{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# **LT Toolbox Tutorial: 2. Advanced Analysis with Trajectory Frames**\n", "\n", "Welcome to the Lagrangian Trajectories Toolbox tutorial! \n", "\n", "The LT Toolbox is a Python library dedicated to the post-processing, visualisation and analysis of Lagrangian water parcel trajectories. The toolbox offers users two structures for working with Lagrangian trajectories: Trajectory Arrays (TrajArrays) and Trajectory Frames (TrajFrames). In this tutorial, we continue exploring TrajFrames, which make use of [Polars](http://pola.rs) blazingly fast DataFrames to store column variables associated with trajectories (e.g. lat, lon, in-situ temperature etc.).\n", "\n", "In this advanced tutorial, we will learn how to:\n", "\n", "+ **Add** new attribute variables using the Polars expressions syntax to your TrajFrame.\n", "\n", "+ **Compute** binned statistics, such as the Lagrangian probability, and grouped statistics, such as meridional heat transport, from Lagrangian trajectories.\n", "\n", "+ **Map** probabilities and properties determined from Lagrangian trajectories.\n", "\n", "To learn about the basics of working with TrajFrames, including simple filtering and using datetimes, users should see **LT Toolbox Tutorial: 1. Getting Started with Trajectory Frames**.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Getting Started\n", "\n", "Let us begin by importing the relevant packages we'll need to get started with the LT Toolbox. \n", "\n", "**Note**: Since lt_toolbox is still undergoing unit testing, the package is not yet available on PyPi, we use pip to install a local development version." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Import relevant Python packages:\n", "import xarray as xr\n", "import numpy as np\n", "import polars as pl\n", "import matplotlib.pyplot as plt\n", "\n", "# Following pip installation as shown on the LT Toolbox github:\n", "import lt_toolbox as ltt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Storing Trajectory Data\n", "\n", "To explore the functionality of the LT Toolbox, we will use example output from a Lagrangian particle tracking experiment using the eddy-rich ORCA0083-GO8p7 JRA55 ocean sea-ice hindcast ([Megann et al. 2022](https://dx.doi.org/10.5285/399b0f762a004657a411a9ea7203493a)). Trajectories were advected forwards-in-time from the full-depth northward inflows across the Overturning in the Subpolar North Atlantic (OSNAP) East array.\n", "\n", "We have provided some example trajectory output for the January 1995 initialisation in the .parquet file format.\n", "\n", "Below we load the output .parquet file as a DataFrame with Polars, before creating a TrajFrame, traj." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\n", "\n", "----- Trajectory DataFrame -----\n", "Trajectories: 2500\n", "Variables: ['id', 'x', 'y', 'z', 'subvol', 'time', 'boxface', 'thetao', 'so', 'mask']\n", "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Defining filepath to our example ORCA0083-GO8p7 output trajectory file:\n", "traj_filepath = \"./data/ORCA0083-GO8p7_JRA55_SPNA_1995_example.parquet\"\n", "\n", "# Open output .parquet file as a DataFrame.\n", "dataset = pl.read_parquet(traj_filepath, use_pyarrow=True)\n", "\n", "# Create a TrajFrame from the DataFrame:\n", "traj = ltt.TrajFrame(source=dataset, condense=True)\n", "\n", "# Here we see how condensing a TrajFrame will store all observations in\n", "# lists per trajectory (rows) instead of in a row per observation.\n", "traj" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Transforming x,y,z variables to lon,lat,depth in a TrajFrame.\n", "\n", "In our example TrajFrame, we have stored the positions of each trajectory as indices referring to the original numerical model grid (eORCA12). However, we can easily transform these model coordinates to geographical coordinates using the **.transform_trajectory_coords()** method which uses bi-linear interpolation to determine each trajectories geographical position." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset> Size: 125MB\n",
       "Dimensions:  (y: 3606, x: 4322, z: 75)\n",
       "Dimensions without coordinates: y, x, z\n",
       "Data variables:\n",
       "    nav_lat  (y, x) float32 62MB ...\n",
       "    nav_lon  (y, x) float32 62MB ...\n",
       "    nav_lev  (z) float32 300B ...\n",
       "Attributes:\n",
       "    file_name:  mesh_mask.nc\n",
       "    TimeStamp:  30/09/2016 08:33:26 +0000
" ], "text/plain": [ " Size: 125MB\n", "Dimensions: (y: 3606, x: 4322, z: 75)\n", "Dimensions without coordinates: y, x, z\n", "Data variables:\n", " nav_lat (y, x) float32 62MB ...\n", " nav_lon (y, x) float32 62MB ...\n", " nav_lev (z) float32 300B ...\n", "Attributes:\n", " file_name: mesh_mask.nc\n", " TimeStamp: 30/09/2016 08:33:26 +0000" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Importing eORCA12 ocean model grid from a NetCDF file using xarray:\n", "ds_grid = xr.open_dataset('/home/snapdragon/HadGEM3-GC31-MM/Proj_Future_Pathways/src/Software/lt_toolbox/docs/tutorials/data/ORCA0083-GO8p7_JRA55_model_grid.nc')\n", "\n", "ds_grid" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\n", "\n", "----- Trajectory DataFrame -----\n", "Trajectories: 2500\n", "Variables: ['id', 'lon', 'lat', 'depth', 'subvol', 'time', 'boxface', 'thetao', 'so', 'mask']\n", "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Transform the TrajFrame to geographical coordinates and store as new TrajFrame object:\n", "traj_geo = traj.transform_trajectory_coords(lon=ds_grid.nav_lon.values,\n", " lat=ds_grid.nav_lat.values,\n", " depth=ds_grid.nav_lev.values,\n", " )\n", "\n", "traj_geo" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### **Adding Attribute Variables with List Expressions**\n", "\n", "Often when we want to add a new attribute variables to our TrajFrame, we would like to compute a complex function using existing attribute variables, for example potential density from temperature and salinity recorded along-stream. Below we show how we can do this using Polars expressions syntax and list operations." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\n", "\n", "----- Trajectory DataFrame -----\n", "Trajectories: 2500\n", "Variables: ['id', 'lon', 'lat', 'depth', 'subvol', 'time', 'boxface', 'thetao', 'so', 'mask', 'sigma0']\n", "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Import the density module from Gibbs Seawater Toolbox (gsw):\n", "import gsw.density as density\n", "\n", "# Compute potential density anomaly (sigma0) from conservative temperature\n", "# and absolute salinity recorded along our trajectories:\n", "traj_geo = traj_geo.add_variable(name='sigma0',\n", " expr=density.sigma0(CT=pl.col('thetao'), SA=pl.col('so')),\n", " list_expr=True,\n", " )\n", "\n", "traj_geo" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### **Computing Grouped Expressions**\n", "\n", "In addition to the more basic computations demonstrated in Tutorial 2, the LT-Toolbox also includes several more advanced computation methods which return n-dimensional arrays as outputs since they do not conform to the tabular structure of our Lagrangian trajectory data. To help manage the resulting n-dimensional arrays produced, a TrajFrame also stores these in an xarray DataSet accessible via the **summary_data** attribute.\n", "\n", "Below we show a simple example of a grouped expression where we group over the water parcel ID variable in an uncondensed TrajFrame. Note, that this example is for illustrative purposes only since we could store this output in our condensed TrajFrame.\n", "\n", "A more common example of grouped expressions is computing an aggregated statistic over groups of water parcel trajectories; for example, we could compute the average transit time grouped over start time if we had released parcels every month for a year." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset> Size: 40kB\n",
       "Dimensions:  (id: 2500)\n",
       "Coordinates:\n",
       "  * id       (id) int64 20kB 1 12 15 21 26 32 ... 28473 28512 28549 28956 28990\n",
       "Data variables:\n",
       "    dMHT     (id) float64 20kB -5.24e+08 -5.837e+09 ... -1.32e+10 -1.097e+10
" ], "text/plain": [ " Size: 40kB\n", "Dimensions: (id: 2500)\n", "Coordinates:\n", " * id (id) int64 20kB 1 12 15 21 26 32 ... 28473 28512 28549 28956 28990\n", "Data variables:\n", " dMHT (id) float64 20kB -5.24e+08 -5.837e+09 ... -1.32e+10 -1.097e+10" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Create an uncondensed TrajFrame from our example DataFrame:\n", "traj = ltt.TrajFrame(source=dataset)\n", "\n", "# Define physical constants:\n", "rho0 = 1025 # Reference seawater density in kg/m^3\n", "Cp = 3992 # Seawater specific heat capacity in J/kg/K\n", "\n", "# Define Polars expression to compute the change in heat\n", "# transport of each water parcel trajectory:\n", "expr = rho0*Cp*pl.col('subvol').first()*(pl.col('thetao').last() - pl.col('thetao').first())\n", "\n", "# Computing the change in heat transport of each water parcel trajectory:\n", "traj = traj.compute_grouped_expr(group='id', expr=expr, alias='dMHT', append=False)\n", "\n", "# Display the summary data of the TrajFrame:\n", "traj.summary_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### **Computing 1-dimensional Binned Statistics**\n", "\n", "A further example of an advanced computation is to compute 1-dimensional binned statistics or histograms using the positions or properties sampled along water parcel trajectories. \n", "\n", "Below we demonstrate how to compute the sum of water parcel volume transport binned according to their initial longitude at the point of release along the Overturning in the Subpolar North Atlantic Program (OSNAP) array in the subpolar North Atlantic.\n", "\n", "We will visualise our results with xarray's in-built plotting wrapper of the matplotlib library." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset> Size: 368B\n",
       "Dimensions:       (lon_ini: 23)\n",
       "Coordinates:\n",
       "  * lon_ini       (lon_ini) float64 184B -30.5 -29.5 -28.5 ... -10.5 -9.5 -8.5\n",
       "Data variables:\n",
       "    subvol_x_ini  (lon_ini) float64 184B 0.01537 0.8793 ... 0.02333 0.0009916
" ], "text/plain": [ " Size: 368B\n", "Dimensions: (lon_ini: 23)\n", "Coordinates:\n", " * lon_ini (lon_ini) float64 184B -30.5 -29.5 -28.5 ... -10.5 -9.5 -8.5\n", "Data variables:\n", " subvol_x_ini (lon_ini) float64 184B 0.01537 0.8793 ... 0.02333 0.0009916" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Define discrete longitude bins:\n", "bin_lon = np.arange(-33, 0, 1).tolist()\n", "\n", "# Compute 1-D summed volume transport in discrete longitude bins:\n", "traj_geo = (traj_geo\n", " # Add volume transport [Sv] per parcel as an attribute variable:\n", " .add_variable(name='subvol_ini',\n", " expr=(pl.col('subvol').list.first() / 1E6),\n", " list_expr=False,\n", " )\n", " # Add the initial longitude of each parcel as an attribute variable:\n", " .add_variable(name='lon_ini',\n", " expr=pl.col('lon').list.first(),\n", " list_expr=False,\n", " )\n", " # Compute volume transport in discrete longitude bins:\n", " .compute_binned_statistic_1d(var='lon_ini',\n", " values='subvol_ini',\n", " statistic='sum',\n", " bin_breaks=bin_lon,\n", " alias='subvol_x_ini',\n", " group=None, # No group-by required.\n", " append=True, # Allow append to existing summary DataSet.\n", " )\n", " )\n", "\n", "# Display the summary data of the TrajFrame:\n", "traj_geo.summary_data" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjcAAAGxCAYAAACeKZf2AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAABnSklEQVR4nO3dd3hUdfY/8PednjYJEEiBUKQjAgoCQVFRRLG7a19FV3RlWXUVyy6uv7VswbViWSxfRSzoIq5dVCIroFKUjhRpQgJpJJBM6tT7+2PmzkxIQmYmd+aWeb+eJ88jkykfHCb35HzO5xxBFEURRERERDphUHoBRERERHJicENERES6wuCGiIiIdIXBDREREekKgxsiIiLSFQY3REREpCsMboiIiEhXGNwQERGRrpiUXoASfD4fSktLkZGRAUEQlF4OERERRUAURdTV1SE/Px8GQ/v5maQMbkpLS1FQUKD0MoiIiCgGJSUl6NWrV7vfT8rgJiMjA4D/f47dbld4NURERBQJh8OBgoKC4HW8PYoGNytXrsQTTzyB9evXo6ysDB9++CEuu+yy4z5mxYoVmDVrFrZt24b8/Hzcf//9mDFjRlSvK21F2e12BjdEREQa01FJiaIFxQ0NDRg5ciReeOGFiO7/yy+/4IILLsDEiROxceNGPPDAA7jzzjvx3//+N84rJSIiIq1QNHMzdepUTJ06NeL7v/TSS+jduzfmzp0LABg6dCjWrVuHJ598Er/+9a/jtEoiIiLSEk0dBV+9ejWmTJnS4rbzzjsP69atg9vtbvdxTqcTDoejxRcRERHpk6aCm/LycuTk5LS4LScnBx6PB1VVVe0+bs6cOcjMzAx+8aQUERGRfmkquAFaFxGJotjm7eFmz56N2tra4FdJSUlc10hERETK0dRR8NzcXJSXl7e4rbKyEiaTCd26dWv3cVarFVarNd7LIyIiIhXQVOamsLAQRUVFLW5bunQpxowZA7PZrNCqiIiISE0UDW7q6+uxadMmbNq0CYD/qPemTZtQXFwMwL+dNG3atOD9Z8yYgQMHDmDWrFnYsWMH5s+fj9deew333nuvEssnIiIiFVJ0W2rdunWYNGlS8M+zZs0CANx4441YsGABysrKgoEOAPTr1w9LlizB3XffjX//+9/Iz8/Hc889x2PgREREFCSIUkVuEnE4HMjMzERtbS07FBMREWlEpNdvTdXcEBEREXWEwQ0RERHpCoMbIhVwe3346VAtvL6k2yUmIpKdpvrcEOlNWW0T3v2hBP/5oRiVdU7cNXkg7po8SOllERFpGoMbogTz+UR8t6cKb685gGU7K1tka/YdblBwZURE+sDghihBjja48P76g1i49gD2VzcGbx/Xryt6ZqXgg42H4GhufwAsERFFhsENURyJooiNJTV4e80BfLalDC6PDwCQYTXhV6f0xG/G98GgnAx8+VMZPth4CHXNHoVXTESkfQxuiOKg0eXBx5tK8faaA9hW6gjefmK+HdeP74NLRuYjzRr6+Nlt/vEhjiZmboiIOovBDZGMdlfU4e01B/DBhkOoc/qzMBaTAReNyMMN4/tgVEFWmxPs7SmB4IbbUkREncbghqiTXB4fvtpWjrfXHMDaX44Eb+/bLRW/GdcHV4zuhS5pluM+R4bN/1HkthQRUecxuCGK0cGjjXj3h2Is+vEgquqdAACDAEwemoMbCvvgtP7ZMBhaZ2naIm1LNbq8cHt9MBvZgoqIKFYMbohi8PAn2/Dm6v2QTnH3yLDimrG9ce3YAuRlpkT9fFLmBvBnb7p2kOkhIqL2MbghipLXJ+KN1fshikDhCd0wrbAPJg/L6VS2xWQ0INViRKPLi7pmN4MbIqJOYHBDFKW6ZjfEQMbmjZvHwmKSZwvJbjOj0eWFo4l1N0REncGNfaIoScFHitkoW2ADAPYU/+8aPDFFRNQ5DG6IolQb6EUjBSNyyWCvGyIiWTC4IYqSFNxkBnrTyMXO4+BERLJgcEMUpbgFN2zkR0QkCwY3RFGSgg/5MzfcliIikgODG6IoBWtubPIGN1KvGwe3pYiIOoXBDVGUQgXF3JYiIlIjBjdEUYpfQbG0LcXMDRFRZzC4IYqSI07BTWh4JjM3RESdweCGKErx35Zi5oaIqDMY3BBFKV6ZG6nPDU9LERF1DoMboijFq+Ym2KGY21JERJ3C4IYoStK2kdzjF6Tnq3d64POJsj43EVEyYXBDFAVRFON+WkoUgXoX626IiGLF4IYoCg0uL7yBrIrcwY0tbMo4626IiGLH4IYoClLWxmwUkGI2yv78HJ5JRNR5DG6IouAIG70gCILsz8/5UkREncfghigK8aq3kWSw1w0RUacxuCGKQrwa+Ens7FJMRNRpDG5UrqbRBVHksWC1iHfmhttSRESdx+BGxZbtqMCoR4vw0op9Si+FAhzxztwEet1wW4qIKHYMblRs88FaAMCP+48ovBKShEYvyNvATyJ1Kea2FBFR7BjcqJh0IS2taVJ4JSSJ/7aUNF+KmRsiolgxuFGxmkYXAKCstlnhlZAk7sFNCudLERF1FoMbFZMupLVNbjSyHb8qBOdK2eJcUMzghogoZgxuVKwm7MRMaQ2zN2oQ9z437FBMRNRpDG5UrDYsuCnn1pQqJGxbikfBiYhixuBGxWobwzI3tSwqVoO4HwW3sUMxEVFnMbhRKVEUW2RuyrgtpQqJ25Zys3kjEVGMGNyoVIPLC48vdHErY+ZGcc1uL5weH4B4NvHzP6/bK6LZ7YvLaxAR6R2DG5WqPabmopQ1N4qTtqQEAciwxqeJX5rFCENg2DhPTBERxYbBjUpJPW4kZWzkpzgp2LDbzDBIEYjMBEFgl2Iiok5icKNSUubGYvS/RTwtpbzQRPD4ZG0k0vPXsksxEVFMGNyolHRSakCPdABAndPD3+QVFu9iYgkb+RERdQ6DG5WSLqR5mbbgxZRjGJSVqOCGjfyIiDqHwY1KSd2JM1PNyMu0AeAATaVJwyzjNXpBEszcsJEfEVFMGNyoVHiWQApumLlRVsK2pTg8k4ioU+JbGUkxqwnU3GSlWJCX5e93whNTykp4zQ0LiomIYsLgRqUcwQupCUZDYFuKmRtF1cZ59IIkvEsxERFFj8GNStU0+fvcZKVagn1PeBxcWfGeKyUJbUsxc0NEFAsGNyoVvgViNftLozg8U1mJ25byfyxZUExEFBsGNyol1dxkpprRJdUCwD88UxRFCEJ8uuPS8SXuKDg7FBMRdQZPS6lUW6elmtzeVjOnKHEcCTstFcjccFuKiCgmDG5UyOsTgw3cslLMsJmN6Jrmz96U1rDuRilSsCFtG8UL+9wQEXWOKoKbefPmoV+/frDZbBg9ejS+/fbb495/4cKFGDlyJFJTU5GXl4ff/va3qK6uTtBq4y/8oiYVl4Z63bDuRgkerw/1Tn9wk6ij4OxQTEQUG8WDm0WLFuGuu+7CX/7yF2zcuBETJ07E1KlTUVxc3Ob9v/vuO0ybNg3Tp0/Htm3bsHjxYvz444+45ZZbErzy+JG6E6dbTTAHBmfmZaYAYCM/pYRvEcX/tJQ/M9Tk9sLl8cX1tYiI9Ejx4Obpp5/G9OnTccstt2Do0KGYO3cuCgoK8OKLL7Z5/zVr1qBv376488470a9fP5x++um47bbbsG7dugSvPH7aKlzNz2LmRknSe5JmMQYDznhJt4a2vVhUTEQUPUWDG5fLhfXr12PKlCktbp8yZQpWrVrV5mMmTJiAgwcPYsmSJRBFERUVFXj//fdx4YUXtvs6TqcTDoejxZea1TT6e9yEBzfBzA1rbhSRqB43AGAyGoIBDouKiYiip2hwU1VVBa/Xi5ycnBa35+TkoLy8vM3HTJgwAQsXLsTVV18Ni8WC3NxcZGVl4fnnn2/3debMmYPMzMzgV0FBgax/D7m1lbkJDs9k5kYRiToGLmGXYiKi2Cm+LQWgVd+W4/Vy2b59O+6880789a9/xfr16/Hll1/il19+wYwZM9p9/tmzZ6O2tjb4VVJSIuv65SZdSLNSWwc3rLlRRqJGL0g4X4qIKHaKNvHLzs6G0WhslaWprKxslc2RzJkzB6eddhruu+8+AMCIESOQlpaGiRMn4u9//zvy8vJaPcZqtcJqtcr/F4iT2sa2am5CBcVs5Jd40oTuRGVuQr1umLkhIoqWopkbi8WC0aNHo6ioqMXtRUVFmDBhQpuPaWxshMHQctlGoxGAP+OjB9JpqcywzE2O3QZBAFweH440uJRaWtIKZm5sidqWYpdiIqJYKb4tNWvWLLz66quYP38+duzYgbvvvhvFxcXBbabZs2dj2rRpwftffPHF+OCDD/Diiy9i3759+P7773HnnXdi7NixyM/PV+qvIau26jssJgOy0/3ZJ25NJV6ia25C86W4LUVEFC3FZ0tdffXVqK6uxqOPPoqysjIMHz4cS5YsQZ8+fQAAZWVlLXre3HTTTairq8MLL7yAe+65B1lZWTj77LPxr3/9S6m/guykuVJZKZYWt+dn2nC4zonSmiYM75mpxNKSVqJGL0hCk8GZuSEiipbiwQ0AzJw5EzNnzmzzewsWLGh12x133IE77rgjzqtSTnsX0rzMFGw+WMvMjQKkDIpUCxNvodNSzNwQEUVL8W0paq2myV9TE35aCgByeRxcMYnfluJ8KSKiWDG4UaH2LqTBLsVs5JdwCQ9uuC1FRBQzBjcqVNPGUXAgfL4UMzeJplQTP3YoJiKKHoMblWl2e+EMDEvMTG0nc8Oam4STMiiJb+LHzA0RUbQY3KiMlCEwGgRkWFsWr0qZmwpHM3w+ffT00QKfT1TstBQLiomIosfgRmVCzeJMrboQ98iwwiAAbq+IqnqnEstLSvUuD6RYMvF9bpi5ISKKFoMblQn2uEm1tPqeyWhAjwzpxBS3phJFGodhMRlgMxsT8ppSh+J6l4dZOiKiKDG4UZmOBjTmBU9Msag4UYL1NgkavQCECopFEahzcmuKiCgaDG5UpqYx0OOmneAmP1B3w8xN4oROSiWu56XNbITV5P94cmuKiCg6DG5UpqMjx3mBRn7lPA6eMIkuJpaEhmcyc0NEFA0GNyojBTfHdieW5GUxc5Noie5xI5FGPbCRHxFRdBjcqExHF9L8TNbcJFporlSCgxv2uiEiigmDG5VprzuxRMrcsJFf4iiVueHwTCKi2DC4UZlIa24qHM3weH0JW1cyU25bivOliIhiweBGZWqa2u9zAwDZ6VaYDAJ8IlBZx0Z+iaBYcBPclmLmhogoGgxuVKajkzlGg4AcO2dMJZISfW6AUEFxHTM3RERRYXCjMsE+N+2clgLCB2iyqDgROmqsGC/BzA2DGyKiqDC4URGfT4xoC0QaoFlWw8xNIii3LSXNl+K2FBFRNBjcqEikAxqlEQylzNwkhBRcsKCYiEgbGNyoiDSg0drBgMY8qeaGmZu4E0UxWAdlT+D4BYBHwYmIYsXgRkU66k4sCfW6YeYm3prdPrgCR+4VOy3FzA0RUVQY3KhIpLUdHJ6ZONJ7YhCAdGtiMzfBbSl2KCYiigqDGxWRuhNnpbTd40Yi1dxU1Tvh8rCRXzwFj4GnmCEIQkJfO3xbShTFhL42EZGWMbhRkUiPHHdLs8BiMkAU/Z2KKX6UOikFhLalPD4RTW5vwl+fiEirGNyoSE1Txz1uAEAQhOAYBjbyi6/aDmZ9xVOqxQijwZ8t4nFwIqLIMbhRkWiyBKHghkXF8aRk5kYQhLCtKdbdEBFFisGNitQGa24iCW4CRcU8Dh5XSo1ekPDEFBFR9BjcqEgwS9DBthTAzE2iKDV6QSL11uG2FBFR5BjcqEhNFPUdoV43zNzEk5LbUgAzN0REsWBwoyLRXEjzmblJCKWDG6nmxsEuxUREEWNwoyKhDsXH73MDcHhmokjbQYkevSAJZm7YyI+IKGIMblQkqsxNoJFfdYMLzeyBEjcOpbelODyTiChqDG5Uwu31od7pzxJEcloqM8UMm9n/9pWz7iZu1LItxeGZRESRY3CjEuHbDpGczBEEIWzGFOtu4kXp4IbbUkRE0WNwoxI1gYtXhs0U7ErbEWnGFDM38aN4n5vgthQzN0REkWJwoxKxZAiCRcUMbuLC7fWh0eWvZ1J+W4qZGyKiSDG4UYlgd+IIGvhJpOPgpTXcloqH2ii3CuOB21JERNFjcKMSMWVu2MgvrqT3JMMa+Vah3IIdirktRUQUMQY3KlHTGJgIntJxjxtJHjM3ceVQePQCEMrccFuKiChyDG5UojbYLI41N2qh9FwpIBTcNLt9cHrYz4iIKBIMblSipsmfuYluW8qfualtcqPRxW0LuYW2CpXpTgwA6bbQa7PXDRFRZBjcqERo9ELkwY3dZka61X/xY/ZGflKdi1InpQDAaBCQYZUmg3NriogoEgxuVCLWNv9S3Q1nTMkvWHOjUI8bCbsUExFFh8GNStRIR8GjDW6y2KU4XpTuTizhfCkiougwuFGJWC+k+czcxI3Ue0jx4CbY64aZGyKiSDC4UQlp/EJmFDU3AJArBTfM3MguOHpB4eCGXYqJiKLD4EYFRFHsROZG2pZi5kZu3JYiItImBjcq0Oz2weXxAQCyUiNv4geED89k5kZuqglubNJpKW5LERFFgsGNCkgXUaNBQJrFGNVjg438WHMjOzU08QOADHYpJiKKCoMbFZAa+GWlmCEI0c0wyg9kbuqcHl78ZOZQQRM/gPOliIiixeBGBTpzKifVYgo+jo385OPziahzRj8SIx44GZyIKDoMblQg1pNSEg7QlF9dswei6P9vxWtuUqRtKWZuiIgiweBGBTpbuBrsUszMjWyk98RmNsBqiq4OSm7SUXCeliIiigyDGxWojbE7sUTqUszgRj7BHjcKj14IXwO3pYiIIsPgRgU6m7kJdSnmtpRc1HIMHAjvc8NtKSKiSDC4UQHptFRmlD1uJMHj4MzcyEZNwY20LVXv9MDrExVeDRGR+jG4UYHaQHO2mGtuAsfBOTxTPmoMbgCgntkbIqIOMbhRgZrGUJ+bWOSHNfITRf5mLweHShr4AYDVZITN7P+osqiYiKhjqghu5s2bh379+sFms2H06NH49ttvj3t/p9OJv/zlL+jTpw+sViv69++P+fPnJ2i18nN0MksgDc9scnuDGQfqHDVlboBQl2IGN0REHVO29SqARYsW4a677sK8efNw2mmn4eWXX8bUqVOxfft29O7du83HXHXVVaioqMBrr72GAQMGoLKyEh6PdtP1Up+brBj73NjMRnRNs+BIgwulNc1Rz6ei1tQyekFit5lwuM7J+VJERBFQPLh5+umnMX36dNxyyy0AgLlz5+Krr77Ciy++iDlz5rS6/5dffokVK1Zg37596Nq1KwCgb9++iVyy7OTIEuRl2nCkwYVyRxOG5dvlWlrSUlvmhpPBiYgip+i2lMvlwvr16zFlypQWt0+ZMgWrVq1q8zGffPIJxowZg8cffxw9e/bEoEGDcO+996KpSZvFtD6fGLqQxpi5AUInpko5QFMW0rFru03x+B9AqNcNuxQTEXVM0Z/cVVVV8Hq9yMnJaXF7Tk4OysvL23zMvn378N1338Fms+HDDz9EVVUVZs6ciSNHjrRbd+N0OuF0OoN/djgc8v0lOqnOKU+bf2mAZhlPTMlCbZmbYJdi1lQREXVIFQXFx07CFkWx3enYPp8PgiBg4cKFGDt2LC644AI8/fTTWLBgQbvZmzlz5iAzMzP4VVBQIPvfIVZSd+IUs7FTbf7zwk5MUed1tshbbtyWIiKKXMSZm1NOOQXLli1Dly5dcPLJJ7cbfADAhg0bInrO7OxsGI3GVlmaysrKVtkcSV5eHnr27InMzMzgbUOHDoUoijh48CAGDhzY6jGzZ8/GrFmzgn92OByqCXDkyhDks9eNrNR0FBwIH8HAbSkioo5EHNxceumlsFqtAIDLLrtMlhe3WCwYPXo0ioqKcPnllwdvLyoqwqWXXtrmY0477TQsXrwY9fX1SE9PBwDs2rULBoMBvXr1avMxVqs1uHa1kboTx3pSSpJr5/BMuYiiqNptqTpmboiIOhRxcPPQQw+1+d+dNWvWLNxwww0YM2YMCgsL8corr6C4uBgzZswA4M+6HDp0CG+++SYA4LrrrsPf/vY3/Pa3v8UjjzyCqqoq3Hfffbj55puRkpIi27oSRa4jx/lhwzOPt61HHWt0eeEJjDlQS3DDbSkiosh1qqDY5XKhsrISPp+vxe3t9adpy9VXX43q6mo8+uijKCsrw/Dhw7FkyRL06dMHAFBWVobi4uLg/dPT01FUVIQ77rgDY8aMQbdu3XDVVVfh73//e2f+Koqp6eREcEmO3QZBAFweH440uNAtXZ2ZKi2QAk6TQUCqJfY6KDnZgwXF3JYiIupITMHNrl27MH369FbHtaWMgdfrjer5Zs6ciZkzZ7b5vQULFrS6bciQISgqKorqNdRKru0Pi8mA7HQrDtc5UVbbzOCmE6TsiD3FrJoMWPAouJOZGyKijsQU3Pz2t7+FyWTCZ599hry8PNVcALSotpPdicPlZ9pwuM6J0pomDO+Z2fEDqE3SCTa1bEkBgD2FmRsiokjFFNxs2rQJ69evx5AhQ+ReT9KR80Kal5mCzQdrWVTcSWobvQCEnZZizQ0RUYdi6nMzbNgwVFVVyb2WpCSdlsqUYR6UNECTx8E7R20npYDQ4My6Zg8nvxMRdSCm4OZf//oX7r//fixfvhzV1dVwOBwtvihycl5IpV435czcdIraRi8AoW0pr09Eoyu6mjYiomQT00/vyZMnAwDOOeecFrfHWlCczOQ6LQWwS7Fc1Ji5STEbYTII8PhEOJrdSLOqJ/AiIlKbmH5CfvPNN3KvI2nJ2eafXYrlobbRC4B/RIk9xYwjDS7UNXuQx3pxIqJ2xRTcnHnmmXKvI2nVyHhaSsrcVDia4fOJMBh4ii0WaszcAP4uxUcaXByeSUTUgYiDmy1btmD48OEwGAzYsmXLce87YsSITi8sGbg8vmD9hBwX0h4ZVhgEwO0VUVXvRI/ASAaKjtrmSkl4YoqIKDIRBzejRo1CeXk5evTogVGjRkEQhDZPbbDmJnJShkAQQqdhOsNkNCDHbkNZbTNKa5sZ3MRIrZkb9rohIopMxMHNL7/8gu7duwf/mzpPuohmWE0wyrSFlJvpD27KapowqiBLludMNmoNbjKs0nFwZm6IiI4n4uBGmvV07H8fz4UXXohXX30VeXl50a8sCdQGJ4J3vseNJD8zBRtRw0Z+naDW4CaYuWlm5oaI6Hhi6nMTqZUrV6KpiSd32hOPi2heoJFfGU9MxSw4W0qGrUI5BWtuWFBMRHRccQ1u6PiCPW5kOCklycvyn5gqZeYmJk6PF81u/5R7tWVuMoIFxczcEBEdD4MbBcVjhlG+lLmpYeYmFi2LvNXVKC+0LcXMDRHR8TC4UZCc3YklUuaGNTexcYQVeautTxC3pYiIIsPgRkHxrLmpcDTD4/XJ9rzJojZwzFptPW6A0JrquC1FRHRcDG4UVCtjd2JJdroVJoMAnwgcrnfK9rzJQo2jFyTSNhm3pYiIjk/24Ca8sd8DDzyArl27yv0SuhGPzI3RICAn0LyvlAM0o6bWY+BA+LYUMzdERMcTU3Bzww03oL6+vtXt+/fvxxlnnBH88+zZs5GVlRXz4vSuptHf5yYzRb4+N0BogCaPg0dPrcfAgVBBMZv4EREdX0zBzfbt23HSSSfh+++/D972xhtvYOTIkcjJyZFtcXoXryyBNECzjJmbqNU2qjdzIx0Fd3p8aHZzxAkRUXtiOuu6du1aPPjggzj77LNxzz33YPfu3fjyyy/x7LPP4uabb5Z7jboVj5obAMgLZG5KmbmJWjDglPk9kUOG1QRBAETRX1RsMxuVXhIRkSrFFNyYTCY89thjsFqt+Nvf/gaTyYQVK1agsLBQ7vXpliiKccvc5DNzEzM119wYDALSrSbUNXvgaHaje4ZV6SUREalSTNtSbrcb99xzD/71r39h9uzZKCwsxOWXX44lS5bIvT7danR54fb6i6/lztzkSo38HAxuohWquVFXAz+JVAvE4+BERO2L6Sf4mDFj0NjYiOXLl2P8+PEQRRGPP/44fvWrX+Hmm2/GvHnz5F6n7kgZArNRQIrM2wuhzA23paIVj67RcgoeB2cjPyKidsWUuRkzZgw2bdqE8ePHAwAEQcCf/vQnrFmzBitXrpR1gXpVEyxctUAQ5O2EK9XcHK53wuVhI79oSE381LgtBYSCLva6ISJqX0zBzWuvvYa0tLRWt48aNQrr168P/vmxxx5DTU1NzIvTs1Bth/zbH93SLLCYDBBFf6diipyam/gBoe0ybksREbVP9iZ+VmuoyPGf//wnjhw5IvdL6EJtk7/HTVaqvD1uAH8mTRrDwBlT0XGofFuK86WIiDoW1/EL4d2KqaV4n8oJBTesu4mU1yeizsltKSIireNsKYXEYyJ4OKmRH0cwRC48G6La4IbbUkREHWJwo5B4n8qRMjflzNxETHpPUi1GmI3q/GhkcFuKiKhD6vwJngRq4tSdWJKXFcjcsOYmYmqeKyWR5ks5mLkhImoXgxuFxLvmJp81N1FTc3diCQuKiYg6FtfgZuLEiUhJSYnnS2iWNKAxbpkbjmCImhaCmwx2KCYi6lDETVYcDkfET2q32wGA4xiOI+6Zm0Ajv+oGF5rdXg5ZjIDauxMD4dtSzNwQEbUn4uAmKyurw066oihCEAR4vd5OL0zvagJ9bjJT5O9z439eM1LMRjS5vSivbUbf7NZNF6klR6A7sT0OjRXlwm0pIqKORfxT/JtvvonnOpJObWN8MzdSI799VQ0oY3ATEW1sS/k/sg0uLzxeH0wqPdVFRKSkiIObM888M57rSCpenxg87RKvmhvAP2PKH9ywqDgS2ghuQmurd3ri0uGaiEjrYs6/19TU4LXXXsOOHTsgCAKGDRuGm2++GZmZmXKuT5fqmhPTLC5YVMzj4BFR+1wpALCYDMHtRkcTgxsiorbElNNet24d+vfvj2eeeQZHjhxBVVUVnn76afTv3x8bNmyQe426I3UnTotzszjpOHhpDTM3kdBCnxuARcVERB2JKXNz991345JLLsH//d//wWTyP4XH48Ett9yCu+66CytXrpR1kXqTqO0PqZEfMzeR0cK2FODfmqpwOBncEBG1I6bgZt26dS0CGwAwmUy4//77MWbMGNkWp1dSd+LMOG8p5DFzE5VgcBPHOig5SPOlpNNdRETUUkx7Ina7HcXFxa1uLykpQUZGRqcXpXehDEF8jxxLNTflDmZuIiHV3Kh/W0pq5MfMDRFRW2IKbq6++mpMnz4dixYtQklJCQ4ePIj//Oc/uOWWW3DttdfKvUbdqW3097jJilOPG0leoJFfTaMbTS72HjoeUQydYNPCthTA+VJERO2JKXXw5JNPQhAETJs2DR6P/wes2WzG73//ezz22GOyLlCPElXbYbeZkW41od7pQWltE/p3T4/r62lZvdMDr08EoP7gJrQtxcwNEVFbYgpuLBYLnn32WcyZMwd79+6FKIoYMGAAUlNT5V6fLtXEea5UuLxMG3ZX1qOsppnBzXFIAafFaIDNrO7GeNK2FAuKiYjaFtNP8TfeeAMNDQ1ITU3FSSedhBEjRjCwiUIiZxhJJ6ZK2cjvuMJHL3Q0ZkRpUpdiDs8kImpbTMHNvffeix49euCaa67BZ599FtyaoshIp6USkbmRet1wOvjxaWFopoTzpYiIji+m4KasrAyLFi2C0WjENddcg7y8PMycOROrVq2Se326lMh+KqEuxczcHI9WetwA3JYiIupITMGNyWTCRRddhIULF6KyshJz587FgQMHMGnSJPTv31/uNeqONDQz3qelgFCvGzbyOz4tjF6Q2LktRUR0XJ1utJKamorzzjsPR48exYEDB7Bjxw451qVrCc3cZEnBDTM3x6OV0QtA+FFwZm6IiNoS87GQxsZGLFy4EBdccAHy8/PxzDPP4LLLLsNPP/0k5/p0qaYp0OcmIaelAttSrLk5Li1tS0nNH9mhmIiobTFlbq699lp8+umnSE1NxZVXXonly5djwoQJcq9Nl5rdXjS7fQASU7yaH8jc1Dk9qGt2B3/rp5a0FNxI2aW6ZjdEUVT96S4iokSLKbgRBAGLFi3Ceeed12K+FHVMqu0wCECGNf7/71ItJmSmmFHb5EZZbTODm3ZoKbiR3kOfCDS4vEhPwL8jIiItiemn4jvvvCP3OpJG+JFjgyExv3HnZdpQ2+RGaU0TBuVw9ldbgnOl4jzvSw42swFmowC3V4Sjyc3ghojoGDHX3CxbtgwXXXQR+vfvjwEDBuCiiy7C119/LefadCnY4yaBGQLpxFQ5T0y1S0uZG0EQwramWHdDRHSsmIKbF154Aeeffz4yMjLwxz/+EXfeeSfsdjsuuOACvPDCC3KvUVekY+CJvIiGuhQzuGmPlpr4AaEuxTwxRUTUWkz57Dlz5uCZZ57B7bffHrztzjvvxGmnnYZ//OMfLW6nlqTMTWZq/HvcSEJdinkcvD21TdqYCC4JNvJjl2IiolZiytw4HA6cf/75rW6fMmUKHA5HpxelZ0psf4S6FDNz0x4t9bkBwkYwMHNDRNRKTMHNJZdcgg8//LDV7R9//DEuvvjiTi9Kz2obAz1uErot5c/ccHhm25rdXrg8/uP5mQnoPSQHDs8kImpfxNtSzz33XPC/hw4din/84x9Yvnw5CgsLAQBr1qzB999/j3vuuSfqRcybNw9PPPEEysrKcOKJJ2Lu3LmYOHFih4/7/vvvceaZZ2L48OHYtGlT1K+rBCUyN/lhjfzYF6W12rDj+ekWbZw84vBMIqL2RfyT/Jlnnmnx5y5dumD79u3Yvn178LasrCzMnz8fDz74YMQLWLRoEe666y7MmzcPp512Gl5++WVMnToV27dvR+/evdt9XG1tLaZNm4ZzzjkHFRUVEb+e0hI5EVySG6i5aXJ74WjyaCY7kShScJNhS9zx/M6Sjqw7mLkhImol4uDml19+icsCnn76aUyfPh233HILAGDu3Ln46quv8OKLL2LOnDntPu62227DddddB6PRiI8++igua4sHJU7l2MxGdE2z4EiDC6W1TQxujqGloZmS8C7FRETUUsx9buTgcrmwfv16TJkypcXtU6ZMwapVq9p93Ouvv469e/fioYceiuh1nE4nHA5Hiy+l1CrQ5wYInw7OuptjaanHjSR4FJzzpYiIWompwODmm28+7vfnz58f0fNUVVXB6/UiJyenxe05OTkoLy9v8zG7d+/Gn//8Z3z77bcRj36YM2cOHnnkkYjuG29K9LkB/CemtpU6UMoBmq1oMbgJHgVn5oaIqJWYgpujR4+2+LPb7cZPP/2EmpoanH322VE/37EFru0VvXq9Xlx33XV45JFHMGjQoIiff/bs2Zg1a1bwzw6HAwUFBVGvUw7BC2mCt4akAZrM3LSmpdELktBRcGZuiIiOFdNP87aOgft8PsycORMnnHBCxM+TnZ0No9HYKktTWVnZKpsDAHV1dVi3bh02btwYbBTo8/kgiiJMJhOWLl3aZnBltVphtVojXle8iKIYNn4hcU38gLBeN8zctKK1Bn5A2FFwnpYiImpFtpobg8GAu+++u9WpquOxWCwYPXo0ioqKWtxeVFSECRMmtLq/3W7H1q1bsWnTpuDXjBkzMHjwYGzatAnjxo3r9N8jnhpcXnh9IoDEX0jz2eumXVobvQBwW4qI6HhkzcPv3bsXHk90afJZs2bhhhtuwJgxY1BYWIhXXnkFxcXFmDFjBgD/ltKhQ4fw5ptvwmAwYPjw4S0e36NHD9hstla3q1FNoIGfxWSAzZzYWu5cO4dntkfTNTcsKCYiaiWm4Ca8fgXwb7eUlZXh888/x4033hjVc1199dWorq7Go48+irKyMgwfPhxLlixBnz59AABlZWUoLi6OZZmqE34RTXQjvfys0AgGNvJrSWujF4DQtpTL60Oz2wub2ajwioiI1COm4Gbjxo0t/mwwGNC9e3c89dRTHZ6kasvMmTMxc+bMNr+3YMGC4z724YcfxsMPPxz1aypBOimV6GPgAJBjt0EQAKfHhyMNLnRLV74GSS20mLlJt5ggCIAo+oMzBjdERCExBTeff/45RFFEWloaAGD//v346KOP0KdPn4iPZycjJS+iFpMB2elWHK5zoqy2mcFNGC028TMYBGRYTXA0e+Bo8qBHhtIrIiJSj5gKPy677DK89dZbAICamhqMHz8eTz31FC677DK8+OKLsi5QT5QYvRAuP9DIr7SGRcXhtJi5AUJ1N+xSTETUUkzBzYYNG4KDLd9//33k5OTgwIEDePPNN1sM2KSWlD6VEzwOzqLiFhwaPC0F+GdhAex1Q0R0rJiCm8bGRmRk+PPgS5cuxa9+9SsYDAaMHz8eBw4ckHWBelLTqEyPG0lesJEfgxuJ2+tDg8sLQIOZm+AIBmZuiIjCxRTcDBgwAB999BFKSkrw1VdfBWdDVVZWwm63y7pAPVF6+4PzpVoLDwykYEErQttSzNwQEYWLKbj561//invvvRd9+/bFuHHjUFhYCMCfxTn55JNlXaCe1Db5+9woVXPDLsWtSQFnutUEk1HRObJRCw7PZM0NEVELMf2qesUVV+D0009HWVkZRo4cGbz9nHPOweWXXy7b4vRG6cwNuxS3JtWraC1rA4TNl+K2FBFRCzH/RM/NzUVubm6L28aOHdvpBemZVHOT6KGZEilzU+Fohs8nwmBgIz+li7w7g9tSRERt01YeXuOUztz0yLDCIABur4jKOqcia1Abpd+TzrBzW4qIqE0MbhJIyQ7FAGAyGtAv2994cUeZQ5E1qI22gxtuSxERtYXBTYJ4vD7UOf3bB0peSEf2ygIAbD5Yo9ga1ESrPW4AwJ4iZW64LUVEFI7BTYKEX4CUDG5G9MoEAGw5WKvYGtREi6MXJFITP3YoJiJqicFNgqjlyPFJgczNloO1EEVRsXWohT62pZi5ISIKx+AmQWoa/T1ulL6Inphvh9EgoKreyU7FCDstpcWj4CksKCYiaguDmwRRS4bAZjZiUI5/dMYW1t0EAwOljud3hpS5aXR54fH6FF4NEZF6MLhJkFqFJ4KHG8m6myC1BJ2xSA/LNrHXDRFRCIObBFHTRXREWN1NslPT+xIts9GAVIsRALemiIjCMbhJkOBEcBVkbkInpmqSvqhYKsaVtni0xm5jl2IiomMxuEkQNbX5H5STAYvJAEezBweqG5VejmJ8PjFUc6OC9yUWweGZbORHRBTE4CZBgpmbFIvCKwEsJgOG5tkBJHczvzqnB1LiSg1BZyykdXNbiqjz/rezAkXbK5ReBsmAwU2CqK22Qyoq3prEdTdStsNqMsBmNiq8mtgE50ux1w1Rpzia3bjtrfX43VvrcKimSenlUCcxuEmQ2iZ/nxs11NwALCoG1LVVGCupSzEzN0Sds7OsDm6vCFEE/reD2RutY3CTIGrL3EhFxT+V1sLrS86iYi2PXpBwvhSRPMKHCRftqFRwJSQHBjcJItXcqOVC2r97OlItRjS6vNh7uF7p5ShCbQFnLDgZnEge20tDwc2avdWod/IXBi1jcJMgaruQGg0Chuf7szebS2qUXYxC1PaexELaUuNRcKLO2VEeCm5cXh++3XVYwdVQZzG4SYBmtxdOj789vlpqboDQ1tTWQ8lZdyPVqWhxrpQkeBScNTdEMfN4ffi5vA4AMHloDwBAEetuNI3BTQJIGQKjQUC6VT0X0hEFWQCAzUlaVKyLzA23pYg6bX91A5weH1ItRtx8ej8AwDc7KzmzTcMY3CRAeL2NIAgKryZkRE9/5mZHmQMuT/J9iHUR3HBbiqjTtpf5szaDczMwtm9XZKaYcbTRjQ3FNcoujGLG4CYB1HoR7dMtFXabCS6PD7sq6pReTsLVSqMXVPa+RIPbUkSdJ52UGppnh8lowNlD/FtTX3NrSrMY3CRATaO/x43aghtBEIL9bpKxU7FDB31uuC1F1HnhwQ0AnBOou/ma3Yo1i8FNAqg1cwOEFRUnYd2Nmt+XSEl9buqdHviStF8RUWdJx8CH5WUAAM4Y1B1mo4B9VQ1J2ypD6xjcJIB0EVXTSSlJKHOTfMGNLpr4BTI3PhFocLHuhiha1fVOVNY5IQjA4Fx/5sZuM2P8Cd0AAMu4NaVJDG4SQM0ZAilzs6uiDk0ur8KrSSw1vy+RspoMsBj9H2N2KSaK3o5AMXGfrqktTrNOHpoDAPh6O7sVaxGDmwQITQRX30U0L9OG7HQrvD4R28Paj+udKIqhPjcqfF8iJQhCaAQD626IonZsvY1EqrtZd+AIjjS4Er4u6hwGNwmg5gGN/qJif/ZmSxIVFTe5vXB7/TUqWs7cAKHhmTwOThS99oKbXl1SMTTPDp/o73lD2sLgJgFqgjU3FoVX0rZkLCoOb6yYZjEqvJrOkTosM3NDFL3t7QQ3AHDuUB4J1yoGNwmg9tqOkUl4HDyYTbOZVNVYMRZSRpC9boii4/R4safSfxpqaOCkVLhzAnU3K3cdhtOTXDWJWsfgJgFqA31u1HhaCgBOCmRu9lU1oC5JLpCOQAM/tQac0bBzW4ooJnsq6+HxibDbTOiZldLq+yf1zESPDCsaXF6s2XdEgRVSrBjcJIDaMzfZ6Vb0zEqBKAI/HUqOomK1vyfRyOC2FFFMpJNSQ/PsbWZwDQYhmL1hQz9tYXATZz6fGOpzo+IL6Uk9k6uoWM1F3tHithRRbNorJg537rBQ3Y0oslGmVjC4ibN6lwdS41g1X0hHFASCm0PJUVSsq+AmkLnhthRRdKTgZthxgpsJ/bORYjairLYZ20qTI7OtBwxu4qw20OPGZjbAZlbvqRypqDhZMjd66E4skY6CM3NDFDlRFCPK3NjMRkwcmA2Ap6a0hMFNnGmltmN4YFuq5EhTUjSs0sr7EolQEz9mbogiVe5oxtFGN4wGAQNz0o9738nDAnU3DG40g8FNnIW6E6uzx40kM8WMftlpAICtSbA1pafMTei0FDM3RJGSsjb9u6d1mFU/e0gPCIL/wEVZbVMilkedxOAmzrSUIQgWFZfUKLuQBAiOXrCp/33pSGhbipkbokiFn5TqSHa6FScXZAEAlu1gt2ItYHATZzVN/i2eTJX2uAkXHMOQBJkbLQWdHeFsKaLoHa8zcVu4NaUtDG7iTEsX0ZGB30ySoahYS+9LR+xhBcU8qkoUmUiKicOdG+h3s2pPNRqczJKqHYObOKtV8UTwY52Yb4dBACocTlQ4mpVeTlzpKrgJ/B3cXhFOj0/h1RCpX5PLi/1VDQDaHrvQlgE90tGnWypcXh++3X04nssjGTC4iTMtXURTLSYM7OH/oG/R+RBN6WSRtKWjZWkWIwyB5qrcmiLq2M8VdfCJQHa6BT0ybBE9RhAETA5kb4q2s+5G7RjcxFnwtJQGam6A0JwpPW9NuTw+NLn9Q/C0EHR2RBAE9rohisL20ui2pCRScPPNz5Xw+rgFrGYMbuJMa51wRwaCm806ztzUhmU3MnRwWgoIKyrmiSmiDkXSmbgtY/p2gd1mwpEGFzYWH43H0kgmDG7irEaaK5Wq7j43khGBTsVbD9botjhVCm4ybCYYDa2H5WlRhjWQueG2FFGHoi0mlpiNBkwa4p81VcRTU6rG4CbOtNYsbkheBsxGAUcb3Th4VJ/NqvTU40bCzA1RZHw+ETvLI+9xc6zJnBKuCQxu4qym0d/nRgunpQDAajJiSK7/A6/XomItFXlHil2KiSJz8GgT6p0eWIwGnNA9LerHnzm4O0wGAXsPN2Df4fo4rJDkwOAmjtxeHxpc2itc1XtRsdayaZEIFhRzvhTRcUnN+wbmpMNsjP4SaLeZMf6EbgDYrVjNGNzEUXjhqlYKioHwouIaZRcSJ6Eib+0fA5eEtqWYuSE6nmg7E7dl8lB/3Q27FasXg5s40mrhqlRU/NMhB3w6PO6ox8xNsEsxC4qJjivWk1LhzgnU3aw7cBRHG1yyrIvkxeAmjrTW40YysEc6bGYD6p0e7At08dQTPdbcZNj8mZs6FhQTHVesJ6XCFXRNxZDcDHh9Ipbv4taUGqkiuJk3bx769esHm82G0aNH49tvv233vh988AHOPfdcdO/eHXa7HYWFhfjqq68SuNrIaTVDYDIacGK+f2tq66EaZRcTB3oMbqRtT25LEbXP0Rw6BdqZzA0QfmqKwY0aKR7cLFq0CHfddRf+8pe/YOPGjZg4cSKmTp2K4uLiNu+/cuVKnHvuuViyZAnWr1+PSZMm4eKLL8bGjRsTvPKOSRPBs1K00eMmnDQhfHOJ/k5Maa2xYiS4LeXnaHbjhf/txuE6p9JLIRXaWeY/Ap6faUNmJzPq0pTwFbsOw+nxdnptJC/Fg5unn34a06dPxy233IKhQ4di7ty5KCgowIsvvtjm/efOnYv7778fp556KgYOHIh//vOfGDhwID799NMEr7xj0tBMLWYIRuj4xJR0okiL70t7pILiZN+WenrpLjy5dBce/mSb0kshFZJjS0oyomcmumdYUe/0YO2+I51+PpKXosGNy+XC+vXrMWXKlBa3T5kyBatWrYroOXw+H+rq6tC1a9d4LLFTpO7Enf0NQQlSUfG2Ugc8Xn1NmtZ15iaJt6U8Xh8+21IKAFi6vRxHWOhJx5AzuDEYhOCpqWU8NaU6igY3VVVV8Hq9yMnJaXF7Tk4OysvLI3qOp556Cg0NDbjqqqvavY/T6YTD4WjxlQharu3o1y0NGVYTnB4fdlXoq1GVlt+X9tjZ5war9lajqt4f0Li9Ij7edEjhFZHaSMfAh+V3PrgBwupudlTqdlyNVim+LQX4pxqHE0Wx1W1teffdd/Hwww9j0aJF6NGjR7v3mzNnDjIzM4NfBQUFnV5zJKRtKa10Jw5nMAgY3lOfRcVSXYoexy80ub1w6yzTFqmPN/mzNtnp/hq399YdVHI5pDIerw8/d2LsQltOG5ANm9mAQzVN2BGo5yF1UDS4yc7OhtFobJWlqaysbJXNOdaiRYswffp0vPfee5g8efJx7zt79mzU1tYGv0pKSjq99khoPUMwokB/E8K9PhF1Tv3V3KRbQw0Jk7HuptntxVfb/D9H5vxqBCwmA3aUOfDTIf3826XO2V/dAKfHh1SLEX26psrynDazEacP6A6ADf3URtHgxmKxYPTo0SgqKmpxe1FRESZMmNDu4959913cdNNNeOedd3DhhRd2+DpWqxV2u73FVyKEJoJr8yI6omcWAH0VFYfPXtJTcGMyGpBmMQJIzhNT3+ysRL3Tg/xMG84Z0gNTAidZFq9LzC8ypH7bA5mVwbkZMMjYVPXcYexWrEaKb0vNmjULr776KubPn48dO3bg7rvvRnFxMWbMmAHAn3WZNm1a8P7vvvsupk2bhqeeegrjx49HeXk5ysvLUVurvt/QtF64Kp2Y+rm8Ds1ufRx1lN6TFLMRFpPi//xlJf07S8bMzSeb/VtSF4/Kh8Eg4Kox/q3njzaV6ubfLnWOnMXE4c4ekgNB8A8arnA0y/rcFDvFf7pfffXVmDt3Lh599FGMGjUKK1euxJIlS9CnTx8AQFlZWYueNy+//DI8Hg/+8Ic/IC8vL/j1xz/+Uam/QruCHYo12OcGAHp1SUGXVDPcXhE7y/WxnywV3OpprpRE6lKcbCemHM1uLNvpb6R2ych8AP5aiPxMG2qb3PyNmgDEL7jpnmHFqIIsABykqSaKBzcAMHPmTOzfvx9OpxPr16/HGWecEfzeggULsHz58uCfly9fDlEUW30tWLAg8Qs/DlEUQx2KNbotJQhC8Ej4Vp1sTWm9Dup4krWR39JtFXB5fBjQIz3YddZoEPDr0b0AsLCY/LaXdn6mVHtCp6YYSKuFKoIbPWpye+EKnFrR4mkpSWhCuPq2/WKh6+AmSUcwSEe+LxmZ3+KU5RWB4Obb3YdRWtOkyNpIHarrnaisc0IQgCG5GbI//7mBGq/v9lSh0ZV828JqxOAmTqSLqMkgIDVQ6KlFJwUyN3opKtZ1cJOEwzMP1zmxam81gNCWlKRPtzSMP6ErRBH473pmb5KZdEy7T9dUpFnl35Ie2CMdBV1T4PL48O3uKtmfn6LH4CZOwieCR9KzR62kouI9lfVocGr/oillNfTU40aSkYTbUku2lsHrEzGyVyb6Zqe1+v6Vo/2FxYvXH4TPxyZrySpe9TYSQRDCBmlya0oNGNzEidZPSkly7Dbk2K3wif5RDFqnl/elLVKRtCOJMjfBLalRPdv8/tSTcpFuNaH4SCN+2M/5P8kq3sENAJwbCG7+t7MSXgbSimNwEyc1Gu5OfKwROtqa0ve2VHLV3JQcacSG4hoIAnDRiLw275NqMeHikf7vvceeN0lrewKCm1P7dUWGzYTqBhc2ldTE7XUoMgxu4sSho4voyOCEcO0XFes5uMlIsvlSUm+bwhO6Icdua/d+VwZ63nyxtbxFE0dKDk6PF3sq/fPx5Jop1Raz0YBJg9nQTy0Y3MRJTZN/gF9WqjZ73ITTU1GxIym2pZLjAv5pILi5dFT+ce93ckEW+ndPQ5Pbi8+3lCViaaQieyrr4fGJsNtMyM9sPwiWw+RhrLtRCwY3caKnDMGIwADN/dWNwWGgWqWnjNqxpG2pZDgt9XN5HXaW18FsFHD+iW1vSUkEIdSxmFtTyUc6KTU0zx73wx1nDuoOk0HA7sp67K9qiOtr0fExuIkTqeZGDxfRLmkWFHRNAQBs1fggQj0FnccKdihOgtNSn2z2FxKfNbhHRE0yLz+lJ4wGARuKa4JbFJQcElFMLMlMMWNsv64AuDWlNAY3caK3i6hUVLxZ41tTodNS+hu/kCxN/ERRDNbbHNvbpj09MmyYNNg/vXnxemZvIuEONCHVOim4iUdn4rawW7E6MLiJk1qNTwQ/llRUvFXDRcWiKAaPSesl6AwnbUvVOz267umysaQGJUeakGoxBi8kkZAKiz/YcAgenVy44+FwnRO3v7MBgx78Av/5objjB6iYKIoJzdwAoeDmx/1HNb+Nr2UMbuJEb5mbk3pmAdB2UXGDyxvsP6GX9yWctC0likC9jlvAf7LJn7WZMiwHKVF0/z57SA9kp1twuM6JFbsOx2t5miWKIhavK8Hkp1fgsy1lEEXg+f/t0XTPlgqHE0cb3TAaBAzMSU/Ia/bulorBORnw+kQs38VBmkphcBMnesvcDO9phyAApbXNOFznVHo5MZHeE7NRQIpZuyMx2mMzG2Ex+T/Seq278Xh9+Cxw4unSdhr3tcdsNOCywGNYWNzSgeoGXP/aWtz3/hbUNrlxYr4dmSlmHKppwjc7tXuB3l7mzzT3754GWwI/85OH+Y+EF/HUlGIY3MSJngqKAX8PlRMC7e23HqpRdjExklLEdpu2R2Icj13nvW5W76tGVb0TXVLNOH1gdtSPl7amlu2oRFW9NoN0OXm8Pry8Yi/Om7sS3++phtVkwOypQ/DxH07DlYHBo2+vPaDwKmMXflIqkaStqRU/H4bLwy1QJTC4iQOfTwwWdWamaL/PjWSkVFRcos26m9B7oo+Asy1SobRem9VJW1IXnJQHszH6H1+DczMwsiALHp+IjzYeknt5mvLToVpc+u/vMeeLnWh2+3DagG5YevcZuO3M/jAZDfjN+D4AgBW7DqPkSKPCq41NIjoTt2Vkryxkp1tR5/Tgh1849kMJDG7ioK7ZAzGwTa2nC6k0RFOrx8H1PFdKEuxSrMNeN81uL778qRxA5Kek2iJlJN5bVwJR1G49SayaXF7MWbIDl/77e2wrdSAzxYwnrhiBt6ePQ59uoeGj/bLTMHFgNkQRWLhWm4XFiS4mlhgMAs4Zwm7FSmJwEwfSRTTVEqqB0IPwTsVavCjorci7LXYd97pZ/nMl6pwe5GXacGrfrjE/z8Uj82E1GbCrol4XI0Wi8f2eKpw3dyVeXrkPXp+Ii0bk4etZZ+LKMQVtbtX+Zpw/e/PeuhI4Pd5EL7dTmlzeYCO9oXkZCX99qVtx0fYKTf681Dr9XHlVRBq9oLeL6In5dhgNAqrqXSirbVZ6OVHT8+gFifR30+O2VHhvG4Mh9pqpzBQzpg7PBZA8hcU1jS7ct3gzfvPqWhQfaURepg2vThuDF647Bd0zrO0+bvLQHsi123CkwRXMmmnFzxV18IlAdroVPTLiO3ahLacPyIbVZMChmib8XFGX8NdPdgxu4kCvGQKb2YhBOf7fgLR4JDw0ekF/DfwkwcyNzral6prd+HqH/9TOxZ3YkpJIhcWfbC5Fs1tbGYloiKKIz7aUYvLTK7B4/UEIAnBjYR8svfuMYGbheExGA64Z6/9/9fYabRUWby+VtqQSn7UBgBSLERMDRe+caZZ4DG7iQG8npcJJzfw2azCdr9egM1zotJS+MjdLt1XA5fGhf/c0nCjDZOfCE7qhZ1YK6po9+GqbtjISkSqtacKtb67D7e9sRFW9CwN7pOP9GRPwyKXDg7VZkbjm1N4wGgT8uP8odpY74rhieSW6M3FbLjvZ33pgwar9bOiXYAxu4kDPF1FpDIMWOxXr+X2RhLal9JW5+Ti4JdVTlmP8BoOAK8eECov1xOcT8ebq/Tj36RX4ekclzEYBd08ehM/uPB2j+3SJ+vlyM204N3C0eeEa7RQWK1VMHO6C4XkYnJOBumYPXvl2r2LrSEYMbuJAbw38wkknprRYVCxt1dij+K1Va4LDM3VUc1NV78T3e6oAAJeM6vyWlOSK0b0gCMCqvdWaPep8rF0VdbjipVX468fb0ODyYnSfLlhy50T8cfJAWE2xN7G7PnAs/MONh9DgVH/g7POJ2FmuTI+bcAaDgFlTBgEAXv9+P3srJRCDmzjQc4ZgUE4GLCYDHM0e7K/W1gVBz++LJLgtpaPgZsnWMnh9Ikb0ykS/7LSOHxChXl1SMaF/N4gi8N8NB2V7XiU4PV48U7QLFz73LTYU1yDdasLfLj0Ri28rxMCcztecTOjfDf2y01Dv9OCjTervD3TwaBPqnR5YjAac0F2+fzOxmDIsByN7ZaLR5cWLy5m9SRQGN3FQ0+g/LZWVqp8GfhKLyRD8TUhrRcVJEdykSEfB1f/bdaSkxn2d6W3TnqsChcWL1x3U7LBRt9eHq19eg2eX7YbbK2Ly0B5YevcZuKGwb6dOlYUzGAT8ZlxvAMDba4pVn7WVmvcNyk2PqdmjnARBwD1TBgMA3lpzAGW1TYquJ1kwuIkDvTeLGxncmtJW3Y3e3xcglLnRy1Hwg0cbse7AUQiCPKekjnXeibnIsJlwqKYJq/dVy/78ifCfH0uwqaQGdpsJ/77uFPzftDHIz0qR/XWuGN0LVpMBO8oc2FBcI/vzyynYmThXuS2pcBMHZmNs365weXx44X97lF5OUmBwEwfSaaksnV5EtVpU7EiCzI3eOhR/utl/hHZ8v27Iscvfq8RmNgYzQos1WFhc7/Tg2a93AQDuPW8wLhyRF7e5aVmpFlw0wv//aqHKj4WroZg4nD9746+9WfRjCYo1tqWvRQxu4kDv2x9SUfFPpbXwaiSV3+z2whkYYKfrzE1KqEOx2rcOIvFxoL5DzkLiY0lbU1/8VB787GrFKyv3oarehX7Zabh2bO+4v9714/2v8dnWMhxtcMX99WKltuAGAMad0A0TB2bD4xPx7LLdSi9H9xjcxIGeT0sBQP/u6Ui1GNHo8mJPZb3Sy4mIlLURBCDDqucmfv5/cx6fiGa3tqcR76qow87yOpiNQrCjcDyM6JWJwTkZcHp8+DRw5FwLKh3N+L+V+wAA9583OCG1JaMKsnBivh0ujw+L16sz0+VoduPgUX9di5I9btpyb6D25sONB7Gnkl2L44nBTRzoPXNjNAgYnh86Eq4FwXobm1m2Iks1SrUYYQz8/bR+YkoqJD5zUI+4FucLQqjnzeL12jk19czXu9Hk9uLk3lk4P47BXzhBEILHwheuLVZlEfbOMn/QkJ9pQ6bKfsEcWZCFc4flwCcCzxQxexNPDG5k5vL40Ojyt3PPStHfaSnJCI0VFUsXeruORy8A/otPhg6GZ4qiGJolFcctKcllJ/eEySBgc0kNfi5X/2/UeyrrsOhHf0O9v1wwNG51Nm25dFQ+MqwmHKhuxHeB/kNqEuxMLEMn63i4Z8ogCALw+dYybCvVxs9PLWJwI7Pa8O0Pm34vpCMKsgAAWw5p48Op92xaOLsOioo3ldSg+EgjUi1GTB7aI+6vl51uxTmB19FCYfFjX+yETwTOOzEHYzoxIT0WqRYTfnWKf6yAGudNqbHeJtyQXDsuDhRmP710l8Kr0S8GNzKrDUwE1/v2x4ie/szNjlIHXB7113YkU3Cjhy7FHwe2pM4dloNUS2J+SbhytL+w+MONh+D2qvff9Jp91fh6RyWMBgH3nz9EkTX8JrA19fWOCtX1bdmu8uAGAO6aPBBGg4BlOyuxofio0svRJQY3MkuWi2ifbqmw20xweX2aSONLQ+v0PHpBovXhmV6fiM8CU5QvTcCWlOSswd3RPcOK6gYX/rezMmGvGw1RFDFnyQ4AwLVjC9C/e7oi6xiUk4Gx/brCJwLv/qCeTJcn7OeRmoObE7qn49eB7NdTS39WeDX6xOBGZsEeNyorZJObIAjBfjdbDtUoupZISFs0eg86gVBdkVaHZ67eW42qeieyUs04fUD3hL2uyWgIbreodWvq861l2HywFqkWI/54ziBF1yIVFv/nh2LVZLr2VzfA6fEh1WJEn66pSi/nuO44eyDMRgHf76nGqr3qq13SOgY3MkuWzA0QVlRcov66m2R6X7Q+X+qTzf7eNheclAeLKbE/oqStqW9+PozKuuaEvnZHXB4fHv/S/1v+bWf0R/cMq6LrOf/EXGSnW1BZ58TX2ysUXYtke+Ck1ODcDNWXBRR0TQ32Jnpq6S5d9KVSEwY3MpMyN8lwEQ1lbrQT3Oi5gZ8k2KVYg/OlnB4vvvipHEB8Zkl1ZECPdJzSOwten4gPN6hrQOTCtQdQfKQR3TOsuGViP6WXA4vJEGyA+PZadRQWq72Y+Fi3TxoAq8mA9QeOYvnPh5Vejq4wuJFZMmUIpMzNroo6NAWOv6tVMgU3wS7FGszcLP/5MOqaPci12zA2waeAJNIF+711Jar5bdrR7MZzga62d08ehDSVNKK8dmxvCALw/Z5q7DusfEPP4DFwjQQ3Pew23DihLwDgyaU/q+bfmx4wuJGZ3rsTh8vLtCE73QqvT8T2MnVnb5JhrpQkNDxTe5mb4ATwUfmKbStcOCIPKWYj9h5uUM2AyJeW78XRRjf6d0/DVYGGg2pQ0DUVkwb7j9AvXFus8GqA7aXaytwAwIwz+yPNYsS2Uge+DGQtqfMY3MgsmTI3/qJif/Zms8rrbpLpfdFqE7+6Zje+3uGv3VBiS0qSYTNj6kn+jr/vq2DEQFltE1777hcAwJ+nDoUpAWMWoiHNm3p//UE0u5XL4FbXO1FZ54QgAENyMxRbR7S6plkw/XT/NuPTRbs0M69P7dT1KdGBmkZ/nxs9dycOd3Kgmd+85XuDvzWpUVJlblK0WVBctL0CTo8PJ3RPw4kKd5eVtqY+3VyGRpeyGbCnl+6C0+PD2L5dE9LQMFpnDuqBnlkpqG1yKzqba0egmLhP11TVbNtFavrEE5CZYsbuyvpgQT11DoMbmSVTbQcA3FDYB0Pz7Kiqd+LqV1Zj3f4jSi+pTaHZUtr6oRcLrW5LSY37LhmZn9BxAm0Z168r+nRLRb3Tgy+2KrdVsLPcgfc3+Oddzb5giOL/X9piNAi4bpw/e/O2gltTWismDpeZYsbvzjgBADD3692qOVqvZQxuZFaTRDU3AJCVasF/fjceY/p0QV2zB9e/thbLf1ZXAzSP14eGQMFzMmRutLgtVV3vDM4pUnJLSiIIAq44RRqmqdzW1GNf7IQoAheelIeTe3dRbB0dufrUApiN/tlcPyl0elLLwQ0A/Pa0vshOt+BAdSPe19AAV7VicCOzZNr+kGSmmPHW9HE4c1B3NLt9uPXNdYqmp48VPmMpGTJqmXHalnr3h2KM+fvX+O3rP+C/6w/K+vxLtpbB6xNxUs9MnKBQ191j/Xp0LwgCsGbfERyobkj463+/pwrLfz4Mk0HAfecNTvjrRyM73Yrzh+cBUG7e1HaNnZQ6VqrFhJlnDQAAPLdst6L1S3rA4EZGoigmTYfiY6VYjPi/aWNw0Yg8uL0i7vzPRixUSe8LaUsqzWKEWWXFmPEgbUs1u32yzP0SRRFPfLUTsz/Yiqp6J775+TDuWbwZY/7+NX735jp8srm003Up0gTwRI5b6Eh+VgomDvR3SE70b9I+n4g5X/jHLFw/vg/6Zqcl9PVjcX1ga+rjTaUJr/dyeXzYGziKPlSl08Ajcd243sjLtKGsthnv/qD86TMt0/9P+gRqdHnhCVS6J1PmRmIxGfDsNSfjN+N6QxSBv3z4E+Yt36N47wZHktVBpYfVFdV18iLj8vhw96JN+Pc3ewEAt515Au6aPBD9u6fB5fFh6fYK3PnuRpzytyL84Z0N+PKnsqh/4zxU04Qf9x+FIAAXjVBPcAMAV472b029t64koR2LP9lcip8OOZBuNeGOswck7HU7Y2y/rhiUk44mtxcfJDgY3F1ZB7dXhN1mQn6mLaGvLSeb2Yg7zh4IAPj3N3sUL2bXMgY3MpLqbSxGA1LMRoVXowyjQcDfLxuOP0zqDwB4/MufA3UDygU4yXQMHPC/B+lWqZFf7D8ca5vcuHH+D/hoUymMBgGP/3oEZk8dirsmD8LXs87EF3+ciD9M6o8+3VLR7Pbh8y1lmPH2Boz+WxHuXrQJy3ZURJQ5krYwx/XrilyVXZjOHZaDHLsVFQ4nLn7+u4RMcG52e/HEV/4xC78/qz+6pSs7ZiFSgiDgN+P886beXluc0M+8dFJqaJ5dlUXX0bhyTC/07pqKqnoX3liljuy3FjG4kVFw8nSKWfMfsM4QBAH3nTcED1wwBADw8sp9+PN/tyrWvyHZTrABoVNhsWZuSmuacOVLq7B6XzXSLEbMv+lUXHVqQfD7giBgaJ4d9503BMvvPQuf3H4afnfGCeiZlYIGlxcfbjyE6W+sw5i/F+G+xZuxctdheNo5ARI6JdUzprXGk81sxLu3jsfAHumocDhx9cur475d8NbqAzhU04Rcuw03n6b8mIVoXH5KT6SYjdhTWY+1vyTu5KTWi4nDmY0G3DXZn715acVezbV0UAsGNzKqaQr0uEmyepv2/O6M/vjXr0+CQQAWrSvB7e9sgNOT+CK5ZMvcAGG9bmKYL7WttBaXz/seuyrq0SPDivdmFOLMQe1P55YmxD9wwVB8e/8k/Pf3E3DThL7okWGFo9mDxesPYtr8HzD2n8vwwIdbsXpvdTDQ3V1Rhx1lDpiNAqYOz43tLxtnJ3RPx4d/OA3nn5gLt1fE7A+2YvYHW+Pyb7mm0YXn/+cfszBryiCkWLSVAbbbzLjsZP/WYiILi7U2dqEjl47qiQE90lHb5MZr3/6i9HI0icGNjJLxpFRHrj61N/593SmwGA344qdy3PLGOjQ4E7OPXNPowjNFu/D4lzsBAFlJ9L4Ej4NH+Vvfyl2HcdVLq1HhcGJQjv+ifmJ+ZsSPNxgEjO7TBQ9fciJWzz4H//ndePxmXG90TbPgSIML76wtxrX/twbj5yzDw59sw4vL/bU8Zw7qji5p6m18mW414cXrT8F95w2GIPhPjl3zyhpUOOStw5m3fC8czR4MzsnAr09Rz5iFaEhbU19tK8fhOmfcX08UxVBwo+Fi4nBGg4BZ5w4CALz23S842uBSeEXaw+BGRsGTUkl0EY3E1JPyMP+mU5FqMeLb3VX4zatrg52c4+FwnROPfbETpz32Pzy7bDcczR6c0D0N0wr7xu011cYenAweeXDz3roS3LzgRzS4vBh/QlcsnjEBPbNSYl6D0SBg/And8I/LT8IPD5yDt6aPxVVjesFuM+FwnRMLVu3HBxv93VgvVkFvm44IgoA/TBqA1286FXabCRuLa3Dhc9/hR5kaV5YcacSC7/cDAP58wRAYFZqt1VnDe2ZiVEEW3F4R762Lf4+gCocTRxvdMBoEDOihjjYCcjj/xFycmG9HvdODl1bsVXo5msPgRkbJuP0RqdMHZmPhLeOQmWLGppIaXP3yGlTK/FtvWW0THv5kG07/1//w0oq9aHB5MSQ3Ay9cdzKK7j4TJ/WKPAOhddK2VCRdikVRxDNFu3D/+1vg8Ym4bFQ+3rh5rKz/jk1GAyYO7I7HrxiJdQ+ei/k3jcGvTu6JdKsJvbum4txhObK9VrydNbgHPr3jdAzJzUBVvRPXvrIGb63e3+kC2qeLdsHl9WFC/2446zjbgFpw/Xh/9uadtcVxr7WThvb2754Gm44OchgMAu6Z4s/evLF6v+w/L/WOwY2MpNNSmay5adPJvbvgvdsK0SPDip8r6vDrl1bJ0hytuLoRsz/YijMe/wYLVu2H0+PDyIIsvDptDL7440RcNCJfs78FxyrSbSm314f73t+CZ5f56zz+MKk/nrl6FKym+F0kLCYDzh6Sg6evHoVNfz0Xy+89C6kWbY3F6NMtDR/MnICLRuTB4xPx/z7ehvvf3xJz47WfDtXiw0AWa/bUoZo/kHDRiDxkpphxqKYp7h3Lw09K6c2kwT1wSu8sNLt9+Pc3e5RejqYwuJERMzcdG5ybgf/+fgJ6d01FyZEmXPHSauwsj23g5p7KOsxatAmTnlqOd38ohtsrYly/rnhr+lh8NHMCJg/L0fxFIlaRbEvVNbtx84If8f76gzAIwD8vPwn3nZfY+UUmowEGjQaeqRYTnr/2ZDxwwRAYBGDx+oO46uXVKK1piup5RDHUsO/SUfm6yDDazMZgj6B4FxZv19FJqWMJgoB7p/i7U7/zQzEOHm1UeEXaweBGRrWsuYlIQddUvD+jEENyM3C4zomrXlqN9Qci7x+yrbQWf1i4Aec+sxIfbDwEr0/EGYO6473bCrHotkJMHNg9aYMaiT1FOgre9rZUeW0zrnxpNb7dXYUUsxGv3jgmOPyQIicIAn53Rn+8efM4dEk1Y8vBWlz8/HdYvbc64udYubsK3++phsVoCF7I9OA3ga2p5bsOo+RI/C7KejoG3pYJA7IxoX83uL0inl/G7E2kGNzIqJbbUhHrYbdh0e8KcUrvLDiaPbj+1bVYuevwcR+zofgopi/4ERc+9x0+31oGUQSmDMvBx384DW/ePBZj+3VN0OrVL0PK3LSxLbWz3IHL532PneV1yE63YtFt43H2EO3UvKjR6QOz8cntp2NYnh3VDS5c/9pazP/ulw7rcLw+EXOW+LM20wr7oKBraiKWmxD9stNw+oBsiKI/6xAPTS4v9lf5t7b1cgy8LfcEgt73NxzEL1Wxb+U3u7346VAtPthwEI99sRPTF/yIMx7/BoMe/AI3zv8Bn20p1c1MK21tdKtcsM9NinqPtKpJZqoZb98yDre9tR7f7q7C9Dd+xLPXnIwLTsoL3kcURaz95Qhe+N+e4NRogwBcOCIff5jUH0Ny9fsDrTNC21ItMzer9lThtrfWo87pQf/uaVjw27G6uqAqqaBrKv77+wl44MOt+HDjITz62XZsPVSLf15+Urv9aj7ceAg7y+tgt5lwu0bGLETj+vG98d2eKrz3YwnumjxQ9lqunyvq4BP9gzu7Z2ijk3MsRvfpgrOH9MD/dlZi7te78Ow1Jx/3/s1uL/ZU1mNPZT12VdRhV0U9dlfWofhII9qLt1fsOowVuw4jM8WMS0fl48rRBRjeU7sdnxncyCgZO+F2VqrFhNduPBV3L9qEz7eW4fZ3NuCfl5+Eq08twIpdh/HC//ZgXWDLymQQcPnJPfH7s/qrZnK0WknbUuGZmw82HMSf/rsFbq+IsX274pVpo5GVykBcTikWI56+aiRG9MrE3z/fgQ83HsKuijq8dP3oVkFks9uLp5b6xyz8YdIAXb4Xk4eGxld8+VM5Lh0lbxfq7aXSllSGrM+rRrPOHYT/7azEJ5tLMfOsARicm4Fmtxf7Djdgd2VdKIip8Acx7R1S65JqxsCcDAzKScegnAwM7JGBzBQzPt9aiv+uP4RyRzPeXH0Ab64+gCG5GbhidC9cdnJPZGtkDIiEwY2MknUieGdZTAY8d+3JsKeY8O4PJfjzB1vxyrf7sO+wP/1qMRpw1am9cNsZ/ZlliJCUualr9kAURfz7mz14cukuAMCFI/Lw1JUjdXVsVk0EQcBvT+uHIbl23P7OBmwrdeCSF77D89eegtMHZgfv9/r3+1FW24yeWSm4cUJf5RYcRyajAdec2hvPLtuNhWuKZQ9u9NaZ+HiG98zEBSflYsnWctz65jqYDAL2Vze0G8RkpZoxqEcGBuakY2CPQCCTk4HsdEub2Zhh+XbMOncwvttThffXH8RX28qxs7wOf/98Bx77YicmDemBK0f3wqQhPWA2qr+ihcGNTERRxAnd01HT6GJBcQyMBgH/vPwkZKZY8NKKvdh3uAEpZiN+M643bj3jBOTY1TVQUe2ko+A1jS488OFWvPuDv5nabWecgD+dP0SzJ5S0pLB/N3x6x+mY8fZ6bDlYi2nz1+LPU4fg1okn4GijG/MCR3vvmTJI14HmtWN744Vv9uCH/Ufwc3kdBufKl2XRezHxsWadOwhf/lSO4rACbbvNFAxcgtmYnHR0T7dGvaVkNAg4c1B3nDmoO2ob3fhkSyneX1eCzQdrUbS9AkXbK5CdbsFlo3riyjEFsr6XchNEJcc1B8ybNw9PPPEEysrKcOKJJ2Lu3LmYOHFiu/dfsWIFZs2ahW3btiE/Px/3338/ZsyYEfHrORwOZGZmora2FnZ7cnwotOS/6w+i3NGMa04t0MxEZLWpqndizN+/Dv7ZIAAPX3JiUnVpVotmtxf/76OfsHj9QQD+HjD2FDPeWVuMoXl2fH7H6boPNme8tR5fbivHtWN74+FLhslSe+PziRjxyFLUOz346q4zVH2hldPKXYex93A9BvbwBzPdM6IPYqK1q6IOi9eV4MONh1BVH+ouP6JXJq4c3QsXj8xP2LZqpNdvxYObRYsW4YYbbsC8efNw2mmn4eWXX8arr76K7du3o3fv1kdTf/nlFwwfPhy33norbrvtNnz//feYOXMm3n33Xfz617+O6DUZ3JDeOT1eDH7wSwCAzWzAc9ecjCknqnMwZTIQRRFvrzmARz7dDk/YPsJb08di4kBtdyOOxHe7q3D9a2uDf86wmdA93YrsdCuyMyzITreiW1rov7PTreiebkW3dAvSrG1vMBRXN+KMJ76BxWTAtkfO08RWida5vT6s+PkwFq8vwbIdlcF/yxajAeeemIMrR/fCxIHd49o0VTPBzbhx43DKKafgxRdfDN42dOhQXHbZZZgzZ06r+//pT3/CJ598gh07dgRvmzFjBjZv3ozVq1dH9JoMbigZ/Gre9zhU04SXrh+Nk3t3UXo5BODH/Ufw+7c3oKreiYkDs/HW9HFKLykhfD4Rv3trPVbsqoTbG90lJ8VsbBH0+L/8g1gXri3G8J52fHZH+5l+io/qeic+2lSKxetKsLO8Lnh7rt2GX53SE1eM7hWXgx+aCG5cLhdSU1OxePFiXH755cHb//jHP2LTpk1YsWJFq8ecccYZOPnkk/Hss88Gb/vwww9x1VVXobGxEWZz63oXp9MJpzM0ndbhcKCgoIDBDema1yfCJ4r8jVZlKhzNWLK1DJeN6qnqSejxIIoiHE0eHK53orreiap6F6rqncGvw3UuVDcE/lznQlMEPVeuHN0LT1w5MgGrp7aIoohtpQ4sXleCjzeXBg/WAMCXd02UvV1HpMGNogXFVVVV8Hq9yMlp2UAsJycH5eXlbT6mvLy8zft7PB5UVVUhLy+v1WPmzJmDRx55RL6FE2mA0SDACH3XcmhRjt2G357WT+llKEIQBGSmmpGZao5ogneD0xMW/LiCQY90m8vjw82nJ+f/S7UQBAHDe2ZieM9MPHDhUCzbUYnF60pQWtOMwTnK1UGp4rTUscVQoiget0Cqrfu3dbtk9uzZmDVrVvDPUuaGiIjUK81qQprVhD7d0pReCkXAajLigpPycMFJeXB6vIo2AFQ0uMnOzobRaGyVpamsrGyVnZHk5ua2eX+TyYRu3bq1+Rir1QqrladuiIiIEkHubtTRUnQz3mKxYPTo0SgqKmpxe1FRESZMmNDmYwoLC1vdf+nSpRgzZkyb9TZERESUXBSvNJw1axZeffVVzJ8/Hzt27MDdd9+N4uLiYN+a2bNnY9q0acH7z5gxAwcOHMCsWbOwY8cOzJ8/H6+99hruvfdepf4KREREpCKK19xcffXVqK6uxqOPPoqysjIMHz4cS5YsQZ8+fQAAZWVlKC4OTZTt168flixZgrvvvhv//ve/kZ+fj+eeey7iHjdERESkb4r3uVEC+9wQERFpT6TXb8W3pYiIiIjkxOCGiIiIdIXBDREREekKgxsiIiLSFQY3REREpCsMboiIiEhXGNwQERGRrjC4ISIiIl1hcENERES6ovj4BSVITZkdDofCKyEiIqJISdftjoYrJGVwU1dXBwAoKChQeCVEREQUrbq6OmRmZrb7/aScLeXz+VBaWoqMjAwIgpDw13c4HCgoKEBJSQlnW6kM3xt14vuiXnxv1Emv74soiqirq0N+fj4MhvYra5Iyc2MwGNCrVy+llwG73a6rf3R6wvdGnfi+qBffG3XS4/tyvIyNhAXFREREpCsMboiIiEhXGNwowGq14qGHHoLValV6KXQMvjfqxPdFvfjeqFOyvy9JWVBMRERE+sXMDREREekKgxsiIiLSFQY3REREpCsMbhLskksuQe/evWGz2ZCXl4cbbrgBpaWlLe5TXFyMiy++GGlpacjOzsadd94Jl8ul0IqTw/79+zF9+nT069cPKSkp6N+/Px566KFW/98FQWj19dJLLym0av2L9H3hZ0YZ//jHPzBhwgSkpqYiKyurzfvwM5N4kbwvev/MJGUTPyVNmjQJDzzwAPLy8nDo0CHce++9uOKKK7Bq1SoAgNfrxYUXXoju3bvju+++Q3V1NW688UaIoojnn39e4dXr186dO+Hz+fDyyy9jwIAB+Omnn3DrrbeioaEBTz75ZIv7vv766zj//PODf46koRTFJpL3hZ8Z5bhcLlx55ZUoLCzEa6+91u79+JlJrI7el6T4zIikqI8//lgUBEF0uVyiKIrikiVLRIPBIB46dCh4n3fffVe0Wq1ibW2tUstMSo8//rjYr1+/FrcBED/88ENlFkSiKLZ+X/iZUd7rr78uZmZmtvk9fmaU0977kgyfGW5LKejIkSNYuHAhJkyYALPZDABYvXo1hg8fjvz8/OD9zjvvPDidTqxfv16ppSal2tpadO3atdXtt99+O7Kzs3HqqafipZdegs/nU2B1yevY94WfGfXjZ0ZdkuEzw20pBfzpT3/CCy+8gMbGRowfPx6fffZZ8Hvl5eXIyclpcf8uXbrAYrGgvLw80UtNWnv37sXzzz+Pp556qsXtf/vb33DOOecgJSUFy5Ytwz333IOqqio8+OCDCq00ubT1vvAzo278zKhPMnxmmLmRwcMPP9xm0Vz417p164L3v++++7Bx40YsXboURqMR06ZNgxjWS7GtSeWiKCoywVzron1vAKC0tBTnn38+rrzyStxyyy0tvvfggw+isLAQo0aNwj333INHH30UTzzxRCL/Srog9/vCz4x8YnlvjoefGXnI/b7o/TPDzI0Mbr/9dlxzzTXHvU/fvn2D/52dnY3s7GwMGjQIQ4cORUFBAdasWYPCwkLk5uZi7dq1LR579OhRuN3uVpE2dSza96a0tBSTJk1CYWEhXnnllQ6ff/z48XA4HKioqOD7EwU53xd+ZuQV7XsTLX5mYiPn+5IMnxkGNzKQgpVYSBkbp9MJACgsLMQ//vEPlJWVIS8vDwCwdOlSWK1WjB49Wp4FJ5Fo3ptDhw5h0qRJGD16NF5//XUYDB0nNjdu3AibzdbucUtqm5zvCz8z8urMz7NI8DMTGznfl2T4zDC4SaAffvgBP/zwA04//XR06dIF+/btw1//+lf0798fhYWFAIApU6Zg2LBhuOGGG/DEE0/gyJEjuPfee3HrrbfCbrcr/DfQr9LSUpx11lno3bs3nnzySRw+fDj4vdzcXADAp59+ivLychQWFiIlJQXffPMN/vKXv+B3v/td0g6ni7dI3hd+ZpRTXFyMI0eOoLi4GF6vF5s2bQIADBgwAOnp6fzMKKSj9yUpPjOKntVKMlu2bBEnTZokdu3aVbRarWLfvn3FGTNmiAcPHmxxvwMHDogXXnihmJKSInbt2lW8/fbbxebmZoVWnRxef/11EUCbX5IvvvhCHDVqlJieni6mpqaKw4cPF+fOnSu63W4FV65vkbwvosjPjFJuvPHGNt+bb775RhRFfmaU0tH7Ior6/8xwKjgRERHpCk9LERERka4wuCEiIiJdYXBDREREusLghoiIiHSFwQ0RERHpCoMbIiIi0hUGN0RERKQrDG6IiIhIVxjcEJFizjrrLNx1110Jea2bbroJl112WVSPEQQBH330UVzWQ0Txw9lSRJQUnn32WUTbkL2srAxdunSJ04qIKF4Y3BBRUsjMzIz6MdJwTiLSFm5LEZEqHD16FNOmTUOXLl2QmpqKqVOnYvfu3cHvL1iwAFlZWfjqq68wdOhQpKen4/zzz0dZWVlEz3/sttRZZ52FO++8E/fffz+6du2K3NxcPPzwwy0ew20pIm1icENEqnDTTTdh3bp1+OSTT7B69WqIoogLLrgAbrc7eJ/GxkY8+eSTeOutt7By5UoUFxfj3nvvjfk133jjDaSlpWHt2rV4/PHH8eijj6KoqEiOvw4RKYjBDREpbvfu3fjkk0/w6quvYuLEiRg5ciQWLlyIQ4cOtcicuN1uvPTSSxgzZgxOOeUU3H777Vi2bFnMrztixAg89NBDGDhwIKZNm4YxY8Z06vmISB0Y3BCR4nbs2AGTyYRx48YFb+vWrRsGDx6MHTt2BG9LTU1F//79g3/Oy8tDZWVlzK87YsSIFn/u7PMRkTowuCEixbV3ikkURQiCEPyz2Wxu8X1BEKI+ARWurefz+XwxPx8RqQODGyJS3LBhw+DxeLB27drgbdXV1di1axeGDh2q4MqISIsY3BCR4gYOHIhLL70Ut956K7777jts3rwZ119/PXr27IlLL71U6eURkcYwuCEiVXj99dcxevRoXHTRRSgsLIQoiliyZEmrrSMioo4IYmc2rImIiIhUhpkbIiIi0hUGN0SkC+np6e1+ffvtt0ovj4gSiNtSRKQLe/bsafd7PXv2REpKSgJXQ0RKYnBDREREusJtKSIiItIVBjdERESkKwxuiIiISFcY3BAREZGuMLghIiIiXWFwQ0RERLrC4IaIiIh0hcENERER6cr/ByWSVtloTOrHAAAAAElFTkSuQmCC", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plotting the summed volume transport in discrete longitude bins:\n", "traj_geo.summary_data.subvol_x_ini.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### **Computing 2-dimensional Binned Statistics**\n", "\n", "We next extend our computation to a 2-dimensional binned statistic using two different positions or properties sampled along water parcel trajectories. \n", "\n", "Below we demonstrate how to compute the average potential density recorded along-stream binned according to the longitude and latitude positions of our trajectories in the subpolar North Atlantic.\n", "\n", "We will again visualise our results with xarray's in-built plotting wrapper of the matplotlib library." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset> Size: 140kB\n",
       "Dimensions:       (lon_ini: 23, lat: 69, lon: 248)\n",
       "Coordinates:\n",
       "  * lon_ini       (lon_ini) float64 184B -30.5 -29.5 -28.5 ... -10.5 -9.5 -8.5\n",
       "  * lat           (lat) float64 552B 51.88 52.12 52.38 ... 68.38 68.62 68.88\n",
       "  * lon           (lon) float64 2kB -66.88 -66.62 -66.38 ... -5.375 -5.125\n",
       "Data variables:\n",
       "    subvol_x_ini  (lon_ini) float64 184B 0.01537 0.8793 ... 0.02333 0.0009916\n",
       "    sigma0_xy     (lat, lon) float64 137kB nan nan nan nan ... nan nan nan nan
" ], "text/plain": [ " Size: 140kB\n", "Dimensions: (lon_ini: 23, lat: 69, lon: 248)\n", "Coordinates:\n", " * lon_ini (lon_ini) float64 184B -30.5 -29.5 -28.5 ... -10.5 -9.5 -8.5\n", " * lat (lat) float64 552B 51.88 52.12 52.38 ... 68.38 68.62 68.88\n", " * lon (lon) float64 2kB -66.88 -66.62 -66.38 ... -5.375 -5.125\n", "Data variables:\n", " subvol_x_ini (lon_ini) float64 184B 0.01537 0.8793 ... 0.02333 0.0009916\n", " sigma0_xy (lat, lon) float64 137kB nan nan nan nan ... nan nan nan nan" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Define discrete depth bins:\n", "bin_lon = np.arange(-70, 0, 0.25).tolist()\n", "bin_lat = np.arange(50, 70, 0.25).tolist()\n", "\n", "# Compute 2-D mean potential density in discrete longitude-latitude bins:\n", "traj_geo = (traj_geo\n", " # Compute volume transport in discrete longitude-latitude bins:\n", " .compute_binned_statistic_2d(var_x='lat',\n", " var_y='lon',\n", " values='sigma0',\n", " statistic='mean',\n", " bin_breaks=[bin_lat, bin_lon],\n", " alias='sigma0_xy',\n", " group=None, # No group-by required!\n", " append=True, # Allow append to existing summary DataSet.\n", " )\n", " )\n", "\n", "# Display the summary data of the TrajFrame:\n", "traj_geo.summary_data" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plotting the mean potential density along-stream in discrete 2-D longitude-latitude bins:\n", "traj_geo.summary_data.sigma0_xy.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### **Computing Lagrangian Probability Statistics**\n", "\n", "The most common 2-dimensional binned statistic in Lagrangian oceanography is the probability density map.\n", "\n", "Below we demonstrate how to compute two types of Lagrangian probability density map for our exmaple trajectories circulating around the North Atlantic Subpolar Gyre. The two types of Lagrangian probability are outlined in the excellent review paper by van Sebille et al. (2018):\n", "\n", "* 'pos' - What is the probability that any given observation of all water parcel trajectories will be found in a discrete longitude-latitude bin:\n", "\n", "$p_{pos} = \\frac{N_{pos}(j,i)}{\\sum_j \\sum_i N_{pos}}$\n", "\n", "* 'traj' - What is the probability that any given water parcel will enter a discrete longitude-latitude bin at least once during its trajectory:\n", "\n", "$p_{traj} = \\frac{N_{traj}(j,i)}{\\sum_j \\sum_i N_{traj}}$\n", "\n", "We will visualise our results with xarray's in-built plotting wrapper of the matplotlib library." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset> Size: 139kB\n",
       "Dimensions:      (lat: 69, lon: 248)\n",
       "Coordinates:\n",
       "  * lat          (lat) float64 552B 51.88 52.12 52.38 ... 68.38 68.62 68.88\n",
       "  * lon          (lon) float64 2kB -66.88 -66.62 -66.38 ... -5.625 -5.375 -5.125\n",
       "Data variables:\n",
       "    probability  (lat, lon) float64 137kB nan nan nan nan ... nan nan nan nan
" ], "text/plain": [ " Size: 139kB\n", "Dimensions: (lat: 69, lon: 248)\n", "Coordinates:\n", " * lat (lat) float64 552B 51.88 52.12 52.38 ... 68.38 68.62 68.88\n", " * lon (lon) float64 2kB -66.88 -66.62 -66.38 ... -5.625 -5.375 -5.125\n", "Data variables:\n", " probability (lat, lon) float64 137kB nan nan nan nan ... nan nan nan nan" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Compute Lagrangian probability in discrete longitude-latitude bins:\n", "traj_geo.summary_data = xr.Dataset()\n", "traj_geo = (traj_geo\n", " # Compute volume transport in discrete longitude-latitude bins:\n", " .compute_probability(bin_res=0.25, # Bin resolution in degrees\n", " prob_type='pos', # Type of Lagrangian probability density.\n", " group=None, # No group-by required.\n", " append=False, # Replace existing summary DataSet.\n", " )\n", " )\n", "\n", "# Display the summary data of the TrajFrame:\n", "traj_geo.summary_data" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plotting the Lagrangian probability in discrete 2-D longitude-latitude bins:\n", "traj_geo.summary_data.probability.plot()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset> Size: 139kB\n",
       "Dimensions:      (lat: 69, lon: 248)\n",
       "Coordinates:\n",
       "  * lat          (lat) float64 552B 51.88 52.12 52.38 ... 68.38 68.62 68.88\n",
       "  * lon          (lon) float64 2kB -66.88 -66.62 -66.38 ... -5.625 -5.375 -5.125\n",
       "Data variables:\n",
       "    probability  (lat, lon) float64 137kB nan nan nan nan ... nan nan nan nan
" ], "text/plain": [ " Size: 139kB\n", "Dimensions: (lat: 69, lon: 248)\n", "Coordinates:\n", " * lat (lat) float64 552B 51.88 52.12 52.38 ... 68.38 68.62 68.88\n", " * lon (lon) float64 2kB -66.88 -66.62 -66.38 ... -5.625 -5.375 -5.125\n", "Data variables:\n", " probability (lat, lon) float64 137kB nan nan nan nan ... nan nan nan nan" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Compute Lagrangian probability in discrete longitude-latitude bins:\n", "traj_geo.summary_data = xr.Dataset()\n", "traj_geo = (traj_geo\n", " # Compute volume transport in discrete longitude-latitude bins:\n", " .compute_probability(bin_res=0.25, # Bin resolution in degrees\n", " prob_type='traj', # Type of Lagrangian probability density\n", " group=None, # No group-by required.\n", " append=False, # Replace existing summary DataSet.\n", " )\n", " )\n", "\n", "# Display the summary data of the TrajFrame:\n", "traj_geo.summary_data" ] }, { "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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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plotting the Lagrangian probability in discrete 2-D longitude-latitude bins:\n", "traj_geo.summary_data.probability.plot()\n" ] } ], "metadata": { "kernelspec": { "display_name": "jlab_LMOC", "language": "python", "name": "jlab_lmoc" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.12" } }, "nbformat": 4, "nbformat_minor": 2 }