{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Analysis using `Xarray`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebooks demonstrates some features of `xarray` that are useful for Climate Data Analysis, including:\n", "\n", "1. Reading in multiple files at a time\n", "2. Averaging over dimensions to calculate an average in space, time, or over ensemble members\n", "3. Calculating a climatology and anomalies for monthly data\n", "4. Writing data to netcdf file" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Monthly Data\n", "\n", "In this notebook, we will work with monthly data as an example. \n", "\n", "We will return to the CMIP5 data, this time for surface temperature (ts), which corresponds to sea surface temperature over the ocean, from the RCP4.5 scenario produced by the NCAR/CCSM4 model. This time, we will read in all of the ensemble members at one time.\n", "\n", "The data are located on the COLA severs in the following directory:\n", "```/shared/cmip5/data/rcp45/atmos/mon/Amon/tas/NCAR.CCSM4/r*i1p1/```\n", "\n", "The filename is:\n", "```tas_Amon_CCSM4_rcp45_r*i1p1_210101-229912.nc```\n", "\n", "The ensemble members are indicated by: r1i1p1, r2i1p1, r3i1p1, r4i1p1, r5i1p1, r6i1p1 in the directory and filename. \n", "\n", "In `xr.open_mfdataset`, we can simply use `*` in the filename and directory name to indicate all the ensemble members." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import warnings\n", "\n", "import numpy as np\n", "import xarray as xr\n", "import pandas as pd\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "warnings.filterwarnings(\"ignore\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Read multiple files using `xr.open_mfdataset`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Set the path and filename using `*` for the ensemble members" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "path='/shared/cmip5/data/rcp45/atmos/mon/Amon/ts/NCAR.CCSM4/r*i1p1/'\n", "fname='ts_Amon_CCSM4_rcp45_r*i1p1_200601-210012.nc'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Read the data" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/shared/cmip5/data/rcp45/atmos/mon/Amon/ts/NCAR.CCSM4/r*i1p1/ts_Amon_CCSM4_rcp45_r*i1p1_200601-210012.nc\n" ] } ], "source": [ "ds=xr.open_mfdataset(path+fname,concat_dim='ensemble',combine='nested',decode_times=True)\n", "print(path+fname)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When reading the data, we need to tell `xarray` how to put the data together. Here, I told it to create a new dimension called `ensemble` for combining the data. " ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
<xarray.Dataset>\n",
       "Dimensions:    (bnds: 2, ensemble: 6, lat: 382, lon: 288, time: 1140)\n",
       "Coordinates:\n",
       "  * lon        (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8\n",
       "  * lat        (lat) float64 -90.0 -89.06 -89.06 -88.12 ... 89.06 89.06 90.0\n",
       "  * time       (time) object 2006-01-16 12:00:00 ... 2100-12-16 12:00:00\n",
       "Dimensions without coordinates: bnds, ensemble\n",
       "Data variables:\n",
       "    time_bnds  (ensemble, time, bnds) object dask.array<chunksize=(1, 1140, 2), meta=np.ndarray>\n",
       "    lat_bnds   (ensemble, lat, bnds) float64 dask.array<chunksize=(1, 382, 2), meta=np.ndarray>\n",
       "    lon_bnds   (ensemble, lon, bnds) float64 dask.array<chunksize=(1, 288, 2), meta=np.ndarray>\n",
       "    ts         (ensemble, time, lat, lon) float32 dask.array<chunksize=(1, 1140, 382, 288), meta=np.ndarray>\n",
       "Attributes:\n",
       "    institution:                  NCAR (National Center for Atmospheric Resea...\n",
       "    institute_id:                 NCAR\n",
       "    experiment_id:                rcp45\n",
       "    source:                       CCSM4\n",
       "    model_id:                     CCSM4\n",
       "    forcing:                      Sl GHG Vl SS Ds SA BC MD OC Oz AA\n",
       "    parent_experiment_id:         historical\n",
       "    parent_experiment_rip:        r1i1p1\n",
       "    branch_time:                  2005.0\n",
       "    contact:                      cesm_data@ucar.edu\n",
       "    references:                   Gent P. R., et.al. 2011: The Community Clim...\n",
       "    initialization_method:        1\n",
       "    physics_version:              1\n",
       "    tracking_id:                  635969e3-0203-402b-a58b-e3630cb58a30\n",
       "    acknowledgements:             The CESM project is supported by the Nation...\n",
       "    cesm_casename:                b40.rcp4_5.1deg.001\n",
       "    cesm_repotag:                 ccsm4_0_beta49\n",
       "    cesm_compset:                 BRCP45CN\n",
       "    resolution:                   f09_g16 (0.9x1.25_gx1v6)\n",
       "    forcing_note:                 Additional information on the external forc...\n",
       "    processed_by:                 strandwg on mirage0 at 20111021\n",
       "    processing_code_information:  Last Changed Rev: 428 Last Changed Date: 20...\n",
       "    product:                      output\n",
       "    experiment:                   RCP4.5\n",
       "    frequency:                    mon\n",
       "    creation_date:                2011-10-21T21:56:36Z\n",
       "    history:                      2011-10-21T21:56:36Z CMOR rewrote data to c...\n",
       "    Conventions:                  CF-1.4\n",
       "    project_id:                   CMIP5\n",
       "    table_id:                     Table Amon (26 July 2011) 976b7fd1d9e1be31d...\n",
       "    title:                        CCSM4 model output prepared for CMIP5 RCP4.5\n",
       "    parent_experiment:            historical\n",
       "    modeling_realm:               atmos\n",
       "    realization:                  1\n",
       "    cmor_version:                 2.7.1
" ], "text/plain": [ "\n", "Dimensions: (bnds: 2, ensemble: 6, lat: 382, lon: 288, time: 1140)\n", "Coordinates:\n", " * lon (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8\n", " * lat (lat) float64 -90.0 -89.06 -89.06 -88.12 ... 89.06 89.06 90.0\n", " * time (time) object 2006-01-16 12:00:00 ... 2100-12-16 12:00:00\n", "Dimensions without coordinates: bnds, ensemble\n", "Data variables:\n", " time_bnds (ensemble, time, bnds) object dask.array\n", " lat_bnds (ensemble, lat, bnds) float64 dask.array\n", " lon_bnds (ensemble, lon, bnds) float64 dask.array\n", " ts (ensemble, time, lat, lon) float32 dask.array\n", "Attributes:\n", " institution: NCAR (National Center for Atmospheric Resea...\n", " institute_id: NCAR\n", " experiment_id: rcp45\n", " source: CCSM4\n", " model_id: CCSM4\n", " forcing: Sl GHG Vl SS Ds SA BC MD OC Oz AA\n", " parent_experiment_id: historical\n", " parent_experiment_rip: r1i1p1\n", " branch_time: 2005.0\n", " contact: cesm_data@ucar.edu\n", " references: Gent P. R., et.al. 2011: The Community Clim...\n", " initialization_method: 1\n", " physics_version: 1\n", " tracking_id: 635969e3-0203-402b-a58b-e3630cb58a30\n", " acknowledgements: The CESM project is supported by the Nation...\n", " cesm_casename: b40.rcp4_5.1deg.001\n", " cesm_repotag: ccsm4_0_beta49\n", " cesm_compset: BRCP45CN\n", " resolution: f09_g16 (0.9x1.25_gx1v6)\n", " forcing_note: Additional information on the external forc...\n", " processed_by: strandwg on mirage0 at 20111021\n", " processing_code_information: Last Changed Rev: 428 Last Changed Date: 20...\n", " product: output\n", " experiment: RCP4.5\n", " frequency: mon\n", " creation_date: 2011-10-21T21:56:36Z\n", " history: 2011-10-21T21:56:36Z CMOR rewrote data to c...\n", " Conventions: CF-1.4\n", " project_id: CMIP5\n", " table_id: Table Amon (26 July 2011) 976b7fd1d9e1be31d...\n", " title: CCSM4 model output prepared for CMIP5 RCP4.5\n", " parent_experiment: historical\n", " modeling_realm: atmos\n", " realization: 1\n", " cmor_version: 2.7.1" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This next step converts the time dimension to a type that can be used later for plotting" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "ds['time'] = pd.to_datetime(ds.time.values.astype(str)) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, the data now has an ensemble dimension of size 6 corresponding to each of our ensemble members. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Calculate the Ensemble Mean" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`xarray` has nice features for performing operations over a specified dimension or set of dimension. One example is `xr.mean` which we can use to average over the `ensemble` dimension to make the ensemble mean." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "ds_emean=ds.mean(dim='ensemble')" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
<xarray.Dataset>\n",
       "Dimensions:   (bnds: 2, lat: 382, lon: 288, time: 1140)\n",
       "Coordinates:\n",
       "  * lon       (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8\n",
       "  * lat       (lat) float64 -90.0 -89.06 -89.06 -88.12 ... 89.06 89.06 90.0\n",
       "  * time      (time) datetime64[ns] 2006-01-16T12:00:00 ... 2100-12-16T12:00:00\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    lat_bnds  (lat, bnds) float64 dask.array<chunksize=(382, 2), meta=np.ndarray>\n",
       "    lon_bnds  (lon, bnds) float64 dask.array<chunksize=(288, 2), meta=np.ndarray>\n",
       "    ts        (time, lat, lon) float32 dask.array<chunksize=(1140, 382, 288), meta=np.ndarray>
" ], "text/plain": [ "\n", "Dimensions: (bnds: 2, lat: 382, lon: 288, time: 1140)\n", "Coordinates:\n", " * lon (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8\n", " * lat (lat) float64 -90.0 -89.06 -89.06 -88.12 ... 89.06 89.06 90.0\n", " * time (time) datetime64[ns] 2006-01-16T12:00:00 ... 2100-12-16T12:00:00\n", "Dimensions without coordinates: bnds\n", "Data variables:\n", " lat_bnds (lat, bnds) float64 dask.array\n", " lon_bnds (lon, bnds) float64 dask.array\n", " ts (time, lat, lon) float32 dask.array" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds_emean" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, the ensemble dimension is no longer shown, but all the metadata is still present in the `xr.Dataset`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Calculate Anomalies for Monthly Data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We often want to calculate anomalies for climate data analysis. `xarray` has a function called `groupby` which allows us to group the data by months. We can then apply a `mean` function over the months to get the climatology and subtract that from the original data to get anomalies." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "ds_climo = ds_emean.groupby('time.month').mean('time')\n", "ds_anoms = (ds_emean.groupby('time.month') - ds_climo)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's convince ourselves that the climatology and anomalies we calculated look like what we expect. We will use `xr.sel` to select a specific latitude and longitude and plot them. Here, we tell `xarray` to select the values `nearest` the latitude and logitude of Fairfax, VA (39N,77W)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "ds_climopt=ds_climo.sel(lon='283',lat='39',method='nearest')\n", "ds_anomspt=ds_anoms.sel(lon='283',lat='39',method='nearest')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot the Climatology" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'NCAR-CCSM RCP4.5 Surface Temperature Climatology at Fairfax, VA')" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(ds_climopt['month'],ds_climopt['ts'][0:12])\n", "plt.title('NCAR-CCSM RCP4.5 Surface Temperature Climatology at Fairfax, VA')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot the Anomalies" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'NCAR-CCSM RCP4.5 Surface Temperature Anomalies at Fairfax, VA')" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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WwHi/YObJMAYuL5Bh0lsAY+I4Ey6AMSm8GMbzexHepqkwPAtjrmYzjIn2S806b4UxYfxDGAJ2HYz2WaLKxBxcTQBwv9lWf420SczK7z4As00TzggYz/B1AF/DEDBh3IHD3v855vnnpTY3CcYA77gQZf3E/H8VDA3icaTNNqEJejYB1z4Po296xWw7c2DMXQDGc/gnjL5ghVnPhxR5bIYhgP8Hw7R+BtLTDIAxSHsNhlPQNvJ3NJDzngnDPPgVjDbS06xjRpgm+BtgmB0rYSyheRuwBfo/ADzFzB8x83oYFpt/moP5tjA07C8CinkKhsnTthARUX8Yc+5/FtsLM38Mo6/w1YosV0qNRhMRInoBhlt9VPOGRtPoIMML8Wxm/r+GLltlj9doNBpNgcHMb8PUnhoaHWtOo9FoNHlFm+Y0Go1Gk1e0RqTRaDSavNJk5og6d+7M/fr1y3c1NBqNpkkxe/bszeai/UZLkxFE/fr1w6xZs/JdDY1Go2lSEJFXzM5GgzbNaTQajSavaEGk0Wg0mryiBZFGo9Fo8ooWRBqNRqPJK1oQaTQajSavaEGk0Wg0mryiBZFGo9Fo8ooWRBqNRpNnquuSeHn2GhRqyLUms6BVo9Fomiv3vrUY//x0JcrbluKYAY06CEJO0BqRRqPR5JlNVdUAgKrqujzXJD9oQaTRaDR5hkD5rkJe0YJIo9FoNHlFCyKNRqNpJBSor4IWRBqNRpN3CtsypwWRRqPRaPKLFkQajUbTSChQy5wWRBqNRqPJL1oQaTQaTZ4p8CkiLYg0Go1Gk1+0INJoNJpGQqHGmotdEBHRQCKaI/zbQUTXSGmOJaLtQprfxF0PjUajaSoQFbZxLvagp8z8DYARAEBERQDWApisSDqdmU+Nu3yNRqPRNC1ybZo7HsByZl6V43I0Go1G00TJtSA6H8DzHucOI6K5RPQWEQ1RJSCiK4hoFhHNqqioyF0tNRqNJo8UtmEuh4KIiFoAOA3AS4rTXwLoy8zDAfwFwKuqPJh5EjOPYuZR5eWFt0eHRqPRFAK51IhOAvAlM2+UTzDzDmbeaf5+E0AJEXXOYV00Go2m0VOgTnM5FUQXwMMsR0TdyHQTIaLRZj0qc1gXjUajabRYTnNcoEF+crJVOBG1AnACgJ8IxyYAADM/CuAcAD8lonoAewCcz4XqQK/RaAqeQp8jyokgYubdAPaSjj0q/H4YwMO5KFuj0Wg0TQsdWUGj0WgaCYVqF9KCSKPRaPJMoUdW0IJIo9FoNHlFCyKNRqPR5BUtiDQajaaRoOeINBqNRpMXCnuGSAsijUaj0eQZLYg0Go2mkVCgljktiDQajSbvFLhtTgsijUaj0eQVLYg0Go1G4pg/fIBHPliW72oUDFoQaTQajcSqyt34wzvfhEpbU5/Eko1VsZRbqLGftSDSaDSaLLh58gKMe3AaNu+syTgPKvBJIi2INBqNJgtmfrsFALCzuj7PNWm6aEFk8l3lblRV1+W7GhqNpomR3tQuewrTMKcFkc3Rf/gA33/0s3xXQ6PRNDESpiTKZn6nwINva0EksnhDPBOOGo2mcLBkSKFqM3HQ7AVRbX0KO7TJLW9s2VWLP09dilRKf6aa3PHVd1vx8dLNea1DLA5vBfqZNHtB9OOnZ2HYbVPyXY2C5cZX5uHBqUvw2YrKfFdF04w586+f4qInPs9P4bZZLQvTnJ1DYUqiZi+Ipi2pyHcVCprdtUkAQL3WiDTNFFuIZNHE9RxRDiCilUQ0n4jmENEsxXkiooeIaBkRzSOiA3NRD41G0zSYsnADNu6oznc1NHmiOId5j2FmL6PtSQD6m/8OAfA383+NRlNgpFKMK56Zjb57tcJH14/Jd3UiQ5bXXAx5FWhghbyZ5k4H8DQbzADQgYi656kuGo0mjyTN3nfN1j15rklmJKx1RNmY5nRkhZzAAKYQ0WwiukJxvieA1cLfa8xjDojoCiKaRUSzKir0XE+hMmvlFuyq0avWmyspswdvql2xJUQK1dEgDnIliI5g5gNhmOCuJKKjpfOqNud6i8w8iZlHMfOo8vLyXNRT08jZvLMG5zz6GX754px8V0WTI1Ip4/9EE5+xj8OsVqiiLCeCiJnXmf9vAjAZwGgpyRoAvYW/ewFYl4u6aHLHg+8uwVffbc1pGXtMr7uv1++IJb//zlmLbbtrXceXbdqJ7yp3x1KGJhqWaa6pyiGKwTRnkSrQSaLYBRERtSaittZvAOMALJCSvQbgEtN77lAA25l5fdx10eSWP7+3FGf+9dMGKSuO7/O7yt34xQtz8PPnv3KdG/vARzj1L9OzL6RAYWZMX1qRUZibpOna3+Q1omzWEcUozJoiudCIugL4mIjmApgJ4A1mfpuIJhDRBDPNmwBWAFgG4DEAP8tBPTQaBzX1hna1bpt6UnyHjp4cilSKcd1LczFn9Tb72Ktz1uLiJ2bihS9W+1zpnR+QnvTPN/ncE6hA5VD87tvMvALAcMXxR4XfDODKuMvONdt216JFcQKtWuTS612TK+J0sy1ktu6uxcuz1+C9RRvx1W/GAQDWmh5va7ZGN29aprlEI5FEUeVQOuhpHgpvJjT7yApxMuKOd3HCA9PyXY1GQdRRY2PoYkiKTnn9S3Mx9Lfv5K0+TZVEzAI91chMc1HvKw6zmpVHoQYg0YIoIms9zDqFRtSPrjF9X1ZdXpq9Bju1W3hk7E4zpl4zbmeFRz5Yhn4T37DnnqISeZCVQb0nTVuOix53x8bTW4UXAIfd8x5++1/Zb0KTCaJ3z11vfI0BN7+Vx9qEIx0TrDA/dj/qkinUJ1Oh0sZt4rTkRVwa0UPvLQVg3FMmZHpfUZwV7n5zMT5e5g48U6gts2AEETNj/fZqPPXZKvvYZ8srUZdM6Y4pA8Qn9tj0b1Gb4UffkOg5Im8G3PIWjr7vg0jXxPXZND5nhWjp7QWtWT0PIw9tmmvmyI1kzuptuOCxGbj/nW8KdX4wKxpqvUOc0wZhQ7Es21SFfhPfwCeKEWs2bNpRjQsmzcDWXe51TPmGGVi3PWTQUbauick0l7JMc41DEkV1w451q/AC7YwKRxBJf2+uqgFgLGQszFefHU3xeyF71Olf+c+/3QIAeH1evEvbHpu+Ap+tqMRLs/1dnFMpxuadNbGWHSdWRx1XE7C95mKSQ9k6D0TXiAy82tXkr9ag38Q3dJgqHwpGEPl1PoU6CsmGODSiPbVJXPT451hesRO19Snfye843lHYDsqaq4h7V9mwcyF/fm8pRt05tdFui2DdR3Vd0o58kVV+MXvNZRL77YNvNmH/W9/OzHklwH374feXAfBevyZSqF1RwQgi+QWzx29NOOLooz9ZthkfL9uMu99YhAG3vIXfvNY4HEmKKJzmFJWw+U1dtBEAsGlH/rSitxdsUIZCAtKDghQD+//mbelc9LLSGlG8prkodfnjlG+wpy6JFRU7M9aIvAZLYdzd0+7bhdkbFY4g8mgGjcQs3eSIU4u0cnp2xneeaeKYP7CqHFT3XK3pYB+N6I15611x+/LVNjdsr8aEZ2fjqn+5QyEB6g41m/djh/iJuTeK8vrEwKuZzhF5tZcopsLCFEOFJIh83nCBDkKyIo5OOsooMA7BF3ZuI5FjjUg1F3Llv75ssLh9QeypM8xtqz2iJMT9vVj5xb0nT5Q2kxK0ssznlvwvVAm4pRurAKSD+xZqX1QwgsiPQtlHZNbKLdi+py6WvOKIrBBG84jTk8qqcpCAsUbmORNEAbPyjaUz8qql6nvJZqCQzJH7diSNyH43GURWsPNwn3v1q7XYaJpYVY/oh//8AgAw+au1jnoUGgUjiFxzRMKBxvruP12+Gb+JuAD3mw1VSmFTXZfEOY9+hh+ZDT9b4jRbNZSziFVKWGcFq4P8rnI3+k18A7NWbsmufGvkHyBcG6o5JlOsjD4Q+D58Bw4Z1KMRzBFZj6GIKHJ7TMeac15XXZfENS/Osb9HVbb1yUba+TQwBSOIvEcajXeS6MLHPsfTwgLcMJz4p2k47++fuY5b9z9r1Vbsrs3ejVT1PP0+4Gw/t0yv/3bzLmww18dY9QvKq8gcmr8+bz2Y2V4B//LsNRnWwsDq7MK2OL9+uaY+ie27s9NuD7n7PRx057uu49bz8RKYcXedKXsdUTz5pedkIpjmhLVMkTUiD81eFvIqTbJeTtNYR8U5pmAEkd/rbW7vfvGGKtcx0f5+yRMzsy5DLYii5RFmRXq2fdOY+z/Eofe8Z5RjHgurEQHOZ+nXUW7bXYsnP/7WXxjHOPK/+ImZGH7HlKzy2LyzBtt8hJmnaS7m7yVX+xFF04jS5sGM54gCRJhSI0qlAtMUAoUjiHxH685zz32+Ck9+/C2+WLkF/Sa+0WjXc0RBvMdZq2LYVVXxOCPbt0M4K8T5XYb1mhPnKuqTHGoO8dcvz8Mdr3+NL7/b5pnGz1nBWc/g8mZ+m52Z0L98//Nxz2PkzDSXQVqOeiHEhdLRy09KprlCDfFTMBvryO9X/Fv+rm6ebMzLnDKsOwBjpf1pw3vkrnINQNwNXJVf5pO8DfX1qU1zcscvmqScfaN3R2lpFn6BNv3ct1XE7UUWhgVrt6OsJGFVQMlf3l8aa5mWUhC3u3oUM5ellTGHG3hMX1qBVZW7cdGhfT0HVGHKr5M1ogJxnJIpII1IfZzIuwO1GlJRvhZ0mGzeWYPquuAV7H6RABpicWamZfi71sdXb6/HIxeRyfu2OhC/K+3yzUS7auqV7yzt1BC5Glkz5euNgXNZz8+MvgurH6kQGlHlzhpsqqrGOws3BEY/kLadUvL3j5bj1lfTjkBpbTmceeziJ2billclRyLzumWbdmL+mu2u9qb6PlzzSIUphwpHEPm1Sq/OLldupVEZdedUXPjYjMB0SZ9WzDEHx45jjsi+zu+ccHL2qq044t73saM6s0l6L9OcXL68sFJ1X/0mvoGfPjvblcbPI06cI9q+pw5DfvsO/jR1iSPN9KUV3jfQAFRV1zV4ENL0Dq3eaQ66cypG3/UefvLMbEz8zzzf/Kx6+w2M7nlrMZ6ZkXYEstKmOHvNfuwDH+F7D3/sbmfKOSLtrAAUkCDyU3m9ztixwfItiQDfuQcLv43A4taIVNkFlVFdl3QEfiQPt1ev8h56bynWbtuD2Sszm+PyWtAql5/wMM3J/fJbCza4yvDru8U5okozqOn/pMCqFz8xE99sdDubNBRV1fUZbyiXKVFjza3ZGnJzygi3Yd1zijlj9235sVVVOzW3MIM3BrC7tj629X5NhYIRRK6JRFb/dlzTyLYwDsLv+2kYQeR/zdH3fYAhwtbcfgsBVbRvWQIAro/0nrcWod/EN4JXtpunZXOYfJWsCYSpXpg0YtDTMNp2PppdVXWd3VYaqvhsNLDVW3bjU4/tOqK0eOvdsKQRLVi7PfBaMUKIGDX99v99LZWhrpFYBjNw3P0fYfjt2XlENjViFURE1JuIPiCiRUS0kIh+oUhzLBFtJ6I55r/fxFkHL3w7KY9TlsmgqImIa5VpbvvuOnxXuTsHzgrR5ogIwKYqdRBPf5fn9O92LQ3fGtk099i0FQAQuDmfbZrzKUNZnxAdM4dJY/5PlH5XxT72qHw4K6RYFAwNVWY0E7hYr2Pv/xAXKrbcBjJz3zY0ovTxu95YFDoPZiOSgkXlLmd792qep/7lY3Rq3cIuf0Mz8NKNStxec/UArmXmL4moLYDZRPQuM38tpZvOzKfGXLYvmfTD9qRtE9GIVCYVa63JjScNcp2rrkuirKQoo7KUZgYfOaB6/lGDi7YtMzQi2eRRVlKE3bVJVNelUFrsfT+2edZlDvHWkERhEKYZhDHNEZG9ot7P7JsvD6q0RtRAc0RCwNEwiKnkNn/5P79ATb0Zty3C8xPbs3hdkCXh02WbHQtoS4vTAws5aoJfXta5wpwhilkjYub1zPyl+bsKwCIAPeMsI1M8vebg3WAtE06QF9WSjVWYtyZ4DifX+HnNWVsLWExbUoFBt76N2asyW4+idt/202yiz9HJedrbM6QY1XVJLN6wAwDsjz/IszCsRiQmuOP1hcoFwhbfVe52XPL3j1bg2827PMpn+/+UrRH5OTcY/09ZuAH/+ORbz3SZcMS973uW2dC7vkouzHsAACAASURBVEfdGM9vYPj+4k2oS1rPOXwdbLMtM3bXJF3Hvbjw8c8d64haCIJIduX3328rXHnNlZwZnYioH4CRAFR682FENJeI3iKiIT55XEFEs4hoVkVFdt5E7k5SGAF5zRHZpjn/L2Tcg9Nw2sOfZFO9WPA3jTnv4dPllQCAGSsyE0QqweKn2Sg1IjuyQjjTnH0dATf8Zx7G/2k6tu6qtbWgMC7uqvLcE8bpAzNWbMFzn3/nqK/I+u17HHlM+Xqjp4ejtWSEOe0t5de2rDyveGa2a77B614slm2q8t2Iba3nOc7aNLeqchfe/Xqjb5ra+pRdPzu8TkgNLGy1wsyLrqrc5ahDioGrX0hvfxFGq7KeU5LZoZHLHnG3/ncB6j2kfDr8VGFKopwIIiJqA+A/AK5h5h3S6S8B9GXm4QD+AuBVr3yYeRIzj2LmUeXl5VnVKYMpoga3lWeLn/u2jDVy81uA6YdK6GS6C26Yassf6BdmZIHddUl7AWZ1Xbg5IldMsBButl6oTGs7q9XrXKx7qEumbLONr0YUolNS1XXB2u0Y+8A0HC5oPR98s8m135HFGmm7h6iOLczsWFt0woPT8H9PzwJgbG8w6s6pmLbEOZC88ZX5OPze97GnNimYLCMVG6JewWnO+7sxaLCSMjPmrXE6D6zbtgfPz/TeK8saTKRS7NCIZKGzvGIXPvhGPaDWGlHMEFEJDCH0HDO/Ip9n5h3MvNP8/SaAEiLqHHc9ZPxcUr06SVsjaiKSKOXTD8u30KLIOCALojVbd+PuNxcFbpOt6iT9BZF3neS8llfsxOotTpOXV5ksjEIDTXO2+7a/7T6SIFLsvhl0+fUvz8M15qjbd46IgV++OMc3L6vu9ckURt35Lu58/Wuc+pePXel++I8vPPc7evSj5Y6/o7pvL6/Y6dCwauvTbWrppips3lmD+95Z7LjGWi+1bU+tXV6Q5cEizs/R8nLz8uBMMeOSJ2fixlfm2zvWfr3OOba2+of6FKOF4Nkka0R+iIKwEInba44APAFgETM/4JGmm5kORDTarENlnPVQ4fVx+UVWqDE/qMawjigMfhqR/PGWmB+M2GkAwLX/notJ01ZgTsCcl0ro+WqdEc4d/8ePcNR9H5jn3BcSkbAGCSg1NaI9PoLo3a832uZTlylOuhcvgarqAO2mIU52S9dX1yVdEc/XmRHBg+aIJgteWCqsZr2rNonNO2vx+MfZzSUxp9tRWCedbzd7baDHdvuSnUjalBp+Urtq0hpR3KGPwmna/kdSDGzdZQggS7Cc/NB0Rxqrf0imUigRNCJVn+O1lUh6/jC4zs2RuL3mjgBwMYD5RGQN5W4C0AcAmPlRAOcA+CkR1QPYA+B8boBhQCadpDXp2TTEkP9kqEzaNOe8xuoLqmv9tYuo7tvqOSKDhetk663ievZ+T1Zn7re3yz1vpt1w5Q7CZZrzqYOMahQvJzvqvg9QUVWD8UO6hbo+nU/w+ww7j2nx0qzV2Lyz1j9P8/ksWp9+L69+tRZtSosxdnBXV3qvQV4yxfZgrj7FWLR+B/bv3g4A0KbMEkT1DueIZIod9zJr5RZ0NF2bo5LJfIt7vSGnIzV43KdV3Rv+M99xXP62AODv01bgJ8fs61muXMR/56zFM5+twjM/OgQtW2Tm4doUiFUQMfPHCOi3mflhAA/HWW4Y/DtJ9TnLxttUBilRnBVsjUgyzVnu3NX14TzQnOX7pc/sKUYxeYnvsd/EN3DxoX2FuqXPySaTsKY5VTtRjeLl/Co81k8B4ZwV7HxT7NLO03ML3sJANItd/7I7PI58qShYrI74GtNE+MXNY5VlqKhPpTWiuau34aQ/T8eSO09Ci+IEWrcwup6dNfX2QODjZZux701vYuW9p9h5nPOoe28tqymH9ZL0TyO1BcViZ8va5mVx8HqH4uJWkQN/p9oDSm02Xr+9Op5o+Y2cgom+7We28ppcXrppJwAfr7oU49qX5mZdt7jws+3L6yYtW3adZJpLu0L7T/w31MZ4VpZyf59euyHGFnOmccYS8y7DfS9ec4buY1Yn5BCYHmWpLE9+849yNrXJFMoSRVIaI9X/5q6HimlLKvD3j1Z4lgHIa2jcQnv60k3236u3us1wXt9WXTJla0RyWbY5tTYZSZMH0iPdG1+Z75suk2gi8hUrKnbZQVa9qhlH5BWVs4Lo5t9EpqkzponEDMge10hH+HOCELxShVcHu3lnTaANX06fyzhekTSiYuNvT40ocOJfcSyi+TPKk5BNc6Kjgxx0UsV3W9TzGIB7vssrG3nvGDGtowPxLMmNn0Yk348qcoTVnG6arO6Uw3TGcpsUNcaa+hRe/Wqd/XeJIhJE0sNLpi7JrjrPWFGJ2vqU451F/SaIgE1V1Zi72n8eM1KuQpgeETHSt5fADGsW9UP1mt6cv8FzINbcKBxB5DVKBWHJxp2+1zKAz5Yr/CkiNI7KnTUYdedU3D/lm/AXRSTK91xkdiiymarMnFSWR7LusqLNEam6BVX6TVVyeBN3GiJ1xINMzX9yPbyeo7V3jFiO8p6jaEQRTHM1Ci01SND89cPlvucB5wJWlgRDTV3S4VChqq/XCoD6ZMrlDHPZP77AvW8ttrXYK56ZHTni+IwVWzD6rvewwmPhsIX1aKrrki4XdRk7WofP4/QSmHF41Vqarfg+n/p0pd3Wmkq8y0wpGEGUjSby4TcVuECxSDFKCJQtpufNlIXuiM0A8MCUb3DGI9ktio0SffuuN4wFkvJah9KS4CgF89dsx1kKV2Cx+LpkCo9NW2ELNGV/rTj2lRRlXOn2LTx3ZmGHzAwjArjmiDx6I9WKfWZg445qx/Pyul7VXvxH08EaUdD2HrNDzC/MWe1MIz6PmvqUo45qQeShEaVYuYBziRRd3GttTfYY93H181/hyN9/4KnRhDXherljxxECTKVZJxLOQLnNmYKZI8om+rS1el5Gbhuyx48zbdrdWMVD7y/LuH4WKyqcI0Rx5C57lG3cYUykysKryHZF9X5e//x0pfK49YzfnL8eKyp24v4p6b12lKY81TGPB8RId/BEomnOWX51XRIvfhFt47awprk356/H788+wLFWJMWMQ+5+L9T1cxSmpGgaURKbd9Zg3IPTHOVny3Kh3WzZVYvdgsdkTX0KxUXpOqrevShrxLlFef7RYs7qbYGb28WB9Wis8FZJZiSkwQADmCpEgfB7nvUeAjcOGZGONZcuP0HU4JHQ80XBaESutSNZ5HX23wxtQB5h+UUpSCg6zri58l9fOv4Wq+c1oSwLnDAjL09hwYzFG3bgZ8996RBCxrlw+bjcZz3qkF6+45xo/+uHy/Hb1xZ6XKVG7Hy27a7FfI/Q/8kU4+bJC1zCT8arzqqwOv5zRM6/a5MpfPRNha1de5WfDXPXbMfNk9M7j9bWpxwRwlURBsS1MU8I65i8Ou6GEEKA8fwqqmrs52i1dTn8kLitiN/T9FsekC1WzuI7L0qQniNqbmQTxkXGMnfIHYW/ILI8u3Ipipw897loclCXK5sbrH7RK/3Kzbs8F7umGNi0Q+2yKuZ37b8NT0NVCaJgfHvBBjwx/VtFqjTH/fEjO9w+M3t6QMr0m/iGsm7ff/QzTJrm7WW2YvMul5eZjCUc65MpvDVf7c1m4es1J72D8X+a7uqQcr2HXU19MnAy/hUPhx3VOpqGhMH4nhBlwmpbVnw5wOgHdgiC6NInZ3rml0yxa2EyEG39nmddFaY5IjLd55vODgCZUjCmOdfe8DHoJrJwUw0AK3fWoH3LEsfmWYAhtK587kv8/Lj+OKBXe8c1m6qqsSdgQanMC9JIdfrSCvzmv2nNoE+nVq75F8D9XLxcoS2Ovf9DzzqkmF1bNFiI2f3nyzXYt0tr3Pe223HjwXfTmpTLm9EaHcL5Ya40I2DL0Y/D8oUwordc9r1IplKOzkJlwrSO/O3D5fjju0tc50VEs5e7LHfev/q3c7lArteC19SnfKM/+FGXTOV1DR4zHHv7zF29DYfv19n1XKtCamj1KVaaJuMdXKbz2q+8DVLc/M1yQAFpRK6FazGovO7Fb86/a+qTOOjOqbh58oL0VsSmsFpRsQtTvt6IX/3bHUts9F3v4Zg/fBipLhOlNRUXP+Ec2XkuOvSIrJDJt8Vs7PCpPufM8KH3lirTBXlCWYivzco7xWzH0IuCvCLej/oko1IwjanMNdatem0HIeKnbXht+CaSa40omeKMv5FMNaI4NAzA3YYvfPxzrNu2x2UFCOvIlEyx8ruI8x0wA23N8EetS4uQYm72jgpAAQkil0YUwygmyO3Xmrh9c/56V1rLjFec4favM7/dgn4T37D3wwnC62ObuXKLQyik91aJ/nxSzNgVUpMLWjCrwqoRERySSLSvZ6IRRWHxhirHXj5e8yBAsAs84L9DaxhybepNpTjjjtZry4MgHvlgmTL6QFRUVo9tu+tcgk6cc/NjVeUupRdilKj3QaSERay1yRT++uHySMFTmyoFI4jcMaSyzzOscKuqqcdOc7MtsZEByGgEDwAvzzY8wz5bsTmjuoo8IJiPslmbl0p5u/Jm+7zl60lxjpnt0EUNxUuz1iiPb9lVi217gju4bEe7Yjy4XJBkzli41yU5I0H5x3eXhBYOfqiK3rBjT8Yhc65/eR7eX7zJdTwuDQ4w6mxlF9U835Rp9oKoR/syAOHXiqhQNejFG3YoQsZ75/Hw+0sd9bBMOpl2nFadbvjPfHunUj/Cmh9sp4oMPi6G9+g57g2/VJO33AAakcxrc9e5jhEZ8cQ+WRYcVN5vjigMP3pqVlbXB5FMMTq1yizoaF0qlXPToR8q0+jl/5yFj5bEu24pzmgpjHQfsXW32szdHGn2guiWUwcDUJjRzIF7mAGpqrPZsqtWESxTVrvSP63Al1YSa8V5ph2R2Paf+nSVd0KTIPV+prnRXNqpInqdmL0/yjg00KBQ+Snm2DuZTIjyRhu7/T+ZykyrAYx1RPncX6ehPFTjLCfF6Xmo/yn6neZKsxdE6ZhWzuPZNh1DhZa1LLmM9JG55q6PttptrsSXNSJr860oBIUvAYJHbef+3YhybD2vHdV1uHnyfKW7qhcMvzVGobPxyT39S9V9r9y8Cx/mbJV+eKK42iYo7c7eGEmmOOM5kPoUx2q2ikq2828dW5WEShfrLXK8c05NhWbvvu21l0i2o5g5q7dhsLm3ileeqgZqddQ15jYL8oh4xB3hJmlFIaeKQSYT2nxg1sdamNi7UytMUOyfoqwTs2fcsVi/VVZrstUhnAMagig6TooNd/aMy8qxQpXizIVJXTI701ym29hbZPtswjoSxakRiaa5QqIABJHxv6sjzvJd/+Ed9xoYuf34BQZNb4QVviLVdUlUVNWgd6dWjuNhRlBhR1nytxulfimf0dx1WW+XkV5lXlOfVAaqXRawBqihiNIBytt0RyXXzhnJlHdQ0yAydVaw6H/zWxlfC0TbqltF2GCmq30iu0dFNM0VEgVkmlNrRFEClwbhVYaIdURc+xKWS56YaW+hLQrSMNpOWI3IZV6M9FFwDucE0vk+6bEdtsqjKR/E2aYs+kiDD4va+hQqPTZgi8Lb1xylPJ7kzIXJjj11ee1UvTw4w+LnQXrKAd3t38srwq19C0Mut4lpzDR7QWSZvuQXLK64jguXr4KiTVlmDutcfVIdoVjFTDMCgHtxbnDjDT06zKLn8HNWiAMr5z0BeyXlnQA5NKhb28hZ+kU3OOjOqZHzEzmqf2cM6tYOt5yyv+tcKoSzgpen4h2vfx27t2QUsrTsYd8ubTzPDczgHYahEM1yQAEIoiLTK61O6CBnrKjEn6aqV/ZnM6KXL/UK/7J9d53tiZdixr1vLXal8yOZcn7e4TSicF+le71VBDd35HaiVRWPqzESpA91ah3dHTqOzde8ePjCAz3PJVPBG9f96oQBnufyOcDPRiPq2q4UV47Zz/N8tm73XljPOpPBSlOm2Qsia0dJMST9AiG68iJpDU42H04Y0xwBuObFr2wzUjLF+HhZuEWp6XydAiKMIAobOVgewUbp9JlzJySYgSc/MUxyjX3UGDS1kEn1cymIrLxV3n5JDvaa85tLeX9R/syl2WhExwwo912T1iKmublThnV3/G19y13alcWSf1MhdkFEROOJ6BsiWkZEExXniYgeMs/PIyLv4VgMWCMXLw8ceQ+fbDq5l2c7vZ9UWRGRY1+aMCNOmRQ7xUWcprlsfDoy2fY5Exq7GT1ojiiTNparETjgr8GlPOKriSR8hORMIaBsQ5ONRlSUIF9zaFxOInIZ1veTacSVpkqsgoiIigA8AuAkAIMBXEBEg6VkJwHob/67AsDf4qyDjPWiRUHk92FlI4ge/sC5uZ0qr+176hwrpuszWKchd/bhNKJwH2WYeS4vnvz4W8d+NIVKbjSi3BkvSs2Rv6rayRCDC1WfOVyKKN9Q7NO5tf07G6+5BJHvOqSykty8D6vO2a6BamrEfbejASxj5hXMXAvgBQCnS2lOB/A0G8wA0IGIussZxYW1FkAMQOknbOK0+oT5Dhau2+HSyoJISi6eYcoJGwnZZZqLoBNNkTYc8+K4QV1C59kUCWpDmUzgZ7oVQxDtW5b4rpdJhdDYVRqRn5aUS/rulfYuzEY7L06QrxbaskU8K19kYfn5CkODLNIaUVb0BCDu07zGPBY1DQCAiK4gollENKuiIrMV82mNKP3C/ZpnbbauNgK5msuQI26HKSe0+3YWGpFMGzOcfasWRY7jjX2Ox2J47w4ZXRd0f5n0j7maIwpyRqkP4TWnClMUdg1O3IjzOtkIoiT7b7/QqqTI81ykcqQBotX/ZBq5vKkStyBSvTm5NYRJYxxknsTMo5h5VHl5eUYVsmy5tfXhTHNXP/9VRuWoyNWamjP/+klkr7mwZgq5ztncwYCuhvtrl7aljuOZdBCVMURjjkqmg9Kgu8ukXeRKIwoiTKw5lZD0E5zXjO2fdb28aFGcFhDZeHDurk36riMqjck05/VdNvYYhHETtyBaA6C38HcvAHLkvjBpYsNSr62QOoD/iDWuWGU/eHwGnvh4ZSx5ychmtjAhWPz2zXHkld2KVgeWnbtMGj025KK9C0b3yfjaTLWQIEGTyd3n0mvOQtX3zVhRiWQK6NymFEf176y8TlU1v/qeMLhrqLIzQfRmk7UNFVeO2RdH7ue+r9013lukFycoNq85r++ywORQ7ILoCwD9iWhvImoB4HwAr0lpXgNwiek9dyiA7cy8PuZ62FgjSadGlPuO8JNllXhe2r47Thzu22G85sLOEcX4aKwPuVRyg21IQZTNBx0leKlI0DPM5PbzpRFNXbQJu2vrUZTwFi5K05xPfVVehUN7xOPc0KI4nXeY7+Kyw/fGoxcf5DpeVVPnqZVcd+LA0NuNLLz9RHx9x4me572+y1xE52jMxCqImLkewFUA3gGwCMC/mXkhEU0goglmsjcBrACwDMBjAH4WZx1krIlYce4nzM6ZjR3xGwtjTg6jEX2+olLhrBCNYwakTajWd1xanD+NKJvPOdN5jkAzaAbSPpdecxZed/vfOetQRN7uzCqh42daUp2Ky7lB1FTWbdvjme7mk/fHvNvGobxtqfK+h/fq4DmIIYTf96p1aTFa+Tg2DO/tIYALSw7FH/SUmd+EIWzEY48KvxnAlXGX60WJQiPKpSCqrU9h+tKG3Yog1DqiEBrReZNmuGKaMQNffrcVw3q2D2W3Fjsrq1ryR9tUwtznyhyWiRwuyZEXlVdVfnfGUNz66gL770SCYtOIVK8/rkcttrV/C7vnDurWFos3VNl/EwHtykrMsp2Fv/7zI9F3r1aeLtQJIpeWH8QTl45ybWL4jx8ejDEDu2BPbcperC2WUUg0e2d1lft2TQ5jlb2/eFPOd80EnC7AYTxswjorfCdFEl66qQpn/fVT3PPW4lACT9UByZ6IFx3SN1Rd4iCbHVtz5YKciddgQ7hDi6bIw/fdy1m+z7oaVd2iCnFVanFQtG95ayy586TAfMR2vp8QKy6Khja0Z3u0LStByxZFWHnvKcr0LYqiec317+IM2fOfnx6OMQONZQwH9e3oSt9UPEvjogAEkVsj+rYyvrDtMmL4oIYi7BqhTNhmLr79eOnmUJqMuPbC+pjkqBZ7l7fGn84bEWMt1XxveA90M0Ol9O7UEv19gliqyNVSjkz6GNksNmZgZl6kURCdCooEjWj/7u0c8fJUJsyoc1qq+TgxiwRRqEHF94b3UNbLT8GIqnyErYtfGeLf1jcjLhfI5862+aDZCyIr1pzoNTcth9tJL69omD1xxHaa7QZifmw1d4ytqU9Cnmb68ZF7u9KLcxlegojQMF5BJw/tZuuNJw/tjkd+cCA6twkfcDR3prlM5oicdbnpZHek7GwR30l9knHGiPTyvgSl65Ag4DShw4/qNReWREhBYnHMgHL07pjWoup85kVFwRfVDJYgIOqUnVyEWKZldm1bmp4pKbTtIJq9ILIaTC61BpGpi8JFF4iTbDcA86NypyGItu+pwzsLNzjODe3pnmgtETog62Oqq3fWz88b7dgYR/pElO70CRjQtS3euebo0NePH5qzgB+RkTWMXAhyMcv6VMox6q9PMT4xg/MuXLdDGtE7u5HLDu/n6ertxc5q95b0oslPFhYq92mGcy7NL2IJefz24+JD+9r18hNeFx7iXjIgpxdfZ1tzrqq8ban9zAtsPWsBCCKzAYRdR5MtDSXwogyqszHjWAtJt+6uwzUvznGcE8OpWOysSXco1qO47Ih+jjQE71FonJO04sduucOWRlgRf/aBPfHBdcd6nh+lsO17MXrvTvbvHyg6KhX/uOxg+7fbay6m5+TRjspKihyd+qrK3dgsbMAnuhfLQvK204ZEDgr6zcYq1zE/r0VV+B1mDr29t0jYNpfeTNP7mmMHluPuMw9wHXeZ5oTnN6pvR9x95gG44/QhuGH8IABAl3al6NiqJFS9mgPNXhBZjbmhBERDESVe2TkH9Q5OFIGzRvbEtOvHYGSfjo4gk4DTNGnZuQd2a4ux+6fnG4i8R/RxDvQTRC6BXRbBtk9E2Ltza7w84TDl+Sir64f0aGf/vviwfspJcJGV956CYULgUNlrLidWQ/OldGtXhn3L27jmQdid1KhLgnDuqF4AgJamoJdNc7LzQ4TqmL+d+XkJqUzWW4Ud+1j3T0TK5/+bUwfjj98frrxWFlzyvV14SB+0LSux23+LogS++s24cBVrBjR7QWS98OYWuymKRhTTInCb4b07oI+pDb3wk0NxvBDE1KERmaa5kkTCUQeCW0DY52LUiBydpfk7kxHzqH6dlNGko6yuP3Sf6B2x2Hm5O7Joz+kXx4cPq3Pc/sb7lO9PfGdiR1ycINxz1jDcd/Yw2/Qp11fcdyfspm+OOSLpnNftewkif2eFcM/SGlgliJSegpcfuTf2alPqOq4q36tIK1vtrNDMsEYvuZxHiYOThnbL6nprolNlLot7TYL4EXZpW4Zrxw20/95Tm3YKsQRRQlqV71edOEf6CSI7/JFYZtd2zs5C/jss8kJdP04cEv79WtqP33uL+px+ecIAOwitF3KWfp5h8mR/UYJw7sG97QGKl7PCBaP7IJEgPHpR8DZkYh6yZdLLnd2rXLlfz+STsKz7RNG/KTlSgqdp2qx/U1lrFxfNXhABxkuftyY7t+pfnTAAt5wSv6eSRVQvI3nLhZYtinD6iB7KziYobzEaQhhks4j4Z3Wde7uNIsXkrtdnFqfQJBLMKUJH8PKEw3HPWWk7/kPnj7R/TzhmX+/MJOIKfNlN2o1zwe1GSBjyyT6TEDCf3HCc/fvB89QmJCDdafuH6UkTJrKC7TNiHh4/tDt6dmjpW1/VHF9RgnD4vnvZywrkMuLUqC0evehA/Pn8EbY5PEHOun0y8Ti8f+0xjmsuGN3HsQ5KfkRe7dyqfyMfN8dOwQiibGlRnEDrgBFlVvlnaT/bVFUDgseqdZ8OZeW9p+Aw037fIeTkqFxV8fGKi1fTm3w5V+UbczfqL61tWXzPWOyUxDr27tTKEQz1kH32sr28DoswlxF1db0X8uuxNC1ypMnea04UbAO6GuYxr3kfIGAAQ2I692mvKO5BAkxEbLdLNxnODMvvPhn/+r9DlemjzJtGeXzjh3bH6SN62sLBsLKkc+jZoSX2KXeuUbvnrAMw7ddj7L/Dvj9rkGc9v7DfZFOnIARRHHsM5WJuWGRTVU1wogCISPkpBglie/4kpFbmdkVN/32qMBfQta0x0i8tLnJoUX5zBLd+b3CgCSksCRJG4iGvifKes4naIOI1UBCf615tWqB7+7TmFEYQyZoWOX67Mzj1gB44uF9HXDlmX1f5fnVTpXN9c4qBR6AgEvIVNe04GL139Dk7yxGjtDgReSAgb3TndesnDO6Kru1KcfkRewMAPr/peCz+3fjIdW1qFIQgiotcmm1rfeLflbcNN4exaP0O5fGg4J3pDz7c1yV3IOJfDwoREx6+cCT++P3h6N2pla2dWHMEXs+yXVkJfnqs0RFmqyUmHOuI/O8tZDIHUeaI/AgTw62kiPDZjcfbaVUmqDeuPtLx9xkjnftNBi0Qbd+qBC9NOBy9zEWhvmFxAuove6mmPc7SxyyT8O/Pdrs7A9GDzkb5PgcLXoxh+fX4gbhmbH+cckD3yFYW9yBPfX1521J8ftNY9Dc11tLiItc2Ks0RLYhCwgDWbosnNFCuVqJX1yWVJq+gVeBWpxb22/IzM4jrR/ZqU4qzDzLceq3DVtowZhS53m/94qhwFbTqBbVJSIVVH6+5F9XRuExzXh2uw8XXrIF1SNVcBndvh86C15b8jDOJIOCF0yPRnVD2Uk1rpum0t5yyP6ZdPwbnHaxeVxU2ekFD7NUEGAtPrxk7AMVFiQwEkfNm8rSrR6Ol4AXRBaPDr7HJRJ1XMUKxBXWJe6oRuwAAHD1JREFUT6cWdqTnZSoKa5oLi9uUFJyBGB4mCEuYyvdtzWuIvHbVEZ75eM0RqcsMrpdMXKY5zzVV5P5tvUuVwCQiZ14+nmJh+lF/jSh9TqkRSbPt1juVIzL0UXh5+uWr4v7vDzPLCJU8FqJ+M7JGVGjRtYMoeEF0z1nDlMct85AFwTAlfHNnbuy1JT4tO+yaAq/V7EEfdNRPws9rzgu5A/W7JeucvF2H6ja81m3Y6dlf05HLjNLBBJnmnvnR6FD5RNleAbZASh+65LC+2Ke8tXhaiUrD8mtbcr0O7peOJCHmpRRE0rsb1c+ILHH8/u7dWQHgjtOHuI6F6ayH92qPDi2N+IFBWnacfX9U7zx58KblkJOCF0RetG+p9lbxCoWfLX7CImyQzJKihN2hPn7JKPu4/EHLq7+tjyTst+Hymgt1jdP85yuIPI6rPn6/fXqMWHPWb//62Z0YAccN6oKRfdxaa5SyAeCo/uHc4j1decXfJGmUwsk7Th+K96891kyXPi4/x6hBROU0dwmha8RTqqYrh9Qa2rM9vr3nZM+lAt8b1gMj+3TAE5eOwr6mUB2psBzIJJntygR9myUNsLlgWLRG5KTxvJlGhufK/xjy3l3r3g9JFTvLrkvIfEX1v2fH9BoNWcgN7NYW+3dvZ0dQjjy68/GaC7rGL+Ukc8vmKCaWUp99YRIkzv34I5b55GUHY/LPnCa/335vsCJ/I9eg9TBBhIm7l5Y/5DonImp+Kck8FrXt+gUaDYpefd7BfTB2/y6OY37trGPrFpj8syNw/P5dMd5c3B3G9JlMpe/Tf1CSu80FNdmjBVFEMhnIyGtjxJ0i0/n6aEQhV7eR0PEmiPDm1Ufh7WuOco1YiYyJ/4cuMBZyWhsFbqqqQe9O7k61n2THd3nNRTHNWeskFGmsdVpR1oMErf4P6w2nWvgqMrJPR3tB8ynDuuPOM4aim+lO3bo0O68mL21Ypd1YSb1ux8+06BcyJ0y9vOaYVPVv37IEj196sOt4GKIMRFIptrcZUZmmLTdoZv95WDEEUUPQEBsdNiW0IPLAqzPMZOV2mG26/bSKSB+m0PEO7tEOg7q1c3u5Sd3QFjPCNgC8+8tj8OIVzgWDZx/YC5celt5VNSg/wO1yLvcRqrkJss+5TuHes5wuvnefeQDuOesAX0FkaERm3iElkV8yq17d2pXhokP72hpotuZarz5JVee0ic5LeKWPy49RJUj8mpZchPiexd9+bffcUb0cUSziJsmMWvP7Ugmazm3T+0/5vadHLgwOORQnWg45KUhBFGanzrg8cGbdMjbUJldiu/z5cfs5zslzRNbEdNj85BGr/D2K/UhZSRHaSBpcXTKFXwnx5IImXuffNg7Trh8jlSnNEflUWnWuQyvnhnZnjOyBC0b38Q9DQ97rlWTCmPDkNC1bmBEQsuxUogxurKRhLnHHV3PMOgVeH3Z7bb9QR/edM9wRxSJuUsy2Y4Rq3Zk1L8QMtGhEprlMQjQ1Z2ITRET0ByJaTETziGgyESlnGoloJRHNJ6I5RDQrrvKjcN7B8W2L0KdTK0c0AZmSRMJ3/kfFL8cOcPwt96V+k67KdUQBGowsJ+X0KTZMLZYADFpo2LasxO6kLaxryEfapM+ppEe4NTFH7tfZjszgmCMKUoiE8C1BWElatcjtglYAOEDafFC87306t3YtBQgr08Kk8/OOtH7226sVurR1RnBoSFoUJWzTnCWIjhOiwVsac4rZ1zTX0GiNyEmcb+ZdAEOZeRiAJQBu9Ek7hplHMPMonzQ5o7SkCNN/PQbv/lK9W+dZB/bEpYf3w+SfHY69WvtvLd2zQ0uc5LOTZyLhnseQ7dEPXTDSIawSZmBH68OXNaIwjg1OG75UJ+lyefGhWxCxI/OwkZBFrM7WehaWgBC1U1lbutpn6wKvjvS+c4YJa5bStrkghwrVc3Olkfc2Mle8hzG9+uEn2A8yN9+znRXsNgG8f92xePVKp1OFc14pu3qF0YhOPiB3cythBgUtWxTZ4YRKio30TwobCoqCKJO9inJGI6pKYyA2QcTMU5jZ2oxmBoBeceUdN2XFCfTu1MoOoyHzwLkj0Ka0GCP7dLQjA3hRlCAM7ekdLqQ4kXBNok40d2G0GD+km6uTe/ZHh2C6GTRR1li89tRx5uFtw5e/bzkci/y9ppxyyD1SVtZGXbeWUrgS0U1aniMSOw75+YQN/xI2soJcB9+8zLKtdUR1WcYyjGLas96l1/ofUdv1M0uGKVIMkvrU5aMd9bQWd2eyz1IQUcRnqxZFdngslbOCpSWl2HudXT7Q7ttOcvVmLgfwlsc5BjCFiGYT0RV+mRDRFUQ0i4hmVVRUZF0pa98Zv9hNr//8SM9zKoiAvnu19txxM5Fw267lD6I4Qa55pESC7I6OmXG/sPbndNPtWoniK3Z7Pzn/ljtS+byskWXiNWeNWq35BFUnKZcr/uW3JgYe6fyifLuuU6z8l7H3V7IFkXEv8sJbAK6da/0I0ylZd2Fp6F7aQscADd7C9l70eTyisJfDGY3euxO+vuNEHB1xC5GoyIFbZVqWFNvfs8p6IWpEcYVkigMtiJxEejNENJWIFij+nS6kuRlAPYDnPLI5gpkPBHASgCuJSG0fA8DMk5h5FDOPKi/PvsFbHbufIIraPoIaVHEigTvPHOpwgXY7D5By0aqlETAD5wia2Q+P6OdZnsrE5J4jciKbllwaUcoKuWN11v5zTsp6eW4LIHhi2aa5tFDwDFsUIhJFIhE+mGl6HOCd0HoO1jjCakfy4uf/XXUk/vPTw13Xz7zpeHx16wmu4/4OF86/n7p8NO48Yyg6eQicSRcfZLc1PyEcppkH7QrbqkVutkUZ3N2wMAzs2hZ/DdhAb/zQbjj/4N645ZT9ccXR7r2kbEGU4ox2580VWgw5ifRmmHksMw9V/PsvABDRpQBOBfAD9vgKmHmd+f8mAJMBhIuDEgM19cZ6mTIfL584AkOK8esSBIwZ2AUfCl5kKlu13z5CspAKNaEu/Ja1rSCNSOWsAAimuQw0IqvTrq5zLuZVTYCLjgPiNuRhaN2iyCHk0utvwr1XX43I2ujPTFTethT3nHUAnrhslCNU/wG92is1ky7typTHoziz9OjQEhcd2tfzfNd2Zbjs8H6B+YRa+5VBBI04+N7wHpj6q2MwdnDXwPd2zkG9UFyUwI+P2kc5aLGOJZnzvqD13FHpwaTWiJzE6TU3HsANAE5jZmWYaiJqTURtrd8AxgFYEFcdgrBMKH4aUa+Owavkn758NIaYYeRVQuGO04fav1XnVR2PysW72BZE6WOWELVGjTInH2CsShdHzPLeMLIcdJ9Xm+bsnTsD5pxUpAVRypGXiDViFU/ZWlIIC9v71x7jcPMW9yMKIkwy6z2I2tgFo/uge/uWKCspwoCubRyaa1gOD7EZX1gTo+MaxbH7zh6Gd67xNEI4iGMzvkzZz3Riyda/oNSaI0rlf47IYWbUcshBnG/mYQBtAbxrumY/CgBE1IOI3jTTdAXwMRHNBTATwBvM/HaMdfDFGo2XeQSrXHnvKWhbFrwj4tEDym1vIZUACWrw4sK6cYONIJAq05yseSy4/UR8aZp2/nLhSFd6ALj2hIGY+9txjg5ZNuPIprSLpRG23OHIdXOPlIO/KmvOpGcH42NUuVVbNnzRnGYH5wwQFcUJSu+SKWhUoT3H7OCo3qSkOSKZKb88xjGXF5YEkd0OZNI7dkbIL+F9zbkH98bAbm1DvTM5EkM+1r5kqzmIc0T53tfHOX+Zt2o0SmIz8jLzfh7H1wE42fy9AkD0LzUm0hpR9vLXUvPDLFaVETWiSWZwUr85Igtx51KvdpxIkGvOonObUiy4/UQcfs972FFd7xI0h0ieT/L8i60RwZojiT5SHj+0G5778SE4zCwrfbvpi61Ow9qJtGvbMiyEerM/kXm3jXN0WGlzXPj1QWEiMNimuZh7ETE4q4xVVn2EdmabOH2EsDwfp6Ipd5Zty4pRVV1va9nJFOOuM4fi3a835q1O4ieuTXNOGs/sXQNgNYRM9pGRP1dLq8lEEKlcj68VIhfY6QKiBlhYMeP8aFNabF+j+gYmHLOvHUvNy33brlcG7ttEhCP265werdvH02ksjejiQ/ti0sUH4awD0zuM+mkE7cpKlNuLOwNzhqgkstOIsuGiQ9XRB4o85gnDkG10EOd+TtSgpjmLhE+b9cPS8tuYcQBTjLwuvAWcQl/LISe5cXtp5MThPWNpRJl0ECqPr/0Vcz5WR6CasBdzsD62oJrY3mSKr2DiSYNcx+TrvLSLTOLvDTMjBhy1X2f86/PvAKQHCIkEYdyQbmbmZh0il2BpRMEmNyCcd11S8pqLiwtH93FForCwNaIoi2bJKex9k0Ywt+Wj77TMwHLZpwzrjoqqGs/rrhs3EBOO3Rc15pxkJt9p3DSCKjRaCkIQ/f7sA1CcSODal+YC8N+Ezgv5iuKitO3Z4olLR2HGisqM63n8oC7oIW0pMOPG49GhlXveyuFtFrDI0e9aFbIGZM032c4Krt0mw+UrMrx3B8y/bZxjTk61pUMmnZ8ocMN++ylbYPm4b1vOCjENZ1//+ZFo2aLIUwiJZSUj9GKy96EyTQjTnMhBfTuicpd3x58r7C3SpWceFKQ0kSC0KyvBlqQR0DcTy0XcOJxwtLeCg4IQROcdbJg9LEEUh0Zkzd+IDfz4/bt67kAJGDtpPv3ZKs/zT1zmDptvbTUg44yEHI6wrsyWCapnh5a46rj9cObIno7zbtMcRaqHhewY4hc8MxOvMSDtfeW3JbWI36NJxTxHNFSKI6fCNs1F6EiDoi8A0bXYuOfFwhK07UUQVlvVGlHjpiAEkUzUIKQqLM+4KB/0HacPdbh2Z4NKIwp9bcB5scMVIyd7Rh8w/87aw0kxQMjE7GcL3ATh4kP7YkiP9nbMNs9rQnQSadNcw3XKVllRNCLVIEkmjNbkvqbhhZHfvGYYLNNemPt86xdHoWOrcJEpMkHUPjOZp27OFKQgimPLYEuYZTpSj5PQ32hIDzJrUveaseqgo/Ll1t/ZWqzCBE995kej8fU6f086tk1oxr0GCSEgXNDTpC2I8yCIomhEYQRRBreQjwn2dNvKrHCrww8TE081Txsr5uu4YfygvGmYjZWCFESyRvTFzWMjRyoOWivUb69WWFmpXNcLwJgPem/xpkhlehH1Gw1K37JFkWfsPON6tddcpp3Fq1cegU+WbVaXZf5vCZej+pfjqP7hwj1FGcHv3bkVFq3fgdY+YWusQUfYgKtxEEa7kbGaZhgtKsqt5MVZwdKIMry+tLgIU391TNbbuav4908Ocy2V8CPMYKdQKUxBJI1G5N1EwxAULuStXxxtRwVWoZoPioIzNE68pjkvvLq1bDuLEb07uPbVsTh0n73w2tx12Lc8eDNDi/R26cFprW0o7jtnOM45qBf6+QQrTQc9DV2VrLGdFTKYI/I3zVkafRaVawAycYSR2S/ERpiZMHrvTpHSh/XgLEQKUhDFYVoJ2h46yBsqW5xrPMJdEzXumut6y7Tnqku0ekThgtG9MWZQObq3jz6iDXrPc35zgr3avk1pMY4b5O1oAqTNZA0ZKiYT05y49uhvPzgQm4Wt4C3CvquHLhiJgeZ2KQ1pkrSwBGZzWAAaNgBvIVKQgigO4nB4yAby+B3q2gyr7rXbaXpOJv5nQkSRhZA4R+SHvP14EDeMH4RWLYrxPb9tOGImkaWzwkkeG9fZJs+AvE4T7jUfLd4e5OSh7LgZYe69Nbh7sLdkoaEFUYbkO4AiKSRR2AWtmY5sR/XrhDfmrUerEmezSQnrdhoTcY/gO7RqgVtPHRzpmgsP6ZPVzqDFWbhv+2pRBeKs0Jg4dVgPjOzTMSfzVU0dLYgyJN9OL+K8UFQBkOk3/cfvD8dVY/ZDe2mBrbXr6rXjBmSWccyIsebyzd1nHhA67dRfHeOKg2g5RkSJNZeJOa+xku38Y2NDCyE1WhBlSWP41COb5jIsp6ykSOniWlyU8PWya3BCuqk3NlST6oeZW0SIcfeCsDpvf4Uo+rPJa/TtpvUqNREpKEF0xdH7YOqi/EXfjZOsFrQ2sQ46U5rDbfbu1CqykPfaUFEko2eTR9NcYzP7auKloATRTSfvj5tO3j+m3BqRs0JEr7nmTtp9uzA7r6IQc0QZyaG8zhE1fNmahkPHmWiqkPJn1EubNY1hjigf5GqOqCkuaFXRL2TcQU3DUVAaUZx0bWcsgg27yj9uHEFPC7TDDaJQIxyHEUS2ebaJqMlxabdL7jypYAcojRktiDKkV8dW+OzG49A1T5ttOb/LcF9Wn06tsHhDVbM3WRX6wsEwIX4yM801/ANNeQXazRAdbLRxogVRFmSy2j8uMvkun/nRIfjqu605jfjQGMg2gkRTx/aaizvoaaYVygbFlvKa5oceHjQjghbfl7ctTe982oxJb4yX54rkibZlxvhS3mRRJCP37Tw8Tx0otDCITRAR0W1EtJaI5pj/TvZIN56IviGiZUQ0Ma7yC41MYs0VGoXipi6zX5e2+NsPDsR95wzLd1Wyxopgsm+5dzBaTdMnbtPcg8x8v9dJIioC8AiAEwCsAfAFEb3GzF/HXI/YaKxdWWOtV2PgoQtG4m8fLi9YjQiAZ4w5G9tXIcoW5A3/QDu1boF/XHYwDuwTvKeUpunS0HNEowEsY+YVAEBELwA4HUCjFUQ/G7Mftu2uw8WH9c13VRwU6GA/FKcO64FThzVcYNKmSFPZGA8Axgzqkp+CNQ1G3HNEVxHRPCJ6kohUQ5ieAFYLf68xjykhoiuIaBYRzaqoqIi5quFo37IEvz9nGFr5bJiWDwrVNVkTD7r1aBoTkQQREU0logWKf6cD+BuAfQGMALAewB9VWSiOedoGmHkSM49i5lHl5flZr9No0T2JJgsKdf5M0ziJNMxn5rFh0hHRYwBeV5xaA6C38HcvAOui1EFjoPsRTUOj25wmV8TpNSfOjp4JYIEi2RcA+hPR3kTUAsD5AF6Lqw6FhO4TNNlgb4wXIbKCNgdrckWcEx/3EdEIGKa2lQB+AgBE1APA48x8MjPXE9FVAN4BUATgSWZeGGMdNBpNCJqSs4Km+RObIGLmiz2OrwNwsvD3mwDejKvcQqVI4ZvcRMKGaRoBlnaj24ymMaAjKzRRWghbleuBqqYh0O1Mkyu0IGqiFBfpV6fJnEw2PtWedppcoXszjaaAiWKa02JIkyu0INJoChCt3GgaE1oQaTQFSFOJvq0pDLQg0mg0odBzRJpcoQWRRlOAaJmiaUxoQdTEaVcmLAWLskxeU9CkIyvoNqPJP40rpLQmEgtvPxEJInyzsQqAXpyoCY82s2kaE1oQNWFalxqvT3cpGo2mKaMFkUZTgCQI6NmhJa4Z2z/fVdFotCDSaAoRIsInE4/LdzU0GgDaWUGj0Wg0eUYLIo1Go9HkFS2INBqNRpNXtCBqBuzXpQ3alRXj2nED810VjUajiYx2VmgGtC4txrzbTsx3NTQajSYjtEak0Wg0mryiBZFGo9Fo8ooWRBqNRqPJK7HNERHRiwCs2fIOALYx8whFupUAqgAkAdQz86i46qDRaDSapkdsgoiZz7N+E9EfAWz3ST6GmTfHVbZGo9Fomi6xe82REdb3XAA6fohGo9FoAsnFHNFRADYy81KP8wxgChHNJqIr/DIioiuIaBYRzaqoqIi9ohqNRqPJP5E0IiKaCqCb4tTNzPxf8/cFAJ73yeYIZl5HRF0AvEtEi5l5miohM08CMAkARo0apbfb0Wg0mmZIJEHEzGP9zhNRMYCzABzkk8c68/9NRDQZwGgASkGk0Wg0muZP3Ka5sQAWM/Ma1Ukiak1Eba3fAMYBWBBzHTQajUbThIhbEJ0PySxHRD2I6E3zz64APiaiuQBmAniDmd+OuQ4ajUajaULE6jXHzJcpjq0DcLL5ewWA4XGWqdFoNJqmjY6soNFoNJq8ogWRRqPRaPKKFkQajUajySt6PyKNRhOau84ciiE92ue7GppmhhZEGo0mND84pG++q6BphmjTnEaj0WjyihZEGo1Go8krWhBpNBqNJq9oQaTRaDSavKIFkUaj0WjyihZEGo1Go8krWhBpNBqNJq9oQaTRaDSavELMTWPjUyKqALAq3/VoQDoD2JzvSuQRff/6/vX9x0NfZi6PKa+c0GQEUaFBRLOYeVS+65Ev9P3r+9f3Xzj3r01zGo1Go8krWhBpNBqNJq9oQdR4mZTvCuQZff+Fjb7/AkLPEWk0Go0mr2iNSKPRaDR5RQsijUaj0eQVLYgaCCLqTUQfENEiIlpIRL8wj3cioneJaKn5f0fz+AlENJuI5pv/HyfkdZB5fBkRPURElK/7CkvU+xeu60NEO4noOuFYQdw/EQ0jos/M9POJqMw83uzvn4hKiOgp8z4XEdGNQl7N6f6/b/6dIqJR0jU3mvf4DRGdKBxvcvcfCDPrfw3wD0B3AAeav9sCWAJgMID7AEw0j08E8Hvz90gAPczfQwGsFfKaCeAwAATgLQAn5fv+4r5/4br/AHgJwHWFdP8wdk+eB2C4+fdeAIoK6P4vBPCC+bsVgJUA+jXD+98fwEAAHwIYJaQfDGAugFIAewNY3pTff9A/rRE1EMy8npm/NH9XAVgEoCeA0wE8ZSZ7CsAZZpqvmHmdeXwhgDIiKiWi7gDaMfNnbLTKp61rGjNR7x8AiOgMACtg3L91rFDufxyAecw817ymkpmTBXT/DKA1ERUDaAmgFsCO5nb/zLyImb9RXHI6DEFcw8zfAlgGYHRTvf8gtCDKA0TUD4bG8zmArsy8HjAaK4AuikvOBvAVM9fA+HjXCOfWmMeaDGHun4haA7gBwO3S5QVx/wAGAGAieoeIviSiX5vHC+X+XwawC8B6AN8BuJ+Zt6D53b8XPQGsFv627rPJ37+K4nxXoNAgojYwzE3XMPOOIPMuEQ0B8HsYI2TAUMdlmowPfoT7vx3Ag8y8U0pTKPdfDOBIAAcD2A3gPSKaDWCHIm1zvP/RAJIAegDoCGA6EU1FM3v/fkkVx9jneJNGC6IGhIhKYDTC55j5FfPwRiLqzszrTbV7k5C+F4DJAC5h5uXm4TUAev1/O3fo0kAYxnH8+1Y1DINJYQarQQauiGC0CQYRcX+FQTSYDAtiWLX5DzhQXDCIrJgW5lRsMgTFZlEEH8P7Dk5hjhXf3e73gZfdce978AvHw957uMRtJ4EnUqDP/PPAqnOuDOSAL+fce1ifhfxt4NLMXsPaM2AOOCYb+deBczP7BF6cc3WgAFwxXPm7aQNTifNOztQ+/3/R1tw/CZ0tR8CtmR0kLlWBUjguASdhfg44BbbNrN6ZHLYv3pxzxXDPzc6aQdZvfjNbMLO8meWBQ2DfzCpZyQ/UgFnn3Eh4T7IItDKU/xFYct4oUATuhjB/N1VgLbwXngZmgOu05u8pdrdEVgZ+m8XwnVCNMJbx3VAXwEP4HQ/zd/F75I3EmAjXCkAT30lTIXwhY5BHv/l/rd3jZ9dcJvIDG/hGjSZQzlJ+YAzfLXkDtICtIc2/gv+X8wE8A7XEmp2Q8Z5EZ1wa8/ca+sSPiIhEpa05ERGJSoVIRESiUiESEZGoVIhERCQqFSIREYlKhUhERKJSIRIRkai+AeP/xGts2ntZAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(ds_anomspt['time'],ds_anomspt['ts'])\n", "plt.title('NCAR-CCSM RCP4.5 Surface Temperature Anomalies at Fairfax, VA')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Make Hovmoller Diagram of EQ Pacific SST Anomalies " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will use `xarray` sel and slice to extract 5S-5N, 60-290 and mean to average over the latitudes" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
<xarray.Dataset>\n",
       "Dimensions:   (bnds: 2, lon: 185, time: 1140)\n",
       "Coordinates:\n",
       "  * lon       (lon) float64 60.0 61.25 62.5 63.75 ... 286.2 287.5 288.8 290.0\n",
       "  * time      (time) datetime64[ns] 2006-01-16T12:00:00 ... 2100-12-16T12:00:00\n",
       "    month     (time) int64 1 2 3 4 5 6 7 8 9 10 11 ... 2 3 4 5 6 7 8 9 10 11 12\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    lat_bnds  (time, bnds) float64 dask.array<chunksize=(1, 2), meta=np.ndarray>\n",
       "    lon_bnds  (time, lon, bnds) float64 dask.array<chunksize=(1, 185, 2), meta=np.ndarray>\n",
       "    ts        (time, lon) float32 dask.array<chunksize=(1, 185), meta=np.ndarray>
" ], "text/plain": [ "\n", "Dimensions: (bnds: 2, lon: 185, time: 1140)\n", "Coordinates:\n", " * lon (lon) float64 60.0 61.25 62.5 63.75 ... 286.2 287.5 288.8 290.0\n", " * time (time) datetime64[ns] 2006-01-16T12:00:00 ... 2100-12-16T12:00:00\n", " month (time) int64 1 2 3 4 5 6 7 8 9 10 11 ... 2 3 4 5 6 7 8 9 10 11 12\n", "Dimensions without coordinates: bnds\n", "Data variables:\n", " lat_bnds (time, bnds) float64 dask.array\n", " lon_bnds (time, lon, bnds) float64 dask.array\n", " ts (time, lon) float32 dask.array" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "eqpac=ds_anoms.sel(lon=slice(60,290),lat=slice(-5,5)).mean(dim='lat')\n", "eqpac" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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S+GPWIRf9mTEnsV+HDxcumEs+x+T2DMrURd+ZHSHD9bpwOn1IWY9mKfpWX2tdQOzj98gJQcp6NEvRX43ZbYU2y+ed+Mwt0mZ5yK9r3kMNF1oo87kMfc5S9Mu4yDqrDBHBHBmSxymVz9C89G00xl4XM3vRd5w6jlxydFLCFoOh4tSlfKfYGw+dp+XoRZTtEkXkHOB3gZ8CFPhl4DngbmAbsA+4XlW/X5x/C7ADOA7crKr3D0nfccag7qEcq0GosmGMtMcQ2SkKeUqaRi+G9vQ/Bfw3Vf0FEdkEvB74OPCgqu4Rkd3AbmCXiFwB3ABcCVwEfEVELlfV4wNtcJzopO7tp0o/Vbop37Cm3gj1Fn0ROQt4N/BhAFU9ChwVkeuAq4vT7gAeAnYB1wF3qeoR4AUReR64Cni4rw2OMxZTFwKLeJnHYUhP/y3AK8DvichfBx4HPgpcoKqHAFT1kIicX5y/FXik9P2DxbFTEJGdwE6A0887Z4CJjhOW1D3+udKlAQhxj8ZocJrstDiRewbwduAjqvqoiHyKxVDOOqTimFadqKp7gb0Am7dvVJ7jOCmw2vucemM0dv5CpVdXX1LVpSGifxA4qKqPFv9/gYXovywiW4pe/hbgcOn8i0vf3wBeHJC+adr4/7u7qBOKWAIy9cYkJl0n38dqBHqLvqq+JCJ/ISI/qarPAdcATxc/NwJ7it/3Fl+5D7hTRG5jMZF7GfDYEONTEWpBV/k63gA4ZXIU29B+6rnTNV9j7Rw21HvnI8DnC8+d7wC/BJwG3CMiO4ADwIcAVPUpEbmHRaNwDLgpV88dF2inzOrD2mebvCkwl3yOSVPd6sMg0VfVJ4F3VHx0zZrzbwVuHZKm41gjlQ+9Mw9C9/Z9Ra7jrBBk+K5mP1RnmqyOAHSJpTXmUK+LvvNjprQ37hB8xy2nD1X1pU8dqvqO75zlnERZrGOEDLDgcTA2Lvi2uba0JWHdloPXRti6cExi9Ppd9LEz/tp26fmq0A4R3lSibX3bxqaHzRuFZtpsU1lXznVlfPvX3tXKhrF2supTH1I5hLjoY0t4Ytoyl3yGIKSou7fXybQtj6HlZr1hjj2Msw4XfcepwIU6f6ZyD0OPRLjoO5PGytCd4/Rl+VYcqi676DuTxjdBcazSdoiz7QKttvXcRd+ZPG0erhwahjP3V8cefPVSWftZW169tCoe4ol0Vz9fTa/q+0Nt6ktdXizRts61bRzanuei7zjYn1gGOHJJv89ipNsmvSOXpJlMfcPoKcYh1pyEedHftOlYp5VtjjMU614fzjxY1sPQ2mde9I8ePaNTpn2LNWcobfzLHSdXzIt+V1x8nVj426YTgradh9kO71gkVo/PRSUMOUzKOs1MtQMX4znvssLdvOjLUTG356V1cY4Rg7svLsBOX6ZSd8bc37fN982Lvm7SUUWrby/eUkNgqYdkyZZVUotKKpdGpx99XUGH1jOPpx8ZS+LtxCV1g1Tn9uiTxvYI5QpaFXe/zXmhyEL0U/fIVkktFkNYN/QTsoxzLp/QuHg7q6SuE+ZFX47aW11nrRFaRxtBd7GPS4jeWmqRcE4m99EA86I/9pi+JUJtXjLX8psKbUQml47IFNjc8ryhGxrFem7Ni/6ccbHOB2ui63Wnmdj3rOxGaWkrUhd9J0usiaw1rO9MZoExymbzgU3m7oWL/gSo88uvEseYlW/zgU3Brj9lYbciADmRe33oElUz5puBedG3OJE7Fl0q+WplCCX2bTddH5LGGNdaYkU4rNhhgamEvi7TlKemztE6J4wQ5WBe9HXT9BawtF0x2+VhiNWjn1qPNHR+chMji0yxDNvkaUinbgjmRX+KWO8ND6Hv8I61VYvW041N7m6i11747CjpPPDSW6OnMbvQyk1Y7SVMVQyaGOqm1peuw1BWsCSuIYRynQh2vfZQMS1/P2YDUHftEA1CjDUB2Yv+WDPwY3ynTFW+hgzhWG0c5075oU7dAMTstY7RIx6adujGocv1xiyf7EV/DKz0HGNPeFnJ51wJ1asL6UE1J24P2Ku25qZZZrDoi8jpwNeA76rqB0TkXOBuYBuwD7heVb9fnHsLsAM4DtysqvcPTd85gaUFILmQ8g0otutsV7yehMNyWYbo6X8UeAY4q/h/N/Cgqu4Rkd3F/7tE5ArgBuBK4CLgKyJyuaoeD2DDZBjbrz5XpjBcZS0P1uyZMindVAeJvohsAH8PuBX41eLwdcDVxd93AA8Bu4rjd6nqEeAFEXkeuAp4eIgNOVIn7OsqQ5s4PGM3DhZEInWDaKEMhhArpn9T7Pm6dJffrTqnb0x7a5TrzWo+2+RxSL0f2tP/d8C/BM4sHbtAVQ8BqOohETm/OL4VeKR03sHi2CmIyE5gJ8Dp550z0ER79LlhY61y9UBu3ViWQepJ2N78TJzLNsaer0n3DTXnhIppb4qVfC7zaC6evoh8ADisqo+LyNVtvlJxrLK5V9W9wF6Azds3endFcuqFde3hdxXctmVhscxSNC4hRXwsn3HnZFJ6DIVg28YrrTZc6do4DOnpvxP4oIi8H3gtcJaI/GfgZRHZUvTytwCHi/MPAheXvr8BvNiUSKg9csvUxbZIRVd7rNkfkxSeEFUPUt+GYFV8vBEYhz7lbL2hCNH77y36qnoLcAtA0dP/dVX9RRH5TeBGYE/x+97iK/cBd4rIbSwmci8DHmtMZ008/SGiZ9XLZSwhXw3INtakUuryHUqo1+2QroFzpO1CvNw6Rn3m8/oQw09/D3CPiOwADgAfAlDVp0TkHuBp4BhwUxvPndWefqpJyzEYY9x+NY22aU6xvFORmxhZJlVZhu4o9Qm+1pcgoq+qD7Hw0kFVvwdcs+a8W1l4+vQm1+X2FqjrScylLF1wbXHmfl3rrdPHU2csb6TQ9WjMeml+RW7V8M5qZMmpCVaXhi2UN47lHlOq9LyBiE9ZTEO4Y07FpTMm5kW/iqmJ/Cpju02m3PQkhrD2EfY2+xE4jgWGPq9ZiH6Oq1T7ikaIfMUor1Sxv4fQxWYXecciVZ2Rqo5Kl/prXvTLO2dZEZM29O1ttt1gper7675TrihzIqf64sybsjtwnZfYkA2XlpgX/XUum1MitNtZyFW3Y9Bl71DHmSKxVt9WYV70+y7OmpJAhBxvrnP/sj7J2SbNKd13x1kS8nkzL/p9mdJwxjqB7prHsiAOFcfQvsOhsGZPKvqO9zrTJ1vRn9JKvD7E9JRJGfbVCYPfH2cd2Yq+V+p4pHRjDD084/XEcU7GvOjnOpHbJThX6EmcELGFqjx+crwPOdpsgVwby6nd7xj3wbzo58qYs/Ft6PowxArdbIUhQfxW51OmJjQwzTxNiZSbqCQhN4FpSxs//b55n0KZxRYi30DGsUaMepal6I8VgnRsquzPPU8hGVoWLtROTNYN6Zbf+vtsgpJDaOWgxNhEpY5UwuDi3o4hawm8jKeB1ThJhw5U7v7K5oZzNp9yJC7mRT/XidyuWPF7t+4Ka8GGHIgVYtgCZ+5/zcoRW3mti/TZ9r7EjBZqXvRT9PTnLCxzzvsqQ8NapySmaEx1eHUMLIR+Ni/6MQkRvCgUXRdO+QM2DEshqaeCl0c8PAxDAqyIrBU7cmcKaxBCkku9muN9mt1EbkiGVJgxKlvXgGK5PKghiD3XMKeyzBm/T8OZlehPqcJMKS+Ok4LYk92r4/cWJnFhZqI/Fl1XbPoOTyfo80Y11Vf+qd/r1Iw9qTpGem0cUVz0I+D+4f2pK6upivs65pbfnIj1TNeFB1l14lgK/Op3mupNtqI/By8W6z7zYxMyzykEtUsQPsc4G4tfXWNs1dWBddeqE/U+9Thb0Z+D6M0hj6mIsXl8E+seasuNgbXAgSnK6toLn13/Yd1nKzzw0ltrP68L0eAum04U+ojelBqmVHlZt3w/NjkOH6VohG5P1PBtPrApSogGF/0SqR+C1AJaNT64PD4FphyaoA+nhjNwbKFRJn9d9EuEErecl+9btm0o5QfIGwAnBiFF+sz9elI9DXVtF/2WhHwLGBK7ZIwIg1ajGLpQO9aJWUeX1x4q/i76LVk39BHq2jHO7UNVHq0EoWtT2b1hyIOzv30ktQkn8b9/YuwAx2HpUu97i76IXAx8DrgQ+Ctgr6p+SkTOBe4GtgH7gOtV9fvFd24BdgDHgZtV9f6mdHIIrWxBEEORS15c3PMmlshaiGLZh6HDOF2+M6Snfwz4NVV9QkTOBB4XkQeADwMPquoeEdkN7AZ2icgVwA3AlcBFwFdE5HJVPT7ABhM0DddYHS7JmbEfbusdDydvjlwS9np1GtNb9FX1EHCo+PtVEXkG2ApcB1xdnHYH8BCwqzh+l6oeAV4QkeeBq4CHu6SbWjBD7KNaFTQttqikLrcciHEPLPvgD6XWfz0xTT7xOTDEPbWuLgcZ0xeRbcDbgEeBC4oGAVU9JCLnF6dtBR4pfe1gcaz+2iNvotJElS3DhhqW37WTx7kRWuynLPRlpiCsltm28Uql8A/Vw8GiLyJvAP4Q+Jiq/kBk7Wt31QeVaikiO4GdAGec/cahJkYn13HEKRJawPvE3be2irUrc2m0rLNaj0J1fgeJvoi8hoXgf15Vv1gcfllEthS9/C3A4eL4QeDi0tc3gBerrquqe4G9AK/devEpDcNqz3pM0Y01gegNxzBiDY+NOR8zNA+hbGtaIZzr/IalEYM2xPInGuK9I8CngWdU9bbSR/cBNwJ7it/3lo7fKSK3sZjIvQx4rE/aKQUyVtoxFmHkTpeAc7k90FXkkodc7JwiITqdQ3r67wT+MfANEXmyOPZxFmJ/j4jsAA4AHwJQ1adE5B7gaRaePzdNwXMnFBaEvksPbowHP1Ya7u45X9atD8jdT78LQ7x3/gfV4/QA16z5zq3ArX3TdOKSugfXd9igq90WGliLzKExtCbu5UZonW1V4UOG1GFfkeuYIXWjM3e8MRyfVy99bcfzh9+j0wZfwXEcx8kGF33HcZwZ4aLvOI4zI3xM33GcSVMV9mTI+W0dDtalNfT7Q3HRd5zE5LrYCepXLNeFIx9z0r5rWk3nD7W9/P26e7/8LHRZueg7TkByFvA+tBGtrp85J4jROLroB6BrBXbXRNt0WQnc9fOhuFjaY+g9H/ueuugHYOjYnWMLy43yWKG4nfbkdi9c9CNiTTyqKucQEUmZvzmsHq3jzP2vSW3CYHwxWD2LOh6+ns9e9C210rFFtO76bfYAttSIdRWMsRoJa3u/pqQp5EGbe5KqPGOHa2jKV5uQDHXUle3sRb9OyMZqEELYEEKQLYl6aJYPS2zxLz+s3pMdTtcwBUuGP7s/OuVIyOejb77aX3993Zu96NfRpvc7BxusUSXcbQU2lhD7PZo+Y93j5SY25U1UQjY4LvoNWOj9WrDBEqGFO0fBbru7VZtdvHynrPDUlXtdea/7Xtlnf6j/vov+zGjarOXM/Zr9sERKEc9RQGNt75hjWfSha/k1nd+0L27yPXKtsW7MNnchC0VTOUyhnNo+FDEah9z3xw1JirJI8VYcYsq3zrMuNJMT/SmIljMOU1tI1XZ5/+q5ZXIc6ipj0f6+ZX3kkqO+IrcvOfp0x2i8xiqHVdtTbmQfCotiskoXG3PIz1QYUtYxhH8Wom9JZNoKbwyBTlUOlsq/iXXlvlwMlVNe5kKsBiy02PZxvy4v0Hr1Ukm+MfpkidkLOnLJqcdCV64p9KxDEbrx7Hq9FIuLxt4HNnYemxd5xVqdHLrutLVzXccjjD0u+hVYCi/QJNhtKkKIzZRzZaxFWeuwthF3DELncbURGdKopCj/dZudL4+nrhMu+iOw+uZQ16h0FeY5CnkfQr0aO/FJLYpDWWd/n3yVn2/v6Rumzaw8+KKrsRnaQHqj4YxNjHUzLvoRsCDmTYuwcsMF10lJn2coVJ0NXfdd9A0yBYHrk4e6B2vdZ1Moq1R4RND2nP3t8dOMNczlom+QnHrmoUS3b57rJmrrrumNRf5j50Ow8Iw11cF1YVKG4qI/MYaEoehaoYYKdUh8AtzJjVB7QnS9jov+xBgiZn2+6+LZjK9+bUeOAdrGjC905JIw84VZi37ur+hjCmbuZVVm7IYmR9FOKaBVQpijoC9JHURvdmEYTj86LVZEWxYAAAVpSURBVMEqU7cQy2qemyZUm+LuNF3HIjkGJ0stVKtYs2fOjC76IvJe4FPA6cDvquqeIdfLSTzaklLwQ4/TT/H+LLHgmuvMi+wmckXkdOA/AtcCB4E/EZH7VPXpvtecQk+yiZBeKCmHlKZ0T5zpYukN7tTga8MZu6d/FfC8qn4HQETuAq4Deov+OqwOj3QhpEimENw5i/wU6t9ciRfAbUE3T7rw9Whs0d8K/EXp/4PA31o9SUR2AjuLf488/ulf++YItoXkTcBfpjaiBznanaPNkKfdOdoMedodwuZLqw6OLfpVTdwpTZmq7gX2AojI11T1HbENC0mONkOedudoM+Rpd442Q552x7T5tBgXreEgcHHp/w3gxZFtcBzHmS1ji/6fAJeJyHYR2QTcANw3sg2O4zizZdThHVU9JiK/AtzPwmXzM6r6VMPX9sa3LDg52gx52p2jzZCn3TnaDHnaHc1mUXUvA8dxnLkw9vCO4ziOkxAXfcdxnBlhVvRF5L0i8pyIPC8iu1Pbsw4RuVhE/ruIPCMiT4nIR4vjnxSR74rIk8XP+1PbWkZE9onINwrbvlYcO1dEHhCRbxW/35jazjIi8pOl8nxSRH4gIh+zVtYi8hkROSwi3ywdW1u2InJLUc+fE5G/m8bqtXb/pog8KyJ/JiJ/JCLnFMe3icgPS2X+O4ZsXlsfjJf13SWb94nIk8XxsGWtquZ+WEzyfht4C7AJ+DpwRWq71ti6BXh78feZwJ8DVwCfBH49tX01du8D3rRy7N8Au4u/dwO/kdrOhjryEosFKKbKGng38Hbgm01lW9SVrwObge1FvT/dkN1/Bzij+Ps3SnZvK59nrKwr64P1sl75/LeAfxWjrK329H8crkFVjwLLcA3mUNVDqvpE8ferwDMsVh7nyHXAHcXfdwA/m9CWJq4Bvq2q+1Mbsoqq/jHwP1cOryvb64C7VPWIqr4APM+i/o9Old2q+mVVPVb8+wiLtTVmWFPW6zBd1ktERIDrgd+PkbZV0a8K12BeSEVkG/A24NHi0K8Ur8WfsTZUwmIl9JdF5PEi7AXABap6CBaNGXB+MuuauYGTHwrLZQ3ryzanuv7LwJdK/28XkT8Vka+KyLtSGbWGqvqQS1m/C3hZVb9VOhasrK2KfqtwDZYQkTcAfwh8TFV/APw28BPA3wAOsXhds8Q7VfXtwPuAm0Tk3akNakuxsO+DwB8Uh6yXdR1Z1HUR+QRwDPh8cegQcImqvg34VeBOETkrlX0rrKsPWZQ18A85uUMTtKytin5W4RpE5DUsBP/zqvpFAFV9WVWPq+pfAbeT6DVyHar6YvH7MPBHLOx7WUS2ABS/D6ezsJb3AU+o6stgv6wL1pWt+bouIjcCHwD+kRaDzMUQyfeKvx9nMT5+eTorT1BTH3Io6zOAnwfuXh4LXdZWRT+bcA3F+NungWdU9bbS8S2l034OMBMpVET+moicufybxWTdN1mU8Y3FaTcC96axsJGTekKWy7rEurK9D7hBRDaLyHbgMuCxBPZVIotNj3YBH1TV/1c6/mZZ7I+BiLyFhd3fSWPlydTUB9NlXfAe4FlVPbg8ELysU8xct5zdfj8LT5hvA59IbU+NnX+bxSvinwFPFj/vB/4T8I3i+H3AltS2lmx+Cwsvhq8DTy3LFzgPeBD4VvH73NS2Vtj+euB7wNmlY6bKmkWDdAj4EYve5Y66sgU+UdTz54D3GbP7eRbj4Mu6/TvFuf+gqDtfB54A/r4hm9fWB8tlXRz/LPDPVs4NWtYehsFxHGdGWB3ecRzHcSLgou84jjMjXPQdx3FmhIu+4zjOjHDRdxzHmREu+o7jODPCRd9xHGdG/H9UeEYybmEQZAAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.contourf(eqpac['ts'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Calculate the Nino3.4 Index" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can use `xarray` sel, slice, and mean to calculate the Nino3.4 index by taking the mean over multiple dimensions" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "ds_nino34=ds_anoms.sel(lat=slice(-5,5),lon=slice(190,240)).mean(['lat','lon'])" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(ds_nino34['ts'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Write data to netcdf file" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "ds_nino34.to_netcdf('nino34.nc')" ] } ], "metadata": { "kernelspec": { "display_name": "Python (aoes)", "language": "python", "name": "aoes" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.7" } }, "nbformat": 4, "nbformat_minor": 4 }