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-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "PMP Json Formatted I/O\n",
-    "======================\n",
-    "\n",
-    "# Reading in PMP's json files\n",
-    "\n",
-    "This section will sohw how to read in json files generated by PMP and select pieces of it.\n",
-    "\n",
-    "We are assuming you're running this notebook from its directory in the [pcmdi_metrics](https://github.com/pcmdi/pcmdi_metrics) repo\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/1Tb/miniconda3/envs/nightly/lib/python3.6/site-packages/dask/config.py:168: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details.\n",
-      "  data = yaml.load(f.read()) or {}\n",
-      "/1Tb/miniconda3/envs/nightly/lib/python3.6/site-packages/distributed/config.py:20: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details.\n",
-      "  defaults = yaml.load(f)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Setup the notebook\n",
-    "from __future__ import print_function\n",
-    "import pcmdi_metrics\n",
-    "json1 = \"../../tests/pcmdi_install_test_results/metrics_results/installationTest/tas_2.5x2.5_regrid2_linear_metrics.json\"\n",
-    "json2 = \"../../tests/pcmdi_install_test_results/metrics_results/installationTest/tos_2.5x2.5_esmf_linear_metrics_2.json\"\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Reader object\n",
-    "\n",
-    "Let's create our Json reader object by pointing it to the desired files"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "J1 = pcmdi_metrics.io.base.JSONs([json1, json2])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Querying the reader object\n",
-    "\n",
-    "Let's query the object, first what are the axes available?\n",
-    "i.e what is the overall json structure of the files read in"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['variable', 'model', 'reference', 'rip', 'region', 'statistic', 'season']"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "J1.getAxisIds()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we get a little more info by getting the actual cdms2 axes that would be generated by reading everything in.\n",
-    "\n",
-    "Note now that the axes length include the **total** possible number of values, for example in this example each file contains **ONE** variable , but the resulting reading would have **TWO** variables"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[   id: variable\n",
-       "    Length: 2\n",
-       "    First:  tas\n",
-       "    Last:   tos\n",
-       "    Python id:  0x7fe15e1d9ba8,    id: model\n",
-       "    Length: 2\n",
-       "    First:  GFDL-ESM2G\n",
-       "    Last:   GFDL-ESM2Gb\n",
-       "    Python id:  0x7fe15e1d9a58,    id: reference\n",
-       "    Length: 2\n",
-       "    First:  SimulationDescription\n",
-       "    Last:   defaultReference\n",
-       "    Python id:  0x7fe15e1d9940,    id: rip\n",
-       "    Length: 2\n",
-       "    First:  r1i1p1\n",
-       "    Last:   r2i1p1\n",
-       "    Python id:  0x7fe15e1d9e10,    id: region\n",
-       "    Length: 6\n",
-       "    First:  NHEX\n",
-       "    Last:   terre\n",
-       "    Python id:  0x7fe15e1d9908,    id: statistic\n",
-       "    Length: 16\n",
-       "    First:  bias_xy\n",
-       "    Last:   std_xyt\n",
-       "    Python id:  0x7fe15e1d9c18,    id: season\n",
-       "    Length: 5\n",
-       "    First:  ann\n",
-       "    Last:   son\n",
-       "    Python id:  0x7fe15e1d9da0]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "J1.getAxisList()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also retrieve only a specific axis"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "   id: statistic\n",
-       "   Length: 16\n",
-       "   First:  bias_xy\n",
-       "   Last:   std_xyt\n",
-       "   Python id:  0x7fe15e1d99b0"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "J1.getAxis(\"statistic\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's print all the values in the axis"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array(['bias_xy', 'cor_xy', 'mae_xy', 'mean-obs_xy', 'mean_xy',\n",
-       "       'rms_devzm', 'rms_xy', 'rms_xyt', 'rms_y', 'rmsc_xy', 'std-obs_xy',\n",
-       "       'std-obs_xy_devzm', 'std-obs_xyt', 'std_xy', 'std_xy_devzm',\n",
-       "       'std_xyt'], dtype='<U16')"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "J1.getAxis(\"statistic\")[:]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Reading in data\n",
-    "\n",
-    "### All of it\n",
-    "\n",
-    "Now let's read **everything** in"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2, 2, 2, 2, 6, 16, 5)"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data = J1()\n",
-    "data.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Getting only some elements\n",
-    "\n",
-    "#### For one dimension\n",
-    "\n",
-    "But we might not be interested in everything, let's subset the **statistics** dimension\n",
-    "Note that the output array now went from 16 in length for the dimenson, down to 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(2, 2, 2, 2, 6, 2, 5)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "array(['rms_xy', 'std_xy'], dtype='<U6')"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data = J1(statistic=['rms_xy','std_xy'])\n",
-    "print(data.shape)\n",
-    "data.getAxis(-2)[:]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### For multiple dimensions\n",
-    "\n",
-    "Of course we can subset multiple axes at once. Notice that now region is smaller as well."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(2, 2, 2, 2, 3, 2, 5)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "array(['NHEX', 'global', 'terre'], dtype='<U6')"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data = J1(statistic=['rms_xy','std_xy'], region=['NHEX', 'global', 'terre'])\n",
-    "print(data.shape)\n",
-    "data.getAxis(-3)[:]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Reordering the elements\n",
-    "\n",
-    "One can also re-order the axes as it is read in. Notice 'terre' is now first."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(2, 2, 2, 2, 3, 2, 5)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "array(['terre', 'NHEX', 'global'], dtype='<U6')"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data = J1(statistic=['rms_xy','std_xy'], region=['terre', 'NHEX', 'global'])\n",
-    "print(data.shape)\n",
-    "data.getAxis(-3)[:]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Sometimes it can be useful to *join* two or many dimensions together.\n",
-    "For example let's merge `model` and `rip` together."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(2, 3, 1, 6, 16, 5)\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/1Tb/miniconda3/envs/nightly/lib/python3.6/site-packages/numpy/ma/core.py:3174: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
-      "  dout = self.data[indx]\n",
-      "/1Tb/miniconda3/envs/nightly/lib/python3.6/site-packages/numpy/ma/core.py:3206: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
-      "  mout = _mask[indx]\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "array(['GFDL-ESM2G_r1i1p1', 'GFDL-ESM2Gb_r1i1p1', 'GFDL-ESM2Gb_r2i1p1'],\n",
-       "      dtype='<U18')"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#### Merging dimensions together\n",
-    "\n",
-    "##### Two dimensions merged in one\n",
-    "\n",
-    "data = J1(merge=['model','rip'])\n",
-    "print(data.shape)\n",
-    "data.getAxis(1)[:]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Notice that the resulting array now has 6 dimensions rather than 7.\n",
-    "\n",
-    "The newly constructed array takes all possible values of `model` and match them with all possible of `rip`.\n",
-    "\n",
-    "Also notice that while it will create all possible combinations of model/rip, `GFDL-ESM2G_r2i1p1` was excluded has the combination contained no valid data.\n",
-    "\n",
-    "#### Order matters\n",
-    "\n",
-    "Let's switch the order we combine these dimensions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(2, 3, 1, 6, 16, 5)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "array(['r1i1p1_GFDL-ESM2G', 'r1i1p1_GFDL-ESM2Gb', 'r2i1p1_GFDL-ESM2Gb'],\n",
-       "      dtype='<U18')"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data = J1(merge=['rip','model'])\n",
-    "print(data.shape)\n",
-    "data.getAxis(1)[:]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Notice that the axis values now start with the values of `rip` combined with with `model` values. Which is the opposite of the previous example.\n",
-    "\n",
-    "### Combining more than 2 dimensions together\n",
-    "\n",
-    "You can also merge more than 2 dimensions together, agin the axis values will be all the possible (not totally empy) matrix combinations"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(2, 2, 1, 2, 312)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "array(['bias_xy_NHEX_ann', 'bias_xy_NHEX_djf', 'bias_xy_NHEX_jja',\n",
-       "       'bias_xy_NHEX_mam', 'bias_xy_NHEX_son', 'bias_xy_SHEX_ann',\n",
-       "       'bias_xy_SHEX_djf', 'bias_xy_SHEX_jja', 'bias_xy_SHEX_mam',\n",
-       "       'bias_xy_SHEX_son', 'bias_xy_TROPICS_ann', 'bias_xy_TROPICS_djf',\n",
-       "       'bias_xy_TROPICS_jja', 'bias_xy_TROPICS_mam',\n",
-       "       'bias_xy_TROPICS_son', 'bias_xy_global_ann', 'bias_xy_global_djf',\n",
-       "       'bias_xy_global_jja', 'bias_xy_global_mam', 'bias_xy_global_son',\n",
-       "       'bias_xy_ocean_ann', 'bias_xy_ocean_djf', 'bias_xy_ocean_jja',\n",
-       "       'bias_xy_ocean_mam', 'bias_xy_ocean_son', 'bias_xy_terre_ann',\n",
-       "       'bias_xy_terre_djf', 'bias_xy_terre_jja', 'bias_xy_terre_mam',\n",
-       "       'bias_xy_terre_son', 'cor_xy_NHEX_ann', 'cor_xy_NHEX_djf',\n",
-       "       'cor_xy_NHEX_jja', 'cor_xy_NHEX_mam', 'cor_xy_NHEX_son',\n",
-       "       'cor_xy_SHEX_ann', 'cor_xy_SHEX_djf', 'cor_xy_SHEX_jja',\n",
-       "       'cor_xy_SHEX_mam', 'cor_xy_SHEX_son', 'cor_xy_TROPICS_ann',\n",
-       "       'cor_xy_TROPICS_djf', 'cor_xy_TROPICS_jja', 'cor_xy_TROPICS_mam',\n",
-       "       'cor_xy_TROPICS_son', 'cor_xy_global_ann', 'cor_xy_global_djf',\n",
-       "       'cor_xy_global_jja', 'cor_xy_global_mam', 'cor_xy_global_son',\n",
-       "       'cor_xy_ocean_ann', 'cor_xy_ocean_djf', 'cor_xy_ocean_jja',\n",
-       "       'cor_xy_ocean_mam', 'cor_xy_ocean_son', 'cor_xy_terre_ann',\n",
-       "       'cor_xy_terre_djf', 'cor_xy_terre_jja', 'cor_xy_terre_mam',\n",
-       "       'cor_xy_terre_son', 'mae_xy_NHEX_ann', 'mae_xy_NHEX_djf',\n",
-       "       'mae_xy_NHEX_jja', 'mae_xy_NHEX_mam', 'mae_xy_NHEX_son',\n",
-       "       'mae_xy_SHEX_ann', 'mae_xy_SHEX_djf', 'mae_xy_SHEX_jja',\n",
-       "       'mae_xy_SHEX_mam', 'mae_xy_SHEX_son', 'mae_xy_TROPICS_ann',\n",
-       "       'mae_xy_TROPICS_djf', 'mae_xy_TROPICS_jja', 'mae_xy_TROPICS_mam',\n",
-       "       'mae_xy_TROPICS_son', 'mae_xy_global_ann', 'mae_xy_global_djf',\n",
-       "       'mae_xy_global_jja', 'mae_xy_global_mam', 'mae_xy_global_son',\n",
-       "       'mae_xy_ocean_ann', 'mae_xy_ocean_djf', 'mae_xy_ocean_jja',\n",
-       "       'mae_xy_ocean_mam', 'mae_xy_ocean_son', 'mae_xy_terre_ann',\n",
-       "       'mae_xy_terre_djf', 'mae_xy_terre_jja', 'mae_xy_terre_mam',\n",
-       "       'mae_xy_terre_son', 'mean-obs_xy_NHEX_ann', 'mean-obs_xy_NHEX_djf',\n",
-       "       'mean-obs_xy_NHEX_jja', 'mean-obs_xy_NHEX_mam',\n",
-       "       'mean-obs_xy_NHEX_son', 'mean-obs_xy_SHEX_ann',\n",
-       "       'mean-obs_xy_SHEX_djf', 'mean-obs_xy_SHEX_jja',\n",
-       "       'mean-obs_xy_SHEX_mam', 'mean-obs_xy_SHEX_son',\n",
-       "       'mean-obs_xy_TROPICS_ann', 'mean-obs_xy_TROPICS_djf',\n",
-       "       'mean-obs_xy_TROPICS_jja', 'mean-obs_xy_TROPICS_mam',\n",
-       "       'mean-obs_xy_TROPICS_son', 'mean-obs_xy_global_ann',\n",
-       "       'mean-obs_xy_global_djf', 'mean-obs_xy_global_jja',\n",
-       "       'mean-obs_xy_global_mam', 'mean-obs_xy_global_son',\n",
-       "       'mean-obs_xy_ocean_ann', 'mean-obs_xy_ocean_djf',\n",
-       "       'mean-obs_xy_ocean_jja', 'mean-obs_xy_ocean_mam',\n",
-       "       'mean-obs_xy_ocean_son', 'mean-obs_xy_terre_ann',\n",
-       "       'mean-obs_xy_terre_djf', 'mean-obs_xy_terre_jja',\n",
-       "       'mean-obs_xy_terre_mam', 'mean-obs_xy_terre_son',\n",
-       "       'mean_xy_NHEX_ann', 'mean_xy_NHEX_djf', 'mean_xy_NHEX_jja',\n",
-       "       'mean_xy_NHEX_mam', 'mean_xy_NHEX_son', 'mean_xy_SHEX_ann',\n",
-       "       'mean_xy_SHEX_djf', 'mean_xy_SHEX_jja', 'mean_xy_SHEX_mam',\n",
-       "       'mean_xy_SHEX_son', 'mean_xy_TROPICS_ann', 'mean_xy_TROPICS_djf',\n",
-       "       'mean_xy_TROPICS_jja', 'mean_xy_TROPICS_mam',\n",
-       "       'mean_xy_TROPICS_son', 'mean_xy_global_ann', 'mean_xy_global_djf',\n",
-       "       'mean_xy_global_jja', 'mean_xy_global_mam', 'mean_xy_global_son',\n",
-       "       'mean_xy_ocean_ann', 'mean_xy_ocean_djf', 'mean_xy_ocean_jja',\n",
-       "       'mean_xy_ocean_mam', 'mean_xy_ocean_son', 'mean_xy_terre_ann',\n",
-       "       'mean_xy_terre_djf', 'mean_xy_terre_jja', 'mean_xy_terre_mam',\n",
-       "       'mean_xy_terre_son', 'rms_devzm_NHEX_ann', 'rms_devzm_SHEX_ann',\n",
-       "       'rms_devzm_TROPICS_ann', 'rms_devzm_global_ann',\n",
-       "       'rms_devzm_ocean_ann', 'rms_devzm_terre_ann', 'rms_xy_NHEX_ann',\n",
-       "       'rms_xy_NHEX_djf', 'rms_xy_NHEX_jja', 'rms_xy_NHEX_mam',\n",
-       "       'rms_xy_NHEX_son', 'rms_xy_SHEX_ann', 'rms_xy_SHEX_djf',\n",
-       "       'rms_xy_SHEX_jja', 'rms_xy_SHEX_mam', 'rms_xy_SHEX_son',\n",
-       "       'rms_xy_TROPICS_ann', 'rms_xy_TROPICS_djf', 'rms_xy_TROPICS_jja',\n",
-       "       'rms_xy_TROPICS_mam', 'rms_xy_TROPICS_son', 'rms_xy_global_ann',\n",
-       "       'rms_xy_global_djf', 'rms_xy_global_jja', 'rms_xy_global_mam',\n",
-       "       'rms_xy_global_son', 'rms_xy_ocean_ann', 'rms_xy_ocean_djf',\n",
-       "       'rms_xy_ocean_jja', 'rms_xy_ocean_mam', 'rms_xy_ocean_son',\n",
-       "       'rms_xy_terre_ann', 'rms_xy_terre_djf', 'rms_xy_terre_jja',\n",
-       "       'rms_xy_terre_mam', 'rms_xy_terre_son', 'rms_xyt_NHEX_ann',\n",
-       "       'rms_xyt_SHEX_ann', 'rms_xyt_TROPICS_ann', 'rms_xyt_global_ann',\n",
-       "       'rms_xyt_ocean_ann', 'rms_xyt_terre_ann', 'rms_y_NHEX_ann',\n",
-       "       'rms_y_SHEX_ann', 'rms_y_TROPICS_ann', 'rms_y_global_ann',\n",
-       "       'rms_y_ocean_ann', 'rms_y_terre_ann', 'rmsc_xy_NHEX_ann',\n",
-       "       'rmsc_xy_NHEX_djf', 'rmsc_xy_NHEX_jja', 'rmsc_xy_NHEX_mam',\n",
-       "       'rmsc_xy_NHEX_son', 'rmsc_xy_SHEX_ann', 'rmsc_xy_SHEX_djf',\n",
-       "       'rmsc_xy_SHEX_jja', 'rmsc_xy_SHEX_mam', 'rmsc_xy_SHEX_son',\n",
-       "       'rmsc_xy_TROPICS_ann', 'rmsc_xy_TROPICS_djf',\n",
-       "       'rmsc_xy_TROPICS_jja', 'rmsc_xy_TROPICS_mam',\n",
-       "       'rmsc_xy_TROPICS_son', 'rmsc_xy_global_ann', 'rmsc_xy_global_djf',\n",
-       "       'rmsc_xy_global_jja', 'rmsc_xy_global_mam', 'rmsc_xy_global_son',\n",
-       "       'rmsc_xy_ocean_ann', 'rmsc_xy_ocean_djf', 'rmsc_xy_ocean_jja',\n",
-       "       'rmsc_xy_ocean_mam', 'rmsc_xy_ocean_son', 'rmsc_xy_terre_ann',\n",
-       "       'rmsc_xy_terre_djf', 'rmsc_xy_terre_jja', 'rmsc_xy_terre_mam',\n",
-       "       'rmsc_xy_terre_son', 'std-obs_xy_NHEX_ann', 'std-obs_xy_NHEX_djf',\n",
-       "       'std-obs_xy_NHEX_jja', 'std-obs_xy_NHEX_mam',\n",
-       "       'std-obs_xy_NHEX_son', 'std-obs_xy_SHEX_ann',\n",
-       "       'std-obs_xy_SHEX_djf', 'std-obs_xy_SHEX_jja',\n",
-       "       'std-obs_xy_SHEX_mam', 'std-obs_xy_SHEX_son',\n",
-       "       'std-obs_xy_TROPICS_ann', 'std-obs_xy_TROPICS_djf',\n",
-       "       'std-obs_xy_TROPICS_jja', 'std-obs_xy_TROPICS_mam',\n",
-       "       'std-obs_xy_TROPICS_son', 'std-obs_xy_global_ann',\n",
-       "       'std-obs_xy_global_djf', 'std-obs_xy_global_jja',\n",
-       "       'std-obs_xy_global_mam', 'std-obs_xy_global_son',\n",
-       "       'std-obs_xy_ocean_ann', 'std-obs_xy_ocean_djf',\n",
-       "       'std-obs_xy_ocean_jja', 'std-obs_xy_ocean_mam',\n",
-       "       'std-obs_xy_ocean_son', 'std-obs_xy_terre_ann',\n",
-       "       'std-obs_xy_terre_djf', 'std-obs_xy_terre_jja',\n",
-       "       'std-obs_xy_terre_mam', 'std-obs_xy_terre_son',\n",
-       "       'std-obs_xy_devzm_NHEX_ann', 'std-obs_xy_devzm_SHEX_ann',\n",
-       "       'std-obs_xy_devzm_TROPICS_ann', 'std-obs_xy_devzm_global_ann',\n",
-       "       'std-obs_xy_devzm_ocean_ann', 'std-obs_xy_devzm_terre_ann',\n",
-       "       'std-obs_xyt_NHEX_ann', 'std-obs_xyt_SHEX_ann',\n",
-       "       'std-obs_xyt_TROPICS_ann', 'std-obs_xyt_global_ann',\n",
-       "       'std-obs_xyt_ocean_ann', 'std-obs_xyt_terre_ann',\n",
-       "       'std_xy_NHEX_ann', 'std_xy_NHEX_djf', 'std_xy_NHEX_jja',\n",
-       "       'std_xy_NHEX_mam', 'std_xy_NHEX_son', 'std_xy_SHEX_ann',\n",
-       "       'std_xy_SHEX_djf', 'std_xy_SHEX_jja', 'std_xy_SHEX_mam',\n",
-       "       'std_xy_SHEX_son', 'std_xy_TROPICS_ann', 'std_xy_TROPICS_djf',\n",
-       "       'std_xy_TROPICS_jja', 'std_xy_TROPICS_mam', 'std_xy_TROPICS_son',\n",
-       "       'std_xy_global_ann', 'std_xy_global_djf', 'std_xy_global_jja',\n",
-       "       'std_xy_global_mam', 'std_xy_global_son', 'std_xy_ocean_ann',\n",
-       "       'std_xy_ocean_djf', 'std_xy_ocean_jja', 'std_xy_ocean_mam',\n",
-       "       'std_xy_ocean_son', 'std_xy_terre_ann', 'std_xy_terre_djf',\n",
-       "       'std_xy_terre_jja', 'std_xy_terre_mam', 'std_xy_terre_son',\n",
-       "       'std_xy_devzm_NHEX_ann', 'std_xy_devzm_SHEX_ann',\n",
-       "       'std_xy_devzm_TROPICS_ann', 'std_xy_devzm_global_ann',\n",
-       "       'std_xy_devzm_ocean_ann', 'std_xy_devzm_terre_ann',\n",
-       "       'std_xyt_NHEX_ann', 'std_xyt_SHEX_ann', 'std_xyt_TROPICS_ann',\n",
-       "       'std_xyt_global_ann', 'std_xyt_ocean_ann', 'std_xyt_terre_ann'],\n",
-       "      dtype='<U28')"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data = J1(merge=['statistic', 'region', 'season'])\n",
-    "print(data.shape)\n",
-    "data.getAxis(-1)[:]\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Merging and subsetting can be combined\n",
-    "\n",
-    "You can also shuffle the order and number of values for each dimension"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(2, 2, 1, 2, 8)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "array(['rms_xy_global_mam', 'rms_xy_global_djf', 'rms_xy_TROPICS_mam',\n",
-       "       'rms_xy_TROPICS_djf', 'cor_xy_global_mam', 'cor_xy_global_djf',\n",
-       "       'cor_xy_TROPICS_mam', 'cor_xy_TROPICS_djf'], dtype='<U18')"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data = J1(merge=['statistic', 'region', 'season'],\n",
-    "          season=['mam','djf'], statistic=['rms_xy', 'cor_xy'], region=['global','TROPICS'])\n",
-    "print(data.shape)\n",
-    "data.getAxis(-1)[:]\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Multiple ***combined dimensions*** can be created (and subsetted) at once\n",
-    "\n",
-    "You can also merge multiple dimensions at once:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(2, 3, 1, 8)\n"
-     ]
-    }
-   ],
-   "source": [
-    "data = J1(merge=[['model','rip'], ['statistic', 'region', 'season']],\n",
-    "          season=['mam','djf'], statistic=['rms_xy', 'cor_xy'], region=['global','TROPICS'])\n",
-    "print(data.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python [conda env:nightly] *",
-   "language": "python",
-   "name": "conda-env-nightly-py"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.7"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": true,
-   "toc_window_display": false
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/pcmdi_metrics/pcmdi/mean_climate_metrics_calculations.py b/pcmdi_metrics/pcmdi/mean_climate_metrics_calculations.py
index 1efdc345d..e6f8cf2c5 100644
--- a/pcmdi_metrics/pcmdi/mean_climate_metrics_calculations.py
+++ b/pcmdi_metrics/pcmdi/mean_climate_metrics_calculations.py
@@ -39,6 +39,8 @@ def compute_metrics(Var, dm, do):
         metrics_defs["zonal_mean"] = pcmdi_metrics.pcmdi.zonal_mean.compute(
             None,
             None)
+#rms_sea_dev_am_xy
+
         return metrics_defs
     cdms.setAutoBounds('on')
     metrics_dictionary = {}
@@ -108,7 +110,7 @@ def compute_metrics(Var, dm, do):
     for stat in ["std-obs_xy", "std_xy", "std-obs_xyt",
                  "std_xyt", "std-obs_xy_devzm", "mean_xy", "mean-obs_xy", "std_xy_devzm",
                  "rms_xyt", "rms_xy", "rmsc_xy", "cor_xy", "bias_xy",
-                 "mae_xy", "rms_y", "rms_devzm"]:
+                 "mae_xy", "rms_y", "rms_devzm_xy","rms_devam_xy"]:
         metrics_dictionary[stat] = {}
 
     metrics_dictionary[
@@ -187,7 +189,7 @@ def compute_metrics(Var, dm, do):
         conv,
         sig_digits)
     metrics_dictionary[
-        'rms_devzm']['ann'] = format(
+        'rms_devzm_xy']['ann'] = format(
         rms_xy_devzm *
         conv,
         sig_digits)
@@ -205,6 +207,24 @@ def compute_metrics(Var, dm, do):
         mae_sea = pcmdi_metrics.pcmdi.meanabs_xy.compute(dm_sea, do_sea)
         bias_sea = pcmdi_metrics.pcmdi.bias_xy.compute(dm_sea, do_sea)
 
+    # CALCULATE SEASONAL MEAN DEVIATION FROM ANNUAL MEAN RMS
+
+        dm_sea_devam =  MV2.subtract(dm_sea,dm_am)
+        do_sea_devam =  MV2.subtract(do_sea,do_am)
+        rms_sea_devam = pcmdi_metrics.pcmdi.rms_xy.compute(dm_sea_devam, do_sea_devam)
+
+    # SEASONAL ZONAL MEANS ######
+        dm_sea_zm, do_sea_zm = pcmdi_metrics.pcmdi.zonal_mean.compute(dm_sea, do_sea)
+
+    # CALCULATE SEASONAL MEAN DEVIATION FROM ZONAL MEAN RMS
+
+        dm_sea_zm_grown, dummy = grower(dm_sea_zm, dm_am)
+        do_sea_zm_grown, dummy = grower(do_sea_zm, do_am)
+
+        dm_sea_devzm =  MV2.subtract(dm_sea,dm_sea_zm_grown)
+        do_sea_devzm =  MV2.subtract(do_sea,do_sea_zm_grown)
+        rms_sea_devzm = pcmdi_metrics.pcmdi.rms_xy.compute(dm_sea_devzm, do_sea_devzm)
+
         # CALCULATE SEASONAL OBS and MOD STD
         stdObs_xy_sea = pcmdi_metrics.pcmdi.std_xy.compute(do_sea)
         std_xy_sea = pcmdi_metrics.pcmdi.std_xy.compute(dm_sea)
@@ -213,6 +233,14 @@ def compute_metrics(Var, dm, do):
         meanObs_xy_sea = pcmdi_metrics.pcmdi.mean_xy.compute(do_sea)
         mean_xy_sea = pcmdi_metrics.pcmdi.mean_xy.compute(dm_sea)
 
+        metrics_dictionary['rms_devzm_xy'][sea] = format(
+            rms_sea_devzm *
+            conv,
+            sig_digits)
+        metrics_dictionary['rms_devam_xy'][sea] = format(
+            rms_sea_devam *
+            conv,
+            sig_digits)
         metrics_dictionary['bias_xy'][sea] = format(
             bias_sea *
             conv,