CAM-Chemistry-plotting.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.tri as tri\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:       (ilev: 33, lev: 32, nbnd: 2, ncol: 48602, time: 12)\n",
       "Coordinates:\n",
       "  * lev           (lev) float64 3.643 7.595 14.36 24.61 ... 957.5 976.3 992.6\n",
       "  * ilev          (ilev) float64 2.255 5.032 10.16 18.56 ... 967.5 985.1 1e+03\n",
       "  * time          (time) object 2013-02-01 00:00:00 ... 2014-01-01 00:00:00\n",
       "Dimensions without coordinates: nbnd, ncol\n",
       "Data variables:\n",
       "    lat           (time, ncol) float64 dask.array<shape=(12, 48602), chunksize=(1, 48602)>\n",
       "    lon           (time, ncol) float64 dask.array<shape=(12, 48602), chunksize=(1, 48602)>\n",
       "    area          (time, ncol) float64 dask.array<shape=(12, 48602), chunksize=(1, 48602)>\n",
       "    hyam          (time, lev) float64 dask.array<shape=(12, 32), chunksize=(1, 32)>\n",
       "    hybm          (time, lev) float64 dask.array<shape=(12, 32), chunksize=(1, 32)>\n",
       "    hyai          (time, ilev) float64 dask.array<shape=(12, 33), chunksize=(1, 33)>\n",
       "    hybi          (time, ilev) float64 dask.array<shape=(12, 33), chunksize=(1, 33)>\n",
       "    date          (time) int32 dask.array<shape=(12,), chunksize=(1,)>\n",
       "    datesec       (time) int32 dask.array<shape=(12,), chunksize=(1,)>\n",
       "    time_bnds     (time, nbnd) object dask.array<shape=(12, 2), chunksize=(1, 2)>\n",
       "    date_written  (time) |S8 dask.array<shape=(12,), chunksize=(1,)>\n",
       "    time_written  (time) |S8 dask.array<shape=(12,), chunksize=(1,)>\n",
       "    ndbase        (time) int32 0 0 0 0 0 0 0 0 0 0 0 0\n",
       "    nsbase        (time) int32 0 0 0 0 0 0 0 0 0 0 0 0\n",
       "    nbdate        (time) int32 20130101 20130101 20130101 ... 20130101 20130101\n",
       "    nbsec         (time) int32 0 0 0 0 0 0 0 0 0 0 0 0\n",
       "    mdt           (time) int32 1800 1800 1800 1800 1800 ... 1800 1800 1800 1800\n",
       "    ndcur         (time) int32 dask.array<shape=(12,), chunksize=(1,)>\n",
       "    nscur         (time) int32 dask.array<shape=(12,), chunksize=(1,)>\n",
       "    co2vmr        (time) float64 dask.array<shape=(12,), chunksize=(1,)>\n",
       "    ch4vmr        (time) float64 dask.array<shape=(12,), chunksize=(1,)>\n",
       "    n2ovmr        (time) float64 dask.array<shape=(12,), chunksize=(1,)>\n",
       "    f11vmr        (time) float64 dask.array<shape=(12,), chunksize=(1,)>\n",
       "    f12vmr        (time) float64 dask.array<shape=(12,), chunksize=(1,)>\n",
       "    sol_tsi       (time) float64 dask.array<shape=(12,), chunksize=(1,)>\n",
       "    nsteph        (time) int32 dask.array<shape=(12,), chunksize=(1,)>\n",
       "    CLDHGH        (time, ncol) float32 dask.array<shape=(12, 48602), chunksize=(1, 48602)>\n",
       "    CLDLOW        (time, ncol) float32 dask.array<shape=(12, 48602), chunksize=(1, 48602)>\n",
       "    CLDMED        (time, ncol) float32 dask.array<shape=(12, 48602), chunksize=(1, 48602)>\n",
       "    CLOUD         (time, lev, ncol) float32 dask.array<shape=(12, 32, 48602), chunksize=(1, 32, 48602)>\n",
       "    ISOP          (time, lev, ncol) float32 dask.array<shape=(12, 32, 48602), chunksize=(1, 32, 48602)>\n",
       "    MEG_ISOP      (time, ncol) float32 dask.array<shape=(12, 48602), chunksize=(1, 48602)>\n",
       "    MEG_MTERP     (time, ncol) float32 dask.array<shape=(12, 48602), chunksize=(1, 48602)>\n",
       "    MTERP         (time, lev, ncol) float32 dask.array<shape=(12, 32, 48602), chunksize=(1, 32, 48602)>\n",
       "    NO            (time, lev, ncol) float32 dask.array<shape=(12, 32, 48602), chunksize=(1, 32, 48602)>\n",
       "    NO2           (time, lev, ncol) float32 dask.array<shape=(12, 32, 48602), chunksize=(1, 32, 48602)>\n",
       "    O3            (time, lev, ncol) float32 dask.array<shape=(12, 32, 48602), chunksize=(1, 32, 48602)>\n",
       "    O3S           (time, lev, ncol) float32 dask.array<shape=(12, 32, 48602), chunksize=(1, 32, 48602)>\n",
       "    OMEGA         (time, lev, ncol) float32 dask.array<shape=(12, 32, 48602), chunksize=(1, 32, 48602)>\n",
       "    PHIS          (time, ncol) float32 dask.array<shape=(12, 48602), chunksize=(1, 48602)>\n",
       "    PMID          (time, lev, ncol) float32 dask.array<shape=(12, 32, 48602), chunksize=(1, 32, 48602)>\n",
       "    PS            (time, ncol) float32 dask.array<shape=(12, 48602), chunksize=(1, 48602)>\n",
       "    T             (time, lev, ncol) float32 dask.array<shape=(12, 32, 48602), chunksize=(1, 32, 48602)>\n",
       "    TGCLDIWP      (time, ncol) float32 dask.array<shape=(12, 48602), chunksize=(1, 48602)>\n",
       "    TGCLDLWP      (time, ncol) float32 dask.array<shape=(12, 48602), chunksize=(1, 48602)>\n",
       "    TROP_P        (time, ncol) float32 dask.array<shape=(12, 48602), chunksize=(1, 48602)>\n",
       "    TROP_T        (time, ncol) float32 dask.array<shape=(12, 48602), chunksize=(1, 48602)>\n",
       "    U             (time, lev, ncol) float32 dask.array<shape=(12, 32, 48602), chunksize=(1, 32, 48602)>\n",
       "    V             (time, lev, ncol) float32 dask.array<shape=(12, 32, 48602), chunksize=(1, 32, 48602)>\n",
       "    Z3            (time, lev, ncol) float32 dask.array<shape=(12, 32, 48602), chunksize=(1, 32, 48602)>\n",
       "    jno2          (time, lev, ncol) float32 dask.array<shape=(12, 32, 48602), chunksize=(1, 32, 48602)>\n",
       "    jo3_a         (time, lev, ncol) float32 dask.array<shape=(12, 32, 48602), chunksize=(1, 32, 48602)>\n",
       "Attributes:\n",
       "    ne:                30\n",
       "    np:                4\n",
       "    Conventions:       CF-1.0\n",
       "    source:            CAM\n",
       "    case:              f.e20.FCHIST.ne30_ne30_mg17.cam6_1_019.GEOS5_nudged.ne...\n",
       "    logname:           tilmes\n",
       "    host:              cheyenne2\n",
       "    initial_file:      /gpfs/fs1/p/acom/acom-climate/tilmes/inputdata/init/ce...\n",
       "    topography_file:   /glade/p/cesmdata/cseg/inputdata/atm/cam/topo/se/ne30n...\n",
       "    model_doi_url:     https://doi.org/10.5065/D67H1H0V\n",
       "    time_period_freq:  month_1"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = xr.open_mfdataset(\"/Users/abanihi/datasets/ACOM/cam_chem/*.nc\")\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:       (ilev: 1, lev: 1, nbnd: 2, ncol: 48602, time: 1)\n",
       "Coordinates:\n",
       "  * lev           (lev) float64 3.643\n",
       "  * ilev          (ilev) float64 2.255\n",
       "  * time          (time) object 2013-02-01 00:00:00\n",
       "Dimensions without coordinates: nbnd, ncol\n",
       "Data variables:\n",
       "    lat           (time, ncol) float64 -35.26 -35.65 -36.26 ... 36.68 36.05\n",
       "    lon           (time, ncol) float64 315.0 315.8 317.2 ... 133.7 136.3 135.0\n",
       "    area          (time, ncol) float64 4.397e-05 0.0001465 ... 0.0003665\n",
       "    hyam          (time, lev) float64 0.003643\n",
       "    hybm          (time, lev) float64 0.0\n",
       "    hyai          (time, ilev) float64 0.002255\n",
       "    hybi          (time, ilev) float64 0.0\n",
       "    date          (time) int32 20130201\n",
       "    datesec       (time) int32 0\n",
       "    time_bnds     (time, nbnd) object 2013-01-01 00:00:00 2013-02-01 00:00:00\n",
       "    date_written  (time) |S8 b'04/25/19'\n",
       "    time_written  (time) |S8 b'12:28:27'\n",
       "    ndbase        (time) int32 0\n",
       "    nsbase        (time) int32 0\n",
       "    nbdate        (time) int32 20130101\n",
       "    nbsec         (time) int32 0\n",
       "    mdt           (time) int32 1800\n",
       "    ndcur         (time) int32 31\n",
       "    nscur         (time) int32 0\n",
       "    co2vmr        (time) float64 0.0003961\n",
       "    ch4vmr        (time) float64 1.824e-06\n",
       "    n2ovmr        (time) float64 3.256e-07\n",
       "    f11vmr        (time) float64 2.353e-10\n",
       "    f12vmr        (time) float64 5.245e-10\n",
       "    sol_tsi       (time) float64 -1.0\n",
       "    nsteph        (time) int32 1488\n",
       "    CLDHGH        (time, ncol) float32 0.24911407 0.27495208 ... 0.24251114\n",
       "    CLDLOW        (time, ncol) float32 0.3409759 0.3670384 ... 0.71152115\n",
       "    CLDMED        (time, ncol) float32 0.14385791 0.15166831 ... 0.33129966\n",
       "    CLOUD         (time, lev, ncol) float32 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
       "    ISOP          (time, lev, ncol) float32 2.2188716e-37 ... 2.9064206e-37\n",
       "    MEG_ISOP      (time, ncol) float32 0.0 0.0 ... 6.7773376e-15 5.9795685e-15\n",
       "    MEG_MTERP     (time, ncol) float32 0.0 0.0 ... 3.0455627e-13 2.536253e-13\n",
       "    MTERP         (time, lev, ncol) float32 1.019557e-37 ... 1.2553998e-37\n",
       "    NO            (time, lev, ncol) float32 8.916914e-09 ... 5.400478e-09\n",
       "    NO2           (time, lev, ncol) float32 9.807102e-09 ... 1.08326725e-08\n",
       "    O3            (time, lev, ncol) float32 6.060612e-06 ... 6.600038e-06\n",
       "    O3S           (time, lev, ncol) float32 6.060612e-06 ... 6.600038e-06\n",
       "    OMEGA         (time, lev, ncol) float32 -3.069033e-05 ... 8.526019e-05\n",
       "    PHIS          (time, ncol) float32 0.0 0.0 0.0 ... 2365.2463 1057.8777\n",
       "    PMID          (time, lev, ncol) float32 364.34708 364.34708 ... 364.34705\n",
       "    PS            (time, ncol) float32 101544.875 101546.055 ... 100809.586\n",
       "    T             (time, lev, ncol) float32 251.58237 251.77411 ... 243.97507\n",
       "    TGCLDIWP      (time, ncol) float32 0.00529009 0.004984511 ... 0.0072655077\n",
       "    TGCLDLWP      (time, ncol) float32 0.053325526 0.06225107 ... 0.2670434\n",
       "    TROP_P        (time, ncol) float32 16670.434 17025.768 ... 28265.703\n",
       "    TROP_T        (time, ncol) float32 213.19145 213.47375 ... 222.747 223.19025\n",
       "    U             (time, lev, ncol) float32 -41.68085 -41.243 ... -6.3770766\n",
       "    V             (time, lev, ncol) float32 -0.16207765 ... -5.1464634\n",
       "    Z3            (time, lev, ncol) float32 38298.234 38312.797 ... 37540.273\n",
       "    jno2          (time, lev, ncol) float32 0.0077576945 ... 0.0052811415\n",
       "    jo3_a         (time, lev, ncol) float32 0.00092947134 ... 0.0003595864\n",
       "Attributes:\n",
       "    ne:                30\n",
       "    np:                4\n",
       "    Conventions:       CF-1.0\n",
       "    source:            CAM\n",
       "    case:              f.e20.FCHIST.ne30_ne30_mg17.cam6_1_019.GEOS5_nudged.ne...\n",
       "    logname:           tilmes\n",
       "    host:              cheyenne2\n",
       "    initial_file:      /gpfs/fs1/p/acom/acom-climate/tilmes/inputdata/init/ce...\n",
       "    topography_file:   /glade/p/cesmdata/cseg/inputdata/atm/cam/topo/se/ne30n...\n",
       "    model_doi_url:     https://doi.org/10.5065/D67H1H0V\n",
       "    time_period_freq:  month_1"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample = ds.isel(time=slice(0, 1), lev=slice(0, 1), ilev=slice(0, 1)).load()\n",
    "sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "triang = tri.Triangulation(sample.lon.data.flatten(), sample.lat.data.flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.tri.triangulation.Triangulation at 0x1123ae828>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "triang"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'lat')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1, ax1 = plt.subplots()\n",
    "ax1.set_aspect('equal')\n",
    "tpc = ax1.tripcolor(triang, sample.O3.values.flatten(), shading='flat')\n",
    "fig1.colorbar(tpc)\n",
    "ax1.set_title(f'time={str(sample.time.values[0])} lev={sample.lev.values[0]}\\n{sample.O3.long_name} {sample.O3.units}')\n",
    "ax1.set_xlabel('lon')\n",
    "ax1.set_ylabel('lat')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}