{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a version of a notebook I (Elise) made to try out some of the things I learned in Jinhui Qin's dask tutorial on calculations I would normally do. I'm posting a version that was improved by Doug. I am guessing this part might be most helpful to people:\n",
    "\n",
    "client = dask.distributed.Client(\n",
    "    n_workers=2, threads_per_worker=2, processes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "!lscpu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "!cat /proc/meminfo | head -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime, timedelta\n",
    "import functools\n",
    "import sys\n",
    "import time\n",
    "\n",
    "import dask\n",
    "import dask.distributed\n",
    "import matplotlib.pyplot as plt\n",
    "import netCDF4\n",
    "import numpy\n",
    "import xarray\n",
    "\n",
    "from salishsea_tools import evaltools\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "print(sys.version)\n",
    "print(xarray.__version__)\n",
    "print(dask.__version__)\n",
    "print(netCDF4.__version__)\n",
    "print(numpy.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "j0, j1, i0, i1 = 230, 470, 0, 200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with netCDF4.Dataset(\"/ocean/eolson/MEOPAR/NEMO-forcing/grid/mesh_mask201702_noLPE.nc\") as mesh:\n",
    "    tmask=numpy.copy(mesh.variables['tmask'][:,:,j0:j1,i0:i1])\n",
    "    e3t0=numpy.copy(mesh.variables['e3t_0'][:,:,j0:j1,i0:i1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "flistmuZ = evaltools.index_model_files(\n",
    "    datetime(2015, 6, 1), datetime(2015, 6, 9),\n",
    "    \"/data/eolson/results/MEOPAR/SS36runs/CedarRuns/testmuZ/\",\n",
    "    \"long\", 10, \"ptrc_T\", 1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "flistmuZ['t_0'][0], flistmuZ['t_n'][len(flistmuZ)-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_depth_avgs(flist, tmask, e3t0):\n",
    "    sum1 = numpy.empty((len(flist)*24*10, j1-j0, i1-i0))\n",
    "    sum1uZ = numpy.empty((len(flist)*24*10, j1-j0, i1-i0))\n",
    "    for ind, row in flist.iterrows():\n",
    "        ds = netCDF4.Dataset(row['paths'])\n",
    "        diatoms = ds.variables['diatoms'][:,:,j0:j1,i0:i1]\n",
    "        uZ = ds.variables['microzooplankton'][:,:,j0:j1,i0:i1]\n",
    "        sum1[(24*10*ind):(24*10*(ind+1)),:,:] = numpy.sum(tmask*e3t0*diatoms, 1)\n",
    "        sum1uZ[(24*10*ind):(24*10*(ind+1)),:,:] = numpy.sum(tmask*e3t0*uZ, 1)\n",
    "    diatom_int = numpy.mean(sum1, 0)\n",
    "    microzoo_int = numpy.mean(sum1uZ, 0)\n",
    "    return diatom_int, microzoo_int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "t0=datetime.now()\n",
    "diatoms_muZ_Int, uZ_muZ_Int = calc_depth_avgs(flistmuZ, tmask, e3t0)\n",
    "t1=datetime.now()\n",
    "print(t1-t0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_depth_avgs_no_loop(flist, tmask, e3t0):\n",
    "    with netCDF4.Dataset(flist) as ds:\n",
    "        diatoms = ds.variables['diatoms'][:,:,j0:j1,i0:i1]\n",
    "        diatoms_int = (diatoms * tmask * e3t0).sum(axis=1).mean(axis=0)\n",
    "        uZ = ds.variables['microzooplankton'][:,:,j0:j1,i0:i1]\n",
    "        uZ_int = (uZ * tmask * e3t0).sum(axis=1).mean(axis=0)\n",
    "    return diatoms_int, uZ_int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "t0=datetime.now()\n",
    "diatoms_int, uZ_int = calc_depth_avgs_no_loop(\n",
    "    \"/data/eolson/results/MEOPAR/SS36runs/CedarRuns/testmuZ/SalishSea_1h_20150531_20150709_ptrc_T_20150531-20150609.nc\",\n",
    "    tmask, e3t0)\n",
    "numpy.testing.assert_equal(diatoms_int, diatoms_muZ_Int)\n",
    "numpy.testing.assert_equal(uZ_int, uZ_muZ_Int)\n",
    "t1=datetime.now()\n",
    "print(t1-t0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with xarray.open_dataset(\"/ocean/eolson/MEOPAR/NEMO-forcing/grid/mesh_mask201702_noLPE.nc\") as mesh:\n",
    "    x_tmask = mesh.tmask.isel(y=slice(j0, j1), x=slice(i0, i1)).data\n",
    "    x_e3t0 = mesh.e3t_0.isel(y=slice(j0, j1), x=slice(i0, i1)).data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_depth_avgs_xarray(flist, x_tmask, x_e3t0):\n",
    "    with xarray.open_dataset(flist) as ds:\n",
    "        diatoms = ds.diatoms.isel(y=slice(j0, j1), x=slice(i0, i1))\n",
    "        diatoms_int = (diatoms * x_tmask * x_e3t0).sum(axis=1).mean(axis=0)\n",
    "        uZ = ds.microzooplankton.isel(y=slice(j0, j1), x=slice(i0, i1))\n",
    "        uZ_int = (uZ * x_tmask * x_e3t0).sum(axis=1).mean(axis=0)\n",
    "    return diatoms_int, uZ_int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "t0=datetime.now()\n",
    "x_diatoms_int, x_uZ_int = calc_depth_avgs_xarray(\n",
    "    \"/data/eolson/results/MEOPAR/SS36runs/CedarRuns/testmuZ/SalishSea_1h_20150531_20150709_ptrc_T_20150531-20150609.nc\",\n",
    "    tmask, e3t0)\n",
    "numpy.testing.assert_allclose(x_diatoms_int, diatoms_muZ_Int)\n",
    "numpy.testing.assert_allclose(x_uZ_int, uZ_muZ_Int)\n",
    "t1=datetime.now()\n",
    "print(t1-t0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "client = dask.distributed.Client(\n",
    "    n_workers=2, threads_per_worker=2, processes=True)\n",
    "client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_depth_avgs_dask(flist, chunks, tmask, e3t0):\n",
    "    with xarray.open_dataset(flist, chunks=chunks) as ds:\n",
    "        diatoms = ds.diatoms.isel(y=slice(j0, j1), x=slice(i0, i1))\n",
    "        diatoms_int = (diatoms * tmask * e3t0).sum(axis=1).mean(axis=0)\n",
    "        uZ = ds.microzooplankton.isel(y=slice(j0, j1), x=slice(i0, i1))\n",
    "        uZ_int = (uZ * tmask * e3t0).sum(axis=1).mean(axis=0)\n",
    "    return diatoms_int, uZ_int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def check_results(diatoms_calc, diatoms_exp, uZ_calc, uZ_exp):\n",
    "    numpy.testing.assert_allclose(diatoms_calc, diatoms_exp)\n",
    "    numpy.testing.assert_allclose(uZ_calc, uZ_exp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "chunks = {\"time_counter\": 1, \"deptht\": 40, \"y\": 898, \"x\": 398}\n",
    "d_diatoms_int, d_uZ_int = calc_depth_avgs_dask(\n",
    "    \"/data/eolson/results/MEOPAR/SS36runs/CedarRuns/testmuZ/SalishSea_1h_20150531_20150709_ptrc_T_20150531-20150609.nc\",\n",
    "    chunks, tmask, e3t0)\n",
    "check_results(d_diatoms_int, diatoms_muZ_Int, d_uZ_int, uZ_muZ_Int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "chunks = {\"time_counter\": 3, \"deptht\": 40*3, \"y\": 898*3, \"x\": 398*3}\n",
    "d_diatoms_int, d_uZ_int = calc_depth_avgs_dask(\n",
    "    \"/data/eolson/results/MEOPAR/SS36runs/CedarRuns/testmuZ/SalishSea_1h_20150531_20150709_ptrc_T_20150531-20150609.nc\",\n",
    "    chunks, tmask, e3t0)\n",
    "check_results(d_diatoms_int, diatoms_muZ_Int, d_uZ_int, uZ_muZ_Int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "chunks = {\"time_counter\": 3, \"deptht\": 40*3, \"y\": 898*3, \"x\": 398*3}\n",
    "d_diatoms_int, d_uZ_int = calc_depth_avgs_dask(\n",
    "    \"/data/eolson/results/MEOPAR/SS36runs/CedarRuns/testmuZ/SalishSea_1h_20150531_20150709_ptrc_T_20150531-20150609.nc\",\n",
    "    chunks, tmask, e3t0)\n",
    "check_results(d_diatoms_int, diatoms_muZ_Int, d_uZ_int, uZ_muZ_Int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "chunks = {\"time_counter\": 4, \"deptht\": 40*4, \"y\": 898*4, \"x\": 398*4}\n",
    "d_diatoms_int, d_uZ_int = calc_depth_avgs_dask(\n",
    "    \"/data/eolson/results/MEOPAR/SS36runs/CedarRuns/testmuZ/SalishSea_1h_20150531_20150709_ptrc_T_20150531-20150609.nc\",\n",
    "    chunks, tmask, e3t0)\n",
    "check_results(d_diatoms_int, diatoms_muZ_Int, d_uZ_int, uZ_muZ_Int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "flistmuZ = evaltools.index_model_files(\n",
    "    datetime(2015, 6, 1), datetime(2015, 6, 19),\n",
    "    \"/data/eolson/results/MEOPAR/SS36runs/CedarRuns/testmuZ/\",\n",
    "    \"long\", 10, \"ptrc_T\", 1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "flistmuZ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "t0=datetime.now()\n",
    "m_diatoms_muZ_Int, m_uZ_muZ_Int = calc_depth_avgs(flistmuZ, tmask, e3t0)\n",
    "t1=datetime.now()\n",
    "print(t1-t0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_depth_avgs_mfdataset(flist, chunks, tmask, e3t0):\n",
    "    kwargs = dict(combine=\"nested\", concat_dim=\"time_counter\", parallel=True)\n",
    "    with xarray.open_mfdataset(flist, chunks=chunks, **kwargs) as ds:\n",
    "        diatoms = ds.diatoms.isel(y=slice(j0, j1), x=slice(i0, i1))\n",
    "        diatoms_int = (diatoms * tmask * e3t0).sum(axis=1).mean(axis=0)\n",
    "        uZ = ds.microzooplankton.isel(y=slice(j0, j1), x=slice(i0, i1))\n",
    "        uZ_int = (uZ * tmask * e3t0).sum(axis=1).mean(axis=0)\n",
    "    return diatoms_int, uZ_int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "chunks = {\"time_counter\": 4, \"deptht\": 40*4, \"y\": 898*4, \"x\": 398*4}\n",
    "mf_diatoms_int, mf_uZ_int = calc_depth_avgs_mfdataset(\n",
    "    [\"/data/eolson/results/MEOPAR/SS36runs/CedarRuns/testmuZ/SalishSea_1h_20150531_20150709_ptrc_T_20150531-20150609.nc\",\n",
    "     \"/data/eolson/results/MEOPAR/SS36runs/CedarRuns/testmuZ/SalishSea_1h_20150531_20150709_ptrc_T_20150610-20150619.nc\",],\n",
    "    chunks, tmask, e3t0)\n",
    "check_results(mf_diatoms_int, m_diatoms_muZ_Int, mf_uZ_int, m_uZ_muZ_Int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "client.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Final:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with xarray.open_dataset(\"/ocean/eolson/MEOPAR/NEMO-forcing/grid/mesh_mask201702_noLPE.nc\") as mesh:\n",
    "    x_tmask = mesh.tmask.isel(y=slice(j0, j1), x=slice(i0, i1)).data\n",
    "    x_e3t0 = mesh.e3t_0.isel(y=slice(j0, j1), x=slice(i0, i1)).data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "flistmuZ = evaltools.index_model_files(\n",
    "    datetime(2015, 6, 1), datetime(2015, 9, 1),\n",
    "    \"/data/eolson/results/MEOPAR/SS36runs/CedarRuns/testmuZ/\",\n",
    "    \"long\", 10, \"ptrc_T\", 1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "flistmuZ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "flistHC = evaltools.index_model_files(\n",
    "    flistmuZ['t_0'][0], flistmuZ['t_n'][len(flistmuZ)-1] - timedelta(days=1),\n",
    "    '/results/SalishSea/hindcast.201905/', 'nowcast', 1, 'ptrc_T', 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "flistHC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_depth_avgs_mfdataset(flist, chunks, tmask, e3t0):\n",
    "    kwargs = dict(combine=\"nested\", concat_dim=\"time_counter\", parallel=True)\n",
    "    with xarray.open_mfdataset(flist, chunks=chunks, **kwargs) as ds:\n",
    "        diatoms = ds.diatoms.isel(y=slice(j0, j1), x=slice(i0, i1))\n",
    "        diatoms_int = (diatoms * tmask * e3t0).sum(axis=1).mean(axis=0)\n",
    "        uZ = ds.microzooplankton.isel(y=slice(j0, j1), x=slice(i0, i1))\n",
    "        uZ_int = (uZ * tmask * e3t0).sum(axis=1).mean(axis=0)\n",
    "    return diatoms_int, uZ_int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "client = dask.distributed.Client(\n",
    "    n_workers=2, threads_per_worker=2, processes=True)\n",
    "client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "chunks = {\"time_counter\": 3, \"deptht\": 40*3, \"y\": 898*3, \"x\": 398*3}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_diatoms_muZ_int, x_uZ_muZ_int = calc_depth_avgs_mfdataset(\n",
    "    [row[\"paths\"] for _, row in flistmuZ.iterrows()], chunks, x_tmask, x_e3t0\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_diatomsHC_int, x_uZHC_int = calc_depth_avgs_mfdataset(\n",
    "    [row[\"paths\"] for _, row in flistHC.iterrows()], chunks, x_tmask, x_e3t0\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "t0=datetime.now()\n",
    "diatomsmuZInt = x_diatoms_muZ_int.compute()\n",
    "t1=datetime.now()\n",
    "print(t1-t0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "t0=datetime.now()\n",
    "diatomsHCInt = x_diatomsHC_int.compute()\n",
    "t1=datetime.now()\n",
    "print(t1-t0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(1, 3, figsize=(15, 4))\n",
    "m0 = ax[0].pcolormesh(diatomsmuZInt)\n",
    "plt.colorbar(m0, ax=ax[0])\n",
    "ax[0].set_title('muZ Diatoms')\n",
    "m1 = ax[1].pcolormesh(diatomsHCInt)\n",
    "plt.colorbar(m1,ax=ax[1])\n",
    "ax[1].set_title('HC Diatoms')\n",
    "m2 = ax[2].pcolormesh(diatomsmuZInt - diatomsHCInt)\n",
    "plt.colorbar(m2,ax=ax[2])\n",
    "ax[2].set_title('Diff')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "t0=datetime.now()\n",
    "uZmuZInt = x_uZ_muZ_int.compute()\n",
    "t1=datetime.now()\n",
    "print(t1-t0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "t0=datetime.now()\n",
    "uZHCInt = x_uZHC_int.compute()\n",
    "t1=datetime.now()\n",
    "print(t1-t0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(1, 3, figsize=(15, 4))\n",
    "m0 = ax[0].pcolormesh(uZmuZInt)\n",
    "plt.colorbar(m0, ax=ax[0])\n",
    "ax[0].set_title('muZ microZ')\n",
    "m1 = ax[1].pcolormesh(uZHCInt)\n",
    "plt.colorbar(m1,ax=ax[1])\n",
    "ax[1].set_title('HC microZ')\n",
    "m2 = ax[2].pcolormesh(uZmuZInt - uZHCInt)\n",
    "plt.colorbar(m2,ax=ax[2])\n",
    "ax[2].set_title('Diff')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "client.close()"
   ]
  },
  {
   "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": []
  }
 ],
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