{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import netCDF4 as nc\n",
    "import re\n",
    "import shutil\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "src='/data/eolson/results/MEOPAR/tmp/SalishSea_03032640_restart_trc.nc'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "f=nc.Dataset(src,'r+')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['nav_lon', 'nav_lat', 'nav_lev', 'time_counter', 'kt', 'ndastp', 'adatrj', 'rnf_pis_NO3_b', 'rnf_pis_NH4_b', 'rnf_pis_Si_b', 'rnf_pis_DIAT_b', 'rnf_pis_PHY_b', 'rnf_pis_MYRI_b', 'rnf_pis_MICZ_b', 'rnf_pis_DON_b', 'rnf_pis_PON_b', 'rnf_pis_bSi_b', 'rnf_pis_TRA_b', 'rnf_pis_DIC_b', 'rnf_pis_TA_b', 'rnf_pis_O2_b', 'sbc_NO3_b', 'sbc_NH4_b', 'sbc_Si_b', 'sbc_DIAT_b', 'sbc_PHY_b', 'sbc_MYRI_b', 'sbc_MICZ_b', 'sbc_DON_b', 'sbc_PON_b', 'sbc_bSi_b', 'sbc_TRA_b', 'sbc_DIC_b', 'sbc_TA_b', 'sbc_O2_b', 'rdttrc1', 'TRNNO3', 'TRNNH4', 'TRNSi', 'TRNDIAT', 'TRNPHY', 'TRNMYRI', 'TRNMICZ', 'TRNDON', 'TRNPON', 'TRNbSi', 'TRNTRA', 'TRNDIC', 'TRNTA', 'TRNO2', 'TRBNO3', 'TRBNH4', 'TRBSi', 'TRBDIAT', 'TRBPHY', 'TRBMYRI', 'TRBMICZ', 'TRBDON', 'TRBPON', 'TRBbSi', 'TRBTRA', 'TRBDIC', 'TRBTA', 'TRBO2'])\n",
      "rnf_pis_MYRI_b\n",
      "sbc_MYRI_b\n",
      "TRNMYRI\n",
      "TRBMYRI\n"
     ]
    }
   ],
   "source": [
    "print(f.variables.keys())\n",
    "for var in f.variables.keys():\n",
    "    if 'MYRI' in var:\n",
    "        print(var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for var in ('rnf_pis_MYRI_b','sbc_MYRI_b','TRNMYRI','TRBMYRI'):\n",
    "    f.variables[var][:]=0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for var in ('rnf_pis_MYRI_b','sbc_MYRI_b','TRNMYRI','TRBMYRI'):\n",
    "    f.variables[var][:]=0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f=nc.Dataset(dst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.max(f.variables['TRNPHY']), np.min(f.variables['TRNPHY'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "display_name": "Python (py39)",
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   "name": "py39"
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  "language_info": {
   "codemirror_mode": {
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
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