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z{O!Uc;%KpL4<@xa;<6-Jp?;Qt?laW&)ev?mHe!sGdUfkka6^Ol>B7kOMh+{PUrC3z ze@W&Z(f!DS+URv z(%hMI=Uo$XkBjr43A0IXO=WCxFyC?veS6-W(BL(;P6Q{!3 + txpipe + rail.estimation.algos.bpz_lite + +python_paths: [] + +stages: + - name: TXSourceSelectorMetadetect + nprocess: 30 + - name: Inform_BPZ_lite + nprocess: 1 + - name: BPZ_lite + nprocess: 30 + - name: CLClusterBinning + nprocess: 1 + - name: CLClusterShearCatalogs + nprocess: 30 + - name: CLClusterEnsembleProfiles + nprocess: 30 + - name: CLClusterDataVector + nprocess: 1 + + + +output_dir: data/cosmodc2/outputs-20deg2-CL +config: examples/cosmodc2/config-20deg2-CL.yml + +inputs: + # See README for paths to download these files + shear_catalog: ./data/cosmodc2/20deg2/shear_catalog.hdf5 + photometry_catalog: ./data/cosmodc2/20deg2/photometry_catalog.hdf5 + fiducial_cosmology: data/fiducial_cosmology.yml + calibration_table: ./data/cosmodc2/20deg2/sample_cosmodc2_w10year_errors.dat + spectroscopic_catalog: ./data/cosmodc2/20deg2/spectroscopic_catalog.hdf5 + cluster_catalog: ./data/cosmodc2/20deg2/cluster_catalog.hdf5 + +resume: True +log_dir: data/cosmodc2/logs +pipeline_log: data/cosmodc2/log.txt + From 557c225a08fa307b9e7e466da2ac8bf62e839292 Mon Sep 17 00:00:00 2001 From: Marina Ricci Date: Thu, 20 Jul 2023 00:00:33 +0200 Subject: [PATCH 02/22] Created placeholder files to run full CL pipeline --- examples/cosmodc2/1deg2-in2p3.sub | 9 ++ examples/cosmodc2/config-1deg2-CL.yml | 89 +++++++++++++++++ examples/cosmodc2/pipeline-1deg2-CL.yml | 49 +++++++++ notebooks/Run_CL_pipeline.ipynb | 127 ++++++++++++++++++++++++ 4 files changed, 274 insertions(+) create mode 100644 examples/cosmodc2/1deg2-in2p3.sub create mode 100644 examples/cosmodc2/config-1deg2-CL.yml create mode 100644 examples/cosmodc2/pipeline-1deg2-CL.yml create mode 100644 notebooks/Run_CL_pipeline.ipynb diff --git a/examples/cosmodc2/1deg2-in2p3.sub b/examples/cosmodc2/1deg2-in2p3.sub new file mode 100644 index 000000000..b4a25860e --- /dev/null +++ b/examples/cosmodc2/1deg2-in2p3.sub @@ -0,0 +1,9 @@ +#!/usr/bin/bash +#SBATCH --time=01:00:00 +#SBATCH --partition=hpc +#SBATCH --ntasks=1 +#SBATCH --cpus-per-task=1 +#SBATCH --mem=128000 + +source /pbs/throng/lsst/users/jzuntz/txpipe-environments/setup-txpipe +ceci examples/cosmodc2/pipeline-1deg2-CL.yml diff --git a/examples/cosmodc2/config-1deg2-CL.yml b/examples/cosmodc2/config-1deg2-CL.yml new file mode 100644 index 000000000..1dc86e808 --- /dev/null +++ b/examples/cosmodc2/config-1deg2-CL.yml @@ -0,0 +1,89 @@ +TXSourceSelectorMetadetect: + input_pz: False + bands: riz #used for selection + T_cut: 0.5 + s2n_cut: 10.0 + max_rows: 1000 + delta_gamma: 0.02 + source_zbin_edges: [0.1, 3.0] + chunk_rows: 100000 + true_z: False + shear_prefix: '' + +Inform_BPZ_lite: + aliases: + input: spectroscopic_catalog + model: photoz_model + zmin: 0.0 + zmax: 3.0 + nzbins: 301 + columns_file: ./data/bpz_riz.columns + data_path: ./data/example/rail-bpz-inputs + spectra_file: CWWSB4.list + prior_band: i + ref_band: i + # Not sure about this + prior_file: hdfn_gen + p_min: 0.005 + gauss_kernel: 0.0 + mag_err_min: 0.005 + inform_options: {'save_train': False, 'load_model': False, 'modelfile': 'BPZpriormodel.out'} + madau_reddening: no + bands: riz + zp_errors: [0.01, 0.01, 0.01] + hdf5_groupname: photometry + + + +BPZ_lite: + aliases: + model: photoz_model + input: shear_catalog + output: source_photoz_pdfs + zmin: 0.0 + zmax: 3.0 + dz: 0.01 + nzbins: 301 + data_path: ./data/example/rail-bpz-inputs + bands: [mag_r, mag_i, mag_z] + err_bands: [mag_err_r, mag_err_i, mag_err_z] + hdf5_groupname: shear/00 + nondetect_val: .inf + columns_file: ./data/bpz_riz.columns + spectra_file: CWWSB4.list + ref_band: mag_i + prior_file: hdfn_gen + p_min: 0.005 + gauss_kernel: 0.0 + zp_errors: [0.01, 0.01, 0.01] + mag_err_min: 0.005 + madau_reddening: false + mag_limits: + mag_r: 29.06 + mag_i: 28.62 + mag_z: 27.98 + +CLClusterBinning: + z_min : 0 + z_max : 1.2 + lambda_min : 5 + lambda_max : 1e3 + +CLClusterShearCatalogs: + max_radius: 5.0 # Mpc + delta_z: 0.2 # redshift buffer + +CLClusterEnsembleProfiles: + #radial bin definition + r_min : 0.2 #in Mpc + r_max : 5.0 #in Mpc + #type of profile + delta_sigma_profile : True + shear_profile : False + magnification_profile : False + #stacking step or not + stack_profile : True + + + + diff --git a/examples/cosmodc2/pipeline-1deg2-CL.yml b/examples/cosmodc2/pipeline-1deg2-CL.yml new file mode 100644 index 000000000..d4c2caf59 --- /dev/null +++ b/examples/cosmodc2/pipeline-1deg2-CL.yml @@ -0,0 +1,49 @@ +launcher: + name: mini + interval: 3.0 + +site: + name: cc-parallel + mpi_command: "mpirun -n" + + +modules: > + txpipe + rail.estimation.algos.bpz_lite + +python_paths: [] + +stages: + - name: TXSourceSelectorMetadetect + nprocess: 1 + - name: Inform_BPZ_lite + nprocess: 1 + - name: BPZ_lite + nprocess: 1 + - name: CLClusterBinning + nprocess: 1 + - name: CLClusterShearCatalogs + nprocess: 1 + - name: CLClusterEnsembleProfiles + nprocess: 1 + - name: CLClusterDataVector + nprocess: 1 + + + +output_dir: data/cosmodc2/outputs-1deg2-CL +config: examples/cosmodc2/config-1deg2-CL.yml + +inputs: + # See README for paths to download these files + shear_catalog: ./data/example/inputs/metadetect_shear_catalog.hdf5 + photometry_catalog: ./data/example/inputs/photometry_catalog.hdf5 + fiducial_cosmology: data/fiducial_cosmology.yml + calibration_table: ./data/example/inputs/sample_cosmodc2_w10year_errors.dat + spectroscopic_catalog: ./data/example/inputs/mock_spectroscopic_catalog.hdf5 + cluster_catalog: ./data/example/inputs/cluster_catalog.hdf5 + +resume: True +log_dir: data/cosmodc2/logs +pipeline_log: data/cosmodc2/log_1deg2.txt + diff --git a/notebooks/Run_CL_pipeline.ipynb b/notebooks/Run_CL_pipeline.ipynb new file mode 100644 index 000000000..affedc086 --- /dev/null +++ b/notebooks/Run_CL_pipeline.ipynb @@ -0,0 +1,127 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "a01235ba-b30a-413c-a381-eef2712dfcb9", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from pprint import pprint\n", + "import numpy as np\n", + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "from IPython.display import Image\n", + "import ceci" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "d5eb6757-e79f-445c-a2c6-4d25af5f6f65", + "metadata": {}, + "outputs": [], + "source": [ + "my_txpipe_dir = \"/pbs/home/m/mricci/throng_mricci/desc/TXPipe\"\n", + "os.chdir(my_txpipe_dir)\n", + "\n", + "import txpipe" + ] + }, + { + "cell_type": "markdown", + "id": "33d82af0-d1c4-43cc-ac0b-a2d6f1e66889", + "metadata": {}, + "source": [ + "# Let's start working with the 1deg2 data file" + ] + }, + { + "cell_type": "markdown", + "id": "b031074a-bc97-4619-bac7-a364dccd5891", + "metadata": {}, + "source": [ + "### Launching a pipeline at CC-IN2P3\n", + "\n", + "Let's have a look at the submission script for this pipeline: `examples/cosmodc2/20deg2-in2p3.sub`:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "d0539f99-806e-481c-9a00-69716e216d74", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "#!/usr/bin/bash\n", + "#SBATCH --time=01:00:00\n", + "#SBATCH --partition=hpc\n", + "#SBATCH --ntasks=30\n", + "#SBATCH --cpus-per-task=1\n", + "#SBATCH --mem=128000\n", + "\n", + "source /pbs/throng/lsst/users/jzuntz/txpipe-environments/setup-txpipe\n", + "ceci examples/cosmodc2/pipeline-20deg2-clmm.yml\n" + ] + } + ], + "source": [ + "! cat examples/cosmodc2/1deg2-in2p3.sub" + ] + }, + { + "cell_type": "markdown", + "id": "64a79cc5-4e43-4146-b3ac-aceb68b56470", + "metadata": {}, + "source": [ + "This will launch a job of up to one hour (it should finish in 30 min) on a single CC-IN2P3 node to run a pipeline.\n", + "\n", + "In a terminal, **navigate to your TXPipe directory and run**:\n", + "\n", + "```\n", + "sbatch examples/cosmodc2/1deg2-in2p3.sub\n", + "```\n", + "to set it running.\n", + "\n", + "## Investigating our pipeline\n", + "\n", + "While that's running, let's have a look at what's in the pipeline.\n", + "\n", + "First, we can use ceci to build a flow-chart showing the pipeline stages:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3218ac13-3d30-4bc9-bd8d-25d40a3eeddd", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "TXPipe-2023-Jul-12", + "language": "python", + "name": "txpipe-2023-jul-12" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 1d82e909335fb6ed230d7380fbf4b2213c277f85 Mon Sep 17 00:00:00 2001 From: Marina Ricci Date: Thu, 20 Jul 2023 20:22:58 +0200 Subject: [PATCH 03/22] added placeholders --- examples/cosmodc2/config-20deg2-CL.yml | 23 ++++++++++++++++++++--- examples/cosmodc2/pipeline-1deg2-CL.yml | 12 ++++++------ examples/cosmodc2/pipeline-20deg2-CL.yml | 2 +- notebooks/Run_CL_pipeline.ipynb | 8 +------- 4 files changed, 28 insertions(+), 17 deletions(-) diff --git a/examples/cosmodc2/config-20deg2-CL.yml b/examples/cosmodc2/config-20deg2-CL.yml index 6a712f301..1dc86e808 100644 --- a/examples/cosmodc2/config-20deg2-CL.yml +++ b/examples/cosmodc2/config-20deg2-CL.yml @@ -63,10 +63,27 @@ BPZ_lite: mag_i: 28.62 mag_z: 27.98 -CLClusterShearCatalogs: - max_radius: 5.0 # Mpc - delta_z: 0.2 # redshift buffer +CLClusterBinning: + z_min : 0 + z_max : 1.2 + lambda_min : 5 + lambda_max : 1e3 CLClusterShearCatalogs: max_radius: 5.0 # Mpc delta_z: 0.2 # redshift buffer + +CLClusterEnsembleProfiles: + #radial bin definition + r_min : 0.2 #in Mpc + r_max : 5.0 #in Mpc + #type of profile + delta_sigma_profile : True + shear_profile : False + magnification_profile : False + #stacking step or not + stack_profile : True + + + + diff --git a/examples/cosmodc2/pipeline-1deg2-CL.yml b/examples/cosmodc2/pipeline-1deg2-CL.yml index d4c2caf59..5fb6395b8 100644 --- a/examples/cosmodc2/pipeline-1deg2-CL.yml +++ b/examples/cosmodc2/pipeline-1deg2-CL.yml @@ -20,14 +20,14 @@ stages: nprocess: 1 - name: BPZ_lite nprocess: 1 - - name: CLClusterBinning - nprocess: 1 +# - name: CLClusterBinning +# nprocess: 1 - name: CLClusterShearCatalogs nprocess: 1 - - name: CLClusterEnsembleProfiles - nprocess: 1 - - name: CLClusterDataVector - nprocess: 1 +# - name: CLClusterEnsembleProfiles +# nprocess: 1 +# - name: CLClusterDataVector +# nprocess: 1 diff --git a/examples/cosmodc2/pipeline-20deg2-CL.yml b/examples/cosmodc2/pipeline-20deg2-CL.yml index 4effa00e7..caa3e8972 100644 --- a/examples/cosmodc2/pipeline-20deg2-CL.yml +++ b/examples/cosmodc2/pipeline-20deg2-CL.yml @@ -45,5 +45,5 @@ inputs: resume: True log_dir: data/cosmodc2/logs -pipeline_log: data/cosmodc2/log.txt +pipeline_log: data/cosmodc2/log_20deg2.txt diff --git a/notebooks/Run_CL_pipeline.ipynb b/notebooks/Run_CL_pipeline.ipynb index affedc086..9fc0d098c 100644 --- a/notebooks/Run_CL_pipeline.ipynb +++ b/notebooks/Run_CL_pipeline.ipynb @@ -85,13 +85,7 @@ "```\n", "sbatch examples/cosmodc2/1deg2-in2p3.sub\n", "```\n", - "to set it running.\n", - "\n", - "## Investigating our pipeline\n", - "\n", - "While that's running, let's have a look at what's in the pipeline.\n", - "\n", - "First, we can use ceci to build a flow-chart showing the pipeline stages:" + "to set it running." ] }, { From b4f2099afd91ef52540e40d29bba98232e66c68d Mon Sep 17 00:00:00 2001 From: Marina Ricci Date: Thu, 20 Jul 2023 21:32:03 +0200 Subject: [PATCH 04/22] Make cl related import explicit --- txpipe/__init__.py | 2 +- txpipe/extensions/__init__.py | 2 +- txpipe/extensions/clmm/__init__.py | 6 +++--- 3 files changed, 5 insertions(+), 5 deletions(-) diff --git a/txpipe/__init__.py b/txpipe/__init__.py index 6c367d4e3..c2cfe7e25 100755 --- a/txpipe/__init__.py +++ b/txpipe/__init__.py @@ -40,5 +40,5 @@ # Here are the stages that mostly will be used for other projects # such as the self-calibration of Intrinsic alignment. from .extensions.twopoint_scia import TXSelfCalibrationIA -from .extensions.clmm import TXTwoPointRLens +from .extensions.clmm.select import CLClusterShearCatalogs from .covariance_nmt import TXFourierNamasterCovariance, TXRealNamasterCovariance diff --git a/txpipe/extensions/__init__.py b/txpipe/extensions/__init__.py index f92e86879..062134ff4 100644 --- a/txpipe/extensions/__init__.py +++ b/txpipe/extensions/__init__.py @@ -1 +1 @@ -from .clmm import * +#from .clmm import * diff --git a/txpipe/extensions/clmm/__init__.py b/txpipe/extensions/clmm/__init__.py index b7900fdcb..95696f585 100644 --- a/txpipe/extensions/clmm/__init__.py +++ b/txpipe/extensions/clmm/__init__.py @@ -1,3 +1,3 @@ -from .ingest import * -from .select import * -from .rlens import TXTwoPointRLens +#from .ingest import * +#from .select import CLClusterShearCatalogs +#from .rlens import TXTwoPointRLens From 4b80cb58576a5bdd4117c17b97dfb16980589b61 Mon Sep 17 00:00:00 2001 From: Marina Ricci Date: Fri, 21 Jul 2023 00:56:46 +0200 Subject: [PATCH 05/22] Initial cluster binning stage --- examples/cosmodc2/config-1deg2-CL.yml | 8 +- examples/cosmodc2/pipeline-1deg2-CL.yml | 4 +- notebooks/Run_CL_pipeline.ipynb | 431 +++++++++++++++++++++++- txpipe/__init__.py | 2 +- txpipe/extensions/clmm/select.py | 103 ++++++ 5 files changed, 535 insertions(+), 13 deletions(-) diff --git a/examples/cosmodc2/config-1deg2-CL.yml b/examples/cosmodc2/config-1deg2-CL.yml index 1dc86e808..a663900ea 100644 --- a/examples/cosmodc2/config-1deg2-CL.yml +++ b/examples/cosmodc2/config-1deg2-CL.yml @@ -63,11 +63,9 @@ BPZ_lite: mag_i: 28.62 mag_z: 27.98 -CLClusterBinning: - z_min : 0 - z_max : 1.2 - lambda_min : 5 - lambda_max : 1e3 +CLClusterBinningRedshiftRichness: + zedge : [0.2, 0.4, 0.6, 0.8, 1.0] + richedge : [5., 10., 20.] CLClusterShearCatalogs: max_radius: 5.0 # Mpc diff --git a/examples/cosmodc2/pipeline-1deg2-CL.yml b/examples/cosmodc2/pipeline-1deg2-CL.yml index 5fb6395b8..167f23692 100644 --- a/examples/cosmodc2/pipeline-1deg2-CL.yml +++ b/examples/cosmodc2/pipeline-1deg2-CL.yml @@ -20,8 +20,8 @@ stages: nprocess: 1 - name: BPZ_lite nprocess: 1 -# - name: CLClusterBinning -# nprocess: 1 + - name: CLClusterBinningRedshiftRichness + nprocess: 1 - name: CLClusterShearCatalogs nprocess: 1 # - name: CLClusterEnsembleProfiles diff --git a/notebooks/Run_CL_pipeline.ipynb b/notebooks/Run_CL_pipeline.ipynb index 9fc0d098c..628b0fbd3 100644 --- a/notebooks/Run_CL_pipeline.ipynb +++ b/notebooks/Run_CL_pipeline.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 2, "id": "d5eb6757-e79f-445c-a2c6-4d25af5f6f65", "metadata": {}, "outputs": [], @@ -49,7 +49,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 3, "id": "d0539f99-806e-481c-9a00-69716e216d74", "metadata": {}, "outputs": [ @@ -60,12 +60,12 @@ "#!/usr/bin/bash\n", "#SBATCH --time=01:00:00\n", "#SBATCH --partition=hpc\n", - "#SBATCH --ntasks=30\n", + "#SBATCH --ntasks=1\n", "#SBATCH --cpus-per-task=1\n", "#SBATCH --mem=128000\n", "\n", "source /pbs/throng/lsst/users/jzuntz/txpipe-environments/setup-txpipe\n", - "ceci examples/cosmodc2/pipeline-20deg2-clmm.yml\n" + "ceci examples/cosmodc2/pipeline-1deg2-CL.yml\n" ] } ], @@ -88,13 +88,434 @@ "to set it running." ] }, + { + "cell_type": "code", + "execution_count": 4, + "id": "5935f013-c29c-4446-b3e9-00d5de2b85cb", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Read the appropriate pipeline configuration, and ask for a flow-chart.\n", + "pipeline_file = \"examples/cosmodc2/pipeline-1deg2-CL.yml\"\n", + "flowchart_file = \"CL_pipeline.png\"\n", + "\n", + "\n", + "pipeline_config = ceci.Pipeline.build_config(\n", + " pipeline_file,\n", + " flow_chart=flowchart_file,\n", + " dry_run=True\n", + ")\n", + "\n", + "# Run the flow-chart pipeline\n", + "ceci.run_pipeline(pipeline_config)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "5b45238d-3568-4d6c-96d8-cf794680bf74", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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JkqSkZ7iVJEmSJCU9w60kSZIkKekZbiVJkiRJSc9wK0mSJElKeoZbSZIkSVLSM9xKkiRJkpKe4VaSJEmSlPQMt5IkSZKkpGe4lSRJkiQlPcOtJEmSJCnpGW4lSZIkSUnPcCtJkiRJSnqGW0mSJElS0jPcSpIkSZKSnuFWkiRJkpT0DLeSJEmSpKRnuJUkSZIkJb30qAvYXy1cuJC8vLzdHl+1ahXz5s3b6bEWLVpQs2bNiipNkiRJkpJOSiwWi0VdxP7o7LPP5p133om7XHp6OsuXL6dx48YVUJUkSZIkJSe7JUfk4osvjrtMamoqp59+usFWkiRJkuIw3EZkwIAB1KhRI+5yl19+eQVUI0mSJEnJzXAbkVq1anHuueeSkZFR7DIZGRmcc845FViVJEmSJCUnw22ELr30UnJycvb4XHp6OgMGDKBOnToVXJUkSZIkJR/DbYT69+9PvXr19vhcXl4el156aQVXJEmSJEnJyXAboYyMDAYOHLjHrsl16tShX79+EVQlSZIkScnHcBuxSy65ZLeuyRkZGVx00UVUr149oqokSZIkKbkYbiN28skn07Rp050ey8nJ4ZJLLomoIkmSJElKPobbiKWmpjJ48GCqVav2/WNNmjShd+/eEVYlSZIkScnFcFsJXHzxxWRnZwOhS/Jll11GWlpaxFVJkiRJUvJIicVisaiLELRt25YFCxYAMHbsWHr27BltQZIkSZKURGy5rSSuuOIKAFq3bm2wlSRJkqRSMtxWEoMGDQLgyiuvjLYQSZIkSUpC6VEXUCVlboG8HNi8HnKzYfvW8Pi2zZCXu/OyeTmQuYVDgSM7tuWidg3g01chvRrUqL37tmvXh9TUwudr1IaMalC7AaSklPtbkyRJkqTKyGtu92TzOli3AjasCr9vWR+C6tYNhbcb1sCmNeG57O2QuRlycyAna69f9qN10KfRPtSdmgbVa0K1GpBRHeo0hPoHQL0mULdhCMB1G4bH6zQItw2aQsNm4TbN/3VIkiRJSk77V7jNyoSVC2DlQli1ENYuCyF2zdLw+9plIczm5ey8Xlp6aC0lBWL5kJcXbpNJWnoIvxBqz80Fihz6lJQQeOs3gSYtoXFLaNAMDjgQmraGZgdD04OhfuNIypckSZKkklS9cLtqESyZCUtmwfJ5Icwumxse37K+cLmCsJefv3uYVdg3BWE4L7swB2dUD+H3wPbQ4pAQeA/qAK0OhYPah+7SkiRJklTBkjPc5uXC4hmw8LsQZBd9B/OmwvI5oYswQHpGaI3MzYEkfItJIT0DSA3XFRMLYfiAA6FNN2jTBVp2gtad4ZDDoWbdqKuVJEmSVIVV/nCbmw1LZ8OscTB7HHz3JcyfEq5tTUkJASsvN7TAqpJIgWrVQ9fn/B0DaNVvDJ16QaejocNRcHDX0PIrSZIkSWWg8oXbpbNh2hcw9XOYNBpWzA/XiKalF7bEKjmlpobjmJMd7tc7ALocD916Q9cToONRdmuWJEmStFeiDbexfJgzASZ/AlM+g6mfwqZ1kJYGpHot7P4gJSUE3tyc0Arf/ig44tQQdrufsufpkCRJkiRpFxUfbjeuDi2y4z6EL9+EjWtCuInl27VYQbUakJ0VWnrbdoPjz4Vjz4b2RzqXryRJkqQ9qphwO38KfPIyjHkLFkwLASUlNVwrK5UoJbTk5+VC3UZw3Nlw0oVwVF+7MEuSJEn6XvmF28UzYPTLMPK5MBVPerUdo+pK+yA9I3RXr1kPel8ApwyCI04rnLZIkiRJ0n6pbMPttk3w0XPw9j/C9DzpGQ4ApfJTcH7VaQD9roQfXgstO0ZdlSRJkqQIlE24XTgtBNr//Rtys8K8sjGvn1UFSssI0w51PwV+dAP0+oGtuZIkSdJ+ZN/C7XdfwtO/C6Md20qryiAtPYTcRgfC4D/AGT8J56YkSZKkKm3vwu2CqfDkLTD2vRAmHBhKlVFKKjRpBT+9F04eGO5LkiRJqpJKF263rId/3BCuq01Ldx5aVX6pqaGbfOsucOPT0OmYqCuSJEmSVA4SD7fjR8B9g2HzersfK/mkpgExuPR2uOS28M8ZSZIkSVVG/HCbmw2P3whvPQakOFCUkltqGrTtBre/Bge2i7oaSZIkSWWk5HCbuRn+eC5M/iwM0iNVBWkZUKsO3PMhdDw66mokSZIklYHiw21uNtzaL4yIbDdkVTVpaVC9Njz8JRzcNepqJEmSJO2j4sPt3/8fvDMsaUdCXpUNH62D1dnQpxF0rRN1Rap00tKhUQt4YirUqhd1NZIkSZL2wZ7D7dTP4Dcnh1FmK9hH6+ClFfDU0nD/jAPg8gPhkuaJb2PmVrhpNvz9ULh+Bry3BtacAvUq6RhCH66FF5bDs8vD/WPrQ6002JYH+cCgZnB1S6iTFp5/dw08sAA+WQ8ZKXBiQ8jOh7wYtK8F1xwEJzUMyy7aDgd/BjVToUsdaJwBKUVe+6uNsCEXftYShnaOX2tuDL7ZBB+sgRMaQL8DwuMfr4PfzYGXukGbmmW0YypCWgb88Br4+WNRVyJJkiRpH+w53N50Wgi4EbbaNhkNa3Jg0UnQqkbp1j1/EnSqDX9uDxtzQxgsTTiOQgyo9zFsyYO8PpC6I4G+sBwunwa9G8CHR4UwCzBxM/T4KgThMTtmt1mWBZdPDUHz8S7w04NgzEa4YSa82yME26K+3ggnfAMtqsPU46B+AuF/zEZ4Ygn8axk82QWuOig8/voq+MUM+PDInVvJl2eF7VdqaRnwwsLQiitJkiQpKaXu9simtTD5k8i7IxcErYYZJS+3qxihpbZg/frplT/YQmhNLag5tUjT6qUt4IKmMHo9fLGh8PGCVtz0IsseWB2e6hr2wc2zw+2S7XBnu92DbWZ+CMJ5sRBSEwm2AMfVh+tb7/74+U1hae+dg+2mXLhsamLbjVY+fPlW1EVIkiRJ2ge7h9v5kyvFdD8pO0JbSsmL7WZTLmyPvvy9klLMm+1YO9zO2xZ/2YNrQI1U2JADW/PgyHrQu+Huy/12NszaBlcfFLp+l0a1BA7K1jy4cDLMzSzdtqORAnMmRF2EJEmSpH2we7jdtjmCMuKbuBlumgWHfA7rc+DKadB4NBzzNczbEaCeWQb/b0b4/dWVcPV3cN+Cwm28tjJcg3vjLOg/Hn4/B7J2BOGlWXDvAjhsDKzIhn7jw7Wqa3Ng2pZwPWmnL8I1rL+bA60/g65fwqh1IUz/aia0+xxafQYfrC3b9/7VhtCa27N+/GUXbw/19KgXWnfb1QzX2xY1ej08sgha14C/dCybGldnw2OLQ1dngP+ugulbYU12OA5/WRgejwH/XALXTodeY8N+nr2t2M1WjPw82Lox4iIkSZIk7Yvdw22jytmHt3n1EHDnZ8Jv58DNbeClw2HmthBSAa44EB7qFH7/UVN4ogvc0ibcf3AhPLQI/tYpBLoXuoUA3G98CFxTtoRwPGMrPL4kDOLUvHoIv02rhe69s7bBnfNCN+Fpx0GjDPjpdyF0X9MSJh0HHWrBz6fv23udsTUE6o/WwRVTYewmeOxQ6BZnxOfV2XDNdyHM3tN+z8tszoUfTwu/P9W1bAbZ+mIDnD85/ONgSVZ4bHAL6F4XGlcLx+HGg8Pj9y0I9Q3tDF/2DPX0/iYMnhWZtAxofFCEBUiSJEnaV7uH23Y9oEbtCEopWfNqhS2Xd7eHLrXDFD8nNYBxm0ped1U23D43jAhcMCDTARnwu7bw6Xp4fjn0PyCM/psXC9e5/uQg+PqYcB1rk2ph4CaAX7YOXX3rpsN5TUOr8VUHQefaoaX0nCbhsdXZe/9e718QQvSNs+D5FXBuk8LX39WULdBnHBw3Fo7/JtQ16ujCUYx39ZtZsCAz7Is+jfa+xqJOaAC3t42/3LIseGghXLZj3Ka0FLigWWgpH766bGrZK7k5cPjJERYgSZIkaV/t3m6XngH9r4LhQyEvJ4KSipe2I5gWHUSpbnpo/SvJVxvDNaC7jrr8wybhdvT6ELgyUsK22+1hKpuC1y7634C6O/ZeRpF6CgZ6WpMTQvHeeLpr4e/jN8F5k+DFFfBGdzi7yc7LdqsDHx2V2HbfXwNPLA1T9dxfRt2RC9RKi7/MlxsgJwZDdmnZ/ulBUDOB9ctNw2Zw9BkRFiBJkiRpX+25U+qgW+HDf0NmbiRz3Za1hdvD7bpdsnrjjBDKlm3fu+3uaVylgsfyy2i3HVkvBNGLJodW113DbaLW54Qu1CnA010KQ3hFmr4VaqeFbsqVRwr89D7IqOzzFUmSJEkqye7dkiHM93njvyq4lPLTdkeL7bxiRu7tVPl6Ye/kqHrhds620PK5N66fGboF/7wVnLqH7siztoU5dstTrbRw7fKSPfwzYU0UnQTS0uGk86Hv5RG8uCRJkqSytOdwC3DieTDkb6Wfi6eMFDQYlzbL7Wn5YxuELsRvrtr58aVZYSCjc/ayNbSsFddIPnNruG1fq7ALdH4p9s/rq+CF5XBITbi3w56X+evCxKb4KY1UIKfItEzd6oR6b5m983KrsuFfS8v2teNKS4fOx8Itz1XwC0uSJEkqDyWPlXveDVC7Pjz4UyAlTJlSQTbteKn1OYVdaLN3BKXcIoluax5k5ofQlELh9bdFR99tnBFGD75+BoxcB6fvaLl8ZFG41va0HfdzYmFAqZzYztfRAmTHdn/tgmmEMosEuIKW1axSzrUbAzbmFr6n2jve84JMuGFm+P2uIiMgr9/R0rlrV+tdrcoO0+6kAP/qWrjdoj5cCyPXQrXi/9Wxk4I6i+6Lgn2wvch+P7A6vJsdRrnemBsGnupZD/6zIkxXNKBpaI3+cgO8eHhir10mUlOhZ3/4/ctQrUb85SVJkiRVeml33HHHHSUu0b4HHHUGjPsQtm+DWClTWymNXh9GC/5kfbg/cxtkpIaQ9sDCEOa25MEx9UJL7D+WwKZcSEkJIfj+BTBpS5iSpkm1MJdrjVQ4pn6YmubhRTB2YxhkqlFGuJ41hTBg098Xw+a8EJBb1ywcEGrsRnhgQdjm5rywnbmZ4bWWZIV6utWFhTseW7QdtubDEXWhYUb89/zxOrhnfqgJwvt6ZzU8uhiGLYHOdcK0PWc2Ds9/sBb+MDdcS7wmJ4ThptXC1EW7+sk0+GYT1E8Ptb64ovDnuWXw5/nw4CI4vC5ceWD8Widuhj8vCNMVbcwLrcEFx2bWtnB8OtUOg3e1rhlGQX5zVRjt+Yi6cH6z0GI+ej38by00yYDHOkOzvRx8q1TS0sOEwZfeDr8Y6nW2kiRJUhWSEoslOGJU5mb4+y/CQFOpaRXaiivts9R0OKhdaK09pHvU1UiSJEkqY4mH2wLffADDfgOLp0NKqiE3jpafxu+i/Oxhha2yUUqmWhOWng4ZNWDQb+H8X9sNWZIkSaqiSh9uIXRNHvUSPP07WL0YiFWJKYNUhaRnhH++nP8rGHgz1GkYdUWSJEmSytHehdsCuTkw4hl442+wcDqkV4Pc7DIsTyqFlB0jYtWsAz8YEoJtoxbR1iRJkiSpQuxbuC1q6mfw5qPwxRtAKuRFMXGp9kvpGeEfLW0PDyN8nzIIqteMuipJkiRJFajswm2Bdcvhw2dg5POwcBpk7GjNtdeyylJBoK3TEE69GPpeDof2iroqSZIkSREp+3Bb1MoF8OVb8P6TsGBqCCR5uV6fq72TUR1ys6BGXTjhXOg9EI7uH84rSZIkSfu18g23RS2cBl+/C2Pfg2lfQF7ejtY3r9FVMVLTwm1+HjRvC8cPgJ79ofupBlpJkiRJO6m4cFvU9q0w8WP49n8wZjisXgQpKZBm2N2vpaaF8yAvF2rUhiP7wjFnwdFnQNPWUVcnSZIkqRKLJtzuas3S0Jo77QuYNAoWfhda69Kr7ejGHGfyVSWn9Gph4LFYDBo1Dy2yXU+Aw06ENocVttxKkiRJUhyVI9zuKnMLzPgavvsSZn0Ls8bB2qXhuYxqIQzlOhpz8kiB9HTIzQVioVW2XXfodAx0PjYE2sYtoy5SkiRJUkQRx98AACAASURBVBKrnOF2T7ashzkTYO5EmDMeZoyFFfNDyy6EVsBYfuF9VbyU1MJRjAta2xs2g/ZHQoejoH0PaHcEtDgk2jolSZIkVTnJE273JC83BNzFM8LPklkwf0r4fevGsEwKkFYt/O71vPsuLT10Fy4aYDOqQfNDoG03aHXojp9O0LIT1KwTbb2SJEmS9gvJHW5LsnldCL4rF8KqhbBiASyfB8vmwOrFYVCrAimpIbSRAvm54Xrf/U3BgF4FAzoV3Qdp6dCoBRzYDlq0g2YHQ7M20LwNND0YmrQM+1CSJEmSIlJ1w208WzaE63g3rIK1y3bcLocNK8Pt6kWwcTVs3bTnrs6paYWj+0JoxYzFou0WnZpapKYddeXnFV9TrbpQr3HoOtz04HDboCkccGC4bdQ8hNpGLQrfpyRJkiRVQvtvuC2N7VtDGN6yfsfPBti8vvB+9vbQDTo3B7Ztgu3bIGsLbNkUukJv3RC2E4vBts1AbPftx2KMXAeH1YFm1YBqNSFtl9GCq9cM1xYDVK8FGdWhbsPwWN1GUK1GWK9W3fBcnQZQp2H4qdtw5/t1GpT3XpMkSZKkCmO4rUTS0tJ44YUXGDRoUNSlSJIkSVJS8UJJSZIkSVLSM9xKkiRJkpKe4VaSJEmSlPQMt5IkSZKkpGe4lSRJkiQlPcOtJEmSJCnpGW4lSZIkSUnPcCtJkiRJSnqGW0mSJElS0jPcSpIkSZKSnuFWkiRJkpT0DLeSJEmSpKRnuJUkSZIkJT3DrSRJkiQp6RluJUmSJElJz3ArSZIkSUp6hltJkiRJUtIz3EqSJEmSkp7hVpIkSZKU9Ay3kiRJkqSkZ7iVJEmSJCU9w60kSZIkKekZbiVJkiRJSc9wK0mSJElKeoZbSZIkSVLSM9xKkiRJkpKe4VaSJEmSlPQMt5IkSZKkpGe4lSRJkiQlPcOtJEmSJCnppUddwP5q4cKF5OXl7fb4qlWrmDdv3k6PtWjRgpo1a1ZUaZIkSZKUdFJisVgs6iL2R2effTbvvPNO3OXS09NZvnw5jRs3roCqJEmSJCk52S05IhdffHHcZVJTUzn99NMNtpIkSZIUh+E2IgMGDKBGjRpxl7v88ssroBpJkiRJSm6G24jUqlWLc889l4yMjGKXycjI4JxzzqnAqiRJkiQpORluI3TppZeSk5Ozx+fS09MZMGAAderUqeCqJEmSJCn5GG4j1L9/f+rVq7fH5/Ly8rj00ksruCJJkiRJSk6G2whlZGQwcODAPXZNrlOnDv369YugKkmSJElKPobbiF1yySW7dU3OyMjgoosuonr16hFVJUmSJEnJxXAbsZNPPpmmTZvu9FhOTg6XXHJJRBVJkiRJUvIx3EYsNTWVwYMHU61ate8fa9KkCb17946wKkmSJElKLobbSuDiiy8mOzsbCF2SL7vsMtLS0iKuSpIkSZKSR0osFotFXYSgbdu2LFiwAICxY8fSs2fPaAuSJEmSpCRiy20lccUVVwDQunVrg60kSZIklZLhtpIYNGgQAFdeeWW0hUiSJElSEkqPuoBdLduay6bsvKjLqHjN2tKlew+OOfNHzFifFXU1kTi0YTlNfZT1HWRPLZ9tq/KofQak1o+6ivi2fgj5G6KuQskotR7U7h91FZIkVVqV7prbtxZsZvp+Gu7mfP0J7XudHHUZkbnliMakpJTDhtfcCWv+WA4bVqXSdhJUPzzqKuKb1xmyZ0RdhZJRxiHQbm7UVUiSVGnZLbkS2Z+DbblLKadWYanU9sOeKSoj+VEXIElSpWa4lSRJkiQlPcOtJEmSJCnpGW4lSZIkSUnPcCtJkiRJSnqGW0mSJElS0jPcSpIkSZKSnuFWkiRJkpT0DLeSJEmSpKRnuJUkSZIkJT3DrSRJkiQp6RluJUmSJElJz3ArSZIkSUp6hltJkiRJUtIz3EqSJEmSkp7hVpIkSZKU9Ay3kiRJkqSkZ7iVJEmSJCW9/SrcZm/bGnUJScn9trst26KuQCofntvxuY8kSaqc9otwO/G913j6ugv5y4BeUZeSVL554zmeuvYC/nb+8Qktn5+Xy6LJ3/DRP+9j9phR5VxdNF4YDv2ugo79o66k8sjNgzET4Y+PwodfRF2NdlXc8fn4Kzj2IliwNNz/+3/g1CvCYxVl1xoqu0T2kZ8HSZKis1+E28P7/4i83Bzyc3PKZHub16wsk+1UdkcNuITcrO3k5+YmtPySaRMY+8ZzjHz8L2xcuaycq4vGoLMgJzf8VGXLVye+7DdT4IlX4c5/wOIV5VeT9k5xx2f9pnB/a2a4P+QiWLsB8vLLr5Zdz6tda4hCac71RPaRnwdJkqKzX4Tb1NQ06jc9sEy2lbV1My/fdl2ZbKuyS01No36zxPdb68N7cvygq8uxouilpUHL5lFXUb42bYHLbk58+eOOgOsHl1892jfFHZ/z+8HST6Br+3A/PQ0OalZ+dezpvNq1hopW2nM9kX3k50GSpOjsF+G2rGRnbuOFm69i3ZL5UZdSaaVlZERdgvbB1ky48AaYu7h061XzsFdqUR+fvT2vylN51hT1/pYkaX+VHnUB+2rVvJlMfP81pn38HlcNfY0377mZBRO+4oBWbTn75j/TutvROy2/ee0q3rz7RuaPH0PDA1tx0V1DaXpIp++fn/rRcOaN+5L0atVYOWc6B3XpzmlX30R6tWpMG/Uuq+bNInPzBt74069ocnB7Trr853HXA/i/3u3oetpZ1KhTH4CZn49gzaJ5XHTXUI4464K473Pl3BlMfP91po4czlVDX+Pr155hwruvUr12Hc695V5ad+/JB4/8iemffEBebg7n3f4gHY8/badtxKsR4LvR7zPj8xHUrNuA3KxMNu3SBTsWizH29WdYPmsay2ZMpkadepxz6300bn1IKY5a5TdpBjz4DHRpD8tXQVYO/OMPOy+zYg387A749BtocxA8/wB0aReei8Vg2MswaSaM/w7q14G//wE6HByeX7kWbn8YWrWARctgzXp48i44oAEsXQnPvQ3Pvw0f/QsuvwVmzofxb4TnE/Hep/DOaMhIh7GT4Sfnw9UXxn/t/46A6XNDd9Grb4dObeHGn5S8Tkle+x988g1UrwZTZ8NRXeEP14X7Bb6dGvbV1kyYswiuOh+uuiC0kqnQ3h7T4qxeBy+/Dz27Qa/Dd35u7GS47eFwbHp2g6F/hENalXxu5uaV/rwqroaSzpuJM8K1769/CONeh1/dE/bLIa3gpb+G20Ts67le3D4qTry/CZIkad8lfbjdtnE900a9z5qFc/nsuX9w4qU/46izL+KNP/2GJ4ecx01vf0PdxqEfWU7WdkY//RD9f3E7ebm5DPvJD3nvoTu48pEXAfj8hX8ydeRwrn78TdLSM9i2cR1DrziTBRO+5uon3qLHWRcy+X9vsnLudM67/cHva4i3XkpKCidd/nNO++mvAVi3dCFfv/Zv2hzRi+5nnp/Q+6zTqAkbVy5jzcK5jHz8Lxw38Cec8uNf8u/rB/H6nTfQ6cS+HDvwx/S77rc886vBvHXPzdw0/NtS1Tjx/dcZ89KTXP3Em6RXq862jev423nHk5pamDI++fcj1GvcjAG/e4D8/DyG/fiHPH7V2dw0/FsyatTc5+NZWQz6DTz5JzjhSMjOgQt+ufPzmdvhnsfh3l+H629PGgw33Q/vDgvP3/cktGgSvvDm5cGJl0LvwTB3BNSqAYN+Dc0OgNuvDcsf8SO44R547j6YMgueeRNmL4THXwnX+Q57BbKyE6v9ubfh/U/h+fshNRX+PAyu+QO0awWnHVvyaw8+J4SNqbPhiT8V2R8lrFOcB58JAWTUMyGQrd0Axw2Cz8fB6GchJQUWLoNTLoepw8M/CK64NfzD4IlX4aSj4MHfJvaeq7p9OaZ78sV4+O3f4LNx8NrDOwfLNevD6/3mxzBvMdx4fzi/Z31Q8rl56U2lO6+KqyHeedO8MUycDvOXhPVv/mnY/vm/gN8/BC/+NbF9ui/nekn7qHYxfwbj/U2QJEn7Lum7JbfpcSytDjsSUlLo/8s/cMjRJ9D1tB9y7u/uJ2d7Jl+/9u/vl01NS+esX/0fTdp0oHn7zrTv1Ztl0ycDsGXdGkb84x56XXAlaemhT1mt+o045Se/Yv74MUx879U9vn4i68ViMY46u3B4zbfvu5X8vDwG/O4BUlJSEnqftRseQOtuRwFwwiVDOPDQw6leuw5dT/8h65YupOeAS2natiPVatWmy8n9Wbd0IVvXr024xpztmbz34B85/pJrSK9W/ftl2vQ47vsaNq1ewRf/GUaPHwwM+zM1jcP6nM3mtauY/un/EnofySAnF2bMgwnTw/1qGaGVrKj0dPjLzXDoIdCtI/Q5LrTGACxbBQ89A5edE+6npcEFZ4SW3uFFBpHufmjh74d1gMkzw+/9T4ITeoQvwJf+MLz21y/DgU3j1756HVx/F9x9QwhBEFr3zusbvljHe+2SlGadVTtav352UQgoEFq+fjcEPv0Wnh8eHnvsBWhUPwRbgNt2BIprBhpsC5THMT3hSLi9mKEDqmXAo7fBGSfCtRfDr38czumnXot/bu5rDYmcN80bh5ZSCPukS7vw+TvpKBg3rfjXK41476OkfbQnif5NkCRJ+ybpW24BUtPSSE1L+z64AXQ99SzSMqqxYvb07x9LS0/faZkadeuTuXkjAIunfEt25jYaNDtop20f2rsfAPO+/eL7UFdUouvV3/H8tFHvMvPzj+h9xfU0a9+5VO8zJS20oBYNxNVr1Qn7IL3wUFarWRuArRvWUrvhAQnVWLtREzavWUnzdofutEx6kWtoF036hvzcXP579292WqbnjwaTUb3qND1kpEPf4+GXf4bv5sKffwUDTt99mYwin54GdWHD5vD7lxNCQB7yx53X+ekFUDP834BRz4TbrZmhi+c3UyC/yAisGRmhW2671qWr/fPxYTttWxY+1qQRvP5I4f14r70npV3nq0lh2VYtdn78h6eE29Fjwxf9pSth2/bC5zscDI0bOspsUeV1TItrLaxXZ+f7P/4R3DW0MDgWd26WRQ2Jnjc7/hTu1HW9bm3YXAZTcifyPuLto10l8jdBkiTtuyoRbvckLT2Dek2akZ9X/JwtRUPi+uVLANi2af1Oy9Ru0IiMGjXZtHrP37ZLs1525jbeeeA2GjRvyenX3Fi6N5TAe9j1sdiOb2SJ1Lh6/mwAUtOLHwll1fxZZNSotVOX7Krq5QdD98ShL4Yukq8+BL2PLn75oodh+rzQNbFoV8dd5eWFbopzF8OvrwwB5quJ+1731NnhS3QstnNN+/rapV1n4Y6ZoNZt3Pnxxg1DoFm2Ktw/40R48V0Y+RWcfmz4B8GWbdD/xETe7f6hvI5potocFFoqM7NKXq4sakj0vClPe/M+4u2jRP4mSJKkfVdlwy1AXk4OjdskNsdEo4NCM8S6JQv3+HyTYrZTmvU+fuKvbFixhMv+9izVatZKqK6ykEiNBaMcb1i+uNj3mlGjJptWLWPjymW7TRG0dcM6ajdoVIZVR6t2Tfjfk6Hl5jf3Q7+rYOJ/QzfkeGrVgCUrYcmK3acNWrM+dMM9a0jobvmfv5Rt3fVqw/as0OK86/Qq2Tmhpau0r52fX/p1CloZ5xUzEm2ntuH2igFhntHLbwldXJetCtdMnnBkYq+zPyiPY1oaqanhNQ7rUPwye3OO7Emi50152dv3EW8fxfub0Ljh3tcsSZIKJf01t8XZsm4Nm9euolufsxNavlW3o6leuw7fjXpvp8c3rVpOzvZMOp/cH4CU1FTycnNLvd6qeTP5/PmhdO59Bl1OOfP75WaPKf8LrhKpsUWHrgBM+ejtnZaJ5eeTn58HQPP2nYnFYnzwyJ07LbNl3RrGvfWfcnwHFSsrO7TYQhh05quXwpfej79ObP1uHUMr2y27DGyzai386w0YOwU+/AJOL7ycmZwciJVB7UcfFm5vf3jnrpRzFsEr7yf22qmpoaWwwN7Ue2z30E30zZE7P17QDfmcU8P97VlhwKBJb8KffgFP3bV7F/D9XVkc032xYGk4Hwb2L36ZvTmv9iTR86aslMW5DvH3Uby/CZIkqWxUmZbbvJxsVsyZTvMd17GOeupv9DjrQlodFgZhyt6eSfb2zJ3WydmeSV5uDrFYjNoNGnHG9bcx/L7fMnfsp7Q7pjcAX7z4BD1+MJB2PU8CoF6T5sz8bATLZ05l+5ZNtOzaI6H13rr3FlLT0zn75nsKa87NYfZXo+lwXGLf2PJycgDIz8v7/rHc7NAPLier8MLFvNyc7/cJkPB7a3vU8Yx760UO6tydI394ESvnzmDBxK/Zun4tE99/nS6nnEnLrj2Y+P7r5GRl0fXUM1m7eD4LJ33DoHseB2D7lnDhaV4J3cGTwVOvw/+7NFzb17I51K8LPXZcIr0tM/wUtW17aEWLxcL1uj27wX/eCeFtQB+YszBcd/fiXwvn1Xzmv3BMt3Cd3ndzYeWaMHBNs8bhC3VefvjCnFGKT+kJR8KZveG/H8HpP4bz+8Gi5TBrQRiRtuCawJJe+8Cm8O4nYcqVjZsLA1VJ62zccb1xwf99GjeEe34dBkIq6HIM8Mhz4ZrJ03bcv3sYjBwTRv1t3gTq1IJWzcu/hS6ZlMUx3fX4QGEX2u1FutKmpYbrVvPywrkfi8GfhsIff17Ya2FP52ZBd+nSnFc9u+1eQ6LnTfaOkcNzC/8UsjUzbK+k7tu72ptzPZF9tOv+jvc3QZIklY20O+64446oiyhq5oZs1mzPi79gEdM//R8rZk8nO3MbE957ldljRlG3cTN+8Os7SUlJYfonH/Dli0+QtXUzqWnpHNipG7O+HMkX/xlGduY2UlJTOfjwo2l9eE9adOzKFy8+zuKp41k05Vtq1WvAmTf88fvrWBs0b8n0T//Hd6Pfo3W3o2nRsSutuh5Z4nqTR7zFZ8/+ncatDiFzy0ZmfzWa70a9x4ih91KvSQs6ndgn7ntcPHU8nz3zGBtXLSNr2xZadDyMdYvn88kzj7Fx5TKyt22lefsubFixhE///SgbViwlO3MbB3bqRs16DeLWCND1tB+wec1Kvvnvc3z92jNUr1Wbuo2b0bxDFw7u3pPGrQ/hsD5ns2n1cuZ/+zmzxoyidsPGnHPrvdQ9oAnLZ05l1NMPsXrBbLZv2cQBLdvQ8MAEJ50ETmxeK+EvpaWy7RPI/AxI7LzKywvTfAwfFVqMnh8Olw+Ac0+Dtz8OX7Q3bQndEHt0gQ8+g4eeha3bQkvQ8T1g4Jlh3dFj4X+fQ5OG8Njt4ctxy2ZhLs0RX8LXk+BHfeHUXmGuzkXLQ3B4/JXwBXrzVmh9YBhAKFHn9Q1zd345Mbx+u9bw2G2ha2S81x54Zgiaw0eF1rNju8MPTi55nY5t4C9Pw4z54Uv9Ia3CNYjHdAujzj78bJgT9KuJoUv2/TcVho8U4Ok34JUP4OX34Lm3wgjKw0eF63Eb1CvFcW54LaQ3K8UKEVn/KOStK9Uq+3JM93R8Vq2FB54OAXndxvDPhFbNQ7faRcvhX/8NoxOP/CqMRDxkx2DvL74Lf//P7ufm3pxXWdl7riHeeTPq67Deuo3h+uxjusGbH8E/Xgyfy5SUMHJyagL9klq3KN25PvBMOLJLyfto4owwVdOun4fz+xX/NyFhaQ2g0Q2lWEGSpP1LSiwWK6vea2XirQWbmb4+zsglu3jjT79iwruv8qevlpRTVaoItxzRuHzC7Zo7Ye2fIVa680rl7x8vwkFNod8JIVRs2RZC09eTYcVquPc38bfxvbaToPrh8ZeL2ryOkD076iqUjDLaQLv5UVchSVKlVWW6JSeze/ofTl52ycHrwj/9g04neDHi/qzlKaG1qyTP3hu6sCaDcdPg7n/C0k/C/TZFZqrq1DZ04ZT2RlX7rEiSpMRUiXCbm51Nfm4usVhsj1PjVHa//WBy1CUoCSwZHXUFZWvq7DA68p+HweXnhu6Z6zeGuU5HfAn3l81sWdoPVbXPiiRJSkzSj5b88ZN/47vR75Ofn8f7D93B4qnjoy5JUgIGnw23XwuPPg+tToVGveDs68K1lA//DmrWiLpCSZIkJZMqcc2tqgavud1/bdsONasnPsrtHnnNrao6r7mVJKlEVaJbsqTkVstWWkmSJO2jpO+WLEmSJEmS4VaSJEmSlPQMt5IkSZKkpGe4lSRJkiQlPcOtJEmSJCnpGW4lSZIkSUnPcCtJkiRJSnqGW0mSJElS0jPcaj+RF3UBkiRJksqR4baSWrNwLku+mxh1GVVHLLfcXyI/H179AGKxcn+p7z34DDzzZsW9Xnl5/BXYsi3qKipISkbUFVRaU2bB5JlRV1GJee5IklSilFisIr+KK1HXXnst06dPZ/To0VGXogQsWrSIK664gi+//JKvv/6aI444okJe9/DDD+ess87i3nvvrZDXKw9z5szh6KOPpnr16tx2220MGTKEatWqRV2WInDxxReTnZ3N66+/HnUpkiQpCdlyW0m1b9+e2bNnR12GEvDqq6/So0cPVq5cyZgxYyos2GZlZTFjxgx69OhRIa9XXtq3b8+8efP48Y9/zC233EKHDh14/PHHycuzK7kkSZISZ7itpNq3b8/y5cvZunVr1KWoGBs3buSyyy7joosu4oILLuCbb77hyCOPrLDXnzJlCjk5OUkfbgEaNWrEvffey6xZs+jfvz8///nPOfzww3n11VejLk2SJElJwnBbSXXo0IFYLMbcuXOjLkV7MHLkSA477DBGjBjB8OHDGTZsGLVr167QGiZMmECdOnVo3759hb5ueWrZsiXDhg1jypQpdO3alYsuuojjjz+eTz/9NOrSJEmSVMkZbiupdu3akZaWZtfkSmb79u3ceuut9OvXj169ejFt2jR+8IMfRFLLhAkT6N69O6mpVe9jfOihh/LKK68wZswYatSowcknn0zfvn2ZNGlS1KVJkiSpkqp634qriOrVq9OyZUvmzJkTdSnaYdq0aRx33HEMHTqUoUOH8tprr3HAAQdEVs+ECROqRJfkkvTq1YuPP/6YESNGsHbtWo488kgGDhzI/Pnzoy5NkiRJlYzhthJzUKnKIRaL8fDDD3PUUUdRo0YNxo0bxzXXXBNpTfn5+UydOrXCBq+KWp8+fRg3bhwvvfQS48eP59BDD2XIkCGsXLky6tIkSZJUSRhuK7EOHTrYchuxRYsWcfrpp3PjjTdy66238vnnn1eKa1xnzZrFli1b9ptwC5CSksKFF17I9OnTefTRR3n77bdp3749t956K5s2bYq6PEmSJEXMcFuJ2XIbrYIpflasWMHXX3/NHXfcQVpaWtRlAWGk5LS0NLp06RJ1KRUuIyODa665hjlz5nDbbbfxz3/+k3bt2nHfffeRlZUVdXmSJEmKiOG2EnM6oGhEPcVPIqZNm0a7du2oWbNm1KVEpnbt2txyyy3MnTuXq666ijvuuINOnTrx+OOPk5+fH3V5kiRJqmCG20rM6YAqXmWY4icR06ZNo2vXrlGXUSkccMAB38+Re8YZZ3DdddfRvXt358iVJEnazxhuKzGnA6o4lWmKn0RMnTrVcLuLVq1aMWzYMCZPnkznzp0ZOHAgJ554Ip9//nnUpUmSJKkCGG4rMacDqhiVbYqfeLKyspgzZ47hthhdunT5fo7c9PR0TjrpJPr27cuUKVOiLk2SJEnlyHBbyTmoVPmpjFP8JGLmzJnk5uZy2GGHRV1KpXbssccyevRoRowYwerVqzniiCMYOHAgCxYsiLo0SZIklQPDbSXndEDlo7JO8ZOIqVOnkp6eTocOHaIuJSn06dOH8ePH89JLLzFu3Dg6derEkCFDWL16ddSlSZIkqQwZbis5W27LXmWe4icR06ZNo2PHjlSvXj3qUpJGamrqTnPkvvXWW7Rr145bb72VzZs3R12eJEmSyoDhtpJzOqCykwxT/CTCkZL3XrVq1b6fI/f3v/89Q4cOpXPnzjz++OPk5uZGXZ4kSZL2geG2knM6oLKRLFP8JGLGjBl07tw56jKSWp06db6fI3fw4MH84he/oGvXrrz66qvEYrGoy5MkSdJeMNxWck4HtG+SbYqfeHJzc5k/fz4dO3aMupQqoXHjxtx7773MnDmTU045hYsvvphevXoxcuTIqEuTJElSKRluKzmnA9p7yTbFTyLmz59Pdna24baMHXzwwQwbNoxJkybRpk0b+vTpQ9++fRk3blzUpUmSJClBhtsk4KBSpZOsU/wkouA8cKTk8tG1a1deeeUVvvjiC7KysujZsycDBw708ydJkpQEDLdJwOmAEpfMU/wkYtasWTRr1owGDRpEXUqVdvzxx/Ppp5/y4YcfMnPmTLp27cqQIUNYtmxZ1KVJkiSpGIbbJGDLbWKSfYqfRMyePdtW2wrUp08fJkyYwAsvvMCIESPo0KEDt956Kxs2bIi6NEmSJO3CcJsEnA6oZFVlip9EzJo1y+ttK1jBHLkzZszgwQcf5F//+hft2rXjvvvuIzMzM+ryJEmStIPhNgk4HVDxqtIUP4mw5TY6BXPkzp07l5tvvpm7776bjh07OkeuJElSJWG4TQJOB7S7qjbFTyIyMzNZvHixLbcRKzpH7qWXXsr1119Pt27dnCNXkiQpYobbJOB0QDurilP8JGLOnDnk5+cbbiuJJk2afD9Hbu/evRk0aBDHHnsso0aNiro0SZKk/ZLhNkk4qFTVnuInEXPnziUlJYV27dpFXYqKaNOmDcOGDWPixIk0bdqU0047jb59+zJ+/PioS5MkSdqvGG6TxP4+HVBVn+InEQsWLKBZs2bUrFkz6lK0B926dWP48OGMGDGCDRs2cPTRRzNw4MD9+nMrSZJUkQy3SWJ/brndH6b4ScTChQtp06ZN1GUojj59+jB27FhefvllJk2aRJcuXRgyZAgrVqyIujRJkqQqzXCbJPbH6YD2pyl+ErFgwQIOPvjgSjmd0AAAIABJREFUqMtQAlJSUrjwwguZNm0ajz32GO+88w7t27fn1ltvZePGjVGXJ0mSVCWlR12AElMwHdCYMWPIyMhg9uzZzJkzhzlz5nD77bfTvXv3qEssUyNHjuTKK68kJyeH4cOHV/mRkBOxcOFC+vXrF3UZKoX09HSuueYaBg8ezKOPPsq9997Lk08+yU033cQvf/lLatSoEXWJkVm7du1uQX/Lli3k5OQwb968nR6vV68ejRs3rsjyJElSEkqJOXdFpTRhwgTGjRvHnDlzmD17NtOmTWP27Nnk5+cD4UtzSkoKubm5rF27loYNG0ZccdnYvn07d9xxBw888AA/+tGPGDZs2H4xEnIiGjVqxN133821114bdSnaS+vWreP+++/nkUceoXHjxtx2221cddVV+2U3+2effZYrrrgioWWffPJJrrrqqnKuSJIkJTvDbSU1dOhQrrvuOjIyMsjNzS12/syWLVuyePHiCq6ufEybNo3Bgwczb948Hnjggf1qJOR4Nm/eTL169Xj33Xc566yzoi5H+2jp0qXceeedPP3003To0IH/+7//48ILL4y6rAq1adMmmjRpQnZ2donLZWT8f/buOzyKsmvg8G9bOkkgIdTQW+i9KiiIAiIiCEgQC4rYe/9Efe34qrzYELGLIiKKIiAiIjZ6r6HX9EZ6su3748mSBJLsJiSZ3ey5r2uvbJmZPTM7M5kzTzORkJBQa27gCSGEEKL6SJtbN3X77bcTGRmJ1WotM7HV6/X079+/hiOret4+xI8rjh8/DiAdStUSTZo0Yd68eezZs4fOnTszadIkBgwYwLp167QOrcYEBwczevRoTCZTmdMYjUZGjBghia0QQgghXCLJrZsymUy8+OKLZSa2oC78evfuXYNRVT0Z4sc1juQ2MjJS20BElWrfvj3ffvstGzZsICAggMsuu4zhw4ezY8cOrUOrEVOmTMFisZT5udVq5cYbb6zBiIQQQgjhySS5dWM33ngjrVu3Rq8v/WcqKCjw6N6DZYgf1x0/fpzw8HDq1KmjdSiiGvTt25c1a9awevVqUlNT6dWrFxMnTrygY6XzrV+/ntzc3BqKsupdffXVBAYGlvm5n58fo0ePrsGIhBBCCOHJJLl1YwaDgZdffrnc0tsePXrUYESui4uLIy0trdTPZIifijtx4oQMA+QFrrjiCrZs2cI333zD9u3biYqKYsaMGSQkJFwwrdlsZvLkyUyYMKHc0k935uvry/XXX4+Pj88Fn5lMJsaPH09AQIAGkQkhhBDCE0ly6+YmTJhAp06dSi3RbNSokVsOj2Gz2ZgyZQozZsy44LM1a9bQuXNnVq9ezbJly5g3b165JTdCOXXqFM2aNdM6DFEDHGPk7tu3j3feeYdly5adGyM3IyPj3HTz58/n1KlT/PLLL0ybNq3cm2DuLDo6utROpcxmM9HR0RpEJIQQQghPJcmtm9PpdLz66qtYrdYS7+v1evr166dRVOV78803+eOPP1i8eDFfffUVoIb4efLJJ7nyyivp168fe/fulbFrKyA2NpYmTZpoHYaoQSaTiTvuuINDhw7xzDPPMG/ePFq3bs2sWbNITU3l2WefxWazYbVa+eqrr7jvvvu0DrlShg0bVupNupCQEK644goNIhJCCCGEp5Lk1gOMHj2aPn36YDQaz73nrp1Jbdu2jaeffhq73Y5Op2PGjBmsXr2aAQMGMHfuXObOnct3330nY9dWUFxcHI0aNdI6DKGBwMBAnnjiCQ4dOsTUqVN57rnnaNu2Lenp6eemsdlsvP/++8yaNUvDSCtHr9cTHR1domqyyWQiOjq63J6UhRBCCCHOJ8mth3jjjTdKtKtzx86ksrOzS4zVabfbKSgoYNy4cQQFBbFz504Z4qeS4uPjadiwodZhCA2Fh4fz1ltvsX79erKysi6ozWG323nqqaeYP3++RhFW3uTJk0tUTXa0JxZCCCGEqAhJbj3E4MGDGTx4cInSW3frTOree+/l5MmTJZJws9lMTk4Oo0ePljFaKykjI4Ps7GwpuRUAfPnll2W2r7Xb7dx5550sXry4hqO6OP379y/Rprxhw4YMGjRIw4iEEEII4YkkufUgs2bNOpc4hoeHu1VJ3pIlS/jss89K7bXVZrMxc+ZMdu7cqUFkni8uLg5AklvBiRMnePfddzGbzWVOY7fbiY6O5tdff63ByC7e1KlTMZlMmEwmpk6dWuYQaEIIIYQQZZGrBw/Sv39/Ro4cCeBWnUmdPn2aadOmodPpypzGbrczYcIEjx6TUyvx8fGAJLcCnn32WafT2O12bDYbY8eOZevWrTUQVdWYMmUKZrNZekkWQgghRKVJcuthXnnlFXQ6ndt0JmW1Wpk4cSK5ubnlDkVisVg4evQoTz/9dA1GVzvExcVhNBrdctgnUXP27t3LggULLmhrWxqbzYbZbGb48OEcPHiwBqK7eFFRUURFRdG6dWu6d++udThCCCGE8EBG55MITeXnQnY6ZKZBQS7dbWlcP+xSeoUa4Z8fwFpYDdhgBP86RfPVqQs+/hAUCkF1wde/WsJ75ZVX2LhxIzabrdzpfHx8KCgo4LPPPuPee++ldevW1RJPbRQXF0dERESpYx0L72Gz2XjllVc4ePAgu3fvJiYm5ty4t0ajEaPRSH5+/rmbTBaLhczMTIYOHcrGjRurdiiprDT112qBnEz1vCAXCvKKpinIU+ev8uRlgaWoivVNl/bAbLHCn8XaDBtN4BdU/nJ8/cHHr+i1j586/wH4B6llgDoXCiGEEKLW0tnLK24T1SvxJJw+CMmn1XPH37ijkJmqLhotBRfMdiAbgo3Q2LcC32X0gYA6UKceNGoFEc0gvGnR36bt1PMK2LRpEwMHDiy1JMlgMJyrHtm2bVvGjRvHFVdcweDBg0sM+SGce/zxx/n999/ZsmWL1qEIN5OUlMT+/fuJiYkhJiaGPbt2snfvPmLj40vccIpq1oS/X3+Eej56lXRmp4O5AHKzVIJpLoCMJJWM5uVATgbYrGDOL0pY83PVe9XoeC6Y7dA2oFq/BvSGoht+Pn5g8gWdHgJDwC9AfRYcrt73CwK/QPDxhcDQwvcCICC48LwarM6tgSHqERCsphdCCCFEjZPktiacTYaDW+DIdjixD47uhNjDRaUaeoMqebXZwFp2RzFVymAEnQFslqILVl9/aNQGWneDZlHQpge06wMhF1aHPXv2LJ07dyYuLu5ccms0GrFYLISEhDBq1ChGjRrFlVdeSURERM2sUy01depU0tPTWbZsmdahiJqQkVLskVz0PCtdJZ3ZZ1XJaWaa+puVpm6E5WWfO5bzbXAwB2KyISYH9mdDPR89szsaMZxrG28Hu13N46TmhdfS69X5WacDim03m12dO8v696k3qAQ3oI4qLQ4MheB6hc8LE+CgUAgOK/YIL3ouhBBCiEqRaslVzW6Dw9th5x9wYCPs/RdSzqjPTD7qQvL8kk6btdpLRC5gtQDn9WycnwvHd8PJferizFFqHNYEOg2EDv2g22XQpgczZszg9OnTAOj1enr16sWYMWMYMWIEPXv2lJ5Oq1B8fDwtW7bUOgxRWWeTIS0eUuMgJQ7SE9UjI0WVlqYlqGkyUiHn7IUJk95QlGCdS0bLP1/46qFLkHoUsZVaE0SUw2arXOJvs6obETkZkHym6P3ivyV29b/g/N9Sp4OAENW0JCQc6jWE4Poq6Q2NUI96DSGsMdRtACH1L2oVhRBCiNpEktuqEH8MtqyCbb/C9jWQnaHaeNlsJS9czB5yYXn+xXPKGfj7e/h3KVjMfJHiz9+HYdrwgYycEM0VE6YQGhqqXby1XHJyMn379tU6DHG+jBTVjCDlDKTGQ0qsSlSTT6nniachM6WoXTyokkCDEdAV3ui6cOisC2hx80tUD1d+S7tdVRvPTlf/W0DtM3oDKiG2lEy4DUaoEwb1m0B4E9XMpG5h8luvobo5GdFMSoSFEEJ4BUluKyvuKGxYBmsWwKGtqr2WjqJSWUsNVS+uKcUuygYH5HJ6gAH0m2Dxv7B+DlweDf2vgba9NA609klJSaFevXpah+F9stLUce54pMSqdvGnYiDhuKoG7KDXq3MAlJ+w2mxg85CbXMJ9WC1l71dWC6QnqMehbWD0Vfuj3abaTDsYfaBuBDRpqxLgsMaq/4WGrYr6YTDIJYEQQgjPJm1uK+JsEqz+ApbPgzOHVOlsbUtiK0MHGAq3RZO2MOoOuPJmqS5XRerUqcOcOXOYNm2a1qHULpYClbSeOaQ6dos9DCf3q+fpiUUlbDq9OtbtNjneRe1gNKn92mJW+zWokuHQCNW5YGQHdS5v0la9btRKJcdCCCGEm5Pk1hU7foef58I/S9Vrm7XsjkREYfU5HQwcA6Pvgp5XaB2RxzKbzfj6+rJkyRKuu+46rcPxTMln4PgelbSeOQinY+DUAUg6U3Rhb/JVbR2LD2UjhLcyGNXx4LiZo9NDWCOV9EZ2UAlv0/bQopMqBRZCCCHchCS3ZbHbYONy+OJ5OLxN/bN3pX2cKGI0qd6fm3WCiY/DsCmFia9wVVJSEhEREfzxxx8MGTJE63DcW06GKoU9vlc1FTi6E47tUr0KQ1Fb15rqkVyI2uj8G0F+gdC4DbTqqpqlNO+ketyXmjtCCCE0IMltadZ9C5/+H8QeUW2XpDOXi6PXq6EzGrWEaa/A4ImFvYUKZw4ePEj79u3ZuXMnXbt21Toc95GeqHojP7AJDm+FIztVm1hQSazBKKWwQtQkg7GoN29QnVm17g5te0OHvtC+r+rdWQghhKhGktwWd3g7vHsv7FsPep2M/VjV9DqwAVH94b53oU1PrSNyexs2bGDAgAGcPHmSyMhIrcPRRn6uqj0Rswn2b4Q9f6lEVodqB2g2A3IaE8Lt6HSqPwZrgTpEwxpDp0ugY3+V7LbpqcZXF0IIIaqIJLcA+Tnw4WOwbC4YDWCR6sfVymBUvUqPvhNmvAG+AVpH5LZWrlzJqFGjyMzMJCgoyPkMtUFuFuz+Uw2rtW21GnfZalUXyXab1KQQwpPpDaoNr9UMBgM06wg9h0P3odB1CPh7yXlOCCFEtZDk9vA2eGkiJJyQNrU1zWCEiOYw81spxS3D119/zS233EJ+fj662lqV21KgqhhvXwObf4FDW1QCa/KFgnzn8wshPJvBCDaLSnxb94R+o1SyG9VfemkWQghRId49qN2yufDe/eq5TRLbGme1QOIJuLcf3PM2XHOX1hG5nbS0NOrWrVv7EtusNPj3R9W+feda1T7W6KMSXQdJbIXwDo4by1YrHNysOoP78j/g4wfdLochE2HgtRBUV9s4hRBCuD3vTG7tdvj8WVj4kjTV05rjoubduyHpFNz6snQ2VYwjua0VHAntH9+oUlq7Xf3Wjn2geGIrhPBejnNBQR5sXQVbVsFb06H75XD5ZJXo1qmnbYxCCCHckvclt3Y7zLkTVn4kia07sQPfzoK0BHj4I0lwC3l8cmsxw/qfYOX80hNaIYQoj6NjR7sNdqxRj/9Nh25D4eo7YMC1atg5IYQQAm9Mbhe+UpjYSk/Ibsdmg18/gwbN4cZntY7GLZw9e5aQkBCtw6i4s0mw9B1V9T8jBQx6VeVQCCEqq/gIBjvWwPbfVFXlMffAtfdCaIR2sQkhhHALeq0DqFF/LobPZ0pi687sNvjyedUWU5CTk0NAgAf1Jp0aD+/fD9GR8M1rkJEM2CWxFUJULZtN1QTJTFXnmimR8N69kBqndWRCCCE05D3JbUYKzJ4uVZE9xezpcDZZ6yg0l5ubi7+/B4wDac6Hr1+Gm1vBzx+o11az1lEJIbyB1QzmAvj5Q7i5NXz1ojoHCSGE8Drek9x+8rQaz1ayW/dnt0NBLnzylNaRaM4jkttju+GuHvDlc5Cfq9rZCiFETbOa1TlowX/gzu5wdJfWEQkhhKhh3pHcJpxQ7WzlottzWMyw8mOIP6Z1JJpy++R2wzK4tw+cOSRVj4WoAVlymDlntULsYbint2qOJIQQwmt4R4dSaxeqweGtVd/W9tcU+CoOvihs5tM/BAIMkGMFG3BDA5jeFIIM6vPlyfDf47AuDUw6uKQuFNjAaoc2AXBHE7i0sHPck3nQ/C/w10PHIAg3QfE+hDechXQL3NkU5kY5j9Vih80Z8EsyDAqFK8PU+7+nwtOH4Zsu0MKd8iijUf12k5/WOhLNuHVyu/FneG4sYFel7W7szv0w7zSEmaCxL+Tb4GAO1DOpfT7NDMdzwVcPucPgzzR48wT8lKTmb+UPj7eA25qAUQe7s9Tnn8dCVCBMbggzW6lp/06HJw9BfD746NX0dYwwrbGa31Nc7HqUdb5xJ8uS4LNY+D5Rvd49ADoHlT19t/WwK0vtR4+1gPsi1fm+Jsw/A9/Gw4EcOHVpzXynR7NaVM/sL98AJl8YMEbriIQQQtQA7yi5/e2Lamv/d2UYfNa5KHn9pw+s6QXr+8L9kfDoIbhmO5gLr/2vDof/tVfPewXD773g7z6wpBvE5cOQLfDRGfX5mXzoGwInB8OWfvBLT1hZ+Hi+NWRaoakfvNbWtVg3Z8D80/DCUTiVV/R+mkW9zj6vRCBO6yZLFjOs/lzjILTltslt8hl4ZbJ67uaJLUCeTR0ziUNg1wB4v/Bm0Khw2NoPjl4COweoJM4ODK4LP3aHqY3UdIEGmNFUJXgAXYLgqjAYEKKOTUdiuycLhm+Fu5rCwUGwbyA81RJ2ZkKs1sdTBVTFepR1vqkKVXVuuqY+fNG56PXbJ8ue9p902Jutnt/cGJ5oUbHE9mJjntZY7ceWCt6jrY7zuOb/G1xlt6s7wq9MVuOoCyGEqPVqf3Jrs8KZg9X6FTogpLAMXF+saHVKI7g+Av5IUxdGDo5E2Fhs2sa+8HEndWH9+CH193QevNBaldgWl2uDm/ao0t6POhZ9tzMDQuC+Zhe+Pz4CzgyGTsVKLDIsMHWPa8utVmcOe3V1crdNbr97Q3XY4iE9j+uBp1qUPD7P1yVIlcDmF1ulD6KgfaAqqf0xqej92HyYdRx+6F4ywfksVh27UxoVfdfkhqpmhSclt1WxHmWdby5WVZ+bAg2q9D7QAAviIKWM0837p2BsffW8bgXrPFVFzAaduplZ099bE8usVjab+h+y+HWtIxFCCFEDan9ymxpXI20BdWVcNLcLVH+P5jiftrkf+Okh3axKUXsGqxKk8z11SFWpnN5ElR5VhE85F/cO2VaYsAuO5FZs2dXCboOUWK2j0IzbJrf//uhRNx0ea6FKZV2ZrvhNpwADfNlZJRb37IezFlULI3o3vN0eGviUnD+hQCXHa1NLvj+l4cWuQc2qqvVw5XxTEdV1bgo1wk2N1I3D+Wcu/DyxAGJy4LJ66nVZ5/DSaHU+rY7vdav/DRVhNcM/P2odhRBCiBpQ+9vcZp/V9Os3pKuSjz4hzqc9laeqnfUMVqW7QaXkNH+kqapzzfzgjXZVE2NSASxKgD7B0C8EfkiE/dmqHeL0fark6tHmqiRn3mlVNXFbpioxfq8DtK3uYViz0qBB82r+EvfktslthmcN0xQV6Np0rUvZ1H2C4aFm8MYJeCgGgo0wMrz0G09D6qrSv/G7YGGXoptPel1RVWiH7xJU23tfvaoG3CsYnm2lXi+Kh9v3QahJta/MsMAnsaoNbI86qtnDmXz4Mk5932+9VG2OmGzY1l+1CV2RDD8ngUkPm87CtCbqhhg4P5ZdXY/KnBNcmaes2Ms6Nznbns62FcD9zeCD0/DeKbXM4jc5Pjqj+kMwl1EDv7x1KivmhAKYeRgi/VT/CslmVRMnrFhNnR+TYHkS1DVBrvXC6sCV+d7q2v5uT4aWE0IIr1D7S27DGtfo1x3Ihr1Z8Fsq3LwHNmXAux1UlcfyJBXAHftU51Gvtil9mkwL3LpXPf+4k7rIvlj/pKsL2PsOwOnCC6cbG0G3OhDuA/M7Fl28zDqu4psbBf/2UfEM3qw6z6pW4R7UC08Vy8nJcc/ktnEbSnZvVrv9p7XqVOrTWHV8P96i9OlubQxjI9TF/4htqoQ3oUB9VnxrzT4B/zsJb7VXN6m+6gKLE+DKbSr5mNQQBoYWTR9shAeblTyP7M5SHVodyIYPT6vO6xoWdpblSOTe7QBz2qu2pXfsU53HgfNj2dX1qMw5wdk85cVe1rnJ2fYsb1s5dAhUfSiczivqYApU849F8aqKdmXWqayYb9gFGVbVXnt+RziWCw/GFC3z63h47Ri83QFmtYXnWsO+7Iv/3urY/u5PB01d7JxCCCGER6v9yW1QXfCtueTg9eOqA5VHD8KCeLi2vupBuTS7s+CKrTBgEwzcrHoiXdu77F5FHzmoenS9sylcUa9q4h0UCjNbOp8uNh/+d6Kogx2DDq5vAPEFqsfRamPyheDwavwC92Wz2SgoKHDP5Hb0naCv/acPhwCD6pAKVG+1mZbSpzPo4LuuKsEKNMDCeIj6F5YUS5YSC2DmEXUcmwozxTATPN1S9dK8IK7oO89XvDRxRJg6fq12lXhNawIb+6pl3ncAXm5T1F52ehMYFwGNfF07ll1Zj8qcE5zNk1RQfuylcWV7lrWtGp+3zAcK2wjPKdax1PJkuCJMbYfKrFN5uhW7WdE5CHZlquc5VvU/5IFmqqmKY50uLXbDw122v0cw6OHqO7WOQgghRA2o/dWSAbpeBltXg62MK9Iq9EmnoufbMmDcTnVh+H03dQe8uC5BqoqcK1Ymq7ZgLfzh9SqqjuzgSo+f/6arKnkz9pd8//Ym4F9dQ2HoDdB1SMUauNUieXl52O1290xur7wFVsyHozs9qu1tZeVYVengFfVUrYxHDqqSq9IYdPBIc5U03LFPDRc2sbB678QGagivbKuqjlrc6MLzwx9pRcmHMyadSniLV6f+O10NQ9ay2Hv1fVSP7KCq77pyLDtbj8qcE5zN4yz20ri6PUvbVucbEQ7tAlScWzKgdzDMPQXvljPUWmXPjWt7q7/ZVpWAbz6r1h3gr3RVBfn8YYmKtxt3l+3v9owmaNEZRkzTOhIhhBA1wDuS2yumwtZVNf61PYNVIjppl7oYPj+5dVWaWbW/0wGfdCzqbbkm7c9WJRdlXdBXC7td/XZeymxWSaPR6IaHqdEHnl8KDw1SHX7V8gT3jv1wS2M1BEzHf1UbzAkNyh+7tbkfrOqpSsLePaX+TmgAJwqHxUk9b5OFm9SNptiLHDZnTxaYbao6bmm3hSp6LJe1HpU5Jzib58Wj5cdemqrcnjpU29t7D6jS2+dagVFffkJc2XOj1a6qCB/JhYebqcRyQ2EXEQcKqx+bytkI7rL93ZrBBPUawQs/qVpAQgghaj3vqFc4YAwEBGtSAtgrWP09nFN2ZyTO3BejqpPdEwmXl1Id+WAOZFVzu9cAg2qLdrqUC8Xk6shrdEBgMAwcWw0LF1UivAm8swlad1el7LXU3NNQYFNJj6PzHVAd6hSvnnwwB946ceH8b3eAJr6q+mxsPrQsLGE8WkaPs+1d7PyqLMFG1THdvqwLPyuwOT+WXV2PypwTnM3jLPbSVPX2vLmx+p2/TYBnj8C9keVPX5ntYLPDqO3qRsTHHUsOwwZFJbQnyknM3WX7uy29AVp3Veeo8KZaRyOEEKKGeEdy6xcI0/9Ldd6LtpeRuMYU3oFvE1B0F95WOK0rue6SRPgqTnVm81oZ/WG8eaLqh9zQo+7gO3QJUvE+cajkdIkF8GkpQ2dcNJ0ebn8d/J30xCW0FRoB//sXbnwWDAZVBdADuHoMbjyreif/uFhJ19gIVQvjZB48Vux4aOmvjsWkgpLL0AFN/FSb+kY+0D9UPV+aWHK6M/mq+vOYwhoeRh1kWVQJn0OWtajqall6F95Qm3mkaD1B3WD7NsH5sezqelTmnOBsHmexw4XnJle3Z1nslLxJEWSA25qoZG5LRsnSeUdIxc/3rmyH82PelKGqeQ8rdrPSbC9abtfC097ihJLLtNnBcR+zMt9bHdvf7RhN6lw05RmYswHqNtA6IiGEEDXIDes7VpMR02DZ+3BsjxrzrgrZUeNfgmo/5eh45Hix3i9fKtYDclrh159fje58iQVw1351Uflpp9I7NPk1BdakuDaGJ8XitBS7cMktvFDJK1b629gXlhfAjkw1z6BQNSTK1/Hqzv7YCHXB8286LOzq2ne7zGiC5p1g5G1VvGBRLQxGldxeNgnmPQIbl6vf0I2rKqcVHgdny2mGfzpPtZmfF6WSp+JebaM64PnwtEp0rw5XN6/89HDtDviuW1FnRX+lqfb3r7dVnfSEm9T89x2ANalFCc7bJ1Xb0KGFr7sEqfaxrx6DiQ3h23jVu+/pPLW8nsEqIbLa1V/HzbNBoWqooh8SYdhWGN9AJeIHs1VcBl35x7Kr6zE8zPk54fzzjbN5wk3lxw4Xnpv6BLu2PUvbVqCGYIvNV/E4Om+6N1L1vnxvZMlboo5zdlqx/caV7XB+zI7E8fM46BsCWzNUaWlCAezKUjdDh9RVvXP3ClZj8O7NVlWXkwrUd11bv+Lf6+w8Xtnt70q/DdXOaASLBXoOhxlvQmQHrSMSQgihAcPzzz//vNZB1AidDnpfBas/A3NB2UWtFfR7qrr4dLSVWpqoxgd855QaSzAqSA3bM7Kww99fUlRVtxOF4xpmWyHCRw1Lcb5pe2FzhqoidzpfdUzleHwZC68cg9knoWsd1R7QmR2Z8Mpx1Z7rrFWVBicWwH9PqKqIqWZVhS/SD5r5q4v3pYmqt+fuddSFzpl81UHLqhSob1IdrTTwqZJNqegNqqR91m8QXE6DRi9QUFDAa6+9xo033kibNmWMD+VOgsNhaDT0ukq1w409rNrm2qp7rCjX2VEJ6VsnVCJxIk+VhEb4qI5zHD48DdP2qcTH3wCRvkW9xR7KUcnBn2nq9dIkdZHfP0QlHwadqsq8KgW+iIUfk+GVNjCjWM3IviFqSJW2fh51AAAgAElEQVQ5J9U4ohvOQj2TaqPvSKZ61FEJzYI4lXzc3wwSC4/R5v6qVPG9U5BpVSWPzfyL1mFcA5WA/ZuujtfW/upYDTCo5Ts7ln9Pdb4ezpZT2vmmhb/z7y4vdrjw3NStjvPtuTC+9G21NBGeOqx+033ZqmS6mZ8aV/ZwDvxfSzVObrZVbYt3TqplHMpRCXL3YPXX2TqdH/PV9dX+tzpV1Q64LkI1Ofk5SSWTExuo4aDi81VHgh+chiCj2ge7BsGAUDUu7fUV/F5XzuOV2f6aMprU//PeI+Cxz2DSkxDinT3sCyGEAJ3dXkVZnqfYtx4eHQJWK9jduW6VF9LpVXL7xlroNEjraDSXkZFBSEgIK1euZMSIEVqHU3En96vaEqs+hYJcQOdWia4QwkPpDYAdfPxVz+3X3A3Na7K3QyGEEO7K+5JbgM0r4T/jVBWmGhgeqCY0/VNVWSzPF52LSpDdjt6gqrc++x30G611NG7B45Nbh7xs+Os7+OMb2LZGJbh6PVhrx7EnhKgBeoMqodXpoecwuOwGGDxB1fQRQgghCnlncgsQswmeugpys+QiW2sGo+o46uWVENVf62jcRq1JbovLSocNy1Siu3U12K3qYlWOQSHE+QxGVcNKZ4Bew2HIJDX6QVCo1pEJIYRwU96b3IJqE/jC9XBir1xca8VggMiO8NwSaFJGd9BeKjMzk+Dg4NqV3BaXkwEbfoatv8DmVZCeqMalxC7HoxDeyDGkmM2q2s32GQG9R0K/qyEwRNvYhBBCeATvTm4BLAXw+bOw6HVVVVLaBNYMvQFsNpjwKNz6kup4SJTgSG5XrFjByJEjtQ6n+p06ANvXwNZfYcdayM1UncXYrGpfEULULvrCfhYsZlV7p9vl0OtK6DEMmkVpHZ0QQggPJMmtw+4/Yc4MOHUQsFdZb8qiFDq9KqV9cB50HaJ1NG7L65Lb4mxWOLQNdq2D/Rtg7z+QFg/owOSjejx3aaRoIYTbMBhVZ47Y1RjZnQZCx0HQdTC07VVUciuEEEJUkveMc+tMl8Ewfy/8tgDmPqiqTEopbtUy+YDJD25+Acbcoy50hCiN3gDt+6iHQ2ocHNwCh7bCvg2w719VuqvTqX3JYkESXiHcgQ58fNWY8lYr+PpDy67QoZ9KYrtcCg1bah2kEEKIWkhKbkuTlQY/zIElsyE/R/WoLFup8vQGNWTD9Q/DuAchqK7WEXkEry65dYXdpoYbOrIDju2GozvhyE6VBINKeA1GKMjTNk4hajODEdVOvvBmcGgEtOoObXtAyy7QuruqYqzTaxqmEEII7yDJbXlys+DnD2DRLMhIUZ0fSUc3rnFUPwupBxOfgNF3gn8draPyKHl5efj7+/PTTz9xzTXXaB2O58hKh+O74dgeOLYLDm9XncblZqnP9XrVcZXFLGNdC+EKnV61f7daimo0+QVC807QtqcqlW3ZGVp0kZ6MhRBCaEqSW1dYzGr4kuUfwrbVKsm1mLWOyj0ZTap6aM/hcPUdatgGo0nrqDyS3W7HZDLx1VdfMWnSJK3D8XzpiXD6IJw5VPQ4sRfijxWV7hoMoDeq6pTSiZXwJo6bPjZLUSmsj5+qPtysIzRtp/pKcDzqNtA2XiGEEKIU0ujRFUYTXDJOPZJOwerP4feFcHJf4d1sqxeXAOmKSrQjO8DQaBh+M0Q00zowj6fT6fD39yc7O1vrUGqH0Aj16HzJhZ+lxBYlvHFHIPEkxB5Rie/ZpKIO5hwJAIA5v+ZiF+JiOXomLtH7uA5CwiCiuUpYG7aARq2hcRv1OryJlhELIYQQFSbJbUXVj4ToZ9Qj9gj8vQT+WKSqPuoorO5YoHWU1ctxd9+Oak912SS4dLy6IBJVKiAggJycHK3DqP3CGqtHab13W8yQfFolvAknIPGEeh53VCW/qXHntevVqZteOl3JapxCVAe9QTUDsdtVjYPilbF8/KBuQ1X62qiVuunYoAU0aK6ehzeVmjVCCCFqFUluL0bj1jDxcfXISIGdf8CONbDpF0g4hhq2xNG2z4Nrf5t8i4ZeadAC+o6E7kPVmITBYVpHV6sFBgZKcqs1o0klB+X17pqXrRLg9ERVCpwar5Le1LjCxPgUnE1WndWVUNjTs16vzhE2i1SH9nY6vdondDpVI6i0XsADQyG0vrrZWr8p1GukHnUbqNLW0AgIa6LGjhVCCCG8iCS3VSU4TJVeXjpevU6JhZhNELNZDVlycEtRhzYmH3UB626dUzkuss2FJc9+gWoolqgB0KEvtO+rSrdEjZGSWw/hFwhN26tHeSwFKgHOSFHJ7tmkoueZKep5WoJKijNSVTJcavXnYkkxqCSoRHVToSlHFWBHD8E2W2Ftl1Jucpp8VbIaEqZKWes1VP9P6oRBSLh6HlK/6HloBBh9anZ9hBBCCA8hyW11CWsMA8eqB6iLmrijcOqAGr7k1AHVk+vpQ5CdXjTfuTZ9hUMrVFWVRr1BtY1Fd2FnOYGhqn1Vq66q3WyzDhAZpaqx6XRV8/2iUiS5rWWMPqoqaHhT1+fJz1WJb1a6Gn87+2zR38y0ku9lpkFWqpo2+yxkZ4Alv+iGVXn0RtDrLhyyxWYtLEW0u98Nuco6VzJqV+fG4uw2sBWWojtj8lG/aWBI4SMU6tSDOnUhIFi9FxCsXjueF/8bHK7GgBVCCCFElZDktqbodKoac+PW0O/qkp/l56qOqlLOqOqLSadUCU5WmrqoTS8s1cnLVm37HCU55gJ2pxdgB7qG+qgLLVB/ffxVaVKdMAgNV3+D6qo7//UjISJSVVurHykXV24sMDBQOpTydr7+4FvBhLg0ORnq3JGTqc4llgKVDJvz1XjeORkqCc7JUK/N+Sr5yyq8+WY1F9U+yc2CgsKbLplni0oli9+oA7BasOdls+iMhbGNjPidP9Sp3a5iKY9fYPk32fwCC8daLSYwRCXpeiPUCVHv+fgXDUfmF1hU+hkUqpZv8gXfAAioo54HBKvXJl81jclXzRdQpyihFUIIIYRbkeTWHfj6q2EWmrar8KyvTJ5MQUEBS5YsqYbAhNak5FZUmYBg9Tekfo1+7a+rVjF5xAi2b99O9+7da/S7hRBCCOFdzr+PLoRwI9KhlPB0s2fPZtiwYZLYCiGEEKLaScmtEG4sICCA5ORkrcMQolJiYmL49ddfWbZsmdahCCGEEMILSMmtEG5MSm6FJ3vzzTdp27YtI0eO1DoUIYQQQngBSW6FcGPS5lZ4qqSkJBYsWMBDDz2EXi//aoQQQghR/eSKQwg3FhQURGZmptZhCFFhH3zwAX5+fkydOlXrUIQQQgjhJSS5FcKN1atXj5SUFK3DEKJC8vPzmTt3LnfddReBgYFahyOEEEIILyHJrRBuLCwsjNTUVOx2u9ahCOGyhQsXkpyczF133aV1KEIIIYTwIpLcCuHGwsLCMJvNUjVZeJQ5c+YwadIkmjZtqnUoQgghhPAiktwK4cbCwsIApGqy8Bhr1qxhx44d3H///VqHIoQQQggvI8mtEG5MklvhaWbPns2QIUPo06eP1qEIIYQQwssYtQ5ACFG2evXqAZLcCs9w8OBBVq5cyffff691KEIIIYTwQlJyK4QbCw4OxtfXl8TERK1DEcKp2bNn07x5c0aPHq11KEIIIYTwQpLcCuHGdDodDRs2JC4uTutQhChXWloaX375JQ8//DAGg0HrcIQQQgjhhSS5FcLNNWrUSJJb4fbmzp2LyWTilltu0ToUIYQQQngpSW6FcHOS3Ap3ZzabmTt3LnfccQdBQUFahyOEEEIILyXJrRBuTpJb4e4WLVpEfHw899xzj9ahCCGEEMKLSXIrhJuT5Fa4u7fffpsJEybQrFkzrUMRQgghhBeToYCEcHOS3Ap3tm7dOjZv3sw777yjdShCCCGE8HJSciuEm2vatClZWVmkpaVpHYoQF5g9ezaDBg2iX79+WocihBBCCC8nJbdCuLkWLVoAcPz4cerWrattMEIUc+zYMX7++WcWLVqkdShCCCGEEFJyK4S7a9asGTqdjhMnTmgdihAlvPXWW0RGRjJ27FitQxFCCCGEkORWCHfn7+9PRESEJLfCraSnp/PZZ5/xwAMPYDAYtA5HCCGEEEKSWyE8QYsWLSS5FW7lww8/RKfTceutt2odihBCCCEEIMmtEB6hRYsWHD9+XOswhADAYrHw3nvvMX36dEJCQrQORwghhBACkORWCI/QokULjh07pnUYQgCwePFizpw5wz333KN1KEIIIYQQ50hyK4QHaNOmDYcOHcJut2sdihDMmTOHcePG0apVK61DEUIIIYQ4R5JbITxA+/btyc7OJjY2VutQhJf7559/2LhxIw899JDWoQghhBBClCDJrRAeoH379gDExMRoHInwdrNnz6Z3794MGDBA61CEEEIIIUqQ5FYIDxAREUHdunU5ePCg1qEIL3b8+HGWLl3Ko48+qnUoQgghhBAXkORWCA/Rrl07KbkVNSIzM7PU9+fMmUOjRo0YN25cDUckhBBCCOGcUesAhOtSUlI4e/ZsifeysrIwm80cPXq0xPvBwcGEh4fXZHiimrVr105KbkWNGD9+PH5+fjz66KMMHjwYUAnvp59+yjPPPIPJZNI4QiGEEEKIC0ly60GWL1/OzTffXOpnrVu3LvH6o48+4rbbbquJsEQNiYqK4sMPP9Q6DOEFTp06RUxMDMuWLaNLly488cQTxMbGYrPZuP3227UOTwghhBCiVDq7jC3iMTIyMqhfvz4FBQXlTmcymUhISKBu3bo1FJmoCT///DNjxowhNTWV0NBQrcMRtVh4eDgpKSkA6PWq9YrJZKJbt26sWLGCsLAwLcMTQgghhCiVtLn1IMHBwYwePbrcKoFGo5ERI0ZIYlsLde3aFbvdzp49e7QORdRidru9RPMHm82GzWYjPz+fbdu20ahRI6ZOncq+ffs0jFIIIYQQ4kKS3HqYKVOmYLFYyvzcarVy44031mBEoqY0a9aMsLAwdu3apXUoohbLyMgo8xxjsVgwm80sXLiQLl26sG7duhqOTgghhBCibJLcepirr76awMDAMj/38/Nj9OjRNRiRqEmdO3eW5FZUq+TkZKfT2Gw2Xn75ZYYMGVIDEQkhhBBCuEaSWw/j6+vL9ddfj4+PzwWfmUwmxo8fT0BAgAaRiZrQtWtXSW5FtUpMTCz3c51OxwMPPMCTTz5ZQxEJIYQQQrhGklsPFB0dXWqnUmazmejoaA0iEjWla9eu7N69G5vNpnUoopYqr+TWYDBw00038dZbb9VgREIIIYQQrpHk1gMNGzas1DFsQ0JCuOKKKzSISNSUHj16kJWVRUxMjNahiFoqKSkJg8FwwfsGg4ExY8bw8ccfo9PpNIhMCCGEEKJ8ktx6IL1eT3R0dImqySaTiejo6HJ7Uhaer2vXrvj7+7Np0yatQxG1VHJyMkZjySHQjUYjV155JYsWLSo18RVCCCGEcAeS3HqoyZMnl6iabDabmTx5soYRiZpgMpno0aOHJLei2iQlJZV4bTKZGDBgAN9//73cPBNCCCGEW5Pk1kP179+fZs2anXvdsGFDBg0apGFEoqb07dtXkltRbZKTk88NBWQymejUqRPLly/Hz89P48iEEEIIIconya0Hmzp1KiaTCZPJxNSpU9Hr5ef0Bn379mXnzp3k5uZqHYqohRISErBarZhMJlq1asVvv/1GnTp1tA5LCCGEEMIpyYY82JQpUzCbzdJLspfp27cvZrOZHTt2aB2KqIXi4+MBVRvk999/JywsTOOIhBBCCCFcI8mtB4uKiiIqKorWrVvTvXt3rcMRNaRVq1aEh4ezceNGrUMRtVBKSgr169dn3bp1NG7cWOtwhBBCCCFcZnQ+SdU7lmkm3yLjdFaFkROisZjNHEjL1zqUWsHHoKNVsI/zCTWk0+kYNGgQf/75Jw8++GCNf3+B1c7RjAvHWRa1g02n55MfV5Ef2ljOK24gzN9Ifb9q6qHalgnZK6tn2UJUlk9H8O1cPcuWfV4UV537mtCMJsntb6eySMm3avHVtY6x79XoLGaWHs/UOpRaIdTHwJ2d3Du5BRgyZAgvvfQSNputxttaZ5ptsr/VUnabjWtfns8ev0j2yG/sFgY2DKB+o4DqWbjlDJyZVD3LFqKywp6B+tWUcMg+L4qrzn1NaEaTasl27Fp8ba1Ut3Ek4c1aaR1GreEp++aQIUNITU1l3759WociahGdXk/TjtLEwV0YdFpHIEQN0/lqHYHwFrKv1VrS5lYID9StWzdCQ0P5888/tQ5FCCGEEEIItyDJrRAeyGAwMHDgQNatW6d1KEIIIYQQQrgFSW6F8FBDhgxh3bp12O2eUZVaCCGEEEKI6iTJrRAe6vLLLychIYE9e/ZoHYoQQgghhBCak+RWCA/Vq1cvIiIi+OWXX7QORQghhBBCCM1JciuEh9Lr9QwfPpxVq1ZpHYoQQgghhBCak+RWCA921VVX8ddff5GVlaV1KEIIIYQQQmhKklshPNiIESOwWCysXbtW61CEEEIIIYTQlCS3Qniw+vXr06NHD6maLIQQQgghvJ4kt0J4uJEjR7JixQqtwxBCCCGEEEJTktwK4eGuvfZajh07xvbt27UORQghhBBCCM1IciuEh+vduzctW7bkhx9+0DoUIYQQQgghNCPJrRC1wLXXXsv333+vdRhCCCGEEEJoRpJbIWqB6667jr1793LgwAGtQxFCCCGEEEITktwKUQtccsklNGzYUEpvhRBCCCGE15LkVohaQK/XM2bMGGl3K4QQQgghvJYkt0LUEhMmTGDLli0cPHhQ61CEEEIIIYSocZLcepiCnGytQ9BMaeuelZrMjpVL+OfrD0k44t3tTYcOHUrTpk1ZsGCB1qFUmDfv155OfjvvtSsGPl8K362CMwmuzZOVU70xCSGE8G6S3HqIHSu+45O7J/DG2H5ah1LjNn//JR/fdT1vjR9Y4v2k44f5/oUHadmzP0e3/M07k4eSn52pUZTa0+v1TJ48mS+++AK73a51OC7x5v3a09X2385mtXBy12Z++2AWh9av1Toct5KbB7c8BTHHYNRgWLwKWl8Jh06UPc/8xTB8GkRdXbWx/L4B+k+C42dcm95ihfU74Ll34Nd/qjYWd+at613dZLtWjmw3UZ0kufUQXUdch9VixmYxV8nyMpNdvM3uBnqNjcaSn4fNYinx/qp3X6ZBmyhCGjRhwgvvcv1/3sE3sI5GUbqHm2++mRMnTvD3339rHYpLqnq/ri6edLzUFE/57YqryO94eu92Nn3/JWs+fIOzCbHVGJXneXEuBAfBhBFQvx588Ro8dhuE1y17nmnjIK8AzjuNX7S0DDgVD9m5rk2/ebdKtF94X83nLbx1vatbdW7XuKSqXV51q0i8sj+K6iTJrYfQ6w2ERDSukmXlZ2ey6Jm7q2RZNUGvNxDSoOS62+12Yv5ejV+QSmb9goLpPnK8FuG5lU6dOtG9e3e+/PJLrUNxSVXu19XF046XmuIJv11xFf0dm3Xtw8AbpldjRJ7rw2+hQ6ui174+8OL9UDe47HkMBmjaoOpjGX8lnFkHndq4Nv2A7nDfjVUfh7vz1vWubtW1XTOyYOrjVb/c6lLReGV/FNVJklsvU5Cbw1eP30bq6WNah3JR8rMzsRTkax2GW5o6dSrffvstubkuFmWIMtWW48XbVfZ3NJhM1RSR50pIgZR08Cm2acwWiE2EpFTt4qoIHy/9Wb11vatbVW/X7FyY8CAcOVW1y60ulY1X9kdRXYxaB+CquIN7+furD2jQqh0ZSQlYzQVc+9Tr5z7f89syjm79F6OPDwmH99OkYzeGTn8Mo48Pu35dypIXHsS/TghPrtxJfnYmW378ml/efoHG7bty1+cryUiMY9vyb9mx4jtu+2AJi2feQ9Lxw9y3cA0BIfWI+fs3Dvz1K3qjkdN7ttP72mj6jJsKqFLETUs+J+7gXmIP7MIvKJgxT84ivFmrslanhMSjMexY+R17f1/BbXO/Y+mrj3N8+wbCIltyzeOv0KxL7xLTZ6YksvTlRzm2bT11G0cy6aW5RLRq79K22Lt2OYlHD5Kbmc73Lz5E/eZtuPSme5zOB/Cfwa3pNHQUfkEhAMT8vZrkk0eZ9NJcuo+6vkrXc98fKznw92r864Riyc8lo1h1wm3LvuHwxj8B2L36J1JOHSMssiVDbrnf6X7iDaKjo3niiSf44YcfiI6O1jocwPnxC+Xv186OsayUJH6d+yqhDZqQHn+a7PRUxj87m4CQek6P7fJU9nhJOHKAHSuXsGfNMm6b+x0bv/uc7csX4xsYxLVPvEazbn345e0X2b/uF6wWM+NmzqbdwKElvtvZ8Qhwet8ONi35nILcHFJOHaP32Cn0GTsFvUGd2stbd5vFWuY2q6pz0r4/VrLombsoyMlm9KMv0X/CrRhMPpzctZkFj9zCgBumc/ltD7q8H5V3Hi5vHyjrdyxvnvJUxW/jidZsgPe+Us8/WQL/bFNt5/Ydhm374D/3wbPFCsd/XAPL16kS3dz8ktUWF62E25+B0GA4tVaV/HzyPTz5JvSIgvXfFE274k/4+Q8wGWHTLpg2HqZPUJ8lpapl9ekC/bqq9xJSYOYciGwEJ2MhOQ0+egnCQi9+G5QXC6jOtdZtVqXZew5Br05qm/gW7ho7D8Dsz6FjG4hLhHwzvP8s7D0MXy2DJb/C6k/gg29gwU9QJxDenalKup56C35aCwVmmP8CjLi0ZGzOvrssrsy3ZQ/MW6QSmcMn4bbxcNv1YDSo3/nGx1VnYbOfgrsnq8Rl/Q4Yd58qoXt6xsVvey05+92Lc3XfLmtf+GE17D+iqtxPnwntW8Kj08BuV7/Bzhh1vIUEwXvPQtvmqkO3L39S+8xvn8JNT6g28du+d32/L28dyzumyoq3ssfhxe6PQnjMf9mFT05n/LP/o3n3vljNBXz12LRzn/391QfsWbOM6R8uxWA0kXM2lbk3j+T49o1Mn/8jXa8cy5YfvybxaAwAvoF1GBQ9gx0rvju3jPjD+9m2bBEpJ4+yackXdL3qOjYt+RxLQQHbl39LzD9rmPTSXHR6PWs//h/fv/Qw9SJb0LrPpaz77G2Cwxsw9un/YrNZmXfraD687RoeW7YFk5+/03XLOZvG3rUrST5xhL++fJ9LptxJr2sm8f2Lj/DRjHE89tNm6oSr+lzm/Dz++OR/jLh/JlaLhXnTRrPif89zy9sLXdoWPUZNYNeqpSQc2c+4mbNd3oY6nY5Lb7qHobc/DEDqmRNs/O4zWnTvRzcXqwO7up47Vi5h/TcfMX3+Uow+vuScTeWtcQPR69VZq+c1N9Bh8JVsX7GYTkOv5vLbHnJpP/EWDRs25Nprr+WDDz5wm+TW2e/ibL92dowtfGo6QfXqM/SZRwB4+4bL+Pm/zzDxpffLPbadqezxElSvPmcTYkk+cYQ1H77BgInTuOzWB/jsvhtY8sKDtL9kOP0n3sqVdz/F5w/dyI+vPs5jy7a4vHydTkd63CnmT7+WBxf/Td3GkSx+9l6Wvvwom7//khY9+jP60ZfKXfdF/3dnmdusqs5JHS8bycBJt/PHp3No3r0fBpO6OmkS1Y3QRpEVSmydnYfL2wfK+h3Lm6csVfXbeKJh/VXV4h9+g5vHwoxJ6v2TcdB8qLoodvj6Z3hnAaz9HPx8VWlv1NVgKKwvNmmkSpD3HVGvg4PgwZtUglfclz/Byj9hweug18Mr8+COZ6F1pLrYfeot+GsrfDenKLm94WFoEAYz71Kvu18HD74KX866uPUvL5ah/VWisuRXtc4mo1rnATfA31vhjy9Ap4MbHoGPXoRBPVWSev0DatkR9eB0PBw8Di+8B3dHw1N3wKgZcPtM1XHX3ZPhxQdgzN1wz4tw5Nei2Fz57tK4Mt+JWLjsJtizDFo0gZufhDufV20mL+2lEtp7p8Br8+GSnkUlcr06QfPGnp/YOvvdz+fqvl3WvnDjGJUg7zkE818smn7WR9CoPsx9DqxWuGQKDL4RjqyG3QdVz+WHTqhmAzeMgnnfQr7zf3MurWN5x1RZ8VbmOKyq/VF4N4+olmy1mEk6fojYmF0AGEw+9L5WXbRnpSaz+v1X6Xf9LRiM6owaEFKPy6Y9xLFt69mxYjFAqUlm8Tvo7QYOpXn3vthsVrqPup7e10Zz9xerMBhN/DTrKa665//Q6dXm6jtuKp2GjqZOeAMykuL55+t59Lh6olqm3kDnK64hMyWR/X+ucmn9WvToT2TnnqDTMeKBZ2nVexCdho7m2qdfx5yXy8bvPisR86iH/kP9Fm1p2CaKNv0GE7t/V4W2xflcmc9ut9Prmknn5vlp1pPYrFbGPv1fdGX916zEeprzclkx+zkGRt+B0cf3XCwtegxwuvzy9hNvc9ddd/HXX3+xe/durUNx6Xcpb7929Rhr1K7TuecN2kQRd2gvUPaxHVy/YaXWx5XjJbBuGM269AJgUPQMGnfoim9gEJ2GjSb1zAn6jJ1CRMt2+AQE0nHICFLPnCA7LcXl5QP8u+hj/IPrUrdxJACXF9546jv+pnPJk7N1L2ubVdU5CaD/xGnoDUY2Lfn83HuHNqyjw+ArXd7m2Wkp5Z6HHcpan/JUZJ6q/G1qE0eJSuFPQ04ePPo6PHCTSmxBldZc2qvkfAF+Fy6reMlLUirc9xK8/GDRsqdPgHHD1UX+oJ4ws4xm1N06FD3v3FYNW3QxnMWSWFhKdeekoiQ/LFQldn9ugQXLVPXtA0dh+371uY9JlY6B6pirf3f1/IGboGdHVWo7bjgcPaVKpaJaQ1AAjBmq3nNUA3flu0vj6nzvfgX1QlQiAfBMYbJyx8SiROKeaPXbzVtUtPzV/8Loyyq1ud2Gs9+9LM727fL2hdLEJsL/PoepY9RrgwGuvwrik2HZWlWKP6iHSnqnjFbL2rgIGkdU3TpW5piqyDxVuT8K7+YRJVxqafIAACAASURBVLcGo4m2/S9j2X//j8SjB7nq3v+j4+WjADi1ewsFuTmENmhSYh7HhdPRLf+cuyh25Xv0BiNhTVuce+/4jg3Y7TbqNml27r3AumHc+MangKqeZrNY+OHlR0osq891N2LyLeXsVga9wYDeYDh3wQTQ6fJRGEw+xB/aXyxGY4lp/OqEkJt5Fqj8tnB1vpDCz/euXU7M378x+Ob7aNAmyuV1dGU9j23fQGZyAg1bdygxn9GFtm/l7SfeZujQobRv35558+bx7rvvahqLK79Lefv1yZ2bnR5j0z9cCqi2ldtXLOb03u3YbbYSMZx/bFeWq8eLzqCuZIrf/PENCAJAbyw69fr4BwKQnZ5CYN0wl5efkRiHOa+oXXV4s1YEhtbjbHzJMVHKWndn26wqzkkAIQ0a02X4GLYvX8xV980kMLQeu1f/yLAZj+EqZ+dhV9anNBWdp6p/m9rqry2qCnLntiXfr2gbu7+3gc0GLZsWvVe/Hix5u+h1aUnE2sL7KNm5qprm5t1qORfDWSw//a6+L7JRyfkcyd0fm1RiMnwgPPCKKtV75SEYO6xoWkeptr5YsUMddXooUSoeFKD+JqepGDbsdO27z+fqfGcS1A0Lh7bNVc/YxXu5bdpQ9Z69YBm8+rD6/NuV8Nw9F36vJ3FlH6wMk7H8feF8/25XCfGM50q+f/v14F94A8lkUgl062YXzl8eV9axMsdUReepyv1ReDePKLkFmPzafNr0G8KGxZ/y5nX9ObZtPQBpcacByMlIKzF9YGg9TH7+ZCRd3N6ecPgAVrO5zHFDE48dxOQXwLiZsy94RA0ZcVHfbTCaCK7fAJu17LETil84V3ZbVGS+gtwcfv7vM4Q2bMqwOx6t2AqVofh6Jh07BIDeWMGroEJl7SfeRqfTMWPGDL744gsyM7Uf+7eiv0vx/dqVY8xms7L24/+x7PWnaNG9H5GdelbbulzMOae0Wg6O9xxJlavLbzfgcnLOpnJkk2p/npd5lvycbNoNLOcKqZjKbLOKnpMcLplyJ5aCfDYt+QKruYDs9BTqNWnuUpzg/DwMlVufis5TU7+NpztwVP01XeTt8z2H1AV9RYfttlpVtcr7X4ZLekHfrhcXhyuxnCgcLSr1bMn3w+uqBDw2Ub1eNBuuGABzF0L7kapEqjylVYxyvGezV+y7KxvzVZeo6qFrNqjX6Zmqfe2IS0rO99DNkJevqsUWmFXy3Sqy/PVzd5XdB11RkX1h/1EI9FfVfs9/jBla9nyucGUdK3NMVXSeqt4fhffymOTWxz+Aae99e64t1Cd3X0/S8UPUK7yTn3q69NHj67dwcXyAMvgG1sFSkH+uvW5xVnOBuqBJjC11HMTs9IvvOtJqNhPu4jpUdltUZL7f579Jevxprnn8FXz8A1yKyxWO9XT0TpoeV7luAsvaT7zRrbfeitVqZeHChVqHclG/i7NjzG6z8dl9k0k4sp/xz82hwXml/lWtus85ri6/5zU3cNV9z/DtzHtY/f6rLH/rWSa/+iHNu/d1+h0Xs80qck5yaNqpB82792XDtx9z4K9fiRp8VYXmd3Yersz6VGaemvhtagNHCe2JCw/ZCgkOVMmSo+1icQVlDK9ss6l2qnsOwccvuT5E0MXG4ij1OlrGv672LdXfQH9Y9VFRu8Mrbyu6GVBZrn53Zee7eawqjb3pCZj5NjwyCxa+qaqFF9eni3rvva9Vx0TXXF6h1XBLldkHXVWRfSHAD04nqHbZ50tOu/C9inC2jpU5piozT1Xvj8J7eURyaykoYMPizwDVwcvdn6/CbrNzZNNfRHbpjW9gEPvWrigxj6NamKNkR28wkJ+Tjc1mPTdNfm42dnv59SqadlSNYFa//1qJ6mopp46xa/WPNGwThd1u55e3XygxX1ZqMlt//LrS6+xYRmZKIl2uuMal6V3dFjq9HqvFUuH5Eo/G8PeCuUQNvoqOl408N92h9WsrtX4OxdezUVvV/m33bz+VmMZus5X47Uq7w1jefuKNQkNDmThxIu+++265JV7V7WJ/F2fH2Km92zi0fi1t+g4+95nVYq6yW+2VPV4qy9XlWwryyTmbxv2L1jH87qcY/9wcl6vhV3abVfScVNyQm+8jIyme5W89S5fhpdSRLIez87Ar63P+71iZbVATv01t0LWw8/7Fv5R832YHa7F/uUYjZGWrEh6HrJyiEsnendXfmXNKVmc8fFJVeS3Npt3w6z8wrFg3DWYzXOzZwFks/bupKsRL15Scz1GFcszlqnOfuYX3Gm8cAxu+Ucv6fePFxebKd1/MfHn5qqRs51I1nvHHL5Vdhfbx21QJ2yOzVDVlT1eZfRCc79vO9gW9XpWmOnRpp05NT7xZ8nsSU+DT7yu3bg7O1tGVY+r8eCtzHFbH/ii8k0cktwBbli44l9wEN2iEX1AdGnfoQmBoPa667xlO7Nx0rgoYwD8L59Pj6om07qP6ym/YpiN5mWf545M5JJ84wu8fvYW1IJ+kE0eIPaA6P7FazNhtVnWBU6h59760HzSMvWuX89Gd41i/6CNWzvkPK2Y/R7errqNN/8to2qkHO1YuYcGjt7J9+bf89sEsFv3fnfSqYGdGVnMB8YeL2rKt/fgteoyaQGRn1QtHQV4uBcXacQGY83JV3Ha7y9siuH5DspITiYvZw7Gt/+Lj5+/SfD++9gR6o5FrHn+1KGaLmUMb/qiy9WzevS8tew1k648Lz3UwdXrvdo7v2Eh2Wgo7Vi7BnJdLQXZW4frnlFh2WfuJt3r44YfZs2cPv/zyi/OJq1F5v4uz/drZMeaoBrt12TfEH97P1p++IfFoDJmpScQf2kdWSlKpx7arKnu8WM3qu2zFrm4cYzOb84saDDlisppVt5auHsdrP3qLI5v+ZM9vP7Fv7QoOb1xH0vHDF8Rf2rq7ss0cMV3MOam4DoOvokHrDjRq19npUDvnc3YedmV9zv8dLXl5TufJy1JV+q2F1bCr+rfxRLmFw4sXL7Vy9MiaV/jZoJ4wpA98+oMa0iYnT7W3+3ur6rzm65/Ve13aqSqFr36oegl+aa5a1sFjaqiTQT1h5GDVO/OwW1VHMo+/oTqruuHqkvE4vttRZffzH1QPsp/9oEqkEpJVZzYJKXC2sKWGpeza9RdwFkt4XVWa9M+2ouqSAG9/qdoJOnrV/XhJUcLTtCGE1FHDwxTfpsXjcmzb3GJtDAtPLec+c/W7z19vV+d7eR6sWa+GaFm6Bn5br4aZKc01l6tSum4dqmboJa25sg+Wtj8527eh/H2hcYTqLGrHATUszqCeqmT8659h/P2qd+Pn3oEpj8Gt49Q8ZrO6eWSuwH7tyjq6ckydH69jf63IcVgd+6PwTobnn3/++Zr+0q1JueRaXb+ParNa2b78Ww78uYqMxDh2rPiOnqMnnSs9jOzUk0btOvHPwg85tWcbJ3dvISA4lJEPPnfuoqdJhy4kHDnA9hWLOblzMwMnTyc7NZn6LdoQ2jCSM/t2sH7Rx+TnZJGfnUVowyYE1g0HoNOw0eRmnuXEzs0c3foPYU1b/j979x1fZXn/f/x1VjYhzBD23kNlCAYQMIL6FcQiCiig/YpaW/Hrqtb+WkuHWkcRHFStLUhBWipqFYsVQSFsIkv2DDOEkb1zcn5/XDkZkAlJTs457+fjEQ455z7nfO77nNzX9bmvxbjnXsYRFIzFYqH3TbeTeu4MR7fGcmDDakIbNWXccy/ToEkFU+ldYu+ar0g4uLdoYpODG1bToGkk//Pkb7FYLOz9bgXrP3qfnIw0rDY7Lbv14cD6b1i3+F1yszKxWK206zuAtn0HVnosIlq0Zu+ar9jz7Ze07TOAqK69Kj2GO7/+jLUfvk3TNh3JSk/h4MZv2bP6S76e9zLhzaLoNjSmRvYToNeo/yHt/Fm2fLKQTf9aQGBIKA2aRtKiS0/a9RtITmYG381/kzMHdpOSeJrQRk2JiGqN1Wav8HtSFUE2CwObV758k7eIjIxk06ZNfPfdd9x///018ppZ+S6+P59d+YaFKvr7rdL3ut9A+tx8R7l/Yw2btyT9wjkObfqWE7vi6DXqNjoOGMq+NV+RlHASV0EBm5d9WObfdlVcyd/LiR++Z+2Ct0hJPE1OZjpRXXtz8cRRvlvwFilnT5ObmUGLzj1JTjjJmvlvkpxwitysTFp260NweESVzmkWLGz9bBG7vv6Mnf/9lG3L/8mGf3zA3u++ouuQUQQ3aMiOFcvKPK9Vdsz6jh7PgfWrauScZC0xsda5YwfpMfyWUutyV1VF5+Gq7E+TNh1KfY7dht1c4XOatevMmoVvc+7YQbLTU2nSuj2NWrapsc+mKqwWaB3moF2DK5uDoFLO85D0dpU3P37GLPeyfR8kXiycMMoCr/zFJK+JF6BfN2jbEu6MMZXd95eaBDcs1My82rcbDLkWurQ1MwLvPmQmIVq/DWZONa/brYNZQqZLOzNja1IqrN9uJpTp1Bbe+n+mm+bmnfDqX03ycDHFPG9wP1Nx/no9bNoBd94MI6833WSPn4Gu7eG1v8K+o6Zy3bFN8ayrlakoFoBBfUxSN+dDE9vG7WZW11eeMQmC02mSks9Xm9aov38O08bDHaMK9+UD0/U0LdMcx8PHzbE9mWBa/fp0NV29X/nA7EtGFlzTHRo1rPy9t+8z4x8v3e/Knof5iPnrMvjnCvjHl7DwM5MAfb7ajH+MCC8+RhaLeY+xI836rdVmsUNwNIRe5UDS8lTzOw8Vf+7lHddre1T83e7YuvzvAkDbKPPYp9+Y7/Q13WHCaLPtt5vhq1ho1sisgRzZFD5abrqDp2WYn7YtzaRQNbGPrSMr/pu6+1azZFDJeP/nxiv7O6zp72OFavu7Jh5jcXmgv+J7ey5yMecqpy70Mct+9wTbli/ldxtPejqUWlXf97NhgJWf9Kpeq1J9t3r1akaNGsWGDRsYPLiMRfmq6UK2k/f3XuUgH7lqG//5V8KbR9FlyEjSL5wjNzOdnMx0TvzwPWnnE7ll5q+u6vVr42/1g5/cxfQ5i4qW+fJVNfXZ2CxwfWQIw6Nqbn6DUnL3wZHqzXgv/uedj6BVcxgdbRKW9EyTQG3aCQnn4OXSE9lz84/h83nFy0BViyUQGj8DzX5X+bZXQt95r1fd72O5avu7Jh7jFUsBebOXbumLs7ArYnkm/u6dOoqm9vjLfnqjkSNHMnjwYF5//XWWLi17rWN/VNXvbLfo+jeY59TeHaz+YDa/+MqsY+xeTxWgabvO7FjxsadCK9fRuPW06tHvssTWmz+HsnjjZ+PvWo8o7uJbng9fNl03/U3cbvjDn+HUd+b3kq3c3TqYbrIlfbcF+ve6wsRWapyvfber+30U/6Tktpb9YsXOKm23Y8UyCvLzcblcZS6lUd/5y356qyeffJJJkyZx8OBBunTpUvkT/EBVv7P10dlDe0k9l8DqD97gutvvJqxJM7JSUzixaysHN37Hrf/3QuUvUon83Nyr/ls9tm0jn/zhaVp06k7C4X089Jd/X7aNN38OZamLz0Zq1slvPR1B/fXDQTNB1IvvwrQ7TBfYpBSzJunX6+GVp2FtHDzyAvTuCj8cgO8WejpqcfO173ZVvo8iXjHm1tet+suf2PrZYvJzs8nNyiCoQUMaNo+q/Ilexhv209fG3Lp1796dRYsWcerUKcaNq95MtZeq7phbqXmRXXqAy8WmpX9j1fuvE7vozxze9B2RnXow4oGZRUtqXama+lvNzcpg2xf/JPVcAj/69Wyad+h6VXF5g5r8bOrbmFvxP326mFl65y2B382D2QvMBD69u8BzM8yyTxlZZvzo6USz7mqPTlfxhvVwzK3UH1X5PlaZxtz6LI25FSnBF8fcus2fP58HH3yQ3bt3061b9Sf0cdOY2/olLzsLe2CQekLUQ1f72WjMrdQnmdkQHFg8sU+t0JhbqaKr/j5qzK3P8pqlgETk6kydOpWuXbvy+9//3tOhSA1yz9ou9Y8+G/ElIUG1nNiKVIO+j1IeJbcifsJms/HCCy+wePFidu70rXGOIiIiIiJKbkX8yN13303fvn2ZNWuWp0MREREREalRSm5F/IjFYmHWrFl88sknbN682dPhiIiIiIjUGCW3In5m3LhxDBo0iF/+8peeDkVEREREpMYouRXxQ7Nnz+abb77h008/9XQoIiIiIiI1QsmtiB8aMmQIkyZN4qmnniI7W2vWioiIbzt//jyrvt3o6TBEpJYpuRXxU6+++ipnz55l9uzZng5FRESkRmVlZfH111/z7LPP0r9/fyIjIxn3o0fJzfN0ZCJSm5TcivipVq1a8eyzz/Liiy9y+vRpT4cjIiJyxQoKCoiLi+OPf/wjN998M40bN2b06NH861//YsCAASxZsoQTh78lwOHpSEWkNtk9HYCIeM4zzzzDX//6V37xi1+wYMECT4cjIiW4PB2ASJ1zVmvrQ4cOsWrVKlauXMmqVau4cOECkZGRxMTEMG/ePGJiYmjdunXxE3L3QVINhyxeqnrfNfEeHklurVg88bbi4xIO7eXo9+vpe/N4Qhs1uaLXsFr867sZFBTEq6++yt13380DDzzAiBEjPB2S1JLDm9cQ2akHYU2aeToUqaICH8tuN+6AE2dg4i2ejkTqLVd+hQ8fP36c1atXs2rVKlavXs2JEycIDQ1l+PDhPP/888TExNCnTx8sflaWV9c3G6F3F4i8sqqSb6jkuybey+JyuXys+BR/9eGHH/LTn/6U7OxsYmJimDx5MuPHjyc8PNzTodV748ePZ8+ePezYsYPg4GBPhyO1wGazsWjRIiZNmuTpUMSP5ObmsnTpUubOncvmzZsZM2YMK1as8HRY4iXOnj3LmjVrWLlyJbGxsezZswe73U6/fv2IiYkhJiaGYcOGERgY6OlQvYrKA/FlGnMrPmPatGmcO3eOZcuW0aBBA2bMmEFkZCRjx47lww8/JDMz09Mh1lvvvPMO586d47e//a2nQxERH5CYmMgf//hHOnXqxLRp02jevDlff/21Elup0Llz5/j888957rnnGDBgAFFRUUyePJm4uDjGjh3L119/TWpqKlu3buXll18mJiZGia2IlKKWW/FZycnJ/Pvf/2bp0qWsWLGC0NBQxo0bx8SJE7nllltwODSrREnz5s1j5syZbNiwgQEDBng6HKlhulIvdWHbtm38+c9/ZuHChQQEBDB9+nSefPJJ2rVr5+nQpB5KT09n48aNrFy5kpUrV7Jt2zYsFgvXXHMN0dHRDB06lNGjR9OwYUNPh+pTVB6IL1NyK37h9OnTLF26lKVLl7J+/XoaN27MhAkTmDp1KtHR0Rqfg5lpcvTo0Zw8eZK4uDhCQ0M9HZLUIFVmpLYUFBSwfPly5s6dy8qVK+natSuPPvooM2bMICQkxNPhST2SkZHBhg0biroZb968mby8PDp27FjUzTgmJoZGjRp5OlSfpvJAfJmSW/E78fHxLFmyhPnz57Nv3z7atm3L+PHjmT59Otddd52nw/OoU6dO0a9fPyZMmMC7777r6XCkBqkyIzUtJSWF+fPnM3v2bE6cOMGoUaOYOXMmt99+uy4YCmDWmo2Li2PdunWsXLmSNWvWkJubWyqZHTVqFE2a+PPMRnVP5YH4MiW34td2797N0qVLWbhwIUeOHKFnz55MnDiR++67j86dO3s6PI9YtmwZEyZMYNmyZdx5552eDkdqiCozUlMOHDjA22+/zQcffIDNZmPSpEk88cQTdO/e3dOhiYdlZ2ezceNGvv32W1atWsWmTZvIzc2lS5cujBw5sugnMjLS06H6NZUH4suU3IpgutWtX7+epUuXsmTJEhITE+nfvz9Tp07lnnvuoUWLFp4OsU499NBDLF26lK1bt9KpUydPhyM1QJUZuRoFBQWsWrWKOXPmsHz5cjp16sSDDz7Iww8/TEREhKfDEw/JzMxkw4YNrFmzhtWrV7N582ZycnJo164dI0eOZNSoUYwcObL0WrPicSoPxJcpuRW5hNPpZPXq1Xz44Yd8+umnZGZmMnjwYKZNm8akSZP8Ymmh7Oxshg4dSm5uLhs3btS4OR+gyoxcibS0ND766CNmz57N/v37uemmm3jooYf40Y9+hM1m83R4UscyMzP5/vvvi7oZx8bGkp2dTVRUFEOHDiUmJobo6Gh69erl6VClAioPxJcpuRWpQHZ2Nl9//TULFy7ks88+w2q1EhMTw8SJE7nrrrt8OumLj49nwIABjB49mkWLFnk6HLlKqsxIdRw6dIi//OUvvPvuu+Tn5zNlyhRmzpyppMXPuCeAio2NZd26daxdu5acnJxSyezNN99Mhw4dPB2qVIPKA/Fldk8HIFKfBQUFMXbsWMaOHVtqaaH//d//ZebMmUVLC916663Y7b7159SuXTsWLFjA2LFjGThwIP/3f//n6ZBEpBa5XC6++eYb3nvvPZYtW0a7du147rnnmDFjBo0bN/Z0eFIH3EvzuJPZkhNARUdHM3fuXMaMGaOlnUSk3vKt2rhILYqIiGDatGlMmzat1NJCd9xxh88uLXTbbbfx0ksv8dRTT9GxY0fGjRvn6ZBEpIalp6ezePFi5s6dy+7du4mOjuajjz7izjvv9LmLdlJayXVmy1qaZ+rUqYwYMYK2bdt6OlQRkSpRt2SRq+QPSws98sgjLFq0iNjYWPr16+fpcOQKqBuaXOro0aO8++67vP/++2RkZHD33Xfz9NNP07dvX0+HJrUkLS2NTZs2lZvMRkdHM3LkSNq0aePpUKUWqTwQX6bkVqQG+erSQrm5uYwZM4bDhw+zbt06VXy8kCoz4hYbG8vcuXP55JNPaNasGQ899BA/+9nPaNq0qadDkxqWmJjI2rVrWbNmDd999x27du0CoHfv3owYMYIbb7yR4cOH67P3MyoPxJcpuRWpBb64tFBKSgojRowgIyOD2NhYmjdv7umQpBpUmfFvOTk5/OMf/+D1119n586d9O/fn5kzZzJ58mQcDoenw5MaEh8fz5o1a1i7di1r165l37592Gw2+vXrx/DhwxkxYgTDhg3TGGo/p/JAfJmSW5Fa5ktLC50+fZro6GiaN2/ON998Q1hYmKdDkipSZcY/nTlzhnfffZe3336b1NRU7rjjDp544gmGDBni6dCkBhw5cqRo8qeVK1dy5MgR7HY7/fr1Izo6mqFDh3LTTTcpmZVSVB6IL1NyK1KHfGFpof379zNs2DCuueYa/v3vfxMUFOTpkKQKVJnxL3FxccyZM4clS5bQuHFj7r//fh577DFatWrl6dDkCjmdTvbt28e6deuIjY1l9erVnDx5ktDQUK655ppS68wGBwd7Olypx1QeiC9TciviISWXFlqxYgWhoaFes7TQ999/T0xMDIMHD+aTTz4hMDDQ0yFJJVSZ8X25ubl89tlnzJ49mw0bNnDdddfx8MMPM23aNF2E8kL5+fns2LGjVMtsUlISDRo04Prrry9KZAcNGkRAQICnwxUvovJAfFn9rT2L+DhvXlrouuuu45tvviEmJobx48fz6aefKsEV8ZCzZ88yf/583nzzTRISErj11lv5+uuviYmJ8XRoUg0ZGRls27atKJFdt24dWVlZtGjRggEDBvDss88SExPDtddei9Vq9XS4IiL1klpuReoZb1paaNOmTYwePZqhQ4eydOlSr+hW7a90pd73fP/997z77rt8+OGHBAUFMW3aNJ566imtSeolLl2WZ8uWLeTm5hIVFVWqi3HPnj3r3QVO8W4qD8SXKbkVqcfKW1po6tSpdOrUydPhAbBlyxZuu+02unbtyhdffEGjRo08HZKUQZUZ3+B0Ovnyyy+ZO3cuK1eupF+/fjz66KPcd999urhUz8XHxxMbG1v0s3v3biwWC7169WL48OEMGzaMYcOG0bJlS0+HKj5O5YH4MiW3Il6gvi8ttG/fPsaMGUNYWBhfffUVrVu39mg8cjlVZrxbcnIyCxYs4E9/+hMnT57ktttu4/HHH+emm25Sq149VFBQwO7du1m7di3r1q1j7dq1nDhxAofDwYABA4iOjmb48OFER0drJmOpcyoPxJcpuRXxMvV1aaEzZ84wZswY0tLS+Oqrr+jatatH4pCyqTLjnfbv388777zDX/7yF+x2O/fffz9PPPEE7du393RoUsKlkz+tWrWKCxcuEBYWRr9+/TSTsdQrKg/Elym5FfFi9W1poaSkJG6//XYOHz7Ml19+We/GCPszVWa8R0FBAcuXL2fu3Ll88803dO7cmZ/+9Kc8+OCDhIaGejo8oXi8rDuZjY2NJTs7u2jyp6FDhxIdHc3111+Pw+HwdLgipag8EF+m5FbER9SXpYXS09OZMGECGzdu5NNPP2XkyJF18r5SMVVm6r/U1FT+9re/8cYbb3D8+HFGjRrFzJkzuf3229X12MNOnz5dlMSuW7eObdu2UVBQQMeOHYmOji5KZnv16uXpUEUqpfJAfJmWAhLxEfVlaaGwsDA+//xzpk+fzpgxY3jjjTd49NFHa+39RLzdwYMHeeutt/jggw+wWq1MnjyZxx9/nJ49e3o6NL915MiRUuvLHjlyBLvdTr9+/YiOjubZZ59lxIgRNGvWzNOhiohICWq5FfFxnlpayOVy8corr/D8888zZcoU3nvvPY018yBdqa9fCgoKWLVqFXPmzGH58uV07NiRGTNm8NBDD2nG8TqWnZ3N1q1bi2YxXrduHcnJyYSHh3PDDTcQHR3NsGHDGDRokM5h4hNUHogvU3Ir4kc8sbTQf/7zH6ZMmUKPHj34+OOPiYqKqpX3kYqpMlM/pKWl8dFHHzFnzhz27NlDdHQ0jz/+OD/60Y+w2WyeDs8vJCQksGXLFuLi4jReVvySygPxZUpuRfxQRUsLTZo0icjIyBp9v4MHD3LHHXeQkpLCxx9/zODBg2v09aVyqsx41pEjR3jvvfd47733yMrKYuLEiTzzzDP06dPHRwL14QAAIABJREFU06H5NKfTye7du4mNjWXDhg2sX7+eI0eOYLPZ6N27N9HR0QwZMoTo6Gg6dOjg6XBF6oTKA/FlSm5F/FxdLS2UnJzMlClTWLVqFfPmzeOBBx6okdeVqlFlxjNiY2OZO3cuy5YtIzIykhkzZvDYY4/RpEkTT4fmk9LT09m+fXtRi+z69eu5ePFiqSV53BNAqfu3+CuVB+LLlNyKSJHaXlrI5XIxa9Ysfvvb33LffffxzjvvEBYWVkPRS0VUmak7OTk5/OMf/+C1115j165d9O/fn5kzZzJlypQ6m7XcX5ScxTguLo7NmzeTl5dHVFRUUSLbv39/dTEWKUHlgfgyJbciUqbaXFroq6++4v777ycoKIjFixczZMiQGoxcyqLKTO07ffo07733Hm+99Rbp6emMGzeOp556iuuvv97TofmE/Px89u/fX5TMrlmzhvj4eOx2O127di1KZm+88UbatWvn6XBF6i2VB+LLlNyKSKVKLi20fv36Glla6OzZs9x///2sXLmSX/7yl/z617/GarXWQvT+Jz4+HqfTWeq+Ll26MHv2bG6//fZS90dFRWkG2KsUFxfHnDlz+Oijj2jatCnTp09n5syZtGzZ0tOhebXU1FQ2b95cNIPxunXryMrKIjw8nEGDBpVaX1bfYZGyqTwQf6PkVkSqpSaXFnK5XMydO5ef//znDB06lA8//JBWrVrVUuT+Y+zYsXzxxReVbme32zlz5gxNmzatg6h8S25uLp999hl/+tOf2LhxY1HX48mTJ6v76xUqubZsbGwse/fuxeVy0bFjx1KJbM+ePWt1rW4RX6LyQPyNklsRuWI1tbTQli1bmDJlCikpKXzwwQeMHTu2FqP2fYsXL+bee++tcBur1crNN9/MihUr6igq35CQkMCCBQuYO3cu58+f54477uDxxx8nOjra06F5lczMTL7//vui5XhWr17N+fPnCQkJ4dprr6V///4MHTqUkSNHqrItchVUHoi/UXIrIletJpYWSktL47HHHmPBggVMnTqV2bNna0bZK5SZmUmTJk3Izs4udxur1crChQuZMmVKHUbmvdxdj5csWUKjRo144IEH+OlPf0qbNm08HZpXOHPmDFu3bi1qld26dSs5OTlERUUVJbLR0dEMHDiQwMBAT4cr4jNUHoi/UXIrIjXqapcW+vLLL3nkkUfIycnh1VdfZdq0aXUUuW+ZNGkSy5YtIy8vr8zHAwMDOX/+vGarroC76/GcOXNYt24d1157LY888ghTp07VuLQKOJ1O9u3bV2oW4z179mCz2ejWrVupWYx79erl6XBFfJ7KA/EnSm5FpNZc6dJCqamp/OpXv+Ktt97i1ltvZd68eWohq6bPP/+ccePGlfmY3W5nwoQJLFmypI6j8g6JiYn87W9/4+233+bUqVPcdtttPP7448TExHg6tHopLS2NTZs2FSWysbGxJCcn06BBA66//vqiRHbYsGFERER4OlwRv6PyQPyJklsRqRNXsrRQbGwsDz74IAkJCcyaNYvHHntMMypXUV5eHk2bNiU1NfWyxywWC5999pnGNl9i+/btzJs3j4ULFxIQEMD06dN58skntazMJUquLbtu3Tq2bdtGQUFBqbVlhw4dyrXXXqu/V5F6QOWB+BMltyJS56qztFBWVhYvvPACs2fPZujQobz55pv07t27zNddsmQJffv2pWfPnnW1K/XajBkzWLBgwWVd0Ro0aMC5c+d8emxjXl4ejz76KC+//HKFY7cLCgpYvnw5c+fOZeXKlXTt2pVHH32UBx98kNDQ0DqMuH5KT09ny5YtbNiwgY0bN7Jx40bOnTtHUFAQ/fv354YbbiA6OpohQ4bQvHlzT4crIuXw5/JA/IuSWxHxqKouLbR161Z+8pOfsH37dh599FFmzZpVqotjdnY2bdq0wWKxsHHjRjp27OiJ3alXVq9ezahRo0rd53A4mD59Ou+//76Hoqp9mZmZTJgwgRUrVvDSSy/x3HPPXbZNSkoK8+fPZ/bs2Zw4cYJRo0Yxc+ZMbr/9dr9dZsblcnHgwIGiJHbDhg388MMPOJ1OWrduzeDBgxkyZAhDhgyhf//+BAQEeDpkEakify0PxP8ouRWReqOypYVcLhcLFy7k5z//OXl5efz617/mZz/7GTabjTfeeIMnnngCq9VKVFQUmzZt8vs1c91dRRMTE0vdv2rVKkaOHOmhqGpXUlISt956K3FxceTn59OiRQtOnDhR1O39wIEDvP3223zwwQdYrVYmT57ME088Qffu3T0ced1LT09n+/btRV2MN27cyPnz53E4HPTt27dorKwmfhLxfv5YHoh/UnIrIvVOZUsLBQYG8vLLLzN79mx69erFK6+8wl133UVKSgpgxhC1adOGzZs3V2kZIl/21FNP8dZbb5GbmwtAs2bNOHPmDDabzcOR1byEhARiYmI4cOBAUdc7i8XCRx99RJMmTZgzZw7Lly+nU6dOPPjggzz88MN+NcHRkSNHiiZ9unSsbMnleAYMGEBQUJCnwxWRGuZP5YH4LyW3IlKvVbS0UL9+/fjlL3/JqlWrANOt0s1isdC+fXvi4uJo1KiRp8L3uK1btzJw4EDAdEF77LHHeP311z0cVc07evQoo0aN4tSpU6XGlFmtVhwOB7m5uYwePZrHH3+cMWPG+PxER2lpaezYsaOoVXbDhg1cuHDhslbZ4cOH0759e0+HKyJ1wF/KA/FvSm5FxGuUtbTQiBEjWLVqVdGV6Eu1b9+eXbt2+fX6fR06dODYsWMAbN68uahy4yt2797NqFGjSEpKKncdx3/+859MnDixjiOrG+51ZePi4spslS25ruzAgQM1cYyIH/P18kBEya2IeKWLFy/y8ccf8+KLLxYV1OWJiopi3759hIeH101w9cxvfvMbZs2aRdu2bYmPj/d0ODVq06ZNjB49mszMTPLz88vcxuFwcM8997Bw4cI6jq7Y4sWLiYyM5Kabbrrq10pNTWXz5s2luhgnJSURGhrKNddcU9TF+MYbb9QMxiJSii+XByKg5FZEvNiFCxdo27YtmZmZlW4bERHBzp07adOmTR1EVr/s27ePHj168Otf/5pZs2Z5Opwas3z5ciZMmEB+fj5Op7PCbe12OydOnKBFixZ1FJ2xf/9+Hn74Yb777juefvppXn311Wo9v2SrrLuL8d69e3G5XJe1yg4aNEgzGItIhXy1PBBxs3s6ABGRK/XKK6+U2w31UsnJyXTp0oVVq1Zxww031HJkNSQzFQqckJMFudnm/5mpJR5PA2fZrZXk50J2BgDdgeu6duCeThGwZql53B4AQRWs4xoWAe4lcQKCICAYrDYILWz9Dgk3v3vIokWLmD59Oi6Xi4KCgkq3t1gsvPvuu7zwwgt1EJ1Zn/nFF1/kj3/8Y9F9sbGxlT4vJSWFLVu2FLXKxsbGkpycTFhYGP369SMmJobf/OY3jBgxgmbNmtXmLohIXcrJhLwc8/+84vM3rgLISCm9bX4eZKVX7XUzkqFEO1aZ5YFbcBjYHJW/pqOM8iMg2JQVAHaHeS0ALKY8EakjarkVEa+UkJBAx44dyc7OpjqnMavVyrx583jooYdqLpicLEi7aH7Sk0wCmp1hKiRZ6eb/2RmQlmQqMFnpkJEEacnm/twsU6nJzYaCArNNDVt5EWIa1/jLmkqOPcAkusFh5jYkHEIjICSs+P/BYRAYYm7DIkzFKDDEPN6gETRobH5CG1b6lnPmzOGJJ56o0ufucDiwWq3k5eXRpEkTTp48Weutm6tWrWLGjBnEx8eXalEOCAggPT0dh8NUHvPz89m/f39Ri2xcXFxRq2zHjh2LWmSHDh3Ktdde6/OTYIl4nMtlksFLz9vui4W52eZ87b7g6E5IszMKE9J0k3imJ4Mz15zjC5zFCWaBC7JKXKDMziiVeNaVWisPqiIwBNznsoAgcBTOAeBOrEMjwG6D8KZgsRYmxhZTTlgs5nGr1ZQVNjsENzDJdFCYKVeCQovLmeAwUz6JX1FyKyJeKS0tjbi4OJ577jm2bNlSpda7kmbMmMFbb711eaKTkwkXEyDpLCQnQlICpJwvTl7TLkBSIqReMBWWjFRT8bmMxRS8Vov5P5gr8E6nufUXFkyFxVLiOBQUgMtpbi/b3gohDUzFJKwxNGwGEc2KEuBffbGJ3y/9qsy3CgwMJCIigkaNGtG0aVOaN29OkyZNaNKkCY0bN6Zx48bceeedNG5cO7W606dP8/Of/5xFixZhtVrL/E6+9tprJCQksHHjRuLi4sjKyiIiIoLBgwcX/Vx//fV+tUSRyFVz5puEMj3J3GYkm6TUfZuVVpyspidDegpkFl58zEiG7EyTtOZmV/w+Fqu5gFfyvA6FCarL/87vdcHuABfFCTGY4+1ymWNdUPGQFGw206ocEl6Y/IZBWDiENytOhkMamGQ52F32NCq8LfH/wJBa3U2pOUpuRcRrZWZmEhkZSXp6FbtnXeKGDi341519icq5aJLZlHOXV25sdlOZcblMIVpZQSo1z2oFq52d6fDfc04a2wpobHfR2IH5CbLTuFEEQY0joVlraBwFjSKhUQto1gaatzG3jVoUd7WuQfn5+bz99ts8//zz5OXlldtV3t3y2r17d/r3769WWZFL5eWYi4mp583FxeRE87s7aU1PgtSL5vG0iyZJzUo1LamXsgBWR3FS5E6GyhvKIf7HajPfD0uJ74gzv+wLFDaH6Y0UWpjwhhdefA2LMPc1aAwNm5qf8KYQ0dz8BPvvSg2eouRWRLxWZmYme/fuNQVRUiKcP2VaWpMKW14vnoFzJ0slrRfz4GSelRM5Fm6IsNA5IJ/2wR7eEalZVpu5KOFyma6B7lLOZjdJb/N20LITNG8LTVubxDeqo/lxVG+ZnK1bt/Lggw+ya9euSnsP2O12Jk6cyOLFi69wx0S8jKug+Fx8MaE4cU05Z87ZSWchOQGSz5nHLh2SYbGav1uLRRcYpf6xWMBqL76A4sy//PtpDzDJb0RTaNLSXGR1J8CNIk2C3LDwscZRxeOW5YopuRUR73HhNMTvgYQjcOYInDoEJ/bBmcPFLa7ubmO4dIVeyme1FU6c4oL8nOIEOLiBSXI79oV2vYqT3tZdzWOFLly4wDPPPMP8+fOx2WzlLkN0qY4dO3L48OGa3x+RupaeZM7JF86Y24uFt+dPQWK8+X/yWdNV160oWcXcr0RV/I3V/TdgNRds8nIpLoAw81hERELTVubCa+Oo4sS3SUtoEmUuzJYoj6Q0JbciUr/k58Gpg3Dsh8KfXXBoG5w/WVxJshVeKc3Lo1ShIFJTbA4zri6/RMtvw6a42vXib2cdPPvvjZxPTcdisWC1WitdisjNYrFw4cIFGjVqVHuxi1ytnCxIOGqS1LPxxbenDpr/p14snZjabKYFy1VgzuEicvWKeiGV8XcV2hAat4CozqYnUmQ70yspsvCnof/Opq/kVkQ858JpOBhnktiju+DIdjh50LS4WixgDwRnnq7uS73hdMHRXBvxmS6OZRYQnw3HCoI5mBvI0fQ8zqVnUlBgilWbzYbD4SA/P79Uy+6KFSsYM2aMp3ZBxFSUE46aZLVkEnvmECTEm/Gsbja7mdQnP7d0K6yI1A/WwskrnXnFF2Mdgab1t2Vh8utOfFt2Nj2RQsI9GnJt0jq3IlI3MlPhyE6TzO7fDHs3wJmj5jGb4/LZc10uyKtk5kqROmazQOdAJ50DgaLG1ywgGwICycuzciLTybH8AOLDWnEsoBnH8hwcvJDO0dNnSUxMZNOmTUpupW6kJ8Gx3XB8T+FQjoNwfB+cPljcEuQe0+rML3tZGme+hniI1GcF+XDplA95OeZv/swR2BkAuHsiFf6Nu4fgtO4K7XoWD8Np0/3yNYy9jFpuRaR2HN8LO1bDrrWwZz0kHjf3OwLLnnRBxBfZHKbi4XJBcAPyuwwgvcv1RAy5BXoMrvYEViKXKXDC6cNwdCcc3mES2fg9pkU2L8dsYyucNfjS8X0i4t/sheN/i4bgWEx357Y9oH1v6NAHOvaD9r28ZjkkJbciUjNO7ocd38L21bBtpVkH1j1jrRJZEcNiMYlGfq6ZOKT7YLguBvqNhO6DzMyaIuVJTzIJ7NFdJpk9sMW0xOblFA7lCCjdOiMicqUcAab+5nQCFohsC136Q6droENfM/FiZPtaWWLvaii5FZErk5MJW7+C9Z/B5uVmGQebzVz5UzIrUjWlkt1A6BUN0XfCkHFmRkzxX1npJnndswF2rzNDOpLOmsfsjuI1OUVE6orFWjgGv3CVgcAQ07rb8wboOcT8NG3t2RCV3IpIlSUnwsbPIXYZbPvGVMjdFXMRuXoWi6k8FDhNl7DhE+GGO0y3MPFdLhecOgB7N5qfnWvMMmeugsKhHHml5yQQEalPSg7BadgMeg81Pz0GQ+fr6nT9XiW3IlKxrHRY809Y/j7s21S8WLlaZ0VqlwWwFXYzbdISxjwAY35sJv0Q73f6MMR9BZu/NHMTZKaa86vVpuV0RMS7WW3mfJafZ4aodewHA2+FgbdA9+vNfbVEya2IlG3/ZvjPX2DlItP9BJTQiniSzW7GPvUdDrc/Yrova0Iq75GdYSbZ2/oVbPjcLL9jsxcO5VD3YhHxcTa7GUoRFAr9x8CgW2HAGGjWpkbfRsmtiBTLz4Vv/g7/eg3i9xZPTiIi9YfVWjT7MmN/Anc9DQ2bejoqKUvSWYj9GNYsNeNmnXnmvJqn86qI+DGrzdwWOE1vpOET4cZ7oPO1V/3SSm5FxHQb+c/78PffmXG1uDTbpog3sNnNWKe7noK7noSwRpU/R2pXTpZJZr/6q+lubLGY86lLY2ZFRMpkd5i6aIv2cPP9ZhjOFU6qqORWxN9t+Q+8+TNIPKYJS0S8lc1uJuy45zmY+LS6K3vC6UPwyRz473zIzjRjpnVOFRGpHrvDDMG57ma4c6YZq1uN5YaU3Ir4q4wUePtnsPLvppujKmEi3s9mg6jO8ItFZj1CqX3xu2HhLFj7sTn+mgxKROTqWe3gyoc2PWHqC6brchWSXCW3Iv7o5AF4/lY4f0IVMRFfY7ObCsATf4Gbp3k6Gt+VngQLfgWfzwOLzYynFRGRmmUpnGei2wCYOa/SC7dKbkX8zckD8PgQs+yEUzN0+rp0J4TZPB2F1DlL4T8zXjXjcaVm7d8Mv7nTzFGg86hIlVWnTLqa8isxF1ZehHO5ENMYeoVd2evUBJXDNcRmN70M7/sV3Pdrk/SWQcmtiD9JT4L/7QEpF+rt0hMp+fDqMViTDEl50D4Y7BboEWoebxkIUYFmm00p5r7/awvTW8I1Da7+tR9pDVtSYcV5iI6A0U1qaUevUmwyPHcQEnIgwGr2o4EdftwS/rcVLDoDC87AD+lwerjn4qzKMf9Zza4CAEC+yzs+x1pnscCsz2DwWE9H4ju++ye8NMX838uXR9P5tmL/vWDOpR+eMb8PbgghNsh0QgEwKRJmtC6duKy6CM8fgiV9zD7XpNp67f+ch1fjYfVFc10sOsLcn+uCyAC4JxKmRBVeM7tC75+CfybAvkw4MazibatafpV3PPZnwDMH4e3u8Ng++PI8nB8B4SWWVr2Sfa7u8a8v5bDPsVhh2F3w/OLiWZdLKDvlFRHf9O7TkHax3ia2X56H7uvguyT4sBfsGgKfXwN/7Qmnc+Clo6ZSMaE5vNbVPKdPGMzuVnlFq6qvvSUV3j8Jvz0CJ7Jrdv/O5NTM6/yQDjfHwU9aw4Fo2HMD/KID7Egz+wIwqQXkFZgfT6nqMa+q6hy/2vwcvYsFXn8AstI9HYhv+CEWXrrXtB54eWKr823lRjeB+b2Lk9d1A+Gb/rBhEMxsA08fhLHbIK9EM1FSvtmXjFr4etTWa9/aFN7sZv7fKwzWDjQ/q/pDiwC47wd4Yv/VvcePW0J2AeRXoUyqavlV3vF4/hD0DoM2QbCgt/kMw+2lvxNXss/VPf71oRz2Sa4Cs8Tau2X3SlJyK+IvUs7DyoX1dozt0SyYtAvaBcOqAaWvijZywPxeMKVFcaHS1GFuGztq9rWHNITHrmz2+Qql5sPUH2rmteafBhdwbxRYCy8rT24B83oUJ7c2C7QOqpn3uxLV/TwrU93jV1ufo9dxFUB6Mqxe7OlIvJ/LBXN/AhYX5i/Qe+l8W3UWoGFhi5+1RDPevVFwV3P4NgnWJRffP6E5nBpeO91ga/O13fsYVCIzCLXBW90h0ApvnTDH9UpVp0yq6rZlHQ8X5uKKe38a2s33razvRHX3ubrH39PlsE8rcMKnb8Kxy//QldyK+It9m6Cgfia2ANN+gLR8+F0ncJTT92lWp+KWPvcmVekmVd3XDriavldlyHDCxJ1wOKtmXu9sLuQUmO5UJd3bomZevyZU95hX5EqPX01/jl7L5YKd33o6Cu935oipSDm9u8UWdL6trvImaO1a2MX6SGbNvZenlLePAVbTTdfpglM11PuoNqXmmxbiksr7TvjKPvstux3Wf3r53R4IRUQ8IfWCmdHTVf8qZrvSzRjSCDvcXMGYq84h1b/KXxOv/Y8EeHAPRDjMWKHUfPjraTPm9doGposamG7Bs49Dz1DT/SmnAN7pAZ8kwt4MM+5sxh7oFgpPtzNXmN89aZ73fZq5ivx2d+gSYgrUhWfg72dgZX9TYdyfAd8Phhsbmfsn7ISP+sCYwv2yWsz7XSohFx7ZY8a+tQ+Cv/cxMULFMYBJpH91yHTvOp4N5/PgLz2hiaP8GBf0rv4xr+h9yjt+FT2nIv86a7pLBlpNF+/+4fDrjuZ3t62p5rhkOOFQphnH/L+tzJjBij7reqmgAJISPR2F97t42tMR1Aidb6t3vq3ofLIx2Zx3BzYsvu9cLvzjLAwMh+sbwvY0M/by40SIux6eOABfnIOOwbCkr7mtyjZX+tpulZ3TynM213TFDbaWboWvrOwA+OwcLD9nWuyznJd3Fa/KebSi8uvS47HgtJlECmDpWbOfnUOgVWDZ34nq7vOl7+f25Xlz3B1W2JwCP24FM1pVbT+q+hlWdrwrOpZeVV5VR4ELzp+67G613Ir4i6gO9XaM2ObCiUo6hVS8HUDbanbxqYnXvqcF3BBR/Hu43Uyq0ueSrkmTdpkC7eft4Y9d4GRhQX5fFPRrAE0D4P2exYXqH4+ZwnNeD1g/0LR2DN9iWjR2pZuCel8GvHfSTFzSItAUSg+0hPHNTUF9y/cwZZcpjOHylpWsAjO+7eUu8N0AOJIFzxwofryiGAAm7YRUJ/yqo4n9aBb8X+E4pPJiXJtU/WNe0fuUd/wqek55ZsfDG8fhT93MOMJFfUwlaPT3xR1N47NhxFb4ZQdY3MdMgPPIXhi8uXgMVnmfdb1kc0Drrp6Owvu17cnVTalTP+h8W73zbUn7MmB3ukmgpv8Am1NNF1Z3bOuSzUXHx/YVx9Mi0CQwR7PgF4dMvEv6wv5M+OWhqm9zpa8NVTunlWV3Oty53XSvndPdHD+3ysqOxQnw8lGY2918Pi90gj0ZVfsM3Soqv8o6HtNbwhuF42jvbG4+/2fbl/+dqM4+l/V+UHxR5K3uMKcbjG0GD+0xk09VZT+q+hlWWlZXcCy9qryqDgvQoc9ldyu5FfEXPYZARHNPR1GmpMIxLZW1uHnytUPKmMa/5BXvPJep+GxLM78HWM0EGuU5nQNvxMPUKPO7zQJ3RZqru5+fg1uamNkbnS4ztuvHrWDTIDPDqM0C/+prkrNQG3yUAD3Wmyu/ZcX4WlfoHmoqYDGN4fvUqsXg1q9EpbJ3GOws3MfyYgwuPFbVPeblvU9NPScxF3512MzQ6u4u2cQBz3eANUmmggLw1nEzttB9xf7/dTS3D7Uyk+lU97P2OGeemVlSrk54E4iZCvZaOFHVIZ1vq3e+LemVY2byq6cPwN8T4I5mZgZlt+gI+FWH0s9pEVDcsvuHzqb1LKYxDIuAuNSqb3Olrw2Vn9NKOpAJMXHQZR1ct8n0FNoxpHRLZGXHM9NpjtHjbYvHszZxmLjcqvIZVlR+lXU8rlRV9rms9zuXa5LdP3QuHo89oxX8qLmZZbwq+1GVz7Cy413RsfS68qrKLBAcBjfdd9kj6pYs4i9sdrj/dzDnETMGrx5pV3gF/2gNjpGqi9cuyWEx3fAe3w970uHFLqZ1tTzrk02h8/De0vc/2Ko4OXRYTKHYqYwlB2wWeKqdKeAe2mOWrLi7sJvy3ZGl4yo57i3CAcn5VY9h9QBzm+E0yd+WFLMERsnXvzTGKznmlb1PTTxnY4rZts0lLUa3NzO33yaZysOpnNJjgbuEmAl1ThRe7a7uZ+1RNgf0HAL9Rno6Et/w8GuwOxbOnai3k/NVRufbYlU937r9tVfx/79PhR/tMBcXl/UzLXZQdmJuKzwHl0zQG9hN61t1trnS167snFZS1xD473UQ873p1tsmqLgbsFtlx3Ntsun+2vuS1vaAEk1qVfkMKyq/oOzjcSWqss9lvV9ssilzOpT4zjQLgI/7ld6usv2o7DOs7HhXdCy9qryqKgtgtcLPP4SQ8MseVnIr4k9unQFb/gMbvwBn/VkOyD3z4NEssz5pZWOA6strX+offUz3n3knTSvq0r4wvFHZ2+7NMK2u7/e8uvdsFwRfXWeuHr91wtxOjCy/82TJ+6sSg9NlukMdzoIn25rCfGNKxTFdyTG/kvep7nPiC5cauXhJTtLUYSotpwsfH9PEVFi/uQg3NTaVkHSnad1xq85n7TE2O4Q2hGcXlj9zilRPw2bw6mp47mYzwVQ9Oo9Wlc63NeO6cHilK9yzE546UJzc1kdVOaeVZLXAot7Qb6MZAzqwoRln6lbZ8Zxz3NyWN6GYW3XPo7V5Fqtsn8vyQ7pZ5sdVzdiqux9V+f5WdCy9oryqKpvdrG373N/h+tvL3ETdkkX8icUCzy+Ba24qc+FrT+kWYiZ4yHcVj9esCXszoE18LXbtAAAgAElEQVRg7bx2WUJtJtFc2Nv8Pvp70x2oLCE2OJltfi51voIGoQOZ8Kf4y++f291MmpGYW7wcUGUqi6HABbdtMwX4Bz2rvvxBdT/PK3mfK3lOh8JWpSPltCp1K7xSP70lvNTFTCrzq8Om4vpRH9Mtza06n7VH2BwQGg6vrYbmWhOpRjVvC/O2wU1TzTnV6l3tBDrfllbR+bYy/QuTn0OZpde6rW+qck67VFQgLOhlkreJO0pfFKzseLpbaOPLeLyk+nYerWifyxJuNzMz7yljGfHcGlzbtirf34qOZX07zlfE3VrbshO8vbXCoTZKbkX8jSMQ/rAcxj9mfrd6/jRgs8C7hTP3/eJQ+YVCar6ZVRCqtsrkLw5CmL36r10WuwXS801roVu6s7gbbE6BuSoKZvKKjYNMAuaeVMJK6YXc+4SZfXj2YOn3ScyFv10++V+RDsHwerwZ61OSBWgVZLoyRQWU//ySKothc6rp7nxT4+LH8lyV92qv7udZlfe59PhdSWyDI8zx+fSSscnuLnvjCltesgvgQq4Zc/W7TiZ5LtmNq7LP2uOsNmjXA+Zth/a9PR2NbwoMgaf/Cn9aC+16Fla86s8Fw4rofFussvOtW3nnlf2FCULnkMpbKT2psnMamOMHpT/rW5ua4S/x2XDfD8WPVXY8+xZebFx69vL3cPeOrq3zaHnf1Uu/E+54Ln1OeftclgGFFzd+dbj4tcBc7Pjn2bKfcyUqO94VHct6X15Vhc0OweHwyGx474dKyzXP12pFpO5ZbeYk8eIKiIisF5WyGxuZqel3pZtZHbeUmAwjOR+WJcKPd8PIwmTGfUU1pYxegSn5ZibIIJupc1b3td2vmV+isOoTZrZ96ahpPf39EVNoHMgonhjig1PFlbHWQWaq/msLC7+WgWbyh+1pZhma6AjT5WlxAkzYYWZcfOEw3LsLHiicxCLPZV6vZIuAw2Im6Lhje+kW2rVJJo5ZHYsntsh0QuYlhXmmE3JdpqC8uUnFMbjragvOmGM3/7S5Qn02F3YW3pYVY3U/z6q8z6XHL8tZ+XMu/RybOuClzmbWy29KFOxzj5uxtqMKP/8/HDWP/+usSYRXXiyuxLpV9Fl7jD3A/C1PfBre3AzN2ng4ID/QKxr+vB1+8xl0vsbcZ6v/E07pfFu18y2Yc6U7xowS41aPlZid/fedi+/PKjznZpfY1p3kl9zHDKfZ1lWNba70tatyTnOPAU2+pLXyxS4wqCH85zzMOmzuq6zsiI4w34O/nYY/nzTlzpZUM3TkXK55Xqaz4s+wsvKrvOPhHqd66Rrql34nMp3V2+ey3i86wiTDnyTCTXFmaNDPD5rJtCa1qPp+VPYZVna8oeJjWS/Lq8pYbabxJbwx3P97WHwCxs80iW4lbL/5zW9+U/sRiki91LIz3P4IBDeA3evMfa4a7EtTTQPDYXILM4byb6fNyfsfCaYy1CHYFDgN7fDvczDriFnbNCEXVlwwhfbCM2apl18cNGMvH29b3G2sqq+9PQ1ePGa67KQ4zRpz7YPN+oq7M8zEReuTYWZbSMwzXfDaBZvtFp4xMxeeyjEzaU5raWbTBGgbbB77NNHMrnlNA5gQabb9Ngm+ugDNHPBWD7N4/EcJ8PYJSHOawrptsJmoAswVV5vFXI396gJ8eBo+Ow8vdoaHW5tt/n3OJG2p+aYV5NoG5ji9cdwUmlYL3NAQ7m5Rfgytg0yS+PVF2JRillYY2diswXc82xSW750qO8bqHPPK3ufuSLO0SMnj9z/NKn5O1xB4Lf7yz3FQQ7MkxJzjZtmSjSlmFtFXuhYn2RbMupr/PGvWNFx4xlRaPj9vxq41sFf8Wdc5mwNwQfR4eOFjGDGpShUAqSEWC7TpBrc9BP3HmNmpjxfO/GKx1rsJ/Nx0vq38fLvqokmw3WP5P00055g3T5g1R3uEwQe9TIID5pzyarxJyC8Wxnso09x3Mc+0Pg8KN6/zzklzfrZYTFLzeiXbBFrNNtV97WERpiWronPallR44YgZspGUb5L5lkHm2NgsZvbe+afNcTuebT7nH7cq/3iCOScn5MD7p0yCG2Y33X77hsGQCOgQYj7fsj7DqpRfNosZolPyeJzPM8sP7Ug3y900CzBLTgVZL/9OJOZWb5/zMctElXy/NkHwo0jz/PXJ5lh0CjbHIcRWtf3ILjD7UdlnOLGC76/TVf7fQ0WP1Ut2hzln9hgMD/wBnvwA+o0wvQ6ryOJy1dOzrojUrQunYemr8Pk8KCgwFTQRP/TOCdPFe3QTOJtjKhxpTpNEJ+SYtQrrBbvDTGh0/e2mElDGen/iIRkpEPsxrPw77PyueEKverrWuPg2rzmniR+ygN1uZp9v0QFungYjp1zV2uxKbkWktJRz8OX7sPxdSDxuKtBeuuSFSHXFpcK47XBq+OWPXcwz3cJ+5snevhaL+bEHwMjJcMfPoPN1HgxIKpWcCOs/g60rIO6/kJVuPr/83MqfK3KV6v05TfyPzQEF+YDFlF9DxsLgsdD52hp5eSW3IlI2lwt2fgtfvgdrPzbdlV0u06or4qMWnIb7d8MfOsO0KIgMhKQ80y3x6wum+3KwJ2arcCdDXfqboQQj7jHDCcS7OPNh70bY+hVs+gIO7zD94G12XUSUWlFvz2niP6w2c1HWmQ+NIk0iO2AMXHsThNX8mkRKbkWkculJsPojWPsv2LXGJLlWK+R73xqPIhVxusz4wvdPmvGFYTboGQY/aW0qhu7JumqfBWw2Uxlo2QmG3w033QvtetVVAFIXUs7D91/DD7Hm3Hp8r+m67AgAp1PdmOWq1Z9zmvgN95AZl8sks72HQ+9ouO5mM7t8LVNyKyLVk54Mm7+E9Z/Api8hN9O0KuWpi534lkwnBNuKJ5qqdVY7UNg7ovtAs47fDeOhlQbE+Y3sDDiwFfZsMJP87VkPaRfNxFR2B+RVcRFrkTLU+TlNfJ97QkNnvrko1/Ea6DMMet5gJoVq0rLOQ1JyKyJXLi8Htq8248i2fQ3Hdpt56x0OJbsilXEvwVXghIbNoP/NppvW9bdDRPOKnyv+48wR05X58HY4FAeHd0LqefOYI8C0jqhLs4jUJovFXIB15gMuCAiC1t2g+yDo0NfcdrrWXITzdKhKbkWkxmSlm0rYtpWwablJdnGZ8WROJxUvhy7i4+wBJpEtcEKDxnDNKOg91Px0vq54Rl2RyqQnw7Ef4GAcxO8xt8d+KG7ZtTmAgsLzrohIFbknLCxwFiayQHjTwiS2D7TtCV37Q5vuxRdo6xkltyJSe1IvmK51+7fAvk2wf7NZIsNiMWuW5WUr3xXfZC3s++d0mqvd7bqbcUfdBkLPIeaKt0hNcubDyQNwfA+cOmj+H78HTh0wyTCYuRLsgWZyMo3nFfFf1sK10AsKE1i7AyLbQ/te0KaHGQ7Ttof53csmL1RyKyJ16/RhOLDFJLy718PRHZCTZR4r2bIl4g0slsLlsvLNjOIWK0R1MpNndBsIXQdCp37muy3iKWkXixPek/sLE9/dpstzbnbhRpbiLoXOXF14FPFmNru5yOrMK17lwmqDJlEmaW3b06wl26oLtOoKzdv6TO8hJbci4lmuAjhz1HSpO74Hju6EQ9vh9CHTEuHuIpOfZ7YV8QiLGUtecgbbxlHQsS907GdmMe7Q21zxDgz2bKgi1ZFyDs7Gm5/EwtvTh+HMITh/ygw3cbM5CmfK1/lYxGMsFvO3aKHwb7EwlbM5oHELiOpoLrJGtjOtsZHtzE/T1ibp9XFKbkWkfnLmm5aGYz+YFoZTB+H4PpP0ZqaabaxWczJ3Oou71ohcDbvDXOV2J7B2BzRrY650t+5W2E2rt1nOICTcs7GK1IWMlOLEN+EonDsBFxMg8bhJfi+egZzM4u0tFjPRFVbTauTUuVmkyqx2sFkvXwrMZjdzNTRtZVpZm7eFxi2LE9fI9uaCq4+0vl4NJbci4n3Sk+DUIZPoun/i90DCEUi9WLyde/kMV4FmExXTJctmLz1RBkBAMES2NYlrq67QsrNZW7ZlZ5PYqrIgUrHsDDh/EpLOwrmTkHwWEk+Y2zNHTQKcnFg6CYbibv1YdJ4W3+Qud3CVvXa1PQBCI6B5a5O4Nmtr1oZt1sbcNm1tbjWDfpUpuRUR35KXYypX506Yn8TjptJ19pjpanfxTOludhbAFmAqWZcmPeIdLNbirlbO/NLdJd3dtJq3NQlrszbmp2lrc7W7WRsIbeiZuEX8TX4upJw3XaGTz5mEN/W8uS/1vGkRdifCqRchM6W4y6WbxWL+3i1W87vO21IXrLbCiQIt5jtZkF88lrWkwGDTwhoRaZLSRpHQsKn5vWHTwp9m5ieiOQSH1f2++DgltyLif7LSTcKbcq6wMpVg/n/htPn/hdPmJz3JVMZKKlWxKudKrFw5q9V0y7JYKPf4WizQoJFZnqBp4dXuhs3MYvERzU1lonGUua9xC4/shojUAFeBSXzTk8352H2blgQZyaVvU86ZGfrTk0xX6uyMyxNjN6vNnGvcCbLLZd5LSbLvuZLPOjAYghpAgwiTqIY3hfAmEBYBYY1M+RMaYW7d94U1MtsEBNXNfkm5lNyKiFQkI8VUmtIumgpU2sXCylWJ39MuQFJi8WPZmZd3v7uUO0Eu2eXV3eJY4Cz7inB9ZLcDlsL9KNwXF2ZfCpwVTzpjDzCViLAICGtceCW7qalMhDUytw0aXfJ7Y4hoVm/X1xOReiQ92SS/Wekm2c1MK058szPM/A2ZqcW/Z6SYc3hmqnlORgpkZZhWupI9fipjsRYmVSXOi24uF0VTUbv8bC1iixVsttK/l+KCAhfVXqM5MNgc75BwCAqFoDAICzfJZlCYuS8kvMTjoabHTnADCHb/HgEhDcyte9Zw8UpKbkVEasullSZ3BSs7ozAJLvx/VrpJZjNSzPOy0sxV5exMyCl8LC2p+DVLtmS6K15lyc4sSi6/uQi9wyDSvSJNYIi5mn0ZiynwrSUqZCHhpuIQFFJYiSic2AJMBcFiNfcHBJn1iwNDTFLqrkQEhZkENijUPBYaXvyaIiLewuUyybL7fO3MN+fr/DxzHs/PNef03GzIzTLL3OVmm4udeTnmNXIyIbfw/+7twZyrU88Xv1daSvG5veTzS8pMq3zW6oKCyy62XlYegLnY6KjCkmXBYZefuy0WkxS6hUaAvXCboDBz3geTNAaFFT8nLKL0861WU6a4E1X39o4AU34EBJk5EtzlTWCIKXNESlByKyLiB2w2G4sWLWLSpEmeDkVERDxI5YH4srIu24uIiIiIiIh4FSW3IiIiIiIi4vWU3IqIiIiIiIjXU3IrIiIiIiIiXk/JrYiIiIiIiHg9JbciIiIiIiLi9ZTcioiIiIiIiNdTcisiIiIiIiJeT8mtiIiIiIiIeD0ltyIiIiIiIuL1lNyKiIiIiIiI11NyKyIiIiIiIl5Pya2IiIiIiIh4PSW3IiIiIiIi4vWU3IqIiIiIiIjXU3IrIiIiIiIiXk/JrYiIiIiIiHg9JbciIiIiIiLi9ZTcioiIiIiIiNdTcisiIiIiIiJeT8mtiIiIiIiIeD0ltyIiIiIiIuL1lNyKiIiIiIiI11NyKyIiIiIiIl5Pya2IiIiIiIh4PSW3IiIiIiIi4vWU3IqIiIiIiIjXU3IrIiIiIiIiXk/JrYiIiIiIiHg9JbciIiIiIiLi9eyeDkBERGpWfHw8TqfzsvsTExM5cuRIqfuioqIIDg6uq9BERKQOqTwQf2NxuVwuTwchIiI1Z+zYsXzxxReVbme32zlz5gxNmzatg6hERKSuqTwQf6NuySIiPmby5MmVbmO1WrnppptUkRER8WEqD8TfKLkVEfEx48ePJygoqNLtpk2bVgfRiIiIp6g8EH+j5FZExMeEhIRwxx134HA4yt3G4XAwbty4OoxKRETqmsoD8TdKbkVEfNC9995LXl5emY/Z7XbGjx9PWFhYHUclIiJ1TeWB+BMltyIiPuiWW24hPDy8zMecTif33ntvHUckIiKeoPJA/ImSWxERH+RwOLj77rvL7IoWFhbG6NGjPRCViIjUNZUH4k+U3IqI+KgpU6Zc1hXN4XBwzz33EBgY6KGoRESkrqk8EH+h5FZExEfdeOONNG/evNR9eXl5TJkyxUMRiYiIJ6g8EH+h5FZExEdZrVbuu+8+AgICiu5r1qwZw4cP92BUIiJS11QeiL9Qcisi4sMmT55Mbm4uYLqgTZ06FZvN5uGoRESkrqk8EH9gcblcLk8HISIitadDhw4cO3YMgM2bNzNw4EDPBiQiIh6h8kB8nVpuRUR83PTp0wFo27atKjIiIn5M5YH4OiW3IiI+btKkSQDcf//9ng1EREQ8SuWB+Dq7pwMQEf+T63RxJDXX02H4j8gO9Ox3LYNuvZN9STmejsZvNAm20yxI49nEN+i87SNUHvgUlTOX05hbEalzF7KdvL83ydNh+JVDm76j8/U3ejoMv3JDixCGR4V4OgyRGqHztu9QeeA7VM5cTt2SRUT8gCoydctm8XQEIiJlU3ngG1TOlE3JrYiIiIiIiHg9JbciIiIiIiLi9ZTcioiIiIiIiNdTcisiIiIiIiJeT8mtiIiIiIiIeD0ltyIiIiIiIuL1lNyKiIiIiIiI11NyKyIiIiIiIl5Pya2IiIiIiIh4PSW3IiIiIiIi4vWU3IqIiMj/b+/O46Kq9z+Ov5gBRBBBEHFD1AgXxO3nklaWmrkvmZVpy72ZLfazsu1mZZll2vLIltvmltYtc72aiVvmkltuuOOuBCqLoOzrzNw/CBIFZtiEgffzLztzzvl+vt8Pfb7znTnnjIiIiN3T4lZERERERETsnha3IiIiIiIiYve0uBURERERERG7p8WtiIiIiIiI2D0tbkVERERERMTuaXErIiIiIiIidk+LWxERO5GZmlLRIYiIiEgRNFdXLMeKDkBEpLTMpmwij4RyYvtv+Lfrws3delZ0SGVqf8gS9v2ykKhTYby27nBFh1MpVPWci1R1+n+4ZDRulZfm6spB39yKiN2LPBLKrmXfs2HmRyREXyjTcyddii7T85VE2373YMrOwpydVdGhlKvijHV55lxEyl9Vr9vFYY+1z97G+EaoLnN1ZafFrYjYvSZtO9N95NgyP29GShIL3xhX5uctLoPBiEe9hhUdRrkq7liXV85F5Mao6nXbVvZY++xtjG+U6jBX2wMtbkWkSjA6OZXp+TLTUvnhlTHER54t0/PK9Uo61mWdcxG5sap73bbH2mdvYyzVj+65FRG7cXzrrxz7fR0GR0ciD4fSaegoOg9/uMB9D65bztIpz1PT3YNXVx8gIyWJPSt+ZM1nU2jYoi1Pz18NwMUTR9j6w9f4Ng8kMTYaU1YmQyd+wJGNq4g5c4K0pCsse2cCPv4B3P7IM1gsFnYtnc/FE0e4cOwgLrVqM+TV96nbpDmJMRfZt2oR+0OWMObrpSye9Ayx504xfsEGXD28rPavsFiulhQXw/KpL3F23w7qNPTjgXe/ol7zFgBFxgaQHBfLuq+m4enbiCtRkaRciefeN2fg6uFV6tit5aeotgsb66KOKcrhX1dyZu92HJ2diT4VRqPW7eg19mUcnZ3z9ok8up9dS+eTmZZKXMRZOg0bTedhozEYHW3OhYhYV9XrtrU+2lvts6awmK21H336GPtXL+XwhpWM+WoJfyyZT+iqxdRwq8XQf02nSbvOrPnsHcI2r8GUncXwSTMI7N6rzPtXVL7N2aZCxz3mzHH2r17Ckd9CGPPVEpZPe4VzoTvx9mvG4Ffeo0lwp3yxFjZXH920moVvPE1magqDXnqXW+77J0YnZ/48uJv/vPgPuo0cS88xz9uUCymYvrkVEbsQumoRoSGLGfKv6Qx++T1a9ujLsndf4PTu3wvcv+3dw/Bv1yXvv2u4uXPrqCepH9A6334LXh1Ll3sepsej4+n/3Jt59zB1GHAfDQKDcPP0YvikGXmT9+Z5n+FUw4Vhr33IU/NCyEhJZuaYwWSlpxF1Kox9KxcSe/Yku5Z+R9u+9+Betx7ZmZk29bGwWHJlZaSzae4n9Ht2Ek/M/pn4yHBCPpmc93pRsQEsmDiWjOQkeo19keGTZnD5fDi/fPgGQKljt5afotoubKyLOqYwW3/4mq0/fs3AF6cwYMLbPPDe1xxa/zNzx43AYrEAcOViBLPGDqXnmBcY+d431GsWyPKpL/HlI/345aM3bMqFiFhXHep2Vat91hQWs7X2a3n5kBB9gUvhp9kw8yOC7xrMhCVbca3tydIpzxMyYzJd7n2E5xZuxrtJc1ZMe6Vc+ldUvosa99SEyxzZuJpL4af5/fsvuW30U9z75gziI8OZ/eTwfPcgFzVXt76zP90feBwA//ZdMTrlLMwbtWqHZwM/LWzLgBa3IlLppVyO4+f3J9L3mddxMOSUrS7DHyao1yDc6/oWepyTS83rtl396bQpO4vYcye5cPwgAEYnZzoNHVXo+RJjo9j24zd0GHh/zrkMRtrcNZikuBjCtqwlsHsv/Nt3wWw20X7ACDoNHcW479ZS26e+1T7aEovB6MiACW/j0/Rm6ge0IqBrDy6EHbQptlwNAoPy/u0b0IqLJ48AlCp2W/NTWNtFKc4xyfGXWP/lNLqO+AdGx5zL9lw9vLjzsQmc3beD/SGLAdi+cA41a9ehTkM/AHo+/kJOzPc+wqCX3i3234WIXK861O2qVvtKypb23ep40yT4/wC4ddSTNGzZlhputQjqPYj48+F0Hjaaes0CcXZ1o/Ud/Yg/H07K5bgy75+1fBc27k073IJfm47g4EC/596keadbCeo1iKGvfUBWehp/LJmXd1xRczXALfc/hsHoyK6l8/O2ndy5mZY97i5xDuRvuixZRCq9c/t3YrGYqdOoSd42tzrePPTRt6U6r9HRiZtvuZOVH75OzJkT9P3/12ndc0Ch+/95YDfm7Gz+O/XFfNs73/MQTjVc8s5pMDri3bhpmcdidHTMm9gBXNw9SEtKsDm2sTOXAzn3TIWGLCbySCgWszlfDCWJ3Zb8WGu7IMU9JuLQHjLTUvH0bZRve+4bhjN7ttFh4P0kxlzM+zYboG6T5rh5epEQdR4o/t+FiFyvOtTtqlb7SsrW9h2MRgAcHBzy9qnhWgsAg+PfSxLnmm4ApFyJw62Od5n3r7B8Wxt3g9GIwWjMNw8H9RyA0cmZqJNhV52/8LkawMO3IcF9hhC6ajF9x0/CzdOLQ+tX0PvJl5HS0+JWRCq96FPHMGVlYbFY8k2KZeHB6bNYMPEJdi7+lsMbVjLqg7k069itwH1jzp7AycWV4ZNmlGkMJYkF8r9BsCU2s9nE5m8/Jz7yLLc99DThoX/w56E9pY7blvyUpO3iHnP5YiQAqYmX82138/TCyaUmibFRAAR268mBNcs4vWsLN3XpQXpSAhmpKQR27513THFzISL5VYe6XRVrX0nY2n5BChq33G25C8sb1b+S5Mro6ERtH1/Mpuxi9fG20U9xYM0ydi39jh6PjCPlShxejfxtilOKpsWtiFR6Ndzcyc7MIObMcXxvapnvNVNWZt49KyXhXNOVx77IuWcq5OM3mTtuBM/+tAmfpjdft6+TS00SYy6QEH0BD9/8j/tPuRKPm6dtDx8pi1iKG5trbU/mjX8QV486jHzvm1LFeS1r+TEYHYvdtsVsLvYxXn99exIfGV7g6z5NAwDoOHgkiZeiWTTpGToNHUVibBQPTpuJf/u/7/UrTS5EpHrU7apY+0rC1vbL+/yl6V9Jxj2XKSuLusXsY+OgDvi378LORXOo1yyAVj36Fut4KZzuuRWRSq9x6/YArP9yer5LhOIiznJw/YpCjzMYjWSkpmA2m/K2ZaSlYLHknCM7M5Odi+cBOQ/JGDd/LRazhdO7ch4E4mAwYMr++9PY+gGtsFgsrPlsSr52kuMvsXfFj6Xqo7VYrLEWW8SRfZzcsZGALj3yXjNlZ8FfD+IoDWv5saXta8e6JPH6BXeihlstjm4Mybc991K1Vnf0AyA7M4PUhMs8u3AzfcZN5N63Ps13WWNpcyEi1aNuV7XaZ6trY7a1/ZK6Ef0r6RyZHH+JpLgYgu8aXMxewR2PjicxNopVH79JcJ8hxT5eCqZvbkWk0vNv34UWt/bmyMZVzH5qOG16D+JK1HkuhZ9m9IdzAUhPTgLAZLr6TU1rDv+6kk1zP6Vtn6EcXL8CU2YGsdEXuHDsIPWat2TP8v/QbeQYDAYjtX0b4FLLnYYtgwGo7VOf47+v5+Lxw6QnJ+LfviuNgzqwf/VSsjIyCOrZn7iIs4Qf2M3IaTNz2s/OwmI2YcrOynfPjS2KiiUzPY3Mq+4lAshKT8tpz2Ih4JY7i4wt9zcJ9678icZtOnL+6AFizhwnKT6WqJNHqeXlU+LYreXnfNgBq21fO9a5bxSLOubanLt5etF3/BusfH9i3mVpANsWzKLDwPu5qfPtAGyc/TGnd23Bu3FT3L3r4ezqhodvo3zfLhSVCxGxrjrU7apY+2xxbcyNgzrY1L4pKwsAs+nvDy6yMzOAnCcM5zJlZ/21f2a59K+gfOdeOlzUuOfGFHUqjPoBrXLanPMxHQbch1+bnIdlWZurr75EuWWPvvje1BJvv2Y2/+yUWGecPHny5IoOQkSql7RsC/supVvf8SpBvQeRlpRA+IHdnNm7De/GzRjy6nScXGpy8fhhNs79hNhzJ0lPTsS7cVPqNPSjUctgok8fIzRkMX8e2E33B8eSEn8Jn6YBeNb3w6uRP6GrFnFsy1oSYy6yP2QJHQc9QOs7+wPgWb8xYVvWcnRTCE2CO9GgRRva9B5EYuxFzu7ZyokdG3GrU5chr07H3duHA2uWsWPhHDJSk8lIScazfiPc6tS1qX9mk6nQWMI2r2H7gllkpCRhMDrSsEUwJ7ZvYNuP35CZloqDwYB/u84E9xlaaGwe9RqSHBfLqYrmdTIAAAOQSURBVD82EXFoL0G9BtC8020c27KWy1GRWMxmdi37rkSxW8uPtbbb3j0Mb79m+ca6xe19ijzGxz+ALd9/cV3O/YI60iAwiG0LZhJxeB9/HtqDa21P+j//Vt6bCgcc2LPiBw6tX8HBdcsJXbWIHQvnELZ5LYHdelHDtVaRfxe2MDhA41pO+LsX7wMOkcpKdbv4fbS32lfT3cOmPl83xoFBVtuPOLyP3+f/m4SYC2SkJtMgsA3xEWfZPP/fJERfIDM1hfoBrbkSFcmWeZ9zJeo8mWmpNGwRTM3anmXWv8LybUuuTmz/jaiTYXkPnDq5YyPudX0Z+MIUHBwcbJur23bCcNWDtWLPnaRVj355v1dfHJpnCuZgsZTBNWkiIsUQl25iVthl6zuKlIOdi+ZSu14Dbu7Wk+S4WDJTk8lITSbi8D6SLsXQ79lJpW7D6ABdfV3p0cC1DCIWqXiq2/bvRtS+ilTe/Vv2zgRCVy3mnZ2RZRQxzHl6BI9++gOOzjWKfazmmYLpsmQRkXI2rV9bTH9delWY+975kha3lu6JleXBnmMvyPmwA2ycM4OJaw8B5P0eIkBd/wAOrFlaUaGJSCVSHWufPffZHmv72b3badSqXYkWtlI4LW5FRMrZxDUHre9USdlz7AWJPhVGYmwUG+d8QsdB91PL24e0xAQiDu3h5M7N9H/+rYoOUUQqgepY+7o98HhFh1liN6K2Z2dmYs7OLtXPW50L3cl/p75E/ZtaEnX6GE/M/rnUcUl+WtyKiEi10X7gfcRFnmPHT7NY98VUnF3d8G3egq4j/sngl6fiYNCPCIhI1VPVa1959++32R9zdNNqzGYTqz+ZTHCfofi16Vjs87h61CE7I53Io/sZ8fbnpf4JQbme7rkVkRtO925JZZCVnoZjDZcSfwJfFN0LJVWN6nbVUZ61rzKo6v3LpXmmYPrmVkREqiUnl5oVHYKIyA1X1WtfVe+fFM2+r0EQERERERERQYtbERERERERqQK0uBURERERERG7p8WtiIiIiIiI2D0tbkVERERERMTuaXErIiIiIiIidk+LWxEREREREbF7WtyKiIiIiIiI3dPiVkREpIxZKjoAERGp0jTPFEyLWxERkTJm1rsOEREpR5pnCuZgsVg0NCIiIiIiImLX9M2tiIiIiIiI2D0tbkVERERERMTuaXErIiIiIiIids8RWFzRQYiIiIiIiIiUxv8AuZ1q4+8w3qIAAAAASUVORK5CYII=", + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Image(flowchart_file)" + ] + }, { "cell_type": "code", "execution_count": null, - "id": "3218ac13-3d30-4bc9-bd8d-25d40a3eeddd", + "id": "040bda88-3a54-44d1-9ac2-d9e30cdb528c", "metadata": {}, "outputs": [], "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f5c9c0f8-31d4-4d96-a998-5b35ef09a025", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "add8cbe8-7e1e-446b-9214-c60cfa598393", + "metadata": {}, + "outputs": [], + "source": [ + "#open cluster catalog" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "c2cd69d0-89a2-4cb3-ae65-0882b7194507", + "metadata": {}, + "outputs": [], + "source": [ + "import h5py" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "3218ac13-3d30-4bc9-bd8d-25d40a3eeddd", + "metadata": {}, + "outputs": [], + "source": [ + "output_dir = '/pbs/home/m/mricci/throng_mricci/desc/TXPipe/data/example/inputs'\n", + "\n", + "filename = output_dir + \"/cluster_catalog.hdf5\"" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "d8c38523-a8f7-4a43-9831-9ae273adeaa6", + "metadata": {}, + "outputs": [], + "source": [ + "f = h5py.File(filename, \"r\")" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "c6d59b2a-8857-44be-93ad-912d8a893ec0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "f.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "1ecd9464-7ef8-4600-b931-8b8c76830564", + "metadata": {}, + "outputs": [], + "source": [ + "dset = f['clusters']" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "f5624d30-cea6-4999-8f53-0ee81030b632", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['cluster_id', 'dec', 'ra', 'redshift', 'redshift_err', 'richness', 'richness_err', 'scaleval']\n" + ] + } + ], + "source": [ + "cols = [col for col in dset]\n", + "print(cols)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "4349ca71-0548-490b-b5c3-5af0ce98f7f2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'richness')" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.semilogy(dset['redshift'][()], dset['richness'][()],'.', alpha=1)\n", + "\n", + "plt.xlabel('redshift')\n", + "plt.ylabel('richness')" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "0b4c65de-82c8-4f99-89de-dd0a598a31f0", + "metadata": {}, + "outputs": [], + "source": [ + "# open for reading\n", + "output_dir = '/pbs/home/m/mricci/throng_mricci/desc/TXPipe/data/cosmodc2/outputs-1deg2-CL'\n", + "filename2 = output_dir + \"/cluster_catalog_tomography.hdf5\"\n", + "#f2 = txpipe.data_types.TomographyCatalog(filename2, \"r\")\n", + "\n", + "f2 = h5py.File(filename2, \"r\")" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "b3274869-a729-45c7-b531-05d4f8ef69da", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "f2.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "7785436c-8652-4c98-9ecb-373abd2dc5d2", + "metadata": {}, + "outputs": [], + "source": [ + "dat2 = f2['provenance']\n", + "dset2 = f2['lens']" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "736b4dc3-381b-45f4-a93e-f9c00b9e5e17", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dset2.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "6fbe636e-052e-47b0-8802-ae4ceae34ea9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "bin_0_0 {'rich_min': -inf, 'z_min': -inf} 5\n", + "bin_0_1 {'rich_min': -inf, 'z_min': -inf} 3\n", + "bin_0_2 {'rich_min': -inf, 'z_min': -inf} 0\n", + "bin_1_0 {'rich_min': 5.0, 'z_min': 0.2} 20\n", + "bin_1_1 {'rich_min': 5.0, 'z_min': 0.2} 8\n", + "bin_1_2 {'rich_min': 5.0, 'z_min': 0.2} 0\n", + "bin_2_0 {'rich_min': 10.0, 'z_min': 0.4} 13\n", + "bin_2_1 {'rich_min': 10.0, 'z_min': 0.4} 3\n", + "bin_2_2 {'rich_min': 10.0, 'z_min': 0.4} 0\n", + "bin_3_0 {'rich_min': 20.0, 'z_min': 0.6} 7\n", + "bin_3_1 {'rich_min': 20.0, 'z_min': 0.6} 3\n", + "bin_3_2 {'rich_min': 20.0, 'z_min': 0.6} 0\n", + "bin_4_0 {'rich_min': inf, 'z_min': 0.8} 0\n", + "bin_4_1 {'rich_min': inf, 'z_min': 0.8} 0\n", + "bin_4_2 {'rich_min': inf, 'z_min': 0.8} 0\n" + ] + }, + { + "data": { + "text/plain": [ + "[None,\n", + " None,\n", + " None,\n", + " None,\n", + " None,\n", + " None,\n", + " None,\n", + " None,\n", + " None,\n", + " None,\n", + " None,\n", + " None,\n", + " None,\n", + " None,\n", + " None]" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[print (i, dict(dset2[i].attrs), dset2[i]['redshift'][:].size) for i in dset2.keys()]" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "38be57ca-e370-480d-a2e0-c2d45baf7b13", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['cluster_id',\n", + " 'dec',\n", + " 'ra',\n", + " 'redshift',\n", + " 'redshift_err',\n", + " 'richness',\n", + " 'richness_err',\n", + " 'scaleval']" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[col for col in dset2['bin_0_0']]" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "160082fd-9809-4144-aabf-66a3fd7b00de", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.semilogy(dset['redshift'][()], dset['richness'][()],'.', alpha=1)\n", + "\n", + "plt.axvline(0.2)\n", + "plt.axvline(0.4)\n", + "plt.axvline(0.6)\n", + "plt.axvline(0.8)\n", + "plt.axvline(1.0)\n", + "plt.axhline(5)\n", + "plt.axhline(10)\n", + "plt.axhline(20)\n", + "\n", + "plt.xlabel('redshift')\n", + "plt.ylabel('richness')\n", + "\n", + "plt.plot(dset2['bin_0_0']['redshift'][:], dset2['bin_0_0']['richness'][:],'m.')\n", + "plt.plot(dset2['bin_0_1']['redshift'][:], dset2['bin_0_1']['richness'][:],'c.')\n", + "\n", + "plt.plot(dset2['bin_1_0']['redshift'][:], dset2['bin_1_0']['richness'][:],'r.')\n", + "plt.plot(dset2['bin_1_1']['redshift'][:], dset2['bin_1_1']['richness'][:],'g.')\n", + "\n", + "plt.plot(dset2['bin_2_0']['redshift'][:], dset2['bin_2_0']['richness'][:],'mx')\n", + "plt.plot(dset2['bin_2_1']['redshift'][:], dset2['bin_2_1']['richness'][:],'cx')\n", + "\n", + "plt.plot(dset2['bin_3_0']['redshift'][:], dset2['bin_3_0']['richness'][:],'rx')\n", + "plt.plot(dset2['bin_3_1']['redshift'][:], dset2['bin_3_1']['richness'][:],'gx')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "f8829286-7aa3-4fe6-8717-73094f8df9f8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dset2['bin_1_2']['redshift'][:].size" + ] } ], "metadata": { diff --git a/txpipe/__init__.py b/txpipe/__init__.py index c2cfe7e25..76c47671c 100755 --- a/txpipe/__init__.py +++ b/txpipe/__init__.py @@ -40,5 +40,5 @@ # Here are the stages that mostly will be used for other projects # such as the self-calibration of Intrinsic alignment. from .extensions.twopoint_scia import TXSelfCalibrationIA -from .extensions.clmm.select import CLClusterShearCatalogs +from .extensions.clmm.select import CLClusterShearCatalogs, CLClusterBinningRedshiftRichness from .covariance_nmt import TXFourierNamasterCovariance, TXRealNamasterCovariance diff --git a/txpipe/extensions/clmm/select.py b/txpipe/extensions/clmm/select.py index 121affa62..fa2437fde 100644 --- a/txpipe/extensions/clmm/select.py +++ b/txpipe/extensions/clmm/select.py @@ -4,9 +4,112 @@ from ...base_stage import PipelineStage from ...data_types import ShearCatalog, HDFFile, PhotozPDFFile, FiducialCosmology, TomographyCatalog, ShearCatalog from ...utils.calibrators import Calibrator +from ...utils import DynamicSplitter from collections import defaultdict import yaml import ceci +import itertools + +#from ...data_types import HDFFile, MapsFile + + +class CLClusterBinningRedshiftRichness(PipelineStage): + name = "CLClusterBinningRedshiftRichness" + parallel = False + inputs = [("cluster_catalog", HDFFile)] + outputs = [("cluster_catalog_tomography", HDFFile)] + config_options = { + "zedge": [0.2, 0.4, 0.6, 0.8, 1.0], + "richedge": [5., 10., 20.], + "initial_size": 100_000, + "chunk_rows": 100_000, + } + def run(self): + initial_size = self.config["initial_size"] + chunk_rows = self.config["chunk_rows"] + + zedge = np.array(self.config['zedge']) + richedge = np.array(self.config['richedge']) + + nz = len(zedge) + nr = len(richedge) + + # add infinities to either end to catch objects that spill out + zedge = np.concatenate([[-np.inf], zedge, [np.inf]]) + richedge = np.concatenate([[-np.inf], richedge, [np.inf]]) + + # all pairs of z bin, richness bin indices + bins = list(itertools.product(range(nz), range(nr))) + bin_names = {f"{i}_{j}":initial_size for i,j in bins} + + # Columns we want to save for each object + cols = ['cluster_id', 'dec', 'ra', 'redshift', 'redshift_err', 'richness', 'richness_err', 'scaleval'] + + + f = self.open_output("cluster_catalog_tomography") + g = f.create_group("lens") + g.attrs['nr'] = nr + g.attrs['nz'] = nz + splitter = DynamicSplitter(g, "bin", cols, bin_names) + + # Make an iterator that will read a chunk of data at a time + it = self.iterate_hdf("cluster_catalog", "clusters", cols, chunk_rows) + + # Loop through the chunks of data; each time `data` will be a + # dictionary of column names -> numpy arrays + for _, _, data in it: + n = len(data["redshift"]) + + # Figure out which bin each halo it in, if any, starts at 0 + zbin = np.digitize(data['redshift'], zedge) - 1 + richbin = np.digitize(data["richness"], richedge) - 1 + + # Find which bin each object is in, or None + for zi in range(1, nz): + for mi in range(1, nr): + w = np.where((zbin == zi) & (richbin == mi)) + # if there are no objects in this bin in this chunk, + # then we skip the rest + if w[0].size == 0: + continue + + # Otherwise we extract the bit of this chunk of + # data that is in this bin and have our splitter + # object write it out. + d = {name:col[w] for name, col in data.items()} + bin_name = f"{zi - 1}_{mi - 1}" #TO CHANGE ? + splitter.write_bin(d, bin_name) + + # Truncate arrays to correct size + splitter.finish() + + # Save metadata + for (i, j), name in zip(bins, bin_names): + metadata = splitter.subgroups[name].attrs + metadata['rich_min'] = richedge[i] + #metadata['rich_max'] = richedge[i+1] + metadata['z_min'] = zedge[i] + #metadata['z_max'] = zedge[i+1] + + f.close() + + + + + + + + + + + + + + + + + + class CLClusterShearCatalogs(PipelineStage): name = "CLClusterShearCatalogs" From 1e98fb2b618617d263e44c9a90e3188271090749 Mon Sep 17 00:00:00 2001 From: Marina Ricci Date: Fri, 21 Jul 2023 03:55:52 +0200 Subject: [PATCH 06/22] small modif to cluster bin stage --- notebooks/Run_CL_pipeline.ipynb | 259 ++++++++++++++++++++++++++----- txpipe/extensions/clmm/select.py | 39 ++--- 2 files changed, 226 insertions(+), 72 deletions(-) diff --git a/notebooks/Run_CL_pipeline.ipynb b/notebooks/Run_CL_pipeline.ipynb index 628b0fbd3..33038f50b 100644 --- a/notebooks/Run_CL_pipeline.ipynb +++ b/notebooks/Run_CL_pipeline.ipynb @@ -291,7 +291,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 128, "id": "0b4c65de-82c8-4f99-89de-dd0a598a31f0", "metadata": {}, "outputs": [], @@ -306,17 +306,17 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 129, "id": "b3274869-a729-45c7-b531-05d4f8ef69da", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 45, + "execution_count": 129, "metadata": {}, "output_type": "execute_result" } @@ -327,28 +327,28 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 130, "id": "7785436c-8652-4c98-9ecb-373abd2dc5d2", "metadata": {}, "outputs": [], "source": [ "dat2 = f2['provenance']\n", - "dset2 = f2['lens']" + "dset2 = f2['cluster_bin']" ] }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 131, "id": "736b4dc3-381b-45f4-a93e-f9c00b9e5e17", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 47, + "execution_count": 131, "metadata": {}, "output_type": "execute_result" } @@ -359,7 +359,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 132, "id": "6fbe636e-052e-47b0-8802-ae4ceae34ea9", "metadata": {}, "outputs": [ @@ -367,21 +367,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "bin_0_0 {'rich_min': -inf, 'z_min': -inf} 5\n", - "bin_0_1 {'rich_min': -inf, 'z_min': -inf} 3\n", - "bin_0_2 {'rich_min': -inf, 'z_min': -inf} 0\n", - "bin_1_0 {'rich_min': 5.0, 'z_min': 0.2} 20\n", - "bin_1_1 {'rich_min': 5.0, 'z_min': 0.2} 8\n", - "bin_1_2 {'rich_min': 5.0, 'z_min': 0.2} 0\n", - "bin_2_0 {'rich_min': 10.0, 'z_min': 0.4} 13\n", - "bin_2_1 {'rich_min': 10.0, 'z_min': 0.4} 3\n", - "bin_2_2 {'rich_min': 10.0, 'z_min': 0.4} 0\n", - "bin_3_0 {'rich_min': 20.0, 'z_min': 0.6} 7\n", - "bin_3_1 {'rich_min': 20.0, 'z_min': 0.6} 3\n", - "bin_3_2 {'rich_min': 20.0, 'z_min': 0.6} 0\n", - "bin_4_0 {'rich_min': inf, 'z_min': 0.8} 0\n", - "bin_4_1 {'rich_min': inf, 'z_min': 0.8} 0\n", - "bin_4_2 {'rich_min': inf, 'z_min': 0.8} 0\n" + "bin_zbin_0_richbin_0 {'rich_max': 5.0, 'rich_min': -inf, 'z_max': 0.2, 'z_min': -inf} 8\n", + "bin_zbin_0_richbin_1 {'rich_max': 10.0, 'rich_min': 5.0, 'z_max': 0.2, 'z_min': -inf} 1\n", + "bin_zbin_0_richbin_2 {'rich_max': 20.0, 'rich_min': 10.0, 'z_max': 0.2, 'z_min': -inf} 0\n", + "bin_zbin_1_richbin_0 {'rich_max': 5.0, 'rich_min': -inf, 'z_max': 0.4, 'z_min': 0.2} 3\n", + "bin_zbin_1_richbin_1 {'rich_max': 10.0, 'rich_min': 5.0, 'z_max': 0.4, 'z_min': 0.2} 5\n", + "bin_zbin_1_richbin_2 {'rich_max': 20.0, 'rich_min': 10.0, 'z_max': 0.4, 'z_min': 0.2} 0\n", + "bin_zbin_2_richbin_0 {'rich_max': 5.0, 'rich_min': -inf, 'z_max': 0.6, 'z_min': 0.4} 3\n", + "bin_zbin_2_richbin_1 {'rich_max': 10.0, 'rich_min': 5.0, 'z_max': 0.6, 'z_min': 0.4} 1\n", + "bin_zbin_2_richbin_2 {'rich_max': 20.0, 'rich_min': 10.0, 'z_max': 0.6, 'z_min': 0.4} 0\n", + "bin_zbin_3_richbin_0 {'rich_max': 5.0, 'rich_min': -inf, 'z_max': 0.8, 'z_min': 0.6} 4\n", + "bin_zbin_3_richbin_1 {'rich_max': 10.0, 'rich_min': 5.0, 'z_max': 0.8, 'z_min': 0.6} 0\n", + "bin_zbin_3_richbin_2 {'rich_max': 20.0, 'rich_min': 10.0, 'z_max': 0.8, 'z_min': 0.6} 0\n", + "bin_zbin_4_richbin_0 {'rich_max': 5.0, 'rich_min': -inf, 'z_max': 1.0, 'z_min': 0.8} 0\n", + "bin_zbin_4_richbin_1 {'rich_max': 10.0, 'rich_min': 5.0, 'z_max': 1.0, 'z_min': 0.8} 0\n", + "bin_zbin_4_richbin_2 {'rich_max': 20.0, 'rich_min': 10.0, 'z_max': 1.0, 'z_min': 0.8} 0\n" ] }, { @@ -404,7 +404,7 @@ " None]" ] }, - "execution_count": 63, + "execution_count": 132, "metadata": {}, "output_type": "execute_result" } @@ -415,7 +415,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 135, "id": "38be57ca-e370-480d-a2e0-c2d45baf7b13", "metadata": {}, "outputs": [ @@ -432,34 +432,41 @@ " 'scaleval']" ] }, - "execution_count": 49, + "execution_count": 135, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "[col for col in dset2['bin_0_0']]" + "[col for col in dset2['bin_zbin_0_richbin_0']]" ] }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 136, "id": "160082fd-9809-4144-aabf-66a3fd7b00de", "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 64, - "metadata": {}, - "output_type": "execute_result" + "ename": "KeyError", + "evalue": "\"Unable to synchronously open object (object 'bin_0_0' doesn't exist)\"", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[136], line 15\u001b[0m\n\u001b[1;32m 12\u001b[0m plt\u001b[38;5;241m.\u001b[39mxlabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mredshift\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 13\u001b[0m plt\u001b[38;5;241m.\u001b[39mylabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrichness\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m---> 15\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(\u001b[43mdset2\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mbin_0_0\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mredshift\u001b[39m\u001b[38;5;124m'\u001b[39m][:], dset2[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbin_0_0\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrichness\u001b[39m\u001b[38;5;124m'\u001b[39m][:],\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mm.\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 16\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(dset2[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbin_0_1\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mredshift\u001b[39m\u001b[38;5;124m'\u001b[39m][:], dset2[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbin_0_1\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrichness\u001b[39m\u001b[38;5;124m'\u001b[39m][:],\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mc.\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 18\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(dset2[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbin_1_0\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mredshift\u001b[39m\u001b[38;5;124m'\u001b[39m][:], dset2[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbin_1_0\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrichness\u001b[39m\u001b[38;5;124m'\u001b[39m][:],\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mr.\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", + "File \u001b[0;32mh5py/_objects.pyx:54\u001b[0m, in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32mh5py/_objects.pyx:55\u001b[0m, in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32m/pbs/throng/lsst/users/jzuntz/txpipe-environments/conda-2023-Jul-12/lib/python3.10/site-packages/h5py/_hl/group.py:357\u001b[0m, in \u001b[0;36mGroup.__getitem__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 355\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid HDF5 object reference\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 356\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(name, (\u001b[38;5;28mbytes\u001b[39m, \u001b[38;5;28mstr\u001b[39m)):\n\u001b[0;32m--> 357\u001b[0m oid \u001b[38;5;241m=\u001b[39m \u001b[43mh5o\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mid\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_e\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlapl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_lapl\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 358\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 359\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAccessing a group is done with bytes or str, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 360\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnot \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;28mtype\u001b[39m(name)))\n", + "File \u001b[0;32mh5py/_objects.pyx:54\u001b[0m, in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32mh5py/_objects.pyx:55\u001b[0m, in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32mh5py/h5o.pyx:190\u001b[0m, in \u001b[0;36mh5py.h5o.open\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mKeyError\u001b[0m: \"Unable to synchronously open object (object 'bin_0_0' doesn't exist)\"" + ] }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -498,24 +505,190 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 71, "id": "f8829286-7aa3-4fe6-8717-73094f8df9f8", "metadata": {}, + "outputs": [], + "source": [ + "import itertools\n", + "\n", + "zedge = [0.2, 0.4, 0.6, 0.8, 1.0]\n", + "richedge = [5., 10., 20.]\n", + " \n", + " \n", + "nz = len(zedge) - 1 \n", + "nr = len(richedge) - 1 \n", + "bins = list(itertools.product(range(nz), range(nr)))" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "id": "6d1fc93f-0ca3-49ff-8e75-de70a4d352ac", + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0" + "(4, 2)" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nz, nr" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "id": "981ca262-fd2c-4d31-9737-627527f52bf0", + "metadata": {}, + "outputs": [], + "source": [ + "bin_names = {f\"zbin_{i}_richbin_{j}\"for i,j in bins}" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "id": "16a92975-e231-4949-92f8-267d2436b9f8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[(0, 0), (0, 1), (1, 0), (1, 1), (2, 0), (2, 1), (3, 0), (3, 1)]" + ] + }, + "execution_count": 95, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bins" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "id": "fa28a39a-d3c8-4789-bc2e-ea1bdc237833", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'zbin_0_richbin_0',\n", + " 'zbin_0_richbin_1',\n", + " 'zbin_1_richbin_0',\n", + " 'zbin_1_richbin_1',\n", + " 'zbin_2_richbin_0',\n", + " 'zbin_2_richbin_1',\n", + " 'zbin_3_richbin_0',\n", + " 'zbin_3_richbin_1'}" ] }, - "execution_count": 62, + "execution_count": 111, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "dset2['bin_1_2']['redshift'][:].size" + "bin_names" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "id": "0aa7c2ad-6c9e-4f17-8486-5612dc892f0e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(0.2, 0.4) (5.0, 10.0)\n", + "(0.2, 0.4) (10.0, 20.0)\n", + "(0.4, 0.6) (5.0, 10.0)\n" + ] + } + ], + "source": [ + "print((zedge[0], zedge[1]) , (richedge[0], richedge[1]))\n", + "print((zedge[0], zedge[1]) , (richedge[0+1], richedge[1+1]))\n", + "print((zedge[0+1], zedge[1+1]) , (richedge[0], richedge[1]))" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "id": "0ca70ce6-3e87-4630-b759-630a7184b06e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "zbin_2_richbin_0\n", + "zbin_1_richbin_1\n", + "zbin_3_richbin_1\n", + "zbin_1_richbin_0\n", + "zbin_0_richbin_0\n", + "zbin_2_richbin_1\n", + "zbin_0_richbin_1\n", + "zbin_3_richbin_0\n" + ] + } + ], + "source": [ + "for yes in bin_names:\n", + " print(yes)" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "id": "3a1404d8-2040-4679-be05-379235242cac", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 0 zbin_2_richbin_0\n", + "0 1 zbin_1_richbin_1\n", + "1 0 zbin_3_richbin_1\n", + "1 1 zbin_1_richbin_0\n", + "2 0 zbin_0_richbin_0\n", + "2 1 zbin_2_richbin_1\n", + "3 0 zbin_0_richbin_1\n", + "3 1 zbin_3_richbin_0\n" + ] + } + ], + "source": [ + "for (i, j), name in zip(bins, bin_names):\n", + " print(i,j, name)\n", + " #metadata = splitter.subgroups[name].attrs\n", + " #metadata['rich_min'] = \n", + " #print(\"richmin\", richedge[j])\n", + " #metadata['rich_max'] = richedge[i+1]\n", + " #metadata['z_min'] = zedge[i]\n", + " #print(\"zmin\", zedge[i])\n", + " #metadata['z_max'] = zedge[i+1]" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e14f8a6d-74b0-4b1e-aa90-8a4d37bab15e", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/txpipe/extensions/clmm/select.py b/txpipe/extensions/clmm/select.py index fa2437fde..1d7868dca 100644 --- a/txpipe/extensions/clmm/select.py +++ b/txpipe/extensions/clmm/select.py @@ -10,8 +10,6 @@ import ceci import itertools -#from ...data_types import HDFFile, MapsFile - class CLClusterBinningRedshiftRichness(PipelineStage): name = "CLClusterBinningRedshiftRichness" @@ -31,8 +29,8 @@ def run(self): zedge = np.array(self.config['zedge']) richedge = np.array(self.config['richedge']) - nz = len(zedge) - nr = len(richedge) + nz = len(zedge - 1) + nr = len(richedge - 1) # add infinities to either end to catch objects that spill out zedge = np.concatenate([[-np.inf], zedge, [np.inf]]) @@ -40,14 +38,14 @@ def run(self): # all pairs of z bin, richness bin indices bins = list(itertools.product(range(nz), range(nr))) - bin_names = {f"{i}_{j}":initial_size for i,j in bins} + bin_names = {f"zbin_{i}_richbin_{j}":initial_size for i,j in bins} # Columns we want to save for each object cols = ['cluster_id', 'dec', 'ra', 'redshift', 'redshift_err', 'richness', 'richness_err', 'scaleval'] f = self.open_output("cluster_catalog_tomography") - g = f.create_group("lens") + g = f.create_group("cluster_bin") g.attrs['nr'] = nr g.attrs['nz'] = nz splitter = DynamicSplitter(g, "bin", cols, bin_names) @@ -66,8 +64,8 @@ def run(self): # Find which bin each object is in, or None for zi in range(1, nz): - for mi in range(1, nr): - w = np.where((zbin == zi) & (richbin == mi)) + for ri in range(1, nr): + w = np.where((zbin == zi) & (richbin == ri)) # if there are no objects in this bin in this chunk, # then we skip the rest if w[0].size == 0: @@ -77,7 +75,7 @@ def run(self): # data that is in this bin and have our splitter # object write it out. d = {name:col[w] for name, col in data.items()} - bin_name = f"{zi - 1}_{mi - 1}" #TO CHANGE ? + bin_name = f"zbin_{zi-1}_richbin_{ri-1}" #TO CHANGE ? splitter.write_bin(d, bin_name) # Truncate arrays to correct size @@ -86,30 +84,13 @@ def run(self): # Save metadata for (i, j), name in zip(bins, bin_names): metadata = splitter.subgroups[name].attrs - metadata['rich_min'] = richedge[i] - #metadata['rich_max'] = richedge[i+1] + metadata['rich_min'] = richedge[j] + metadata['rich_max'] = richedge[j+1] metadata['z_min'] = zedge[i] - #metadata['z_max'] = zedge[i+1] + metadata['z_max'] = zedge[i+1] f.close() - - - - - - - - - - - - - - - - - class CLClusterShearCatalogs(PipelineStage): name = "CLClusterShearCatalogs" From 90d701960934d0c1f84b3a5e29580e2ff6222dc8 Mon Sep 17 00:00:00 2001 From: Marina Ricci Date: Sat, 22 Jul 2023 02:17:52 +0200 Subject: [PATCH 07/22] finalized CLbinning stage --- notebooks/Run_CL_pipeline.ipynb | 390 +++++++++-------------------- txpipe/extensions/__init__.py | 2 +- txpipe/extensions/clmm/__init__.py | 2 +- txpipe/extensions/clmm/select.py | 30 +-- 4 files changed, 130 insertions(+), 294 deletions(-) diff --git a/notebooks/Run_CL_pipeline.ipynb b/notebooks/Run_CL_pipeline.ipynb index 33038f50b..b7aa05c0d 100644 --- a/notebooks/Run_CL_pipeline.ipynb +++ b/notebooks/Run_CL_pipeline.ipynb @@ -149,33 +149,74 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "040bda88-3a54-44d1-9ac2-d9e30cdb528c", + "execution_count": 6, + "id": "08da239c-1860-47a4-a39f-74ecf1ccfb26", "metadata": {}, "outputs": [], - "source": [] + "source": [ + "# we can also use the notsbook interface for small data\n", + "##TOFIX: this saves the output in '.', do not account for the path given...." + ] }, { "cell_type": "code", - "execution_count": null, - "id": "f5c9c0f8-31d4-4d96-a998-5b35ef09a025", - "metadata": {}, - "outputs": [], - "source": [] + "execution_count": 7, + "id": "274c2ea4-b366-4e3d-8690-efa8af3f6d96", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Options for this pipeline and their defaults:\n", + "{'zedge': [0.2, 0.4, 0.6, 0.8, 1.0], 'richedge': [5.0, 10.0, 20.0], 'initial_size': 100000, 'chunk_rows': 100000}\n" + ] + } + ], + "source": [ + "print(\"Options for this pipeline and their defaults:\")\n", + "print(txpipe.extensions.CLClusterBinningRedshiftRichness.config_options)\n", + "\n", + "step3 = txpipe.extensions.CLClusterBinningRedshiftRichness.make_stage(\n", + " # This is the initial cluster catalog - RAs, Decs, richess, redshift, etc.\n", + " cluster_catalog=\"./data/example/inputs/cluster_catalog.hdf5\",\n", + " # This fiducial cosmology is used to convert distance separations to redshifts\n", + " fiducial_cosmology=\"./data/fiducial_cosmology.yml\",\n", + " \n", + " # This is the output for this stage\n", + " cluster_tomography=\"./data/cosmodc2/outputs-1deg2-CL/cluster_catalog_tomography.hdf5\",\n", + "\n", + " # This contains all the options for this stage. You can override them here, as we do with the max_radius below.\n", + " config=\"examples/cosmodc2/config-1deg2-CL.yml\", \n", + ")\n" + ] }, { "cell_type": "code", - "execution_count": 39, - "id": "add8cbe8-7e1e-446b-9214-c60cfa598393", - "metadata": {}, + "execution_count": 8, + "id": "0df094c5-3e3a-4335-89ff-4df52bfb8a43", + "metadata": { + "tags": [] + }, "outputs": [], "source": [ - "#open cluster catalog" + "step3.run()\n", + "step3.finalize()" + ] + }, + { + "cell_type": "markdown", + "id": "6174784d-ba23-4e33-bbf9-559295cb9d0f", + "metadata": {}, + "source": [ + "# Open cluster catalog inpout and outputs" ] }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 9, "id": "c2cd69d0-89a2-4cb3-ae65-0882b7194507", "metadata": {}, "outputs": [], @@ -185,7 +226,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 10, "id": "3218ac13-3d30-4bc9-bd8d-25d40a3eeddd", "metadata": {}, "outputs": [], @@ -197,7 +238,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 11, "id": "d8c38523-a8f7-4a43-9831-9ae273adeaa6", "metadata": {}, "outputs": [], @@ -207,7 +248,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 12, "id": "c6d59b2a-8857-44be-93ad-912d8a893ec0", "metadata": {}, "outputs": [ @@ -217,7 +258,7 @@ "" ] }, - "execution_count": 40, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -228,7 +269,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 13, "id": "1ecd9464-7ef8-4600-b931-8b8c76830564", "metadata": {}, "outputs": [], @@ -238,7 +279,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 14, "id": "f5624d30-cea6-4999-8f53-0ee81030b632", "metadata": {}, "outputs": [ @@ -257,7 +298,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 15, "id": "4349ca71-0548-490b-b5c3-5af0ce98f7f2", "metadata": {}, "outputs": [ @@ -267,7 +308,7 @@ "Text(0, 0.5, 'richness')" ] }, - "execution_count": 43, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, @@ -291,13 +332,15 @@ }, { "cell_type": "code", - "execution_count": 128, + "execution_count": 16, "id": "0b4c65de-82c8-4f99-89de-dd0a598a31f0", "metadata": {}, "outputs": [], "source": [ "# open for reading\n", - "output_dir = '/pbs/home/m/mricci/throng_mricci/desc/TXPipe/data/cosmodc2/outputs-1deg2-CL'\n", + "#output_dir = '/pbs/home/m/mricci/throng_mricci/desc/TXPipe/data/cosmodc2/outputs-1deg2-CL'\n", + "output_dir = '.'\n", + "\n", "filename2 = output_dir + \"/cluster_catalog_tomography.hdf5\"\n", "#f2 = txpipe.data_types.TomographyCatalog(filename2, \"r\")\n", "\n", @@ -306,7 +349,7 @@ }, { "cell_type": "code", - "execution_count": 129, + "execution_count": 17, "id": "b3274869-a729-45c7-b531-05d4f8ef69da", "metadata": {}, "outputs": [ @@ -316,7 +359,7 @@ "" ] }, - "execution_count": 129, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -327,7 +370,7 @@ }, { "cell_type": "code", - "execution_count": 130, + "execution_count": 18, "id": "7785436c-8652-4c98-9ecb-373abd2dc5d2", "metadata": {}, "outputs": [], @@ -338,17 +381,17 @@ }, { "cell_type": "code", - "execution_count": 131, + "execution_count": 19, "id": "736b4dc3-381b-45f4-a93e-f9c00b9e5e17", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 131, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -359,7 +402,7 @@ }, { "cell_type": "code", - "execution_count": 132, + "execution_count": 20, "id": "6fbe636e-052e-47b0-8802-ae4ceae34ea9", "metadata": {}, "outputs": [ @@ -367,55 +410,24 @@ "name": "stdout", "output_type": "stream", "text": [ - "bin_zbin_0_richbin_0 {'rich_max': 5.0, 'rich_min': -inf, 'z_max': 0.2, 'z_min': -inf} 8\n", - "bin_zbin_0_richbin_1 {'rich_max': 10.0, 'rich_min': 5.0, 'z_max': 0.2, 'z_min': -inf} 1\n", - "bin_zbin_0_richbin_2 {'rich_max': 20.0, 'rich_min': 10.0, 'z_max': 0.2, 'z_min': -inf} 0\n", - "bin_zbin_1_richbin_0 {'rich_max': 5.0, 'rich_min': -inf, 'z_max': 0.4, 'z_min': 0.2} 3\n", - "bin_zbin_1_richbin_1 {'rich_max': 10.0, 'rich_min': 5.0, 'z_max': 0.4, 'z_min': 0.2} 5\n", - "bin_zbin_1_richbin_2 {'rich_max': 20.0, 'rich_min': 10.0, 'z_max': 0.4, 'z_min': 0.2} 0\n", - "bin_zbin_2_richbin_0 {'rich_max': 5.0, 'rich_min': -inf, 'z_max': 0.6, 'z_min': 0.4} 3\n", - "bin_zbin_2_richbin_1 {'rich_max': 10.0, 'rich_min': 5.0, 'z_max': 0.6, 'z_min': 0.4} 1\n", - "bin_zbin_2_richbin_2 {'rich_max': 20.0, 'rich_min': 10.0, 'z_max': 0.6, 'z_min': 0.4} 0\n", - "bin_zbin_3_richbin_0 {'rich_max': 5.0, 'rich_min': -inf, 'z_max': 0.8, 'z_min': 0.6} 4\n", - "bin_zbin_3_richbin_1 {'rich_max': 10.0, 'rich_min': 5.0, 'z_max': 0.8, 'z_min': 0.6} 0\n", - "bin_zbin_3_richbin_2 {'rich_max': 20.0, 'rich_min': 10.0, 'z_max': 0.8, 'z_min': 0.6} 0\n", - "bin_zbin_4_richbin_0 {'rich_max': 5.0, 'rich_min': -inf, 'z_max': 1.0, 'z_min': 0.8} 0\n", - "bin_zbin_4_richbin_1 {'rich_max': 10.0, 'rich_min': 5.0, 'z_max': 1.0, 'z_min': 0.8} 0\n", - "bin_zbin_4_richbin_2 {'rich_max': 20.0, 'rich_min': 10.0, 'z_max': 1.0, 'z_min': 0.8} 0\n" + "bin_zbin_0_richbin_0 {'rich_max': 10.0, 'rich_min': 5.0, 'z_max': 0.4, 'z_min': 0.2} 5\n", + "bin_zbin_0_richbin_1 {'rich_max': 20.0, 'rich_min': 10.0, 'z_max': 0.4, 'z_min': 0.2} 3\n", + "bin_zbin_1_richbin_0 {'rich_max': 10.0, 'rich_min': 5.0, 'z_max': 0.6, 'z_min': 0.4} 20\n", + "bin_zbin_1_richbin_1 {'rich_max': 20.0, 'rich_min': 10.0, 'z_max': 0.6, 'z_min': 0.4} 8\n", + "bin_zbin_2_richbin_0 {'rich_max': 10.0, 'rich_min': 5.0, 'z_max': 0.8, 'z_min': 0.6} 13\n", + "bin_zbin_2_richbin_1 {'rich_max': 20.0, 'rich_min': 10.0, 'z_max': 0.8, 'z_min': 0.6} 3\n", + "bin_zbin_3_richbin_0 {'rich_max': 10.0, 'rich_min': 5.0, 'z_max': 1.0, 'z_min': 0.8} 7\n", + "bin_zbin_3_richbin_1 {'rich_max': 20.0, 'rich_min': 10.0, 'z_max': 1.0, 'z_min': 0.8} 3\n" ] - }, - { - "data": { - "text/plain": [ - "[None,\n", - " None,\n", - " None,\n", - " None,\n", - " None,\n", - " None,\n", - " None,\n", - " None,\n", - " None,\n", - " None,\n", - " None,\n", - " None,\n", - " None,\n", - " None,\n", - " None]" - ] - }, - "execution_count": 132, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ - "[print (i, dict(dset2[i].attrs), dset2[i]['redshift'][:].size) for i in dset2.keys()]" + "[print (i, dict(dset2[i].attrs), dset2[i]['redshift'][:].size) for i in dset2.keys()];" ] }, { "cell_type": "code", - "execution_count": 135, + "execution_count": 21, "id": "38be57ca-e370-480d-a2e0-c2d45baf7b13", "metadata": {}, "outputs": [ @@ -432,7 +444,7 @@ " 'scaleval']" ] }, - "execution_count": 135, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -443,30 +455,23 @@ }, { "cell_type": "code", - "execution_count": 136, + "execution_count": 22, "id": "160082fd-9809-4144-aabf-66a3fd7b00de", "metadata": {}, "outputs": [ { - "ename": "KeyError", - "evalue": "\"Unable to synchronously open object (object 'bin_0_0' doesn't exist)\"", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[136], line 15\u001b[0m\n\u001b[1;32m 12\u001b[0m plt\u001b[38;5;241m.\u001b[39mxlabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mredshift\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 13\u001b[0m plt\u001b[38;5;241m.\u001b[39mylabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrichness\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m---> 15\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(\u001b[43mdset2\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mbin_0_0\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mredshift\u001b[39m\u001b[38;5;124m'\u001b[39m][:], dset2[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbin_0_0\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrichness\u001b[39m\u001b[38;5;124m'\u001b[39m][:],\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mm.\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 16\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(dset2[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbin_0_1\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mredshift\u001b[39m\u001b[38;5;124m'\u001b[39m][:], dset2[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbin_0_1\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrichness\u001b[39m\u001b[38;5;124m'\u001b[39m][:],\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mc.\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 18\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(dset2[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbin_1_0\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mredshift\u001b[39m\u001b[38;5;124m'\u001b[39m][:], dset2[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbin_1_0\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrichness\u001b[39m\u001b[38;5;124m'\u001b[39m][:],\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mr.\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", - "File \u001b[0;32mh5py/_objects.pyx:54\u001b[0m, in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mh5py/_objects.pyx:55\u001b[0m, in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32m/pbs/throng/lsst/users/jzuntz/txpipe-environments/conda-2023-Jul-12/lib/python3.10/site-packages/h5py/_hl/group.py:357\u001b[0m, in \u001b[0;36mGroup.__getitem__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 355\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid HDF5 object reference\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 356\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(name, (\u001b[38;5;28mbytes\u001b[39m, \u001b[38;5;28mstr\u001b[39m)):\n\u001b[0;32m--> 357\u001b[0m oid \u001b[38;5;241m=\u001b[39m \u001b[43mh5o\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mid\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_e\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlapl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_lapl\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 358\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 359\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAccessing a group is done with bytes or str, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 360\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnot \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;28mtype\u001b[39m(name)))\n", - "File \u001b[0;32mh5py/_objects.pyx:54\u001b[0m, in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mh5py/_objects.pyx:55\u001b[0m, in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mh5py/h5o.pyx:190\u001b[0m, in \u001b[0;36mh5py.h5o.open\u001b[0;34m()\u001b[0m\n", - "\u001b[0;31mKeyError\u001b[0m: \"Unable to synchronously open object (object 'bin_0_0' doesn't exist)\"" - ] + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -490,202 +495,31 @@ "plt.xlabel('redshift')\n", "plt.ylabel('richness')\n", "\n", - "plt.plot(dset2['bin_0_0']['redshift'][:], dset2['bin_0_0']['richness'][:],'m.')\n", - "plt.plot(dset2['bin_0_1']['redshift'][:], dset2['bin_0_1']['richness'][:],'c.')\n", + "plt.plot(dset2['bin_zbin_0_richbin_0']['redshift'][:], \n", + " dset2['bin_zbin_0_richbin_0']['richness'][:],'m.')\n", + "plt.plot(dset2['bin_zbin_0_richbin_1']['redshift'][:], \n", + " dset2['bin_zbin_0_richbin_1']['richness'][:],'c.')\n", "\n", - "plt.plot(dset2['bin_1_0']['redshift'][:], dset2['bin_1_0']['richness'][:],'r.')\n", - "plt.plot(dset2['bin_1_1']['redshift'][:], dset2['bin_1_1']['richness'][:],'g.')\n", + "plt.plot(dset2['bin_zbin_1_richbin_0']['redshift'][:], \n", + " dset2['bin_zbin_1_richbin_0']['richness'][:],'r.')\n", + "plt.plot(dset2['bin_zbin_1_richbin_1']['redshift'][:], \n", + " dset2['bin_zbin_1_richbin_1']['richness'][:],'g.')\n", "\n", - "plt.plot(dset2['bin_2_0']['redshift'][:], dset2['bin_2_0']['richness'][:],'mx')\n", - "plt.plot(dset2['bin_2_1']['redshift'][:], dset2['bin_2_1']['richness'][:],'cx')\n", - "\n", - "plt.plot(dset2['bin_3_0']['redshift'][:], dset2['bin_3_0']['richness'][:],'rx')\n", - "plt.plot(dset2['bin_3_1']['redshift'][:], dset2['bin_3_1']['richness'][:],'gx')\n" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "id": "f8829286-7aa3-4fe6-8717-73094f8df9f8", - "metadata": {}, - "outputs": [], - "source": [ - "import itertools\n", - "\n", - "zedge = [0.2, 0.4, 0.6, 0.8, 1.0]\n", - "richedge = [5., 10., 20.]\n", - " \n", - " \n", - "nz = len(zedge) - 1 \n", - "nr = len(richedge) - 1 \n", - "bins = list(itertools.product(range(nz), range(nr)))" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "id": "6d1fc93f-0ca3-49ff-8e75-de70a4d352ac", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(4, 2)" - ] - }, - "execution_count": 72, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "nz, nr" - ] - }, - { - "cell_type": "code", - "execution_count": 85, - "id": "981ca262-fd2c-4d31-9737-627527f52bf0", - "metadata": {}, - "outputs": [], - "source": [ - "bin_names = {f\"zbin_{i}_richbin_{j}\"for i,j in bins}" - ] - }, - { - "cell_type": "code", - "execution_count": 95, - "id": "16a92975-e231-4949-92f8-267d2436b9f8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[(0, 0), (0, 1), (1, 0), (1, 1), (2, 0), (2, 1), (3, 0), (3, 1)]" - ] - }, - "execution_count": 95, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bins" - ] - }, - { - "cell_type": "code", - "execution_count": 111, - "id": "fa28a39a-d3c8-4789-bc2e-ea1bdc237833", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'zbin_0_richbin_0',\n", - " 'zbin_0_richbin_1',\n", - " 'zbin_1_richbin_0',\n", - " 'zbin_1_richbin_1',\n", - " 'zbin_2_richbin_0',\n", - " 'zbin_2_richbin_1',\n", - " 'zbin_3_richbin_0',\n", - " 'zbin_3_richbin_1'}" - ] - }, - "execution_count": 111, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bin_names" - ] - }, - { - "cell_type": "code", - "execution_count": 104, - "id": "0aa7c2ad-6c9e-4f17-8486-5612dc892f0e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(0.2, 0.4) (5.0, 10.0)\n", - "(0.2, 0.4) (10.0, 20.0)\n", - "(0.4, 0.6) (5.0, 10.0)\n" - ] - } - ], - "source": [ - "print((zedge[0], zedge[1]) , (richedge[0], richedge[1]))\n", - "print((zedge[0], zedge[1]) , (richedge[0+1], richedge[1+1]))\n", - "print((zedge[0+1], zedge[1+1]) , (richedge[0], richedge[1]))" - ] - }, - { - "cell_type": "code", - "execution_count": 120, - "id": "0ca70ce6-3e87-4630-b759-630a7184b06e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "zbin_2_richbin_0\n", - "zbin_1_richbin_1\n", - "zbin_3_richbin_1\n", - "zbin_1_richbin_0\n", - "zbin_0_richbin_0\n", - "zbin_2_richbin_1\n", - "zbin_0_richbin_1\n", - "zbin_3_richbin_0\n" - ] - } - ], - "source": [ - "for yes in bin_names:\n", - " print(yes)" - ] - }, - { - "cell_type": "code", - "execution_count": 121, - "id": "3a1404d8-2040-4679-be05-379235242cac", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0 0 zbin_2_richbin_0\n", - "0 1 zbin_1_richbin_1\n", - "1 0 zbin_3_richbin_1\n", - "1 1 zbin_1_richbin_0\n", - "2 0 zbin_0_richbin_0\n", - "2 1 zbin_2_richbin_1\n", - "3 0 zbin_0_richbin_1\n", - "3 1 zbin_3_richbin_0\n" - ] - } - ], - "source": [ - "for (i, j), name in zip(bins, bin_names):\n", - " print(i,j, name)\n", - " #metadata = splitter.subgroups[name].attrs\n", - " #metadata['rich_min'] = \n", - " #print(\"richmin\", richedge[j])\n", - " #metadata['rich_max'] = richedge[i+1]\n", - " #metadata['z_min'] = zedge[i]\n", - " #print(\"zmin\", zedge[i])\n", - " #metadata['z_max'] = zedge[i+1]" + "plt.plot(dset2['bin_zbin_2_richbin_0']['redshift'][:], \n", + " dset2['bin_zbin_2_richbin_0']['richness'][:],'mx')\n", + "plt.plot(dset2['bin_zbin_2_richbin_1']['redshift'][:], \n", + " dset2['bin_zbin_2_richbin_1']['richness'][:],'cx')\n", + " \n", + "plt.plot(dset2['bin_zbin_3_richbin_0']['redshift'][:], \n", + " dset2['bin_zbin_3_richbin_0']['richness'][:],'rx')\n", + "plt.plot(dset2['bin_zbin_3_richbin_1']['redshift'][:], \n", + " dset2['bin_zbin_3_richbin_1']['richness'][:],'gx')\n" ] }, { "cell_type": "code", "execution_count": null, - "id": "e14f8a6d-74b0-4b1e-aa90-8a4d37bab15e", + "id": "7b711df4-aefe-4f12-a477-c52e739ec9b7", "metadata": {}, "outputs": [], "source": [] diff --git a/txpipe/extensions/__init__.py b/txpipe/extensions/__init__.py index 062134ff4..f92e86879 100644 --- a/txpipe/extensions/__init__.py +++ b/txpipe/extensions/__init__.py @@ -1 +1 @@ -#from .clmm import * +from .clmm import * diff --git a/txpipe/extensions/clmm/__init__.py b/txpipe/extensions/clmm/__init__.py index 95696f585..defbc6b2a 100644 --- a/txpipe/extensions/clmm/__init__.py +++ b/txpipe/extensions/clmm/__init__.py @@ -1,3 +1,3 @@ #from .ingest import * -#from .select import CLClusterShearCatalogs +from .select import CLClusterShearCatalogs, CLClusterBinningRedshiftRichness #from .rlens import TXTwoPointRLens diff --git a/txpipe/extensions/clmm/select.py b/txpipe/extensions/clmm/select.py index 1d7868dca..984a5ebca 100644 --- a/txpipe/extensions/clmm/select.py +++ b/txpipe/extensions/clmm/select.py @@ -28,18 +28,20 @@ def run(self): zedge = np.array(self.config['zedge']) richedge = np.array(self.config['richedge']) - - nz = len(zedge - 1) - nr = len(richedge - 1) - + + nz = len(zedge) - 1 + nr = len(richedge) - 1 + # add infinities to either end to catch objects that spill out zedge = np.concatenate([[-np.inf], zedge, [np.inf]]) richedge = np.concatenate([[-np.inf], richedge, [np.inf]]) - + # all pairs of z bin, richness bin indices bins = list(itertools.product(range(nz), range(nr))) bin_names = {f"zbin_{i}_richbin_{j}":initial_size for i,j in bins} + #bin_names = [f"zbin_{i}_richbin_{j}" for i,j in bins] + # Columns we want to save for each object cols = ['cluster_id', 'dec', 'ra', 'redshift', 'redshift_err', 'richness', 'richness_err', 'scaleval'] @@ -59,12 +61,12 @@ def run(self): n = len(data["redshift"]) # Figure out which bin each halo it in, if any, starts at 0 - zbin = np.digitize(data['redshift'], zedge) - 1 - richbin = np.digitize(data["richness"], richedge) - 1 + zbin = np.digitize(data['redshift'], zedge) - 2 + richbin = np.digitize(data["richness"], richedge) - 2 # Find which bin each object is in, or None - for zi in range(1, nz): - for ri in range(1, nr): + for zi in range(0, nz): + for ri in range(0, nr): w = np.where((zbin == zi) & (richbin == ri)) # if there are no objects in this bin in this chunk, # then we skip the rest @@ -75,7 +77,7 @@ def run(self): # data that is in this bin and have our splitter # object write it out. d = {name:col[w] for name, col in data.items()} - bin_name = f"zbin_{zi-1}_richbin_{ri-1}" #TO CHANGE ? + bin_name = f"zbin_{zi}_richbin_{ri}" #TO CHANGE ? splitter.write_bin(d, bin_name) # Truncate arrays to correct size @@ -84,10 +86,10 @@ def run(self): # Save metadata for (i, j), name in zip(bins, bin_names): metadata = splitter.subgroups[name].attrs - metadata['rich_min'] = richedge[j] - metadata['rich_max'] = richedge[j+1] - metadata['z_min'] = zedge[i] - metadata['z_max'] = zedge[i+1] + metadata['rich_min'] = richedge[j+1] + metadata['rich_max'] = richedge[j+2] + metadata['z_min'] = zedge[i+1] + metadata['z_max'] = zedge[i+2] f.close() From 4e849561cf5715605e032f3ab431b27ded0b6c16 Mon Sep 17 00:00:00 2001 From: Marina Ricci Date: Sat, 22 Jul 2023 02:30:00 +0200 Subject: [PATCH 08/22] Change filename --- txpipe/extensions/clmm/bin_cluster.py | 95 +++++++++++++++++++++++++++ txpipe/extensions/clmm/select.py | 86 +----------------------- 2 files changed, 96 insertions(+), 85 deletions(-) create mode 100644 txpipe/extensions/clmm/bin_cluster.py diff --git a/txpipe/extensions/clmm/bin_cluster.py b/txpipe/extensions/clmm/bin_cluster.py new file mode 100644 index 000000000..8140893c7 --- /dev/null +++ b/txpipe/extensions/clmm/bin_cluster.py @@ -0,0 +1,95 @@ +# -*- coding: utf-8 -*- +import os +import gc +import numpy as np +from ...base_stage import PipelineStage +from ...data_types import ShearCatalog, HDFFile, PhotozPDFFile, FiducialCosmology, TomographyCatalog, ShearCatalog +from ...utils.calibrators import Calibrator +from ...utils import DynamicSplitter +from collections import defaultdict +import yaml +import ceci +import itertools + + +class CLClusterBinningRedshiftRichness(PipelineStage): + name = "CLClusterBinningRedshiftRichness" + parallel = False + inputs = [("cluster_catalog", HDFFile)] + outputs = [("cluster_catalog_tomography", HDFFile)] + config_options = { + "zedge": [0.2, 0.4, 0.6, 0.8, 1.0], + "richedge": [5., 10., 20.], + "initial_size": 100_000, + "chunk_rows": 100_000, + } + def run(self): + initial_size = self.config["initial_size"] + chunk_rows = self.config["chunk_rows"] + + zedge = np.array(self.config['zedge']) + richedge = np.array(self.config['richedge']) + + nz = len(zedge) - 1 + nr = len(richedge) - 1 + + # add infinities to either end to catch objects that spill out + zedge = np.concatenate([[-np.inf], zedge, [np.inf]]) + richedge = np.concatenate([[-np.inf], richedge, [np.inf]]) + + # all pairs of z bin, richness bin indices + bins = list(itertools.product(range(nz), range(nr))) + bin_names = {f"zbin_{i}_richbin_{j}":initial_size for i,j in bins} + #bin_names = [f"zbin_{i}_richbin_{j}" for i,j in bins] + + + # Columns we want to save for each object + cols = ['cluster_id', 'dec', 'ra', 'redshift', 'redshift_err', 'richness', 'richness_err', 'scaleval'] + + + f = self.open_output("cluster_catalog_tomography") + g = f.create_group("cluster_bin") + g.attrs['nr'] = nr + g.attrs['nz'] = nz + splitter = DynamicSplitter(g, "bin", cols, bin_names) + + # Make an iterator that will read a chunk of data at a time + it = self.iterate_hdf("cluster_catalog", "clusters", cols, chunk_rows) + + # Loop through the chunks of data; each time `data` will be a + # dictionary of column names -> numpy arrays + for _, _, data in it: + n = len(data["redshift"]) + + # Figure out which bin each halo it in, if any, starts at 0 + zbin = np.digitize(data['redshift'], zedge) - 2 + richbin = np.digitize(data["richness"], richedge) - 2 + + # Find which bin each object is in, or None + for zi in range(0, nz): + for ri in range(0, nr): + w = np.where((zbin == zi) & (richbin == ri)) + # if there are no objects in this bin in this chunk, + # then we skip the rest + if w[0].size == 0: + continue + + # Otherwise we extract the bit of this chunk of + # data that is in this bin and have our splitter + # object write it out. + d = {name:col[w] for name, col in data.items()} + bin_name = f"zbin_{zi}_richbin_{ri}" #TO CHANGE ? + splitter.write_bin(d, bin_name) + + # Truncate arrays to correct size + splitter.finish() + + # Save metadata + for (i, j), name in zip(bins, bin_names): + metadata = splitter.subgroups[name].attrs + metadata['rich_min'] = richedge[j+1] + metadata['rich_max'] = richedge[j+2] + metadata['z_min'] = zedge[i+1] + metadata['z_max'] = zedge[i+2] + + f.close() diff --git a/txpipe/extensions/clmm/select.py b/txpipe/extensions/clmm/select.py index 984a5ebca..52195cbf8 100644 --- a/txpipe/extensions/clmm/select.py +++ b/txpipe/extensions/clmm/select.py @@ -4,95 +4,11 @@ from ...base_stage import PipelineStage from ...data_types import ShearCatalog, HDFFile, PhotozPDFFile, FiducialCosmology, TomographyCatalog, ShearCatalog from ...utils.calibrators import Calibrator -from ...utils import DynamicSplitter from collections import defaultdict import yaml import ceci -import itertools - -class CLClusterBinningRedshiftRichness(PipelineStage): - name = "CLClusterBinningRedshiftRichness" - parallel = False - inputs = [("cluster_catalog", HDFFile)] - outputs = [("cluster_catalog_tomography", HDFFile)] - config_options = { - "zedge": [0.2, 0.4, 0.6, 0.8, 1.0], - "richedge": [5., 10., 20.], - "initial_size": 100_000, - "chunk_rows": 100_000, - } - def run(self): - initial_size = self.config["initial_size"] - chunk_rows = self.config["chunk_rows"] - - zedge = np.array(self.config['zedge']) - richedge = np.array(self.config['richedge']) - - nz = len(zedge) - 1 - nr = len(richedge) - 1 - - # add infinities to either end to catch objects that spill out - zedge = np.concatenate([[-np.inf], zedge, [np.inf]]) - richedge = np.concatenate([[-np.inf], richedge, [np.inf]]) - - # all pairs of z bin, richness bin indices - bins = list(itertools.product(range(nz), range(nr))) - bin_names = {f"zbin_{i}_richbin_{j}":initial_size for i,j in bins} - #bin_names = [f"zbin_{i}_richbin_{j}" for i,j in bins] - - - # Columns we want to save for each object - cols = ['cluster_id', 'dec', 'ra', 'redshift', 'redshift_err', 'richness', 'richness_err', 'scaleval'] - - - f = self.open_output("cluster_catalog_tomography") - g = f.create_group("cluster_bin") - g.attrs['nr'] = nr - g.attrs['nz'] = nz - splitter = DynamicSplitter(g, "bin", cols, bin_names) - - # Make an iterator that will read a chunk of data at a time - it = self.iterate_hdf("cluster_catalog", "clusters", cols, chunk_rows) - - # Loop through the chunks of data; each time `data` will be a - # dictionary of column names -> numpy arrays - for _, _, data in it: - n = len(data["redshift"]) - - # Figure out which bin each halo it in, if any, starts at 0 - zbin = np.digitize(data['redshift'], zedge) - 2 - richbin = np.digitize(data["richness"], richedge) - 2 - - # Find which bin each object is in, or None - for zi in range(0, nz): - for ri in range(0, nr): - w = np.where((zbin == zi) & (richbin == ri)) - # if there are no objects in this bin in this chunk, - # then we skip the rest - if w[0].size == 0: - continue - - # Otherwise we extract the bit of this chunk of - # data that is in this bin and have our splitter - # object write it out. - d = {name:col[w] for name, col in data.items()} - bin_name = f"zbin_{zi}_richbin_{ri}" #TO CHANGE ? - splitter.write_bin(d, bin_name) - - # Truncate arrays to correct size - splitter.finish() - - # Save metadata - for (i, j), name in zip(bins, bin_names): - metadata = splitter.subgroups[name].attrs - metadata['rich_min'] = richedge[j+1] - metadata['rich_max'] = richedge[j+2] - metadata['z_min'] = zedge[i+1] - metadata['z_max'] = zedge[i+2] - - f.close() - + class CLClusterShearCatalogs(PipelineStage): name = "CLClusterShearCatalogs" From b2a3f06385ace1a617a067327332a2a7eeb5ebae Mon Sep 17 00:00:00 2001 From: Marina Ricci Date: Sat, 22 Jul 2023 02:37:30 +0200 Subject: [PATCH 09/22] added imports --- txpipe/__init__.py | 2 +- txpipe/extensions/clmm/__init__.py | 3 ++- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/txpipe/__init__.py b/txpipe/__init__.py index 76c47671c..41bcf692a 100755 --- a/txpipe/__init__.py +++ b/txpipe/__init__.py @@ -40,5 +40,5 @@ # Here are the stages that mostly will be used for other projects # such as the self-calibration of Intrinsic alignment. from .extensions.twopoint_scia import TXSelfCalibrationIA -from .extensions.clmm.select import CLClusterShearCatalogs, CLClusterBinningRedshiftRichness +from .extensions.clmm import CLClusterShearCatalogs, CLClusterBinningRedshiftRichness from .covariance_nmt import TXFourierNamasterCovariance, TXRealNamasterCovariance diff --git a/txpipe/extensions/clmm/__init__.py b/txpipe/extensions/clmm/__init__.py index defbc6b2a..de68def3d 100644 --- a/txpipe/extensions/clmm/__init__.py +++ b/txpipe/extensions/clmm/__init__.py @@ -1,3 +1,4 @@ #from .ingest import * -from .select import CLClusterShearCatalogs, CLClusterBinningRedshiftRichness +from .select import CLClusterShearCatalogs +from .bin_cluster import CLClusterBinningRedshiftRichness #from .rlens import TXTwoPointRLens From ad869988b9ddc96203d2f6295b12c039c3702e9d Mon Sep 17 00:00:00 2001 From: Marina Ricci Date: Tue, 17 Oct 2023 15:27:21 +0200 Subject: [PATCH 10/22] update notebook? --- notebooks/Run_CL_pipeline.ipynb | 12 ++---------- 1 file changed, 2 insertions(+), 10 deletions(-) diff --git a/notebooks/Run_CL_pipeline.ipynb b/notebooks/Run_CL_pipeline.ipynb index b7aa05c0d..dd7d7a614 100644 --- a/notebooks/Run_CL_pipeline.ipynb +++ b/notebooks/Run_CL_pipeline.ipynb @@ -160,7 +160,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "id": "274c2ea4-b366-4e3d-8690-efa8af3f6d96", "metadata": { "tags": [] @@ -195,7 +195,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "id": "0df094c5-3e3a-4335-89ff-4df52bfb8a43", "metadata": { "tags": [] @@ -515,14 +515,6 @@ "plt.plot(dset2['bin_zbin_3_richbin_1']['redshift'][:], \n", " dset2['bin_zbin_3_richbin_1']['richness'][:],'gx')\n" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7b711df4-aefe-4f12-a477-c52e739ec9b7", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { From bbd3f622f0c7ae7169ce5d5ca603c023d8653149 Mon Sep 17 00:00:00 2001 From: Marina Ricci Date: Tue, 17 Oct 2023 19:43:21 +0200 Subject: [PATCH 11/22] add rlens in the import to not break test --- txpipe/extensions/clmm/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/txpipe/extensions/clmm/__init__.py b/txpipe/extensions/clmm/__init__.py index de68def3d..e69c33016 100644 --- a/txpipe/extensions/clmm/__init__.py +++ b/txpipe/extensions/clmm/__init__.py @@ -1,4 +1,4 @@ #from .ingest import * from .select import CLClusterShearCatalogs from .bin_cluster import CLClusterBinningRedshiftRichness -#from .rlens import TXTwoPointRLens +from .rlens import TXTwoPointRLens From 68fad00e95db5b5ddc62fc746aef11d6c0cc8d20 Mon Sep 17 00:00:00 2001 From: Marina Ricci Date: Wed, 18 Oct 2023 19:33:10 +0200 Subject: [PATCH 12/22] make NERSC vs CC more clear --- 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zR@kBrQh|C91+0(Ld{tJ$F{ihiWq^u;B9x6qG~XSJC&PR5i=FVXQ4KCI`R4&g=DbN+ zO?rXK%B{z9g5b`>h$SOjR(yWSoI|abaL~$7=IlqPjw6r)n+xpk#u=KeeVh@ilAxBa zAWw|v0<)2PR9TjI|Lxco=eL1++-(-$eTyM-!$o5*KR+EIqfL*B7Qx5v3a2sjaF};< zxp52c{iM=E^~+)Ix3HekPJi)OI~lUn?3LtaUz!ShuXOBG38V7B@ySR@p;^A7vZ?0* zNaCHVn|)ETrK*DhvJk=@QAF}a>c#>G-g+j>QXH-d{9D(^Cq2<}&TXrau;rfw5*{F8 zx%rqI?PgrZ?y_EdN#28%h-{xN#soRLa? z$1C3H)vfuGeK9jyfq?YoY`H;Jh(DhdB={c7k;Wc^$xMi=I;Ac~4B8ivD|Oy1^1Dl- zO1*dI0AKr7RW0ZF35B?%j$f-n=9S3wXD{eOm6@C@mJV5I+v zA0E2+sp~~xm!}l*vu>}is?zyJ7tpt`z0mh<5MZ1nt3Tiq)K^ txpipe rail.estimation.algos.bpz_lite diff --git a/examples/cosmodc2/pipeline-20deg2-CL.yml b/examples/cosmodc2/pipeline-20deg2-CL.yml index caa3e8972..5d058a98a 100644 --- a/examples/cosmodc2/pipeline-20deg2-CL.yml +++ b/examples/cosmodc2/pipeline-20deg2-CL.yml @@ -1,12 +1,19 @@ -launcher: - name: mini - interval: 3.0 - +#this step depends on where you run +#for CCin2p3 site: name: cc-parallel mpi_command: "mpirun -n" +#for NERSC +#site: +# name: cori-batch +# image: ghcr.io/lsstdesc/txpipe-dev + +#all the following steps should not depend on where you run +launcher: + name: mini + interval: 3.0 modules: > txpipe rail.estimation.algos.bpz_lite diff --git a/examples/cosmodc2/pipeline-20deg2-clmm-nersc.yml b/examples/cosmodc2/pipeline-20deg2-clmm-nersc.yml deleted file mode 100644 index 8b80c78b6..000000000 --- a/examples/cosmodc2/pipeline-20deg2-clmm-nersc.yml +++ /dev/null @@ -1,43 +0,0 @@ -launcher: - name: mini - interval: 3.0 - -site: - name: cori-batch - image: ghcr.io/lsstdesc/txpipe-dev - - -modules: > - txpipe - rail.estimation.algos.bpz_lite - -python_paths: [] - -stages: - - name: TXSourceSelectorMetadetect - nprocess: 30 - - name: Inform_BPZ_lite - nprocess: 1 - - name: BPZ_lite - nprocess: 30 - - name: CLClusterShearCatalogs - nprocess: 30 - - - -output_dir: data/cosmodc2/outputs-20deg2 -config: examples/cosmodc2/config-20deg2-clmm.yml - -inputs: - # See README for paths to download these files - shear_catalog: ./data/cosmodc2/20deg2/shear_catalog.hdf5 - photometry_catalog: ./data/cosmodc2/20deg2/photometry_catalog.hdf5 - fiducial_cosmology: data/fiducial_cosmology.yml - calibration_table: ./data/cosmodc2/20deg2/sample_cosmodc2_w10year_errors.dat - spectroscopic_catalog: ./data/cosmodc2/20deg2/spectroscopic_catalog.hdf5 - cluster_catalog: ./data/cosmodc2/20deg2/cluster_catalog.hdf5 - -resume: True -log_dir: data/cosmodc2/logs -pipeline_log: data/cosmodc2/log.txt - diff --git a/examples/cosmodc2/pipeline-20deg2-clmm.yml b/examples/cosmodc2/pipeline-20deg2-clmm.yml deleted file mode 100644 index b286a0a8d..000000000 --- a/examples/cosmodc2/pipeline-20deg2-clmm.yml +++ /dev/null @@ -1,43 +0,0 @@ -launcher: - name: mini - interval: 3.0 - -site: - name: cc-parallel - mpi_command: "mpirun -n" - - -modules: > - txpipe - rail.estimation.algos.bpz_lite - -python_paths: [] - -stages: - - name: TXSourceSelectorMetadetect - nprocess: 30 - - name: Inform_BPZ_lite - nprocess: 1 - - name: BPZ_lite - nprocess: 30 - - name: CLClusterShearCatalogs - nprocess: 30 - - - -output_dir: data/cosmodc2/outputs-20deg2 -config: examples/cosmodc2/config-20deg2-clmm.yml - -inputs: - # See README for paths to download these files - shear_catalog: ./data/cosmodc2/20deg2/shear_catalog.hdf5 - photometry_catalog: ./data/cosmodc2/20deg2/photometry_catalog.hdf5 - fiducial_cosmology: data/fiducial_cosmology.yml - calibration_table: ./data/cosmodc2/20deg2/sample_cosmodc2_w10year_errors.dat - spectroscopic_catalog: ./data/cosmodc2/20deg2/spectroscopic_catalog.hdf5 - cluster_catalog: ./data/cosmodc2/20deg2/cluster_catalog.hdf5 - -resume: True -log_dir: data/cosmodc2/logs -pipeline_log: data/cosmodc2/log.txt - diff --git a/notebooks/Run_CL_pipeline.ipynb b/notebooks/Run_CL_pipeline.ipynb index dd7d7a614..13b6310e7 100644 --- a/notebooks/Run_CL_pipeline.ipynb +++ b/notebooks/Run_CL_pipeline.ipynb @@ -23,7 +23,10 @@ "metadata": {}, "outputs": [], "source": [ + "#user specific paths\n", "my_txpipe_dir = \"/pbs/home/m/mricci/throng_mricci/desc/TXPipe\"\n", + "output_dir = '/pbs/home/m/mricci/throng_mricci/desc/TXPipe/data/example/inputs'\n", + "\n", "os.chdir(my_txpipe_dir)\n", "\n", "import txpipe" @@ -160,7 +163,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "274c2ea4-b366-4e3d-8690-efa8af3f6d96", "metadata": { "tags": [] @@ -170,16 +173,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "Options for this pipeline and their defaults:\n", - "{'zedge': [0.2, 0.4, 0.6, 0.8, 1.0], 'richedge': [5.0, 10.0, 20.0], 'initial_size': 100000, 'chunk_rows': 100000}\n" + "Options for this pipeline and their defaults (this may be override by config file):\n", + "{'zedge': [0.2, 0.4, 0.6, 0.8, 1.8], 'richedge': [5.0, 10.0, 20.0], 'initial_size': 100000, 'chunk_rows': 100000}\n" ] } ], "source": [ - "print(\"Options for this pipeline and their defaults:\")\n", + "print(\"Options for this pipeline and their defaults (this may be override by config file):\")\n", "print(txpipe.extensions.CLClusterBinningRedshiftRichness.config_options)\n", "\n", - "step3 = txpipe.extensions.CLClusterBinningRedshiftRichness.make_stage(\n", + "pip_stage = txpipe.extensions.CLClusterBinningRedshiftRichness.make_stage(\n", " # This is the initial cluster catalog - RAs, Decs, richess, redshift, etc.\n", " cluster_catalog=\"./data/example/inputs/cluster_catalog.hdf5\",\n", " # This fiducial cosmology is used to convert distance separations to redshifts\n", @@ -188,22 +191,43 @@ " # This is the output for this stage\n", " cluster_tomography=\"./data/cosmodc2/outputs-1deg2-CL/cluster_catalog_tomography.hdf5\",\n", "\n", - " # This contains all the options for this stage. You can override them here, as we do with the max_radius below.\n", + " # This contains all the options for this stage. You can override them here, \n", + " #as we do with the max_radius below.\n", " config=\"examples/cosmodc2/config-1deg2-CL.yml\", \n", ")\n" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "id": "0df094c5-3e3a-4335-89ff-4df52bfb8a43", "metadata": { "tags": [] }, "outputs": [], "source": [ - "step3.run()\n", - "step3.finalize()" + "pip_stage.run()\n", + "pip_stage.finalize()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "009eb8b0-1c54-497f-a419-055e869e2ece", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Actual options used for this pipeline (as defined in config file or default):\n", + "{zedge:[0.1, 0.4, 0.6, 0.8],richedge:[5.0, 10.0, 20.0, 25.0],initial_size:100000,chunk_rows:100000,cluster_catalog:./data/example/inputs/cluster_catalog.hdf5,fiducial_cosmology:./data/fiducial_cosmology.yml,cluster_tomography:./data/cosmodc2/outputs-1deg2-CL/cluster_catalog_tomography.hdf5,config:examples/cosmodc2/config-1deg2-CL.yml,aliases:{},}\n" + ] + } + ], + "source": [ + "print(\"Actual options used for this pipeline (as defined in config file or default):\")\n", + "print(pip_stage.config)" ] }, { @@ -211,12 +235,20 @@ "id": "6174784d-ba23-4e33-bbf9-559295cb9d0f", "metadata": {}, "source": [ - "# Open cluster catalog inpout and outputs" + "# Open cluster catalog input and compare to binning outputs" + ] + }, + { + "cell_type": "markdown", + "id": "b7101483-b7a2-45ce-a8b1-c6030e15da0d", + "metadata": {}, + "source": [ + "## Open cluster catalog input " ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "id": "c2cd69d0-89a2-4cb3-ae65-0882b7194507", "metadata": {}, "outputs": [], @@ -226,60 +258,55 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "id": "3218ac13-3d30-4bc9-bd8d-25d40a3eeddd", "metadata": {}, "outputs": [], "source": [ - "output_dir = '/pbs/home/m/mricci/throng_mricci/desc/TXPipe/data/example/inputs'\n", - "\n", - "filename = output_dir + \"/cluster_catalog.hdf5\"" + "filename_in = output_dir + \"/cluster_catalog.hdf5\"" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "id": "d8c38523-a8f7-4a43-9831-9ae273adeaa6", "metadata": {}, "outputs": [], "source": [ - "f = h5py.File(filename, \"r\")" + "f_in = h5py.File(filename_in, \"r\")" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "id": "c6d59b2a-8857-44be-93ad-912d8a893ec0", "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] } ], "source": [ - "f.keys()" + "print(f_in.keys())" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "id": "1ecd9464-7ef8-4600-b931-8b8c76830564", "metadata": {}, "outputs": [], "source": [ - "dset = f['clusters']" + "dset_in = f_in['clusters']" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "id": "f5624d30-cea6-4999-8f53-0ee81030b632", "metadata": {}, "outputs": [ @@ -292,13 +319,13 @@ } ], "source": [ - "cols = [col for col in dset]\n", + "cols = [col for col in dset_in]\n", "print(cols)" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "id": "4349ca71-0548-490b-b5c3-5af0ce98f7f2", "metadata": {}, "outputs": [ @@ -308,7 +335,7 @@ "Text(0, 0.5, 'richness')" ] }, - "execution_count": 15, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, @@ -324,15 +351,23 @@ } ], "source": [ - "plt.semilogy(dset['redshift'][()], dset['richness'][()],'.', alpha=1)\n", + "plt.semilogy(dset_in['redshift'][()], dset_in['richness'][()],'.', alpha=1)\n", "\n", "plt.xlabel('redshift')\n", "plt.ylabel('richness')" ] }, + { + "cell_type": "markdown", + "id": "bfd77d78-6514-4c26-babb-24f3f167956e", + "metadata": {}, + "source": [ + "## Open binning output" + ] + }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "id": "0b4c65de-82c8-4f99-89de-dd0a598a31f0", "metadata": {}, "outputs": [], @@ -341,68 +376,62 @@ "#output_dir = '/pbs/home/m/mricci/throng_mricci/desc/TXPipe/data/cosmodc2/outputs-1deg2-CL'\n", "output_dir = '.'\n", "\n", - "filename2 = output_dir + \"/cluster_catalog_tomography.hdf5\"\n", + "filename_out = output_dir + \"/cluster_catalog_tomography.hdf5\"\n", "#f2 = txpipe.data_types.TomographyCatalog(filename2, \"r\")\n", "\n", - "f2 = h5py.File(filename2, \"r\")" + "f_out = h5py.File(filename_out, \"r\")" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "id": "b3274869-a729-45c7-b531-05d4f8ef69da", "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] } ], "source": [ - "f2.keys()" + "print(f_out.keys())" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "id": "7785436c-8652-4c98-9ecb-373abd2dc5d2", "metadata": {}, "outputs": [], "source": [ - "dat2 = f2['provenance']\n", - "dset2 = f2['cluster_bin']" + "dat_out = f_out['provenance']\n", + "dset_out = f_out['cluster_bin']" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "id": "736b4dc3-381b-45f4-a93e-f9c00b9e5e17", "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] } ], "source": [ - "dset2.keys()" + "print(dset_out.keys())" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "id": "6fbe636e-052e-47b0-8802-ae4ceae34ea9", "metadata": {}, "outputs": [ @@ -410,68 +439,77 @@ "name": "stdout", "output_type": "stream", "text": [ - "bin_zbin_0_richbin_0 {'rich_max': 10.0, 'rich_min': 5.0, 'z_max': 0.4, 'z_min': 0.2} 5\n", - "bin_zbin_0_richbin_1 {'rich_max': 20.0, 'rich_min': 10.0, 'z_max': 0.4, 'z_min': 0.2} 3\n", + "bin_zbin_0_richbin_0 {'rich_max': 10.0, 'rich_min': 5.0, 'z_max': 0.4, 'z_min': 0.1} 5\n", + "bin_zbin_0_richbin_1 {'rich_max': 20.0, 'rich_min': 10.0, 'z_max': 0.4, 'z_min': 0.1} 3\n", + "bin_zbin_0_richbin_2 {'rich_max': 25.0, 'rich_min': 20.0, 'z_max': 0.4, 'z_min': 0.1} 0\n", "bin_zbin_1_richbin_0 {'rich_max': 10.0, 'rich_min': 5.0, 'z_max': 0.6, 'z_min': 0.4} 20\n", "bin_zbin_1_richbin_1 {'rich_max': 20.0, 'rich_min': 10.0, 'z_max': 0.6, 'z_min': 0.4} 8\n", + "bin_zbin_1_richbin_2 {'rich_max': 25.0, 'rich_min': 20.0, 'z_max': 0.6, 'z_min': 0.4} 0\n", "bin_zbin_2_richbin_0 {'rich_max': 10.0, 'rich_min': 5.0, 'z_max': 0.8, 'z_min': 0.6} 13\n", "bin_zbin_2_richbin_1 {'rich_max': 20.0, 'rich_min': 10.0, 'z_max': 0.8, 'z_min': 0.6} 3\n", - "bin_zbin_3_richbin_0 {'rich_max': 10.0, 'rich_min': 5.0, 'z_max': 1.0, 'z_min': 0.8} 7\n", - "bin_zbin_3_richbin_1 {'rich_max': 20.0, 'rich_min': 10.0, 'z_max': 1.0, 'z_min': 0.8} 3\n" + "bin_zbin_2_richbin_2 {'rich_max': 25.0, 'rich_min': 20.0, 'z_max': 0.8, 'z_min': 0.6} 2\n" ] } ], "source": [ - "[print (i, dict(dset2[i].attrs), dset2[i]['redshift'][:].size) for i in dset2.keys()];" + "[print (i, dict(dset_out[i].attrs), dset_out[i]['redshift'][:].size) for i in dset_out.keys()];" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 22, "id": "38be57ca-e370-480d-a2e0-c2d45baf7b13", "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "['cluster_id',\n", - " 'dec',\n", - " 'ra',\n", - " 'redshift',\n", - " 'redshift_err',\n", - " 'richness',\n", - " 'richness_err',\n", - " 'scaleval']" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "['cluster_id', 'dec', 'ra', 'redshift', 'redshift_err', 'richness', 'richness_err', 'scaleval']\n" + ] } ], "source": [ - "[col for col in dset2['bin_zbin_0_richbin_0']]" + "print ([col for col in dset_out['bin_zbin_0_richbin_0']])" + ] + }, + { + "cell_type": "markdown", + "id": "99017d4e-0ad1-44da-ace2-4fa54fb28b2e", + "metadata": {}, + "source": [ + "## Compare the two " ] }, { "cell_type": "code", - "execution_count": 22, - "id": "160082fd-9809-4144-aabf-66a3fd7b00de", + "execution_count": 33, + "id": "82695f43-ab54-4efd-bc1d-633c1db44d33", "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "The file contains 9 keys corresponding to 3 redshift bins times 3 richness bins\n" + ] + } + ], + "source": [ + "print('The file contains',len(dset_out.keys()), 'keys corresponding to',\n", + " len(pip_stage.config.zedge) - 1, ' redshift bins times', \n", + " len(pip_stage.config.richedge) - 1,'richness bins')" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "2b146a75-3aff-4fa7-be65-679c6f49bf6e", + "metadata": {}, + "outputs": [ { "data": { - "image/png": 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", "text/plain": [ "
" ] @@ -481,39 +519,24 @@ } ], "source": [ - "plt.semilogy(dset['redshift'][()], dset['richness'][()],'.', alpha=1)\n", - "\n", - "plt.axvline(0.2)\n", - "plt.axvline(0.4)\n", - "plt.axvline(0.6)\n", - "plt.axvline(0.8)\n", - "plt.axvline(1.0)\n", - "plt.axhline(5)\n", - "plt.axhline(10)\n", - "plt.axhline(20)\n", - "\n", + "#plot data from input catalog\n", + "plt.semilogy(dset_in['redshift'][()], dset_in['richness'][()],'k.', alpha=1)\n", "plt.xlabel('redshift')\n", "plt.ylabel('richness')\n", "\n", - "plt.plot(dset2['bin_zbin_0_richbin_0']['redshift'][:], \n", - " dset2['bin_zbin_0_richbin_0']['richness'][:],'m.')\n", - "plt.plot(dset2['bin_zbin_0_richbin_1']['redshift'][:], \n", - " dset2['bin_zbin_0_richbin_1']['richness'][:],'c.')\n", + "#plot bin limits as defined in the config file\n", + "[plt.axvline(i,linestyle='dashed', color='black') for i in pip_stage.config.zedge]\n", + "[plt.axhline(i,linestyle='dotted', color='black') for i in pip_stage.config.richedge]\n", "\n", - "plt.plot(dset2['bin_zbin_1_richbin_0']['redshift'][:], \n", - " dset2['bin_zbin_1_richbin_0']['richness'][:],'r.')\n", - "plt.plot(dset2['bin_zbin_1_richbin_1']['redshift'][:], \n", - " dset2['bin_zbin_1_richbin_1']['richness'][:],'g.')\n", + "#overplot data from output file to make sure the bins are ordered correctly\n", + "markers=['s','o', 'D', 'P', '^']\n", "\n", - "plt.plot(dset2['bin_zbin_2_richbin_0']['redshift'][:], \n", - " dset2['bin_zbin_2_richbin_0']['richness'][:],'mx')\n", - "plt.plot(dset2['bin_zbin_2_richbin_1']['redshift'][:], \n", - " dset2['bin_zbin_2_richbin_1']['richness'][:],'cx')\n", - " \n", - "plt.plot(dset2['bin_zbin_3_richbin_0']['redshift'][:], \n", - " dset2['bin_zbin_3_richbin_0']['richness'][:],'rx')\n", - "plt.plot(dset2['bin_zbin_3_richbin_1']['redshift'][:], \n", - " dset2['bin_zbin_3_richbin_1']['richness'][:],'gx')\n" + "for i in range(len(pip_stage.config.zedge)-1):\n", + " for j in range(len(pip_stage.config.richedge)-1):\n", + " plt.scatter(dset_out['bin_zbin_'+str(i)+'_richbin_'+str(j)]['redshift'][:], \n", + " dset_out['bin_zbin_'+str(i)+'_richbin_'+str(j)]['richness'][:], marker=markers[j], label='bin_zbin_'+str(i)+'_richbin_'+str(j))\n", + " \n", + " plt.legend(fontsize='x-small')" ] } ], From c3d1b701eb76238ad5b40deb3bdad3c7afc1d644 Mon Sep 17 00:00:00 2001 From: Marina Ricci Date: Wed, 18 Oct 2023 19:47:27 +0200 Subject: [PATCH 13/22] small modif notebook --- notebooks/Run_CL_pipeline.ipynb | 37 +++++++++++++++++---------------- 1 file changed, 19 insertions(+), 18 deletions(-) diff --git a/notebooks/Run_CL_pipeline.ipynb b/notebooks/Run_CL_pipeline.ipynb index 13b6310e7..d54382d6b 100644 --- a/notebooks/Run_CL_pipeline.ipynb +++ b/notebooks/Run_CL_pipeline.ipynb @@ -45,14 +45,18 @@ "id": "b031074a-bc97-4619-bac7-a364dccd5891", "metadata": {}, "source": [ - "### Launching a pipeline at CC-IN2P3\n", + "### Launching a pipeline\n", "\n", - "Let's have a look at the submission script for this pipeline: `examples/cosmodc2/20deg2-in2p3.sub`:" + "Let's have a look at the submission script for this pipeline:\n", + "- to work at CCin2p3 we can use: `examples/cosmodc2/1deg2-in2p3.sub`:\n", + "- to work at NERSC we can use: `examples/cosmodc2/1deg2-nersc.sub`:\n", + "\n", + "Let's continue with the CCin2p3 example :" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 38, "id": "d0539f99-806e-481c-9a00-69716e216d74", "metadata": {}, "outputs": [ @@ -81,7 +85,7 @@ "id": "64a79cc5-4e43-4146-b3ac-aceb68b56470", "metadata": {}, "source": [ - "This will launch a job of up to one hour (it should finish in 30 min) on a single CC-IN2P3 node to run a pipeline.\n", + "This will launch a job of up to one hour (it should finish in 30 min) on a single CC-IN2P3 node to run a pipeline. After the first run, the output files are created and following runs take much less time.\n", "\n", "In a terminal, **navigate to your TXPipe directory and run**:\n", "\n", @@ -93,7 +97,15 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, + "id": "0808d9ea-e647-4777-8399-f573efa9826a", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 39, "id": "5935f013-c29c-4446-b3e9-00d5de2b85cb", "metadata": { "tags": [] @@ -105,7 +117,7 @@ "0" ] }, - "execution_count": 4, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } @@ -152,18 +164,7 @@ }, { "cell_type": "code", - "execution_count": 6, - "id": "08da239c-1860-47a4-a39f-74ecf1ccfb26", - "metadata": {}, - "outputs": [], - "source": [ - "# we can also use the notsbook interface for small data\n", - "##TOFIX: this saves the output in '.', do not account for the path given...." - ] - }, - { - "cell_type": "code", - "execution_count": 7, + "execution_count": 40, "id": "274c2ea4-b366-4e3d-8690-efa8af3f6d96", "metadata": { "tags": [] From 737f6fb42668cf6d52e518b25060a4db5ef9c49b Mon Sep 17 00:00:00 2001 From: Camille Avestruz Date: Thu, 19 Oct 2023 07:41:30 -0700 Subject: [PATCH 14/22] separated the in2p3 vs. the nersc yml versions for the 1deg2-cl so the comment/uncommenting happen in the notebook level --- ...eg2-CL.yml => pipeline-1deg2-CL-in2p3.yml} | 0 examples/cosmodc2/pipeline-1deg2-CL-nersc.yml | 57 +++++++++++++++++++ 2 files changed, 57 insertions(+) rename examples/cosmodc2/{pipeline-1deg2-CL.yml => pipeline-1deg2-CL-in2p3.yml} (100%) create mode 100644 examples/cosmodc2/pipeline-1deg2-CL-nersc.yml diff --git a/examples/cosmodc2/pipeline-1deg2-CL.yml b/examples/cosmodc2/pipeline-1deg2-CL-in2p3.yml similarity index 100% rename from examples/cosmodc2/pipeline-1deg2-CL.yml rename to examples/cosmodc2/pipeline-1deg2-CL-in2p3.yml diff --git a/examples/cosmodc2/pipeline-1deg2-CL-nersc.yml b/examples/cosmodc2/pipeline-1deg2-CL-nersc.yml new file mode 100644 index 000000000..fd74bee0a --- /dev/null +++ b/examples/cosmodc2/pipeline-1deg2-CL-nersc.yml @@ -0,0 +1,57 @@ +#this step depends on where you run +#for CCin2p3 +# site: +# name: cc-parallel +# mpi_command: "mpirun -n" + +#for NERSC +site: + name: cori-batch + image: ghcr.io/lsstdesc/txpipe-dev + + +#all the following steps should not depend on where you run +launcher: + name: mini + interval: 3.0 + +modules: > + txpipe + rail.estimation.algos.bpz_lite + +python_paths: [] + +stages: + - name: TXSourceSelectorMetadetect + nprocess: 1 + - name: Inform_BPZ_lite + nprocess: 1 + - name: BPZ_lite + nprocess: 1 + - name: CLClusterBinningRedshiftRichness + nprocess: 1 + - name: CLClusterShearCatalogs + nprocess: 1 +# - name: CLClusterEnsembleProfiles +# nprocess: 1 +# - name: CLClusterDataVector +# nprocess: 1 + + + +output_dir: data/cosmodc2/outputs-1deg2-CL +config: examples/cosmodc2/config-1deg2-CL.yml + +inputs: + # See README for paths to download these files + shear_catalog: ./data/example/inputs/metadetect_shear_catalog.hdf5 + photometry_catalog: ./data/example/inputs/photometry_catalog.hdf5 + fiducial_cosmology: data/fiducial_cosmology.yml + calibration_table: ./data/example/inputs/sample_cosmodc2_w10year_errors.dat + spectroscopic_catalog: ./data/example/inputs/mock_spectroscopic_catalog.hdf5 + cluster_catalog: ./data/example/inputs/cluster_catalog.hdf5 + +resume: True +log_dir: data/cosmodc2/logs +pipeline_log: data/cosmodc2/log_1deg2.txt + From 1924ac3626e4eba641d2666254cab3ec624861e8 Mon Sep 17 00:00:00 2001 From: Camille Avestruz Date: Thu, 19 Oct 2023 07:43:33 -0700 Subject: [PATCH 15/22] separated the in2p3 vs. the nersc yml versions for the 20deg2-cl so the comment/uncommenting happen in the notebook level --- ...g2-CL.yml => pipeline-20deg2-CL-in2p3.yml} | 0 .../cosmodc2/pipeline-20deg2-CL-nersc.yml | 56 +++++++++++++++++++ 2 files changed, 56 insertions(+) rename examples/cosmodc2/{pipeline-20deg2-CL.yml => pipeline-20deg2-CL-in2p3.yml} (100%) create mode 100644 examples/cosmodc2/pipeline-20deg2-CL-nersc.yml diff --git a/examples/cosmodc2/pipeline-20deg2-CL.yml b/examples/cosmodc2/pipeline-20deg2-CL-in2p3.yml similarity index 100% rename from examples/cosmodc2/pipeline-20deg2-CL.yml rename to examples/cosmodc2/pipeline-20deg2-CL-in2p3.yml diff --git a/examples/cosmodc2/pipeline-20deg2-CL-nersc.yml b/examples/cosmodc2/pipeline-20deg2-CL-nersc.yml new file mode 100644 index 000000000..05b6807aa --- /dev/null +++ b/examples/cosmodc2/pipeline-20deg2-CL-nersc.yml @@ -0,0 +1,56 @@ +#this step depends on where you run +#for CCin2p3 +# site: +# name: cc-parallel +# mpi_command: "mpirun -n" + +#for NERSC +site: + name: cori-batch + image: ghcr.io/lsstdesc/txpipe-dev + + +#all the following steps should not depend on where you run +launcher: + name: mini + interval: 3.0 +modules: > + txpipe + rail.estimation.algos.bpz_lite + +python_paths: [] + +stages: + - name: TXSourceSelectorMetadetect + nprocess: 30 + - name: Inform_BPZ_lite + nprocess: 1 + - name: BPZ_lite + nprocess: 30 + - name: CLClusterBinning + nprocess: 1 + - name: CLClusterShearCatalogs + nprocess: 30 + - name: CLClusterEnsembleProfiles + nprocess: 30 + - name: CLClusterDataVector + nprocess: 1 + + + +output_dir: data/cosmodc2/outputs-20deg2-CL +config: examples/cosmodc2/config-20deg2-CL.yml + +inputs: + # See README for paths to download these files + shear_catalog: ./data/cosmodc2/20deg2/shear_catalog.hdf5 + photometry_catalog: ./data/cosmodc2/20deg2/photometry_catalog.hdf5 + fiducial_cosmology: data/fiducial_cosmology.yml + calibration_table: ./data/cosmodc2/20deg2/sample_cosmodc2_w10year_errors.dat + spectroscopic_catalog: ./data/cosmodc2/20deg2/spectroscopic_catalog.hdf5 + cluster_catalog: ./data/cosmodc2/20deg2/cluster_catalog.hdf5 + +resume: True +log_dir: data/cosmodc2/logs +pipeline_log: data/cosmodc2/log_20deg2.txt + From 7eb84e93302ca3277f36db5cd97c5548a37d901c Mon Sep 17 00:00:00 2001 From: Camille Avestruz Date: Thu, 19 Oct 2023 08:52:02 -0700 Subject: [PATCH 16/22] added minor comment/uncomment lines and markdown text to disambiguate steps between NERSC/IN2P3. Now, there are separate files for each. --- notebooks/Run_CL_pipeline.ipynb | 239 ++++++++++++++++++++++++-------- 1 file changed, 178 insertions(+), 61 deletions(-) diff --git a/notebooks/Run_CL_pipeline.ipynb b/notebooks/Run_CL_pipeline.ipynb index d54382d6b..7d647b2bd 100644 --- a/notebooks/Run_CL_pipeline.ipynb +++ b/notebooks/Run_CL_pipeline.ipynb @@ -4,7 +4,9 @@ "cell_type": "code", "execution_count": 1, "id": "a01235ba-b30a-413c-a381-eef2712dfcb9", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "import os\n", @@ -16,16 +18,31 @@ "import ceci" ] }, + { + "cell_type": "markdown", + "id": "f1a90282-ed70-4496-bc4b-bc2d2e8ee302", + "metadata": {}, + "source": [ + "Make sure to change your path in the next cell that leads to your TXPipe directory. See examples for IN2P3 and NERSC below." + ] + }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 23, "id": "d5eb6757-e79f-445c-a2c6-4d25af5f6f65", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ - "#user specific paths\n", - "my_txpipe_dir = \"/pbs/home/m/mricci/throng_mricci/desc/TXPipe\"\n", - "output_dir = '/pbs/home/m/mricci/throng_mricci/desc/TXPipe/data/example/inputs'\n", + "#user specific paths -- IN2P3 example\n", + "#my_txpipe_dir = \"/pbs/home/m/mricci/throng_mricci/desc/TXPipe\"\n", + "#output_dir = '/pbs/home/m/mricci/throng_mricci/desc/TXPipe/data/example/inputs'\n", + "\n", + "#user specific paths -- NERSC example\n", + "my_txpipe_dir = \"/pscratch/sd/a/avestruz/TXPipe\"\n", + "output_dir = '/pscratch/sd/a/avestruz/TXPipe/data/example/inputs'\n", + "\n", "\n", "os.chdir(my_txpipe_dir)\n", "\n", @@ -51,7 +68,7 @@ "- to work at CCin2p3 we can use: `examples/cosmodc2/1deg2-in2p3.sub`:\n", "- to work at NERSC we can use: `examples/cosmodc2/1deg2-nersc.sub`:\n", "\n", - "Let's continue with the CCin2p3 example :" + "If we use the CCin2p3 example :" ] }, { @@ -80,6 +97,44 @@ "! cat examples/cosmodc2/1deg2-in2p3.sub" ] }, + { + "cell_type": "markdown", + "id": "1e514aff-1f8f-4988-ad30-8e46fee8ebad", + "metadata": {}, + "source": [ + "If we use the NERSC example:" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "daebcfa9-dcd4-4e57-8468-b5a6bb4ab3ba", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "#!/bin/bash\n", + "#SBATCH -A m1727\n", + "#SBATCH -C cpu\n", + "#SBATCH --qos=debug\n", + "#SBATCH --time=00:30:00\n", + "#SBATCH --nodes=1\n", + "#SBATCH --ntasks-per-node=32\n", + "\n", + "source $CFS/lsst/groups/WL/users/zuntz/setup-txpipe\n", + "\n", + "tx ceci examples/cosmodc2/pipeline-1deg2-clmm-nersc.yml\n" + ] + } + ], + "source": [ + "! cat examples/cosmodc2/1deg2-nersc.sub" + ] + }, { "cell_type": "markdown", "id": "64a79cc5-4e43-4146-b3ac-aceb68b56470", @@ -87,25 +142,30 @@ "source": [ "This will launch a job of up to one hour (it should finish in 30 min) on a single CC-IN2P3 node to run a pipeline. After the first run, the output files are created and following runs take much less time.\n", "\n", - "In a terminal, **navigate to your TXPipe directory and run**:\n", + "In a terminal, **navigate to your TXPipe directory on IN2P3 and run**:\n", "\n", "```\n", "sbatch examples/cosmodc2/1deg2-in2p3.sub\n", "```\n", - "to set it running." + "to set it running.\n", + "\n", + "If you are **on NERSC, you will instead run**:\n", + "```\n", + "sbatch examples/cosmodc2/1deg2-nersc.sub\n", + "```" ] }, { - "cell_type": "code", - "execution_count": null, - "id": "0808d9ea-e647-4777-8399-f573efa9826a", + "cell_type": "markdown", + "id": "a06ad5c8-120d-4695-b303-992152daf8d3", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "Below, you will need to select the appropriate yaml file to comment/uncomment for `pipeline_file`, depending on if you are in IN2P3 or on NERSC. " + ] }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 24, "id": "5935f013-c29c-4446-b3e9-00d5de2b85cb", "metadata": { "tags": [] @@ -117,14 +177,15 @@ "0" ] }, - "execution_count": 39, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Read the appropriate pipeline configuration, and ask for a flow-chart.\n", - "pipeline_file = \"examples/cosmodc2/pipeline-1deg2-CL.yml\"\n", + "#pipeline_file = \"examples/cosmodc2/pipeline-1deg2-CL-in2p3.yml\"\n", + "pipeline_file = \"examples/cosmodc2/pipeline-1deg2-CL-nersc.yml\"\n", "flowchart_file = \"CL_pipeline.png\"\n", "\n", "\n", @@ -140,7 +201,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 25, "id": "5b45238d-3568-4d6c-96d8-cf794680bf74", "metadata": { "tags": [] @@ -148,12 +209,12 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "" ] }, - "execution_count": 5, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -164,7 +225,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 5, "id": "274c2ea4-b366-4e3d-8690-efa8af3f6d96", "metadata": { "tags": [] @@ -175,7 +236,7 @@ "output_type": "stream", "text": [ "Options for this pipeline and their defaults (this may be override by config file):\n", - "{'zedge': [0.2, 0.4, 0.6, 0.8, 1.8], 'richedge': [5.0, 10.0, 20.0], 'initial_size': 100000, 'chunk_rows': 100000}\n" + "{'zedge': [0.2, 0.4, 0.6, 0.8, 1.0], 'richedge': [5.0, 10.0, 20.0], 'initial_size': 100000, 'chunk_rows': 100000}\n" ] } ], @@ -200,7 +261,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 6, "id": "0df094c5-3e3a-4335-89ff-4df52bfb8a43", "metadata": { "tags": [] @@ -213,9 +274,11 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "id": "009eb8b0-1c54-497f-a419-055e869e2ece", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "name": "stdout", @@ -249,9 +312,11 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 8, "id": "c2cd69d0-89a2-4cb3-ae65-0882b7194507", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "import h5py" @@ -259,9 +324,11 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 9, "id": "3218ac13-3d30-4bc9-bd8d-25d40a3eeddd", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "filename_in = output_dir + \"/cluster_catalog.hdf5\"" @@ -269,9 +336,11 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 10, "id": "d8c38523-a8f7-4a43-9831-9ae273adeaa6", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "f_in = h5py.File(filename_in, \"r\")" @@ -279,9 +348,11 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 11, "id": "c6d59b2a-8857-44be-93ad-912d8a893ec0", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "name": "stdout", @@ -297,9 +368,11 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, "id": "1ecd9464-7ef8-4600-b931-8b8c76830564", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "dset_in = f_in['clusters']" @@ -307,9 +380,11 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 13, "id": "f5624d30-cea6-4999-8f53-0ee81030b632", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "name": "stdout", @@ -326,9 +401,11 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 14, "id": "4349ca71-0548-490b-b5c3-5af0ce98f7f2", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { @@ -336,13 +413,13 @@ "Text(0, 0.5, 'richness')" ] }, - "execution_count": 16, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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cY3QmTJiQal0AAqwklMeF1QWMkULQxR105s6dm846AABp0DpG6uQWHcZIISiSGqNTXV2tzZs3t9u+efNmbdmyJeWiAADOYIwUgi6poDNt2jTt37+/3fa//OUvmjZtWspFAQCcM6m8jzbMHKUld1+lDTNHcbNGBEpS99GpqanR0KFD220fMmSIampqUi4KAOAsxkghqJJq0encubM+//zzdtsjkYhycpLKTgAAAI5LKuiMGTNGs2bNUjQajW07fPiwZs+erTFjxjhWHAAAQCqSan55/PHHVVlZqb59+2rIkCGSpB07dqi4uFi/+93vHC0QAAAgWUkFnV69eun999/XK6+8oj/96U/Ky8vTlClT9JOf/KTdzQMBAADckvSAmvz8fP3DP/yDk7UAAAA4Ku6g88Ybb2jcuHHKzc3VG2+8cdrn3nLLLSkXBgAAkKq417rKysrSgQMHdM455ygr69RjmC3LUnNzs2MFphNrXQHwqki0SbX1jepflM+0cOAkaVnrqqWlRZL017/+VZWVlfrtb3+riy66KLVKAQDtVFXXadaynWqxpSxLmj/xcm7yByQp4enlubm5+uCDD5SdnZ2OegAg0CLRpljIkb5Zo2r2sl2KRJvcLQzwqaTuo3Pbbbfp+eefd7oWAPCFSLRJm/bUpyV8nG61cQCJS2rW1fHjx/X8889r1apVGj58uPLz89t8f+HChY4UBwBek+5uJVYbB5yVVNDZtWtXbK2r3bt3t/me9e0KuQBgmlN1K1UO6OHYgOHW1cZnL9ulZttmtXEgRUkFnbVr1zpdBwB43um6lZwMIpPK+6hyQA/tqz+qfkVdCTlACliBEwDilMluJVYbB5yR1GBkAAii1m6l7G+76OlWAryPFh0ASADdSvHjpofwAoIOTosTFdAe3Upnxk0P4RWBDDrhcFjhcNg3S1W4hRMVgGRkYnYaEK9AjtGZNm2aampqVF1d7XYpnsXdWQEki5sewksCGXRwZpyoACSrdXbaibjpIdxC0EGHOFH5RzqXIwCSwew0eEkgx+jgzLg7qz8wjgpexey0+DDhI/0s27btMz/NTA0NDQqFQopGoyooKHC7HE+KRJs4UXlUJNqkigVr2t28bsPMUewrwAf4oJK8RK7fdF3htEpCeRpx/tlcOD2IcVSAfzHhI3MIOoBPMY4K8C8+qGQOQQfwKQZ8Av7FB5XMYTAy4GMM+AT8iQkfmUPQAXyO5QgAf+KDSmYQdAAAcAkfVNKPMToAAMBYBB0AAGAsgg4AADAWQQcAABiLoAMAAIxF0AEAAMYi6AAAAGMRdAAAgLEIOgAAwFgEHQAAYCyCDgAAMBZBBwAAGIugAwAAjEXQAQAAxiLoAA6IRJu0aU+9ItEmt0sBAJwgx+0CAL+rqq7TrGU71WJLWZY0f+LlmlTex+2yAACiRQdISSTaFAs5ktRiS7OX7aJlBwA8gqADpKC2vjEWclo127b21R91pyAAQBsEHSAF/YvylWW13ZZtWepX1NWdggAAbRB0gBSUhPI0f+Llyra+STvZlqVHJ16mklCey5UBACQGIwMpm1TeR5UDemhf/VH1K+pKyEmzSLRJtfWN6l+Uz3sN4IwIOoADSkJ5XHQz4Lfv7tGCN/8smxluAOJE1xUAX/jtuj2av/KbkCMxww1AfAg6ADwvEm3Sgjf/3G47M9wAnAlBB4Dn1dY3yu5ge5YlZrgBOC2CDgDP62gavyT987iLGRsF4LQIOgA87+Rp/FmSZo27WPdUnu9uYUCasY5e6ph1BcAXmMaPoGEdPWfQogMYxuRPgCWhPI04/2xCDozX0Tp6s17faeRxnW4EHcAgVdV1qliwRrc+t1kVC9aoqrrO7ZIAJKGjdfRaJL20YZ8b5fgaQQcwBCupA+boX5SvDsbf6/kNezmmE0TQAQzBSupIB5O7Qr2sJJSnu6/p3257iy2O6QQxGBkpYd0h72idgn1i2GEldaSCwbDumnJ1fz2/oZZjOkW06CBpjAfxFlZSh5PoCnUfx7QzaNFBUk51Eqwc0IOD0EVMwYZTTtcVyt9V5nBMp46gg6RwEvQuVlKHE+gK9Q6O6dTQdYWkdHRLfk6CgDnoNoETvDCYnRYdJKX1JDh72S412zYnQcBAdJsgFV4ZzG7Ztt3RosCB0NDQoFAopGg0qoKCArfL8aVItImTIACgjUi0SRUL1rTr+twwc5Qj14pErt+06CAlifQdMxUdAILBS+M4CTrICK80YSI5hFSgYxwbHfPSYHaCDtLOhKnoQT6ZEVKBjnFsnJqXxnESdJB2XmrCTIbpJ7PThTgTQiqQDhwbZ+aVwewEHaSdl5owE2X6yexMIc7vIRVIF46N+HjhHkDcRwdp5+f7cZi8UGY8t/jnfkne44X7koBjw09o0UFGeKUJM1E7/xJtt82Uk1k8n0i91M8O87tR/YRjwz8IOsgYLzRhJiISbdIv3/xzu+0P3niRr36PU4m3S9GvIdU0pnej+hHHhj/QdQWcQkctHpI0sPdZGa8lXol0ayTSpVgSytOI88/mRO4ik7tR/Yxjw/to0QFOwW+DqJPp1uATqX/47e8R8ApadIBT8NMg6ngGFp8Kn0j9wU9/j4CXGNGi8/3vf1/vvPOOvvvd72rp0qVulwOD+KXFg6muweCXv0fAS4xo0bnvvvv08ssvu10GDOWHFg+mugaHH/4eAS8xIuiMGjVK3bt3d7sMwDV0awBAx1wPOu+++65uvvlm9ezZU5ZlacWKFe2es2jRIvXv319dunTRsGHDtH79+swXCnjcpPI+2jBzlJbcfZU2zBzF/VUAQB4IOo2NjRo0aJCeeuqpDr9fVVWlBx54QHPmzNH27dt1zTXXaNy4caqrq0v4/zp27JgaGhrafAEmoVsDANpyPeiMGzdOjzzyiCZOnNjh9xcuXKg777xTd911ly655BI98cQTKi0t1dNPP53w/zV//nyFQqHYV2lpaarlp4zbuQMAkD6uB53TOX78uLZu3aqxY8e22T527Fht2rQp4debNWuWotFo7Gv//v1OlZqUquo6VSxYo1uf26yKBWtUVZ14KxUAADg1T08vr6+vV3Nzs4qLi9tsLy4u1oEDB2KPb7jhBm3btk2NjY3q3bu3li9frvLy8nav17lzZ3Xu3DntdceD27kDAJB+ng46rSyr7bxZ27bbbPvDH/6Q6ZJSxn1P4LZItEm19Y3qX5TP3xwAY3k66BQVFSk7O7tN640kHTx4sF0rj99wO3e4iVWwAQSFp8fodOrUScOGDdOqVavabF+1apVGjhzpUlXO4L4ncEsqy0UAgN+43qLz1Vdf6eOPP449rq2t1Y4dO1RYWKg+ffpo+vTpmjx5soYPH64RI0bo2WefVV1dnaZOnepi1c7gdu5wA92mAILE9aCzZcsWjRo1KvZ4+vTpkqTbb79dixcv1qRJk3To0CHNmzdPkUhEl112mVauXKm+ffu6VbKjSkJ5XFwM4KfxLnSbAggSy7Zt+8xPM1NDQ4NCoZCi0agKCgrcLgc+dfJ4lzuv7q+fXt3f04GnqrpOs5ftUrNtx7pNGaMDwC8SuX4TdAg6SEEk2qSKBWvadQVZkhb8IPkBvploIYpEm+g2BeBLiVy/Xe+6ckM4HFY4HFZzc7PbpcDnOhrvIkm2kr8vUqZmRNFtCiAIPD3rKl2mTZummpoaVVdXu10KfK51vEtHWgf4JoIZUQDgrEAGHcAprbcJ6CjsJDPA93QzotwUiTbp9+9/pv/3p78QugD4SiC7rgAntd4m4KWNtXr+3Vq1KPn7ImV6RlQ8Y4Gqqus08/Wdai0p1fFHAJBJDEZmMDIc5MQA30zNiIpnLNCpBltnSdo46/o2v6OfptgD8DcGIwMucWKAbyZuJBnvorKnGmzdIrW5waDJS0oQ4AB/I+gAHpTuGVHx3h25o6406ZsWndbutHhDkx+ZHOCAoGAwMhBAHc0W62gsUOtga+uE51qS5v/g8liIOdMA6ki0SZv21PtuEDMz4AAz0KIDBFBrgDl5LFBHLTCtXWnbPvlSti0N6/edM7b6tIYmP7eInCrAbfvkS30n37muLLrGgPRiMDKDkRFgqQ6ebr1I7/w0qsfe+rBNaKoc0KPdQOZsy9KGmaN8cUHvaCC2ZUmyv7khpBPBzc9BEO4LckhmMDKAuKQyFujki/Q/j7tYA3udFQtNm/bU+3qV9JNbvbIsyf425Eipj0UyeWwT0o+QHL9AjtEJh8MqKytTeXm526XAg/w6piSTOrpIP/bmh21ahuIdB+Rlk8r7aMPMUVpy91X69Y8H6+Tm71Ru5ujVm0PC+xg/lphABh2WgMCpVFXXqWLBGt363GZVLFijquo6t0vypHgu0q0tItnfjmRO9iaKbisJ5WnE+WdreL9CR4ObCUEQZ5aOD06E5MTQdQV8i66E+MV7B+dM3BMoUxIZwO3G68F70tW9lOk7qPsdQQf4Vrz3ljmVdA8M9NLAw0Qu0iatku50cDMpCKKtdH5wIiQnhqADfCuVT0npHhjoxYGHQb1IOx3cTAqC+D+pfnA6k6Aef8kI5BgdoCPJjilJ98BALw88bB2/wkkWaCsTY7A4/uJDiw5wgmQ+JaX7k1u6Xz8dvNTNBriB7iXvIOgAJ0m0KyHdAwP9NvDQi91sgBvoXvIGuq6AFKV7GrWfpml7uZsNcAPdS+6jRQdwQLo/ufnlk6Efu9niQVcc4F8EHcAh6Z4944fZOX7rZosHXXGAv9F1BcAxfupmiwddcYD/0aIDwFF+6WaLh6ldcUCQBDLohMNhhcNhNTc3u10KYCQ/dLPFw8SuOCBoAtl1xaKeqWOFbwSBaV1xXsd5BekQyBadTDB5lgaDM9PL5L+dZLj9fpjUFedlnFeQLgSdNDD5gPXjCt9uXygTYfLfTjK88n6Y0hWXafEee348r8A/Atl1lU6ZnqWR6abe0w3O9KKq6jpVLFijW5/brIoFa1RVXed2SafEDJ+2vPJ+0J2SnESOPb+dV+AvtOg4LJOzNNz4tOunwZl++5TIDJ+2vPB+eKVFyW8SPfb8dF6B/9Ci47BMrFgrufdp10+DM/32KTFTfzt+4fb74ZUWJT9K9Njz03kF/kOLjsM6WrH2wRsvUm19Y+z7TnDz065fBmf67VMiqx235fb74YUWJb9K5tjzy3kF/kPQSYMTD9j3/3JYv3zzz443fbt9EffD4Ey3L5TJ4GTflpvvh9vHmJ8le+z54bwC/7Fs27bP/DQzNTQ0KBQKKRqNqqCgwPHXj0SbVLFgTbsT5YaZoxw5mKuq69qdSBg/0F4k2kRw8DAvz4rjGEsNxx7SJZHrNy06aZTupm8+/ceHT4ne5fXBvhxjqeHYgxcQdNIoE03fnEjgto5aZOJppfHLrDiOMcDfCDpp5McxIkAiOmqRkRRXKw2DfQFkAkEnzWj69hYvjwfxgxPfP0ntWmRmvb5TOqEV83StNAz2hck413gHQScDaPr2Bq+PB/G6k9+/O6/u365FpkWS4mylocUTpuJc4y2BnHUVDocVDofV3Nys3bt3p23WFdIn0U9L6Z4BZ7qO3r8sS7LttrkmS2rToiOd+X1mZg5MwrkmMxKZdRXIOyNPmzZNNTU1qq6udrsUJCGZ9av8dpdkr+no/Wuxpbsr+7e5m+38H1ye8B1uS0J5GnH+2VwEYATONd5D1xV8JdmZOowHSc2p3r8pFf01paJ/uxYZxqUhqDjXeE8gW3TgX8l+WmItndSc7v3rqEWGVhoEFeca76FFB76SyqclL8yA8/NMDC+8f/C+dPyN++244VjxFoIOfCXVmTpuzoDz+0wMv11skHnp+Bv363HDbFvvCOSsq1bpXusK6eO3mTp+n4nh14sNMicdf+N+P26QPsy6gvH8NgbEiZkYkWiTNu2pVyTa5HB1Z/5/OxoAnuk64G3pmG3EDCY4ga4rIANSnYnhZosKSzUgHumYbcQMJjiBFh3gBOlqNUllJobbLSqtF5sTcbHBydIx24gZTHACLTrAt9LdapLsTAy3W1RYqgHxSsdsIy/OYGJgvr8QdAAlfyPCeF/7xJNioq/nheZ7L15s4E3pmG3kpRlMDMz3H7quAKVv0GMyy1WczCvN934bAA44ze1uZCSHFh1A6Wk1cbKViBYVeFlQunLc7kZGcgg6gNIzDsXpk6KXmu+BVkHqyvFCNzISR9ABvuV0qwknRZgunWPbvIiB+f5E0AFO4GSrCSdFmC6IXTl0I/sPQQdII06KMFlQWy3pRvaXQM66CofDKisrU3l5udulIACYrQRTeWVGIHA6LOrJop4AkBK/LbIL/0vk+k3XFQAgJXTlwMsC2XUFAACCgaADAACMRdABAADGIugAAABjEXQAAICxCDoAAMBYBB0AAGAsgg4AADAWQQcAABiLoAMAAIxF0AEAAMYK9FpXreuZNjQ0uFwJAACIV+t1O551yQMddI4cOSJJKi0tdbkSAACQqCNHjigUCp32OZYdTxwyVEtLiz777DN1795dlmXFtjc0NKi0tFT79+8/4/LvSD/2h/ewT7yHfeI97JP0sW1bR44cUc+ePZWVdfpROIFu0cnKylLv3r1P+f2CggL+OD2E/eE97BPvYZ94D/skPc7UktOKwcgAAMBYBB0AAGAsgk4HOnfurLlz56pz585ulwKxP7yIfeI97BPvYZ94Q6AHIwMAALPRogMAAIxF0AEAAMYi6AAAAGMRdAAAgLECGXQWLVqk/v37q0uXLho2bJjWr19/yucuW7ZMY8aMUY8ePVRQUKARI0boD3/4QwarDYZE9smJNm7cqJycHA0ePDi9BQZQovvk2LFjmjNnjvr27avOnTvr/PPP14svvpihaoMh0X3yyiuvaNCgQeratatKSko0ZcoUHTp0KEPVmu3dd9/VzTffrJ49e8qyLK1YseKMP7Nu3ToNGzZMXbp00Xnnnadnnnkm/YVCsgPm3//93+3c3Fz7ueees2tqauz777/fzs/Ptz/55JMOn3///ffbv/zlL+333nvP3r17tz1r1iw7NzfX3rZtW4YrN1ei+6TV4cOH7fPOO88eO3asPWjQoMwUGxDJ7JNbbrnFvvLKK+1Vq1bZtbW19ubNm+2NGzdmsGqzJbpP1q9fb2dlZdm//vWv7b1799rr16+3L730UnvChAkZrtxMK1eutOfMmWO//vrrtiR7+fLlp33+3r177a5du9r333+/XVNTYz/33HN2bm6uvXTp0swUHGCBCzpXXHGFPXXq1DbbLr74YnvmzJlxv0ZZWZn98MMPO11aYCW7TyZNmmT/y7/8iz137lyCjsMS3SdvvvmmHQqF7EOHDmWivEBKdJ/86le/ss8777w225588km7d+/eaasxqOIJOg8++KB98cUXt9l2zz332FdddVUaK4Nt23aguq6OHz+urVu3auzYsW22jx07Vps2bYrrNVpaWnTkyBEVFhamo8TASXafvPTSS9qzZ4/mzp2b7hIDJ5l98sYbb2j48OF67LHH1KtXLw0YMEA///nP1dTUlImSjZfMPhk5cqQ+/fRTrVy5UrZt6/PPP9fSpUt10003ZaJknOS///u/2+2/G264QVu2bNFf//pXl6oKhkAt6llfX6/m5mYVFxe32V5cXKwDBw7E9RqPP/64Ghsb9aMf/SgdJQZOMvvko48+0syZM7V+/Xrl5ATqTzgjktkne/fu1YYNG9SlSxctX75c9fX1+sd//Ed98cUXjNNxQDL7ZOTIkXrllVc0adIkff311/rb3/6mW265Rb/5zW8yUTJOcuDAgQ7339/+9jfV19erpKTEpcrMF6gWnVaWZbV5bNt2u20dWbJkiX7xi1+oqqpK55xzTrrKC6R490lzc7NuvfVWPfzwwxowYECmygukRI6TlpYWWZalV155RVdccYXGjx+vhQsXavHixbTqOCiRfVJTU6P77rtPDz30kLZu3aq33npLtbW1mjp1aiZKRQc62n8dbYezAvVxuKioSNnZ2e0+AR08eLBd0j5ZVVWV7rzzTr322msaPXp0OssMlET3yZEjR7RlyxZt375d9957r6RvLrK2bSsnJ0dvv/22rr/++ozUbqpkjpOSkhL16tVLoVAotu2SSy6Rbdv69NNPdeGFF6a1ZtMls0/mz5+viooKzZgxQ5I0cOBA5efn65prrtEjjzxCC0KGnXvuuR3uv5ycHJ199tkuVRUMgWrR6dSpk4YNG6ZVq1a12b5q1SqNHDnylD+3ZMkS3XHHHXr11Vfp33ZYovukoKBAO3fu1I4dO2JfU6dO1UUXXaQdO3boyiuvzFTpxkrmOKmoqNBnn32mr776KrZt9+7dysrKUu/evdNabxAks0+OHj2qrKy2p/js7GxJ/9eSgMwZMWJEu/339ttva/jw4crNzXWpqoBwbRi0S1qnaL7wwgt2TU2N/cADD9j5+fn2vn37bNu27ZkzZ9qTJ0+OPf/VV1+1c3Jy7HA4bEcikdjX4cOH3foVjJPoPjkZs66cl+g+OXLkiN27d2/7hz/8of3BBx/Y69atsy+88EL7rrvucutXME6i++Sll16yc3Jy7EWLFtl79uyxN2zYYA8fPty+4oor3PoVjHLkyBF7+/bt9vbt221J9sKFC+3t27fHpvufvD9ap5f/0z/9k11TU2O/8MILTC/PkMAFHdu27XA4bPft29fu1KmTPXToUHvdunWx791+++32tddeG3t87bXX2pLafd1+++2ZL9xgieyTkxF00iPRffK///u/9ujRo+28vDy7d+/e9vTp0+2jR49muGqzJbpPnnzySbusrMzOy8uzS0pK7L//+7+3P/300wxXbaa1a9ee9trQ0f5455137CFDhtidOnWy+/XrZz/99NOZLzyALNumDRMAAJgpUGN0AABAsBB0AACAsQg6AADAWAQdAABgLIIOAAAwFkEHAAAYi6ADAACMRdABAADGIugA8LXrrrtODzzwgGPPtSxLK1asiD3+85//rKuuukpdunTR4MGDk64TgDsIOgBwgkgkonHjxsUez507V/n5+frwww+1evVqLV68WGeddZZ7BQJISI7bBQCAJB0/flydOnVyuwyde+65bR7v2bNHN910k/r27etSRQBSQYsOAFdcd911uvfeezV9+nQVFRVpzJgxqqmp0fjx49WtWzcVFxdr8uTJqq+vj/1MY2OjbrvtNnXr1k0lJSV6/PHH273uokWLdOGFF6pLly4qLi7WD3/4wzbfb2lp0YMPPqjCwkKde+65+sUvftHm+yd2XVmWpa1bt2revHmyLEvXXXedpkyZomg0KsuyZFlWu58H4C0EHQCu+bd/+zfl5ORo48aNWrBgga699loNHjxYW7Zs0VtvvaXPP/9cP/rRj2LPnzFjhtauXavly5fr7bff1jvvvKOtW7fGvr9lyxbdd999mjdvnj788EO99dZbqqysbPd/5ufna/PmzXrsscc0b948rVq1qsP6IpGILr30Uv3sZz9TJBLRG2+8oSeeeEIFBQWKRCKKRCL6+c9/np43B4Aj6LoC4JoLLrhAjz32mCTpoYce0tChQ/Xoo4/Gvv/iiy+qtLRUu3fvVs+ePfXCCy/o5Zdf1pgxYyR9E1p69+4de35dXZ3y8/P1ve99T927d1ffvn01ZMiQNv/nwIEDNXfuXEnShRdeqKeeekqrV6+OveaJzj33XOXk5Khbt26xLq1QKCTLstp1cQHwJoIOANcMHz489u+tW7dq7dq16tatW7vn7dmzR01NTTp+/LhGjBgR215YWKiLLroo9njMmDHq27evzjvvPN1444268cYb9f3vf19du3aNPWfgwIFtXrukpEQHDx508tcC4CF0XQFwTX5+fuzfLS0tuvnmm7Vjx442Xx999JEqKytl2/YZX6979+7atm2blixZopKSEj300EMaNGiQDh8+HHtObm5um5+xLEstLS2O/U4AvIWgA8AThg4dqg8++ED9+vXTBRdc0OYrPz9fF1xwgXJzc/U///M/sZ/58ssvtXv37javk5OTo9GjR+uxxx7T+++/r3379mnNmjWO1dmpUyc1Nzc79noA0ougA8ATpk2bpi+++EI/+clP9N5772nv3r16++239dOf/lTNzc3q1q2b7rzzTs2YMUOrV6/Wrl27dMcddygr6/9OY7///e/15JNPaseOHfrkk0/08ssvq6WlpU33Vqr69eunr776SqtXr1Z9fb2OHj3q2GsDcB5BB4An9OzZUxs3blRzc7NuuOEGXXbZZbr//vsVCoViYeZXv/qVKisrdcstt2j06NG6+uqrNWzYsNhrnHXWWVq2bJmuv/56XXLJJXrmmWe0ZMkSXXrppY7VOXLkSE2dOlWTJk1Sjx49YoOpAXiTZcfT8Q0AAOBDtOgAAABjEXQAAICxCDoAAMBYBB0AAGAsgg4AADAWQQcAABiLoAMAAIxF0AEAAMYi6AAAAGMRdAAAgLEIOgAAwFj/H8ZHQ0KRV8x2AAAAAElFTkSuQmCC", + "image/png": 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", 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" ] @@ -368,9 +445,11 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 15, "id": "0b4c65de-82c8-4f99-89de-dd0a598a31f0", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "# open for reading\n", @@ -385,9 +464,11 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 16, "id": "b3274869-a729-45c7-b531-05d4f8ef69da", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "name": "stdout", @@ -403,9 +484,11 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 17, "id": "7785436c-8652-4c98-9ecb-373abd2dc5d2", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "dat_out = f_out['provenance']\n", @@ -414,9 +497,11 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 18, "id": "736b4dc3-381b-45f4-a93e-f9c00b9e5e17", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "name": "stdout", @@ -432,9 +517,11 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 19, "id": "6fbe636e-052e-47b0-8802-ae4ceae34ea9", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "name": "stdout", @@ -458,9 +545,11 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 20, "id": "38be57ca-e370-480d-a2e0-c2d45baf7b13", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "name": "stdout", @@ -484,9 +573,11 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 21, "id": "82695f43-ab54-4efd-bc1d-633c1db44d33", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "name": "stdout", @@ -504,13 +595,15 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 22, "id": "2b146a75-3aff-4fa7-be65-679c6f49bf6e", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -539,13 +632,37 @@ " \n", " plt.legend(fontsize='x-small')" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c8d129d1-43f9-4111-af3d-1d7aa42a9c31", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c3d36570-e6ff-4d2c-ba1a-ea354aaa4877", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7077bf36-77a3-4c3b-8e49-47687f989807", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "TXPipe-2023-Jul-12", + "display_name": "TXPipe", "language": "python", - "name": "txpipe-2023-jul-12" + "name": "txpipe" }, "language_info": { "codemirror_mode": { @@ -557,7 +674,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.9.16" } }, "nbformat": 4, From 3169a04dc310ce44e51350b5ca059cdd434d0aec Mon Sep 17 00:00:00 2001 From: Marina Ricci Date: Thu, 19 Oct 2023 18:53:38 +0200 Subject: [PATCH 17/22] corrected file name in sub files --- examples/cosmodc2/1deg2-in2p3.sub | 2 +- examples/cosmodc2/1deg2-nersc.sub | 3 +-- examples/cosmodc2/20deg2-in2p3.sub | 2 +- examples/cosmodc2/20deg2-nersc.sub | 3 +-- 4 files changed, 4 insertions(+), 6 deletions(-) diff --git a/examples/cosmodc2/1deg2-in2p3.sub b/examples/cosmodc2/1deg2-in2p3.sub index b4a25860e..a104d767c 100644 --- a/examples/cosmodc2/1deg2-in2p3.sub +++ b/examples/cosmodc2/1deg2-in2p3.sub @@ -6,4 +6,4 @@ #SBATCH --mem=128000 source /pbs/throng/lsst/users/jzuntz/txpipe-environments/setup-txpipe -ceci examples/cosmodc2/pipeline-1deg2-CL.yml +ceci examples/cosmodc2/pipeline-1deg2-CL-in2p3.yml diff --git a/examples/cosmodc2/1deg2-nersc.sub b/examples/cosmodc2/1deg2-nersc.sub index 00f32b676..3ed6809a4 100644 --- a/examples/cosmodc2/1deg2-nersc.sub +++ b/examples/cosmodc2/1deg2-nersc.sub @@ -7,5 +7,4 @@ #SBATCH --ntasks-per-node=32 source $CFS/lsst/groups/WL/users/zuntz/setup-txpipe - -tx ceci examples/cosmodc2/pipeline-1deg2-clmm-nersc.yml +tx ceci examples/cosmodc2/pipeline-1deg2-CL-nersc.yml diff --git a/examples/cosmodc2/20deg2-in2p3.sub b/examples/cosmodc2/20deg2-in2p3.sub index 717d168cd..e628081c4 100644 --- a/examples/cosmodc2/20deg2-in2p3.sub +++ b/examples/cosmodc2/20deg2-in2p3.sub @@ -6,4 +6,4 @@ #SBATCH --mem=128000 source /pbs/throng/lsst/users/jzuntz/txpipe-environments/setup-txpipe -ceci examples/cosmodc2/pipeline-20deg2-clmm.yml +ceci examples/cosmodc2/pipeline-20deg2-CL-in2p3.yml diff --git a/examples/cosmodc2/20deg2-nersc.sub b/examples/cosmodc2/20deg2-nersc.sub index d2cabb3f3..50a4d2d56 100644 --- a/examples/cosmodc2/20deg2-nersc.sub +++ b/examples/cosmodc2/20deg2-nersc.sub @@ -7,5 +7,4 @@ #SBATCH --ntasks-per-node=32 source $CFS/lsst/groups/WL/users/zuntz/setup-txpipe - -tx ceci examples/cosmodc2/pipeline-20deg2-clmm-nersc.yml +tx ceci examples/cosmodc2/pipeline-20deg2-CL-nersc.yml From 5fc4bf219267b101dfd8df20300cd6a026bd186c Mon Sep 17 00:00:00 2001 From: Marina Ricci Date: Thu, 19 Oct 2023 18:59:04 +0200 Subject: [PATCH 18/22] corrected typos in pipeline files --- examples/cosmodc2/pipeline-1deg2-CL-in2p3.yml | 4 ++-- examples/cosmodc2/pipeline-1deg2-CL-nersc.yml | 4 ++-- examples/cosmodc2/pipeline-20deg2-CL-in2p3.yml | 14 +++++++------- examples/cosmodc2/pipeline-20deg2-CL-nersc.yml | 14 +++++++------- 4 files changed, 18 insertions(+), 18 deletions(-) diff --git a/examples/cosmodc2/pipeline-1deg2-CL-in2p3.yml b/examples/cosmodc2/pipeline-1deg2-CL-in2p3.yml index 5aa02277e..da407a347 100644 --- a/examples/cosmodc2/pipeline-1deg2-CL-in2p3.yml +++ b/examples/cosmodc2/pipeline-1deg2-CL-in2p3.yml @@ -30,8 +30,8 @@ stages: nprocess: 1 - name: CLClusterBinningRedshiftRichness nprocess: 1 - - name: CLClusterShearCatalogs - nprocess: 1 +# - name: CLClusterShearCatalogs +# nprocess: 1 # - name: CLClusterEnsembleProfiles # nprocess: 1 # - name: CLClusterDataVector diff --git a/examples/cosmodc2/pipeline-1deg2-CL-nersc.yml b/examples/cosmodc2/pipeline-1deg2-CL-nersc.yml index fd74bee0a..e99595bc1 100644 --- a/examples/cosmodc2/pipeline-1deg2-CL-nersc.yml +++ b/examples/cosmodc2/pipeline-1deg2-CL-nersc.yml @@ -30,8 +30,8 @@ stages: nprocess: 1 - name: CLClusterBinningRedshiftRichness nprocess: 1 - - name: CLClusterShearCatalogs - nprocess: 1 +# - name: CLClusterShearCatalogs +# nprocess: 1 # - name: CLClusterEnsembleProfiles # nprocess: 1 # - name: CLClusterDataVector diff --git a/examples/cosmodc2/pipeline-20deg2-CL-in2p3.yml b/examples/cosmodc2/pipeline-20deg2-CL-in2p3.yml index 5d058a98a..d53f969e1 100644 --- a/examples/cosmodc2/pipeline-20deg2-CL-in2p3.yml +++ b/examples/cosmodc2/pipeline-20deg2-CL-in2p3.yml @@ -27,14 +27,14 @@ stages: nprocess: 1 - name: BPZ_lite nprocess: 30 - - name: CLClusterBinning - nprocess: 1 - - name: CLClusterShearCatalogs - nprocess: 30 - - name: CLClusterEnsembleProfiles - nprocess: 30 - - name: CLClusterDataVector + - name: CLClusterBinningRedshiftRichness nprocess: 1 +# - name: CLClusterShearCatalogs +# nprocess: 30 +# - name: CLClusterEnsembleProfiles +# nprocess: 30 +# - name: CLClusterDataVector +# nprocess: 1 diff --git a/examples/cosmodc2/pipeline-20deg2-CL-nersc.yml b/examples/cosmodc2/pipeline-20deg2-CL-nersc.yml index 05b6807aa..4c0d33dac 100644 --- a/examples/cosmodc2/pipeline-20deg2-CL-nersc.yml +++ b/examples/cosmodc2/pipeline-20deg2-CL-nersc.yml @@ -27,14 +27,14 @@ stages: nprocess: 1 - name: BPZ_lite nprocess: 30 - - name: CLClusterBinning - nprocess: 1 - - name: CLClusterShearCatalogs - nprocess: 30 - - name: CLClusterEnsembleProfiles - nprocess: 30 - - name: CLClusterDataVector + - name: CLClusterBinningRedshiftRichness nprocess: 1 +# - name: CLClusterShearCatalogs +# nprocess: 30 +# - name: CLClusterEnsembleProfiles +# nprocess: 1 +# - name: CLClusterDataVector +# nprocess: 1 From f26c32e33be00cd2d8095241f885c0f5c599f257 Mon Sep 17 00:00:00 2001 From: Marina Ricci Date: Thu, 19 Oct 2023 19:01:24 +0200 Subject: [PATCH 19/22] corrected config file for 20deg2, now exact same as 1de2 --- examples/cosmodc2/config-20deg2-CL.yml | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/examples/cosmodc2/config-20deg2-CL.yml b/examples/cosmodc2/config-20deg2-CL.yml index 1dc86e808..9fdd92ea9 100644 --- a/examples/cosmodc2/config-20deg2-CL.yml +++ b/examples/cosmodc2/config-20deg2-CL.yml @@ -63,11 +63,9 @@ BPZ_lite: mag_i: 28.62 mag_z: 27.98 -CLClusterBinning: - z_min : 0 - z_max : 1.2 - lambda_min : 5 - lambda_max : 1e3 +CLClusterBinningRedshiftRichness: + zedge : [0.1, 0.4, 0.6, 0.8] + richedge : [5., 10., 20.,25.] CLClusterShearCatalogs: max_radius: 5.0 # Mpc @@ -84,6 +82,7 @@ CLClusterEnsembleProfiles: #stacking step or not stack_profile : True + From 47183c1c3be6e46639915d6bf3ddbfef156a0537 Mon Sep 17 00:00:00 2001 From: Marina Ricci Date: Thu, 19 Oct 2023 20:43:02 +0200 Subject: [PATCH 20/22] updated notebook to clarify jupyer vs pipeline process 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zAWw|v0<)2PR9TjI|Lxco=eL1++-(-$eTyM-!$o5*KR+EIqfL*B7Qx5v3a2sjaF};< zxp52c{iM=E^~+)Ix3HekPJi)OI~lUn?3LtaUz!ShuXOBG38V7B@ySR@p;^A7vZ?0* zNaCHVn|)ETrK*DhvJk=@QAF}a>c#>G-g+j>QXH-d{9D(^Cq2<}&TXrau;rfw5*{F8 zx%rqI?PgrZ?y_EdN#28%h-{xN#soRLa? z$1C3H)vfuGeK9jyfq?YoY`H;Jh(DhdB={c7k;Wc^$xMi=I;Ac~4B8ivD|Oy1^1Dl- zO1*dI0AKr7RW0ZF35B?%j$f-n=9S3wXD{eOm6@C@mJV5I+v zA0E2+sp~~xm!}l*vu>}is?zyJ7tpt`z0mh<5MZ1nt3Tiq)K^ python_paths: [] stages: - - name: TXSourceSelectorMetadetect - nprocess: 1 - - name: Inform_BPZ_lite - nprocess: 1 - - name: BPZ_lite - nprocess: 1 +# - name: TXSourceSelectorMetadetect +# nprocess: 1 +# - name: Inform_BPZ_lite +# nprocess: 1 +# - name: BPZ_lite +# nprocess: 1 - name: CLClusterBinningRedshiftRichness nprocess: 1 # - name: CLClusterShearCatalogs @@ -39,19 +39,19 @@ stages: -output_dir: data/cosmodc2/outputs-1deg2-CL +output_dir: ./data/cosmodc2/outputs-1deg2-CL config: examples/cosmodc2/config-1deg2-CL.yml inputs: # See README for paths to download these files - shear_catalog: ./data/example/inputs/metadetect_shear_catalog.hdf5 - photometry_catalog: ./data/example/inputs/photometry_catalog.hdf5 - fiducial_cosmology: data/fiducial_cosmology.yml - calibration_table: ./data/example/inputs/sample_cosmodc2_w10year_errors.dat - spectroscopic_catalog: ./data/example/inputs/mock_spectroscopic_catalog.hdf5 + #shear_catalog: ./data/example/inputs/metadetect_shear_catalog.hdf5 + #photometry_catalog: ./data/example/inputs/photometry_catalog.hdf5 + #fiducial_cosmology: ./data/fiducial_cosmology.yml + #calibration_table: ./data/example/inputs/sample_cosmodc2_w10year_errors.dat + #spectroscopic_catalog: ./data/example/inputs/mock_spectroscopic_catalog.hdf5 cluster_catalog: ./data/example/inputs/cluster_catalog.hdf5 resume: True -log_dir: data/cosmodc2/logs -pipeline_log: data/cosmodc2/log_1deg2.txt +log_dir: ./data/cosmodc2/logs +pipeline_log: ./data/cosmodc2/log_1deg2.txt diff --git a/examples/cosmodc2/pipeline-1deg2-CL-nersc.yml b/examples/cosmodc2/pipeline-1deg2-CL-nersc.yml index e99595bc1..f2a3d6c5f 100644 --- a/examples/cosmodc2/pipeline-1deg2-CL-nersc.yml +++ b/examples/cosmodc2/pipeline-1deg2-CL-nersc.yml @@ -22,12 +22,12 @@ modules: > python_paths: [] stages: - - name: TXSourceSelectorMetadetect - nprocess: 1 - - name: Inform_BPZ_lite - nprocess: 1 - - name: BPZ_lite - nprocess: 1 +# - name: TXSourceSelectorMetadetect +# nprocess: 1 +# - name: Inform_BPZ_lite +# nprocess: 1 +# - name: BPZ_lite +# nprocess: 1 - name: CLClusterBinningRedshiftRichness nprocess: 1 # - name: CLClusterShearCatalogs @@ -39,19 +39,19 @@ stages: -output_dir: data/cosmodc2/outputs-1deg2-CL -config: examples/cosmodc2/config-1deg2-CL.yml +output_dir: ./data/cosmodc2/outputs-1deg2-CL +config: ./examples/cosmodc2/config-1deg2-CL.yml inputs: # See README for paths to download these files - shear_catalog: ./data/example/inputs/metadetect_shear_catalog.hdf5 - photometry_catalog: ./data/example/inputs/photometry_catalog.hdf5 - fiducial_cosmology: data/fiducial_cosmology.yml - calibration_table: ./data/example/inputs/sample_cosmodc2_w10year_errors.dat - spectroscopic_catalog: ./data/example/inputs/mock_spectroscopic_catalog.hdf5 +# shear_catalog: ./data/example/inputs/metadetect_shear_catalog.hdf5 +# photometry_catalog: ./data/example/inputs/photometry_catalog.hdf5 +# fiducial_cosmology: ./data/fiducial_cosmology.yml +# calibration_table: ./data/example/inputs/sample_cosmodc2_w10year_errors.dat +# spectroscopic_catalog: ./data/example/inputs/mock_spectroscopic_catalog.hdf5 cluster_catalog: ./data/example/inputs/cluster_catalog.hdf5 resume: True -log_dir: data/cosmodc2/logs -pipeline_log: data/cosmodc2/log_1deg2.txt +log_dir: ./data/cosmodc2/logs +pipeline_log: ./data/cosmodc2/log_1deg2.txt diff --git a/examples/cosmodc2/pipeline-20deg2-CL-in2p3.yml b/examples/cosmodc2/pipeline-20deg2-CL-in2p3.yml index d53f969e1..80370648c 100644 --- a/examples/cosmodc2/pipeline-20deg2-CL-in2p3.yml +++ b/examples/cosmodc2/pipeline-20deg2-CL-in2p3.yml @@ -21,12 +21,12 @@ modules: > python_paths: [] stages: - - name: TXSourceSelectorMetadetect - nprocess: 30 - - name: Inform_BPZ_lite - nprocess: 1 - - name: BPZ_lite - nprocess: 30 +# - name: TXSourceSelectorMetadetect +# nprocess: 30 +# - name: Inform_BPZ_lite +# nprocess: 1 +# - name: BPZ_lite +# nprocess: 30 - name: CLClusterBinningRedshiftRichness nprocess: 1 # - name: CLClusterShearCatalogs @@ -38,19 +38,19 @@ stages: -output_dir: data/cosmodc2/outputs-20deg2-CL -config: examples/cosmodc2/config-20deg2-CL.yml +output_dir: ./data/cosmodc2/outputs-20deg2-CL +config: ./examples/cosmodc2/config-20deg2-CL.yml inputs: # See README for paths to download these files - shear_catalog: ./data/cosmodc2/20deg2/shear_catalog.hdf5 - photometry_catalog: ./data/cosmodc2/20deg2/photometry_catalog.hdf5 - fiducial_cosmology: data/fiducial_cosmology.yml - calibration_table: ./data/cosmodc2/20deg2/sample_cosmodc2_w10year_errors.dat - spectroscopic_catalog: ./data/cosmodc2/20deg2/spectroscopic_catalog.hdf5 + #shear_catalog: ./data/cosmodc2/20deg2/shear_catalog.hdf5 + #photometry_catalog: ./data/cosmodc2/20deg2/photometry_catalog.hdf5 + #fiducial_cosmology: ./data/fiducial_cosmology.yml + #calibration_table: ./data/cosmodc2/20deg2/sample_cosmodc2_w10year_errors.dat + #spectroscopic_catalog: ./data/cosmodc2/20deg2/spectroscopic_catalog.hdf5 cluster_catalog: ./data/cosmodc2/20deg2/cluster_catalog.hdf5 resume: True -log_dir: data/cosmodc2/logs -pipeline_log: data/cosmodc2/log_20deg2.txt +log_dir: ./data/cosmodc2/logs +pipeline_log: ./data/cosmodc2/log_20deg2.txt diff --git a/examples/cosmodc2/pipeline-20deg2-CL-nersc.yml b/examples/cosmodc2/pipeline-20deg2-CL-nersc.yml index 4c0d33dac..c9d8b0cdd 100644 --- a/examples/cosmodc2/pipeline-20deg2-CL-nersc.yml +++ b/examples/cosmodc2/pipeline-20deg2-CL-nersc.yml @@ -21,12 +21,12 @@ modules: > python_paths: [] stages: - - name: TXSourceSelectorMetadetect - nprocess: 30 - - name: Inform_BPZ_lite - nprocess: 1 - - name: BPZ_lite - nprocess: 30 +# - name: TXSourceSelectorMetadetect +# nprocess: 30 +# - name: Inform_BPZ_lite +# nprocess: 1 +# - name: BPZ_lite +# nprocess: 30 - name: CLClusterBinningRedshiftRichness nprocess: 1 # - name: CLClusterShearCatalogs @@ -38,19 +38,19 @@ stages: -output_dir: data/cosmodc2/outputs-20deg2-CL -config: examples/cosmodc2/config-20deg2-CL.yml +output_dir: ./data/cosmodc2/outputs-20deg2-CL +config: ./examples/cosmodc2/config-20deg2-CL.yml inputs: # See README for paths to download these files - shear_catalog: ./data/cosmodc2/20deg2/shear_catalog.hdf5 - photometry_catalog: ./data/cosmodc2/20deg2/photometry_catalog.hdf5 - fiducial_cosmology: data/fiducial_cosmology.yml - calibration_table: ./data/cosmodc2/20deg2/sample_cosmodc2_w10year_errors.dat - spectroscopic_catalog: ./data/cosmodc2/20deg2/spectroscopic_catalog.hdf5 + #shear_catalog: ./data/cosmodc2/20deg2/shear_catalog.hdf5 + #photometry_catalog: ./data/cosmodc2/20deg2/photometry_catalog.hdf5 + #fiducial_cosmology: ./data/fiducial_cosmology.yml + #calibration_table: ./data/cosmodc2/20deg2/sample_cosmodc2_w10year_errors.dat + #spectroscopic_catalog: ./data/cosmodc2/20deg2/spectroscopic_catalog.hdf5 cluster_catalog: ./data/cosmodc2/20deg2/cluster_catalog.hdf5 resume: True -log_dir: data/cosmodc2/logs -pipeline_log: data/cosmodc2/log_20deg2.txt +log_dir: ./data/cosmodc2/logs +pipeline_log: ./data/cosmodc2/log_20deg2.txt diff --git a/notebooks/Run_CL_pipeline.ipynb b/notebooks/Run_CL_pipeline.ipynb index 7d647b2bd..9cb3e2e41 100644 --- a/notebooks/Run_CL_pipeline.ipynb +++ b/notebooks/Run_CL_pipeline.ipynb @@ -15,7 +15,9 @@ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "from IPython.display import Image\n", - "import ceci" + "import ceci\n", + "import h5py\n", + "import yaml" ] }, { @@ -28,7 +30,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 3, "id": "d5eb6757-e79f-445c-a2c6-4d25af5f6f65", "metadata": { "tags": [] @@ -36,13 +38,10 @@ "outputs": [], "source": [ "#user specific paths -- IN2P3 example\n", - "#my_txpipe_dir = \"/pbs/home/m/mricci/throng_mricci/desc/TXPipe\"\n", - "#output_dir = '/pbs/home/m/mricci/throng_mricci/desc/TXPipe/data/example/inputs'\n", + "my_txpipe_dir = \"/pbs/home/m/mricci/throng_mricci/desc/TXPipe\"\n", "\n", "#user specific paths -- NERSC example\n", - "my_txpipe_dir = \"/pscratch/sd/a/avestruz/TXPipe\"\n", - "output_dir = '/pscratch/sd/a/avestruz/TXPipe/data/example/inputs'\n", - "\n", + "#my_txpipe_dir = \"/pscratch/sd/a/avestruz/TXPipe\"\n", "\n", "os.chdir(my_txpipe_dir)\n", "\n", @@ -54,178 +53,22 @@ "id": "33d82af0-d1c4-43cc-ac0b-a2d6f1e66889", "metadata": {}, "source": [ - "# Let's start working with the 1deg2 data file" - ] - }, - { - "cell_type": "markdown", - "id": "b031074a-bc97-4619-bac7-a364dccd5891", - "metadata": {}, - "source": [ - "### Launching a pipeline\n", - "\n", - "Let's have a look at the submission script for this pipeline:\n", - "- to work at CCin2p3 we can use: `examples/cosmodc2/1deg2-in2p3.sub`:\n", - "- to work at NERSC we can use: `examples/cosmodc2/1deg2-nersc.sub`:\n", - "\n", - "If we use the CCin2p3 example :" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "d0539f99-806e-481c-9a00-69716e216d74", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "#!/usr/bin/bash\n", - "#SBATCH --time=01:00:00\n", - "#SBATCH --partition=hpc\n", - "#SBATCH --ntasks=1\n", - "#SBATCH --cpus-per-task=1\n", - "#SBATCH --mem=128000\n", - "\n", - "source /pbs/throng/lsst/users/jzuntz/txpipe-environments/setup-txpipe\n", - "ceci examples/cosmodc2/pipeline-1deg2-CL.yml\n" - ] - } - ], - "source": [ - "! cat examples/cosmodc2/1deg2-in2p3.sub" - ] - }, - { - "cell_type": "markdown", - "id": "1e514aff-1f8f-4988-ad30-8e46fee8ebad", - "metadata": {}, - "source": [ - "If we use the NERSC example:" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "daebcfa9-dcd4-4e57-8468-b5a6bb4ab3ba", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "#!/bin/bash\n", - "#SBATCH -A m1727\n", - "#SBATCH -C cpu\n", - "#SBATCH --qos=debug\n", - "#SBATCH --time=00:30:00\n", - "#SBATCH --nodes=1\n", - "#SBATCH --ntasks-per-node=32\n", - "\n", - "source $CFS/lsst/groups/WL/users/zuntz/setup-txpipe\n", - "\n", - "tx ceci examples/cosmodc2/pipeline-1deg2-clmm-nersc.yml\n" - ] - } - ], - "source": [ - "! cat examples/cosmodc2/1deg2-nersc.sub" + "# Let's start working with the 1deg2 data file on Jupyter" ] }, { "cell_type": "markdown", - "id": "64a79cc5-4e43-4146-b3ac-aceb68b56470", + "id": "f4e0ef7e-9659-4a03-b0ca-ab856c5e5f54", "metadata": {}, "source": [ - "This will launch a job of up to one hour (it should finish in 30 min) on a single CC-IN2P3 node to run a pipeline. After the first run, the output files are created and following runs take much less time.\n", + "First we will do some runs on the 1 deg^2 example data set with around 80k galaxies. This is small enough that we can do it all in jupyter.\n", "\n", - "In a terminal, **navigate to your TXPipe directory on IN2P3 and run**:\n", - "\n", - "```\n", - "sbatch examples/cosmodc2/1deg2-in2p3.sub\n", - "```\n", - "to set it running.\n", - "\n", - "If you are **on NERSC, you will instead run**:\n", - "```\n", - "sbatch examples/cosmodc2/1deg2-nersc.sub\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "a06ad5c8-120d-4695-b303-992152daf8d3", - "metadata": {}, - "source": [ - "Below, you will need to select the appropriate yaml file to comment/uncomment for `pipeline_file`, depending on if you are in IN2P3 or on NERSC. " + "The data set, which is based on CosmoDC2, contains pre-computed photo-z and and contains a RedMapper cluster catalog for the field." ] }, { "cell_type": "code", - "execution_count": 24, - "id": "5935f013-c29c-4446-b3e9-00d5de2b85cb", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Read the appropriate pipeline configuration, and ask for a flow-chart.\n", - "#pipeline_file = \"examples/cosmodc2/pipeline-1deg2-CL-in2p3.yml\"\n", - "pipeline_file = \"examples/cosmodc2/pipeline-1deg2-CL-nersc.yml\"\n", - "flowchart_file = \"CL_pipeline.png\"\n", - "\n", - "\n", - "pipeline_config = ceci.Pipeline.build_config(\n", - " pipeline_file,\n", - " flow_chart=flowchart_file,\n", - " dry_run=True\n", - ")\n", - "\n", - "# Run the flow-chart pipeline\n", - "ceci.run_pipeline(pipeline_config)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "5b45238d-3568-4d6c-96d8-cf794680bf74", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Image(flowchart_file)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, + "execution_count": 48, "id": "274c2ea4-b366-4e3d-8690-efa8af3f6d96", "metadata": { "tags": [] @@ -247,11 +90,9 @@ "pip_stage = txpipe.extensions.CLClusterBinningRedshiftRichness.make_stage(\n", " # This is the initial cluster catalog - RAs, Decs, richess, redshift, etc.\n", " cluster_catalog=\"./data/example/inputs/cluster_catalog.hdf5\",\n", - " # This fiducial cosmology is used to convert distance separations to redshifts\n", - " fiducial_cosmology=\"./data/fiducial_cosmology.yml\",\n", " \n", " # This is the output for this stage\n", - " cluster_tomography=\"./data/cosmodc2/outputs-1deg2-CL/cluster_catalog_tomography.hdf5\",\n", + " cluster_catalog_tomography=\"./data/example/cluster_catalog_tomography.hdf5\",\n", "\n", " # This contains all the options for this stage. You can override them here, \n", " #as we do with the max_radius below.\n", @@ -261,7 +102,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 49, "id": "0df094c5-3e3a-4335-89ff-4df52bfb8a43", "metadata": { "tags": [] @@ -274,7 +115,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 50, "id": "009eb8b0-1c54-497f-a419-055e869e2ece", "metadata": { "tags": [] @@ -285,7 +126,7 @@ "output_type": "stream", "text": [ "Actual options used for this pipeline (as defined in config file or default):\n", - "{zedge:[0.1, 0.4, 0.6, 0.8],richedge:[5.0, 10.0, 20.0, 25.0],initial_size:100000,chunk_rows:100000,cluster_catalog:./data/example/inputs/cluster_catalog.hdf5,fiducial_cosmology:./data/fiducial_cosmology.yml,cluster_tomography:./data/cosmodc2/outputs-1deg2-CL/cluster_catalog_tomography.hdf5,config:examples/cosmodc2/config-1deg2-CL.yml,aliases:{},}\n" + "{zedge:[0.1, 0.4, 0.6, 0.8],richedge:[5.0, 10.0, 20.0, 25.0],initial_size:100000,chunk_rows:100000,cluster_catalog:./data/example/inputs/cluster_catalog.hdf5,cluster_catalog_tomography:./data/example/cluster_catalog_tomography.hdf5,config:examples/cosmodc2/config-1deg2-CL.yml,aliases:{},}\n" ] } ], @@ -294,6 +135,48 @@ "print(pip_stage.config)" ] }, + { + "cell_type": "code", + "execution_count": 19, + "id": "8ee60427-f399-4fbe-ab97-39ea2387d40e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'./data/example/inputs/cluster_catalog.hdf5'" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pip_stage.config['cluster_catalog']" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "22639ea6-38d4-4061-acb4-e402939a3fd4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'./data/example/cluster_catalog_tomography.hdf5'" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pip_stage.config['cluster_catalog_tomography']" + ] + }, { "cell_type": "markdown", "id": "6174784d-ba23-4e33-bbf9-559295cb9d0f", @@ -312,31 +195,28 @@ }, { "cell_type": "code", - "execution_count": 8, - "id": "c2cd69d0-89a2-4cb3-ae65-0882b7194507", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "import h5py" - ] - }, - { - "cell_type": "code", - "execution_count": 9, + "execution_count": 21, "id": "3218ac13-3d30-4bc9-bd8d-25d40a3eeddd", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "./data/example/inputs/cluster_catalog.hdf5\n" + ] + } + ], "source": [ - "filename_in = output_dir + \"/cluster_catalog.hdf5\"" + "filename_in = pip_stage.config['cluster_catalog']\n", + "print(filename_in)" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 22, "id": "d8c38523-a8f7-4a43-9831-9ae273adeaa6", "metadata": { "tags": [] @@ -348,7 +228,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 23, "id": "c6d59b2a-8857-44be-93ad-912d8a893ec0", "metadata": { "tags": [] @@ -368,7 +248,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 24, "id": "1ecd9464-7ef8-4600-b931-8b8c76830564", "metadata": { "tags": [] @@ -380,7 +260,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 25, "id": "f5624d30-cea6-4999-8f53-0ee81030b632", "metadata": { "tags": [] @@ -401,7 +281,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 26, "id": "4349ca71-0548-490b-b5c3-5af0ce98f7f2", "metadata": { "tags": [] @@ -413,13 +293,13 @@ "Text(0, 0.5, 'richness')" ] }, - "execution_count": 14, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -445,26 +325,38 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 27, "id": "0b4c65de-82c8-4f99-89de-dd0a598a31f0", "metadata": { "tags": [] }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "./data/example/cluster_catalog_tomography.hdf5\n" + ] + } + ], + "source": [ + "filename_out = pip_stage.config['cluster_catalog_tomography'] #output_dir + \"/cluster_catalog_tomography.hdf5\"\n", + "print (filename_out)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "da3761cd-c7ed-47a2-a0bc-562cd7ad3381", + "metadata": {}, "outputs": [], "source": [ - "# open for reading\n", - "#output_dir = '/pbs/home/m/mricci/throng_mricci/desc/TXPipe/data/cosmodc2/outputs-1deg2-CL'\n", - "output_dir = '.'\n", - "\n", - "filename_out = output_dir + \"/cluster_catalog_tomography.hdf5\"\n", - "#f2 = txpipe.data_types.TomographyCatalog(filename2, \"r\")\n", - "\n", "f_out = h5py.File(filename_out, \"r\")" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 29, "id": "b3274869-a729-45c7-b531-05d4f8ef69da", "metadata": { "tags": [] @@ -484,7 +376,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 30, "id": "7785436c-8652-4c98-9ecb-373abd2dc5d2", "metadata": { "tags": [] @@ -497,7 +389,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 31, "id": "736b4dc3-381b-45f4-a93e-f9c00b9e5e17", "metadata": { "tags": [] @@ -517,8 +409,8 @@ }, { "cell_type": "code", - "execution_count": 19, - "id": "6fbe636e-052e-47b0-8802-ae4ceae34ea9", + "execution_count": 32, + "id": "9ad81296-06fb-4c27-bef9-116e8bf7dee8", "metadata": { "tags": [] }, @@ -545,7 +437,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 33, "id": "38be57ca-e370-480d-a2e0-c2d45baf7b13", "metadata": { "tags": [] @@ -563,18 +455,10 @@ "print ([col for col in dset_out['bin_zbin_0_richbin_0']])" ] }, - { - "cell_type": "markdown", - "id": "99017d4e-0ad1-44da-ace2-4fa54fb28b2e", - "metadata": {}, - "source": [ - "## Compare the two " - ] - }, { "cell_type": "code", - "execution_count": 21, - "id": "82695f43-ab54-4efd-bc1d-633c1db44d33", + "execution_count": 34, + "id": "9b7670e8-3672-41c4-bd24-f17b1b3a5622", "metadata": { "tags": [] }, @@ -593,9 +477,17 @@ " len(pip_stage.config.richedge) - 1,'richness bins')" ] }, + { + "cell_type": "markdown", + "id": "99017d4e-0ad1-44da-ace2-4fa54fb28b2e", + "metadata": {}, + "source": [ + "## Compare the two " + ] + }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 35, "id": "2b146a75-3aff-4fa7-be65-679c6f49bf6e", "metadata": { "tags": [] @@ -603,7 +495,7 @@ "outputs": [ { "data": { - "image/png": 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sqqqqMHPmTMTHxyMxMREXL16UymWqHPa8h3HjxiE0NBQAEB8fj8rKSrN5bcEeHSIicguWBDntetOzc/ToURQWFkKr1SI7OxvFxcZBU2lpKU6ePAm1Wo2RI0fiiSeeMHk8RkFBAQoKClBdXY20tDTk5uaioqICn3/+OU6dOoXw8HBMnDgRs2bNQlhYmNF7T5w4gUOHDuHWrVsYP348HnroIZNlraqqQlxcnPT3yMjIbk8wP3z4MEpKShAcHIzCwkIpffHixcjKysKCBQuMDvQ0Vw573kNH27dvR1paWo/5rMEeHSIicnk6gw4rP1vZc8YOVn62EjqDzurPio2NRXx8PBQKBbKzs3HgwAGj11NSUjBgwAAEBgZi+PDhZntQAKC1tRU5OTlYvXo1hg4dCgCYMGECNBoNfH198cADD+DgwYNd3peRkQG1Wo2IiAgIIaDTmb4PUzvEKBTmTnYHMjMzERwc3CX9008/RW5uLgBArVZDrVZ3Ww573kO7d955B5999hkee+yxbvNZi4EOERG5PF+lL5758TNWveeZHz/T6/11TAUS/v7+0s9KpRKtra1m379s2TKkpKRgxowZFl/f1Gfo9XqT+YYMGWI0ebe73hwAZg9mNRccWVKO3t4DAHz11Vd48sknUVxcbPQ+e2CgQ0REbuH+mPtRkGx+TkxHBckFNk9IPnXqFI4ePQoA2LFjB1JTU226zttvv42SkhKsXGncE7V//35UVVVBp9OhuLgYKSkpNl0fAGbOnInXX38der0ex48fh0KhgEajsfo6ycnJ2L59O4C2zfk6Dl+ZYs97uHz5MubMmYPXXnvNprL3hIGODJRKJZKSkpCUlMRt070c24K8WL/ycsX6tSTY6U2QAwAJCQlYs2YNxowZg/79++P++2271vr161FRUYHExEQkJCTgpZdeAgCkpqYiPz8fWq0WU6ZMQVJSks1l1Wq1SE9Pxx133IE5c+bghRdesOk6zz//PIqKiqDVapGSkoKamppu89vzHlavXo0rV64gLy8PCQkJyMrKsvlapvAICB4BQUTkEqw5AsLcxOTeBjnkmngEBBEReRVTPTsMcsgULi8nIiK31B7UrPxsJZ758TNOC3JqamqQnp5ulBYWFmbXTQaPHTuGefPmGaVptVqTG/RZk9eRnFUuDl3JMHTV0NAg7WtQVlaGwMBAu12b3AvbgrxYv/JydP3y9HIypzdDV+zRkYEQAufPn5d+Ju/FtiAv1q+83KV+GeRQdzhHh4iIiDwWe3SIiMittFRUQN/NaeqqoCD4fb8LMREDHSIichstFRX4euq0HvON2P1vBjsEgENXRETkRrrrybElX2cdT+DuKCMjw6brdbZv3z7k5OR0Sa+qqsL8+fOtvt6FCxeQkpKCgIAAaUNCS+3cuRMbNmww+3rH0807svc9AG0HoEZHR2P06NE4dOiQTdcwhz06REREPbDnUnFTNBqNTcusQ0JCsHbtWuzcudOq9+n1esyePdvqz+uOrfdw7NgxvPfeezh9+jTKysqQl5dn12CHPToyUCgUiIuLQ1xcXLenyJLnY1uQF+tXXt5av83NzcjOzsaoUaOwcOFCGAwGREREAGjrzcjMzMSsWbMQExODggLzx1EsX74cCQkJSEhIwMCBA/Hss88CaNt3Z+rUqYiNjcWKFSsAGPckFRYWIicnB+np6RgxYgS2bt1q9jP69euH8ePHw9e355VnhYWFePDBBzFt2jTk5uYa9dhUVVVh5syZiI+PR2JiIi5evCiVy1Q57HkP77zzDubOnQuVSoWxY8dCr9d3eyK8tdy+R+fixYuYN28erl69Ch8fHzzzzDP42c9+5tQyBQYG4sSJE04tA7kGtgV5sX7l5a31e/ToURQWFkKr1SI7OxvFxcVGr5eWluLkyZNQq9UYOXIknnjiCZOnghcUFKCgoADV1dVIS0tDbm4uKioq8Pnnn+PUqVMIDw/HxIkTMWvWLISFhRm998SJEzh06BBu3bqF8ePH46GHHrLLvR0+fBglJSUIDg5GYWGhlL548WJkZWVhwYIFRgd6miuHPe+hqqpK2q8JACIjI3s8hd0abt+j4+Pjg/Xr16OsrAwffPABHnvsMdTbODZLREQUGxuL+Ph4KBQKZGdn48CBA0avp6SkYMCAAQgMDMTw4cO77X1obW1FTk4OVq9ejaHfT46eMGECNBoNfH198cADD+DgwYNd3peRkQG1Wo2IiAgIIaDT6exyb5mZmQgODu6S/umnnyI3NxcAoFaroVaruy2HPe/B1B5N9uxBdPsencGDB0tR38CBAxEaGopvv/3WZHRNRERkDVO/hP39/aWflUolWltbzb5/2bJlSElJwYwZMyy+vqnP0Ov1Fg1P9cTc70ZzgYWpcnTW23sYMmQILl26JP3dnr05gAv06Hz88ceYNWsWNBoNFAoF/vnPf3bJ8+KLL0rbPicmJmL//v0mr/XFF1/AYDDgtttuk7nU3WtoaMDo0aMxevRoNDQ0OLUs5FxsC/Ji/crLW+v31KlTOHr0KABgx44dSE1Ntek6b7/9NkpKSrBy5Uqj9P3796Oqqgo6nQ7FxcVISUnpdZl7Kzk5Gdu3bwfQdtxCx+ErU+x5DzNnzsTrr78OvV6P48ePQ6FQQKPR2Hy9zpwe6NTX1yM+Ph4bN240+fobb7yBX/3qV/jtb3+L0tJSTJgwAdOmTcOFCxeM8tXU1GD+/PnYvHmz2c9qbm5GbW2t0R85CCFQVlaGsrIyl942neTHtiAv1q+8XLF+VRb21luaz5SEhASsWbMGY8aMQf/+/XH//bYdFrp+/XpUVFQgMTERCQkJ0vLv1NRU5OfnQ6vVYsqUKUhKSrK5rA0NDYiMjMTatWuxbNkyREdH23Sd559/HkVFRdBqtUhJSUFNTU23+e15D1qtFunp6bjjjjswZ84cvPDCCzZfyxSXOtRToVCguLgY9913n5Q2fvx43HXXXfjzn/8spY0aNQr33Xcf/vCHPwBoC2Duuece/Nd//VeXk1E7+t3vfifNeu/I3od61tfXo0+fPgCAuro6DqN5MbYFebF+5eXo+rX0UE/ujOx9PPZQz5aWFpSUlHTZsGjKlCn45JNPALT9i2PBggVIS0vrNsgB2sZKH3/8cenvtbW1Th/mIiIi6zCIIWu4dKBz/fp16PV6DBo0yCh90KBBuHz5MgDg4MGDeOONN6DVaqX5Pf/3f/+HsWPHdrmev7+/0eQoIiKi3qqpqUF6erpRWlhYmF03GTx27FiXf8xrtVqTG/RZk9eRnFUulw502nWeDS6EkNJSU1NhMBicUSwiIiIMGDAAR44ckfUzxo4da/FnWJPXkZxVLqdPRu5OWFgYVCqV1HvT7urVq116eYiIiIg6c+keHT8/PyQmJuL99983mvX+/vvv495773ViybqnUCgQFRUl/Uzei21BXqxfebF+yRM4PdCpq6vDuXPnpL+Xl5fjyJEjCA0Nxe23347HH38c8+bNQ1JSEu6++25s3rwZFy5cwCOPPOLEUncvMDAQFRUVzi4GuQC2BXmxfuXl6vVrMAhUn72J+tpmBIX4Y3BMPyiVDMjImNOHrr744gvceeeduPPOOwEAjz/+OO68804sX74cADBnzhysX78eBQUFSEhIwMcff4x3331X+lcGERF5n69Lr+KVpz/BP9eV4v0tZfjnulK88vQn+Lr0aq+u2/Fgyo4yMjJ6dd12+/btQ05OTpf0qqoqzJ8/3+rrXbhwASkpKQgICJD26bHUzp07sWHDBrOvdzz0syNXugdLOL1HZ/LkyT1uRJWfn4/8/HwHlYiIiFzZ16VXsfvl413S6282Y/fLxzF10RiMuHOgXT/TniuoTNFoNDatPgoJCcHatWuxc+dOq96n1+sxe/Zsqz+vO46+B0s5vUfHEzU2NmLcuHEYN25cj9tok2djW5AX61derli/BoPA/jfOdpvnQNFZGAy274Xb3NyM7OxsjBo1CgsXLoTBYEBERASAtt6MzMxMzJo1CzExMSgoKDB7neXLlyMhIQEJCQkYOHCgtGFtTU0Npk6ditjYWKxYsQKAcU9SYWEhcnJykJ6ejhEjRmDr1q1mP6Nfv34YP368RedgFRYW4sEHH8S0adOQm5tr1GNTVVWFmTNnIj4+HomJibh48aJULlPlcNY92MLpPTrOsGnTJmzatMnk4WT2YDAY8MUXX0g/k/diW5AX61derli/1Wdvov5mc7d56m40o/rsTQy5o79Nn3H06FEUFhZCq9UiOzsbxcXFRq+Xlpbi5MmTUKvVGDlyJJ544gmTu0YXFBSgoKAA1dXVSEtLQ25uLioqKvD555/j1KlTCA8Px8SJEzFr1iyEhYUZvffEiRM4dOgQbt26hfHjx+Ohhx6y6V46O3z4MEpKShAcHIzCwkIpffHixcjKysKCBQuMglpz5XDmPVjLK3t0Hn30UZSVleHw4cPOLgoREVmhvrb7IMfafKbExsYiPj4eCoUC2dnZOHDggNHrKSkpGDBgAAIDAzF8+HBUV1ebvVZraytycnKwevVqDP1+R+cJEyZAo9HA19cXDzzwAA4ePNjlfRkZGVCr1YiIiIAQAjqdzub76SgzMxPBwcFd0j/99FPk5uYCANRqNdRqdbflcOY9WMsrAx0iInJPQSGW7W5vab6emJpD2nGHfaVSidbWVrPvX7ZsGVJSUjBjxgyLr2/qM+w1AmHuvDJz2wdYUg5H34O1GOgQEZHbGBzTD0H9ug9i+vRvW2puq1OnTuHo0aMAgB07diA1NdWm67z99tsoKSnBypUrjdL379+Pqqoq6HQ6FBcXIyUlxeay2ktycjK2b98OoO0AzZ7mZLniPZjDQIeIiNyGUqnAhDkx3eZJzY7p1X46CQkJWLNmDcaMGYP+/fsbbVhrjfXr16OiogKJiYlISEiQlk6npqYiPz8fWq0WU6ZMQVJSks1lbWhoQGRkJNauXYtly5YhOjrapus8//zzKCoqglarRUpKCmpqarrN74r3YI5C9LS224NZc8y7Nerr69GnTx8AbRsimusqJM/HtiAv1q+8HF2/TU1NKC8vx7BhwxAQENBt3q9Lr2L/G2eNJib36e+P1OwYuy8tJ+fr3Das+f3tlauuHKHz7HPyXmwL8mL9ystV63fEnQMxLD6cOyNTjxjoyCAoKAjXrl1zdjHIBbAtyIv1Ky9Xr1+lUmHzEnJ7qqmpQXp6ulFaWFiYXTcZPHbsGObNm2eUptVqTW7QZ01eR3JWuTh0JcPQFRERWc+aoSvyLr0ZuuJkZCIiIvJYDHRk0NjYiMmTJ2Py5Mkus206OQfbgrxYv/Jyl/rVd7OPDRHn6MjAYDDgo48+kn4m78W2IC/Wr7zcoX6P7d2DD//6ItIfzsfYn0xxdnHIBXllj86mTZsQFxeHcePGObsoRERko2N792DPSxugb23Fnpc24NjePc4uErkgrwx0eNYVEZF7aw9yOrJHsNPxBO6OMjIyenXddvv27UNOTk6X9KqqKsyfP9/q6124cAEpKSkICAiQNiS01M6dO7Fhwwazr3c83bwje99DcXExtFottFot7rnnHly6dMnqa3SHQ1dERORWTAU57drT7T2MZc+l4qZoNBqbllmHhIRg7dq12Llzp1Xv0+v1mD17ttWf1x1b7yEyMhL79u1DaGgotm3bhmeeeQZbt261W7m8skeHiIjcU3dBTrve9uw0NzcjOzsbo0aNwsKFC2EwGBAREQGgrTcjMzMTs2bNQkxMDAoKCsxeZ/ny5UhISEBCQgIGDhyIZ599FkDbvjtTp05FbGwsVqxYAcC4J6mwsBA5OTlIT0/HiBEjuv2l369fP4wfPx6+vr493ldhYSEefPBBTJs2Dbm5uUY9NlVVVZg5cybi4+ORmJiIixcvSuUyVQ573sO4ceMQGhoKAIiPj0dlZWWP92IN9ugQEZFbsCTIadebnp2jR4+isLAQWq0W2dnZKC4uNnq9tLQUJ0+ehFqtxsiRI/HEE0+YPB6joKAABQUFqK6uRlpaGnJzc1FRUYHPP/8cp06dQnh4OCZOnIhZs2Z12YH6xIkTOHToEG7duoXx48fjoYcesvo+TDl8+DBKSkoQHByMwsJCKX3x4sXIysrCggULjFbYmSuHXPewfft2pKWl2eVe27FHRyaBgYEIDAx0djHIBbAtyIv1Ky9XqV99ays+/OuLVr3nw7++aNPS89jYWMTHx0OhUCA7OxsHDhwwej0lJQUDBgxAYGAghg8fjurqarPXam1tRU5ODlavXo2hQ4cCACZMmACNRgNfX1888MADOHjwYJf3ZWRkQK1WIyIiAkII6HQ6q+/DlMzMTAQHB3dJ//TTT5GbmwsAUKvVUKvV3ZZDjnt455138Nlnn+Gxxx7r7W0aYaAjg6CgINTX16O+vp6HDHo5tgV5sX7l5Ur1q/LxQfrD+Va9J/3hfKh8ejdwYerwAH9/f+lnpVKJ1m6CqWXLliElJQUzZsyw+PqmPkOv11ta5G6Z+x4VCtNnhFlSDnvcw1dffYUnn3wSxcXFRu+zBwY6RETkFsb+ZAqmPLLYorxTHlls84TkU6dO4ejRowCAHTt2IDU11abrvP322ygpKcHKlSuN0vfv34+qqirodDoUFxcjJSXFpuvbU3JyMrZv3w6g7biFnjaItOc9XL58GXPmzMFrr70GjUZj83XMYaBDRERuw5JgpzdBDgAkJCRgzZo1GDNmDPr374/777/fpuusX78eFRUVSExMREJCgrT8OzU1Ffn5+dBqtZgyZQqSkpJsLmtDQwMiIyOxdu1aLFu2DNHR0TZd5/nnn0dRURG0Wi1SUlJQU1PTbX573sPq1atx5coV5OXlISEhAVlZWTZfyxQe6inDoZ5NTU346U9/CgB46623eDidF2NbkBfrV16Orl9rDvU0NzG5t0EOuabeHOrJVVcy0Ov1ePfdd6WfyXuxLciL9SsvV67f9mCmY7DDIIdMYaBDRERuqT2ocfZZVzU1NUhPTzdKCwsLs+smg8eOHcO8efOM0rRarckN+qzJ60jOKpdXBjqbNm3Cpk2bXO5fKEREZJ2xP5mCuAlpvV5d1RsDBgzAkSNHZP2MsWPHWvwZ1uR1JGeVyysnI/OsKyIiz+HMIIdcn1cGOkREROQdGOgQERGRx2J/HxERuRXd9UaIZvNzLBX+KviGqR1YInJl7NGRQVBQEIQQEEI4fdt0ci62BXmxfuXlivWru96IK6u/wNUXSs3+ubL6C+iud7+zrzkdT+DuKCMjo7dFB9B2+nlOTk6X9KqqKsyfP9/q6xUXF0Or1UKr1eKee+7BpUuXLH7vzp07sWGD+UNSO55u3pG97wFoOwA1Ojoao0ePxqFDh2y6hjns0SEiIrfRXU+OLfksZc+l4qZoNBqblllHRkZi3759CA0NxbZt2/DMM89g69atPb5Pr9dj9uzZthTVLFvv4dixY3jvvfdw+vRplJWVIS8vz67BDnt0iIiIOmhubkZ2djZGjRqFhQsXwmAwICIiAkBbb0ZmZiZmzZqFmJgYFBQUmL3O8uXLkZCQgISEBAwcOBDPPvssgLZ9d6ZOnYrY2FisWLECgHFPUmFhIXJycpCeno4RI0Z0G7iMGzcOoaGhAID4+HhUVlaazVtYWIgHH3wQ06ZNQ25urlGPTVVVFWbOnIn4+HgkJibi4sWLUrlMlcOe9/DOO+9g7ty5UKlUGDt2LPR6fbcnwluLPToyaGpqkjZF+r//+z9uS+/F2BbkxfqVl7fW79GjR1FYWAitVovs7GwUFxcbvV5aWoqTJ09CrVZj5MiReOKJJ0wO7RUUFKCgoADV1dVIS0tDbm4uKioq8Pnnn+PUqVMIDw/HxIkTMWvWLISFhRm998SJEzh06BBu3bqF8ePH46GHHuqx3Nu3b0daWlq3eQ4fPoySkhIEBwejsLBQSl+8eDGysrKwYMECowM9zZXDnvdQVVWFuLg46e+RkZG4dOkSBg8e3OM9W4I9OjLQ6/V488038eabb3JTQi/HtiAv1q+8vLV+Y2NjER8fD4VCgezsbBw4cMDo9ZSUFAwYMACBgYEYPnx4t70Pra2tyMnJwerVqzF06FAAwIQJE6DRaODr64sHHngABw8e7PK+jIwMqNVqREREQAgBnU7XbZnfeecdfPbZZ3jssce6zZeZmYng4OAu6Z9++ilyc3MBAGq1Gmq1utty2PMeTB25qVAour0Pa7BHh4iIyAxTv4T9/f2ln5VKJVpbW82+f9myZUhJScGMGTMsvr6pz9Dr9fD19TWZ96uvvsKTTz6J//znP0bvM8XcpHJzgYWpcnTW23sYMmSI0SRqe/bmAOzRISIiMnLq1CkcPXoUALBjxw6kpqbadJ23334bJSUlWLlypVH6/v37UVVVBZ1Oh+LiYqSkpNhc1suXL2POnDl47bXXoNFobL5OcnIytm/fDqBtyLLj8JUp9ryHmTNn4vXXX4der8fx48ehUCh6dS+dMdAhIiLqICEhAWvWrMGYMWPQv39/3H///TZdZ/369aioqEBiYiISEhLw0ksvAQBSU1ORn58PrVaLKVOmICkpyeayrl69GleuXEFeXh4SEhKQlZVl03Wef/55FBUVQavVIiUlBTU1Nd3mt+c9aLVapKen44477sCcOXPwwgsv2HwtUxTCXJ+TF6itrUXfvn1x69YthISE2O269fX16NOnDwCgrq7OZfafIMdjW5AX61dejq7fpqYmlJeXY9iwYWYnPrfvo9OTQUuTuGmgB+ncNqz5/c05OkRE5DZ8w9QYtDSJOyOTxbwy0Nm0aRM2bdrkVasIiIg8hasFMTU1NUhPTzdKCwsLs+smg8eOHZOW+rfTarUmN+izJq8jOatcHLqSYehKCIGGhgYAQGBgoF2XyZF7YVuQF+tXXo6uX0uGrsg7cejKxSgUCs4VIABsC3Jj/cqL9UuegKuuiIiIyGMx0JFBc3MzFixYgAULFqC5udnZxSEnYluQF+tXXq5evwaDAeXl5Th27BjKy8thMBicXSRyQZyjw+XlJCO2BXmxfuXlisvL25WVlWH37t2ora2V0kJCQjB16lSjc5PIM/Rmjg57dIiIyK2UlZWhqKjIKMgB2v7xWlRUhLKyMpuv3fEE7o4yMjJsvmZH+/btQ05OTpf0qqoqzJ8/3+rrFRcXQ6vVQqvV4p577jE6SqEnO3fuxIYNG8y+3vF0847sfQ8XLlxASkoKAgICpE0V7YmTkYmIyG0YDAbs3r272zy7d+9GbGwslEr7/VvenkvFTdFoNDYts46MjMS+ffsQGhqKbdu24ZlnnsHWrVt7fJ9er8fs2bNtKapZtt5DSEgI1q5di507d9q1PO3Yo0NERG7j/PnzXXpyOqutrcX58+dt/ozm5mZkZ2dj1KhRWLhwIQwGAyIiIgC09WZkZmZi1qxZiImJQUFBgdnrLF++HAkJCUhISMDAgQPx7LPPAmjbd2fq1KmIjY3FihUrABj3JBUWFiInJwfp6ekYMWJEt4HLuHHjEBoaCgCIj49HZWWl2byFhYV48MEHMW3aNOTm5hr12FRVVWHmzJmIj49HYmIiLl68KJXLVDnseQ/9+vXD+PHjzR5a2lvs0SEiIrdRV1dn13ymHD16FIWFhdBqtcjOzkZxcbHR66WlpTh58iTUajVGjhyJJ554wuT8pYKCAhQUFKC6uhppaWnIzc1FRUUFPv/8c5w6dQrh4eGYOHEiZs2ahbCwMKP3njhxAocOHcKtW7cwfvx4PPTQQz2We/v27UhLS+s2z+HDh1FSUoLg4GAUFhZK6YsXL0ZWVhYWLFhgdKCnuXLIdQ9yYI8OERG5jfbJ0fbKZ0psbCzi4+OhUCiQnZ2NAwcOGL2ekpKCAQMGIDAwEMOHD0d1dbXZa7W2tiInJwerV6/G0KFDAQATJkyARqOBr68vHnjgARw8eLDL+zIyMqBWqxEREQEhBHQ6Xbdlfuedd/DZZ5/hscce6zZfZmYmgoODu6R/+umnyM3NBQCo1Wqo1epuyyHHPciFgQ4REbmNqKioHlfZhISEICoqyi6fZ2phsr+/v/SzUqlEa2ur2fcvW7YMKSkpmDFjhsXXN/UZ3R1Z9NVXX+HJJ59EcXGx0ftMMbdyztyu15aUwx73ICcGOjIIDAzE1atXcfXqVQQGBjq7OOREbAvyYv3KyxXrV6lUYurUqd3mmTp1aq8mIp86dQpHjx4FAOzYsQOpqak2Xeftt99GSUkJVq5caZS+f/9+VFVVQafTobi4GCkpKTaX9fLly5gzZw5ee+01aDQam6+TnJyM7du3A2hbyt1x+MoUe96D3BjoyEChUCA8PBzh4eE8e8fLsS3Ii/UrL1et37i4OGRnZ3fp2QkJCUF2dnav99FJSEjAmjVrMGbMGPTv3x/333+/TddZv349KioqkJiYiISEBGnpdGpqKvLz86HVajFlyhQkJSXZXNbVq1fjypUryMvLQ0JCArKysmy6zvPPP4+ioiJotVqkpKSgpqam2/z2vIeGhgZERkZi7dq1WLZsGaKjo22+lincMFCGDQOJyH1VVlbi7NmziImJQWRkpLOL41WsPdTTYDDg/PnzqKurQ58+fRAVFWXXJeXkOrhhoItpbm7Go48+ikcffdQlt00nx2FbkJe963fLli2IiopCWloaoqKisGXLFjuU0n25evtVKpUYNmwYxo4di2HDhjHIIZPYo8MjIEhGbAvysmf9VlZWIioqyui8JJVKhYqKCq/t2XHlIyBcSU1NDdLT043SwsLC7LrJ4LFjxzBv3jyjNK1Wa3KDPmvyOlJvytWbHh3uo0NEBODs2bNdDoXU6/U4d+6c1wY6ZJkBAwbgyJEjsn7G2LFjLf4Ma/I6krPK5ZX9fJs2bUJcXBzGjRvn7KIQkYuIiYnpMvShUqnsPjGS7M9gcM7+LOQevDLQefTRR1FWVobDhw87uyhE5CIiIyOxefNmqFQqAG1Bzssvv8zeHBdXVbUD+z4ai6qqHc4uCrkoDl0REX0vLy8PmZmZOHfuHKKjoxnkuLiqqh04eWoZAPH9fwGN5mfOLRS5HK/s0SEiMicyMhKTJ09mkOPiOgY5bdqCnd727HQ8mLKjjIyMXl233b59+5CTk9MlvaqqCvPnz7f6esXFxdBqtdBqtbjnnntw6dIli9+7c+dObNiwwezrHQ/97MiV7sESDHSIiMitdA1y2tkn2DHFniuoTNFoNDatioqMjMS+fftw9OhRzJ07F88884xF79Pr9Zg9ezYWL15s9Wea4+h7sBQDHRmo1WqUl5ejvLxcOhiNvBPbgrxYv/Jyxfo1H+S0632w09zcjOzsbIwaNQoLFy6EwWBAREQEgLbejMzMTMyaNQsxMTEoKCgwe53ly5cjISEBCQkJGDhwIJ599lkAbcvRp06ditjYWKxYsQKAcU9SYWEhcnJykJ6ejhEjRmDr1q1mP2PcuHEIDQ0FAMTHx6OystJs3sLCQjz44IOYNm0acnNzjXpsqqqqMHPmTMTHxyMxMREXL16UymWqHM66B1twjo4MlEqldEoteTe2BXmxfuXlavXbc5DTrndzdo4ePYrCwkJotVpkZ2ejuLjY6PXS0lKcPHkSarUaI0eOxBNPPGFyj6GCggIUFBSguroaaWlpyM3NRUVFBT7//HOcOnUK4eHhmDhxImbNmoWwsDCj9544cQKHDh3CrVu3MH78eDz00EM9lnv79u1IS0vrNs/hw4dRUlKC4OBgFBYWSumLFy9GVlYWFixYYHTOlblyOPMerMUeHSIicnkGgw6nTj+DnoOcdgKnTj9j09Lz2NhYxMfHQ6FQIDs7GwcOHDB6PSUlBQMGDEBgYCCGDx+O6upqs9dqbW1FTk4OVq9eLQWNEyZMgEajga+vLx544AEcPHiwy/syMjKgVqsREREBIQR0uu7v45133sFnn32Gxx57rNt8mZmZCA4O7pL+6aefIjc3F0BbT157D565cjjzHqzFQEcGLS0tePLJJ/Hkk0+ipaXF2cUhJ2JbkBfrV16uVL9KpS9i71gJwNLDRRWIvWMllErfXn2uqcMD/P39O5RLidbWVrPvX7ZsGVJSUjBjxgyLr2/qM/R6vdnP+Oqrr/Dkk0+iuLjY6H2mmNvd2tyhrZaUw9H3YC0GOjLQ6XRYvXo1Vq9e3WMES56NbUFerF95uVr9ajQ/w6jYP6DnYEeBUbF/sHmp+alTp3D06FEAwI4dO5CammrTdd5++22UlJRg5cqVRun79+9HVVUVdDodiouLkZKSYtP1AeDy5cuYM2cOXnvtNWg0Gpuvk5ycjO3btwNoO26h4/CVKa54D+Yw0CEiIrfRc7DTuyAHABISErBmzRqMGTMG/fv3x/3332/TddavX4+KigokJiYiISEBL730EgAgNTUV+fn50Gq1mDJlCpKSkmwu6+rVq3HlyhXk5eUhISEBWVlZNl3n+eefR1FREbRaLVJSUlBTU9Ntfle8B3N4qCcP9SQZsS3Ii/UrL1c+1NP0xOTeBznkmnpzqCd7dIiIyO107dlhkEOmcXk5ERG5pfag5tTpZxB7x0qnBTk1NTVIT083SgsLC7PrJoPHjh3DvHnzjNK0Wq3JDfqsyetIzioXAx0iInIp1syo0Gh+hoiI+3q9uqo3BgwYgCNHjsj6GWPHjrX4M6zJ60i9KVdvZtkw0CEiIpfg6+sLhUKBa9euITw83OySZ9PML10m9yaEwLVr16BQKODra31Ay0BHBmq1GsePH5d+Ju/FtiAv1q+8HF2/KpUKkZGRqKysREVFheyfR+5DoVAgMjISKpXK+vdy1ZX9V10REZHt9Hq9S+zbQ67D19fXKMix5vc3e3SIiMilqFQqm/7lTmQKAx0ZtLS04Pe//z0A4Omnn4afn5+TS0TOwrYgL9avvFi/5Ak4dMUNA0lGbAvyYv3Ki/VLroobBvZg06ZNiIuLw7hx45xdFCIiIpKRVwY6jz76KMrKynD48GFnF4WIiIhk5JWBDhEREXkHBjpERETksRjoEBERkcdioENEREQei/voyCAgIACHDh2SfibvxbYgL9avvFi/5Als2kdn9+7d6NOnD1JTUwG0Ldf+y1/+gri4OGzatAn9+/e3e0HlwCMgiIiI3I/s++g8+eSTqK2tBQAcO3YMTzzxBKZPn45vvvkGjz/+uC2XJCIiIrI7m4auysvLERcXBwB46623MHPmTPz+97/Hl19+ienTp9u1gO6opaUFzz//PABgyZIl3Dbdi7EtyIv1Ky/WL3kCm4auQkNDceDAAcTFxSE1NRXz58/HwoULUVFRgbi4ODQ0NMhRVrvjERAkN7YFebF+5cX6JVcl++nlqampePzxx5GSkoJDhw7hjTfeAACcOXMGkZGRtlySiIiIyO5smqOzceNG+Pj44M0338Sf//xnDBkyBADw73//G1OnTrVrAYmIiIhsZVOPzu23345du3Z1SV+3bl2vC0RERERkLzb16Hz55Zc4duyY9Pe3334b9913H55++mm0tLTYrXBEREREvWFToLNo0SKcOXMGAPDNN98gJycHgYGB2LFjB37961/btYBEREREtrIp0Dlz5gwSEhIAADt27MDEiRPxt7/9DYWFhXjrrbfsWT4iIiIim9k0R0cIAYPBAAD44IMPMHPmTADAbbfdhuvXr9uvdG4qICAAe/fulX4m78W2IC/Wr7xYv+QJbNpHJy0tDbfddhsyMjKQl5eHsrIyREdH46OPPkJubi4qKipkKKr98QgIIiIi9yP7ERDr16/Hl19+iV/+8pf47W9/i+joaADAm2++ieTkZFsuSUTkVJWVldi7dy8qKyudXRQisiObenTMaWpqgkqlgq+vr70uKSu5enR0Oh02b94MAFi4cKHb1AfZH9uCvOxVv1u2bMHChQthMBigVCqxefNm5OXl2bOobontl1yVNb+/bQ50bt68iTfffBNff/01nnzySYSGhuLLL7/EoEGDpA0EXR2PgCC5sS3Iyx71W1lZiaioKGneIQCoVCpUVFR4/U7vbL/kqmQ/AuLo0aNIT09Hv379UFFRgf/6r/9CaGgoiouLcf78ebzyyis2FZyIyNHOnj1rFOQAgF6vx7lz57w+0CHyBDbN0Xn88cfxi1/8AmfPnjWaiT9t2jR8/PHHdiscEZHcYmJioFQaPwpVKpU095CI3JtNgc7hw4exaNGiLulDhgzB5cuXe10oIiJHiYyMxObNm6FSqQC0BTkvv/wye3OIPIRNQ1cBAQGora3tkn769GmEh4f3ulBERI6Ul5eHzMxMnDt3DtHR0QxyiDyITT069957LwoKCqDT6QAACoUCFy5cwFNPPYWf/vSndi0gEZEjREZGYvLkyQxyiDyMTYHO6tWrce3aNQwcOBCNjY2YNGkSoqOjERwcjP/3//6fvctIREREZBObhq5CQkJw4MAB/Oc//8GXX34Jg8GAu+66CxkZGfYunyw2bdqETZs2Qa/Xy3J9f39/7Nq1S/qZvBfbgrxYv/Ji/ZInsOuGge6GR0AQERG5H9n30QGADz/8EB9++CGuXr3aZQ+KrVu32npZIiIiIruxKdB59tlnUVBQgKSkJAwePBgKhcLe5XJrOp0Or732GgDgwQcf5LbpXoxtQV6sX3mxfskT2DR0NXjwYKxatQrz5s2To0wOwyMgSG5sC/Ji/cqL9UuuSvbTy1taWnhKOREREbk8mwKdhx9+GH/729/sXRYiIiIiu7Jpjk5TUxM2b96MDz74AFqttsu47dq1a+1SOCIiIqLesPn08oSEBADA8ePHjV7jxGQiIiJyFTYFOnv37rV3OYiIZFdZWYmzZ88iJiaGRz0QeQmb5ugQEbmbLVu2ICoqCmlpaYiKisKWLVucXSQicgCbenTq6+vxxz/+0eyGgd98841dCueu/P39UVRUJP1M3ottQV6W1m9lZSUWLlwoPasMBgMWLVqEzMxM9ux0g+2XPIFNgc7DDz+Mjz76CPPmzeOGgSb4+PjgZz/7mbOLQS6AbUFeltbv2bNnu/yDTK/X49y5cwx0usH2S57ApkDn3//+N/71r38hJSXF3uUhIrK7mJgYKJVKo2BHpVIhOjraiaVyb5zvRO7Cpjk6/fv3R2hoqL3L4jFaW1uxY8cO7NixA62trc4uDjkR24K8LK3fyMhIbN68GSqVCkBbkPPyyy/zF3QPzNUv5zuRO7HpCIhXX30Vb7/9NrZv347AwEA5yuUQ7VtIV1VVISIiQhqCa2lpgU6ng4+Pj9G4dH19PQBArVZDqWyLEXU6HVpaWqBSqRAQECDla982vba2FsHBwWbzAkBDQwOEEAgICJAexK2trWhuboZSqYRarbYpb2NjIwwGA/z9/eHj09Z5p9fr0dTUZFVehUJh9D03NTVBr9fDz89P2kPJmrwGgwGNjY0AYLSlfHNzM1pbW+Hr6ws/Pz+r8woh0NDQAAAIDAzs8n1ak9eS796SvI2NjVJbuHLlCkJCQnr87u3RTtq/z962k87fZ2/bibnv09Z2otPpjOo3KCio23bS3gsxZMgQDBkyxKrv3tZ2Yu77tCavs54RBoNBqt9r165BrVbj2rVrGDFihFHvmFKpxPnz56XAkc8Iy58Rtnz3fEb4WneEk7BQQkKCuPPOO6U/wcHBok+fPmLMmDFG6Xfeeaell3S6W7duCQACgLh69aqU/txzzwkA4uGHHzbKHxgYKACI8vJyKW3dunUCgJg7d66UVldXJ1330KFDUvrmzZsFAHHvvfcaXTcqKqpL3ldffVUAEBkZGUZ54+LiBACxd+9eKa24uFgAEMnJyUZ5k5KSBACxa9cuKW3Pnj0CgIiPjzfKO2nSJAFAFBUVSWkHDhwQAER0dLRR3unTpwsAYtu2bVJaaWmpACA0Go1R3qysLAFAbNy4UUo7c+aMACD69u1rlDc3N1cAEKtWrZLSKisrBQDh4+NjlDc/P18AECtWrJDSbty4IdV7S0uLlL506VIBQCxdulRKa2lpkfLeuHFDSl+xYoUAIPLz840+z8fHRwAQlZWVUtqqVasEAJGbm2uUt2/fvgKAOHPmjFFbACCysrKM8mo0GgFAlJaWSmnbtm0TAMT06dON8kZHRwsA4sCBA1JaUVGRACAmTZpklDc+Pl4AEHv27JHSdu3aJQCIpKQko7zJyckCgCguLpbS9u7dKwCIuLg4o7wZGRkCgHj11VeltEOHDgkAIioqyijvvffeKwCIzZs3S2nHjx8XAERYWJhR3rlz5woAYt26dVJaeXm5ACACAwON8j788MMCgHjuuee61G/nR9qSJUsEAPH0009LaR3fU1dXJ6U//fTTAoBYsmSJ0TXkeEYIIURYWJgAII4fPy6ludozomNdpaamCgBi+fLlXeq88+fxGdHGkmdEu40bN/IZISx/RrT//r5165boicVzdO677z5LsxIRkYcaPHhwl/lOADjfiVyWTUNXnoJDVxy6MpeXQ1eeOXTVm++eQ1dtQ1f+/v7Yvn07Fi1aBL1eD6VSiRdeeAH5+fndfp98RnDoyllDVzYFOocPH4bBYMD48eON0j///HOoVCokJSVZe0mnsGqMzwodA526ujqjL4u8C9uCvFi/8uqufisrK3Hu3DlER0dzUjc5nDW/v21adfXoo4/i4sWLXdIvXbqERx991JZLEhGRG4mMjMTkyZMZ5JDLsynQKSsrw1133dUl/c4770RZWVmvC0VERERkDzZtGOjv748rV65g+PDhRunV1dXSmJw38/Pzw7Zt26SfyXuxLciL9Ssv1i95Apvm6OTk5ODy5ct4++230bdvXwDAzZs3cd9992HgwIHS2SiuTq45OkRErs5g0EGp9HV2MYhsIvscnTVr1uDixYuIiorCT37yE/zkJz/BsGHDcPnyZaxZs8amQhMRkWNUVe3Avo/Goqpqh7OLQiQ7m8aZhgwZgqNHj+K1117DV199BbVajV/84hf4+c9/Li0R82atra147733AACZmZkczvNibAvyYv1ar6pqB06eWgZAfP9fQKMxfXAn65c8AffR4fJykhHbgry8oX7tOcTUMcj5gQKjYv9gMtjxhvol92TN72+Lw/OdO3di2rRp8PX1xc6dO7vNO3v2bEsvS0REZlRV7cCp088g9o6VZntdrLlWe5Bz7VorLlXqMCTSF+HhPj327BC5M6uOgLh8+TIGDhzY7XEQCoUCer3eHmUjIvJa1gwxWXOtf79bi3XrrsNgAJRK4LHHwjBtegiDHfJYFk9GNhgMGDhwIHQ6HSZOnIiTJ0/CYDB0+cMgh4iod7oOMbUFO7ZMHu7ck9Me5ACAwQCsW3cd16619uoziFyZ1auufH19ceLECelsDCIisp/OgcmR0kabAxGDQYdTp59Be8B0qVKHTmdxwmAALl3Sff83gVOnn4HBoAORp7Bpefn8+fPx17/+1d5lISLyap2HmB6cewFLl1bjwbkX8O93a2FtsKNU+iL2jpUA2g6kHBLpC6Wycx5gyJD2yc4KxN6xkvvrkEexaa1gS0sL/vrXv+L9999HUlJSl5n4a9eutUvhiIi8hSVDTEnjAq2ePNye5+SpZQgP98Fjj4V1maMTHu6D7lZfEbkzmwKd48ePS2ddnTlzxui19qPsvZmfnx82btwo/Uzei21BXp5Sv9YMMbUFJW1DTBER91nU+9Ix2Jk2PQRJ4wJx6ZIOQ4b4dhvkeEr9knfjPjo8AoKIXEDnHp0H514wCnaUSuC1v93eq94Xa/fRIXJVsh8BQeSWDHqgfD9w7M22/xq4QpBch0bzM4yK/QMAhTTE1D6fxl5DTB0/ow2DHPJ83M8bbbt/BgcHS8NuLS0t0Ol08PHxgb+/v1E+AFCr1VB+/wTS6XRoaWmBSqVCQEAAAECv12PPnj0AgIyMDOlYDFN5AaChoQFCCAQEBEir2VpbW9Hc3AylUgm1Wm1T3sbGRhgMBvj7+0tbt+v1ejQ1NVmVV6FQIDAwUMrb1NQEvV4PPz8/6d6syWswGNDY2AgARvO7mpub0draCl9fX6mb3Jq8Qgg0NDQAAAIDA42/z6P/hO9/lsOvofqHvAGDgXtWIjDxZzZ995bkFUJg//79aG1txY9+9CP4+fn1+N3bo520f5+9bSedv8/ethNz36et7USlUmH//v0QQiAxMREqlap37aSb79OavD09I8zlDQ+/Dw0NTTh7boXREFNYmAoDBvhArwfGjG4LTGx9RrQHNadOP4Oo259B377T0draavIZ4efnh/379wMAkpKSoFAoHPqMuHTpEi5duoSYmBhERkbK+4zQ6ezSTqx9RvTUTviMMJ/XYsKL3bp1S6CtD1dcvXpVSn/uuecEAPHwww8b5Q8MDBQARHl5uZS2bt06AUDMnTtXSqurq5Oue+jQISl98+bNAoC49957ja4bFRXVJe+rr74qAIiMjAyjvHFxcQKA2Lt3r5RWXFwsAIjk5GSjvElJSQKA2LVrl5S2Z88eAUDEx8cb5Z00aZIAIIqKiqS0AwcOCAAiOjraKO/06dMFALFt2zYprbS0VAAQGo3GKG9WVpYAIDZu3CilnTlzRgAQffv2Ncqbm5srAIhVq1ZJaZWVlQKA8PHxMcqbn58vAIgVK1ZIaTdu3JDqvaWlRUpf+ov7BACx9G4/cfGxPuI/8wPFN4uDpLw3Pn1NyrtixQoBQOTn5xt9no+PjwAgKisrpbRVq1YJACI3N9cob9++fQUAcebMGaO2AEBkZWUZ5dVoNAKAKC0tldK2bdsmAIjp06cb5Y2OjhYAxIEDB6S0oqIiAUBMmjTJKG98fLwAIPbs2SOl7dq1SwAQSUlJRnmTk5MFAFFcXCyl7d27VwAQcXFxRnkzMjIEAPHqq69KaYcOHRIARFRUlFHee++9VwAQmzdvltKOHz8uAIiwsDCjvHPnzhUAxLp166S08vJyAUAEBgYa5X344YcFAPHcc891qd/Oj7QlS5YIAOLpp5+W0jq+p66uTkp/+umnBQCxZMkSo2vI8YwQQoiwsDABQBw/flxKa39GZGYmiQ8+HCE++HC4+ODD4WLQoLb2969//V7K29tnhF7f0uMzomNdpaamOvwZ0f5HqVSKv/71r/I9I5YubXtGLF0qpbW0tPzwjLhxQ0q39zOi3caNG/mMEJY/I9p/f9+6dUv0xCuHrjZt2oS4uDiMGzfO2UUhuRn0QPnHAIBjV/WIWl+HtFcaMGJD/Q95PnyWw1jkUgIChnQaYmoTHp5ht89w5SXkly9fNvq7wWDAokWLpJ4QImtwMnLfvqiqqkJERITduqU7HoRXW1uL4OBgs3kBDl21k6Vbunw/WrbOQMVNA2I31htNwVQCOPnLIMSEKqFY8C9g2AS7d0s3NjZKbeHKlSsICQnxmm7phoZGXDpdg5YmgX6hfTA4ph8AYdehK51OZ1S/QUFBbj101Tlv+1lXUbf/DyIi7nf4M8JgMEj1e+3aNajVaoc8Iz788ENkZHQN6t577z2kpKRw6MpDnhG9GbqyZjIyAx2eXu7Zjr0JvJWHveWtSHulocvLe3MDMXmoD/DTLcDYLLt/vLe2ha9Lr2L/G2dRf7NZSgvq548Jc2Iw4s6Bdvscb6hfe55ebi1n1W9lZSWioqJg6LDsTKVSoaKiApGRkQ4pA7k2rroiatdnEAAgZoASyk5bPKkUQHSo0igf9d7XpVex++XjqL/ZjBt113DmUilu1F1D/c1m7H75OL4uversIroVVx5ikktkZCQ2b94s9TSoVCq8/PLLDHLIJlx1RZ4tKhkI0SAS1dg8MwCLdjVBL9qCnJdnBiAyRAWEaNryUa8ZDAL73zgLAPjk1Lv4+8frIIQBCoUSP5/4GJJjp+NA0VkMiw+HsnPkSdRBXl4eMjMzce7cOURHR3tlkFNZWYmzZ89Kq87INgx0yLMpVcDU/wWK5iPvLn9kRvvg3LcGRIcq24IcAJj6x7Z81GvVZ29KPTntQQ4ACGHA6x+vw6jIcQDCUX32Jobc0d+5hSWXFxkZ6bW/4Lds2YKFCxfCYDBAqVRi8+bNyMvLc3ax3BIDHRn4+vpi1apV0s/kZHGzgexXgN2/QSSqEBny/XBViKYtyImbLdtHe1tbqK9tm5Nz7ValFOS0MwgDrtVeQv8+4VK+3vK2+nU01q9zVFZWSkEO8MOqs8zMTK8N/HqDgY4M/Pz88OSTTzq7GNRR3GwgdgZw/hOg7krbnJyoZNl7crytLQSFtK0sCe8bCYVCaRTsKBVKhIcMMcrXW95Wv47G+nWOs2fPGk3EBtpWLp07d46Bjg04GZm8h1IFDJvQtrpq2AQOV8lgcEw/BPXzR/8+4fj5xMegVLQ9YpQKJXImPob+fcLRp7//90vNiciUmJgYadl5O5VKhejoaCeVyL2xR0cGer0eX375JQDgrrvuklYOkPfxtragVCowYU4Mdr98HMmx0zEqchyu1V5CeMgQ9O8TDgBIzY6x20Rkb6tfR2P9Okf7qrNFixZBr9dz1VkvcR8d7qNDMvLWtmBqH50+/f2Rms19dNwJ69e5KisrvXrVWXes+f3NHh0isrsRdw7EsPi21VX1tc0ICmkbruKSciLLefOqM3tioENEslAqFVxCTkROx8nIRERE5LEY6BAREZHHYqBDREREHouBDhEREXksTkaWga+vL1asWCH9TN6LbUFerF95sX7JE3AfHRn20SEiIiL5WPP7m0NXRERE5LE4dCUDg8GAkydPAgBGjRrV5cwS8h5sC/Ji/cqL9UuegENXPAKCZMS2IC/Wr7xYv+SqOHRFREREBAY6RERE5ME4R4fIixgMggdtEpFXYaBD5CW+Lr2K/W+cRf3NZiktqJ8/JsyJwYg7BzqxZERE8uHQFZEX+Lr0Kna/fBz1N5txo+4azlwqxY26a6i/2YzdLx/H16VXnV1EIiJZsEeHyMMZDAL73zgLAPjk1Lv4+8frIIQBCoUSP5/4GJJjp+NA0VkMiw/nMBYReRwGOjLw9fXF0qVLpZ/Je7lCW6g+e1PqyWkPcgBACANe/3gdRkWOAxCO6rM3MeSO/k4po61coX49GeuXPAEDHRn4+fnhT3/6k7OLQS7AFdpCfW3bnJxrtyqlIKedQRhwrfYS+vcJl/K5E1eoX0/G+iVPwECHyMMFhfgDAML7RkKhUBoFO0qFEuEhQ4zykWkGgwHnz59HXV0d+vTpg6ioKO4UTOQGGOjIwGAw4MKFCwCA22+/nQ9DL+YKbWFwTD8E9fMHEI6fT3wMr3+8DgZhgFKhRM7Ex9C/Tzj69G9bau5uHFW/J06cwL/+9S80NDRIaSEhIZg6dSri4uJk+UxX4Artl6i3eAQEj4AgGblKW2hfdQUAN+qu4VrtJYSHDEH/PuEAgKmLxrjlEnNH1O+ePXvwySefAGh7ZtTU1GDAgAHSMyM7O9tjgx1Xab9EnVnz+5s9OkReYMSdAzF10ZjvV1+FSwFOn/7+SM3mPjrmHD9+XApyPvnkE3zwwQcQQkChUGDmzJm46667sHv3bsTGxrK3g8hFMdAh8hIj7hyIYfHh3BnZQgaDAe+++y4A4ODBg/jggw+k14QQ2LVrF6KjowEA58+fx7Bhw5xSTiLqHgMdIi+iVCrcbgm5s5w/fx4NDQ2ora01CnLaCSHw7bffIiQkBHV1dU4oIRFZgn2tREQmtAcvNTU1Jl9XKBQIDQ0FAGkeCxG5HgY6REQmtAcvAwYMgELRdXgvIyMDISEhCAwMRFRUlKOLR0QWYqBDRLIzGAQunb6BM4cv49LpGzAYXH+xZ1RUFEJCQhASEoKZM2caBTsZGRlITk4GAMyYMYMTkckpKisrsXfvXlRWVjq7KC6Nc3Rk4OPjg/z8fOln8l5sC/Kemi5n/SqVSkydOhVFRUW46667EB0djW+//RahoaHSctbk5GSMHj3arp/rSth+XdeWLVuwcOFCGAwGKJVKbN68GXl5ec4ulkviPjoy7KNDRG267N9zqxLhfSPdav+esrIy7N69G7W1tVJaYGAgpk+fjjFjxjixZOStKisrERUVBYPhh13OVSoVKioqEBkZ6cSSOQ730SEip/OUU9Pj4uIQGxvL4x/IZZw9e9YoyAEAvV6Pc+fOeU2gYw0GOjIQQuD69esAgLCwMJMTGcnFGPTA+U+AuitAn0FAVDKgVPX6st7cFhxxarqj6lepVHrlPjne3H5dmblVfty52jQGOjJoaGjAwIFt3fHcNt0NlO0Edv8GqK36IS1EA0z9XyBudq8u7c1twRGnpntz/TqCveu3srISZ8+eRUxMDHseesHcvk319fUOLol7YN8rebeynUDRfKC2CpW1Buwtb0VlrQGorW5LL9vp7BK6rc6npnfEU9O9z5YtWxAVFYW0tDRERUVhy5Ytzi6S24qJiekydKpSqaSduskYAx3yXgZ9W08OBLZ82YKo9XVIe6UBUevrsOXL73sZdj/Vlo+s1n5qev8+baemK78Pdjzh1HSyTmVlpbRCCGg7XmPRokVcFm2jyMhIbN68GSpV2/C6SqXCyy+/zF4yMzh0Rd7r/CdST87CXU1o39rFIIBFu5qQGe2DSFxqyzdsgnPL6oaUSgUmzInB7pePIzl2OkZFjutyanpqdoxLT0Qm++DkWfvLy8tDZmYmzp07h+joaNZjNxjokPequwIAOFtjQOf96/QCOPetAZEhSikfWY+nphPww1BL5+XQHGrpncjISAY4FmCgQ96rzyAAQMwAJZQKGAU7KgUQHao0ygdAttVZnoynplP7UMuiRYug1+s51OJFXGECOgMd8l5RyUCIBpGoxuaZAVi0qwl60RbkvDwzAJEhqrbVV1FtW/3LuTrL0/HUdOJQi/dxld2bGejIwMfHB7m5udLP5KKUqrYgpWg+8u7yR2a0D859a0B0qLItyAGAqX9sy9e+OgsClbUGnK0xIGaAEpH4fnVW9ismgx22BfszGITUO+QTAMyfnwuFgvUrB3u3Xw61eA9zE9AzMzMd3gZ4BASPgCCTPTVD2oKcuNltw1XrxwC1VdjyZYs0cVmpADbPDEDeXf5tPTu/OuYSw1gdAwFPGyaS89wsIrKfvXv3Ii0tzWT65MmTe319HgFBZI242UDsDPNzb9xodZa7BwLdBWnmzs0CwrH75eNucW4WeSdXmKfiaK40AZ2BjgyEEGhoaADQdvgft013A0qV+SClF6uzHNkW3D0Q6C5IGxYfbvLcLECB7NT/xviRU7D/jTMuf26Wu+GzrPdcZZ6Ko7nSBHQOXckwdFVfXy+dRcJt6T1A+X5g+0xU1hoQtb6uy+qsil/1aQt0cnd1CZYc1RYMBoGtS/ejuaHV7AGaffr7Y97/S3bJQKCnU87HzRqKw+9U4EbdNSz/29wuR0oAwJqHdmHOr5M56dlGBoOhy8GljY2NfJb1Ak8Zb6sDOSagc+iKyJ6sXZ3lBCXvVqC5oRU36q7hbx+tBdAWjdnzAE25WHLK+dH/tO2ga+rcrI56c26WNysrK8Pu3btRW1srpYWEhGDixIlOLJX740aJrjEBnYEOUU+sWZ3lBAaDwFd7LwIA9h17C+1BjvS6nQ7QlIslp5z3R1vPTvu5WeaCHZ6bZb2ysjIUFRUBaPtXck1NDQYMGAAA+Mc//uHMork9V5qn4s141hWRJeJmty0hDxmMyBAlJg/1aRuuCtGYXVruKNVnb6K5vq035z9H3+zyugIKpx+gaTAIXDp9A2cOX8al0zdg6DD+Z8kp5wDgH+TT5dwsBX4YhuvTj+dmWctgMGD37t0AgC+//BLr16/HK6+8gvXr1+PLL790cuncH8+kcg3s0SGyVE+rs5zEKFBA1yl3afE/Q/8+4fAP8nFKINDTSrDOp5x3DHY6nnIe/5PbcGhXudG5WX0DB2DlGwsAAHf/dIRLzj9yZefPn0dtbS1qa2uxa9cutE/ZFEJg165diIqKcnIJ3R83SnQ+BjpE1uhudZaTdBcoKBRKTB7zAABAmxbp8EDAkpVgw+LDEdTPH0Bbb83rH6+DQRi6nHKeOH0oQocEGZ2b1axrlD5reLzrrihzVXV1dQCAmpoadF6XIoTAt99+64xieRxXmKfizRjoELm5wTH9egwUAoJ8kTRtmEPLZckk4wNFZzEsPtziU847n5ul8GnFE1sdelsepX1F1YABA6BQKIyCHYVCgdDQUGcVjchuPCLQuf/++7Fv3z6kp6fjzTe7zlFwNJVKhaysLOln8l6OaAtKpaLHQGHy/3eHw3tzLJlk3L4SzJpTzjuem9XU1MT/13ohKipKWpo7c+ZMafhKoVBg5syZ6Nu3L7RaLWJiYli/5LY8Yh+dvXv3oq6uDtu3b7cq0OEREORJTM2FMRUoOMqZw5fx/pYynLlUig27lnZ5ffGsNRipScA9eXEYOS4CgGcfX+GqOq+6+vbbbxEaGio9E7OzsxEXF+fMIhJ14XX76PzkJz/Bvn37nF0MIqfqPKzj7EDB0knGHVeC8ZRzx4uLi0N2dra0+qr9l0ZISAimTp3KIIfcntMDnY8//hh/+tOfUFJSgurqahQXF+O+++4zyvPiiy/iT3/6E6qrqzF69GisX78eEya41oRQIlfgSoGCJXOH+vTnknBXEBcXh9jY2C47IyuV3IGE3J/TA536+nrEx8fjF7/4BX760592ef2NN97Ar371K7z44otISUnByy+/jGnTpqGsrAy333673coQHBwsnePS0tICnU4HHx8f+Pv7G+UDALVaLT0AdDodWlpaoFKpEBAQIOVrn+RXW1uL4OBgs3kBoKGhAUIIBAQESOPgra2taG5uhlKphFqttilvY2MjDAYD/P394ePT9lXr9Xo0NTVZlVehUCAwMFDK29TUBL1eDz8/P/j6+prNW369Ht/eqoPeoIev7w95DQYDmpoaEeTng9FRPwypNDc3o7W1Fb6+vvDz85PyNja2razpuP28qbzmzuVp/z6tyWvJd29J3o5b6F+5cgUhISE9fvf2aCft32dv20nn796avC0tzUicrcHebWeluUNXb11Ev6Bw9AsKA/DDJGNTbcqS716n0xnVb1BQUK/bibnv09Z2Yu77tCavo54Rw4YNk77P5uZmGAwGqX6vXbsGtVpt12eEubzW/H/v7s8IW757T3lGWPPdm8prMeFCAIji4mKjtB/96EfikUceMUqLjY0VTz31lFHa3r17xU9/+tNur9/U1CRu3bol/bl48aJA2zay4urVq1K+5557TgAQDz/8sNH7AwMDBQBRXl4upa1bt04AEHPnzpXS6urqpOseOnRISt+8ebMAIO69916j60ZFRXXJ++qrrwoAIiMjwyhvXFycACD27t0rpRUXFwsAIjk52ShvUlKSACB27dolpe3Zs0cAEPHx8UZ5J02aJACIoqIiKe3AgQMCgIiOjjbKO336dAFAbNu2TUorLS0VAIRGoxFCCPHNtToR9ZtdIvCOFAFAhN7ziIj6zS4R9ZtdQvNfLwsAQuEfJL65ViddIzc3VwAQq1atktIqKysFAOHj42NUhvz8fAFArFixQkq7ceOGVO8tLS1S+tKlSwUAsXTpUimtpaVFynvjxg0pfcWKFQKAyM/PN/o8Hx8fAUBUVlZKaatWrRIARG5urlHevn37CgDizJkzRm0BgMjKyjLKq9FoBABRWloqpW3btk0AENOnTzfKGx0dLQCIAwcOSGlFRUUCgJg0aZJR3vj4eAFA7NmzR0rbtWuXACCSkpKM8iYnJ3f5f2/v3r0CgIiLizPKm5GRIQCIV199VUo7dOiQACCioqKM8t57770CgHjuf1aLbb85IDYu+lD89mdbBAARrO4nzn15Rco7d+5cAUCsW7dOSisvLxcARGBgoNF1H3744bbrPvdcl/rt/EhbsmSJACCefvppKa3je+rqfmh/Tz/9tAAglixZYnQNOZ4RQggRFhYmAIjjx49Laa72jOhYV6mpqXZ9RrTLysoSAMTGjRultDNnzggAom/fvkZ5PfEZ0W7jxo1e+4zYvHmzlHb8+HEBQISFhRnl7fyMuHXrlgAgbt26JXri0v2SLS0tKCkpwZQpU4zSp0yZgk8++cTq6/3hD39A3759pT+33XabvYpKndQ3txr9Xd9Uh6bzR9Fae73bfOR5Bt4egvm/T8Z9j92Jux8YAaBtl2NXPkmdiDyHS626UigURnN0qqqqMGTIEBw8eBDJyT8cmPj73/8e27dvx+nTpwEAmZmZ+PLLL1FfX4/Q0FAUFxdj3LhxXa7f3NyM5uYfVqTU1tbitttuQ1VVFSIiIjh0Zcehq+OXbmHmCwcgWlvw3Vfv48YHf27/ktF/yi/RJ24SAODdJzIwZkhf6fvxtG5pbx66srRbuqWiAvXfftuW19fXOG9TE1RBQejfYUIsh64c94zg0BWHrjrmdaWhK49bddXewNqJ7/d5aPfee+9ZdB1/f3+jxtYuKCjI6Hp+fn5SY++crzPfDg9mUzpO5jOXt+MX387Hx0dqJLbm7dhQ26lUKpP3YU3ejv9j9ZRX31CLGx++9EOCELixZxMChyfCJyTMKK+p70epVJq8rqm8CoXCZF5T36c1eQHT3701edvTO9edqe/THu3E1PfpCu2k8/fZUlGBr6dO65Kvs6Dd/4bf0KEAjL97nU73Q55OQU7nvO0c/d2b+z6tyeusZ0T7L+T2vJ3LbI9nhCXtpJ0nPyOs+e696RnRXV5LufTQVVhYGFQqFS5fvmyUfvXqVQwaNMhJpSJrtN6oAjp3GgoDWm9WOadA5FL0HX6RAsBlnQ6fN9TjcocAxlQ+IiJLuXSg4+fnh8TERLz//vtG6e+//77RUBa5Lp/+GqBTjxwUSvj00zinQOSy3rp5ExnffI1fXLyIjG++xls3bzq7SETkAZw+dFVXV4dz585Jfy8vL8eRI0cQGhqK22+/HY8//jjmzZuHpKQk3H333di8eTMuXLiARx55xIml7p5KpcL06dOln72ZT0gYQjP/G9++txEQBkChRGjmL7sMW3kqV2kLLRUV0NfXo7WqGvqmxi6vKwPU8NUMhiooSBoicqTLOh1WXLmM9i0FDQBWXLmMlKAgRHQzNOwq9eupWL/kCZwe6HzxxRf4yU9+Iv398ccfBwDk5uaisLAQc+bMQU1NDQoKClBdXY0xY8bg3XffRVRUlLOK3KOAgAD861//cnYxXEZw/BSoh92F1ptV8Omn8ZogB3CNtmDpPJh2IzrMh3GU87oWGDqlGQBc0LV0G+i4Qv16MtYveQKnBzqTJ09GTwu/8vPzkZ+f76ASkT0E+Rs3LZ+QMJMBTud8ZH+m5sGc17UgUKFEgzAgytfPKJgwNx+mvVfInN70BkX5+kEJGAU7SgC3+3adyElEZA3+liFZDAsLwt6lk7vdJyfI3wfDwkyvUCJ5vHXzptEQEdAWUDw7KAI/7dfP7Pss7RWytTcowtcXzw6KkMrWXqbuenOIiCzBQEcG9fX1GDiwbTO0q1evml1u7OkYxLhWW+g8D6adJfNhzPUKWdobZImf9uuHlKAgXNC14PZO1zXHlerXE7F+yRN4ZaCzadMmbNq0CXq9XrbPaN9oishV2oKpeTDtLJkP065jr5AlvUHdUXX6xRnh62uyDPqbN1F38CBav/3WKL2+qUmq37pPP0VQRoZN5SDzXKX9EtnKKwOdRx99FI8++qi0syKRNzA1D6adpfNhbF0dZY7f0KG4bctfcTHv4W7zmXu9wfDD3VTmP4qgbVvRJyXF6nIQkefyykCHyBt1ngfTzpr5MLaujuqOqlNvkLlhsY6ONTaipLEBo/1/2C1VGRKJ5gu34De0rkt+hb8KvmFdd2wlIs/HQIfIi3ScB6NWKNEoDBbPhwHkXx1lybDY09VV+GdtbZf3Bk38DRq/UqPxq1KT1x60NMklgx3d9UaIZvPD6AzSiHqHgQ6Rh7N0Hoy5/J3fK9fqKEuGxY41NpoMcjqqrr2K8huVGNY/EoNDfjghvbtgwll01xtxZfUXPeZz1SCNyB0w0CHycH5Dh2LE7n/bbWdkW1ZHWcKSYbGSxu4nxr7+1S785r3VMAgDlAol/jdzKXLiZwIAdFeN3+sKPSWdgy93CtKI3AUDHRkolUpMmjRJ+pm8l6u0BSl4GT3apvdb2ivUXW9QTywZFktUdz2dGQDGDBqJK3XXpSAHAAzCgKfeW41Jw36EwSEDceON013e50o9JZYGaZYEaPYaDnOV9kvUGwx0ZKBWq7Fv3z5nF4NcgKe0hY69Qub09pwsS4bFxqrVuC8kxGj46v6I4diQ+1d8cv5LKchppxcGVNy8BAAu3VNSXXvVqiCtuwDNnsNhntJ+ybsx0CEiizji/CtLhsV+P1iDn/frj1K9Cilpv0GCZhQAYFj/SCgVSqNgR6VQ4qvqU8h5/TGTPSWuovxGpckgreTScYTe6GdVgMbhMCJjDHSI3JCc5045mrWTpYG2np34vrcj6Psgp91/Jf0MfzlcBAMEVAolnpq0CH/46GWzPSWuwlSQpoAC+TufhYCwOUDrbjiMPFNlZSXOnj2LmJgYREZGOrs4LoGBjgzq6+sx9PtfMhUVFdw23YvJ0RbkPnfK0SwdFmu5dMloZ2R9nQo1hxtx90vZaG5tQYOu6fugQIFHxuXgocQssz0lFTcvuVSgMzhkIP43cymeem819N8HJUIICLQdeGxLgNbTcJgl+CxzL1u2bMHChQthMBigVCqxefNm5OXlObtYTueVgY4jjoC4fv26bNcm92LvtmDruVP27gWy5/Usydc5T8ulOtQc/gTfNt4ySjcIgb8cLsJDiVlmh7OG9htiUbkcKSd+JiYN+xEqbl7C9fobyN/5O6PXrQ3Q7BXk8VnmHiorK6UgBwAMBgMWLVqEzMxMr+/Z8cpAh0dAkKew9Nwpe/cCuXqvUvsv9Ltvv9Oop0SlUOKPmUtdpjdH4a8y+vvgkIEYHDIQ1bVXex2guVOQ583sNdR09uxZKchpp9frce7cOa8PdLhekMhNmdtg77JO1yWvqV6gzxvqu+S19PRxe1/P3jr+Qs+Jn4lPHylC0c+fx6ePFLnUHBXfMDUGLU3CwP++EwP/+070n3MHgB+GslSKtke0LQGaPa5B8tqyZQuioqKQlpaGqKgobNmyxeZrxcTEdNkCQKVSITo6urfFdHte2aND5AlsPXfKnqePy3E9WymhkCYhd/6F3t5T0lnnHhVnMLfEu+NQ1tB+Q2wKUOxxDZKHvYeaIiMjsXnzZixatAh6vR4qlQovv/yy1/fmAAx0iNyWLedO2fv0cXtfrzf2Pvx/uFJfY9Ev9NB5o+A7KMhlNgtsZ24oq6d89r4GyU+Ooaa8vDxkZmbi3LlziI6OZpDzPQY6RG7KlnOn7H36uBynmVuq8y/qiOBwDB9wu1GauT1kfPoFuFyQA/wwlNWbXY3tcQ2SX/tQU8dgxx5DTZGRkQxwOmGgIwOlUomkpCTpZ/JecrcFa8+dsvfp43KfZt4d3zA1Ih5LQuJ/7gIADMq/Ez7fCWkXYXfdQ8YeAYi9ghg+y+TDoSbHYaAjA7VajcOHDzu7GOQC5GgLvTl3yt6nj8t5mrklQm4LxRdHSqS/t1yqA2CfPWQcyV5nU9kbn2Xy4lCTYzDQIXIzvT13yt6nj8t1mnlvuMtGgYB9z6Yi98OhJvkx0CFyQ9buTWPv08cdcZp5b7jTHjI8m4pIXgx0ZNDQ0IC4uDgAQFlZGQIDA51cInIWV2kL9j593BGnmVuic/22P9A6H6ngLnvIuNq8Ildpv0S9wUBHBkIInD9/XvqZvJcrtQV7Bx2ucI5Wd/XrbnvIuOK8Ildqv0S2YqBDXqn8ej3qm1vNvh7k74NhYTzA0N248x4y7jSviMideGWg44hDPT2dOwcK5dfr8ZPV+3rMt3fpZJe9B0ez94GgcrHHHjLOWgHlTvOKiNyJVwY6PNSzd+QOFOQOojpfu7X2OlpvVMGnvwY+IWFm8zmCKwYUrn6AZ2e9CUKcuQLKXecVuRJ7HZBJnsUrAx3qHTkDBUf3tnz31R58+94LgBCAQoHQzP9GcPyUXl/XFq4aUJg6wPO8rgVRnZaSO+sAT3tyxgqo1ptN0s/uNq/IlWzZskU6O0qpVGLz5s3Iy8tzdrHIBTDQoV6xd6DgyN6W1trrP5QdAITAt+9thHrYXUaf5SjuEFC4ygGejuCIFVC664349v9OGqW507wiR7Ckl8beB2SSZ2GgIwOFQiEtyVQoFE4ujXx6ChTOXa3r1TCT3L0trTeqfih7O2FA680quwU6trYFVwwoXOkAz3Zy/b/mqBVQlvYghc4b5ZTNAp39LLO0l0aOAzLJczDQkUFgYCBOnDjh7GLIrqdA4VdvHAFg2zCTI3pbfPprAIXC+B4USvj009jl+oBtbcEVAwrAuQd4miPX/2vOWAHVXQ+ST78AWT6zJ858llnTSyPXAZnkGXhKG9lMChSMKKDwMX4o2zLM1F0QZS8+IWEIzfxvQPH9/wYKJUIzf+mUYauOugsonKn9AM+OHHWApxx01xvRcqnO6I/uagOAH1ZAdSTnCihzPUjVtVdl+Tx30F0vTWftB2SqVG3Dezwgkzpijw7ZrD1Q+Pa9jYD0r1+By68+0ethJkf0tgBAcPwUqIfd1dYL1U/j9CAHcO6J4N1x9gGe9tTT6ipHr4DiHjpdWdtLwwMyyRwGOjJoaGjAuHHjAACHDx/26G3Tg+OnwC98KC7/3xMA7DfM1CWIsmNvS5C/cbP3CQkzed3O+WxhS1tw5YDC1Q7wNFW/luyDY8ncGEeugHLVPXSc+Sxr76VZtGgR9Hq9Rb00PCCTTGGgIwMhBMrKyqSfPU3nAEDomiAFOVJi7yf1ytXbMiwsCHuXTnbIhoe2tgVXCihc+QDPjvXbfOk7oKWhyyomU0LnjZJ+7m5ujKNWQLnqHjrOfpaxl4bsgYEOWa09UPjq4k386o0jdh1mclRviyvueGxpQNFaVY1GOG7zQFc5wBPouqFi3ZXvpJ+v/fkrBPoZr0wyuw9Oi0F6vbvVVf3n3AHfgca9GHLtjMw9dExjLw31FgMdssmwsCCpR8Sew0yO7G1xNR0DCl1VNS7993+bzFfZId1Rmwe6wo7HpjZUbOpjHEx3DGw+Kj/U4z44Pc2N8R0YCL8hfeS5Ibj32VxE7oKBDtmFPYeZPDGIsZS5gMIVNw90NFMbKp797ob09zeP/RvPfLgBBmGAAm2rAcX3Q6rm9sFx9twYe5zNRe6Jx1U4DgMdspkjJ/V6gpbz56HsZp6DueEfV9w80Nk61km7//ngeSmwEZ3njMH0KiZXmBvDIMb78LgKx/LK30A8vdw+vHmYyRbf3Hc/ApXdb13VeSjKVTcPdKbOddLOVHDTkbmeGs6NIUficRWO55WBjtynlysUCkRFRUk/ezIGMd1rbwtCp0PHlmDpUJQr7kbsbKbqxBQFFFAoFDCY6KlR+BkHnJwbY5o3PcschcdVOJ5XBjpyCwwMREVFhbOLQS6gvS00njiBip9mAbBuKMpRmwd2Xs3UmaNWVVnCVJ2oFEo8NWkR/vjRy0ZDUOZ6anz6BXBujAX4LLM/HlfheAx0iBzI2qEoR2weaGo1kymOWuHVk8510h7U5MTPxL2j0rsENuZ6arw9iCHnsGUjROodBjpEDmTLUJTcmweaWs3kiiu8dFXV0s/tdXJRFYRRGcuNghpTgU3ovFHSwZgMcryLK65u4kaIjsVARwaNjY2YOHEiAODjjz+GWs2HqrdqbwuGxkb81WCweCjKWbsRWzOs5sjhrpaKii77CkX4+qKfoQnzty8AFEq8OmocApQqRG58Ab6aH/bXYWBjO2c9y+wVnLjy6iZuhOg4DHRkYDAY8MUXX0g/k/cyagsxIy0einLGbsTWDKs5erjLXK/TQJUPjjfUAQBab12EZtMmBI0b2evPozbOeJbZKzjh6iZqx0CHyMEsHYoyFyC096To6+vReOJEl9dtDYCsGVZz5nBXx16nzuuAfDSD7f555Dj2DE64uonaMdAhcgJbh6Lk7EmxdYWXIzc07Nzr5HlH5no3ewYnXN1E7RjoEDnI8H8WI8CGnZE7krMnxZYVXo7e0NDSPXTIPdkzOOHqJmrHQIfIQfyioqC24+Rhe/SktFRUoPmbb6S/W7vCy9EbGprqdSLPYe/ghKubCGCgQ+SW7NGTYm4YzJphNUdtaNixbB17nRTg8JWnsXdw4szVTa64tN0bMdCRSViY7ad3k2eRoy3YoyfF0mGwwX9ahcCxY00OqzliQ8POOvY6hat88P9dvCDbZ5FznmWesPTalZe2exsGOjIICgrCtWvXnF0McgFytQV796R0NwzmP3x4t3OH5N7QEOh+X6GD0TFm81HvOPJZ5km9H1za7loY6BC5IXv2pNgyDOboDQ2dsa8QOY6n9X5wabtrYaBD5Kbs1ZNiyzCYMwIPBjGeyRN7P7i03bUw0AFQX1+P4OBgKBRt24+1tLRAp9PBx8cH/v7+RvkAQK1WQ6lUAgB0Oh1aWlqgUqkQENB2lk5jYyOmTJkCANi9ezeCvv9Xram8ANDQ0AAhBAICAqBSqQAAra2taG5uhlKpNNp23Zq8jY2NMBgM8Pf3h49P21et1+vR1NRkVV6FQoHAwEApb1NTE/R6Pfz8/OD7/S9Ca/IaDAY0NjYCgFQ3ANDc3IzW1lb4+vrCz8/P6rxCCDQ0NABoO3W58/dpTV5LvntL8jY3N2PatGkwGAz4xz/+gT59+vT43XfXTpoVCuiFgOr78ob5+CBEpYISxlpUKhjq6y1qJxEqny6TehUAwlU+aOz0r9KO7aQ98Ojuu6+vr7fou7e1nej1ekybNg1CCLz11ltQq9W9bifmvk9b24m579OavM56RgDAtGltE9b/8Y9/wN/f3+7PiBMnTpjs/Th27Bj69+/vls+I9tVjHXupOq4e6+13b4920v7d97addP4+e/u7xJpnhMWEF7t165ZA2/NdXL16VUp/7rnnBADx8MMPG+UPDAwUAER5ebmUtm7dOgFAzJ07V0qrq6uTrnvo0CEpffPmzQKAuPfee42uGxUV1SXvq6++KgCIjIwMo7xxcXECgNi7d6+UVlxcLACI5ORko7xJSUkCgNi1a5eUtmfPHgFAxMfHG+WdNGmSACCKioqktAMHDggAIjo62ijv9OnTBQCxbds2Ka20tFQAEBqNxihvVlaWACA2btwopZ05c0YAEH379jXKm5ubKwCIVatWSWmVlZUCgPDx8THKm5+fLwCIFStWSGk3btyQ6r2lpUVKX7p0qQAgli5dKqW1tLRIeW/cuCGlr1ixQgAQ+fn5Rp/n4+MjAIjKykopbdWqVQKAyM3NNcrbt29fAUCcOXPGqC0AEFlZWUZ5NRqNACBKS0ultG3btgkAYvr06UZ5o6Oj2777HTtEw/HjouH4cfHqmjUCgJiQlCSlNZeXi/j4eAFA7NmzR3r/rl27BACRlJQkhBCi4fhxUXZHrLgzQC0ACMX3ZVQC4qH+/QUAMcLPTzQcPy5dIyMjQwAQr776qpR26NAhAUBERUUZlffee+8VAMTmzZultOPHjwsAIiwszCjv3LlzBQCxbt06Ka28vFwAEIGBgUZ5H374YQFAPPfcc13qt/MjbcmSJQKAePrpp6W0ju+pq6uT0p9++mkBQCxZssToGnI8I4QQIiwsTAAQxzvUr6s9IzrWVWpqqizPiBkzZnT5DpVKpUc8I37729+a/P++4zOi3caNG+32jDhw4ICUVlRUJACISZMmGeW15BnRLjk5WQAQxcXFUtrevXsFABEXF2eU11HPiPbf37du3RI96fwPQa+wadMmxMXFYdy4cc4uCpHVfAcPhnr0aKhHj4bfbbcBAJRBQT+k2TjE8+ygCBTedhs+GD4CE/v0sWOJicxr7zlo7zFRqVRYuXKlM4tkN3379gVg3DNBjqcQoputWj1cbW0t+vbti6qqKkRERNitW7q+vh59vv9FUVtbi+DgYLN5AQ5dtXOXbmlrhq4aGxultnDlyhWEhIS4TLd0+z46TQYDDAD8FAr4fF8PeiHQLAQUAMbseU8KnhzZLW3Jd6/T6YzqNygoiENXdnxGGAwGqX6vXbsGtVot2zPi6tWrOH/+PKKjo6HRaLzmGcGhK9ueEe2/v2/duoWQkBB0h4GOhRVljY6BTl1dHaN5L+bqbaH9gFBzXH0lk6vXr7tj/ZKrsub3NycjE3kxVw5iiIjswSvn6BAREZF3YI+OTDqOQ5J3Y1uQF+tXXqxfcncMdGQQFBQkTTYj78a2IC/Wr7xYv+QJOHRFREREHouBDhEREXksBjoyaGpqwowZMzBjxgw0NTU5uzjkRGwL8mL9yov1S56A++hwHx2SEduCvFi/8mL9kquy5vc3e3SIiIjIYzHQISIiIo/FQIeIiIg8FgMdIiIi8lgMdIiIiMhjefXOyO0Lzmpra+163Y47idbW1kKv19v1+uQ+2BbkxfqVF+uXXFX7721LFo579fLyyspK3Hbbbc4uBhEREdng4sWLiIyM7DaPVwc6BoMBVVVVCA4OhkKhcHZxnKq2tha33XYbLl68aNc9hYh1KzfWr7xYv/Jh3dpOCIHvvvsOGo0GSmX3s3C8euhKqVT2GAl6m5CQEP4PJxPWrbxYv/Ji/cqHdWubvn37WpSPk5GJiIjIYzHQISIiIo/FQIcAAP7+/lixYgX8/f2dXRSPw7qVF+tXXqxf+bBuHcOrJyMTERGRZ2OPDhEREXksBjpERETksRjoEBERkcdioENEREQei4GOl3jxxRcxbNgwBAQEIDExEfv37zeb9x//+AfuuecehIeHIyQkBHfffTfee+89B5bW/VhTvx0dPHgQPj4+SEhIkLeAbs7a+m1ubsZvf/tbREVFwd/fHyNGjMDWrVsdVFr3Ym3dvvbaa4iPj0dgYCAGDx6MX/ziF6ipqXFQad3Lxx9/jFmzZkGj0UChUOCf//xnj+/56KOPkJiYiICAAAwfPhwvvfSS/AX1dII83uuvvy58fX3FX/7yF1FWViaWLFkigoKCxPnz503mX7Jkifjf//1fcejQIXHmzBmxbNky4evrK7788ksHl9w9WFu/7W7evCmGDx8upkyZIuLj4x1TWDdkS/3Onj1bjB8/Xrz//vuivLxcfP755+LgwYMOLLV7sLZu9+/fL5RKpXj++efFN998I/bv3y9Gjx4t7rvvPgeX3D28++674re//a146623BABRXFzcbf5vvvlGBAYGiiVLloiysjLxl7/8Rfj6+oo333zTMQX2UAx0vMCPfvQj8cgjjxilxcbGiqeeesria8TFxYlnn33W3kXzCLbW75w5c8T//M//iBUrVjDQ6Ya19fvvf/9b9O3bV9TU1DiieG7N2rr905/+JIYPH26UtmHDBhEZGSlbGT2FJYHOr3/9axEbG2uUtmjRIvHjH/9YxpJ5Pg5debiWlhaUlJRgypQpRulTpkzBJ598YtE1DAYDvvvuO4SGhspRRLdma/1u27YNX3/9NVasWCF3Ed2aLfW7c+dOJCUlYdWqVRgyZAhGjhyJpUuXorGx0RFFdhu21G1ycjIqKyvx7rvvQgiBK1eu4M0338SMGTMcUWSP9+mnn3b5PjIzM/HFF19Ap9M5qVTuz6sP9fQG169fh16vx6BBg4zSBw0ahMuXL1t0jTVr1qC+vh7Z2dlyFNGt2VK/Z8+exVNPPYX9+/fDx4f/C3bHlvr95ptvcODAAQQEBKC4uBjXr19Hfn4+vv32W87T6cCWuk1OTsZrr72GOXPmoKmpCa2trZg9ezZeeOEFRxTZ412+fNnk99Ha2orr169j8ODBTiqZe2OPjpdQKBRGfxdCdEkz5e9//zt+97vf4Y033sDAgQPlKp7bs7R+9Xo95s6di2effRYjR450VPHcnjXt12AwQKFQ4LXXXsOPfvQjTJ8+HWvXrkVhYSF7dUywpm7LysqwePFiLF++HCUlJdi9ezfKy8vxyCOPOKKoXsHU92EqnSzHf056uLCwMKhUqi7/Qrt69WqXfzl09sYbbyAvLw87duxARkaGnMV0W9bW73fffYcvvvgCpaWl+OUvfwmg7RezEAI+Pj7Ys2cP0tLSHFJ2d2BL+x08eDCGDBmCvn37SmmjRo2CEAKVlZWIiYmRtczuwpa6/cMf/oCUlBQ8+eSTAACtVougoCBMmDABzz33HHsceikiIsLk9+Hj44MBAwY4qVTujz06Hs7Pzw+JiYl4//33jdLff/99JCcnm33f3//+dyxYsAB/+9vfOP7eDWvrNyQkBMeOHcORI0ekP4888gjuuOMOHDlyBOPHj3dU0d2CLe03JSUFVVVVqKurk9LOnDkDpVKJyMhIWcvrTmyp24aGBiiVxr82VCoVgB96Hsh2d999d5fvY8+ePUhKSoKvr6+TSuUBnDYNmhymfQnpli1bRFlZmfjVr34lgoKCREVFhRBCiKeeekrMmzdPyv+3v/1N+Pj4iE2bNonq6mrpz82bN511Cy7N2vrtjKuuumdt/X733XciMjJSZGVliRMnToiPPvpIxMTEiIcffthZt+CyrK3bbdu2CR8fH/Hiiy+Kr7/+Whw4cEAkJSWJH/3oR866BZf23XffidLSUlFaWioAiLVr14rS0lJp+X7n+m1fXv7YY4+JsrIysWXLFi4vtwMGOl5i06ZNIioqSvj5+Ym77rpLfPTRR9Jrubm5YtKkSdLfJ02aJAB0+ZObm+v4grsJa+q3MwY6PbO2fk+ePCkyMjKEWq0WkZGR4vHHHxcNDQ0OLrV7sLZuN2zYIOLi4oRarRaDBw8WDz74oKisrHRwqd3D3r17u32Wmqrfffv2iTvvvFP4+fmJoUOHij//+c+OL7iHUQjB/kYiIiLyTJyjQ0RERB6LgQ4RERF5LAY6RERE5LEY6BAREZHHYqBDREREHouBDhEREXksBjpERETksRjoEBERkcdioENEbm3y5Mn41a9+Zbe8CoUC//znP6W/nzp1Cj/+8Y8REBCAhIQEm8tJRM7BQIeIqIPq6mpMmzZN+vuKFSsQFBSE06dP48MPP0RhYSH69evnvAISkVV8nF0AIiIAaGlpgZ+fn7OLgYiICKO/f/3115gxYwaioqKcVCIi6g326BCRU0yePBm//OUv8fjjjyMsLAz33HMPysrKMH36dPTp0weDBg3CvHnzcP36dek99fX1mD9/Pvr06YPBgwdjzZo1Xa774osvIiYmBgEBARg0aBCysrKMXjcYDPj1r3+N0NBQRERE4He/+53R6x2HrhQKBUpKSlBQUACFQoHJkyfjF7/4BW7dugWFQgGFQtHl/UTkWhjoEJHTbN++HT4+Pjh48CD++Mc/YtKkSUhISMAXX3yB3bt348qVK8jOzpbyP/nkk9i7dy+Ki4uxZ88e7Nu3DyUlJdLrX3zxBRYvXoyCggKcPn0au3fvxsSJE7t8ZlBQED7//HOsWrUKBQUFeP/9902Wr7q6GqNHj8YTTzyB6upq7Ny5E+vXr0dISAiqq6tRXV2NpUuXylM5RGQXHLoiIqeJjo7GqlWrAADLly/HXXfdhd///vfS61u3bsVtt92GM2fOQKPRYMuWLXjllVdwzz33AGgLWiIjI6X8Fy5cQFBQEGbOnIng4GBERUXhzjvvNPpMrVaLFStWAABiYmKwceNGfPjhh9I1O4qIiICPjw/69OkjDWn17dsXCoWiyxAXEbkmBjpE5DRJSUnSzyUlJdi7dy/69OnTJd/XX3+NxsZGtLS04O6775bSQ0NDcccdd0h/v+eeexAVFYXhw4dj6tSpmDp1Ku6//34EBgZKebRardG1Bw8ejKtXr9rztojIhXDoioicJigoSPrZYDBg1qxZOHLkiNGfs2fPYuLEiRBC9Hi94OBgfPnll/j73/+OwYMHY/ny5YiPj8fNmzelPL6+vkbvUSgUMBgMdrsnInItDHSIyCXcddddOHHiBIYOHYro6GijP0FBQYiOjoavry8+++wz6T03btzAmTNnjK7j4+ODjIwMrFq1CkePHkVFRQX+85//2K2cfn5+0Ov1drseEcmLgQ4RuYRHH30U3377LX7+85/j0KFD+Oabb7Bnzx489NBD0Ov16NOnD/Ly8vDkk0/iww8/xPHjx7FgwQIolT88xnbt2oUNGzbgyJEjOH/+PF555RUYDAaj4a3eGjp0KOrq6vDhhx/i+vXraGhosNu1icj+GOgQkUvQaDQ4ePAg9Ho9MjMzMWbMGCxZsgR9+/aVgpk//elPmDhxImbPno2MjAykpqYiMTFRuka/fv3wj3/8A2lpaRg1ahReeukl/P3vf8fo0aPtVs7k5GQ88sgjmDNnDsLDw6XJ1ETkmhTCkoFvIiIiIjfEHh0iIiLyWAx0iIiIyGMx0CEiIiKPxUCHiIiIPBYDHSIiIvJYDHSIiIjIYzHQISIiIo/FQIeIiIg8FgMdIiIi8lgMdIiIiMhjMdAhIiIij/X/A8DatAqGrVw0AAAAAElFTkSuQmCC", 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", 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" ] @@ -633,36 +525,293 @@ " plt.legend(fontsize='x-small')" ] }, + { + "cell_type": "markdown", + "id": "93633ac1-206d-42ac-88a8-b33c7b6656ac", + "metadata": {}, + "source": [ + "# Now let's do the same using the pipeline approach\n", + "\n", + "Here we will use the 20deg2, but we can also use the 1deg2 files (just need to change 20deg2 to 1deg2 in the name of the files)" + ] + }, + { + "cell_type": "markdown", + "id": "3e233ec3-29eb-45b1-ae97-7e03f3719478", + "metadata": {}, + "source": [ + "### Launching a pipeline\n", + "\n", + "Let's have a look at the submission script for this pipeline:\n", + "- to work at CCin2p3 we can use: `examples/cosmodc2/1deg2-in2p3.sub`:\n", + "- to work at NERSC we can use: `examples/cosmodc2/1deg2-nersc.sub`:\n", + "\n", + "If we use the CCin2p3 example :" + ] + }, { "cell_type": "code", - "execution_count": null, - "id": "c8d129d1-43f9-4111-af3d-1d7aa42a9c31", + "execution_count": 46, + "id": "9660328e-0f08-4e92-9350-d4831c308e55", "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "#!/usr/bin/bash\n", + "#SBATCH --time=01:00:00\n", + "#SBATCH --partition=hpc\n", + "#SBATCH --ntasks=30\n", + "#SBATCH --cpus-per-task=1\n", + "#SBATCH --mem=128000\n", + "\n", + "source /pbs/throng/lsst/users/jzuntz/txpipe-environments/setup-txpipe\n", + "ceci examples/cosmodc2/pipeline-20deg2-CL-in2p3.yml\n" + ] + } + ], + "source": [ + "! cat examples/cosmodc2/20deg2-in2p3.sub" + ] + }, + { + "cell_type": "markdown", + "id": "94939a10-4dab-4cc2-9570-3de230423b48", + "metadata": {}, + "source": [ + "If we use the NERSC example:" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "dc09313d-ab83-4ab8-8d39-3cf1d495ebd5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "#!/bin/bash\n", + "#SBATCH -A m1727\n", + "#SBATCH -C cpu\n", + "#SBATCH --qos=debug\n", + "#SBATCH --time=00:30:00\n", + "#SBATCH --nodes=1\n", + "#SBATCH --ntasks-per-node=32\n", + "\n", + "source $CFS/lsst/groups/WL/users/zuntz/setup-txpipe\n", + "tx ceci examples/cosmodc2/pipeline-20deg2-CL-nersc.yml\n" + ] + } + ], + "source": [ + "! cat examples/cosmodc2/20deg2-nersc.sub" + ] + }, + { + "cell_type": "markdown", + "id": "53430447-8c6d-413f-9138-9806050f01fe", + "metadata": {}, + "source": [ + "This will launch a job of up to one hour (it should finish in 30 min) on a single CC-IN2P3 node to run a pipeline. After the first run, the output files are created and following runs take much less time.\n", + "\n", + "In a terminal, **navigate to your TXPipe directory on IN2P3 and run**:\n", + "\n", + "```\n", + "sbatch examples/cosmodc2/20deg2-in2p3.sub\n", + "```\n", + "to set it running.\n", + "\n", + "If you are **on NERSC, you will instead run**:\n", + "```\n", + "sbatch examples/cosmodc2/20deg2-nersc.sub\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "b61f90b0-7f33-4fea-8ab7-310a1e00a6ca", + "metadata": {}, + "source": [ + "Below, you will need to select the appropriate yaml file to comment/uncomment for `pipeline_file`, depending on if you are in IN2P3 or on NERSC. " + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "af7452ac-b01e-454d-a521-3426392ca4dc", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Read the appropriate pipeline configuration, and ask for a flow-chart.\n", + "pipeline_file = \"examples/cosmodc2/pipeline-20deg2-CL-in2p3.yml\"\n", + "#pipeline_file = \"examples/cosmodc2/pipeline-20deg2-CL-nersc.yml\"\n", + "flowchart_file = \"CL_pipeline.png\"\n", + "\n", + "\n", + "pipeline_config = ceci.Pipeline.build_config(\n", + " pipeline_file,\n", + " flow_chart=flowchart_file,\n", + " dry_run=True\n", + ")\n", + "\n", + "# Run the flow-chart pipeline\n", + "ceci.run_pipeline(pipeline_config)" + ] }, { "cell_type": "code", - "execution_count": null, - "id": "c3d36570-e6ff-4d2c-ba1a-ea354aaa4877", + "execution_count": 52, + "id": "a88eb44a-3a78-41f1-bffe-b1aa8d061842", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Image(flowchart_file)" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "59f26536-3416-4229-8664-bea506599179", "metadata": {}, "outputs": [], - "source": [] + "source": [ + "## Open the corresponding pipeline file to load correct input/output file names" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 54, "id": "7077bf36-77a3-4c3b-8e49-47687f989807", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], - "source": [] + "source": [ + "with open(pipeline_file, 'r') as file:\n", + " pipeline_content = yaml.safe_load(file)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "80ed821b-354d-4ae2-8240-cf28e2f3909f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "./data/cosmodc2/20deg2/cluster_catalog.hdf5\n" + ] + } + ], + "source": [ + "#open input cluster catalog\n", + "filename_in = pipeline_content['inputs']['cluster_catalog']\n", + "print(filename_in)\n", + "f_in = h5py.File(filename_in, \"r\")\n", + "dset_in = f_in['clusters']" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "6346d5b9-5196-4342-8a67-a69ca84705cc", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "./data/cosmodc2/outputs-20deg2-CL/cluster_catalog_tomography.hdf5\n" + ] + } + ], + "source": [ + "#open output binning output\n", + "filename_out =pipeline_content['output_dir']+\"/cluster_catalog_tomography.hdf5\"\n", + "print (filename_out)\n", + "f_out = h5py.File(filename_out, \"r\")\n", + "dat_out = f_out['provenance']\n", + "dset_out = f_out['cluster_bin']" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "604121fa-0cc8-4844-92c7-ec8d80642385", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#plot data from input catalog\n", + "plt.semilogy(dset_in['redshift'][()], dset_in['richness'][()],'k.', alpha=1)\n", + "plt.xlabel('redshift')\n", + "plt.ylabel('richness')\n", + "\n", + "#plot bin limits as defined in the config file\n", + "[plt.axvline(i,linestyle='dashed', color='black') for i in pip_stage.config.zedge]\n", + "[plt.axhline(i,linestyle='dotted', color='black') for i in pip_stage.config.richedge]\n", + "\n", + "#overplot data from output file to make sure the bins are ordered correctly\n", + "markers=['s','o', 'D', 'P', '^']\n", + "\n", + "for i in range(len(pip_stage.config.zedge)-1):\n", + " for j in range(len(pip_stage.config.richedge)-1):\n", + " plt.scatter(dset_out['bin_zbin_'+str(i)+'_richbin_'+str(j)]['redshift'][:], \n", + " dset_out['bin_zbin_'+str(i)+'_richbin_'+str(j)]['richness'][:], marker=markers[j], label='bin_zbin_'+str(i)+'_richbin_'+str(j))\n", + " \n", + " plt.legend(fontsize='x-small')" + ] } ], "metadata": { "kernelspec": { - "display_name": "TXPipe", + "display_name": "TXPipe-2023-Jul-12", "language": "python", - "name": "txpipe" + "name": "txpipe-2023-jul-12" }, "language_info": { "codemirror_mode": { @@ -674,7 +823,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.10.12" } }, "nbformat": 4, From 0ef05eec35c19051982fde1e486c6cc55b5c75b1 Mon Sep 17 00:00:00 2001 From: Marina Ricci Date: Thu, 19 Oct 2023 20:47:27 +0200 Subject: [PATCH 21/22] remove old png --- 20deg2.png | Bin 52029 -> 0 bytes 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 20deg2.png diff --git a/20deg2.png b/20deg2.png deleted file mode 100644 index 60f816a1fb9cf1922811d7ccac3a73bba144c517..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 52029 zcmd42WmH^SyERB~*PuZ{a0u=mT!I7%?(Qyy6C?x(5D2cp-5m;dhv313I|L~R*1I_8 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b/notebooks/Run_CL_counts_pipeline.ipynb similarity index 100% rename from notebooks/Run_CL_pipeline.ipynb rename to notebooks/Run_CL_counts_pipeline.ipynb