto.html) and executing pipeline tasks in python as well as on the command line.\n", + " 3. The sequence of steps involved in the DRP pipeline.\n", "\n", "### Logistics\n", "This notebook is intended to be runnable on `lsst-lspdev.ncsa.illinois.edu` from a local git clone of https://github.com/LSSTScienceCollaborations/StackClub.\n", @@ -191,7 +188,7 @@ "\n", "All command-line tasks write output datasets to a Butler repository. The --rerun flag here tells the tasks to write to `processCcdOutputs`.\n", "\n", - "TODO: Further unpacking, see https://github.com/LSSTScienceCollaborations/StackClub/blob/project/processccd/kadrlica/ImageProcessing/ProcessCcd.ipynb" + "For information on further unpacking, see Alex Drlica-Wagner's [Pipeline Tasks](https://github.com/LSSTScienceCollaborations/StackClub/blob/project/processccd/kadrlica/ImageProcessing/PipelineTasks.ipynb) notebook on unpacking command-line tasks." ] }, { @@ -201,7 +198,15 @@ "outputs": [], "source": [ "from stackclub import where_is\n", - "where_is(processCcdTaskInstance, in_the=\"source\")" + "\n", + "from lsst.pipe.tasks.processCcd import ProcessCcdTask, ProcessCcdConfig\n", + "\n", + "processCcdConfig = ProcessCcdConfig()\n", + "processCcdTaskInstance = ProcessCcdTask(butler=butler)\n", + "\n", + "where_is(processCcdTaskInstance, in_the=\"source\")\n", + "\n", + "ProcessCcdTask.parseAndRun()" ] }, { @@ -212,13 +217,15 @@ "source": [ "\"\"\"\n", "# Running this in python might look something like this:\n", - "\n", + "from stackclub import where_is\n", "processCcd.py\n", "from lsst.pipe.tasks.processCcd import ProcessCcdTask\n", "\n", "processCcdConfig = ProcessCcdConfig()\n", "processCcdTaskInstance = ProcessCcdTask(butler=butler)\n", "\n", + "where_is(processCcdTaskInstance, in_the=\"source\")\n", + "\n", "ProcessCcdTask.parseAndRun()\n", "\"\"\"" ] @@ -352,6 +359,11 @@ "metadata": {}, "outputs": [], "source": [ + "# We pass a datasetRefOrType and a DataId (dict) to the butler\n", + "# datasetRefOrType : deepCoadd_forced_src\n", + "# see all options at\n", + "# /opt/lsst/software/stack/stack/miniconda3-4.3.21-10a4fa6/Linux64/obs_subaru/16.0+1/python/lsst/obs/hsc\n", + "\n", "rSources = butler_coadd.get('deepCoadd_forced_src', {'filter': 'HSC-R', 'tract': 0, 'patch': '1,1'})\n", "iSources = butler_coadd.get('deepCoadd_forced_src', {'filter': 'HSC-I', 'tract': 0, 'patch': '1,1'})\n", "print('{} sources with forced photometry measured from coadds'.format(len(rSources)))" @@ -453,7 +465,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In order to associate individual visits with the MJD of the exposure, we go back to the raw images stored in the Butler repository. This may be replaceable with cleaner code that takes advantage of some Butler feature that accomplishes the same goal." + "In order to associate individual visits with the MJD of the exposure, we go back to the raw images stored in the Butler repository. This may be replaceable with cleaner code that takes advantage of some Butler feature that accomplishes the same goal.\n", + "\n", + "Note: this should be improved" ] }, { @@ -462,6 +476,9 @@ "metadata": {}, "outputs": [], "source": [ + "# get visitInfo (for exposure time, obs Date, coords, info on observatory)\n", + "# also look into butler registry butler.query_metadata()\n", + "\n", "# associate each visit ID with an MJD\n", "# store in lookup hashtable\n", "visit_to_mjd = {} \n", @@ -482,7 +499,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Doing forced photometry on individual exposures saves the source tables in different files. Here we query the Butler for all the data and store the tables together. This may be replaceable with cleaner code that takes advantage of some Butler feature that accomplishes the same goal." + "Doing forced photometry on individual exposures saves the source tables in different files. Here we query the Butler for all the data and store the tables together. This may be replaceable with cleaner code that takes advantage of some Butler feature that accomplishes the same goal.\n", + "\n", + "Note: this should be improved" ] }, { @@ -507,8 +526,7 @@ " sources = butler_ccd.get('forced_src', data_id_dict)\n", " source_table = sources.asAstropy().to_pandas()\n", " source_table['visit'] = fields[2].split(':')[1]\n", - " # TODO : fix this\n", - " source_table['mjd'] = [visit_to_mjd[key] if key in visit_to_mjd else 56598.2 for key in source_table['visit'] ] # this is obviously problematic\n", + " source_table['mjd'] = [visit_to_mjd[key] if key in visit_to_mjd else 56598.2 for key in source_table['visit'] ] # TODO: this is problematic. fix this\n", "\n", " if fields[0] == 'filter:HSC-R':\n", " r_tables.append(source_table)\n", From 0ad58fc53ceb2508bf75f994c8a05ac6c792c498 Mon Sep 17 00:00:00 2001 From: Justin Myles Date: Fri, 21 Sep 2018 21:55:05 +0000 Subject: [PATCH 13/15] adjust lightcurve --- ImageProcessing/Re-RunHSC.ipynb | 476 ++++++++++++++++++++++++++++++-- 1 file changed, 451 insertions(+), 25 deletions(-) diff --git a/ImageProcessing/Re-RunHSC.ipynb b/ImageProcessing/Re-RunHSC.ipynb index dd312587..632d2ad1 100644 --- a/ImageProcessing/Re-RunHSC.ipynb +++ b/ImageProcessing/Re-RunHSC.ipynb @@ -68,9 +68,194 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ": 'HSC Re-Run: Making Forced Photometry Light Curves from Scratch\n", + "Owner: **Justin Myles** (@jtmyles)\n", + "Last Verified to Run: **2018-09-13**\n", + "Verified Stack Release: **16.0**\n", + "\n", + "This project addresses issue #63: HSC Re-run\n", + "\n", + "This shell script runs the command-line tasks from the tutorial at pipelines.lsst.io for analysis\n", + "from raw images through source detection and forced photometry measurements. It is an intermediate\n", + "step toward the end-goal of making a forced photometry lightcurve in the notebook at\n", + "StackClub/ImageProcessing/Re-RunHSC.ipynb\n", + "\n", + "Running this script may take several hours on lsst-lspdev.\n", + "\n", + "Recommended to run with \n", + "$ bash Re-RunHSC.sh > output.txt\n", + "'\n", + "\n", + "\n", + "# Setup the LSST Stack\n", + "source /opt/lsst/software/stack/loadLSST.bash\n", + "eups list lsst_distrib\n", + "setup lsst_distrib\n", + "\n", + "\n", + "# I. Setting up the Butler data repository\n", + "date\n", + "echo \"Re-RunHSC INFO: set up the Butler\"\n", + "\n", + "setup -j -r /project/shared/data/ci_hsc\n", + "DATADIR=\"/home/$USER/DATA\"\n", + "mkdir -p \"$DATADIR\"\n", + "\n", + "# A Butler needs a *mapper* file \"to find and organize data in a format specific to each camera.\" \n", + "# We write this file to the data repository so that any instantiated Butler object knows which mapper to use.\n", + "echo lsst.obs.hsc.HscMapper > $DATADIR/_mapper\n", + "\n", + "# The ingest script creates links in the instantiated butler repository to the original data files\n", + "date\n", + "echo \"Re-RunHSC INFO: ingest images with ingestImages.py\"\n", + "\n", + "ingestImages.py $DATADIR $CI_HSC_DIR/raw/*.fits --mode=link\n", + "\n", + "# Grab calibration files\n", + "date\n", + "echo \"Re-RunHSC INFO: obtain calibration files with installTransmissionCurves.py\"\n", + "\n", + "installTransmissionCurves.py $DATADIR\n", + "ln -s $CI_HSC_DIR/CALIB/ $DATADIR/CALIB\n", + "mkdir -p $DATADIR/ref_cats\n", + "ln -s $CI_HSC_DIR/ps1_pv3_3pi_20170110 $DATADIR/ref_cats/ps1_pv3_3pi_20170110\n", + "\n", + "\n", + "# II. Calibrate a single frame with processCcd.py\n", + "date\n", + "echo \"Re-RunHSC INFO: process raw exposures with processCcd.py\"\n", + "\n", + "# Use calibration files to do CCD processing\n", + "# Does calibration happen here? What is the end result of the calibration process?\n", + "# What specifically does this task do?\n", + "processCcd.py $DATADIR --rerun processCcdOutputs --id\n", + "\n", + "\n", + "# III. (omitted) Visualize images.\n", + "\n", + "\n", + "# IV. Make coadds\n", + "\n", + "# IV. A. Make skymap\n", + "# A sky map is a tiling of the celestial sphere. It is composed of one or more tracts.\n", + "# A tract is composed of one or more overlapping patches. Each tract has a WCS.\n", + "# We define a skymap so that we can warp all of the exposure to fit on a single coordinate system\n", + "# This is a necessary step for making coadds\n", + "date\n", + "echo \"Re-RunHSC INFO: make skymap with makeDiscreteSkyMap.py\"\n", + "\n", + "makeDiscreteSkyMap.py $DATADIR --id --rerun processCcdOutputs:coadd --config skyMap.projection=\"TAN\"\n", + "\n", + "# IV. B. Warp images onto skymap\n", + "date\n", + "echo \"Re-RunHSC INFO: warp images with makeCoaddTempExp.py\"\n", + "\n", + "makeCoaddTempExp.py $DATADIR --rerun coadd \\\n", + " --selectId filter=HSC-R \\\n", + " --id filter=HSC-R tract=0 patch=0,0^0,1^0,2^1,0^1,1^1,2^2,0^2,1^2,2 \\\n", + " --config doApplyUberCal=False doApplySkyCorr=False\n", + "\n", + "makeCoaddTempExp.py $DATADIR --rerun coadd \\\n", + " --selectId filter=HSC-I \\\n", + " --id filter=HSC-I tract=0 patch=0,0^0,1^0,2^1,0^1,1^1,2^2,0^2,1^2,2 \\\n", + " --config doApplyUberCal=False doApplySkyCorr=False\n", + "\n", + "# IV. C. Coadd warped images\n", + "# Now that we have warped images, we can perform coaddition to get deeper images\n", + "# The motivation for this is to have the deepest image possible for source detection\n", + "date\n", + "echo \"Re-RunHSC INFO: coadd warped images with assembleCoadd.py\"\n", + "\n", + "assembleCoadd.py $DATADIR --rerun coadd \\\n", + " --selectId filter=HSC-R \\\n", + " --id filter=HSC-R tract=0 patch=0,0^0,1^0,2^1,0^1,1^1,2^2,0^2,1^2,2\n", + "\n", + "assembleCoadd.py $DATADIR --rerun coadd \\\n", + " --selectId filter=HSC-I \\\n", + " --id filter=HSC-I tract=0 patch=0,0^0,1^0,2^1,0^1,1^1,2^2,0^2,1^2,2\n", + "\n", + "\n", + "# V. Measuring Sources\n", + "\n", + "# V. A. Source detection\n", + "# As noted above, we do source detection on the deepest image possible.\n", + "date\n", + "echo \"Re-RunHSC INFO: detect objects in the coadd images with detectCoaddSources.py\"\n", + "\n", + "detectCoaddSources.py $DATADIR --rerun coadd:coaddPhot \\\n", + " --id filter=HSC-R tract=0 patch=0,0^0,1^0,2^1,0^1,1^1,2^2,0^2,1^2,2\n", + "\n", + "detectCoaddSources.py $DATADIR --rerun coaddPhot \\\n", + " --id filter=HSC-I tract=0 patch=0,0^0,1^0,2^1,0^1,1^1,2^2,0^2,1^2,2\n", + "\n", + "# V. B. Merge multi-band detection catalogs\n", + "# Ultimately, for photometry, we will need to deblend objects. \n", + "# In order to do this, we first merge the detected source catalogs.\n", + "date\n", + "echo \"Re-RunHSC INFO: merge detection catalogs with mergeCoaddDetections.py\"\n", + "\n", + "mergeCoaddDetections.py $DATADIR --rerun coaddPhot --id filter=HSC-R^HSC-I\n", + "\n", + "# V. C. Measure objects in coadds\n", + "# Given a full coaddSource catalog, we can do regular photometry with implicit deblending.\n", + "date\n", + "echo \"Re-RunHSC INFO: measure objects in coadds with measureCoaddSources.py\"\n", + "\n", + "measureCoaddSources.py $DATADIR --rerun coaddPhot --id filter=HSC-R\n", + "measureCoaddSources.py $DATADIR --rerun coaddPhot --id filter=HSC-I\n", + "\n", + "# V. D. Merge multi-band catalogs from coadds\n", + "date\n", + "echo \"Re-RunHSC INFO: merge measurements from coadds with mergeCoaddMeasurements.py\"\n", + "\n", + "mergeCoaddMeasurements.py $DATADIR --rerun coaddPhot --id filter=HSC-R^HSC-I\n", + "\n", + "# V. E. Run forced photometry on coadds\n", + "# Given a full source catalog, we can do forced photometry with implicit deblending.\n", + "date\n", + "echo \"Re-RunHSC INFO: perform forced photometry on coadds with forcedPhotCoadd.py\"\n", + "\n", + "forcedPhotCoadd.py $DATADIR --rerun coaddPhot:coaddForcedPhot --id filter=HSC-R\n", + "forcedPhotCoadd.py $DATADIR --rerun coaddForcedPhot --id filter=HSC-I\n", + "\n", + "# V. F. Run forced photometry on individual exposures\n", + "# Given a full source catalog, we can do forced photometry on the individual exposures.\n", + "# Note that as of 2018_08_23, the forcedPhotCcd.py task doesn't do deblending,\n", + "# which could lead to bad photometry for blended sources.\n", + "# This tasks requires a coadd tract stored in the Butler to grab the appropriate \n", + "# coadd catalogs to use as references for forced photometry.\n", + "# It has access to this tract because we chain the output from the coaddPhot subdirectory\n", + "\n", + "date\n", + "echo \"Re-RunHSC INFO: perform forced photometry on individual exposures with forcedPhotCcd.py\"\n", + "\n", + "forcedPhotCcd.py $DATADIR --rerun coaddPhot:ccdForcedPhot --id filter=HSC-R --clobber-config --configfile=/project/shared/data/ci_hsc/forcedPhotCcdConfig.py &> ccd_r.txt\n", + "forcedPhotCcd.py $DATADIR --rerun ccdForcedPhot --id filter=HSC-I --clobber-config --configfile=/project/shared/data/ci_hsc/forcedPhotCcdConfig.py &> ccd_i.txt\n", + "\n", + "\n", + "# VI. Multi-band catalog analysis\n", + "# For analysis of the catalog, see part VI of StackClub/ImageProcessing/Re-RunHSC.ipynb\n", + "date\n", + "echo \"Re-RunHSC INFO: parse output of forcedPhotCcd.py\"\n", + "\n", + "# The following grep & sed commands clean up the output log file used to determine \n", + "# which DataIds have measured forced photometry. The cleaner output is stored\n", + "# in a new file, data_ids.txt, that is used in Re-RunHSC.ipynb\n", + "grep 'forcedPhotCcd INFO: Performing forced measurement on DataId' ccd_r.txt ccd_i.txt > data_ids.txt\n", + "sed -i 's/ccd_[i,r].txt:forcedPhotCcd INFO: Performing forced measurement on DataId(initialdata={//g' data_ids.txt\n", + "sed -i 's/}, tag=set())//g' data_ids.txt\n", + "sed -i 's/'\"'\"'//g' data_ids.txt\n", + "sed -i 's/ //g' data_ids.txt\n" + ] + } + ], "source": [ "! cat Re-RunHSC.sh" ] @@ -339,7 +524,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -355,9 +540,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6800 sources with forced photometry measured from coadds\n" + ] + } + ], "source": [ "# We pass a datasetRefOrType and a DataId (dict) to the butler\n", "# datasetRefOrType : deepCoadd_forced_src\n", @@ -378,7 +571,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -401,7 +594,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -426,9 +619,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plt.style.use('seaborn-notebook')\n", "plt.figure(1, figsize=(4, 4), dpi=140)\n", @@ -454,7 +658,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -472,7 +676,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -506,9 +710,49 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903334, 'ccd': 23, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", + "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903334, 'ccd': 22, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", + "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903334, 'ccd': 16, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", + "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903334, 'ccd': 100, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", + "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903336, 'ccd': 24, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", + "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903336, 'ccd': 17, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", + "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903338, 'ccd': 25, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", + "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903338, 'ccd': 18, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", + "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903342, 'ccd': 100, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", + "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903342, 'ccd': 10, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", + "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903342, 'ccd': 4, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", + "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903344, 'ccd': 11, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", + "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903344, 'ccd': 5, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", + "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903344, 'ccd': 0, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", + "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903346, 'ccd': 12, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", + "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903346, 'ccd': 6, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", + "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903346, 'ccd': 1, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", + "{'filter': 'HSC-I', 'pointing': 671, 'visit': 903986, 'ccd': 23, 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0}\n", + "{'filter': 'HSC-I', 'pointing': 671, 'visit': 903986, 'ccd': 22, 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0}\n", + "{'filter': 'HSC-I', 'pointing': 671, 'visit': 903986, 'ccd': 16, 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0}\n", + "{'filter': 'HSC-I', 'pointing': 671, 'visit': 903986, 'ccd': 100, 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0}\n", + "{'filter': 'HSC-I', 'pointing': 671, 'visit': 903988, 'ccd': 24, 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0}\n", + "{'filter': 'HSC-I', 'pointing': 671, 'visit': 903988, 'ccd': 23, 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0}\n", + "{'filter': 'HSC-I', 'pointing': 671, 'visit': 903988, 'ccd': 17, 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0}\n", + "{'filter': 'HSC-I', 'pointing': 671, 'visit': 903988, 'ccd': 16, 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0}\n", + "{'filter': 'HSC-I', 'pointing': 671, 'visit': 903990, 'ccd': 25, 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0}\n", + "{'filter': 'HSC-I', 'pointing': 671, 'visit': 903990, 'ccd': 18, 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0}\n", + "{'filter': 'HSC-I', 'pointing': 671, 'visit': 904010, 'ccd': 100, 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0}\n", + "{'filter': 'HSC-I', 'pointing': 671, 'visit': 904010, 'ccd': 10, 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0}\n", + "{'filter': 'HSC-I', 'pointing': 671, 'visit': 904010, 'ccd': 4, 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0}\n", + "{'filter': 'HSC-I', 'pointing': 671, 'visit': 904014, 'ccd': 12, 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0}\n", + "{'filter': 'HSC-I', 'pointing': 671, 'visit': 904014, 'ccd': 6, 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0}\n", + "{'filter': 'HSC-I', 'pointing': 671, 'visit': 904014, 'ccd': 1, 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0}\n" + ] + } + ], "source": [ "data_id_fields = [('filter', str), ('pointing', int), ('visit', int), \n", " ('ccd', int), ('field', str), ('dateObs', str), \n", @@ -538,9 +782,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plt.style.use('seaborn-notebook')\n", "fig, axarr = plt.subplots(1, 2, figsize=(8, 4), dpi=140)\n", @@ -594,9 +849,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "9742 i band objects, 30400 measurements\n", + "8321 r band objects, 32717 measurements\n", + "1769 objects with 5 epocs\n" + ] + } + ], "source": [ "iSources = pd.concat(i_tables) \n", "rSources = pd.concat(r_tables)\n", @@ -620,36 +885,197 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "e.g. objectId: 141733921084 (showing select rows from table)\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
idcoord_racoord_decobjectIdbase_PsfFlux_fluxbase_PsfFlux_fluxSigmavisitmjd
2787765181299278482155.599023-0.0064431417339210849120.533865151.51688190398656598.221156
5177765198522097341505.599023-0.0064431417339210849886.496949156.20124690398856598.222126
7727765215744916201015.599023-0.0064431417339210849071.808927141.83446690399056598.223046
17765386942312611865.599023-0.00644314173392108427701.5898041142.20247890401056598.250471
847765421387950326615.599023-0.0064431417339210849585.718591143.35626590401456598.200000
\n", + "
" + ], + "text/plain": [ + " id coord_ra coord_dec objectId base_PsfFlux_flux \\\n", + "278 776518129927848215 5.599023 -0.006443 141733921084 9120.533865 \n", + "517 776519852209734150 5.599023 -0.006443 141733921084 9886.496949 \n", + "772 776521574491620101 5.599023 -0.006443 141733921084 9071.808927 \n", + "1 776538694231261186 5.599023 -0.006443 141733921084 27701.589804 \n", + "84 776542138795032661 5.599023 -0.006443 141733921084 9585.718591 \n", + "\n", + " base_PsfFlux_fluxSigma visit mjd \n", + "278 151.516881 903986 56598.221156 \n", + "517 156.201246 903988 56598.222126 \n", + "772 141.834466 903990 56598.223046 \n", + "1 1142.202478 904010 56598.250471 \n", + "84 143.356265 904014 56598.200000 " + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "obj = objids[1]\n", "print('e.g. objectId:', obj, '(showing select rows from table)')\n", - "iSources[iSources['objectId'] == obj][['id','coord_ra','coord_dec','objectId','base_PsfFlux_flux','visit','mjd']]" + "iSources[iSources['objectId'] == obj][['id','coord_ra','coord_dec','objectId','base_PsfFlux_flux','base_PsfFlux_fluxSigma', 'visit','mjd']]" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.01810787, 0.0172214 , 0.01704176, 0.04494329, 0.01630116])" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# http://slittlefair.staff.shef.ac.uk/teaching/phy217/lectures/stats/L18/index.html\n", + "yerr = 1.09 * iSources[iSources['objectId'] == obj]['base_PsfFlux_fluxSigma'].values / iSources[iSources['objectId'] == obj]['base_PsfFlux_flux'].values\n", + "yerr" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plt.style.use('seaborn-notebook')\n", "plt.figure(3, figsize=(4, 4), dpi=140)\n", "plt.title('objectId: {}'.format(obj))\n", - "plt.scatter(iSources[iSources['objectId'] == obj]['mjd'].values, \n", + "plt.errorbar(iSources[iSources['objectId'] == obj]['mjd'].values, \n", " iCcdCalib.getMagnitude(iSources[iSources['objectId'] == obj]['base_PsfFlux_flux'].values),\n", - " s=6, c='k')\n", + " yerr=yerr,\n", + " markersize=6, color='k', fmt='.')\n", "\n", - "plt.ylabel('$i$')\n", + "plt.ylabel('$i$' + ' mag')\n", "plt.xlabel('MJD')\n", "\n", "plt.subplots_adjust(left=0.125, bottom=0.1)\n", "ax = plt.gca()\n", "ax.ticklabel_format(useOffset=56598, style='plain', axis='x', useMathText=True)\n", + "ax.invert_yaxis()\n", "plt.show()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From e6c3586f33e2a6f10c5564b7b1b8b87d3c8b93ad Mon Sep 17 00:00:00 2001 From: Phil Marshall Date: Fri, 28 Sep 2018 11:44:47 -0600 Subject: [PATCH 14/15] Cleared outputs, added summary, ready to merge --- ImageProcessing/Re-RunHSC.ipynb | 476 +++----------------------------- 1 file changed, 36 insertions(+), 440 deletions(-) diff --git a/ImageProcessing/Re-RunHSC.ipynb b/ImageProcessing/Re-RunHSC.ipynb index 632d2ad1..c6e77690 100644 --- a/ImageProcessing/Re-RunHSC.ipynb +++ b/ImageProcessing/Re-RunHSC.ipynb @@ -16,9 +16,9 @@ "\n", "### Learning Objectives:\n", "After working through and studying this notebook you should be able to understand how to use the DRP pipeline from image visualization through to a forced photometry light curve. Specific learning objectives include: \n", - " 1. Using the `butler` to fetch data\n", + " 1. How the command line tasks are configured, executed and linked together in a complete pipeline.\n", " 2. [Configuring](https://pipelines.lsst.io/v/w-2018-12/modules/lsst.pipe.base/command-line-task-config-howto.html) and executing pipeline tasks in python as well as on the command line.\n", - " 3. The sequence of steps involved in the DRP pipeline.\n", + " 3. The actual sequence of steps involved in the DRP pipeline.\n", "\n", "### Logistics\n", "This notebook is intended to be runnable on `lsst-lspdev.ncsa.illinois.edu` from a local git clone of https://github.com/LSSTScienceCollaborations/StackClub.\n", @@ -68,194 +68,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ": 'HSC Re-Run: Making Forced Photometry Light Curves from Scratch\n", - "Owner: **Justin Myles** (@jtmyles)\n", - "Last Verified to Run: **2018-09-13**\n", - "Verified Stack Release: **16.0**\n", - "\n", - "This project addresses issue #63: HSC Re-run\n", - "\n", - "This shell script runs the command-line tasks from the tutorial at pipelines.lsst.io for analysis\n", - "from raw images through source detection and forced photometry measurements. It is an intermediate\n", - "step toward the end-goal of making a forced photometry lightcurve in the notebook at\n", - "StackClub/ImageProcessing/Re-RunHSC.ipynb\n", - "\n", - "Running this script may take several hours on lsst-lspdev.\n", - "\n", - "Recommended to run with \n", - "$ bash Re-RunHSC.sh > output.txt\n", - "'\n", - "\n", - "\n", - "# Setup the LSST Stack\n", - "source /opt/lsst/software/stack/loadLSST.bash\n", - "eups list lsst_distrib\n", - "setup lsst_distrib\n", - "\n", - "\n", - "# I. Setting up the Butler data repository\n", - "date\n", - "echo \"Re-RunHSC INFO: set up the Butler\"\n", - "\n", - "setup -j -r /project/shared/data/ci_hsc\n", - "DATADIR=\"/home/$USER/DATA\"\n", - "mkdir -p \"$DATADIR\"\n", - "\n", - "# A Butler needs a *mapper* file \"to find and organize data in a format specific to each camera.\" \n", - "# We write this file to the data repository so that any instantiated Butler object knows which mapper to use.\n", - "echo lsst.obs.hsc.HscMapper > $DATADIR/_mapper\n", - "\n", - "# The ingest script creates links in the instantiated butler repository to the original data files\n", - "date\n", - "echo \"Re-RunHSC INFO: ingest images with ingestImages.py\"\n", - "\n", - "ingestImages.py $DATADIR $CI_HSC_DIR/raw/*.fits --mode=link\n", - "\n", - "# Grab calibration files\n", - "date\n", - "echo \"Re-RunHSC INFO: obtain calibration files with installTransmissionCurves.py\"\n", - "\n", - "installTransmissionCurves.py $DATADIR\n", - "ln -s $CI_HSC_DIR/CALIB/ $DATADIR/CALIB\n", - "mkdir -p $DATADIR/ref_cats\n", - "ln -s $CI_HSC_DIR/ps1_pv3_3pi_20170110 $DATADIR/ref_cats/ps1_pv3_3pi_20170110\n", - "\n", - "\n", - "# II. Calibrate a single frame with processCcd.py\n", - "date\n", - "echo \"Re-RunHSC INFO: process raw exposures with processCcd.py\"\n", - "\n", - "# Use calibration files to do CCD processing\n", - "# Does calibration happen here? What is the end result of the calibration process?\n", - "# What specifically does this task do?\n", - "processCcd.py $DATADIR --rerun processCcdOutputs --id\n", - "\n", - "\n", - "# III. (omitted) Visualize images.\n", - "\n", - "\n", - "# IV. Make coadds\n", - "\n", - "# IV. A. Make skymap\n", - "# A sky map is a tiling of the celestial sphere. It is composed of one or more tracts.\n", - "# A tract is composed of one or more overlapping patches. Each tract has a WCS.\n", - "# We define a skymap so that we can warp all of the exposure to fit on a single coordinate system\n", - "# This is a necessary step for making coadds\n", - "date\n", - "echo \"Re-RunHSC INFO: make skymap with makeDiscreteSkyMap.py\"\n", - "\n", - "makeDiscreteSkyMap.py $DATADIR --id --rerun processCcdOutputs:coadd --config skyMap.projection=\"TAN\"\n", - "\n", - "# IV. B. Warp images onto skymap\n", - "date\n", - "echo \"Re-RunHSC INFO: warp images with makeCoaddTempExp.py\"\n", - "\n", - "makeCoaddTempExp.py $DATADIR --rerun coadd \\\n", - " --selectId filter=HSC-R \\\n", - " --id filter=HSC-R tract=0 patch=0,0^0,1^0,2^1,0^1,1^1,2^2,0^2,1^2,2 \\\n", - " --config doApplyUberCal=False doApplySkyCorr=False\n", - "\n", - "makeCoaddTempExp.py $DATADIR --rerun coadd \\\n", - " --selectId filter=HSC-I \\\n", - " --id filter=HSC-I tract=0 patch=0,0^0,1^0,2^1,0^1,1^1,2^2,0^2,1^2,2 \\\n", - " --config doApplyUberCal=False doApplySkyCorr=False\n", - "\n", - "# IV. C. Coadd warped images\n", - "# Now that we have warped images, we can perform coaddition to get deeper images\n", - "# The motivation for this is to have the deepest image possible for source detection\n", - "date\n", - "echo \"Re-RunHSC INFO: coadd warped images with assembleCoadd.py\"\n", - "\n", - "assembleCoadd.py $DATADIR --rerun coadd \\\n", - " --selectId filter=HSC-R \\\n", - " --id filter=HSC-R tract=0 patch=0,0^0,1^0,2^1,0^1,1^1,2^2,0^2,1^2,2\n", - "\n", - "assembleCoadd.py $DATADIR --rerun coadd \\\n", - " --selectId filter=HSC-I \\\n", - " --id filter=HSC-I tract=0 patch=0,0^0,1^0,2^1,0^1,1^1,2^2,0^2,1^2,2\n", - "\n", - "\n", - "# V. Measuring Sources\n", - "\n", - "# V. A. Source detection\n", - "# As noted above, we do source detection on the deepest image possible.\n", - "date\n", - "echo \"Re-RunHSC INFO: detect objects in the coadd images with detectCoaddSources.py\"\n", - "\n", - "detectCoaddSources.py $DATADIR --rerun coadd:coaddPhot \\\n", - " --id filter=HSC-R tract=0 patch=0,0^0,1^0,2^1,0^1,1^1,2^2,0^2,1^2,2\n", - "\n", - "detectCoaddSources.py $DATADIR --rerun coaddPhot \\\n", - " --id filter=HSC-I tract=0 patch=0,0^0,1^0,2^1,0^1,1^1,2^2,0^2,1^2,2\n", - "\n", - "# V. B. Merge multi-band detection catalogs\n", - "# Ultimately, for photometry, we will need to deblend objects. \n", - "# In order to do this, we first merge the detected source catalogs.\n", - "date\n", - "echo \"Re-RunHSC INFO: merge detection catalogs with mergeCoaddDetections.py\"\n", - "\n", - "mergeCoaddDetections.py $DATADIR --rerun coaddPhot --id filter=HSC-R^HSC-I\n", - "\n", - "# V. C. Measure objects in coadds\n", - "# Given a full coaddSource catalog, we can do regular photometry with implicit deblending.\n", - "date\n", - "echo \"Re-RunHSC INFO: measure objects in coadds with measureCoaddSources.py\"\n", - "\n", - "measureCoaddSources.py $DATADIR --rerun coaddPhot --id filter=HSC-R\n", - "measureCoaddSources.py $DATADIR --rerun coaddPhot --id filter=HSC-I\n", - "\n", - "# V. D. Merge multi-band catalogs from coadds\n", - "date\n", - "echo \"Re-RunHSC INFO: merge measurements from coadds with mergeCoaddMeasurements.py\"\n", - "\n", - "mergeCoaddMeasurements.py $DATADIR --rerun coaddPhot --id filter=HSC-R^HSC-I\n", - "\n", - "# V. E. Run forced photometry on coadds\n", - "# Given a full source catalog, we can do forced photometry with implicit deblending.\n", - "date\n", - "echo \"Re-RunHSC INFO: perform forced photometry on coadds with forcedPhotCoadd.py\"\n", - "\n", - "forcedPhotCoadd.py $DATADIR --rerun coaddPhot:coaddForcedPhot --id filter=HSC-R\n", - "forcedPhotCoadd.py $DATADIR --rerun coaddForcedPhot --id filter=HSC-I\n", - "\n", - "# V. F. Run forced photometry on individual exposures\n", - "# Given a full source catalog, we can do forced photometry on the individual exposures.\n", - "# Note that as of 2018_08_23, the forcedPhotCcd.py task doesn't do deblending,\n", - "# which could lead to bad photometry for blended sources.\n", - "# This tasks requires a coadd tract stored in the Butler to grab the appropriate \n", - "# coadd catalogs to use as references for forced photometry.\n", - "# It has access to this tract because we chain the output from the coaddPhot subdirectory\n", - "\n", - "date\n", - "echo \"Re-RunHSC INFO: perform forced photometry on individual exposures with forcedPhotCcd.py\"\n", - "\n", - "forcedPhotCcd.py $DATADIR --rerun coaddPhot:ccdForcedPhot --id filter=HSC-R --clobber-config --configfile=/project/shared/data/ci_hsc/forcedPhotCcdConfig.py &> ccd_r.txt\n", - "forcedPhotCcd.py $DATADIR --rerun ccdForcedPhot --id filter=HSC-I --clobber-config --configfile=/project/shared/data/ci_hsc/forcedPhotCcdConfig.py &> ccd_i.txt\n", - "\n", - "\n", - "# VI. Multi-band catalog analysis\n", - "# For analysis of the catalog, see part VI of StackClub/ImageProcessing/Re-RunHSC.ipynb\n", - "date\n", - "echo \"Re-RunHSC INFO: parse output of forcedPhotCcd.py\"\n", - "\n", - "# The following grep & sed commands clean up the output log file used to determine \n", - "# which DataIds have measured forced photometry. The cleaner output is stored\n", - "# in a new file, data_ids.txt, that is used in Re-RunHSC.ipynb\n", - "grep 'forcedPhotCcd INFO: Performing forced measurement on DataId' ccd_r.txt ccd_i.txt > data_ids.txt\n", - "sed -i 's/ccd_[i,r].txt:forcedPhotCcd INFO: Performing forced measurement on DataId(initialdata={//g' data_ids.txt\n", - "sed -i 's/}, tag=set())//g' data_ids.txt\n", - "sed -i 's/'\"'\"'//g' data_ids.txt\n", - "sed -i 's/ //g' data_ids.txt\n" - ] - } - ], + "outputs": [], "source": [ "! cat Re-RunHSC.sh" ] @@ -524,7 +339,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -540,17 +355,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "6800 sources with forced photometry measured from coadds\n" - ] - } - ], + "outputs": [], "source": [ "# We pass a datasetRefOrType and a DataId (dict) to the butler\n", "# datasetRefOrType : deepCoadd_forced_src\n", @@ -571,7 +378,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -594,7 +401,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -619,20 +426,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.style.use('seaborn-notebook')\n", "plt.figure(1, figsize=(4, 4), dpi=140)\n", @@ -658,7 +454,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -676,7 +472,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -710,49 +506,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903334, 'ccd': 23, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", - "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903334, 'ccd': 22, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", - "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903334, 'ccd': 16, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", - "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903334, 'ccd': 100, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", - "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903336, 'ccd': 24, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", - "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903336, 'ccd': 17, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", - "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903338, 'ccd': 25, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", - "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903338, 'ccd': 18, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", - "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903342, 'ccd': 100, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", - "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903342, 'ccd': 10, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", - "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903342, 'ccd': 4, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", - "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903344, 'ccd': 11, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", - "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903344, 'ccd': 5, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", - "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903344, 'ccd': 0, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", - "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903346, 'ccd': 12, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", - "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903346, 'ccd': 6, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", - "{'filter': 'HSC-R', 'pointing': 533, 'visit': 903346, 'ccd': 1, 'field': 'STRIPE82L', 'dateObs': '2013-06-17', 'taiObs': '2013-06-17', 'expTime': 30.0, 'tract': 0}\n", - "{'filter': 'HSC-I', 'pointing': 671, 'visit': 903986, 'ccd': 23, 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0}\n", - "{'filter': 'HSC-I', 'pointing': 671, 'visit': 903986, 'ccd': 22, 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0}\n", - "{'filter': 'HSC-I', 'pointing': 671, 'visit': 903986, 'ccd': 16, 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0}\n", - "{'filter': 'HSC-I', 'pointing': 671, 'visit': 903986, 'ccd': 100, 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0}\n", - "{'filter': 'HSC-I', 'pointing': 671, 'visit': 903988, 'ccd': 24, 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0}\n", - "{'filter': 'HSC-I', 'pointing': 671, 'visit': 903988, 'ccd': 23, 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0}\n", - "{'filter': 'HSC-I', 'pointing': 671, 'visit': 903988, 'ccd': 17, 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0}\n", - "{'filter': 'HSC-I', 'pointing': 671, 'visit': 903988, 'ccd': 16, 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0}\n", - "{'filter': 'HSC-I', 'pointing': 671, 'visit': 903990, 'ccd': 25, 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0}\n", - "{'filter': 'HSC-I', 'pointing': 671, 'visit': 903990, 'ccd': 18, 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0}\n", - "{'filter': 'HSC-I', 'pointing': 671, 'visit': 904010, 'ccd': 100, 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0}\n", - "{'filter': 'HSC-I', 'pointing': 671, 'visit': 904010, 'ccd': 10, 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0}\n", - "{'filter': 'HSC-I', 'pointing': 671, 'visit': 904010, 'ccd': 4, 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0}\n", - "{'filter': 'HSC-I', 'pointing': 671, 'visit': 904014, 'ccd': 12, 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0}\n", - "{'filter': 'HSC-I', 'pointing': 671, 'visit': 904014, 'ccd': 6, 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0}\n", - "{'filter': 'HSC-I', 'pointing': 671, 'visit': 904014, 'ccd': 1, 'field': 'STRIPE82L', 'dateObs': '2013-11-02', 'taiObs': '2013-11-02', 'expTime': 30.0, 'tract': 0}\n" - ] - } - ], + "outputs": [], "source": [ "data_id_fields = [('filter', str), ('pointing', int), ('visit', int), \n", " ('ccd', int), ('field', str), ('dateObs', str), \n", @@ -782,20 +538,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.style.use('seaborn-notebook')\n", "fig, axarr = plt.subplots(1, 2, figsize=(8, 4), dpi=140)\n", @@ -849,19 +594,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "9742 i band objects, 30400 measurements\n", - "8321 r band objects, 32717 measurements\n", - "1769 objects with 5 epocs\n" - ] - } - ], + "outputs": [], "source": [ "iSources = pd.concat(i_tables) \n", "rSources = pd.concat(r_tables)\n", @@ -885,128 +620,9 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "e.g. objectId: 141733921084 (showing select rows from table)\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
idcoord_racoord_decobjectIdbase_PsfFlux_fluxbase_PsfFlux_fluxSigmavisitmjd
2787765181299278482155.599023-0.0064431417339210849120.533865151.51688190398656598.221156
5177765198522097341505.599023-0.0064431417339210849886.496949156.20124690398856598.222126
7727765215744916201015.599023-0.0064431417339210849071.808927141.83446690399056598.223046
17765386942312611865.599023-0.00644314173392108427701.5898041142.20247890401056598.250471
847765421387950326615.599023-0.0064431417339210849585.718591143.35626590401456598.200000
\n", - "
" - ], - "text/plain": [ - " id coord_ra coord_dec objectId base_PsfFlux_flux \\\n", - "278 776518129927848215 5.599023 -0.006443 141733921084 9120.533865 \n", - "517 776519852209734150 5.599023 -0.006443 141733921084 9886.496949 \n", - "772 776521574491620101 5.599023 -0.006443 141733921084 9071.808927 \n", - "1 776538694231261186 5.599023 -0.006443 141733921084 27701.589804 \n", - "84 776542138795032661 5.599023 -0.006443 141733921084 9585.718591 \n", - "\n", - " base_PsfFlux_fluxSigma visit mjd \n", - "278 151.516881 903986 56598.221156 \n", - "517 156.201246 903988 56598.222126 \n", - "772 141.834466 903990 56598.223046 \n", - "1 1142.202478 904010 56598.250471 \n", - "84 143.356265 904014 56598.200000 " - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "obj = objids[1]\n", "print('e.g. objectId:', obj, '(showing select rows from table)')\n", @@ -1015,20 +631,9 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.01810787, 0.0172214 , 0.01704176, 0.04494329, 0.01630116])" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# http://slittlefair.staff.shef.ac.uk/teaching/phy217/lectures/stats/L18/index.html\n", "yerr = 1.09 * iSources[iSources['objectId'] == obj]['base_PsfFlux_fluxSigma'].values / iSources[iSources['objectId'] == obj]['base_PsfFlux_flux'].values\n", @@ -1037,20 +642,9 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.style.use('seaborn-notebook')\n", "plt.figure(3, figsize=(4, 4), dpi=140)\n", @@ -1071,30 +665,32 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "## Summary\n", + "\n", + "As the plots above show, we have completed an end-to-end processing of the `ci_hsc` test dataset, following the steps in the http://pipelines.lsst.io \"getting started\" tutorial. We saw how the command line tasks take care of all teh book-keeping, and how they are configured and run. " + ] } ], "metadata": { "kernelspec": { - "display_name": "LSST", + "display_name": "Python 2", "language": "python", - "name": "lsst" + "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 3 + "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.2" + "pygments_lexer": "ipython2", + "version": "2.7.12" } }, "nbformat": 4, From 33fb944f535d35b42b4fa8841465db02dd0eb434 Mon Sep 17 00:00:00 2001 From: Phil Marshall Date: Fri, 28 Sep 2018 11:48:44 -0600 Subject: [PATCH 15/15] Replaced processEimage with Re-runHSC --- ImageProcessing/README.rst | 17 ++++++++--------- 1 file changed, 8 insertions(+), 9 deletions(-) diff --git a/ImageProcessing/README.rst b/ImageProcessing/README.rst index 36bdd516..e83204e6 100644 --- a/ImageProcessing/README.rst +++ b/ImageProcessing/README.rst @@ -2,7 +2,7 @@ Image Processing ================ This folder contains a set of tutorial notebooks exploring the image processing routines in the LSST science pipelines. See the index table below for links to the notebook code, and an auto-rendered view of the notebook with outputs. - + .. list-table:: :widths: 10 20 10 10 @@ -14,13 +14,12 @@ This folder contains a set of tutorial notebooks exploring the image processing - Owner - * - **ProcessEimage.ipynb** - - How to process a simulated "e-image" using the DM Stack. - - `ipynb `_, - `rendered `_ - - .. image:: https://github.com/LSSTScienceCollaborations/StackClub/blob/rendered/ImageProcessing/log/ProcessEimage.svg - :target: https://github.com/LSSTScienceCollaborations/StackClub/blob/rendered/ImageProcessing/log/ProcessEimage.log + * - **Re-run HSC** + - End-to-end processing of the ``ci_hsc`` test dataset using the DM Stack. + - `ipynb `_, + `rendered `_, `bash script `_, - - `Alex Drlica-Wagner `_ + .. image:: https://github.com/LSSTScienceCollaborations/StackClub/blob/rendered/ImageProcessing/log/Re-runHSC.svg + :target: https://github.com/LSSTScienceCollaborations/StackClub/blob/rendered/ImageProcessing/log/Re-runHSC.log + - `Justin Myles `_