diff --git a/environments/environment_iti.yaml b/environments/environment_iti.yaml index c0febdb..5c291f7 100644 --- a/environments/environment_iti.yaml +++ b/environments/environment_iti.yaml @@ -1,56 +1,53 @@ name: rs_tools_iti channels: - conda-forge - - pytorch - - nvidia + # - pytorch + # - nvidia dependencies: - python=3.11 # Standard Libraries - numpy # Numerical Linear Algebra - # - scipy # Scientific Computing - # - scikit-image - xarray - - rioxarray - - goes2go - - earthaccess - - eumdac - - pyinterp - # - conda-forge::torchgeo - pandas # Data structure + - scikit-image - scikit-learn # Machine Learning # PLOTTING LIBRARY - matplotlib # standard plotting library - seaborn # Stats viz library # Storage - netCDF4 - - zarr - - h5netcdf - - pyhdf + # - zarr + # - h5netcdf + # - pyhdf # - nvidia/label/cuda-12.2.0::cuda - - pytorch-cuda + # - pytorch-cuda # - h5py # GUI - ipywidgets - ipykernel - tqdm - pip + - sunpy>=2.0 + - astropy + - aiapy + - drms - pip: - - toolz - - typer - - einops + # - toolz + # - typer + # - einops # PYTORCH - - torch + - torch>=1.8 - torchvision - lightning # - git+https://github.com/spaceml-org/InstrumentToInstrument.git@development # formatting - - black - - pylint - - isort - - flake8 - - mypy - - pytest - - pre-commit + # - black + # - pylint + # - isort + # - flake8 + # - mypy + # - pytest + # - pre-commit # Notebook stuff - autoroot - pyprojroot @@ -58,9 +55,9 @@ dependencies: # logging - loguru # plotting - - celluloid - - corner - - tabulate + # - celluloid + # - corner + # - tabulate # experiment - hydra-core - hydra-zen diff --git a/notebooks/2.0-test-normalization.ipynb b/notebooks/3.0-test-normalization.ipynb similarity index 100% rename from notebooks/2.0-test-normalization.ipynb rename to notebooks/3.0-test-normalization.ipynb diff --git a/notebooks/dev/multi-sat/3.0-test-normalization.ipynb b/notebooks/dev/multi-sat/3.0-test-normalization.ipynb new file mode 100644 index 0000000..47cddab --- /dev/null +++ b/notebooks/dev/multi-sat/3.0-test-normalization.ipynb @@ -0,0 +1,10096 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/freischem/.conda/envs/rs_tools_iti/lib/python3.11/site-packages/goes2go/data.py:519: FutureWarning: 'H' is deprecated and will be removed in a future version. Please use 'h' instead of 'H'.\n", + " within=pd.to_timedelta(config[\"nearesttime\"].get(\"within\", \"1H\")),\n", + "/home/freischem/.conda/envs/rs_tools_iti/lib/python3.11/site-packages/goes2go/NEW.py:188: FutureWarning: 'H' is deprecated and will be removed in a future version. Please use 'h' instead of 'H'.\n", + " within=pd.to_timedelta(config[\"nearesttime\"].get(\"within\", \"1H\")),\n" + ] + } + ], + "source": [ + "import autoroot\n", + "from rs_tools._src.preprocessing.normalize import normalize\n", + "\n", + "%matplotlib inline\n", + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import xarray as xr\n", + "\n", + "norm_dir = '/home/freischem/outputs/miniset/20240517-0928/normalization'\n", + "goes_norm = xr.open_dataset(f'{norm_dir}/goes_norm.nc')" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([134.38202 , 65.178566 , 37.20662 , 2.272993 ,\n", + " 5.744725 , 1.5239868 , 0.77519065, 2.815666 ,\n", + " 7.9387918 , 13.896899 , 47.947056 , 43.477985 ,\n", + " 82.78253 , 93.38069 , 100.64417 , 88.33139 ],\n", + " dtype=float32),\n", + " array([83.57160446, 70.23925393, 47.01136956, 6.13335517, 6.55724009,\n", + " 1.74397223, 0.245557 , 0.69149961, 1.84830359, 3.10537042,\n", + " 11.73538443, 8.68892694, 18.23192365, 20.14717129, 20.09120789,\n", + " 14.56906235]))" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "goes_norm['mean'].values, goes_norm['std'].values" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "msg_norm = xr.open_dataset(f'{norm_dir}/msg_norm.nc')" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([ 1.695779 , 0.6518643, 52.94932 , 45.511276 , 86.90486 ,\n", + " 97.99122 , 80.530754 , 2.6322215, 2.830637 , 3.2238169,\n", + " 14.109417 ], dtype=float32),\n", + " array([ 1.65503616, 0.23466894, 15.14200648, 10.67773555, 22.5324939 ,\n", + " 23.94238188, 14.97517086, 2.73353005, 3.34456361, 0.8772509 ,\n", + " 3.74212206]))" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "msg_norm['mean'].values, msg_norm['std'].values" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "goes_files = os.listdir('/mnt/disks/data/miniset/goes16/geoprocessed/')\n", + "goes_files = ['/mnt/disks/data/miniset/goes16/geoprocessed/' + f for f in goes_files]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# f = goes_files[0].split('/')[-1]\n", + "# f" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# ls /mnt/disks/data/miniset/goes16/geoprocessed/" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# get_list_filenames('/mnt/disks/data/miniset/goes16/geoprocessed/', ext='nc')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# from iti.data.geo_utils import get_split, get_list_filenames\n", + "\n", + "\n", + "# splits_dict = { \n", + "# \"train\": {\"years\": [2020], \"months\": [10], \"days\": list(range(1,20))},\n", + "# \"val\": {\"years\": [2020], \"months\": [10], \"days\": list(range(20,32))},\n", + "# }\n", + "\n", + "# get_split(goes_files, splits_dict['train'])\n", + " \n", + "\n", + "# # def get_files(self):\n", + "# # # Get filenames from data_dir\n", + "# # files = get_list_filenames(data_path=self.data_dir, ext=self.ext)\n", + "# # # split files based on split criteria\n", + "# # files = get_split(files=files, split_dict=self.splits_dict)\n", + "# # return files\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import xarray as xr\n", + "# ds_goes_files = xr.open_mfdataset(goes_files[:100])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "goes_time = xr.open_dataset(goes_files[1])" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [], + "source": [ + "msg_time = xr.open_dataset(msg_files[1])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[1.64, 3.92, 8.7, 9.66, 10.8, 12.0, 13.4, 0.64, 0.81, 6.25, 7.35]" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(msg_time.band_wavelength.values)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "ds_goes = normalize(goes_files[:10])" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "msg_files = os.listdir('/mnt/disks/data/miniset/msg/geoprocessed/')\n", + "msg_files = ['/mnt/disks/data/miniset/msg/geoprocessed/' + f for f in msg_files]" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [], + "source": [ + "from iti.data.geo_editor import BandSelectionEditor\n" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(11, 1289, 891)" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_dict['data'].shape" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "7\n" + ] + }, + { + "ename": "IndexError", + "evalue": "index 0 is out of bounds for axis 0 with size 0", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[80], line 5\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;66;03m# Get indexes of bands to select\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# indexes = [np.where(source_bands == wvl)[0][0] for wvl in target_bands]\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m wvl \u001b[38;5;129;01min\u001b[39;00m target_bands:\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwhere\u001b[49m\u001b[43m(\u001b[49m\u001b[43msource_bands\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mwvl\u001b[49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m)\n", + "\u001b[0;31mIndexError\u001b[0m: index 0 is out of bounds for axis 0 with size 0" + ] + } + ], + "source": [ + "source_bands = data_dict[\"wavelengths\"]\n", + "# Get indexes of bands to select\n", + "# indexes = [np.where(source_bands == wvl)[0][0] for wvl in target_bands]\n", + "for wvl in target_bands:\n", + " print(np.where(source_bands == wvl)[0][0])" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([ 1.64, 3.92, 8.7 , 9.66, 10.8 , 12. , 13.4 , 0.64, 0.81,\n", + " 6.25, 7.35]),\n", + " 3.89)" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "source_bands, wvl" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [], + "source": [ + "data = msg_time.Rad.compute().to_numpy()\n", + "target_bands=[0.64, 3.92, 7.35, 9.66, 13.4]\n", + "data_dict = {\"data\": data,\n", + " \"wavelengths\" : msg_time.band_wavelength.compute().to_numpy()}\n", + "editor = BandSelectionEditor(target_bands=target_bands)\n", + "data_bands_order1=editor.call(data_dict=data_dict)\n", + "\n", + "\n", + "\n", + "target_bands=[7.35, 9.66, 13.4, 0.64, 3.92]\n", + "data_dict = {\"data\": data,\n", + " \"wavelengths\" : msg_time.band_wavelength.compute().to_numpy()}\n", + "editor = BandSelectionEditor(target_bands=target_bands)\n", + "data_bands_order2=editor.call(data_dict=data_dict)" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1715683597.6426992" + ] + }, + "execution_count": 93, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import time\n", + "time.time()" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([ 0.64, 3.92, 7.35, 9.66, 13.4 ]),\n", + " array([ 7.35, 9.66, 13.4 , 0.64, 3.92]))" + ] + }, + "execution_count": 92, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_bands_order1['wavelengths'], data_bands_order2['wavelengths']" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [], + "source": [ + "import wandb\n" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [], + "source": [ + "logging_config = {}\n", + "wandb_id = logging_config['wandb_id'] if 'wandb_id' in logging_config else None\n", + "log_model = logging_config['wandb_log_model'] if 'wandb_log_model' in logging_config else False\n" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mlilli-freischem\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Path /home/freischem/outputs/miniset/wandb/ wasn't writable, using system temp directory.\n", + "wandb: WARNING Path /home/freischem/outputs/miniset/wandb/ wasn't writable, using system temp directory\n" + ] + }, + { + "data": { + "text/html": [ + "wandb version 0.17.0 is available! To upgrade, please run:\n", + " $ pip install wandb --upgrade" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "Tracking run with wandb version 0.16.6" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "Run data is saved locally in /var/tmp/wandb/run-20240514_104954-blrv8mxq" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "Syncing run wandb-test to Weights & Biases (docs)
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + " View project at https://wandb.ai/lilli-freischem/msg-goes-miniset" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + " View run at https://wandb.ai/lilli-freischem/msg-goes-miniset/runs/blrv8mxq" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "run = wandb.init(project='msg-goes-miniset', \n", + " name='wandb-test', \n", + " entity='lilli-freischem', \n", + " id=None, \n", + " dir='/home/freischem/outputs/miniset/')" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'/home/freischem/outputs/miniset/20240514-1058'" + ] + }, + "execution_count": 117, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from datetime import datetime\n", + "time_str = datetime.now().strftime(\"%Y%m%d-%H%M\")\n", + "base_dir = '/home/freischem/outputs/miniset'\n", + "\n", + "import os\n", + "os.path.join(base_dir, time_str)" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [], + "source": [ + "from lightning.pytorch.loggers import WandbLogger\n", + "\n", + "\n", + "wandb_logger = WandbLogger(project='msg-goes-miniset', name='wandb-test', offline=True,\n", + " entity='lilli-freischem', id=None, dir='/home/freischem/outputs/miniset/', log_model=False)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": {}, + "outputs": [], + "source": [ + "# print(wandb_logger" + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "metadata": {}, + "outputs": [], + "source": [ + "from iti.data.editor import RandomPatchEditor" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": {}, + "outputs": [], + "source": [ + "data = msg_time.Rad.compute().to_numpy()\n", + "target_bands=[0.64, 3.92, 7.35, 9.66, 13.4]\n", + "data_dict = {\"data\": data,\n", + " \"wavelengths\" : msg_time.band_wavelength.compute().to_numpy()}\n", + "editor = BandSelectionEditor(target_bands=target_bands)\n", + "data_dict=editor.call(data_dict=data_dict)\n", + "\n", + "editor = RandomPatchEditor(patch_shape=(256, 256))\n", + "patches=editor.call(data=data_dict['data'])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((11, 1289, 891), (16, 3687, 504))" + ] + }, + "execution_count": 123, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "msg_time.Rad.shape, goes_time.Rad.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Size: 272B\n", + "Dimensions: (band_wavelength: 16, band: 16)\n", + "Coordinates:\n", + " * band_wavelength (band_wavelength) float32 64B 0.47 0.64 ... 12.27 13.27\n", + " * band (band) int8 16B 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16\n", + "Data variables:\n", + " mean (band) float32 64B 144.0 74.45 43.39 ... 89.34 96.57 85.23\n", + " std (band) float64 128B 97.21 80.72 53.49 ... 23.48 23.76 17.25" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds_goes" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "from iti.data.editor import Editor\n", + "\n", + "import numpy as np\n", + "\n", + "class MeanStdNormEditor(Editor):\n", + " \"\"\"\n", + " Normalise each band in the data using the mean and std from the norm_ds.\n", + " \"\"\"\n", + " def __init__(self, norm_ds, key=\"data\"):\n", + " \"\"\"\n", + " Args:\n", + " norm_ds (xarray.Dataset): Dataset with normalization values (mean and std)\n", + " key (str): Key in dictionary to apply transformation\n", + " \"\"\"\n", + " self.key = key\n", + " self.norm = norm_ds\n", + "\n", + " def call(self, data_dict, **kwargs):\n", + " data = data_dict[self.key]\n", + " # use wavelengths and only normalise the bands that we have in the data\n", + " data_wavelengths = data_dict[\"wavelengths\"]\n", + " # Get indeces of bands to select\n", + " indeces = [np.where(self.norm.band_wavelength == wvl)[0][0] for wvl in data_wavelengths]\n", + " \n", + " # extract relevant means and stds\n", + " means = self.norm['mean'][indeces].values\n", + " stds = self.norm['std'][indeces].values\n", + "\n", + " # check that number of channels equals number of means & stds\n", + " assert data.shape[0] == means.shape[0]\n", + " assert data.shape[0] == stds.shape[0]\n", + "\n", + " # apply normalization\n", + " data = (data - means[:, None, None]) / stds[:, None, None]\n", + " \n", + " # Update dictionary\n", + " data_dict[self.key] = data\n", + " return data_dict" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.Dataset> Size: 275MB\n",
+       "Dimensions:          (x: 504, y: 3687, time: 1, band_wavelength: 16, band: 16)\n",
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+       "  * x                (x) float32 2kB 3.108e+06 3.109e+06 ... 3.611e+06 3.612e+06\n",
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+       "  * time             (time) <U16 64B '2020-10-26 14:05'\n",
+       "  * band_wavelength  (band_wavelength) float32 64B 0.47 0.64 ... 12.27 13.27\n",
+       "  * band             (band) int8 16B 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16\n",
+       "    latitude         (y, x) float64 15MB ...\n",
+       "    longitude        (y, x) float64 15MB ...\n",
+       "    cloud_mask       (y, x) float32 7MB ...\n",
+       "Data variables:\n",
+       "    Rad              (band, y, x) float32 119MB ...\n",
+       "    DQF              (band, y, x) float32 119MB ...\n",
+       "Attributes: (12/30)\n",
+       "    naming_authority:          gov.nesdis.noaa\n",
+       "    Conventions:               CF-1.7\n",
+       "    standard_name_vocabulary:  CF Standard Name Table (v35, 20 July 2016)\n",
+       "    institution:               DOC/NOAA/NESDIS > U.S. Department of Commerce,...\n",
+       "    project:                   GOES\n",
+       "    production_site:           WCDAS\n",
+       "    ...                        ...\n",
+       "    timeline_id:               ABI Mode 6\n",
+       "    date_created:              2020-10-26T14:09:56.5Z\n",
+       "    time_coverage_start:       2020-10-26T14:00:19.7Z\n",
+       "    time_coverage_end:         2020-10-26T14:09:50.5Z\n",
+       "    LUT_Filenames:             SpaceLookParams(FM1A_CDRL79RevP_PR_09_00_02)-6...\n",
+       "    id:                        fc5ae74c-413e-4ee6-8b54-bfd6d004f270
" + ], + "text/plain": [ + " Size: 275MB\n", + "Dimensions: (x: 504, y: 3687, time: 1, band_wavelength: 16, band: 16)\n", + "Coordinates:\n", + " * x (x) float32 2kB 3.108e+06 3.109e+06 ... 3.611e+06 3.612e+06\n", + " * y (y) float32 15kB 501.3 1.503e+03 ... 3.693e+06 3.694e+06\n", + " * time (time) U.S. Department of Commerce,...\n", + " project: GOES\n", + " production_site: WCDAS\n", + " ... ...\n", + " timeline_id: ABI Mode 6\n", + " date_created: 2020-10-26T14:09:56.5Z\n", + " time_coverage_start: 2020-10-26T14:00:19.7Z\n", + " time_coverage_end: 2020-10-26T14:09:50.5Z\n", + " LUT_Filenames: SpaceLookParams(FM1A_CDRL79RevP_PR_09_00_02)-6...\n", + " id: fc5ae74c-413e-4ee6-8b54-bfd6d004f270" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "goes_time" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "data_wavelengths = np.array([0.47, 1.38, 1.61,])\n", + "indeces = [np.where(ds_goes.band_wavelength == wvl)[0][0] for wvl in data_wavelengths]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "means = ds_goes['mean'][indeces].values\n", + "stds = ds_goes['std'][indeces].values" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "data = goes_time.Rad[indeces].compute().to_numpy()" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_norm = (data - means[:, None, None]) / stds[:, None, None]\n", + "\n", + "np.isclose(((data[2] - means[2]) / stds[2]), data_norm[2]).all()" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-0.38646985, -0.53684598, -0.428241 , ..., -0.71228487,\n", + " -0.7039306 , -0.67886792],\n", + " [-0.31963594, -0.55355446, -0.64545097, ..., -0.6955764 ,\n", + " -0.6955764 , -0.68722212],\n", + " [-0.63709677, -0.59532561, -0.67051372, ..., -0.68722212,\n", + " -0.6955764 , -0.68722212],\n", + " ...,\n", + " [-0.4198868 , -0.40317825, -0.23609364, ..., 0.47401596,\n", + " 0.42389061, 0.40718205],\n", + " [-0.25280219, -0.12748873, 0.14820094, ..., 0.39047366,\n", + " 0.37376526, 0.43224481],\n", + " [-0.25280219, 0.08972139, 0.23174324, ..., 0.32363975,\n", + " 0.39882785, 0.38211946]])" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "((data - means[:, None, None]) / stds[:, None, None])[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-0.38646984, -0.536846 , -0.42824098, ..., -0.71228486,\n", + " -0.7039306 , -0.67886794],\n", + " [-0.31963593, -0.5535545 , -0.64545095, ..., -0.69557637,\n", + " -0.69557637, -0.6872221 ],\n", + " [-0.63709676, -0.5953256 , -0.6705137 , ..., -0.6872221 ,\n", + " -0.69557637, -0.6872221 ],\n", + " ...,\n", + " [-0.4198868 , -0.40317824, -0.23609364, ..., 0.47401595,\n", + " 0.4238906 , 0.40718204],\n", + " [-0.2528022 , -0.12748873, 0.14820093, ..., 0.39047366,\n", + " 0.37376526, 0.4322448 ],\n", + " [-0.2528022 , 0.08972139, 0.23174325, ..., 0.32363975,\n", + " 0.39882785, 0.38211945]], dtype=float32)" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(data[0] - 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" + ], + "text/plain": [ + " Size: 440B\n", + "Dimensions: (band: 11, band_wavelength: 11)\n", + "Coordinates:\n", + " * band (band) xr.Dataset:\n", + " return ds.mean(spatial_variables)\n", + "\n", + "\n", + "files = goes_files[:10]\n", + "\n", + "temporal_variables = [\"time\"]\n", + "spatial_variables = [\"x\",\"y\"]\n", + "\n", + "preprocess_mean = partial(spatial_mean, spatial_variables=spatial_variables)\n", + "\n", + "# calculate mean\n", + "ds_mean = xr.open_mfdataset(files, preprocess=preprocess_mean, combine=\"by_coords\", engine=\"netcdf4\")\n", + "\n", + "# ds_mean = ds_mean.mean(temporal_variables)\n", + "\n", + "def preprocess_std(ds: xr.Dataset):\n", + " # calculate the mean\n", + " ds = ((ds - ds_mean)**2).std(spatial_variables)\n", + " return ds\n", + "\n", + "\n", + "ds_std = xr.open_mfdataset(goes_files[:10], preprocess=preprocess_std, combine=\"by_coords\", engine=\"netcdf4\")\n", + "\n", + "# ds_std = ds_std.mean(temporal_variables)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.Dataset> Size: 275MB\n",
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+       "  * band             (band) int8 16B 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16\n",
+       "Data variables:\n",
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<xarray.Dataset> Size: 400B\n",
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+       "  * band_wavelength  (band_wavelength) float32 64B 0.47 0.64 ... 12.27 13.27\n",
+       "  * band             (band) int8 16B 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16\n",
+       "Data variables:\n",
+       "    Rad              (band, time) float64 128B 87.13 71.13 46.68 ... 20.72 15.15\n",
+       "    DQF              (band, time) float64 128B 0.02006 0.0 0.006225 ... 0.0 0.0
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+       "  * band             (band) int8 16B 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16\n",
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<xarray.Dataset> Size: 480B\n",
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+       "    std              (band) float32 128B dask.array<chunksize=(24,), meta=np.ndarray>\n",
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<xarray.Dataset> Size: 272B\n",
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+       "  * band_wavelength  (band_wavelength) float32 64B 0.47 0.64 ... 12.27 13.27\n",
+       "  * band             (band) int8 16B 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16\n",
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+       "    std              (band) float32 64B dask.array<chunksize=(8,), meta=np.ndarray>\n",
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<xarray.Dataset> Size: 208B\n",
+       "Dimensions:          (band_wavelength: 16, band: 16)\n",
+       "Coordinates:\n",
+       "  * band_wavelength  (band_wavelength) float32 64B 0.47 0.64 ... 12.27 13.27\n",
+       "  * band             (band) int8 16B 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16\n",
+       "Data variables:\n",
+       "    std              (band) float32 64B dask.array<chunksize=(8,), meta=np.ndarray>\n",
+       "    mean             (band) float32 64B dask.array<chunksize=(8,), meta=np.ndarray>
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<xarray.Dataset> Size: 208B\n",
+       "Dimensions:          (band_wavelength: 16, band: 16)\n",
+       "Coordinates:\n",
+       "  * band_wavelength  (band_wavelength) float32 64B 0.47 0.64 ... 12.27 13.27\n",
+       "  * band             (band) int8 16B 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16\n",
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+       "    std              (band) float32 64B dask.array<chunksize=(8,), meta=np.ndarray>\n",
+       "    mean             (band) float32 64B dask.array<chunksize=(8,), meta=np.ndarray>
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<xarray.Dataset> Size: 208B\n",
+       "Dimensions:          (band_wavelength: 16, band: 16)\n",
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+       "  * band_wavelength  (band_wavelength) float32 64B 0.47 0.64 ... 12.27 13.27\n",
+       "  * band             (band) int8 16B 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16\n",
+       "Data variables:\n",
+       "    Rad              (band) float32 64B dask.array<chunksize=(8,), meta=np.ndarray>\n",
+       "    DQF              (band) float32 64B dask.array<chunksize=(8,), meta=np.ndarray>
" + ], + "text/plain": [ + " Size: 208B\n", + "Dimensions: (band_wavelength: 16, band: 16)\n", + "Coordinates:\n", + " * band_wavelength (band_wavelength) float32 64B 0.47 0.64 ... 12.27 13.27\n", + " * band (band) int8 16B 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16\n", + "Data variables:\n", + " Rad (band) float32 64B dask.array\n", + " DQF (band) float32 64B dask.array" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds_std" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "Could not find any dimension coordinates to use to order the datasets for concatenation", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[11], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m ds \u001b[38;5;241m=\u001b[39m \u001b[43mxr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcombine_by_coords\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43mds_mean\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mds_std\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/opt/conda/envs/rs_tools/lib/python3.10/site-packages/xarray/core/combine.py:958\u001b[0m, in \u001b[0;36mcombine_by_coords\u001b[0;34m(data_objects, compat, data_vars, coords, fill_value, join, combine_attrs)\u001b[0m\n\u001b[1;32m 954\u001b[0m grouped_by_vars \u001b[38;5;241m=\u001b[39m itertools\u001b[38;5;241m.\u001b[39mgroupby(sorted_datasets, key\u001b[38;5;241m=\u001b[39mvars_as_keys)\n\u001b[1;32m 956\u001b[0m \u001b[38;5;66;03m# Perform the multidimensional combine on each group of data variables\u001b[39;00m\n\u001b[1;32m 957\u001b[0m \u001b[38;5;66;03m# before merging back together\u001b[39;00m\n\u001b[0;32m--> 958\u001b[0m concatenated_grouped_by_data_vars \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mtuple\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m 959\u001b[0m \u001b[43m \u001b[49m\u001b[43m_combine_single_variable_hypercube\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 960\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mtuple\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mdatasets_with_same_vars\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 961\u001b[0m \u001b[43m \u001b[49m\u001b[43mfill_value\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfill_value\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 962\u001b[0m \u001b[43m \u001b[49m\u001b[43mdata_vars\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata_vars\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 963\u001b[0m \u001b[43m \u001b[49m\u001b[43mcoords\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcoords\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 964\u001b[0m \u001b[43m \u001b[49m\u001b[43mcompat\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcompat\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 965\u001b[0m \u001b[43m \u001b[49m\u001b[43mjoin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mjoin\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 966\u001b[0m \u001b[43m \u001b[49m\u001b[43mcombine_attrs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcombine_attrs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 967\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 968\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mvars\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdatasets_with_same_vars\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mgrouped_by_vars\u001b[49m\n\u001b[1;32m 969\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 971\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m merge(\n\u001b[1;32m 972\u001b[0m concatenated_grouped_by_data_vars,\n\u001b[1;32m 973\u001b[0m compat\u001b[38;5;241m=\u001b[39mcompat,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 976\u001b[0m combine_attrs\u001b[38;5;241m=\u001b[39mcombine_attrs,\n\u001b[1;32m 977\u001b[0m )\n", + "File \u001b[0;32m/opt/conda/envs/rs_tools/lib/python3.10/site-packages/xarray/core/combine.py:959\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 954\u001b[0m grouped_by_vars \u001b[38;5;241m=\u001b[39m itertools\u001b[38;5;241m.\u001b[39mgroupby(sorted_datasets, key\u001b[38;5;241m=\u001b[39mvars_as_keys)\n\u001b[1;32m 956\u001b[0m \u001b[38;5;66;03m# Perform the multidimensional combine on each group of data variables\u001b[39;00m\n\u001b[1;32m 957\u001b[0m \u001b[38;5;66;03m# before merging back together\u001b[39;00m\n\u001b[1;32m 958\u001b[0m concatenated_grouped_by_data_vars \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(\n\u001b[0;32m--> 959\u001b[0m \u001b[43m_combine_single_variable_hypercube\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 960\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mtuple\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mdatasets_with_same_vars\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 961\u001b[0m \u001b[43m \u001b[49m\u001b[43mfill_value\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfill_value\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 962\u001b[0m \u001b[43m \u001b[49m\u001b[43mdata_vars\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata_vars\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 963\u001b[0m \u001b[43m \u001b[49m\u001b[43mcoords\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcoords\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 964\u001b[0m \u001b[43m \u001b[49m\u001b[43mcompat\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcompat\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 965\u001b[0m \u001b[43m \u001b[49m\u001b[43mjoin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mjoin\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 966\u001b[0m \u001b[43m \u001b[49m\u001b[43mcombine_attrs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcombine_attrs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 967\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 968\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m \u001b[38;5;28mvars\u001b[39m, datasets_with_same_vars \u001b[38;5;129;01min\u001b[39;00m grouped_by_vars\n\u001b[1;32m 969\u001b[0m )\n\u001b[1;32m 971\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m merge(\n\u001b[1;32m 972\u001b[0m concatenated_grouped_by_data_vars,\n\u001b[1;32m 973\u001b[0m compat\u001b[38;5;241m=\u001b[39mcompat,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 976\u001b[0m combine_attrs\u001b[38;5;241m=\u001b[39mcombine_attrs,\n\u001b[1;32m 977\u001b[0m )\n", + "File \u001b[0;32m/opt/conda/envs/rs_tools/lib/python3.10/site-packages/xarray/core/combine.py:619\u001b[0m, in \u001b[0;36m_combine_single_variable_hypercube\u001b[0;34m(datasets, fill_value, data_vars, coords, compat, join, combine_attrs)\u001b[0m\n\u001b[1;32m 613\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(datasets) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m 614\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 615\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAt least one Dataset is required to resolve variable names \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 616\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfor combined hypercube.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 617\u001b[0m )\n\u001b[0;32m--> 619\u001b[0m combined_ids, concat_dims \u001b[38;5;241m=\u001b[39m \u001b[43m_infer_concat_order_from_coords\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mdatasets\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 621\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m fill_value \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 622\u001b[0m \u001b[38;5;66;03m# check that datasets form complete hypercube\u001b[39;00m\n\u001b[1;32m 623\u001b[0m _check_shape_tile_ids(combined_ids)\n", + "File \u001b[0;32m/opt/conda/envs/rs_tools/lib/python3.10/site-packages/xarray/core/combine.py:144\u001b[0m, in \u001b[0;36m_infer_concat_order_from_coords\u001b[0;34m(datasets)\u001b[0m\n\u001b[1;32m 139\u001b[0m tile_ids \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m 140\u001b[0m tile_id \u001b[38;5;241m+\u001b[39m (position,) \u001b[38;5;28;01mfor\u001b[39;00m tile_id, position \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(tile_ids, order)\n\u001b[1;32m 141\u001b[0m ]\n\u001b[1;32m 143\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(datasets) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m concat_dims:\n\u001b[0;32m--> 144\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 145\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCould not find any dimension coordinates to use to \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 146\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124morder the datasets for concatenation\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 147\u001b[0m )\n\u001b[1;32m 149\u001b[0m combined_ids \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mdict\u001b[39m(\u001b[38;5;28mzip\u001b[39m(tile_ids, datasets))\n\u001b[1;32m 151\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m combined_ids, concat_dims\n", + "\u001b[0;31mValueError\u001b[0m: Could not find any dimension coordinates to use to order the datasets for concatenation" + ] + } + ], + "source": [ + "ds = xr.combine_by_coords([ds_mean, ds_std])" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "import xarray as xr" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "from functools import partial\n", + "from typing import List\n", + "\n", + "def spatial_mean(ds: xr.Dataset, spatial_variables: List[str]) -> xr.Dataset:\n", + " return ds.mean(spatial_variables)\n", + "\n", + "preprocess = partial(spatial_mean, spatial_variables=['x', 'y'])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "ds_mean = xr.open_mfdataset(goes_files[:100], preprocess=preprocess, combine=\"by_coords\", engine=\"netcdf4\")" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ 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+       "Dimensions:          (band_wavelength: 16, band: 16)\n",
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+       "  * band_wavelength  (band_wavelength) float32 64B 0.47 0.64 ... 12.27 13.27\n",
+       "  * band             (band) int8 16B 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16\n",
+       "Data variables:\n",
+       "    Rad              (band) float32 64B dask.array<chunksize=(8,), meta=np.ndarray>\n",
+       "    DQF              (band) float32 64B dask.array<chunksize=(8,), meta=np.ndarray>
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<xarray.DataArray 'Rad' (band: 16)> Size: 64B\n",
+       "dask.array<mean_agg-aggregate, shape=(16,), dtype=float32, chunksize=(8,), chunktype=numpy.ndarray>\n",
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" + ], + "text/plain": [ + " Size: 64B\n", + "dask.array\n", + "Coordinates:\n", + " * band (band) int8 16B 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds.mean(['x', 'y']).mean(['time']).Rad" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "rs_tools_iti", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/dev/multi-sat/4.0-resolution-investigation.ipynb b/notebooks/dev/multi-sat/4.0-resolution-investigation.ipynb new file mode 100644 index 0000000..a099427 --- /dev/null +++ b/notebooks/dev/multi-sat/4.0-resolution-investigation.ipynb @@ -0,0 +1,210 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], + "source": [ + "import autoroot\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import xarray as xr\n", + "import os\n", + "from glob import glob\n", + "from rs_tools._src.utils.io import get_list_filenames\n", + "\n", + "\n", + "%matplotlib inline\n", + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "msg_path = '/mnt/disks/data/miniset/msg/geoprocessed'\n", + "goes_path = '/mnt/disks/data/miniset/goes16/geoprocessed'" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "goes_filenames = get_list_filenames(goes_path, ext='nc')\n", + "msg_filenames = get_list_filenames(msg_path, ext='nc')" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/mnt/disks/data/miniset/msg/geoprocessed/20201001151243_msg.nc\n", + "/mnt/disks/data/miniset/goes16/geoprocessed/20201001150019_goes16.nc\n" + ] + } + ], + "source": [ + "print(msg_filenames[2])\n", + "print(goes_filenames[2])" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "msg_test = xr.open_dataset(msg_filenames[2])\n", + "goes_test = xr.open_dataset(goes_filenames[2])" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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g8wU8e12Ohm4HNxzJ88sSm4SY20cki4zZG7fQOXSvKcCx/19tcPwmCCaKjU8XdJ8+JO9uyOdmhbh+oFhtEd7KMXtDkn5M2tfYCDrXNNmohY3uB4GvyvTaCuXmAP3FG9KnX0L9LiOEFfgJ3VKOAutk8a3FlSV6ZUB5bgWjNXYyRR2PJB6IQrJLa4wvBUwvOVQJgy+W9J6ds/cXE2wQMLlgyNubzLY15397CodDXJajj4aY1QEuDnFhQHx7TLu3Qt7R6Fx+H52fXtf+zG8Atb2FvX2AjiPG73mMaFiQPHULe3Qsi7+MA/R3v2olqCQ5+RzA7u2jJ1N5rIoF9g5wSpN97TnKRNHagTKSsEMfT9n+jS7pQBEfOxbrmvk5R5kELL7hCp0/vIqdzlCzORiNHbSlkKQVyUHB4AsL0o0W6nDK50/p+pz9DTCZgTG467cIH1wlXQ2wb71I+48s5eHRCag3IAMbeYGdHjYIYdPAuF2WScaQpgIFCwL53kB7p8TkjjLStD9zE4qC/rMz5tsJvU/dpthe4Wary60/3+bck2nDIRCGFBs9guEcdTSiN8uwcYiLDUVLE4T3PcDdWxxSPnoF8wdPEf//PkfS7cDaALu1inGuHtly1dFQjYktw8ONQZHXY146GWC6HdzR0LeFLb3fv0q5vY6ezFGLDLt/gN5YJ9gdYQYRbjoneD4leEeX3vWC5HM3sJl0+ZRWmFmGOhoJhiDLML0uLFKCwWUo78cAd212/5BwnEKnjZvOpAo4nqD7PWi3YDYHyhrvh7MC1KgJI+T8V8bIHe8DxHJrHXduBf3HT2OLApXlmCTGHY8ofaDpkohis48qHSow2KNjLvzvX8RV4+Wl5P/28Bg1mUKv23AKAK7fJRzl2PQ+LPzuzc/fuyw/ga610xl270DIIpzUAtDq5PO09midBtOvwgBXlJhbh9hWgLp0XjZHK6G8eVuOBqUwqyuovMD88TMk14aS4lUTyUkin1O9ZxDIBFHpN9j2Bi4MsP0WelGgh69TfoB7YbrbRbXaqDgWlE6anoj8T8z250UD4jSmQfzoCqWr5YgoLdiS8DMvSFpY3bXWgbLN4Gkqg6bu+i2cZxpx1sJ0Vn+OunKR6SOrOAOdFyaw3mf6YJdwVKKzkvBgig3uo4Lv2uxkgpstcLO5BFxb6/KDCvRRehyf1vXGcJ4joBnxcs1zqwxgOsPNF9j9Q8/3E0gZuQKaTqbNuLl/jYpC8q+5KHwBlZdRijJRjB4I2P+GATYydJ6foEtLuh5RDlqU/ZcYYLlHduY9gCtKYQfTWopAB0dyJzsnd7y3Kg84Mf8PDTTcY/XrIwGEvmXQx+4doLRCtdvYK+fQxzLnZ3f26mBS8AgFwdEcBl2YzVBXLlIMWvSeHhFMu8QHC8zxTHAFpSOYypyAGd+fDbx7q+bv/xSipRPHglY1A0j9Wq3RUYRdAmja+QKzMhBU8XyBynL0F65SvPVhZtsx/d+c4kYTHM7HFxk8cxWnFKqVYDsxOi+ZXe5hFiVmkuKSEDXPCA8s0WQOzlGo08MEnvkjAOfzd3/nVvOAKop8c0idoGxRSYyOY1QUorodGQSpikMba7I5olC+lMIeHKHWVmo+IZdlhC/skRzkqFar/ozqcytSCTeZop+7ibm+R+fZY+L9OWo6R08WEAbYJMLN57heGzeentrlOfMboFoopVTD0lGdv2HYzAQ6W9O6oP3o2HAkrvv8lizacFSTTFCROwQBlDL7p4wAT9xkQrQzFrYv3TSiKnCn83UGlwrkjJ199HAqVDF7B/L9LJUNdDQ6SUp1j+3sHwFKydSmp26x/mIq/N3oTp75LhdYF1VwWJZwcCyxgS/cuNLWE8JuNkMbjZsvpF8Qx3LsvHBDGkm+hkBeyBFgl1rTZSnlZK2w+4cyW4jQzqndA2xVKzhFO/MeoKrqVZF9la65zLdYX2Lw0s0XuIVPF63FVfMBfqgUkGHRiuJtvqjv9OoYAGBrww+Z2uZzlucPq6liPzVcWcVWpu4knzoFO/MbQLVamO2tk65+6a7X7bbnADby/yT25WDr+YQEOOIWaU0fp6Kw4RbwG0sFAbrdRm+ui+tuJbB3iN6U9q/zvINwB+Xr8lBpKGmkK5rJZWVMU6U8BbvnG+ADH/jAialbpRTb29v1z51zfOADH+DChQu0Wi3e/e5389nPfvbEe6Rpyt//+3+fjY0NOp0Of+2v/TWuX79+V7+PWyxwR8MTnLu1WYedzXxxp8T6Kt4JlhCk+lfFDyoK0VsbcG4D9eClJu+fz+VrPEGt9GU8rCgotwaoBy4K5KvTkZihIqyCZnjUv3c1uCo/k/Fy99XGEPK1X/u13Lp1q/769Kc/Xf/sp37qp/jpn/5pPvShD/Hkk0+yvb3Ne9/7Xsbjcf2cJ554gl/5lV/hl37pl/id3/kdJpMJf+Wv/BXKuzgPXZrJwvo7VAVBPcxZU7XphsC5yttVEMhUDtT8QhWblzs4EkKH1Xa9oEA98EleoNoyLWRu7EMYSO8hzxoaucobeJCJXl2RsfWL20JI7fkJXJbV3EOnYafyzkEQnLjrK3PO8a/+1b/in/7Tf8p3fdd3AfBzP/dznDt3jn/7b/8tP/ADP8BwOORnfuZn+IVf+AX+8l/+ywD8m3/zb7h8+TIf+9jH+PZv//ZX9st4gua6AQMNVfvyuevxgkopSKRszDIj6DIH8CLFHRwSjCeyMaoF8m7eDkf1XeyKAvfctZotVBm/Ac5voXymUVPNjCf1hBDnNiAw6PGMYhDB0Sv7s79cOxUP8PTTT3PhwgUeeugh/vbf/ts8++yzADz33HPcvn2bb/u2b6ufG8cx3/zN31yrgPzBH/wBeZ6feM6FCxd4y1ve8pJKIZWlacpoNDrxBchdlHsOfiX5uOACT9K/VrCxKgXEGB/Ry3ROxRrulqqHLstwmXgYm6bY+UKyjDyXTZJl2IpwuizrqWFXFLB/KFlF9bj3HAD2wiazx9bZf+cGN77rCkdvXXnVa/Jyds89wDvf+U5+/ud/nscee4ydnR1+/Md/nG/6pm/is5/9bM39/1IqIC+8IKMvt2/fJooiVldXX/Scl1IKqexlFUO0aqLtwuKmtintVu7eGFk0n9fXRR2aiLxm+fIdQlXhBl/OtG64g5ZoaKuqoluksugVNA2aOKMsiQ4zzMISjDPc3ikNBnIKG+B973tf/f+3vvWtPP744zzyyCP83M/9HO9617uAO6JgeFkVkFfynDsVQ0ajEZcvX/YFm5N/plJK5vl9EYco9FhB1/T+PUuIm86kBVxRwgVBjRgG0K1EOoVx1DB6LZM6LZNJv5QtZwaB8AS4KMDMc4JnhBYku7zyJa/Nq7FTTwM7nQ5vfetbefrpp+u44EupgGxvb5NlGUdHRy/7nJeyOI7p9/snvoA6kKusPpuzXNRDPP+/SpLmzjYGlxfYo2NJB/NCNkRVFo6iuvPnSoGXszqQIK7Tbsijq/5/EDRfHWlNq1aCaiXoTlsCRF9TUEWJ00rIorSC1T5F5/TGw099A6Rpyuc+9znOnz/PQw89xPb29gkVkCzL+PjHP16rgLz97W8nDMMTz7l16xaf+cxnXpFSyAlbyvurjl7d+s0y7GRKeXRcn/cuTX3qZzwHoK8HzOc1BKyuK1Ru/tpNKR1PZ+Id/JfydQZVqYWUpXicLJevdotya1WAKYsF7vCI4OouZvdIPMrBEdHO5O7+7i/D7vkR8I/+0T/ir/7Vv8oDDzzA7u4uP/7jP85oNOJ7v/d7UUrxxBNP8MEPfpBHH32URx99lA9+8IO0222+53u+B4DBYMB/+9/+t/zDf/gPWV9fZ21tjX/0j/4Rb33rW+us4BWZVigdnGjL1uZBH/VZ7Tt/0ir2gyFVRa5KBe8gm67fp7KlWEEFgaR+IPz/06ngD/1RoYzBJRH62m1cKs/TKwOJDzxoxC1SOPoqIoq8fv06//V//V+zv7/P5uYm73rXu/jEJz5Rq3z8yI/8CPP5nB/6oR/i6OiId77znfzar/3aCUWsf/kv/yVBEPA3/+bfZD6f863f+q387M/+LOYu+PKUanr8Tt/B6lXBtKrnQkMF+xLn9p2yL8BJcYg4EnZQTznviqIZQzse+s8tms+NY9R45hFFVgJOpHhVxxIvQXV/L+3MK4a8J/6bBG5pny/1+oETHL7L5deqfl/XDpY33x2cv5WpIGjqApU8jJeDqQCg1WtUFMLGGuwe1Oyjy2TTKgxk41pLGcJ/Pvq5+4ohd2NK4RddNyXWShdwKRYAX5VTTdpYNWTqxyp1kOquXyrjgl84o1HtVo0hlHhh6XUV34/SsviVQEUVf1Tdw+Xg9RQ7gme+HexKmQ6uUDmwxBHgp36qn6mKIdSPjr2k611O6yr+YfyRUM0RzuZyXHjVD+Xdfe3alarnEOr3rP7vnEDC1lZQgQeb7r58/ePV2tn3AGapuWNPnnY1SETfcZfDSZ7eqtXrnKR81koaV/USKhDpbO4nirPqA5rj5dw65dseOXmUwIva0XptFTbWZHBUKQiW0MmnYGfeA6h+H7UQVO6dd/eJSiA0BZ5qMnjpLMcYIZkKw7rUW5/bUVh3FOuYIArRQYvy0iZlZLCRoWgZgvNbqP1DAYhUsUEY1p9thyN01V3c3ZfNpe8fAXdviwV6sC7wq4oEwrt2BScWHDgxIYSztQtXUYStCkdLd60rSxRNa1f1uthLm8y329hQsffnAjb+qMQZyFua6LhDmBeCJI48JM2TUlQU8242O0ktf4oxwJk/AggC3GQCgy6q26ldukqWQBZVQFcxhEMjEVdN8hRL535l3nvU7OOVcohSqBKOHg1I9qH32X2hiOlAcDjF7R8KVnHQl1RQazi/KVKxayvyHj4eOc0UEF4PHsCWEMYScRfCvqnabciF3rWaGajrArlqGMVDwQS4duJjBF89XKRSsJn5yuB4qVI3X6C/cJXW/Dz9/iqDzxyipnPUrV3O7/awx35MrChq7gESQSG7RSoCkicg6hp45fWPL9deBxtA0LdVDKC6Hbn4nq69QvLWm8CYunSsOh2Z1j0sod9DhT3R81lbwYWBuG1fJ6hz+XYL1e9RtqWok292iA6Hgjg6Ol76vZbSxywXyfkskxGyO2Tlv+oAIa8lc0WBq5C4gOq0hex590DIoKoYoGrLZlkT9U+n8rO8gPmC6dsfYHQ5oGwpujcsvS+G6EkKB0eQisjD7G2XhPz55ozBr39eiB/mJyt7L7KqBrBUlgYaqppTtDO/AXS/jxrO6tzf3rwt538co9pS+LETGbyounUuiYQQKk1FraPd4ta3nWd6EdDw0L8fokdzuTvzAmeMHAnthOTGBLV3KEWkflck45ZIqF7OVBCc3CTGoJIElcQUR/d5Au/aXJahBn20F2KqFL5UUeDWV0kvrVC0DSazjB4ImZ1TRCMoW2C9Jy5bjjKGYKbY/FSBORj7VrKIRap+V6J2D+9yvp9g54fNEOpyYWnJE9Qp6Z2Rvo8TCNpNBfMU7MxvANXvogrtmUAkome1z/DPbTJ6QNi40jVL2Yb2NUX3hmPj9/YpVtrceHcHG8LqU46Vzw7Ze8cKN75Zo7/pIo/8f0boeS50b8cTieTbLVgs4NK2TPSMJzWG8EXRfNVAWupELn+PMRCF2E4Lu3IR/q/TuT5nfgO88N0XeOATJeFzOwBkj51nuh0zPa9Z/5Oc+DBlcrnFdDsgHjrWf28PFwcEz95iu3WZ0ZWIIlGkm202nzxi+MZVVr72gL23r7P2uQVmlmF7HQg0+mAkx8vc8/+029hLW9LunS+aQlHVkYS6AFSxj4jkrBKVkclMmEcO70vG3LW1DhxOK6Zff5nx5YBsoChjUCXsvzUknIRs/84RwbxH0da4MGBxvkvQT3AaNv7giGK1xXwjIpxEXPzNghcGAx7+YkbeCwjGKdM39AnHJfH1HQksq4YOBfrqLSnwVJlGJUpVZRgV5hAfoPY6qNlCso08g9t7WHt/NvCubfQQBLqFyR3zLcX8QkG8PqcoDMZYpsOYbLBK/3nL6h8dQZZjUsvuN7Zp7VrQXcLjlNYuFJ2Q5NaMCx/rkg0044uGdKXP/tsUD/2HVGICjwRyzqHXVqSlXBFLGq9hqDRu0EUvs5EWhSx+Xoi+0GRaVylVfF848q7tG77pC+x/U4ENFOufLdn8hCF8skfrU214psPFBw7IVhxpX3B4+YUB6WpI+7bFZDJHqKwj3BkRHcwpVmKiscUaCBaO4cOa9o5i7+s7EvX71q5SCtvrSEaxTETRbqE6bYrVNsWFtRqT6DK526sB1PqogBc1se6lnXkPEOmCqJ9iUsN8XeMUrH8mJ9lfoNKcp5Mttn/fcvSYId9sM98IWaxpykjRvVUSDTOcVqSXVrChomgbwmmJLiHrK9Y+V9K+naIXvkFUQcfLEnVb0jfnnLj+pYGUYG/M4sFVzFFHpOuMERxBp4UaTTxhpB8hM19FkLDXmn32YJtsGAscrIBsTTF+ICDrd2jt5fSfVgwf0ugM5hshZaRwCqKxIzouoLBM3tBlsaaJRo7uzZQy1ISTktaepn1LGEP01V0pHIEnpPKz/1Dfyc45yRIAwgHzjYDZ1hbrH5sKULXbxrUicB2BrXtEkHL3yaLv2v7S+S9y++KE3Qd7TLOI8V6P7udiFmuK4SMxs0sFZq4xc0V6pLEhFB1FPLQE81KUwJC73SzAzApMYXGxoa0gW40YXgnZnm2ink2b7t3y0KfvKeDl4gHUbEH36oK8H4pY5XQORYkNNHa9ixmn2FaIKiylzuHG6VyfMx8D/O7OQ2zEUx7qHzBLQ9Q0wBpYbFpmb8jYvHKEjRz5qmX6HROO3+RIVxxFS2EjTbGSEE4t/edLbAjjB9u4UOO0IjpKsaGis1syfGMftb4qHcEq2KthYw6iZvQMYyjXe8wuJBRtOetdt012ZZ10Q4ZKXWgoWyGzy10O3npfO/iube+5NX4tkxn9MtOofg43DGymPP7w80yKmOKKJs0DZqMEU4ALYbatgJhg7ogmFl1AuqYYvUGTdbv0r2aUicYsHK3jhTB5VaAQY8CEUn30iOTiDRcIr+43LCBaMbqiSdccym4yX9fEQwn2glnJYjMh72jGlzSL6L5w5F2biy3aOPKZdOc21keE33zMZBGjlaMdZFzoj9ifdcizAC4UlKUmnwVkKwHJvkYVIjI533TYyBHOQZWO6ChDFRYzXkjurpSMdvc62HZCMUiwkcYaxWw7pM8GweEUdTxGz3LiY0d8DGWk0BlMtzWdWyVmknH8jhbTCw7bKk9tMhheBxsgPAgwhy1U4IhGinJT8Y6tm/z601/Dc6M13rJ2i7f0blKuakbnEg7zDgdph6dunqOcGbK+zBOEY4WNHMFUMb6k0EVIMDeowhGGBhOHktPrARhF3o9R1pENAg7fGLDyxZLwhT0BfAYByjnauyVmYbGhpns1Je+FZH3DzfcMmH79HBS4aYDS99PAu7bODVi9lWMjIYu6eW6N/3R7QHAYcNsqumHGleSQ2KSkNuB8PCQtA4rMoKw0ggDcTBENFTpVBDOYnjMEM033VkHeC0lXI6JhTtEJKGOFDRXd56dMLkaoErpfFNCIMxIM2nbEYsXQvSU6Q3kvJF0JOHyzJn0gJQxLbGlQscUG9zfAXVv3ekn7uRG2k1B2Qh76VZheSphvKLLjFk8fXYQ3w4XOkEE4J/QynQ9d3OfZxTn01ICGbNUSjjXhDEzmGD0M/WfBpCU6s7hAMb0QkxzkaK1FOUwp0oEmGjvy1QRYkazCKFReEs4d842A1n6BmZccvCUiu5LS6S+YTyPsLADjcPclY+7ekr15DfbIe6GIRltQhaO1D/GR5pn8Ek+vb6GMw2YGNTEEE01/T1FGsNiQs9+GjtaeQ+fQe0E4fgHMcEG21SFdUczXY0zm6N4UHcBw6kiGJfHtMcWgBUmAzgXy3Xt6zOJ8G51ZlHXkPQjigjw32FmAnhps20J8HxV812YmKWphodsiuT0T8eZWSLIf4HwSPHheU7RD4ebVChc4gmmGso7JpYjpQxa90AQTRWtfZvta+1IiNnPRCghHKclRyNGjiu515PiINCvPzNFpgZotUN2Y6QNtOldnuNCQbSaUsUYXjmwlIBxDmga4QmFGAcqBVQ7S+5jAuzdP6qizAjURuHUYBAStWAKyosSudHAqIRzn6HkhLnpRoBYp/XzAdLtN0YX2jiPem4Glfg4gJFDTlP4XLGl/QDy2OA3JrRl6kQkj+GqP2cU2oyuG1k5A3gtIVww4sIFhsaopI2AUECw0wVRR+PhDz+8DQu7aVFmCNajhpGH+1Bq1yGA6FxLowBAbhZ7nUrOfZqiJjHCbScqF37GkqzHzDblcOitQ8xSnFco6bDeR1xYlm79vcVHA9HIHNBQrbZRzqLQk7yichtn5mCJRFIl4inRFU3qUeu85I/93UCagco2Znh40/MxvADee4ZQvxTornTfPCFJLxE3mBPO09hbLpncO0UFAcEPRGnTRB8cQR7gwQM1TYfI6GAnpg9bo3SMIQ3rTlLKXML3cIpyWxAcpqoRoJJ3HvKNo71rSFUW67nAG4kNFfOTQhSPvKPIuxLsGfXx/A9y9ld5NL0/bKu1nBPymKBviqAqtIzRx1ayATO3oo5Hg/ZxDVQxfaQa2RMWRHDHOoYoCtchwq22iUUnWNxStFigI5rBYVwyeK6S4tBFgUgGo2ED+7b2QstiIsMaQrkPROb3Lc/Y3ADQ4e6WgCqhLixzm1P86Z2VTRC/PyaNaCS4wcgTkjdwrvuVb0b4pTyUbDjPyToIziqyrSI4sZi7VwXhU0NrXhBNFuirHQ/+5OcE4JX8gJhv4tz7F4aAz3wwCmtm6SvKl4gxytiZvplp8aEAZyoMyKkBGFEq/fpGdpHBXChcGuE6r2WxZLpXA1YhwKmlePHIoByYDXTiKtkEXjmDhMAvY+ExOdPMYNc8IUkf3hiMagj49ptjXgQcwAehIINYVz48FqP7v5/Gr42AZfbMUD6gowvZaqGIJtVuWMspVFJQrbXRaoNstOV6cQ88y3IZkFzYICayljDXBwrFYMdgQ+s9nOKMokpBw5KlluglpT5P1FSgIT48j6nWwAWwpvDxaAQa1rA5itOfjzZcw+xV1jG2OC9/LV6lXFTMaphIjOO81gv2xULzmBWjZHHo8p7UTkvcj0oGmSBTx2JEclphFiU5Lwp0h5aCDDTVFN8QMOozeOCDvKmwMRSLp52nZ62MDhLEnZtL+QG30gNEKVUlGLd/9laZwRRg9noDns3aeNwDrhGDSGNx4KgOeaeqbQgraCVhh+0wiTdY3BDNLOMww41QCSWvRWUGZKKbbIdHqCsNHBLoWH0lgGM7uQ8Lu3qwDpVGB9kQPwshZgTFZUupE24ZEWktrF1s2/D6V4ES21J+v4getcIMuaoinf3Go2QIzdbhOi1ZW0n42k03o1UhUluM6LRYXe+BgvqVYrCuCOURDmUYyqSKYn94GOPtBoF+gis6VXkemeMDr9xmfw/sFr9TClZbnezpZQfj4yeGyFM9QeQilUWEolG/VptJKMIF5gRpO0DsyJl6stlGLVCBgpQyehscps01NmUifoX3bEo0teVdgaNHwPiDkrk30fSwYarVu120L/t7P9xGFqCgSVU8Euu3STIgllBaSicXixLFRZw3ac/wmkV/U0kvAajkKimVIeJvw+V15LySwVJkmW41RVlrXvasylzDfCsl7jvgQgv3TiwJfsQf4rd/6Lf7qX/2rXLhwAaUUv/qrv3ri5/dKEeTo6Ij3v//9DAYDBoMB73//+zk+Pn7Ff2DF8lnP8ccRaraQXD4MZaHSVBa/urMrFG/pqeK9S6/+PuHvq8rKBlWVlrWWz6n4BcIlxpBWS8bAZ/OGncxKEcpkluTYsfqFlNbNCco6CVWcIh41xFWnYa94A0ynU77u676OD33oQy/583ulCPI93/M9fOpTn+KjH/0oH/3oR/nUpz7F+9///lf+FwaBJ3HuyMW/vSd34MGxFHKMEY0fP76lup0X3bnL9HI1b6CumERKTy6d11SwKDnnXenZP0srUrPV4oPAvhOBj0e3x7R3UuIXDlG5dCR1ITUDgZmdnqN+VUyhSil+5Vd+he/8zu8E5AJduHCBJ554gh/90R8F5G4/d+4cP/mTP1krgmxubvILv/AL/K2/9bcAuHnzJpcvX+YjH/kI3/7t387nPvc53vzmN/OJT3yCd77znQB84hOf4PHHH+epp57ijW9845/6u1VMod+6+r2EgzUhci6tTOb4oM61YqnnR6Gwdi0WEuB5sejqbxTNAcedl6oe214OJMHHBqoJGkMJOlWSSOk4CFBtTwxlDHZNaHL13jF2a5Xxo8IGOnzYsPr5gtanrvKx5z/02mcKvVeKIL/7u7/LYDCoFx/gXe96F4PB4GVVQ15OMUT1OhTnViQqb7coHj4vbBzjiXiBNMVNZkLOvBTd18yidy7uslWPV0KSZSn1gijyQ6LedfvRMGlCxbDax0Uh5aVNsoe2wII+knM+X2vhQUnEB04UxU+RJ/CeboAvpQhS/ezLUQS5ffs2W1tbL3r/ra2tl1UN+Ymf+Ik6XhgMBiIWAcwf2WRxzpdo1waENw9raph6Jr/TkoVZ7gF43YATOn/QkEnak0LS9c8r+Zc4bkgo2y3hBVbae56IYrPH9FIbVVpcEuD6Hc8pqImGBU5B70Yhben89GrBp5IG3gtFkJd6/pd6n3/yT/4Jw+Gw/rp27RoAeVfOz/KNl0nPy52nVgfojtC6qTjGjSe1HLwrS5GNnQubSF019JJutRYAnAjOTsjNOeEJrqnmjJGSs/M0sVFAthLRuTrBjFL0LMNGAdNLbcKx9BCSw4Lk5hiVF75xdTp2T6OLZUWQ8+fP14+/nCLIshfY3d2tBSG2t7fZ2dl50fvv7e29rGpIHMfELyGw2HvqkNAaXBhgxob8XJ/w9lCCOajn906c71WdwFop6ih18i5c2oRVb8E5JxXFOJaF9gohNYXsYiEYhMBgDsZ0do9Ba+ygA7cO4YFzmNRiplmNAFJzPxMYnB4k7J56gHulCPL4448zHA75/d///fo5v/d7v8dwOHzlqiFHI5jN4XCIGk4Ibw9hPMVN575pI4ygNWVsNdNX3d0VfezynV/ZnUSO1vP6VIRUb7iEXhnU6SJlSbnWhdlcPE4rRs1SVCuhTAKSvQVqkaOnC/R41iz8a4kreDKZ8Mwzz9TfP/fcc3zqU59ibW2NBx544J4ogrzpTW/iO77jO/j+7/9+/uf/+X8G4O/8nb/DX/krf+XLygCWTQUG4ggWqUT5aVa7ahUEOKgjf5QWRZBl0aeXysGr3j80bj/3KmMAOsClGWb3GNdrw3yOCgyu08IcSrCnwhDmKcwXEAREz+5Qaw0BBAZnHG62wNnTiwFe8Qb45Cc/yXve8576+0qp63u/93v52Z/92XumCPKLv/iL/IN/8A/qbOGv/bW/9rK1hy9lLs9x5Uzy/CQRIWiLpHUFEvgZW5d4X5QV3/l9ReVW8QsuM38BtURtWeImJcwW0jLOcwh7kMuC240BarqoEUcqkE2j4qj+V80XkjmEp1exP/OKId+69f8gKJSkZ61WowR+Z2B1B53bsrp3bUt6QLX5SeBGbUQ32AJo6gDaoDotQQc/0KF1a06wO8TNF9KbqMgpq5Jz1TQqS4pQ8Z/3f+ZU6gBnvhcgJeBQqnGzWf2wiiNZtKJo7tCyRClp+CgLhEGjFFrdJ/rFd6PIzUnPAJCFU7rpKvrPyy6tka5HhJOSYOe4qTv4I0UobavPaxBK93kCX43ZUqY7tccD+j6/W+LfbTR9HKodQRJjD47Qq6uSKRyPGhEIOHksLAeLVROountrhXAjreLS0b4+RQ89HXzhwSOx70kURV01lN/TYxW/WtLA16RZR4X3qMq5yiJ3exBAqwVFgU0zVJJgt1ZRCwFq2N194e7ptBt1URCvsAwGhYYartoEugnoVBRilSI4mKDSTBa/JoPyv5snqXaZbZjKvZKpx7Cdip39DVDZnbV8Xw1USkmg5QWe9GiG8wMjKkkgTbGTKer8Fmoe4eZzKeoYU3sFZUx957pchKZFSURqAQRGMACLrAGo+nYxzkqb2v9ayui6Le1qqpn7ghF3bc5H9icLPb5q5zn5UBIgsrbC+M9to3oCxFftBM5LSdrd2sW14gYDAE3gVpZydith9VKxkFHU6uDtRPAHXhyy3gTae4zUp6jLamF2ScDqvmrYvTO1DPMGT8qc4XRMudYh92yhINiBWjqu18W2Y/TY4Kw0iFRN5aabeYLU1l2+SkxKHY9FcaQopFZQUcf4IpTLC1S18K4hka5/v1O019UGWBaKqvsK3m0rpQluHLJ2/QA3GkvrthWhpr4/cDxEjSei8ll5AGO87pBXC13SGlBVBqC8S69mEKz0+9EKMn+2a13rFCyLWMn5L8QWp2Wvjw1QkTcu3U1VCdcttWqZTHEPbGMvrFG2Q+KnbgiGwDN+4wNCl8vkj5vOIc+aKL0qDfsAri4zRx4N1IpRaY6bzST19KmeMn6NrW08ShCgCsElKH16y3TmY4Daluv7dwpI0mwIGwe88H/rEUwyz/idiDR8KxHsX68t/88LKTNX+kJJjO73UKsDQRgNek0aWBWQxlOZM1QK5+OMCl5e9xSs7yAWRd1QUknr1C7L68MDVFYHbfbEY0r5Lh6gckv3muPoTT1am48QjnPMPEdHoYyE7R3K66NQkMDdDqrfk5p+5VXSDKZ+GMWJZpHqtCRGGE8lBtjZrzUBsE6Ywd2SkERpocykDf1aagZ91ZnWJyeDl+VaK4tCWSDk7N76rV3mD68RzAtU6dBfvC5nuS/wqEFPXl8dH7O5oIB8b0DFEc5WCCHBDbpWjIs8bXwYSIeyLCVoDCPZBHeWmb1ekXuphtQ9sjO/AWT86yUeXz6rqwJOYFA7hzBf0M4LWKTsfftDrOWXUZ9/AaX9/EBpRQuo25YF9STRbjr1qZyq27+CPDY467BxQDD0UPReV2BpylcA60qlxyIaA61EXp/NT+36nPkNcMLuVPrG1/G1gijCTWaSk5elKIFFIZsfu0p5bkVEoeZzcec9Dc7AaCJTwO0WhAGq6+nikxjbaaGPx5L6+TqAOZrKxvGqoEShbABbSgVwOaX03samU9x9suhXYUrk3JkvpM+/vAmqWYFFA96sii6uLCGVMTBTFHK399pCElGJOeQi9cZ44tvKHkA6mQlZREU6AQI5ny3ExVf1gqKQDmXmy9LV4hdFQyqtFMrc1w28ewt9DV8rgW4tq4RWaJ8lwWfmS1U3a31HMMft7KP7PVy/C4dDcMvqnv5Y8It2oqUcBOJpum2U1rg4hLAjolKlFTdvjHiWwJeXtQKnpD6xpF5+GnbmN4Cbz3EuaOrzRVG7Wpd7Kdmq3ao0arkFXF34vGjOeD/8oeIIWzV0rGvUXcMAlRfidfJcgKVeVRTPGuKMAT+ehi8Pq25bUks/TVSXf4051WbQma8DqFYLvbku9Xkv5wLUquD4US+X5ZKKVXecz92Xizw1ytdZ7HRWv5+rmjoVC1kU4tZXmp9Xo2lpJpvDWmyvjWvLhqAspfzsN2I9flaXg+9nAXdvRdGc2WGA7nVlGCTNGkhXlXtXMrFL6l7gc/LlFK2CigdBXUyqvUYlBBXo+r3qkvN8LvCvLIcL61CUTct4uoROXh5LK8saF3AadvY3QAXUALkzl7l9luDfJ1+ims6cXnp99bMoRPv0jjyXGMPPBVTntrp6u6k0xrFkF86hrLShzf4It0gbUGoUyntXR4b/3aUlfDqXBl4PG8C7ZeecIH6ri7tcXavuQmPq9rFSnkgi9ylYdSdrr+ZtTI0vVJ2OdP2CAMaTJYSRLwxFIdbD0Zz2yOPhSGKJTlvErXsdIa6sgKmVx6nGzU/JznwMIFG+L9FWmP2XQv46V0vA14uQpo1rr856BLjpJpMmYPODnm48aUbCKvOATxVFzcdV4+VQcxZwNJRZgWrjVF3FIGiwhqdgZ94DuKIQIqeXK6dWqSC8COtXzwcsqX9XXTu0lqBu0BOp2CCQyL8scURNLFF9bkU66Vu8DqQA5ZVG6wByefHrY+X0GELOvgeouoB3AjmrhVnODJatWpCXMRWLxq/ttskvb9QUtMtZAyBpZRyKJ1n+XapIXwvYRCVJ0z30v5fzpJOvKYKIrzq70+UvX8w7/3/nV/X6ZasyBj/n72IjYM9YRsJxTWuYhx9AdbvkF1deuqBj/VEQGKkDgDxnuY1cPXZKduaPAODu7qA7L3q1KNb6xVQorSm7EWbnWOoKnlTKZRmqlaAWQhkXPbuHayUwmTYppjGSHdgSN5o0krN+RN3d6bVOyc7+BrC2gYV/uVbp9t4BGHFV2dgYKdrEEeENmf9TRyMJ1gIj1b28gOOhxAzGSAPISedRD/q+E2lPQMRVu43qdYSJ/OCoZhhR6j4e4N7blxoA9XeoWw7aqtRw2T0vUrmTF/44yHNJ76xFRQNUtytMY7Y5hpSHg9vd/fr3qLIGt1jIom+sYi9vY4ZTwSeGIRyf0mU4nbd9DVk1d1d9haEQNAWBuGmf05+wKiV8OXNW+v1KSWGpoo1PU6GWCQMIA6znJXIXz6GWuAvs8bCJTayUoZ1HH2O0EEiGmnKti728jV27t/OAy/b68ABLC6yCQIovSSTn9nQO05l04SqXX7n/5enfyiq0r1YNxKyQx1Sng1sshHMwMKjtTTg8Jl9vER23myzhzvRSKXH/VaXSmFqM0nValPqVnmFfvp19D+Cnd1UUolcGAtdqJxBHMqY1nzfdOiVVPlWhel4KRVylb1kuix2GDQ+Qh4m5okRZx/RrNlCDPtHz4u71xlpDNbPsYTy/YEUgSVlKTAGQF+ijhmLvXtvZ3wAgm2DQZ/HYuYaEwbmGB0irxkVHoeTlraTpDHISQQy+wJQtCUbMF0sTRxluOqP7x7cEiDKTyiFZLkDSioBK3liOgYpSrihwo4kUf9IMNZ7i5otTuzRnfgNUzRuA+Nqx/P/WriB0fCfQ+Tn8WubdzwWqCpbtod91jd56ShnPAaS0Fh4g6yje9CBqZSAewTrc2qBhKc1y2TjLAeUy31C1CQCso7y4IewmZXHnn3XP7OxvgG4Ht7nG5Gu3mLx5XUiZtILdA+x4cpLnxzoZxMxzOSaMromkVSUJDz6g9M0ipT37h+T/4e1jbL8td/4ixRkl08WqGfKs3kfFEbrXRYVSRlYVabWPM8zOsTSCXo6n8B7Ymd8AxcV19HRO+5pw86RbHQFvKuXpWpMT7t0tUtkYe4eAb8aEUvYVIojljeDZQEsrpdyyxPbbqFsHsinyDDVL61r+ckNIrQ7qCWWUro+HeiTcS9G7QfdUr8+ZzwLMOBVuwFv79G4f4AY90f0JA1wggxnq2q2T3MDVHeq7fSqJcc6ibDP0WYtFVGaFEEIfjnGbqxLBT6YC+4oiaLcE8uWc4BStQ60OcIsU1e/ijoaCMqpKzc7JrOHO/ot5i+6hnfkN4LSGw5G4ZJqioJvNpRq3vYnqdXFVbl5ZWfr0zp4EaTjbTP4u9+nzHLbW5TMWmWy6XheXRNhuQtEJCY8X6OFUNt7RsB7/WkYA47kGVL+Hm8xQ7QRVLm20e2xnfgPo4RhKTwvjZ+5UktQ/V6OpzwT00iKLG1YeTVzLx1jnz+MlNrFlqJhzAvOaL6DbloGRo2N0EBB3O9huG9tpMb/co/1FH4B2Eso4ILhxUHsR14phtqhnCuxgGw5P5/qc+Q3g2i1UYuQuSzNJ0SYTrxTmp4OSCHVYNvMB1WvLUqZ1tIZyCerNUl0Amhbv/rEEcq1EdAWzTHD/nRYUJXo4wS1SOrMF6QNrxF/cBaUwi5xyexVz04+mK1WDSyhKzOHxqV2fM78BVJriMl8BDAy0+7g4wkUhLgkwe0PUfCHntJeEeVHeXZ3LcJIVZNkqbkBncRsD9K68h1ukklVUQyOrfZxSRLsTOQJGAjkzM6GfIQopzq9ixmMpDN2cYdVrKA380xRDvu/7vq8WVai+3vWud514zldSMcSNpz66tpJTFyVqtkBPZpj9UTOqrTWq08Je3m569y/Vh3+Z1nINGQf0/hDXaWEfOI8a9HDbm6hOW7KO2UJqDqNpQ00XRbjJVAClSSJCEdWw6Jf4zHthr3gD/GmKIQDf8R3fwa1bt+qvj3zkIyd+/hVVDKncfDWUkeW40Rh3eCx3eppJP8AXi8zu0Zd+v6oYtAwjWyaVrErE0zl6MqfYXhG2kPlC+g1KoQrRFSATfiI3n3uiKoObTDDX907+CaeoGPKK3/l973sf73vf+77kc+I4rpnD77ThcMjP/MzP8Au/8As1N/C/+Tf/hsuXL/Oxj32sVgz56Ec/ekIx5H/5X/4XHn/8cT7/+c+/Yr5g5gvsxqrg8adz2Qyp8PQ56yTNC4x04cZLAk3Ld94yP/CX4u+p1EWyDI5ygrkQQDnnYH1VPj8MZBNWg6mZVyVrJY3eQBgInX23Qxnx1dUO/s3f/E22trZ47LHH+P7v/352d3frn32lFUOcZ+DSRyMYjiVI8zoAKvIInkUqQVu3/SWFo1mO/L/UUVDBwksr57gXolJFKYCRqodgrdDEumZ6SHU8NCyXAVHXihk/tvqSn3Uv7J5vgPe973384i/+Ir/xG7/Bv/gX/4Inn3ySb/mWbyH1RZOvtGJI1Xhx4wluNseOxrgsk67efC53Z1FghyPUPJV4IQy82oeuCZtO2EsNlLg7zmzrFUm8XqFKEiGFKPyEkJY4w83nNfGkW6SSoVSA0LKEW7skB19F4+GVEBTAW97yFt7xjndw5coV/uN//I9813d918u+7l4ohlTM5SBk0ZcvX5aq2xILeI25W6SNS6+i/P3DkwxgL1WDv/POr+oAd/5b/Q1liRuN5f2SWLgCrJUCUDV3sHSkuCw/2SDKC8LPXX2Zq/bq7dR7AefPn+fKlSs8/fTTwEnFkGW7U1XkbhRD+v3+iS9AKnR2Se6tLGXxoenuLfcCloSeKuKHl7OagPIOT1Bv0qq5o5V4ncy3d631mMIXK5wAJzMQ507yFN9jO/UNcHBwwLVr12oJma+4YgjIIoZ+nKu0L3bryqN7fOAlKV3pa/Ouef6XSseWlEZOCEgtTwFluZSg5wvBE0ZhA0C9w1Q1DaQUqtV+5X/zl2n3VDFkbW2ND3zgA3z3d38358+f5/nnn+fHfuzH2NjY4G/8jb8BfOUVQ2rWjiU6NvvIZfQXr714TtDX5ZWf3JGfCUmEoqoMNroC9VOcOwk89hNEJzqHlaeoSKBnc48Z8JxAlTeoIGdBIM0gQHUS2H9lf/aXa/dUMeTDH/4wn/70p/n5n/95jo+POX/+PO95z3v45V/+5T8zxRC0hrUVIWicTCEIMIcj7J2VPGvrM1mFASqM5Tw2RhozR8eoMG6YReEkrOslxCVOVAsrFFI1Mm6EuLIipHBZJhtmYw1mc+y5NdQLN2Xz/imKa6/GzrxiyF++9P8kIGwIIED6+zVKp2Lo8NKy1qI8QTS7B8IBGIVSUfSgTZdm4iWWPMKJAHAZT1htag8vXz5+VL8n2kATgX0pD11DK/ILawSfex7imKJI7yuG3LUFBgof/UchrtPCRSGzh/p0//BGrRxOGOA211A3dkS8+fBIUrHhqNYWcLasKV+cJ5CoKd2XM4rqe99hVEGAqzzAEgCVKjtotwSpPJ2RP7AhYpKB0MXS66Dm9xFBd23u8Bg3nfo7N4WjES4J6Hz+ADedSWrWbeGSSGjdAiFxrNOzZTfu73gVLKt66BejfP3iqyisiSNOsI5Um6EaKPXClThH8NRVVFqiUykK2dUu2eX1U7s+Z94DuLIU2bXS1uPWejRHzRbYohCuv6OhBHVGw8aajGZ5iLZqtaSJlGYC966AnIVQyulBX+b6i6Lu59dWsXwtUcuf4P6B2puQujry19d3sVfOUZxfZXqpRZHfRwW/OqtIHyoUz9HIj2xJlY44Rm1vUj72AC6JpQwbRjUs3AXC2kmeNWQN1UIHgZzbd/YJqkreSxBT32m1yISXriPPMLePKNoheUvTvTZ72de+WjvzG0C12i8at1ZG4x68iOp2IIllIDOOMC/sSAyQi6Jo3UY+Gkrubp00lq5sNzN+h0fYw6OTd/WJcXRX08rULfIgODEq5sqyEZkIAwGRLBaE4wzlHGV0etKxZ/4IYH2AUhEc+0mb+QKX5+i9YwFqKoWbztAeFOKyXGBdfvCjEoOyWe4XLkJf3a05BgGfuy/1B5areGWJwgBCHF2DT+9QIasyioqnCEBPU5KjBFXeB4Xevd3aBRPL3J5vSKluR2oCntDBpZmc/5442q720YHBLkOxqukd8N5hiVCy8hQv1SRaEqpQRqOcfI6rMIjV63xmIFmCn1XYPaDtHDmvIUTQV5vVuPuybKBedw6LGpn5dw9dhCBA7x81pA3LBZ2ylIJNp4NaW5XxMa3RnfZLL35ly9QwQSAxRyUstYw6Wm4igWzQ/WP0ZHovL8kJO/MeQPe6qKiNOxr6se1IcnsQjYBUJF+U0qjrO+IFOj0p/VaR/dLgaCUPZ3f2lqaDi2bhKzjZHcROyhg5XmCJmnap+xkGqF4PN5vVzSoAlWfSOj4lO/MbAJAFrwScFotak8cdHksqtzIQKPbxCJwIRLG1gTYGN5vXZVqZAm5DYNDGYEdjXwPwDaZzG1J42j04ySa23CCqqGOh2QBaS7xxPJRNtsxlfMp25o8AezyUtuvGmpAvBF4kooq6q2rdZHaie+eSELvalzTvykUp2wJuPBZugWpA1KOKMAY1W8hYt28lqyhqXD3UimA1MUV19ssP5f0rwsk4rkfJTpMm7sx7ANXuSDo3mclG0FoCwLKUYC4IsNNZ3ZzBWkkNr96ShXUOdTSSux2EMr7SDbp4jmK1TXhb0sQaCmY87KuaHVw+5/3iO5VKagm1p1BVIFqWuFnhySw6EAEHp3N9zrwHqISb8KPcQs9iPdgjav5vRQFcAjup3rk0qwNBvbaKSnzuXuv+aoKjmRSK/ON1RmF8P6AiqKjG1Ae+K7pcEazl5iLpVyxxBrjZHJV+FUHCXnOWpbjcX+iKxjWMBKxZTeJWbOBOo5MA4gg7m9VUbi7PKR7eJnzByiL6OkGx0iJ45qZfvBC0XyhjYG2AGs98C3hSd/4oStEdCCPxOmkmHiHLUGkqR0cQSGcSxAsV99PAV2cnWrJCx0Ic1505ZbQMbRhdF4dqDgArYM3w1rG45iiErTVUuyWPpan0CoyQSuhOWz5n/9hLwnm8YStm9sYtn1FoKSt74YnlRpLL8wZGHoWySSqNwVOws78BTHCifq+SWC5shfitaOGDAM5tYnst3FCie5cXMp1rDO7oWIKx3QNsS9rKWIvyQJeqxuBW+7C1LgSQh0e4Xkd6BWFAmWjhD+h3BXZWcQKFwYkeAtahux3JWvpdXCu686+6Z3bmN4DqtiWa9oGZyz3ky2vzqLZX5ex3cUmIPp7I+b0MzNze9HdtgYojzPU9mf8fjnGzmXiM+UKGSgo5UiqyByqyJyDvaIZvGggyuEoBK4XQOG4IqOIYu7Uqv99sQdn6ErMKr9LOfAzgkhg3XDReoIKI+xxbBYHg9+apzO1FobSAi0KgWtMZjEQDQPd7TN92ERsrup87REUh+faAYNcPoZQl3LhdawyrdgtaCS4w5GttWvsFNlQiGKUVSnmyiTSVTVA2JFPq9kH9ePD09Zf56169nX0PUFGxrvRh0KsXl1aCardwWS4ewkfcAOX2qpzn/Z6ARDtt9OY6LssJxzmjBwKKjS5kOeHtoUjEVTOCFfFUmtX5+/gtm6TrEcG0oHXdj57FMbWieCxUNYQBanVFaha+de3StJkWOgU7+x4gDFExMBzjVvu4fgdlLa7bljt/MsPFISrPvUJIAYXFra+Qr7YIP/uCHBtOCKSiawd0zsc4rbDrfdR04ad+5wITq1RHqm5fXtD9Yqump9PHk3piqKozMOhhWxHqumAS7UYfPZ42G2ntq2g07LVmduARPYCazOWCaw2HQ1SaYVf7qNIjeao5wsmcxfku+aBh7XJTYRKx+4cMfv0pRg8m3PjWVVwnEVn4Qb8OKivgp/JTyerqLdTV20IYNZpI5hFHcmdrg2tF6J3D+v31sze81qGvDyzFEffazvwGULkfzAhDudNHXsLdWSFxSrMmN4caBZw8f0jnT3YlDby4hbtywUPKBNa19ukhnduWfK3F+E1rTa7u3bqKItTynVuKHK2KI1QUYTdWpfgUGPTxRIZQKlsdyIbyk0WVNtFp2Jk/AiisLLx1uG6rHsxQaYabzlFjL/jsOf9wTqhkdg+wVR+/sKjjcRO4aYVVinBmCQ9mxE8di8s3wjBCEEgWMJt77KCV+kASU672MMMp6vYebmudYtAivLYvo+CtluALd/blPXyMQnYfEnbXVqx4Yog4xEYG200Eix+F1IKM1sLqAPvgNsVmvyaMqGYJ1fFYjoC8qItDswc6zNY9Y7gnfqhBJUpLS9cY0KIP4NYGkBdCQDGbS9CnFGYqbOMqSaQu4H8nVxQyyTybvTQPwT2yM78BgnGKso58q0u+EpOtxrhAo3wJlkr/zzkoLGa8oLy0idverCuI1fFQTe+6LKP71CHpmmL8hr4QRKeZzPdvrDVA0KIQPeA8x3Zi5l97Abu5UiOLpg8N/PvPZPF9N1I6ln5k7A462XttZ34DqEWOCwzWaA7fGFHGWuDZs4XEBZV8K2CTAPaPKWODPh7LQimhl6t789Vgx84e539nSu/poaRvFcdfXsj/nWcUn85Q7Rbm+h5mVqDmmYyaDbp0P3UDNc+kZOzLwSqOhEW0mjcIg5fmKrpHdvZjgOEYCImHYy5+MZZW8Na60MU5Cy1fGp7MYLMPm6vCC1DxCVrP/gUn4V4Xtwn2RpJGVjwCQUBxcQ2zP/axRCqL6zeHWciwyfitWyR7KcHuoXiIqluZZug4lpglCGSYxYNRT8vO/gZYLEBJZO7SVKL667c9Dt/B3iFOK8gLguMZthOjs1LcdxxD3JFCkmf1qOr3+VobZzrEV/34eFnieh3m2wndoR8BN0aCwMMjKSxd20VFEcleip4XqG4bN5nVmYbaORDo2WQuVcWqqOROrxt45jeAK0v5K60T6taigMlUqn7GSCewvyLf39hBx7HclUpj1/voicDI0fJchyB7oht+FmC+gDhi9taL4BzJbiqbKI5lgQ+OPBxN4TZWIcsJ9saoRcb+uy+z8Rsv4IqC2YN9WqFBp7lsOD+SDqDcl5CveZV25mMAQO7EOJZmjXUC+qiGNFotAXSsr9RsIG46g7WB3IFpJp3DKMQNeuI5olBgYfOF5O9FyfHDIXnPEBzN0NeEFMvNJJuQzl8qNYcwkGPBaAbPzqUy2e2QdzTFQALU9IE1SSM9nY1qtU7t0px5D6A31lDlksBT/QMPBZ/OxL1XtQDvulWl2FkFYfMFxVqHwDmJK67flrqA7+fbGObrmvZqm3A8k9Gzatz7aChHRBwJW1lZ4gY9gv0JaEW5NWDlD3awKx0Ov26VvAPb1zpCJ6M0qNPzAGd+A5RrPYL9eU3UxNoANZ1LjT3LPIS7FFh4FEngVRQwmTH+iw8D0H12jBpPCZ/fEX4f62rRR9Xr4sYTLvzGEZNH+owebrEyX8HgPcl8UUPJah2gIIDDoTxmNPp2Bs5ipnPWFgVHX7fC4soq0XEbfTylbBt4aXK0V21nfgOoL7yAi3w3zegaI1gHgRWdGzTTvZ4oqvvMEDVLhf7dWezEQ8q3NnBRKOPkTmILbu7TH05xnRbZZge9aMPCxw/rq/LZO/ueiGIhiKAw8l1EoZlVWqOPRnRvtCnahtnFNt3hjPn5NvzR6VyfMx8D1Iwcfi6AGzsnp3S0jwU6bbKHtlCtlq/Xh4IM3j2Q51ZHgnXYTgIa5l+zja00f/2cvzoe40wzKaxaLSk6zVPJIo6G8pmtljSfvNYQ+JhBKYJhSvuFEZ0XJozfusnwodO7T8/8BqhGw1SSwGrfD2iWtcRLffEnE6Lrh3LHOke5vXoCRKJWBzJlNOhjjsbka23mmyEYJalclbaVJeHhgtGb1+R4iMNaQ8AtmtSwwhxWBaRaKn6xwAxlbtEphc4c9vQQYWf/CKhBmUUhY999r8GjdY24cUWBsmAPjmqErtkdCsV7rBvGzyjEjSWWiJ9zxFcN7vBIav/OobwimLl9QP/mnkwGd9pSJsbPISYxrigl1vBHjap7Eg40OK3ItweEu2OCeUL/hftB4F2biiJUuyWDHaUPwqoLbgwuXdTDobrXlRaxEeImlcS4LG1Gw5JYYoY8lbQuivwiNhVC1WrJgnsyCTdr3t8528z9LU8GV4okIF3AvJA6w2xO7BzZ5ftp4N2bs37xS3HpHhOoAkELK4TAUV04J63XrXW4KSyl9tyaIH6Ox/I+x0NBAidxMynsZwZdXkjOP/XHQUuAny7PcdbPHvjWsIpCVKcH01nDX9hq+Z6AlSDRS8xxeEz7+JT0YngdxAAoT9ZkjNC1Lok/ue11UQmNIyaPrsBwAgfH4tKLAnVrn6N3bKHaSZ0ZKGOwD56Hc5vCMFJ9RtLM8qmOIJErQYhKog7VaBQut35rs85XHRWVODUVHe0p2SvaAD/xEz/BN37jN9Lr9dja2uI7v/M7+fznP3/iOc45PvCBD3DhwgVarRbvfve7+exnP3viOV9JxRBMIHe2j/xr8QVjUDd2ZQagtPT+yCfaRYFa8Vx88wWrT+4wffO2LKCPJ/R4QbHeaVBA2xuyGbSfOraCNqoIJ1SrJV6ier5StSRdFQcIU7i8ph5SWdIjPC17RRvg4x//OH/37/5dPvGJT/Drv/7rFEXBt33btzGt+uXAT/3UT/HTP/3TfOhDH+LJJ59ke3ub9773vYzHjQDyV1QxZKWHCgJ0r9twBftonaJoABjTOW42Q4WhKH9WI937h7RfGDbYAA/tCq8d1P0F9o9Fa8B6VTKtBRJe8QAUwv1fxQL1lx8PAzwoJK5dv6tAo6dsr4opdG9vj62tLT7+8Y/zl/7SX8I5x4ULF3jiiSf40R/9UUDu9nPnzvGTP/mT/MAP/ADD4ZDNzU1+4Rd+oaaWv3nzJpcvX+YjH/lIrRjy5je/+YRiyCc+8Qkef/xxnnrqqS+LL7hmCr34g5jUSnA2mTSRd7vVTOd6nT67f4heW2nuRqhJJVQcCZJo6vF5g67Qz2ZZnUpWxR3lBSmrs1y1281EchSemP2riCarNBAQr1F5iDSjIOc/H/7sqTCFvqoYYDiUcuba2hogxNG3b98+oQYSxzHf/M3fXCt9fKUVQ+zRcaMTlAuThwoCmQRaImei8HfecFRvDJXEshhx5Mmk5vUdWi9+5dbzrCaldotUgCCVUET1HF9ldGlWU81XwtT1iBrI+V8hlSrPcEp21xvAOccP//AP8xf+wl/gLW95C0Ct5nEnp/+daiBfScWQqusHnobFi0epRVbn5QSBIG+teArVaTexQl7g+h0Z/qwW1JaC1UtT+d5ZWTxP8YZpiB/sal8KT3GEu3gOzkvwqNot9MpAXtPtoAa9uiRMUeDmc+zGwAevr8Hp4L/39/4ef/zHf8z/9r/9by/62Z2qHl9K6ePlnnM3iiHD4bD+unbtGoBM9DhXn7mUDa2r81Rw+C8VRZLGgbjyJIEoxHZiGf40Xia+SimVlsmdIED1u80IeGnFYywWqBs7srDdNhglsHRAhaF4kLKUdNHLxgE1Ha3eH3p62tfYBvj7f//v8x/+w3/gv/yX/8KlS5fqxyulsDvv0jvVQL6SiiHWz9urTltq75Ve0GTaCDwURR2lu7LETqby3Om07sVXnoMgkLs0TetA0uU5bjSRCeLxxLeIba05XCGA1e2Duiro+h0ZVwuEj6B6HGuFldQY+fx2C3f+xd7wXtkr2gDOOf7e3/t7/Pt//+/5jd/4DR566KETP3/ooYfY3t4+oQaSZRkf//jHa6WPr7RiiNo/knKsl2mrqViq6LuKgcOoPhIqiVhXWtx8Ljp+aSZTPf6Or1K8WhGk0haGekMRhbLxlIKjoR9J19IfyAsIRIuAKg7AB51r/mhIEslabu+91J92T+wV5Rl/9+/+Xf7tv/23/B//x/9Br9er7/TBYECr1UIpxRNPPMEHP/hBHn30UR599FE++MEP0m63+Z7v+Z76uV9JxRA3neLWNgQbWEHBddOtAxry56KoSZ1UFEob92gkZ3il/xNVQZ0MgdQI4JKGEwhk4NMDO8lzcfk+0GT/ULyCMRSXN1ClQx8NpbK4tsr8Yp/kmqenzTM4RUjYK9oAH/7whwF497vffeLx//V//V/5vu/7PgB+5Ed+hPl8zg/90A9xdHTEO9/5Tn7t137tz1Yx5MAfN/VdFjbMW0sCT0oroWyJQkndQk/PohUuQ+5a1dzpKA260QSqQCLVcyoamlqLcKkHUXUOndHo2QK2N9BHI5zRxPtzXBygisLPLJxePeDMK4Z86xueIExdPXhBFKLiWOr6ntFLtZL6nLbHQ7nbKze/PPOndF27Bxq3X2ENqkKPRx+jlTCI5EuUdNDoC7QSyrUu6WYbnCMc55hZLsGf7yxSFJSx5mM3/t+vvTrAV4Udj7HDUZ2mkeXY0VhSQl+/V502izduS6pXVQnTtMH1OynJuovnmnO+0v+BhmImy8Vt+/Ywxkj3L4kpLq77oVTPCewcbjxBv3Cb9hf2iI5Swqv76L1j4QiqPFQQ1DWK07Az3w108xmYpCn7RiFK6ea89iRQyTO7Urmr7mpjIG4ujz23JqrgraSpIELNJo4tmzTVWQk08wKMww3HmPnCkz10GgYwrSQNHY0xWuEGXdjZ9yPsHjiiFE69RppBX62mPAy7zvnTtOHwqfLw6Uymha5cqFlBq/EulEaP57KAYViTPQJ1waie4Hk5ock0RXW7NTMpzor38U0fhhOyraq7qJpYYkl38DTs7G8Ar9Xj5gtq/Z44prgkOjzOOVlQz8qlh1NwDvvIRWn7+nTRHUnZ26WZpGll07Spu3yeTIIwqkkfwaeFvqy7zPlnJ9Pm9bakFitc9gDOvvYKQV9NplotyHLpBlaaPYD5/DXQSub2AXc8lELQ8RC7MUDlJXoo3cETTZooRI1ndR2/LjMr786tq6XpVeLLzn4+0FXC0XGMWl1Br600EnV5QXTjWErRUYTy3IC1lO0p2ZnfAGSpnLUVEkcJC5dLU3H1rUQqeBVbaBBgWyGUTqTkKoRPWUqpuChqAkfCoAZrOE84SRw3xJShLxGHoQSKthTuX2dxVb9/vjSscjw+MYhCEKA9uOS07OwHgXmBMwHKlALLLhYNM7evD1Rj4PKNwzz1AmrQx8WRD+wMqiNTu0ymOGZUIhRA0/ELgjrXV502LFI/fCJBovJK5hgjSmVBIHf7slr4cFTXDqo4w6Wnpxp25jdA7abLskEGeUDGi0QfQSJ37Wf9A9NU+oyRARGfKsq4uMcWVrSvRkvRLtBelcwXgWxZo4/ll/KbRxvpKoLnFQpxqQVkDsHdCRk7BTv7R8AyaLMCX1RneT00ssTbXxV1yhIXNarjVXevLtCAp3i1TR/fR+9VBbAifsbjDyp4WCVYUXsNqHkCAXnPCql0ylnAmfcArihkwBKapotH3Dht0JUecMX1W1pUIAwi87ds0znqCJ9QlsuCGYPS2gNJqk/xGcHUB4dRKCwgRosKidJweCxnuVaNjpEBKiLoCqGU5acKAr3TzvwGUK0Wympxt9Op3JFRWE/puE7fu3nXYPY91Vv72WPwaqFVGogtcPYlkLqV6IOP/N140pBBVjg/awUKDnVFkINjAYvEoUwRv5TdZwh5FZbnYA2qEwm5Q6ctTN9ZLguwxBBCJdRgLbrTwR0O5U73ZBKABHWVssgylk+phv7Vk0mpWPCBbpo2PEBVTcC6usvn0gxu7zc8xt5EhyAAe3qCEWc/BtA+XSvKGmfPwXEjAx9H0va9o4Ln0lTgYlWbuN+FC1v1ouvVlWbxq9IxyAJXRFGlrRFGqtel0QoomrnEqsHkbK0UopTwDqhBX0SqTtHOvgdQeikNCxp+Xs/l62ZzyHNx/9UG8RJxlXBkBdlitpDFabcoLq1jnvPU84uFT++0DwhL8QZQTw9VXEAVpkApJTCxoZBHq05L+IwrDwFSNCptMyV0Cnb2N0CRgQI21lCTmXQG8TV8K2VgweyLbq9bCEdvLeYA8pyqwgegFOaZGxKwVc8JA1ksrVBxS+7myot4T1GhjN1kAmursvhp6h+bSmVy0Bf4+fFwCXdwepfnzB8BFcef29nHjidNccXP8i139tBGxKGUpI212HPbz/JXVb9FKp7CU84DvtRbNMdBmvrqn6up6OtSsJGpYhVHsL1Zk1VUhSJ37IPBMGjS0lOyM78BlsUbgSX4d9IoeFcTSWkqQBGtRBvYy79Lqdg/xzd86m5i/VhTtHFZLp0/aOhjoVEH6XZ8wOhp6hYLKnVQVyzFBiBNpVO0M78BdK9XCzbW5hk/XJo2ebkxjW6gFeqY+jVxfGI2kFYiSN7Foq7uqdWBeJdlsGkQvJjhyw+hKC0Qc5XEFG9+ENVtU57fkCKR9tXHSjL2FCuCZ34DuDxHrQ4aBe9ery4DV+NXWOuDNd/6rVC9cVwPa1KphCKYQpkjCCWTSCSOcEXhPYOnny8KceNhIJvD2ZpKxs3n0i9IYtEeDAPM4UhmCToiNFVR1dzXDn41Vmn7RqE/u20dB9jRuCkBVxBvaKhZXaMTWFUKVRRhhyN5P99bcEUhDOCVxNtRWc8BusOjJi7wvQGXZuikheu0KNY7BE/flI+rAsI0RZVLS3O/EPQqzAQ1Zx9a16XWWtO30gj0ptdWpdiTpvUU0YmefBRKUK4NKhYUMdXkkVaovBBCCF/rF4p5LTQwWtVTwi7NUJMZ4WxR8xeqjTXK1Q5m51hiFT9G/qdNVb0aO/sbAN9QyQs501sJytfcXzR0qZRUDqtz3VPLyOL5qt+yJrCpWsKhDxxPZhWq3RYqmQomXmEGvKCEWyyaimG3gxtP0aMxdIU91A1Hfoj0PiLo7i1LhYxxQ1i7qnKr8+Va8P0C39N3s3ndE3DjRuFLJUkD+aqQw0o3DKNBIKIQnjIeryJWD5lWqCIf1TsPDVdV8WiRSsHHo5LqCuIpgkHg9bABIhF0IvUooCSpBRmUv4MrwQYZxVqScV1m5nC2IXasjoTcB46eAILdAwn+fG/BDkeoFdH/qeFkVZaQpiIRFwR+EzSwM+KY8uEL8nn2Pibw1Zktmzq9H9asJ3WUQLhUu+Ul25fEmYKgniNUlYx8XBFFBCeyBKqiUlXGVRqiEN1p4w79ee7z/7oVHEb1Ge+ck/x/Y03u+CzHPHuz2VinmAWc+Q3g0hS3fygpWq9bl37RWriAqoWv5vuX4oJKadyOxjXJBKXFXdwURTBfKKobSaop+tQsIJXrLwrKNT/Vk6aS3qWZ3OFV2nk8krQ1jgRh1GnL45trp3Z9zvwGqCP8LJdBUT+ECeAqsEaFvslyCcqMaRjA8qw5LuIYFUci/jhueJFUFDVFICdnu53OvOiDrgc89DU/Nh8GdRGpUgVVofcg5zYgDJm940HSN5yT3+M+IujVmRR9AjmLtQZ/R1cNGmdLv/Ba+gZaXDhxLGzfUVQTTbsqS1DaT/xKedctZGzcKV03jZQxqJU+7ui4qQwmMe5oUesMuelMKGWnnnhiZx/nHJ0/vNr8AbP0pf+we2BnfgPoTgdmeT1j57KFh3hLLd+Nx1Lt89h9Vd1tt/eEnCGO/WCpn+SJI+HvKQop+hSloHmGIxwNi0nNB3hwJAjj+fxk70D7wpOzkgE4T2TlRSKd5xsSD3J6qOAzfwS4XEa26upeFbxBQ/UCshkqlg7fCLLTmZ/0tdLTz0QQ2rWkkaNmvjqYVuSOSwvvPCLYGIF6Z7mv7+uGMNqW4uLbLWlMReHJzt/yhjklO/MeoFL9sNOZLGR1tpdL2UEoZJK635M0bomZSyUJjCdSASy9JvA8bQSg86yGiNcgkMXCs414MchqAUuLOziq+xJ6ZSAkVFUbuaKdWV7w5abQKdiZ3wCq10EVGioJOIBeR6Z8Fqlg9ooS1fZaPmlQTw3DUmOoKiFHYc0hWJNEV3QwcYRrJ6jbvsvoeQl1tyM4wbUBXL8tAaV1MhPgS83LnkMpc7IDeIoo4TO/AdxkhiNsSKIrXsAkgc1VGM/qu92NJ+KqQSDbZQkZDdmDMs2CxZEEgVP/+iyX+n5R4Dzs3Pr3ctOZbK69I9ksQYC9so05nAiplLL1ea/Kpo9Qwc1VuwVHL/7b7oWd+RgAJRe8FmWwrm7yqNG0SQnHEwnCvAsWqlcj/3qXrNotadVWI91e9KGeAKrQwU46hMp4ourYg0s9FF3FMflK4oO/pbvbj5fXjarqsVO0s78BvCYPeGSuD9bcfE5xQSpvLgwoH76AHXQl5dtcF+i4p5eXjWGlqFQVjbQSTsAgEI6/0UQ8wnwu0f3WutQN/DAISDyhuh3yBzaxgT/nvei0WltFddsScwSmnk2omU1Oyc78ESCwbtX07a31838a88KO7+XPMGFA2YsxG6uohfcK/i0EpuXJIuJYAr/MNoMcFaPY1KuCp5l4iI1VGE1hNkOtDHBhgI0jwp0hwSimvLCOuX0EsZR/3WQiJeLZvPFaYcjSCNI9t7O/AbRCOYVaGQhws53gbomwY9W6dc6hrt3CXDwn49pGC6Wc0VI6bsWo6+mJer9amg5SQLnaxpQlRB1sK6Tox+SdgO5nfF4fhTAcozZW5XPHU4wXnmA2F88Sx6heR0AkppGvd9H9XsBdm+p6mvjCEzOOJg1AtFrAtqh1uNA0IE6lpDXsR8jV+mpT7s0b7H5VXwie35HPCTR6vKCMDWUszCT14GdRwO09KfbkmRxFq74/YAwMujUlTU1E2WmdnF6+x3bmPYCbL0CH9WQw7Zbw75UWNegJF+Bo0vAATaZSFl4u6Fy71dDL1Hz/GlWqOm93eQ47ewI6UYr2PJWCUa8jpeWd/aZ0XEHDum2cUQ3s/3jk2Uw9KNQKgYTK5y/7971aO/MeQGYDPTZ/OpOy7eYa7vI5XDshfWC1KQ0vlvAC4CN8zyfspNDjfEVPtVqojbWG+8enb+7SOTi3IRLw42kd5KF0LUmHMdIEynLUrQMvZK0b/qAgqLOME3K3p2CvaAN8OZIx3/d939cMVPivd73rXSee85WUjJFeu7/r5iIApUZT1M4h6mhE8oUdLxWX43odX+Era2CGMkYaScuMHaWVcbKggY1zfksWdrqQEvHRUFq9h8eo8QwVGOkltFp+5lC0hLFlAyfbXK8zD4xu+AiK1wgg5MuRjAH4ju/4Dm7dulV/feQjHznx86+oZMyy+fTPLRYorSX6tiLgoNpJc9a2Erkj+12JD5RuWsV+U7vSioi0b/+q43EzJWRt814V4ijPm8kg36JWt3Y9/az/vhKgrBBHp6gVVNk9lYwB8QDHx8f86q/+6ku+5rQkY9I0JV1yl6PRiMuXL/OtK+/H2KCZEKIBeqAV9tIm5nAC8wXlxQ2woMczXEfm/G0cYJ6+XheMVOQBnItUArkKO1BaKQNnOeVqDzSowqJu7jeADxBiKs8SKoGevwerzuAy9az3RGXLvDapYu+UjKnsN3/zN9na2uKxxx7j+7//+9nd3a1/dlqSMS+rGGICX7YNmske62TxypJ8kOA8TNvc2MccT1BpjlrkuNAQ3Dg40ZxxRSHjY14D0GW53OGDavxbifSrhXytJeihTluwAJH0CmpQqVY+szBN6zeOTyw+RjcQtlOwu94ALyUZA/C+972PX/zFX+Q3fuM3+Bf/4l/w5JNP8i3f8i313XlakjEvpxhSuXjgJAzcz98Fs1xoXJDWsTseyUUPDCrzlHBRdGK0rCaO8u9PlqNmC3Hhno3UHE+IXzhETeaNSERVTPIUcLU2YFGgul3U6kDeUyuhr7t4zreTG8W1e213nQZWkjG/8zu/c+Lxyq0DvOUtb+Ed73gHV65c4T/+x//Id33Xd73s+71ayZg4jonj+MU/cBZlghqOrSKas9WWBM/viEbwqhSKVF5QrnXl/1nRCEh12lCVeaHhFfS/F16dVHU6Nc9ABTJVgcFVYlWTmWwIrcCVuHlW1wncdCabIY5wgx56thBgyCnyA9yVB3g5yZiXsvPnz3PlyhWefvpp4PQkY17WIk/caEsqCbdl0IVLM7mDsxw1muBGY/QLt4UltBBCqRotHPqgsdL9rayifweRggk9ijgImqZSK5HjomIBr9DE1mcpFatIxRo2mWG7iQSn5vTKNa9oA/xpkjEvZQcHB1y7do3z588DX3nJmIoQGqU8+eMSJLxW+yi9aFMui1IUwtrp73LVaS+hd1/mklVoYF8AcpXO4GgsuID5oh78kItpm9giyyXla7dgbSDpaJqih1OpBK6tvLK/+RXYPZWMmUwmfOADH+C7v/u7OX/+PM8//zw/9mM/xsbGBn/jb/yN+rlfSckYcZ9GKF+6Cflam3g697N3QQMPWyaKqLQCp3Pc2oDJowN6n7qNW19B3dyRzbBsWsvmaifymtCgdg9xKz3ZGEEg4+BqiewhjGpCqSqLIC9QgO0m0DkH8ww1ncPsNRID/GmSMcYYPv3pT/PzP//zHB8fc/78ed7znvfwy7/8y392kjFFAYHc9erWAUEUiAx8RQlnHaot7rmODbSni5nM4PYenUCTPrSBXpSEw47MB8CJY8AOusyv9HB6DZNaYruOunUgKOCiFO9SIZF7PVzie/6Hxw3CKAhQYYCeLHBaoUorQlUuf+V/95dpZ18yZuX9BM67/dBH3h7ihfM8fmsrqOlc8IBWSKJZ91mKc3A0xD50AX00gf1DeXxp2NN5kefxX3yY9s05wbV9eWnhj5OK+r0Cj6yvyFHhaWRUpyW8ws7VmoF4bIEKQwqb8bHb//pU6gBnvhmENtJO91pBxJEMdVTjXUUhblb7IU3twSNZ3gyHBgH6+dtNgcZTuimlpNvo7+7eZ/ZR3pMI20j1VTRqotUEUE0oFVKu9zAHwFRYwRxF3XZ2nRaM7s8F3L0pD9hstbz4s2j6uiQSLb8k8VBt7SXglYhAhgH2gfMQaCgsKi/Jtjokzx/ghiPpESQJ6RvOET91Q8AjRyMpL4chKs9xFaTEWVQQNekfSCeynWC1tI9lorghqLAbq+jjMe54dKqk0Wd/A0AdzZN76LXWqKKUdK7aFGEosUEFCj0eo4HR123SuTpDX7tNMp7Wo+MC9dLELxzg+l1UmpFfWCNdj+l4ilkOj72CSNDMHGgjcUErRk3n4jHiCOJI2sJKiWDFLG3oZ9Lpi/+me2Rnvx1cSnfPjSfN9G4QCOJn0JcFLQoZ+gh8zl+laFHI4MmbBFd9KTsTAciKDxhroSixgzYYQ3jzEJNZ8rW2NIq8m69q/iqJJa8vCtEQtq5pEWc5dr2PXe+LbE1lzgnT+CnZ2fcASuOsO4G0dROJAZRz0hXsdiXgmi3kYme5nL3W1gIOamOtvmtZpLJwfvhTPzMVZ1+WJL8/ktSyJpXUjUJ5t4Wap2SPncfMCvRoLmKVswUqMOTrbVRu0XFAcHVXOIzLsh4/OA17HXiAsiZxcIuls9YjfN183sjCgEjHOScsnns+4o9jbL/N/jeuUWwNcP0urA68Cpjn9V2eJFru33ueARf7WcQ4Yno+ximYP7RKubWCaifMvu4yh18TY9KSYG/UYBhOGRZ+9j0ANOxfy32EqgpnHTXff8USkuVUsm2q05Fq4M4hndsdFlst9FrC6ErA4IuZaPwuUtlE1VlfE1AoWO1jWxFqKoUm22/Re36GOZyQpAVlN8ZeXKNoabZ+f4S5fSRMJq1WXWJWp6gdfPY9ANL/P4Hxs04qcXEs4I+Kyy8Msat9lMf7s9IXQuc4kjN+lHH4poDZZsD6p2ckzx9K/WDUUNAC9YyfCgLSyyscf+2gBoioNMccTHCdhPGjfVRpyXsh4bTEJqH8Hp4zoG4J31l5vId29j1AGEIYN0ibyj3nmah4Zrn06fO8QfHEkWQJw4nMAk6noDRmmrHx6Yh4Z4Z+3nP7VYuklAyQjJqybf7QNrffGbP5Rzmuk6Amc1SuGL9lExtAPCzR85zQaPJeSHA4xUUhdtDGHIwlQwkMpK8RSNhXpSnP8VONg1W6vaUVokjrOYOqTp0R3sDy/Jpg9Gdz1OoK5aOX2P+GFZKbY8ztgwYIWtO6LhE9eQu+cI0Hf+55wlHB7T+/IkjgOMRp0CVgHWUnIhuEhOMcdTSC4Rh9dUeOFOdQeYEdnF4WcPY3ADTI2khkX09YXkhW4As7ap7BfIEezmTCx2hcYAj2xxQtxe2/tMb06y8341oe0eOyXBpMd9LCxRHTizG96wXp+R6Lc20mF6XnkdyaEBxMwUFwPK9LwdWXm86lQXT74NSuzdk/ArQCNESmbtnqbgc7Wcq1KxXwMMAlIWpmJLfvJMLrs38E3Q6bfzQjOJqhjkaSWrZbUlqutP7uAHGqKMJ2E3ThUCXotMQsYO0piG9OUPOU6dds0ro5lUHV6oWVMJW1NZfhadmZ3wAqiWBeQn8gkK0ggLwZ/Kjdv0f1aCfNoDI2lKsxyXUN6305myPN+GtW6X7RCNgTZPGr1m61AcIAVgeUgzbzCx1u/XlFONZEwzaDZ0vat+bYbkS63aF1Y4I+njScBMZgL2wye6BD+/oUs3Pc0Nacgp35DUDhQRj7RyIalVDn1s65E2IcKgwpV3voWUreC2ndnKDiiMWVNYqWQecWk9p64tgVLxGdG4N94DzjR3vkLcXeny/YvrzHeJ4wfaFHGRlmmx1Wn06xkUYfjmXGoIKCWwcaup87kKKTXQKOnIKd/Q1QcQJ4wgeXZQ1ZhBEuPpcNpVhkDGbvmMVj26CpETnxC4eEgzbz8x26f3RTmD2Wpd2LppFDFJKtJ+RtRbqqIHDsH/Uo5gEqcqRrjpVnLDot6Ty3L53EIEBR1L+vORg3eIVWAuV96di7NrdIBa0d+Pw6MI2mn9IC3fJiUtOvu0j7mUPMomD4SJsiOU//t54FwBQlnVkmG8g6VL8jYA1oXL8Xp45vT8lWAkYPa1bW5TlFS7OYRxQmYnYuoLVvxDtVSGXnpNS8yHCtmOLiGnqRM7vcQR1O4YXTuT5nPgtwWVYre7lWTLG9IgtYSbPNPG1cK0FnlvnDazit6F4vGD1oBNcfGFmgOMRtrsH6CrZS/KzcczU4GkdMHhuw842adKtkpT0niXKsVRSjiPDY0LklG8au9Cg3+pKJtGIfhEaoRYbKS46/pkcZKmx4est05j2AUgrV76Gcwx0eExSlb7IA7ZbgAiYT3HROvD9HTebk2wNQcPE/H6PmGXZrFT2cUrZC9KJg9tAmrVuzJn7QCpUk2O115ufaTM4biq0UE1kOpm0W84h8EoF2FF2LDYWP0CWBII/B0834r7xAWUc8tORdTeuF41O7Pmd+AxAEpI+eI5jkgrWrUDrV5K+HZpNnqDSnODfg9uNtNv9Q+AFdy3cRFynBs7dQ7RZms4ULPE0MBtZWcGHA4VsHlAkM31gStfNGjCSwmBshTkHes4wva+LjgGCYokczavHqwg+FdlrY0BAfpHSeW1Auk1jf68tzau/8GjEVRUTXjijODVChkcCuquApJdCvCuplHcHxnPZOi6OviVFviFj/3AI9L9CtRChmWzHWKIaPtOm0HyKYZEyutNEl5F0wC3CRIztKUEmJaWUUyjHfLmhfDQjHmjKWmEBNF1Js0rpmJnFhgO21cZFG5VXGcT8NvGtzWQYHx+hBG1U6ydtBunz49q+3fLOLzko2fusGO996keEbobMTUUYxK9MMJjOyc12OHw2ZbTvyTsR8K8YZaO02i5TcDEgfTkm6KYGxZIUj6GcsNjQXftsRH+cEo7TmIqooaXBOehBrXcK9qaiLZfl9suhXZZ7Y2dzYr6dyVKdN9tAWOisxE6/MEUeEOyPUbEH+4BZrn5vRvRWBAuUUO39hjfU/TtCLkvXPpqx+XjHfDJgZRXwE4yuw8nmwIURDSKcBRZIzS0OMscIjbCDrafp/dNioilRl39KCy6Ud7WnoXCvCJSEu0fDSI5Gv2s7+BjBGhKMqTF4cg9ZEz+1K1G1F59eeW8NpjVaKa+9tc/HjC9p/8AL20ibX3zsgGziO39ginCjiA4XO4dzvDUkO24TjgnCS4DSYDJx26IUin0QUgQPtcKkhSBVZX7F4aJ349gR13HQOVacl00NKEV7dw673KTsR00staQx97nQuz9nfAEsz96rdJnt4k3B3InLt3vJHL7D7jjYbn06Zv7FH54ZjsR4ShyEqLQlmkK5AdHlKnht02iY5cLjQUMaa+UbCYkOhU7nL0zWHMxDthNgQbOSoUoaiDQdvjllTkFhbE07XRaXAQGZxoWHnnR2w0P/MfTzA3ZsndVTtNhhN+NQNqf5VVcAkZnYhYfNTC4JJRni4YPJIFxxMvv4ioysBozeWtG4YFtc7uMCRDxy9q1B0QubrWhbZQLbhMKnCzBVOy2Yo+iWEFgpFGWiyXNOeerLKOERN5s1RAPJ7AXqe07lpQUHrxuTULs+Z3wCqlUB/VTpt+4e+HJzX6dbswQH7b1Ns/FHI4NYIlWZ0vwhoWGy1ybsQ7xlmlwoGTwUM31wQbE8YLrpAhElhdl7RvuUoY0U4kbt9+ChkgxLdzbGZQaWaYKYJJgqTObKeIZiEqLQlncDCE0ZMZgJiyXJW/nAPgHJyehvgzFcCD//CZWYPr6LSTNK+1ZVmOGM4IdmZsfkpS2s/h8Mhbr6g7EdgIdmdoSwkexCODIPnClSpyHNDer7g+GtAWUeyL1F6NHLEI4vOHTpXoMDOA9TUEI404VChCyhjRZEoXFXhq+Bi1Xi4tTIzMFvIyHp+eoigM+8BBs9MZLjjT4QRxB0dA6BaLexqn4Ov6zO+onjg/7uQCZ5QACPDr+mx8idDggmY3NF/BkaXA6LNMUFgyZQjU46DMMQZBw7CsQYMnevyvZ5rdK4IZgqdQTCHcOqIh472zQXB8ULy/0pQMgxPcg1UWcspcgWfeQ9ACYMvLrw2j+T+atAne8N5VJrRfz4jWy2ZXG7V3DxHjyYoB05r1j+3AAujR2DyoENrh9EWE1jQUAzk7gxHmqIt7j/vKqIRRMcas5DiUDSC5NDRuVWS7OcE41QYQAIjgZ9XJ3dF4aXlPM2eH1s7LTvzHkDf2CO4ugfGkJ8boLb6TB5o0//8ELXIiA7nDJ5aYXYOVjst3HRO92ZBOjCovCT6k+usLc6jbJv9bywJnCIvDHluIFeYsaF9U7P+JznpwDA9ryg6kOw7nFLYSBGO5c5v7RUk+4uahApPPilgVWEyVe2WNJ6iEJUXXlH0fiHo7q0swBrUao/ZxYTFqmbjkyPU9R2cUuidgu3fsswf6NUED+1nDsm+fpNykBBOW4TXD9iYZUwvrVKuK4xx4BR6aogP5e7Mu5rOzZSilcj5DwQzh5qI62/v5oSTAj1Nxe0Xfi7RiO6gKireIkkHVSpt5/ul4FdrYYhKuuTbK8w2NNu/fYg6HuO2N8Fail6COZ7RfmHE8VvXWP29BQ5YrCmybot+y9D64j7TB3vMLxYEpaYsNeXcEKYKVQIK6QAmsVQCR46iDbqAeGgJJ5b4MMXsjxqEUqUtvMjlTgevZaChyGUcrKoQniJJ1NnfAP0u08fOYyPF9sduY3ttise2KRJD68YYnfmAKzSEM19wCQOSI8fwYY0uI8LJgKPHAghFIMoWGpUazFwqguHUkRxawqllsWrIu4pgBigIZ5b4YIEezWtMAUmEa8fo4VTu/EUqFcusEPqZulvpdYvue4C7t+x8n9lWwGJDYRbrtJ/aQY8D4jQXvL0GlELvHRMNYuH1d04CtbkhnJSMHmozu2hRxmFzjSs1yoLOobXvaO8WBJOcMjF0blmm50N0AdGoJBrlqNJKU0cpWfCilEaQJ6JGS/BZcw/dgQFUp0gUeeY3gHIOG8DaUzntP74u/PxZjuu2yLY66KwkGk5xrZgyNoSVcsiiJJgWBAcTpuc3cdrBVC6XzhXRsaa942jtF0THKeZoJhM+6x2Sw5JgURKMM6Ghhfpsx9qaVVQdj5d0C209tKI6bVwYiLaBMaBODxp+5jdA9MwO/Vab1jN70hq+tVezgeisRBcW229TdiLKSDN7sM/wwZBw4lh5WgY+s64iHCqcQcCiGcQH4vp14cj7EWac4gKNCxTxUYoNRTzC9gQ8Yg4noDxPwHTeEFEUXnl82c17z1Bur6PyEibDU7s+Z34DEIfo0pE+uEG015Ihj6LAtRN0VjK71MYsYvKuYXJBE44d6Yrk8t1bIco6dA7tHUXRgjKBYAbhxJF3FFkvJJw5ZlurlJFi8FyKXgjKSOUWSoc5HjVk0vO0ximKCIVgAZznKlBhiAsM5WqHvB8RHS1IV1bhudO5PGd+A2SX1xg9FjN8A1z6L4Z2LoRNxUqLoze2ybuK5EgxuaiZXbDERxpVQDgGnVucUiRDS1ZonBYvoHPp+88uKIrEEQ01uoD+CxYcqOmCYOTP8zSrxSRqnL91DX9QRUoVhtiNAemGUM6Hk1xGzwHXfQkK3HtkZ74SmHcCjr7WYhYQjnJcKyLf6jJ5oEXRlqJNuqJZbDiUBRs4wjEEC0fRMpStQB4PAQeLdcd8U0a98p7FXlyAgt41S/fqjPj5fWnoeAramjGsEn6omMJO8BMg00idiPEDETbU6Lm81vZb6Oz02sGvaAN8+MMf5m1vexv9fp9+v8/jjz/Of/pP/6n+uXOOD3zgA1y4cIFWq8W73/1uPvvZz554j6+kWgjA8KGQwRcMV/7TjPD2EDVdYKY5JpWybTR0FC1o7Sm6L2g61yE58rN8mcXMC6JhQXJkae9azv2+ZePTjs5OSe9ZjdqNiY6hcyPFjNOTYg9lWTN9VEoky2QVKhDCKFb6uMBgphnB3GEyK/pFSYwezdH5a4Qg4tKlS/x3/91/xyc/+Uk++clP8i3f8i389b/+1+tF/qmf+il++qd/mg996EM8+eSTbG9v8973vpfxuEG+fKXVQjq3S3rXC8YPtpg9ukG51sXGAYtVTdGC+NgRHzvWP5vTuWVpHVhaBwXt/YK8F5APIsysoH1rQf+ZMYPPHNL/k2NaO3M2PjNn6/dh8HyOTkWT0LUTqesb06R1y4Wc5RQvjpi9eZvZI6uoLEcfT1h9cof45kgmlI/H4BzB7ukFga+aKXRtbY3//r//7/lv/pv/hgsXLvDEE0/woz/6o4Dc7efOneMnf/In+YEf+IFTUwupPuulFEPe/t0/Tr7dRllHZ8eiC8d027BYUygLrT1HclwSjkvKWGMWFrMoKFsBec+gSmn5RkcZ4bUDcA67MaDoxQRHcwg0Ki9xSqE8zFwtsnoE7UR0r43k9MqrjgLl5kDKw+NZUxfwA6yEIa7bopyN+djzH3ptKYaUZckv/dIvMZ1Oefzxx3nuuee4ffv2CSWQOI755m/+5lrl47TUQuDlFUPKWGFjIWQoEuUHLuScD8eOMoKsoynahnBcEMxybCR0MO0bc1q357ReGBO+sAdFQbm5QrbWwhmFa4VSzZunqIW4f5UXtbzMMi19vfhRSLm9Sn5BqGj1aI7TWiaDAkNxeRO73hdaGudAa/Jzg7tdpj/VXvEG+PSnP0232yWOY37wB3+QX/mVX+HNb35zzRx+J5//nUogp6EWAi+vGNK7nhKO5Ew3ucNphQ3ABRLNm1Q2hyoc4f4EPc+lQVNY9HCG2T1GDyd1rq6cw6QlKChjI5NFZYkqSlSaCbCz9PX+yvUHgbCNDLpkl9aYPtAh2J/g4gjXilBeH9Cu9ijbYd0NxDlsO2K2nbzs3/1q7RWngW984xv51Kc+xfHxMf/u3/07vvd7v5ePf/zj9c/vVPT4UiofL/ecV6oWAi+vGGKmOSaHogU6d+LOR1K3t6E0c4K5Jbk9FTfcTrBBh2IQE6U5zKV+r/ICgoD5+Q7hrMApRZCWjYp4UTYahcsWBNDvMn7zBtGoQGeW7rPC/5NtdbGBIjpYoMIeNglQ5VIHMApRaU7/C6+h6eAoinjDG94AwDve8Q6efPJJ/of/4X+oz/3bt2/X4hDwYiWQSi1k2Qvs7u7WQhD3VC0EOb9N5kgHmsWqob1bYNISk1tU4bChFnWviZd3KUpaV4ciIwuiHbQQOFl+ZRM0OK0IJhlqnkvbtv4wdfL/xkBPFETi45zjRxJM6mjvBTgF4TinaEfkqwllov3MICw2I5K9DJ0WmEkq3EGnZK+6DuCcI01THnroIba3t08ogWRZxsc//vF6cb/iaiGACzTBwmIyOe+LljRyguMFwe6IaG9KuFexgpsludayBmq4Tov0kS3m52JwsgF0VkKgJeqHk1RuSip6Kgxl8xQl0RdusfnbO/SuppSRJpwULDZjRg8EDB+KSFek8RQfZnSuzQh3RgQ3D1GHQwGMnpK9Ig/wYz/2Y7zvfe/j8uXLjMdjfumXfonf/M3f5KMf/ShKKZ544gk++MEP8uijj/Loo4/ywQ9+kHa7zfd8z/cAfwZqIfi7dWaJjzVZX6EcFB1DdIBg8sdCEGVX+6jxFBcGZJdWsYEimBao1TZlElAmRthBFELzejyhXO9BFGCTPno8k7Fu8HzERsa+iwI1LhvNQiszA9MLMbNzhqyHDJTkijKMSI5L4oMM43EDrtITPCV7RRtgZ2eH97///dy6dYvBYMDb3vY2PvrRj/Le974XgB/5kR9hPp/zQz/0QxwdHfHOd76TX/u1X/uzUwsBspWI9iQnjDV525B1NTiID2P0OBKpmChELVJct02+0cZpxe7bY9aeMrRvzAiGKdFOhm3LpLCeCJBTD2eC7olCiQGqtK8oJDgMDCoXIgi72kdlOeGNQyaXL5B1FcmBpb0rMch83RCPLcG0JBjJ0GhNG6NOr2B75hVDvv5v/7/oTgLCYU66EWGNYralWXsqJX52T0q24HPzQBi7I0O6mRDvLTATD9/yItAVitdFIeVaBzOcC/N3GODCANdJ0KMZLjDS+y9LOSaKUoSottYoVlroWS5eo7QyCxgKl6HyJeTa7WtFGTg+dv3D9xVD7sZGDyrcUcAgd7RuLzDjlO7zAVhk5s6IoJTriKBEuiUeoHVjio0MZT/BBlpax7Nc0DyhEWVRrbCdGK01ZS9msZlgUkuSetbRNMOudFHzTLqAWkFREt44FBSQ33RqkTZEk4Hxx4UomtvVPqUu4Pqf+qfelZ35DZBeSWnPEspIkxxOUdM5uiXSMSoIcP0uNglFPFJLPSBY5KhZii5Cyl4sfX7rhEW0RMgknUOnBelmm6zfJesq8o5i8HyB7SYU3ZDw0MjiT+dS6EkiKRTNF0266IEgWNEorI8SrWotYhW+RppBX40W7ka+m+cvchRiVzrYKKC8uIHtS/tVT+ZgwaSl8PKFoicQHM+IdicEw0Vd1lV5gSpK9CzDGZhckMyis2MpEkW6FqMKGR7Nt3r1Xa1S0Rk+USuoGkS+GiixQyDNIp9+uvj07tMz7wEe+E9z8osR8VHuwRe5CDUUpVcOc7XrNccT9FjjKu7/4USg2+0EZxR6InoDLg5r6fn25/cpWluY1JH2NdHUkezMRDO4HZNvtjAbfaGedU66f1negD+hpqtRRSkVQKOhVEJoObGYxWskC/hqNJWXxEcyiePasSxCFfgZXef5thtjDidS8avOYo/akbpAAYHBrnaZXemTdzTxcUlya0Lv6RG2FZLsOFRpKboRdCOylYgyVqSbbSKjMZMU2xGxCHM4kRTULjnhaizMzwM451BlKapjp2RnfgPYyBDNC1xocCEEx4XAs3pt8vUOZTugjDXBrBTotpLhTJtEzB9ZpXVjCoFmsZEwejBgvtmggrrXFWXSIzouCCYZLtAsNtqkA0M8LCnamvm6IphpIMFtJEzOB7SOLL1JKizmVniAnLWCHjaCG3TZEiT8vl7A3Vu4M0L3+kLHVlqZxWsnTB9eYbptyDuK/tWScOQl3qOQxcUu880AGyjio5C9r2uTrkPekV5Ca1cRjh06l0UKxinOaNL1mOm5gMWaIm8r+tdycAZlId6fc/TmHq0ji9Mwf2BAcjvARoZgbySt5ErdrChlJlBr+V6ZL/Unvio78xtAOYcez6kk3clyWGR0noX2Vc30oT7B3Mr5HoWUg4Rb3xSRDyztGwqTSf0+OhZqmGjiiMalFJQAnTkmD3YpWooyVDgF3RuWwRfGqLzEpC2C4wUuCuhdSzl+Q0zeVbRvK3TeokxEiEKNprWcrNIaZ6Q9LE2m+zRxd2/Wkj6yTvzCYdOsKQrUeIYqCrqlY/rwgHyri5lkpKsRrR1IDiSyn24LckiXMuW7WFeEM0iOSrKeJt5foFZjgrmiTDTBtKR1fSwBZBSi2xEqL7FxQLoSkvUEXZyuKKKJIfr/t3cmMXZdVb//7eZ0t3WV27IdjF94fDxClCcSAWEAJANEpDAgCAEzJkgZIIFgAkjIQXoomTKgkaIMCAzCAGagoGSQiEkmifSeH+blM5+T2I776m7Vvafde7/BOvdWFWk+x58rDuVaUsnV3Do+dfc6e6/mv/7/UT2jifWDTDIL5/GdBF3WErdc+4CIR/9LWggkpy/PUrjQagYrYwjzQ6gbmkwzPpRSdzOCBuUEBKpreZrLoSJd9sQjR90zxGsOF2viNY8ZV2QrE0IcMf7IgHi1ksV3DsqAWWwdoajJrpTk8wJGTVYC+bzBxQpdinQMQLMnw64W6KqRzxfHuzHAf8kaR+j1CJ1EOnxXlwEIwx7j40PqnqbqKkyBtIoLoYSvhoIGThZLbJmgSwFrJmdyqRnEEc0gpRmm5AdTmR+ogvTzI0uoa2kMtX0C5RVmXIHKmHvVUQ405ZyinDOkixYzKlBVg14VpVGfRJhJLQhjt+sAN2zlRw7SHOyj60B6tYQ9GcoHqj2JdPbKgMoUKgSidQkGg1F0l3MJHOuGNEhTycUa1Y0IiaHcm7B+yGIqSEaOZKmWqaH1Yjb1E1qqFxpH6HcgBOZeLVCNZ+ULQlbhI7j6P1OGr1spVV9bQ00KzNpkBgn7Zy2im2k73gGCUnijqDuayb4OthA4uG6kC9c9u040zsAH0n9cmdXwCaLeEbKE6NIK6DnGR1LWPhRRd2UqOF3y9N/IsVdGUieo6hnhs7JWoODt5K8qa3CeaFKS37mXuMUkdq7JERSNHPbKqOUu9rP0cBa8bpPteAdoOobsao12HpcYvFEEI+DQaNww/lBPqF4vV8LKkZfSiQuyACpHyBvPLzJY7+E6sfD4Vl7St3EbobcKoVgzuw7QCla0aOG2i5idXSW9KBT0ei2nPjyHvbb+1sWHbV18uA0coOpr4jVNcmaFKLL4TkzTbcetXSBebTBFQ3RpFSa5PLWwITenpCmD0ejRRJo70zJyO9I9a+a0r/WdWKbONy1eszDH5EiH3n+sopfXUU0z296jN66Ko0R2Y2gUNrb+3SPgxm3w2oRkraV9d4FqLkG5gG480bL08kNVCYP41NoWsWznsgUHYyj/2z7MpMGMS1RhpFPnvJSUlYJCCKBVZCSQ2zcUbGGQ/9fFiuJwnyyvUI3Z4AtuS8++l6KXNo4RgNDLYLSybe/PjncAe3EZFXeEdDkx0u6d1Njzi4SyImyGWxkjT3ML6QpFuYHrszIrMCV0KD48Bz6IyETVbBwFMCOAyo8NWT9qmfv3gmKvhSCYxPJD7UxA47GLOXpdACTF4T5xGoFW2MurMiI+zAjF+J//rJv3/mzblT8o1m7DenFEKCtSownOy8Ibg1J2hrubEjTQNDK3F8fSx29p2vSkajkANN4ofEcTbJfkWo52UluQyR7hBkAh08KVlH+LeRk/N5Vm9XgEAfrnI3p/K1HOUw0MS//WJ7vmmR8VqHGOWVzH7QaB/wULgbBJcWMzzm46ZyAkjW0LOLTNmDSZiUr4LELVDlXUNPsGNKnU5qORI1iFTywu7eETQ7RU4Dvt610gGgeq+ZgmUZgykK54olHD4HVFMWdoUlErCwaiNYedkyC1nu8QVUIgFZJ4296ene8Avu3Bw9b+/zS/3jSnF+JIlEPzdqt3IuBgigrfE3SOXcyxRlEc6lIPDHbdYdYKVCnnuR92yA8kkl6uNygXMKUnGkhTKF5t0GVD53xJ51xATQQaVh8cgBKEsIshRFrO/0lJsZDC/9uet2fnO0C7uFMiRmBjm44jCeKcI0QWnEOP2iJOK+Em+sEGvSzEEqqscHNdksWC/FCH5Y8m2DtiulcasvPr6PWC/inh+TUhEMURvpPSAxk+LQVbqCcFQSv5/5XCjgp83CFZDSTLjmgplx7CsINLdrOAG7YQR9C0Z6g1G2XVshJgyFT6vSmlfj/72m3g9KCVnbdgpSXsE0t2acJkf59oEmhSTWg1iWZtXRCnmhSY1OLjjdG1YPSMHNLN99pxMEhWHNnFsWgbxSI0lV3d7QbesAniVjT5UAo/7Ip62HQKqKi2llynObdWoFtUUONmjqAaJ/MAnYRmkDL37znVnoi6a2h6MarooMc5NMh1fQDfoNcKTGLxsUZ1IlQmixusJj+YEq1J/SE7vza7P9U4zNI6we4GgTduTkidaWRoQy81LSxs02siu3E8tKydoZPKuZ/Xku+36l7AzFGCUbjM0GQab2XiqOn2SS8Y9LiAyNIMUuyomDGIucTIVHHrZ3VPU3c0um75ArqJQNZTO3PGhk3zhzfZdr4DwFtk3WfW1vqVDxsS8tOizvRJn37fB+H3bzX+glIU+yIm+zV2AvG6EETpJuC6EW4QU3cs8WqF68b4xNKkEu27WOMShW6C0MmueeruFBuYYPoR3gi+ACBsXxngNnCA6bb+z7l0mwmolrxxSt0SsgTfgjhmdXwjMDKCQL1DbCn2pdLLr2TRTSnXj1ZkawlGEzc19SAWtjGjcJmm6mmaRGHLgDeiHqIdwlmgZYdQXsbXfCRO4s0uJOzGTb9NPb1t2qAUIY0JsRUK97wkxJZqLqXuW1EDS6wMaWhF04vwVuES2fKjsSdZld0AJSxkZnkszhZZXD+lGqTk85Z0RUgllYd4HPBGFlyYTBWmhqACVU8BClMxcypT7MYAN27RlA5kg3wZa3B7+xQHMtmGK098Psf3OpT7O6zdEeMS6BiLywzlQLfqogjgA/BWEYwAQ00LBFFegsmQRjR7UiYHEyb7210mCNIoGkt24COFrr2wjSMTwwAqaPzsc6kLhG0c39nxDlAe24uaKFTpJCMwhvLokHxfhEsUyaojXasJ3ZTJHX2KeWH7NmWg7mjqDtRdUB5UA6bVf9OtI6DAxfLUKh+jfKCcT8j3WnwkdPGmAjvx4jxamErIxYl05dEuUHdty0rqCEY0hXQj13bRbh3ghm2yP8asGKJRie9ErB/rUMxrgoJ4TWBcaEXTTyiHerbY3irKoRBGRmN58k3NDAo+bfW6SFHOaYKWp9qlKVUb0KXLMtVjcykL+1jTZGb2hLtketYLmZV2cn0Xt5iF2qOC2tYBvh3vALbwqCANnJX/3qXJhOfXR/KGB6NoukLZkq44okkbebcLChLk2dyjS48KUPeMnN9eFqvuSDCX79PEI4GGZ0sOO3GSGVSuDfA0TUecb9ocsrkmW3Ioj7y2DrihonO5phpY8nlF0+xqBt2wdc5PiLylONrHxZCsTiFhAVt6fKxQXpFenICRYE8XNa6bSBMn07L4dWi3Zi3fKzw2lyc8mhiqvqbcI0ojyXJoaWUjvAFbWtKrFdXAyOyAAVMFbB7wkTSVXNruBkaRLDu8VYwPSkfR7gaBN256bQLDPYwPWnwsCw/y5JcDGeHSTcD1YuxI4Nj4AP2UoCUWUE6e+iZV1JkiHgdM7ts0TsgnlQuoRuE6UA0Vdd+208aBcqgIuu3oKQkGm1RRzikIUO2RHUh5iNYa6oEln9eoIFS2+fZxRe98Byg/NEf+4QH5AaGEGy8YTC4Cj53LzWxLr3uGKDMkl8eoSoo/TaaJR1JEUi7QO1syWUjQDmH1sqIKBu3iTQLpcqDuKHy725QDRbwWZsHcNKIv5xSmlPx/7aghWg90LzWMD8cELTMJ8bKXFLF82z/tptiOd4DJwYhqT/smBsmpo1xigGLe0qSyWNk14Q2s9nYk9bJyVtu8waxX0t9vPJnV5AdiMIIsNoXHx+IMtpA8v+kq5k+V1ANL3VXy/1lF1ZPgEiSj8Fae8Lovvzs+aKkGisE5cbppsMj2JQE73wGaRGHzVtp1LRC3Zddyj5JoW0PniqdzqaTuWaqhbecFRE7WTucDWuxf0PI7sxQtlvTNFl5SxSBwcZ+IA3WuSIDXpKqdIfC4WFH1JXCc9v+rntQZTBUkrhg31F1L0Iry5tICbbEd7wDaIXl4EUiWG3QdqLtSv/eRPInagV0a46M+4wWBakUGqQP0ByTLNWZcoxpP3TfYQrZm5aWMayeye9ixI1qrWPm3Lnbs0HWgGhjs2JNda3CxxlRSD/CREENWPY0tpZroklZedlRLPJJHTI52iNd2J4Nu2KJ1j0lkgZqOweaeeM2R77WYQhS/83mF/cgcpvRtmgh1t32CrzohibKa4mCGclJYbNI2RZwEijlDlIu2wNrxLspBfH6ZkCakb7YTyZGVEnHdzEAgvpMS70nxiUG31DR2eSJUMkajtUbXgWj8ASGK/Fc0lyhcT5OueuJRQ90XrsCqL0eDaSeFqr6GniZaFzJHUESFJ3tzTIi0kDc4SC+PaYYJxTDBpVLP771ZSQqXCNtndmE86y0ALWS82uAAbk1XNfHq+kaZ2hqhjKtFpRwgWYopzfalgTdVMeRb3/qWkBtt+vjMZz6z5Rrvt2LIeEFLabfwVANL1dMbXbhGjofuJUfvXEm67IjHHlsEOldq+q+NUc6hK4frxVR9jevFeKsYvFHSe9PRO19RDURbAAWdfywJKmg64TNFBr0dsnfK/tFiFaY8QVNTzmPWS+z69uEBbqpiCMCXvvQlLl68OPv485//vOUa77diiK4ksMr3WsqBxuYB7QIulWAuGPCxwscaO27QbWCGUhQHM8bHB0zu6KHzht4bE5kiWq+xqyWd10fESzm6EUgYQEhb1lDYMuDxn5oRdDDObyCJQpAAVH9AegFf/vKXt3z9s5/9jF/96le89NJL3HXXXYDQth86dOhtf391dZWnnnqK3/72tzNu4N/97nfccccdPP/88zPFkGeffXaLYsiTTz7J/fffz6uvvvqe+YKzJY+JQxu0QdXT1F1Z/LqvWnp2jYsi0RNQsq3XPUntggJbCvmTLhp8ajHrFThPvTAgenMFO85EGLp0Mh3cvAup0zs5xJQOBmSkfApEmTrDNtlNUwyZ2gsvvMCBAwf46Ec/yre//W2uXLky+9l2KoaUZcloNNryAZJ3V33F+h2K0YcVdU9J9O+kJhDUpu5ekNfbsm3TBll8XQWKAx0BijSeYAy+m9JklubAgGhUEl2dEF1Y3qCefadhjrcje5jiDqdHwRSWNkUnfVB2ABDFkPvvv5+iKOj1ejPFEICHHnqIr33taxw7dozXXnuNn/zkJzz44IO8/PLLJEmyrYohjz/+OD/96U/f8v2gke1eSXWNAL0LbtbMQSlMKbm5QnJ73SipwFWiEOJjGQfPD2Z0zq+jXIPvxUSjCjORWQM3TNFrm9g83g7F806L/w6m6gYah9k+5dibpxjy8Y9/fCYEBfCJT3yC++67j2PHjvGnP/2JRx555B2veTMUQ370ox/x/e9/f/b1VDRKNxAqiMbC8dO76KR6lwhvgG5h4t5KV89bUF768ckoELSgf3SsRGUkNpi8wqyV+NiKYJRRjD6cMn81FrLHLBE+gBYZNNsN6nc5GqavaUmpN4+Hq3L7POA9HwFTxZD77ruPxx9/nHvuuYef//znb/vahYUFjh07xunTp4GtiiGb7Z9VRW5EMSRJkll2Mv0AcJHIvTaZbPnTrp7JBYihW/1A5WW3iNfbWULXduxKT/dSjalCW/s3hCymPNTDdSORiosMvQsyNh4G3Y2xMqOpD+0RIurNkPOpvdPXbhOLqFKEaPvUw2+aYsjb2eLiIufOnZtJyNwaxRD5N1mRZo2PFS5RVENJCWdAzVTNFrzpSFWuHEr71kwaIY/yARWgms9oOgbXEYr3ek+KXauoDwnbWHWgS31knsnHDqKrRnh+pqPgU9s8+z9jCd80k/A+2U1TDFlfX+exxx7jq1/9KgsLC7z++uv8+Mc/Zt++fXzlK18Bbo1iiCnBGTC5gDK9Vbi4feKVokkkINRBdgbV1vjLeUiWwVtNeVcHHwkZtEuNLH6iMNMew+UxvhNR7k1mIJL0qsdHbWoYRxsDKDAjhvb9VPSKxkVbfAJwm5jEBUKm6g+IfPy7KYbkec7Jkyd5+umnWVlZYWFhgQceeIDf//73t1QxJCiIVwK2CC0qRzpvNvdSFLLC2xevB9KrDbZwKGdQjaKYh6gNAO1EUkSXCshTN4ItUHlJyGKavvT7vYFkucFHUvev5lKiyGAvLjPdcP2wB4DrxKDBHexKQDkqWlqacutusY00cTteMeTer/4vIpuiXKDJ1Axvr7wMc3gDTaoxdSBdatC1bwc4FNVAMzqmSVYC+/5Pjl0aEyJDM8xoupZoVBFdXKE5OGSyILTzpvTSzFkrKfd3AEgvrImqyNSMJkSW8tg85ZzF5lJyVrUTbsDLq1sqgo0vef6NX+wqhtyo6bpN54wUgZQPRBNp6MRr0sNVXnaIpiNysfFiSbRu8DZpaeUiyvk54lFNuScSONh6iZvvUQ9i6q6ie7EmWiowy2vgPIkLGyNeU9pXEM7iuYxyj7z9ydVCFn+QEqyCOJKh1mlVsPqAHAH/imYLj4plCscWgWgiOT8Kem+W5PsFgUOAYk5m+HvncnTjKQ4kdK402LxhvJAwOaBJFzXdSzXpf1wFoLpjL1XfEI0D0UqBWR3PNIZcP5GdwmpMVRN6HVEh1Qo1Ken/Q+oQPja4bkw1H2NyL+wiAFbLnKDfFYy4YVO+Tf90mJE15Psj6q5QvVU9jSkDLhVot3Zt/T0IjEt58LWmf2ZMPEpFRPrCUstA2qHpGHQTyC7mmMW1DeBImlAPIsEVjloi6n4irODtsKmqG8YfO9ASQyg5mpqA6yaUe5M21giYC7vDoTduQTD88VKJCqL3qxuY/79rlHtTOlca4pWKahgTjCIaC05w9JEeLlJEE6nxq9qTXCtE6KFt3TbzXezEiezsuCBkCfXeLsoF4RGKNJ2zI2gc1ZEhTUdSvejCMiGywgugBVamXMAUDcFo8kMp5VBIqnsXHG4+27a3Z8c7gG4C8UqOXp3Q7B/gUkPnYo7LInQTSM6NUHmJXUmE07dxVEfmsLlHeS3awusVqmpwUYLvd1Cpo5nLqHuW9GouMnGRZXJ8D01Hz4pN2UXR/PX9lHIuIp/TzK/UhMiSH58j32fpXJbzPVoSDkK/p0vnYk52pT0GQqDcvjrQzncAO2kwSxN8P6MeRNhxg728KuXacT4LsFT7r+gGakwhswDJYkFxsEP65jo+syjrKY90qQbt0ZFFFP/jAE2maTJJGb1pFUr7kYBElnNM7unlXjgC9nSo+4Ymkz6EXRYGENEczGWcfVOVMOzCwm/c7EpB+eG9NJlIv4p2UCp1fK3xHeHlM2slamUNVdTo0hGUEXHnXoQtRCfYG029J5LxsCCwsfp4StWTAFOAou3YWJBzvckMuk5Ir+aMP9Sl3JPQuwDdsxOyS4ZoaSJBozUwqSX9a4msQiKPvqq2Dxe+4x2g3psRl45sKUdNSnwvww1jUIomS2m6Bm8V6ZLF9BN0LoBMTEa1J5YATUlwllxeR4Uudc+inSx0nW2gjoV/OLRPdgs6UYrSxphS3mpbBHTeCP5vVFAdlKJQfH5lS+4/E6WcVNuKB9jxDuCtourEhPmEZDnBjCuhYNNKyrqZps4Uk/0JNk/Y+79XxTlSQ9MR+pagwBQGPbHiDE6i86qn0Q6yqw02dzRdIwJUxQa2oBxoupcbTC5iEy5WTI50UCHQfX1d2MAaEYwCWuoZwQGoWhyi3teF09vz/ux4B8j3R9A+sXZiiC5OMFZTzYvs+3Rax8cKNQlUezPB9wUpFNWd6ZROgU8tTcfIONi6o0kU5VDBPks81rPfCVqu61KZOSBA3bc0iZoNeehKMoWpnmCwZiZjN9MTBnwnZrKwWwd4zzatcJemxHjF3N8nmNWcfG8GqqZ2ijIzlO3Ili6gdoHlQ4Dycp4r8BU0WnH1o7H0E0yFUdIXYFKjlBZCMBWI1x12JEdDsdcSnCKPIaRgqwBNjbMKZwLaBvKjhqSXSXoa6xnLSHRtDK4mNIF8f4/Sllv+pptpO7YXcObMGe68885bfRs31c6dO8fRo0dv6jV37A4wPz8PwNmzZxkOh7f4bm7cpsimU6dOcfjw4Zt+/R3rALpF3w6Hw5veQbsVduTIkdnfdDNtx6uH79q7264D3Oa2Yx0gSRJOnDhBkmxjHfV9sO3+O3ZsFrBr12c7dgfYteuzXQe4zW3XAW5z23WA29x2HeA2tx3rAL/85S85fvw4aZpy77338te//vVW39LMHnvssbcwqWzmVAgh8Nhjj3H48GGyLOMLX/jCFhIOuD6mleuysAPtmWeeCVEUhSeffDKcOnUqfPe73w3dbje88cYbt/rWQgghnDhxItx1113h4sWLs48rV67Mfv7EE0+Efr8f/vCHP4STJ0+Gr3/962FhYSGMRqPZax599NFw5MiR8Nxzz4VXXnklPPDAA+Gee+4JTdO8p3vZkQ7wqU99Kjz66KNbvvexj30s/PCHP7xFd7TVTpw4Ee655563/Zn3Phw6dCg88cQTs+8VRRGGw2H49a9/HUIIYWVlJURRFJ555pnZa958882gtQ7PPvvse7qXHXcEVFXFyy+/vIWFBOCLX/ziuzKMvN92+vRpDh8+zPHjx/nGN77BmTNnAHjttde4dOnSlvtPkoTPf/7zs/u/HqaV67Ud5wDXrl3DOfcWLoHNLCS32j796U/z9NNP85e//IUnn3ySS5cu8dnPfpbFxcXZPb7b/V8P08r12o5tB/8zm0j4TxhG3k976KGHZp/ffffd3H///dx555385je/mdHq3cj938jfuON2gH379mGMecuTsJmF5INm3W6Xu+++m9OnT8+ygXe7/+thWrle23EOEMcx99577xYWEoDnnnvuhhhG3g8ry5K///3vLCwscPz4cQ4dOrTl/quq4sUXX5zd//UwrVy3vccA9l/CpmngU089FU6dOhW+973vhW63G15//fVbfWshhBB+8IMfhBdeeCGcOXMmvPTSS+Hhhx8O/X5/dn9PPPFEGA6H4Y9//GM4efJk+OY3v/m2aeDRo0fD888/H1555ZXw4IMP7qaBm+0Xv/hFOHbsWIjjOHzyk58ML7744q2+pZlN8/ooisLhw4fDI488Ev72t7/Nfu69DydOnAiHDh0KSZKEz33uc+HkyZNbrpHnefjOd74T5ufnQ5Zl4eGHHw5nz559z/eyiwe4zW3HxQC79t5s1wFuc9t1gNvcdh3gNrddB7jNbdcBbnPbdYDb3HYd4Da3XQe4zW3XAW5z23WA29z+P8X5OBmLeIIqAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.imshow(goes_test.Rad.data[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Size: 8B\n", + "array(0.00464154)\n", + " Size: 8B\n", + "array(39.98519969)\n", + " Size: 8B\n", + "array(-44.99025902)\n", + " Size: 8B\n", + "array(-20.04057225)\n" + ] + } + ], + "source": [ + "print(goes_test.latitude.compute().min())\n", + "print(goes_test.latitude.compute().max())\n", + "print(goes_test.longitude.compute().min())\n", + "print(goes_test.longitude.compute().max())" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Size: 8B\n", + "array(0.)\n", + " Size: 8B\n", + "array(44.90658452)\n", + " Size: 8B\n", + "array(-78.44849834)\n", + " Size: 8B\n", + "array(-14.55963817)\n" + ] + } + ], + "source": [ + "print(msg_test.latitude.compute().min())\n", + "print(msg_test.latitude.compute().max())\n", + "print(msg_test.longitude.compute().min())\n", + "print(msg_test.longitude.compute().max())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}