{
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   "cell_type": "markdown",
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   "source": [
    "# PredictionEnsemble Objects\n",
    "\n",
    "One of the major features of `climpred` is our objects that are based upon the `PredictionEnsemble` class. We supply users with a `HindcastEnsemble` and `PerfectModelEnsemble` object. We encourage users to take advantage of these high-level objects, which wrap all of our core functions.\n",
    "\n",
    "Briefly, we consider a `HindcastEnsemble` to be one that is initialized from some observational-like product (e.g., assimilated data, reanalysis products, or a model reconstruction). Thus, this object is built around comparing the initialized ensemble to various observational products. In contrast, a `PerfectModelEnsemble` is one that is initialized off of a model control simulation. These forecasting systems are not meant to be compared directly to real-world observations. Instead, they provide a contained model environment with which to theoretically study the limits of predictability. You can read more about the terminology used in `climpred` [here](terminology.html).\n",
    "\n",
    "Let's create a demo object to explore some of the functionality and why they are much smoother to use than direct function calls."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
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       "                var nbb_formatted_code = \"# linting\\n%load_ext nb_black\\n%load_ext lab_black\";\n",
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    "# linting\n",
    "%load_ext nb_black\n",
    "%load_ext lab_black"
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     "data": {
      "text/plain": [
       "<xarray.core.options.set_options at 0x7f9ebae60d90>"
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      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 2;\n",
       "                var nbb_unformatted_code = \"%matplotlib inline\\nimport matplotlib.pyplot as plt\\nimport xarray as xr\\nimport numpy as np\\n\\nfrom climpred import HindcastEnsemble, PerfectModelEnsemble\\nfrom climpred.tutorial import load_dataset\\nimport climpred\\n\\nxr.set_options(display_style=\\\"text\\\")\";\n",
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   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import xarray as xr\n",
    "import numpy as np\n",
    "\n",
    "from climpred import HindcastEnsemble, PerfectModelEnsemble\n",
    "from climpred.tutorial import load_dataset\n",
    "import climpred\n",
    "\n",
    "xr.set_options(display_style=\"text\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now pull in some sample data that is packaged with `climpred`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## HindcastEnsemble\n",
    "\n",
    "We'll start out with a `HindcastEnsemble` demo, followed by a `PerfectModelEnsemble` case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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     "end_time": "2020-01-06T18:15:47.840210Z",
     "start_time": "2020-01-06T18:15:47.823392Z"
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     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 3;\n",
       "                var nbb_unformatted_code = \"hind = climpred.tutorial.load_dataset(\\n    \\\"CESM-DP-SST\\\"\\n)  # CESM-DPLE hindcast ensemble output.\\nobs = climpred.tutorial.load_dataset(\\\"ERSST\\\")  # ERSST observations.\";\n",
       "                var nbb_formatted_code = \"hind = climpred.tutorial.load_dataset(\\n    \\\"CESM-DP-SST\\\"\\n)  # CESM-DPLE hindcast ensemble output.\\nobs = climpred.tutorial.load_dataset(\\\"ERSST\\\")  # ERSST observations.\";\n",
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    "hind = climpred.tutorial.load_dataset(\n",
    "    \"CESM-DP-SST\"\n",
    ")  # CESM-DPLE hindcast ensemble output.\n",
    "obs = climpred.tutorial.load_dataset(\"ERSST\")  # ERSST observations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to add a \"units\" attribute to the hindcast ensemble so that `climpred` knows how to interpret the lead units."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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     "end_time": "2020-01-06T18:15:48.153858Z",
     "start_time": "2020-01-06T18:15:48.151726Z"
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   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 4;\n",
       "                var nbb_unformatted_code = \"hind[\\\"lead\\\"].attrs[\\\"units\\\"] = \\\"years\\\"\";\n",
       "                var nbb_formatted_code = \"hind[\\\"lead\\\"].attrs[\\\"units\\\"] = \\\"years\\\"\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
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       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
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    "hind[\"lead\"].attrs[\"units\"] = \"years\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we instantiate the `HindcastEnsemble` object and append all of our products to it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-06T18:15:49.785084Z",
     "start_time": "2020-01-06T18:15:49.769174Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<climpred.HindcastEnsemble>\n",
      "Initialized Ensemble:\n",
      "    SST      (init, lead, member) float64 ...\n",
      "Observations:\n",
      "    None\n",
      "Uninitialized:\n",
      "    None\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/aaron.spring/Coding/climpred/climpred/utils.py:122: UserWarning: Assuming annual resolution due to numeric inits. Change init to a datetime if it is another resolution.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 5;\n",
       "                var nbb_unformatted_code = \"hindcast = HindcastEnsemble(\\n    hind\\n)  # Instantiate object by passing in our initialized ensemble.\\nprint(hindcast)\";\n",
       "                var nbb_formatted_code = \"hindcast = HindcastEnsemble(\\n    hind\\n)  # Instantiate object by passing in our initialized ensemble.\\nprint(hindcast)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
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       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
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   "source": [
    "hindcast = HindcastEnsemble(\n",
    "    hind\n",
    ")  # Instantiate object by passing in our initialized ensemble.\n",
    "print(hindcast)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we just use the `add_` methods to attach other objects. See the API [here](api.html#add-and-retrieve-datasets). **Note that we strive to make our conventions follow those of `xarray`'s**. For example, we don't allow inplace operations. One has to run `hindcast = hindcast.add_observations(...)` to modify the object upon later calls rather than just `hindcast.add_observations(...)`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-06T18:15:50.078063Z",
     "start_time": "2020-01-06T18:15:50.046372Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<climpred.HindcastEnsemble>\n",
      "Initialized Ensemble:\n",
      "    SST      (init, lead, member) float64 ...\n",
      "Observations:\n",
      "    SST      (time) float32 ...\n",
      "Uninitialized:\n",
      "    None\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 6;\n",
       "                var nbb_unformatted_code = \"hindcast = hindcast.add_observations(obs)\\nprint(hindcast)\";\n",
       "                var nbb_formatted_code = \"hindcast = hindcast.add_observations(obs)\\nprint(hindcast)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
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       "<IPython.core.display.Javascript object>"
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   ],
   "source": [
    "hindcast = hindcast.add_observations(obs)\n",
    "print(hindcast)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can apply most standard `xarray` functions directly to our objects! `climpred` will loop through the objects and apply the function to all applicable `xarray.Datasets` within the object. If you reference a dimension that doesn't exist for the given `xarray.Dataset`, it will ignore it. This is useful, since the initialized ensemble is expected to have dimension `init`, while other products have dimension `time` (see more [here](setting-up-data.html)).\n",
    "\n",
    "Let's start by taking the ensemble mean of the initialized ensemble so our metric computations don't have to take the extra time on that later. Just using deterministic metrics here, so we don't need the individual ensemble members. Note that above our initialized ensemble had a `member` dimension, and now it is reduced. Those `xarray` functions do not raise errors such as `ValueError`, `KeyError`, `DimensionError`, but show respective warnings, which can be filtered away with `warnings.filterwarnings(\"ignore\")`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-06T18:15:50.873957Z",
     "start_time": "2020-01-06T18:15:50.866688Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/aaron.spring/Coding/climpred/climpred/classes.py:380: UserWarning: Error due to verification/control/uninitialized: xr.mean(('member',), {}) failed\n",
      "ValueError: Dataset does not contain the dimensions: ['member']\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<climpred.HindcastEnsemble>\n",
       "Initialized Ensemble:\n",
       "    SST      (init, lead) float64 -0.2121 -0.1637 -0.1206 ... 0.7286 0.7532\n",
       "Observations:\n",
       "    SST      (time) float32 ...\n",
       "Uninitialized:\n",
       "    None"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 7;\n",
       "                var nbb_unformatted_code = \"hindcast = hindcast.mean(\\\"member\\\")\\nhindcast\";\n",
       "                var nbb_formatted_code = \"hindcast = hindcast.mean(\\\"member\\\")\\nhindcast\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
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       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
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   "source": [
    "hindcast = hindcast.mean(\"member\")\n",
    "hindcast"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Arithmetic Operations with PredictionEnsemble Objects\n",
    "\n",
    "`PredictionEnsemble` objects support arithmetic operations, i.e., `+`, `-`, `/`, `*`. You can perform these operations on a `HindcastEnsemble` or `PerfectModelEnsemble` by pairing the operation with an `int`, `float`, `np.ndarray`, `xr.DataArray`, `xr.Dataset`, or with another `PredictionEnsemble` object.\n",
    "\n",
    "An obvious application would be to area-weight an initialized ensemble and all of its associated datasets (like verification products) simultaneously."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 8;\n",
       "                var nbb_unformatted_code = \"dple3d = climpred.tutorial.load_dataset(\\\"CESM-DP-SST-3D\\\")\\nverif3d = climpred.tutorial.load_dataset(\\\"FOSI-SST-3D\\\")\\narea = dple3d[\\\"TAREA\\\"]\";\n",
       "                var nbb_formatted_code = \"dple3d = climpred.tutorial.load_dataset(\\\"CESM-DP-SST-3D\\\")\\nverif3d = climpred.tutorial.load_dataset(\\\"FOSI-SST-3D\\\")\\narea = dple3d[\\\"TAREA\\\"]\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
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       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dple3d = climpred.tutorial.load_dataset(\"CESM-DP-SST-3D\")\n",
    "verif3d = climpred.tutorial.load_dataset(\"FOSI-SST-3D\")\n",
    "area = dple3d[\"TAREA\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we load in a subset of CESM-DPLE over the eastern tropical Pacific. The file includes `TAREA`, which describes the area of each cell on the curvilinear mesh."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/aaron.spring/Coding/climpred/climpred/utils.py:122: UserWarning: Assuming annual resolution due to numeric inits. Change init to a datetime if it is another resolution.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<climpred.HindcastEnsemble>\n",
       "Initialized Ensemble:\n",
       "    SST      (init, lead, nlat, nlon) float32 ...\n",
       "Observations:\n",
       "    SST      (time, nlat, nlon) float32 ...\n",
       "Uninitialized:\n",
       "    None"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 9;\n",
       "                var nbb_unformatted_code = \"hindcast3d = HindcastEnsemble(dple3d)\\nhindcast3d = hindcast3d.add_observations(verif3d)\\nhindcast3d\";\n",
       "                var nbb_formatted_code = \"hindcast3d = HindcastEnsemble(dple3d)\\nhindcast3d = hindcast3d.add_observations(verif3d)\\nhindcast3d\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
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     },
     "metadata": {},
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   ],
   "source": [
    "hindcast3d = HindcastEnsemble(dple3d)\n",
    "hindcast3d = hindcast3d.add_observations(verif3d)\n",
    "hindcast3d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can perform an area-weighting operation with the `HindcastEnsemble` object and the area `DataArray`. `climpred` cycles through all of the datasets appended to the `HindcastEnsemble` and applies them. You can see below that the dimensionality is reduced to single time series without spatial information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<climpred.HindcastEnsemble>\n",
       "Initialized Ensemble:\n",
       "    SST      (init, lead) float64 -0.3539 0.1947 0.3623 ... 0.662 1.016 1.249\n",
       "Observations:\n",
       "    SST      (time) float64 24.76 24.48 23.73 24.68 ... 24.78 24.21 24.92 25.95\n",
       "Uninitialized:\n",
       "    None"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 10;\n",
       "                var nbb_unformatted_code = \"hindcast3d_aw = (hindcast3d * area).sum([\\\"nlat\\\", \\\"nlon\\\"]) / area.sum([\\\"nlat\\\", \\\"nlon\\\"])\\nhindcast3d_aw\";\n",
       "                var nbb_formatted_code = \"hindcast3d_aw = (hindcast3d * area).sum([\\\"nlat\\\", \\\"nlon\\\"]) / area.sum([\\\"nlat\\\", \\\"nlon\\\"])\\nhindcast3d_aw\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hindcast3d_aw = (hindcast3d * area).sum([\"nlat\", \"nlon\"]) / area.sum([\"nlat\", \"nlon\"])\n",
    "hindcast3d_aw"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**NOTE**: Be careful with the arithmetic operations. Some of the behavior can be unexpected in combination with the fact that generic `xarray` methods can be applied to `climpred` objects. For instance, one might be interested in removing a climatology from the verification data to move it to anomaly space. It's safest to do anything like climatology removal before constructing `climpred` objects.\n",
    "\n",
    "Naturally, they would remove some climatology time slice as we do here below. However, note that in the below example, the intialized ensemble returns all zeroes for `SST`. The reasoning here is that when `hindcast.sel(time=...)` is called, `climpred` only applies that slicing to datasets that include the `time` dimension. Thus, it skips the initialized ensemble and returns the original dataset untouched. This feature is advantageous for cases like `hindcast.mean('member')`, where it takes the ensemble mean in all cases that ensemble members exist. So when it performs `hindcast - hindcast.sel(time=...)`, it subtracts the identical initialized ensemble from itself returning all zeroes. We are hoping to implement a fix to this issue in the future.\n",
    "\n",
    "In short, any sort of bias correcting or drift correction should be done _prior_ to instantiating a `PredictionEnsemble` object. Alternatively, detrending or removing a mean state can also be done _after_ instantiating a `PredictionEnsemble` object. But beware of unintuitive behaviour. Removing a `time` anomaly in `PredictionEnsemble`, does not modify `initialized` and therefore returns all `0`s."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/aaron.spring/Coding/climpred/climpred/classes.py:375: UserWarning: Error due to initialized:  xr.sel((), {'time': slice('1960', '2014', None)}) failed\n",
      "ValueError: dimensions or multi-index levels ['time'] do not exist\n",
      "  warnings.warn(f\"Error due to initialized:  {msg}\")\n",
      "/Users/aaron.spring/Coding/climpred/climpred/classes.py:375: UserWarning: Error due to initialized:  xr.mean(('time',), {}) failed\n",
      "ValueError: Dataset does not contain the dimensions: ['time']\n",
      "  warnings.warn(f\"Error due to initialized:  {msg}\")\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<climpred.HindcastEnsemble>\n",
       "Initialized Ensemble:\n",
       "    SST      (init, lead, nlat, nlon) float32 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0\n",
       "Observations:\n",
       "    SST      (time, nlat, nlon) float32 0.01611 0.01459 0.0161 ... 1.543 1.49\n",
       "Uninitialized:\n",
       "    None"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 11;\n",
       "                var nbb_unformatted_code = \"hindcast3d - hindcast3d.sel(time=slice(\\\"1960\\\", \\\"2014\\\")).mean(\\\"time\\\")\";\n",
       "                var nbb_formatted_code = \"hindcast3d - hindcast3d.sel(time=slice(\\\"1960\\\", \\\"2014\\\")).mean(\\\"time\\\")\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hindcast3d - hindcast3d.sel(time=slice(\"1960\", \"2014\")).mean(\"time\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To fix this always handle all `PredictionEnsemble` datasets `initialized` with dimensions `lead` or `init` and `observations/control` with dimension `time` at the same time to avoid these zeros."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/aaron.spring/Coding/climpred/climpred/classes.py:375: UserWarning: Error due to initialized:  xr.sel((), {'time': slice('1960', '2014', None)}) failed\n",
      "ValueError: dimensions or multi-index levels ['time'] do not exist\n",
      "  warnings.warn(f\"Error due to initialized:  {msg}\")\n",
      "/Users/aaron.spring/Coding/climpred/climpred/classes.py:375: UserWarning: Error due to initialized:  xr.mean(('time',), {}) failed\n",
      "ValueError: Dataset does not contain the dimensions: ['time']\n",
      "  warnings.warn(f\"Error due to initialized:  {msg}\")\n",
      "/Users/aaron.spring/Coding/climpred/climpred/classes.py:384: UserWarning: xr.sel((), {'init': slice('1960', '2014', None)}) failed\n",
      "ValueError: dimensions or multi-index levels ['init'] do not exist\n",
      "  warnings.warn(msg)\n",
      "/Users/aaron.spring/Coding/climpred/climpred/classes.py:384: UserWarning: xr.mean(('init',), {}) failed\n",
      "ValueError: Dataset does not contain the dimensions: ['init']\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<climpred.HindcastEnsemble>\n",
       "Initialized Ensemble:\n",
       "    SST      (init, lead) float64 -0.2046 -0.1688 -0.1335 ... 0.6326 0.6463\n",
       "Observations:\n",
       "    SST      (time) float32 -0.3864 -0.3373 -0.17 ... 0.2632 0.3611 0.4653\n",
       "Uninitialized:\n",
       "    None"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 12;\n",
       "                var nbb_unformatted_code = \"hindcast - hindcast.sel(time=slice(\\\"1960\\\", \\\"2014\\\")).mean(\\\"time\\\").sel(\\n    init=slice(\\\"1960\\\", \\\"2014\\\")\\n).mean(\\\"init\\\")\";\n",
       "                var nbb_formatted_code = \"hindcast - hindcast.sel(time=slice(\\\"1960\\\", \\\"2014\\\")).mean(\\\"time\\\").sel(\\n    init=slice(\\\"1960\\\", \\\"2014\\\")\\n).mean(\\\"init\\\")\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hindcast - hindcast.sel(time=slice(\"1960\", \"2014\")).mean(\"time\").sel(\n",
    "    init=slice(\"1960\", \"2014\")\n",
    ").mean(\"init\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: Thinking in initialization space is not very intuitive and such combined `init` and `time` operations can lead to unanticipated changes in the `PredictionEnsemble`. The safest way is subtracting means before instantiating `PredictionEnsemble` or use `HindcastEnsemble.remove_bias()`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PredictionEnsemble.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`PredictionEnsemble` also have a default `.plot()` call showing all datasets associated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='time', ylabel='SST'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rV9jgfdXU1ChTUlIsQG9vhdFoNBuNRnN4eHj6M888EwMAL7/8crTVajUZDAbz008/HRuo+/zzz0/S6/XWmTNnGs6ePasayzF2d3cLS5Ys0VutVpPJZOqPraamRmmz2VLNZrPJbDabSktLgwBAkiSsWrUqPikpyXL//fcnt7a2jvohQXFxceLs2bOdCoVi2LkomzdvjsjPz58S+L1p06aI1atXTwaAoqIiXVpamsloNJqXLVuWEEgAli9fHm+1Wk3JycmWgeclLi4u7dlnn42x2Wyp27dvH1Xvx42Mx3sWCCGEEELICPbsOR16+bJdcSvbjI0N8T3yiLl7tOXPnDmjOXHixHm9Xu+z2WzG0tLS4AULFjgC24uKipp27NgRVV1dfXpwXa1WK+3fv79Op9NJzc3N8unTpxuXLVvWKQhD36suLi5uAIDa2lrlggULUtauXdv24YcfhtbV1alPnjx5hnOOefPmJX/yySfBwcHB0t69e3WVlZWnfT4fpk6das7IyHACwBtvvBEJAOvXr28Z7rhefPHFmOzs7O7du3fXt7a2yjIzM025ubndsbGx4hdffFGr1Wp5ZWWlaunSpfdUVVWd2blzZ1hdXZ2qpqbmVGNjoyItLc3y+OOPtwFAfn7+lCNHjoQM3sfDDz/cvnHjxiujOc/5+fntFovF7PF4GlUqFd+1a1fE1q1bG7788kv1nj17dGVlZdUqlYqvWLEi/p133pn41FNPtb311ltN0dHRflEUMXPmzNRjx45ppk+f7gIAtVotlZeX14xm3yOhZIEQQgghhAwpLS2tJykpyQcAFovFee7cOeVo60qSxAoKCiYfPXo0WBAEXLt2TdnY2CiPj48fdmyM0+lkeXl5SZs3b75oMBi8b775ZtThw4dDzWazuW+7UF1drbbb7cKiRYs6Q0JCJACYP39+Z6CNGyUJAYcOHQr99NNPwwoLCycBgMfjYXV1dcqEhARffn5+wunTpzWCIKChoUEFAJ9//nnIo48+2i6Xy6HX630zZsywB9ratm3bpdGek+GEhoZK9913n724uHhCWlqa2+fzsaysLNfGjRsjq6qqtOnp6SYAcLvdQlRUlAgA7733nm7Hjh0RoiiylpYWRUVFhTqQLKxatarj68YUQMkCIYQQQshdaCw9ALeLSqXqHzojk8kgiiIbbd2tW7fq2tra5JWVlWdUKhWPi4tLc7lcNxwCv3LlyoQHHnigY/HixXYA4JyjoKCg+bnnnmsdWG7Dhg1RfS/wvSmcc+zZs6cuPT3dM3D9unXrYqOionwffPDBBUmSoNFobIFtw+3vVvQsAMCaNWtaX3311UkGg8G9YsWK1r442ZIlS9refvvtpoFlq6urlVu2bIkuLy8/ExkZ6c/Ly9O73e7+cxtIom4FmrNACCGEEEJumlwu5x6P57or6a6uLllERIRPpVLxjz/+OOTy5cs37JV47bXXIh0Oh2zgBfbChQu7d+7cGdHV1SUAwIULFxRNTU3yOXPmOPbv3x/mcDhYR0eHUFpaGjaWmLOzs7s3bdoUHZiDceTIEU0g5piYGJ9MJkNRUdFEv98PAJg9e7Z99+7dOlEU0dDQoDh69Gh/crBt27ZL1dXVpwcvY0kUAGDOnDk9zc3Nyr17907Mz89vB4CcnJzukpKS8KamJjkAXL16VVZbW6vs6OiQaTQaSafT+S9duiQ/dOjQhLHsayyoZ4EQQgghhNy05cuXt5hMJrPVanV+9NFHFwLrV69e3b5w4cJkq9VqslgszsTERPeN2tmyZcskhULBjUajGQCeeOKJlvXr17ecOnVKPW3aNCPQOw/i/fffvzBr1iznQw891G61Wi1xcXGerKys/nkUo5mz8Prrr19es2ZNvNFoNHPO2eTJkz0HDx6sKygouJaXl5e0b9++8FmzZtk1Go0EACtXruz87LPPQlNTUy2JiYnurKws+3BtD3bx4kX5tGnTzD09PTLGGN+6dWv0mTNnqnQ63XV3/xcvXtxx8uRJbWRkpB8AbDab+6WXXmqaO3euQZIkKBQKXlhYeHHu3Lk9VqvVmZKSYomPj/fYbDbH9Xu+NRjn356XBGZmZvKysrLxDoMQQgghZESMsXLOeebAdRUVFfXp6emtw9Uh327Z2dnJBQUFVx988MFRJyO3QkVFRUR6erp+qG00DIkQQgghhJBx1NraKtPr9Va1Wi3d6URhJDQMiRBCCCGEkHEUERHhr6+vrxrvOIZCPQuEEEIIIYSQIVGyQAghhBBCCBkSJQuEEEIIIYSQIVGyQAghhBBCCBnSbUsWGGPbGWPXGGNVA9b9M2OsiTF2om9ZNEzdHMZYDWOsjjH2wu2KkRBCCCGEfFVGRoZxpDKPPfZYQnl5uRoAXnjhhUljra/VajMAoL6+XpGTk3PPzcYasG7duthXXnkl+kZlSkpKQrKzs5O/7r4AIC8vT//uu++Gj6bsv/3bv+kMBoPZYDCYMzIyjH/4wx80tyKGO+V29izsAJAzxPrNnPOpfctvBm9kjMkAvA1gIQAzgKWMMfNtjJMQQgghhPQ5fvx49UhliouLG2w2mxsACgsLY8ZaP0Cv1/sOHDhwfuxRfnMkJyd7jhw5UlNbW3v6H/7hHy6vXbs24U7tWxTFr93GbUsWOOeHAbTfRNUsAHWc8/Occy+A/wDw4C0NjhBCCCGEDClw17+kpCQkKysrNScn557ExERLbm5uoiT1vnQ4Kysr9fDhw9onn3wyzuPxCEaj0Zybm5s4sH5XV5cwY8YMg9lsNhkMBvOuXbvCBu+rpqZGmZKSYgF6eyuMRqPZaDSaw8PD05955pkYAHj55ZejrVaryWAwmJ9++unYQN3nn39+kl6vt86cOdNw9uxZ1ViOsbu7W1iyZInearWaTCZTf2w1NTVKm82WajabTWaz2VRaWhoEAJIkYdWqVfFJSUmW+++/P7m1tXXUrx/44Q9/2BN4I3N2dnbPlStXlIPLbN68OSI/P39K4PemTZsiVq9ePRkAioqKdGlpaSaj0WhetmxZQiABWL58ebzVajUlJydbBp6XuLi4tGeffTbGZrOlbt++fVS9HzcyHu9ZeIoxtgpAGYBnOOcdg7bHAbg04HcjgOnDNcYYWwNgDQDEx8ff4lAJIYQQQsbHnj0IvXwZilvZZmwsfI88gu7Rlj9z5ozmxIkT5/V6vc9msxlLS0uDFyxY4AhsLyoqatqxY0dUdXX16cF1tVqttH///jqdTic1NzfLp0+fbly2bFmnIAx9r7q4uLgBAGpra5ULFixIWbt2bduHH34YWldXpz558uQZzjnmzZuX/MknnwQHBwdLe/fu1VVWVp72+XyYOnWqOSMjwwkAb7zxRiQArF+/vmW443rxxRdjsrOzu3fv3l3f2toqy8zMNOXm5nbHxsaKX3zxRa1Wq+WVlZWqpUuX3lNVVXVm586dYXV1daqamppTjY2NirS0NMvjjz/eBgD5+flTjhw5EjJ4Hw8//HD7xo0brwxc9//+3/+LyM7O7hpcNj8/v91isZg9Hk+jSqXiu3btiti6dWvDl19+qd6zZ4+urKysWqVS8RUrVsS/8847E5966qm2t956qyk6OtoviiJmzpyZeuzYMc306dNdAKBWq6Xy8vKa4Y5/LO50svBvAP43AN73uQnAE4PKsCHq8eEa5Jz/HMDPASAzM3PYcoQQQgghZGzS0tJ6kpKSfABgsVic586du+6u+HAkSWIFBQWTjx49GiwIAq5du6ZsbGyUx8fHDzs2xul0sry8vKTNmzdfNBgM3jfffDPq8OHDoWaz2dy3Xaiurlbb7XZh0aJFnSEhIRIAzJ8/vzPQxo2ShIBDhw6Ffvrpp2GFhYWTAMDj8bC6ujplQkKCLz8/P+H06dMaQRDQ0NCgAoDPP/885NFHH22Xy+XQ6/W+GTNm9L9ledu2bZeG289AH3/8cciuXbsifv/73183TCs0NFS677777MXFxRPS0tLcPp+PZWVluTZu3BhZVVWlTU9PNwGA2+0WoqKiRAB47733dDt27IgQRZG1tLQoKioq1IFkYdWqVYNvxt+0O5oscM6vBr4zxn4BoGSIYo0Apgz4PRnA5dscGiGEEELIXWUsPQC3i0ql6r8RK5PJIIriUDd1h7R161ZdW1ubvLKy8oxKpeJxcXFpLpfrhkPgV65cmfDAAw90LF682A4AnHMUFBQ0P/fcc60Dy23YsCGKsVGHch3OOfbs2VOXnp7uGbh+3bp1sVFRUb4PPvjggiRJ0Gg0tsC24fY3mp6FY8eOaZ588smE/fv3n500aZJ/qHbWrFnT+uqrr04yGAzuFStWtPbFyZYsWdL29ttvNw0sW11drdyyZUt0eXn5mcjISH9eXp7e7Xb3n9tAEnUr3NFHpzLGBk6AeQjAUK+1/hOAFMZYImNMCeBHAD66E/ERQgghhJCxkcvl3OPxXHcl3dXVJYuIiPCpVCr+8ccfh1y+fPmGvRKvvfZapMPhkA0curNw4cLunTt3RnR1dQkAcOHCBUVTU5N8zpw5jv3794c5HA7W0dEhlJaWho0l5uzs7O5NmzZFB+ZgHDlyRBOIOSYmxieTyVBUVDTR7++9rp89e7Z99+7dOlEU0dDQoDh69Gh/crBt27ZL1dXVpwcvgeM4e/ascsmSJUnbt2+/cO+993quj6bXnDlzepqbm5V79+6dmJ+f3w4AOTk53SUlJeFNTU1yALh69aqstrZW2dHRIdNoNJJOp/NfunRJfujQoQljOf6xuG09C4yxXwO4H0AEY6wRwD8BuJ8xNhW9w4rqAaztKxsL4Jec80Wcc5Ex9hSATwHIAGznnJ+6XXESQgghhJCbt3z58haTyWS2Wq3Ojz766EJg/erVq9sXLlyYbLVaTRaLxZmYmOi+UTtbtmyZpFAouNFoNAPAE0880bJ+/fqWU6dOqadNm2YEeudBvP/++xdmzZrlfOihh9qtVqslLi7Ok5WV1T+PYjRzFl5//fXLa9asiTcajWbOOZs8ebLn4MGDdQUFBdfy8vKS9u3bFz5r1iy7RqORAGDlypWdn332WWhqaqolMTHRnZWVZR+u7cFeeumlmM7OTvnf/d3fJQC9yVVVVdWZocouXry44+TJk9rAhGibzeZ+6aWXmubOnWuQJAkKhYIXFhZenDt3bo/VanWmpKRY4uPjPTabzTFUe7cC4/zbM8w/MzOTl5WVjXcYhBBCCCEjYoyVc84zB66rqKioT09Pbx2uDvl2y87OTi4oKLj64IMPjjoZuRUqKioi0tPT9UNtozc4E0IIIYQQMo5aW1tler3eqlarpTudKIxkPB6dSgghhBBCCOkTERHhr6+vH2ou77ijngVCCCGEEELIkChZIIQQQgghhAyJkgVCCCGEEELIkChZIIQQQgghhAyJkgVCCCGEENIvIyPDOFKZxx57LKG8vFwNAC+88MKksdbXarUZAFBfX6/Iycm552ZjDVi3bl3sK6+8En2jMiUlJSHZ2dnJX3dfAJCXl6d/9913w0dTdteuXWEGg8FsNBrNVqvV9OmnnwbfihjuFEoWCCGEEEJIv+PHj1ePVKa4uLjBZrO5AaCwsDBmrPUD9Hq978CBA+fHHuU3xwMPPNAdeKvztm3b6n/84x8n3Kl9i6L4tdugZIEQQgghhPQL3PUvKSkJycrKSs3JybknMTHRkpubmyhJEgAgKysr9fDhw9onn3wyzuPxCEaj0Zybm5s4sH5XV5cwY8YMg9lsNhkMBvOuXbvCBu+rpqZGmZKSYgF6eyuMRqPZaDSaw8PD05955pkYAHj55ZejrVaryWAwmJ9++unYQN3nn39+kl6vt86cOdNw9uxZ1ViOsbu7W1iyZInearWaTCZTf2w1NTVKm82WajabTWaz2VRaWhoEAJIkYdWqVfFJSUmW+++/P7m1tXXUrx+YMGGCJAi9l9x2u11gjF1XZvPmzRH5+flTAr83bdoUsXr16skAUFRUpEtLSzMZjUbzsmXLEgIJwPLly+OtVqspOTnZMvC8xMXFpT377LMxNpstdfv27aPq/bgRes8CIYQQQshdaE8ZQi93QnEr24wNg++RTHSPtvyZM2c0J06cOK/X6302m81YWloavGDBAkdge1FRUdOOHTuiqqurTw+uq9Vqpf3799fpdDqpublZPn36dOOyZcs6AxfOgxUXFzcAQG1trXLBggUpa9eubfvwww9D6+rq1CdPnjzDOce8efOSP/nkk+Dg4GBp7969usrKytM+nw9Tp041Z2RkOAHgjTfeiASA9evXtwx3XC+++GJMdnZ29+7du+tbW1tlmZmZptzc3O7Y2Fjxiy++qNVqtbyyslK1dOnSe6qqqs7s3LkzrK6uTlVTU3OqsbFRkZaWZnn88cfbACA/P3/KkSNHQgbv4+GHH27fuHHjFQD41a9+FfZP//RPce3t7YoPPvjg7OCy+fn57RaLxezxeBpVKhXftWtXxNatWxu+/PJL9Z49e3RlZWXVKpWKr1ixIv6dd96Z+NRTT7W99dZbTdHR0X5RFDFz5szUY8eOaaZPn+4CALVaLZWXl9fc8I87SpQsEEIIIYSQIaWlpfUkJSX5AMBisTjPnTunHG1dSZJYQUHB5KNHjwYLgoBr164pGxsb5fHx8cOOjXE6nSwvLy9p8+bNFw0Gg/fNN9+MOnz4cKjZbDb3bReqq6vVdrtdWLRoUWdISIgEAPPnz+8MtHGjJCHg0KFDoZ9++mlYYWHhJADweDysrq5OmZCQ4MvPz084ffq0RhAENDQ0qADg888/D3n00Ufb5XI59Hq9b8aMGf1vWd62bdulkfa3atWqzlWrVnV+8sknwa+88krcvHnzagduDw0Nle677z57cXHxhLS0NLfP52NZWVmujRs3RlZVVWnT09NNAOB2u4WoqCgRAN577z3djh07IkRRZC0tLYqKigp1IFlYtWpVx0gxjRYlC4QQQgghd6Gx9ADcLiqVige+y2QyiKJ4/RiaYWzdulXX1tYmr6ysPKNSqXhcXFyay+W64RD4lStXJjzwwAMdixcvtgMA5xwFBQXNzz33XOvAchs2bIgaajjPaHHOsWfPnrr09HTPwPXr1q2LjYqK8n3wwQcXJEmCRqOxBbYNt7/R9CwELFy40LF69WpVc3OzPCYm5itJ05o1a1pfffXVSQaDwb1ixYrWvjjZkiVL2t5+++2mgWWrq6uVW7ZsiS4vLz8TGRnpz8vL07vd7v5zG0iibgWas0AIIYQQQm6aXC7nHo/nuivprq4uWUREhE+lUvGPP/445PLlyzfslXjttdciHQ6HbOAF9sKFC7t37twZ0dXVJQDAhQsXFE1NTfI5c+Y49u/fH+ZwOFhHR4dQWloaNpaYs7Ozuzdt2hQdmINx5MgRTSDmmJgYn0wmQ1FR0US/3w8AmD17tn337t06URTR0NCgOHr0aH9ysG3btkuBCcwDl8BxVFVVqQL7+Z//+R+tz+dj0dHR1/WuzJkzp6e5uVm5d+/eifn5+e0AkJOT011SUhLe1NQkB4CrV6/KamtrlR0dHTKNRiPpdDr/pUuX5IcOHZowluMfC+pZIIQQQgghN2358uUtJpPJbLVanR999NGFwPrVq1e3L1y4MNlqtZosFoszMTHRfaN2tmzZMkmhUHCj0WgGgCeeeKJl/fr1LadOnVJPmzbNCPTOg3j//fcvzJo1y/nQQw+1W61WS1xcnCcrK6t/HsVo5iy8/vrrl9esWRNvNBrNnHM2efJkz8GDB+sKCgqu5eXlJe3bty981qxZdo1GIwHAypUrOz/77LPQ1NRUS2JiojsrK8s+XNuD/frXvw4vLi6eKJfLuVqtlnbu3Hl+uHkbixcv7jh58qQ2MjLSDwA2m8390ksvNc2dO9cgSRIUCgUvLCy8OHfu3B6r1epMSUmxxMfHe2w2m2PIBm8BxjkfudQ3RGZmJi8rKxvvMAghhBBCRsQYK+ecZw5cV1FRUZ+ent46XB3y7ZadnZ1cUFBw9cEHHxx1MnIrVFRURKSnp+uH2kbDkAghhBBCCBlHra2tMr1eb1Wr1dKdThRGQsOQCCGEEEIIGUcRERH++vr6qvGOYyi3rWeBMbadMXaNMVY1YN2/MsaqGWMnGWN7GWNhw9StZ4xVMsZOMMZoXBEhhBBCCCHj4HYOQ9oBIGfQulIAVs75vQBqAfzDDepnc86nDh7LRwghhBBCCLkzbluywDk/DKB90Lrfcs4Dj4o6CmDy7do/IYQQQggh5OsZzwnOTwD4ZJhtHMBvGWPljLE1N2qEMbaGMVbGGCtraRnxhX2EEEIIIYSQURqXZIEx9o8ARADvD1PkPs759wAsBPC3jLG/GK4tzvnPOeeZnPPMyMjI2xAtIYQQQsh3R0ZGhnGkMo899lhCeXm5GgBeeOGFSWOtr9VqMwCgvr5ekZOTc8/Nxhqwbt262FdeeSX6RmVKSkpCsrOzk7/uvgAgLy9P/+6774aPpc7nn3+ulclktrHWG293PFlgjP01gL8CsJwP85IHzvnlvs9rAPYCyLpzERJCCCGEfHcdP368eqQyxcXFDTabzQ0AhYWFMWOtH6DX630HDhw4P/Yov1lEUcTzzz8/edasWV13er9f1x1NFhhjOQCeB5DLOXcOUyaIMRYS+A5gPoC78lFShBBCCCHfNoG7/iUlJSFZWVmpOTk59yQmJlpyc3MTJUkCAGRlZaUePnxY++STT8Z5PB7BaDSac3NzEwfW7+rqEmbMmGEwm80mg8Fg3rVrV9jgfdXU1ChTUlIsQG9vhdFoNBuNRnN4eHj6M888EwMAL7/8crTVajUZDAbz008/HRuo+/zzz0/S6/XWmTNnGs6ePasayzF2d3cLS5Ys0VutVpPJZOqPraamRmmz2VLNZrPJbDabSktLgwBAkiSsWrUqPikpyXL//fcnt7a2jun1Axs3box68MEHOyIiIoa8et+8eXNEfn7+lMDvTZs2RaxevXoyABQVFenS0tJMRqPRvGzZsoRAArB8+fJ4q9VqSk5Otgw8L3FxcWnPPvtsjM1mS92+ffvX7sW4be9ZYIz9GsD9ACIYY40A/gm9Tz9SAShljAHAUc75jxljsQB+yTlfBCAawN6+7XIA/845P3C74iSEEEIIuRvt6UboZR8Ut7LNWAV8j4Sie7Tlz5w5ozlx4sR5vV7vs9lsxtLS0uAFCxY4AtuLioqaduzYEVVdXX16cF2tVivt37+/TqfTSc3NzfLp06cbly1b1ikIQ9+rLi4ubgCA2tpa5YIFC1LWrl3b9uGHH4bW1dWpT548eYZzjnnz5iV/8sknwcHBwdLevXt1lZWVp30+H6ZOnWrOyMhwAsAbb7wRCQDr168fdjLriy++GJOdnd29e/fu+tbWVllmZqYpNze3OzY2Vvziiy9qtVotr6ysVC1duvSeqqqqMzt37gyrq6tT1dTUnGpsbFSkpaVZHn/88TYAyM/Pn3LkyJGQwft4+OGH2zdu3HjlwoULio8//jj8D3/4Q81jjz0WNFQ8+fn57RaLxezxeBpVKhXftWtXxNatWxu+/PJL9Z49e3RlZWXVKpWKr1ixIv6dd96Z+NRTT7W99dZbTdHR0X5RFDFz5szUY8eOaaZPn+4CALVaLZWXl9fc4E87arctWeCcLx1i9bZhyl4GsKjv+3kA6bcrLkIIIYQQMjppaWk9SUlJPgCwWCzOc+fOKUdbV5IkVlBQMPno0aPBgiDg2rVrysbGRnl8fPywY2OcTifLy8tL2rx580WDweB98803ow4fPhxqNpvNfduF6upqtd1uFxYtWtQZEhIiAcD8+fM7A23cKEkIOHToUOinn34aVlhYOAkAPB4Pq6urUyYkJPjy8/MTTp8+rREEAQ0NDSoA+Pzzz0MeffTRdrlcDr1e75sxY0b/W5a3bdt26Ub7evLJJ6e8/vrrjXL58JfdoaGh0n333WcvLi6ekJaW5vb5fCwrK8u1cePGyKqqKm16eroJANxutxAVFSUCwHvvvafbsWNHhCiKrKWlRVFRUaEOJAurVq3qGOkcjBa9wZkQQggh5C40lh6A20WlUvXPL5XJZBBFkY227tatW3VtbW3yysrKMyqVisfFxaW5XK4bDoFfuXJlwgMPPNCxePFiOwBwzlFQUND83HPPtQ4st2HDhqi+USg3hXOOPXv21KWnp3sGrl+3bl1sVFSU74MPPrggSRI0Go0tsG24/Y3Us3Dy5MmgVatW3QMAHR0d8oMHD06Qy+V85cqVnQPLr1mzpvXVV1+dZDAY3CtWrGjti5MtWbKk7e23324aWLa6ulq5ZcuW6PLy8jORkZH+vLw8vdvt7j+3gSTqVhjPR6cSQgghhJBvOLlczj0ez3VX0l1dXbKIiAifSqXiH3/8ccjly5dv2Cvx2muvRTocDtnGjRuvBNYtXLiwe+fOnRFdXV0CAFy4cEHR1NQknzNnjmP//v1hDoeDdXR0CKWlpWFjiTk7O7t706ZN0YE5GEeOHNEEYo6JifHJZDIUFRVN9Pv9AIDZs2fbd+/erRNFEQ0NDYqjR4/2Jwfbtm27VF1dfXrwEjiOpqamysCycOHCjk2bNl0cnCgAwJw5c3qam5uVe/funZifn98OADk5Od0lJSXhTU1NcgC4evWqrLa2VtnR0SHTaDSSTqfzX7p0SX7o0KEJYzn+saCeBUIIIYQQctOWL1/eYjKZzFar1fnRRx9dCKxfvXp1+8KFC5OtVqvJYrE4ExMT3TdqZ8uWLZMUCgU3Go1mAHjiiSda1q9f33Lq1Cn1tGnTjEDvPIj333//wqxZs5wPPfRQu9VqtcTFxXmysrL651GMZs7C66+/fnnNmjXxRqPRzDlnkydP9hw8eLCuoKDgWl5eXtK+ffvCZ82aZddoNBIArFy5svOzzz4LTU1NtSQmJrqzsrLsw7X9dSxevLjj5MmT2sjISD8A2Gw290svvdQ0d+5cgyRJUCgUvLCw8OLcuXN7rFarMyUlxRIfH++x2WyOkdq+WWyYp5d+I2VmZvKysrLxDoMQQgghZESMsXLOeebAdRUVFfXp6emtw9Uh327Z2dnJBQUFVx988MHbkowMp6KiIiI9PV0/1DYahkQIIYQQQsg4am1tlen1eqtarZbudKIwEhqGRAghhBBCyDiKiIjw19fX35XvFaOeBUIIIYQQQsiQKFkghBBCCCGEDImSBUIIIYQQQsiQKFkghBBCCCGEDImSBUIIIYQQ0i8jI8M4UpnHHnssoby8XA0AL7zwwqSx1tdqtRkAUF9fr8jJybnnZmMNWLduXewrr7wSfaMyJSUlIdnZ2clfd18AkJeXp3/33XfDR1O2pKQkJCQkZKrRaDQbjUbzs88+G3MrYrhT6GlIhBBCCCGk3/Hjx6tHKlNcXNwQ+F5YWBjz+uuv9791eTT1A/R6ve/AgQPnxx7lN0tmZqbj4MGDdXd6v6IoQi7/epf71LNACCGEEEL6Be76l5SUhGRlZaXm5OTck5iYaMnNzU2UJAkAkJWVlXr48GHtk08+GefxeASj0WjOzc1NHFi/q6tLmDFjhsFsNpsMBoN5165dYYP3VVNTo0xJSbEAvb0Vgbvv4eHh6c8880wMALz88svRVqvVZDAYzE8//XRsoO7zzz8/Sa/XW2fOnGk4e/asaizH2N3dLSxZskRvtVpNJpOpP7aamhqlzWZLNZvNJrPZbCotLQ0CAEmSsGrVqvikpCTL/fffn9za2npLb7hv3rw5Ij8/f0rg96ZNmyJWr149GQCKiop0aWlpJqPRaF62bFmCKIoAgOXLl8dbrVZTcnKyZeB5iYuLS3v22WdjbDZb6vbt20fV+3Ej1LNACCGEEHIXOoJLoe1wK25lmzqoffdhSvdoy585c0Zz4sSJ83q93mez2YylpaXBCxYscAS2FxUVNe3YsSOqurr69OC6Wq1W2r9/f51Op5Oam5vl06dPNy5btqxTEIa+Vx3oraitrVUuWLAgZe3atW0ffvhhaF1dnfrkyZNnOOeYN29e8ieffBIcHBws7d27V1dZWXna5/Nh6tSp5oyMDCcAvPHGG5EAsH79+pbhjuvFF1+Myc7O7t69e3d9a2urLDMz05Sbm9sdGxsrfvHFF7VarZZXVlaqli5dek9VVdWZnTt3htXV1alqampONTY2KtLS0iyPP/54GwDk5+dPOXLkSMjgfTz88MPtGzduvAIAx48fD05NTTVHR0f73nrrrUuZmZnugWXz8/PbLRaL2ePxNKpUKr5r166IrVu3Nnz55ZfqPXv26MrKyqpVKhVfsWJF/DvvvDPxqaeeanvrrbeaoqOj/aIoYubMmanHjh3TTJ8+3QUAarVaKi8vrxnxDzwKlCwQQgghhJAhpaWl9SQlJfkAwGKxOM+dO6ccbV1JklhBQcHko0ePBguCgGvXrikbGxvl8fHx4nB1nE4ny8vLS9q8efNFg8HgffPNN6MOHz4cajabzX3bherqarXdbhcWLVrUGRISIgHA/PnzOwNt3ChJCDh06FDop59+GlZYWDgJADweD6urq1MmJCT48vPzE06fPq0RBAENDQ0qAPj8889DHn300Xa5XA69Xu+bMWNG/1uWt23bdulG+5o5c2ZPQ0PDyQkTJkjFxcUT8vLykhsaGr7yArbQ0FDpvvvusxcXF09IS0tz+3w+lpWV5dq4cWNkVVWVNj093QQAbrdbiIqKEgHgvffe0+3YsSNCFEXW0tKiqKioUAeShVWrVnWMdA5Gi5IFQgghhJC70Fh6AG4XlUrFA99lMhlEUWSjrbt161ZdW1ubvLKy8oxKpeJxcXFpLpfrhkPgV65cmfDAAw90LF682A4AnHMUFBQ0P/fcc60Dy23YsCGKsVGHch3OOfbs2VOXnp7uGbh+3bp1sVFRUb4PPvjggiRJ0Gg0tsC24fY3Us+CTqeTAusee+yxrnXr1sU3NzfLY2JivpI0rVmzpvXVV1+dZDAY3CtWrGjti5MtWbKk7e23324aWLa6ulq5ZcuW6PLy8jORkZH+vLw8vdvt7j+3gSTqVqA5C4QQQggh5KbJ5XLu8Xiuu5Lu6uqSRURE+FQqFf/4449DLl++fMNeiddeey3S4XDIAkN3AGDhwoXdO3fujOjq6hIA4MKFC4qmpib5nDlzHPv37w9zOByso6NDKC0tDRtLzNnZ2d2bNm2KDszBOHLkiCYQc0xMjE8mk6GoqGii3+8HAMyePdu+e/dunSiKaGhoUBw9erQ/Odi2bdul6urq04OXwHFcvHhRHtjPwYMHtZIkITo6+rrelTlz5vQ0Nzcr9+7dOzE/P78dAHJycrpLSkrCm5qa5ABw9epVWW1trbKjo0Om0WgknU7nv3TpkvzQoUMTxnL8Y3HbehYYY9sB/BWAa5xza986HYBiAHoA9QAe5Zxf103CGMsB8DMAMgC/5Jy/frviJIQQQgghN2/58uUtJpPJbLVanR999NGFwPrVq1e3L1y4MNlqtZosFoszMTHRfaN2tmzZMkmhUHCj0WgGgCeeeKJl/fr1LadOnVJPmzbNCPTOg3j//fcvzJo1y/nQQw+1W61WS1xcnCcrK6t/HsVo5iy8/vrrl9esWRNvNBrNnHM2efJkz8GDB+sKCgqu5eXlJe3bty981qxZdo1GIwHAypUrOz/77LPQ1NRUS2JiojsrK8s+XNuD7dq1K3z79u1RMpmMq9Vq6Ve/+tX54eZtLF68uOPkyZPayMhIPwDYbDb3Sy+91DR37lyDJElQKBS8sLDw4ty5c3usVqszJSXFEh8f77HZbI4hG7wFGOd85FI30zBjfwHAAeBXA5KFNwC0c85fZ4y9ACCcc/78oHoyALUAfgigEcCfACzlnF83cWawzMxMXlZWdouPhBBCCCHk1mOMlXPOMweuq6ioqE9PT28drg75dsvOzk4uKCi4+uCDD446GbkVKioqItLT0/VDbbttw5A454cBtA9a/SCA9/q+vwdg8RBVswDUcc7Pc869AP6jrx4hhBBCCCHfOq2trTK9Xm9Vq9XSnU4URnKnJzhHc86bAYBz3swYixqiTByAgbPKGwFMH65BxtgaAGsAID4+/haGSgghhBBCyO0XERHhr6+vrxq55J13N05wHmqq+bBjpTjnP+ecZ3LOMyMjI29jWIQQQgghhHy33DBZYIw9dYv3d5UxFtPXdgyAa0OUaQQwZcDvyQAu3+I4CCGEEEIIISMYqWfhiVu8v48A/HXf978G8F9DlPkTgBTGWCJjTAngR331CCGEEEIIIXfQbRuGxBj7NYA/AEhljDUyxvIBvA7gh4yxs+h92tHrfWVjGWO/AQDOuQjgKQCfAjgD4D8556duV5yEEEIIIYSQoY2ULNzLGOseYrEzxm74VkHO+VLOeQznXME5n8w538Y5b+Ocz+Wcp/R9tveVvcw5XzSg7m845wbOeRLn/NVbcJyEEEIIIWQUMjIyjCOVeeyxxxLKy8vVAPDCCy9MGmt9rVabAQD19fWKnJyce2421oB169bFvvLKK9E3KlNSUhKSnZ2d/HX3BQB5eXn6d999N3y05UtKSkKMRqM5OTnZMm3atNRbEcOdMtLTkCo55xl3JBJCCCGEEDLujh8/Xj1SmeLi4obA98LCwpjXX3+9/63Lo6kfoNfrfQcOHDg/9ii/OVpbW2V///d/H3/gwIGzKSkp3sDbmO8EURQhl3+93d2NT0MihBBCCCHjJHDXv6SkJCQrKys1JyfnnsTEREtubm6iJEkAgKysrNTDhw9rn3zyyTiPxyMYjUZzbm5u4sD6XV1dwowZMwxms9lkMBjMu3btChu8r5qaGmVKSooF6O2tMBqNZqPRaA4PD09/5plnYgDg5ZdfjrZarSaDwWB++umnYwN1n3/++Ul6vd46c+ZMw9mzZ1VjOcbu7m5hyZIleqvVajKZTP2x1dTUKG02W6rZbDaZzWZTaWlpEABIkoRVq1bFJyUlWe6///7k1tbWUV+B//KXv9T95V/+ZUdKSooXAOLi4sTBZTZv3hyRn5/f/4CfTZs2RaxevXoyABQVFenS0tJMRqPRvGzZsgRR7K2+fPnyeKvVakpOTrYMPC9xcXFpzz77bIzNZkvdvn37qHs/hjPSge7+ujsghBBCCCFjdwlHQt1oV9zKNtXQ+abgvhsOJR/ozJkzmhMnTpzX6/U+m81mLC0tDV6wYIEjsL2oqKhpx44dUdXV1acH19VqtdL+/fvrdDqd1NzcLJ8+fbpx2bJlnYIw9L3qQG9FbW2tcsGCBSlr165t+/DDD0Pr6urUJ0+ePMM5x7x585I/+eST4ODgYGnv3r26ysrK0z6fD1OnTjVnZGQ4AeCNN96IBID169e3DHdcL774Ykx2dnb37t2761tbW2WZmZmm3Nzc7tjYWPGLL76o1Wq1vLKyUrV06dJ7qqqqzuzcuTOsrq5OVVNTc6qxsVGRlpZmefzxx9sAID8/f8qRI0dCBu/j4Ycfbt+4ceOV2tpatc/nY1lZWak9PT3CT37yk2tPPfVU28Cy+fn57RaLxezxeBpVKhXftWtXxNatWxu+/PJL9Z49e3RlZWXVKpWKr1ixIv6dd96Z+NRTT7W99dZbTdHR0X5RFDFz5szUY8eOaaZPn+4CALVaLZWXl9eM4k88opGShRbGWArn/CxjjAHYDiAPQD2AxznnX96KIAghhBBCyN0nLS2tJykpyQcAFovFee7cOeVo60qSxAoKCiYfPXo0WBAEXLt2TdnY2CiPj4+/7s56gNPpZHl5eUmbN2++aDAYvG+++WbU4cOHQ81ms7lvu1BdXa222+3CokWLOkNCQiQAmD9/fmegjRslCQGHDh0K/fTTT8MKCwsnAYDH42F1dXXKhIQEX35+fsLp06c1giCgoaFBBQCff/55yKOPPtoul8uh1+t9M2bM6H/L8rZt2y4Ntx8AEEWRnTx5UvvFF1/U9vT0CN///veNf/EXf+G49957PYEyoaGh0n333WcvLi6ekJaW5u5LLlwbN26MrKqq0qanp5sAwO12C1FRUSIAvPfee7odO3ZEiKLIWlpaFBUVFepAsrBq1aqOkc7BaI2ULPw9gB1935cCuBdAIoAMAD8D8INbFQghhBBCCPmzsfQA3C4qlar/xbgymQyiKA718twhbd26VdfW1iavrKw8o1KpeFxcXJrL5brhEPiVK1cmPPDAAx2LFy+2AwDnHAUFBc3PPfdc68ByGzZsiOq9j31zOOfYs2dPXXp6umfg+nXr1sVGRUX5PvjggwuSJEGj0dgC24bb30g9C5MnT/ZGRESIoaGhUmhoqDR9+nR7WVmZdmCyAABr1qxpffXVVycZDAb3ihUrWvviZEuWLGl7++23mwaWra6uVm7ZsiW6vLz8TGRkpD8vL0/vdrv7z20giboVRpqzIHLOfX3f/wrAr/qeaPQ7AEG3KghCCCGEEPLNJJfLucfjue5KuqurSxYREeFTqVT8448/Drl8+fINeyVee+21SIfDIdu4cWP/ZOmFCxd279y5M6Krq0sAgAsXLiiamprkc+bMcezfvz/M4XCwjo4OobS0NGwsMWdnZ3dv2rQpOjAH48iRI5pAzDExMT6ZTIaioqKJfr8fADB79mz77t27daIooqGhQXH06NH+5GDbtm2XqqurTw9eAsfxyCOPdP7hD38I9vl8sNvtwvHjx4PT0tJcg2OaM2dOT3Nzs3Lv3r0T8/Pz2wEgJyenu6SkJDwwKfrq1auy2tpaZUdHh0yj0Ug6nc5/6dIl+aFDhyaM5fjHYqSeBanvTcsdAOYCGPgYU83tCooQQgghhHwzLF++vMVkMpmtVqvzo48+uhBYv3r16vaFCxcmW61Wk8VicSYmJrpv1M6WLVsmKRQKbjQazQDwxBNPtKxfv77l1KlT6mnTphmB3nkQ77///oVZs2Y5H3rooXar1WqJi4vzZGVl9c+jGM2chddff/3ymjVr4o1Go5lzziZPnuw5ePBgXUFBwbW8vLykffv2hc+aNcuu0WgkAFi5cmXnZ599FpqammpJTEx0Z2Vl2Ydre7Dvfe977nnz5nUZjUaLIAhYuXJly7Rp04Y8F4sXL+44efKkNjIy0g8ANpvN/dJLLzXNnTvXIEkSFAoFLywsvDh37tweq9XqTElJscTHx3tsNptjqPZuBcY5H34jY38FYCsAGYCPOef/q2/9bADrOed/ebsCuxmZmZm8rKxsvMMghBBCCBkRY6ycc545cF1FRUV9enp663B1yLdbdnZ2ckFBwdUHH3xw1MnIrVBRURGRnp6uH2rbSD0LVwHMAGDnnHcwxlahd4LzVQBrbmmUhBBCCCGEfAcFnshkMpmcdzpRGMlIycJWAPP6EoW/APA6gL8DMBW9E5wfub3hEUIIIYQQ8u0WERHhr6+vrxrvOIYyUrIg45y3931/DMDPOecfAPiAMXbitkZGCCGEEEIIGVcjPQ1JxhgLJBRzAfz3gG137FXVhBBCCCGEkDtvpAv+XwP4nDHWCsAF4AsAYIwlA+i6zbERQgghhBBCxtENkwXO+auMsc8AxAD4Lf/zo5ME9M5dIIQQQgghhHxLjTQMCZzzo5zzvZzzngHrajnnX97e0AghhBBCyJ2WkZFhHKnMY489llBeXq4GgBdeeGHSWOtrtdoMAKivr1fk5OTcc7OxBqxbty72lVdeib5RmZKSkpDs7Ozkr7svAMjLy9O/++674aMp+/LLL0cbjUaz0Wg0p6SkWGQyme3q1auyWxHHnTBiskAIIYQQQr47jh8/Xj1SmeLi4gabzeYGgMLCwpix1g/Q6/W+AwcOnB97lN8c//t//++rgbc6/8u//EvjtGnT7NHR0f47sW9RFL92G5QsEEIIIYSQfoG7/iUlJSFZWVmpOTk59yQmJlpyc3MTJUkCAGRlZaUePnxY++STT8Z5PB7BaDSac3NzEwfW7+rqEmbMmGEwm80mg8Fg3rVrV9jgfdXU1ChTUlIsQG9vReAOfHh4ePozzzwTA/TembdarSaDwWB++umnYwN1n3/++Ul6vd46c+ZMw9mzZ1VjOcbu7m5hyZIleqvVajKZTP2x1dTUKG02W6rZbDaZzWZTaWlpEABIkoRVq1bFJyUlWe6///7k1tbWm3rQz69//WvdkiVL2gev37x5c0R+fv6UwO9NmzZFrF69ejIAFBUV6dLS0kxGo9G8bNmyhEACsHz58nir1WpKTk62DDwvcXFxac8++2yMzWZL3b59+6h6P27kjj/RiDGWCqB4wKp7ALzCOf+/A8rcD+C/AAReGf4h53zDHQqREEIIIWT8ufaEQrqsuKVtCrE+aB7pHm3xM2fOaE6cOHFer9f7bDabsbS0NHjBggWOwPaioqKmHTt2RFVXV58eXFer1Ur79++v0+l0UnNzs3z69OnGZcuWdQrC0Peqi4uLGwCgtrZWuWDBgpS1a9e2ffjhh6F1dXXqkydPnuGcY968ecmffPJJcHBwsLR3715dZWXlaZ/Ph6lTp5ozMjKcAPDGG29EAsD69etbhjuuF198MSY7O7t79+7d9YEXouXm5nbHxsaKX3zxRa1Wq+WVlZWqpUuX3lNVVXVm586dYXV1daqamppTjY2NirS0NMvjjz/eBgD5+flTjhw5EjJ4Hw8//HD7xo0brwR+2+124fDhwxN++ctfXhxcNj8/v91isZg9Hk+jSqXiu3btiti6dWvDl19+qd6zZ4+urKysWqVS8RUrVsS/8847E5966qm2t956qyk6OtoviiJmzpyZeuzYMc306dNdAKBWq6Xy8vKaYf+wY3DHkwXOeQ16X+oGxpgMQBOAvUMU/YJz/ld3MDRCCCGEEDJAWlpaT1JSkg8ALBaL89y5c8rR1pUkiRUUFEw+evRosCAIuHbtmrKxsVEeHx8/7NgYp9PJ8vLykjZv3nzRYDB433zzzajDhw+Hms1mc992obq6Wm2324VFixZ1hoSESAAwf/78zkAbN0oSAg4dOhT66aefhhUWFk4CAI/Hw+rq6pQJCQm+/Pz8hNOnT2sEQUBDQ4MKAD7//POQRx99tF0ul0Ov1/tmzJjR/5blbdu2XRrN+fiP//iPCTabzTHUEKTQ0FDpvvvusxcXF09IS0tz+3w+lpWV5dq4cWNkVVWVNj093QQAbrdbiIqKEgHgvffe0+3YsSNCFEXW0tKiqKioUAeShVWrVnWMJqbRGO93JcwFcI5z3jDOcRBCCCGE3F3G0ANwu6hUqsCTMCGTySCKIhtt3a1bt+ra2trklZWVZ1QqFY+Li0tzuVw3HAK/cuXKhAceeKBj8eLFdgDgnKOgoKD5ueeeax1YbsOGDVGMjTqU63DOsWfPnrr09HTPwPXr1q2LjYqK8n3wwQcXJEmCRqOxBbYNt7/R9iz853/+p+7RRx+9bghSwJo1a1pfffXVSQaDwb1ixYrWvjjZkiVL2t5+++2mgWWrq6uVW7ZsiS4vLz8TGRnpz8vL07vd7v5zG0iiboXxnrPwI/S+y2EoMxhjFYyxTxhjluEaYIytYYyVMcbKWlpGTCQJIYQQQsgtJJfLucfjue5KuqurSxYREeFTqVT8448/Drl8+fINeyVee+21SIfDIRt4gb1w4cLunTt3RnR1dQkAcOHCBUVTU5N8zpw5jv3794c5HA7W0dEhlJaWho0l5uzs7O5NmzZFB+ZgHDlyRBOIOSYmxieTyVBUVDTR7+/tBJg9e7Z99+7dOlEU0dDQoDh69Gh/crBt27ZLgQnMA5eBx9HW1ib74x//GLJs2bLO4WKaM2dOT3Nzs3Lv3r0T8/Pz2wEgJyenu6SkJLypqUkOAFevXpXV1tYqOzo6ZBqNRtLpdP5Lly7JDx06NGEsxz8W49azwBhTAsgF8A9DbP4SQALn3MEYWwRgH4CUodrhnP8cwM8BIDMzkw9VhhBCCCGE3B7Lly9vMZlMZqvV6vzoo48C802xevXq9oULFyZbrVaTxWJxJiYmum/UzpYtWyYpFApuNBrNAPDEE0+0rF+/vuXUqVPqadOmGYHeeRDvv//+hVmzZjkfeuihdqvVaomLi/NkZWX1z6MYzZyF119//fKaNWvijUajmXPOJk+e7Dl48GBdQUHBtby8vKR9+/aFz5o1y67RaCQAWLlyZednn30WmpqaaklMTHRnZWXZh2t7KO+//37YD37wg+7Q0NAb3vFfvHhxx8mTJ7WRkZF+ALDZbO6XXnqpae7cuQZJkqBQKHhhYeHFuXPn9litVmdKSoolPj7eY7PZHDdq9+tgf37P2p3FGHsQwN9yzuePomw9gEzOeeuNymVmZvKysrJbFCEhhBBCyO3DGCvnnGcOXFdRUVGfnp5+w+sd8u2VnZ2dXFBQcPXBBx8cUzLydVVUVESkp6frh9o2nsOQlmKYIUiMsUmsb2AYYywLvXG23cHYCCGEEEIIuSNaW1tler3eqlarpTudKIxkXIYhMca0AH4IYO2AdT8GAM75OwAeAfATxpgIwAXgR3y8ukAIIYQQQgi5jSIiIvz19fVV4x3HUMYlWeCcOwFMHLTunQHftwDYcqfjIoQQQgghhPzZeD8NiRBCCCGEEHKXomSBEEIIIYQQMiRKFgghhBBCCCFDomSBEEIIIYT0y8jIMI5U5rHHHksoLy9XA8ALL7wwaaz1tVptBgDU19crcnJy7rnZWAPWrVsX+8orr0TfqExJSUlIdnZ28tfdFwDk5eXp33333fDRlG1ra5PNmTMnOTU11ZycnGz52c9+NnHkWncPShYIIYQQQki/48ePV49Upri4uMFms7kBoLCwMGas9QP0er3vwIED58ce5TfHv/7rv0ampqa6ampqTh8+fLjmlVdemeJ2u6974/XtIIri126DkgVCCCGEENIvcNe/pKQkJCsrKzUnJ+eexMRES25ubqIk9b6AOCsrK/Xw4cPaJ598Ms7j8QhGo9Gcm5ubOLB+V1eXMGPGDIPZbDYZDAbzrl27wgbvq6amRpmSkmIBensrjEaj2Wg0msPDw9OfeeaZGAB4+eWXo61Wq8lgMJiffvrp2EDd559/fpJer7fOnDnTcPbsWdVYjrG7u1tYsmSJ3mq1mkwmU39sNTU1SpvNlmo2m01ms9lUWloaBACSJGHVqlXxSUlJlvvvvz+5tbV11E8UZYzBbrfLJElCd3e3MGHCBFGhUHzllQCbN2+OyM/PnxL4vWnTpojVq1dPBoCioiJdWlqayWg0mpctW5YQSACWL18eb7VaTcnJyZaB5yUuLi7t2WefjbHZbKnbt28fVe/HjYzLo1MJIYQQQsgITu0Jhf2y4pa2GRLrg+WR7tEWP3PmjObEiRPn9Xq9z2azGUtLS4MXLFjgCGwvKipq2rFjR1R1dfXpwXW1Wq20f//+Op1OJzU3N8unT59uXLZsWacgDH2vuri4uAEAamtrlQsWLEhZu3Zt24cffhhaV1enPnny5BnOOebNm5f8ySefBAcHB0t79+7VVVZWnvb5fJg6dao5IyPDCQBvvPFGJACsX7++ZbjjevHFF2Oys7O7d+/eXd/a2irLzMw05ebmdsfGxopffPFFrVar5ZWVlaqlS5feU1VVdWbnzp1hdXV1qpqamlONjY2KtLQ0y+OPP94GAPn5+VOOHDkSMngfDz/8cPvGjRuvrF+//lpOTk5ydHT0vT09PbLt27efl8lkXymbn5/fbrFYzB6Pp1GlUvFdu3ZFbN26teHLL79U79mzR1dWVlatUqn4ihUr4t95552JTz31VNtbb73VFB0d7RdFETNnzkw9duyYZvr06S4AUKvVUnl5ec3If+GRUbJACCGEEEKGlJaW1pOUlOQDAIvF4jx37pxytHUlSWIFBQWTjx49GiwIAq5du6ZsbGyUx8fHDzs2xul0sry8vKTNmzdfNBgM3jfffDPq8OHDoWaz2dy3Xaiurlbb7XZh0aJFnSEhIRIAzJ8/vzPQxo2ShIBDhw6Ffvrpp2GFhYWTAMDj8bC6ujplQkKCLz8/P+H06dMaQRDQ0NCgAoDPP/885NFHH22Xy+XQ6/W+GTNm9L9ledu2bZdutK99+/ZNsFqtrj/84Q+1p0+fVi1YsMAwf/78UzqdTgqUCQ0Nle677z57cXHxhLS0NLfP52NZWVmujRs3RlZVVWnT09NNAOB2u4WoqCgRAN577z3djh07IkRRZC0tLYqKigp1IFlYtWpVx0jnYLQoWSCEEEIIuRuNoQfgdlGpVP3DZWQyGURRHPVY+61bt+ra2trklZWVZ1QqFY+Li0tzuVw3HAK/cuXKhAceeKBj8eLFdgDgnKOgoKD5ueeeax1YbsOGDVGM3fywf8459uzZU5eenu4ZuH7dunWxUVFRvg8++OCCJEnQaDS2wLbh9jdSz8J777038YUXXrgiCAKsVqtnypQpnoqKCnV2drZzYPk1a9a0vvrqq5MMBoN7xYoVrX1xsiVLlrS9/fbbTQPLVldXK7ds2RJdXl5+JjIy0p+Xl6d3u9395zaQRN0KNGeBEEIIIYTcNLlczj0ez3VX0l1dXbKIiAifSqXiH3/8ccjly5dv2Cvx2muvRTocDtnGjRuvBNYtXLiwe+fOnRFdXV0CAFy4cEHR1NQknzNnjmP//v1hDoeDdXR0CKWlpWFjiTk7O7t706ZN0YE5GEeOHNEEYo6JifHJZDIUFRVN9Pv9AIDZs2fbd+/erRNFEQ0NDYqjR4/2Jwfbtm27VF1dfXrwEjiOuLg4729/+9tQALh06ZL8/PnzaqPR6B0c05w5c3qam5uVe/funZifn98OADk5Od0lJSXhTU1NcgC4evWqrLa2VtnR0SHTaDSSTqfzX7p0SX7o0KEJYzn+saCeBUIIIYQQctOWL1/eYjKZzFar1fnRRx9dCKxfvXp1+8KFC5OtVqvJYrE4ExMT3TdqZ8uWLZMUCgU3Go1mAHjiiSda1q9f33Lq1Cn1tGnTjEDvPIj333//wqxZs5wPPfRQu9VqtcTFxXmysrL651GMZs7C66+/fnnNmjXxRqPRzDlnkydP9hw8eLCuoKDgWl5eXtK+ffvCZ82aZddoNBIArFy5svOzzz4LTU1NtSQmJrqzsrLsw7U92Kuvvtq8fPlyvcFgMHPO2T//8z83xsTEDDkUa/HixR0nT57URkZG+gHAZrO5X3rppaa5c+caJEmCQqHghYWFF+fOndtjtVqdKSkplvj4eI/NZnMM1d6twDjnI5f6hsjMzORlZWXjHQYhhBBCyIgYY+Wc88yB6yoqKurT09Nbh6tDvt2ys7OTCwoKrj744IOjTkZuhYqKioj09HT9UNtoGBIhhBBCCCHjqLW1VabX661qtVq604nCSGgYEiGEEEIIIeMoIiLCX19fXzXecQyFehYIIYQQQgghQ6JkgRBCCCGEEDIkShYIIYQQQgghQxqXZIExVs8Yq2SMnWCMXff4ItarkDFWxxg7yRj73njESQghhBBCyHfZePYsZHPOpw5+ZFifhQBS+pY1AP7tjkZGCCGEEPIdlZGRYRypzGOPPZZQXl6uBoAXXnhh0ljra7XaDACor69X5OTk3HOzsQasW7cu9pVXXom+UZmSkpKQ7Ozs5K+7LwDIy8vTv/vuu+GjKdvS0iL74Q9/mGQwGMxpaWmmP/3pT+pbEcOdcrcOQ3oQwK94r6MAwhhjMeMdFCGEEELIt93x48erRypTXFzcYLPZ3ABQWFj4lWu00dQP0Ov1vgMHDpwfe5TfHC+99FLMvffe66ytrT39q1/96sL/9//9f/F3at+iOOS738ZkvJIFDuC3jLFyxtiaIbbHAbg04Hdj37rrMMbWMMbKGGNlLS3DvqiPEEIIIYSMQuCuf0lJSUhWVlZqTk7OPYmJiZbc3NxESZIAAFlZWamHDx/WPvnkk3Eej0cwGo3m3NzcxIH1u7q6hBkzZhjMZrPJYDCYd+3aFTZ4XzU1NcqUlBQL0NtbYTQazUaj0RweHp7+zDPPxADAyy+/HG21Wk0Gg8H89NNPxwbqPv/885P0er115syZhrNnz6rGcozd3d3CkiVL9Far1WQymfpjq6mpUdpstlSz2Wwym82m0tLSIACQJAmrVq2KT0pKstx///3Jra2to379QE1NjXr+/Pl2AMjIyHA3NjYqL1269JX6mzdvjsjPz58S+L1p06aI1atXTwaAoqIiXVpamsloNJqXLVuWEEgAli9fHm+1Wk3JycmWgeclLi4u7dlnn42x2Wyp27dvH1Xvx42M13sW7uOcX2aMRQEoZYxVc84PD9jOhqgz5KumOec/B/BzoPcNzrc+VEIIIYSQcfDRnlBcvay4pW1Gx/qQ+0j3aIufOXNGc+LEifN6vd5ns9mMpaWlwQsWLHAEthcVFTXt2LEjqrq6+vTgulqtVtq/f3+dTqeTmpub5dOnTzcuW7asUxCGvlddXFzcAAC1tbXKBQsWpKxdu7btww8/DK2rq1OfPHnyDOcc8+bNS/7kk0+Cg4ODpb179+oqKytP+3w+TJ061ZyRkeEEgDfeeCMSANavXz/sXeQXX3wxJjs7u3v37t31ra2tsszMTFNubm53bGys+MUXX9RqtVpeWVmpWrp06T1VVVVndu7cGVZXV6eqqak51djYqEhLS7M8/vjjbQCQn58/5ciRIyGD9/Hwww+3b9y48YrVanXt3r07bMGCBY6DBw9qm5ubVfX19copU6b03/bPz89vt1gsZo/H06hSqfiuXbsitm7d2vDll1+q9+zZoysrK6tWqVR8xYoV8e+8887Ep556qu2tt95qio6O9ouiiJkzZ6YeO3ZMM336dBcAqNVqqby8vGaUf+YbGpdkgXN+ue/zGmNsL4AsAAOThUYAUwb8ngzg8p2LkBBCCCGEpKWl9SQlJfkAwGKxOM+dO6ccbV1JklhBQcHko0ePBguCgGvXrikbGxvl8fHxw46NcTqdLC8vL2nz5s0XDQaD980334w6fPhwqNlsNvdtF6qrq9V2u11YtGhRZ0hIiAQA8+fP7wy0caMkIeDQoUOhn376aVhhYeEkAPB4PKyurk6ZkJDgy8/PTzh9+rRGEAQ0NDSoAODzzz8PefTRR9vlcjn0er1vxowZ/W9Z3rZt26Xh9gMAGzZsaF6zZk18X6+Jy2g0OuVy+VducIeGhkr33Xefvbi4eEJaWprb5/OxrKws18aNGyOrqqq06enpJgBwu91CVFSUCADvvfeebseOHRGiKLKWlhZFRUWFOpAsrFq1qmOkczBadzxZYIwFARA45/a+7/MBbBhU7CMATzHG/gPAdABdnPPmOxwqIYQQQsj4GUMPwO2iUqn6L2plMhlEURxq9MeQtm7dqmtra5NXVlaeUalUPC4uLs3lct1wCPzKlSsTHnjggY7FixfbAYBzjoKCgubnnnuudWC5DRs2RDE26lCuwznHnj176tLT0z0D169bty42KirK98EHH1yQJAkajcYW2Dbc/kbqWdDpdNKePXvqgd7hTFOmTElLTU31DC6/Zs2a1ldffXWSwWBwr1ixorUvTrZkyZK2t99+u2lg2erqauWWLVuiy8vLz0RGRvrz8vL0bre7/9wGkqhbYTzmLEQD+B/GWAWAPwLYzzk/wBj7MWPsx31lfgPgPIA6AL8A8OQ4xEkIIYQQQkYgl8u5x+O57kq6q6tLFhER4VOpVPzjjz8OuXz58g17JV577bVIh8Mh27hx45XAuoULF3bv3LkzoqurSwCACxcuKJqamuRz5sxx7N+/P8zhcLCOjg6htLQ0bCwxZ2dnd2/atCk6MAfjyJEjmkDMMTExPplMhqKiool+vx8AMHv2bPvu3bt1oiiioaFBcfTo0f7kYNu2bZeqq6tPD14Cx9Ha2ipzu90M6J2bkJWVZdfpdNddzM+ZM6enublZuXfv3on5+fntAJCTk9NdUlIS3tTUJAeAq1evympra5UdHR0yjUYj6XQ6/6VLl+SHDh2aMJbjH4s73rPAOT8PIH2I9e8M+M4B/O2djIsQQgghhIzd8uXLW0wmk9lqtTo/+uijC4H1q1evbl+4cGGy1Wo1WSwWZ2JiovtG7WzZsmWSQqHgRqPRDABPPPFEy/r161tOnTqlnjZtmhHonQfx/vvvX5g1a5bzoYceardarZa4uDhPVlZW/zyK0cxZeP311y8HhgZxztnkyZM9Bw8erCsoKLiWl5eXtG/fvvBZs2bZNRqNBAArV67s/Oyzz0JTU1MtiYmJ7qysLPtwbQ924sQJdX5+fqIgCDwlJcX9/vvv1w9XdvHixR0nT57URkZG+gHAZrO5X3rppaa5c+caJEmCQqHghYWFF+fOndtjtVqdKSkplvj4eI/NZnMM1+bXxXqvy78dMjMzeVnZde94I4QQQgi56zDGyge/b6qioqI+PT29dbg65NstOzs7uaCg4OqDDz446mTkVqioqIhIT0/XD7Xtbn3PAiGEEEIIId8Jra2tMr1eb1Wr1dKdThRGMl6PTiWEEEIIIYQAiIiI8NfX11eNdxxDoZ4FQgghhBBCyJAoWSCEEEIIIYQMiZIFQgghhBBCyJAoWSCEEEIIIYQMiZIFQgghhBDSLyMjwzhSmcceeyyhvLxcDQAvvPDCpLHW12q1GQBQX1+vyMnJuedmYw1Yt25d7CuvvBJ9ozIlJSUh2dnZyV93XwCQl5enf/fdd8NHU/b48ePqqVOnGpVK5fcGx7hnz55QvV5vjY+Pt7744ouThmtjPFGyQAghhBBC+h0/frx6pDLFxcUNNpvNDQCFhYUxY60foNfrfQcOHDg/9ii/OaKiosSf/exnF9euXXt14HpRFPH000/H/+Y3v6mtra099cEHH+gCCdjt5vP5Rl2WkgVCCCGEENIvcNe/pKQkJCsrKzUnJ+eexMRES25ubqIkSQCArKys1MOHD2uffPLJOI/HIxiNRnNubm7iwPpdXV3CjBkzDGaz2WQwGMy7du0KG7yvmpoaZUpKigXo7a0wGo1mo9FoDg8PT3/mmWdiAODll1+OtlqtJoPBYH766adjA3Wff/75SXq93jpz5kzD2bNnVWM5xu7ubmHJkiV6q9VqMplM/bHV1NQobTZbqtlsNpnNZlNpaWkQAEiShFWrVsUnJSVZ7r///uTW1tZRv34gLi5OnD17tlOhUHzlTciHDh0KSkhI8JjNZq9areYPP/xw+549e75yjjo6OoS4uLg0j8fDAKC9vb3/96lTp1Q/+MEPUiwWi8lms6UeP35cDQD//u//PuHee+81mkwm88yZMw2XLl2SA729L0uXLk247777Uh5++OHE0cZP71kghBBCCLkLnd6zJ9R++bLiVrYZEhvrMz/ySPdoy585c0Zz4sSJ83q93mez2YylpaXBCxYscAS2FxUVNe3YsSOqurr69OC6Wq1W2r9/f51Op5Oam5vl06dPNy5btqxTEIa+V11cXNwAALW1tcoFCxakrF27tu3DDz8MraurU588efIM5xzz5s1L/uSTT4KDg4OlvXv36iorK0/7fD5MnTrVnJGR4QSAN954IxIA1q9f3zLccb344osx2dnZ3bt3765vbW2VZWZmmnJzc7tjY2PFL774olar1fLKykrV0qVL76mqqjqzc+fOsLq6OlVNTc2pxsZGRVpamuXxxx9vA4D8/PwpR44cCRm8j4cffrh948aNV4aL4dKlS8q4uDhv4PfkyZO9x44dCx5YJjw8XJoxY4b9P//zPyesXLmyc/v27bpFixZ1qFQqvnr16oSf//znDWlpaZ7//u//DvrJT34Sf/To0dof/vCHjh/96EfVgiDgrbfeitiwYcOkX/ziF40AcPLkSe2xY8eqg4OD+eB4hkPJAiGEEEIIGVJaWlpPUlKSDwAsFovz3LlzytHWlSSJFRQUTD569GiwIAi4du2asrGxUR4fHy8OV8fpdLK8vLykzZs3XzQYDN4333wz6vDhw6Fms9nct12orq5W2+12YdGiRZ0hISESAMyfP78z0MaNkoSAQ4cOhX766adhhYWFkwDA4/Gwuro6ZUJCgi8/Pz/h9OnTGkEQ0NDQoAKAzz//POTRRx9tl8vl0Ov1vhkzZvS/ZXnbtm2XRntOBuL8+ut1xth1K9esWdPy05/+dNLKlSs7d+3aFfGLX/yivqurSzh+/HjwkiVLkgLlvF4vA4ALFy4oFy9ePLmlpUXh9XqFKVOmeAJlcnJyOseSKACULBBCCCGE3JXG0gNwu6hUqv4LS5lMBlEU2Wjrbt26VdfW1iavrKw8o1KpeFxcXJrL5brhEPiVK1cmPPDAAx2LFy+2A70X1AUFBc3PPfdc68ByGzZsiGJs1KFch3OOPXv21KWnp3sGrl+3bl1sVFSU74MPPrggSRI0Go0tsG24/d1sz0J8fLy3qampP/lqbGxUxsbGXjeZYP78+T1/93d/p9q/f3+w3+9n06ZNc7e3twshISHiUD06Tz31VPzf//3fX1m+fHlXSUlJyIYNG/qHbgUFBUnDxTMcmrNACCGEEEJumlwu54Ex9QN1dXXJIiIifCqVin/88cchly9fvmGvxGuvvRbpcDhkAy+wFy5c2L1z586Irq4uAQAuXLigaGpqks+ZM8exf//+MIfDwTo6OoTS0tKwscScnZ3dvWnTpujAHIwjR45oAjHHxMT4ZDIZioqKJvr9fgDA7Nmz7bt379aJooiGhgbF0aNH+5ODbdu2Xaqurj49eLlRotDXZk99fb26urpa6Xa72YcffqjLy8vrHKrsj370o7a/+Zu/uWfFihWtAKDT6aTJkyd7t2/fHg70zqn4wx/+oAEAu90ui4+P9wHAjh07Jo7lvAyFehYIIYQQQshNW758eYvJZDJbrVbnRx99dCGwfvXq1e0LFy5MtlqtJovF4kxMTHTfqJ0tW7ZMUigU3Gg0mgHgiSeeaFm/fn3LqVOn1NOmTTMCvfMg3n///QuzZs1yPvTQQ+1Wq9USFxfnycrK6p9HMZo5C6+//vrlNWvWxBuNRjPnnE2ePNlz8ODBuoKCgmt5eXlJ+/btC581a5Zdo9FIALBy5crOzz77LDQ1NdWSmJjozsrKsg/X9mAXL16UT5s2zdzT0yNjjPGtW7dGnzlzpkqn00mbNm26mJOTY/D7/Vi2bFlrZmbmkOcoPz+/7ac//Wlcfn5+e2Ddr3/96/P/63/9r4Sf/vSnMaIosoceeqh9xowZrn/8x3+8vHTp0qTo6GhvZmZmz8WLF8c0+XswNtR4qW+qzMxMXlZWNt5hEEIIIYSMiDFWzjnPHLiuoqKiPj09vXW4OuS76d133w3/r//6r7B9+/ZdGLn02FVUVESkp6frh9pGPQuEEEIIIYTcpf76r/96ysGDByeUlJScHY/93/FkgTE2BcCvAEwCIAH4Oef8Z4PK3A/gvwAEsqcPOecb7mCYhBBCCCGEjLv33nvvEoCbeuLSrTAePQsigGc4518yxkIAlDPGSjnng2dzf8E5/6txiI8QQgghhBCCcXgaEue8mXP+Zd93O4AzAOLudByEEEIIIYSQGxvXR6cyxvQAMgAcG2LzDMZYBWPsE8aY5QZtrGGMlTHGylpaRnwHByGEEEIIIWSUxi1ZYIwFA/gAQAHnfPBLR74EkMA5Twfw/wDsG64dzvnPOeeZnPPMyMjI2xYvIYQQQggh3zXjkiwwxhToTRTe55x/OHg757ybc+7o+/4bAArGWMQdDpMQQggh5DuvpqZGmZKSMuwojzvl97//vaa4uHhC4Pf7778/4cUXX5w0njF9F4zH05AYgG0AznDO3xqmzCQAVznnnDGWhd6kpu0OhkkIIYQQQm4Tn88HhUIxpjplZWXasrKyoMcee6wLAJYvX94FoOt2xEf+bDx6Fu4DsBLAHMbYib5lEWPsx4yxH/eVeQRAFWOsAkAhgB/xb9Pb4wghhBBC7lL//M//HJ2SkmJJSUmxbNiwIQoARFHEww8/rDcYDOacnJx77Ha7AABPPvlkXFJSksVgMJjXrFkzGQAuX74sX7BgQZLVajVZrVbTb3/72yAAWLduXezSpUsT7rvvvpSHH3448d577zWWlZWpA/vNyspK/eKLL7QHDx7UZmRkGE0mkzkjI8NYUVGhcrvd7LXXXov9+OOPw41Go/kXv/hFeGFh4cRVq1bFA0Btba1yxowZBoPBYJ4xY4bh7NmzSgDIy8vTP/7441MyMjKMkydPTnv33XfDAaChoUGRmZmZajQazSkpKZYDBw4E39mz/M1xx3sWOOf/A4CNUGYLgC13JiJCCCGEkLsPY8x2u9rmnJcPtf6LL77Q/vu///vE8vLyM5xz2Gw209y5c+319fXqrVu31s+fP79nyZIl+n/913+N/Nu//dvW3/zmN+Hnz5+vEgQBra2tMgBYu3btlHXr1l1dsGCB4+zZs8oFCxaknD9//hQAnDx5Unvs2LHq4OBg/i//8i9R77//vi4zM/NyQ0OD4tq1a4of/OAHzvb2duGPf/xjtUKhwL59+0LWr18/+dNPPz33D//wD5fLysqCfvWrX10EgMLCwomBuH/84x/HL1u2rO3v/u7v2v7v//2/E3/yk59M+d3vfncOAK5evaooKyurPnHihPqhhx5K/pu/+ZuO7du36+bOndv105/+9IooiggkP+R69AZnQgghhBACADh06FDwokWLOkNDQyUA+Mu//MuOgwcPhkyaNMk7f/78HgBYuXJlW2FhYdTLL798VaVSST/60Y8S/vIv/7IrMDzoyJEjoWfPntUE2nQ4HLKOjg4BAHJycjqDg4M5AKxatapj3rx5hs2bN1/+1a9+Ff7AAw90AEB7e7vsscceS6yvr1czxrjP57vhTWYAOH78eNAnn3xyDgB+8pOftP/Lv/zL5MC23NzcTplMBpvN5m5ra1MAwPe///2etWvX6n0+n/DII490zJw503WrzuG3DWVRhBBCCCEEADDcqO/eKadf/a1QKHDixIkzeXl5nfv27Qu7//77UwJtlJWVnamurj5dXV19+tq1ayfDw8MlAAgKCpICbSQmJvrCwsLEY8eOaT788EPdypUr2wHg+eefj5s9e7b97Nmzpz7++OM6r9f7ta5X1Wp1/0EFjm/hwoWOw4cP18TFxXkff/zxxC1btkwctoHvOEoWCCGEEELuQpzz8tu1DLfPOXPmOH7zm9+E2e12obu7W/jNb34Tnp2dbW9ublb+7ne/CwKAf//3f9fNnDnT0dXVJfT1AnS98847l86cOaMFgFmzZnX/9Kc/jQq0+fvf/14z3P4eeeSR9o0bN06y2+2yrKwsFwB0d3fLJk+e7AWArVu39j8NMzQ01O9wOIa8ds3IyOj55S9/Gd5XR5eZmem40bmtra1VxsXF+Z555pnWFStWtH755ZfaG5X/LqNkgRBCCCGEAABmzZrlXLZsWdv3vvc9k81mM61cubIlIiLCf88997i3b98+0WAwmDs6OuTPPvtsS2dnpywnJyfFYDCYf/CDH6T+n//zfy4BwM9//vNLX375ZZDBYDAnJSVZtmzZMuyLsFasWNGxf/9+3YMPPtgeWPf8889f+ed//ufJ3/ve94x+v7+/7MKFC+21tbWawATnge3827/928WdO3dGGAwG869//euJRUVFl250nJ9++mmI2Wy2mEwm83/913+Fr1+//upNn7RvOfZteshQZmYmLysrG+8wCCGEEEJGxBgr55xnDlxXUVFRn56e3jpeMZHvpoqKioj09HT9UNuoZ4EQQgghhBAyJEoWCCGEEEIIIUOiZIEQQggh5O4hSZI04qNCCblV+v55k4bbTskCIYQQQsjdo6qlpWUCJQzkTpAkibW0tEwAUDVcGXopGyGEEELIXUIUxdVXrlz55ZUrV6ygm7rk9pMAVImiuHq4ApQsEEIIIYTcJWw22zUAueMdByEBlCwQQggh5DtF9HjgczrBJWnIBZxjQnz8eIdJyF2BkgVCCCGEfDNIEiC6AJ8T8DoBexvg6AScTsDtBFxOwOmEv8cOqasLot0BnysIPocDPocdYk8PxB4HuMcDSSbHpYyZQ+5GUCjwF//4j3f22Ai5S1GyQAghhJBR4xzgAMABiQNuH+ARexe3D1ArOSJDObzgcEt+eLgHHskNt+SFmnHEygXImRwC5PBDgp/7IPq6IXnb4Hddg9RzFZL9MiRXC+BpB3ydkPl6oHXYIXA3mOQDIIJBBAQJ3CXA92UYuN8Pye+HJEnwg4HLZBC5Cm3dqeCCAEVwMBSxU6AMCYVywgTIQkMRnJEFJghDLoSQXpQsEEIIId8wIgfcHHBJvZ9qBgQLvZ+s7xk6Xp8Eu90DUfTDD9a7SBzuHhc8rk54nK2A5IXfLwLMC/id8Hqd6OyU0NIhQ6tTgVafAh0+NTp9GnT5NOj2qtHjU0MIk6CIFeHnDBIbuAgIjrIjKrWlL9JAZtH7NUq4iu+py8EFBjAOGfdDBj/kXESYtwsG+7nebIQB0DJwDQMXGdRdIuJq2uGXFPBzFUQeApGr4WMaiHwC3BMNkIWFQxY+EYqwMCiDgqEICoYyNBT3RMdDNWECBJlsHP5ShHzzUbJACCGE3GmS1Dt0pqcbsLdBtHfgWnsP2lwiWlwM551KnO9R4pJLhatuLTo8avgkAaLI4JcYJD8D9wOSyCD5en+LHsDvESF5REhuH7joBfd5wQQ/BKUEppAABQdTSGDy3u9clEPyqeD3hEByx8DvU4EJckAmA1OowBQqAIDAJCjkPigVHqgVXoSHdyAmvhkymQSZ4IeGeaCCB2ruRbC8BxPae6CACAX3QcFEyLkIBSQESS6E+R3wQw4uySFBAQly+KFGiEeF6NZwMHUkoImGEBwNeWgkhMgwKO7RQTNrcm9c7M8ZEWP0dFFCbjdKFgghhHwr+eGDyJ3wS05AlMBcHjBXN/yODjg6O9Hd3Qmn0wPOZJAEGfyCAn5BDq8gh4cp4RTkEGU++OR++GR+eAU/vEyChwFeLsHv8cHvEuH3ihC9HH6vH34fg09kkCn9YEoJTMHBlQxQAVwFiDIZunqC0dUSivaroehs1sJxVQVnWyi89hD4RQEiBAAcjElggh8ywQ2ZYAfz+wG/BMZ7h+AwiBAggnM/4PeBQwITOOQMYAJ6H7qpFMAggHsFwClA8glgXABjrPeZnJIESBLkSh+UKj9UGglKlQSVmsP8vVBkZ09EbKgCkSFayLVqMIUcXJBDyaYgWG5BsEwLFZODYYSLds4BvxdgMkBGlx6EfJOMy7+xjLEcAD8DIAPwS87564O2s77tiwA4ATzOOf/yjgdKCCHfZZz3DQlhkEQRPpcL3p4e+JxO+Hp6IPl8YFyCj3vhZj7YBR+6mYR2UUSnW4Qkk+AXJDg5Q0uHClda1Wjv0MLlkUGh9UGp9kGh9UKh9kAu94L7RUAUoYIPKu6DQuKQeTlkXkAmckgig9snh8uvgNungMevgEfsW/xKuCU5PJISPsjhlZTwSkqIkgJ+LgdkAjiTgwsTwYUIMDmHoJAAee/xQdY79IUxgAm925jAIXkFSF4BfjeD2OWBv8MFqbMHktMLplFDCNJACNZAFqQBkwsQ5BxMLkHyyHrruiVwhwuSwwVu74HkdMPvEsFFEVzyQhA8kKv9vedDI0Kj9UEr9yFY4UOIXEKIHAjVKBCkViFYq0RQsAoarQIqlQJKtRYKpQJKtRyhIcEIDQlCWEgQQoLkCAnmCAkWoVJ5wYRggIUBTAbOAa8XcDh6l6AgQKcDOJfg8fjhdov9S0iIEpGRQbfmnyXGALnq1rRFCLmj7niywBiTAXgbwA8BNAL4E2PsI8756QHFFgJI6VumA/i3vk9CyE2SJA7OOWSyb+bEPc45JInD7+eQydiYjoODQ4QEAQyyQe84kuCHR3RBIVNCxhT9d0g9HqCrC+jpAaKiei+qxosEP0S4IEpOcL8bkOzgnjagpxnouQq4eyeCctHbO5jd17v4vSJElwiXQ4Sbc7g0CrhUGvSo5OhRqOBQqNAjV0IUAA4/wERwSACTwAUJEhh8EOCVK+AVVHBIWth9obB7Q+HwBcEpauHmSri6GRyXJLgvi/A0e+Fr88DvFuB3y+B3KyH55ADkYDJAEDy949H9fnDuB/dLAJeBQw0m44AAcD/rXUZxbhgACABjHjDmhsAkMHDIBD8EcMiYHzIGyOQyyOQKyGQKyOQqyPousuUqBQTeuydBknrH13OASRyS6Iff7YLP6YbX7QaTOGQMUCkYgoPU8Dq7wNtlEOQKQBCgCQqCNigICpUaks8Nr7sHHpcbYBwcDEKIDEFxQYiZpIZ+shIGvQoJk1WYMEGJ4GAlgoKUUCgECELvP9+CwHqTl1s41IYxQKXqXSZOHLhFgFYrQKtV3LJ9EUK+HcajZyELQB3n/DwAMMb+A8CDAAYmCw8C+BXnnAM4yhgLY4zFcM6b73y4hHwzcN57Id3T40V7uwutrS5cverC5ctOXG524fIVF5xuCZApICiVgFwJCApIMiX8TAGlWoFJ0QrExigREytHSBjAGYdf4tAoGBLDgxAXJkD9Na8lPB4RnZ1udHd74HT64HKJcLl8sNt9uHbNh6vXPGhv64Hkd0PyeyD5PfD5vXA5PfC6PfD4RPg4+iZWCuidHsn6fsugDubQhgOacAlBEyVoI/3QRIhQan0QvRIcrUBPi4SedgmOdo6eNsDdzSB6leB+NSSfGn6fFlxSQyZTQSZTITQkFEnJOmRkhMOaGgyrUY6JoX+eSDoUPyRwAAIYuCjC7/bA0dODq44OXHE44PR74GR+OLiIbq8PPT43HC4RLq8Xfu6CTOkCk7khCRwiB/wQIDHA65XD6VTA45LD7ZTB4wyD1xMOt1MGd7cc7i453F0y+BwMPgeD6OTwe3ondjIF712UgKAEmAoQVIAgl6H3fwdyMCgASQH4VeB+BcAFSH6ASxyMS2BMhAAJAvODu10Qu53gbh8YkyATBKg0GkzQ6qBSyxA0wYXQIBcmBDkQHuTGBI0PYVovNEoBTAgChxZcUkD0MPi8AjxuBu4XoFLLISjlkOQCJMEPP/NBVIiA4EeQAgiSA0FyhhCFDEFyAcEyQCMoIAsOhhA6AYJGCz8H/H4Jfj+H1+uH3e6B3e6Fw+FFd7cHdrsPfr8foujp70AZuEhS799RpVIiLk6H+PgQJCSEIj4+BBMnasAYA+ccXV0eXLni6F+uXnWgs7MDYWFqREeHYtKkWERHB2HSpGCEhalpjD0h5BtnPJKFOACXBvxuxPW9BkOViQNwXbLAGFsDYA0AxN+lL1DpdvU+Xs7n8aGr04WONieuXnGiqcmJK1dc6OkRoVLJoFLJoFDKIFfKIFPKIChlUCplCFEIUMgECELvwmQMXADAGORc6H3YRN9z7Hr/J9d75zUoSIXgYDWCg1VQKmV9ZQCZDAgJ6V00mhtf8NxtXC4fOjvdsNu9UKlkCAlRIThYCaXyz0+58HoBheLWHVfg4lYuF/r3dav+hy9JgOgHfCLQbffh2lUnrlzrQcs1J7rtPjjdfvS4RDhdElxuEW63Hy6PBK9Xgs8DuLwSejx+OL0iXF4JHpHD52Pwehl83t4Lai5XAwo1oAgDFwRIog/c5wX3d4FLXoD7AIEDEgP3/vm4BIUARTCDIkSAQssglzMo5QJCtQwRQQzRoQrEhKoRHiLAL+PwC4BXxuFiEuyiB3afFx6fD36PE363E94eD7w9Xnjdfvj9gF9k8DkZfE4ZvE45RK8SYHJAUIBDDhFKiDwYfh4OPxS9Q0iYHJwJAJfAuQTw3rvgTPADQu99cH7FB8npAfdLYAAYeO/4bLkMkk/suxjsHdTNFBoIKg0EhRpcJgEyH5jMC2h8APNCkHsB3gOxqwtln1/Gf3wqQCZXQ65WIyhCjonxMghyCaJXguiT4PNx+EUOUeSQ/IDoBfxeDr+Pw+8DJJ8E+P2A5O998ZMkgQf+xYQMDALAFQAPBpdY313uvr/JV26zs+s+ex/3KIcgl0Mul0EZLENQCIN2MkPQBEAhEyC5GCSXDJJLgORhECQJzOnvvaj2SfD7JUAQAbghyN0Q5BwyGaASGJS9I3kgZ4CMAQqBISRahcmTo6DXh0Kvn4D4+CCEhjIEBwPBwb3/rblb+f0SHA4vnE7fsGVkMgETJ2qG7cVijCEsTI2wMDWMxoj+9ZLEIQjfoP+wEkLIDYxHsjDUf0EH9zaPpkzvSs5/DuDnAJCZmTmaXuub1trqhChKYAx93cN/7iL+/HPg6lU/PB4PPB4vPB4v3G4P3G4vTtZ7cLXVDY/LB78I+H0AB4NSpYZHoYVPUIBLfkh9C5c8gOSHKPkgSSKYIEFgEmQCh0zGIRMkyGR+CIK/t7scAOMcjPddGPXeBgTnDJIkQOIMTFCAyVUAk0EmyKCRA1q5DCEqBUJDlNCFqaELlSEslEEbLEAmlyAIIgARjPkBiAA4QkI00Gi0UKk0UKm08HMt7E4FOu29fyFJ9MPtcsPZ44Lb6YbL6YbH44VSyaBSM6g0AhSq3u9KFSAoPJAggTMJEPzwSyL8kgif6IPX5+u9sPV6IDqd8Lk84KIHciaC+/3w+YAepwx2hxxejxxejxI+rwqiqIRWyTExVEREiB+6EBHBIRzqCSKUQRxyQQGZFARIQbCLIXD4NejyKdDllKGtw4X2Nhe6utzo6PbC0eODy8PhlQRA4JApJMhVHIogBmUwgzKYQz2BQy3zQ8NFaCU/tJxDKwkIYgxqCPCKKrR7tOjyKNDtUcDhk6PHK4fLK4fXK8LrdUP0uuD3+8DRO/SCCzKwvqENTBB6L2wFBoEJgCADmBISEwDGwGSs926xHJCrJcg1HMqJMoRO5NBESNCGuBCibcEETScmKO3Qyl0IkvdAy5zQ+D1QeFzgDhFeD0e3R4NrXeG41h6Gjs4gdHUGwdGpgqdLBq9fiR6fHNdEBWpEAZDcYNwNzvsueHnf20+51PfvBQcYB6AEk4X0PVlFCblKAZlaDplSAc6UQKgcsskM8lA/ZMF+yIIlKEN8UMhEqJkHQcyNIMGOILgRBC+0EBGkZFDJBagVMqjkaiiYAnKoIGcaMOjgY2rYXTK0dXJ0dvrR2e6Dw+FDyIRgTIgIhS4qCGETVVBpJKhUElRqESFBvc+HVyq8EFgPfN5OeJwd6Olqw6VrV3CxzYPGVhkuNwSj7aIKPVflON+o+PPFvMAhMA4WWAQOmZxBqeSQK3rzNZWSQyGXoJQDKjmHigEKJkAlCFAKcsgVSsjlKsjVGijUWii0GshVSjCFvPe/N5xDq5YhNEiBULUCE4IVCNfKERqkgFIpYEIoEKnrvREw0mPiRRGw23uHWgkCEBbWe4EvCL29VG63iJ4eHySJQ6EQIJd/dfmm3yGXyQRMmKDGhAnqW942JQqEkG+T8UgWGgFMGfB7MoDLN1Hmjtuz5zSuXHEMue3Mmd7JYoGbhJwDCoUCSqUSGiiRHBuBsIlahOk00EVpEaZTQx0s4KIAdAmAXOq9rpJJffPsJOAad6Bd9MLlkcHhFOB0M/j9HD4JEP0uiNwNCELvBSZjkPpn54n9F9nc4wV8bnCfp/e32wXJ7UNnD4PoFMAdDLydA3UczMcBX28MChmDUmBQywRoZAqoBQWYJMHpaYfT54EXHCIY/AAEpQyCWga/X4ToF8FZ3wUvY+CCAKZQQhIFSCLA/b3j5nl/f7/Ufw4DI5R77wT3LhAEMHkQmGIioFCCydUQ1EoIGjm4i/feIRd9YJIXTOGCIHeDKTy9kxHbFRBdACTWO3ERHEzoTaI4F/qOoAPg7RDkHILCD/TFDJUaglYLebQc8lAZQkIY4OXwdgG+Lg6XUwJvF8Gv+SD5fOB+gEsMksT+fPGI3omSEDgg+MEEEUxwgin8vUmH2gd5GKANlUMZIoc6VIA2REJwKEeIxgmVTIQgkyDIJTCZBLms93GHghxQyb0IV3QiXN6JCUIPQtCDIDghFwCFX0JwN0dQJ0dwpwDVVQ0Y1IBcAaZQg2tDAe0EIGgyeNAEsPCJEOKCwRQKcJ8H8PSAuRxgbgfg7YHf3YpuVw8cohNO0Y1uLqJNKeCifCIu+iehSwyGxu+BVhQR7PciyCtC6weUPiXkUEGjUoJDCx/3wcvVcDMNXIzBrxAQO8mLKJ0MYSEy6ILlCFVJCBI8UMsYgiZOgjws7DZ3fQVufQ+eeKnDV/8T9GccHI5rl9BcXYbz585CqdRAp4tCeEw8tJPioAgNglzBIJcLUDANZFDexvhvnlwOhIf3LoMxxqDRKKDR0Ph1Qgj5rhuPZOFPAFIYY4kAmgD8CMCyQWU+AvBU33yG6QC67ob5CgsWJMHtDgxj4F/5lCTeO2QoRNk/NEYu/7oTSYO/8kuSALsb6HQCjU41Oj0MTCaDIGO91zwCIMkAv9D7FBKNQvj/27v7EMvqOo7j7+99nJmdfdDVDR9zEw0f0mwXkZAKI4ugNGRBtOyJUrIoMMUtob+CwuqPIBMpH3rAKDPM0kREMkSKXc1001CRTJRd3Yd2dseduQ/f/jhn8bJeJ6+4M3Pnvl8wnHN/52F+w/1w537P+Z1zaFSLIQNVitvpVaJFi1m2d6bY0drOyzO7eXmmxba9wY7dwa59NaZmxpjd02R2T432VJWpPXV27amQ+4LqZIfK5Czj9WkqsQe608TMNOxrUZmoUp2o01hWoTkO4+NJo96h0Z2iEbM0aFNrt4hpiBmImSD3VUigS50ONdpUyajTiipt6kTUydniriLFrQln6My26bSD+mSXsdUdmqs71A9JsjFOVpfTrdaoZodKp0W22rSmurzyEuzbXuGVHRUqtQ6N8RaNZR1qY22qzTZZrdDNGqtXtDjm0L0cs2wbRzRmWN2ANVWYqFSo1ZdRq6+i1lhBl0PYO72SnbvG2LGzy2ynzUxzitn6bqbYzc7OLDs7HXa2qtSqbQ5v7mV1c5rVjWkmaTHW7VLJZJway5lkWXcFE9VDqFVXUGOMSrVJNJrE2DiV8QliYqJ8r6vF2YR5Pqp7xLz+tsUtCJavOZbla47lxPctdG8kSTq45r1YyMx2RHwZuIfiK+6NmbklIi4rl18P3EVx29SnKW6d+tn57mc/a9f2OQQ3jyoVWDlR/LyduY74zfW21oEJYBWvd+S01W2zvbuLre3tbG1t46XZnbw0M8XeTouxTpfJdpfJNkzOJitnk8nZZKKdrOw2WFlp0KzWiWqDSmWMqDSJahMmVsH4MmiOwdhYMW02h+uCiQO8/rFnSZKkpSFevbhu+K1fvz43bdq00N2QJEn6vyJic2auX+h+SHMZzhuuS5IkSTroLBYkSZIk9WWxIEmSJKkviwVJkiRJfVksSJIkSerLYkGSJElSXxYLkiRJkvqyWJAkSZLU15J6KFtEvAT8e6H7MY8OA15e6E5oqJgZDcrMaFBm5o17e2YevtCdkOaypIqFURMRm3zyowZhZjQoM6NBmRlpaXEYkiRJkqS+LBYkSZIk9WWxMNxuWOgOaOiYGQ3KzGhQZkZaQrxmQZIkSVJfnlmQJEmS1JfFgiRJkqS+LBYWmYi4MSK2RcTjPW2nR8RDEfFYRNwZESt6lp1WLttSLh8r29eVr5+OiB9GRCzE36ODa5C8REQ9Im4p25+IiI0925iXERERx0TE/WUGtkTEV8v2QyPi3oh4qpwe0rPNxjIb/4qID/e0m5sRMGhmIuJDEbG5zMbmiDinZ19mRhoyFguLz83ARw5o+wlwdWa+C/gdcCVARNSAXwCXZeYpwAeAVrnNj4EvAieUPwfuU0vDzbzBvAAbgGbZvg64NCKOK5eZl9HRBq7IzJOAs4DLI+Jk4Grgvsw8AbivfE257ELgFIpcXBcR1XJf5mY0DJQZigeyfaz8rPk08POefZkZachYLCwymfkAsOOA5ncCD5Tz9wIXlPPnAv/IzEfLbbdnZicijgBWZOZDWVzB/jPg/IPeec27AfOSwLKyyBwHZoHd5mW0ZOaLmflwOT8FPAEcBZwH3FKudguvZuA84FeZOZOZzwJPA2eam9ExaGYy85HMfKFs3wKMRUTTzEjDyWJhODwOfLyc3wAcU86fCGRE3BMRD0fEVWX7UcDzPds/X7ZpNLxeXm4D9gIvAs8B38vMHZiXkVWeWToD+Cvwtsx8EYovh8CacrWjgP/0bLY/H+ZmBL3BzPS6AHgkM2cwM9JQslgYDp+jOO27GVhOcUQYoAacDVxcTj8RER8E+o0B9R65o+P18nIm0AGOBNYCV0TEOzAvIykiJoHfAl/LzN1zrdqnLedo1xI1QGb2r38K8F3g0v1NfVYzM9IiZ7EwBDLzycw8NzPXAbcCz5SLngf+nJkvZ+Y0cBfwnrL96J5dHA28gEbCHHm5CPhTZrYycxvwILAe8zJyIqJO8aXvl5l5e9m8tRwmQjndVrY/z6tnp+DVfJibETJgZoiIoymumbokM3v/Z5kZachYLAyBiFhTTivANcD15aJ7gNMiYqIch/5+4J/l6eCpiDirvNPEJcAdC9B1LYA58vIccE4UllFcqPikeRkt5Xv8U+CJzPxBz6LfU1yMSjm9o6f9wnLM+VqKi1L/Zm5Gx6CZiYhVwB+BjZn54P6VzYw0nHyC8yITEbdS3NXoMGAr8C1gEri8XOV2ig/gLNf/JLCR4lTuXZl5Vdm+nuJOOePA3cBX0jd7yRkkL+UQgpuAkymGA9yUmdeW+zEvIyIizgb+AjwGdMvmb1CMQf81cCxFYbmhvKaFiPgmxfC2NsUQlLvLdnMzAgbNTERcQ/F/6ame3ZybmdvMjDR8LBYkSZIk9eUwJEmSJEl9WSxIkiRJ6stiQZIkSVJfFguSJEmS+rJYkCRJktSXxYKkkRQRqyLiS+X8kRFx20L3SZKkxcZbp0oaSRFxHPCHzDx1ofsiSdJiVVvoDkjSAvkOcHxE/J3i4VEnZeapEfEZ4HygCpwKfB9oAJ8CZoCPlg+eOh74EXA4MA18ITOfnO8/QpKkg8lhSJJG1dXAM5n5buDKA5adClwEnAl8G5jOzDOAh4BLynVuoHj67Drg68B189FpSZLmk2cWJOm17s/MKWAqIv4L3Fm2PwacFhGTwHuB30TE/m2a899NSZIOLosFSXqtmZ75bs/rLsXnZgXYVZ6VkCRpyXIYkqRRNQUsfzMbZuZu4NmI2AAQhdPfys5JkrQYWCxIGkmZuR14MCIeB659E7u4GPh8RDwKbAHOeyv7J0nSYuCtUyVJkiT15ZkFSZIkSX1ZLEiSJEnqy2JBkiRJUl8WC5IkSZL6sliQJEmS1JfFgiRJkqS+LBYkSZIk9fU/aQw9+6YgKxwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 13;\n",
       "                var nbb_unformatted_code = \"hindcast.plot()\";\n",
       "                var nbb_formatted_code = \"hindcast.plot()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hindcast.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have a huge bias because the initialized data is already converted to an anomaly, but uninitialized historical and observations is not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='time', ylabel='SST'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 14;\n",
       "                var nbb_unformatted_code = \"hindcast.remove_bias(alignment=\\\"same_verif\\\").plot()\";\n",
       "                var nbb_formatted_code = \"hindcast.remove_bias(alignment=\\\"same_verif\\\").plot()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hindcast.remove_bias(alignment=\"same_verif\").plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We still have a trend in all of our products, so we could also detrend them as well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Detrend\n",
    "\n",
    "Here we use a kitchen sink package called `esmtools`. It has a few vectorized stats functions that are `dask`-friendly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can leverage `xarray`'s `.map()` function to apply/map a function to all variables in our datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='time', ylabel='SST'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 15;\n",
       "                var nbb_unformatted_code = \"from climpred.stats import rm_poly\\n\\nhindcast_detrended = hindcast.map(rm_poly, deg=2, dim=\\\"init_or_time\\\")\\nhindcast_detrended.plot()\";\n",
       "                var nbb_formatted_code = \"from climpred.stats import rm_poly\\n\\nhindcast_detrended = hindcast.map(rm_poly, deg=2, dim=\\\"init_or_time\\\")\\nhindcast_detrended.plot()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from climpred.stats import rm_poly\n",
    "\n",
    "hindcast_detrended = hindcast.map(rm_poly, deg=2, dim=\"init_or_time\")\n",
    "hindcast_detrended.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And it looks like everything got detrended by a quadratic fit! That wasn't too hard."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Verify"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we've done our pre-processing, let's quickly compute some metrics. Check the metrics page [here](metrics.html) for all the keywords you can use. The [API](api.html#analysis-functions) is currently pretty simple for the `HindcastEnsemble`. You can essentially compute standard skill metrics and a reference persistence forecast."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-06T18:15:55.096705Z",
     "start_time": "2020-01-06T18:15:54.630606Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (lead: 10, skill: 2)\n",
       "Coordinates:\n",
       "  * lead     (lead) int32 1 2 3 4 5 6 7 8 9 10\n",
       "  * skill    (skill) &lt;U11 &#x27;initialized&#x27; &#x27;persistence&#x27;\n",
       "Data variables:\n",
       "    SST      (skill, lead) float64 0.003274 0.004149 ... 0.01109 0.008786</pre>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (lead: 10, skill: 2)\n",
       "Coordinates:\n",
       "  * lead     (lead) int32 1 2 3 4 5 6 7 8 9 10\n",
       "  * skill    (skill) <U11 'initialized' 'persistence'\n",
       "Data variables:\n",
       "    SST      (skill, lead) float64 0.003274 0.004149 ... 0.01109 0.008786"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 16;\n",
       "                var nbb_unformatted_code = \"hindcast_detrended.verify(\\n    metric=\\\"mse\\\",\\n    comparison=\\\"e2o\\\",\\n    dim=\\\"init\\\",\\n    alignment=\\\"same_verif\\\",\\n    reference=\\\"persistence\\\",\\n)\";\n",
       "                var nbb_formatted_code = \"hindcast_detrended.verify(\\n    metric=\\\"mse\\\",\\n    comparison=\\\"e2o\\\",\\n    dim=\\\"init\\\",\\n    alignment=\\\"same_verif\\\",\\n    reference=\\\"persistence\\\",\\n)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hindcast_detrended.verify(\n",
    "    metric=\"mse\",\n",
    "    comparison=\"e2o\",\n",
    "    dim=\"init\",\n",
    "    alignment=\"same_verif\",\n",
    "    reference=\"persistence\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we leverage `xarray`'s plotting method to compute Mean Absolute Error and the Anomaly Correlation Coefficient against the ERSST observations, as well as the equivalent metrics computed for persistence forecasts for each of those metrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-06T18:15:57.438157Z",
     "start_time": "2020-01-06T18:15:56.024458Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 17;\n",
       "                var nbb_unformatted_code = \"plt.style.use(\\\"ggplot\\\")\\nplt.style.use(\\\"seaborn-talk\\\")\\n\\ncolor = \\\"#7570b3\\\"\\n\\nf, axs = plt.subplots(nrows=2, figsize=(8, 8), sharex=True)\\n\\nfor ax, metric in zip(axs.ravel(), [\\\"mae\\\", \\\"acc\\\"]):\\n    handles = []\\n    result = hindcast_detrended.verify(\\n        metric=metric,\\n        comparison=\\\"e2o\\\",\\n        dim=\\\"init\\\",\\n        alignment=\\\"same_verif\\\",\\n        reference=\\\"persistence\\\",\\n    )\\n    (p1,) = result.sel(skill=\\\"initialized\\\").SST.plot(\\n        ax=ax, marker=\\\"o\\\", color=color, label=\\\"initialized forecast model\\\", linewidth=2\\n    )\\n    (p2,) = result.sel(skill=\\\"persistence\\\").SST.plot(\\n        ax=ax, color=color, linestyle=\\\"--\\\", label=\\\"persistence\\\"\\n    )\\n    handles.append(p1)\\n    handles.append(p2)\\n    ax.set_title(metric.upper())\\n\\n\\naxs[0].set_ylabel(\\\"Mean Error [degC]\\\")\\naxs[1].set_ylabel(\\\"Correlation Coefficient\\\")\\naxs[0].set_xlabel(\\\"\\\")\\naxs[1].set_xlabel(\\\"Lead Year\\\")\\naxs[1].set_xticks(np.arange(10) + 1)\\n\\n# matplotlib/xarray returning weirdness for the legend handles.\\nhandles = [i.get_label() for i in handles]\\n\\n# a little trick to put the legend on the outside.\\nplt.legend(handles, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.0)\\n\\nplt.suptitle(\\\"CESM Decadal Prediction Large Ensemble Global SSTs\\\", fontsize=16)\\nplt.show()\";\n",
       "                var nbb_formatted_code = \"plt.style.use(\\\"ggplot\\\")\\nplt.style.use(\\\"seaborn-talk\\\")\\n\\ncolor = \\\"#7570b3\\\"\\n\\nf, axs = plt.subplots(nrows=2, figsize=(8, 8), sharex=True)\\n\\nfor ax, metric in zip(axs.ravel(), [\\\"mae\\\", \\\"acc\\\"]):\\n    handles = []\\n    result = hindcast_detrended.verify(\\n        metric=metric,\\n        comparison=\\\"e2o\\\",\\n        dim=\\\"init\\\",\\n        alignment=\\\"same_verif\\\",\\n        reference=\\\"persistence\\\",\\n    )\\n    (p1,) = result.sel(skill=\\\"initialized\\\").SST.plot(\\n        ax=ax, marker=\\\"o\\\", color=color, label=\\\"initialized forecast model\\\", linewidth=2\\n    )\\n    (p2,) = result.sel(skill=\\\"persistence\\\").SST.plot(\\n        ax=ax, color=color, linestyle=\\\"--\\\", label=\\\"persistence\\\"\\n    )\\n    handles.append(p1)\\n    handles.append(p2)\\n    ax.set_title(metric.upper())\\n\\n\\naxs[0].set_ylabel(\\\"Mean Error [degC]\\\")\\naxs[1].set_ylabel(\\\"Correlation Coefficient\\\")\\naxs[0].set_xlabel(\\\"\\\")\\naxs[1].set_xlabel(\\\"Lead Year\\\")\\naxs[1].set_xticks(np.arange(10) + 1)\\n\\n# matplotlib/xarray returning weirdness for the legend handles.\\nhandles = [i.get_label() for i in handles]\\n\\n# a little trick to put the legend on the outside.\\nplt.legend(handles, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.0)\\n\\nplt.suptitle(\\\"CESM Decadal Prediction Large Ensemble Global SSTs\\\", fontsize=16)\\nplt.show()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use(\"ggplot\")\n",
    "plt.style.use(\"seaborn-talk\")\n",
    "\n",
    "color = \"#7570b3\"\n",
    "\n",
    "f, axs = plt.subplots(nrows=2, figsize=(8, 8), sharex=True)\n",
    "\n",
    "for ax, metric in zip(axs.ravel(), [\"mae\", \"acc\"]):\n",
    "    handles = []\n",
    "    result = hindcast_detrended.verify(\n",
    "        metric=metric,\n",
    "        comparison=\"e2o\",\n",
    "        dim=\"init\",\n",
    "        alignment=\"same_verif\",\n",
    "        reference=\"persistence\",\n",
    "    )\n",
    "    (p1,) = result.sel(skill=\"initialized\").SST.plot(\n",
    "        ax=ax, marker=\"o\", color=color, label=\"initialized forecast model\", linewidth=2\n",
    "    )\n",
    "    (p2,) = result.sel(skill=\"persistence\").SST.plot(\n",
    "        ax=ax, color=color, linestyle=\"--\", label=\"persistence\"\n",
    "    )\n",
    "    handles.append(p1)\n",
    "    handles.append(p2)\n",
    "    ax.set_title(metric.upper())\n",
    "\n",
    "\n",
    "axs[0].set_ylabel(\"Mean Error [degC]\")\n",
    "axs[1].set_ylabel(\"Correlation Coefficient\")\n",
    "axs[0].set_xlabel(\"\")\n",
    "axs[1].set_xlabel(\"Lead Year\")\n",
    "axs[1].set_xticks(np.arange(10) + 1)\n",
    "\n",
    "# matplotlib/xarray returning weirdness for the legend handles.\n",
    "handles = [i.get_label() for i in handles]\n",
    "\n",
    "# a little trick to put the legend on the outside.\n",
    "plt.legend(handles, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.0)\n",
    "\n",
    "plt.suptitle(\"CESM Decadal Prediction Large Ensemble Global SSTs\", fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PerfectModelEnsemble\n",
    "\n",
    "We'll now play around a bit with the `PerfectModelEnsemble` object, using sample data from the MPI perfect model configuration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-06T18:15:58.493102Z",
     "start_time": "2020-01-06T18:15:58.487299Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.Dataset>\n",
      "Dimensions:  (area: 3, init: 12, lead: 20, member: 10, period: 5)\n",
      "Coordinates:\n",
      "  * lead     (lead) int64 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20\n",
      "  * period   (period) object 'DJF' 'JJA' 'MAM' 'SON' 'ym'\n",
      "  * area     (area) object 'global' 'North_Atlantic' 'North_Atlantic_SPG'\n",
      "  * init     (init) int64 3014 3023 3045 3061 3124 ... 3175 3178 3228 3237 3257\n",
      "  * member   (member) int64 0 1 2 3 4 5 6 7 8 9\n",
      "Data variables:\n",
      "    tos      (period, lead, area, init, member) float32 ...\n",
      "    sos      (period, lead, area, init, member) float32 ...\n",
      "    AMO      (period, lead, area, init, member) float32 ...\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 18;\n",
       "                var nbb_unformatted_code = \"ds = load_dataset(\\\"MPI-PM-DP-1D\\\")  # initialized ensemble from MPI\\ncontrol = load_dataset(\\\"MPI-control-1D\\\")  # base control run that initialized it\\n\\nds[\\\"lead\\\"].attrs[\\\"units\\\"] = \\\"years\\\"\\n\\nprint(ds)\";\n",
       "                var nbb_formatted_code = \"ds = load_dataset(\\\"MPI-PM-DP-1D\\\")  # initialized ensemble from MPI\\ncontrol = load_dataset(\\\"MPI-control-1D\\\")  # base control run that initialized it\\n\\nds[\\\"lead\\\"].attrs[\\\"units\\\"] = \\\"years\\\"\\n\\nprint(ds)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds = load_dataset(\"MPI-PM-DP-1D\")  # initialized ensemble from MPI\n",
    "control = load_dataset(\"MPI-control-1D\")  # base control run that initialized it\n",
    "\n",
    "ds[\"lead\"].attrs[\"units\"] = \"years\"\n",
    "\n",
    "print(ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-06T18:15:59.750652Z",
     "start_time": "2020-01-06T18:15:59.695144Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<climpred.PerfectModelEnsemble>\n",
      "Initialized Ensemble:\n",
      "    tos      (period, lead, area, init, member) float32 ...\n",
      "    sos      (period, lead, area, init, member) float32 ...\n",
      "    AMO      (period, lead, area, init, member) float32 ...\n",
      "Control:\n",
      "    tos      (period, time, area) float32 ...\n",
      "    sos      (period, time, area) float32 ...\n",
      "    AMO      (period, time, area) float32 ...\n",
      "Uninitialized:\n",
      "    None\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/aaron.spring/Coding/climpred/climpred/utils.py:122: UserWarning: Assuming annual resolution due to numeric inits. Change init to a datetime if it is another resolution.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 19;\n",
       "                var nbb_unformatted_code = \"pm = climpred.PerfectModelEnsemble(ds)\\npm = pm.add_control(control)\\nprint(pm)\";\n",
       "                var nbb_formatted_code = \"pm = climpred.PerfectModelEnsemble(ds)\\npm = pm.add_control(control)\\nprint(pm)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pm = climpred.PerfectModelEnsemble(ds)\n",
    "pm = pm.add_control(control)\n",
    "print(pm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our objects are carrying sea surface temperature (`tos`), sea surface salinity (`sos`), and the Atlantic Multidecadal Oscillation index (`AMO`). Say we just want to look at skill metrics for temperature and salinity over the North Atlantic in JJA. We can just call a few easy `xarray` commands to filter down our object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 20;\n",
       "                var nbb_unformatted_code = \"pm = pm[[\\\"tos\\\", \\\"sos\\\"]].sel(area=\\\"North_Atlantic\\\", period=\\\"JJA\\\", drop=True)\";\n",
       "                var nbb_formatted_code = \"pm = pm[[\\\"tos\\\", \\\"sos\\\"]].sel(area=\\\"North_Atlantic\\\", period=\\\"JJA\\\", drop=True)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pm = pm[[\"tos\", \"sos\"]].sel(area=\"North_Atlantic\", period=\"JJA\", drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can easily compute for a host of metrics. Here I just show a number of deterministic skill metrics comparing all individual members to the initialized ensemble mean. See [comparisons](comparisons.html) for more information on the `comparison` keyword."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-06T18:16:03.306736Z",
     "start_time": "2020-01-06T18:16:01.502565Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.plot.facetgrid.FacetGrid at 0x7f9ec2547790>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1004.3x432 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 21;\n",
       "                var nbb_unformatted_code = \"METRICS = [\\\"mse\\\", \\\"rmse\\\", \\\"mae\\\", \\\"acc\\\", \\\"nmse\\\", \\\"nrmse\\\", \\\"nmae\\\", \\\"msss\\\"]\\n\\nresult = []\\nfor metric in METRICS:\\n    result.append(pm.verify(metric=metric, comparison=\\\"m2e\\\", dim=[\\\"init\\\", \\\"member\\\"]))\\n\\nresult = xr.concat(result, \\\"metric\\\")\\nresult[\\\"metric\\\"] = METRICS\\n\\n# Leverage the `xarray` plotting wrapper to plot all results at once.\\nresult.to_array().plot(\\n    col=\\\"metric\\\", hue=\\\"variable\\\", col_wrap=4, sharey=False, sharex=True\\n)\";\n",
       "                var nbb_formatted_code = \"METRICS = [\\\"mse\\\", \\\"rmse\\\", \\\"mae\\\", \\\"acc\\\", \\\"nmse\\\", \\\"nrmse\\\", \\\"nmae\\\", \\\"msss\\\"]\\n\\nresult = []\\nfor metric in METRICS:\\n    result.append(pm.verify(metric=metric, comparison=\\\"m2e\\\", dim=[\\\"init\\\", \\\"member\\\"]))\\n\\nresult = xr.concat(result, \\\"metric\\\")\\nresult[\\\"metric\\\"] = METRICS\\n\\n# Leverage the `xarray` plotting wrapper to plot all results at once.\\nresult.to_array().plot(\\n    col=\\\"metric\\\", hue=\\\"variable\\\", col_wrap=4, sharey=False, sharex=True\\n)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "METRICS = [\"mse\", \"rmse\", \"mae\", \"acc\", \"nmse\", \"nrmse\", \"nmae\", \"msss\"]\n",
    "\n",
    "result = []\n",
    "for metric in METRICS:\n",
    "    result.append(pm.verify(metric=metric, comparison=\"m2e\", dim=[\"init\", \"member\"]))\n",
    "\n",
    "result = xr.concat(result, \"metric\")\n",
    "result[\"metric\"] = METRICS\n",
    "\n",
    "# Leverage the `xarray` plotting wrapper to plot all results at once.\n",
    "result.to_array().plot(\n",
    "    col=\"metric\", hue=\"variable\", col_wrap=4, sharey=False, sharex=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is useful to compare the initialized ensemble to an uninitialized run. See [terminology](terminology.html) for a description on \"uninitialized\" simulations. This gives us information about how *initializations* lead to enhanced predictability over knowledge of external forcing, whereas a comparison to persistence just tells us how well a dynamical forecast simulation does in comparison to a naive method. We can use the `generate_uninitialized()` method to bootstrap the control run and create a pseudo-ensemble that approximates what an uninitialized ensemble would look like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-06T18:16:03.557852Z",
     "start_time": "2020-01-06T18:16:03.308311Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<climpred.PerfectModelEnsemble>\n",
       "Initialized Ensemble:\n",
       "    tos      (lead, init, member) float32 13.46 13.64 13.72 ... 13.55 13.57\n",
       "    sos      (lead, init, member) float32 33.18 33.15 33.05 ... 33.18 33.26\n",
       "Control:\n",
       "    tos      (time) float32 13.5 13.74 13.78 13.86 ... 13.12 12.92 13.08 13.47\n",
       "    sos      (time) float32 33.23 33.19 33.2 33.21 ... 33.15 33.22 33.16 33.18\n",
       "Uninitialized:\n",
       "    tos      (lead, init, member) float32 13.18 13.62 13.56 ... 12.92 13.2 13.19\n",
       "    sos      (lead, init, member) float32 33.22 33.18 33.15 ... 33.09 33.0 33.04"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 22;\n",
       "                var nbb_unformatted_code = \"pm = pm.generate_uninitialized()\\npm\";\n",
       "                var nbb_formatted_code = \"pm = pm.generate_uninitialized()\\npm\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pm = pm.generate_uninitialized()\n",
    "pm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-06T18:16:03.562894Z",
     "start_time": "2020-01-06T18:16:03.560057Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 23;\n",
       "                var nbb_unformatted_code = \"pm = pm[[\\\"sos\\\"]]  # Just assess for salinity.\";\n",
       "                var nbb_formatted_code = \"pm = pm[[\\\"sos\\\"]]  # Just assess for salinity.\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pm = pm[[\"sos\"]]  # Just assess for salinity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we plot the ACC for the initialized, uninitialized, and persistence forecasts for North Atlantic sea surface salinity in JJA. We add circles to the lines if the correlations are statistically significant for $p <= 0.05$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f9ec21bbeb0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 748.8x514.8 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 24;\n",
       "                var nbb_unformatted_code = \"def plot_result(acc, pval, skill, color, label, linestyle=\\\"-\\\"):\\n    \\\"\\\"\\\"Helper function for cleaner plotting code.\\\"\\\"\\\"\\n    acc.sel(skill=skill)[\\\"sos\\\"].plot(color=color, linestyle=linestyle)\\n    # mask if pval <=0.05\\n    masked_acc = acc.sel(skill=skill)[\\\"sos\\\"].where(pval.sel(skill=skill)[\\\"sos\\\"] <= 0.05)\\n    masked_acc.plot(marker=\\\"o\\\", linestyle=\\\"None\\\", color=color, label=label)\\n\\n\\nreference = [\\\"persistence\\\", \\\"uninitialized\\\", \\\"climatology\\\"]\\nacc_result = pm.verify(\\n    metric=\\\"acc\\\", comparison=\\\"m2e\\\", dim=[\\\"init\\\", \\\"member\\\"], reference=reference,\\n)\\npval_result = pm.verify(\\n    metric=\\\"p_pval\\\", comparison=\\\"m2e\\\", dim=[\\\"init\\\", \\\"member\\\"], reference=reference,\\n)\\n\\n# ACC for initialized ensemble\\nplot_result(acc_result, pval_result, \\\"initialized\\\", \\\"red\\\", \\\"initialized\\\")\\nplot_result(acc_result, pval_result, \\\"uninitialized\\\", \\\"gray\\\", \\\"uninitialized\\\")\\nplot_result(\\n    acc_result, pval_result, \\\"persistence\\\", \\\"black\\\", \\\"persistence\\\", linestyle=\\\"--\\\"\\n)\\nplot_result(\\n    acc_result, pval_result, \\\"climatology\\\", \\\"tan\\\", \\\"climatology\\\", linestyle=\\\"--\\\"\\n)\\nplt.title(\\\"North Atlantic JJA ACC\\\")\\nplt.legend()\";\n",
       "                var nbb_formatted_code = \"def plot_result(acc, pval, skill, color, label, linestyle=\\\"-\\\"):\\n    \\\"\\\"\\\"Helper function for cleaner plotting code.\\\"\\\"\\\"\\n    acc.sel(skill=skill)[\\\"sos\\\"].plot(color=color, linestyle=linestyle)\\n    # mask if pval <=0.05\\n    masked_acc = acc.sel(skill=skill)[\\\"sos\\\"].where(pval.sel(skill=skill)[\\\"sos\\\"] <= 0.05)\\n    masked_acc.plot(marker=\\\"o\\\", linestyle=\\\"None\\\", color=color, label=label)\\n\\n\\nreference = [\\\"persistence\\\", \\\"uninitialized\\\", \\\"climatology\\\"]\\nacc_result = pm.verify(\\n    metric=\\\"acc\\\", comparison=\\\"m2e\\\", dim=[\\\"init\\\", \\\"member\\\"], reference=reference,\\n)\\npval_result = pm.verify(\\n    metric=\\\"p_pval\\\", comparison=\\\"m2e\\\", dim=[\\\"init\\\", \\\"member\\\"], reference=reference,\\n)\\n\\n# ACC for initialized ensemble\\nplot_result(acc_result, pval_result, \\\"initialized\\\", \\\"red\\\", \\\"initialized\\\")\\nplot_result(acc_result, pval_result, \\\"uninitialized\\\", \\\"gray\\\", \\\"uninitialized\\\")\\nplot_result(\\n    acc_result, pval_result, \\\"persistence\\\", \\\"black\\\", \\\"persistence\\\", linestyle=\\\"--\\\"\\n)\\nplot_result(\\n    acc_result, pval_result, \\\"climatology\\\", \\\"tan\\\", \\\"climatology\\\", linestyle=\\\"--\\\"\\n)\\nplt.title(\\\"North Atlantic JJA ACC\\\")\\nplt.legend()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_result(acc, pval, skill, color, label, linestyle=\"-\"):\n",
    "    \"\"\"Helper function for cleaner plotting code.\"\"\"\n",
    "    acc.sel(skill=skill)[\"sos\"].plot(color=color, linestyle=linestyle)\n",
    "    # mask if pval <=0.05\n",
    "    masked_acc = acc.sel(skill=skill)[\"sos\"].where(pval.sel(skill=skill)[\"sos\"] <= 0.05)\n",
    "    masked_acc.plot(marker=\"o\", linestyle=\"None\", color=color, label=label)\n",
    "\n",
    "\n",
    "reference = [\"persistence\", \"uninitialized\", \"climatology\"]\n",
    "acc_result = pm.verify(\n",
    "    metric=\"acc\", comparison=\"m2e\", dim=[\"init\", \"member\"], reference=reference,\n",
    ")\n",
    "pval_result = pm.verify(\n",
    "    metric=\"p_pval\", comparison=\"m2e\", dim=[\"init\", \"member\"], reference=reference,\n",
    ")\n",
    "\n",
    "# ACC for initialized ensemble\n",
    "plot_result(acc_result, pval_result, \"initialized\", \"red\", \"initialized\")\n",
    "plot_result(acc_result, pval_result, \"uninitialized\", \"gray\", \"uninitialized\")\n",
    "plot_result(\n",
    "    acc_result, pval_result, \"persistence\", \"black\", \"persistence\", linestyle=\"--\"\n",
    ")\n",
    "plot_result(\n",
    "    acc_result, pval_result, \"climatology\", \"tan\", \"climatology\", linestyle=\"--\"\n",
    ")\n",
    "plt.title(\"North Atlantic JJA ACC\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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