Chapter02_NC_and_bpch_diagnostics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter 2: NetCDF and BPCH diagnostics in GEOS-Chem"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Prerequisite**: Minimal xarray knowledge in [Chapter01_NetCDF_xarray.ipynb](./Chapter01_NetCDF_xarray.ipynb).\n",
    "\n",
    "As every GEOS-Chem user knows, [BPCH](http://wiki.seas.harvard.edu/geos-chem/index.php/List_of_diagnostics_archived_to_bpch_format) has been the major diagnostics format. An important structural update in [GEOS-Chem v11-02](http://wiki.seas.harvard.edu/geos-chem/index.php/GEOS-Chem_v11-02) is the ability to write [NetCDF diagnostics](http://wiki.seas.harvard.edu/geos-chem/index.php/List_of_diagnostics_archived_to_netCDF_format). This is a big thing -- it allows [GCHP](http://wiki.seas.harvard.edu/geos-chem/index.php/GEOS-Chem_HP) to use **exactly the same diagnostics interface** and output **all available diagnotics as in GEOS-Chem-classic**. The new user interface is also much more user-friendly than the old interface.\n",
    "\n",
    "**Xarray is able to read both formats**. So you don't need to use IDL/gamap at all even if you need the old BPCH module. This is made possible by the [xbpch package](http://xbpch.readthedocs.io/en/latest/) written by Daniel Rothenberg."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import cartopy.crs as ccrs\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "import xbpch\n",
    "\n",
    "import warnings; warnings.filterwarnings(\"ignore\")  # hide some xbpch warnings\n",
    "\n",
    "from gamap_colormap import WhGrYlRd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NetCDF diagnostics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We provide a pre-generated diagnostics file in this directory. To configure NetCDF diagnotics for your own GEOS-Chem simulation, please see [GEOS-Chem wiki](http://wiki.seas.harvard.edu/geos-chem/index.php/List_of_diagnostics_archived_to_netCDF_format). To quickly test the new NC diagnotics module without setting up the latest version of GEOS-Chem on your own, you can use my [GEOS-Chem-on-cloud project](http://cloud-gc.readthedocs.io)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:         (ilev: 73, lat: 46, lev: 72, lon: 72, time: 3)\n",
       "Coordinates:\n",
       "  * time            (time) datetime64[ns] 2013-07-01T00:20:00 ...\n",
       "  * lev             (lev) float64 0.9925 0.9775 0.9625 0.9475 0.9325 0.9175 ...\n",
       "  * ilev            (ilev) float64 1.0 0.985 0.97 0.955 0.94 0.925 0.91 ...\n",
       "  * lat             (lat) float64 -89.0 -86.0 -82.0 -78.0 -74.0 -70.0 -66.0 ...\n",
       "  * lon             (lon) float64 -180.0 -175.0 -170.0 -165.0 -160.0 -155.0 ...\n",
       "Data variables:\n",
       "    hyam            (lev) float64 ...\n",
       "    hybm            (lev) float64 ...\n",
       "    hyai            (ilev) float64 ...\n",
       "    hybi            (ilev) float64 ...\n",
       "    P0              float64 ...\n",
       "    AREA            (lat, lon) float32 ...\n",
       "    SpeciesConc_CO  (time, lev, lat, lon) float32 ...\n",
       "    SpeciesConc_O3  (time, lev, lat, lon) float32 ...\n",
       "    SpeciesConc_NO  (time, lev, lat, lon) float32 ...\n",
       "Attributes:\n",
       "    title:         GEOS-Chem diagnostic collection: inst\n",
       "    history:       \n",
       "    format:        not found\n",
       "    conventions:   COARDS\n",
       "    ProdDateTime:  \n",
       "    reference:     www.geos-chem.org; wiki.geos-chem.org\n",
       "    contact:       GEOS-Chem Support Team (geos-chem-support@as.harvard.edu)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_nc = xr.open_dataset(\"new-NC-diagnostics.nc4\")\n",
    "ds_nc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The corresponding config file in GEOS-Chem's run directory (called `HISTORY.rc`, which is also used for GCHP!) looks like:\n",
    "\n",
    "```\n",
    "COLLECTIONS: 'inst',\n",
    "              ::\n",
    "  inst.template:  '%y4%m2%d2.nc4',\n",
    "  inst.frequency:  002000,\n",
    "  inst.duration:   240000,\n",
    "  inst.mode:      'instantaneous',\n",
    "  inst.fields:    'SpeciesConc_NO',           'GIGCchem',\n",
    "                  'SpeciesConc_O3',           'GIGCchem',\n",
    "                  'SpeciesConc_CO',           'GIGCchem',\n",
    "                   ::\n",
    "```\n",
    "\n",
    "Here I let the model to output the instantaneous concentrations (`inst.mode: 'instantaneous'`) of 3 species: NO, O3 and CO, with a frequency of 20 minutes (`inst.frequency`). A new file is generated every 24 hours (`inst.duration`)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The sample NC file contains 3 time slices, since I run the model for only 1 hour."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'SpeciesConc_CO' (time: 3, lev: 72, lat: 46, lon: 72)>\n",
       "[715392 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2013-07-01T00:20:00 2013-07-01T00:40:00 ...\n",
       "  * lev      (lev) float64 0.9925 0.9775 0.9625 0.9475 0.9325 0.9175 0.9025 ...\n",
       "  * lat      (lat) float64 -89.0 -86.0 -82.0 -78.0 -74.0 -70.0 -66.0 -62.0 ...\n",
       "  * lon      (lon) float64 -180.0 -175.0 -170.0 -165.0 -160.0 -155.0 -150.0 ...\n",
       "Attributes:\n",
       "    long_name:         Dry mixing ratio of species CO\n",
       "    units:             mol mol-1 dry\n",
       "    averaging_method:  instantaneous"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_nc['SpeciesConc_CO']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is nothing special about this diagnotics file. It is just a normal NetCDF, so you can use all techniques you learned in the previous chapter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x1118f9cc0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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zlaLnbOkzPzhxakzH6bfvmDk3Gd9Po5vmHJnqRnaN7zexIY28mdMKJjovbQmg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\n7/Adx3FGBO/wHcdxRgTv8B3HcUaE/w9LeUs6EelumgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1118331d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# a quick and dirty plot\n",
    "ds_nc['SpeciesConc_CO'].isel(time=2, lev=0).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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voPTEznlpEZ999D4Tp06vCbGp85wKBcsWzmfPTzvQNzDAw8ub/kOG8efJ87g0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QAAAR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\nsnVMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111929e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make it look a bit nicer\n",
    "fig = plt.figure(figsize=[6, 5])\n",
    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
    "ax.coastlines()\n",
    "ax.gridlines(linestyle='--')\n",
    "\n",
    "ds_nc['SpeciesConc_CO'].isel(time=2, lev=0).plot(ax=ax, cmap=WhGrYlRd, \n",
    "                                                 cbar_kwargs={'shrink': 0.5, 'label': 'mol/mol'})\n",
    "ax.set_title('surface CO');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# BPCH diagnostics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This section is mainly for existing GEOS-Chem users. If you just start to learn GEOS-Chem, I recommend skipping this legacy BPCH module and only use the NetCDF diagnotics as shown above."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ND49 (timeseries) is no doubt the most-used BPCH diagnostics. To replicate the same configuration as the NC diag module above, my `input.geos` file would look like:\n",
    "\n",
    "```\n",
    "%%% ND49 MENU %%%       :\n",
    "Turn on ND49 diagnostic : T\n",
    "Inst 3-D timeser. file  : tsYYYYMMDD.bpch\n",
    "Tracers to include      : 1 2 4\n",
    "Frequency [min]         : 20\n",
    "IMIN, IMAX of region    :   1  72\n",
    "JMIN, JMAX of region    :   1  46\n",
    "LMIN, LMAX of region    :   1  72\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A pre-generated BPCH file is available in this repository. `xbpch.open_bpchdataset()` is able to read BPCH files into xarray formats, so you can use all the xarray skills you've learned to analyze BPCH data!\n",
    "\n",
    "Since BPCH is not self-descriptive (unlike NetCDF), you also need to provide `tracerinfo.dat` and `diaginfo.dat` which contain tracer names and other metadata. I added them into this repository, but for your own BPCH data you should use the `*.dat` files in the corresponding run directory so the model versions would match."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:      (lat: 46, lev: 72, lon: 72, nv: 2, time: 3)\n",
       "Coordinates:\n",
       "  * lev          (lev) float64 0.9925 0.9775 0.9624 0.9473 0.9322 0.9171 ...\n",
       "  * lon          (lon) float64 -180.0 -175.0 -170.0 -165.0 -160.0 -155.0 ...\n",
       "  * lat          (lat) float64 -89.0 -86.0 -82.0 -78.0 -74.0 -70.0 -66.0 ...\n",
       "  * time         (time) datetime64[ns] 2013-07-01T00:19:41.250000 ...\n",
       "  * nv           (nv) int64 0 1\n",
       "Data variables:\n",
       "    IJ_AVG_S_NO  (time, lon, lat, lev) float32 dask.array<shape=(3, 72, 46, 72), chunksize=(1, 72, 46, 72)>\n",
       "    time_bnds    (time, nv) datetime64[ns] 2013-07-01T00:19:41.250000 ...\n",
       "    IJ_AVG_S_O3  (time, lon, lat, lev) float32 dask.array<shape=(3, 72, 46, 72), chunksize=(1, 72, 46, 72)>\n",
       "    IJ_AVG_S_CO  (time, lon, lat, lev) float32 dask.array<shape=(3, 72, 46, 72), chunksize=(1, 72, 46, 72)>\n",
       "Attributes:\n",
       "    modelname:    GEOSFP\n",
       "    halfpolar:    1\n",
       "    center180:    1\n",
       "    res:          (5.0, 4.0)\n",
       "    Conventions:  CF1.6\n",
       "    source:       old-ND49-diagnostics.bpch\n",
       "    tracerinfo:   tracerinfo.dat\n",
       "    diaginfo:     diaginfo.dat\n",
       "    filetype:     CTM bin 02\n",
       "    filetitle:    GEOS-CHEM DIAG49 instantaneous timeseries\n",
       "    history:      14:58:18 05-10-2018: Processed/loaded by xbpch-0.3.2 from o..."
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_bp = xbpch.open_bpchdataset(\"old-ND49-diagnostics.bpch\", \n",
    "                               tracerinfo_file='tracerinfo.dat', \n",
    "                               diaginfo_file='diaginfo.dat')\n",
    "ds_bp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is just a normal xarray object! You can do variable selections and any other operations as usual."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'IJ_AVG_S_CO' (time: 3, lon: 72, lat: 46, lev: 72)>\n",
       "dask.array<shape=(3, 72, 46, 72), dtype=float32, chunksize=(1, 72, 46, 72)>\n",
       "Coordinates:\n",
       "  * lev      (lev) float64 0.9925 0.9775 0.9624 0.9473 0.9322 0.9171 0.902 ...\n",
       "  * lon      (lon) float64 -180.0 -175.0 -170.0 -165.0 -160.0 -155.0 -150.0 ...\n",
       "  * lat      (lat) float64 -89.0 -86.0 -82.0 -78.0 -74.0 -70.0 -66.0 -62.0 ...\n",
       "  * time     (time) datetime64[ns] 2013-07-01T00:19:41.250000 ...\n",
       "Attributes:\n",
       "    number:          4\n",
       "    category:        IJ-AVG-$\n",
       "    name:            CO\n",
       "    full_name:       CO tracer\n",
       "    molwt:           0.028\n",
       "    C:               1\n",
       "    tracer:          4\n",
       "    hydrocarbon:     False\n",
       "    chemical:        True\n",
       "    original_shape:  (72, 46, 72)\n",
       "    origin:          (1, 1, 1)\n",
       "    scale_factor:    1000000000.0\n",
       "    units:           ppbv"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_bp['IJ_AVG_S_CO']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Besides tracers, the BPCH file also contains a variable for time boundaries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'time_bnds' (time: 3, nv: 2)>\n",
       "array([['2013-07-01T00:19:41.250000000', '2013-07-01T00:19:41.250000000'],\n",
       "       ['2013-07-01T00:40:18.750000000', '2013-07-01T00:40:18.750000000'],\n",
       "       ['2013-07-01T01:00:00.000000000', '2013-07-01T01:00:00.000000000']],\n",
       "      dtype='datetime64[ns]')\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2013-07-01T00:19:41.250000 ...\n",
       "  * nv       (nv) int64 0 1\n",
       "Attributes:\n",
       "    units:    hours since 1985-01-01 00:00:00"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_bp['time_bnds']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is not too useful, so let's just drop it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:      (lat: 46, lev: 72, lon: 72, time: 3)\n",
       "Coordinates:\n",
       "  * lev          (lev) float64 0.9925 0.9775 0.9624 0.9473 0.9322 0.9171 ...\n",
       "  * lon          (lon) float64 -180.0 -175.0 -170.0 -165.0 -160.0 -155.0 ...\n",
       "  * lat          (lat) float64 -89.0 -86.0 -82.0 -78.0 -74.0 -70.0 -66.0 ...\n",
       "  * time         (time) datetime64[ns] 2013-07-01T00:19:41.250000 ...\n",
       "Data variables:\n",
       "    IJ_AVG_S_NO  (time, lon, lat, lev) float32 dask.array<shape=(3, 72, 46, 72), chunksize=(1, 72, 46, 72)>\n",
       "    IJ_AVG_S_O3  (time, lon, lat, lev) float32 dask.array<shape=(3, 72, 46, 72), chunksize=(1, 72, 46, 72)>\n",
       "    IJ_AVG_S_CO  (time, lon, lat, lev) float32 dask.array<shape=(3, 72, 46, 72), chunksize=(1, 72, 46, 72)>\n",
       "Attributes:\n",
       "    modelname:    GEOSFP\n",
       "    halfpolar:    1\n",
       "    center180:    1\n",
       "    res:          (5.0, 4.0)\n",
       "    Conventions:  CF1.6\n",
       "    source:       old-ND49-diagnostics.bpch\n",
       "    tracerinfo:   tracerinfo.dat\n",
       "    diaginfo:     diaginfo.dat\n",
       "    filetype:     CTM bin 02\n",
       "    filetitle:    GEOS-CHEM DIAG49 instantaneous timeseries\n",
       "    history:      14:58:18 05-10-2018: Processed/loaded by xbpch-0.3.2 from o..."
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_bp = ds_bp.drop('time_bnds')\n",
    "ds_bp = ds_bp.drop('nv')  # also drop the redundant dimension\n",
    "ds_bp\n",
    "\n",
    "# running this cell twice will lead to an error since the variables are already dropped"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the dimension order is `(time, lon, lat, lev)`, not the common order `(time, lev, lat, lon)`. This is because BPCH has a weird data structure. The order can be corrected by the [transpose()](http://xarray.pydata.org/en/stable/generated/xarray.Dataset.transpose.html) method.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:      (lat: 46, lev: 72, lon: 72, time: 3)\n",
       "Coordinates:\n",
       "  * lev          (lev) float64 0.9925 0.9775 0.9624 0.9473 0.9322 0.9171 ...\n",
       "  * lon          (lon) float64 -180.0 -175.0 -170.0 -165.0 -160.0 -155.0 ...\n",
       "  * lat          (lat) float64 -89.0 -86.0 -82.0 -78.0 -74.0 -70.0 -66.0 ...\n",
       "  * time         (time) datetime64[ns] 2013-07-01T00:19:41.250000 ...\n",
       "Data variables:\n",
       "    IJ_AVG_S_NO  (time, lev, lat, lon) float32 dask.array<shape=(3, 72, 46, 72), chunksize=(1, 72, 46, 72)>\n",
       "    IJ_AVG_S_O3  (time, lev, lat, lon) float32 dask.array<shape=(3, 72, 46, 72), chunksize=(1, 72, 46, 72)>\n",
       "    IJ_AVG_S_CO  (time, lev, lat, lon) float32 dask.array<shape=(3, 72, 46, 72), chunksize=(1, 72, 46, 72)>\n",
       "Attributes:\n",
       "    modelname:    GEOSFP\n",
       "    halfpolar:    1\n",
       "    center180:    1\n",
       "    res:          (5.0, 4.0)\n",
       "    Conventions:  CF1.6\n",
       "    source:       old-ND49-diagnostics.bpch\n",
       "    tracerinfo:   tracerinfo.dat\n",
       "    diaginfo:     diaginfo.dat\n",
       "    filetype:     CTM bin 02\n",
       "    filetitle:    GEOS-CHEM DIAG49 instantaneous timeseries\n",
       "    history:      14:58:18 05-10-2018: Processed/loaded by xbpch-0.3.2 from o..."
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_bp = ds_bp.transpose('time', 'lev', 'lat', 'lon')\n",
    "ds_bp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "People often want to convert BPCH data to NC format. Since xarray is a universal interface to different formats, such a conversion is trivial:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ds_bp = xbpch.common.fix_attr_encoding(ds_bp) # fix a bug mentioned at http://xbpch.readthedocs.io/en/latest/usage.html#save-to-netcdf\n",
    "ds_bp.to_netcdf('from_bpch.nc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We do get a fully-fledged NetCDF file!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "netcdf from_bpch {\n",
      "dimensions:\n",
      "\tlev = 72 ;\n",
      "\tlon = 72 ;\n",
      "\tlat = 46 ;\n",
      "\ttime = 3 ;\n",
      "variables:\n",
      "\tdouble lev(lev) ;\n",
      "\t\tlev:_FillValue = NaN ;\n",
      "\t\tlev:standard_name = \"atmosphere_hybrid_sigma_pressure_coordinate\" ;\n",
      "\t\tlev:axis = \"Z\" ;\n",
      "\tdouble lon(lon) ;\n",
      "\t\tlon:_FillValue = NaN ;\n",
      "\t\tlon:long_name = \"longitude\" ;\n",
      "\t\tlon:units = \"degrees_east\" ;\n",
      "\tdouble lat(lat) ;\n",
      "\t\tlat:_FillValue = NaN ;\n",
      "\t\tlat:long_name = \"latitude\" ;\n",
      "\t\tlat:units = \"degrees_north\" ;\n",
      "\tfloat IJ_AVG_S_NO(time, lev, lat, lon) ;\n",
      "\t\tIJ_AVG_S_NO:_FillValue = NaNf ;\n",
      "\t\tIJ_AVG_S_NO:number = 1LL ;\n",
      "\t\tIJ_AVG_S_NO:category = \"IJ-AVG-$\" ;\n",
      "\t\tIJ_AVG_S_NO:name = \"NO\" ;\n",
      "\t\tIJ_AVG_S_NO:full_name = \"NO tracer\" ;\n",
      "\t\tIJ_AVG_S_NO:molwt = 0.03 ;\n",
      "\t\tIJ_AVG_S_NO:C = 1LL ;\n",
      "\t\tIJ_AVG_S_NO:tracer = 1LL ;\n",
      "\t\tIJ_AVG_S_NO:hydrocarbon = 0LL ;\n",
      "\t\tIJ_AVG_S_NO:chemical = 1LL ;\n",
      "\t\tIJ_AVG_S_NO:original_shape = 72LL, 46LL, 72LL ;\n",
      "\t\tIJ_AVG_S_NO:origin = 1LL, 1LL, 1LL ;\n",
      "\t\tIJ_AVG_S_NO:scale_factor = 1000000000. ;\n",
      "\tdouble time(time) ;\n",
      "\t\ttime:_FillValue = NaN ;\n",
      "\t\ttime:bounds = \"time_bnds\" ;\n",
      "\t\ttime:units = \"seconds since 2013-07-01 00:19:41.250000\" ;\n",
      "\t\ttime:calendar = \"proleptic_gregorian\" ;\n",
      "\tfloat IJ_AVG_S_O3(time, lev, lat, lon) ;\n",
      "\t\tIJ_AVG_S_O3:_FillValue = NaNf ;\n",
      "\t\tIJ_AVG_S_O3:number = 2LL ;\n",
      "\t\tIJ_AVG_S_O3:category = \"IJ-AVG-$\" ;\n",
      "\t\tIJ_AVG_S_O3:name = \"O3\" ;\n",
      "\t\tIJ_AVG_S_O3:full_name = \"O3 tracer\" ;\n",
      "\t\tIJ_AVG_S_O3:molwt = 0.048 ;\n",
      "\t\tIJ_AVG_S_O3:C = 1LL ;\n",
      "\t\tIJ_AVG_S_O3:tracer = 2LL ;\n",
      "\t\tIJ_AVG_S_O3:hydrocarbon = 0LL ;\n",
      "\t\tIJ_AVG_S_O3:chemical = 1LL ;\n",
      "\t\tIJ_AVG_S_O3:original_shape = 72LL, 46LL, 72LL ;\n",
      "\t\tIJ_AVG_S_O3:origin = 1LL, 1LL, 1LL ;\n",
      "\t\tIJ_AVG_S_O3:scale_factor = 1000000000. ;\n",
      "\tfloat IJ_AVG_S_CO(time, lev, lat, lon) ;\n",
      "\t\tIJ_AVG_S_CO:_FillValue = NaNf ;\n",
      "\t\tIJ_AVG_S_CO:number = 4LL ;\n",
      "\t\tIJ_AVG_S_CO:category = \"IJ-AVG-$\" ;\n",
      "\t\tIJ_AVG_S_CO:name = \"CO\" ;\n",
      "\t\tIJ_AVG_S_CO:full_name = \"CO tracer\" ;\n",
      "\t\tIJ_AVG_S_CO:molwt = 0.028 ;\n",
      "\t\tIJ_AVG_S_CO:C = 1LL ;\n",
      "\t\tIJ_AVG_S_CO:tracer = 4LL ;\n",
      "\t\tIJ_AVG_S_CO:hydrocarbon = 0LL ;\n",
      "\t\tIJ_AVG_S_CO:chemical = 1LL ;\n",
      "\t\tIJ_AVG_S_CO:original_shape = 72LL, 46LL, 72LL ;\n",
      "\t\tIJ_AVG_S_CO:origin = 1LL, 1LL, 1LL ;\n",
      "\t\tIJ_AVG_S_CO:scale_factor = 1000000000. ;\n",
      "\n",
      "// global attributes:\n",
      "\t\t:modelname = \"GEOSFP\" ;\n",
      "\t\t:halfpolar = 1LL ;\n",
      "\t\t:center180 = 1LL ;\n",
      "\t\t:res = 5., 4. ;\n",
      "\t\t:Conventions = \"CF1.6\" ;\n",
      "\t\t:source = \"old-ND49-diagnostics.bpch\" ;\n",
      "\t\t:tracerinfo = \"tracerinfo.dat\" ;\n",
      "\t\t:diaginfo = \"diaginfo.dat\" ;\n",
      "\t\t:filetype = \"CTM bin 02\" ;\n",
      "\t\t:filetitle = \"GEOS-CHEM DIAG49 instantaneous timeseries\" ;\n",
      "\t\t:history = \"14:58:18 05-10-2018: Processed/loaded by xbpch-0.3.2 from old-ND49-diagnostics.bpch\" ;\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "!ncdump -h from_bpch.nc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparing two formats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we can compare BPCH and NetCDF diagnostics to make sure they are the same:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PPjWBJ58cg5QSV7caxN6IJjM9nZycHH27idNepaioiDr1/OnUoxcWFjqQkvFDK07+3ufh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\n71+9IB0wsy+Gtr4hoY+ynpNTSTduXb5AuqjIyF26siKHVFJcDIkJCYw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nyi4WBGGS0lW2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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ace5f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=[10, 4], subplot_kw={'projection': ccrs.PlateCarree()})\n",
    "\n",
    "# Plot the previous NetCDF diagnostics on the left side\n",
    "ds_nc['SpeciesConc_CO'].isel(time=2, lev=0).plot(ax=axes[0], cmap=WhGrYlRd, \n",
    "                                                 cbar_kwargs={'shrink': 0.5, 'label': 'mol/mol'})\n",
    "axes[0].set_title('NetCDF diagnostics')\n",
    "\n",
    "\n",
    "# Plot BPCH diagnotics on the right side\n",
    "ds_bp['IJ_AVG_S_CO'].isel(time=2, lev=0).plot(ax=axes[1], cmap=WhGrYlRd, \n",
    "                                              cbar_kwargs={'shrink': 0.5, 'label': 'ppbv'})\n",
    "\n",
    "axes[1].set_title('BPCH diagnostics')\n",
    "\n",
    "for ax in axes:\n",
    "    ax.coastlines()\n",
    "    ax.gridlines(linestyle='--')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "They look pretty much the same (although the units are different). A more rigorous comparison can be done by `np.allclose()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# notice the unit conversion\n",
    "np.allclose(ds_nc['SpeciesConc_CO']*1e9, ds_bp['IJ_AVG_S_CO'], rtol=1e-5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With a relative error tolerance of 1e-5 (0.001%), two data fields are the same. But they are not bit-wise identical, due to inevitable numerical noise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Don't be to strict! 1e-6 is too small.\n",
    "np.allclose(ds_nc['SpeciesConc_CO']*1e9, ds_bp['IJ_AVG_S_CO'], rtol=1e-6)"
   ]
  }
 ],
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