{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can run this notebook in a [live session](https://binder.pangeo.io/v2/gh/pangeo-data/climpred/main?urlpath=lab/tree/docs/source/alignment.ipynb) [<img src=\"https://mybinder.org/badge_logo.svg\" alt='binder badge'>](https://binder.pangeo.io/v2/gh/pangeo-data/climpred/main?urlpath=lab/tree/docs/source/alignment.ipynb) or view it [on Github](https://github.com/pangeo-data/climpred/blob/main/docs/source/alignment.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Verification Alignment\n",
    "\n",
    "A forecast is verified by comparing a set of initializations at a given lead to\n",
    "observations over some window of time. However, there are a few ways to decide *which*\n",
    "initializations or verification window to use in this alignment.\n",
    "\n",
    "One must pass the keyword ``alignment=...`` to the {py:meth}`.HindcastEnsemble.verify` method to set the behavior for aligning forecasts with the verification product. Note that the alignment decision only matters for {py:class}`.HindcastEnsemble`. {py:class}`.PerfectModelEnsemble` are perfectly time-aligned by design, equating to our `\"same_inits\"` keyword.\n",
    "\n",
    "The available keywords for hindcast alignment are:\n",
    "\n",
    "*  `\"same_inits\"`: Use a common set of initializations that verify\n",
    "   across all leads. This ensures that there is no bias in the result due to the state\n",
    "   of the system for the given initializations.\n",
    "\n",
    "*  `\"same_verifs\"`: Use a common verification window across all leads. This ensures\n",
    "   that there is no bias in the result due to the observational period being verified\n",
    "   against.\n",
    "\n",
    "*  `\"maximize\"`: Use all available initializations at each lead that verify against\n",
    "   the observations provided. This changes both the set of initializations and the\n",
    "   verification window used at each lead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 1;\n",
       "                var nbb_unformatted_code = \"# linting\\n%load_ext nb_black\\n%load_ext lab_black\";\n",
       "                var nbb_formatted_code = \"# linting\\n%load_ext nb_black\\n%load_ext lab_black\";\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": [
    "# linting\n",
    "%load_ext nb_black\n",
    "%load_ext lab_black"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 2;\n",
       "                var nbb_unformatted_code = \"from climpred import HindcastEnsemble\\nfrom climpred.tutorial import load_dataset\\nfrom esmtools.stats import rm_trend\\n\\nimport matplotlib.pyplot as plt\\n\\nplt.style.use(\\\"fivethirtyeight\\\")\\n%matplotlib inline\\n\\nimport numpy as np\\n\\nimport warnings\\n\\n# Supress datetime warnings for this page.\\nwarnings.filterwarnings(\\\"ignore\\\")\";\n",
       "                var nbb_formatted_code = \"from climpred import HindcastEnsemble\\nfrom climpred.tutorial import load_dataset\\nfrom esmtools.stats import rm_trend\\n\\nimport matplotlib.pyplot as plt\\n\\nplt.style.use(\\\"fivethirtyeight\\\")\\n%matplotlib inline\\n\\nimport numpy as np\\n\\nimport warnings\\n\\n# Supress datetime warnings for this page.\\nwarnings.filterwarnings(\\\"ignore\\\")\";\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 import HindcastEnsemble\n",
    "from climpred.tutorial import load_dataset\n",
    "from esmtools.stats import rm_trend\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.style.use(\"fivethirtyeight\")\n",
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import warnings\n",
    "\n",
    "# Supress datetime warnings for this page.\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 3;\n",
       "                var nbb_unformatted_code = \"def create_hindcast_object():\\n    \\\"\\\"\\\"Loads in example data from CESM-DPLE and ERSST observations and detrends.\\\"\\\"\\\"\\n    hind = load_dataset(\\\"CESM-DP-SST\\\")[\\\"SST\\\"]\\n    verif = load_dataset(\\\"ERSST\\\")[\\\"SST\\\"]\\n\\n    # Bias-correct over same period as CESM-DPLE.\\n    verif = verif - verif.sel(time=slice(1964, 2014)).mean(\\\"time\\\")\\n\\n    # Remove linear trend.\\n    hind_dt = rm_trend(hind, dim=\\\"init\\\").rename(\\\"SST\\\")\\n    verif_dt = rm_trend(verif, dim=\\\"time\\\").rename(\\\"SST\\\")\\n\\n    # Create `HindcastEnsemble` object from `climpred`.\\n    hindcast = HindcastEnsemble(hind)\\n    hindcast = hindcast.add_observations(verif)\\n    hindcast_dt = HindcastEnsemble(hind_dt)\\n    hindcast_dt = hindcast_dt.add_observations(verif_dt)\\n    return hindcast, hindcast_dt\";\n",
       "                var nbb_formatted_code = \"def create_hindcast_object():\\n    \\\"\\\"\\\"Loads in example data from CESM-DPLE and ERSST observations and detrends.\\\"\\\"\\\"\\n    hind = load_dataset(\\\"CESM-DP-SST\\\")[\\\"SST\\\"]\\n    verif = load_dataset(\\\"ERSST\\\")[\\\"SST\\\"]\\n\\n    # Bias-correct over same period as CESM-DPLE.\\n    verif = verif - verif.sel(time=slice(1964, 2014)).mean(\\\"time\\\")\\n\\n    # Remove linear trend.\\n    hind_dt = rm_trend(hind, dim=\\\"init\\\").rename(\\\"SST\\\")\\n    verif_dt = rm_trend(verif, dim=\\\"time\\\").rename(\\\"SST\\\")\\n\\n    # Create `HindcastEnsemble` object from `climpred`.\\n    hindcast = HindcastEnsemble(hind)\\n    hindcast = hindcast.add_observations(verif)\\n    hindcast_dt = HindcastEnsemble(hind_dt)\\n    hindcast_dt = hindcast_dt.add_observations(verif_dt)\\n    return hindcast, hindcast_dt\";\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 create_hindcast_object():\n",
    "    \"\"\"Loads in example data from CESM-DPLE and ERSST observations and detrends.\"\"\"\n",
    "    hind = load_dataset(\"CESM-DP-SST\")[\"SST\"]\n",
    "    verif = load_dataset(\"ERSST\")[\"SST\"]\n",
    "\n",
    "    # Bias-correct over same period as CESM-DPLE.\n",
    "    verif = verif - verif.sel(time=slice(1964, 2014)).mean(\"time\")\n",
    "\n",
    "    # Remove linear trend.\n",
    "    hind_dt = rm_trend(hind, dim=\"init\").rename(\"SST\")\n",
    "    verif_dt = rm_trend(verif, dim=\"time\").rename(\"SST\")\n",
    "\n",
    "    # Create `HindcastEnsemble` object from `climpred`.\n",
    "    hindcast = HindcastEnsemble(hind)\n",
    "    hindcast = hindcast.add_observations(verif)\n",
    "    hindcast_dt = HindcastEnsemble(hind_dt)\n",
    "    hindcast_dt = hindcast_dt.add_observations(verif_dt)\n",
    "    return hindcast, hindcast_dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 4;\n",
       "                var nbb_unformatted_code = \"hindcast, hindcast_dt = create_hindcast_object()\";\n",
       "                var nbb_formatted_code = \"hindcast, hindcast_dt = create_hindcast_object()\";\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_dt = create_hindcast_object()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The user can simply change the alignment strategy by passing in the keyword `alignment=...`. {py:meth}`.HindcastEnsemble.plot_alignment` shows `valid_time` dates that are verified against observations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.plot.facetgrid.FacetGrid at 0x148fc5c10>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 5;\n",
       "                var nbb_unformatted_code = \"hindcast.plot_alignment(edgecolor=\\\"w\\\")\";\n",
       "                var nbb_formatted_code = \"hindcast.plot_alignment(edgecolor=\\\"w\\\")\";\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_alignment(edgecolor=\"w\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the choice of alignment strategy changes the lead-dependent metric results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 6;\n",
       "                var nbb_unformatted_code = \"f, axs = plt.subplots(ncols=2, figsize=(12, 4), sharex=True)\\nfor alignment in [\\\"same_inits\\\", \\\"same_verifs\\\", \\\"maximize\\\"]:\\n    hindcast.verify(metric=\\\"acc\\\", comparison=\\\"e2o\\\", dim=\\\"init\\\", alignment=alignment)[\\n        \\\"SST\\\"\\n    ].plot(label=alignment, ax=axs[0])\\n    hindcast_dt.verify(metric=\\\"acc\\\", comparison=\\\"e2o\\\", dim=\\\"init\\\", alignment=alignment)[\\n        \\\"SST\\\"\\n    ].plot(label=alignment, ax=axs[1])\\n\\naxs[0].legend()\\naxs[1].legend()\\naxs[0].set(\\n    ylabel=\\\"anomaly\\\\ncorrelation coefficient\\\",\\n    xlabel=\\\"lead year\\\",\\n    xticks=np.arange(1, 11),\\n    title=\\\"SST with trend\\\",\\n)\\naxs[1].set(\\n    ylabel=\\\"anomaly\\\\ncorrelation coefficient\\\", xlabel=\\\"lead year\\\", title=\\\"detrended SST\\\"\\n)\\n\\nf.suptitle(\\\"Verification with Different Alignment Methods\\\", fontsize=14, weight=\\\"bold\\\")\\nplt.subplots_adjust(top=0.85)\\n\\nplt.show()\";\n",
       "                var nbb_formatted_code = \"f, axs = plt.subplots(ncols=2, figsize=(12, 4), sharex=True)\\nfor alignment in [\\\"same_inits\\\", \\\"same_verifs\\\", \\\"maximize\\\"]:\\n    hindcast.verify(metric=\\\"acc\\\", comparison=\\\"e2o\\\", dim=\\\"init\\\", alignment=alignment)[\\n        \\\"SST\\\"\\n    ].plot(label=alignment, ax=axs[0])\\n    hindcast_dt.verify(metric=\\\"acc\\\", comparison=\\\"e2o\\\", dim=\\\"init\\\", alignment=alignment)[\\n        \\\"SST\\\"\\n    ].plot(label=alignment, ax=axs[1])\\n\\naxs[0].legend()\\naxs[1].legend()\\naxs[0].set(\\n    ylabel=\\\"anomaly\\\\ncorrelation coefficient\\\",\\n    xlabel=\\\"lead year\\\",\\n    xticks=np.arange(1, 11),\\n    title=\\\"SST with trend\\\",\\n)\\naxs[1].set(\\n    ylabel=\\\"anomaly\\\\ncorrelation coefficient\\\", xlabel=\\\"lead year\\\", title=\\\"detrended SST\\\"\\n)\\n\\nf.suptitle(\\\"Verification with Different Alignment Methods\\\", fontsize=14, weight=\\\"bold\\\")\\nplt.subplots_adjust(top=0.85)\\n\\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": [
    "f, axs = plt.subplots(ncols=2, figsize=(12, 4), sharex=True)\n",
    "for alignment in [\"same_inits\", \"same_verifs\", \"maximize\"]:\n",
    "    hindcast.verify(metric=\"acc\", comparison=\"e2o\", dim=\"init\", alignment=alignment)[\n",
    "        \"SST\"\n",
    "    ].plot(label=alignment, ax=axs[0])\n",
    "    hindcast_dt.verify(metric=\"acc\", comparison=\"e2o\", dim=\"init\", alignment=alignment)[\n",
    "        \"SST\"\n",
    "    ].plot(label=alignment, ax=axs[1])\n",
    "\n",
    "axs[0].legend()\n",
    "axs[1].legend()\n",
    "axs[0].set(\n",
    "    ylabel=\"anomaly\\ncorrelation coefficient\",\n",
    "    xlabel=\"lead year\",\n",
    "    xticks=np.arange(1, 11),\n",
    "    title=\"SST with trend\",\n",
    ")\n",
    "axs[1].set(\n",
    "    ylabel=\"anomaly\\ncorrelation coefficient\", xlabel=\"lead year\", title=\"detrended SST\"\n",
    ")\n",
    "\n",
    "f.suptitle(\"Verification with Different Alignment Methods\", fontsize=14, weight=\"bold\")\n",
    "plt.subplots_adjust(top=0.85)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These alignment keywords also extend to reference forecasts (e.g. `reference=\"persistence\"`), which uses the identical set of initializations (and alignment strategy) in its computation. Below, the dashed lines represent the persistence forecast for the given alignment strategy, while the solid lines denote the initialized anomaly correlation coefficient (as in the above plots)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 7;\n",
       "                var nbb_unformatted_code = \"COLORS = [\\\"#008FD5\\\", \\\"#FC4F30\\\", \\\"#E5AE38\\\"]\\nf, axs = plt.subplots()\\n\\nfor alignment, color in zip([\\\"same_inits\\\", \\\"same_verifs\\\", \\\"maximize\\\"], COLORS):\\n    result = hindcast_dt.verify(\\n        metric=\\\"acc\\\",\\n        reference=\\\"persistence\\\",\\n        comparison=\\\"e2o\\\",\\n        dim=\\\"init\\\",\\n        alignment=alignment,\\n    )\\n    result.sel(skill=\\\"initialized\\\").SST.plot(label=alignment, color=color)\\n    result.sel(skill=\\\"persistence\\\").SST.plot(linestyle=\\\"--\\\", color=color, lw=3)\\n\\naxs.set(\\n    ylabel=\\\"anomaly\\\\ncorrelation coefficient\\\",\\n    xlabel=\\\"lead year\\\",\\n    xticks=np.arange(1, 11),\\n    title=\\\"Detrended SST Verification with Persistence\\\",\\n)\\nplt.legend()\\nplt.show()\";\n",
       "                var nbb_formatted_code = \"COLORS = [\\\"#008FD5\\\", \\\"#FC4F30\\\", \\\"#E5AE38\\\"]\\nf, axs = plt.subplots()\\n\\nfor alignment, color in zip([\\\"same_inits\\\", \\\"same_verifs\\\", \\\"maximize\\\"], COLORS):\\n    result = hindcast_dt.verify(\\n        metric=\\\"acc\\\",\\n        reference=\\\"persistence\\\",\\n        comparison=\\\"e2o\\\",\\n        dim=\\\"init\\\",\\n        alignment=alignment,\\n    )\\n    result.sel(skill=\\\"initialized\\\").SST.plot(label=alignment, color=color)\\n    result.sel(skill=\\\"persistence\\\").SST.plot(linestyle=\\\"--\\\", color=color, lw=3)\\n\\naxs.set(\\n    ylabel=\\\"anomaly\\\\ncorrelation coefficient\\\",\\n    xlabel=\\\"lead year\\\",\\n    xticks=np.arange(1, 11),\\n    title=\\\"Detrended SST Verification with Persistence\\\",\\n)\\nplt.legend()\\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": [
    "COLORS = [\"#008FD5\", \"#FC4F30\", \"#E5AE38\"]\n",
    "f, axs = plt.subplots()\n",
    "\n",
    "for alignment, color in zip([\"same_inits\", \"same_verifs\", \"maximize\"], COLORS):\n",
    "    result = hindcast_dt.verify(\n",
    "        metric=\"acc\",\n",
    "        reference=\"persistence\",\n",
    "        comparison=\"e2o\",\n",
    "        dim=\"init\",\n",
    "        alignment=alignment,\n",
    "    )\n",
    "    result.sel(skill=\"initialized\").SST.plot(label=alignment, color=color)\n",
    "    result.sel(skill=\"persistence\").SST.plot(linestyle=\"--\", color=color, lw=3)\n",
    "\n",
    "axs.set(\n",
    "    ylabel=\"anomaly\\ncorrelation coefficient\",\n",
    "    xlabel=\"lead year\",\n",
    "    xticks=np.arange(1, 11),\n",
    "    title=\"Detrended SST Verification with Persistence\",\n",
    ")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll be using the same example data as above. `climpred` will be aligning the following initialization and verification dates:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "initialization dates: \n",
      "CFTimeIndex([1954-01-01 00:00:00, 1955-01-01 00:00:00, 1956-01-01 00:00:00,\n",
      "             1957-01-01 00:00:00, 1958-01-01 00:00:00, 1959-01-01 00:00:00,\n",
      "             1960-01-01 00:00:00, 1961-01-01 00:00:00, 1962-01-01 00:00:00,\n",
      "             1963-01-01 00:00:00, 1964-01-01 00:00:00, 1965-01-01 00:00:00,\n",
      "             1966-01-01 00:00:00, 1967-01-01 00:00:00, 1968-01-01 00:00:00,\n",
      "             1969-01-01 00:00:00, 1970-01-01 00:00:00, 1971-01-01 00:00:00,\n",
      "             1972-01-01 00:00:00, 1973-01-01 00:00:00, 1974-01-01 00:00:00,\n",
      "             1975-01-01 00:00:00, 1976-01-01 00:00:00, 1977-01-01 00:00:00,\n",
      "             1978-01-01 00:00:00, 1979-01-01 00:00:00, 1980-01-01 00:00:00,\n",
      "             1981-01-01 00:00:00, 1982-01-01 00:00:00, 1983-01-01 00:00:00,\n",
      "             1984-01-01 00:00:00, 1985-01-01 00:00:00, 1986-01-01 00:00:00,\n",
      "             1987-01-01 00:00:00, 1988-01-01 00:00:00, 1989-01-01 00:00:00,\n",
      "             1990-01-01 00:00:00, 1991-01-01 00:00:00, 1992-01-01 00:00:00,\n",
      "             1993-01-01 00:00:00, 1994-01-01 00:00:00, 1995-01-01 00:00:00,\n",
      "             1996-01-01 00:00:00, 1997-01-01 00:00:00, 1998-01-01 00:00:00,\n",
      "             1999-01-01 00:00:00, 2000-01-01 00:00:00, 2001-01-01 00:00:00,\n",
      "             2002-01-01 00:00:00, 2003-01-01 00:00:00, 2004-01-01 00:00:00,\n",
      "             2005-01-01 00:00:00, 2006-01-01 00:00:00, 2007-01-01 00:00:00,\n",
      "             2008-01-01 00:00:00, 2009-01-01 00:00:00, 2010-01-01 00:00:00,\n",
      "             2011-01-01 00:00:00, 2012-01-01 00:00:00, 2013-01-01 00:00:00,\n",
      "             2014-01-01 00:00:00, 2015-01-01 00:00:00, 2016-01-01 00:00:00,\n",
      "             2017-01-01 00:00:00],\n",
      "            dtype='object',\n",
      "            length=64,\n",
      "            calendar='proleptic_gregorian',\n",
      "            freq='AS-JAN')\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 8;\n",
       "                var nbb_unformatted_code = \"print(f\\\"initialization dates: \\\\n{hindcast.get_initialized().init.to_index()}\\\")\";\n",
       "                var nbb_formatted_code = \"print(f\\\"initialization dates: \\\\n{hindcast.get_initialized().init.to_index()}\\\")\";\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": [
    "print(f\"initialization dates: \\n{hindcast.get_initialized().init.to_index()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "verification dates: \n",
      "CFTimeIndex([1955-01-01 00:00:00, 1956-01-01 00:00:00, 1957-01-01 00:00:00,\n",
      "             1958-01-01 00:00:00, 1959-01-01 00:00:00, 1960-01-01 00:00:00,\n",
      "             1961-01-01 00:00:00, 1962-01-01 00:00:00, 1963-01-01 00:00:00,\n",
      "             1964-01-01 00:00:00, 1965-01-01 00:00:00, 1966-01-01 00:00:00,\n",
      "             1967-01-01 00:00:00, 1968-01-01 00:00:00, 1969-01-01 00:00:00,\n",
      "             1970-01-01 00:00:00, 1971-01-01 00:00:00, 1972-01-01 00:00:00,\n",
      "             1973-01-01 00:00:00, 1974-01-01 00:00:00, 1975-01-01 00:00:00,\n",
      "             1976-01-01 00:00:00, 1977-01-01 00:00:00, 1978-01-01 00:00:00,\n",
      "             1979-01-01 00:00:00, 1980-01-01 00:00:00, 1981-01-01 00:00:00,\n",
      "             1982-01-01 00:00:00, 1983-01-01 00:00:00, 1984-01-01 00:00:00,\n",
      "             1985-01-01 00:00:00, 1986-01-01 00:00:00, 1987-01-01 00:00:00,\n",
      "             1988-01-01 00:00:00, 1989-01-01 00:00:00, 1990-01-01 00:00:00,\n",
      "             1991-01-01 00:00:00, 1992-01-01 00:00:00, 1993-01-01 00:00:00,\n",
      "             1994-01-01 00:00:00, 1995-01-01 00:00:00, 1996-01-01 00:00:00,\n",
      "             1997-01-01 00:00:00, 1998-01-01 00:00:00, 1999-01-01 00:00:00,\n",
      "             2000-01-01 00:00:00, 2001-01-01 00:00:00, 2002-01-01 00:00:00,\n",
      "             2003-01-01 00:00:00, 2004-01-01 00:00:00, 2005-01-01 00:00:00,\n",
      "             2006-01-01 00:00:00, 2007-01-01 00:00:00, 2008-01-01 00:00:00,\n",
      "             2009-01-01 00:00:00, 2010-01-01 00:00:00, 2011-01-01 00:00:00,\n",
      "             2012-01-01 00:00:00, 2013-01-01 00:00:00, 2014-01-01 00:00:00,\n",
      "             2015-01-01 00:00:00],\n",
      "            dtype='object',\n",
      "            length=61,\n",
      "            calendar='proleptic_gregorian',\n",
      "            freq='AS-JAN')\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 9;\n",
       "                var nbb_unformatted_code = \"print(f\\\"verification dates: \\\\n{hindcast.get_observations().time.to_index()}\\\")\";\n",
       "                var nbb_formatted_code = \"print(f\\\"verification dates: \\\\n{hindcast.get_observations().time.to_index()}\\\")\";\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": [
    "print(f\"verification dates: \\n{hindcast.get_observations().time.to_index()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use the standard python library `logging` to log the initializations and verification dates used in alignment at each lead. The user can check these logs to ensure that the expected initializations and verification dates are being retained. See the logging section on this page for more details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 10;\n",
       "                var nbb_unformatted_code = \"import logging\\n\\n# Print log to screen with initializations and verification dates.\\nlogger = logging.getLogger()\\nlogger.setLevel(logging.INFO)\";\n",
       "                var nbb_formatted_code = \"import logging\\n\\n# Print log to screen with initializations and verification dates.\\nlogger = logging.getLogger()\\nlogger.setLevel(logging.INFO)\";\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": [
    "import logging\n",
    "\n",
    "# Print log to screen with initializations and verification dates.\n",
    "logger = logging.getLogger()\n",
    "logger.setLevel(logging.INFO)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Same Verification Dates\n",
    "\n",
    "`alignment=\"same_verifs\"`\n",
    "\n",
    "The `same_verifs` alignment finds a set of verification dates that can be verified against over all leads. It also requires that the verification data have an observation at each initialization being retained. This is so that the reference forecast, such as persistence, uses an identical set of initializations in deriving its forecast. Notice in the logger output that a common set of verification dates spanning 1965-2015 are used, while the initialization window slides one year at each lead.\n",
    "\n",
    "`alignment=\"same_verifs\"` used in {cite:p}`Boer2016,Hawkins2014,Smith2013`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:initialized | lead: 01 | inits: 1963-01-01 00:00:00-2014-01-01 00:00:00 | verifs: 1964-01-01 00:00:00-2015-01-01 00:00:00\n",
      "INFO:root:initialized | lead: 02 | inits: 1962-01-01 00:00:00-2013-01-01 00:00:00 | verifs: 1964-01-01 00:00:00-2015-01-01 00:00:00\n",
      "INFO:root:initialized | lead: 03 | inits: 1961-01-01 00:00:00-2012-01-01 00:00:00 | verifs: 1964-01-01 00:00:00-2015-01-01 00:00:00\n",
      "INFO:root:initialized | lead: 04 | inits: 1960-01-01 00:00:00-2011-01-01 00:00:00 | verifs: 1964-01-01 00:00:00-2015-01-01 00:00:00\n",
      "INFO:root:initialized | lead: 05 | inits: 1959-01-01 00:00:00-2010-01-01 00:00:00 | verifs: 1964-01-01 00:00:00-2015-01-01 00:00:00\n",
      "INFO:root:initialized | lead: 06 | inits: 1958-01-01 00:00:00-2009-01-01 00:00:00 | verifs: 1964-01-01 00:00:00-2015-01-01 00:00:00\n",
      "INFO:root:initialized | lead: 07 | inits: 1957-01-01 00:00:00-2008-01-01 00:00:00 | verifs: 1964-01-01 00:00:00-2015-01-01 00:00:00\n",
      "INFO:root:initialized | lead: 08 | inits: 1956-01-01 00:00:00-2007-01-01 00:00:00 | verifs: 1964-01-01 00:00:00-2015-01-01 00:00:00\n",
      "INFO:root:initialized | lead: 09 | inits: 1955-01-01 00:00:00-2006-01-01 00:00:00 | verifs: 1964-01-01 00:00:00-2015-01-01 00:00:00\n",
      "INFO:root:initialized | lead: 10 | inits: 1954-01-01 00:00:00-2005-01-01 00:00:00 | verifs: 1964-01-01 00:00:00-2015-01-01 00:00:00\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 11;\n",
       "                var nbb_unformatted_code = \"skill = hindcast.verify(\\n    metric=\\\"acc\\\", comparison=\\\"e2o\\\", dim=\\\"init\\\", alignment=\\\"same_verifs\\\"\\n)\";\n",
       "                var nbb_formatted_code = \"skill = hindcast.verify(\\n    metric=\\\"acc\\\", comparison=\\\"e2o\\\", dim=\\\"init\\\", alignment=\\\"same_verifs\\\"\\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": [
    "skill = hindcast.verify(\n",
    "    metric=\"acc\", comparison=\"e2o\", dim=\"init\", alignment=\"same_verifs\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we include a figure of a simpler alignment case with annual initializations from 1990 through 2000 and three lead years. We verify this hypothetical initialized ensemble against a product that spans 1995 through 2002.\n",
    "\n",
    "Two conditions must be met when selecting the verification window:\n",
    "\n",
    "\n",
    "1. There must be a union between the initialization dates and verification dates. This\n",
    "   is represented by the black vertical lines in the top panel below, which leave out\n",
    "   1990-1994 initializations since there aren't observations before 1995. This logic\n",
    "   exists so that any reference forecasts\n",
    "   (e.g. a persistence forecast) use an identical set of initializations as the\n",
    "   initialized forecast.\n",
    "   \n",
    "2. A given verification time must exist across all leads. This is to ensure that at each\n",
    "   lead, the entire set of chosen verification dates can be verified against. This is\n",
    "   represented by diagonals in the top panel below (and the dashed black lines).\n",
    "   Without the first stipulation, this would set the verification window at 1995-2001.\n",
    "   \n",
    "This leaves us with a verification window of [1998, 1999, 2000, 2001] which can be verified against across all leads (and have a complimentary persistence forecast with the same set of initializations used at each lead)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![sameverifs](images/alignment_plots/same_verifs_alignment.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Same Initializations\n",
    "\n",
    "`alignment=\"same_inits\"`\n",
    "\n",
    "The `same_inits` alignment finds a set of initializations that can verify over all leads. It also requires that the verification data have an observation at each initialization being retained. This is so that the reference forecast, such as persistence, uses an identical set of initializations in deriving its forecast. Notice in the logger output that a common set of initializations spanning 1955-2005 are used, while the verification window slides one year at each lead.\n",
    "\n",
    "`alignment=\"same_inits\"` used in {cite:p}`Marotzke2016`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:initialized | lead: 01 | inits: 1954-01-01 00:00:00-2005-01-01 00:00:00 | verifs: 1955-01-01 00:00:00-2006-01-01 00:00:00\n",
      "INFO:root:initialized | lead: 02 | inits: 1954-01-01 00:00:00-2005-01-01 00:00:00 | verifs: 1956-01-01 00:00:00-2007-01-01 00:00:00\n",
      "INFO:root:initialized | lead: 03 | inits: 1954-01-01 00:00:00-2005-01-01 00:00:00 | verifs: 1957-01-01 00:00:00-2008-01-01 00:00:00\n",
      "INFO:root:initialized | lead: 04 | inits: 1954-01-01 00:00:00-2005-01-01 00:00:00 | verifs: 1958-01-01 00:00:00-2009-01-01 00:00:00\n",
      "INFO:root:initialized | lead: 05 | inits: 1954-01-01 00:00:00-2005-01-01 00:00:00 | verifs: 1959-01-01 00:00:00-2010-01-01 00:00:00\n",
      "INFO:root:initialized | lead: 06 | inits: 1954-01-01 00:00:00-2005-01-01 00:00:00 | verifs: 1960-01-01 00:00:00-2011-01-01 00:00:00\n",
      "INFO:root:initialized | lead: 07 | inits: 1954-01-01 00:00:00-2005-01-01 00:00:00 | verifs: 1961-01-01 00:00:00-2012-01-01 00:00:00\n",
      "INFO:root:initialized | lead: 08 | inits: 1954-01-01 00:00:00-2005-01-01 00:00:00 | verifs: 1962-01-01 00:00:00-2013-01-01 00:00:00\n",
      "INFO:root:initialized | lead: 09 | inits: 1954-01-01 00:00:00-2005-01-01 00:00:00 | verifs: 1963-01-01 00:00:00-2014-01-01 00:00:00\n",
      "INFO:root:initialized | lead: 10 | inits: 1954-01-01 00:00:00-2005-01-01 00:00:00 | verifs: 1964-01-01 00:00:00-2015-01-01 00:00:00\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 12;\n",
       "                var nbb_unformatted_code = \"skill = hindcast.verify(\\n    metric=\\\"acc\\\", comparison=\\\"e2o\\\", dim=\\\"init\\\", alignment=\\\"same_inits\\\"\\n)\";\n",
       "                var nbb_formatted_code = \"skill = hindcast.verify(\\n    metric=\\\"acc\\\", comparison=\\\"e2o\\\", dim=\\\"init\\\", alignment=\\\"same_inits\\\"\\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": [
    "skill = hindcast.verify(\n",
    "    metric=\"acc\", comparison=\"e2o\", dim=\"init\", alignment=\"same_inits\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we include a figure of a simpler alignment case with annual initializations from 1990 through 2000 and three lead years. We verify this hypothetical initialized ensemble against a product that spans 1995 through 2002.\n",
    "\n",
    "Two conditions must be met to retain the initializations for verification: \n",
    "\n",
    "1. There must be an observation in the verification data for the given initialization.\n",
    "   In combination with (1), initializations 1990 through 1994 are left out. This logic\n",
    "   exists so that any reference forecast (e.g. a persistence forecast) use an identical set of initializations as the\n",
    "   initialized forecast.\n",
    "\n",
    "2. All forecasted times (i.e., initialization + lead year) for a given initialization\n",
    "   must be contained in the verification data. Schematically, this means that there must\n",
    "   be a union between a column in the top panel and the time series in the bottom panel.\n",
    "   The 2000 initialization below is left out since the verification data does not\n",
    "   contain 2003.\n",
    "   \n",
    "This leaves us with initializations [1995, 1996, ..., 1999] which can verify against the observations at all three lead years."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![sameinits](images/alignment_plots/same_inits_alignment.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Maximize Degrees of Freedom\n",
    "\n",
    "`alignment=\"maximize\"`\n",
    "\n",
    "The `maximize` alignment verifies against every available observation at each lead. This means that both the initializations and verification dates could be different at each lead.  It also requires that the verification data have an observation at each initialization being retained. This is so that the reference forecast, such as persistence, uses an identical set of initializations in deriving its forecast.\n",
    "\n",
    "Notice in the logger output that the initialization window shrinks from 1955-2014 (N=60) at lead year 1 to 1955-2005 (N=51) at lead year 10. Similarly, the verification window spans 1956-2015 at lead year 1 and 1965-2015 at lead year 10. However, using the other two alignment strategies (`same_verifs` and `same_inits`), there is a fixed N=51 to ensure constant initializations or verification dates, while the number of samples is extended to as high as 60 with this alignment strategy.\n",
    "\n",
    "`alignment=\"maximize\"` used in {cite:p}`Yeager2018`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:initialized | lead: 01 | inits: 1954-01-01 00:00:00-2014-01-01 00:00:00 | verifs: 1955-01-01 00:00:00-2015-01-01 00:00:00\n",
      "INFO:root:initialized | lead: 02 | inits: 1954-01-01 00:00:00-2013-01-01 00:00:00 | verifs: 1956-01-01 00:00:00-2015-01-01 00:00:00\n",
      "INFO:root:initialized | lead: 03 | inits: 1954-01-01 00:00:00-2012-01-01 00:00:00 | verifs: 1957-01-01 00:00:00-2015-01-01 00:00:00\n",
      "INFO:root:initialized | lead: 04 | inits: 1954-01-01 00:00:00-2011-01-01 00:00:00 | verifs: 1958-01-01 00:00:00-2015-01-01 00:00:00\n",
      "INFO:root:initialized | lead: 05 | inits: 1954-01-01 00:00:00-2010-01-01 00:00:00 | verifs: 1959-01-01 00:00:00-2015-01-01 00:00:00\n",
      "INFO:root:initialized | lead: 06 | inits: 1954-01-01 00:00:00-2009-01-01 00:00:00 | verifs: 1960-01-01 00:00:00-2015-01-01 00:00:00\n",
      "INFO:root:initialized | lead: 07 | inits: 1954-01-01 00:00:00-2008-01-01 00:00:00 | verifs: 1961-01-01 00:00:00-2015-01-01 00:00:00\n",
      "INFO:root:initialized | lead: 08 | inits: 1954-01-01 00:00:00-2007-01-01 00:00:00 | verifs: 1962-01-01 00:00:00-2015-01-01 00:00:00\n",
      "INFO:root:initialized | lead: 09 | inits: 1954-01-01 00:00:00-2006-01-01 00:00:00 | verifs: 1963-01-01 00:00:00-2015-01-01 00:00:00\n",
      "INFO:root:initialized | lead: 10 | inits: 1954-01-01 00:00:00-2005-01-01 00:00:00 | verifs: 1964-01-01 00:00:00-2015-01-01 00:00:00\n"
     ]
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       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 13;\n",
       "                var nbb_unformatted_code = \"skill = hindcast.verify(\\n    metric=\\\"acc\\\", comparison=\\\"e2o\\\", dim=\\\"init\\\", alignment=\\\"maximize\\\"\\n)\";\n",
       "                var nbb_formatted_code = \"skill = hindcast.verify(\\n    metric=\\\"acc\\\", comparison=\\\"e2o\\\", dim=\\\"init\\\", alignment=\\\"maximize\\\"\\n)\";\n",
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    "skill = hindcast.verify(\n",
    "    metric=\"acc\", comparison=\"e2o\", dim=\"init\", alignment=\"maximize\"\n",
    ")"
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   "cell_type": "markdown",
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   "source": [
    "Here, we include a figure of a simpler alignment case with annual initializations from 1990 through 2000 and three lead years. We verify this hypothetical initialized ensemble against a product that spans 1995 through 2002.\n",
    "\n",
    "Two conditions must be met when selecting initializations/verifications at each lead:\n",
    "   \n",
    "1. There must be a union between the initialization dates and verification dates. This\n",
    "   is represented by the black vertical lines in the top panel below, which leave out\n",
    "   1990-1994 initializations since there aren't observations before 1995. This logic\n",
    "   exists so that any reference forecasts\n",
    "   (e.g. a persistence forecast) use an identical set of initializations as the\n",
    "   initialized forecast.\n",
    "   \n",
    "2. The selected initializations must verify with the provided observations for the given lead.\n",
    "   This is shown by the hatching in the figure below. The 2000 initialization is left out\n",
    "   at lead year 3 since there is no observation for 2003.\n",
    "\n",
    "This leaves us with the following alignment:\n",
    "\n",
    "* LY1 initializations: [1995, 1996, 1997, 1998, 1999, 2000]\n",
    "\n",
    "* LY2 initializations: [1995, 1996, 1997, 1998, 1999, 2000]\n",
    "\n",
    "* LY3 initializations: [1995, 1996, 1997, 1998, 1999]\n"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "![maximize](images/alignment_plots/maximize_alignment.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logging\n",
    "\n",
    "``climpred`` uses the standard library [`logging`](https://docs.python.org/3/library/logging.html) to store the initializations and verification dates used at each lead for a given computation. This is used internally for testing, but more importantly, can be activated by the user so they can be sure of how computations are being done.\n",
    "\n",
    "To see the log interactively, e.g. while working in Jupyter notebooks or on the command line use the following:"
   ]
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   "execution_count": 14,
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       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 14;\n",
       "                var nbb_unformatted_code = \"import logging\\n\\nlogger = logging.getLogger()\\nlogger.setLevel(logging.INFO)\";\n",
       "                var nbb_formatted_code = \"import logging\\n\\nlogger = logging.getLogger()\\nlogger.setLevel(logging.INFO)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
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       "                        break;\n",
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       "            }, 500);\n",
       "            "
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    "import logging\n",
    "\n",
    "logger = logging.getLogger()\n",
    "logger.setLevel(logging.INFO)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
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     "text": [
      "INFO:root:initialized | lead: 01 | inits: 1963-01-01 00:00:00-2014-01-01 00:00:00 | verifs: 1964-01-01 00:00:00-2015-01-01 00:00:00\n",
      "INFO:root:initialized | lead: 02 | inits: 1962-01-01 00:00:00-2013-01-01 00:00:00 | verifs: 1964-01-01 00:00:00-2015-01-01 00:00:00\n",
      "INFO:root:initialized | lead: 03 | inits: 1961-01-01 00:00:00-2012-01-01 00:00:00 | verifs: 1964-01-01 00:00:00-2015-01-01 00:00:00\n",
      "INFO:root:initialized | lead: 04 | inits: 1960-01-01 00:00:00-2011-01-01 00:00:00 | verifs: 1964-01-01 00:00:00-2015-01-01 00:00:00\n",
      "INFO:root:initialized | lead: 05 | inits: 1959-01-01 00:00:00-2010-01-01 00:00:00 | verifs: 1964-01-01 00:00:00-2015-01-01 00:00:00\n",
      "INFO:root:initialized | lead: 06 | inits: 1958-01-01 00:00:00-2009-01-01 00:00:00 | verifs: 1964-01-01 00:00:00-2015-01-01 00:00:00\n",
      "INFO:root:initialized | lead: 07 | inits: 1957-01-01 00:00:00-2008-01-01 00:00:00 | verifs: 1964-01-01 00:00:00-2015-01-01 00:00:00\n",
      "INFO:root:initialized | lead: 08 | inits: 1956-01-01 00:00:00-2007-01-01 00:00:00 | verifs: 1964-01-01 00:00:00-2015-01-01 00:00:00\n",
      "INFO:root:initialized | lead: 09 | inits: 1955-01-01 00:00:00-2006-01-01 00:00:00 | verifs: 1964-01-01 00:00:00-2015-01-01 00:00:00\n",
      "INFO:root:initialized | lead: 10 | inits: 1954-01-01 00:00:00-2005-01-01 00:00:00 | verifs: 1964-01-01 00:00:00-2015-01-01 00:00:00\n"
     ]
    },
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     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 15;\n",
       "                var nbb_unformatted_code = \"skill = hindcast.verify(\\n    metric=\\\"acc\\\", comparison=\\\"e2o\\\", dim=\\\"init\\\", alignment=\\\"same_verifs\\\"\\n)\";\n",
       "                var nbb_formatted_code = \"skill = hindcast.verify(\\n    metric=\\\"acc\\\", comparison=\\\"e2o\\\", dim=\\\"init\\\", alignment=\\\"same_verifs\\\"\\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",
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       "            "
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    "skill = hindcast.verify(\n",
    "    metric=\"acc\", comparison=\"e2o\", dim=\"init\", alignment=\"same_verifs\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `INFO` level reports the minimum and maximum bounds for initializations and verification dates. To see every single initialization and verification date used, set the level to `DEBUG`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "```{eval-rst}\n",
    ".. bibliography::\n",
    "  :filter: docname in docnames\n",
    "```"
   ]
  }
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