From 81018f84907555f089fd374bf3e0ed2ab69c31cc Mon Sep 17 00:00:00 2001 From: jzhu4 Date: Thu, 12 Aug 2021 17:45:26 -0800 Subject: [PATCH 01/15] update the example dataset url --- smallbaselineApp_hyp3.ipynb | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/smallbaselineApp_hyp3.ipynb b/smallbaselineApp_hyp3.ipynb index e342a9a..c33f7d9 100644 --- a/smallbaselineApp_hyp3.ipynb +++ b/smallbaselineApp_hyp3.ipynb @@ -121,7 +121,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "case data can be obtained through either downloading from the stagged server or producing with hyp3-sdk. As far as producing data from hyp3-sdk, we provide the prep_ts_hyp3 notebook( )." + "The example dataset is from 2019 Ridgecrest, CA earthquake. The dataset can be obtained through either downloading from the stagged server or producing with hyp3-sdk. As far as producing data from hyp3-sdk, we provide the prep_ts_hyp3 notebook at the tutorial directory of (https://github.com/ASFHyP3/hyp3-docs/tree/develop/docs )." ] }, { @@ -146,7 +146,8 @@ " # Check if a stage file from S3 already exist, if not try and download it\n", " zip_file = os.path.join(hyp3_dir, zip_file_name)\n", " if not os.path.isfile(zip_file):\n", - " !aws s3 cp s3://jzhu-hyp3-dev/hyp3-mintpy-example/{zip_file_name} {zip_file_name}\n", + " !wget https://jzhu-hyp3-dev.s3.us-west-2.amazonaws.com/hyp3-mintpy/{zip_file_name}\n", + " #!aws s3 cp s3://jzhu-hyp3-dev/hyp3-mintpy-example/{zip_file_name} {zip_file_name}\n", " # verify if download was succesfull\n", " if os.path.isfile(zip_file_name):\n", " import zipfile, glob\n", From beb0f95a6c50b1355992bd8637968cfe16ea5f4a Mon Sep 17 00:00:00 2001 From: Zhang Yunjun Date: Mon, 13 Sep 2021 18:09:11 -0700 Subject: [PATCH 02/15] Update smallbaselineApp_hyp3.ipynb --- smallbaselineApp_hyp3.ipynb | 8 +++----- 1 file changed, 3 insertions(+), 5 deletions(-) diff --git a/smallbaselineApp_hyp3.ipynb b/smallbaselineApp_hyp3.ipynb index c33f7d9..bea04ee 100644 --- a/smallbaselineApp_hyp3.ipynb +++ b/smallbaselineApp_hyp3.ipynb @@ -33,10 +33,7 @@ "import matplotlib.pyplot as plt\n", "# verify mintpy install\n", "try:\n", - " #from mintpy.objects.insar_vs_gps import plot_insar_vs_gps_scatter\n", - " #from mintpy.unwrap_error_phase_closure import plot_num_triplet_with_nonzero_integer_ambiguity\n", - " #from mintpy import workflow, view, tsview, plot_network, plot_transection, plot_coherence_matrix\n", - " from mintpy import view, tsview\n", + " from mintpy import workflow, view, tsview\n", "except ImportError:\n", " raise ImportError(\"Can not import mintpy!\")\n", "\n", @@ -146,8 +143,9 @@ " # Check if a stage file from S3 already exist, if not try and download it\n", " zip_file = os.path.join(hyp3_dir, zip_file_name)\n", " if not os.path.isfile(zip_file):\n", - " !wget https://jzhu-hyp3-dev.s3.us-west-2.amazonaws.com/hyp3-mintpy/{zip_file_name}\n", + " # Download the staged dataset from AWS S3 bucket or from [Zenodo](https://zenodo.org/record/5502403).", " #!aws s3 cp s3://jzhu-hyp3-dev/hyp3-mintpy-example/{zip_file_name} {zip_file_name}\n", + " !wget https://jzhu-hyp3-dev.s3.us-west-2.amazonaws.com/hyp3-mintpy/{zip_file_name}\n", " # verify if download was succesfull\n", " if os.path.isfile(zip_file_name):\n", " import zipfile, glob\n", From 28a766d06de5868e032d275348093be0b30f4f7e Mon Sep 17 00:00:00 2001 From: Zhang Yunjun Date: Mon, 13 Sep 2021 18:11:10 -0700 Subject: [PATCH 03/15] Revert "Update smallbaselineApp_hyp3.ipynb" This reverts commit beb0f95a6c50b1355992bd8637968cfe16ea5f4a. --- smallbaselineApp_hyp3.ipynb | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/smallbaselineApp_hyp3.ipynb b/smallbaselineApp_hyp3.ipynb index bea04ee..c33f7d9 100644 --- a/smallbaselineApp_hyp3.ipynb +++ b/smallbaselineApp_hyp3.ipynb @@ -33,7 +33,10 @@ "import matplotlib.pyplot as plt\n", "# verify mintpy install\n", "try:\n", - " from mintpy import workflow, view, tsview\n", + " #from mintpy.objects.insar_vs_gps import plot_insar_vs_gps_scatter\n", + " #from mintpy.unwrap_error_phase_closure import plot_num_triplet_with_nonzero_integer_ambiguity\n", + " #from mintpy import workflow, view, tsview, plot_network, plot_transection, plot_coherence_matrix\n", + " from mintpy import view, tsview\n", "except ImportError:\n", " raise ImportError(\"Can not import mintpy!\")\n", "\n", @@ -143,9 +146,8 @@ " # Check if a stage file from S3 already exist, if not try and download it\n", " zip_file = os.path.join(hyp3_dir, zip_file_name)\n", " if not os.path.isfile(zip_file):\n", - " # Download the staged dataset from AWS S3 bucket or from [Zenodo](https://zenodo.org/record/5502403).", - " #!aws s3 cp s3://jzhu-hyp3-dev/hyp3-mintpy-example/{zip_file_name} {zip_file_name}\n", " !wget https://jzhu-hyp3-dev.s3.us-west-2.amazonaws.com/hyp3-mintpy/{zip_file_name}\n", + " #!aws s3 cp s3://jzhu-hyp3-dev/hyp3-mintpy-example/{zip_file_name} {zip_file_name}\n", " # verify if download was succesfull\n", " if os.path.isfile(zip_file_name):\n", " import zipfile, glob\n", From 3e17eb5fd351e367a6a9a3dbc6ce1f20b40715df Mon Sep 17 00:00:00 2001 From: jzhu4 Date: Mon, 20 Sep 2021 10:05:07 -0800 Subject: [PATCH 04/15] modify smallbaselineApp_hyp3.ipynb --- smallbaselineApp_hyp3.ipynb | 77 ++++++++++++++++++++++++++++++++----- 1 file changed, 68 insertions(+), 9 deletions(-) diff --git a/smallbaselineApp_hyp3.ipynb b/smallbaselineApp_hyp3.ipynb index c33f7d9..0dd7b32 100644 --- a/smallbaselineApp_hyp3.ipynb +++ b/smallbaselineApp_hyp3.ipynb @@ -23,9 +23,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Go to work directory: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/mintpy\n" + ] + } + ], "source": [ "%matplotlib inline\n", "import os\n", @@ -92,9 +100,28 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "# vim: set filetype=cfg:\n", + "mintpy.load.processor = hyp3\n", + "##---------interferogram datasets:\n", + "mintpy.load.unwFile = /media/jzhu4/data/hyp3-mintpy/Ridgecrest/hyp3/*/*unw_phase_clip.tif\n", + "mintpy.load.corFile = /media/jzhu4/data/hyp3-mintpy/Ridgecrest/hyp3/*/*corr_clip.tif\n", + "##---------geometry datasets:\n", + "mintpy.load.demFile = /media/jzhu4/data/hyp3-mintpy/Ridgecrest/hyp3/*/*dem_clip.tif\n", + "mintpy.load.incAngleFile = /media/jzhu4/data/hyp3-mintpy/Ridgecrest/hyp3/*/*inc_map_clip.tif\n", + "\n", + "write configuration to file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/mintpy/Ridgecrest.txt\n" + ] + } + ], "source": [ "CONFIG_TXT = f'''# vim: set filetype=cfg:\n", "mintpy.load.processor = hyp3\n", @@ -126,9 +153,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "S3 pre-staged data retrieval was successfull\n" + ] + } + ], "source": [ "# verify / prepare input dataset\n", "\n", @@ -181,9 +216,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Traceback (most recent call last):\r\n", + " File \"/home/jzhu4/projects/work/hyp3/hyp3-mintpy/MintPy/mintpy/save_kmz.py\", line 21, in \r\n", + " from pykml.factory import KML_ElementMaker as KML\r\n", + "ModuleNotFoundError: No module named 'pykml'\r\n", + "\r\n", + "During handling of the above exception, another exception occurred:\r\n", + "\r\n", + "Traceback (most recent call last):\r\n", + " File \"/home/jzhu4/apps/anaconda3/bin/smallbaselineApp.py\", line 24, in \r\n", + " import mintpy.workflow # dynamic import of modules for smallbaselineApp\r\n", + " File \"/home/jzhu4/projects/work/hyp3/hyp3-mintpy/MintPy/mintpy/workflow/__init__.py\", line 40, in \r\n", + " importlib.import_module(root_module + '.' + module)\r\n", + " File \"/home/jzhu4/apps/anaconda3/lib/python3.7/importlib/__init__.py\", line 127, in import_module\r\n", + " return _bootstrap._gcd_import(name[level:], package, level)\r\n", + " File \"/home/jzhu4/projects/work/hyp3/hyp3-mintpy/MintPy/mintpy/save_kmz.py\", line 23, in \r\n", + " raise ImportError('Can not import pykml!')\r\n", + "ImportError: Can not import pykml!\r\n" + ] + } + ], "source": [ "! smallbaselineApp.py --work-dir {work_dir} {configName}" ] From ab5e0e09c6be6d2b00289e8421b621610e2eb9b7 Mon Sep 17 00:00:00 2001 From: jzhu4 Date: Mon, 20 Sep 2021 10:06:21 -0800 Subject: [PATCH 05/15] modify smallbaselineApp_hyp3.ipynb --- smallbaselineApp_hyp3.ipynb | 73 ++++--------------------------------- 1 file changed, 8 insertions(+), 65 deletions(-) diff --git a/smallbaselineApp_hyp3.ipynb b/smallbaselineApp_hyp3.ipynb index 0dd7b32..3e66141 100644 --- a/smallbaselineApp_hyp3.ipynb +++ b/smallbaselineApp_hyp3.ipynb @@ -23,17 +23,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Go to work directory: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/mintpy\n" - ] - } - ], + "outputs": [], "source": [ "%matplotlib inline\n", "import os\n", @@ -100,28 +92,11 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { "scrolled": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "# vim: set filetype=cfg:\n", - "mintpy.load.processor = hyp3\n", - "##---------interferogram datasets:\n", - "mintpy.load.unwFile = /media/jzhu4/data/hyp3-mintpy/Ridgecrest/hyp3/*/*unw_phase_clip.tif\n", - "mintpy.load.corFile = /media/jzhu4/data/hyp3-mintpy/Ridgecrest/hyp3/*/*corr_clip.tif\n", - "##---------geometry datasets:\n", - "mintpy.load.demFile = /media/jzhu4/data/hyp3-mintpy/Ridgecrest/hyp3/*/*dem_clip.tif\n", - "mintpy.load.incAngleFile = /media/jzhu4/data/hyp3-mintpy/Ridgecrest/hyp3/*/*inc_map_clip.tif\n", - "\n", - "write configuration to file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/mintpy/Ridgecrest.txt\n" - ] - } - ], + "outputs": [], "source": [ "CONFIG_TXT = f'''# vim: set filetype=cfg:\n", "mintpy.load.processor = hyp3\n", @@ -153,17 +128,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "S3 pre-staged data retrieval was successfull\n" - ] - } - ], + "outputs": [], "source": [ "# verify / prepare input dataset\n", "\n", @@ -216,33 +183,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Traceback (most recent call last):\r\n", - " File \"/home/jzhu4/projects/work/hyp3/hyp3-mintpy/MintPy/mintpy/save_kmz.py\", line 21, in \r\n", - " from pykml.factory import KML_ElementMaker as KML\r\n", - "ModuleNotFoundError: No module named 'pykml'\r\n", - "\r\n", - "During handling of the above exception, another exception occurred:\r\n", - "\r\n", - "Traceback (most recent call last):\r\n", - " File \"/home/jzhu4/apps/anaconda3/bin/smallbaselineApp.py\", line 24, in \r\n", - " import mintpy.workflow # dynamic import of modules for smallbaselineApp\r\n", - " File \"/home/jzhu4/projects/work/hyp3/hyp3-mintpy/MintPy/mintpy/workflow/__init__.py\", line 40, in \r\n", - " importlib.import_module(root_module + '.' + module)\r\n", - " File \"/home/jzhu4/apps/anaconda3/lib/python3.7/importlib/__init__.py\", line 127, in import_module\r\n", - " return _bootstrap._gcd_import(name[level:], package, level)\r\n", - " File \"/home/jzhu4/projects/work/hyp3/hyp3-mintpy/MintPy/mintpy/save_kmz.py\", line 23, in \r\n", - " raise ImportError('Can not import pykml!')\r\n", - "ImportError: Can not import pykml!\r\n" - ] - } - ], + "outputs": [], "source": [ "! smallbaselineApp.py --work-dir {work_dir} {configName}" ] From c9f8baf742a33ec48e5741e44a4d9de38e0fa963 Mon Sep 17 00:00:00 2001 From: jzhu4 Date: Mon, 20 Sep 2021 11:44:09 -0800 Subject: [PATCH 06/15] modify the smallbaselineApp_hyp3.ipynb to point inc_angele to lv_theta --- smallbaselineApp_hyp3.ipynb | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/smallbaselineApp_hyp3.ipynb b/smallbaselineApp_hyp3.ipynb index 3e66141..42a0602 100644 --- a/smallbaselineApp_hyp3.ipynb +++ b/smallbaselineApp_hyp3.ipynb @@ -93,9 +93,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "scrolled": true - }, + "metadata": {}, "outputs": [], "source": [ "CONFIG_TXT = f'''# vim: set filetype=cfg:\n", @@ -105,7 +103,7 @@ "mintpy.load.corFile = {hyp3_dir}/*/*corr_clip.tif\n", "##---------geometry datasets:\n", "mintpy.load.demFile = {hyp3_dir}/*/*dem_clip.tif\n", - "mintpy.load.incAngleFile = {hyp3_dir}/*/*inc_map_clip.tif\n", + "mintpy.load.incAngleFile = {hyp3_dir}/*/*lv_theta_clip.tif\n", "'''\n", "print(CONFIG_TXT)\n", "configName = os.path.join(work_dir, \"{}.txt\".format(proj_name))\n", From a1bfdeeca0621156b78d57e0854331c421bd7f4f Mon Sep 17 00:00:00 2001 From: jzhu4 Date: Tue, 21 Sep 2021 15:31:26 -0800 Subject: [PATCH 07/15] add mintpy.load.waterMaskFile option --- smallbaselineApp_hyp3.ipynb | 1 + 1 file changed, 1 insertion(+) diff --git a/smallbaselineApp_hyp3.ipynb b/smallbaselineApp_hyp3.ipynb index 42a0602..58a3380 100644 --- a/smallbaselineApp_hyp3.ipynb +++ b/smallbaselineApp_hyp3.ipynb @@ -104,6 +104,7 @@ "##---------geometry datasets:\n", "mintpy.load.demFile = {hyp3_dir}/*/*dem_clip.tif\n", "mintpy.load.incAngleFile = {hyp3_dir}/*/*lv_theta_clip.tif\n", + "mintpy.load.waterMaskFile = {hyp3_dir}/*/*/water_mask_clip.tif\n", "'''\n", "print(CONFIG_TXT)\n", "configName = os.path.join(work_dir, \"{}.txt\".format(proj_name))\n", From c3c4ae5bb2dc58434c316398a945813836bfe5fa Mon Sep 17 00:00:00 2001 From: Zhang Yunjun Date: Tue, 21 Sep 2021 18:01:21 -0700 Subject: [PATCH 08/15] typo fix --- smallbaselineApp_hyp3.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/smallbaselineApp_hyp3.ipynb b/smallbaselineApp_hyp3.ipynb index 58a3380..c99b653 100644 --- a/smallbaselineApp_hyp3.ipynb +++ b/smallbaselineApp_hyp3.ipynb @@ -104,7 +104,7 @@ "##---------geometry datasets:\n", "mintpy.load.demFile = {hyp3_dir}/*/*dem_clip.tif\n", "mintpy.load.incAngleFile = {hyp3_dir}/*/*lv_theta_clip.tif\n", - "mintpy.load.waterMaskFile = {hyp3_dir}/*/*/water_mask_clip.tif\n", + "mintpy.load.waterMaskFile = {hyp3_dir}/*/*water_mask_clip.tif\n", "'''\n", "print(CONFIG_TXT)\n", "configName = os.path.join(work_dir, \"{}.txt\".format(proj_name))\n", From 6b3d9e72dd508b20e1aae332160bcf25fdebf97b Mon Sep 17 00:00:00 2001 From: jzhu4 Date: Thu, 5 May 2022 21:25:57 -0800 Subject: [PATCH 09/15] update the smallbaselineApp_hyp3.ipynb --- smallbaselineApp_hyp3.ipynb | 1317 ++++++++++++++++++++++++++++++++--- 1 file changed, 1204 insertions(+), 113 deletions(-) diff --git a/smallbaselineApp_hyp3.ipynb b/smallbaselineApp_hyp3.ipynb index 58a3380..7a734b4 100644 --- a/smallbaselineApp_hyp3.ipynb +++ b/smallbaselineApp_hyp3.ipynb @@ -4,12 +4,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# InSAR time series analysis with HyP3 and MintPy\n", + "# Time series analysis of hyp3 InSAR products by MintPy\n", "\n", - "This notebook shows how to do time-series analysis using HyP3 product with MintPy. It requires `hyp3_sdk` and `MintPy`:\n", - "\n", - "+ run `conda install --yes -c conda-forge hyp3_sdk ipywidgets` to install `hyp3_sdk`\n", - "+ check the [installation page](https://github.com/insarlab/MintPy/blob/main/docs/installation.md) to install `MintPy`" + "This notebook shows how to do time-series analysis with HyP3 InSAR product by MintPy. We assume you have already got the hyp3 InSAR products somewhere. This steps for the analysis are: clip the hyp3 INSAR product, define the config.txt file, run the time series analysis, and display the results. The sample hyp3 INSAR data are at https://jzhu-hyp3-dev.s3.us-west-2.amazonaws.com/hyp3-mintpy-example/2018_kilauea.zip. Asfar as how to produce the hyp3 INSAR product, we provide the detail steps in the tutorial(https://github.com/ASFHyP3/hyp3-docs/tree/develop/docs). \n" ] }, { @@ -18,7 +15,28 @@ "source": [ "## 0. Initial setup of the notebook\n", "\n", - "The cell below performs the intial setup of the notebook and must be **run every time the notebook (re)starts**. It imports necessary modules and defines the processing location." + "To run this notebook, you'll need a conda environment with the required dependencies. You can set up a new environment (recommended) and run the jupyter server like:\n", + "\n", + "conda create -n hyp3-mintpy python=3.8 asf_search hyp3_sdk \"mintpy>=1.3.2\" pandas jupyter ipympl\n", + "\n", + "To make you conda env accessible in the jupyter notebook, you need to do:\n", + "\n", + "conda activate hyp3-mintpy\n", + "conda install -c conda-forge tensorflow\n", + "conda install -c anaconda ipykernel\n", + "python -m ipykernel install --user --name=hyp3-mintpy\n", + "\n", + "To run your notebook, just:\n", + "\n", + "conda activate hyp3-mintpy\n", + "jupyter notebook smallbaselineApp_hyp3_new.ipynb\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import modules and set environment variables" ] }, { @@ -27,51 +45,82 @@ "metadata": {}, "outputs": [], "source": [ - "%matplotlib inline\n", "import os\n", + "from pathlib import Path\n", + "import glob\n", + "import zipfile\n", + "from dateutil.parser import parse as parse_date\n", + "from osgeo import gdal\n", "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "# verify mintpy install\n", - "try:\n", - " #from mintpy.objects.insar_vs_gps import plot_insar_vs_gps_scatter\n", - " #from mintpy.unwrap_error_phase_closure import plot_num_triplet_with_nonzero_integer_ambiguity\n", - " #from mintpy import workflow, view, tsview, plot_network, plot_transection, plot_coherence_matrix\n", - " from mintpy import view, tsview\n", - "except ImportError:\n", - " raise ImportError(\"Can not import mintpy!\")\n", + "from mintpy import view, tsview\n", + "\n", + "os.environ['WEATHER_DIR'] = '/media/jzhu4/data/mintpy_data/weather_data'\n", "\n", "# utils function\n", - "def configure_template_file(outName, CONFIG_TXT): \n", - " \"\"\"Write configuration files for MintPy to process HyP3 product\"\"\"\n", - " if os.path.isfile(outName):\n", - " with open(outName, \"w\") as fid:\n", - " fid.write(CONFIG_TXT)\n", - " print('write configuration to file: {}'.format(outName))\n", "\n", - " else:\n", - " with open(outName, \"a\") as fid:\n", - " fid.write(\"\\n\" + CONFIG_TXT)\n", - " print('add the following to file: \\n{}'.format(outName))\n", + "def get_intersect_rectangle_geotiffs(filelist):\n", + " '''\n", + " :param data_dir: data directory storing the hyp3 products.\n", + " :process get the smallest overlap retangular area to clip the geotiff files.\n", + " :return:\n", + " '''\n", + " corners = [gdal.Info(str(dem), format='json')['cornerCoordinates'] for dem in filelist]\n", "\n", - "# define the work directory\n", - "#work_dir = os.path.abspath(os.path.join(os.getcwd(), 'mintpy')) #OpenSARLab at ASF\n", - "proj_name = 'Ridgecrest'\n", - "proj_dir = os.path.join('/media/jzhu4/data/hyp3-mintpy', proj_name) #Local\n", - "hyp3_dir = os.path.join(proj_dir, 'hyp3')\n", - "work_dir = os.path.join(proj_dir, 'mintpy') #Local\n", + " ulx = max(corner['upperLeft'][0] for corner in corners)\n", + " uly = min(corner['upperLeft'][1] for corner in corners)\n", + " lrx = min(corner['lowerRight'][0] for corner in corners)\n", + " lry = max(corner['lowerRight'][1] for corner in corners)\n", + " return [ulx, uly, lrx, lry]\n", + "\n", + "def prepare_hyp3_product(data_dir):\n", + " filelist = glob.glob(f\"{data_dir}/*/*_dem.tif\")\n", + " insect_box = get_intersect_rectangle_geotiffs(filelist)\n", + " #files_for_mintpy = ['_water_mask.tif', '_corr.tif', '_unw_phase.tif', '_dem.tif', '_lv_theta.tif', '_lv_phi.tif']\n", + " files_for_mintpy = ['_water_mask.tif', '_corr.tif', '_unw_phase.tif', '_dem.tif', '_lv_theta.tif']\n", + " list_product_dirs = [f.path for f in os.scandir(data_dir) if f.is_dir()]\n", + "\n", + " for product_dir in list_product_dirs:\n", + " for file_suffix in files_for_mintpy:\n", + " product_dir = Path(product_dir)\n", + " src_file = product_dir / f'{product_dir.name}{file_suffix}'\n", + " dst_file = product_dir / f'{src_file.stem}_clipped{src_file.suffix}'\n", + " gdal.Translate(destName=str(dst_file), srcDS=str(src_file), projWin=insect_box)\n", + "\n", + "def unzip_files(zip_file, data_dir):\n", + " if os.path.isfile(zip_file):\n", + " with zipfile.ZipFile(zip_file, 'r') as fzip:\n", + " fzip.extractall(data_dir)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define the parameters and create directories" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "project_name = 'Ridgecrest'\n", + "\n", + "project_home = '/media/jzhu4/data/hyp3-mintpy'\n", + "\n", + "work_dir = Path(project_home) / project_name\n", + "\n", + "data_dir = work_dir / 'data'\n", "\n", - "if not os.path.isdir(proj_dir):\n", - " os.makedirs(proj_dir)\n", - " print('Create directory: {}'.format(proj_dir))\n", - " \n", - "if not os.path.isdir(hyp3_dir):\n", - " os.makedirs(hyp3_dir)\n", - " print('Create directory: {}'.format(hyp3_dir))\n", - " \n", "if not os.path.isdir(work_dir):\n", " os.makedirs(work_dir)\n", " print('Create directory: {}'.format(work_dir))\n", " \n", + "if not os.path.isdir(data_dir):\n", + " os.makedirs(data_dir)\n", + " print('Create directory: {}'.format(data_dir))\n", + " \n", "os.chdir(work_dir)\n", "print('Go to work directory: {}'.format(work_dir))" ] @@ -87,7 +136,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### 1.1 Prepare the template file" + "### 1.1 Load the hyp3 InSAR data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The example dataset is from 2018 Kīlauea volcano. The dataset can be obtained through either downloading from the stagged server or producing with hyp3-sdk. Here we provide the sample dataset at https://jzhu-hyp3-dev.s3.us-west-2.amazonaws.com/hyp3-mintpy. " ] }, { @@ -96,33 +152,27 @@ "metadata": {}, "outputs": [], "source": [ - "CONFIG_TXT = f'''# vim: set filetype=cfg:\n", - "mintpy.load.processor = hyp3\n", - "##---------interferogram datasets:\n", - "mintpy.load.unwFile = {hyp3_dir}/*/*unw_phase_clip.tif\n", - "mintpy.load.corFile = {hyp3_dir}/*/*corr_clip.tif\n", - "##---------geometry datasets:\n", - "mintpy.load.demFile = {hyp3_dir}/*/*dem_clip.tif\n", - "mintpy.load.incAngleFile = {hyp3_dir}/*/*lv_theta_clip.tif\n", - "mintpy.load.waterMaskFile = {hyp3_dir}/*/*/water_mask_clip.tif\n", - "'''\n", - "print(CONFIG_TXT)\n", - "configName = os.path.join(work_dir, \"{}.txt\".format(proj_name))\n", - "configure_template_file(configName, CONFIG_TXT)" + "file = 'Ridgecrest.zip'\n", + "\n", + "file_url = f'https://jzhu-hyp3-dev.s3.us-west-2.amazonaws.com/hyp3-mintpy-example/{file}'\n" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ - "### 1.2 Load the data produced from HyP3" + "!wget {file_url} -P {data_dir}" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ - "The example dataset is from 2019 Ridgecrest, CA earthquake. The dataset can be obtained through either downloading from the stagged server or producing with hyp3-sdk. As far as producing data from hyp3-sdk, we provide the prep_ts_hyp3 notebook at the tutorial directory of (https://github.com/ASFHyP3/hyp3-docs/tree/develop/docs )." + "print(f'downloaded file is {data_dir}/{file}')" ] }, { @@ -131,100 +181,1141 @@ "metadata": {}, "outputs": [], "source": [ - "# verify / prepare input dataset\n", - "\n", - "os.chdir(hyp3_dir)\n", - "\n", - "use_staged_data = True\n", - "\n", - "zip_file_name ='Ridgecrest.zip'\n", - "\n", - "if all(os.path.isfile(os.path.join(work_dir, 'inputs', i)) for i in ['ifgramStack.h5', 'geometryGeo.h5']):\n", - " print(\"Required inputs for mintpy already exists.\")\n", - "\n", - "else:\n", - " if use_staged_data:\n", - " # Check if a stage file from S3 already exist, if not try and download it\n", - " zip_file = os.path.join(hyp3_dir, zip_file_name)\n", - " if not os.path.isfile(zip_file):\n", - " !wget https://jzhu-hyp3-dev.s3.us-west-2.amazonaws.com/hyp3-mintpy/{zip_file_name}\n", - " #!aws s3 cp s3://jzhu-hyp3-dev/hyp3-mintpy-example/{zip_file_name} {zip_file_name}\n", - " # verify if download was succesfull\n", - " if os.path.isfile(zip_file_name):\n", - " import zipfile, glob\n", - " \n", - " with zipfile.ZipFile(zip_file, 'r') as fzip:\n", - " fzip.extractall(hyp3_dir)\n", - " # unzip zip files extracted from the zip_file\n", - " files = glob.glob(\"./????_*.zip\")\n", - " for file in files:\n", - " with zipfile.ZipFile(file) as f:\n", - " f.extractall(hyp3_dir)\n", - " \n", - " print('S3 pre-staged data retrieval was successfull')\n", - "\n", - " else:\n", - " msg = 'No staged data. Setting use_staged_data = False and re-run this cell.'\n", - " print(msg)\n", - "\n", - " else:\n", - " print(\"Using HyP3-sdk to download and prepare the input data for MintPy\")\n", - " print(\"please refer the notebook\")\n", - " os.chdir(os.path.dirname(work_dir))" + "unzip_files(f'{data_dir}/{file}', data_dir)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### 1.3 Run Time-series Analysis application" + "### 1.2 Cut geotiff files for mintpy analysis" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ - "! smallbaselineApp.py --work-dir {work_dir} {configName}" + "prepare_hyp3_product(data_dir)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### 1.4 Display the analysis results\n", + "### 1.3 Prepare the template file" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "707" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mintpy_config = work_dir / 'mintpy_config.txt'\n", + "mintpy_config.write_text(\n", + "f\"\"\"\n", + "mintpy.load.processor = hyp3\n", + "##---------interferogram datasets:\n", + "mintpy.load.unwFile = {data_dir}/*/*_unw_phase_clipped.tif\n", + "mintpy.load.corFile = {data_dir}/*/*_corr_clipped.tif\n", + "##---------geometry datasets:\n", + "mintpy.load.demFile = {data_dir}/*/*_dem_clipped.tif\n", + "mintpy.load.incAngleFile = {data_dir}/*/*_lv_theta_clipped.tif\n", + "#mintpy.load.azAngleFile = {data_dir}/*/*_lv_phi_clipped.tif\n", + "mintpy.load.waterMaskFile = {data_dir}/*/*_water_mask_clipped.tif\n", + "\"\"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.4 Run Time-series Analysis application" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "___________________________________________________________\n", + "\n", + " /## /## /## /## /####### \n", + " | ### /###|__/ | ## | ##__ ## \n", + " | #### /#### /## /####### /###### | ## \\ ## /## /##\n", + " | ## ##/## ##| ##| ##__ ##|_ ##_/ | #######/| ## | ##\n", + " | ## ###| ##| ##| ## \\ ## | ## | ##____/ | ## | ##\n", + " | ##\\ # | ##| ##| ## | ## | ## /##| ## | ## | ##\n", + " | ## \\/ | ##| ##| ## | ## | ####/| ## | #######\n", + " |__/ |__/|__/|__/ |__/ \\___/ |__/ \\____ ##\n", + " /## | ##\n", + " | ######/\n", + " Miami InSAR Time-series software in Python \\______/ \n", + " MintPy v1.3.3, 2022-04-14\n", + "___________________________________________________________\n", + "\n", + "--RUN-at-2022-05-05 20:15:43.276408--\n", + "Current directory: /media/jzhu4/data/hyp3-mintpy/Ridgecrest\n", + "Run routine processing with smallbaselineApp.py on steps: ['load_data', 'modify_network', 'reference_point', 'quick_overview', 'correct_unwrap_error', 'invert_network', 'correct_LOD', 'correct_SET', 'correct_troposphere', 'deramp', 'correct_topography', 'residual_RMS', 'reference_date', 'velocity', 'geocode', 'google_earth', 'hdfeos5']\n", + "Remaining steps: ['modify_network', 'reference_point', 'quick_overview', 'correct_unwrap_error', 'invert_network', 'correct_LOD', 'correct_SET', 'correct_troposphere', 'deramp', 'correct_topography', 'residual_RMS', 'reference_date', 'velocity', 'geocode', 'google_earth', 'hdfeos5']\n", + "--------------------------------------------------\n", + "Project name: mintpy_config\n", + "Go to work directory: /media/jzhu4/data/hyp3-mintpy/Ridgecrest\n", + "copy default template file /home/jzhu4/apps/anaconda3/envs/hyp3-mintpy/lib/python3.8/site-packages/mintpy/defaults/smallbaselineApp.cfg to work directory\n", + "read custom template file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/mintpy_config.txt\n", + "update default template based on input custom template\n", + " mintpy.load.processor: auto --> hyp3\n", + " mintpy.load.unwFile: auto --> /media/jzhu4/data/hyp3-mintpy/Ridgecrest/data/*/*_unw_phase_clipped.tif\n", + " mintpy.load.corFile: auto --> /media/jzhu4/data/hyp3-mintpy/Ridgecrest/data/*/*_corr_clipped.tif\n", + " mintpy.load.demFile: auto --> /media/jzhu4/data/hyp3-mintpy/Ridgecrest/data/*/*_dem_clipped.tif\n", + " mintpy.load.incAngleFile: auto --> /media/jzhu4/data/hyp3-mintpy/Ridgecrest/data/*/*_lv_theta_clipped.tif\n", + " mintpy.load.waterMaskFile: auto --> /media/jzhu4/data/hyp3-mintpy/Ridgecrest/data/*/*_water_mask_clipped.tif\n", + "copy mintpy_config.txt to inputs directory for backup.\n", + "copy smallbaselineApp.cfg to inputs directory for backup.\n", + "copy mintpy_config.txt to pic directory for backup.\n", + "copy smallbaselineApp.cfg to pic directory for backup.\n", + "read default template file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/smallbaselineApp.cfg\n", + "\n", + "\n", + "******************** step - load_data ********************\n", + "\n", + "load_data.py --template /media/jzhu4/data/hyp3-mintpy/Ridgecrest/smallbaselineApp.cfg /media/jzhu4/data/hyp3-mintpy/Ridgecrest/mintpy_config.txt --project mintpy_config\n", + "processor : hyp3\n", + "SAR platform/sensor : unknown from project name \"mintpy_config\"\n", + "--------------------------------------------------\n", + "prepare metadata files for hyp3 products\n", + "prep_hyp3.py /media/jzhu4/data/hyp3-mintpy/Ridgecrest/data/*/*_unw_phase_clipped.tif\n", + "prep_hyp3.py /media/jzhu4/data/hyp3-mintpy/Ridgecrest/data/*/*_corr_clipped.tif\n", + "prep_hyp3.py /media/jzhu4/data/hyp3-mintpy/Ridgecrest/data/*/*_dem_clipped.tif\n", + "prep_hyp3.py /media/jzhu4/data/hyp3-mintpy/Ridgecrest/data/*/*_lv_theta_clipped.tif\n", + "prep_hyp3.py /media/jzhu4/data/hyp3-mintpy/Ridgecrest/data/*/*_water_mask_clipped.tif\n", + "--------------------------------------------------\n", + "searching interferometric pairs info\n", + "input data files:\n", + "unwrapPhase : /media/jzhu4/data/hyp3-mintpy/Ridgecrest/data/*/*_unw_phase_clipped.tif\n", + "coherence : /media/jzhu4/data/hyp3-mintpy/Ridgecrest/data/*/*_corr_clipped.tif\n", + "number of unwrapPhase : 11\n", + "number of coherence : 11\n", + "--------------------------------------------------\n", + "searching geometry files info\n", + "input data files:\n", + "height : /media/jzhu4/data/hyp3-mintpy/Ridgecrest/data/S1AA_20190610T135156_20190622T135157_VVP012_INT80_G_ueF_1262/S1AA_20190610T135156_20190622T135157_VVP012_INT80_G_ueF_1262_dem_clipped.tif\n", + "incidenceAngle : /media/jzhu4/data/hyp3-mintpy/Ridgecrest/data/S1AA_20190610T135156_20190622T135157_VVP012_INT80_G_ueF_1262/S1AA_20190610T135156_20190622T135157_VVP012_INT80_G_ueF_1262_lv_theta_clipped.tif\n", + "waterMask : /media/jzhu4/data/hyp3-mintpy/Ridgecrest/data/S1AA_20190610T135156_20190622T135157_VVP012_INT80_G_ueF_1262/S1AA_20190610T135156_20190622T135157_VVP012_INT80_G_ueF_1262_water_mask_clipped.tif\n", + "--------------------------------------------------\n", + "updateMode : True\n", + "compression: None\n", + "x/ystep: 1/1\n", + "--------------------------------------------------\n", + "create HDF5 file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/ifgramStack.h5 with w mode\n", + "create dataset /unwrapPhase of in size of (11, 2859, 3633) with compression = None\n", + "[==================================================] 20190809_20190821 0s / 0s\n", + "create dataset /coherence of in size of (11, 2859, 3633) with compression = None\n", + "[==================================================] 20190809_20190821 0s / 0s\n", + "create dataset /date of in size of (11, 2)\n", + "create dataset /bperp of in size of (11,)\n", + "create dataset /dropIfgram of in size of (11,)\n", + "add extra metadata: {'PROJECT_NAME': 'mintpy_config'}\n", + "Finished writing to /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/ifgramStack.h5\n", + "--------------------------------------------------\n", + "create HDF5 file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/geometryGeo.h5 with w mode\n", + "create dataset /height of in size of (2859, 3633) with compression = lzf\n", + "create dataset /incidenceAngle of in size of (2859, 3633) with compression = lzf\n", + " convert incidenceAngle from Gamma (from horizontal in radian) to MintPy (from vertical in degree) convention.\n", + "create dataset /waterMask of in size of (2859, 3633) with compression = lzf\n", + "prepare slantRangeDistance ...\n", + " geocoded input, use incidenceAngle from file: S1AA_20190610T135156_20190622T135157_VVP012_INT80_G_ueF_1262_lv_theta_clipped.tif\n", + " convert incidence angle from Gamma to MintPy convention.\n", + "create dataset /slantRangeDistance of in size of (2859, 3633) with compression = lzf\n", + "Finished writing to /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/geometryGeo.h5\n", + "time used: 00 mins 3.2 secs.\n", + "\n", + "No lookup table info range/lat found in files.\n", + "Input data seems to be geocoded. Lookup file not needed.\n", + "Loaded dataset are processed by InSAR software: hyp3\n", + "Loaded dataset is in GEO coordinates\n", + "Interferograms Stack: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/ifgramStack.h5\n", + "Geometry File : /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/geometryGeo.h5\n", + "Lookup Table File : None\n", + "--------------------------------------------------\n", + "All data needed found/loaded/copied. Processed 2-pass InSAR data can be removed.\n", + "--------------------------------------------------\n", + "updating ifgramStack.h5, geometryGeo.h5 metadata based on custom template file: mintpy_config.txt\n", + "\n", + "\n", + "******************** step - modify_network ********************\n", + "Input data seems to be geocoded. Lookup file not needed.\n", + "generate /media/jzhu4/data/hyp3-mintpy/Ridgecrest/waterMask.h5 from /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/geometryGeo.h5 for conveniency\n", + "create HDF5 file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/waterMask.h5 with w mode\n", + "create dataset /waterMask of bool in size of (2859, 3633) with compression=None\n", + "finished writing to /media/jzhu4/data/hyp3-mintpy/Ridgecrest/waterMask.h5\n", + "\n", + "modify_network.py /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/ifgramStack.h5 -t /media/jzhu4/data/hyp3-mintpy/Ridgecrest/smallbaselineApp.cfg\n", + "No lookup table info range/lat found in files.\n", + "read options from template file: smallbaselineApp.cfg\n", + "No input option found to remove interferogram\n", + "Keep all interferograms by enable --reset option\n", + "--------------------------------------------------\n", + "reset dataset 'dropIfgram' to True for all interferograms for file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/ifgramStack.h5\n", + "All dropIfgram are already True, no need to reset.\n", + "\n", + "plot_network.py /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/ifgramStack.h5 -t /media/jzhu4/data/hyp3-mintpy/Ridgecrest/smallbaselineApp.cfg --nodisplay -d coherence -v 0.2 1.0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "read options from template file: smallbaselineApp.cfg\n", + "read temporal/spatial baseline info from file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/ifgramStack.h5\n", + "open ifgramStack file: ifgramStack.h5\n", + "calculating spatial mean of coherence in file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/ifgramStack.h5 ...\n", + "read mask from file: waterMask.h5\n", + "[==================================================] 11/11 0s / 0s \n", + "write average value in space into text file: coherenceSpatialAvg.txt\n", + "number of acquisitions: 7\n", + "number of interferograms: 11\n", + "shift all perp baseline by 88.67054748535156 to zero mean for plotting\n", + "--------------------------------------------------\n", + "number of interferograms marked as drop: 0\n", + "number of interferograms marked as keep: 11\n", + "number of acquisitions marked as drop: 0\n", + "save figure to pbaseHistory.pdf\n", + "save figure to coherenceMatrix.pdf\n", + "save figure to coherenceHistory.pdf\n", + "max perpendicular baseline: 111.93 m\n", + "max temporal baseline: 24.0 days\n", + "showing coherence\n", + "data range: [0.88170004, 0.975093]\n", + "display range: [0.2, 1.0]\n", + "save figure to network.pdf\n", + "\n", + "\n", + "******************** step - reference_point ********************\n", + "Input data seems to be geocoded. Lookup file not needed.\n", + "\n", + "generate_mask.py /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/ifgramStack.h5 --nonzero -o /media/jzhu4/data/hyp3-mintpy/Ridgecrest/maskConnComp.h5 --update\n", + "input ifgramStack file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/ifgramStack.h5\n", + "--------------------------------------------------\n", + "update mode: ON\n", + "1) output file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/maskConnComp.h5 NOT exist.\n", + "run or skip: run.\n", + "calculate the common mask of pixels with non-zero unwrapPhase value\n", + "[==================================================] 11/11 0s / 0s \n", + "create HDF5 file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/maskConnComp.h5 with w mode\n", + "create dataset /mask of bool in size of (2859, 3633) with compression=None\n", + "finished writing to /media/jzhu4/data/hyp3-mintpy/Ridgecrest/maskConnComp.h5\n", + "\n", + "temporal_average.py /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/ifgramStack.h5 --dataset coherence -o /media/jzhu4/data/hyp3-mintpy/Ridgecrest/avgSpatialCoh.h5 --update\n", + "output file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/avgSpatialCoh.h5\n", + "--------------------------------------------------\n", + "update mode: ON\n", + "1) output file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/avgSpatialCoh.h5 NOT exist.\n", + "run or skip: run.\n", + "calculate the temporal average of coherence in file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/ifgramStack.h5 ...\n", + "[==================================================] lines 2859/2859 \n", + "create HDF5 file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/avgSpatialCoh.h5 with w mode\n", + "create dataset /coherence of float32 in size of (2859, 3633) with compression=None\n", + "finished writing to /media/jzhu4/data/hyp3-mintpy/Ridgecrest/avgSpatialCoh.h5\n", + "time used: 00 mins 1.4 secs\n", + "\n", + "Input data seems to be geocoded. Lookup file not needed.\n", + "\n", + "reference_point.py /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/ifgramStack.h5 -t /media/jzhu4/data/hyp3-mintpy/Ridgecrest/smallbaselineApp.cfg -c /media/jzhu4/data/hyp3-mintpy/Ridgecrest/avgSpatialCoh.h5\n", + "--------------------------------------------------\n", + "reading reference info from template: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/smallbaselineApp.cfg\n", + "no input reference y/x.\n", + "reference point selection method: maxCoherence\n", + "--------------------------------------------------\n", + "calculate the temporal average of unwrapPhase in file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/ifgramStack.h5 ...\n", + "[==================================================] lines 2859/2859 \n", + "random select pixel with coherence > 0.85\n", + "\tbased on coherence file: avgSpatialCoh.h5\n", + "y/x: (1681, 1987)\n", + "Add/update ref_x/y attribute to file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/ifgramStack.h5\n", + "{'REF_Y': '1681', 'REF_X': '1987', 'REF_LAT': '3907880.0', 'REF_LON': '471640.0'}\n", + "touch avgSpatialCoh.h5\n", + "touch maskConnComp.h5\n", + "Done.\n", + "\n", + "\n", + "******************** step - quick_overview ********************\n", + "Input data seems to be geocoded. Lookup file not needed.\n", + "\n", + "temporal_average.py /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/ifgramStack.h5 --dataset unwrapPhase -o /media/jzhu4/data/hyp3-mintpy/Ridgecrest/avgPhaseVelocity.h5 --update\n", + "output file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/avgPhaseVelocity.h5\n", + "--------------------------------------------------\n", + "update mode: ON\n", + "1) output file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/avgPhaseVelocity.h5 NOT exist.\n", + "run or skip: run.\n", + "calculate the temporal average of unwrapPhase in file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/ifgramStack.h5 ...\n", + "[==================================================] lines 2859/2859 \n", + "create HDF5 file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/avgPhaseVelocity.h5 with w mode\n", + "create dataset /velocity of float32 in size of (2859, 3633) with compression=None\n", + "finished writing to /media/jzhu4/data/hyp3-mintpy/Ridgecrest/avgPhaseVelocity.h5\n", + "time used: 00 mins 1.7 secs\n", + "\n", + "\n", + "unwrap_error_phase_closure.py /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/ifgramStack.h5 --water-mask /media/jzhu4/data/hyp3-mintpy/Ridgecrest/waterMask.h5 --action calculate --update\n", + "open ifgramStack file: ifgramStack.h5\n", + "number of interferograms: 11\n", + "number of triplets: 5\n", + "calculating the number of triplets with non-zero integer ambiguity of closure phase ...\n", + " block by block with size up to (2860, 3633), 1 blocks in total\n", + "reference pixel in y/x: (1681, 1987) from dataset: unwrapPhase\n", + "[==================================================] line 0 / 2859 \n", + "mask out pixels with zero in file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/waterMask.h5\n", + "mask out pixels with zero in file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/avgSpatialCoh.h5\n", + "write to file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/numTriNonzeroIntAmbiguity.h5\n", + "create HDF5 file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/numTriNonzeroIntAmbiguity.h5 with w mode\n", + "create dataset /mask of float32 in size of (2859, 3633) with compression=None\n", + "finished writing to /media/jzhu4/data/hyp3-mintpy/Ridgecrest/numTriNonzeroIntAmbiguity.h5\n", + "plot and save figure to file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/numTriNonzeroIntAmbiguity.png\n", + "time used: 00 mins 3.0 secs\n", + "Done.\n", + "\n", + "\n", + "******************** step - correct_unwrap_error ********************\n", + "phase-unwrapping error correction is OFF.\n", + "\n", + "\n", + "******************** step - invert_network ********************\n", + "Input data seems to be geocoded. Lookup file not needed.\n", + "\n", + "ifgram_inversion.py /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/ifgramStack.h5 -t /media/jzhu4/data/hyp3-mintpy/Ridgecrest/smallbaselineApp.cfg --update\n", + "use dataset \"unwrapPhase\" by default\n", + "--------------------------------------------------\n", + "update mode: ON\n", + "1) NOT ALL output files found: ['timeseries.h5', 'temporalCoherence.h5', 'numInvIfgram.h5'].\n", + "run or skip: run.\n", + "save the original settings of ['OMP_NUM_THREADS', 'OPENBLAS_NUM_THREADS', 'MKL_NUM_THREADS', 'NUMEXPR_NUM_THREADS', 'VECLIB_MAXIMUM_THREADS']\n", + "set OMP_NUM_THREADS = 1\n", + "set OPENBLAS_NUM_THREADS = 1\n", + "set MKL_NUM_THREADS = 1\n", + "set NUMEXPR_NUM_THREADS = 1\n", + "set VECLIB_MAXIMUM_THREADS = 1\n", + "reference pixel in y/x: (1681, 1987) from dataset: unwrapPhase\n", + "-------------------------------------------------------------------------------\n", + "least-squares solution with L2 min-norm on: deformation velocity\n", + "minimum redundancy: 1.0\n", + "weight function: var\n", + "calculate covariance: False \n", + "mask: no\n", + "-------------------------------------------------------------------------------\n", + "number of interferograms: 11\n", + "number of acquisitions : 7\n", + "number of lines : 2859\n", + "number of columns : 3633\n", + "--------------------------------------------------\n", + "create HDF5 file: timeseries.h5 with w mode\n", + "create dataset : date of |S8 in size of (7,) with compression = None\n", + "create dataset : bperp of in size of (7,) with compression = None\n", + "create dataset : timeseries of in size of (7, 2859, 3633) with compression = None\n", + "close HDF5 file: timeseries.h5\n", + "--------------------------------------------------\n", + "create HDF5 file: temporalCoherence.h5 with w mode\n", + "create dataset : temporalCoherence of in size of (2859, 3633) with compression = None\n", + "close HDF5 file: temporalCoherence.h5\n", + "--------------------------------------------------\n", + "create HDF5 file: numInvIfgram.h5 with w mode\n", + "create dataset : mask of in size of (2859, 3633) with compression = None\n", + "close HDF5 file: numInvIfgram.h5\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "calculating weight from spatial coherence ...\n", + "reading coherence in (0, 0, 3633, 2859) * 11 ...\n", + "convert coherence to weight in chunks of 100000 pixels: 104 chunks in total ...\n", + "convert coherence to weight using inverse of phase variance\n", + " with phase PDF for distributed scatterers from Tough et al. (1995)\n", + " number of independent looks L=41\n", + "chunk 1 / 104\n", + "chunk 2 / 104\n", + "chunk 3 / 104\n", + "chunk 4 / 104\n", + "chunk 5 / 104\n", + "chunk 6 / 104\n", + "chunk 7 / 104\n", + "chunk 8 / 104\n", + "chunk 9 / 104\n", + "chunk 10 / 104\n", + "chunk 11 / 104\n", + "chunk 12 / 104\n", + "chunk 13 / 104\n", + "chunk 14 / 104\n", + "chunk 15 / 104\n", + "chunk 16 / 104\n", + "chunk 17 / 104\n", + "chunk 18 / 104\n", + "chunk 19 / 104\n", + "chunk 20 / 104\n", + "chunk 21 / 104\n", + "chunk 22 / 104\n", + "chunk 23 / 104\n", + "chunk 24 / 104\n", + "chunk 25 / 104\n", + "chunk 26 / 104\n", + "chunk 27 / 104\n", + "chunk 28 / 104\n", + "chunk 29 / 104\n", + "chunk 30 / 104\n", + "chunk 31 / 104\n", + "chunk 32 / 104\n", + "chunk 33 / 104\n", + "chunk 34 / 104\n", + "chunk 35 / 104\n", + "chunk 36 / 104\n", + "chunk 37 / 104\n", + "chunk 38 / 104\n", + "chunk 39 / 104\n", + "chunk 40 / 104\n", + "chunk 41 / 104\n", + "chunk 42 / 104\n", + "chunk 43 / 104\n", + "chunk 44 / 104\n", + "chunk 45 / 104\n", + "chunk 46 / 104\n", + "chunk 47 / 104\n", + "chunk 48 / 104\n", + "chunk 49 / 104\n", + "chunk 50 / 104\n", + "chunk 51 / 104\n", + "chunk 52 / 104\n", + "chunk 53 / 104\n", + "chunk 54 / 104\n", + "chunk 55 / 104\n", + "chunk 56 / 104\n", + "chunk 57 / 104\n", + "chunk 58 / 104\n", + "chunk 59 / 104\n", + "chunk 60 / 104\n", + "chunk 61 / 104\n", + "chunk 62 / 104\n", + "chunk 63 / 104\n", + "chunk 64 / 104\n", + "chunk 65 / 104\n", + "chunk 66 / 104\n", + "chunk 67 / 104\n", + "chunk 68 / 104\n", + "chunk 69 / 104\n", + "chunk 70 / 104\n", + "chunk 71 / 104\n", + "chunk 72 / 104\n", + "chunk 73 / 104\n", + "chunk 74 / 104\n", + "chunk 75 / 104\n", + "chunk 76 / 104\n", + "chunk 77 / 104\n", + "chunk 78 / 104\n", + "chunk 79 / 104\n", + "chunk 80 / 104\n", + "chunk 81 / 104\n", + "chunk 82 / 104\n", + "chunk 83 / 104\n", + "chunk 84 / 104\n", + "chunk 85 / 104\n", + "chunk 86 / 104\n", + "chunk 87 / 104\n", + "chunk 88 / 104\n", + "chunk 89 / 104\n", + "chunk 90 / 104\n", + "chunk 91 / 104\n", + "chunk 92 / 104\n", + "chunk 93 / 104\n", + "chunk 94 / 104\n", + "chunk 95 / 104\n", + "chunk 96 / 104\n", + "chunk 97 / 104\n", + "chunk 98 / 104\n", + "chunk 99 / 104\n", + "chunk 100 / 104\n", + "chunk 101 / 104\n", + "chunk 102 / 104\n", + "chunk 103 / 104\n", + "chunk 104 / 104\n", + "reading unwrapPhase in (0, 0, 3633, 2859) * 11 ...\n", + "use input reference value\n", + "convert zero value in unwrapPhase to NaN (no-data value)\n", + "skip pixels (on the water) with zero value in file: waterMask.h5\n", + "skip pixels with unwrapPhase = NaN in all interferograms\n", + "skip pixels with zero value in file: avgSpatialCoh.h5\n", + "number of pixels to invert: 6411068 out of 10386747 (61.7%)\n", + "estimating time-series via WLS pixel-by-pixel ...\n", + "[==================================================] 6411068/6411068 pixels 777s / 15s\n", + "converting LOS phase unit from radian to meter\n", + "--------------------------------------------------\n", + "open HDF5 file timeseries.h5 in a mode\n", + "writing dataset /timeseries block: [0, 7, 0, 2859, 0, 3633]\n", + "close HDF5 file timeseries.h5.\n", + "--------------------------------------------------\n", + "open HDF5 file temporalCoherence.h5 in a mode\n", + "writing dataset /temporalCoherence block: [0, 2859, 0, 3633]\n", + "close HDF5 file temporalCoherence.h5.\n", + "--------------------------------------------------\n", + "open HDF5 file numInvIfgram.h5 in a mode\n", + "writing dataset /mask block: [0, 2859, 0, 3633]\n", + "close HDF5 file numInvIfgram.h5.\n", + "--------------------------------------------------\n", + "update values on the reference pixel: (1681, 1987)\n", + "set temporalCoherence on the reference pixel to 1.\n", + "set # of observations on the reference pixel as 11\n", + "roll back to the original settings of ['OMP_NUM_THREADS', 'OPENBLAS_NUM_THREADS', 'MKL_NUM_THREADS', 'NUMEXPR_NUM_THREADS', 'VECLIB_MAXIMUM_THREADS']\n", + "remove env variable OMP_NUM_THREADS\n", + "remove env variable OPENBLAS_NUM_THREADS\n", + "remove env variable MKL_NUM_THREADS\n", + "remove env variable NUMEXPR_NUM_THREADS\n", + "remove env variable VECLIB_MAXIMUM_THREADS\n", + "time used: 13 mins 16.8 secs.\n", + "\n", + "Input data seems to be geocoded. Lookup file not needed.\n", + "\n", + "generate_mask.py /media/jzhu4/data/hyp3-mintpy/Ridgecrest/temporalCoherence.h5 -m 0.7 -o /media/jzhu4/data/hyp3-mintpy/Ridgecrest/maskTempCoh.h5\n", + "update mode: ON\n", + "run or skip: run\n", + "input temporalCoherence file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/temporalCoherence.h5\n", + "read /media/jzhu4/data/hyp3-mintpy/Ridgecrest/temporalCoherence.h5\n", + "create initial mask with the same size as the input file and all = 1\n", + "all pixels with nan value = 0\n", + "exclude pixels with value < 0.7\n", + "create HDF5 file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/maskTempCoh.h5 with w mode\n", + "create dataset /mask of bool in size of (2859, 3633) with compression=None\n", + "finished writing to /media/jzhu4/data/hyp3-mintpy/Ridgecrest/maskTempCoh.h5\n", + "time used: 00 mins 0.1 secs.\n", + "number of reliable pixels: 5971001\n", + "\n", + "\n", + "******************** step - correct_LOD ********************\n", + "Input data seems to be geocoded. Lookup file not needed.\n", + "No local oscillator drift correction is needed for Sen.\n", + "\n", + "\n", + "******************** step - correct_SET ********************\n", + "Input data seems to be geocoded. Lookup file not needed.\n", + "No solid Earth tides correction.\n", + "\n", + "\n", + "******************** step - correct_troposphere ********************\n", + "Input data seems to be geocoded. Lookup file not needed.\n", + "Atmospheric correction using Weather Re-analysis dataset (PyAPS, Jolivet et al., 2011)\n", + "Weather Re-analysis dataset: ERA5\n", + "\n", + "tropo_pyaps3.py -f /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries.h5 --model ERA5 -g /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/geometryGeo.h5 -w /media/jzhu4/data/mintpy_data/weather_data\n", + "weather model: ERA5 - dry (hydrostatic) and wet delay\n", + "weather directory: /media/jzhu4/data/mintpy_data/weather_data\n", + "output tropospheric delay file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/ERA5.h5\n", + "output corrected time-series file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries_ERA5.h5\n", + "read dates/time info from file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries.h5\n", + "time of cloest available product: 14:00 UTC\n", + "\n", + "------------------------------------------------------------------------------\n", + "downloading weather model data using PyAPS ...\n", + "common file size: 759240 bytes\n", + "number of grib files existed : 7\n", + "number of grib files to download: 0\n", + "------------------------------------------------------------------------------\n", + "\n", + "update mode: ON\n", + "output file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/ERA5.h5\n", + "1) output file either do NOT exist or is NOT newer than all GRIB files.\n", + "run or skip: run\n", + "open geometry file: geometryGeo.h5\n", + "reading incidenceAngle data from file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/geometryGeo.h5 ...\n", + "reading height data from file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/geometryGeo.h5 ...\n", + "--------------------------------------------------\n", + "create HDF5 file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/ERA5.h5 with w mode\n", + "create dataset : date of |S8 in size of (7,) with compression = None\n", + "create dataset : timeseries of in size of (7, 2859, 3633) with compression = None\n", + "close HDF5 file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/ERA5.h5\n", + "\n", + "------------------------------------------------------------------------------\n", + "calculating absolute delay for each date using PyAPS (Jolivet et al., 2011; 2014) ...\n", + "number of grib files used: 7\n", + "[==================================================] ERA5_N30_N40_W130_W110_20190821_14.grb 28s / 4s\n", + "\n", + "------------------------------------------------------------------------------\n", + "correcting relative delay for input time-series using diff.py\n", + "diff.py /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries.h5 /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/ERA5.h5 -o /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries_ERA5.h5 --force\n", + "/media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries.h5 - ['/media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/ERA5.h5'] --> /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries_ERA5.h5\n", + "the 1st input file is: timeseries\n", + "--------------------------------------------------\n", + "grab metadata from ref_file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries.h5\n", + "grab dataset structure from ref_file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries.h5\n", + "create HDF5 file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries_ERA5.h5 with w mode\n", + "create dataset : bperp of float32 in size of (7,) with compression = None\n", + "create dataset : date of |S8 in size of (7,) with compression = None\n", + "create dataset : timeseries of float32 in size of (7, 2859, 3633) with compression = None\n", + "close HDF5 file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries_ERA5.h5\n", + "read from file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/ERA5.h5\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "* referencing data from ERA5.h5 to y/x: 1681/1987\n", + "* referencing data from ERA5.h5 to date: 20190610\n", + "read from file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries.h5\n", + "--------------------------------------------------\n", + "open HDF5 file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries_ERA5.h5 in a mode\n", + "writing dataset /timeseries block: [0, 7, 0, 2859, 0, 3633]\n", + "close HDF5 file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries_ERA5.h5.\n", + "time used: 00 mins 1.9 secs\n", + "\n", + "\n", + "******************** step - deramp ********************\n", + "No phase ramp removal.\n", + "\n", + "\n", + "******************** step - correct_topography ********************\n", + "Input data seems to be geocoded. Lookup file not needed.\n", + "\n", + "dem_error.py /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries_ERA5.h5 -t /media/jzhu4/data/hyp3-mintpy/Ridgecrest/smallbaselineApp.cfg -o /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries_ERA5_demErr.h5 --update -g /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/geometryGeo.h5\n", + "read options from template file: smallbaselineApp.cfg\n", + "--------------------------------------------------\n", + "update mode: ON\n", + "1) output file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries_ERA5_demErr.h5 NOT found.\n", + "run or skip: run.\n", + "save the original settings of ['OMP_NUM_THREADS', 'OPENBLAS_NUM_THREADS', 'MKL_NUM_THREADS', 'NUMEXPR_NUM_THREADS', 'VECLIB_MAXIMUM_THREADS']\n", + "set OMP_NUM_THREADS = 1\n", + "set OPENBLAS_NUM_THREADS = 1\n", + "set MKL_NUM_THREADS = 1\n", + "set NUMEXPR_NUM_THREADS = 1\n", + "set VECLIB_MAXIMUM_THREADS = 1\n", + "open timeseries file: timeseries_ERA5.h5\n", + "--------------------------------------------------------------------------------\n", + "correct topographic phase residual (DEM error) (Fattahi & Amelung, 2013, IEEE-TGRS)\n", + "ordinal least squares (OLS) inversion with L2-norm minimization on: phase\n", + "temporal deformation model: polynomial order = 2\n", + "--------------------------------------------------------------------------------\n", + "add/update the following configuration metadata to file:\n", + "['polyOrder', 'phaseVelocity', 'stepFuncDate', 'excludeDate']\n", + "--------------------------------------------------\n", + "create HDF5 file: demErr.h5 with w mode\n", + "create dataset : dem of in size of (2859, 3633) with compression = None\n", + "close HDF5 file: demErr.h5\n", + "--------------------------------------------------\n", + "grab dataset structure from ref_file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries_ERA5.h5\n", + "create HDF5 file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries_ERA5_demErr.h5 with w mode\n", + "create dataset : bperp of float32 in size of (7,) with compression = None\n", + "create dataset : date of |S8 in size of (7,) with compression = None\n", + "create dataset : timeseries of float32 in size of (7, 2859, 3633) with compression = None\n", + "close HDF5 file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries_ERA5_demErr.h5\n", + "--------------------------------------------------\n", + "grab dataset structure from ref_file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries_ERA5.h5\n", + "create HDF5 file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseriesResidual.h5 with w mode\n", + "create dataset : bperp of float32 in size of (7,) with compression = None\n", + "create dataset : date of |S8 in size of (7,) with compression = None\n", + "create dataset : timeseries of float32 in size of (7, 2859, 3633) with compression = None\n", + "close HDF5 file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseriesResidual.h5\n", + "open geometry file: geometryGeo.h5\n", + "read 2D incidenceAngle, slantRangeDistance from geometry file: geometryGeo.h5\n", + "read mean bperp from timeseries file\n", + "skip pixels with ZERO in ALL acquisitions\n", + "skip pixels with NaN in ANY acquisitions\n", + "skip pixels with ZERO temporal coherence\n", + "skip pixels with ZERO / NaN value in incidenceAngle / slantRangeDistance\n", + "number of pixels to invert: 6032690 out of 10386747 (58.1%)\n", + "estimating DEM error pixel-wisely ...\n", + "[==================================================] 6032690/6032690 452s / 9s\n", + "--------------------------------------------------\n", + "open HDF5 file demErr.h5 in a mode\n", + "writing dataset /dem block: [0, 2859, 0, 3633]\n", + "close HDF5 file demErr.h5.\n", + "--------------------------------------------------\n", + "open HDF5 file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries_ERA5_demErr.h5 in a mode\n", + "writing dataset /timeseries block: [0, 7, 0, 2859, 0, 3633]\n", + "close HDF5 file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries_ERA5_demErr.h5.\n", + "--------------------------------------------------\n", + "open HDF5 file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseriesResidual.h5 in a mode\n", + "writing dataset /timeseries block: [0, 7, 0, 2859, 0, 3633]\n", + "close HDF5 file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseriesResidual.h5.\n", + "roll back to the original settings of ['OMP_NUM_THREADS', 'OPENBLAS_NUM_THREADS', 'MKL_NUM_THREADS', 'NUMEXPR_NUM_THREADS', 'VECLIB_MAXIMUM_THREADS']\n", + "remove env variable OMP_NUM_THREADS\n", + "remove env variable OPENBLAS_NUM_THREADS\n", + "remove env variable MKL_NUM_THREADS\n", + "remove env variable NUMEXPR_NUM_THREADS\n", + "remove env variable VECLIB_MAXIMUM_THREADS\n", + "time used: 07 mins 41.6 secs.\n", + "\n", + "\n", + "******************** step - residual_RMS ********************\n", + "\n", + "timeseries_rms.py timeseriesResidual.h5 -t /media/jzhu4/data/hyp3-mintpy/Ridgecrest/smallbaselineApp.cfg\n", + "read options from template file: smallbaselineApp.cfg\n", + "remove quadratic ramp from file: timeseriesResidual.h5\n", + "read mask file: maskTempCoh.h5\n", + "--------------------------------------------------\n", + "grab metadata from ref_file: timeseriesResidual.h5\n", + "grab dataset structure from ref_file: timeseriesResidual.h5\n", + "create HDF5 file: timeseriesResidual_ramp.h5 with w mode\n", + "create dataset : bperp of float32 in size of (7,) with compression = None\n", + "create dataset : date of |S8 in size of (7,) with compression = None\n", + "create dataset : timeseries of float32 in size of (7, 2859, 3633) with compression = None\n", + "close HDF5 file: timeseriesResidual_ramp.h5\n", + "estimating phase ramp one date at a time ...\n", + "[==================================================] 7/7 5s / 0s\n", + "finished writing to file: timeseriesResidual_ramp.h5\n", + "time used: 00 mins 6.4 secs.\n", + "\n", + "calculating residual RMS for each epoch from file: timeseriesResidual_ramp.h5\n", + "read mask from file: maskTempCoh.h5\n", + "reading timeseries data from file: timeseriesResidual_ramp.h5 ...\n", + "[==================================================] 7/7 1s / 0s\n", + "save timeseries RMS to text file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/rms_timeseriesResidual_ramp.txt\n", + "read timeseries residual RMS from file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/rms_timeseriesResidual_ramp.txt\n", + "--------------------------------------------------\n", + "date with min RMS: 20190821 - 0.0049\n", + "save date to file: reference_date.txt\n", + "--------------------------------------------------\n", + "date(s) with RMS > 3.0 * median RMS (0.0230)\n", + "20190704 - 0.0247\n", + "20190716 - 0.0256\n", + "save date(s) to file: exclude_date.txt\n", + "create figure in size: [5.0, 3.0]\n", + "save figure to file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/rms_timeseriesResidual_ramp.pdf\n", + "\n", + "\n", + "******************** step - reference_date ********************\n", + "\n", + "reference_date.py -t /media/jzhu4/data/hyp3-mintpy/Ridgecrest/smallbaselineApp.cfg /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries.h5 /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries_ERA5.h5 /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries_ERA5_demErr.h5\n", + "read reference date from file: reference_date.txt\n", + "input reference date: 20190821\n", + "--------------------------------------------------\n", + "change reference date for file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries.h5\n", + "reading data ...\n", + "referencing in time ...\n", + "--------------------------------------------------\n", + "open HDF5 file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries.h5 in r+ mode\n", + "writing dataset /timeseries block: (0, 7, 0, 2859, 0, 3633)\n", + "close HDF5 file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries.h5.\n", + "update \"REF_DATE\" attribute value to 20190821\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--------------------------------------------------\n", + "change reference date for file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries_ERA5.h5\n", + "reading data ...\n", + "referencing in time ...\n", + "--------------------------------------------------\n", + "open HDF5 file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries_ERA5.h5 in r+ mode\n", + "writing dataset /timeseries block: (0, 7, 0, 2859, 0, 3633)\n", + "close HDF5 file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries_ERA5.h5.\n", + "update \"REF_DATE\" attribute value to 20190821\n", + "--------------------------------------------------\n", + "change reference date for file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries_ERA5_demErr.h5\n", + "reading data ...\n", + "referencing in time ...\n", + "--------------------------------------------------\n", + "open HDF5 file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries_ERA5_demErr.h5 in r+ mode\n", + "writing dataset /timeseries block: (0, 7, 0, 2859, 0, 3633)\n", + "close HDF5 file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries_ERA5_demErr.h5.\n", + "update \"REF_DATE\" attribute value to 20190821\n", + "time used: 00 mins 23.8 secs.\n", + "\n", + "\n", + "******************** step - velocity ********************\n", + "\n", + "timeseries2velocity.py /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries_ERA5_demErr.h5 -t /media/jzhu4/data/hyp3-mintpy/Ridgecrest/smallbaselineApp.cfg -o /media/jzhu4/data/hyp3-mintpy/Ridgecrest/velocity.h5 --update\n", + "read options from template file: smallbaselineApp.cfg\n", + "open timeseries file: timeseries_ERA5_demErr.h5\n", + "exclude date:['20190704', '20190716']\n", + "--------------------------------------------------\n", + "dates from input file: 7\n", + "['20190610', '20190622', '20190704', '20190716', '20190728', '20190809', '20190821']\n", + "--------------------------------------------------\n", + "dates used to estimate the velocity: 5\n", + "['20190610', '20190622', '20190728', '20190809', '20190821']\n", + "--------------------------------------------------\n", + "update mode: ON\n", + "1) output file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/velocity.h5 NOT found.\n", + "run or skip: run.\n", + "estimate deformation model with the following assumed time functions:\n", + " polynomial : 1\n", + " periodic : []\n", + " step : []\n", + " exp : {}\n", + " log : {}\n", + "add/update the following configuration metadata:\n", + "['startDate', 'endDate', 'excludeDate', 'bootstrap', 'bootstrapCount']\n", + "--------------------------------------------------\n", + "create HDF5 file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/velocity.h5 with w mode\n", + "create dataset : velocity of in size of (2859, 3633) with compression = None\n", + "create dataset : velocityStd of in size of (2859, 3633) with compression = None\n", + "add /velocity attribute: UNIT = m/year\n", + "add /velocityStd attribute: UNIT = m/year\n", + "close HDF5 file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/velocity.h5\n", + "reading data from file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries_ERA5_demErr.h5 ...\n", + "skip pixels with zero/nan value in all acquisitions\n", + "number of pixels to invert: 6032690 out of 10386747 (58.1%)\n", + "estimating time functions via linalg.lstsq ...\n", + "estimating time function STD from time-series fitting residual ...\n", + "--------------------------------------------------\n", + "open HDF5 file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/velocity.h5 in a mode\n", + "writing dataset /velocity block: [0, 2859, 0, 3633]\n", + "close HDF5 file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/velocity.h5.\n", + "--------------------------------------------------\n", + "open HDF5 file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/velocity.h5 in a mode\n", + "writing dataset /velocityStd block: [0, 2859, 0, 3633]\n", + "close HDF5 file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/velocity.h5.\n", + "time used: 00 mins 1.8 secs.\n", + "\n", + "timeseries2velocity.py /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/ERA5.h5 -t /media/jzhu4/data/hyp3-mintpy/Ridgecrest/smallbaselineApp.cfg -o /media/jzhu4/data/hyp3-mintpy/Ridgecrest/velocityERA5.h5 --update --ref-date 20190821 --ref-yx 1681 1987\n", + "read options from template file: smallbaselineApp.cfg\n", + "open timeseries file: ERA5.h5\n", + "exclude date:['20190704', '20190716']\n", + "--------------------------------------------------\n", + "dates from input file: 7\n", + "['20190610', '20190622', '20190704', '20190716', '20190728', '20190809', '20190821']\n", + "--------------------------------------------------\n", + "dates used to estimate the velocity: 5\n", + "['20190610', '20190622', '20190728', '20190809', '20190821']\n", + "--------------------------------------------------\n", + "update mode: ON\n", + "1) output file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/velocityERA5.h5 NOT found.\n", + "run or skip: run.\n", + "estimate deformation model with the following assumed time functions:\n", + " polynomial : 1\n", + " periodic : []\n", + " step : []\n", + " exp : {}\n", + " log : {}\n", + "add/update the following configuration metadata:\n", + "['startDate', 'endDate', 'excludeDate', 'bootstrap', 'bootstrapCount']\n", + "--------------------------------------------------\n", + "create HDF5 file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/velocityERA5.h5 with w mode\n", + "create dataset : velocity of in size of (2859, 3633) with compression = None\n", + "create dataset : velocityStd of in size of (2859, 3633) with compression = None\n", + "add /velocity attribute: UNIT = m/year\n", + "add /velocityStd attribute: UNIT = m/year\n", + "close HDF5 file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/velocityERA5.h5\n", + "reading data from file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/ERA5.h5 ...\n", + "referecing to date: 20190821\n", + "referencing to point (y, x): (1681, 1987)\n", + "skip pixels with zero/nan value in all acquisitions\n", + "number of pixels to invert: 10386747 out of 10386747 (100.0%)\n", + "estimating time functions via linalg.lstsq ...\n", + "estimating time function STD from time-series fitting residual ...\n", + "--------------------------------------------------\n", + "open HDF5 file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/velocityERA5.h5 in a mode\n", + "writing dataset /velocity block: [0, 2859, 0, 3633]\n", + "close HDF5 file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/velocityERA5.h5.\n", + "--------------------------------------------------\n", + "open HDF5 file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/velocityERA5.h5 in a mode\n", + "writing dataset /velocityStd block: [0, 2859, 0, 3633]\n", + "close HDF5 file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/velocityERA5.h5.\n", + "time used: 00 mins 13.7 secs.\n", + "\n", + "\n", + "******************** step - geocode ********************\n", + "dataset is geocoded, skip geocoding and continue.\n", + "\n", + "\n", + "******************** step - google_earth ********************\n", + "creating Google Earth KMZ file for geocoded velocity file: ...\n", + "\n", + "save_kmz.py /media/jzhu4/data/hyp3-mintpy/Ridgecrest/velocity.h5 -o /media/jzhu4/data/hyp3-mintpy/Ridgecrest/velocity.kmz\n", + "data coverage in y/x: (0, 0, 3633, 2859)\n", + "subset coverage in y/x: (0, 0, 3633, 2859)\n", + "update LENGTH, WIDTH, Y/XMAX\n", + "update/add SUBSET_XMIN/YMIN/XMAX/YMAX: 0/0/3633/2859\n", + "update Y/X_FIRST\n", + "update REF_Y/X\n", + "read mask from file: maskTempCoh.h5\n", + "masking out pixels with zero value in file: None\n", + "colormap: jet\n", + "plotting data ...\n", + "figure size : [15.25, 12.00]\n", + "show reference point\n", + "writing /media/jzhu4/data/hyp3-mintpy/Ridgecrest/velocity.png with dpi=600\n", + "writing /media/jzhu4/data/hyp3-mintpy/Ridgecrest/velocity_cbar.png\n", + "writing /media/jzhu4/data/hyp3-mintpy/Ridgecrest/velocity.kml\n", + "remove /media/jzhu4/data/hyp3-mintpy/Ridgecrest/velocity.kml\n", + "remove /media/jzhu4/data/hyp3-mintpy/Ridgecrest/velocity.png\n", + "remove /media/jzhu4/data/hyp3-mintpy/Ridgecrest/velocity_cbar.png\n", + "merged all files to /media/jzhu4/data/hyp3-mintpy/Ridgecrest/velocity.kmz\n", + "\n", + "\n", + "******************** step - hdfeos5 ********************\n", + "save time-series to HDF-EOS5 format is OFF.\n", + "\n", + "******************** plot & save to pic ********************\n", + "Input data seems to be geocoded. Lookup file not needed.\n", + "view.py --dpi 150 --noverbose --nodisplay --update --memory 4.0 velocity.h5 --dem inputs/geometryGeo.h5 --mask maskTempCoh.h5 -u cm\n", + "view.py --dpi 150 --noverbose --nodisplay --update --memory 4.0 temporalCoherence.h5 -c gray -v 0 1\n", + "view.py --dpi 150 --noverbose --nodisplay --update --memory 4.0 maskTempCoh.h5 -c gray -v 0 1\n", + "view.py --dpi 150 --noverbose --nodisplay --update --memory 4.0 inputs/geometryGeo.h5\n", + "view.py --dpi 150 --noverbose --nodisplay --update --memory 4.0 inputs/ifgramStack.h5 unwrapPhase- --zero-mask --wrap -c cmy\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "view.py --dpi 150 --noverbose --nodisplay --update --memory 4.0 inputs/ifgramStack.h5 unwrapPhase- --zero-mask\n", + "view.py --dpi 150 --noverbose --nodisplay --update --memory 4.0 inputs/ifgramStack.h5 coherence- --mask no -v 0 1\n", + "view.py --dpi 150 --noverbose --nodisplay --update --memory 4.0 avgPhaseVelocity.h5\n", + "view.py --dpi 150 --noverbose --nodisplay --update --memory 4.0 avgSpatialCoh.h5 -c gray -v 0 1\n", + "view.py --dpi 150 --noverbose --nodisplay --update --memory 4.0 maskConnComp.h5 -c gray -v 0 1\n", + "view.py --dpi 150 --noverbose --nodisplay --update --memory 4.0 timeseries.h5 --noaxis -u cm --wrap --wrap-range -5 5\n", + "view.py --dpi 150 --noverbose --nodisplay --update --memory 4.0 timeseries_ERA5.h5 --noaxis -u cm --wrap --wrap-range -5 5\n", + "view.py --dpi 150 --noverbose --nodisplay --update --memory 4.0 timeseries_ERA5_demErr.h5 --noaxis -u cm --wrap --wrap-range -5 5\n", + "view.py --dpi 150 --noverbose --nodisplay --update --memory 4.0 velocityERA5.h5 --mask no\n", + "view.py --dpi 150 --noverbose --nodisplay --update --memory 4.0 numInvIfgram.h5 --mask no\n", + "copy *.txt files into ./pic directory.\n", + "move *.png/pdf/kmz files to ./pic directory.\n", + "time used: 00 mins 44.8 secs.\n", + "Explore more info & visualization options with the following scripts:\n", + " info.py #check HDF5 file structure and metadata\n", + " view.py #2D map view\n", + " tsview.py #1D point time-series (interactive) \n", + " transect.py #1D profile (interactive)\n", + " plot_coherence_matrix.py #plot coherence matrix for one pixel (interactive)\n", + " plot_network.py #plot network configuration of the dataset \n", + " plot_transection.py #plot 1D profile along a line of a 2D matrix (interactive)\n", + " save_kmz.py #generate Google Earth KMZ file in raster image\n", + " save_kmz_timeseries.py #generate Goodle Earth KMZ file in points for time-series (interactive)\n", + " \n", + "Go back to directory: /media/jzhu4/data/hyp3-mintpy/Ridgecrest\n", + "\n", + "################################################\n", + " Normal end of smallbaselineApp processing!\n", + "################################################\n", + "Time used: 23 mins 29.4 secs\n", + "\n" + ] + } + ], + "source": [ + "! smallbaselineApp.py --work-dir {work_dir} {mintpy_config}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Display the analysis results\n", "\n", "There are a few scripts used to display the analysis results. There are in the MINTPY_HOME/mintpy. Here we show two majoy disaply scripts." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ - "os.chdir(proj_dir)" + "%matplotlib widget\n", + "from mintpy import view, tsview" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 44, "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "run view.py in MintPy version v1.3.3, date 2022-04-14\n", + "input file is velocity file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/velocity.h5 in float32 format\n", + "file size in y/x: (2859, 3633)\n", + "num of datasets in file velocity.h5: 2\n", + "datasets to exclude (0):\n", + "[]\n", + "datasets to display (2):\n", + "['velocity', 'velocityStd']\n", + "data coverage in y/x: (0, 0, 3633, 2859)\n", + "subset coverage in y/x: (0, 0, 3633, 2859)\n", + "data coverage in lat/lon: (312640.0, 4042400.0, 603280.0, 3813680.0)\n", + "subset coverage in lat/lon: (312640.0, 4042400.0, 603280.0, 3813680.0)\n", + "------------------------------------------------------------------------\n", + "colormap: jet\n", + "figure title: velocity\n", + "figure size : [15.00, 8.00]\n", + "dataset number: 2\n", + "row number: 1\n", + "column number: 2\n", + "figure number: 1\n", + "read mask from file: maskTempCoh.h5\n", + "----------------------------------------\n", + "Figure 1 - velocity.png\n", + "reading data as a list of 2D matrices ...\n", + "[==================================================] velocityStd 0s / 0s \n", + "data range: [-745.02423, 731.78046] cm/year\n", + "display range: [-745.02423, 731.78046] cm/year\n", + "masking data\n", + "plotting ...\n", + "[==================================================] velocityStd 0s / 0s \n", + "data range: [-745.02423, 731.78046] cm/year\n", + "display range: [-393.91623, 544.49414] cm/year\n", + "show colorbar\n", + "showing ...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jzhu4/apps/anaconda3/envs/hyp3-mintpy/lib/python3.8/site-packages/mintpy/view.py:1355: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n", + " fig.tight_layout()\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "0a5b072f70eb4b38a7a35396a36d7bbf", + "version_major": 2, + "version_minor": 0 + }, + "image/png": 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\n", + " Figure 1 - velocity.png\n", + "
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\n", + " " + ], + "text/plain": [ + "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "view.main(['mintpy/velocity.h5'])" + "view.main([f'{work_dir}/velocity.h5'])" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 45, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tsview.py /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries.h5\n", + "open timeseries file: timeseries.h5\n", + "exclude date:['20190704', '20190716']\n", + "No lookup table info range/lat found in files.\n", + "data coverage in y/x: (0, 0, 3633, 2859)\n", + "subset coverage in y/x: (0, 0, 3633, 2859)\n", + "data coverage in lat/lon: (312640.0, 4042400.0, 603280.0, 3813680.0)\n", + "subset coverage in lat/lon: (312640.0, 4042400.0, 603280.0, 3813680.0)\n", + "------------------------------------------------------------------------\n", + "estimate deformation model with the following assumed time functions:\n", + " polynomial : 1\n", + " periodic : []\n", + " step : []\n", + " exp : {}\n", + " log : {}\n", + "reading timeseries from file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries.h5\n", + "reference to date: 20190821\n", + "read mask from file: maskTempCoh.h5\n", + "data range: [-78.073875, 93.78707] cm\n", + "display range: [-69.005554, 51.61355] cm\n", + "figure size : [8.29, 6.00]\n", + "display data in transparency: 1.0\n", + "plot in geo-coordinate\n", + "plotting image ...\n", + "plot scale bar: [0.2, 0.2, 0.1]\n", + "plot reference point\n", + "showing ...\n", + "\n", + "------------------------------------------------------------------------\n", + "To scroll through the image sequence:\n", + "1) Move the slider, OR\n", + "2) Press left or right arrow key (if not responding, click the image and try again).\n", + "------------------------------------------------------------------------\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "d2f3c87a7aae4c2ea072a96c806a80f9", + "version_major": 2, + "version_minor": 0 + }, + "image/png": 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+K3p7e/G2t70Np5xyCh566CFcccUVeNe73oU333wTS5YswV577YWjjz4aw8PD2H///TFp0iRceeWVGB4exjnnnIPu7m7cd999o3x1BEEQBEEQBNEcSPCMEZYuXYp//dd/jW0/7bTTcNNNN2FoaAhf//rX8eMf/xivvfYapkyZgoMOOghf+9rXgrU6Vq1ahc9+9rO477770NnZiaOOOgpXXHEFubQRBEEQBEEQ4xYSPARBEARBEARBjFvaRnsABEEQBEEQBEEQzYIED0EQBEEQBEEQ4xZyaWtxhoeHsWrVKnR1dYExNtrDIQiCIAiCaAqcc2zYsAEzZsxAWxv9Jk80DhI8Lc6qVaswc+bM0R4GQRAEQRDEiLBy5Upsv/32NR1brVaxY3s7brj3XsyfP7/BIyPGKiR4Wpyuri4A4o+/u7t7lEfTmqxfvx4zZ86k16gFoPeidaD3onWg96I1oPehdbC9F3K7nPvUwhNPPIHXAFy8YAHmky8X4UOCp8WRaWzd3d30BZ0CvUatA70XrQO9F60DvRetAb0PrYPtvagnhf/Cgw/GJAD/gEiRo3IAAiDTAoIgCIIgCGKc8A8A7wGwAQgWbCcIEjwEQRAEQRDEmGft2rVYBWAPADsAuOeee0Z5RESrQIKHGPMUCgVccsklKBQKoz2ULR56L1oHei9aB3ovWgN6H1qHZr0Xv/vd7zAVQBeAtwP43uc+19D+ibEL45wqulqZ9evXo6enBwMDA5RzTBAEQRDEuKXeOc+7GEMngHkA1gC4HsCGcplELkERHoIgCIIgCGJswznHPyAiOwAwFUARwPLly0dvUETLQIKHIAiCIAiCGNM8++yzqAB4m/+cAdgZwFff+97RGxTRMpDgIQiCIAiCIMY0/77PPpiF6Horb4dwbSMIEjwEQRAEQRDEmOYFiIiOyk4A3gDw2muvjfyAiJaCBA9BEARBEAQxZtm4cSNeQVzwTASwHYD77rtv5AdFtBQkeAiCIAiCIIgxy9KlS9EDoNewb2cAV3z84yM8IqLVIMFDEARBEARBjFn+433vi0V3JDtDpLt5njeCIyJaDRI8BEEQBEEQxJjlH4ins0lmQDi2PfHEEyM3IKLlIMFDEARBEARBjEn+8Y9/YADAjpb9bRDmBRcedNCIjYloPUjwEARBEARBEGOSe++9F28DUEhoszPInnpLhwQPMebpx4TRHgJBEARBEKPAVZ/5jDWdTfJ2AK8B6O/vH4EREa0ICR5izNOLzQCAoQEG3i9u8jFBEARBEOOTwcFBvAR7/Y6kG8BUAL/73e+aPyiiJSHBQ4xpShUH5RJDuRSKm6oT3g8NiO3lkhBB8jlBEARBEGObhx56CAUA0zK0fTuAS088sckjIloVd7QHQBD1UuzkGBpgcL1Q7KhIMeTJT7sielwPYFUAU3nzB0oQBEEQRMP46uGH4+0QLmxp7AzgziaPh2hdKMJDjHlKFQeub6/veuFN4lSj7dt7eHBfdQDuIkiDk9EiNWJEEARBEETrsevHP47JGdtOBlBub2/eYIiWhiI8xJjGcx10lIdQKQhhI6M8rmF9sUIFYL0c5RJDsUfcy4CQjAwVO4UYUkVPsZOjVHHgVIcBiPNI0UQQBEEQxOjRhmyTWZrwbtnQ+0+MWcolBscVQUo1iqNGe6SQcapApQCgFNb0AGGaW3BfYih2cnhuGzoLXngehP0AMNYCkQgiCIIgCIJoPUjwEGMWIT5E1MVzxXPWy4E+Bu5/sm11PYCI3JRLDO09HO0QwkZGeDoLHkoVcaDl8BhSBKnRJdZLIoggCIIgmoUDIEuiGk14t2yohocYs0iRA0QjL9z1jQg0nGrYTgqbaH9tKJdYKHSqw0GURz2njlozpKfSSWts6RBHTnEEQRAEQRAjCwleYsxSqIjojZrO1t7DxVo8yidbihA1vS1MaQs1v6zRAYQRQtbIjhpBko89N3SPg0HglEsMntsGz3XQ7QxivdcBAOh2BjOelSAIgiAIquEhskDvPzEmKZcYHIMiGRpgsQ91UkqbpFRx0OmnuDnVYbGvAKNbmxpZMkV8ip0cnjQ98PeroiysGxID68eEYMwyuqSiR5kIgiAIghC0gVLaiHTo/SfGJNKRjfVG1+AxubNJQqERRnWkoHEAlKsssl0VROoxAhENUl3dpKixiaT4NXiB6InvGw7OqYogz3VQ9ZVeLzYbjyUIgiAIgiBCSPAQYxIpbIa0RUSTcKqh8HCqwyhXQ1Gj2k5LVFtqmYImzQw8t030IQVTNXoewCxyVKTY6cVmrEcHnGq2SI7reag6DvoxIbaPRBBBEASxJUEpbUQW6P0nxhzlEkMB8YiOLcKj1vCozm6yLyDuxKaKo1DUCJEEP/IiRU8S+ho+EjVi1I8J6HUGUao6xv22KJAJkwjy4GIbbMjcB0EQBEGMFciljcgCvf/EmKPYyVEGQ6ES3Z4W4ZFpb6qYScJUdwOImprQdCAUPXptjxQ74nncEFFGdGSKmuc6kShPHqFjw/P/xN9Al3E/CSGCIAiCIMY7JHiIMUtazU4SWUWP2h4Qx+g1OkLMhKKn2MkxWHFiBgR5BI1tnxRHrhcVS7WyGpNj26ZjXV19EgRBEMRIkTWlrf6fEImxDAkeYkwyWGwXRf/V4cDAQJJVCKkiRo/m6NvsfcTNC3SkeLHV6Lieh35HOLVljerUK3SSkCLIU/49bIe1TTvfWGQJDo5tOxwPj8JICIIgCIJIgwQPMSZxqh46Cx6GykJg2KyndVQ3N3Vdnmjf5mNNESHVvCDi2IYwjU2P6qjubKpwUdfjaQSe5c/by/g7lwMvaP8aphjbbGlCaAkOtgobkwjy4GAeHmz2sAiCILZYstpSZ2lDjF9I8BBjkkqhAx2GBT0lugBSjQtUsaPuMyFtr9OQTm4qqrV0uM1sRS3FSdVxgnQ1nUans9nw4MCBF9wnIYWQKo6A8S2EpOixCRydxTg0eCxfVxJBBEEQjYFS2ogskOAhxhylioOuctwoIImkCJApBS5or4gdWfcT3sfd3lQL6zw4qBod1lSBpAqhLGLHQTUW5ckS3ZGT8iztq3DgGsTRa5iCqnbsDuhLPXerI6M7UuyowifLayvbqCJIQiKIIAiCIJoDCR5izNFZ8MQaOqXG9KdHf1TCdXui9yo2C+usoke1jVZFT9Z1eZLQRY8qZpKPy9YOCEWPPA4QE3u5TQqfF7Gtvy/6tbMLVmY6TyuwBAfDgYfD8RiW4OBcYicNPRKk9ktiiCAIwgzZUhNZoPefGHMMDTDABbj/6VUFSxb0dknPTW5uughS20WNEOxr75gwRXhM5E1la5bo0SM4sq0DL3asrR8PDv6KHWNt3oEXUsc3WhyGx3ALPoCTLGltjUB/vSgiRBAEQRC1Q4KHGFOs9zowAUJY6BEZvTZHJ20/EO1TNTXIu26PKnzi7fxf7221PA6sdTyA2Fev6GkmabU/WcbyJ7wdVTjYB38Ptj2J2dgXf27YOPNyCz6AaZDpbFMjaW2AWZQ0ExJBBEEQZFpAZIMEDzGm6HYGMQQGz0Vs4VGbmJFCJ03sJLXTRY/+3HPb0FEeTjxOj/iIMatREK3epoaFR3Ux5MGFg3genp4y1UgcJb1NTW1TzydFj2yjj0NGj57BrgAQCJ8nMTtouz+eBQA8infiQDzd8OvQmabUIE3T6pGk+JiHB1OFz2SswzrD+keNwHZuEkIEQYxXyLSAyAIJHmJMIsUO8+fy3LU7scn7pAiPbZ85QhPfbhIzauqbngbnBel4HjwnfpIkt7Yk9MiPFDu1RneS0tpceJH6naombkxts6a62cacZKbwKN6JKhwcghX2C2oCItJjNiNQhYbc1yyxkwQJIYIgCGJLhgQPMaYYGmCBsGHVsI5HfWwSLzbr6SRL6ixpbKb28ehPuL29Isap970akzEdG/AGuvKd0/8TNkVxGkWa6AGiYsczCJta2Bd/xpOYDQDBvTqmR/HO4LktynMLPoAZeD14fhgeq2ksWWp13sQUbK3ZcS/GoUFER74uWaJAI4U6Dvl+HYmlozQagiCI/GQ1LaCUti0bEjzEmEJdOJRbPr36GjtJ6WxpUR25sKgqYkzCRraz7ZdwV5xTGhhUHQcOqphY2YQ3CqHYMa21kxTxsaWujQS6eUEjxI6KrNtRRc/+eBZPYC94cHAgno6IH5WZvgPcIViBZTgAy3BAsE81WQBqF0MSXewAYYqbGplqFbEDROut5D1FgwiCGEtQShuRBRI8xJhiU6cQFoNFUTMjndFchFEeNbWtFhMDU30OEE9ZM7VVUdfskc+j/TpBzU1nwcMmdEVEi8mYwJSyJqM8qujR1+yxGQXYFhjVBUseEWOrx8mLblKwL/6MJ7BXrJ0turMcc3KdTxVDknpFkMlqulVQRViWsZFJAkEQBDFWybc6IkGMMpVCBzy3DZ0FLxQ7nhA7Q4V8fSWJIdtipqq1tBQwMrqj7nOqdjGktsvjtqa39eAaFhZ10YvNkW1S/GSNAOmCxWQqkBWbdXWWmiIHVWMqGwA8gb2wP57FgXg6InbuwNEA4mJnGQ4wipe0ib661o6NpNdmDaYBGL9pYotxaHC7AR8b7eEQBLEFIl3a0m70C/+WDQkeYszhuQ7We0L4tPdwAEC5U+yrpIge14vX7cht6k0Spqm1BULFtJ6Ovk+N5iQtWgqY63CShJBJ6ATjRRXrvY7Y8arokbfwmLiLmu25CdcfURKqaAojUnYDAiB0ZrsHc/EkZgfix4EXpLTJiI9MaTsev7FGdvQIjrSTbhbz8CCmYU1se9JrOgGbmjmkCHoqW73MxEpjFEiKxmatWUQQBEEQaZDgJcYUMgXM9Tw41WEMlRlcxBf+jB8n7tW0tywLlaYtFgogFt2RFtWeCxQ7Ocol5m83tweAN9AVTIP1dLTIeJQ/Wb0GRe534WVyeKvXwS0NUypbnnM9g13hANgWq4Lj1BQ3aUutIoRPtmiUKYXNhs3GO0m8qJP/xTgU8/Ag7sHcxPNsxsTMY2oEzUizk9dqwiR6mi08CYIY32St4aEJ75YNvf/EmEIu2gmEtTVSzKhiRzcukKiubrYPvy6EVIHiuQ6cqhcxKUhCih2ViDiCG5k0b4MNWI8Ofxz5a2YceDFL67R+GrUoqR4pklbUcp8HB7tgJZ7HzJr6T1p0NGkdnkOwAssxJ3aNh+GxTKJHva56DBmEAKqvbDZrvU2z+2gkpjonEkEEQWQlq0sbTXi3bOj9J8Y0qshRIzyqm5sJ7oZr+MjnEjX6Y4rGSNFjQt1nquHR+xJiI5wE92MCoDmz2QSLajigT2Lz1AaZ+jQ9T5rs2ybQE7EZmzABrn/sK5jqj9S19ictrqVYkqltWTgQT2M55gQiR96byCt2bPubsXiribwmA2l92fpoxKK0SVGetHHJ403jIyFEEARB1AIJHmLMsN7rAFwxIao6DjrKQ/7jsI3nRkVO1RFr3wBxG2ubrbVKTKBUPXhufCIohZEUO1J82Y0LHFQdJ4g6bMIETMa6WLusC4nGhUrYLq9dtdpXlkVCTRNTuW0QHUF9TzRSkh5VkqLnGewaiB7duU2aFwBhHY/aa16ntizUI3LyCJVahE0jxVA9wseBhyU4OJNAsb2epmux1QGRECKILRdKaSOyQO8/MaaQAqAXm1EGQ3sPB+9nMbMCKYJcD0AVgBtdnDQvUuQ4Vc8Y3Yk6tA1HIk/ycdJCphOxORAnev1NUrTGlG6li6IsoqfWtLa0dC91mxQwSVEkHV306GltFT/97wnshQPxrHU9HomextaFDdiQc7HXkSJJuNiETVaxk0cUZRE+tv5GyqiAaoMIYstFurSlQRPeLRt6/4kxQ2hW4KEExzr9qjpAe09oFtAOBKJHbaOnu2UxMQDidTy62JGo6wHlQa/BiZw74U9WnQSb7KoBoOBVak53s52zA4PBYxNS0JhMDJLS2lRsaW0FDMac2gBY09n0NLZaxU7WVL9GkSUCYjKxSOtDRY/s6f2YhE+jaoEa+RqqaXGyb3UfrR1EEASx5UGChxgTrPc6fDc2LxAVuiObdEdr7+EYGmDB9IkX/XqdqRzoFyIoSeyokRg1VU2N8qjnVNvq4zKJHT0lTp/s5TEc0PsBTGJHpihVrf2Z3N+ifSbZZEcnx/r16BGeWngR22IdtgoiPDfjRMxWoj0eHKzBtIgN9F1YgCk1nS2dkarbSTtXVvGRZbw2B7q0Wp9mYhJ1pgihut/Uh4pune3Ao2gQQYxhsqa0jdy3NtGKkOAhxgxVx0G3M4hS8LU1DLefoeqEi3/KdXl0QTNUANz+uGOa6Df6XK27MRkWiDbKBD9mSx03T1D7tdUBma63lhqcyHgzfsWnpbSl/QKf5Ty62MmTRrcOWwWPn8RszFb62t9PZdPXvJmCN5XxifOsxRRMwdpM50wib0TCFDFJExR5x9MsTNc5ki5vaVbg9YpPD04sJU5en2mxWoIgCGLsQYKHGBN0O4N4A13oxwT0Fjy8gS5MLm+MtBkstgMlhkIlugBpsZNbxQ4QipI0RzXALFZMFtVmo4JoxMgUbenFZqxHR8TQwCZ6bHUcpjoa2Y94XtXa20VVnsJ1te10rMNqTA6Ok2InGvlxI8dmiQTJxUdVREpbtL3ehxRXjRA7pjFnIZpymC4e8/SfZFZg+zzopEVKmslIn890ftP1L8MBkQip5BCsGPExEgRhJqstdZY2xPglfVVFgmgByiWGbbABvdiMUsXBxMqmiFCRIsRz27Cpsw2e2xYIlqEBIXZYNX4DohGYepHObDomAeR60UnUNtiAfkzIlMam1lrkmSyaxI6pX/Ox6b/qm1LZ6l2ANAtrMC22rQsbjG2b8at91ohHnverEZGLrGJHxwmO9mLPGx3dGW2xk4RNoC/HnOC2DAfkWsSWIIjGIk0L0m71/te59NJLwRjDeeedF2zjnGPhwoWYMWMGJkyYgLlz5+JPf/pTnWcimgEJHmJMUOzkWO91YL3Xgc6Ch86CFwiVYidPdE9rLwlRw13Fpa0qbqwqIjzq4qVJwicpFc2pRlPpdJFT7OSJ1/iGUkAfjX7oIkWffjrYxp/cZ53g2kwN9D7USXOeielrluqZcAItneQaY++sp7MBcUMCKXRGY3KaJBT097JWsliIJ42vln31UO/1Npp63wMpfJbhACzBwSPmUEcQRPN54okn8KMf/Qh77713ZPvll1+Ob3/727j66qvxxBNPYPr06Zg3bx42bDD/4EaMHpTSRowJShUHHdVhbOosBs9RFCYFpYpjXCA0OHZKGzzXwYSNYt0eF350x40vOArYXdVsi40CMv1t2LA9fCxd42xsgw0R0QPoRen2tLM30BUTJmr6lJqSo6aw2epoaknVUkWMem5XmTCbbKnrJakvmXrUjPV4TOIyLR2tEYt6po1JTttrSWUbKVpJ6JhQP8PRVDf7DxHqsRJV9OjXTG5xBNEYmr0Oz8aNG3HyySfjuuuuw9e//vVgO+ccV155JS6++GIcf/zxAICbb74Z06ZNw89//nN84hOfqPGMRDOgCA8xJnCqwyh28iClzXPFbfOkdnQWPGP0RK2rcaoeNk/SMngrAOsVx7GqEDp5LaR1RHpdbcdKsWP7pdmDG9leNUxu8xSYNzqtLKwRip+vCsdYx1ML+kQzacIu047k41r6r5XRiJoA9te3VURGq4wjiVrrmfKkhC7GoViMQ3EP5gY3giDy4zCg3U2/yQSN9evXR26VSiWx/3POOQfHHHMM3vve90a2v/TSS1i9ejXmz58fbCsUCjjssMPw8MPk/NhqUISHGBMUO8W6Ops6iyhACJjOgof1XgfKJRYIHqc6jMFiuzEaIwwHxOP2GZpAmsqBAaa1t0eN6iG2do8hvStNuFT9yIktBSfdqre+qI6JpMJ5/Rx5xZYaiapHqKW70SVHvtL7H/loiYnRdlYbb5hd9uKfj1qEkvqDhUn0HIml2QdKEEQinuehp6cnsu2SSy7BwoULje1vvfVWPPnkk3jiiSdi+1avXg0AmDYtWkM6bdo0vPLKK40ZMNEwSPAQY4pCZRCdhXBS0e0MohRYRYuITkdZpK7pYqWz4KFcZSJ1rU+Im6GC+COQxga26IxcbDQJk1ubrZ2K63mByZhtHRFHETdS7NhQ60WSf3GuBlEj/fi84idN7OyAPryCqUHf8tymc9WyVo+pH5vbW5KY0dP9TPuTzmkaV3hsazmRbUk0O5XQds5GnM8W+SEhRBCCtjbAzTCbdYYBhzt46623ItsLhYKx/cqVK3HuuefivvvuQ7FYtPbLWPTHUs55bBsx+pDgIVoePYJTgoOJpWFUCsKKekJ5GENlBq/YlhiVWe91oKsSurMB8fV6bKSJHYlpIVJzO9+CWVlkVJ1gqxMl06TdtC1JBCXRiImZPpEOhY24FyYGjZ1s2hagVLfVInps2H7Rr+WXfGLsEa3rCevhkto3k3swN/bZPhb3N/WcBDEe6O7uztRuxYoV6Ovrw5w5YTq053n4/e9/j6uvvhp/+9vfAIhIz7bbbhu06evri0V9iNGHBA/R8jjVsODfqQKFyjCqju96VmLwXPF4sOIEURZ98VAZodnUKYwO2tcDvDN6Hil+1ChPLWltUuiY1+eJ9rXJmYiuiuLm4sgoQ7ToP0vUQ438JEWKxGlqr1NJilrohgnJ/ZijPFmvV54nLUKVV/TohemNrnWyjXM02FIiPrqZw0gI1Wa/v6bP9N04wtiWhBAxnmlrA9oz/Km1MyDPv74jjjgCzz77bGTbGWecgd133x0XXHABdtppJ0yfPh2LFy/Gu971LgDA4OAgli1bhssuuyzHFRAjAQkeouWRttHSUID1hguJytqeoQEGR/k0t/dweIorWkd5KCpAtAi2GulRxZJ4HgqYrMhzqfcmJnqb/HOoA/D7MEzMChjEJkwIJqpS4MjJj7zXHcOi9+nf+PVO1kZigrgv/ownMbspoqdZtILIGY9kEdpprnVJKX+m7WmfG1sd1Wi8/6oQIvFDjDfypLTlETxdXV3Yc889I9s6OzsxZcqUYPt5552HRYsWYZdddsEuu+yCRYsWYeLEifjYxz6W4wqIkYAED9HyyBQ0F4qrWi/H0ABDe09oPlDs5OD9TKy5088Qrt85HGlTLok6HlYNbamrjhA97ZWoVbUkq/OaGtHJIpBMqXJJNT2bMAEFDEb7MEzGKuhQPN3Mk7Uk4ZPn1++8hPVI9hfVJFB0cfIkZkdEjz5uPRKli8NmkKVOppVEz1iv6zFFGLNcj0kgNeJ1GKn3Ne0zfCzut0Z7CIJoHOeffz42b96MT3/603jrrbdwwAEH4L777kNXV1f6wcSIQoKHGBNwF2AlAL3i+dAAw2CxDe0I18DxSkLkSBHk+uJHLiTquUo9UCcC4wIAMQGkksdmOimNLclBTqXqxMWAFACqgFHTc3RrapPY0QWDFD2NinCkTeR1EWM6t9pHntQ29XggeY2cPP3md5NrbfEwmgKn0WIgr/22WfjnT8EUx9XjFFif4LV9dkdC0BNEK+K0CdvpNNob8NW3dOnSyHPGGBYuXGh1eSNaB1qHh2h9qn6UZ6YQMtJRzakOo1xigWtbwbfS5/0saNPew0XkpocHqWWLcSh4fyh2WDVqZKCuxaPWAenYTAlskR0hhnyx4t9L8wL5WBU7Jtcx1ZggFDtylRsvcgvG6e+Xj9PsdOVzddt2WIvtsNZ4Xep4mk3W2iNTZEt9XqvBA9Ea1PJZs1m42/ZFf1wI/370Wy3jSHqehC5mTJ9jFx7uwdzIPvmY0tmIcQmDyIrIciO2WEjwEK2PMj8fGmBBvY2M3JRLDMWS39QL095ke1XAONVhzB1YDlaGWHjU74O7YXRH1gyp5zCJG1M0J4stdbwfs8GAeq/vU29izJ4yKfPQgUqG1Kqo+FEjR/rj1zDFd1rLhi1VyFX6lWPQ26ikCZN7MDdx0hkXf+Fzte/D8FhkPI3Ella4pdGo12Ck0saacZ6sYicpSiN/+HANn2NA2FVLy2r9RxKCIIgtFUppI1qfCoCZoj5HfmBlrY0UJtwNU9J4P0N7CRjqDgXQeq8D3b61NfoYeBGB6EEhGuFR09o8NzQx6CgPJ0Z8TNuzrN8j22VFn8C7nheeQxV31mhONfZctss7KbUVgudZ28ckvLKsHwSEa5E8g11zpRnp51iGA/xxpy/ISuKldtJeuywio1k1UEkRQZVa0tlsP1wkIUVPllQ1XdSY1u4h4UOMW9pAs1kiFfqIEK1PAUEKmhqRUVEFC+vlQFm0rxSAIvwUMkdEfLxOP/3NFTcpltS+ZP9hhMdsW53FmMAmepyql1no2CImtoiE63moOk5DUm7kObNMMtOcrlSbbdM6OtnNEsR1PYNdI9tsk9E8gkteR6NodWOAVhrfSBg6ZDWWCB9nq3PLW1NkQxc26vMsouVuHEH/2AmCIDQopY1oaXg/i4gb+Vivu+EuwlqfVSwi5UsVB57roFwSRgeAqNNR+7Ih09t02+qsqWtqrY68mfZbjzemZFVR8CooeJVodAdC6Mht8rHcru5XiRoZJEVWkuti1D7SCsfTJm5Z6m1exwzsg79rx9nrKkzjD1PsoseZaqEk6rVlnczWckyzqNduvJnjzyqqs6JX3Kh96NeiPw7TSt3Ez5VpTDa3QJVmikwyLiC2KGSEJ8uN2GIhwUOMKXRxIoUPq0J8mSn3UqR0Fjx0O4OBUJHRmogrmyGlzU2Zj4i+wpu9Xcrk3hT9yTEZMgkp9bkuehLHYjhvnl/DI2No8uR4W6zK1V4fjyq+VOGjts/aV620SmSlFWi0Fbpp0q8LH/289RgKqOiW6HmwCfy8KWnqdVbhBOmfBDHuyCp46HeALRoSPMSYQI/oAAhECneBoYK48aJfx1MENnW2odjJUar4v9S60dQ0PZVNbkvD9cJUNxVdANld3JK/dW2F/DK6I4VL1RGubtZ+LELLdkwei948pEWNbM+zRHmewa6xKE/eWh7TsUmmESbqmSiPhujJMz5bNGS0IlX1nlf/LOlpdKZrC2vcskcPs7xGWd97qr8hCIKoDxI8REsTETpV7ZaCUx1GqeIEttXSfAAQhgVM6UcKpUjEyIAp6uO5bbGbiW5n0Lg9Sx1PaAbgpgodID2qZEMVPTYDAtv4kiZvaSk+WWqNkkSPRBc/trHYJqP11Dzp/epuerZzNko4jMYv+KMpfNJQ3cx0ZzM9ZTGrYUESrZS2SBBbFG0gW2oiFcpoJFobmaKmo22TQkSmqVWd0Da65H/LdfZwwLepbp8hHNt4PwvEjUnk2BYjNRkYqOiW1U7Vw3p0RIRIVqGT9GtzljQ1SdVxEtsnOayZtmcdr41aFhZNO0aIn+xfa+pY1b7V6zCJtUZNaPV+dGeuPJicufIwEoYBrYj6ntqET5oQtomdWl5TWype1s+EydWNIkTEuCarS1v+VSOIcQRFeIjWxlBjgxk88lyPyjA/ba2jLL7dpPAolxg8VyxCOjTAUC75a/RM5cbUNil2TFGfaoY5jBrpkeJGFTlZozDqhMyDg22wIX0C5tf16OdIiwyp5zMZJjQKU7+mVD7TOeuZvCVNPrOIr0YLAvX69JoLW8G93rZZ48lDo+qZGvkZy/sa2c0FxOcy6+fOFD3KYoCRhu16dsXfMvdBEASxpUKCh2htqhBr5Xj+DRAubHKf3laKnl6O9opvP63gVH1r6mA9Hd/yeioHpvKgFqjqiHsgXJRUbtfFjhRUWZ3bTJEdNU0tyVrZgYc30JXpPPq5TNEdm/tUFmcpPVkr7di0yV0t65sA8TS2ehYPNa9bZL7O8BhzmlqjxZEufhopepJSDhtdY9RoYZOETTCqpKUcAtlcBfNeUy2fD9N1/B27GduqYybDAmJcQy5tRAbo7SdaGw+hsJHpbaobmweRl6t/mfX56/b0chQhRE3VEZGfqiOEEOsVkR7Wy1EuMSGO/PmE64k1fGT7YDiWvxhV9GRZm0clS9QlDdOaPvnW+cm21khyH9HUoCwF29XIRDv/GLLU7Mgx5aFR7dU0KfV1aVT6mD4BblTqkj6+RqXyNULoNOK1sx2v9p0W8cn7WW1UqielqhEEQeSHIjxEayOFzSwO7MIBP+oSzDXk//5K9BgZleH9DOgLxQ6guLT1M7ieiPgUS0LYtPfwwLK62MkjQ7GJnVyXI6M4BiEiy6qzINtWnWjaWq1mBYCaUpYcIclqU92oNWxMyIneM9g1cGqT4ierCMpKvdEa3chAvW80SWlwpnElPTdRr2Bp1HXXElExRchM6/PI/sNtbuy1PAQrrOMykee1zhO5o/V2CAIAQzbDAprxbtHQ25+BSy+9FIwxnHfeeYntli1bhjlz5qBYLGKnnXbCD37wg1ib22+/HbNnz0ahUMDs2bNx5513NmnU44RZHCgjTGOb4YseB+Je9ddXU9qqvhNbGcBUbnRXkzU5rifS1WQbmc425BscAPnEjp7aJsWNHslRRU+t0RU1TU2P6OiiKlv9Tih61Fu8XTantUb9om/C9uv2k5gdeV6vWNEfN9qNK4s4aVbfeSzDk7ZnfZ+ztMvzmcnaVv8xwVQTVVXe3/hNiJ5DsMIqduSxea+hGTTzM0UQLQeltBEZIMGTwhNPPIEf/ehH2HvvvRPbvfTSSzj66KNx6KGH4qmnnsJFF12Ez33uc7j99tuDNo888ghOPPFEnHrqqXjmmWdw6qmn4oQTTsBjjz3W7MsYm7zEgOcZUPSfrxLRGi6/uKbyIJITfJHN4GHK2wwu7KZ9JzbdeEBdh0emrg0NsGC/64mITyMpVEJr6iz20iakCCl4lfTGDSJtpXkgKtqyTpZVm2B5nlpR7anzErqzRY0TTGurNJKRmpSmTfDlfVYx1ywzC7XPRooo0c782dItq1Wir42L5ZiD5ZiDZTgg1q5VbKmrcHAs7h+18xMEQbQiJHgS2LhxI04++WRcd9112GqrrRLb/uAHP8Db3vY2XHnlldhjjz1w1lln4eMf/zi+9a1vBW2uvPJKzJs3DxdeeCF23313XHjhhTjiiCNw5ZVXNvlKxiCrWHx1ZDe0iNYjMIF1dF8oWFSho2J6Lp3d2tcLkeO5CGp75IKl9eJ6HiqFDgDxaEt6Glk02uJ6Hpyql2hznUVM5bG1bha66NEfm56rqClseSJlVThYhRmowkEfphmPT5q4mvaZDAVMqVOj+Qt8llS3dIOJeIpWlkl+mjlAs1D/vlRxY1qXx+wg6MFBNVEU5RuP+fNci027ibtxRO4xEcSYJWuEh4KeWzQkeBI455xzcMwxx+C9731vattHHnkE8+fPj2xbsGAB/vCHP2BoaCixzcMPP5za//r16yO3SmXkft0fFeT/cSVtTYoaPc2s6vgiRrOwZr3cvFDpVB4uPAptnZ2CEErFASGodKEjn5sEkDQrSDMtMAkRfaKtTtBUkSNvWbC10/vJ228atglttC4iOvmPtrOvZq9Tb9H2VKyJ3Gc5p21fFve00U41asS5k+pRxsI6PvpaR6YEtritdDU49hCswGEwR+XTUiBVskSm8tT1SdS/iSoccmgjWppKpRKb3+SGFh4lMkCCx8Ktt96KJ598Epdeemmm9qtXr8a0adMi26ZNm4ZqtYo333wzsc3q1atT+585cyZ6enqCW9ZxjVlkjY6PKnY8N1pTI2twhnRDA/lYurlJ+ljQhvuRHLkWj3xe7okOx3OFs1t7D0+M9uhr7+hRF1VUJAkMD25s4i9T4PTIjjxXFtJETZrwsYsR8/a0CXDSBDxrrYg0LkgaR9LxJrJYTSeJNtu2VsImFlvBmKDRUSAPrjHNKyn1UhU9YdKbi2U4IJbSJsesjz2LU6EKua4RWxqXXnppZG4zc+bM0R4SMU6hEi4DK1euxLnnnov77rsPxWIx/QAfxljkOec8tt3URt9mG1N3d3fwvFAoJLQeRxgiO7rgsC4C6tf7sCqEeJLHzeBC9EzlqA4wuP0MlQLg+MYFQwMMRb92h/eH703VAdx+JkqEnHAcUnypltRJAkQXPVXHgc2SWZ/AZ4nCyAiS3laeK0sfsk3MaMHylZH2K7QpRc1DuHp8WEMjbZurvujLb0Gsv5Ymi2V57lpESSMWi7T3LcZdTy1TEvKaTWmEjUS1467l2Ga2149VrcLT+mz066Wfx/Z5Mf0AItuTSCLGMhdeeCG+8IUvBM/Xr1+fX/TIlLY0Wvs3KKLJUITHwIoVK9DX14c5c+bAdV24rotly5bhqquuguu68AwTxunTp8ciNX19fXBdF1OmTElso0d9THR3d0du417weAispmWNTXtF3EyOa4HVtGJiwF0hTLg0MYC/vZ9hqACUSyxYb0eKF2lNPTTARA1Qb+jwpp7X9cLnSREfU/qaHp2R4iLrJFeKKRlBUsWVTezo57KNxdQ+4gSnWVebfom3rdliqo8IPbC8zJNw06/ipsVHs7ye+mRRNy0YabKOux7i12yuX9HJG3VpRr2OBwfz8GBq36Y0ybuwIBCUSaYNSZG8Wqy8G0We1Db5Hte7thZBNJtCoRCb3xBEMyDBY+CII47As88+i6effjq47bfffjj55JPx9NNPwzFMYg866CAsXrw4su2+++7Dfvvth/b29sQ2Bx98cPMuZiyykoW/xFT8mzIHVNfUkeltapRHihy5jalix1XT4trAeuMpakMDLDAtwKpQ9JgiSbroCRcgDetjTOvkZFkvR5+sqMIjawqbPJftfJ6b3yku3a3N3N822OAfn89VK0tqW5pDW5IJQtov5EnrCo0XbGKnUaLFXiUTvyVxJJbiHsxNbJNuyhC3pzaNwfYYCD+bh+NhHI6H/W3ZneVkn0njzipWktNCmyueCaIlIFtqIgP09hvo6urCnnvuGdnW2dmJKVOmBNsvvPBCvPbaa/jxj38MAPjkJz+Jq6++Gl/4whdw9tln45FHHsF///d/45Zbbgn6OPfcc/Ge97wHl112GY477jjcdddd+N3vfofly5eP3MWNBRxEP5kzfGvoPhasl8OqgKdEZqSTmiqGJDLiw3o5KiUWpJ11FjyUKg4m+u2loGnv4WiHiAA5nQAGWOiH4LeR52DV6FBN6/Xoa+Oookfuk+lm0ZdBcZbSIjN6W1tER54rj0CqhSwpTG+gKxxXzlS1tPZ6apytD4mayiSPV62pa0mnqyUdLZp61/zJqV6wL85rFpT6a2TCC16z5olBD9HCe/0a5L4kZ7K4+174XumfGdeQ4qaPR7IMBySmv41URCgptU0ViGRgQIxLKKWNyAAJnhp5/fXX8eqrrwbPZ82ahd/85jf4/Oc/j2uuuQYzZszAVVddhQ996ENBm4MPPhi33norvvzlL+MrX/kK3v72t+O2227DAQfEC2C3aGZwkU4mnytW0+09wipaGhqoAkMVPbKttK6WxxU7OdZ7HXCqQux0lEVERkZ9AAADLGJOYFt0tOpo/gjBfCOs5el2BrHe64gcZxM9QLT+JDIpU2pvTBEZdZtJ/NQjfGwRIDFWJ/PEWLpdhddVn+ippb5HPVYdG5AserIKANGfWfio202/3pvqNBopiExiR5wjPrmvp46mkeJnHh7EPZiLezAXb2EyuvwoIRBezz2Ya53IJ9mMi/cy7o4oxUPaNdmEsS2lM4laozumftT391jcj7txRLDNFB2jdXsIgtgSYFxW1hMtyfr169HT04OBgYEtI7d1FUO5Rzii6dEUWWsjBUixUwijilbOpAuVwWI7OspD8Ny2wFhACh01UjOkiCjZt74vCXlc2Y8imcRFNL0tbnIgxUWSTbVtMuSgGq23MaSxqWJLPWeakYEuqOTzQcVKL63wuwODscm+Xh8Rr6uwmSSY6ypMER7Zx774cyT1TVQNRc9XjfTrRvYlOW9V4RjHGtY6ZZ/AJh3TqCiQHuHR07GyRCZGqrD/TUzBVlhXswFE+Dw9+qev1xPfL+4Px8NYjjmxz0jaubLWHCV9Xmr9DCSlbppeWxJBxGiQd85z5plnYubvb8DCDOtOv7gJ2P2hdgwODqY3JsYdFOEhWosZHEUAQ9WoO1p7D0fRd1ZzfSEyVBW1Pqo4MtFRFusgyfqajvJwJO1N2lGr5gS8woJzu/42mS6XdK6ykjIHRCM8opZmONJeih4Z6QmEh38OPdqTRlYXNhWTkYEuwEwpd2KY4a/bNic2iWmyp7eRURbdrS3tvKYojY6pzqc2F7jaI0sqWZ3MjsO9uAsL6j5fI8aiYnsdaukraVwn4ZcARMpa1rTBrGInaqJhXlhUFzsOPN+W2ix28tKo6E4jkBEhIJoiqBqKzMODozI2grBCKW1EBkjwEC3F0IBwTlM/mKwMoOJbTPtr6EjxUSmEqWxAKFp02+joYqCh6HA9AFWAIboAqTQ6kBbUKibRI6NJ+qKj8XS2NkXkSIODUPSoJNlVq5gmfzaTgjSzBD3FTh0LkM0W29q3H8MB8kc86hE9ptdnH/wdz2DXWL9qLZAtrU0/XxJZIlRpxzRD7KjpbVnrVfLsqxe976T6nLx92UhyCTQZV+SJejUSm0V1Pci/GdPrrKbFeXCwGIcG+9S/AxJCBEG0MuTSRrQU7ZXQhppVfbEjRYn/P17eyxQ3ky20ahsNIBJZGSy2W+tymNIX19rottQSm1FBt2MOm0fFTjg+PfqTBVOqW1bXNenept4abW5gszs2ijS/jWniKe2a9ePSbK5taTxprm61kGUSqrt9jaStcRq2CJzeJq9rW70ub/dgLrbGWgAi2iX6rO23OpsFdx5HQBNZr7Fxka/wE54VU51TVfnLsqGLIPVapUMdACzGobEbQYwI5NJGZIDefqI1Mc0dFXtpuSYPEBcmMkVNj8KoNTODxXY85hyAuQPL0e7bVeuCCkhOX9NRoztO1UOp6gBKzYw6DnGPyL0gHKO6KGlwDkuxu0q0jicqovQIlI4uevRUNvVxcp1BtHBbje7Uix4lMkVb9IiPxFSjYxqvHiU6HA9jCZpjH9/IFDATSWl+aoF+mqtYXpFj25Z2raZjD8TTAES0Ky3Fz1YjlgXTGG3vT62pjY1KicxCPEWt9vPqhgh6tMeENElQXz+KBBEEMRqQ4CFaC9OcWP6P9pR7R7k3pKJJu2jWy4EBhsFifKI/d2B5cE6m7dMFkxrRkdv1yI4qqMJtcbGjR6TUNDz1OFnTo4oMfbKf5OKVN2KkR3eyRIrSogJZJvKmCaBay2M7p+74prex9QuYU9bMY7OnFK7BNEzDGuWczf86rTWdKUn0hH1nEySAucg9bT0j9TxJ76uJezDXP6eLu7AADqpYh60wGW9lOmfauQGzi53aXhXFafVqtnM2Q+gkfSbSUgGlMYHaThVJOibRk22M4WtgE0kkhIiaaUM2LU85TVs0JHiI1kL+L1WiOVYcADO4qOuBOdIzNCD2dZSH0d4jLKm7nUHfXAAolrQ+q9Fz6mv6BMNUanayCos87Uw1PcEQE4RIWo2NHIMuzEypbDajgkh/lqiAKV0ofmxcTGQxIMjyC3majXQoqOJ1OvIaVESRenSbFDt53cNM6OdOEh15RY+cpGYRPaaJvByTiqmvpLVgdBox8T8F/2tIt0r/l2aOBoavadxEIN2YIc2VrdkRHdtnQgoaKRLT2qn9Aea/3XrEbhIkhIiayWpaQDPeLRp6+4nWYiYHXvLjLbroUZ/L6M4qFmxj+v/mqSK6AwiBMlhx4ABYjw44bhsmloZFKpt6jH8ufUFRibpeT1p6WBqq2QIQiiuxPSpM9EVK5ZwjaYKXR4xlHrM/YdLTwexObckTc1OURZ1k29LR9OPNfZsL8pMcu0yOcbY28fVXshhMJEcC1MfquPU0rkZGenShYnPXS0v7M/XVKOR55OsgJ+jH4v4gze1uHJH42bQRFy9xI4ss9U210AixLNGjvfK5/NyY1vcyCUZZJ5UH0+KtjcIkhEgEEQSRFxI8ROuhz+OSnjsAL/pip4poulsfg6ukpunr3lQKIr2svWwYg2uuBdId32yCwjPU7oQObfJY/3KcqCW2ei7VyU0XQaqLW6qwUNYvMo3VhNmG2u6YZtuvRp2S+rTVTZgm2FHhkSx61D70Y23t1WPShEyWyIk+hiyor4GpZiWrRbM++bQJFdFX/fUlzRA9pjQr1aZa7qs9wmO/Zn0h0kbW8zQLVewch3txB44O9h2P3+AuLIi8Vra6qFqEdZK1d6Ow1dOpJgrEFgTZUhMZoIxGovXwlJtEChodPbLjf6HxTnHPesW6uqqbW8w9TX5RJvxPFkLHP4XBCEDexHPh0CaFhLzPGm2RDnNRB7doG6fqBUIiXruj1w1F77OOpRYLanUSri+EmtR/0qRKnSyZFoOUx++ClbnHm3butPFkpdbJ8EhPorNEMpppsGBDChqZmgVE07CyOpYlmSmo7mNpAlrvy5TW2QoCSI/WHI/fGNtJYZSHY3F/xA0xyRWxkSR9/pbg4NiN2AJgEP/7s9yILRYSPETr4iAqdNQ5iKvtU+es/v/Dcqeo4fFcsXCpjJj0Y4KfItYWjXpoX4YyumOKjOg20nqURK6/o0Z6VFGUBdcTi6oWKsqlace7nmcVFY1IZ6tn3R3TsVn6S5t020TPi9g2EFqm4/WJkqsJJtuxpnPp20Ir7fxpZmlWz/VOnvPWXGRJARtJTAtzZl2bKI9VtP4ZMTsiNlbImKMhjV1jBwiFjoz02K5Bih5V/NjEX9a1kbKmlDaKqPG4Y7TLJstsgtjyoJQ2onWR6WmA+ZNq+/Q6IupT9FPSXA8YqjIUezhKFeF8Vil0oGvAlMsm+o2nspnT2NQam9gwUhb51Kk6wmpbnls3TBARmjCtLal/U1SoFmwpaEB8IpSlhiXS3h+/PIdpomebHOnpbWr7lXgbZuJVyxjNZgZx97b8Zg2qEYI8ty1dz4aeemfaF9+ennZkSjMzpbblTffKc75aMIk1D04wgU+zqE7DLGLNhfomd7K8bnMmsqZE1oIppU19bDtGJ09q2yrMwAysAmD/DDfDij3P6+7AwxIcbHwfKS1uDEIpbUQGSPAQrYfmlJYkbIL2QLR+xz+m6vjRnRJDO8JIzMRSOYjcuF7cllpisp5OIu/CnfpiprrTnLqv6oS1OCbRNdKYXdbCSZEUMq7nKVEuQ7TBiwsf0xo7WU0FdsRL2AF9eBHbWsZtXqNH7TPPRN/mLBcuZhlejyn1yYZd4NQ2WdQn7UmGAyZRqI81bZJej+hJ6zvLejxpmOzT9Ul9LePPKxJrETtJwtjG8fgNrsOpmOIv4qpTz2upIsUOMHLRwFrq40zPpRDS95FJAkGMfUjwEK3FX1n4qUz6H2b7sVH73+96Iq1NRnccAJ0FD0NVFnFJUxceBaIubaroCSM6oZlAXsEhzQvaezh4PwuiOTKiZLPCVkVP2Fe8nS26YzMt0BcbjZ4z3Zq6VqLpfk5E+KSZI5gEizpxfAVTMRkbsA6TrVGnuMtadrvrpPZmIZYv+pWXrL/AJ4kQfXtSFCPrJN20to1pPHkm/fJ1b9QEXadVRY6NJAEsU9PWYgruwNGYUtd5oiYHd+OIukRto6I8jRRVtr70FDgPDo7E0oadl6gTsqUmMkA1PETrkfX/lz6/U4+byoP9cv0dSanioL0ihIMqNFRkpMXubNYWuc+KUx1GR1kIEt4v4kr6GOTjqmMeWziGsE8pHPKk0alRoqTjkuqEguNTJttZI1/mFLpsrk9rsXVk8rUOk1PHpmb76/06MBdhm87dgagRhl7E3WixYy6+z1a4n3eyrdet1DJZlxVSpudZ+9Ov7Tjci7WYknnCmyWa5sFtWnqZxNa/WvqfRJLxgo5MX9OjOlKg5RULUkCp9Tv6+9psouYSo5Oj5MCL1APdg7nBjRgFpOBJu1FK2xYN6V2itXCU+1048DwLU9yyLkYKACsZUFA2Vz10FvyJah8L1uhRoytqt7b6HZWkxTtVARG6tCmF94ZIlIqphsdGUh2RiikyVEuEKok8ESF1vDLKFBFWfjf6Oj3h7mhNxb74MwDgNUyJpaWpEZY1mIqtLWk9AKCmdWWllvS3vH0nFX/X8ku5Wrck7/P+Wp/VGltS64TYJALuwNGYiQ3YgC5jtCDv+jtAtutQxXEe8QEkX38zo4BJpgVJf1c6Yq2jEFNKaB7qifKMtgue7fw20UMRIYIYXSjCQ7QW6v++l5T0NnUekiWdzRc70pa6s+ChVHHA+1kQvZGCouK3lREVKXZs6WK1MrE0HESVWDW81YpqW61ji0wl92efeDQ6rS1iSR2kB3rBTW0T1sLEndaCPuDhNT9hZzusVVzTPPRhWqSfaegzHm+yX5ZRnpj1tyUiZEM6wNUzSUtzcgsf53vz89oJm9pmjS41Cvlab0CXcX/tNuBuEOUx3WTfo2U7ndXwQucGfAxAKHxs9tRJ/TSivmm8UMt7T05xTaQN4geyLDdii4UiPETr8FcW/UJSozpAaEqQ9qn193MXgJ82tqGnHRPKw7HIietBW2Mn3p0uKNKiIqboDgBs6mxDR9nvayoH+liiSYFOUnqbiqwR0vszHV9vdMe20KhqWACYxZTnOugoD/n7o4uqJp3HtJjoGkyNCRk5IdvWL6JOciAzOa4lGTKoUadancxqn5Sn/yqeVtNjc23Lgi1KUY+dsvpa5410yMm7rOnJGnVJcsHTxyNRa5LyRidGIt3LxB042k/ubByNEjujsabTSJD1ulTRI48hl7gaIJc2IgMkeIjWQRc7OgUAs7iI/JjaKMdLE4JyJzBYbEe3M4ghxYtN1s+wXg6UWEzo5ImQqMX38rlOWJzvD72fRcYvIz02AWQTO6prmxQ68bV67GPPmtJmS1VTJ9Zq1Eav+VFfk6R6oPA6whQ3k3ubeB4KHyl2VvtTu+2wDqsxGR4c7IA+vIKpSnvXOiGRk151cirbHoin8SjeqVhXR8diEj+qsDC5v9nSyNTzZ51c1joBH61f6m2iptZIUZboV9J29fOU9jrK9y3ra55V7DTb3OIOHI3j8ZvIfXwMI/d5SDLGaGX0dNpGjl+1y1YhpziCqA8SPERr8LxiDK2nsamLjD7PgCLMaW0yAuTX/AwVgGKnEDRD1bjxdNUBMMBifwVSlOiObOH+NuWxE7k3ERoKDKPYyTE0EB2L7g4H2IWPuX95L8cpoyViX9bIkByrLuBEX1EXtaC9QezkwRSN0kkSPWJb/NyrA8MCPz0OwglsJ7yOF7FtYrTIhAMPj+KdqdcTG3uKsAKyOZkl7df7jQq7+hdjSiqyz0ujJvP6+2VbWyZPbZXpsU6W96DWiF8jSJp8y9dIfa3WYkrE0CDp2vN+nmoR7TrqDwliDK33E/1IiTVbChwJIZBLG5EJquEhWgPbF5EUOoAQOhm/sFgvR3uPEBeFir1dKG70+/Q/DdWMIIs7mtqnLkJMAkcVQklRmqR9eqRKTd+TZKlL0mtr8ggc1UFO7SfdZCEaMVJremwTr22wAQAwHeuCbVL8qJEevQ9TTY7+XBVPtWJKnTsJv6yp2DtpX/PXgXGhV7iY2oxkXU9edIc+uS18HL2uNPFqq+3J8/omvV553OiSPgPH4zc4Hr/BL/A+nI2fZB4bAByL+wGYr6nWiX9SXZ6+f6xFgvJQq5ij2iCCyEbr/jcitixsDmwyuiO3FyAiObZProzy+PUxLnzx4++WqWwSGR2RQqCWmhZV+KTZL5dLLPi3FogeJ1x0VIock5mBFDYmB7esjm4yBS6NLDbSMvJiEj9ZxYy6ZpDZJML+mpom2UMDDE5PEW+gC9OxAW+gC12ljVjb2YsnMRvT4GEVZmAq1hj71H8hV9PfVmImZmAVPEMammmyok8KbZPZX+B9sW1Z1qbJkg7UqCiPjl7PpG7Xr7MRoidtMmhLz8pK0sKjJvts0/s/WjU6eVE/b2mvmXw95Hso7ajdDCIky2c4TexsaaSt8ZUVXfSM+/oghmz1OfQT/xYNvf1Ea5AkYmRkJ1AKiEZ+gKgLi5/OJkWDmkKmrnETj37E/xyyurLpE3PbhF+6tCUJFDXaY3JzSzpWjxzJtuoxJmOGWqk1lc2EmppnWyMoLbq0oacojkEV/ZgAQKQ1vokpgW31DKwKnMmcyK0aee4iGgmaiZV+39EogNxmixLphL1G09pGa8JcX8SqdnGT1e0sab8qdJKcx7L2q4/f9p7o2xvx3qW9do0SAXr9SZaJtR7tUq836X1McgAksTMyqK/nMhyAZTgAyzEnuI0Lsq7DQz/xb9HQ20+0DlVE63Pkp3OmYlQgXdpk1KfgP57Bg/V1ZBRHCgeZ2ub5X3iDRVXY1G4zrSLFTrcziFIljPiI++g5ZITGJFzy1O7YqDX6I8kSqcpL1gVRo2IsdG4LTR9C97ckq+y12BpT8Ca28aM8MyBS2+QRHhxsh7WBlbU0FBD7oqYG0qTAhG5gkPeX2SwT53pNBWpxbEsaj9pv7WPSo19xkwhTOxP6GjN5qXeCPZJCtdn1QbYaoGNxf2SxUSD+uTkSSzMvvJkclWysCcB4YB4eDKI2jf4MLMccww8XzviNBhFbLCR4iNZAChn492X/fiYXi4jO4sCquMGAFDhMETtyXR2Zoub4aWTFTrEmT6nixNzJkhYXzRrlmbBxCOsndQB+0b+5+D98HCx4qjSRz9X0Ngmrhtt1YZQkovKYFthIc6HL3k92gak60OnEFinVmOanrPU7E+AA6MVmvOGv2fJnzMZu+FsggDw42LqyFusKk8P+E53c7IJInYjoC3vWSq3F31kmjjLtKJ8xQn3/NuqJ6Oj8Au/DR/BrANFoT33rHWVbTDU95TD+OjUjvbAW9Emz7bMixbIudiTqa9AIsaOOx9S2FU0LmoVq1KCKnXr7E31GP+Mm18klONjYT0sKIbKlJjJAgodoDdSUNhm9qUKInZlCqKCCMJID//Eqhmon0N7ru585QtjwfoZNnULESFvqckkc16G5mqXVtNhEjypqPNfB5kntyjFR9zZVYMm+1PQ6HduCpLbtJtGUhFrLk7duyRQByhLBsYkd6SYnUWuV1O3q+2QaQ7cziPVeR2SbjAT1IxQ+wAGRyYQDLyJ2sk3I4qJHP1ZOzqSoMC08mqXOQcUUjUkTNmnRHdNze4F+/Jfg8Dyj404mRY+oTUlOQTMvmpo82U/DJJCSLLdbQfTkqRVRx2yK9EiaYXO+JQmcLNQT/cp7rM0ZzySERl0EkUsbkQF6+4nWRAoeIEhVwywe7B7qBNr7GHgxTFlr7+Eolxi8EkOxlwN+JAcAymWmTfKjk2l1Mq4KALPQUcWLMunVCvjV5/Fo0jBstTRVB2jX96nGDRmpJ7KjppCZxEz2FLX4Oj9Za4j0xVLFcdE0N9UwolR1wpQ1w7o/UvjshXBCKm155cRPnax5WipbkqGBDduELWlSmCaCTKKngo5YIXkj0oKSxpKUlpaVkV6HxTYpt0cUkqM9qrDJGvlqZCpgvcebJsHmbfFITx+mxcw/jsX9mSM9eTGL0vEliEzX2MgfEvTPuSpmbWtAZbEEb0kRRBAaJHiI0ed5Fv1hVv1U+tt5PwtSuaoFPwowlYtUNoVCRbiylRSxo0dyVLGTZFygip1iJw8iRHJffIHPqNhR71XURUj1CEZEDKgiR6lbkulsptQ2MY7w3iR68iyqmlXYiLbm9YrypLEFC7NariG+0KrNHEJs7yyI+1LFQbczCAD4K3bEVKzBVv1lOL0mVzEnKLZWFwvVJwQuPFRQSJyIqZMV06KpEl1UqEIjS1RAto8LNk87bza1aRI5pvWP8i7qadqnb2u0+DG9tibS3O7Cds1dHLTRZEkhzCJ6dGxOhyPFeBM7zSLtvZTfL1lMM/K85lIEjYjwaQO5tBGp0NtPtA7SiU3e/FocSJeyqRyVgpjwVh3hvqZO+MslBtYrokCdBU8TL6rIaTOuR6OiT9JVsWNuH/2HIsWPrc4kLY0sJmQU8ZNncVJZ19Msl7awT3MkzETy6y7uVZGjp+fZ3NxMlCoOShUHnQUveDzDWwXX87ChR6Qgqk5r8l51aou6uYVObvJYfZvaR+z6In2F53OVW3DdyjnSyJoW18i1cUzRAptbV1ZHNlO/9bTLtwaOk2mcWV6/ZkzGs4rLvOPQP6e1RAfl+jx344iGOg7qfytbKnmNPGo7R/r3TN73gaI8RCsxdn6mIsYnKzUjAtNEX9LH4BSikZr2CoJ0NgywqDBx2xRhodocm9PX8hJGjqJ9yFoS1UlMFT5h3U+0NigmRCyvS61Obmq0RI1wmaJVeci2cGm+PvM4yyWdf21nL6aU+lFSIwhKKlzwvljmEFl+1TTX75gL1vOKDVNKWZoZQhZ77CzjSJoANfvX9bRUN/380TqebGRJR8xTYG/bXu9kPYugqfX9sEUn01+bMOrowTXW9djc//LUkpBjW3OwpS2OWaiGh8gAvf1E6yDnV+r/bi/6vL0EtLtAudNPMwOLTKalE1u5xNBZ8ERNjz+Zt5sPRAWQKmTypGNJ9MJ5IF7fE4oedRzZ+relssl9QH5RlFf05HVbk+hCxmawoF+HLT3Pdh7J1IH+mNNbUiqcC7PddZjW5vlxHi+IAQHi/ZY1PabaH1W41Junb3Jtk5PLrGsBZcE0AcpSt1PPr/tZFnSVTMY6zMODgcjJKnrka5WUdpeFPJGoZrmNNcsowiTW9HomU/2SLpKTLM/rJUttSaMYKeHVjPPYvm9qoWXTCMmljcgApbQRo4/+ReVBOLJ5ECJI3is1LMWSaOpURZSH9zO4XnSRUUBMbjsLXmwybBMXetTGJgJkipy4yVohL3IPhAtlup6XWA8jx5O2KKkJ0+KkNjc3lVjNUAaS0shMr3Fa//q1mq6DGV6b6OsfPU97T2huYRqHvIbozQtuhcpg5H0reJVI2ppMQZMpbh0YRAGDUNPbwkVL9VS36Auip7bpzm9p4qEZzlgqpjQv/XlVufJGWnCnsQ6T8Qu8L3LeW/ABfAS/Th2LuUYpns6WlJ5XD7VMOJuR2pWlvie+zT6zNO0bzUV1G0kj3ves52km8RTGqvExQYw3KMJDtA6qMxsQETnqNib39TG4ShG/jADItDZpXFCuMnhFWUCPSP2OmOzG60psUR8bMkLS7QwKtzDNNtkkhsJ9iV3HyBq9SYoE5T1v0vVHxUT2Pq2iTN2u1S0pm6zwflbDF1v0+owRr5R5SAGD/mSlgkG/AC1uiJBsIKCKCBNZ14hphAlAUrqUPj5TUX+tk6csUYEqHJyEX+IWfCA2JnVbIxntX7ebcf6kz4a6rlG96A6CeTBd90hGd+T51L+H0UizqzWap463XrEzWtbzqTBki9604NCJkYMEDzH66BNcVejoIqiACNLMAH0M7VVgSNsvIzxSBJkK5tNcy5Im++qxwXliNswp+fB+pEWNdjBd+AFW0wLTIqXq/qAbw5d9ew+Hl2LIkIQambKdKzVipYpakx03EPmm0q81Lb0vGEfCENS1fyRC9ISPHdcXsbIOKphKOKho/XnKxCBuZR1/s0wLl0b7iwsneUyaQKi3FkVFFTpyTKb+a3UyU69JR16jXGi0WcIGiF5TIyZ4+ms0EpPGrO50KupntlaxY/qMq5HIRgiGkZx0j8YEv1H1X+nnad66UGRYQLQalNJGjC76xL6sbNMjPOLHc7FtBhf3FQjjA4QTXzXVzKkOg/ez3FEUHf3XfpPLm1ovpJPFUcxIhnFnSV/TBYic4KspgHKM0spZTfnKi8kdzpR6F3uvAZHCqN7UdgbUfo3nSGmn3tor4c31hM159PMkUt70NLcwpc2ediRT4ABEXNncYGJtX6wybXtoYa37wJm3R/vJlqqj2mTrFttqUlvSmNNIOk6eQQqdzZiY+zym1yzf8fHXN08tTzPSorJOikcqtWw8pUU18r3K21ejxE5eoZ32/rWkiYSs4Um75Xw7L730Uuy///7o6urC1KlT8YEPfAB/+9vfIm0451i4cCFmzJiBCRMmYO7cufjTn/5U9yURjYcEDzG6qBNe9Wdy+QVVVPbL71kPYjFS+dyP6rBerqWrhevyqHUdNlGi1+3oIimtqD9L0X/UlS2aNqWKkiBqkWE+l5bilhZt0WtcyiWWOYXNhk10qPszI4VPUgqcKTKk3qby+LaUGyuHIkiKn45yWPPjetE6HVXQmGp1ACF0hDgS7Tv8x+Glpk9Ko4YI8Ql8PN3MLnLSHMCyT+bd4N60rlGeyX6adbbc14HBSHsPLk7CLy1jMPeZVH9Ui7BplqjRSZt05q1fApKii/muxfbeNavOrJlkdQm0MVKfhzzEvwvMjpJJtJzoySp4cv4Os2zZMpxzzjl49NFHsXjxYlSrVcyfPx+lUiloc/nll+Pb3/42rr76ajzxxBOYPn065s2bhw0bNjTk0ojGQSltRGujprZJZKRHazM0wODAnhoma3FsC2RKOsr2/Vmd29T6nWgtz3BE6KT2VWeEJ0nsqM5neRYj1duajAfUx7GUO0OaWrYTp+w3vQ7ymJVa2p7eVk+dlNv845n/GWv3Uw+r1WF4rkxzE85uDjxswgR0+B9OOZGo+IrcgYdBdEB1fJOPpc1BfPjmF0lNGdPdshxUjelMWdNkbJMzk0V20ljzTqCz2OSegv+1nh8AfooP4xT8L36KDyeO0zahq9VVzGZuUOuEudb3qB6S6jPqTUNLc99zcnw+xzJ5XsdG1QqZ+omnWMb/JtLSUpvlPNhK3HPPPZHnN954I6ZOnYoVK1bgPe95DzjnuPLKK3HxxRfj+OOPBwDcfPPNmDZtGn7+85/jE5/4xGgMm7BAER5i9NGjNyYc5V79tV/5jpWpSACCBUhVZKoWEHftUu9VTFEbz1/fR9+nHt/tDGrHRP8ZiEhBtF9jrUvCL1NJURQ96mPqO1qvYnY809nQOSlTO32c0cHIAfj3auqaCVO6m/rYlAKH8HG5F+kRnbJlm9xeQZBOyarRiE9HeShIc5uIzUEEp4CK7+Am09+q8OAEoidLjUg0/hCmjOWZlJsmm7VMTkJbbfMbb4uGNPLX8J/iwxExY2ov96dFidKiPmlkaWN6LfJGipoZJdBTHhvXb/S1TXutxuNkuZWvKauQypOeqH5SR5w2iLlA2s3/l71+/frIrVLRqzDNDAwMAAB6e3sBAC+99BJWr16N+fPnB20KhQIOO+wwPPww1TC1GiR4iNEnyy/3snZHtpX/S9UaD5m6BOHUBYTPg1MFaWvR5/pjSVoERhU/6vHqWjxO1UNHeSgQOQXLd6sUIGnpYHlJMw2Qr4Vu52yiq7Qxtq3qmA0RIkJFTzszzUVNwkePDKnCx/YaabU/xT7ExYxNLOm3XXj42SsjED5qupsUPhM2DqHgCZHToYgdaVtdwCCqUB2TqrFfWvMUmieZH8jj0swM1Amveq+ORxcAtpS1tP5rTQ2zHXMK/hen4bbEMWQhWk0VnaTbJuq29EF9v3499WJLT8xL9P0cmYl51voh0/ueds2NFGyNEpmtHKlqdprdiBsW5Ehp8zwPPT09kdull16aegrOOb7whS/gkEMOwZ577gkAWL16NQBg2rRpkbbTpk0L9hGtA6W0EaOL+j/B9P2ru7bZmCGc2lRYr79tKgfvZ2C9PEhXk5P8zoKHEpxYGpts04jFOJ3qMIqdPBRhCNPJTItyJrqOpb0O2vE2saNHdwDdwCDcX8t6PdGTWR4D6WI3b3+29qbzmOp+dJ72XxM1yibfg4q4Z1WxGC4AeO4QUAQ8R3q4iRd6EyYCQBD96VByMr1Ym6giNqWSqdEWW5QiK6ZJZZLA0Y+zPde3JRVP53WRuxknBmJH3t+ME+HBwWm4TYsCuUGam208JnSxmCRukt4jW3/10IjJalL6XtZ0vLyT+lpSBrOKHrmtXqGRlNpnw/aa5OnL1HYk0/xM1vK19dO6kS0AcBwHb731VmRboVCwtA75zGc+gz/+8Y9Yvnx5bB9j0bkH5zy2jRh9KMJDjB5/tXwh1FLbIWs0+piIkPQqAqiPBSluMoohU7KGBkSBvtwuncVsNS2e68TS00R/4YKlxU6uLWopIk4Nidy4QtDYRJG6JpEx6pIRNdqTZfFQazqejt4u6xj1CI3OLG43LkjqJ++tgkh6G8r+NZUATOV+tGcomKDsjpfhwUEBlSDa04EKDsEK6DU+4uWo+vfR1JCsk8QskxRb5CJPNCcpTcv263jSr8q2FK60X9tvxonBvRQ+sj85fgfVmNiR6KmCkqxiJ+t+2abZTmk74uWm9d2o6FKzacSYsgrwLK9JGNHNlu5latOMiEzSWGoVO6NGTpe27u7uyC1N8Hz2s5/Fr371KzzwwAPYfvvtg+3Tp08HgFg0p6+vLxb1IUYfEjxE62FLY1ILy2VOrvpcnZiuYvEJbx8LRJCc0HuuSEni/Syo/wGSJ/lJ6+o41WGUS8wXPUCxk6evQ5MRm9DhmgiSQqee86rRnrxExmgynNBT1+T/8axiUI20yNvzLL5N4mnbbDU7anv9ptf0KOltKEEIrpcYWNkX0xDubX/FjkF62wRswkRswiFYgeWYg8PwmP+LtHnSbRcH8Q9BXHS4/qXGq4Aktu16epcpvSiL8MnqfpYH9bgb8LHg/jTcFkR41Lbh9dhS7qK/ao8UzRI9L2NH6778Ft7ZU9DyMBLW2I0QCI2MqmQVULVElxpxbsIM5xyf+cxncMcdd2DJkiWYNWtWZP+sWbMwffp0LF68ONg2ODiIZcuW4eCDDx7p4RIpjDEZT4xbHISCRv1+Vj+h+qQUCc/lxFoKoT4hgHhR1F3wfhbt2gHaeznQz1B1wgiPGrnxXMcodkypbOWScIwrlxgcKUAMl61SdcwiRTqd6duyoi9UmoTq3Jalre1c8cYwp7M5WhsY2qn78mCqB6pa9qltTGNQz19RtjkA9vOjYbM48DxDO4CtvDLcHg+u48HxU9UA4HXMwBPYCx4mYgkOBuD403EPGyBS3rJdmurMFk17Mbm31YpZQNg/IFlrWtQISt4JXlqq1Q34GJBj8qg6VI2U6MkSsWvG5DfvGOpNqbI54tXqhpf//PEFX/NcS7PfA53xIHZGZcFRGeFJI+dX4jnnnIOf//znuOuuu9DV1RVEcnp6ejBhwgQwxnDeeedh0aJF2GWXXbDLLrtg0aJFmDhxIj72sY/lvgyiuZDgIUYf0y/8qlgBopECR2kjj9M/yfJ4D8DMMD2L+TU9QfxiQAic9mwmLUZUq+roYwT3qlFBkqCwiR4buohJ6lsVTvIcum11mtipOWqUZDBQKzYRZTqvSeyYxmRqo9hTG9uqVERbVgK6y0OY0NmPdT2D8OBgA7owEyvxDryAZ7Ar1mErHI6HsRiHwoGHidgcTA7TbKpNvwabU7bM6THy2CxOcWk1PqbxmVAnvurxtomvbJNkaSzHfwM+ho/j5wCAO3A01mGydRymvuRr4aCaKZ2tUSvU2yb+9U5UTTVFrbQg6EiuydOoSE+efhpRS9RMWnlsNcGQLT06Z07TtddeCwCYO3duZPuNN96I008/HQBw/vnnY/Pmzfj0pz+Nt956CwcccADuu+8+dHV15TsZ0XRI8BCjh+kLKsv3sMmhy/SLvNqXH+GBK6I7VUektXmuSDsDRBpXey8HtOL9wWKbtW5HX6xUPlajQU51GJWC6Euvd7EJDD0tzRTlybLgqG2NHF34ZCGX2KlV4NgiK1nmakliR21ji+KofegpcElRp98w4GgO/IIBMwCshbjfhaP9rwyTsRHreiahA5Vg0rQP/g4AuAdzMQGDOAyPYQkOxiA6gomqFCXVYFIeih31Pomkmhl9f1rbeGpaPMqkEhdV5vSxPPUxNvHjwMN1OBVn4yeB2DFFJ9JeM9MaRtHzucbHtYqJJNOAVsJWl6LuH3eTaI280Z6RNBwwMd7fj5GA83TnUsYYFi5ciIULFzZ/QERdkOAhRoe/slDw6LU5Oqa0NlNUR28rIzwvMaATgZObFDuAEDvlEoNTFVGeoQEmhJC/LUxtU5x3lBQ2VfRITOJIdWlThYgaaVEjLJHtiKeKZUlPM7WV/ZjS3GziK03omMRYgEmE6G5n8rEJvZ4r6RxJbWWqZFpamy50kqJB0rjABXCn/xkrAZgEoMdvsztH+/MM21Q2oty5MRDXAPAkZmMyOlBBActwALqwCfvjWdyDubFIiRrxMaXpNAq9rySRo7bPEiEC4ml3YpteuxQ9R5I4coP+xPbrcCqgTTTzTVLtkZukRWDzMhIpXWkLxepjqHdMeSb4tuhdK9NqInQ0OQyPYRkOGO1hhDQppY0YX9DbT4wOsmYHyDbp1dOXbJ9cW9sKgD4G7oYT+FLFQWfBCyIvmMoxWHHQnjRsQ72OFD02W2rPbUO5xICC3QxBF0D6du7GhYVJaEjRZBMpsp+kPmohtabIJkbk+6W+Z2lpakn9J6WpqY9NUUK5P4s4Mp2vCKAAcS0bAewCEf3pBFABinuGNumiSwcH4mkAwDPYNYj6VP3kqgo6YqltaQKj3klZVoFjOpceiTELHVPUJfkDmCSO0qyV9XPZUB20xpxDVR2YrLTz1PYA6dGeJBGZR2Q1qp5mJKNRzT6P7TVp9nlbSuwA2QUPadYtGnJpI1qHJDvJtBQ20z41auRA1O5UEVhUy0iM6/kT9lUMnQXPt6qOiwZpMS0ex/clIRcmbe+pzbXNJChsQqURrnBynFn6Mo5Dr7dKQo+qpLUz3dL6VdHT2uRNOrCpYseDEMtl5VZC6M5WAjDg39YC6Pfv10OkuB3NgcM48Ko/SZjKA0e5/fEsAOBFbGuc9ImlSoWRdZJLmu54ZnJmS7pVUAju9UU41b7VcVT81DubE5u+PavTm+36wn3RxUH1c52Nn8TGM15/mf8Ift2UfpPeVxM2B7w858vKaKZptWqKmG53nTbOVr0Ogmg2W85PWURroUd3kiI2ehqbmgplEkP6sS6EgxYgJpz+r+zdzmBQz+P6fVUHWGShTZnWZhI7aspbVkoVB45v+S+NDCqFqKmBiaTFSPNEaNIiMTJCpC6SWhN6ZER9n2zOeyaSUspM5zO1tfVhiuCYIjymuh+5vawdDwiR1wlxndcz4CwODACH/epx4P1hs2U4AIfhMTjw8A68AABYjck4FusAANfi9GByL4vpRfQnHvGQv6Kn1dUkkaWeR68nykta5CdtbGoUxhQ1uBan41O4KXisprylTfRsY0g/rjYDg7ToRtpr8wu8L/UcaWltWciSfmZ6fU31W/VEeka7TijP31PyGjfJ9VC1nqPWNX4ayWF4rKn9W2lDth/XxudvH0RGKMJDjDwvsbjrmn4D7ClHtglz2hxMLk46VdTtDPkObWoUY7AY/5NQxY6KSezo9TwmZLSn4gufYincpy9OmiRQEveVDNuq5rV8ZD+muiLWyyN1P/pNwrOIGDV1MSkyo5NH7OjY/r/rYkdGeCqIih0Z3VH3l7RbP6JRnlUAXgfQ59+uYOL+r/75dokWwu6APqz2C+2n+2LnOpyKzZioRFcKSgQmjLaIKFAheGyKxqTdAMTa26JBpshPUr/i5Q2jMqboS76+4v2ajASuxln4FG4yRpnMfSXXINmiUTon4ZeJ+1WyiJ085I3M1Np/PSQJ5VqjSWOJLNGweXiw4efVo0AEsSVCER5idNG//2W9jb4trY6nmtBGLqKs2FPLlDW9UF+mpqkiKGkRUh2biYE0PdANDarV4eAykpzYWBXAVG6MvBiP64yPLSkSZKsHkgu15ln3x0gtxyf9f87Sn3q8KVoDRMWN7bFtnxRDFUQFUhVCBBUgIj2T/Mfa51r9NVQKHQD4Or6IL+MnuByfjfy6rqasAUAFhVyRBduv7Gl1O2JbPO0sCb22QDwPz5+0Lo957HEL7bQ6oKtxFmCIvuhCxxRJyTs5lOe/BR/IdZyJvIYUSWsfSUloaiNf87wixhaNyRqBaVRNT620Slqc7b1djEObcr5G0HJmBRKq4SEyQIKHGB2kOJHfx3pUR/9/qKaw6fUhHkKho6e9qefwU9nKJQav2AavOoxCxa/p8Q0NgFDgsGp4DlUcZU1jM7m1SaQAGiy2AeVhuJ5ZkESiMprY0Q0IbMeaSDpWd4rLQt2CCIgLEXVbUtqiiTSxo4uYirJN7i9rz9U0NlXsVLRt+rl7IARPD6KcwID/iduefhnfwtU4C4PKWjKe0ukgOpTWHTVMzrP/km5Lq6r1l35V+ITbkq2gAXMqXFaBop7PNlHX+8hTlG87V5oYtdlt11v/YrpGXdzUKyqyih7ba9WodYxqIU9qnCn9LOn9ySb4GjPrNn1us45FTYXNQ0uKHYBc2ohM0NtPjB5qepo+ScyChzAFzoOYVMpJrB7pWcUCW+qiL3qKnRy8wiJiBwgn+kMF+6RfiiJZ75MmgqT4Ue2tw33R8wJxAWGykdb3m+ynk1BFjzxWFTs1iRiTaElr1yhM57OlRaqPK8o9AJzDRRpalqhOCdFUNzXaI49bC+AvHNifiVsnxO3/uFX0rMFUeHCxCP+Br+AiAOEktRKELAX6xDFt4UzTL/u2KECW6E8aulhR0QVQ0i//tv70Seh38Qmo1tSfwfW4GmcFwirJ1c08OU+uT7GRZK+tkzWykVVo2vprRE1P2jl0Rrv2plbSarpqua6xnpJHEGMZEjzE6CLnAFk+iapAUqM7UB47hrYKQwWgHeH6OwUoaWO9HM5AuFaOKkRY1ddQTih29LQ3VfQkRXfU/SYBBMQjMFmMCUwCJc3UIC1KlETimGxGAZIkswkdNSrjatuyHGdCChj9vopQ7JzPgf9gUbGji54SQuEjb0A8OjTNr1vrBvAEF8+nMWCNEDuX4vO4EN8JhvefWAQA+CK+Dg9dAJJ+yXX8RU1lbU2+ibhpmzoxuwyX4AJ8LbFPG2r0JkmoZC1uB+JRirRojEhtyxZVqFf0JP3qniaAGpXOJV+fRkVzkgR0PaJntKI89Yiv+tIeazPFSKLWY5thvT5qhgUAwJAtXY2q1rdoSPAQI4tvyRv75FkESmS7ut/T9ptqgRylnSecx9p7o7+ms14eOrX1h4uhul4oYAIXNwNqqptTjZseVB0HrucF91LodDuDWO914MHCoTiivCwcjyJQpBhRt9nqfPKktunPs4ipTE5wcgxpYicrqhi2iR09JTKpn6rlpqa6SaGziAHXsGiqm0xzU0WPmtYmoz0ewMtA1QOG/H6rVWBCEXDLwOZJDI+XgLmc48+MYTOACy0rem9AFzw4uB6fwafx7VgqinRvE9ucyL2OKR3HNmFX+SK+Dln2bOs3L6r4SRI+NnQhZRq/6Zd42W+a45h6fNJ222uSJwLU7Em/Kkp0MZRGlvQ4kwDNKhrTxrulMg8PZqrlqTX1Mg+NcpVrKpTSRmSA3n6iddD/75vqN1xtu/wEVxCmtEnUxw7AygBWMgx1CzFT7OQoVRx0KE5t0VodsZioGslJWtjTltZWdZzIPQD0YwJcAIdWHgz6NQmbJBMDdX8W0ZOGbuCQFSaFg44pipMW2VFfV1Xg6I/1PvT3Q//syP1Hc+BOFhc6PQDW+I8XMfF8FaJ1O1Lo6PU7MspTBngJ2FwRAmeznyI35HfbXeb4JxOCf2cAf2QMe/tCZwVjmKOIngW4C5OxDpswAw6q+DB+ignoik2OZUwnr4GBPLYRtTlJkYQ8ky4pfLIWvotj7AYIuiGB2q/tOk3iKU8UR2+Xt4i/WZP9Zri26elx6tizip56ojy1RrBGK8Uurf5HpZHGBfVAKXjEeIIEDzGyqGlnOvov+kltZDu5bRYXttMyqqP35UDU8ECktLXL4bgOXE84swX2zJCpa8NGYaOKnqwiQUZ5dDzXQXuPF3Ffy7K2TmrEJSFVUD02bfyZ1/jR37OkOYwasTFFa6oA9uTAcyw+fv2YqRCWzyo2sVMF8Cv/dT7JFxfX++JnrdKm6vepp7rJaI9MV6sgWsvjCZGzuQxs9s85BGCz3/WfGQs+d4AwcgOApYyhV7uEe3EcPoyf4tf4CA7FYnSgggoKKPiFRnLC5sBDByr+pK8jdVKlHmeLopiOr/UX+/TxhHU1eSZXpkiQKhoAcxRJXncel7B6Iha1ih4VdTyNrMFJQp5nJKItpkhX1vOOtUhQHtHTCLKKcxsjPd6aIZc2IgMkeIiRJ+27t5Yf/OQaOx6E/fRKFvajR34AlEsMg8V2dJSHwoiNXJQ0A7oQ0qM7TtXzxVQodOTjXmxGPyYEaW7rvQ5MUNLmTOvk5FlcNIJB+KT1J69Nb6OLo5ixgSou1XOr+03bTTzHzO30bTaxYztOCphbWNz+HEqbboQpanr9jprapt0mbuToZwxDyiml4JEarx3ABAit9ivG8BqATxlS2v4Xp6Cr9AZmdk7Bn7FvIHxceOjAIADg1/gITsTNESGTRJrBQRZM69fo0Rk5gc8zYa51kVBb/U+SeKlF9Ojn07eb9icdm5WREDgSXVzJ85veQ5vJRZ73MS+Nei0aIdZrpRnRpWZGrPLapBNEq0KChxibuDBPoKuIL2zqAkOdfo0OxMS92MNRBFBGKHB4vziuUhD1OPnW34Hv2DbsLyxqSZnxPKxHh6hfdwZRqjr+MVEBkVfg1GMLLZ3mJHrKnknoSHKZHsgZvxRGtuiOfkwSap2W6TjTvh6EQklPgZTH9cEsdkyLk8poTgVY74ZiZwjRy5SpbfL5GgDrIcTOQsZwMoAv85twG04LhrShcxtMrnjYtvAiujAVE7EZE7EJDjxM83PwNqALosJG9GwTGI4fBZKP12IKpgShLT0KEv9lOIvLmG2NnCTxkxRtsmGahOliSx+Xnt6WZCJQi5ubPh59nGn7WhXb61DrOj42dBGapd96jBmyih7Z1rS9kdQbjdH7ymIP3oi+gVE2LACohofIBL39xMgiJ73y+7KRPxg5CBd4lP0qTmsVxc3X822pC9qv/MKdrQ3AcEwE6LheOPm3pYap9TuuF/0nsd7rCIZZqJitqE11PDoxwTGVCxvuGpDiJ0nkWM+tpqk5yJaWCESFht7GlN6YJoJMqWyq8hhIGZMiYiL3eoqbQfRI0z1V9KgcwDke8yNAgIj2fNuv6/kZEBE7AHAwlmBd4XB0rF2P16fshJl4HoPoQAcG8SDmAQA2YSKkeYEDz4/9mGti1AnlBGzCJkxInSyGx9vX/DFFTeS51X3qJFYXVnkWJk0yEDD1ZUtvq5XsqWpJa/bE61lURmudmjykiZK8k/haa3psaxqlny9fNDFpG5AsxJPOZ4ua1ko9phEm1L/llqQN5NJGpEKChxhdPAC789C9TWJzbCvCnirlIVoj5AKYGrqw6dbR5RKD19mGiaWwhseLLnEiTpvhi1QKAj21TRc5WZFCwmRNLYWQKogiwiNF7Ohr7qiYolqZFyDVxYnJdEAnb3qjKSqkbjeJHZ2PcCzBwTgcD4vn1xher29x4JMs7EO9qUJHOf+QZZ4mRQ4ALPXvbYJI52EcLtqXOzC5shpAF6oFB69jJ2yLF7E11qIDk1DAYDAZsQkc15+26AwifXIl+/klTsKH8VNtXzQylOYWlbTQqC5WJEnGBPI40/lM0aVz8UN8F5+wCg5bLU2Wc5tItqpOXsS0lUVP1kVP81LPddeTopkmRPJi6i+PgUc956nXcMN0jtEyeyCIRkGChxhZ9E+cAyF2soaj9f8/VcM+1XK5nwkx4IRixHPb0FEexmBR3EuGCjK6o3RviezkQdbzAH4aW8VBZ8FDqeIE46lWh9GuiRtV2Eh0IZQpncxgQa1eV57UPZ3ElDb1tUs7R5p7m+zDJHL05zaxI4XKLQw46SBhWHAWN7c5zyIatYiOHp1yHaDdC+t2AOHApmITOwv9Op5H8U4ciKcj+/h2RQDTwZ4BvJ3fADqB17ETgJ2wK57BYETw6CInGvkRbaIf4vTaHpEKdzTuADDRKKrkuQBzrUy9EyabAEpb30cXPd/Gp4PWtUwA9XS3eqM9aaJHpVUEULPEjs6xuB9344iG92tCfhaOxNJcLmmNEAF6vVCev5Va7OLThJxNrLWs6KGUNiIDFOAjRo6/WiaRWb+o9HamCa3667tSD+N64cRe1sx0Fjy093DFhhrwXFFTo4oAdQFSVezINXIAmQon2/uTqKoHp+qhs+AFj0sV0X+5xPzzCMHV3sONqWsmsdMI9GuRiNcgez+xlLZYh/KE2nZdvMg2tsieqb3eRj2fPi4tMnT4TY+Ixz/UPpNfM6+Hk3hOn3Zl7O0IDQqA8LLmGswJvsA5dlSeH4incTtjuIrF/17at1+PDZ3bRLb9HftgA7qwAV3YhAnY7Ff6DKKATZjo3yYEt0F0YDMmBDfxPDxGHldBIXYT2zsw6N+ExHHhwUXVl1bhdifYLx2/9O3iZXSCCdi/4oHguvRJmTyPvIVvgxOZgHtKf+F9eC5z3+rxou2ncFPs9VfPqR+v30zY9rVKMXjeehz5buQ5tpnXKj8L9dQV3YO5ie+hTqOup1FCohGpZ7WIqFFFCp60W2v8mRGjBOldYmQJf1oNUaM0JtTtad+36heaG3ckkxEcz3WAEhPRHoS1O041egJV7OiYBIoeIQJE6pw6LCmmZL2MUx3GUFlYassojym6oy8eaiUhWqL3Z7PWVo0MbOsOpWKLtGSJ9sh2Wa9TfZwmwk4JRUeQ2naF7wp3SUI6YA+Ec5vkWQ7sysKatIoQPVUP2N5fVHQNEFhOH8J5kNJ2FOe4jjFMALAcc3C6IoS+wRgu1oTR5MpqrCtMR9fkDRAWcgJnTQnetE68jN0xFa9CreWxGRnEozvmyJCprdjmBGlw6vmAaDqSyShAx4ODH+JcfBaX4zu4EF/UxpXkgqZbUtsWwNSjTSYjg/i4XFyNs6B/KeWxTs5Tv5PWfqTI66iX97i81BPdSXp/Gm23nfw5zR5FVD/ztX4eGmWM0QqfR4JoJBThIUYOB+H8wUG2X1tsVsZ6XYjsz0MkUqCmcLX3iEmkiKx4GCy2BxEW6bAm9uePdNSCHkVyPbtBgVq/kzeNLVaDYkmHS7reTOsNmYSpLdKiC17D+GJ9JpHnmFsYcJMQHofjYSzDAcC/+wLDFuGpIm528C4mvKXlaR3AdcX9P/01d+ZxHkR5ZB1PFcBv/bV3TuEch2AFbmIMP/X3S7HzDSXCs64wHQCw1tkuMgRvWicAgK0Afox/w9q1W+PNgSlYW5qCtZUpWOttHUR/NqALf8a+fiRoYnCTkSHZZrOyT71V0BFEd2QkZzCI/qjbxbYqnMh2NdoDhBOzT+C78ODis7g8Fp0xRUzM28Koj/oLvynSo0ec9H6TMEWXktvb99cS6bGvnRSPfDWDkbTIbiRq5EdeQ55rMUVNstRumcwIsrYfK4y6QxsAMIRzgLQbscVCER5iZHhJqdPJUq8h2wFxcSOfq7/uy/89jrbdgFx/p7PggVeEoUGhEqaliZv4LUBPb8uCWrMTpreFtUIyeqI6vEmXNt2VzRTlMUV/cqO9pvF1hOro2yZ2IicAEucL6nss7/XooB7RUfuzRXyU/TK6c9g1jwPlFFc722d2uR/l8fe1u0DVj/IMAXheS0szpbQtZSwS4VmoHLOQMSy8DkGt0a54BlfhXByJpQAAdheAIsAXAOyxpcAkYPAdAHutjPaiqNpx3Alw/Q9bF96A404GAHQUBq1RHTU6FO6TC5uGEaEwmiR/m64q0ZO4NW6HUkmkRlnkRO97OB+fxeXBOFRMv1zHa3dC4wE9kpO1hiZvXU+aQ1itpgU20or6m2V2YBMIzV6ctJ46njRRk8XWOlqvNrYiHrWaFKjHpx27DAe0hughiBRI8BAjgy5aTJEbk7AB4p9SUz+WczKlWaniKEKkDeUSQ7GXw/Vd3LJgS+8Ktw0HqXG66FHxXMBTIkpA9rV3al6EVCWlD31tHtM1xwRXnjlW2v9fV7u39W87Z9pYTuE4/CYGVBhwDhcpbeq5beNzEVmwdDnm4BD/MXP94KILuFVhXjDLFzJ/9EXMcsZQBfAyhLHBzgD6tVNsB+A1hCYGKn/HPoAvdgCAHwew23zhM0lcN7sfwI4FDJULgFsV5gjKG9jmenBcD5s2ToTj+ilv/v6O4iAcJxQugJgsqU5uodCJipwODPryJyqWots6YgIJAK7HZ/AJfDdyrfqv3SZRom6PptCZ09dMZgr6sUnnCPeZRUXSBDqP4Kq/BqOxoidJOOQVO3mvbSRMC5LWiLLR7GjMWBNXo0pW0wKK8GzRkOAhRoa0+py0/816ypIujoBY/Y60pAaia/BIip0cQwMs6GawKOt7wuhOregiRzUyMPWtCi5dXKhRnYZRBXgxuYkuevL2H3msv88ywuNY2ptQIz0ydVG2N9WEJY39pywcxzUsTGkDgG9w4IvMLnzkOKrAIXs9Cfw9PJYBaJ/MgigPIKI8LkJXtrmcY63LMKUqjrtNiwK9ZhvzTQzLzxC1QADQVXoDGzq3AT/RP/ftYmz8OIDdz4QAcv2EuuAeGAYw7L92Qy4X6gwAXC8QQ45bhet6gQACgIKv9KRkkNvkczlpNEV8dBx42IBJ6MJG3IRP4XRcCxcOPoHvwvHfWNvaO/J4fbvJnU1ii/SoE928kZgkUWGbQGcVM2ntorVS9jQ3lTQBlFTv0kjqEXQj6dpmox6hk3Ttps903tfKVPfWbOHUEtEdcmkjMkBvPzEyqJPepJQ2U1THVr+T9H9HWX8HEBP3wWL0AGkmEKayOYEgMbm1ATUW7wMRMwSTkFL71aNItgVJ60XtR1+jSG5Tx6a2t6bV2QSCCVNaWxaxovJuDixTBIPp+LQUynM0E4OLfQe3qzlwdjzVbfnSfXHIIU+Ksa8FsAcD/sKBtzHgVY72daJuR1oLuAB20aI1r3nAFACLDU5sMrJzE2Poh3Bxe54x7MI5DjkdeDv+hBfwjphbG/+QuGe3+SfdCLFulRS2pkhpkQFo90NT7Rh2OYbdKoZ88VMpFwLxM+h2CDGkRIBEmlolFvEpREywBaKt4E3MQAGVYFIu608KqMSOiw7ZnMomt9miPfJYPYVOX6NHP1Yffx7RY6ORoiffee0CSL3+tBS9WlLY8kzck17PkRY7ughuRFQn/X01C/t5eNBql51FRNVKo0wQCGK0IcFDjDw2AVNPBoYS9eEuAD+y097DUS75xeKOA9fzfIvouJixRV/Esf5pMn7f625vtn5VJ7SsSNGRWs+T9Jpm/MsfLLYB5eGY6NHHUDd6vY4+Rts5HlJqw/L+Lz6di7V4JJczHH4+B76htJFGGxX//lcchxzhH/OEL2L2YvHxKsxSxM4KxrAZ4To98zgPRM9tjOFlf/sFnAd1PWtdIXZkVOiFs/dEx38NYHBK6NYGAOwa/0ERoXHHRuW5imu5BQKoHcPFqPjRIz+eL3zkBE4VPuIU8WiPSGmLFmd/ALfAxQR0YDAWTdAnv6bJtl3oRCerJnc208Kkej9pNCJ9LI/IahTNqPVJE0tjlZE2EzC951nFTjNFScuKH0ppIzJALm1E81nJwl+ZTdi+qGy/9stJcQXWAnbWKyaLQwMMj3X+CwaL7RGxA8C4Fo0UQqp7WxJ5hIqk2MlR7ORGNzj5XO1XNzJQUYVHfHCWAfj9DRV89zrLj+qqc53tHEaxo0dXkl5DU0qbPM72XDcnSDrOxilcOLWdxYFrGJbgYOB8rWbmPBaO/3/8fe9Xth3CgP2ZsKcGgBfD4/mUeOTmecYwh3McwjnmKSJIPn4NwN4QYkdFpr49Lq/7Oo4/TXkHpuLVSDt+DoTAeVO7rYZ4fVcb9qm3dcr9RgDrGLCuHdhYxPDGiRjaOAHljROxaeNEbNo4AZtLE7C5MjFwaduMiYpjW+jeFrqqCXe0NZjqy5/odjmZUtfzGUQBJmc00zo46uP4czc4Vm+j7zP1mUYj3NFs59Ov5xT8b93n0rEtJJpkVDCSTIws5WumCxtHYCSNIem9lvdZPn+jKTpayVGOtwHcyXYjtlxI8BDNx/Qlo0+EbVEfKPcmASTrN/QUsL5wwnlA6XE4VQ8d5aGI9bSKun6O6tImn9swRXz0SI7nOv6tLTBLGBpgwTjUsajPbWKqFpEVDjjah+cC5U5ddNm/FjKn0s3i2c0lsuyXr5EtBQ4IP2fzMywe+lMWRniqwOFXPAIsYsB/MGFTDQBXcpHWBgCnMuAO/5qmKOdyIFza9mIitc2HGa5HT2sLzuOzA8T6PFcxhmv9qI/q2HYU5/gjYxiazLDdpH+iD28Lz/c1gF3oP1knbvxchILnOeWxTezI20bD440M2OiLn3IHBssFVMoFeFUHg5UODHrChlpYULvK4qSFyEKl6zA5SHdT923GRHhBBMa1CpOsC47K59F99YuePJO8JFGQNuFN4qf4cOYx5CWPkFHtnetd7DOJY3E/NmGCdZ9kAyY15fzNop7P1mgRNYr3WqN+hyAyQiltxMhh+rSliR7bcSbUInh5qB/FqQTuaWF0J1rDotpGRyf8qoU0EE1v0+tpZNTGqQ4nCgfrJSg1Neq55Pnkc5PJQSS9zJbiZjhX0jhti4+mOrSpNuS2ManROSmA0yJGWbbfx8x96fOJm5RUtCqAizjwHwyHXfI48DW/zcUMuE5sD9puRGhosNwXMe/yramnMWANFzeNPzKG9XxfbMeexCzOg8nCbxnDUZzjQ74g+pwveq5iDO/V+tjbb9OubeeXmF8K/p8AOwNKypryWN2m3srK9rJ/X1W2VdsxXHUxXKzAqzroKFaEy1sR8HyDgw5lDIMoAKhgEyYG26To0QWM2kZHT0NTj7Olo8VrMOIpbHraWD3pbc0k7zjyts8b5bEdI49r5NhsmGp66k3Vy2JV3SjSRE6rfPaAqEsj0Fopi7wtPRsDALzW15REE6EIDzEyOJabTtKXlh4xUKNEsj8lXUtSKSAQO2o0Zagg9xmGG6S2mcWOxFTDIs+R1eVNijLXi0Z4QkEibnKs+niNER9TfYYyZkleURZZ+LSq3IDkVLMksaP2ZTpWfa478ZnQI4MKyz74L0ABQMnf4K9vAxfA5b6o+RoXQgd+u88wYKXffiqAX3HgXg4s9Y89hgFPcWFcYBA6kr39RUZncY7ljGGty/BnX+wAIuKzwo/ofI5zfM5Pf1vqbxuazLBpkp9GZ4D5Io2dAbBTxeO7cQT4jRA+2PK22nCzRXvWGbZthB/xiUZ7BssdGKx0oIJCbFHSFTgEap0PEJ3syciOukipTGcLU99qj/ao51OPs00401Lm6qVWu+Ms546+To7xtchCM6M2QPbXsRajglpSDJt9vbVSz+etUWJJFzsAcCCebkjfBDFSUISHaC6rDAXdehqSh7iY0dPW1D5c5TiJjO74v0RLkSLtqFWxowoKVQDpQkJdGDQJU5qXGukRfRt+PU34X5Tp1yotGpQFOVZ93aCwT8dfQ6jNj1JZ+rcJGCAqQk3XqIudNEyRGtmHLSokt2v7D7vzcfG8qGw/hwPfZaFBARCutXOlJmCuNgia/7OInGOY2HcCC2uAfHohanOmKNsOw2MAB37FGN7vi6BrGcOnZFTHd3/DTnHBwy4E+KX+kyrAfyIeypQfvhhge/nXPRnRaM8kiEjOJNgjPeprFonYhdEeAMG6PijAd2ETzmy74hm46MAEP7oCRC10pZmAOuGU+z3/kerglhTtkf2J/fr6O+E6PGo7U19JZP3lvdZFOZP6r/VX/5GKFuS53kY536nHNopao1eNJk0cZ9nfqHS5Rr6+jWQ4a4SnNYdPjBAU4SFGBlNRujoxTmor2wPi1/mkibQXjUIUS0ChIowC1OL8SiGM3rSXhJsbEI8EqbUusUtyojc1UqNjcm1ThZTsI2xvP69JuMnjkwwObOfSMYkzeVysb61+KrYvLbITOYHyWE+Hs5GWtqb3p7eTLm2dEGvxyHZTIaI8FzBhXvBJv90XzdEVI1II/U9cEM02LCoKAH/2xY6M9EixI53cXmIsYo4gCcQOQrGjwrog6nqeAPAPAP9E9noe/VbW70W0ZyiI9nTA83NHKijgBbwD0rFNpLeF6EYFMqJzG06LRSr0CE74OP6BN7WN30ejR2mk1VzkmQzWcr56+x3N1KhmL+DZh2l195GVZtcsNZJGveetXl80zICq05Z689pyfH8T4w7Su0Rzkd+3MvoiyfMDnvopTfv+Vs7D/F/4VROD9h6x2KijtpkhJpDSnU2mjeliIy2ioouIeJTH/PtCHhOCuJV2vA3rFWsQpdlFh9dTW71RImnvtSnlzVbjlUf06JEmQ5Qnsr8E4Id+/c13/X+Gi5io5zHRbd68DAc0pIB3Nuf4p+/mJvknY5g3VVhaz5kKUSc0A1j81CGYhweN/bBDASxfA86nibS2DfeDsU0AJgIvA7J7tgdEZEdGedS6He3GLwDYf/snqGq3IgAUMVwcwiCAQnEQFaeAzaUJmNr5KrpQQLh2T/hG678+O3Dwa3zEt6qO/mGIFDchfQoYjPy6He2z6g/RbmGtR3pUko7X0X9hN0Unao3y5Dmv3FZ7f/HXr5E0M7okFyPtwkasw+SmnMOGLnpGo64lLRo4EuchmstVV12V+5gzzjgDXV1dTRjN2IUEDzEyqMLHgz1KAyQbGeRBnfD6DA2wwGxA7b9cYih4cfEhhYDN3S2NaPpcKCxkqlhdjmsKalSJ95t/xWrEYqV1Y3vfk2p+0qI9ec0OVDqVdudw4ArLL4BnMxFd7INwbPtJVBAdhseAIxhwfwaHuBS25xxrGMM0/36CA2ANxxx//z8Zw/ZrOObJAy5mwF8gXOR8fvngAhyHewGoTlb/AGNdALrBWDeAdmBrCIHj3/gj4TjYGQgFzySAfUPcA4gLniDVrR3Drifc29CBCZ2bUUAFmzABr2MnvB1/iqWjyehPB0Qm4Qdwi3+KaHqbii5covtc/5jQnEA3MrAdq1PbIpvZRE+W82edZCZNbE/DbbgZJ8a2N0PY2DCtk9Qo7sYROBb34y4saGi/tdAMcZuFkRIjrRrp4W1t8Nz0f3CeW//382hw3nnnYfvtt4fjZHv9V65ciWOPPZYEj0YrTIGILQXTZLdR/3P1vi2f7EDs+OflRWBTxUGHLz7aezgwwDBYDB3dgPxCJzY8Ldqjj0lFiiB5jIoaZUpyTZOpbbUsCmpKv4uNU4oM9fs36//bJLErSRp30rG2+p4s33TXKM5yl7PoujwzAXyVi9S2CoyiJ6vYSY0GvU2InbUuw3oA3Z5YfHTKFOClvnAhUymKUAVwB8cSHIzD8TAABGKHsT+D89l+x88DmAhRQdQLYBrwZrdIS5uMMDL6rwhETqRmR8UodnzcAjZtDN+krskbAABT3Ncwzamggo5gUiilRwEVDKIDHQA2oCtYd0WflOsTrniEyJA6ahA90T7tUZ54W3NkpdY6lEaKnrznsqXgqYKx0TS6LklyN47w//Tzjb3Ri682Q+zkEb3NFJatzHBbG7wMYmDYGQaQzUyo1fjDH/6AqVOnZmpLQscM1fAQzUdOiPTvX7VIvFZUtzd1slsFYFhQk2mTM1YFJpaG0d7Dgzqe9h6OjrKM6AzHRIpaq6PW7MjjgeTaG/Wxqd7HtE1POZNtIq5pGZHHhtGnaN+2Gh5JEClKeu80E4pHd9knut/m0mc6Xt9mOtYFcJhBcKi1YppbnbWtCyF2pFPb5UyIHUB8pgoJ/RyjRIg+Zo4WmcTOUmW9HbwqzrXZE2v3bIK/+OgajlmcizoeAEOyvQiI4Pv4NNihAPu80vEhs8HYTWDsJnD+IXB+FDg/AGKZ0+cBrAHKPKjfYXvB7NSm3tT9pvqeMsNwuQOO62G46qBS7kCl3AHH8fyaGTewn5aTsgoKEM5sYh0f6db2S5xUc52N7uKm7tfv9T7Ox/cynSN5W7Yvt1rqefJOZk/Dbbnai3O6sWswbcvWV21OcXmR1TVpyKhnq4sdSdY1e0bqdSZGlksuuQSTJk1Kb+hz0UUXobe3t4kjGpuQ4CFGDlutRRJJqU5SMLmGbfKxPEZLYQv6rkZrfMolhnIpOlHVozumon/Xs6eSSVRBJI/JiinalEXo6GlsaYYFDWEmF7dZ4fUe+NIz+fvJMq9S2yzzX39Hu+nt1M+N/rjHf/xdBnyDi3qeMkIDA7k/Wnsfojq2/TxDxOdAMea5BhODv/ODAADb90DU7QDAdgx9AB5jDH/hh4htvkC6/f9OAQ6EECLw63P+AXB+Ojg/HYxdK7az28D5XAD9ED7VfeBDAH9DPOXP+qltG2FeiHSj9lhJicNGAG8CfIf2wLFtc08vXNcTC5T6tTySQX/Fni5sCITNIDoi6Wy/xEmR10U1N0ib4GURPbJdVetLih5TcbrN9jppLLUsRNoI1HS22owQ3JjQqdWtazxOxuWr02zi1uxpn/3kfUm3sQYHS72msXptgBA8Eyfa1yjTufDCCzF58uTmDWiMQoKHaD62mhw9RSrP/1CTmNF/zdfPowsEv76HuwCmhhPOMP1MOLzVgikFTpol2ASUegPMUaKsSDGkOrdlETpO1TOmtGUSZw6E0LFhup6srmrqdl3Q6hEcU6RH5f3cLnpO4eHjK1g06qPfPsnEGj318Kh9rIdOFgU1Dw0gWN/n+VXAAZzjAM4xDw9iMQ4VjXdi4N9m+PU3j0BktdLIj4JrwNhCcC4mwJzPA9APzqeBsfVi2wbh6MZmISp2pLjRoz9lxIUPALYCGCwXUJy0Cc4aseiRVxUTjs2YCAci2iNX69FFzKC/fs/RuAMAUsVNVLw42vPkPyS1rS560sg7gWqW6Mm7IGmjzmtiLLiXybqfehgpoZNGnol8nkhpUjuZOksQYwkSPETzWMmSJ8BANPKSFrGYpfXlTzy5XE9E7VP25Sn3amG/cgzrjYod9V6KhKzRmCzt1PV9kkSISRypzxNNCJSUOfX69DQ86UxnWyQ1T+3SozO0tLWVhjWY0kSPGpkxiQwbpkiPrd1vmDm640LYVHdCGBi4EI8vUgSSPq4qhKHBGdo59ec18OA6EeFRoz8v80PwRyX9rZstFw8qAO7nOHb/JeAn+zurAH8eYOwbYDMBzhdG+mfs2xD+1ADn3f42LsSLtKxeB3sqm/q4LM4XbAPgTevE5p5edBTDXw1WYhesxC4ARFRnEybgTWyNdZgcET0ytc2DgwW4yypakhYbNU3YklLb5DmqcPA1XICv44uRbTYr4iQBYbPMtomCWsVH2nG6aYEpWtBI0oTdaP/SXq/YaUWa8ZqO9vuUlWG0Rf5Gbbexcj1JrF27Fueccw5mz56NrbfeGr29vZEbYafeCgqCSGalNvHTP3Fy0qg/NvGS1pfiwBYp0Nf7MdV7yGN6oyJK2lYDQhSogkQXMyajALWdTcwkiRz9nEBUdOgLgVqNCZTXWaba6SYHskkWe2q5zpA81kRk5W31fc8imtIiPfp7K+/Vui1obdS+PcOxpvO6EJP3HzLgXH9BUrWG52IGXOY/Ps+/xh/4z89gwI3+4xstQj9hMdIszMOD+BUY9oZIazvAF0N/XAXsDQBPKH3uDl/oXAzGfgrG/gEAfpRnIYD14PwLQXPG1gCYIG7l9vCHA2leoBsZTEI0ulYG+JnxMW/uCf8JT8Wr6MPbMIgOrMXW8Pwoj0SdkMh0NxkBkhGhqElB/MNldmQLi9OTTAzUdrbz5HXishXG2/oZCwXn6vWchF8CAG7BB6ztP4Jf4xd4X+b+s5gJ5DUcUF/ru3FE5uPGEnFjivon+GPh8ziMtkxpll6LX0cWTjnlFLzwwgs488wzMW3aNDBW/49rWwokeIiRQa+hAKJuWnn7kdbWCsbJv6O11c7F+xkqBbEwKYBA7ABR4SEn/JH+FcGVOGSDiLG1U+9Nx5giLqbrVqM/rGpuI7ebFjnVIz7tPTy1RgkAsIoF6xoF0b2VrHY3Pv01UAWLLmxcQxu1H130APFxqe+nFD1X+KKnClHXc7H/uADgSkVg3MiF6JF9qi5uH2OipidhMVLs7x/7lNinp40sZwyHcI4nATzJGN4NsSCpww/C4UoUSLq38V+Hx3J+Cu7BXBzF5oLzhWBsYfz8WO/fugF0AdWJInojxY5co0dNk5ORLrkvhc2lCZjZ+Xzwi6sUHhvQhS5sUISK2OPBQQUFFFDBL3ESPoyfxiZfMprgahO9JDGjnhsI3d7kMboY0SfX+jltE019naBa1+hJsuVuFFkntlnFjvoa/QLvwwRswmbNrKL2sY7+1GW0LKhHA/3zTIwey5cvx/Lly7HPPvukNyYiUEobMTqYvjdrmRQrLmX62joxNBEg76XYKZdYkO5lWngUMKSRWc6lp8K5HiJOcLZj9DoewDyWpHQ4XewkwapRMwVbepsqBK2pdKtYeO8gHt0D8r/HWioiXAB78mhkQabA6STVAc1XanX01LbTxb4lnxApZfh3JaVtkb8ej5pG+UXlOm/kcbtqwG5gcIJy7BM8EDvYI/7a/QXAZcqveQ8BeBxCGN3mb/+jtKo+hOEezAV7F8C2Ahi7PThOih2Z4sbYdWBsMYRz2xoII4M1ANYDVR7W7siUNj2dTanhYT8Lx9tVegNdpTci17ChcxtU4eB17IQCBoPoTgcGA6c2Obmqao+Pxh2RWh41DU220VPc1HvxONpeb6u3iaZ9mdPT9LZJaXVpfeRhNNJzTBGVpMgOEF6fFDsjRWi10Lxam6RUx9GimZ+LVk0Jy2paMNyi48/D7rvvjs2bN4/2MMYkJHiIkcf2v8dWp6HX7hhIFDtysuojJ+ysiohZQbGTo1IQaW7qAqESKTAitsxudJtNiNTijKYKHVNkx5RipkZykqI+ke39Qugl1QsB0WvIZE1tEz0qpjodebM5oT1nSZM0RfH0yKJss8QwLu24w69/RER5ACF6zjeIpIsZ8C3D51OKnrNTrl9Geg7U2v2FAztFt53NOS7gHNsp24YALGQMfwFwE2PYm3OsYAx4HjjywGXgTwFYtx6cfwhHse8DEEJHRnmE+JH/PDdDiJ71/uO1ECutrgfAQ4OCdbA7tG0E2O0A+z+gUi6gWnUwubIakyur0bFWGCNsrkzEtnjRj+hUIUwKOoK0NVXMxF2psrmz6eQp6s6LacKb10QhbcI8UhPNpNdVFTvyGqTYkZEeeZ/lPNG+s62PZBoLkUyjncpaUfSIlLYsLm1jf8r7/e9/HxdffDGWLVuGtWvXYv369ZEbYWfsv/tEa2P69T3L96X6v06t3Un7P6dMmHlRubniNuRPosudAKbG07TkJF+uwwNEhYcqehJNA+Rw/fYyilIusUhEJSniI00LVOGxokeEsW3mCLaIjtxuGrPcFzUyEI9VwWc1ZFBFio4llTC2zXR8WqQu6/n0do62z1W2neS/H6dw4CwOfIKHogcQttQXcVHTowlpAMK6WseyHk+ERzmwq99Oip8eADsx/FOJ3gDANIhlQ9v9QxdyjoWc42UAKxhDFcCaPgQOcJx3g7EVEC/oBF/ofEMxMdgA4B8A/ghhVb3ev++HED1r/cdDUVc2m+gBwI8BhtZ1wau6qJQ74FUdOG4VXaU3sGnjRAx6BazyZqCvMi0wMQAQEzTh4/CFVm2q9X2APXKjnkNtayvcN0eLsqdSteLEsF5Ue2r1tZDCJy3aY+/XZuJgX/cnrLXKn97WjKhPq0V5ksTNePxsbilMnjwZAwMDOPzwwzF16lRstdVW2GqrrTB58mRstdVWoz28loYEj4Vrr70We++9N7q7u9Hd3Y2DDjoIv/3tbxOPueaaa7DHHntgwoQJ2G233fDjH/84sv+mm24CYyx2K5czJL+PNWRak8RBVPzY0pCAaJ2F6QZ/v81uGuHEvlIIoy6VgpjAs14OpyrER9UBShUxEClG1PQtIC469EiHGt0xIY0R5Po++jo/SegLnB7QX8N6NnIc1XRBpF6nTYyp/chrD14H03ulipAkcaSjCpGkNqbzOYZ9eht9exXAncp7cxMTNwdC9FzDwjoVaWSgju88xdBAxZbOdqr2Ofi7305aVT/FgRc5tp8lnu7oi/X3c45pCOwFIvwawrZ6jf98fZFhrcsALAPne4PzCyCk0u6BeQHnF0OksFUhxE4fRKSnz7/1K7dN4jVYh1DgaNbU/EMiysPfzuC4VWzu6UW16gTmBYViRVhU+7eZeB6yHkWN4gBywhsVP3dhQSzKk0X0JEVZsqSe1UtalKeVSBOKQPYoSy01SqZzJ71mjUwVNDFeXd3Gi/AZ7+vwqJx88sno6OjAz3/+c9x///1YsmQJlixZggceeABLliwZ7eG1NK39rTuKbL/99viv//ov7LzzzgCAm2++GccddxyeeuopvOMd74i1v/baa3HhhRfiuuuuw/7774/HH38cZ599Nrbaaiu8732hO013dzf+9re/RY4tFovNvZhWRhaT66R9MhN+wVejGHqkotgpBI0arZhYGka5yiL7qk4+S+bI0Pzjk6I3ckxeSVgk6+dS7aR5PwPrjUajVNe0RqKOfWiARf492M6pvt6RdEEgFBLyfZbPIydNGZQchF7LA0Nf+nGqMYZtTAWEJgQegF8woGQZ1zn+e3q+f/9V5T2+0vB+X8cDN7clVx6Ew89+RGwD4rU+BzJgFwhdoS5g+qJIb+sui21/Zgwn+iYF1/pRn6sYw7v5IfgLW45/MiYS0lyGfg/YhXNwwI/yAMB78Wt+UTCJY+wqANLN7RsQ6Wx+RAfd/r3KBKDMQitqeV+FSGn7BsAvFi3Ld/eC7QzwA3rBXhlC+6QCpkx5E4NeAV7VQUdxEBsqXegoDAYT6KTJrwcHP8GpsW26ExuQvag8blAQNyCIPs/uujYWHK5sjOTYs57LZP6QJLzke6Le18rWWIs3McW6f6waGIzlz+iWyHPPPYennnoKu+2222gPZcxBER4L73vf+3D00Udj1113xa677opvfOMbmDRpEh599FFj+5/85Cf4xCc+gRNPPBE77bQTPvrRj+LMM8/EZZddFmnHGMP06dMjty2erP+DTO08BBNZdYFNdfFOVewAQLu/LIiMmsianaEBBs8VkSB5bBbU80lBoIudconFUtRMKWuyD9UuWz7WLbRjNUUKMTc548Cj+9TUNj3KpaKn85nMFiJtTJEeGB6nPTe9/+o5TNEjNZLoau0B4HAeCjEZUXIh1t+Rx/kmBpFIDxC6t6XRC+BKjsPP88WOTHs7Qbl/PwOmQgRUpNjZy99/SPQcs/06neWMYUcAP/UFzuNsOfoBPMcPw3oAf/GEbHmJMSxlDJzPATAHwBCOxf1gbCkAgPPPAegGY9f60R41pa0fQgBt8O83Iaj5kZ+fsnarAuxrALsCIurzT4DdD+Af7Rh6uRur/7QT1jqiEslxfBczL56aZou6bPKL32uZvOb9hTctPa4e8qbiZe+3cePMUs9TCyPp0iZf13rEzt04Am9iyohHetSxt5o5QiuRfR2esT/l3W+//bBy5crRHsaYZOy/+yOA53m49dZbUSqVcNBBBxnbVCqVWKRmwoQJePzxxzE0FP5CunHjRuywww7Yfvvtceyxx+Kpp55q6tjHBGn/++TEytbOFz0y1UpdWFOKHCloShUH5U4E5gSqiBDiKDQrSIrwmNzUVNRozNCAEDt6epp+a+/hiVEb3s8iwsq6Ls5ULXKgRjeqiAkdIJ6aZxqHvs1WwyTHKG2vgzFI0aCmk5lImsuYUiFNAsokfExt5CKlqlj6IAc+wkU9jwPgFhYdvx/lWfbv/xI1VriYCREEYAkODjYv+ar/nSEjQJdx4JPKOjwugF9xYAqAexUTAznO5VwIsGkMmCZS1Ob4aW1TAWwPYEf/MQCsZsvwGoAnIZzdliFcvJRfzcD5IWBsEzif67uzAcAEcP4pxcHtYghhsxmhicFmRD48anRHFz0u4gYH1fCevcAx8OZk9OFtWFcQP/p4nrCglouOyrQ13a0NCCe8eqqKnvqkLzSqYkqTSnJrS8JUEyJH1ihsLnAmV7hWSeFpZtQjLc2t0eeRYmckRM+RWBqcSz7eGS807XxrMK1pfY8Ech2eLLexzmc/+1mce+65uOmmm7BixQr88Y9/jNwIO2P/3W8izz77LA466CCUy2VMmjQJd955J2bPnm1su2DBAlx//fX4wAc+gH333RcrVqzADTfcgKGhIbz55pvYdtttsfvuu+Omm27CXnvthfXr1+O73/0u3v3ud+OZZ57BLrvsYuxXortvFAoFFAo2G6tRZpXyi7T+CTOtkZKELVKh96ukLrFyuH9oQKaMCRMCYbssJu9liHEWO3mwHZDtMo5PHZLlf7tpu8lEQIokVgXQx6LCpY+BTeVwFSHFerlopyKPjQ0C4Wsv2wHWbwCb05xMeVPXFlLFoXqtqmNc8FhPJ7Mh29k+L3qam56yJq9RfWxKcZP7PQhRMQDgNww4mgN3Miw76V9w2C2PCxMDn2U4AIfhMRyGx4BzlTF9I2yjrqETWU/nPBZ/zQsQ9TwVZZv628leDHiWi/s+oLcHGJrM0Ou/D+0AuiCqboQtgUhCa0coU25nDNsDwUKlnE/07+f5958CY98OTimET7vfU9W/34xYeptJ9GwE4AL8P0WUh58GsNv89pMBrAP4PgzsNaBj7XphZtDjwfNCUeMEb7CwrZaTd+nqZnb1GpnUnCzRjVrGoYqD9JoWPfo1ssImbdHPJKFju7Za37+8C5DmRe27GQuWqovkAqHYkfeAED7y3EdiKe7B3OC+EUwLqv5Gnkqlgkol/PIjp7FkTjzxRADAxz/+8WAbYwycczDG4HmUnmiDBE8Cu+22G55++mmsW7cOt99+O0477TQsW7bMKHq+8pWvYPXq1TjwwAPBOce0adNw+umn4/LLL4fjiC+yAw88EAceeGBwzLvf/W7su++++N73voerrroqcSwzZ86MPL/kkkuwcOHC+i+ymZg+XaZttsmvOtnN+kk1tC1UhAga6hTiRkZKnKqIqsh0M88VbfX0tDyLhgZW14roSFoLJyYIgFiEhveLSXJ1gAWXlra+ToAaGXNgFDp6pMYm3GRES98vX7Ms9T25RY86trRr1oXRO/3X8WkWPScQrSdyARzGhVW13PYbYVZw2J2Ph85tPofhsfDJNf66PGdxYXBweoqFuozy/If/+fikf/yN2nFLeZjK1glgLhMiaCrASiItsxsANgITPZFoJmXJa/D3QUgUF8ArAD4kFye9noGdzQG8BOCv4PwoMHYbOP+CtiCpi9ALTq/jQTRqqIoefxFS9jXxmH0XQugAQfSHPQZguoOhjRMwuEM7JlcG4SgfoLAuR6S6uYbHYoS2ybN9EqxOrKNWy05k8inrgvTzqH2bojqmx6MZaalXCJqOr1fsjLW6ERnVeR0zsC1WNaxfBx7m4UEsxqERgZM0Bil2Gkme92MeHmzouS+99FJ87Wtfq6sPaVqQxvAY+szZeOmll0Z7CGMWSmlLoKOjAzvvvDP2228/XHrppdhnn33w3e9+19h2woQJuOGGG7Bp0ya8/PLLePXVV7Hjjjuiq6sLW2+9tfGYtrY27L///nj++edTx7Jy5UoMDAwEtwsvvLCua2sqSelKQPboTh6xk/Jdx+Uv5ooQ0de4capRsZNVVMQm+np0Ziq333wCoTSViwjZKib66WNB2l0shU1eixoZUCb93FWuGwBm8Nh7I0UIq8ZrhHRXNzWNThWBtawxFGA61lHu9fqbtM+C2uY5Fl2zR92n1xM9xMzObQ6izm0QEZ6Ac/waoCt8owOd6y01PtLs4Af+e3I2E1Ee1cK6x79/1E9pk7cecd/eKaI9vT1AtyNKhT7EOXoBnO7f9yJ0clsqFy09iwP/C/yanwVgZwAA5ycqgxP+b8LRTXrBdSuP3fC1k5+1RwD+awiXtmsRqecBEEbr1iE89k0hptgrQ9i0cSI2bZzgR3DiaWZq7YLpcb2CotbaiKzHjaXJvYlGCDb9NTCl3JlTD9OdtrKkKtWSziRFhoyunI2f5O4jeUwO7sFczERYkyGjNvq9/lglqwBqtFBqBBdeeGFkblNLfUr2dXjGfg3UDjvskHgj7JDgyQHnPBJ6NdHe3o7tt98ejuPg1ltvxbHHHou2NvPLzDnH008/jW233Tb13NIeW95aNp3NRK3ZBupxTsoNCCap+po7QDiZl9vUdXFcL2puICf1XHuuom9LsnxOxCB+YvSxUJTo55BRi6k8lgLGyn56n3yN/FRDrrxGsfP08uA8pjaqvbd6k/vkvcnIIIKtvsYxbNOP0YWLra2pvdqPLnpcCBMDF8B8//79XNT0KEQiPDcxsVbPv/PQwc00ZhsXMOBqv1aoiNDC+v2agPo/LiJBRb/PAoBOgHUCrgNMKApJ8hJj2BtC3JyoiJ4vcA4XwPOMATsxDP8/5k/mXDD2KzB2ne/WJkVNL+7CAgDTfDEkt7eD84ngHOAb/LFVAfYu5fGpiBoa6GmpG5X9G9uBcjuGNk6AV3WxsdQFzwvrdmwLekbrduI1LCbkvjQXuGZNipLOa3J3a7T5QLOuSx17UnRHPb86BbW1yzvmrKJHvaXV40iho6aU2cgqfnXRcSSWYne8bNyvih5d+MjnempbkqhJSoEbLTFQKBRi8xvCzqWXXoobbrghtv2GG26ImWQRUUjwWLjooovw4IMP4uWXX8azzz6Liy++GEuXLsXJJ58MQPwq8W//9m9B+7///e/46U9/iueffx6PP/44PvrRj+K5557DokWLgjZf+9rXcO+99+LFF1/E008/jTPPPBNPP/00PvnJT4749Y0Jsvyir7ZFfE0c6bomHdg8V+xb73WgUBFRnU2dbYFZgQlbBEONeETEQZJ4ycIMHrrPqb+QQ9lWRXRCvEqpDfFFD5eT42DAGcYmo1NKuywRHF30ZEIXPVmiObbtJgGsn8N0rC56lrH4tt8w4FeGSM0tTERc5Ho9JtJS3OQ5fsBDu2pARO26ASxg4iaRAleKHhdgLtDuCuFTReChhsWMBX5rtzGGQ+T6PC9y3Ln2KN9hrg/AdhDWB73+Y7G06QfY/4MQRMvB+SHgfG+IhUu1a/I/j0z6ucgaOpPYAUIzAymIXG78VUGmsMn7NEwCSCVLlKV+97Bs4kun1d23oql64ZsqBU7WhTyzvia1Tr7zFqYLUR/HZBCQRfSkYaq7qbUOZxI2RI5/E1vjSCzFcswJRE8eM4JJ2BATmWMlQjIMltGlrTXHn4cf/vCH2H333WPb3/GOd+AHP/jBKIxo7ECCx8KaNWtw6qmnYrfddsMRRxyBxx57DPfccw/mzRNFvq+//jpeffXVoL3nebjiiiuwzz77YN68eSiXy3j44Yex4447Bm3WrVuH//f//h/22GMPzJ8/H6+99hp+//vf41/+5V9G+vJGFv3/j23CWmf/NtcwIJq+5rlAtzPo21EDnhuePMvaNolt6hU7EgdC+ABh1Kbib/ODjLwHcXGgPI6shTMjKmBsi6faqBRCu24TntsWsfHWLb2tQkgfty4aZVpZ0rEmVOGzJ08WPrpAKiJ0b4P//P3K+/orJtLaTvLd3PT+fmgRR5L/YMLRDQiNDr7IgvV6AAjLNRfCtc2FED33cuHY9qhyzk4ABSF22t1o0pks/ZVVOFL0/JYxHI/fAOdzcC7T83ohhM72/v0USOED9IKxtWBsLUSqG4PMjpOihj/r3//av/+euEUiPXLB0o2IprxtFJ15VQfVBMU88oX52c6nipW8gmmkXaNqeQ1t1zRa687kWa9Hf26qPTK9B//A2yPPGyF0ALO40UXQo3hnJhG0EV2RPk7B/wbbZZ1PHjMCtb9WFjdbOqtXrzZmBW2zzTZ4/fXXR2FEY4eR/bYdQ/z3f/934v6bbrop8nyPPfZItZj+zne+g+985zv1Dq21MTmEZcVBvIg942SX+5EcWY8jzQhk5Ea6tHluaFW93usAiu1wqh46ysNBX7ppgY4s0FfTy7gbr4EJ6GNiYjcjhxiqIHS7k9da8PvqBKQNt3p+iWoHzdQokT8W1zWnmrmyvf8esqlciYy1wakOY/Mk8XqFr2eb73A3HHG2k8JSPgb81005X0SQ6aR9jvy0vdixNjc39Rj9nFVtfxXCxEDdfx+L9H3Yrx4H3u8/KUAIn58y4eb2CcP7rBofFBFdsPQSFtpbn8fCwn+JKjSP8fc7CCMpRYB5gFsBUAlrdgAE0x059NsZEwLok0zomfkA8Cd/b69/r/q8yeey1/ZA7DAGYJLonO0FwF9SjN8JsI8A/BcA/ybAvuIPYDKE2JmM8BqDqE8Rw5PKAITw8Rw314RaNTmQ90D+9WKSirdtKXFJi5bmIetCqfWQd2x5zApsx9ebZpg0nuzjqMYem4SOTRh5cPEnvB3CAN5OPe+hFCnrAoePkMlYZ9wuWY45gWCRAipv5GgkPn/Ngvu21Gl4Y/T6VGbOnImHHnoIs2bNimx/6KGHMGPGjFEa1diABA8x8iRNOiVOuF+fzOvbqk44uZaTc68kZmWhHXXYfmiAYbDYBgdApdCBjrJwoMoS3bERiJ1VTAgbVazIc8t9SaxkwEwepCupPDF1T+zX/xxYL4+s8yPPL7fFojUuIilvrCoebupsi70+0sTgyal7YC9F7ADiXr5WEnm8jh7tMdl8R0RZErIvD3FraclMDrykpfVJnmfhcVraX0Q07ceBx/zX9SEWpmXN5yLiM58L4VNBKELuVM55iuW9vZ4JowCI9XkOP5+HC5aez4GvcSF61OuV13gCi6Y2ykVJD/GFr3xN/DmlNJKWj7sRrp7TD2Ff3Q7gtz8EjpIW1U8xsHcNRF4czrvBmOxJfc+H/Hb+eMvhHv5s/NLZpxCIIpQRiCL/NNrNgVd14VU9eK6DqiPsqatw0BHvOjONmMhl7SO/oIh++Fth0pn1Ghox1jRBpLfNQ5pdddShL93a+h/YOZPgkxG/rK+N6rpmEylJYmcSNmAjujATK7ESUTfXrNbV+uKso/0ZzIs0LUhjPEStzjrrLJx33nkYGhrC4YcfDgC4//77cf755+Pf//3fR3l0rQ0JHqI1MYgca1PtuzkSzSmFE2uZklXs5Bis+F/snheJQKhRibR6lIiVtMQkaBTR8wT2wv59z8VT32TtzEwe9iNFit92Pylo5Ho9vsiRYksdb3uPJoq0v3TWy9ExEO4fLLYB5WG4nriuffFnDCEUjaro0UWOaVtT0A0NPOXxSibW0JHOZiYxrR6nih1V9Ehh9G4uRI+M9Mh7KXrgi5cCgjV78EElynMLC6M6Z/FA9Bx+zSMA/IjOub7wkXU5cqxqdEeO9VfK50Wv6fGvd0IB2FwGql54Oe0I63pcAKdIa+pLmEi96wUwFeDP9gB7ciV6w8G5jObINXgkmxFEfaosED1sJoRL2xsA3gTYSRBiR4qirRGs0aNGp1RBJ9PaHD/Ko67DI9EtovVtNvQokDy+ljVcshyXNkE3Hd/MiWb6ePKfuxbRo0fBmjGuvJgiOnpUSK0Pasa6P7oweRNb4xT8r1WwSIGzEV1GYZPVka1q+dsy0Youb1sa559/Pvr7+/HpT38ag4Pi+7FYLOKCCy5obffeFoBqeIiRR/2V3RTZUVN4Yr8CI+JAJm/t/dqtJNaHkalulYJcYBQoVRx0Fvx1OKrK5ElGh7R6lGDYXvQeUKIpvoU0gOjCqxr7r3pOXEMfC+2n5XWuYtGb0rce0QFC4SPvpfMcIKJYrJcbzRS4K46RpguuB3SUhwNDh6rjL9gKIfzELRQ0usGDKjDDbdGxqoYRunub7hpndJFzDTdVkDoI61509PogmzmCXK/nAP91fLd//5D22s8X2w/Hw0Ls/MYXML/yoy6/YNFx/FR5LiN3LoDvsvDzXtTuXQgTAz/SuRiHiuMPYVh87yHR/j0EdV2bfbGzAWKRURmbOZHz6J+anxaJCkTopw9gyxCslyPrdIQ+qkJUBK2BWOVH3vcDGAqjN35tDtsF0dod+PerEU1jk11PBjB5CHA9DGu/MqjGBYCY/FYiXxDRtuq9eBz/QNgK8Ak7suzbhFoUrmP7RT1rIXz9JhL5TAz0qI960/vNimkhUR0Z5Xm7b5agRnxsx6/EzOB1vxtH4EgsjURq7sYRuBtHWA0xqnBwJJbiWNyf6lbX6sh1eNJuw+NgyssYw2WXXYY33ngDjz76KJ555hn09/fjq1/96mgPreUZ++8+MbYwiRzTc1M7QEzu5ARPvQGxAvjQQlmImHKJRUSBrD1p7+GR1C0TtnQ3pk/edLFjMzFQj9NrdfTH+nnUbhwRrRkaYHgCewXCQl6nKiqC2hw/IsR6OVgvDyJfprQzG7bXyiR61H7TzAz0dDybGAIQNwsAzGvpmF5X1dTA1O45BvzB8L48xER6m9wmFys9mocizIWo6QGE+JGiRgof2aaI0GZabiso9+cx4Ccc+B+OeccvB44Q45l3zPJIyqfqhrY955jFOQ7wIznzOA9S13YGwgVP5fFVCLe/NQBfxfDLtxaISIwC590QS5f243b+Jf/xGgjBsx4R0eNfD38QUbOCSRB+CHKsalrrRgh7av/D4Cp/bDZban17q7ucNQqblXMW8goL1d8qC/VEp8ZiqlGamJKfybR6GlXc7IKVwfNT8L8RsfMo3hnrXwoV/d40FtPfiBRF9ZoyjDbNXofn+9//PmbNmoVisYg5c+bgwQcfbPAV5GfSpEnYf//9seeee46tZUpGERI8xMjRjB9T5US1gGBtkqFusd6OFDEd5WFMLImi+nKJRQruJepjvQA/cjoP9jVxTP/7dAGUNicwTeJ99PNJsSCNBfYqPRfsGxpgGBoQURwpcALx4KfBychQsZOjvSe8RYbrqpEvJ+JolyR61PV5dJGj7suDKoZiwkdF/Z8mUwTV19U1tNVFj+rsJtvI+p7D/MjLMkUQ3cfC4z/IRbTnVywUNrrQKSAuzhzlXrUUP4OJ553x1+Sepw4LHm+u+OvsAFju3z/m3y+WZgVfVV4PGeGpQnwu1wPHXX4f+Hf86zoSYNK3ADsDPz0Ex+M3+CW/EmGkZ71/46G42eibGKwTxgWBwNmI8DugCuBNgB+BmH11pVyAJ8UPouLHbJmb7cOUVIAvz9PI9KlaJldJwk1PA6tH/KRRS+SrlUVn3khPo1CFhk30qOvpJHEgno5tU4VKmnjRBWnac52xvnhurdx2220477zzcPHFF+Opp57CoYceiqOOOiri0ttsjj/+eKxfvz69oc/JJ5+Mvr6+Jo5obEKCh2gcWZy1TJNOT7nXbzryl3kXgSVv8Ku4/z/a9YD2ikhpm1gajkRnpJAp+TU8g8X24LFTHUZHeRiFijhepnu1VxJEjh7hMV2zfr11YKrXMYkHNcIzNBB/X4YGWLA+Ee9nwb1wtos7rAkR6Nc9+MJH3MI1jMJ0wLbI9jRMjnGJi5YqWKM98nOyisWd2vT2erRH8k4eprnJdurxUvg4CBcqdSFS3FQh/kHfvtokdNSUNjWtTW4/j4WfcblfGf+RWBoZs/RSqwJYypiI9LyNYd5XgbnfMryAn+DRlDQgMF944bfb4u9DM/EGugD0goslyHDcffcB+AuE6OmDiPRsEDuV1DaUAXYUIkIoYkcta5Vk2tu6dkARO54n7l/AO2LDtk3OktbBsQkQvS+nCeKnmWQVPrY2o32dzRRuOllET56oViORkaAnsJdxHyD+3t/0Q7BqNKfWCI2atJdGK0fhmrkOz7e//W2ceeaZOOuss7DHHnvgyiuvxMyZM3Httdc24UrM3HXXXXjjjTewfv361NvAwAB+/etfY+PGjekdb2GM/E8exLglt+sWEBU7eXAQLTZXzsn0x1OFRXXkcL8QX63hcaqI2U0Hts6mvxRThKeqPYdmbmD7vlWvw3CuYGKv1PJEndCixgHlkph0S8FicnaTfUgXNZnaJvvS+wdEzVM0yuP429oi20NxZDc00N3bpN03IO6l2FG3JzKVh+YPUnwnGRZAe+wA2EVLQXQhoj2AcHH7g0h1W/Lug3C43A9EFyxVme/X+Khj0PuvQhgb/FAZ81d5aFctIzCqSJMiZX+/zYscEwH0+xGduZxjhXQgeNUf/w+ZME9Q3eq+y0KB5UIEay4XZgY7QYxrvdcB/lon8FcAu3NgPge/l4Et+AmE2FHCVNWJobiRUSrVhKEcbc5+BpHu5vr3ZYbhYmhcUHUcvB1/slTtRNEL6OMRneTanayTbr3ovpbCfduE2jb+PIJGn9SlHVuPlXa9mFPpmjuWrEYV0qggrU0WbJ+RNZiGg/EwXvDX/bG5qqlpbzLj9G4cEevTFGUba45rrcTg4CBWrFiBL33pS5Ht8+fPx8MPPzxi4+CcY9dddx2x841XSPAQDUNOqnX3sshzdTIq93cCrJTSuToxVb+/5WN/wiZT2eQ6MMXecD0eWZgfRiScyJoygEHsyHMnIUWKQewACNafYbKtOn4H8f4V0WNaEFSPehQ7OUoVJ5aWB4SvhUnsFDt5JPqjixYV1alNx3SMfG1lv/rxutGB6pRnQt2ui5/g82WKMOqC2IHZnloibcFfYsAsHoqd51jkfT78D48A+8H82ZDnAMIaH32//LzIcd3EotGbK/xrKfp9Vf3j7uXAx1gYIVFsoQFRwwMA/2QMc3hUvA1dALSv853hdDc7z++vVzmnP56ugSFR57MWIk1vDwjRwwHGfup30B7eyu2h6PGFDX8AYKcijCJJIaTW6wVr8wh7aunQlgXbJDZtQmqbDCattZM8jnTxkEXs2I+NtjH9Yl2LYBgN0TMaYicvSaKnEVGgaVgTiB0galIguRkn4jTcFhNCWT4vW4rYyb4Oj/h70dPDCoWCsQ7mzTffhOd5mDZtWmT7tGnTsHr16jpGnI8HHngg9zHbbbddE0YytiHBQzQMdVJtmqgDiE5KZURILqQIxCM9HsJojr7f8P/G9fuUE2kpdmTEIjrpjkZ3YtgiO/q51XV3DMfmWt9HnQgniB01+iLETigw9IiKTG/TU9uGBkITh3KwblGYtiaRERx9e3RcDlxPm4xpoie6Lxr50YVP2jYVaaUteQhz8G7lOdMjgTP990sKn5lcCB257zlffDzPwoiPjPQ8x0Sqm+QAHpobqBzGwxofVQCZhHFVawOIVLMrmBAaBQDfUs75c//xMf6Y/yKeq5Ol7b8WH1L7Ov+48zlwjWbIIM+vi5BfMLAeCIEkRYoSBeP8FDB2k3oWAFOBdUpanwuwf4WI4AChVbUudoLPvhuktWWhmZP1WiJBUdtr3YI5+yTZdF36Qp22qM5oMF4n1ybR04yUNxndeR4zIyJoGuz1PzrNfA9a2ZI66zo8w3DgeR56enoi2y+55BIsXLjQehxj0e94znlsWzM57LCwVvPVV1/FzJkzjWNauXIl3va2t43YuMYaVMNDjC7/P3t/Hy3HVZ0J40+r+nbfe+UryZIt+QNh2bJljYyQsPBYFvbIyCi2wY4NGSDJhOAMYYXwMiQhhMSsIZBkMSTDxwRI4IVkQSDMC4SvEBwjrFhjx/oZCyMhI6wl28jI1liWZUtIuuhK3epS//6o3qf22bXPqVPdfXXv1a1nrV7dXXXq1Knq6qr9nL33s7XZb3nfct3DeZ5EZ5vKsUSSOmolNXgGjyWSyzwXxeyqo9IGpIZ0mxvGNLMuX3JcHhlqFZF4D4RGdoA0v4Yfj4QM6SOBAiJBiWR3uj0ng/a+ij1QpdCBvS57+5GCB7RMeydQrg/lN73i0DZrfUbemkDX2l5m/P+skhCby9qpYf9EZ/2uSurxARLyQzk+1Bflk22ppCFd1XTdhl9amyi6ufJ56Hr+jJCy1kD7W52MzxgkX64A85Aqsn1UXJ8frwD/Tzt5aRMJQHqtH0FCuij3pookRO9rvM+TAMaQxMMdhMnn+UX21f5HAC+wZSeA9hthixnQkAqQnhBoctV+kYBkMOMhaCChGap8f6H5SN0iS6xO/1zo6fLudENWZDYIR+i5yhN1oAkLTnZK9B9RFOHIkSPWy1W/5pxzzkEURRlvzoEDBzJen9OFiy++GM8//3xm+aFDh3DxxRdPwIimDkrCU6Iv0EKmrPW+Z4JvHRcpoO98Ha9rUk2/06z/SZOXwjZzEAOvchgXUuBhaT4yBuW4teOAXneGf9fIjkscQH6ncDeNMAzMTsLhJCGy+4uslwbp3QlFqLiBS+mNk6OTRyoW8eHg5/IRLAGqwKYLrkkWcJJ9ccfbQySIiMvSdvIiEPmh6+7lbZu48OuWPCNV4Kb7709lrWm5FCWg63gmklweRZ0Nb6gkxUhnA3hIHO+vtZPt/7SdJDPLn+b3Oh6ev1V+cxnGGSMhOsfY+iPs+90VzNj/66KT4wBOpmFt7KWJGFQ+AzGpUDH1eHgNHqA/RnFaryfcqO9WyGA8Vd9CSU+IWG8/MJlV2sYTRQhUCOnpB6bbbxFahyfumLyzZs2yXi5Z51qthlWrVmHjxo3W8o0bN2LNmjXjflwaXN6lX/ziFxgcHFS2KEEoQ9pKjAsqc9NcHS/ZIfBQH/n85dtTiBtBhn7Nb2dCtzQiQzk+9E7LMtBsAVfYHR9bZ1yUB0Tkqxp3cnkY2tU0Mb9dTdbTOXMTHf8DjZOImfXYKNHF1cSbA7AwNrHtzHqMo3Et02crSlrmkRtqx9vSePM8RK6iphI/nHklXn4s9eTw3/DkkVRA4MHZ/xH/6dAPAKTn9KWHngAArDvwfevaaQ92fpuFjEBc3U7JD5Dm9kRISA9t/xP2mV/L/DPP3SFixEUUWkgU3b5VSQjBHZ1xnIXEK/N7bFz/1Pn8zTZwfed43wF8+7W/hNv+/p6EkHy0glv+sA28R5y8j4uxcsiQuxYS6epj7PPMTrsvJSF1r13wLXwDL0Kax3MSCekZSKWotTDNQaTiBkKaGgBawsPTTbgO5faECguE5u6EGrm9hNu5Qtp8np90fBMTXtbteQZOTx7RZCg0W1TkIkJszk0eyZ1uROd04F3vehfe9KY34eUvfzmuueYafPazn8XTTz+Nt73tbad9HEASXve+970Pw8PDZl0cx9iyZQtWrlx5Wsc01VASnhLjgvYhO1mbG/4aKhrZ4Z6dPJUzpGSBg6SlAZs8SLJjjaObZyL3+HjGSAIGchmNg0iPK5qHkx1JLGTeDQkFnDhWUYc0ODOrXkdkSMvlCfXiVOPYjE3m9vC8HsLMeoxjiBxet6zQAQCL7Njt089RC1hz5AeZcylzfujaMyS9BTsva6HwoPxMqK5d1k7IjwuSRGhEh+O1bH9fqyQEw2fH3Je2vy05knTdRyvAHyrKc/TuCx/lYWavbyehcuSVIpISA1/Hb6CCn8EmO4ShRMTgMGzCU0Xq6ZH5PNSkantVWoiQpeFu475bAzqZCw6/CbhkrbvdPg/dHFe3YVjjgYkgY6eT6FTZ9dorpGcxj/RUERtCRfs/U3OrOE5hRtD57uY3eeMb34iDBw/iz//8z/Hss8/iJS95Ce6++25cdNFF3Qy1a/zoRz8CkHh4duzYgVotvRvWajWsWLEC7373u0/rmKYaSsJT4rQgyMsDZJO3AT3Jm3/voDK3bYpw1hudZQrRIk8Ol0U2ctQu8QKxr9yxBxyv9N5oYVuclHHjn5MdwPacEEnRJKG5d2dwZjtDeo41IsysczGHhEjNipo41KnyUjR8jUhPK4owF8dxFDVrvEfjWnLq1JyeLEFyhcDJY5Wy1+l4kveB2UxVsBMGd7IODJCS4AWCKOxVFNeAJMdnaTvN9dEIEPfs8He+bl1nf5sq6efXdzw+oYW0vyTECH5PHAN5d+rIHguJJ0jESFTkgMTD0wBwMdIxbaoAOIWE6BARHUKS0zMLwFzgxLBNeLhXR5Id9puRoRohrRVSxGND0HNkWlZ+jsu7kzU63cazTwo7dFyh2/YCOgYf8QmVby4KH2ErSuZ8xzFR3hy6Njnp6BdCwhjpmuLEq1fSM5kFCwASLch/6J7q8vd4+9vfjre//e1dbdsvkFLbb/3Wb+HjH/84Zs2aNaHjmYooc3hKTAq0KacB0IULOHhb3sehijFyeZFQHjImi5DSssoJpMacNsvNczP4Ok3EgB9TB5VW1tOkoTk4w7wo78aX57IdLzPeHXq5BQeScC8iOyeOVTIeHh7+BqTk6ZApaZkQGHq5YHl1UEUjqiNG1fTDxzsranpD9DiB8+X76PlM6YsU6YAk1I1yfoDk2tmENYYEYT5TIjvEPDoRMrVkLCW3KhIP0NJO2Bu983yzlW270OjVnZybH1aSnCFJbqhwaR6+XLGvV+35z/OE5ItEFiLRh7zmjyGpOxojkZCaBbR/NAPt9jDa7Wsx+8SlAH7aafRTAHsAPAe0TgL7kXh7DsMuRMr7F9C8E1mvSriamvTgyHo33Xp3upWjdo3RB8pJ8K23v1cz3/vt5QktYJnsvzciIMUD+PeihUOLFN8sClefvRKhIqRwOnh4phM+//nPl2SnS5SEp8Tkhs+AY7Pk7WpaOJOIBSccmjQ0LatwRTaC/M73KQ3CAIR4uKKW5qXQ/6LVOEY1jvHy+IcAkCEgJMzg2p7kuqXwAXl4ZtbTEDmN2BiVKw/pMWNhJzJG1SJMrShSSZAUSMjLWbLG1jmesZl2AmdyzMnnNUd+YJaTytt1R75vtSeiY0QQ6PemELMI2Ljw2iT0jbw/9KLvPxPL6TCWdkjOyo436OVtW8BAk7p24cudtr/WTvN+fqcjovAZ1s/fV1IxBFKI46/fYMepeYD4McRIxAsOIhl7A8D/rwI8UcHh+nlIyM5TAJ7ovP/fzgt2Xg+RHU8oaeJp4KnHRYiDTmySw0m9OtLDk+fdiVHFOTiId+FTeBc+pey3ezGA8fLsxMy07wVVcZ4kWXg9vpPbh6sGT1iBVf/vX+T4JBk4naQnD3nnqFfBCa47N9URLlow9Y/12LFjeN/73oc1a9bg0ksvxSWXXGK9SrhRhrSV6Bk+hTYKM+OoxqmXI0NEZMgaLXOhmiUTlZYtL011WGiZRXSg7E8uY4aqLKpq7p+x+I60vTVcJZ8ISEOvkneSys7WvuHeGwoVs+sJpYVUXbVuCFlvTkp6AGBWR7yA5+RwY8IkhDvEDOh7K4osQ5H64IaLVsdHwq7ro58TjnqjqYb1yVA367x0BA8GoKi9kdeH1ZJaf2BzZxCOQVONHwLti4fHRUjC4SSB/omQwXZhHpKwt9d28mwGkRYyJQL0pYrt3dGu+a91tuFhmZzc0zuFvh0AcFln+dWd/WxJjvUv2r/A+yrvAfA/kYS6jSE5q2MAOsm2rv95FZihzFDkhYtVmVnjW85D2WQ/GkHSjMo6GjiMOfgY3q70oYfPSfTDuO5neFgR+Mb+Ndw6LvsE/GQnlOjknXdfDg7fVsuRoWUh+TP9CDXrBWeS5ye8Ds/Un+P/7d/+bdx///1405vehPPPP/+01gOa6igJT4meUZnbdpKe9qFKxhjkIV0q8eF5DgjzjjTqqRGbqbvSmT22RkheHRmuQ4UX+fZMgc2QnUi0gfs7eZ4ohM4X0ibzdaRnQ4oIkLHfiiLUG02nqhmQFSnQQtcIY9EwGhgCmAdnLo7jeYxkx9w54DjKxvxzElONY+u8ZcJslO1lX5qnhy+bFTWTnCArpymr/OaSID9+1gBqJ04i7oT9EVk34XD7KomaW4cMtQ9VMkIZ6YCRkKPO779h/lrcdOD+ZBl5VfZ2iAiQkKMnxPcQ/FKn3d0dgQPpnfxaR/VtPlsmiboctxRWAJLjoP/Iic73ewH8SwUn/5/OOeoQn9fiW7i8/Rg+gXfij/GXAIBbK58EWGik2Zf2DiCqkii1HbrEQR4fTmoksXGRnexseaszBHu53K+bLKWf34G/x9/gt9Ux56EIUfG1myiltvFCKNnx3kMCzhcZzqHESC7jRCkvn0cjPUV+tzPBY1GiGL773e/iX//1X/GKV7xioocy5VASnhI9I68GT3A/3HsiPDcuj5BUOCNUeOX2KtLcHGno0bKF7VR9S/tX8LwKbT1bJgla1Epyc8hzI9cVBffsRK1TaA4OdL6fMiIH5NngHg4KY5OQJGJW1ERLGqa8feckJopB2dyAZF3LjFWOOyQMTgMXTgCyHiUARk5bI0apdyjr9XHBEKMjlcSgv6CN1pEKBjqLXWRfW37jofvTa6Oaeh4N9lVSyWcg9STNV4jP3kqWEL26nQgIUHjYISSen9e3EzIE1rcmxEGem1isI6IzE6k0NZ3eDjnj+VEPYzkOYiFej+8gwqsBALcO3YskpK2SyGxrOUTm1UZUjRFV0+TrJPCGf4/V7xpx0UjLeJIdABmycxzDGMKY2k+36FapbSIV2rpFHmkMIZVuiXE34eiGUPRTpc2F00l0JrtgAQCcQmXcVNomG84++2zMnTt3oocxJTH1/XslJj0kSZEFIyWoCCflVkiPkFye5L4kn7lYgaX6RC/5zCMDj4cdUYJ5jngCjVMWDdXGTeTDddza8oTAKLOIwtB3EQjNsNcM/YQ4Ja+d0TITxkb7mduRGSayQcaiP2k6DV2TBhb1W7Rq+SFBwmQukPYiSGEEV9HWqBVjcGbbyHmn7RNiz/OATh6pGOED7brUlvPXyXpan6ldBXBBG+1B+5o6WUcmJBSATcB3VYDtnTbr2onH59XtxKMzE8A9leQ7iRJUYX+Wr7p4UftG531m58VD5GgoWISrsAM34T5swPWooYFRjKB9HGi3X5Rsc1bnNeh6NVAfbCCKYtTQBFdok+Aqbi7iwttll4eRHS2p/ffwGXVMcp8ADNnpNTleerEk3oyvBvSRJQcL8Jz5P/Lkfw3jlfeRdz/pBaFkR1vveoXsU+Y7hYyrFzLTa17PmeYVPBPxF3/xF/jTP/1TjI2N5TcuYWHqTfWUmHSwcmYc0EK5uCQ0gdegcckKy365Glu72vHuEHh4T5UtswbC3jthPFZNHyJLiheHe574OaCxa6SHh1TZuTs8pycVHCBpaAlaVkQmWqu9w43+ZfFO85nCww5FKdHgs8REeuRDUntgG4NFrNLC5FzGTRM1DGEMUi53+NgJS6CAEzQiPYcwhLlRE0dZJZeoFaNRr6GOZrrvaoSTRyqYObuNY2KwlZkx4k4Y4LFGhMhzzcfH0hwdec7T3zW9DgZmdyTVGZFpDibhdQCMRwkA7sINuGV+GmJnRBR2VewCnr/UBu6vAGvbiaAAJ0nkzTGDQtbb80vthCxJzIbt6akiyRm6o42l9z+FL639zxjGGBqYgxGMYhjH8SX8Z4xgFP98HLj9t76XCBec03nNQUqCzmpj8Kwx1AabqKOBOhoWmbG9Ocn3Wuf383l2XAIFMsTNfnfffCLE+Dh+x2pvr+/Cdcv6LrKc4wt4Y+H9/Qa+ji/jdrGv/PH7clBej+90lcvD7x3Z+0pxmezxzFPJm/TRECpV3V2tpd6O1ZezNtnRDpalnvom70c/+lHs3r0bCxYswKJFizAwMGCt37ZNr1FXoiQ8JfqEENJD4CTGF6ImoSmtyTo7PCQODdFYhhCx3ByzDRtHFSzsSCE9cqx8fIlRm3hTUjIzw3p3EZ/YIk7hD1QexubCiWMVteCqFDCgthgcwPCxE2gODmQ8SediFPsxJ/NwliEh9nd7x1pOkAs1NDNeowgtQ3bm4rjxAiX92nH9hzo5SUQQZ9ZjxHHNnGPych0/awDNRva8y/PjO8+Z7ZScLCkhPjC7jeOxXVqzOZg8zJ7DfFyEAwCAW3Bv2uCCdhr6xkPcftJZNhMp6SHlN43caKFt91f0UE7y/MxE8h9bgIRo3V8BYuAcvIAfYznOwUHU0USMCKM4CyMYBQD88+dvBADc/vHvpYTnPABz2hiYM4rhs45jOBrDEMZQQxPDnXdOZOqdP7c00iQhyi7Lem60a9cVxsbbyM/psu7JjgvdGKE+o5mPUZKdInCRnn4IF+iTKeGk53Qk5XdDesZjvzL/qJv+pjJCRQviMyCo6fbbb5/oIUxZlISnRG84oMwAO6ARFoJGdDQPkBWuJtpX+Kxz3jOR5yoAac6Q6DPjMeKbxGm+DM/PSZbxmy9flxrXPORMKzLK++Bkw6pxkxE1SJfTPswhs30BtueBjHnuTSJjm945Qo2OvAdp3kwmzxeSOBejhjCln6tmGe+bEyTu9UEUGULXrQx2CMhbJkmjUb+jdUp4YjWOcREOGDI3F8dxNK5hVtTxTHFvT4yEfJC62yAS2eifMDEE/tNJTw/gfypESPsBEm/PZR0P0r5k2x/hZRjBKGJEeA7zMYoRHMAC/C7+Ad/Gjem2Kzv7OgvAnJMYFGSnjsTLk4S1tSwiI701Lu+P1i45RN2rk3zOJzu/h8+YPB17fXcJ87L/vGXd9DNehUSlx6LXkD3ffz7v/PZL+Uw/f/o9oUhbYPyIWFGyExLOV4oiTD68//3vn+ghTFmUhKdEb2jBmmV2JXHz0K48r45GdDIQM9IV7s2hMDaeoH1ZRx6Y378Z6aFQurgKDB5LN63IfhSiBRBBsXNkQopp2tAT6V1GMgCnCMBYNIxhjGXUyrgHiHsefCCxASlPnRAL+m6Pod9KQzEinIfDeB4jpv1+zDH7fx4jOLfjQdiI67AS2xEhRrMTwkZhT1rYw+jMs9i4swZVL0ILFLL3PEaASFYUZccXkedKCQeM4s6xptsT2SFFupn1OFGPIwJDogZEfHyiG/KQXTalMiGAKhIy9YpO+NtM4M6f/DW+9JJf6RxLDcM4jjk4jD/Ah3B9Z7N34WM46+XPG2GCoXpCcGpoiPemCWuroaEKFIQQneRzNnwt+zmMEHwcv3NaTMHxmnnvF/npZ4J+tyFVeXkysv/QcWjLQwlAr2QhZKLI970fmEpkh+rw5GEqHZMPhw8fxte//nXs3r0bf/RHf4S5c+di27ZtWLBgAS688MKJHt6kRUl4SvQGdgX55KkJMgTMldcDZEkShZkBwtPD83RoWZUtqyIlO5y8AJYhKL01lZbog71XWklCebptZL0n42chB6J+joT0xmjIM1J4on4NjaT+jSJ6kBf2xvvjtXSKIEaEg5iH+Z0wLLnOtY0G/vDfjpVYjh3m+3k4bMLqElIALBf9XYiDZh2115SYEiMl9QQRfMcu23NCJT1QNAZ3X2Hn5RnMQ4QY5+GwGduxRoSonlzO5h+4r5JMRuyqJIVOgbQQKofLw9MSy+j/czFTNCQFtx9WUgGD2cDleAx7sAhRZ9Z9BKOIEOM+XI8XcA4ux2NYMPM5NFCzziHl5FDYGicy9c5yTmyS7VzkJxu+Ru35O29jn4bTH8rWTe5Gb/vrnvhIosFr0IyHF6PbcfbrfGqeJ58BHdrOtc1kwHo8MNFDCMJ0qsPz4x//GK961aswe/Zs7NmzB29961sxd+5cfOtb38JTTz2FL37xixM9xEmLkvCU6A30DJqfhM1U5uty0gAPt7K7sHNWcnYXMc9PA+7ZaMAmPTJ0p2oLJCT7Tm6GtHyAyFTERAwYAUo9UAlBatTt/It0zDoJou+8vowkKFrBT3MIwuPiqoHDC5FyyBwSbb+y/zSHJv/hMg8H1eWu2U/XcjIC92OOITMESSI4AaI+92OOWU7tfftJPhe7NWrn5Whcw7lRE/sxB89gHnYIssaPV5shd4X2UC5PQnzqQJS6Nxs4hSgCqkTMf1YB5iIlP7ywKEESHJ6rI9tRIVV+egaR5gV1iNEwxjAHh3G481twrwvl5YxgFGMYRhM1y0gOCUtzeXVcwgO9kp085Bnjef29Ff+Iz+HXA/eV/Z/4DGX632r1jPpBdoqsS/YZHrbnGl+34YHdtvdNxoTcC/sdHlaGm01fvOtd78Idd9yB//k//ydGRkbM8ptvvhm//uth95DpipLwlOgdpl5I8iYFDFwkJo/cSOWyatzJp6HnkvYsJO+OrBgP2IaeIF92oc9Tdv/aOFsJAUpXnUK1mhAQjXxIouNqw70qvK22ndYnYBsJXBlOenY0ssOV3+hYyFNBZSBd0BJns6IF3c1gy+3Ow2EAidEPADuwHDfhPjyDebgQh83yC3EQz2BehxzohotvJraoEcSXjUYjGMU8tFDDRTiAFuZDFiW8CAfwJM7HJXgWT+J81k/6m1+CZ83n/Zhj2lNfTUZ6iMQO1GOcPFLBwMWJ17Uyv+N9HbSFPir8Esyznyh0tY5kguNAJZ1ImAnj4Tk5E5iDwx2NtVqna55/00INDdTQwHEMo4UITdSdBE/m3lBfn8K78Af4UBDR4WNIv4cb03neHZ+nJIQAuMiO6//C/2sh/6deiU2I1yZEgSyE7GgErTgB7W97P6F0T+AA0oPsPz+hv+d6PICNuC63v249RpPN05SH6VSH5+GHH8ZnPpOVxL/wwguxf//+CRjR1EFJeEqMOzRVMLmeIFXLAKDeYIYZJztanxR6w9vQd0mA2NiogGfUim0vUmdbE97W8fho+062jcA9IWadMDjyQtMI3HPAvQ+uIoKZujZMGU5TYgsZAxcBIE+JhpCHua+dtpwIi1xPywjLscMs4+v459D9Fg1FkW20h2pCZlzLgSewEPKikv0+gYW4rEP0gMTT8xTmI0Kc5ApFI4jQMgIJszrFQBv1Thmdeud/xbvlYaM8PFWoHwJA60iq2jaAJKSzqvwPkiK7yfiHcVwQ9qij3JYsr6PZCWs7bqiMBk5oiOjQ94/gv+O9+NPOd53o8HfeTiKP7Ohj616ooFfDcjwN09DcGNc2EnlkR/NAhaAfEyr9hiTIRUhPCPLIznQhOtMRg4ODOHr0aGb5Y489hnPPPXcCRjR1MPUDGktMLC7IVoHX5KmpOKj20toReJ2dpAF0whHBLpQIGE9PezD9DMDyDFVjSbgixBTqVu1sK7bxgRfV5PCFSPHcG61gp+5JqHr7NO1YTtGxRoSZ9fAHmvQYRYjxPEYsA/I8ZoC7+3ETDf7SIIkNIS2RGOFCHMyduePt+StkXHkv2b+rTdp/VXm5t9mFRQCAy7DXfJbjfQbzOtslvxmRnqNxDYMzk/9oXJ2B5mDy4sV/B2a3EVcTQtSKklw8zG/jZD35XJnbRqOetBuY3UZzcAaONSKzDb3GZs7A6OwBjNWHMYZhRIg74gOp0lqtI0hAIW0jGMUcHO5ITzeMFLV88b7Iq/PH+DNEiPFe/Cn+B/48Q3Y0z0/yuXuyo3kjukU3xmU/DdJewtLy8Hp8x/peJIwtBPyfIlGUUEwmIz90LK7cGtc5CdmvJGnr8GDhfiYKVIcn73WqD2RzonHbbbfhz//8z3HyZFKjrVKp4Omnn8af/Mmf4Fd+5VcmeHSTG6WHp0Rv2JdNgq60AHRCaMhTIsUJiiATekP35YXtZP9AKitNqNrCAkbsQPStyWFTCFhmv6xvvi86xjT0LhZemqgzs5cNe3F5a2T4WOhDTJNfBtLwNSI9Rzv1XmRYG8lSV2NREd7x+0lvj2uGNUaEx3A5LsdjwQZJA3ZRUED3njzViaV0EZhhHMeFGMVTnVo21N63DQc/Dz6SlF2W/p6XYW/Hi6O3952TK7Abj2IxrsBuAAkBGsIYLsIBk8/zFObjQpYzRbLVpBAXoQXUa4aQcyLcjGtAx7sZV9MipwOz08kMIk1H4xrQ2ZauIwp9pONIw9hSEYIWIuu3rCJGAzVEneMmosPJnsvzpoWvvQ/vzeTpyM/UVkM3np08jKdiWCjy8pOkylq/xAZ4HZ68cMBuSKN2b5zqOS1FfuuNuA75Majd7XcyEcBQhNfhmdrXCAB85CMfwatf/WrMnz8fx48fx9q1a7F//35cc801+OAHPzjRw5vUKD08JboHkY35bcsD0q4io9ZWjXWZaddyQitSFNnIm9MhW+3BVGiAv1yFUNvVlERxD8+sqJkYbx0vD7U1CHwOSLJD725Phz7QvAdPXp8WgXJIZGfr+LAck47niQxaelG+BUk+S6+E5q04D4exFluc4+UelzEMA4AxkMmQl2QjRpIDw/ezoyNrsAPLDRkYwxCewnzU0cSTOF/1ovggPUI+bww/TxxPYb6jffr9CuxGDU1DbAgbcL0hPUuxB0uxBxfhgEqgKPyQZKtjRDgXo5iL45iL4/j36Dq0ogizoqa53qntzHps/f534QbzmWoAzYqaJnTzEIbQiOqgPK8matax2YVCm5nreRjHMQxZc6dhvDpDOI7/hTuFlyf19tCvQmccsMkQJ0T04uBzvxyuWfJ+GIJFZuDHg+wAOmn3nYdewqP6RXZ8ExNF/svjhYkgCc9hwbj0O9HnsoQfs2bNwubNm/GNb3wDf/mXf4l3vOMduPvuu3H//fdj5syZEz28SY3Sw1Oie9DVs88vRW1twp4L5PVxeX8sIkRkh+23zQogyj64IlzU6qiuNWwCw2vvAOjMhNN2M9BqnbLHIPZh5fooSD03tkEsY9Vd4WljGMIwjqt960my2WTfZJy2EhzNyvPZfk3AgHuqtLwknl9j9sVmirmR8gQWYhhjGcNFGlljGE4ktZEkstfQ6Hhk8j0iSXJ/Qmhuwb14EudjJ5ZjGXZiJ5ZhGXZmtimS9KuFpvnA2x/vkDhtf/ycXIa9eARLAERYgcc765fgESzBig7pSccWYRcWYSn2mLFQLaL9mIMGatiOq/FyzDOk8Rbca7anQq1ASpBMMVMAV+MHpi+uPzhWH7aO8byOSAQnhBqInMTGRG2hBmAx9uIxLOlcuxHQ8fQAwJ/hjzGMrMGr5ehkPxcTEXAZrd0Y6uNZXLIbb29RaLP+RQzhkDEWHVcRuevQ89RPouLqa7wIxJvxVWzoVLa6CfeZz64xFDnWtdjS4+hOL0Lr8JwJIW2EdevWYd26dRM9jCmFkvCU6B6k1KSEtEnviqzRw8mCVo+H1mkqUtS3iyhRPZ3kPULtxElnfZ+0bYR6nNSuqZ04qXSKrEwvUsJEfaTN0xPAQy6SB09WwEATJ9DIjjvMh/eRNXTy6uj4iqQSyJim4p+88KdEyxxrMj4iMVo7jhoamTYEUjQDYKma0fHehRtweYfwkZLZJUjygJZhZ0YJLQQhJMf3oI0RGe8MwTVbvQ3LAABXdoiOxCNYgjpi/BSLcXHH0/Mkzu+Qnr2mXXLshwEAFzlHBjRRw4U4aBVtpc/2b5wowZG8N68pxHO4KIeIwto0VT9OdJJtYtyG7+Hj+B3EsMOr0n6JCOnoheRoffiW96PWTgj6aYirkyAO8pC3X9cEQF5YWb+Op0joXUhIcFEi0A0kWSxKHvP6BpIQN9mjFjbrCjeeimFsHKcwI1Cl7cwIavrBD36A++67DwcOHMCpU3bB8o997GMTNKrJj5LwlOgZ7Srw87mDmHvgRLJgfhuVAxXbmyLIDr1rpMciO1KNTcmdoe10iWk7J4cg2yb9RKx2DfPeSGWrgkg9LzS3rUvY0mw5hSXl98uND13NjfavfdcksDkkSTqEIVD4kqbU5qslo405D8l5S7cnEkOfY1RNWNdl2AtgYec9AYWRoWNea0poRQzY0BpEcv2jWKwa8UX3G6GFBmpYiL24Ars7nqARrMDjeBLn4ziGcQV2WzLWRPAixCbED0jFIIi00u/JR+kSpEiuz4S6pKF6w8xvkxIfftzk3SFjtYaEdP0d3oQayDtU7VzByXfa3nfd9FJHx99vd0Zgr96d8RhTKGLP+Q7J9SiSD9KLB6pozpHPqJ+qpCfJ48kibwKGj4Evn+qkZ7rgf/yP/4H//t//Oy6//HIsWLAAlUpqW/HPJbIoCU+J7tGpv1OZ28bZPGfngPtP5xMvyISHtcR7pw6IlMzVPEQSvJYPB9WmMaFbrdgQJMAhWtCpwTPQ8WRJ0YNqHAORLTzgEiuQAgf7Mcc8knR569g7U8ff0/Z2H3Nx3ORkZA7NUduHZv2BxOA9r0N6fA/uXpKJ+Tac5KQEJ1nG81hIGCAVCFBccpn9FLsFFiU73RCdhzt5SDU0TFgb3/bKTmjeCjxuVNtGMWI8gk/i/KQ2DxJSEaOKS/CsJdaAzgy/Nguc1DIatZbbgg9Dpi1XmKPjlct8pLcGMraobYxGwO/GMR41WsYjjK3XEKt+G6Q+0uAjPpMJmqfK5b2aaKM+5F7ZK4rcayf6fPQTpzoqbXkoer+fjPj4xz+Oz33uc7jjjjsmeihTDmeGf6/ExKJDQkgMAMiGtJGQgARfXmnZLwC27HXHa9SuJuprtO3YTJLanZHx7mj7iy1Pz4ygcC5SZmszkQTKAdIga48QIrRQjxuWlyVdZz98ZB8+I8T1oNMS7KmIqNw//zy3UyeFkt73Y45JSuehTr59+qAl8HNIL8UTWKg+rB7DElyGvbgLN+AJLMRjWJJps7MTKtYruAEvDXrtWDjZ4XlaBzrEQ/ttZL4XkBAcWncldprQNyDJeaLvMSI8giU43vG2ADDER4o1kPeFZAIaqFky37ym0V24ATEiQ6SaqOM4hjufU6EC+3hTT48MOcsKCsSWBDVJVCfCBInggU9sNu3bdUbtlwu+Nt2SHV8OGN+nbKvBbSyPX5hdbx7a8FyjXgxRuu64sEie9PxEIXJcr+PRf1Hk3ZNLTA7MmDEDr3jFKyZ6GFMSJeEp0R3Ii3MASS4PRAX3uZ1lx/K9L1atHQpjyzSyldgIVDSUXgB9p9C02GrLQ9kGZ7atujTO8C72LJZKcBy0r7SmTkpQ+ANdKyzKoRnUeQg16uz+s6SHwMPq0nAnN9EKNSx8x0IhV6RAluTgPGvID4WrUT2aXViESzuKZrfgXlyGvbgF93bIUXo2SArb99LGqbXRPCIcmqElf+P5HWW5kHNF3p6rsANXYQcexnLEiLANyyziE6Nq1O1aSPKGbEKWpQo7sBzk/YlRzUhcP4X5aKBm8p9InU+SprT+UFpTyHeOCGlruxdaB8CQnl6IjERRo7Nfnp1+eG9cxNg3xqKEiGroaOcl1Bguanjfhu/hFtyLGFVLWIPjMM4262SbXu8/p9PIH2+vypnitSkCEi3Ie506A0zeP/iDP8Df/u3fTvQwpiSmvn+vxMRgfjup6YJTKfmZ3wY6oW3tQ5WEEMzyd2PIjnwm0/e9SXV3Hr4mt+eozIxNJNOxhjC8Ot6fBqtHsh9zUGPhbESa8iDD2JJl+gyuD9wY8T10fWSjG6NE668ax5gVNXEIQyaEzbXfUIM9ZDl9T/JNEk9ChDRPh4PIzlLsMe8ArGXdJFz1YvC4a/PoXr4iuAo7ACRhbjU0cBUeN6RHgoh1hBYewRJUkYataO0v7ZAiCgukMEASfYg7f6QYUUd0ITvbT0SH2nECrIVjSsSI8Gv4Z3wZt6vtWsiKfNB2Ra97SXRC0K0IQrf76xZaPkweEQKyx8HJTrfoZttv40br822415JFB4A5+LlZJtf1A9o1pbUh9Ps3Ddl/6Dhc//le9jmZMZ3q8Lz73e/Ga17zGixevBjLli3DwMCAtf6b3/zmBI1s8mPq090SE4MDFcx84lRCTA6kyzgGZrcxOnvQKmDIYcjO/Labekd6PR3uXeHelhPHKjjWiAzZSWrqRCZ0jYpqUh0R6X1J8nfSvnmYngSF0EnhA0DPp5GFRUNQxIjLQxI6RKFCSWhdNY4tz9YhDJkcH3vmXSrL2UZpv3EJnsXjuNx8jxGZfBUA1ufxQp53xxcyo5GAovsljw55eVZ0yA4HESI+PuO5Y+PWPANEFmk5iUQ0GdGRIWv8xZXYuiE7tPxruBW/hn/ufG9Zo03r67ivP76sHx6gtD9dDCHEo9ON4VpsbCGEK9yz83p8x5CdicZBzLMIUC8omt/l8/z201juhWScCUZ7ie7x3/7bf8P/+T//B0uWLMG8efMwe/Zs61XCjdLDU6I7kDfngjZQtT08RD4GAIwcOYGTyIoYWJ6ZAxVsnn8lXnFomx3aJkLJpCpbow6LbDSRem+kApksqJmMIbYmrV31aABkFN5k7g8PC2uKpGs+ixYy66pBzsR18zBODdvYO4ZDGDI1cNJt01Alvi+tGGgv4Lk7izvhatTnUuwxnoafYRFuwb1M7jklRNzz5ZvB/CkWm5C4vGMoclw+Nbci/UhyI7+7ltEYIrTUBG4aA/faEM0gHMewQx7a9iDIayJVVUsFN7TZZurnNnzPWv5r+Gd8Cf/ZWkY1nXzXbL+SkX37GK/aOkWQ74Ho7v7yNdwKICE+38Srg8eS72mwr5cQzMPB4GOQ9b7y0A3RGC+CEXL+QuC6x4V6eXx9TBWE1+GZ+nP8X/ziF/GNb3wDr3nNayZ6KFMOJeEp0R32VYClbaci2+jsQYwc8ROdSgs4NH8Qc3Ec1/6sgvZsRiyqdruQC7VRT2ac6b5nERtkZZhdtWl4sVKXAlxcjVRFM56HkCzTQm+q3u+6gZglO708IDsdALDPD6GGhjmeELITRriqJg+Hh6tJ2WwARm2Ne3J4LZuLDfmhQ0nO2RUdAvMoFqtjprYAgsmOhpAwtrw+V2M7HsJK5z5814AGO4SK1WAqcEz5xVTd6+V+NJVBIN9I5cf9Vvwj/g5vUskG72O86+P0Q+a6m/Z5pL3fKEJ2+Lvexl1vzN93/m/Jfw/+uR/eZnlMU5kIhJKeqXyMQJE6PFPfOzZ37lwsXrw4v2GJDErCU6I49lWMRLSFAxVU5rcxiiGMHElq8gwcBU528ngy+ToXtDEXwMkjFVRnZ+vwEGRIGZGQqAWTcxNXgWo1Rhylij+aElqyfZqrwmdEW5HbwyOV3TSMHPsFAKA5OIBWZOc26PkL+t+vV8+AbzubOHW8ACYMj8topx4pIKnJQuIF3Xp1IrTwBBaa852SqWQfwxjDE1iIGhq4jNWMIdTQNISJkx0agyzw6YLtcSt+XvV6Q5LE6mSLw0V2fLK6QNY4qaGheBWLzfTnEx13zkyRc0gGGCWdfxOvxhwcxjo8yMLaUiP/c/h107vcjyZH3G8U8er0m+x022+3iBDjdbgbQDjxyfbhv+a0yY0i2+f9HrTe9R/Kw+k2/It4YbrdxhcZoB3vejxQaDwlTi8+8IEP4P3vfz8+//nPY3h4eKKHM6VQEp4S3WFfh+zIK+hABXPnt9GOk9C2k1K0YH47LUJ6qGLU3GRODiEjStDK7pbIT+3ESWAQaESp8adWGe/k70j4inACsCSvpfeoduKkCXNrRZEa1kbwz4rq3h3XA87OEXInqOv7yoabELGRBq5Phto/Pvv8+2Z9xzBsFQ59FIsRAdiDRViIvYiRJM+TF4d7c4rCd46KGs4ucQJtH0W8LS6Djf82PActG/aYjquoB6Qb0s2NTR9uwn3W94MdCWwOjVy5/jc0rm4MXN9Yff1JwjxeZKcoivzO2vF9E682pGc89ll0m27DCMfLI9cv8Humj5AU3T5kuzMRoXV4Tp0BJu8nPvEJ7N69GwsWLMCiRYsyogXbtm2boJFNfkz9X7/ExKIFHJs3I5V3PlAxnp4BJN4bApEVK0RMCXsjaGRHq+/DvS9RK8Zwawxj9WHwkCwgKZ55FLVMsc9kX7FRaYtaqReJxsq9Okm7GHE1SkgWYJEdoH8zd7Zh637Ahc62+9bHiDr1VxLjkurv/BSLcXEnwV0LY3Mbot0XgtuFRYiQ5O0shU1qHsES7MGluEJskxCk4uc9z0D3EQdXvo783o33wVUYshsDqWiOSxEPWNFcgbtwA6pICoxGAF6Hu/E13GryR34Dd+PLuB1Uwycvf81nxHVL0kLq6uTtO2/byYhRJkf/OtxtCJD0+Mi8REI/QwtDCCcfTx4m0/l3jbeb+1eJ6YXbb799oocwZVESnhK94YI2ZgIJ0ZnfTl4d0tOuwuSJDDQAHALa1Qpi5vyICjwfXWpphOZgMtPh8vAcwhDgyNtJxnLKGg8XSYhapxBXZ6B2IpWtjqunMoVOjZcossM3QuPwuaEZYlx1+4DUPE6yfyI/F2OPqcuibe9+eNsha7Qs3S77gz6F+TgOt5v+CuzGI1iCS/FTADDyy1dgN57AQjRRc24rUYTo+JcV8+pIIl7ESMxPWs/uv5dE7VDDMs8LqRHA2/C9jCH9ug7Z8fVdxNgNbVtUZrooJpOx7cMIRs1v8gW8ESOASnryJjm6IT4TlSc12aFNPmie/emMUNGC+AwQLXj/+98/0UOYspj6v36J048LmMz0vkoa3iaKkVZaCdEZaHTIytI2KnPbndybpKlWz4YgxQK0gp/VGKg3KJ8n7uTOROKBkBZb9CHx0rjXJ6QnHTd5daLWKePpaUWpl6feaKLeaCJCy1I8y0M3BIaOmkNKCMuimJpy1nA8pq57CvPNeS0WP571iMjfgsLYYkQ4jmEjlzyEMTyKxSaMjROHFqKOp+ditJCEuhHZkfLJrnOiwXWtaMedvc70fWn9hnpcNIU1+cqDtk3ey7Vtses4Pcb1eMDkBtAxkfTwN/Fqc+1+DbeqUtRyhByuo9CQt77X9r2gF+9R0j4s2T9EWhsA3oyvms9fwBvxOtyNUUdtLonQey4fVy9wTQaFTBAUvaeFjKXXa0brw/X5dI5rsoLq8BS5t01lHD58GH//93+PO++8E4cOHQKQhLI988wzEzyyyY2S8JQojn0VIAbMffOCdiJEQN6d+VDV204eqVghbpz0uIiGRnJoeR6SmS+9jozdrupUbJNjpfESaidOGgJXO3ES9UYT1ThGvdE0bWRuUF5YGZAfUhR6A/e10fbRiOoqccr2G27M5P0Gu7DIGgMVEb0IBzJER2IRfmbG6CMyruMA8kmOiwD49iOJTjcINQJP50M85FjkuNfhQWzEdQCS/J2bcJ8RLeAgaWQO13UTYriFkh/f9r3A5TnNa1vUKM27v3H4/h9a7s7hTu7eCEbxMJZbJIiDh8JJxBbN0lX7+gF5Dosi1DAOuefKMY0HeN+hEx/atvQ9QlwKFkwB/PjHP8aSJUvwV3/1V/jIRz6Cw4cPAwC+9a1v4c4775zYwU1ylISnRHege2eM1MOzrwLruTs/8QQdmj+Iytw2Bg5k83KKhLS5wPN46o0mhuMxU2CTF9bUvQ36g6IixmXnCdG7HQJHqnFS6S1qJWOgwo7JvsOTwvk2Lk+DCxFi1NFEHc3MzO55OGz1wYtISqPhIqaaJguodmPQU0HL3YrgQIzIytlZgcfN50ewJKh/bmKFFAjl+/YZPHkzhv0gOjT+yYisVzA5C7Scn+u12GLakSG1AdfjLtyADbjebM/7ImTzltzEJwR5xMY3m94LxtfgDSc6HK7/AiecRH7+K/4/83knlmET1gBI6ifRC0gI0elC3v2uV/gIk+/+Ldt141GQ9/pexhoyrjPF43MKlYxnX3udCSFt73rXu3DHHXfgiSeewODgoFl+880349///d8ncGSTH1P/1y9xerGvAhzNaUPS0x0vz9wDJ5LPlyUEaGB2271tAKpxtghp4mE5ZQiHCSdTZKZTU1gQh2rAA6YjaEBeHX4stnR1ZL1Ipto1KxdiaGvLXA9IV8I7ANSReJ+eEepYREK0/ih/J0YVY4rCnau2kDa7S4QKABZ1wtcojI3wM1Z/5wksVJXYXISCJ6Jr+TPabLPPkxNKgvi+84xRn9erKNkJNVhcx9NNyAe1baJmhRJy3I+rEaFljGQg8fDU0exsTfLkbtITQnwkYZFH6WqnoYjnKK/NZAXJf2sgj8438WrzAhIC9GZ8FXux0Gr/bdxoER9pYnJQm34Vie0nuvGs9RPaf0+OpRdvfj+3mYxod1TaQl5THQ8//DB+53d+J7P8wgsvxP79+ydgRFMHJeEpUQwXtAGSmo4A572yCiNi0K4mOTwkR90+VLFIC8/pcYHaSw+RhE18UgJEXh47gT4N6TLGeEeEoF21Q+lcYzwpVOa4mhuFydH7uRj1zqyFLvMhL6wLyCdasTBb0nZUL+e4JR9t9+3PU9Eerkuxx4S1LcUeXIHdprDoI1iCZif/qAhcREcbj3Y+fP2GnGOCa67RhfHy7BQh0r4+XF4t7XwDMPLsFNJG/fhCZ+T17iI+Idv6lnVj4BYJTwtpJz1k4ePo3jXOPTmvx3esz8uwE0BKfICEuBLxoZC2b+NGk4PFP3PcgnsRoYXb8D1EaOEu3JB7/Yegm9/MdV/tluT0kxhpv30v12U3BOZMID3TBYODgzh6NDvr/Nhjj+Hcc8+dgBFNHUx9ulvi9GKfW0YagH1FkXIbgMrcpP4OvRdBHskhCWnAnQtEnp4qUklqXisnW6sn7YuIjhyH3BeJGCTbpw8QksF+HiNwM0TRF/zFMUMfUD5Pj9ZGyk67jLEnxExvCCSxIFC9HfpMY1qBx43ymiYfLH8zvVhn/4q7agII2j7ledTQa82YkO27NWLyrr0QdTuuQgckIW0bcR3W4wGrDg+1k9eZZsDJ8+qSCNfgIxWhhEMzmn3/kSLI9xh1RxK0/w2RnK/hVkN+aNlqbDftiOQcxDxEiFVSQxjFCObg5+Y75WjJ97twQ1fHMVnQbw9Qkeum2+s0tO91eLDwdpMFJFoQ0m6q47bbbsOf//mf45/+6Z8AAJVKBU8//TT+5E/+BL/yK78ywaOb3Jj6v36JyYn5bWyb/x+SmjyM7HBowgPcizMwu62SHSlkQOTERXZqJ051XieNtyfJqUlDYHgdHkDvqzI3UZmT406ITvJXkiF0nPhQFLHtWQoLleoHNENVKp9p4U7dgquvhYRjSDyCJRjDEIYwlrsvTgB4Hk3evrTfwBX/XQRVxLgWW52BFaHwqc1px8OPqReE9uEKGYlRRR0N1YjagOtNDg+HNtv+OtxtLfOdwzwPmm82Xw9gzPcCyDC6UIR5ivI9ghKu64w+VxFbRMc3Fjr3rkKkt+F7WIi9uA3f8+bwEMkhufFbcK8qWuFCL96YoiGNpwuu/1dIyKVvXWg4Zq8erhITh4985CN4/vnnMX/+fBw/fhxr167FpZdeipGREXzwgx+c6OFNalTa7XZvCRUlxhVHjx7F7NmzceTIEcyaNSt/g/EG9/CQPdkS3wGj2KYVCh2Y3cbJIxWT/8LDwshb4/PquBTaNJIi1dW4F4bq9dTjhkV2XNsPHAVOztI9O8l7J1xNyRui9VINziWcwEPfXOs0aIY5n+WtIsZOLMMy7DQzhi328NVq5vQS96wZ4y5EiLEHi3AT7jNS1OTx+RkW4WLs8RKPrIcqK0iQN5ZQYjPeogLj4SHqFS61PhdiRFiHB7EJazIkU/YXEv7ounZ85yo0rDIPvYYMufpytynm0aHrYRQjwQICEWLMwWETvkbEZhPW4DDmWOOk/BvK2SFvDy0PxaNYjBdwDkZxVmbsvnGGYioZ7/I6co29X5Neeedmsnh4ito8b3nLW/DDhQew4gO/nNt29Mnn8a9LP4Bms5nbdrJj06ZN2LZtG06dOoUrr7wSr3rVqyZ6SJMepYenRDgOKGSHPku7okN2yKtDHhlOdk4eqQB7KxkRA/rOPULcm+OCTW5mWHV1iOwk7fTEcp5/AwCDM+1xnZib9COX81whTnZ4HhGHlKn2gXtaXLOCoR4IMiw42bH3lRUecBm0/ayoDgB7sRBLsQfnY58hOVyo4OKOqIHPQxJCduR57MaLM9Fkp1/7KHLMRXOiSKGNBAt8xpb7Ggs7z75rohdlt34jbAa+O48OkISl/Vf8f4XGNMy8pyRSIMkOkNZMitBSQ9vIiyPfv4zb8SgWG+/Oj7Eca7EFt+BejGLEK2ddFP34XYt6PXrZp/Q+aThdZGeqI7QOz6nTcG89XVi3bh3e/e534z3veU9JdgJREp4SxcDJzXy3c5A8O5SvQx4b8uaY91nZxH8ubgDYpCkEnLhw0sOJBw9tc21L46JwuebgAADgxDE9B8kWSjjVWWa34fsjQ8/1MOKCAdo6abCGJ9Jnw798yAu9cwsYZNtfgd1q24XYi0ex2BhvrnYhcKmvyfF0E6Y2HgUSCUMY6xvZ8YXl+cLjXH1whIQK3o+rAaSzxjHcQgV5YWjaZw1FfhsZihYStsa/FzGMT1fYkAxV84HGsw8XONdJcNJDvxcRG1eI2q/hn/EcFhhlOK4Q92v4Z5zNpPGLQPtN+o2QPvtFSFwGepGx5PWft+8SUwPvfOc78YlPfCKz/G/+5m/w+7//+6d/QFMIJeEpUQxEcqjI6DFkwtYIlPNC4EpredLUPE9HEiIX9JC2UxaJ0Twusr0LtRMnPdu5xyQ9RxTWliexy2vISMOzaB4I7x+wE8FpbxJEZLhkNA99o7FrAgauB6gmL91Lu3R/+bKj0mgoklPD27gMXkr4Xo3tGMIYVmO7lQS+Gtut88Jzk1Zju1VvKA959SZ824X2ryE0vJGOU4azcdITWmvHbuMnD67cHldfef2HEhq5fSghClHvc12f3ZJv33H7c0Tkvcr+vglrVJGC5zqS9hIbcL0lYNELxstg7yVnqN9j6LZv+s+dyaQmtA5P6wwweb/xjW/gFa94RWb5mjVr8PWvf30CRjR1MPV//RKnD0RyGmzZzHSdkZ9m3h3ppaHPXJqakx8uRhAC8r7YhUFt0uIiMTIELVmW7V/fNpsfFApXSJtmOEnoeRT6rGAIqD/+SKihgQgtPInzEaGFJ7BQNYhCZJ7lum4e2uTtoXcZuhZKcnznxmdUVpnBIcfP+3wIK7Ea2/EQVhrywonbQ1hpbUtthjCGzViFzVjlHN/pRK9kRwOptHF5aiCMoBQ10PsRchhi/PNxduPFCQld42ID2nJ///p545/DvVTusd6FG3AXbsAYhlQVNu7VkWIVG3C9dwxFQr26vQfmoehv20/S06t3EUDmP3cmYjrV4Tl48CBmz56dWT5r1iy88MILEzCiqYOS8JQohvltoA6Tz0N5OnlS09b6vcnnVpRsG+rBIfC8HBfIk8MV1NxtY7NN0THo62bAVm6zc3x4OJ1GfhKp3tQDw43uGBEaOXVpun3gux6ieQU0Q/cbwc4bCs0j4Tk9RUQJikKqWrmIjjSsIsR4DgvMeiI9D2GlyVEgbw/3Aj2ElWghKpRoPl7w/RY+Q8EVLsgRIfYaXT7jPSQpXrtuXd6jXnLPdI9sf3PZfOhG5c9lIIeQuaLnbBjHM/LTt+BeleRsxHXqmIp6104nuiWLRfcxXn374KuLVWJy4dJLL8WGDRsyy7/73e/ikksumYARTR1Mfbpb4vTgZxW0Z3c8NUyBLY/saOvbM5kXZ28FAwvbOIQhjBw5kTuMELLjAnlk5LY834ZC0BKypO0/JTG+9fx72v8pSOGEuBoZo0Ir2ClD0Ey/Oca9y5tCy2toookaLsIBPCVCTrS+ZV2VtG26jAqIhiKkXg1Hr2TH553hY3K15yDCAgBXYYf5TF6eEYxiFCNWSButH8GoITuEfiZvny5IslNDw6ptVQSuayHNG8mvQSLbuPosUr/HtY1rXT9nkPsZslZkfbcYwxA2YQ3W4UErp+cm3JcJX/Plp4T81uOFPC90nteMLwu9J3V7rHI717NiOmA61eF517vehXe84x14/vnnsW7dOgDAvffei49+9KP467/+64kd3CRHSXhKhOHiNipgnpoqMDZzBoZZyJokNq0IwJEK0FFn47V4BtDJzVmYfB85ciJXVrobyBye0G34vlOSZYshuBBXIytUzkV6SMba7JeRCtfDyxWixXNq0sKP6cObF8nk2z2DeajCL5AQiiSXx50vxMEN0TqaaLDioiFwEbDQ9Xx83ULm5/CQNnQIzMNYjmGMWeSHyE0VMVZjuxrKll4HWeO8qFfB1Vcv0H7Ta7EVm7EqQ3q0a9hFNn0EuBvjMIRUh1wr/Ubeb9EL2TmMOZjTpRhArxjDUGYZeXikp4eIkIZucme4xzWkPTC+5KAb0qMRrpD/j+YZm47EZzrgv/7X/4pGo4EPfvCD+Iu/+AsAwKJFi/DpT38av/mbvznBo5vcKAlPiWBwwtI+VMHMeoz2sWxBUS1M7eSRCgYc4Wsnj1QyZGdwZhsnjmWXd4MknIx/B4BTzJMzI+PlIUiyo9XSobC0qBUbEsNJjyQ2GqpxjFYUZYwv/nDTqqan7XTDOIT0pNtEToM61CD0zYDSPq/AbjyCJWZ9A7XM+IoiayRUvesl+Pm4CjtwF27AAhyw2lyFHXgYyy3vDic6WdJje3+A7G8nyY4mDZ4dqzy28QurCv3dqd7OOmw1UtQ+aEabLa6hkxB+LbkMv24MvbyaU+HXP5+06C+RyjNkab2L7ISSiF4I8gh+YeXx+P7PLrJTFEWT+ovmC51u5HmZXPduzVMWsq8zAW1Ugo7lTDne3/3d38Xv/u7v4vnnn8fQ0BDOOuus/I1KnAH+vRKnDdKL4/s8MLuNgWP29pzsaEVHOVzSzy4U8QRxmWpOdmRfGtkxfbCI+laUkKBGvcb6iK3PcTUyL45ZUVgBNB7T7TIiQuSCZV9RZwacyI52fGmoXdYjpZMj/YFM4GQnRtVSJ3PlkoSQPBdCZ3z5eZEyu0R2CKuxHS9gntWGyI5rmRQtkOjWOLZ/I3eCbtH+i7QnkhNCdkLhDyPzhx3x9UXyXkJ0nkL7yDsGPr5+oN/GXDfXIy8oGoKQ/6Yv3LOomIBPBGA8cLpzcYBi4bETMb7xwCnMCFJpi88wk/fcc88tyU4BnFm/folxA1dc4+Sm0oJdkLSDk0cqwAVtS4GNfz55pIKBo/q+UoJhv58u2B4em+y0oshpCHBPj91flPlOy47GtUKFSAGurKY/rEhlzYXzcBiP4fJkLIhwkfBk+KrTyzC6ZBw66ZHJ/dk8pMSbxAkQh0ZypPHabTI63+ZK7LReALANy3AVdpj2nOwQcTkHB83nh7Hc/Ba0jEa2GatyBQH64QnweYOKnqNux7MOD6KGBmodKcdejfAQmepQcMWzXkhGt9eadq3KMbnGRsf5Otw9YQbqeBfcdYELenST1D8ZjPqiBFcrIQAAzKBJREFU+/cpzrmOh9qS0AfVwCpx5uHrX/863vCGN2D16tW48sorrVcJN0rCU6Kv4OFtJ49ULA8OL+QJJEVHCeRVGZzZFnkufvLD5aHps/T2cI+NS7CAIMkOkRPy4gBAPU4JRYSWUVvTZK5DwtkIIcQnUoyi1Fvjl8nl26zFFgCJUU6iBUU8Nuk626CmeTRXHLoMW5Ljk0YVJwqawVWULHCBCNpuG5aZF1cTexjLLdKT9pGMgyuvxYgwjDGL7PgwmSVSuxkXGVebsAZN1LsWL5BwFbTkKBKi5FLimyiE1k8CErLzTbwaQNZLm2fUd2vw82uBQk4n6pyFePW011RAN+F4rm24t3W6kJ5T00iW+hOf+AR+67d+C/Pnz8ePfvQj/Mf/+B8xb948PPnkk7j55psneniTGiXhKRGMSsc+5bVyeN0dwM7zyQN5fAZmtw3ROHGsYkiPBo2g5OX5hLShduln+6/BZaRrJ06i3mhiuDGGeqPZGVeskptsnZ8sMeJhbdU4zhAB+eD2hbYlN/XIuS2QCBVkxim8JkWg5ZQQ8ZF99wIyCFOBBdd+3SFIK/C42Y5MIu7dmYeDABKvT4TY8vRchR3mxT0+lLNAoSQhZCe7LF91r5t1fJ8hD/1uc7VkKNs6PNizdwdIzm1YGJmb9ORdf/004MeTEHwTr8brcDdeh7ut5aczd6GIqmIIuByyTxpZ/obckD9TSI0v/zFkG62tJD2usL6pDsrhyXud6vP1OxH41Kc+hc9+9rP4m7/5G9RqNbznPe/Bxo0b8c53vhNHjhyZ6OFNapSEp0QuTh6poBXZxMYHn0z1wOw2olbybrw/exOSMzgzIUCUv5PWrzkVXCPHRW54bRxZJ0dr50IritAcHMjk5Mh3Ds3zw3E0TnN/pCCC1Y/i/QB4uBnPwfEro2mkx96X7bFxjYHauvuw1ZN6NQi7yeXh6x/BEvPwI3DyciV2YhuWmc9XYqfq6YkRWbVGZO6OC/I3ohc/r9qDmpa7+9XDX0LG4Vum7YdDziCvw4NBs8oulTYXehVmCCE9vmvTN2fM2/SCbran4/KFQBFON+nJO54IaX0mIjtjGM60cRnyBLcAS/+N217rOXVDYPImjvg5cv335PKp5gErkeLpp5/GmjXJ9T80NITR0STk801vehO+/OUvT+TQJj1KwlMiGNyzQ99d7Vw4eaRikx1kQ9u0kDRtOYfPi8MJTNZDlBIcjehwoqKREQp14yFvtB+NqLmIkdzeB58kad52LmOFkybuBaC+l2JP0D6oD98+rsDu4L58CDE8pEcjRtXy1Egisw3LLNIDAAcd5PCcjjeIiowWQbdGWohR242xVyTUg/onI4obU5uwBhtxXVfV3fvtPegWPlLjay+XFd1nCCikjaOI0Vr0uujGsA+d1IgRoYbEu03FSEMLzbowirPGhejo4+g2fzCf9IT8pq526/CgJVywCWuMp0e7N1B481QG1eHJf019k/e8887DwYPJs+eiiy7CQw89BAD42c9+hnY7LLpmumLq//olJhSS3LQioJpzr3ZJU9PygdntTlibTXK6kajWyI7mLfJ5dYj0aDk2FOZGnzVPTrrfzmwsIzsuoiOVnnxGbOhyejhKw9K1/SV4NqCmSguX4NnMstRUzAosPIrFpyUPoIgRT16eK7ETj2KxtYwXTASSMCuX8prPaHep3bmW+cCvB5dxlE+eiv+h5L60GkKE9XggE6bEPX4SvYsJFA8ZGk8UOZb8MMh0/Zdxe2b9ZJqpL0Jcj2PIHNt6PKASOgnt+iGjfQS/6Plc+DzWvm3GUx5eOybf/58LPRDyvGIlpgbWrVuH73znOwCAt7zlLfiDP/gDrF+/Hm984xvx2te+doJHN7kx9TO4Sow7qrFNbOR3jfQU7dfy+vysgpNzOzk+eys4MTdZnNbmsaWkfR4c+p6n9OZr5xMeUMlKp+5PKEHjRErrL1Ezi4Nn7kOKU16IgyasjQjLkzjfrL8MewGkhIYXFZUhWLRdVgbZ/UCOkSRAS0ECaSyFtpHLq4jNeYhRtdTXJMjLQ6FrYxhGhFgVLACSEDZZb0eOhTDeXoteDZde1NioyCjNHnOvzno8gI24LkN46NrpZtz8mu4mD2oyohuCV4cuZd9P0jOexrsLG3A9hpFeO0VEGO7H1ep6ea/ywRZ96TZkreW8//Jrttv/gL2v/JwdH4qcm8mOU6gE3Wun4j1C4rOf/SxOnUrslbe97W2YO3cuNm/ejFtvvRVve9vbJnh0kxulh6dEMKpxvvcm3MjPEiNTm6dDcE4eqZhwNyI76X70PJxuJKw5SbKJVDbszEVOpHcnrfXj3zf1V7QeD+XD0DIXXCFOT2G+eUA8hfnGWL0MexGhZZGfhOy4iAt/iLeE0RCWIOvLm6B3n7oWV7iimj5XYDcitLACj+NK7CxUH+Yq7DAECUBGNptITl4YGxdX8Hl3xhNFQ9zWYos3xIX62YQ1+AVG0EQNMaJMLgZ910LbfGMJJYh5NXpcOQohQS/9NAK79VrJcfDPUrSgaL/299NPbLRx8OV0zZDhrp0L1zXKl6/FFnU/vebh5CHUQ9TLdea7ru/H1cHX8ZlAAACgHazSNvWPd8aMGahW0+fuG97wBnziE5/AO9/5TtRqNc+WJUoPTwkv2ocqJkyt0gJOetRm00KdyTs39n1eIVmElHt7Bma3ARECx2WrJaTnh7flpMnVR5ZA6TdIIjs0m+cSJQglgFy4gPo9txOW8LxSSM4VribzVexZcbfHZQkeA5B6ayLEeArzzV54Tg89WpdiD57AQms/fgUwO0HfVWfHte4K7MYTWIgm/Df1KuJOWFraBxlPnMhIkPqa9OwQidqGZbn7JmhKcvI302ZY88LUJLrN5/LhflxtGY5EFtfhQTODvAlrTP4FH0c3uTtFcAvuNWIREv31cPQ+A++DX6DBLxISEvZVBCH/3f7uL52o8V3vdN1pv4P06GjL6fNabLGWc8+vhhAPeTfQ9jfe15nsv+j9pcTkwI9//OPgti996UvHcSRTG6WHp0QuyKsTQnZcy/KEDLR6PfSZCJHmLZHCAD6Pz4ljFVG3J92WFOLy4FVRU8YivVAuYtSNaEFa8Sb1+dB6ms3KC/3RDOY8rwy1eQILTegbgEwuj4bQB62sMxR1SAzfH29L7R/FYtOeiEoetDA3DUXJDodGduidf3a1D4VruzwvIIecPadteX0PuW49HkANTa+0cFHIGjwhNXkmI3hI5umqYxOa19Utyen1GOg64febmiNUT6Lof8NFjjj6Ec7WLXohHyEeXNn/ejyg3nOmMsJFC8bvePfs2YO3vOUtuPjiizE0NITFixfj/e9/P5pN+7p++umnceutt2LmzJk455xz8M53vjPTRmLlypV42ctehpUrV3pfL3vZy8bt+M4ElB6eEl5wohJXddIhPTp2PZvs8rwwLwkiQIdnn4VzMdohLu5cHV1t7ZR3vzxcLt3Grq2jiRZQ4VGCzC/SIPudFTVxCEOZdtyz43pA2bHqetFQmfuQzui6PSyAXwKaDCUKd4tRNZ/zoIeZsHPI9ivbPorFuAK78QiWYAUeNwIDNHbyAkVoYRcWBanLXYmdJmztKjyObViGGFHG06Pl7Ejwc+YKYwsVLZAeoJCHdUj+g9afK0QoITeR8epINFFDhNi00zw8NCaOFiK89OnP419eeG1mOeFz57wC8188aL5Lz87p9kx0g4lUnnN5D7LXVfHzR0VIXfl0vjEBsEIg6XMTNfVacfXhb6NfG5LYjJc3xzUm17n2eXq0e0DI76r1z0MGY0R9naCYSFAdnjycGsc5/l27duHUqVP4zGc+g0svvRQ/+clP8Na3vhXHjh3DRz7yEQBAHMd4zWteg3PPPRebN2/GwYMH8eY3vxntdhuf/OQnnX3/7Gc/G7dxTydM3idFiUkDmbcjiQsl6BO5oe+c6AzObCPuFBWVKm2hRGjOkV/gJCqI2DYSofV6XODEaeaBUzg2n49TIQgessPD5qQowpboalwdb3Hm7sgHNn9g0Y1dMzicxyUehHnehKp4yHKjIBUxyNunJFppnzU00ETd+5DWjmEp9phwtYT07AYAQ3x2YRGACMMYwwh+EdQvkCSCL8UePIIluBKP42EsxwZcj5twn1OgQEIjO72i6IxkN2RHA4WuUSigRnZcydGawdpAzUq2P/T0MVx6+cU4ecL9fx0YnIH/97HVFunRkEd8xjtsyIeinpB+hxqFkp5uIEVCQsbCQcY2Jzn03q/zIK8NGX4rSYiP/PjCbbsdF4EL06Rt/BND9va6oIzclta7JjBKdI+bbroJN910k/l+ySWX4LHHHsOnP/1pQ3juuece7Ny5E3v37sUFF1wAAPjoRz+KO+64Ax/84Acxa9Yste+LLrpo/A9gGqAMaSvhBeXekKeHyMvgzDYGZrctosMhyY4L0hukgULaXN8BYPvMFbnHwvdDY3KRrKh1CsfmzxDLYk+uDjN2PXV9CGsaibF4NK5l8ndc8CX889AQ10Obx2/L+jSyDc3gUmAc3zcXNNDHGVYfx3VMPmNnFxbhCuw2x30XbjBeHxp/jAijGMEozsotsEqggocrOmQHAObgcLBAAUdeAVjCeMTRd0t2ZNiPVlA0hrv2jjSe+MzxRlyHYRy39n30hZNesgMAJ0+cwtEXTuYdjkG/E9H7LWBwulAk/8t1fcp6XHnohgRQvSYiORdgX+E+OHxiBHLypeiEhFZg9lpsxXFRKDV0jO713Qln+DzB3Hvrm8CYypgMIW0ajhw5grlz55rv3//+9/GSl7zEkB0AuPHGG9FoNLB169bgfh977DG84x3vwA033IBXvepVeMc73oHHHnusr2M/E1ESnhJd4cSxSiYMTJIHTmCoLeXk8Lwc6SmibV3FRE8eqWRI1MpjjzjHqhUt5WPX1mviBfQC7Po7dpv8kLZ0v+whyvqaa4xD94x1UP+ddk3UM8SFz3Jmi3NG6gOettVydXzGhkvJzXccrnVLsQcbcL0Z1y24Vy1kGiPCJXgWLzDCQ0RG31/LtLkKOzIPRp9Bx1Xi7Dwqf+hayMPXZQAVJYquBz55cUKKD1KOxRwcNgYTESUypFzqbN0mS/cj54XvuxuD8nQRn7BQrWJjyfMYUJs88hOCXj0f+3BBbptefgcZzsbrnBUly1XE2IxVGMIYAOBi7MG12OpUljwdcJEeKeSwDg9OWTLfLxw9etR6NRqNvu9j9+7d+OQnP2lJRe/fvx8LFiyw2p199tmo1WrYv39/UL9f//rX8ZKXvARbt27FihUr8NKXvhTbtm3DS17yEnzta1/r6zGcaSgJTwknThyrZHJ4aDl5eeQ6F0nhpEKKFND6HbP/QxL6JrZPau/Y+9GKl2rwkTC+LO2fhAaiDMkJhUsuO1QyW+bzaHHbRUkPkH0Ia2EVBDJeWojQ6EgPE57E+Rnjge/Plx8gyZX24KW8G758Hy4w32/CfbgCu1Wiw8nHI1hiwqi2YRlqsB9qj2IxtmEZtmGZ5dkhb04NTVNg1GW8FDHyuplddMXsy2uh27CtIjO9RIq40h2X/vXlXvQ6w1rEgNTkf4sSRL3f8TMSe5nR76WfsH2FE4FuSA/3Bo63Id6NF5CHDsv3a5HMyu/EMlOEV5PQ167dIl60bv4/Wlv6D4dMcEwlUB2evFeMCHEcY/bs2dbrQx/6kLPvD3zgA6hUKt7XD3/4Q2ubffv24aabbsLrX/96/PZv/7a1rlLJ2i7tdltdruE973kP7rzzTnz/+9/Hxz72MXzsYx/Dgw8+iPe+97344z/+46A+pivKHJ4STviKZxLp4XC1JXKiCRjQfgZmt7HsWAUnYP/pd8x8CZYf+4m1/eHZZ2HOkfD8DB840UmXidn9KPXqSEgBgrgaOcPesvsOn1H1Gbz8gawZvlFnPT10bWPZjmFP49rpAVtl7ewcItrWJX/dTe2ZGhqdPBwbMsRqM1bhWmy1wtmuYPk8dD4o5+dKRo62YRmuxE40kMoOEtmRY/WFsrlydkJFCXyI2G8Vum03hqJLeW4jrkOMCDfhvsy6TViDCLHJcVqLLUGJ5r2EkxQjluP7WPOFD/XaXz/aFenjdIT48GtZGwO/dsYwZMIfXf2cTnCPjXxvITIkhzw9skiy7EuTq3eB35tD/z+yHX1ehwdxP67GXbgBt+DeIOW6qQSqw5OHGBGiKMLPf/5za3m97pagfcc73oFf/dVf9fa7aNEi83nfvn145StfiWuuuQaf/exnrXbnnXcetmyxyebPf/5znDx5MuP5cWH//v34zd/8zczy3/iN38CHP/zhoD6mK0rCU8ILjfRQPRtN2YzgEyLgtXVM6JqjLyI7ADB4BDgxGxg59ovg+jZyTBrh0kBFRqNW7JWLJoIjSQ8hJOeHUI2TfflrRPgfepykaGgwA9fUEELLCBFoykU0N5a3b26QNGE/QEKSpCm3iOr7AFBlqIF0ZlUDqbgBVDQ0MqpuQLYWjzuUJ8JqbMfDWI64cxZcM7W+fiYLIsSZ+jqc0PAq7XzWfRcWmXAjCoHjdXg24PpJc+STWbGtG+SFfXbj9fWhH2IGWp+huAX3Tqn8kuMYNmSHw3WvkNB+P817LkVfOLLCC9l7NakoDuN4ps7WdIRLHEDDOeecg3POOSeo7TPPPINXvvKVWLVqFT7/+c9jxgw7quOaa67BBz/4QTz77LM4//wkF/aee+5BvV7HqlWrgvZx/fXX44EHHsCll15qLd+8eTOuu84/8TTdcWY9HUr0HRox4ERHy1mRuThSte3EsYq5HdOygWMALmgbz1ESxmb3fWK2f6zcGySPIWrZhUdp7MkY3Dk3cTVSPTuh0Dw+RHZaUaR4R/KL07kN76wXKEJsQrkanjoyRHaS/bXYwzctNKrtg9oDOsnxfaf+tbEACdmRtX4kWozMkOcGSMQHiPTwz4Bdd8d3XDGijkLbDjOTKz0N12LruBlooTO6IaB25JUBsgnMLtW1pdiDpZ3P8lgTVbjimIHe1BT7gV4Iarfb9ptMFPGcheRRZXPFwsK/+pWvEvpfOl1S0nnEhcjOtdhqvM78XiG3JS+PNqnVLdnUCj+7PD20nE9unAkg0YI8jOek1L59+3D99dfjxS9+MT7ykY/g+eefN+vOO+88AMAv/dIvYdmyZXjTm96ED3/4wzh06BDe/e53461vfWswCfvlX/5l/PEf/zG2bt2K1atXAwAeeughfO1rX8Of/dmf4V/+5V+stiVSlISnhBO2gtoMI7MspZeTd5vkSAzObKN9KMkJ0rw+J2YD6JARIju035n1GCeOVTA68yyMHHOHsrnIDr3HCtmRn5NjSR865Omhdw2at0YLg+My1db2Iqk/xGPgCvGR20ZooY5mJw+HPDqxISc8ZA1IBAmewnylZ328+nd/wn9W8lo/r3nS15zEaCCPDidE2hjzHoJkwEjEqGaKcK7FFmvZOjwYXJumaBhbUbJTR0Od2XUZPnlGUbdheuMFeS3zZa7xyGN4He7GN/HqnP30RpTGg/S49sXXS3IfIoQRgn6QnaL7dtXPGY+6OhpxaaCOOhpYje3YjFWde2Zyv+Jen7xQTDtEmH4n/zFoXiHurddIz5kuUDAZ6vDcc889+OlPf4qf/vSneNGLXmSta7c7Ik1RhH/913/F29/+drziFa/A0NAQfv3Xf93IVofg7W9/OwDgU5/6FD71qU+p64AkVyjuYbL2TEQpWlAiFyEyyy75aS5U0IqyNX0IJIKg9XGskdzIzsVooeT/oiDiElcjtKJIDWWT3hpq5wp748td4+bkhL+7IB++PgO5jmbGS+PaR4QYz2CelevTrxkxGQPvw2XYa7w6mneHEx3qL/E0tPAIlljr7dC2LLTj48IHmsFCeUtS7pXHy9P7JqzBejxgQsQaSnFFHj7myqnxgdfL4cv4uNZiixFgkJCz6nSuNLLDl/Hjos+hhQxnn1PFwKD/fzwwOAPD5wwWqDWVL0mcXWcnyeeRHcLr8Z2gdr59hSDk/zcPL+T20c3sd4QWbsG9zvZFlcj48a/HA+Z7qDpddrx20r+P3PaimqZtV+94zWky5CIcMBLVWoibBq5Wmc2tKiZm4NrmTCc6kwl33HEH2u22+uJ48YtfjLvuugtjY2M4ePAgPvnJT3pziCROnToV9CrJThalh6eECgr94h6dqAU0B2c482C0nB5Zo6clnrtU50eKIMysx4bo0DiONaJMAU9CkYKjqpeFeWmIpJiHR2QvJ9ITqt4mw9oo56cax0CkCwf4IA1ATUiAHnQN1Ix3h+Aq+Ckf7NTvJXi2o8wWg88S89lDVzI0nx3V+ufvEprxT0VHgdSDQ6CQtm1YZrw61IbWcWlqF9nRPqfbpB40HgqmeUPksvV4wCIXRHx45XPKiykSppRWrXeTniKQ51WCh8JJOWqC5sHimP/iQXz+sSvx8xfc/9tZ5wxg/osHCs3X64If/X3MRYjxTbza/ELjnbuVN0t/EEl+wTy8YD67oHl1fGGqd+EGAMWLHPfTsxDShya8Mh4gzw5XaSPSI8l+SEFTX76mbEd9ar+Zz3PYb5GNyYhTmBF0ffajaOxkxOHDhzFnzpyJHsakR+nhKRGE5uBAh7ycckpPEzT1Npdnx2xzKLuMSMzOOgtFUjw8M49kQ8VCvUAkO21ITjUlO6pBHEXZbcQDi3t1eEgb9yDJ9XnV4jm41KksPJq89LoSFMqWDW1peY0KSXb2YJGT7PAR0FhdkNuSZ2cT1uAJLMQV2G0U23ZhkaW+BmS9NldiJx7BEuu4SXYasJXYtLAQ38PQnknOJgSHkAoeUpcHfl7yvCcxIotgyEKgeXkRsoiohPSYPYIlph33YMliozR2fhwc8188iEuvHHG+5r940DvufqIXw/x0zaLnGauS7OhekeIGnzZh4ftfk/HtOi95ZJijm3Mr73uaTHS3kJ5SV7grH0ce+Soile2rYeYTX0m3T7Y7k/J3phv+6q/+Cl/96lfN99e//vWYO3cuLrzwQjzyiLseYYnSw1MiB0Q6aifSiucu7wrl22BvBZhrNcl4djLLFyYkiXuJElW2U1gmVLWk9+fY7GzInfQEhXiANAIjw8BiVKE9VzixoZwfDhkuJ7fJ7AP6zJxvti5NiLVnWBMDpMXydmyDhPfpzg1Kz81C7M30z2cY5cNYi4EnWsS/E7jXgLJ4qDYPh+aJiFE1Xh5t/K5jyyM7Wj+aZ+ebeDVeh7tVg0Kqw7lyeAhEEHg76WWjZRtxneVBorGFgEt707i5AMSzuAArOm2fxQVG9ICOPVFsa+BabDXHQGPjY9e8QFRE1oci3oWJQhHxgF720e02Mmetm76K1pzyXc/02XeN9kok+yWkIO9fRHJIsGQdHnQSHxeRcY1Ny4/MTlzZXnyfN80nynIm4VSgLPWpM+A8fOYzn8GXvvQlAMDGjRvxb//2b9iwYQP+6Z/+CX/0R3+Ee+65Z4JHOHlREp4SKpqDA4haMWonTqEpYu0b9RrqjST0RgoYHEOEGhMbkbLWFMImvxPRIe/QiWMVoKPKJglO+1AFmOn34IR6fLikdN4Ncy6OYz/mpNsycuIjMS5oeT+aF8FloJCRJWcwW0r7rJiB7VkCZF0Z/4OB98+JDvfwuAxV7VguwgEAiVBBjAhLsQcLsdfU0ZFFRp/CfLMN4RnMw5U4aL5vw7LMceSFsflAggRaSBfhdbjb2wcZe9zgc/2+vK1rRlySIq7CxCG/83FrBVyJ7DyCJbipo3JHZGczVqGJOmpomH6bqJvzcRduMAVfKUxPO++k8BZijIWQnmz+glSu8ntRQ0lLDQ0zecBFDiYqXyIkLEoTLnBNUIR4G/LUy+T9gZ9Xee1rY+Bjda3X95uMPY/sdEuiOdnZjFWowe/lcV2TrjFp2/lIT/K5P568qYxQ0YIz4bw8++yzWLgwmQq866678IY3vAG/9Eu/hEWLFuHqq8+s+kr9RhnSViKDJFcmRtSyyc7MenJjJbJDoDAzIhUDs9uIWmnOT9ImaSs9PS7Pz+ARezwURhZXI4zOHuj0mXpNZLgYXxaCapyGghHkg2Q/5hiDPnmlJIJCnriAAX13wQ5Mq4r+7AA11406z5uSTUROeq11snu0/fHjkeN1QTMWfQYFH/cleBZAEjpHZIePnerqJPk7CSTZAYALGdnh22vf+dikcfQLjABIjJprsTUjTqB9vh9Xe4v5kSdDhnU1OyIGmtGihbBJgqWFjmlteSgL9/7khRaRF20FHjd9/AIjmfwgupY2YY0pHJlcZzrZ6RahM+NaLZMQhBjWXH49VOQAyCfD3bb1/S9HO9cyIU8kIM8gLyI+IvdLoZaSfPeLKI5X3o52rCRB7YMrnyxG1RJGydsX347jdElzl5hcOPvss7F3714AwIYNG/CqV70KQKIEVwoV+FESnhIZELEZnNnuJNwnXpyjcS1DJAZntjPhYscakSE6tRPpOilbPTC77RQ+ODFbV31L+8rmw/iW0WeNBJGgAM+nsaVtbcKhJe43UbOIAs8B4qRHhs35jMEWIlEoVPMI6Ua8Bm7skNEmZ2IT4uIWUNBIBD8PKUETeU1iJpdmKGNUjfw0jelRLLbIDYE8EZSXo+XE0PLQ4qIEMjTIiJHGzFpsya1O7tsHL/Apl9NMtxYGJwnNBlyfUUejcW3A9dYYuGHpWq4RJS6kQHgES0xbGjP9XuvwoPWZH8fpIDscRZTa3InksZNwELELRRHiwvFl3N7VdhIjGFWXu0hGaA5hr6BrMOuJHt8Z+DS7MWw/ecfqIz2uXEpAI+jpvbmK2HltFhG3ceFMzN+hOjx5rzMhpO11r3sdfv3Xfx3r16/HwYMHcfPNNwMAtm/fnilGWsJGSXgc+PSnP42XvvSlmDVrFmbNmoVrrrkG3/3ud73b/O3f/i3+w3/4DxgaGsLll1+OL37xi5k23/jGN7Bs2TLU63UsW7YM3/rWt8brELpGUgeHEwW9Tk1cjXCsEWFwZtuQJA4uVCCV3eIqgL0Vs5wruVl5PB7Sw0FEgr9LIQIaewjp4ZAhR9pDWkvc58saUR2NKK19I0kC307bv7ZfjhZswsG3cyFG1BExSH4U7eEuZ8rdhlJ27P7QoWQdyU4nhU/tvnmolczXIUIjiQ9fzsfGwb1gfAacwlRc5MRXnbzbvAggJQlEXDhp4OFstY7EOIEU3dZiSyc8LCwhWRYclZiHg5kwuhV43Mq3iRBn9tNELaM6FwJ+TK5zz+G6Tot5cvwJ5ZSLJdFEPYjE0LHzc8a9QRFijGDUHLvsk0IC89CvWf7QBHtCHmmQE0RyYsQ9jvGboc4jORp54QSJK7MBiZfHFc4mffdyAi15r2bu1aGkJzSEcbqg3a4gPhUFvKa+yfu//tf/wjve8Q4sW7YMGzduxFlnnQUgCXXjdXhKZFHm8Djwohe9CH/5l39pGPMXvvAF3HbbbfjRj36EK664ItP+05/+NO6880783d/9Ha666ir84Ac/wFvf+lacffbZuPXWWwEA3//+9/HGN74Rf/EXf4HXvva1+Na3voU3vOEN2Lx586SKvYyrM1A7cRJHB/WaIFHrFAZntnE0TtYfa0QW4SEZ5srcNnAkS2qI9JyYay+XIKnqmfXY7MsFLhSg5sYw7w+RHi4VTevV2jvGK+EPBwE0D0jqySFDP/FuhD34XeFpchx5uTt8+0h5mNqELlwxTo5L9sHHx88frSfvjut8kLy0r2/pzSkCPgP+EFbiWketmjy4PDQSGjFJhQZ0QkWeFUm4iOjoCmn6+UxzbmpOcnEldhrSshHXoYEahnEcNaR5F+tFPhOQkhWelF5EjStGFCRi0CuKkoTX4W5swPUY69RZCQljozo9Wlv6/am/kGKnLvRDcttFduhelZevUxTdTA70MqHg7zfNj6EwNe7J1a4VTnK08x8iQc2fB3ab2PwX0npo/t9YOzdnQq5KCR0DAwN497vfnVn++7//+6d/MFMMlbasilTCiblz5+LDH/4w3vKWt2TWrVmzBq94xSvw4Q9/2Cz7/d//ffzwhz/E5s2bAQBvfOMbcfToUctTdNNNN+Hss8/Gl7/8ZXWfR48exezZs3HkyBHMmjVLbdNv2PVvdO+ILMCZLLND2xLidEqVsCYxAyI71RhoKLW30qKnyX5nRU2L/PAQMacijqaGJkiPrMPjIwopadHD0ugBJBOFpReIt/XBhMblGB5yBpPPJNJ3WibrvQxhzJAmnp/kG49cxvN45Fg5aUzeZe0he6azhchbD0YjQkB4vR0OqpYOJLO33GD3QRr2oUU3Q1C0P9dYNMLBrzdJ0jbgeosIcTEEwhiGrIKURKISIqSPIVSKONRQ66dqm3at34bvmc/k7QkhJeSpobbye4QYt+F7+DZuVLcfxlgm74YwnrP2vrArTQggRHY+2TZM2CBsjHn7cstRE1zXDREdV+hqXoiZnIxynbM88Rg5Zi08ODsGu6+8/9BkD2kravO85S1vwf++4GwMvi9LAiROPfkUTrz0OjSbYR7UyYJ/+Zd/wc0334yBgQH8y7/8i7ftL//yL5+mUU09lB6eAMRxjK997Ws4duwYrrnmGrVNo9HA4KBdN2JoaAg/+MEPcPLkSQwMDOD73/8+/uAP/sBqc+ONN+Kv//qvx2vohXHiWAWozlAJBi2rnThpCpLycDe+LGqdMjV7NPjq+PhAY9ElpO0HNKEVRRnSo5Ed3i5PBYfDRWykES/78q0vIgDA17u8LSEPTU6Q+LGEGKFZQkNy2Dbp0WY2L8Ne7MIiS3pa1tiR8JGdIliN7XgIK8E9IqFEw1djpigk0eiW7NC2nFy4yIaWj0L5OUBiGMliqQAwjOPOcDiXDHU3dVdO9yw1Jznfxo3mezdkZxhjGMMwvoZbUUMTr8P3TB8a2SES5CI74wmfR8IFmhzJIz6uCZ1+k51kX/akVwuRkY4exhgAu1Aox2asQow6NmMV1govD/WdjMOtlpa2jcw6X+HlPBQp9FrizMbtt9+O/fv3Y/78+bj99tud7SqVSilc4EFJeDzYsWMHrrnmGpw4cQJnnXUWvvWtb2HZMr144I033oi///u/x+23344rr7wSW7duxec+9zmcPHkSL7zwAs4//3zs378fCxYssLZbsGAB9u/fnzuWo0ePWt/r9TrqdcUl0kdErdiKjCGvyMGZczGncViVek49MjO8tW8GZ7Zx8kjFWha1EiEDyuGR3h2JapyOr5vQDtmvJEXSmyNDP7KytylZ8XluNKEBHvLFvRwSmoERmrPDx8jHbx+DDGuJIImc/Ozbl4zlp3fuLQOydXZ83h0XNLKTd04ewkqrkOBEoYamRTRCwUPPNALmIxpkDNbQwHEMg/Jyis4AU/6Rdq61cRQNd5MYL+8OkZxv40bEiHAXbsAtuNfp4eHLeA6OK8fH95/5Nm70endCIQ3wvMmOXnKAupV25iGdIbWiQg1617EMY8yMk5OdC7AP+3CB+U4TAEmbMPGGJIyYngPZex2RHmrvOmda+GAv53cqhbQ1Gg00Gunki7R1QnDq1AzErYA6PPHUOS8cp06dUj+XKIapn8E1jrj88suxfft2PPTQQ/jd3/1dvPnNb8bOnfoM8vve9z7cfPPNWL16NQYGBnDbbbfhjjvuAABELC+kUrGN/Ha7nVmmYeHChZg9e7Z5fehDH+r+wDyImXcHgJo7I+V/80AS1RyJMEL6vRUlZGcbluHwzNmqWAEJEbSiCHNx3Hh55nZkcM0xmLCBqrWt1g9/yXaATXok8pJHQx469HCTD3X9oWgn/fIZVvthW7XOQSJMoD9k+bapiEH2wU3Q8nB8CeOuvCceNvcEFuJJnK9u3y1iRLgKOwyZWY3tFrFZje0Z49KXhJwHmhHOU3LzQRMC0AxCn8oaJ0D8swZeQ2c9HsgVM3AhUWyz7xP0u/NxcKJTlOzwa6if+ST2PmxJei4/DaRE5nW425CdEAGDb+NGZwgbx1iHdMoXjSd0/KdTrphLVRP4vUtOkvDvG3Fd5p7GEZIzmYeHsNIaD79fkxy+hP+ebgu8cFEC/T6X3ov5/VrzFPWCUNGOyRjO9qEPfciybajGTBG0T1UQt6Kg11TGqVOn8LnPfQ633HILXvKSl2D58uW47bbb8MUvfhFldko+SsLjQa1Ww6WXXoqXv/zl+NCHPoQVK1bg4x//uNp2aGgIn/vc5zA2NoY9e/bg6aefxqJFizAyMoJzzjkHAHDeeedlvDkHDhzIeH007N27F0eOHDGvO++8s/cDdGBW1LRyXGToF9XFIcysx5ZoAYWzJZ+T8LWB2W2LxCQeoCR3h9TcThyr4ErsxHk4bOURScSo4hCGzMPkeYzgXCG/6iI9vro4fF22No5ek8bW8MnW8OGqafSdXi3zmIys9oCt8iP3GyORq5bKRzInhqvBZdsm72SoasSG3iXJidBCDQ1EiK2wKH4OaKsmakyyWx8D4DY+uoUkN4DtzXkIKzGCUSt/h0DERyM/nNCQdDaf+SchgaJYhwdVFTjNQFmHBzMeEy5TnQf5W3Oiw8lOUeKTB3leioTtyTFr/41eEaNqERMKgyJopOXbuNH8N4jY8Fc/UDzB3y9d7LpncPAcLYKchPF5oOW6Ikn1oUTHZ+THqOJabMVqbEcVsVFW4155KT7gmsCS9y0ge3yc9ISMX3rhOIpKZ2s1p6ZSmNudd95p2TZUY6aEjXa7jV/+5V/Gb//2b+OZZ57B8uXLccUVV2DPnj2444478NrXvnaihzjpUYa0FUC73bZcrxoGBgbwohe9CADwla98BbfccgtmzEh45TXXXIONGzdaeTz33HMP1qzJNyxIHvt0gBOauBphLBrGSGMUcTXCrKiJYy1b4YyTE5nXA3Tcr/sqwAUJ6TlxrGLyewZmJyQoPlbB4CEAM5PmM+uxGYckIvydQ8s74fki3c6iyfC0IgU5Zf0ZHq6m5cxouTvcG+IKl3Mpq0nCJY1ELddGHrMvPI8fG/cOacdRRYwG6saQpO1Imno88DCW4yrsAADL25Pk7dghLlp8PxczAGyltCuwGxtxHV4nDPdu83mKhLRp++CV66mNK5eH/6YuL1KEGPfjakRIjrsbAuQTK/ARw9CQnG7Dfjh47g6QEpubcJ8hs8m724inbXyCBOMNrTBltypud+EGc6/g51cjPePlccuD9LzLsOP0vxw5Pbfa+fHV3OKhahKhqmq0fT8U9s4E9CM8Pwlpy78PtOOpO8f/D//wD/j3f/933HvvvXjlK19prdu0aRNuv/12fPGLX8Rv/uZvTtAIJz/Kf5wD733ve3HzzTdj4cKFGB0dxVe+8hXcd9992LBhA4BkVuKZZ54xtXYef/xx/OAHP8DVV1+Nn//85/jYxz6Gn/zkJ/jCF75g+vy93/s9/Kf/9J/wV3/1V7jtttvw7W9/G//2b/9mVNwmE8jD04oiDMdjaNRrqMYdeWiHtDOATF6P+X5B0u5YI1LNk8GZbUN20m31UATCGIYAJDUr9mMOGqihihjn4TCex4hqfNtiBG4CxAlExB5kcjbNHx8fW3N+QDbfRlMq42QIsIlEUouFina6b/A1NMDrPHBoYgLa2OVnPawv673SZj/JGKh3PEJLsQdPYOG4kh0AhuxwyDAXIJ/4uMALf8YIk6Z2gXtauulHXg9SHa2I50leE8kx6teS3I5f793m6RQBN8yLKHMByTUtyQoRoMR7Yx/j63B3LqHhpKco+ZkoQ1jzOHRDJImAhqi3yXtHcSEDPcyYlvP/sfxP++79IWGp/HzxY9VIT55MtXyuSKLpEkbQnkv2fsNU20pMDXz5y1/Ge9/73gzZAYB169bhT/7kT/C///f/LgmPB1OX7o4znnvuObzpTW/C5ZdfjhtuuAFbtmzBhg0bsH79egBJkaenn37atI/jGB/96EexYsUKrF+/HidOnMCDDz6IRYsWmTZr1qzBV77yFXz+85/HS1/6UvzDP/wDvvrVr06aGjwnjlVMeBqFpFXjGLUTJ606NwAsDxB9pu321Bdl+j7WiIwnSJIiEimgNscaEY7GNbSixKMkb/bnYhSjOMsYVmMYQtx5yNJDYgxDZjl/ybh8czyBRoYMF3GFjCXrI9NC5tvw8IkILTRQy4Qx8PybFiKchVEMY0wtSpjdd9WEq1URo46maoTI8Av7XNWcM5ppvk8yxuMYVskOxxXYjSuwGxFiU3+nWzyM5VZxUQ5ey+VhLMdDWGnJVAOJAUQvCUl2fASIkvX5ud+ENT3l9HRLmtbjAUSIlZo8ukfIZwhp13PIuLoxroqEO/ngMrJDPBCSpHCiwv/deQTGFcoWmmNBbfuFCC3MwWEA2j3CH9ZGCAkfdOVYjaex7RZaSe+tvEioBhmCq43XdX3K3JwiYWh8ew6NPPFx0v6zY8ovRnom41RcQetklP+awjk8P/7xj3HTTTc5199888145JFHTuOIph7KOjyTHKezDg8VEE0EBWZkinS61NIAGE8PhaJJaMupgCnfP5eI5mFcfLaK8lfS2eQqLsNePIX5FumRDwabniQPkLk4juc7yeu+GUP+cNXq2ljHJR74MoeH+k3b27k/8niJ4PDjIiJEx5N+Tvpqoo5Gh/CMYNQQIKkKZx+rL48na1Ro50Dz7qR9xOb8XYa9eBSLcQV2Z85fHniYGpBVKJPftZo8svYOFR1M5GmrWIstJuyNHxMZUJzIyPPGiQHV9shDP+v4cA+Ry8OihZp1G/ffb6N2PIxkzRC9Bfd6CUy34WncQ0Rw/X/GE3LWn84rV5fT/tsEbnDX0DQeda2dvOelY0iWa/LmWrtuvDwuVTotNM11j+fj8Y1BtvHlEIXsU7bxeSh7uWZo3JNRtECimzo8n5tzPvCH78vv/KknMbB2xZSrwwMkOeVPPfUUzj9fF/jZt28fLr744ty0i+mM0sNTwiDNm5mhhqsBQCOqY1bURO3ESbUPqepGniKXCMGJYxXj1dHIDmCHyWhGeYSWUflqMLUoGV4j83BI/IAvk5BKZK6HTtaDk+bapGEOdmgdn/VNM3VS44E8MzU00eqQHPKqRIg74W1ZQ4HIUBUxhjsKdpzsaHARNxfZkb+HlF6lz7yfy7DXnL9dWGTGvAuLsAuLnGMD7BlaGaYmyY2LOPDtHsJKK5mZ9lFFnCE7mifIR2I24rrCnh3NIOwW0qjRBA00ItQt0ZhKCdIcd+EGp8EKFA9H49v5yI5rGS13rZO5RrLtAjxnrbdFV9LfVsprhxjS6/BgcK4O94gR5PWWd62FerloQmottlj3aikeIv+v/D4lvSf+cUlyp587TcgmBC5hiH4Q5KlAdkq4EccxqlVPCH0UodU6fQqNUxHT1wdawguN7EStGHU0cKwVIR4cAGAXJtWg1eqR62bWYxPClmmjEhx3yESvxpeMp6ZloZB1clxEgoel+TwkySNzyITiSdIlyVzqibIJkIvs5M3M8j55G3lc0hiSZAeAM2dH1uDRoIWeaZBkiLw7V2GHITEa+PipHYkckNcn6nzmAgaU3M8hDYtQL0+/DRLuNYoQe7091L5bhOQLZK/ViQkvIQWyu3BDX/vtRyiadv8h5JGo55Cv9pnsIz9fhv8fbsJ9Jkz0Ftyrnre8PuU6nsPj8u6E5jNxxTXy0t6Pq7G+85/jExB0frU8I/lsCUG3v7nr2PT6auEeqGmLUzOS2hZ5mKJ1eIBENOuOO+5wCjyUnp18lISnhIHLC0PKbARXnZ5EeU3vg5ZHLVvC2uT2VKNMnlAeKLwNCMsD4GFwruXJe/jfgj+subdDC2OjduQfSf0n2bHK0Lxk22xoHQ8T48toO+6F8VVDd50TjiZq6vmTxkPk+Exkp5/hWz5IssPHmVdsVP4mnLAQAQKSNH5JZnhIWQjRGS/QOb4LN6DuIBc+shNiGMvikdr1QX1pnycC0mB3KRwWQT/zbmQYLcEVYieVyuRyuieEnndJdgDgfOzDs7gAG3C92QOXoK4iq/Yov0tRjg24HjUlH7EbUBhqUkg3DWdbiy0ZYZFeRBmA/v3WeYSOFzbl9/USCk5VwgjPFM7hefOb35zbphQs8KMkPCUApPkzs6Km+U44cawCKJ4aTalNI0dUyyeuRlYuz8yDp4B5MzIeHj20LHmgnofDeAbzIJXM8h7mPMTMlfRP78Vn+bIPe1lfR2tPZEczFLLb5IfFuAylkJj0vHUuzw7gLp7KcRn2Gg/PejxgeXu6VWuTuTwStO5hLDej5oaf9OQQtKrsHNdiqxqyli4bn4dqEdlqICWWt+Bebw2cPM+O7z8RGoaXd31Pdfg8M/3CeNT14SRK896kAiAXALC9PUX2EyPCI1iCgzjHXL81NLEOD6r/pbzzyfNzKHxtM1ZhHfPwAFnRkAhxIRXGEIT+9prMt9yOT8r0M5ytxNTH5z//+YkewpRH+U8qYUBk5xnMw5zOMu6xeaE+D+c0DloKbTL0LStMEBsidTSu4Whcw6wOwTm6IGl7CEMAy9s5F6PYb0ZgG0jPYF7adyAx8XkvXKRJzoj6Zkh9fdB7neXbSM+OFu4hv3PvUyI5LUP5ssXnXMfPl0uSRliBx/EoFgefY/Iluc41kRoq1snFybuVpvaRHQIXLJDEJkJs+qDPvrA3jhipBDXNJJM3x0WGevX2SKKTJ18tvTdSqEAquEny4go16gWnm+z0WqNnvJBnxLomL+T6kD5ciBB3pLeTNkR2XMIjRHZqaKLZKQFQ5PwexDnOdWuxJUNCuPgAB7Wj/+BmrMJi7MMleNaEnPJJFPqfuGvr9CIGkNaFy/s9Qs/VeFyzZ7x36FQFaFXy28UBbUqcsSgJTwmDY40IUesU5nS8OZLQXIQDOFqtZZTbtHwfAidMnPQAyHh0yBh6BvMQwU1MgGzBQa3+g4tQ+IiG9GQUMdB4SJsULKC6OK4wNm32T+s/L3zFBRdhk8upT0l2tO3zHqJaONsLOMcyYHZhkZXDQ4QoRL0thERIUiS34WFvBMrReQgrLZLE8wR4eA736CQ5Pfmkh89Oh0AjNy6ywz1BPrJDv083dX9cXp+QpO/xJj3dGIz9mEV35f91mxPoqq8Sijyyk9eGQERnA643n+l3DBUyoOvF/t+koAK3BPLCHMew9V9qooYhJP/FqJO7Q233McJEHuXnMN+0o/5JnGQMw6hheuQ9TGRobYkSkwWlSlsJACkxGZzZtmrr8PejcQ21EycRtU6hduJkJl8nrs4wqmwATK6OFDZoRZGT7ADSo2F7QqSRpamCaeskZEiczMWh8Llui+8N4ziGcdzEqDc7emtUb4fn1UhlNm3s2RwlnezEZstsATo6f7WO8psWP0/KQrxgqszxya5xJ6HHiLweHClYQLV6QuB7iMu6O9o2nORQe19uj6bWxo04+u4bXzd1eQA4DUUJSV7W4wFDdngYm7yeQkPT+LWg5X6FbM/RbQ0TFyaK7Iw3JDHxqby5lmmiAID7PsnFHbTfJ/Rc0z2HYx0exDo8qAog8P/YEMaMAlsi3zJm/ocL8bQRc4lRtWruJAToAus45bEOY8xa362yWolJgFMAWoGvEtMWJeEpASBVZUu8PLEJb5NoDg6gOTiAsZmDqmdHU2UjsiPzdIaPneh8tkPC6DuXmG4hESiQBTqlwcSXu+DLTTkPh41h8BTmW4QrZPZaEgB+LFzBbRjHnWQsz9tjkw9eFyhruPmMUrkupLigK8zJRYAkobkA+7z7KApX8dFQXIUd5sUhvTscu7DIeHu4xyaZpU6uQlkDxEXOuiVAGojs8KRwIjLcw7MeD3RFVEK3CRWkGI/QnVCPA0e/BQfyPK9SBtmfR9dfMiYnjVyFLoGsuEMVscnfiRGpuZDa/tbhQfMuifUtuFcNWbsfV+NabEUTdcuzSt6cTViDPbjYeGi4J8yW4w4ryJkfHhip/2Ftu5BCraEo8vtnp6Gyk1VnLEIJzxke2VfCj5LwlAAAIyhAoPo0rSgyYWtEVqSSWlyN0Khnpam5V0cjUKMzz3LOFocQF8LZ8WHruyRDEvTA14jMfswRY6oGJeUDCVnikMIEQGpUcHW5C3EwM37fuJPPvIaP/Z73oHM9EH3S1TK8LQ8RYlVu2lW8sN8Iye/R4BIy4FiKPZn6PUBy/rlRxmm4L0nalWCdB2k8kmeHJ4UDtihBhNhIaYf+Fq7rSPO4TgZMNOkB8g1Vl9fF1ZfszzfJ4e/L76XJm4Tx9cWxHg+YY+PXtTxeCmFN+kuPM0KrM4EQ4xedwtCEa7EVNTQsT6b8z1FO3VpsMfdxfryue6w81/z+SCGpmqd3PNAvslvW3ylRIkFJeEoASDw8R+Ma4mqEPfVFqMbpg6kRJWED1ThG7cRJ1BtNxKhiZj02Xp5qHJtQOAp14yTnEIa8YWwSIUYLbf9CNC9jvLsMfmno04OPt9e8F679832QoILmOaHZUE2y+hnMyyUdfCwu8uFOcM4SIH7s9ErnRu3Sony/PgNNnn8qJkpGzS4swgo87j2+orgSO7vazhXylidXreFabDWGEJDKVruMIun5IW8QnTstvExTaONhbvTSFLR4LR6+XcgMPd8uDzT+buv59GtWvN99dYtw70J3Y+Wkh8LPXOu1fbnGJu8DRc4lvwZC6lFR+CoX/SBCQcTlLIwCSCcWeN4OkFz/vD4WeYVSryt5nG3SQ8u0MGAXnsBCbMYqc43TOF2enm7hG4s2WTUtvDg+lCFtJQJQEp4SRpKasDBOcy6qcYzhRifWuRWjOTiAqHUK52JU1OAROSadnB/A9gi5wq40hDxsXaRGfqeXFpblCluLEGMYY96HiTRW5Kw53592LN3OktuhY3rOjgZO9DhcRfiyBEt/YtA5Woo9oFA28vC02LrJgm49QCEgAuQCX0dGGQCjJKUpSrlyeCh8jV41NC1y5CJI3BsUMgMcEqbmakOz/fJ/1M/QHw3j3T9hovI+uEdXK0oqc1NckJMwefcj3+QHXyaL8spaVtzrya93/p+QnlFJekhAJEbVhMH5hAiKEhx5rJQXNIQxVBFnxqeJ5vSK/LDHaU52gCRU7WTAqyQ80xol4SmBqHXKytvh5IU+cwGCwZlts4625W1lXZ65OA7A9uwUuUlrhktemI1GbHxhWdLbw5fJdtr46bucNecEo4FaZltXSJmrH+24Q5NtXcnhPGQvz5PTTZjFCjyOR7Ck8HanAw9jeealgctVP4SVwfLVGnyha/z3zQtx48QlNByOk571eMB4hnykJ0YU5LXhbST5of5vwn2mmCXhdBGTPHRDXHxy8KcbMiRMQltWZLJEW0/fzxe5eVo/nMhci604jmE11DMNRbO9pPSZe1IJSVFkf+hoCIoKF8hrdwzDznC57scU9t8IuX+XKDGdURKeEpbQgMy1SQjNKRxr2Hk6FP7WHBxQ+mM3+wN+3XstpjzvYcFjskPhewDQA4ITDz6GPAEE2u5CHDRtL8RB85Kzp5F4SGrHzAUbQmb38mYstSJ2mqHpnrnVDYEYEZodIgckYWvPYYHV5hEssULZeNy+BkmOXCSk3/ARcR7qRp/zSA+fhabQFxcorI17d6hyvEtBjZMUGjd5bHiYm7adVtPHtY4gSYzvfLnksAEEF67sFzQy5SNY/SQu40WCpBeHQ94LbLVH+z/smrghhHh+IsQ4gAWFjWy6njZjVcbrCcAKcSNIEQNCDc2uJmN8yCM/2gRSv2Sus6p8xYjotPP6tJF4eUJeJaYtSsJTwuBYIzJEBkhyd+LqDEOIeF4P9+bMrCcvTnRIevrQ/EE8jxHzMDq3E48N6DdlaYTLh0rejdwV35xHGlIa4vak5OEZzEMViQjBM5hnFUkFYPbAVdg0MnURDgTvk2cjacuz7VuZHB3XrKDvgS+JDscCPCe+28eTJzst83zGM/xMU2kjgpXn7cnL95E5PL5QN57fECPCQcwzBAiA6onhZEa248tdhEn2lxfa5vLy0DUgr58GakYOO0/2Wnp9xgs+GfuiyKuvk0d2uiFD2v8xzxiW9cqSnEJSN/Nv6wvDDQlr42NegOfM2Lmnh2MttqCJmgl546Fvm7HKIhQ8T4cQGq6mn0df3aKW8SL5QPt15e/1g5SFEEuZr1eiRImS8JQQSMLUkvC24cZYR6HtFOJqhFlR06i3Ebnhym5EclpRlAljA4Dhxhj2Y05neRihcIW7uHNJsio72mf6bufsZIt6hpImDk50iARpRI73SetiRHgK851j1mbyXOOS6zQxAtmvn+QkPUiio9XO4V6cg5iHXVgUXOvFB5Kh3oZl6mcOX5gXD2G7H1dnvDUPYzliRGr42mpsLyxuYCtVpedWEiAiOfNw0PL60Ot+XG1Ijfa787aAv6iozOOh34e2cRHaUNSZSlyMyCI18rPP69OP8JzJEjYn0Y/aLzKsLq8/+v/X0bDa8kkl7R7BlddcpEd6Afn1/QLmGbEB8l4SeCjaEMZwHMNWP9o9lHtNXZLRoc8JWubDUKdujwtcJELzQtF4s5IQYflE6X7y81aB/vxvpgxKWeoSASgJzzTH0bhmFQsFUhJDpCZqpW2JyEhpaolDGMJcHLc8OqP1ka7GOB71OoqgX+EB/AHEiQf36PA23PDwzaZqyzWy4+8jzOiS+UYRYjyBhXgCC02bpdiDCDF2YRF2YRFewDk9n0MiNVdip3mn5fwzR0h18auww7RroG6IzJgwuHrJ2QFso5Q8PZRzwJXaAFum2mfISONSenekdC/hESwx+6D2pKrFSY9WmNaFkAkB6pvIDuXzuMgOHedkD89Jc+Bs3cOi6JX05G2fF9qnye/zz+Th0yZLaJn0AibXcDWTd0PeGpcogbz2NC8V/SeJPLnOf5Hz6ldH8xOTvN8gL7+oWwJUAqVKW4kglIRnmiNqxSZkjULTZP2cgdltk9tD6+bieBKy1vH4HFJqejyPETyPEZyLUa+npdB4HUa7Dy6PT7eJ+a5ip7TOPQ573UU4gCriTOibbxu5jofjyeUcmpCDKxwuu58qjmPYmvGvo6n+FqTERiptIxjFWmzBPBzMDa/ghOVhLMcjWGLyebj8tJSiltv5QOuvwg5zLFRkdC22GCNqGGOIEBtvTlGPzkNYqYbk0Dt9JnUpzRtF4+MkhsiZPO+agAEZmfSdvG4HOwSUvEdcwICTpHV4MEOqfGptrv/TejyA9XjAFEMFUsN4A65HhBgtQe758U8UeiUwvewzBEWMYk5s6CzTeeaeE1+eTt44fffmEFENTgi0c+C6t8pQN8AOoy1eq6g3whEhqcelFVk+XSFtJUqUyKIkPCUApDLSxxpJkdFjjeThQnk9UmKaEx8iO+T9kZ8BYN6RIzgPh7syYkIV2gB3bDY3pvLydLQHOX/Yajk2nPiEeqQ40XEVGpV5P0CW0MiMHN8MbIQWaiyUJU1+1j1AFMKmteEy0zEi6/ujWIxdWGSKqj6H+cbgfgbzLGECjdRwA+YRLMmEr2khbCE1eWQ+EBEcHrpG57aX3CH+e3LhAjL8QupscBApolA28gi55Ks5aAacwg7l70ykJwljq5vPWgiiK49HEmnXNuTdkbWBXP3x76fL0ItRVQ1uV85OyP+9aDida/9p6Kt9/XRTN0e7/xE5pfVzWDFlm4ympEBOItFLXou+a14P8so/Dleb5zC/Z+IiUYSMbsYq7MMFPavGlQhEGdJWIgAl4Znm4IVC6ftYPQnn4cIEtRMnTZskDC69c8zFcYv4xKiaz3OO/ALPYwQDs9s4caziNVrIOAayXhQzPq/HI+zhdgmexSV4Vl3HiQH/TmMCYOXYaAh5UNOxUp/ag55/5vHjtjenqp6TrCfHLrHKPTQyjE7rm5PFFzAPFMpG23BVOSAVJngUi/EoFmMFHjfLLsRBS5iAPksixCHJjMvjI8fBwcUI8jxBPrITEt6mVWNPiiC2mKFYzYTdheZjERlx1fSR8M288/A2Ireu3J+Qejza/jbiOnVsZGDX0TR9czIkr89+k54i+YHaum69LOMNLVSNwO+p/BzfhPuwDcus0LXDmOO8PxBckyW617IYAQkhQK7l9oSO5pf3v7qBb6xFatDlT8rpOZjTFmVIW4kAlIGi0xiHMJS5AFpRlBQVrdZMnZ3BmW0crdbsAqLVCNU4xlHUMlLWw40xNOqJos3A7DbORRLedu7MNtB5gPqIi2/GNO/hK9uSj4Rv9yTO926nQXpOuNIafW8hyggTSND6ZzDP6dU5D4fxTIdUnNeZYeUkq4aGOS4f6JEpwz30h7EUdEi9RjIU7hwchPToaODruTT1fswxx8WRyHgn4IQoDW2zj5eIzsNYjqs6nznJ4OFrUnnNZ0jkeXbywtt41Xf6jbgKmyyy2C0oLA0A1mELNuI6K/eBvsscIQ0RYjO+rIcnPVeahyfU2HLlVFGfpOiWV/Mn7/7RC27BvbgLN+Tsv5X73+PQyIfrHpe3Pg8yHLCIJHdy3udZ/WjwhQXz6yzvmusWq7E9M+mgkbp0jC21TSi6+S30e3ty3TRRM/9T/r/j58pVaNjub5qTnBIlCqL08ExjzMXxTMHR4WMnrBweyu9pRZGRpSbiw70+HLxeD5CQHf4O2N4cgi+XRUJ6IgD3zCwtJwPe5d2R/fJtCDxsrSWMirwHqsv4oPwF2h+RIfqc51GSIE8OkJAjOT5eBJX2uRcvZsdM5zMlOpdhr3UeiMzswqLcujpAVmq6KJqom8+u0LWQoqGnA1XEJk8nQgu7sMgYNTLEpV+5bdzIbKKG+3E11uMBr3ADreNExxYtyJcu7sXo2ojrrHo96/GAOY48T1I/Q9yqiHEL7s0sC0GoJ0Am4eeFbCXTDb0VNQ05Bu7B1ZYXRbdkvgipCPk/uzzl3cDnWdLW5R0LXQv8vylrEfG6WCHiK9MeZUhbiQBU2u12e6IHUcKNo0ePYvbs2Thy5AhmzZrV9/6PNSKmxtYxutn3uBoZglONY4v4zIqaOBpnPTyk0HYIQ4hRxbkYNWQnRmRm9zWCE/LgSw11nehcgmfxJM5HhEQB7SnMx0U4gCewkJGfbJy+O3bf/cCUpEeu4yTDNQvZzUNTngNaFiHGSEckQvM45RkyWanWZJuksJ9unNN+8urrcHCPRx4ewRJrXKMYwQhG0UStkJiAixDxYykqTuADiSFwbMYq1TsQamDKmV8yVDV5a5phjxEZSWvXrLs/VDS7jq4H30y0q0+NzNyPq9FELcjDE7KPblB0Jp/fL3ye3RBj23d/0PYL6J6LIh5wau8656cjbIrfb/POk+9+2f3+w4he6LVBE1gSJMed7CP5/fgkCK2XeVHc01o0uoHvYyqhqM3zlre8BZ8bWwj82gfyO9//JAbesRTNZrgCZYkzB6WHZxrjEIYy5Ebm8xCZIZJTjVOP0CEMIWolqm6HMGReUrDgeYxgrJPTEyHGfszBfhEbHgLpbcmLtSayEyPqECB9xpSirkNnUbXcIm09fQaKhZb0Gs7C++djzStompdIT8prS7HHkJsrsNscB3l6Qjw+/EEc0j5CC1diJ67ETqzFFlyJnYVkk305OyRKESFW24XMVJPoARc/aDCvFK/L0YviF/fCyHAYmcOTCiREZpsis+6k2Mbrr3CQMeYKu5Hb8IR4bmBvxHXmOLjH53SjitjUDgqFJlzQjUHebYK+azuZe6eBllMoIQDMwWHn7z0e4PecvPsenzxy5Xj60Es+WJ43TnvnoP9/E3UkoW3JmOn/GCJu4Mrr4cu5134qkp2uUebwlAhASXimMeqN5OFOHhwuREAg8YFZUdO0ewbzTHFRqtlTjWNDdIgA8YfZRTiQkacG9NC2XvEEFiJGFU/ifOP14JAhbaRa5gIl5csHqxbuVpSs8IfjhTgYNDssz2GEFoYxhjk4jGGMYRQj5sVDwYqExl2Gvaz/ZAxUV2cXFlniApz8dBOz7/MM0X60kDgKbQsxFlx5ObScDF2tXYjhoHmG6mgY8qNVXqfcmqQmT5ojlWeIkQIW9+DwPjmBABJvDBeukDguJOXX4UHEiMy1wwkWkRYiUbzIKb1zo4sf0yasMWFsnNBIsQINtN+Qtr3AJ3rRLbqZwCCvMn/l9ZMXekjredFXTkDX4wGMYsT83vL+5vIq8Gutm6R/SXp891FfGJmPBEmiU4TsUFvar/ZflmOyVTXTX5HCRKkGUd55yivWPF65bFMOMYCTAa+S8ExrlIRnmoKHslXj2CizkfemFUUmfK0ax4b4tKLIKIZJqWpCI6pnvAVUk0c+aJ7BPMw7dgg/wyLvA11LqufvcrntwbG3lYY/Dw+TUqZSfrWFSNnGzomR4MclCV6SqJ8QHQq94/1rJIsfZw0No7gWI8KFHUEBIno8h6eJ7O/Cwc0FXkg0XZ/+drRP6Z1Ziy2GwPgkpEPhyv3hfWoGiAZOZuhznlqbhI9cSdLD6/fwmVwqPMq9MmuxJTdenxtq3Jsjw1d4LgzB1+8Qjlt9b8IaDGEM6/Cgdbw1NFSPDld0W4cHncZkjMgiLRJUD8gVYsW9PxtxHWJElvFeBLz4KYf08NyCezP5PQRp2Gt5fXwdkHr9iuRFSQO93pmgyfMKafuooWmdR170lf+u9DvyPrIy07aXIlmW3l96rV3Uradb275fxKCKpOBptx451/3XJfOdV7usn7lsJUqc6SgJzzQFCQskSmxpguzR2FZja0VRJkeH41gjmYtsRRGexwgoZwdIH3jnYhTnYhRzjvwis/0YhnFw5lxcnKP4lQ1HsOV9+fL8QprZh59GnPgDyP6ckg6eE+SSiAZSY0jLW+IEjH/mM+V6OJ8d091AzVKg0zxB3HPjgybLTaCwtgZqiBBjFxapXppRjJgwtH6jH32OYRhXYUeheju+kCeesyPD24hwyGKgBPpO5EczekOMNi5eIJfniXvQftbhQVyLrR2Z6tRDyD8TuMgBT7SuoZEhN3n5OS4ZbOnVoT66ITs34T4n2eH7INyFG3AXbnCSHh/kLH8LEeqdGX7N86xB3veq5m7kN3R9pJPW03nk+SL00mTJpTfRBT5xVKSIar/r5mT30R3pyduum9wvQhN1a8JG7ivPw0OQz4Jp5/lpI/Hy5L1OTdQAS0wGlIRnmoIECCgkLQ/c2DFx61FkiBN/uJFAwVwcN+SHavEQzsNhnIfDwQZ4so+sAc/fw/rIf6hm9xObbWUf/HMqO1oPfuBoYWYXWVXC+SwlJ3Z2knQDNRzHMEiCmxtKvObGU5gfPCMoSQ/9ViQTXUcTDdSc8tQur0JRjw/38mgemSJeJE5w1mILHsZy3I+rncTH5dFxhe5pyx/CyqBQP056NOT9bppXhyuxaf8TqdTm6isEPKeHJ2nTOkl2NGOOlhHR0XKPiJRswPXGQyGJjEZmQgjSBlyvenXypKpdSKdBsudekp68fJuquQ9F1vZFZvntbA97Wwo3JO/ZRlxnhSzy8WoFjPVjLE5geiE9cluXhzy7XTGSIAsL57XJ7i8ZJ4mJaGNzeXfyfuuJyH+bcJQ5PCUCUBKeEqamDgArjI2jhobJ+aEHW73RNESHe3MIFMamyVLvxxw8g3kmdErL5fkFa5/s135A++SlM8foDVWzlc7y+gld10Q9Q35kcVUOIjrkpbkEz6qzpRFiK/SN5nz5g9CXl+QiUYRL8CwixCbBlrALiwCgc1RNLMUe1NE0yzXSIUPeHsViyzsTIljAUcQbE9ofER8N0pjhZAnISuTW0VDzCLi4QEjsPtA/Vay12GKJJiR9t8x43HLu4fvn4W+Up0D7IsNNGtaawps08ui7JGTr8YAhNqGeHh6+xcH7uQn3OduFeCGKShiHeUzSCYtutve1D/mNJVFdiy3Oc9BrGJtEUe9JuEcpm+hP+wsRReC/5WasyrTJz7dqmW1dYyPiqa33QebIlShRIkFJeKYpaidOYi6OmzwcUmnTQPk7Y/Vh60FHxUU5nhckhQgQhbXFiIx3BwCGO/lAmqfjrM62LnUxKTPtgiQ0MiyNfy/u/XCRiqy8M+CvNUTngMbgKpAaIzLqc9q+KNyNH8tFOIA9uJgdbfIi4/QSPIu9eDEuwbN4CvPRRF313FBSN+2bt5FhZhoBaoiwKC0UjgsihKAfYXNXYQfux9WZAqUh4KFrmloXz+0hwz1GNahGTjcx+lKtjY+Fg65dIiZaAUTNE6CBwt/W4UH8giW+y3fqhxtkRGo24Hqv6hvHRlyXISYbcD3msQkFjQjxbbTtaZlc163XwUXGuMLgHBxWzy0XsqB37XfIhrv6fyuXsU+/i/x9aBt6T0UNpKxC/0LSiqi3Fe87rL/Q3EAN3db+cREc7fcC7AmCaZ3PU9bhKRGAkvBMU8TVGZboABcwIMEC09bxIItRzUhQR2gZkjPcGDMEiKSo5WeC9uiUONzZJiUnNtkJmXX0xTrzZP9QT0/eA56TD1mroyrGAshwtqRvCiXLErcwA6OKGM9gHi7FT42HaTcuZWO0k94vwgHU0GAErGXOyQo8jqXYY53HpdhjvDz0TpCExkVMtmGZRXQobG4blmWMd1f4Wq8CCUS+Q8nOw1hukZnV2G5dv1y0gINECwBObOyQoF4q1PO+yKM0hmFjwF2LrZYxp0lau/v2G9JAauRzLw6Bh0gBCRGiWe6bcJ+6XuLLuF2dwb4J92U8gJoXSJIZnhMU4jHyeRGkce7yFjWZGtxhcS9M9pG9N/DlPm9PaHiWPIdceGIT1ph98DwqnyhFr9Amo/qBbmWsuffFtU0IKXKRFYJ2PZESotyGltNvxT3kpK5YokQJHWXh0UmO8So8eqyR3BgpB2cujmM/5pgZfwKFUvGHDw9bOyQkbWmb7LIIe7EQCzvGu6/46GHMMd4dIK2nw/vy7YvGK99jRKihYT7L/nyJ4mkeT1iyMR+L7JNQRYwLcdDy1vD9uI6vyH6k7PUzmGdID51TTrKexPkmMV8q0u3Fi42B/giWmNwaIjmuXB4f+ANbGhU8d8dVqHQblhkSxT8XhY/khITRkYdnNbZbYW70nY5N5rbI74Bf6YrWuQqISlA77s15CCvRQD3Th2wrxyLhmhyQSe+uz/y7liTPQYVJAXeOwkZch/l4zqns1y3uwg2Z8+AiPd0WxZS5PBza/SkW/81u+tNAeVPz8AIO4hxVuMB1TRYFPy5tTDxfyFfYtVt0U/BZbrMa283/twqbjMZmqsh+3kRw5XRVjay8D/I60MRApnJIW1eFRw8sBG79QH7nzz+JgT8rC49OV5QenmmIo3HNKjJKstM8Od+V40Kg3Bzy8nBPDw9j4++c7DyDeSrZuRAHMQeHcREOGENcyjVfcGy/elz+wqKRui757g8FkCSkCLK5Q+msHUlR2+39+ygi8iDJzn7MwYU4aGbNL8IB4/0BEvJJQgQX4mDmAb4QT5ucKyJFu7AoQ3R2YVGQp4XITgN1Zy0e8vSMMALMcSV24n5c3RPZ6QU8nI2+r8Z2NFC3vDvkVZHkRovhD6llUoTsAOl/+H5cjdXYbtZxEpnM6ofnYGihVrImT7Jv9zXNyQ4PZ9PyRjRJa+5BWY8H+kp2SKENAG7D98zybshOkeKY8rPv/tONt0ULjZNenBV4XCWgPH+nlxA2Isuh45eFnPsBV25OaDgaTWaEtueeHioRIM9hQiiz58VHdilnZyqTnJ5RhrSVCEBJeKYheK0d/j1CC8cxXKivCC0cwpDx9MSoWiIFMSLsxxxQ7g6QhLS5HnbPYJ55EHGi8xTmYw8uBgDsm3metX85Ho7LsFedsdZCQ6QBJ9fL/UijNHmI68ZqEcPAR860+jj2uJJq8VXEpuAkkZ3HcDkA4HI8BiDx5vwUl6KBmiFePxMhaVUkAgmX4FlLJIKTHBnGthR7gsjHFdiNK7DbtF2Bx43Byt9X4HGrP+6N2YZlTjLUL/D9hYS7PYSVlqhBC5FFbLqN7wds5bXQ9vR+LbYiRmTV/klr+bQy3l1aXmQ/1B+RF6qtQ9CMaAol5IQpQmydsy/jdnW/N+G+jIJa0RwwCU50iNjfhRtySagL3FjPC6nKExXgXpE0D69p2vn69uUJcYTkUGkS5T748lA0cO8OIUQZrSh85yvUA6SFrQL2MeTlwfGyB3J7ufwC7DOeHS7VnucZKlFiuqMMaZvkGM+Qtpn1OFN3J8Qw5w8hyvnxbUtkh/a5X+TiSGjqZWSQu7bRRAL0B3xYuA5vKz001G8SHhdZ6zWiYxsp2fo2/Yi7lsfKi5FqaKBmyKTMpwJSLxyQDW2LUTWepvtxNebg5z3NrD+KxbgCu42h6is2qhGpfnh3OJGR104DNaeBJVXaCBTqUkWcMdR4iJsP0iNUdDZdI0a8Tg95VqSCm64cld03JzlEWsY6EyZy39KIlnljgF3HSEIqT8nvG3B910VINXQjQ+0isSFGtW9GH8gawi7hAl++j++eM4LR3NBN6RHUvIya8EWeZ8pH8rjC4HihG89ckQkLCmsLgUu0QIP8P2jLpyK6CmnbtxC4+QP5nb/wJAb+sgxpm64oPTzTEEfjGmbWY5w4VgGQkJa5OJ6Roo7QQj1umM/8nd/AuVy1nAWlcLb9mIPR+ojx9iTtY5NPoslSE7RcEx9otppLM2s5Ma4ZW62YKBc04G1cx22vSw0UPpMXOttJILlo7QUkD+E6mhjGcZXsnddRg4oQY7iTs/UU5hvPGwcnQdwok1Lga7EFK/B4V7Pqj2KxRXa4h4dC2QiPYImT7MSIsA3LCimruSATv6/CDkNSNGgGOi3TjCJNwpbgm7WWQgfcm0g1dugzgRudsiApT0rnYwsdj8z/GcOwRXa4EEKoAMNqbHd6F6QR5zLqNuD6rmvmcPA6PP1UH5Noiest3WeWBOihTi12v2tCEokkfKrm9bDEiFThBA0uFb1z8AIAnQTxgqau43NhPMhOnjgOwUdU8yYsuDevl7A/X84or5sEpCp70xJlSFuJAJSEZ5rhaFxDI0rCEeKq/fNzTw+B2gI62aHio/KmTjLU3JvDH7ZcmhpIQrUuxEH8DItMGJXEU5jvzd/hpIO8G0kxztSoaKJm4qfTEbnDz3wP5tCHdjb0LcSLZhs3MaqWTDV/YBPJqaGJ4Xgs0w+ByCaXB6eQHf57aASIkBQubWVC67r18FDuzgo8bhEWTn5cIM/OVdhh3vPwMJarxEgrPNpChIewEpuxKiNGIEGKbER2aB8uj4UGX34PJw7342o1/Iy20Yi39LjQdbEZq0x4kvQo8c+yTz4WCrNzF5/MihpIUH8u4YK8MCt+nXcbLihBBUhDpO9d8HkIaJy8Db9HuiZEeE4PjYmKm9Y6dzgX+eGQy4qERPHffS22mP8xJ+U8/JKuX1eoMJ8Es5dXC4ev5bUPzQPKu47y+omRFqHOElg9JDrvswt03kLCEUuUmK4oCc80w6yoieHGGA5hyCi0taIIhzBkPdCJGOj5L1nDJlVCS16yHs+FOGi9CKQaNowxPIN51sNKPnBiRFb+jgvpg6ZqHvz0QBgy+QKtwh4WGoMPmuclXafX7pE5Q1peER1XStH0XACNoHIiQ3k8EWI8j5EMueHf6beSEtoaSfRB5vgQpFABEY5QbxGX9nVBm3F2ESNJaOharCL2kh0NdJ34kprlcpf0u7ac/0/yZsE5OeGGKo2RyNNmrMK12Ioq4kJeDZK+doXEkZQxv7alIVxHwzLWfPV4tOXdzmzz2jtAEsrGDf+7cANuwb24Dd/Lvea7kT8OKUgq83ZkuxhVq76V6x6keXlkW3luN2OV+Y34mLRwSZdyoFymhbO5vBg0hiKg9t0QVM1r3gt8x9Wt55CkqTlIkrof4dFTEjGAk4GvEtMWZQ7PJMd45PA8jxGci1FVUhpIbsT0cE9Ij27g+nJ2tM8uuHJNNIPBl8Pjy4/h6/l3GeLmUlSTJITXpuHLQ8dLbetomt5c0I5BzmjbhEn3JkVoWWp6z2MEYxjKeNP2Y44qG87lrC/CATyJ8zMhbr1gG5YhQivXs0NG+zYswxiG+pbAnEdqinhrQvrj/VK+D5Bc99zb4ssRk7gWW50S3hxEUDRjks4n9bMJawwpcsnA03ayXxoPgYziJmpYjwcyuR6k1kZwSVZry0KO27d9HvoRKkdw5e74ID3STUFyNMh7sbyf2eG22XuZDJEsIphB2/iQd/9M19nXWBGEbOP6PWh8Ibk88rnDw9lCiB1vF/qd+uPS1FM9rK2rHJ6nFgLrP5Df+cEnMfDXZQ7PdEVJeCY5+k14uJoaoCfxc8NfIwkccpuQG7SPBFFdGg3y4Wg/zLNj08ZJ/dhhIRFqHeLh2qeL8LiOMa8PAJmEdu0Y8/rnD+o8ssMfvrxvEijQ8qiewTyMYRiXYa8hP758qyewEHuxsLAxyfEwlgeFp40nOAEBipMdDiI+/LeWfXNyJEUOQg08XqNHg8zjAWDIDBVKpXbcA0OfQwiY9DzR+ENmsvNyFgg1NNBEvadrzAefAIKL9BSt6cL/t2Ssuv7vBFrPyWcRwpB4vmvW/UgjPBHijIIfwTW5wOtyAel100sBXT72XuD6D2m1lUKFJ/hzTyNLnCi56tLJbbT12nf++SbcZ4hOjKivwh0Tha4Iz56FwA0fyO/80JMY+ERJeKYrypC2aYSjcTYESMqL0oOxHjfUm+5cHM8YPNIzYvfnDvHS4CI7EpLs0DuXnU4f5HK8MtTMTcI0IyRGUkeB4rO1scnQEW05KaXRd5fUqxbixkNhqMd63DDCE/yYk5ydqjUePg5eh4feObkhRTYf0SFchr09G6L9JDvSyxIqbHAttqKFyMrL6XafWh887IlyhGg5H0MRSNECAic6nLxsxirU0MC12IoG6ngIK72GKh9P3ZNDxNtLssND6ei7fQxZo1GCyA7ti4ekcfCitj7w7YuSHV/iu5Ycz8UQCDJ3Ju+ead/P8u+pPJTWde+zSRBvn3pWfJBe2VBPkGyn30/1ZwvVtspDkQmDBupqOKI79LDKxAnse37LnFM5Sah73Tjk78QlqPmeSinqDkJFC3rjziWmOErCM43QitLZJq4hQ+APukaUhLLxEKgaGjga15B6c7KF0ySkJ4UvCzGgCa4HoS+sLhYPHPkgJyW3RM1NhoUlrxoaGMJYJglYMzRsY02aO1lSFqOKJ7DQuV727Ysrj9BCK4owK2oackPLqR4SP4/yQUv98xA2Ip+chEoPXNHcFiAxRGWeTkjeTkgxUwlJNIqQqaKEg84F3ycVJuU5Llq/XL2NrgGeG+MaC63jbcjA0zw6Mt+GxrgWW9BAPVOQVBqktF3DU4eFk6VrsdUy+HxJ1ZoxzsGLmm7CGlyLrdiENSpB2YjrsA8XBBmEtH2/pa0lqoixAdc7CZKcEHEZw0lbt9eAgzwQ9CLvkEam+AQR9+bR97z/Q5HwNbkNHes6PKiSJe1+ysMmtfV5zygpGMAnnjiR0SA96by9tl2emIfvtwb0OjtTPXTtTEaj0cDKlStRqVSwfft2a93TTz+NW2+9FTNnzsQ555yDd77znaXH6TShJDzTCNyoJpDstOvhkBjL6XJNyU2CJ75rXiJaxo1roFjRPAphowfJZdjLjPnsA07KQ0tyY5OfrOKbLUMdZ/rUYuI5SASCj18/zmS/VDxUm1nUzhM9pEksgmZuNS9UdgYyadtALfPA1jxuPOenm1CvFqLMjDCXtpbvhF5r7Yw3XBLVq7E9Q3ak8cj/Y9Lg44ad3G4zVlmkkzwqiSKWLpPOx3AttppxS5IjiRN5hbRxy31o+wVSAQMSnPDlhWh5O/K7zPnZiOvMGEO8ytyQDCE7t+Be5/8yFJr3RqM6cptukTW+0334jGx+Lfqk2QFbGMOFvP0RNmGNt90CPGfGVET2nOMC7AMp22X9MynxIyJDsHN2bIpTFNr50CYGAZvc8GKjJTqYZLLU73nPe3DBBRdklsdxjNe85jU4duwYNm/ejK985Sv4xje+gT/8wz88PQOb5igJzzQFGSmNqO40vgFkbuiutueyavckf5w3S0nQZsS08Cvav+YNISUwe5usMUYyzvw9q0BmGxzUV7q+ZbxCGmJEgiC1MIQxQZT8D8gGaqa2SYPV0SCywc+Zy3vjOv+0LR13LPq6CAcsUuOSCO8W/Hxzrw2dYyJDvCYPteWv8cB49Qu4VeAAmyQQWZHhRJL40HIiLCGhO9JI34Q1TkOW+udGL68HRHB5gfh32o5ks3nOHJEeeZ1q3iBJcGTb9XgA12Ir1uMBU/tFMwxlocYQ43EYx53hcz74JkN8pCwkvI/3IcPTCPw3r3ba6KFy2TBhjhF2j9fQRM1JXlPpcr/FyX/bGJG5Lvj7UuwBkO9R4qD/Et3vSWyFvufdkyXp6YX0FokUIEhSz9sSAToT8ne6xiQKafvud7+Le+65Bx/5yEcy6+655x7s3LkTX/rSl/Cyl70Mr3rVq/DRj34Uf/d3f4ejR4+O/+CmOUrCM43BvSRaPQBS9aK8HVouc02I7JyLUePdCZmRdBEd7bsvLEGGZskHOk/W10IOxjq1eiRxoG1dXpI8IifDBuV5znqS9Lsxjf8SPIsncT7Ic3MRDpjjIFLU7Pip+CwhDyvUQKGFF+EAIsQZMvMU5psXwVUEVtbnIbjyKbjXxuXBoRo9V2Kn9QoBr7vzMJbnhuCNpxdJq+UjSQ+9JKmRbbVl9Fm7jsg45MIEQEIg+DIKwXNtT1iLLagjzfOjfYeJE2TrXN2Pq433R0OE2CI7RGb4cbjC5Xifm7AGG3GdMRL5uyQz8ns3uWnSo+wal2/7EE+VbM//6/xeFyHGPEcosdyH9PhJGXmtrbacF6LlyLt/8t+U3qWqH72HhgKSx9V1H3B5bGS+pLYuHZcuL2/vJ+w3DWlT1t6ZPHjuuefw1re+Ff/4j/+I4eHhzPrvf//7eMlLXmJ5f2688UY0Gg1s3VoshLpEcYxP+egSkxKaq1y7OZ+HwyY0iurzaCFdHCR1zREhNgpgEj7J6byZRg5Jvojs2Aa/jNVOjmcMQ7gEzxrvEJEn7s2R8CvBuRWmfA8u6RHi++LHzwuPRmgZeWhJUKrKw5R7cagNgffzC4zgrM7vyH+HIvLTT2ChETpIx5vsLzSRXELm3pAnJo+gyO0ixF4VOClqEJLz8xBWGtJAn3k+z0NYiQgxrsIOZwggl3QGbIWzIrlEWvFQ6ZWRRUM3YY35ffj4+L41g5a3TfraKr5vwTfxarwOd5v+gPS6HsYYxjBsKcLR/0DuTxIbXk8oBBT+JkmLPVtuf79J5EgkSmpuaKTJhW5D1IoQHyCd1OLLpIfBRQ40kquBrpMhjKnXq8wl00iLhIvALsBzTD0uezwyL0YbKzpb5cGVd6ORSqm4GUL89X2O73VxxiJGWI2dzk8qvSn1eh31ujsvMQTtdht33HEH3va2t+HlL3859uzZk2mzf/9+LFiwwFp29tlno1arYf9+vah6if6h9PBME+zHHPPZNQNGy4nsxMzh7xMHABLvzvMYsfbTDdmxl1Vz23DvhQzp4A99rW8SDZD7S8Ph8h+Kcmxym25m8fjsnzbbyH8XIjtaPQg6F5SbQ+AkkaLXqR8iO09hviE5EWJn8dDssbQM2eHndin2YBcW4Qrsds4UhwgXPISVlrenKK7CjlwSQ21iRE6PB18uCQ6BPq/GdrNPLQ+G59RQv1TsVBqPISIRXChAI0tEFMgApUTxvNA0wB1G5ApzI7JD/dEruS7rpk+ey6MZmrx/LbGdPEQaONFxXcdcBUtLBg+tccL/u74QoyL3Ft53L+D75OcvxNtA4Yjy96fr0Seuwa8tys+RXnm5f+7J43gOC9h2WgFsd5gxEK7sJmGTmezEoT2GsLgp1zO4xPgijmPMnj3ben3oQx9ytv/ABz6ASqXiff3whz/EJz/5SRw9ehR33nmnd/+VSiWzrN1uq8tL9BdlHZ5Jjn7W4eFkxAfuFcnL7yHjeaQxipn12NqPdvPOC2Gz12WLg2bbJNtTbQ4gCe9KFImaZpw2mbE9MZxcaOE2sk0ofEZEiBABH4c2Lp5HxItWys9Eavi4qI1UXdNqAxEojI08Qi5ID88uLDKEh+LvXdiGZSqRcS2fDOAeniLQiI9GaKTqW+i+8rxDmmBAN8UlQ/oNacvrFYUapa593Y+rESPKDUPj4W18GQDU0LT6prahyeIuj283xKUXssPzV4hcNDs5gtJTIb3jsg8g60XshkBI740Mu3V5eHz34bwJMtfxFIWsu6OFD4aFdqaTZL68Ht5OXn/UdrxqUk0EuqrDs3MhcM0H8js//CQGvrQUL7zwgrXY5+F54YUXMu0lFi1ahF/91V/Fd77zHYu4xHGMKIrwX/7Lf8EXvvAF/Omf/im+/e1v45FHHjFtfv7zn2Pu3LnYtGkTXvnKV+YfQ4muURKeSY6JIjwceihclHlYEvZiIRYy1TQOmWif9qcXDrUllmPvfjUDQ3o75DJXjD0nc7S+6ENS9nkcwxjCGACb8FyIg3gG88y54YSFxpm8VzPHIMd0CZ7FU5ifORd2qF6iAke5O3zfGiTRIYLkIj1P4nwrBI7PqkvC8ygWe3MDxgPSG9MtYSm6Ty4uIGWh8/YvJZN5H3J7SSy52IELm7HK1Leh8K9+wEe6tNDCjbgOc3BY9cIVLUh7P65GHY2+/LacGGl1cwh03jSj1IXTEY7ESQ/tk7xspJinhWrRcl6bibw9BE1ZUEIjppzQuJ45EnR90v2Y30f1yIFskdwik00+8NDhbkiPi/D42nJESPPaiAydCeiK8PxkIXD1B/I7P/IkBr48PoVHn376aStUbt++fbjxxhvx9a9/HVdffTVe9KIX4bvf/S5uueUW/N//+39x/vlJiPpXv/pVvPnNb8aBAwf6Uly+hBsl4Znk6Bfh8ZGd83AY+zHHvAPJzbQeN9CI3Dky9M635/vSbtLkUYgdDwXNo6P1wz06EtpDiOew8JlO2qfWh/SsdDsr6HqYNVHHcIcA5Xl4knd/ZXU6d5dhryEl1P4SPItHsRg1NHEZ9uJJnI+9eDEW4WeZ/WhjjxEF5/FIwgOgU28oUgkPwRfqJmWsNVD+TT8Ll/YbGgF4CCsLeTV63WcIAaJxAeHS46H9jgeIDIWSNVfIlIRUc5PLfSFUNBsvCZHLuzGekOSA/69biPALjIh7YnZMsiAt3W/Ik8bPKb8W6PrjOWOknqcZ6Zq4QYwI8/EcDmCBNcY80sPv303U1AkniSIESCM9RQlPHlztNKJ9JmCqEh6JPXv24OKLL8aPfvQjrFy5EkDi8Vm5ciUWLFiAD3/4wzh06BDuuOMO3H777fjkJz857mOa7ihzeKYBJNnhdXJ87STZAeyQB3qn7XykirRvSGUs2T4bpsVDz5L92Qm3afuq+oCusQrwcv0wjnfmM5uZ/mRbkq2mujzdIrbolf3gqqGBFiJLdpqOXdvmMuzFUuzJrB/DkCFnFErWRN28LsGzeAILO9LYLUNI1mILLsIBsW9bDY97lLRCpASXOhvvh5/zXViUETB4BEusFy0LITscUngA6K5AqtZHt/34yACv0zMe0GSiz/EU/eWz96ux3TJAfcd/LbbiIM4x38dT4luCSK7P8ONEg/J6tNo+tIyfB2mYu2bT6f/iygeivml2nm9zOsH/11XEOAujaKCGJlN4lCDCwnNnZG4VHRPPlaG6UDxnDMiemw243vIicSnrCDEOdPJ37PtfFQ3UM8qb/D5GY62hacYk1To5qQ+Vm5Zt0hGFhT9H4kg0hFwbZd4PJl0dHheiKMK//uu/YnBwEK94xSvwhje8AbfffrsqYV2i/yg9PJMc/fDwkHAAhU4B/plJ1w2YVNj2Y05mVus8HMbRuIaxaNjqC9BDs2QbGpMM2boMe7ELi6x1ElqImvzu814kn23PEpeTDulLIi/3ibfRQhq0MEIiMxQG9igWWyRN9nMZ9pp8GiIk5N0hEBnSjt8ec2yFthFkWJum0ObDo1gMXoiUSBBfxsUMJPnhuT3Sw9Mvj48MGdNCq7g6W55HpFsFtqLtff0QqL9NWIMRjOaeq/H2RoWCvAS+fCG+TnoXXJ4gLW+E/leh7bWCqVxCW/YNjL+XR5NZlvezMQxjFCOgkNc8L/g5OOjMyZPHTOee7y9GFFw7hvrRJo58+S8aXEREIzkyzFjvz35e0hhbsCMEfGORE328L21/ss2ZEs4GdOnh+dFCYNUHAjp/EgPfOj0enhKTDyXhmeToB+F5HiMmTpvQbVy5i6Bwr9HRuIbRaMR8p4fGRTggVNHcBIZyaJZijwmHcsWX8wcoCRXI/n2hYL7jJZDniJL6m6hnHla9gsidb0yc9Eiyw7dzGVKXYW/GSNS8M64wP8rh4e/9gMzl0RTbfJ4ejdzQMhKx6CWXQ0pPA+GhXhxEWkJCr57AQhzEPKzGdpXsyLAh+lyEdPXahqNojk033rtQcInrkGPg4ViSxOT9TqF1UGQeYFFjuBdo/2dJelqIcLxT6JjW1zpebhc0Q911vmRRUZLx9pGezVil5DRmFTTlvb8oZGgc3x/BRXok4eKEh8ZLY5Ttad+uScA8wiM9imcCSsJTYrxQhrRNA0iyAyQ3yvNw2Nycz8Nhi7TIsDUg8fDIEDJ634855sW9PKQQFiMJZyPZYi18S+4bSHM/ap0ihxFiE4ct2+aFqWnb+JZp54GMAgpe6AcouCI5P+4QB/LU7MIiNDpkMEaU+X25l056W57AQqzFFjyJ8/EkzrfITn4B1PQcaeFtsj+5X8AtCyzzd6qIsQKPWy+ObVhmhUxJrw4tuwo7UHcYQkXC06TctK94IUEzhMn45nkOcix0DJdhL8YwbHmEgKy0NX0mg4zGyj15fB9PYKGTBPC+pRqXbMfHCiTnu0jY33iQHa3AZQi4kT7M7i8u4532wWsYEbT/sD7JEhYq20tILSGfXNnhvOl26X3OFeq1FltMOFuMCBtxXUbY4SGstELe+P424Hq1fhHJstP/jfZfR8NSqOT95UGGA/OogxYisy8tj0crWu3qH0DmPLq3yRYlzTsGjjOJ7HSNNpJwtbzXqYkaYInJgNLDM8nRq4dHq4MDwFLoIuQ9QCik7XmMGKO6HjcwK2piP+ZkbsQ+RTa+PBYPEfKiVBHjCuzOhLS5wr8kNI+QNhMYGlZChgeJJfAZW22mM49k8XARGV7iU60jjGEIdTTNb6Htl++fS0PLUDd5jBIkQhASsrYJa7Cw06ZIeBuH5vHRSA+gFx/tVsb6dCi2cbjkfR/GcoxixBiIktRwaNd5N8ewCWuMsa9JYd+FG3AL7rXGQ2Mu6t2R6DVcTxZt5cjrlxMk7vkMETYoSq60mlq9tOtl3wBM6BoZ6mNmMie9pyQ5j25xACn5T1iPB7AJa1BDw/wG5F3Wfm/u7dEEDbhsuSsErChZkNvQcYYIF2giEHJfrrGGEDRt0lHb9kwSLAC69PBsWwi87AP5nY8+iYFvlx6e6YrSwzNN8QzmWWTnQhy0bqxheSoRxqLhjFgBnwVzkR3AvnHTwyOtG5PMuD2KxZkHqesBQPHk1L7JxADkQ1z25YtVp+Uxqsa74zof8rsWcy7bU4hGE3VQsm3IrO4wjmdmkjn2dsgJiR0QceTQCIlLje1JnK96jIg00fs6PGj2y9uFgHJ4pMdH8wb4io/GiPAwlptXKEKJAvdi0GdXkj5PgucGsuY9ob5iRBjGGB7CSifZiZCE6cWdmWm6FlZje+a4XZ+BNHyQz77zNtTfLbgXd+EGMxZusJJ3p1v0mhPk2j6kX05yyEPkK2RKCA1l8yGbK5ctpsmXh67XXhrkfdH1ziHJABeWkUp2MRKPOGCfL/673I+r8TCWG7KzAderXguNhIR4ebgHPOvfsckOIUS0gEO798t+fd770H7lsjON7HSNUNGC3p2lJaYwSsIzTXGhUGl6BvNwIQ5aoW3ypn0IQ3geI9DAQ77ksvR71pCPkMSJX4HdGMZxDGEMIxjFMMYwjOOmXYOpB7nCEihcioe8cfKV7MtWXbPD3tKQLjfxSdrHiLr2XnBI0sO/X4JncQmeFSQxO66l2GM9RKmPhR0PDr0oxI17aTQiQqF1QOrNovAoGQpHxOZRLFbPB/0mMuxOhrbRd02aWlNz82EblpnfScvp6RXSC0SfNfK1Gass5aomaob0cKOPFx2V/ctQIlIZvAo78DCWZwgKLYsQYxuWmXUyz4nOIyeTLsJH25CHh4PCl06XZ0yG9mnE0ReyB4R5ZpI8oDjTlghRhDi3/oyPdHAVMd89Z7zgC7nS7t2+cC76f9N54SBPD4DM+dqA67EWWzCKEdNG5vRswppOIdlqkOeFYy225Ob2uM69JD3Sy5ZVhnNPbuneeveEWN6EI63vB+k+IzBFVNpKTCz6k4RQYlKDwsM4ZKhbhNgrKx2jinMxqhIejexchAPGSLYTMu3kTHo4Jm2z3h8erpNIOCdiAfyYZLhbIiiQLKP8jSQHyPaeaDV/7HPSMu2pHfUDJGShBrd6XAh8M5ScmJCaGpB6ZUheOvG8pHV2aDw8ZE071iewEE3UcAV2m33JvA9KWKb98HHI43DV1dCOTao7kQdKW1cUnHgk5Kd/ZAeApcYWEgJHCfTXYqsJD5JGepKcDcs7s5oRk+MYzlwrRGwIBzEP89hEBj8PnPQcxhzMw0FcicfxKBaj0SG1V2InHsESXCW8aVq4Gj9m/ptvwzLEiMa1FhKRGZeIgws8jIqrt4XiYSzHGIatejT03g1Z4feXbrYF3KGveeD3Tr3/YpYhTTKlJC69L27EdagJckihbURuaFKAq+lp8N2vtdBkru7G4fLoU96ajDjQ2tJ61zhc22vHoIUla1ENeWHSJUqUcKP08EwD5LnnQx5uEVpO7w4HzVgl9XZobpp7LrLvFM7VRA3HMYzjGM6EISRtk76SsLJ0tk97MMgHiDQMpDfHpUpGBn/aLkYTNVD+TrcqZaGhDZxcSA8KhZ4RGbkCu3EZ9qKGRoa88Ics76eGpuWdoT7lcXEPjxzbE1iIpdiDhUJlDkjJCyc0Gii3SC4DgBdYbRcg8UhoNXs4SNSAjH7p7ekFRB442XkYy62QLhmGFqOKzVjlNE65ihyJLRBW4HGsxnZr2ZXYmbl+NLLDvTr0IoKyDcsM2aG23NvzMJZjI66zBCFkmCCF8T2M5YbsjCfI2ye9fpuxyniEXZLfsviqlijvwv24GmOd0CxOcnr1zIRsGyJxHwJ3vpDfiA75Tfm5leFw6/FAJjeKSI38DcgDKq9tnyctNKcTgFX7aC22OElznjgBoajHCQgThcluoz8vSsGCDmIAJwNfJaYtSsJzBuNJnB90Q87LNQGAuSy8TG4LSMnQqvpwDXHf00gS6eeaIS4jGMUIRjEHhzEHhzGMMS+R04qLFoHmedHi2lNiV+zBx9uPYch8vsyR7M/JguZhIdIjl9HDlULPdmERdmGR8ehchr1WGFmiilc1fXFS5QKNNUJskZYiOR2cGPFlu7BINXa4cptPzGA8il9SzgyQFiPlvyepS23GKu/sMIGu45B8IyIfvuOqd0j6NixTQ/oexnJciZ2mHYGHxpFXRxpU3PCifmhcdA56JZa+3CO6VumdDNQRjFoerSri3OvvJtyXG5IGJKptoTkxeaiyO2Qo+hnqRvdlHubLRVNc5CH51bV7enI0j2IxzmYqn3Sd8OtnE9ZgI65DEzWT38PDzTZjlSmOzPugsMLQySkCL17Kc11ILW4DrvdOwvDz1E2bUNKkeW+0NqVHx4NSpa1EAErCcwbjEjyLi3Agk6/jQst6xNgeGFJh0268boGCbD9AGu+tJczSi0sJk1eHCFCj42FJakQk+T9aIirtj+ftyPwdDm177Rj4dkmOTWRtHwrqjwx2HoLGw9lIdICg5cpQ3g2FnfHxXcZyeYiUXCbye1wg4vMEFlr5RFKUQIMM9aJjICMjLxeH2sl3jjwxg8QTEmYwFiFHVIATyCqaAWn41LXYas0kN5lHBch6X3l+jWs88vhk7hAPUSNPF+U18X1QOznhoBEOGdam5QhR372GD/L9aJ85kaHzOtrxPvPzKUkpB1d0yyMxYx2PsgtFBBe68Qi4EFv0yR+drq1P7/eJ95vCLV33MBfpARIvLPfE0vnkIYN0v7sJ91n3Vp6jA7iUCFvWOv572eF9SYFYTmSJFBPp2YQ12IQ1OAujHWn/sMh+H0nVZCJCoZ3vktyUKNF/lDk80wQa6XkG86xcGM2gB8JuyLSdqyicREMksdN+ZMgbfZZVq9OwsrhjyNthbNpxaMektdFkSWWCMc/jQQ8PJ05kJPFwSUBr4V/83CXnIzXeyNPDpaWT3J2sLDWQzRGS5CtvfDKcSI6XPDcrmEy2PD7e7jkswFJkc3s4YXLVdKE2ZHxJTwBHqIx1DU00O8VMOfhvwNfx3JIaGk5paSAlFpSHpslrk2T3ldjplIPmZIl7uiSJ0v5vHIdZXl+SM5SFJFK9eni+jRtxG76nrnPlTNH15sqnkkVjhzCGFXg8I2Wdl9enQcsdckk4yzahhjG//xB8RF62dx1XOlmT3NdooilRjHR747MCDDZp5iHEaQhg0kaGsXHvCyc+8ngod5NDIz0yL2ttp9CvxLXYynJ88nOh+klWJbRnrjynJTwg0YI8lCpt0xqlh2ca40IcNB6gC3HQzOAT8eGeG/5qoKbe/F0PSc3zoYV15HmQkoc2jaGeaa+FofH9Sw+OCy6yI0HLl2KP8ZqEKrfRGCi8zO432f8YhlQPDJE8rU/65UjlDbC9NLRdDU1LBIFC3S7DXkMSYlS93h9+rLQtkHoxKOdLEyHQQtioHyqoSpLkC/Ccun8+v/0oFmMXFuFRLHbm9czBz53HosHnYdHq1PDrltZLQ8tHdgjkIbkSO9X/E4VzcU+Kj6zlea54OJrEHBx27qebOkchcJEdytsJCZWUxEeSEk6QuTfuWmxFhBZGOjXHCL2ElRWVOPZ5OHgbX2gcJwDcAyT74V6eZD1/Auj3Srt9mtso2xOJoH2uw4NWG67GRl6Xtdii3K3TMeZ50zRvnUsAYRPWOK/7XhDqbfNNupUoUaL/KAlPCQuXYa+RQ+ZkiEgAJ0Ga614+9Ph3jXTwB/YV2J0bDlBDA8MYy+QfpPuzH3ZFlXViRLgIBxzErWUZDxJPYGFuTHjSj30+JIg8rcDjqnz0ng65kDk7/Dhou9CQFyJtQGIM7sIixIis3B8Jvmwp9hiVP5qxPYh5VhsZmiYTz6kfILkW6EWeHnoRIsRWOCOBjFlJelbgcURoBYeuycR/H8gIX43tJrmdS1ID6XXH8xakGAHHw1iOYYw598/FBGi83RhwUu2NL+eQbfqVK6Vdxxo0yXIXOClaje2WB0jeXzRxg9EAgRYf5D5cBEW7F2kTLj4PRFFCpYEIBXnGJYnRiU96X1mLLYjQyuREEUnhy9fjAdyE+4yqI9U8ov+Kq7ZMjKhwodcQpPf+qlie/R7i/dMlqO37sDaxp/fl/m1LwQKGUpa6RAAq7Xa7PdGDKOFG0arDpxsuYyVPPpRc9nTT56IHdaOMZufiEOgBzJdTwqqUkfbFpBOaqGcSXvl+qa8xDFm1gbT2eWpkeWPR6uRcJkLPXDk8BCkdLcNZZLif3Ge30MLTgGQmlcfPFy2Wp/W7DctQRwOUk7QNyzBspHEjXIHdeBSLM8YjkSEtVKwXkJwuD7nahmUYwxCOY9jI9krjlKu81dDACjzuJTjauXC1D5059hleNTSsvCPZpyQ7/TynG3A95uGgIXbyO4dPHpxLiANhoUlEgihZPg95IVE8fC0k3E32zRGhZfrgx+Xq0+Xd4WORv6vt4ZeGf7J8HR60CAhJr3NvGg8b49tyjGAUV2FH5j7hAhGyPHBykYeQ/8oF2Id9uAAAsBBPYx8uyBxv3li08dgThNwz5w8NP1MJT1Gb5y1veQs+938WAos/kN/52JMYeHgpmk1/baYSZyZKD0+JnkDeIP4C+I1bD1PTyA4Ai+w0mEobIS9Hx+XdIZllPg5aJslOFXHHy2PP6NVZjRsJOp5eyQ7hESxR+4oRWWTmCSy0FNUoDI2HdmQNJnschzDkVIXTvDAuzBN5YrTtBdhn9aN5ayRkKB2XI34Ui41hTeecyA4nA+QR0NTc+mGYcw8SzWC/gHkmR+FK7MS12Ir1eCAzu78a2zO/w0Gc4/UmPYzl5vi4PDSXsdZy42gsLvAcHP4C0skA+k4iCfQi4hhyPrVjC1Wlk995kVZOaKRAAV/XQmS8PfSfGMGos64PEB7K1k3+Rx7Z8XkU+LYREklzemVzy8IKa9rb5CvJUUhYnU02ATChgQT6b/DQYwpvW4cHMYoR4+XpBnycId6ubkPILsNecy6J+ISQHQ5fXlT6PV8+vESJEsVREp4SfQcRH1IXW4o9lkeH5wjxWc9hHDdy0pwMkRGnhYKlBCg7C8rxFOZnxsmX0ZguwgE0UOvITdtELe+B48ohCgUPiRvGcRPaBiQPW/IgyTA3Ot/cSyPj9n0qVJrk+FLswUZchyuwGxtxnbqdJCznirwHIhycnJJRo3mCeH90HOTRkHLEIUTsUSx2ihn0A1rft+DeTKV4wM4pIQOcK7JdhR1YhwczqmSaN0MKFXDScC22mu0OYh6uxE4cxDw8hJUWSeQvHronFdeAhPTw5S6PDokoaJBjJpJO4X8aZI0gTqgjxFYYIb3TS2I1tuM42xcp541ixBit3HiVXopeEBK2JuE2etP/MK/bFLJP33rXRA7lNNF+SeJ5Hl4wYWirsd3IP8ttAS4cElsFRuk9QpwpBJuXaynPD00oaOdV8151Ay2crqjABYed76R703zblGAoZalLBKAkPCVOCzj5odyMFXgcVSQS1ERySHpaqrL5wEMc0jCG5BEohRTogXERDphXE3WLNFyCZ41xRF6mpE9dxMH1ANWIme/8UDsufsBlqkOge4VcoguRMz8HSJSsHsLKQqETWl8Udsb3S4IEcjs6TvIA8e0exWKzXhKg57DAtKFiqjzng5MTl2LZeIMb49wop7ER4aBrirwV5GEhLxbBp4r2KBYbgrAeD1h5Q3I7WZyUIL1H9O4zGPn2XI2LL9+I63AJnsXDWG4ZyZLEkngB1TmikD7qjwiRJjtN4OeZruPNWGVehGux1UkSHsLKIM+BLRIdm36lER4SxqZNUPDvVGfoIay0iGqR2lcS/Hemexc/dlI6vB9X4zDOBmCHnvFzys/xYZxtERpOjiRRWostIkzOT3wk+O/kKkHgCynLQ54IRAhCjmk8xBTOaITm8JQqbdMaJeEpMaGgpHROhjjZyQvHStpE6gMoqS3RyPRBDxNS9KLvj2CJUSm7AruNZ4lUw/i2Pugzd+l2vNCodiwcrlo8vHDokzjfEAHuAUr603MLaD+SoPDv12JrZrb8eZbM7Qvhk4IGHFyQQG5PY+ciCnw7nkz+HBYYI3kBnjPXTyK5rSe4c5LTz4KOvYKKhGrX13EMW0U3Ca4CnYA7wZ97YfLC5ySR4sRILpd9bcR1qqfrYSw3xGMOK1TpGjP9vnQd8t9/LbaoNZBCwL02MapWkVi+jv8HfMIDGiEiUtIPUQFJdgiu4yePlpZzSCRMI9+AfV1wcZmqmYpKx0J5PFxOmhPJGNUMuckTHmg6wjLTPv33YP572PV6dO2304nQ/eUJ25yp+TslSownSsJTYtJhBR638gQSda3wBwV5SdJlrY73qG7ydh7FYkQd7xJ5HOghKXM+eK7GFdideWRyw9w3k0jtqdYFX88JS15ui1zPCZ0uc60rtfFxu3JqaH+07lyMms9EVl19hkCTp9a8WbRPbgwtwHPW70Hb8QKIBJKqDs03kei1mCaQTcR+GMuNkeobkzRINc+OFv4mw82KEAItd4Z7hOh8bMYqdd+aQfYM5pnPD2GlCbVzYQOuxxXYbdXQkfuR2/PcHqnSR+vOFkTLRXw3Y5UhyJwccA9NKJnhHp9Q2Inu6echjFliF8l6+15Dnlk+uUKKaTLvRJ7Dq7DDunb4Pa2K2IS5EdEhrMUWU7eJF47O1sXZYmpiUR/8NR/PWZ4fd95kck6kgigfK5137kXqleT4woN94+Tb87H4Q/fC1NymPUqVthIBKFXaJjkmu0rbZMAuLDLCA1zUgAQUdmGRIUFJbk4VYxiyHspLsceoepFSnFSCI/BirQBUT0URSILGj4u3AaB6L0LC3WT/POSLPmv7A3QvFUGOxaXUlrcOSI6DHvAyDE7DIQyp+UcynE3iYSzHKEa6TpJ2oRsFOg0+EtBAPZMrwSE9M/ycP4rFzpyZ0GKhmlQ1XRd5fTyCJSbElKuLudTVAF19bRcWWQVRJbT+eD9Ud8eVcK4pjcntCXmJ//w/4ytAysHbuYxlTV2N9jWEMStfCUjOSUiCPe/3KuwwIZZA4pE7jDmWJ2wzVlljXIstqkqb65qV63h/a7ElM0nA8wHT93xBBq44x9t3k4OTp8Yn96tFH4Ts1yeQcyZ7eLpSabtnIbDwA/mdn3gSAz8pVdqmK0oPT4kpj6XYY9UOAuzCngQiO7EgLBxVxLgSO43BHSE24Ve8DcejWIy9gTk2GvLU3ajNZdhrFQzVipJq4AIA9JLnR+bUyPwk+fClc0KqaRrk8jwCQ94yH1nhxpRGdoCsWhyQGOrk5bkKOzAPL3jHouFruNW7fh0e9MrphoB7JYYwZuSsKf/HR3YAm3Qkstip4UveMJfymVasVeufb5+Xx8bB5a0BWJ4b/q61ITyElUFeRNmnRlYAWAptFP7lIzShXjItd497HHyFQ915KOnnlvhf8vOviWlo4+bHTpM/XLGPcqcIkmRuw7LOeRszHhkZzvYQVnqvWRniJj1gcgJBEjwKZ5YEgnt9XPLaWoheCFxkpR+hi+k+Sq9OIcQATga8Jk8Uc4kJQEl4SpxxIDECIM0FSVTOqkYUgecO0cOFExsyqiiPJ09ueqGnhg030HgIlyQYvu0AZOSoOXzkxFUQlYemcc+YNhYZdiGJDn3nRUI174/vPNLxPoIl5rMsVnottqphexwHWegUkBz/VdiBKmJVotoHTmBej+9Y6zSC0K2HhwzFJuqG3KzA49ZMvUYMfAY4l47+/7d37+FRFWn+wL+5k5AQkJAbdxWQGG6CcheEBXTFdR5mGXUQYVedQQW8jLvz87LKrCvooOvM6C6Ozoqwo7KuiuN4QXBYUJbrgGiAFRBQkCGAkIRoMCFJ/f5o66ROdZ1Ld7o7Sff38zz9JOk+53T1Od1Qb79Vb8lbChqs6yaDFxkM6lkBwJ7R2YCh1iR5lSyLLId/yTbtQIljEOVUaEDt/Oqv0y0wciIDnc/QyzrHagZnA4biG2VekCypbBp6Jp/XKVDxy2lfPThqut+9U24azub1vPIcyJ+1SMcYbEct0q1skBy+J9+P6rwfGRx+hl7Wv5HyeNI4bEEtMhyvqZOOqLCCJKfCIupQsAakYBy2GIMe03BedSicbG9Lz+cLZegaK7UZ+K3SxlOX0BjwUMLogyNB2RqgqfytnpEIZD1SreyHDAy8yk/r98nOujo/JzAMr87KIqWgwTUgkEGb001Sh3yoj5u+Fdcrp6nUv+V2ptdaq31rr2aK9MBEvladWvZ6Ny5AFs5iP7rbjqUHjfoQPfk86uR+2TGX24Yz9FDN2ugZnuaWvFYDB/kNu5o9MQU2+hAvt+FgazA2qMOYhRrUICuoJHadUvFM/lSLG2zDAKuaoslmDEYd0q32bcMApKAeZ5FlDJJ08hxnoNaWlTF1kJ3WtFHv1zM6X6Oz1dF1Gtol14NZj+HWNvVIwQYMtWXaJLegx2korFeFNq/OrFtmyIu6Ro9eilsdnibv24YBVkCgb1/zff7xInxhbaMHVE5tcBtatx7DMQj7sB7DrVLq6po9Tv/+6gvEuq1hJNunr7/UnHk5OnsbmwJW035ugQ6DG6LIYcBDCU9O0NUDoYtxAJdgjxUkyYpu8j8op/+M1E56LdLxhaFimdqBNy36qZZldpsTox5Xbuf3m0I1wDMFgaZvHfXXnKEt2iqPpWfPJPW1yueX49Hl33VIRx8cMQZTTvRz+An6Bi0oq2aO/JCdcJm10TM8QPjFDGRVNiDwumSmBQgEKnKhTMmpopapPTLI6YxTQcUQ1CFu2zAgaI0dILQMiqmynBwKpWaMTJ8b9bXIktsysPDK6KjnRp1Top8ftePt1KGV1eTchl5VI8cKfvS2O1Ffs59S1PrnzVQNThdKxTGv6zoG2631iYCm7PZuXBCU+XLLgpkWHQVgBZPq4+p6PHoBBHmO5efQKVuY8v3QNKDpHI3DFshiNYHsT4p1n3w+cwl9/0Pc9KDKPvcqxdYeN6Zhem4iPfcwLrBoAfnAgIfIJz+L9akBjVz4spc2PEt2JEyFCnReQ8DU4Mi0cKepbfJ3OafDNA9HBjJyno4eqJmCQ1P2TB+apu8jqYUG5HPKYNO0vZNLUYY1GItB2NfsDIxbx0J2vvxO9tepgYh8XfJYk/ARRmCnrQOvd/acviVXK2xloQY7UGKsALcNA1CJjrb26x1mr45XA1Jc592YFgFVgx7TvqbJ9ipT4KBnLSR1LRinjIN+v9opVrdRO7fpqPNVAMCpzX4yPTq1mIBbgOMV9KjnUw1e1IBDBszyOfXPnangw9uYCMD+WrdhADZgqDVPJwX1tvV49ABH/tTnwckvLuTaP8Gv2R7spKLBylaloME6nlo6Gwhk/eT7Q/23/SyyPEtjq5yCHqftQimUwLk8RJET/jLBRAnGqcOtBguyIMB+dLf+y9RLLg9yCEa8qouZ+M1YrMFYa56RaX0boCnoUH/K1xb4mWLbXv7uNtldHc6n3ufUBvnzLyjGxb5emd0ajA2qYGRq3yfo26yAKJR91QyKW4AkK6yppYZlB1UPBPSOulqdrWkYW5btb73z1BGVIWeo9MpdUjVykINq2zA0OS9IH5om91XvN1UWA+xFB2Qn22mifz1SrHLH1chBAzIwARuNwckYbA96X6zBWCvDJOWg2va3rPAINHVuw53PYyqj7Pa4G3U+i6m6pDxW4HwGflfn8KRADWSaXp865E1+btXz+QdMQWd8jY4IXCMZ+ASqr5VZQY1e0ED9Ww6jG/d94DVBy7S5BQh6sKNqCrQajPuYzg/QlGUMLTBpqsSmV/GU702KIpnh8cKiBQmNGR6iZlKzG3J+ifxPVZ9cbxq+JrcLNdgJhVpU4TP0sjp7Ts8pAze1Sp3Tt8dunX/TkLxal29P9WFuoZqEj4IyBzLbMwj7rEptfgMWvwGBU3U206Rrr3lETvN4TH+b9rkEe5CFGttaVqfQ2fH6mdb1kfSOsynYAZoCA7V9Z5EVlMlR15DxM3TOVF3NlDGRUlCPS7Dn+0xNg/Wtvl4JTH0PyGyD6T1XjRwrS+E03ClSnVnTMD0/QpnnoR5XDZqzcNYW3KhD1jJQh80YbFVpk4+9jYnohUPWMTZgKHLwDQB7xsb0fGrWLQX1nusTmYbOOr0u9Tl0oczTaU4hA1OVOCd+szgcyuaiAazSRp4Y8BBFwfk4FlRQQHbm9bk3n6EX6pBurDz2JfJtf5e7rD/iRM+yXIQvbB3+z9DLlsmRgZscOCMf0wOXeqT4KmWscws2TAFYKM9hWrdFf26/ZXsB/0PWQumMVCMnqCCAfC7TQpJuQ8RU+vtK2oESTMJHjoubqkGd+hx65T69pLVXe/TXAQSCIH0hSnm/TmZ13IoUmBad1IdLNbUl1erYV6KTtU0d0q2Oudrxduqgm7h1bEPNAJnW4fEbAJnKYetkwKLOqVHfG+pQwEAbUvA1Ogdd66n4EwZhH+SCpoB5HpQ8z/Jz7DYc0BRkqPN51Pt0TguQxlI46/q4YdECoshhwEMUI3rJa0lfe2Y/uqMcHbEf3a3y2lKhtkq8E7Xzq5bWVivFqc+v/8f6CfqiFhnWTd6nPh4LbtkYtaMu2+NnMrmJab9wCxKoLsEea5iRZAqi1Odyyuo4lXIGAp1Ip+GNamlqdZ0VtR1y3RVJ7TirbTdVjpMli/2SnVW9KIVOzTiEwu2b+RpkBk2an4SPjAGSaU4QEMiGOFUBMwU+4c7XkdSgx8+8Kkmd66MfU13wUy8moGZ4nII1OXRNXWfHbTHX9RhuzcFxKnjgZC1GGTOoTqWopVCDD1MJa/ftTUVd7O+95q7Nox6/uWt8xTWWpSYfGPAQtRIyG9QHR1CJjsbCA04ZHjWIUefIfIZeSEWDlbHR58+o2Ru16MAg7LO+Q89ArVWAQZKZkkgutmfilg1SO+z6dqYOrB9+59uopbS9yGBK7bw4HbsBKbZ5JPqckkp0tAIfNeh0qpglgxI5tE0yBXP1Lu3zE0jK/b22lVk4t2FKaltCKRBgIhfFBBA0vA2wv1f8vG+qkWMb3mbqKOsZKKfOtF5i2ik4sq8jY+9o+138VS/uYCoaIIMWeZNZvKn4k+1YcojsCOy0holK6zHcNpRTnv8GpNgKCMggS31OE1mOWr5WOTdHL0XtxX0to9AzM34q40VqOJvEYW0OWKWNfEgSQoiWbgQ5O3PmDHJzc1FVVYUOHTq0dHOojZKBTXPmCZmKKjR38n80tMY2mahBhz6EzWstGhlUqMUK3OxASVDnqhbpxvV/9Lbp7QMCgdgg7LMN9XIrKqBzKlIANC0I2dxAJ7xObHiD/NXnMh1Db4tpsr3fTJB+fv0Me1KvnxqE6dS2j8F22/tQJ9978vMmS6nnoNrK5mSg1lZeWnbY12M4OqLC1+dUZqPUAgjyd79BjNd1jeRQNKfnCnUNHpNECHhC7fPcfPPNeOG17kDuAu+D1x9E2tcXoa6urvkNpTaHAU8rx4CHQiWzOGqA4rbmjqRWbSrACdQh3bat/BZXTv5vC0FFS9OLFsgsiynDond+1GCnEh3RURnOaOrkXooy6/nkAqOm45r2M2W21LlFAII6v/paQXrgo/ObDYzEHAy3DqxpbSm/nejmtkUd3qgGPeG85lACH6eqbU7nST0HaklsoOn9or5POqISfXAkaA6dnjEzrWHmVrRA/TdJ5fVea3q+puppTuR8JhOv4FV/PFAK2zvgCbfcNAOeYDfffDNeeLU7kLPA++D1B5FWyYAnUXFIG1GcUdfKUUtLq0PWTIt4qmP45XA6dTu10lmiBTtOc5b0gEbPxqjDyNTf5RwavQiALO2sH6ujNndL7zCp5aIvwR7bAqNO1OdX2yELKejt0zlVxtKrfPnlNRfI7/wKp22chn01ICXsIZChaLCCDHuwYXrN4WQcQulEp2pBl9xfbZtTG0wFN+qQjlUYDwDaXLDAwD45ZE4OA5PDGdXnNw1rMxWRUIMd03vMKZvmJFCW239WpmmwYvA+bs8V6UCaiELDgIcojjktCKr/rRcykH/LoCkRqR03pwBPr3xmGvqjzp+RnUR5k+dWdjRrkBWU/dGPOQI7g4ogqPtswwBbu0wLVcp9TJkmpyCnXptfVGcoOjAG27EGY40LfnplMTZjMFLRYOzEqveFPhnde36LfXvz8U2vN9xjuT0ezgKV7s8RWvU4/XzVK4Ga060aObgS66x95GdHDU7kXB65Vo2a2ZELhTq9ZjXLoy4WKt8z5tfRlG3xcy71gCQaAYo8JquvRRjLUpMPDHiIKKh6m1q5Tf/PWWZ54l24Fd9MdqAkaNjYpSizMjH6XB31nK/C+KDiBWr2xZSNMa3/ox53GwYgCzW2feRaOvo3+KbAbwOGIh21xmyOXM/mm+/XsFHpQY+cFK+f62+QA8DeYXb7Zt2J30BH386t461qKkXg3oENPr69mphXhTB9mwakOmSGvIM7vaCC2gl3+sz7GXKnV+1Tg149K2PKAKqlpk0VycZgu/H8OLXNNNzMK/DR32MpqPedVQw3QHW7ZmpQmQjD2cLGKm3kAwMeIgJgLpdtuh9wr56WaPxMrr8Ee5CCBluQopeiTkGD1bm5FGVWEHAl1uEssqy/5dAhAEhHLRqQYhuGBrh39GVnVs8mXYQvgoa4qUGZSq+upZ4D2anNRrUxW6N2UGUlOXXux2YMRvb3wZfTufVbQrg536Trx05HrWN73IalhTtfw9QG/TGn9X9M5an9Poc+x8iN6bimrIusqtaAVNdhazLo6a5VqJT7j8MW13LZftodamDiVRjBPjfHX1BlPpZzUExEzRfZVbKIiBKM7LB5LXpah3TjEDK9IIGkrr+j6ohK5dv0QOCpZmUkvdOrH6MSHY3D19RgZxsGYML3f5uCl2+QgxQ02DJQcjtThkeqR4qt06ourrobF6AihAV2/UxM93ec4PPldexYLHLpFfTIIWLB6/fYCwTIgFotkqAfW18zSqU+h1sGyBTsmI4rh6V1RCXGKF+qBAKbI9bv+rEC16J1BwPhvifleWKwEyJZltrPdpSwmOEhIooAp2BHztNxerwjKpGFGlsnRw1yTEUMdOkua9roLsEe9MGRoLk/JqaASJ3TdSXWIRM1QcHQJ+iLbFRbQ9NM1OyEfG0bMBQV6Bg0Wd3PHB61kxnJ4MftOSPNbSFL09wZtW3Oa/gEB3Km4EgtqtC0XGnw+kKmdqpMWSc12FGzPCOw05ZBlnN9JPV3dX9Jvi9CWTQ02sUDTMMV7Y97zetisBMyv+vwcA5PQmPA42DJkiUYOHAgOnTogA4dOmDkyJF47733XPd56aWXMGjQIGRlZaGoqAh/93d/h1OnTlmPv/jii0hKSgq6fffdd9F+OUTUQtQCEabqbEBgeJn+Dbg6t0X/KW3GYGzDAM8J9WonagdKrP2AQFZIL1rh5Cg6W69nA4biE/QNKhu8BmNRjRxrWJuXeqRgBHZiFcYHlRvWj+3WWYxGR1Z2uE0d7+YKDjDMwYTTRHc9iPAa4maaK+I2iV7vuEt6EQOdPtvI7bqoi+Oq1AIFbmSxAyd6IO73PeKnwqDbujqmgLk583zkvDgiCh+HtDno1q0bHnvsMVx44YUAgGXLluHaa6/Fxx9/jIsvvjho+w0bNuCmm27CU089hWuuuQZHjx7FnDlzcMstt2DlypXWdh06dMDevXtt+7Zr1y66L4ZajdmzZ6OyshJvvvlmSzeFWpgpe6J2/uQQuU/Q11qkU96n/vQ6vmlxUj2zU4Msa3FSt1LUXdH0BU4Oqq25XLKDuAZjrc6ZWqWtaSiS2QYMRTacO7hO96vr56gilZGRxw78dF5jxY0eTNgDCeesjCkIkcOdvOYmOWV73Oe++J/vpA5v8zOsT538b1p0Va7pJO9zCjj0fZ3mToUToOqV4EJlmoMTyjltGkbJDE/I/J5mAX7Nn8B46R1cc801+Ou//mv07dsXffv2xaOPPors7Gxs3rzZuP3mzZvRq1cvzJ8/H71798aYMWPw05/+FH/+859t2yUlJaGwsNB2o9bp6NGjuPHGG9G5c2dkZWVh8ODB2L696T9iIQQWLFiA4uJiZGZmYvz48di9e3cLtpjaOrVqmczmDMI+jMBOZKLGCnJS0OA5HE0tjKCWwjZVcGtACmqQ6RrsSLJowiDss7I80iR8FNRZlJPNTd9we32TrmZ6TEFM8MKW4X2LrndMJ2BjyB1P9Vt8t9cVSic4eFK8WzEK72BMr9Dml74QqVvgpB5ffR59qKHalnqkYAy2B2Uw1fNoCpRkBkk9bgrqfWcFgaZy4/o+blkZ/b5wM4CmhViJKDoY8PjQ0NCAFStW4Ntvv8XIkSON24waNQpfffUV3n33XQghcPz4cbz22mu4+uqrbdt988036NmzJ7p164apU6fi448/9tWGM2fO2G61tf7H7FPoKioqMHr0aKSlpeG9997Dnj178OSTT6Jjx47WNr/85S/xr//6r3jmmWewbds2FBYWYtKkSaiu9h7GQxQqmU0ZgZ3WsDjZUfITqEhZqLEGOKn0TrosSS1vskOYibMAAsHGGGy3Ah/TcdZjOM4i03ZctbMo93PqWKoZIsmpcxluVsdUilmWRfYKetTOv9rplhP1nYZ9mdZG8uInM2TeL7iktfqYPF4ox1Tb7hT81Nva6//ayAB6A4b6WhBWvkf0wEdWD/QqZy7nv8mgWm9rHr72nENmKqMd2NZ5sVun7RNNbW1tUP8mZCxLTT4w4HFRVlaG7OxsZGRkYM6cOVi5ciVKSszrW4waNQovvfQSrrvuOqSnp6OwsBAdO3bE008/bW1z0UUX4cUXX8Rbb72FV155Be3atcPo0aOxf/9+z7Z0794dubm51m3RokURe50U7PHHH0f37t2xdOlSXHbZZejVqxcmTpyICy4ITNgWQuBXv/oVHnjgAUybNg2lpaVYtmwZampq8PLLL/t+nu3btyM/Px+PPvooAGDBggUYPHgwXnjhBfTo0QPZ2dm47bbb0NDQgF/+8pcoLCy0bU+JTZ0PpAY9suPkNF/IRGaD9AyN/NZdD2QkOXRNn8czBttxFpm2BSmb2ufvm3N1jsY4bEE1srEK43130EMLDMLvhOpBTwNSrEIUboFPYB9zLyzUICSwj/eEeLUggffxzNvL+9Vsjt9r6mYzBluBvdNkfz1D5TasMxROw9i+Rp5VUlt9LzYgxVpMdQI2uq6T43Ud3a51vM/fWbRoka1v07179/AOJHzcKKEx4HHRr18/7Ny5E5s3b8Ztt92GWbNmYc+ePcZt9+zZg/nz5+Ohhx7C9u3bsWrVKhw6dAhz5syxthkxYgRuvPFGDBo0CGPHjsWrr76Kvn372oIiJ0eOHEFVVZV1u++++yL2OinYW2+9hWHDhmH69OnIz8/HkCFD8Pzzz1uPHzp0COXl5Zg8ebJ1X0ZGBsaNG4eNG/0tELdu3TpMnDgRv/jFL/DAAw9Y9x84cADvvfceVq1ahVdeeQUvvPACrr76anz11VdYv349Hn/8cTz44IOOwyspcckAR2aBdG4T34FA0JOBOmzDAGshUpWsopWDamv+jOwE6ouPrsJ4K9iRw9o6osJ2PL2TnIJ6x2/T12M4cvCNLYAKJyuh3/THTRq0Tr1XZbQUNNgCS7fAx29n2G0ukGyb+tPpNfh5TsnvvBJ9fo7fSfqmbJ/MJqYEDXqzZ2jCHZ7npy1OZICzHsNtAc5ajLKyPE6Bj1fgm6juu+8+W9/myJEjLd0kilMsWuAiPT3dKlowbNgwbNu2Db/+9a/x29/+NmjbRYsWYfTo0fiHf/gHAMDAgQPRvn17jB07Fv/yL/+CoqKioH2Sk5Nx6aWX+srwyGpxFBsHDx7EkiVLcM899+D+++/H1q1bMX/+fGRkZOCmm25CeXk5AKCgoMC2X0FBAb788kvP4//hD3/AzJkz8dvf/hY33HCD7bHGxka88MILyMnJQUlJCa644grs3bsX7777LpKTk9GvXz88/vjjWLduHUaMGBG5F01xyxT8yLk8eqdWlquuRo6tMMJmDMaY77M91d+Xm3abu6AHO4GsUCfrcaeCA+q8Hfn4OGzBJ+iLQdgXNHzINA+iOZ3KUNdBCS7BHJigbwoW1HVWnCqjeQVd4dJfl9s6TU77yaDAbQ6PEz2wlT/VYEkWLgilIEIsqe/HtRiFCdho+wk4D2/T+Ql0E2FuT0ZGBjIy3KtMEkUCMzwhEEI4zp2pqalBcrL9dKakpFj7OR1v586dxmCIWlZjYyMuueQSLFy4EEOGDMFPf/pT3HrrrViyZIltu6SkJNvfQoig+3RbtmzBD3/4Qyxbtiwo2AGAXr16ISenaf2SgoIClJSU2N5fBQUFOHHiRDgvjQhAYD0e9aZWddOpk8ntlcsCP+UNsM+9kPMx5N8ywyODHbXscwrqUemy4Ogp5Bnvl9+oe32DHulv1v2WyHbKzIQyv6N5AZxzW/w+j2k/pwpp6nM6Zbac5hOppbVjGeyEOv8rUHUwcE7UoCec68SMD1FsMMPj4P7778dVV12F7t27o7q6GitWrMC6deuwatUqAIE07NGjR7F8+XIAgapuskM8ZcoUHDt2DHfddRcuu+wyFBcXAwB+8YtfYMSIEejTpw/OnDmD3/zmN9i5cyf+7d/+rcVeJ5kVFRUFzdfq378/Xn/9dQCwquuVl5fbAtYTJ04EZX10F1xwATp37mwNVUtPT7c9npaWZvs7KSnJeF9jI5eNpsgyZYLUMtiBIW32uTwyaGkKelKtv4FOtg6dnJ8RCIQ6BWV4OqLSdmyd6dtzv9+oA+FXxQrnm3a10++HKRNketwpk6Wvy6O2QR1qZspgeT23zi0YsWdygrNKTUUi6oO2D97fuwy1n3box3PbJhTqe28tRvlunb5ej37OndbzIaLwMcPj4Pjx45g5cyb69euHiRMnYsuWLVi1ahUmTZoEADh27BgOHz5sbT979myrYldpaSmmT5+Ofv364Y033rC2qaysxE9+8hP0798fkydPxtGjR/Hhhx/isssui/nrI3ejR48OWi9p37596NmzJwCgd+/eKCwsxJo1a6zH6+rqsH79eowa5d4By8vLw9q1a3HgwAFcd911OHfuXORfAFEEmQoXAIEO2VqMsoKTamRbj8lASGZg1LkNg7DPmg8BmEsC68/hxjw7x61THpm5FH47zKFmVtThcKHO+XFrn9tCo37aGuo6RM4Zo3rj76Zt9GDHX7EFp3lM7uWmw+FWrMDcBj/rFgVes6ngBxGFhxkeB//xH//h+viLL74YdN+8efMwb948x32eeuopPPXUU81tGsXA3XffjVGjRmHhwoX40Y9+hK1bt+K5557Dc889ByCQYbnrrruwcOFC9OnTB3369MHChQuRlZWFH//4x57Hz8/Px9q1a3HFFVfghhtuwIoVK5Cayo8jtS1enT19jo86sVvNDjmRnUN1joTJJHyENRjrp8kh8w5W3BckVefs6N/cux3bK2AzZYDCWejS67m8hBI4yNpu6hygeqTY5vKonOYKeZ3z5vIztC/QZu9g3M9xiSj6mOEhMrj00kuxcuVKvPLKKygtLcUjjzyCX/3qV5gxY4a1zT/+4z/irrvuwu23345hw4bh6NGjWL16tW3+jZvCwkKsXbsWZWVlmDFjBhoa4n+CKiUmNTBKQYNWyMC90+znG3S/wY6f9W+chom5reWiVqpTs2B+Ajqn7E40Jqw7nWf9nDjPg2rKjJiyJKZzFE4n3xQUubU/XH7mS+nnRd7kgqWReu5wquiR1ADgnM8bJaok4TSjnlqFM2fOIDc3F1VVVazSRkRxxc/Ckn6qWZmYAoZwKqDpc04C2zsFDsHbNJXv9j+Mzm85aK8y42qbQgkWwpnrZJofo5fUNmV4gEBA4zR3x7SAqZ/sjt8y0zJDYwr4/F4Hv0INahJxSFuofZ6bb74ZL7xQCOBBH0c/hLS0wairq2t2O6nt4RgaIiJqEaYMiJ8gSOVURjoSw76cn7Pe2EFveh77+kFebXV+ntC2Nxc2CG34V6TnNnllrNSARm2nHuzoj0dCoNpasOhk15i1IWpJDHiIiKjVcBoGps6VCCUQ8BPkuB3PaX+v4W0qdd0WwP8QvHDapg/D039vGpLmndVyek59O/1cmObkeF0HvUhBcznNCwrvWM3L9ISzXyJmd8LXCH/D1TikLZEx4CEiolbPNJfHz4TxSH1b35yJ8usxHBNsw6dCH4pn2t5rSJvfNvsdwmaad9Kc8xvttXbCGc7nfrzwMm6ROBa5aQR8fTY5TzaRMeAhIqI2yRQErcHYsDrhpjVj1Pv9HtNvZsGpspxzRim8zpoe9Mj2Oa3HE4kA0ev1uwU60QiCIhn4+A1UGMwQtS4MeKhFCCFQU1MDAMjKykJSUlILt4gihdeWWtIkfBR0n5oJchqSpQYBbtW7wsnyhFO+OJzAI5SFSNXX6yeoMy0gatreaShbcwI2te0tjcFOaySrtHlpHe8hahksS00toqamBtnZ2cjOzrY6xxQfeG2ptZmAjbZskGkle1OH2qtDL5ewDN5PrdZm7vx6zeNpbqfZvGhp8KKbplLd+r6mYEe933TcWC7sGisMZIjartb1rwkREVGUmLINaubGFPT4GdpmqigmOQ0dC6W9TtyO7TbR3s+QuVDmGIVCH2JnyuCYAp3mLjYai+DJ7ZwxWIomFi0gb8zwEBFR3PPOqPhblNNJU47DXLks8Bz+1+Lx2se+tk2D7X4/xQe8bn4Dm1DXCgrsE7ngQ8+wNdiuRNMiqU2PR6/Smtu5DyVQZIW2UMmiBV636BcteOeddzB8+HBkZmYiLy8P06ZNsz1++PBhXHPNNWjfvj3y8vIwf/58rgsUI8zwEBFRQjJVNZP368PeTGvb1CPFOKRNZnwiWRrZrd1O9+kiVWFMD4i8qrw5lco2/e32XKZskLwGThk2/fz7zbZEatFRrwydilmgtu3111/HrbfeioULF2LChAkQQqCsrMx6vKGhAVdffTW6dOmCDRs24NSpU5g1axaEEHj66adbsOWJgQEPERHFNTW7o89B0Tu2poUyzUUM3IdYqUGP0zFC4ZZ18VOFTH9+p2N5tVMNBP102N2CHS9OQad+7r2CHqe2OWlOsNOc6xyNBU8Tg9+iBdEb0lZfX48777wTixcvxs0332zd369fP+v31atXY8+ePThy5AiKi4sBAE8++SRmz56NRx99FB06dIha+4hD2oiIKMHok/Dd/gbcgx9TJzswGyj4GM2f/xJcbCHwd6ox2FGHqPnlZ5HWcIQS7DRVk3MOqPTXG6lgJxzhnGe3Y1GoWn5I244dO3D06FEkJydjyJAhKCoqwlVXXYXdu3db22zatAmlpaVWsAMAU6ZMQW1tLbZv3x61tlEAAx4iIiJFA1JQh3THzqcp6FFv4fI7/8bUhlCfJ/CzqdWmx5vTTv04+twaPWDRK965BT1qdbhoCCV48ZMRC+V4nL8TfWfOnLHdamtrm33MgwcPAgAWLFiABx98EG+//TY6deqEcePG4fTp0wCA8vJyFBQU2Pbr1KkT0tPTUV5e3uw2kDsGPERElFDC6dDr0/qB8Drcbtki07A6v2vjOFWZ059bDXbsjwUHPaaS1V5CCxaazp85K9YU9LSm4V6RzOhQJMgqbV63ejQ0NCA3N9d2W7RokeORFyxYgKSkJNfbn//8ZzQ2NgIAHnjgAfzwhz/E0KFDsXTpUiQlJeG///u/reOZ1qUTQnC9uhjgHB4iIkoYke44h1Iu2VQMwalt+u9+O9dOhRJCfd2RmL/iXVbb+7yZXk9LBj/NCXK85lExgIq+lJQUVFRU2O7LyMhw3H7u3Lm4/vrrXY/Zq1cvVFdXAwBKSkpsxz3//PNx+PBhAEBhYSG2bNli27eiogLnzp0LyvxQ5DHgISKihBFKyWW/+8mgx2nifHOqtTktABo4rn3ImNN+zXlOJ3oQ5lTO2p6FCq6KF27QY+JWWjyccuByX/2+cJgLX9jv43C2cMk5PF4C24RSHCAvLw95eXme2w0dOhQZGRnYu3cvxowZAwA4d+4cvvjiC/Ts2RMAMHLkSDz66KM4duwYioqKAAQKGWRkZGDo0KG+20ThYcBDREQUAlMn3qtD7tRp986CNBg733pbTB1z02PNWbzTiVNnPlqZGL1UddPQN+dz7OeYodyfiRqcRZZxu9Y0/C4x+K3SFvn3vtShQwfMmTMHDz/8MLp3746ePXti8eLFAIDp06cDACZPnoySkhLMnDkTixcvxunTp3Hvvffi1ltvZYW2GGDAQy1CCGH9/u2337ZgSyjS1OupXmeilhKJle7dyjg7lUt2O04oQYIezLiV1Va3k/tOwEasx3CrjU3HDQ4QwpmsL8+vKbvj1PZQeAWTXufd7fqH+75Qgx3VlViHVRgfscwQtR2LFy9GamoqZs6cibNnz2L48OFYu3YtOnXqBCAwnO6dd97B7bffjtGjRyMzMxM//vGP8cQTT7RwyxMDAx5qETU1NdbvHLsav2pqapCdnd3SzaAEF+rcEp3fymmBjrW98+3UWU9Hna+gxy1g8BOEAcBajDJ2uf0EO37XpHELvEINekwLjMrncA5cms676TkiEfRKk/CRbW0n9firMN5qgxPTsEfTArbkV2hD2qIlLS0NTzzxhGsA06NHD7z99ttRbQeZMeAhIqK4ZuqMhxL0OHVe9eOGO3TKqTPuliVwK9fs9bxuQ+PU8xJONibUwMJfVsycidIzXU2BkjnjFAkyqMlGDb5Bju35vLiVLG9OOXOSVdq8RDfgodaNAQ+1iLy8PBw/fhwAkJWVxZKMcUQIYWXw/Ez2JIqmwFAu+1yWSHzT76dctFcVLrdsjSnYiVQH3p6RCj1YcwqITMcNL2hyH57m1jav+0J53G3bs8gKaosczubHVPwJb2Oi7+cnouZhwEMtIjk5Gfn5+S3dDIoSDmOj1sTvcKtQjxfOfnomxi3Qcb+vKesR7pow6n7yXPh9bX5KKkcyw9KcSncm4cxVMt2vBndy7o5pHz2Dowc7U/EnX+0hE79D2jhsMJFx4VEiIkoIbkO5QuE1vMykEh2tbfXsTahtiESwI/dVf7pt53e4nPzbKXDzc64akGrd7PsHd2ojcf2ag9XZiNoGZniIiCihmNaQaW5n1fTNvnrcjqgMylJ4zQ1yYlqI00/HO1rzWtQMWqivyRTE2AM6/wu76szlw8Mfxhbu9vW295q9Sh5Fgt+y1H62oXjFgIeIiOKSLMXsRyyCHnU79XH1p+l44VaUc5vXYpo/5DRMLZz5MZEUiaFsfjJUkazk5kckh+glNg5pI28c0kZERHFJzzhEcviRGgyoz+NezMDcwdXnd4Qy1MzPdk5COQdO59HpmP7nyJgWCo1ugKNu4/d1hcspu0NEscVPHxERxTW3YVahDm3Tsy6mrECowZTXGiyRHormtzCCqR1yW7c2+c10NT3WeooRuNED01DWzmGwE01+y1JzSFsiY4aHiIji2gRsdHws1PVsTPuok/GDF8usD7qFwmmiv991dUz0jJSfYMdrqJzb9vrzRmvYWLSCHXnl/N6vPu7HtXg/pPaQTs7h8bpxzlQiY8BDRERxbS1GuT4eSsDgZwiUV3ATeKzBFmzIzrE+TM1PgKAPp4tmliNS+8dyrowfbkFNuPtKzO4QtTwGPEREFPdCWWNGcgt6QglIgvd3n8sTagbFT5W0UOjnys8iq+EIZZ6N6bVFqiS1GqyowYvfDI3TcShWZNECrxuLFiQyfu1ARERxyWkom1fGR4rVuipuGSa3NvipKhZO5TGvOU+mYYDhnqtQ9/XKvHkdz3Q+UtEQFKCEG7AEF6Awd7OiVSKciMwY8BARUUIxBUJrMarZ2YpodGDlcd0W8/RblMHt+H7bov70u184pbLDFe4xTEFPNER6/ScCWLSA/GDAQ0RECc8UBK3BWMcOqVMnXV0oU3aiZTWveq2zq+/vNmQrnI5xpObJNOc4TmsQ+R3OFo12xWq9HT9zd1iwIBK4Dg95Y8BDRERkMAkfGe9Xh8SZSjXrQY/klkXwCgT04CeSHXa/ZbXdyniHmq1wKl3t9Fik+R3aZqIGMk6FKViogKh14SeSiIgoBH6zQWp2R/4t+e3Um+bLqD/V9YDU+5vDbYiZ2u5IzuUxPX9L8Ap69EBGDW6dtmm6n4UMokOWpfbCstSJjAEPERFRM5myQRsw1Nh5TkH99x1l50VLnYoDqD91DUjBJHyENRgb1mswHc+kNQQ10dzfPRNXbwx6nB6jWPA7pI0BTyLjJ5OIiCgKxmC78f4NGBp0nynw8VNxzLRvtPgd+ua0T6zb2xymeVeSU2ATarAzDe+G1zgiChkDHiIiohgyBUJ6lThTNsfP2jiRyu7onKqr+V2vx+kYkWiH6XGvYhN+uQU+Xu1wC/BYnS2S/FZpY4YnkTHgISIiamFOawatwngAznN5WhO/c3gmYKPvtZD04zdHuPs7ZXnsx04Neo7WeI2IEhUDHiIiolbqSqwz3h+tTI5JKB13P2vq6FXuIpXtaEtr3LAcdSRxDg95Y8BDRETUxpiKJMhsEOBveFmoc3B0bvv7Of4EbMQajPW1rTrEz7St15A7r6BNZnFSW3GQRE5YpY28MeAhIiKKA6Zs0BqMtVVuMw25CicT4jdY8s70uAcyzRXqsDJ1+JperU2txuYkVouaElFoGPAQERHFKZkJ0jNCajbI77o74XA7tmnhUbftopGlcStG4PSYae0dldc5Y3W2SOOQNvLGgIeIiCjBOGWDosHvELRw7jc9pi/46iSUymtOa+84YaYnNl544VP478rW4ty5pGg2h1oxBjxERERknBe0BmMjkuVpznA1p9Lcbm3Sh6a5H995qFpzFhJl0BN9L798D37843t8bn0IQI9oNodaMQY8REREZORUHCHUIMapgIDXcZyCHb+m4k94GxODgh4ZEKkBjd/gprVXgEskkyZNAnAcQDWAHI+tD+DXv54f/UZRq8SAh4iIiHwLt1S2ac6O3229HncanvY2JlqPR6ICm9/5RE44fyey8vLyABQBOABgsMuWdQC+wJVXXhmLZlErxICHiIiIms2UDQJCmxvkFEg4ZVRCmYfTXM0dnsasULRcCOBzuAc8XwLIQZ8+fWLSImp9GPAQERFR1HitGeQnkAhlTR05jE2vptac4MhvsOM0b4fBTvRs2LAQY8ZMQqBaW7LDVp8DuBBJSSxakKgY8BAREVFMOQ2LUwMhL17D2FqKKejhvJ/oGT58OALBzjEAXR22+hwrV/4udo2iVocBDxEREbUKpkBIBkGhBg1ua+U4uRbv4w+YYrvPz3wivwuxUuSlpqYCOB+BLI4p4KkAUIEJEybEtF3UujDgISIiolbLLQiKpAak4A38NRDC8Dmn41BsPf/8Pbj11n8GMM7w6AEA3dGhQ4cYt4paEwY8RERE1KY4DYlzG87WnDV13PhdG4iiZ8qUKQB+AuAsgEzt0c+xcOFPY98oalUY8BAREVFcmIo/Bd0XzpwezrlpW7p37w4gD4HFRUuURxoAHPw+IKJExoCHiIiI4pYpCPIzTyecBVJN2zB4ihVZnloNeL4CkIbBgwe3SIuo9WDAQ0RERAnlWrxvvD8wh8dOXWzUK3Bx2qYBKZiOP4bRUvLr/fcXYMqU6QAEAFl++nMAFyA52alcNSUKBjxEREREAKbhXeP9/41rADQv6KHouvzyywHUADgJIP/7ez/H73+/uOUaRa0GAx4iIiIiF6bsjCkbRC2nXbt2AHohUJUtH8A3AMoxefLklmwWtRIMeIiIiIhC5JUNotj71a/m4q67ngYwEoHApxBdunRp4VZRa8BBjdTm1dbWYsGCBaitrW3ppiQ8XovWg9ei9eC1aB1idR2m44+YhnetG+fuBIvWtQhUY/sSwDkEAp4LI3p8aruShBCipRtBzs6cOYPc3FxUVVVx0SwHPEetB69F68Fr0XrwWrQOvA6th9O1aO41EkIgObkTgKkAVuLDD1dh7NixkWs4tVnM8BARERFRm5eUlIRAVmcDgAaMGDGihVtErQUDHiIiIiKKC2+88QACw9p6Iy0traWbQ60Eixa0cnLE4ZkzZ1q4Ja2XPDc8Ry2P16L14LVoPXgtWgdeh9bD6VrIv5sz22LChAkAkvHb394d9jEo/nAOTyv31VdfoXv37i3dDCIiIqKYOHLkCLp16xb2/gcPHkRhYSGysrIi2CpqyxjwtHKNjY34y1/+gpycnO/HphIRERHFHyEEqqurUVxcjORkzrqgyGHAQ0REREREcYvhMxERERERxS0GPEREREREFLcY8BARERERUdxiwENRt2TJEgwcOBAdOnRAhw4dMHLkSLz33nvW47Nnz0ZSUpLtpi8WVltbi3nz5iEvLw/t27fH3/zN3+Crr76ybVNRUYGZM2ciNzcXubm5mDlzJiorK23bHD58GNdccw3at2+PvLw8zJ8/H3V1dbZtysrKMG7cOGRmZqJr167453/+52aVyGytFi1ahKSkJNx1113WfUIILFiwAMXFxcjMzMT48eOxe/du2368FpFnuhb8XMTGggULgs5zYWGh9Tg/E7HjdS34mYido0eP4sYbb0Tnzp2RlZWFwYMHY/v27dbj/FxQmyOIouytt94S77zzjti7d6/Yu3evuP/++0VaWprYtWuXEEKIWbNmiSuvvFIcO3bMup06dcp2jDlz5oiuXbuKNWvWiB07dogrrrhCDBo0SNTX11vbXHnllaK0tFRs3LhRbNy4UZSWloqpU6daj9fX14vS0lJxxRVXiB07dog1a9aI4uJiMXfuXGubqqoqUVBQIK6//npRVlYmXn/9dZGTkyOeeOKJKJ+l2Nq6davo1auXGDhwoLjzzjut+x977DGRk5MjXn/9dVFWViauu+46UVRUJM6cOWNtw2sRWU7Xgp+L2Hj44YfFxRdfbDvPJ06csB7nZyJ2vK4FPxOxcfr0adGzZ08xe/ZssWXLFnHo0CHxwQcfiM8//9zahp8LamsY8FCL6NSpk/jd734nhAj8J3bttdc6bltZWSnS0tLEihUrrPuOHj0qkpOTxapVq4QQQuzZs0cAEJs3b7a22bRpkwAgPvvsMyGEEO+++65ITk4WR48etbZ55ZVXREZGhqiqqhJCCPHv//7vIjc3V3z33XfWNosWLRLFxcWisbGx+S+8FaiurhZ9+vQRa9asEePGjbM62Y2NjaKwsFA89thj1rbfffedyM3NFc8++6wQgtci0pyuhRD8XMTKww8/LAYNGmR8jJ+J2HK7FkLwMxErP//5z8WYMWMcH+fngtoiDmmjmGpoaMCKFSvw7bffYuTIkdb969atQ35+Pvr27Ytbb70VJ06csB7bvn07zp07h8mTJ1v3FRcXo7S0FBs3bgQAbNq0Cbm5uRg+fLi1zYgRI5Cbm2vbprS0FMXFxdY2U6ZMQW1trZWq37RpE8aNG4eMjAzbNn/5y1/wxRdfRPZktJA77rgDV199Nf7qr/7Kdv+hQ4dQXl5uO88ZGRkYN26cdQ55LSLL6VpI/FzExv79+1FcXIzevXvj+uuvx8GDBwHwM9ESnK6FxM9E9L311lsYNmwYpk+fjvz8fAwZMgTPP/+89Tg/F9QWMeChmCgrK0N2djYyMjIwZ84crFy5EiUlJQCAq666Ci+99BLWrl2LJ598Etu2bcOECRNQW1sLACgvL0d6ejo6depkO2ZBQQHKy8utbfLz84OeNz8/37ZNQUGB7fFOnTohPT3ddRv5t9ymLVuxYgV27NiBRYsWBT0mX5/p9avnh9ciMtyuBcDPRawMHz4cy5cvx/vvv4/nn38e5eXlGDVqFE6dOsXPRIy5XQuAn4lYOXjwIJYsWYI+ffrg/fffx5w5czB//nwsX74cAP+voLYptaUbQImhX79+2LlzJyorK/H6669j1qxZWL9+PUpKSnDddddZ25WWlmLYsGHo2bMn3nnnHUybNs3xmEIIJCUlWX+rv0dyG/H9xEfTvm3JkSNHcOedd2L16tVo166d43am1+/12nktQuPnWvBzERtXXXWV9fuAAQMwcuRIXHDBBVi2bJk1IZ6fidhwuxb33HMPPxMx0tjYiGHDhmHhwoUAgCFDhmD37t1YsmQJbrrpJms7fi6oLWGGh2IiPT0dF154IYYNG4ZFixZh0KBB+PWvf23ctqioCD179sT+/fsBAIWFhairq0NFRYVtuxMnTljf4hQWFuL48eNBxzp58qRtG/3bnoqKCpw7d851GzlkQv8Gqa3Zvn07Tpw4gaFDhyI1NRWpqalYv349fvOb3yA1NdXxGzH9PPNaNJ/XtWhoaAjah5+L2Gjfvj0GDBiA/fv3WxXC+JloGeq1MOFnIjqKioqsERhS//79cfjwYQDg54LaJAY81CKEENYwBN2pU6dw5MgRFBUVAQCGDh2KtLQ0rFmzxtrm2LFj2LVrF0aNGgUAGDlyJKqqqrB161Zrmy1btqCqqsq2za5du3Ds2DFrm9WrVyMjIwNDhw61tvnwww9tJS9Xr16N4uJi9OrVKzIvvoVMnDgRZWVl2Llzp3UbNmwYZsyYgZ07d+L8889HYWGh7TzX1dVh/fr11jnktYgMr2uRkpIStA8/F7FRW1uL//u//0NRURF69+7Nz0QLUq+FCT8T0TF69Gjs3bvXdt++ffvQs2dPAODngtqm2NRGoER23333iQ8//FAcOnRIfPrpp+L+++8XycnJYvXq1aK6ulr87Gc/Exs3bhSHDh0S//M//yNGjhwpunbtGlTeslu3buKDDz4QO3bsEBMmTDCWtxw4cKDYtGmT2LRpkxgwYICxvOXEiRPFjh07xAcffCC6detmK29ZWVkpCgoKxA033CDKysrEG2+8ITp06BC35S31ymCPPfaYyM3NFW+88YYoKysTN9xwg7HUKK9F5KnXgp+L2PnZz34m1q1bJw4ePCg2b94spk6dKnJycsQXX3whhOBnIpbcrgU/E7GzdetWkZqaKh599FGxf/9+8dJLL4msrCzx+9//3tqGnwtqaxjwUNT9/d//vejZs6dIT08XXbp0ERMnThSrV68WQghRU1MjJk+eLLp06SLS0tJEjx49xKxZs8Thw4dtxzh79qyYO3euOO+880RmZqaYOnVq0DanTp0SM2bMEDk5OSInJ0fMmDFDVFRU2Lb58ssvxdVXXy0yMzPFeeedJ+bOnWsrZSmEEJ9++qkYO3asyMjIEIWFhWLBggVxW9pSD3gaGxvFww8/LAoLC0VGRoa4/PLLRVlZmW0fXovoUK8FPxexI9cPSUtLE8XFxWLatGli9+7d1uP8TMSO27XgZyK2/vjHP4rS0lKRkZEhLrroIvHcc8/ZHufngtqaJCG4FC0REREREcUnzuEhIiIiIqK4xYCHiIiIiIjiFgMeIiIiIiKKWwx4iIiIiIgobjHgISIiIiKiuMWAh4iIiIiI4hYDHiIiIiIiilsMeIiIiIiIKG4x4CEiIiIiorjFgIeIiIiIiOIWAx4iIiIiIopbDHiIiIiIiChuMeAhIiIiIqK4xYCHiIiIiIjiFgMeIiIiIiKKWwx4iIiIiIgobjHgISIiIiKiuMWAh4iIiIiI4hYDHiIiIiIiilsMeIiIiIiIKG4x4CEiIiIiorjFgIeIiIiIiOIWAx4iIiIiIopbDHiIiIiIiChuMeAhIiIiIqK4xYCHiIiIiIjiFgMeIiIiIiKKWwx4iIiIiIgobjHgISIiIiKiuMWAh4iIiIiI4hYDHiIiIiIiilsMeIiIiIiIKG6ltnQDiIgiSQiBc+fOoaGhASkpKUhLS0NSUlJLN4uIiIhaCAMeIooL586dw/Hjx3H06FGcPXvWuj8zMxNdu3ZFQUEB0tLSWrCFRERE1BLiYkjb7Nmz8YMf/KClm0FELeT06dPYvHkzDhw4gOzsbJSUlGDgwIEoKSlBdnY2Dhw4gM2bN+P06dMt3VQiIopTjY2NYe+7aNEiXHrppcjJyUF+fj5+8IMfYO/evRFsXWJjhoeI2rTTp0+jrKwMnTp1Qr9+/ZCRkWF7PD8/H7W1tdi7dy/KysowYMAAnHfeeS3UWiIiiieNjY1ITk7GkSNHUFVVhdLS0rCOs379etxxxx249NJLUV9fjwceeACTJ0/Gnj170L59+wi3OvHEXcAzfvx4DBgwACkpKVi2bBnS09PxyCOPYMaMGZg7dy5ee+015Ofn45lnnsFVV10FAGhoaMBPfvITrF27FuXl5ejRowduv/123HnnndZx6+vrcc8992D58uVISUnBLbfcgvLyclRVVeH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+ "text/html": [ + "\n", + "
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"CONFIG_TXT = f'''# vim: set filetype=cfg:\n", - "mintpy.load.processor = hyp3\n", - "##---------interferogram datasets:\n", - "mintpy.load.unwFile = {hyp3_dir}/*/*unw_phase_clip.tif\n", - "mintpy.load.corFile = {hyp3_dir}/*/*corr_clip.tif\n", - "##---------geometry datasets:\n", - "mintpy.load.demFile = {hyp3_dir}/*/*dem_clip.tif\n", - "mintpy.load.incAngleFile = {hyp3_dir}/*/*lv_theta_clip.tif\n", - "mintpy.load.waterMaskFile = {hyp3_dir}/*/*water_mask_clip.tif\n", - "'''\n", - "print(CONFIG_TXT)\n", - "configName = os.path.join(work_dir, \"{}.txt\".format(proj_name))\n", - "configure_template_file(configName, CONFIG_TXT)" ->>>>>>> c3c4ae5bb2dc58434c316398a945813836bfe5fa ] }, { @@ -209,7 +193,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -225,20 +209,9 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "707" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "mintpy_config = work_dir / 'mintpy_config.txt'\n", "mintpy_config.write_text(\n", @@ -264,869 +237,11 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": null, "metadata": { "scrolled": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "___________________________________________________________\n", - "\n", - " /## /## /## /## /####### \n", - " | ### /###|__/ | ## | ##__ ## \n", - " | #### /#### /## /####### /###### | ## \\ ## /## /##\n", - " | ## ##/## ##| ##| ##__ ##|_ ##_/ | #######/| ## | ##\n", - " | ## ###| ##| ##| ## \\ ## | ## | ##____/ | ## | ##\n", - " | ##\\ # | ##| ##| ## | ## | ## /##| ## | ## | ##\n", - " | ## \\/ | ##| ##| ## | ## | ####/| ## | #######\n", - " |__/ |__/|__/|__/ |__/ \\___/ |__/ \\____ ##\n", - " /## | ##\n", - " | ######/\n", - " Miami InSAR Time-series software in Python \\______/ \n", - " MintPy v1.3.3, 2022-04-14\n", - "___________________________________________________________\n", - "\n", - "--RUN-at-2022-05-05 20:15:43.276408--\n", - "Current directory: /media/jzhu4/data/hyp3-mintpy/Ridgecrest\n", - "Run routine processing with smallbaselineApp.py on steps: ['load_data', 'modify_network', 'reference_point', 'quick_overview', 'correct_unwrap_error', 'invert_network', 'correct_LOD', 'correct_SET', 'correct_troposphere', 'deramp', 'correct_topography', 'residual_RMS', 'reference_date', 'velocity', 'geocode', 'google_earth', 'hdfeos5']\n", - "Remaining steps: ['modify_network', 'reference_point', 'quick_overview', 'correct_unwrap_error', 'invert_network', 'correct_LOD', 'correct_SET', 'correct_troposphere', 'deramp', 'correct_topography', 'residual_RMS', 'reference_date', 'velocity', 'geocode', 'google_earth', 'hdfeos5']\n", - "--------------------------------------------------\n", - "Project name: mintpy_config\n", - "Go to work directory: /media/jzhu4/data/hyp3-mintpy/Ridgecrest\n", - "copy default template file /home/jzhu4/apps/anaconda3/envs/hyp3-mintpy/lib/python3.8/site-packages/mintpy/defaults/smallbaselineApp.cfg to work directory\n", - "read custom template file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/mintpy_config.txt\n", - "update default template based on input custom template\n", - " mintpy.load.processor: auto --> hyp3\n", - " mintpy.load.unwFile: auto --> /media/jzhu4/data/hyp3-mintpy/Ridgecrest/data/*/*_unw_phase_clipped.tif\n", - " mintpy.load.corFile: auto --> /media/jzhu4/data/hyp3-mintpy/Ridgecrest/data/*/*_corr_clipped.tif\n", - " mintpy.load.demFile: auto --> /media/jzhu4/data/hyp3-mintpy/Ridgecrest/data/*/*_dem_clipped.tif\n", - " mintpy.load.incAngleFile: auto --> /media/jzhu4/data/hyp3-mintpy/Ridgecrest/data/*/*_lv_theta_clipped.tif\n", - " mintpy.load.waterMaskFile: auto --> /media/jzhu4/data/hyp3-mintpy/Ridgecrest/data/*/*_water_mask_clipped.tif\n", - "copy mintpy_config.txt to inputs directory for backup.\n", - "copy smallbaselineApp.cfg to inputs directory for backup.\n", - "copy mintpy_config.txt to pic directory for backup.\n", - "copy smallbaselineApp.cfg to pic directory for backup.\n", - "read default template file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/smallbaselineApp.cfg\n", - "\n", - "\n", - "******************** step - load_data ********************\n", - "\n", - "load_data.py --template /media/jzhu4/data/hyp3-mintpy/Ridgecrest/smallbaselineApp.cfg /media/jzhu4/data/hyp3-mintpy/Ridgecrest/mintpy_config.txt --project mintpy_config\n", - "processor : hyp3\n", - "SAR platform/sensor : unknown from project name \"mintpy_config\"\n", - "--------------------------------------------------\n", - "prepare metadata files for hyp3 products\n", - "prep_hyp3.py /media/jzhu4/data/hyp3-mintpy/Ridgecrest/data/*/*_unw_phase_clipped.tif\n", - "prep_hyp3.py /media/jzhu4/data/hyp3-mintpy/Ridgecrest/data/*/*_corr_clipped.tif\n", - "prep_hyp3.py /media/jzhu4/data/hyp3-mintpy/Ridgecrest/data/*/*_dem_clipped.tif\n", - "prep_hyp3.py /media/jzhu4/data/hyp3-mintpy/Ridgecrest/data/*/*_lv_theta_clipped.tif\n", - "prep_hyp3.py /media/jzhu4/data/hyp3-mintpy/Ridgecrest/data/*/*_water_mask_clipped.tif\n", - "--------------------------------------------------\n", - "searching interferometric pairs info\n", - "input data files:\n", - "unwrapPhase : /media/jzhu4/data/hyp3-mintpy/Ridgecrest/data/*/*_unw_phase_clipped.tif\n", - "coherence : /media/jzhu4/data/hyp3-mintpy/Ridgecrest/data/*/*_corr_clipped.tif\n", - "number of unwrapPhase : 11\n", - "number of coherence : 11\n", - "--------------------------------------------------\n", - "searching geometry files info\n", - "input data files:\n", - "height : /media/jzhu4/data/hyp3-mintpy/Ridgecrest/data/S1AA_20190610T135156_20190622T135157_VVP012_INT80_G_ueF_1262/S1AA_20190610T135156_20190622T135157_VVP012_INT80_G_ueF_1262_dem_clipped.tif\n", - "incidenceAngle : /media/jzhu4/data/hyp3-mintpy/Ridgecrest/data/S1AA_20190610T135156_20190622T135157_VVP012_INT80_G_ueF_1262/S1AA_20190610T135156_20190622T135157_VVP012_INT80_G_ueF_1262_lv_theta_clipped.tif\n", - "waterMask : /media/jzhu4/data/hyp3-mintpy/Ridgecrest/data/S1AA_20190610T135156_20190622T135157_VVP012_INT80_G_ueF_1262/S1AA_20190610T135156_20190622T135157_VVP012_INT80_G_ueF_1262_water_mask_clipped.tif\n", - "--------------------------------------------------\n", - "updateMode : True\n", - "compression: None\n", - "x/ystep: 1/1\n", - "--------------------------------------------------\n", - "create HDF5 file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/ifgramStack.h5 with w mode\n", - "create dataset /unwrapPhase of in size of (11, 2859, 3633) with compression = None\n", - "[==================================================] 20190809_20190821 0s / 0s\n", - "create dataset /coherence of in size of (11, 2859, 3633) with compression = None\n", - "[==================================================] 20190809_20190821 0s / 0s\n", - "create dataset /date of in size of (11, 2)\n", - "create dataset /bperp of in size of (11,)\n", - "create dataset /dropIfgram of in size of (11,)\n", - "add extra metadata: {'PROJECT_NAME': 'mintpy_config'}\n", - "Finished writing to /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/ifgramStack.h5\n", - "--------------------------------------------------\n", - "create HDF5 file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/geometryGeo.h5 with w mode\n", - "create dataset /height of in size of (2859, 3633) with compression = lzf\n", - "create dataset /incidenceAngle of in size of (2859, 3633) with compression = lzf\n", - " convert incidenceAngle from Gamma (from horizontal in radian) to MintPy (from vertical in degree) convention.\n", - "create dataset /waterMask of in size of (2859, 3633) with compression = lzf\n", - "prepare slantRangeDistance ...\n", - " geocoded input, use incidenceAngle from file: S1AA_20190610T135156_20190622T135157_VVP012_INT80_G_ueF_1262_lv_theta_clipped.tif\n", - " convert incidence angle from Gamma to MintPy convention.\n", - "create dataset /slantRangeDistance of in size of (2859, 3633) with compression = lzf\n", - "Finished writing to /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/geometryGeo.h5\n", - "time used: 00 mins 3.2 secs.\n", - "\n", - "No lookup table info range/lat found in files.\n", - "Input data seems to be geocoded. Lookup file not needed.\n", - "Loaded dataset are processed by InSAR software: hyp3\n", - "Loaded dataset is in GEO coordinates\n", - "Interferograms Stack: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/ifgramStack.h5\n", - "Geometry File : /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/geometryGeo.h5\n", - "Lookup Table File : None\n", - "--------------------------------------------------\n", - "All data needed found/loaded/copied. Processed 2-pass InSAR data can be removed.\n", - "--------------------------------------------------\n", - "updating ifgramStack.h5, geometryGeo.h5 metadata based on custom template file: mintpy_config.txt\n", - "\n", - "\n", - "******************** step - modify_network ********************\n", - "Input data seems to be geocoded. Lookup file not needed.\n", - "generate /media/jzhu4/data/hyp3-mintpy/Ridgecrest/waterMask.h5 from /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/geometryGeo.h5 for conveniency\n", - "create HDF5 file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/waterMask.h5 with w mode\n", - "create dataset /waterMask of bool in size of (2859, 3633) with compression=None\n", - "finished writing to /media/jzhu4/data/hyp3-mintpy/Ridgecrest/waterMask.h5\n", - "\n", - "modify_network.py /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/ifgramStack.h5 -t /media/jzhu4/data/hyp3-mintpy/Ridgecrest/smallbaselineApp.cfg\n", - "No lookup table info range/lat found in files.\n", - "read options from template file: smallbaselineApp.cfg\n", - "No input option found to remove interferogram\n", - "Keep all interferograms by enable --reset option\n", - "--------------------------------------------------\n", - "reset dataset 'dropIfgram' to True for all interferograms for file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/ifgramStack.h5\n", - "All dropIfgram are already True, no need to reset.\n", - "\n", - "plot_network.py /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/ifgramStack.h5 -t /media/jzhu4/data/hyp3-mintpy/Ridgecrest/smallbaselineApp.cfg --nodisplay -d coherence -v 0.2 1.0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "read options from template file: smallbaselineApp.cfg\n", - "read temporal/spatial baseline info from file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/ifgramStack.h5\n", - "open ifgramStack file: ifgramStack.h5\n", - "calculating spatial mean of coherence in file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/ifgramStack.h5 ...\n", - "read mask from file: waterMask.h5\n", - "[==================================================] 11/11 0s / 0s \n", - "write average value in space into text file: coherenceSpatialAvg.txt\n", - "number of acquisitions: 7\n", - "number of interferograms: 11\n", - "shift all perp baseline by 88.67054748535156 to zero mean for plotting\n", - "--------------------------------------------------\n", - "number of interferograms marked as drop: 0\n", - "number of interferograms marked as keep: 11\n", - "number of acquisitions marked as drop: 0\n", - "save figure to pbaseHistory.pdf\n", - "save figure to coherenceMatrix.pdf\n", - "save figure to coherenceHistory.pdf\n", - "max perpendicular baseline: 111.93 m\n", - "max temporal baseline: 24.0 days\n", - "showing coherence\n", - "data range: [0.88170004, 0.975093]\n", - "display range: [0.2, 1.0]\n", - "save figure to network.pdf\n", - "\n", - "\n", - "******************** step - reference_point ********************\n", - "Input data seems to be geocoded. Lookup file not needed.\n", - "\n", - "generate_mask.py /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/ifgramStack.h5 --nonzero -o /media/jzhu4/data/hyp3-mintpy/Ridgecrest/maskConnComp.h5 --update\n", - "input ifgramStack file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/ifgramStack.h5\n", - "--------------------------------------------------\n", - "update mode: ON\n", - "1) output file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/maskConnComp.h5 NOT exist.\n", - "run or skip: run.\n", - "calculate the common mask of pixels with non-zero unwrapPhase value\n", - "[==================================================] 11/11 0s / 0s \n", - "create HDF5 file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/maskConnComp.h5 with w mode\n", - "create dataset /mask of bool in size of (2859, 3633) with compression=None\n", - "finished writing to /media/jzhu4/data/hyp3-mintpy/Ridgecrest/maskConnComp.h5\n", - "\n", - "temporal_average.py /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/ifgramStack.h5 --dataset coherence -o /media/jzhu4/data/hyp3-mintpy/Ridgecrest/avgSpatialCoh.h5 --update\n", - "output file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/avgSpatialCoh.h5\n", - "--------------------------------------------------\n", - "update mode: ON\n", - "1) output file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/avgSpatialCoh.h5 NOT exist.\n", - "run or skip: run.\n", - "calculate the temporal average of coherence in file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/ifgramStack.h5 ...\n", - "[==================================================] lines 2859/2859 \n", - "create HDF5 file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/avgSpatialCoh.h5 with w mode\n", - "create dataset /coherence of float32 in size of (2859, 3633) with compression=None\n", - "finished writing to /media/jzhu4/data/hyp3-mintpy/Ridgecrest/avgSpatialCoh.h5\n", - "time used: 00 mins 1.4 secs\n", - "\n", - "Input data seems to be geocoded. Lookup file not needed.\n", - "\n", - "reference_point.py /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/ifgramStack.h5 -t /media/jzhu4/data/hyp3-mintpy/Ridgecrest/smallbaselineApp.cfg -c /media/jzhu4/data/hyp3-mintpy/Ridgecrest/avgSpatialCoh.h5\n", - "--------------------------------------------------\n", - "reading reference info from template: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/smallbaselineApp.cfg\n", - "no input reference y/x.\n", - "reference point selection method: maxCoherence\n", - "--------------------------------------------------\n", - "calculate the temporal average of unwrapPhase in file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/ifgramStack.h5 ...\n", - "[==================================================] lines 2859/2859 \n", - "random select pixel with coherence > 0.85\n", - "\tbased on coherence file: avgSpatialCoh.h5\n", - "y/x: (1681, 1987)\n", - "Add/update ref_x/y attribute to file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/ifgramStack.h5\n", - "{'REF_Y': '1681', 'REF_X': '1987', 'REF_LAT': '3907880.0', 'REF_LON': '471640.0'}\n", - "touch avgSpatialCoh.h5\n", - "touch maskConnComp.h5\n", - "Done.\n", - "\n", - "\n", - "******************** step - quick_overview ********************\n", - "Input data seems to be geocoded. Lookup file not needed.\n", - "\n", - "temporal_average.py /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/ifgramStack.h5 --dataset unwrapPhase -o /media/jzhu4/data/hyp3-mintpy/Ridgecrest/avgPhaseVelocity.h5 --update\n", - "output file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/avgPhaseVelocity.h5\n", - "--------------------------------------------------\n", - "update mode: ON\n", - "1) output file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/avgPhaseVelocity.h5 NOT exist.\n", - "run or skip: run.\n", - "calculate the temporal average of unwrapPhase in file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/ifgramStack.h5 ...\n", - "[==================================================] lines 2859/2859 \n", - "create HDF5 file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/avgPhaseVelocity.h5 with w mode\n", - "create dataset /velocity of float32 in size of (2859, 3633) with compression=None\n", - "finished writing to /media/jzhu4/data/hyp3-mintpy/Ridgecrest/avgPhaseVelocity.h5\n", - "time used: 00 mins 1.7 secs\n", - "\n", - "\n", - "unwrap_error_phase_closure.py /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/ifgramStack.h5 --water-mask /media/jzhu4/data/hyp3-mintpy/Ridgecrest/waterMask.h5 --action calculate --update\n", - "open ifgramStack file: ifgramStack.h5\n", - "number of interferograms: 11\n", - "number of triplets: 5\n", - "calculating the number of triplets with non-zero integer ambiguity of closure phase ...\n", - " block by block with size up to (2860, 3633), 1 blocks in total\n", - "reference pixel in y/x: (1681, 1987) from dataset: unwrapPhase\n", - "[==================================================] line 0 / 2859 \n", - "mask out pixels with zero in file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/waterMask.h5\n", - "mask out pixels with zero in file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/avgSpatialCoh.h5\n", - "write to file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/numTriNonzeroIntAmbiguity.h5\n", - "create HDF5 file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/numTriNonzeroIntAmbiguity.h5 with w mode\n", - "create dataset /mask of float32 in size of (2859, 3633) with compression=None\n", - "finished writing to /media/jzhu4/data/hyp3-mintpy/Ridgecrest/numTriNonzeroIntAmbiguity.h5\n", - "plot and save figure to file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/numTriNonzeroIntAmbiguity.png\n", - "time used: 00 mins 3.0 secs\n", - "Done.\n", - "\n", - "\n", - "******************** step - correct_unwrap_error ********************\n", - "phase-unwrapping error correction is OFF.\n", - "\n", - "\n", - "******************** step - invert_network ********************\n", - "Input data seems to be geocoded. Lookup file not needed.\n", - "\n", - "ifgram_inversion.py /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/ifgramStack.h5 -t /media/jzhu4/data/hyp3-mintpy/Ridgecrest/smallbaselineApp.cfg --update\n", - "use dataset \"unwrapPhase\" by default\n", - "--------------------------------------------------\n", - "update mode: ON\n", - "1) NOT ALL output files found: ['timeseries.h5', 'temporalCoherence.h5', 'numInvIfgram.h5'].\n", - "run or skip: run.\n", - "save the original settings of ['OMP_NUM_THREADS', 'OPENBLAS_NUM_THREADS', 'MKL_NUM_THREADS', 'NUMEXPR_NUM_THREADS', 'VECLIB_MAXIMUM_THREADS']\n", - "set OMP_NUM_THREADS = 1\n", - "set OPENBLAS_NUM_THREADS = 1\n", - "set MKL_NUM_THREADS = 1\n", - "set NUMEXPR_NUM_THREADS = 1\n", - "set VECLIB_MAXIMUM_THREADS = 1\n", - "reference pixel in y/x: (1681, 1987) from dataset: unwrapPhase\n", - "-------------------------------------------------------------------------------\n", - "least-squares solution with L2 min-norm on: deformation velocity\n", - "minimum redundancy: 1.0\n", - "weight function: var\n", - "calculate covariance: False \n", - "mask: no\n", - "-------------------------------------------------------------------------------\n", - "number of interferograms: 11\n", - "number of acquisitions : 7\n", - "number of lines : 2859\n", - "number of columns : 3633\n", - "--------------------------------------------------\n", - "create HDF5 file: timeseries.h5 with w mode\n", - "create dataset : date of |S8 in size of (7,) with compression = None\n", - "create dataset : bperp of in size of (7,) with compression = None\n", - "create dataset : timeseries of in size of (7, 2859, 3633) with compression = None\n", - "close HDF5 file: timeseries.h5\n", - "--------------------------------------------------\n", - "create HDF5 file: temporalCoherence.h5 with w mode\n", - "create dataset : temporalCoherence of in size of (2859, 3633) with compression = None\n", - "close HDF5 file: temporalCoherence.h5\n", - "--------------------------------------------------\n", - "create HDF5 file: numInvIfgram.h5 with w mode\n", - "create dataset : mask of in size of (2859, 3633) with compression = None\n", - "close HDF5 file: numInvIfgram.h5\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "calculating weight from spatial coherence ...\n", - "reading coherence in (0, 0, 3633, 2859) * 11 ...\n", - "convert coherence to weight in chunks of 100000 pixels: 104 chunks in total ...\n", - "convert coherence to weight using inverse of phase variance\n", - " with phase PDF for distributed scatterers from Tough et al. (1995)\n", - " number of independent looks L=41\n", - "chunk 1 / 104\n", - "chunk 2 / 104\n", - "chunk 3 / 104\n", - "chunk 4 / 104\n", - "chunk 5 / 104\n", - "chunk 6 / 104\n", - "chunk 7 / 104\n", - "chunk 8 / 104\n", - "chunk 9 / 104\n", - "chunk 10 / 104\n", - "chunk 11 / 104\n", - "chunk 12 / 104\n", - "chunk 13 / 104\n", - "chunk 14 / 104\n", - "chunk 15 / 104\n", - "chunk 16 / 104\n", - "chunk 17 / 104\n", - "chunk 18 / 104\n", - "chunk 19 / 104\n", - "chunk 20 / 104\n", - "chunk 21 / 104\n", - "chunk 22 / 104\n", - "chunk 23 / 104\n", - "chunk 24 / 104\n", - "chunk 25 / 104\n", - "chunk 26 / 104\n", - "chunk 27 / 104\n", - "chunk 28 / 104\n", - "chunk 29 / 104\n", - "chunk 30 / 104\n", - "chunk 31 / 104\n", - "chunk 32 / 104\n", - "chunk 33 / 104\n", - "chunk 34 / 104\n", - "chunk 35 / 104\n", - "chunk 36 / 104\n", - "chunk 37 / 104\n", - "chunk 38 / 104\n", - "chunk 39 / 104\n", - "chunk 40 / 104\n", - "chunk 41 / 104\n", - "chunk 42 / 104\n", - "chunk 43 / 104\n", - "chunk 44 / 104\n", - "chunk 45 / 104\n", - "chunk 46 / 104\n", - "chunk 47 / 104\n", - "chunk 48 / 104\n", - "chunk 49 / 104\n", - "chunk 50 / 104\n", - "chunk 51 / 104\n", - "chunk 52 / 104\n", - "chunk 53 / 104\n", - "chunk 54 / 104\n", - "chunk 55 / 104\n", - "chunk 56 / 104\n", - "chunk 57 / 104\n", - "chunk 58 / 104\n", - "chunk 59 / 104\n", - "chunk 60 / 104\n", - "chunk 61 / 104\n", - "chunk 62 / 104\n", - "chunk 63 / 104\n", - "chunk 64 / 104\n", - "chunk 65 / 104\n", - "chunk 66 / 104\n", - "chunk 67 / 104\n", - "chunk 68 / 104\n", - "chunk 69 / 104\n", - "chunk 70 / 104\n", - "chunk 71 / 104\n", - "chunk 72 / 104\n", - "chunk 73 / 104\n", - "chunk 74 / 104\n", - "chunk 75 / 104\n", - "chunk 76 / 104\n", - "chunk 77 / 104\n", - "chunk 78 / 104\n", - "chunk 79 / 104\n", - "chunk 80 / 104\n", - "chunk 81 / 104\n", - "chunk 82 / 104\n", - "chunk 83 / 104\n", - "chunk 84 / 104\n", - "chunk 85 / 104\n", - "chunk 86 / 104\n", - "chunk 87 / 104\n", - "chunk 88 / 104\n", - "chunk 89 / 104\n", - "chunk 90 / 104\n", - "chunk 91 / 104\n", - "chunk 92 / 104\n", - "chunk 93 / 104\n", - "chunk 94 / 104\n", - "chunk 95 / 104\n", - "chunk 96 / 104\n", - "chunk 97 / 104\n", - "chunk 98 / 104\n", - "chunk 99 / 104\n", - "chunk 100 / 104\n", - "chunk 101 / 104\n", - "chunk 102 / 104\n", - "chunk 103 / 104\n", - "chunk 104 / 104\n", - "reading unwrapPhase in (0, 0, 3633, 2859) * 11 ...\n", - "use input reference value\n", - "convert zero value in unwrapPhase to NaN (no-data value)\n", - "skip pixels (on the water) with zero value in file: waterMask.h5\n", - "skip pixels with unwrapPhase = NaN in all interferograms\n", - "skip pixels with zero value in file: avgSpatialCoh.h5\n", - "number of pixels to invert: 6411068 out of 10386747 (61.7%)\n", - "estimating time-series via WLS pixel-by-pixel ...\n", - "[==================================================] 6411068/6411068 pixels 777s / 15s\n", - "converting LOS phase unit from radian to meter\n", - "--------------------------------------------------\n", - "open HDF5 file timeseries.h5 in a mode\n", - "writing dataset /timeseries block: [0, 7, 0, 2859, 0, 3633]\n", - "close HDF5 file timeseries.h5.\n", - "--------------------------------------------------\n", - "open HDF5 file temporalCoherence.h5 in a mode\n", - "writing dataset /temporalCoherence block: [0, 2859, 0, 3633]\n", - "close HDF5 file temporalCoherence.h5.\n", - "--------------------------------------------------\n", - "open HDF5 file numInvIfgram.h5 in a mode\n", - "writing dataset /mask block: [0, 2859, 0, 3633]\n", - "close HDF5 file numInvIfgram.h5.\n", - "--------------------------------------------------\n", - "update values on the reference pixel: (1681, 1987)\n", - "set temporalCoherence on the reference pixel to 1.\n", - "set # of observations on the reference pixel as 11\n", - "roll back to the original settings of ['OMP_NUM_THREADS', 'OPENBLAS_NUM_THREADS', 'MKL_NUM_THREADS', 'NUMEXPR_NUM_THREADS', 'VECLIB_MAXIMUM_THREADS']\n", - "remove env variable OMP_NUM_THREADS\n", - "remove env variable OPENBLAS_NUM_THREADS\n", - "remove env variable MKL_NUM_THREADS\n", - "remove env variable NUMEXPR_NUM_THREADS\n", - "remove env variable VECLIB_MAXIMUM_THREADS\n", - "time used: 13 mins 16.8 secs.\n", - "\n", - "Input data seems to be geocoded. Lookup file not needed.\n", - "\n", - "generate_mask.py /media/jzhu4/data/hyp3-mintpy/Ridgecrest/temporalCoherence.h5 -m 0.7 -o /media/jzhu4/data/hyp3-mintpy/Ridgecrest/maskTempCoh.h5\n", - "update mode: ON\n", - "run or skip: run\n", - "input temporalCoherence file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/temporalCoherence.h5\n", - "read /media/jzhu4/data/hyp3-mintpy/Ridgecrest/temporalCoherence.h5\n", - "create initial mask with the same size as the input file and all = 1\n", - "all pixels with nan value = 0\n", - "exclude pixels with value < 0.7\n", - "create HDF5 file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/maskTempCoh.h5 with w mode\n", - "create dataset /mask of bool in size of (2859, 3633) with compression=None\n", - "finished writing to /media/jzhu4/data/hyp3-mintpy/Ridgecrest/maskTempCoh.h5\n", - "time used: 00 mins 0.1 secs.\n", - "number of reliable pixels: 5971001\n", - "\n", - "\n", - "******************** step - correct_LOD ********************\n", - "Input data seems to be geocoded. Lookup file not needed.\n", - "No local oscillator drift correction is needed for Sen.\n", - "\n", - "\n", - "******************** step - correct_SET ********************\n", - "Input data seems to be geocoded. Lookup file not needed.\n", - "No solid Earth tides correction.\n", - "\n", - "\n", - "******************** step - correct_troposphere ********************\n", - "Input data seems to be geocoded. Lookup file not needed.\n", - "Atmospheric correction using Weather Re-analysis dataset (PyAPS, Jolivet et al., 2011)\n", - "Weather Re-analysis dataset: ERA5\n", - "\n", - "tropo_pyaps3.py -f /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries.h5 --model ERA5 -g /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/geometryGeo.h5 -w /media/jzhu4/data/mintpy_data/weather_data\n", - "weather model: ERA5 - dry (hydrostatic) and wet delay\n", - "weather directory: /media/jzhu4/data/mintpy_data/weather_data\n", - "output tropospheric delay file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/ERA5.h5\n", - "output corrected time-series file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries_ERA5.h5\n", - "read dates/time info from file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries.h5\n", - "time of cloest available product: 14:00 UTC\n", - "\n", - "------------------------------------------------------------------------------\n", - "downloading weather model data using PyAPS ...\n", - "common file size: 759240 bytes\n", - "number of grib files existed : 7\n", - "number of grib files to download: 0\n", - "------------------------------------------------------------------------------\n", - "\n", - "update mode: ON\n", - "output file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/ERA5.h5\n", - "1) output file either do NOT exist or is NOT newer than all GRIB files.\n", - "run or skip: run\n", - "open geometry file: geometryGeo.h5\n", - "reading incidenceAngle data from file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/geometryGeo.h5 ...\n", - "reading height data from file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/geometryGeo.h5 ...\n", - "--------------------------------------------------\n", - "create HDF5 file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/ERA5.h5 with w mode\n", - "create dataset : date of |S8 in size of (7,) with compression = None\n", - "create dataset : timeseries of in size of (7, 2859, 3633) with compression = None\n", - "close HDF5 file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/ERA5.h5\n", - "\n", - "------------------------------------------------------------------------------\n", - "calculating absolute delay for each date using PyAPS (Jolivet et al., 2011; 2014) ...\n", - "number of grib files used: 7\n", - "[==================================================] ERA5_N30_N40_W130_W110_20190821_14.grb 28s / 4s\n", - "\n", - "------------------------------------------------------------------------------\n", - "correcting relative delay for input time-series using diff.py\n", - "diff.py /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries.h5 /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/ERA5.h5 -o /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries_ERA5.h5 --force\n", - "/media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries.h5 - ['/media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/ERA5.h5'] --> /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries_ERA5.h5\n", - "the 1st input file is: timeseries\n", - "--------------------------------------------------\n", - "grab metadata from ref_file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries.h5\n", - "grab dataset structure from ref_file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries.h5\n", - "create HDF5 file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries_ERA5.h5 with w mode\n", - "create dataset : bperp of float32 in size of (7,) with compression = None\n", - "create dataset : date of |S8 in size of (7,) with compression = None\n", - "create dataset : timeseries of float32 in size of (7, 2859, 3633) with compression = None\n", - "close HDF5 file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries_ERA5.h5\n", - "read from file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/ERA5.h5\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "* referencing data from ERA5.h5 to y/x: 1681/1987\n", - "* referencing data from ERA5.h5 to date: 20190610\n", - "read from file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries.h5\n", - "--------------------------------------------------\n", - "open HDF5 file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries_ERA5.h5 in a mode\n", - "writing dataset /timeseries block: [0, 7, 0, 2859, 0, 3633]\n", - "close HDF5 file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries_ERA5.h5.\n", - "time used: 00 mins 1.9 secs\n", - "\n", - "\n", - "******************** step - deramp ********************\n", - "No phase ramp removal.\n", - "\n", - "\n", - "******************** step - correct_topography ********************\n", - "Input data seems to be geocoded. Lookup file not needed.\n", - "\n", - "dem_error.py /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries_ERA5.h5 -t /media/jzhu4/data/hyp3-mintpy/Ridgecrest/smallbaselineApp.cfg -o /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries_ERA5_demErr.h5 --update -g /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/geometryGeo.h5\n", - "read options from template file: smallbaselineApp.cfg\n", - "--------------------------------------------------\n", - "update mode: ON\n", - "1) output file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries_ERA5_demErr.h5 NOT found.\n", - "run or skip: run.\n", - "save the original settings of ['OMP_NUM_THREADS', 'OPENBLAS_NUM_THREADS', 'MKL_NUM_THREADS', 'NUMEXPR_NUM_THREADS', 'VECLIB_MAXIMUM_THREADS']\n", - "set OMP_NUM_THREADS = 1\n", - "set OPENBLAS_NUM_THREADS = 1\n", - "set MKL_NUM_THREADS = 1\n", - "set NUMEXPR_NUM_THREADS = 1\n", - "set VECLIB_MAXIMUM_THREADS = 1\n", - "open timeseries file: timeseries_ERA5.h5\n", - "--------------------------------------------------------------------------------\n", - "correct topographic phase residual (DEM error) (Fattahi & Amelung, 2013, IEEE-TGRS)\n", - "ordinal least squares (OLS) inversion with L2-norm minimization on: phase\n", - "temporal deformation model: polynomial order = 2\n", - "--------------------------------------------------------------------------------\n", - "add/update the following configuration metadata to file:\n", - "['polyOrder', 'phaseVelocity', 'stepFuncDate', 'excludeDate']\n", - "--------------------------------------------------\n", - "create HDF5 file: demErr.h5 with w mode\n", - "create dataset : dem of in size of (2859, 3633) with compression = None\n", - "close HDF5 file: demErr.h5\n", - "--------------------------------------------------\n", - "grab dataset structure from ref_file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries_ERA5.h5\n", - "create HDF5 file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries_ERA5_demErr.h5 with w mode\n", - "create dataset : bperp of float32 in size of (7,) with compression = None\n", - "create dataset : date of |S8 in size of (7,) with compression = None\n", - "create dataset : timeseries of float32 in size of (7, 2859, 3633) with compression = None\n", - "close HDF5 file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries_ERA5_demErr.h5\n", - "--------------------------------------------------\n", - "grab dataset structure from ref_file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries_ERA5.h5\n", - "create HDF5 file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseriesResidual.h5 with w mode\n", - "create dataset : bperp of float32 in size of (7,) with compression = None\n", - "create dataset : date of |S8 in size of (7,) with compression = None\n", - "create dataset : timeseries of float32 in size of (7, 2859, 3633) with compression = None\n", - "close HDF5 file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseriesResidual.h5\n", - "open geometry file: geometryGeo.h5\n", - "read 2D incidenceAngle, slantRangeDistance from geometry file: geometryGeo.h5\n", - "read mean bperp from timeseries file\n", - "skip pixels with ZERO in ALL acquisitions\n", - "skip pixels with NaN in ANY acquisitions\n", - "skip pixels with ZERO temporal coherence\n", - "skip pixels with ZERO / NaN value in incidenceAngle / slantRangeDistance\n", - "number of pixels to invert: 6032690 out of 10386747 (58.1%)\n", - "estimating DEM error pixel-wisely ...\n", - "[==================================================] 6032690/6032690 452s / 9s\n", - "--------------------------------------------------\n", - "open HDF5 file demErr.h5 in a mode\n", - "writing dataset /dem block: [0, 2859, 0, 3633]\n", - "close HDF5 file demErr.h5.\n", - "--------------------------------------------------\n", - "open HDF5 file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries_ERA5_demErr.h5 in a mode\n", - "writing dataset /timeseries block: [0, 7, 0, 2859, 0, 3633]\n", - "close HDF5 file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries_ERA5_demErr.h5.\n", - "--------------------------------------------------\n", - "open HDF5 file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseriesResidual.h5 in a mode\n", - "writing dataset /timeseries block: [0, 7, 0, 2859, 0, 3633]\n", - "close HDF5 file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseriesResidual.h5.\n", - "roll back to the original settings of ['OMP_NUM_THREADS', 'OPENBLAS_NUM_THREADS', 'MKL_NUM_THREADS', 'NUMEXPR_NUM_THREADS', 'VECLIB_MAXIMUM_THREADS']\n", - "remove env variable OMP_NUM_THREADS\n", - "remove env variable OPENBLAS_NUM_THREADS\n", - "remove env variable MKL_NUM_THREADS\n", - "remove env variable NUMEXPR_NUM_THREADS\n", - "remove env variable VECLIB_MAXIMUM_THREADS\n", - "time used: 07 mins 41.6 secs.\n", - "\n", - "\n", - "******************** step - residual_RMS ********************\n", - "\n", - "timeseries_rms.py timeseriesResidual.h5 -t /media/jzhu4/data/hyp3-mintpy/Ridgecrest/smallbaselineApp.cfg\n", - "read options from template file: smallbaselineApp.cfg\n", - "remove quadratic ramp from file: timeseriesResidual.h5\n", - "read mask file: maskTempCoh.h5\n", - "--------------------------------------------------\n", - "grab metadata from ref_file: timeseriesResidual.h5\n", - "grab dataset structure from ref_file: timeseriesResidual.h5\n", - "create HDF5 file: timeseriesResidual_ramp.h5 with w mode\n", - "create dataset : bperp of float32 in size of (7,) with compression = None\n", - "create dataset : date of |S8 in size of (7,) with compression = None\n", - "create dataset : timeseries of float32 in size of (7, 2859, 3633) with compression = None\n", - "close HDF5 file: timeseriesResidual_ramp.h5\n", - "estimating phase ramp one date at a time ...\n", - "[==================================================] 7/7 5s / 0s\n", - "finished writing to file: timeseriesResidual_ramp.h5\n", - "time used: 00 mins 6.4 secs.\n", - "\n", - "calculating residual RMS for each epoch from file: timeseriesResidual_ramp.h5\n", - "read mask from file: maskTempCoh.h5\n", - "reading timeseries data from file: timeseriesResidual_ramp.h5 ...\n", - "[==================================================] 7/7 1s / 0s\n", - "save timeseries RMS to text file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/rms_timeseriesResidual_ramp.txt\n", - "read timeseries residual RMS from file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/rms_timeseriesResidual_ramp.txt\n", - "--------------------------------------------------\n", - "date with min RMS: 20190821 - 0.0049\n", - "save date to file: reference_date.txt\n", - "--------------------------------------------------\n", - "date(s) with RMS > 3.0 * median RMS (0.0230)\n", - "20190704 - 0.0247\n", - "20190716 - 0.0256\n", - "save date(s) to file: exclude_date.txt\n", - "create figure in size: [5.0, 3.0]\n", - "save figure to file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/rms_timeseriesResidual_ramp.pdf\n", - "\n", - "\n", - "******************** step - reference_date ********************\n", - "\n", - "reference_date.py -t /media/jzhu4/data/hyp3-mintpy/Ridgecrest/smallbaselineApp.cfg /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries.h5 /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries_ERA5.h5 /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries_ERA5_demErr.h5\n", - "read reference date from file: reference_date.txt\n", - "input reference date: 20190821\n", - "--------------------------------------------------\n", - "change reference date for file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries.h5\n", - "reading data ...\n", - "referencing in time ...\n", - "--------------------------------------------------\n", - "open HDF5 file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries.h5 in r+ mode\n", - "writing dataset /timeseries block: (0, 7, 0, 2859, 0, 3633)\n", - "close HDF5 file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries.h5.\n", - "update \"REF_DATE\" attribute value to 20190821\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "--------------------------------------------------\n", - "change reference date for file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries_ERA5.h5\n", - "reading data ...\n", - "referencing in time ...\n", - "--------------------------------------------------\n", - "open HDF5 file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries_ERA5.h5 in r+ mode\n", - "writing dataset /timeseries block: (0, 7, 0, 2859, 0, 3633)\n", - "close HDF5 file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries_ERA5.h5.\n", - "update \"REF_DATE\" attribute value to 20190821\n", - "--------------------------------------------------\n", - "change reference date for file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries_ERA5_demErr.h5\n", - "reading data ...\n", - "referencing in time ...\n", - "--------------------------------------------------\n", - "open HDF5 file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries_ERA5_demErr.h5 in r+ mode\n", - "writing dataset /timeseries block: (0, 7, 0, 2859, 0, 3633)\n", - "close HDF5 file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries_ERA5_demErr.h5.\n", - "update \"REF_DATE\" attribute value to 20190821\n", - "time used: 00 mins 23.8 secs.\n", - "\n", - "\n", - "******************** step - velocity ********************\n", - "\n", - "timeseries2velocity.py /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries_ERA5_demErr.h5 -t /media/jzhu4/data/hyp3-mintpy/Ridgecrest/smallbaselineApp.cfg -o /media/jzhu4/data/hyp3-mintpy/Ridgecrest/velocity.h5 --update\n", - "read options from template file: smallbaselineApp.cfg\n", - "open timeseries file: timeseries_ERA5_demErr.h5\n", - "exclude date:['20190704', '20190716']\n", - "--------------------------------------------------\n", - "dates from input file: 7\n", - "['20190610', '20190622', '20190704', '20190716', '20190728', '20190809', '20190821']\n", - "--------------------------------------------------\n", - "dates used to estimate the velocity: 5\n", - "['20190610', '20190622', '20190728', '20190809', '20190821']\n", - "--------------------------------------------------\n", - "update mode: ON\n", - "1) output file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/velocity.h5 NOT found.\n", - "run or skip: run.\n", - "estimate deformation model with the following assumed time functions:\n", - " polynomial : 1\n", - " periodic : []\n", - " step : []\n", - " exp : {}\n", - " log : {}\n", - "add/update the following configuration metadata:\n", - "['startDate', 'endDate', 'excludeDate', 'bootstrap', 'bootstrapCount']\n", - "--------------------------------------------------\n", - "create HDF5 file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/velocity.h5 with w mode\n", - "create dataset : velocity of in size of (2859, 3633) with compression = None\n", - "create dataset : velocityStd of in size of (2859, 3633) with compression = None\n", - "add /velocity attribute: UNIT = m/year\n", - "add /velocityStd attribute: UNIT = m/year\n", - "close HDF5 file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/velocity.h5\n", - "reading data from file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries_ERA5_demErr.h5 ...\n", - "skip pixels with zero/nan value in all acquisitions\n", - "number of pixels to invert: 6032690 out of 10386747 (58.1%)\n", - "estimating time functions via linalg.lstsq ...\n", - "estimating time function STD from time-series fitting residual ...\n", - "--------------------------------------------------\n", - "open HDF5 file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/velocity.h5 in a mode\n", - "writing dataset /velocity block: [0, 2859, 0, 3633]\n", - "close HDF5 file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/velocity.h5.\n", - "--------------------------------------------------\n", - "open HDF5 file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/velocity.h5 in a mode\n", - "writing dataset /velocityStd block: [0, 2859, 0, 3633]\n", - "close HDF5 file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/velocity.h5.\n", - "time used: 00 mins 1.8 secs.\n", - "\n", - "timeseries2velocity.py /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/ERA5.h5 -t /media/jzhu4/data/hyp3-mintpy/Ridgecrest/smallbaselineApp.cfg -o /media/jzhu4/data/hyp3-mintpy/Ridgecrest/velocityERA5.h5 --update --ref-date 20190821 --ref-yx 1681 1987\n", - "read options from template file: smallbaselineApp.cfg\n", - "open timeseries file: ERA5.h5\n", - "exclude date:['20190704', '20190716']\n", - "--------------------------------------------------\n", - "dates from input file: 7\n", - "['20190610', '20190622', '20190704', '20190716', '20190728', '20190809', '20190821']\n", - "--------------------------------------------------\n", - "dates used to estimate the velocity: 5\n", - "['20190610', '20190622', '20190728', '20190809', '20190821']\n", - "--------------------------------------------------\n", - "update mode: ON\n", - "1) output file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/velocityERA5.h5 NOT found.\n", - "run or skip: run.\n", - "estimate deformation model with the following assumed time functions:\n", - " polynomial : 1\n", - " periodic : []\n", - " step : []\n", - " exp : {}\n", - " log : {}\n", - "add/update the following configuration metadata:\n", - "['startDate', 'endDate', 'excludeDate', 'bootstrap', 'bootstrapCount']\n", - "--------------------------------------------------\n", - "create HDF5 file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/velocityERA5.h5 with w mode\n", - "create dataset : velocity of in size of (2859, 3633) with compression = None\n", - "create dataset : velocityStd of in size of (2859, 3633) with compression = None\n", - "add /velocity attribute: UNIT = m/year\n", - "add /velocityStd attribute: UNIT = m/year\n", - "close HDF5 file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/velocityERA5.h5\n", - "reading data from file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/inputs/ERA5.h5 ...\n", - "referecing to date: 20190821\n", - "referencing to point (y, x): (1681, 1987)\n", - "skip pixels with zero/nan value in all acquisitions\n", - "number of pixels to invert: 10386747 out of 10386747 (100.0%)\n", - "estimating time functions via linalg.lstsq ...\n", - "estimating time function STD from time-series fitting residual ...\n", - "--------------------------------------------------\n", - "open HDF5 file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/velocityERA5.h5 in a mode\n", - "writing dataset /velocity block: [0, 2859, 0, 3633]\n", - "close HDF5 file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/velocityERA5.h5.\n", - "--------------------------------------------------\n", - "open HDF5 file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/velocityERA5.h5 in a mode\n", - "writing dataset /velocityStd block: [0, 2859, 0, 3633]\n", - "close HDF5 file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/velocityERA5.h5.\n", - "time used: 00 mins 13.7 secs.\n", - "\n", - "\n", - "******************** step - geocode ********************\n", - "dataset is geocoded, skip geocoding and continue.\n", - "\n", - "\n", - "******************** step - google_earth ********************\n", - "creating Google Earth KMZ file for geocoded velocity file: ...\n", - "\n", - "save_kmz.py /media/jzhu4/data/hyp3-mintpy/Ridgecrest/velocity.h5 -o /media/jzhu4/data/hyp3-mintpy/Ridgecrest/velocity.kmz\n", - "data coverage in y/x: (0, 0, 3633, 2859)\n", - "subset coverage in y/x: (0, 0, 3633, 2859)\n", - "update LENGTH, WIDTH, Y/XMAX\n", - "update/add SUBSET_XMIN/YMIN/XMAX/YMAX: 0/0/3633/2859\n", - "update Y/X_FIRST\n", - "update REF_Y/X\n", - "read mask from file: maskTempCoh.h5\n", - "masking out pixels with zero value in file: None\n", - "colormap: jet\n", - "plotting data ...\n", - "figure size : [15.25, 12.00]\n", - "show reference point\n", - "writing /media/jzhu4/data/hyp3-mintpy/Ridgecrest/velocity.png with dpi=600\n", - "writing /media/jzhu4/data/hyp3-mintpy/Ridgecrest/velocity_cbar.png\n", - "writing /media/jzhu4/data/hyp3-mintpy/Ridgecrest/velocity.kml\n", - "remove /media/jzhu4/data/hyp3-mintpy/Ridgecrest/velocity.kml\n", - "remove /media/jzhu4/data/hyp3-mintpy/Ridgecrest/velocity.png\n", - "remove /media/jzhu4/data/hyp3-mintpy/Ridgecrest/velocity_cbar.png\n", - "merged all files to /media/jzhu4/data/hyp3-mintpy/Ridgecrest/velocity.kmz\n", - "\n", - "\n", - "******************** step - hdfeos5 ********************\n", - "save time-series to HDF-EOS5 format is OFF.\n", - "\n", - "******************** plot & save to pic ********************\n", - "Input data seems to be geocoded. Lookup file not needed.\n", - "view.py --dpi 150 --noverbose --nodisplay --update --memory 4.0 velocity.h5 --dem inputs/geometryGeo.h5 --mask maskTempCoh.h5 -u cm\n", - "view.py --dpi 150 --noverbose --nodisplay --update --memory 4.0 temporalCoherence.h5 -c gray -v 0 1\n", - "view.py --dpi 150 --noverbose --nodisplay --update --memory 4.0 maskTempCoh.h5 -c gray -v 0 1\n", - "view.py --dpi 150 --noverbose --nodisplay --update --memory 4.0 inputs/geometryGeo.h5\n", - "view.py --dpi 150 --noverbose --nodisplay --update --memory 4.0 inputs/ifgramStack.h5 unwrapPhase- --zero-mask --wrap -c cmy\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "view.py --dpi 150 --noverbose --nodisplay --update --memory 4.0 inputs/ifgramStack.h5 unwrapPhase- --zero-mask\n", - "view.py --dpi 150 --noverbose --nodisplay --update --memory 4.0 inputs/ifgramStack.h5 coherence- --mask no -v 0 1\n", - "view.py --dpi 150 --noverbose --nodisplay --update --memory 4.0 avgPhaseVelocity.h5\n", - "view.py --dpi 150 --noverbose --nodisplay --update --memory 4.0 avgSpatialCoh.h5 -c gray -v 0 1\n", - "view.py --dpi 150 --noverbose --nodisplay --update --memory 4.0 maskConnComp.h5 -c gray -v 0 1\n", - "view.py --dpi 150 --noverbose --nodisplay --update --memory 4.0 timeseries.h5 --noaxis -u cm --wrap --wrap-range -5 5\n", - "view.py --dpi 150 --noverbose --nodisplay --update --memory 4.0 timeseries_ERA5.h5 --noaxis -u cm --wrap --wrap-range -5 5\n", - "view.py --dpi 150 --noverbose --nodisplay --update --memory 4.0 timeseries_ERA5_demErr.h5 --noaxis -u cm --wrap --wrap-range -5 5\n", - "view.py --dpi 150 --noverbose --nodisplay --update --memory 4.0 velocityERA5.h5 --mask no\n", - "view.py --dpi 150 --noverbose --nodisplay --update --memory 4.0 numInvIfgram.h5 --mask no\n", - "copy *.txt files into ./pic directory.\n", - "move *.png/pdf/kmz files to ./pic directory.\n", - "time used: 00 mins 44.8 secs.\n", - "Explore more info & visualization options with the following scripts:\n", - " info.py #check HDF5 file structure and metadata\n", - " view.py #2D map view\n", - " tsview.py #1D point time-series (interactive) \n", - " transect.py #1D profile (interactive)\n", - " plot_coherence_matrix.py #plot coherence matrix for one pixel (interactive)\n", - " plot_network.py #plot network configuration of the dataset \n", - " plot_transection.py #plot 1D profile along a line of a 2D matrix (interactive)\n", - " save_kmz.py #generate Google Earth KMZ file in raster image\n", - " save_kmz_timeseries.py #generate Goodle Earth KMZ file in points for time-series (interactive)\n", - " \n", - "Go back to directory: /media/jzhu4/data/hyp3-mintpy/Ridgecrest\n", - "\n", - "################################################\n", - " Normal end of smallbaselineApp processing!\n", - "################################################\n", - "Time used: 23 mins 29.4 secs\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "! smallbaselineApp.py --work-dir {work_dir} {mintpy_config}" ] @@ -1142,7 +257,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1152,184 +267,20 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": null, "metadata": { "scrolled": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "run view.py in MintPy version v1.3.3, date 2022-04-14\n", - "input file is velocity file: /media/jzhu4/data/hyp3-mintpy/Ridgecrest/velocity.h5 in float32 format\n", - "file size in y/x: (2859, 3633)\n", - "num of datasets in file velocity.h5: 2\n", - "datasets to exclude (0):\n", - "[]\n", - "datasets to display (2):\n", - "['velocity', 'velocityStd']\n", - "data coverage in y/x: (0, 0, 3633, 2859)\n", - "subset coverage in y/x: (0, 0, 3633, 2859)\n", - "data coverage in lat/lon: (312640.0, 4042400.0, 603280.0, 3813680.0)\n", - "subset coverage in lat/lon: (312640.0, 4042400.0, 603280.0, 3813680.0)\n", - "------------------------------------------------------------------------\n", - "colormap: jet\n", - "figure title: velocity\n", - "figure size : [15.00, 8.00]\n", - "dataset number: 2\n", - "row number: 1\n", - "column number: 2\n", - "figure number: 1\n", - "read mask from file: maskTempCoh.h5\n", - "----------------------------------------\n", - "Figure 1 - velocity.png\n", - "reading data as a list of 2D matrices ...\n", - "[==================================================] velocityStd 0s / 0s \n", - "data range: [-745.02423, 731.78046] cm/year\n", - "display range: [-745.02423, 731.78046] cm/year\n", - "masking data\n", - "plotting ...\n", - "[==================================================] velocityStd 0s / 0s \n", - "data range: [-745.02423, 731.78046] cm/year\n", - "display range: [-393.91623, 544.49414] cm/year\n", - "show colorbar\n", - "showing ...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jzhu4/apps/anaconda3/envs/hyp3-mintpy/lib/python3.8/site-packages/mintpy/view.py:1355: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n", - " fig.tight_layout()\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "0a5b072f70eb4b38a7a35396a36d7bbf", - "version_major": 2, - "version_minor": 0 - }, - "image/png": 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\n", - " Figure 1 - velocity.png\n", - "
\n", - " \n", - "
\n", - " " - ], - "text/plain": [ - "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "view.main([f'{work_dir}/velocity.h5'])" ] }, { "cell_type": "code", - "execution_count": 45, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tsview.py /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries.h5\n", - "open timeseries file: timeseries.h5\n", - "exclude date:['20190704', '20190716']\n", - "No lookup table info range/lat found in files.\n", - "data coverage in y/x: (0, 0, 3633, 2859)\n", - "subset coverage in y/x: (0, 0, 3633, 2859)\n", - "data coverage in lat/lon: (312640.0, 4042400.0, 603280.0, 3813680.0)\n", - "subset coverage in lat/lon: (312640.0, 4042400.0, 603280.0, 3813680.0)\n", - "------------------------------------------------------------------------\n", - "estimate deformation model with the following assumed time functions:\n", - " polynomial : 1\n", - " periodic : []\n", - " step : []\n", - " exp : {}\n", - " log : {}\n", - "reading timeseries from file /media/jzhu4/data/hyp3-mintpy/Ridgecrest/timeseries.h5\n", - "reference to date: 20190821\n", - "read mask from file: maskTempCoh.h5\n", - "data range: [-78.073875, 93.78707] cm\n", - "display range: [-69.005554, 51.61355] cm\n", - "figure size : [8.29, 6.00]\n", - "display data in transparency: 1.0\n", - "plot in geo-coordinate\n", - "plotting image ...\n", - "plot scale bar: [0.2, 0.2, 0.1]\n", - "plot reference point\n", - "showing ...\n", - "\n", - "------------------------------------------------------------------------\n", - "To scroll through the image sequence:\n", - "1) Move the slider, OR\n", - "2) Press left or right arrow key (if not responding, click the image and try again).\n", - "------------------------------------------------------------------------\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "d2f3c87a7aae4c2ea072a96c806a80f9", - "version_major": 2, - "version_minor": 0 - }, - "image/png": 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", - "text/html": [ - "\n", - "
\n", - "
\n", - " Cumulative Displacement Map\n", - "
\n", - " \n", - "
\n", - " " - ], - "text/plain": [ - "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "tsview.main([f'{work_dir}/timeseries.h5'])" ] From 5499af7824c9c9276c697c6c0ad29921a36f8463 Mon Sep 17 00:00:00 2001 From: jzhu4 Date: Thu, 19 May 2022 14:20:49 -0800 Subject: [PATCH 11/15] change reference url --- smallbaselineApp_hyp3.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/smallbaselineApp_hyp3.ipynb b/smallbaselineApp_hyp3.ipynb index 9409f95..635e23b 100644 --- a/smallbaselineApp_hyp3.ipynb +++ b/smallbaselineApp_hyp3.ipynb @@ -6,7 +6,7 @@ "source": [ "# Time series analysis of hyp3 InSAR products by MintPy\n", "\n", - "This notebook shows how to do time-series analysis with HyP3 InSAR product by MintPy. We assume you have already got the hyp3 InSAR products somewhere. This steps for the analysis are: clip the hyp3 INSAR product, define the config.txt file, run the time series analysis, and display the results. The sample hyp3 INSAR data are at https://jzhu-hyp3-dev.s3.us-west-2.amazonaws.com/hyp3-mintpy-example/2018_kilauea.zip. Asfar as how to produce the hyp3 INSAR product, we provide the detail steps in the tutorial(https://github.com/ASFHyP3/hyp3-docs/tree/develop/docs). \n" + "This notebook shows how to do time-series analysis with HyP3 InSAR product by MintPy. We assume you have already got the hyp3 InSAR products somewhere. This steps for the analysis are: clip the hyp3 INSAR product, define the config.txt file, run the time series analysis, and display the results. The sample hyp3 INSAR data are at https://jzhu-hyp3-dev.s3.us-west-2.amazonaws.com/hyp3-mintpy-example/2018_kilauea.zip. As far as how to produce the hyp3 INSAR product, we provide the detail steps in the tutorial(https://github.com/ASFHyP3/hyp3-docs/blob/develop/docs/tutorials/hyp3_insar_stack_for_ts_analysis.ipynb). \n" ] }, { From a8bca3d13fa4ec422fd852464e3755cb86223c16 Mon Sep 17 00:00:00 2001 From: jzhu4 Date: Fri, 20 May 2022 14:21:32 -0800 Subject: [PATCH 12/15] improve the notebook, add type hint to the functions --- smallbaselineApp_hyp3.ipynb | 172 ++++++++++++++++++++++-------------- 1 file changed, 108 insertions(+), 64 deletions(-) diff --git a/smallbaselineApp_hyp3.ipynb b/smallbaselineApp_hyp3.ipynb index 7298cf3..247284e 100644 --- a/smallbaselineApp_hyp3.ipynb +++ b/smallbaselineApp_hyp3.ipynb @@ -34,62 +34,13 @@ "For your convinience, we provide the ERA5 data at https://jzhu-hyp3-dev.s3.us-west-2.amazonaws.com/hyp3-mintpy-example/2019_ridgecrest_era5_data.zip, you can download the and unzip the file to the 'your_weather_dir' directory on you local machine, and setup the environment variable. e.g. export WEATHER_DIR='your_weather_dir'.\n" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Import modules and set environment variables" - ] - }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "import os\n", - "from pathlib import Path\n", - "import glob\n", - "import zipfile\n", - "from dateutil.parser import parse as parse_date\n", - "from osgeo import gdal\n", - "import numpy as np\n", - "from mintpy import view, tsview\n", - "\n", - "# utils function\n", - "\n", - "def get_intersect_rectangle_geotiffs(filelist):\n", - " '''\n", - " :param data_dir: data directory storing the hyp3 products.\n", - " :process get the smallest overlap retangular area to clip the geotiff files.\n", - " :return:\n", - " '''\n", - " corners = [gdal.Info(str(dem), format='json')['cornerCoordinates'] for dem in filelist]\n", - "\n", - " ulx = max(corner['upperLeft'][0] for corner in corners)\n", - " uly = min(corner['upperLeft'][1] for corner in corners)\n", - " lrx = min(corner['lowerRight'][0] for corner in corners)\n", - " lry = max(corner['lowerRight'][1] for corner in corners)\n", - " return [ulx, uly, lrx, lry]\n", - "\n", - "def prepare_hyp3_product(data_dir):\n", - " filelist = glob.glob(f\"{data_dir}/*/*_dem.tif\")\n", - " insect_box = get_intersect_rectangle_geotiffs(filelist)\n", - " #files_for_mintpy = ['_water_mask.tif', '_corr.tif', '_unw_phase.tif', '_dem.tif', '_lv_theta.tif', '_lv_phi.tif']\n", - " files_for_mintpy = ['_water_mask.tif', '_corr.tif', '_unw_phase.tif', '_dem.tif', '_lv_theta.tif']\n", - " list_product_dirs = [f.path for f in os.scandir(data_dir) if f.is_dir()]\n", - "\n", - " for product_dir in list_product_dirs:\n", - " for file_suffix in files_for_mintpy:\n", - " product_dir = Path(product_dir)\n", - " src_file = product_dir / f'{product_dir.name}{file_suffix}'\n", - " dst_file = product_dir / f'{src_file.stem}_clipped{src_file.suffix}'\n", - " gdal.Translate(destName=str(dst_file), srcDS=str(src_file), projWin=insect_box)\n", - "\n", - "def unzip_files(zip_file, data_dir):\n", - " if os.path.isfile(zip_file):\n", - " with zipfile.ZipFile(zip_file, 'r') as fzip:\n", - " fzip.extractall(data_dir)\n" + "from pathlib import Path" ] }, { @@ -105,24 +56,15 @@ "metadata": {}, "outputs": [], "source": [ - "project_name = 'Ridgecrest'\n", + "from pathlib import Path\n", "\n", - "project_home = Path.cwd()\n", + "project_name = 'Ridgecrest_t1'\n", "\n", - "work_dir = Path(project_home) / project_name\n", + "work_dir = Path.cwd() / project_name\n", "\n", "data_dir = work_dir / 'data'\n", "\n", - "if not os.path.isdir(work_dir):\n", - " os.makedirs(work_dir)\n", - " print('Create directory: {}'.format(work_dir))\n", - " \n", - "if not os.path.isdir(data_dir):\n", - " os.makedirs(data_dir)\n", - " print('Create directory: {}'.format(data_dir))\n", - " \n", - "os.chdir(work_dir)\n", - "print('Go to work directory: {}'.format(work_dir))" + "data_dir.mkdir(parents=True, exist_ok=True)\n" ] }, { @@ -175,6 +117,21 @@ "print(f'downloaded file is {data_dir}/{file}')" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import zipfile\n", + "\n", + "def unzip_files(zip_file, data_dir):\n", + " if os.path.isfile(zip_file):\n", + " with zipfile.ZipFile(zip_file, 'r') as fzip:\n", + " fzip.extractall(data_dir)\n" + ] + }, { "cell_type": "code", "execution_count": null, @@ -191,13 +148,100 @@ "### 1.2 Cut geotiff files for mintpy analysis" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### get the minumum overlap of the files" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import PosixPath\n", + "from typing import List, Union\n", + "from osgeo import gdal\n", + "\n", + "\n", + "def get_minimum_overlap(filelist: List[Union[str, PosixPath]]) -> List[float]:\n", + " \"\"\"Get the minimum overlap of the geotiff files in the filelist.\n", + " \n", + " Arg:\n", + " filelist: a list of geotiff file names. The file names can be strings or Path objects.\n", + " \n", + " Returns:\n", + " [ulx, uly, lrx, lry], a list which includes the upper-left x, upper-left y, lower-right x, \n", + " and lower-right y.\n", + " \"\"\"\n", + " corners = [gdal.Info(str(dem), format='json')['cornerCoordinates'] for dem in filelist]\n", + "\n", + " ulx = max(corner['upperLeft'][0] for corner in corners)\n", + " uly = min(corner['upperLeft'][1] for corner in corners)\n", + " lrx = min(corner['lowerRight'][0] for corner in corners)\n", + " lry = max(corner['lowerRight'][1] for corner in corners)\n", + " return [ulx, uly, lrx, lry]\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "files = data_dir.glob('*/*_dem.tif')\n", + "\n", + "overlap = get_minimum_overlap(files)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### clip the files with overlap" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path, PosixPath\n", + "from typing import List, Union\n", + "\n", + "def clip_hyp3_products_to_minimum_overlap(data_dir: Union[str, PosixPath], overlap: List[float]) -> None:\n", + " \"\"\"Clip all geotiff files in the directory with the overlap.\n", + " \n", + " Args:\n", + " data_dir:\n", + " name of a directory which includes the geotiff files.\n", + " overlap:\n", + " a list which includes the upper-left x, upper-left y, lower-right-x, and lower-tight y.\n", + "\n", + " Returns: None\n", + " \"\"\"\n", + "\n", + " files_for_mintpy = ['_water_mask.tif', '_corr.tif', '_unw_phase.tif', '_dem.tif', '_lv_theta.tif', '_lv_phi.tif']\n", + "\n", + " for extension in files_for_mintpy:\n", + "\n", + " for file in data_dir.rglob(f'*{extension}'):\n", + "\n", + " dst_file = file.parent / f'{file.stem}_clipped{file.suffix}'\n", + "\n", + " gdal.Translate(destName=str(dst_file), srcDS=str(file), projWin=overlap)\n" + ] + }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "prepare_hyp3_product(data_dir)" + "clip_hyp3_products_to_minimum_overlap(data_dir, overlap)" ] }, { From 20c6bbdabf924b7dcef3239147860ca862faca39 Mon Sep 17 00:00:00 2001 From: jzhu4 Date: Mon, 23 May 2022 08:53:05 -0800 Subject: [PATCH 13/15] improve the smallbaselineApp_hyp3.ipynb --- smallbaselineApp_hyp3.ipynb | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/smallbaselineApp_hyp3.ipynb b/smallbaselineApp_hyp3.ipynb index 247284e..0222e9b 100644 --- a/smallbaselineApp_hyp3.ipynb +++ b/smallbaselineApp_hyp3.ipynb @@ -58,7 +58,7 @@ "source": [ "from pathlib import Path\n", "\n", - "project_name = 'Ridgecrest_t1'\n", + "project_name = 'Ridgecrest'\n", "\n", "work_dir = Path.cwd() / project_name\n", "\n", @@ -161,12 +161,12 @@ "metadata": {}, "outputs": [], "source": [ - "from pathlib import PosixPath\n", + "from pathlib import Path\n", "from typing import List, Union\n", "from osgeo import gdal\n", "\n", "\n", - "def get_minimum_overlap(filelist: List[Union[str, PosixPath]]) -> List[float]:\n", + "def get_minimum_overlap(filelist: List[Union[str, Path]]) -> List[float]:\n", " \"\"\"Get the minimum overlap of the geotiff files in the filelist.\n", " \n", " Arg:\n", @@ -209,10 +209,10 @@ "metadata": {}, "outputs": [], "source": [ - "from pathlib import Path, PosixPath\n", + "from pathlib import Path, Path\n", "from typing import List, Union\n", "\n", - "def clip_hyp3_products_to_minimum_overlap(data_dir: Union[str, PosixPath], overlap: List[float]) -> None:\n", + "def clip_hyp3_products_to_minimum_overlap(data_dir: Union[str, Path], overlap: List[float]) -> None:\n", " \"\"\"Clip all geotiff files in the directory with the overlap.\n", " \n", " Args:\n", From 0aa5c9a3f242a6257629df5fbae87530f4d2ce8c Mon Sep 17 00:00:00 2001 From: jzhu4 Date: Mon, 23 May 2022 08:56:19 -0800 Subject: [PATCH 14/15] improve the smallbaselineApp_hyp3.ipynb --- smallbaselineApp_hyp3.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/smallbaselineApp_hyp3.ipynb b/smallbaselineApp_hyp3.ipynb index 0222e9b..1f0df3f 100644 --- a/smallbaselineApp_hyp3.ipynb +++ b/smallbaselineApp_hyp3.ipynb @@ -29,7 +29,7 @@ "To run your notebook, just:\n", "\n", "conda activate hyp3-mintpy\n", - "jupyter notebook smallbaselineApp_hyp3_new.ipynb\n", + "jupyter notebook smallbaselineApp_hyp3.ipynb\n", "\n", "For your convinience, we provide the ERA5 data at https://jzhu-hyp3-dev.s3.us-west-2.amazonaws.com/hyp3-mintpy-example/2019_ridgecrest_era5_data.zip, you can download the and unzip the file to the 'your_weather_dir' directory on you local machine, and setup the environment variable. e.g. export WEATHER_DIR='your_weather_dir'.\n" ] @@ -209,7 +209,7 @@ "metadata": {}, "outputs": [], "source": [ - "from pathlib import Path, Path\n", + "from pathlib import Path\n", "from typing import List, Union\n", "\n", "def clip_hyp3_products_to_minimum_overlap(data_dir: Union[str, Path], overlap: List[float]) -> None:\n", From 1ab3ae49f462d16417eb13918af661af3476882f Mon Sep 17 00:00:00 2001 From: jzhu4 Date: Mon, 23 May 2022 08:58:44 -0800 Subject: [PATCH 15/15] improve the smallbaselineApp_hyp3.ipynb --- smallbaselineApp_hyp3.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/smallbaselineApp_hyp3.ipynb b/smallbaselineApp_hyp3.ipynb index 1f0df3f..c833770 100644 --- a/smallbaselineApp_hyp3.ipynb +++ b/smallbaselineApp_hyp3.ipynb @@ -152,7 +152,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### get the minumum overlap of the files" + "### Get the minumum overlap of the files" ] }, { @@ -200,7 +200,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### clip the files with overlap" + "### Clip the files with overlap" ] }, {