02_ocetrac.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 02_ocetrac.ipynb\n",
    "### Use Ocetrac to identify and track MCS objects "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from ocetrac import model as oce"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import data files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "Dimensions:           (lat: 720, lon: 1440, time: 478, month: 12)\n",
       "Coordinates:\n",
       "  * time              (time) datetime64[ns] 1981-09-01 1981-10-01 ... 2021-06-01\n",
       "  * lat               (lat) float32 -89.88 -89.62 -89.38 ... 89.38 89.62 89.88\n",
       "  * lon               (lon) float32 0.125 0.375 0.625 ... 359.4 359.6 359.9\n",
       "  * month             (month) int64 1 2 3 4 5 6 7 8 9 10 11 12\n",
       "Data variables:\n",
       "    lsmask            (lat, lon) float32 dask.array&lt;chunksize=(720, 1440), meta=np.ndarray&gt;\n",
       "    ssta_notrend      (time, lat, lon) float64 dask.array&lt;chunksize=(478, 720, 1440), meta=np.ndarray&gt;\n",
       "    ssta_trend        (time, lat, lon) float64 dask.array&lt;chunksize=(478, 720, 1440), meta=np.ndarray&gt;\n",
       "    mean              (time, lat, lon) float64 dask.array&lt;chunksize=(478, 720, 1440), meta=np.ndarray&gt;\n",
       "    trend             (time, lat, lon) float64 dask.array&lt;chunksize=(478, 720, 1440), meta=np.ndarray&gt;\n",
       "    seas              (time, lat, lon) float64 dask.array&lt;chunksize=(478, 720, 1440), meta=np.ndarray&gt;\n",
       "    ssta_stn_notrend  (time, lat, lon) float64 dask.array&lt;chunksize=(478, 720, 1440), meta=np.ndarray&gt;\n",
       "    prct_notrend      (month, lat, lon) float64 dask.array&lt;chunksize=(12, 720, 1440), meta=np.ndarray&gt;\n",
       "    mcs_ssta_notrend  (time, lat, lon) float64 dask.array&lt;chunksize=(478, 720, 1440), meta=np.ndarray&gt;\n",
       "    ssta_stn_trend    (time, lat, lon) float64 dask.array&lt;chunksize=(478, 720, 1440), meta=np.ndarray&gt;\n",
       "    prct_trend        (month, lat, lon) float64 dask.array&lt;chunksize=(12, 720, 1440), meta=np.ndarray&gt;\n",
       "    mcs_ssta_trend    (time, lat, lon) float64 dask.array&lt;chunksize=(478, 720, 1440), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    description:  preprocessed monthly averaged OISST v2.1 anomalies</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-68f1e68e-3daf-4f2a-b49c-5b08bbdd1712' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-68f1e68e-3daf-4f2a-b49c-5b08bbdd1712' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 720</li><li><span class='xr-has-index'>lon</span>: 1440</li><li><span class='xr-has-index'>time</span>: 478</li><li><span class='xr-has-index'>month</span>: 12</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-9abf888c-eed6-4537-a727-2729bc05b706' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9abf888c-eed6-4537-a727-2729bc05b706' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1981-09-01 ... 2021-06-01</div><input id='attrs-d30e2d42-be1b-4f05-ab41-e83c12bfa9af' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d30e2d42-be1b-4f05-ab41-e83c12bfa9af' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-29fbe194-6988-49f4-8c10-df62b19b1f2d' class='xr-var-data-in' type='checkbox'><label for='data-29fbe194-6988-49f4-8c10-df62b19b1f2d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1981-09-01T00:00:00.000000000&#x27;, &#x27;1981-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1981-11-01T00:00:00.000000000&#x27;, ..., &#x27;2021-04-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-05-01T00:00:00.000000000&#x27;, &#x27;2021-06-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-89.88 -89.62 ... 89.62 89.88</div><input id='attrs-97eac394-292c-460a-92c3-526d201df175' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-97eac394-292c-460a-92c3-526d201df175' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9efe42c3-98f3-40c1-b1f0-b136466eaf05' class='xr-var-data-in' type='checkbox'><label for='data-9efe42c3-98f3-40c1-b1f0-b136466eaf05' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-89.875, -89.625, -89.375, ...,  89.375,  89.625,  89.875],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.125 0.375 0.625 ... 359.6 359.9</div><input id='attrs-b90d57f1-541e-4d0d-ad27-afb1cc543362' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b90d57f1-541e-4d0d-ad27-afb1cc543362' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-df79bee4-3f5c-4366-bc10-6888470e0eea' class='xr-var-data-in' type='checkbox'><label for='data-df79bee4-3f5c-4366-bc10-6888470e0eea' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1.25000e-01, 3.75000e-01, 6.25000e-01, ..., 3.59375e+02, 3.59625e+02,\n",
       "       3.59875e+02], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>month</span></div><div class='xr-var-dims'>(month)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 8 9 10 11 12</div><input id='attrs-8119345b-b0a0-44b6-85c6-a68321a83a2d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8119345b-b0a0-44b6-85c6-a68321a83a2d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-08bebda2-91fa-488e-b34d-ef58149e1bd6' class='xr-var-data-in' type='checkbox'><label for='data-08bebda2-91fa-488e-b34d-ef58149e1bd6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-695178b5-632b-4bd3-bc3b-b288cb3183a9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-695178b5-632b-4bd3-bc3b-b288cb3183a9' class='xr-section-summary' >Data variables: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lsmask</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(720, 1440), meta=np.ndarray&gt;</div><input id='attrs-cd5c05dd-dc3f-4f9b-b7c9-b18a9a6d6599' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cd5c05dd-dc3f-4f9b-b7c9-b18a9a6d6599' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b1f79863-13d4-4521-a91f-f6dbafc57034' class='xr-var-data-in' type='checkbox'><label for='data-b1f79863-13d4-4521-a91f-f6dbafc57034' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
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       "<table>\n",
       "  <thead>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 3.96 MiB </td> <td> 3.96 MiB </td></tr>\n",
       "    <tr><th> Shape </th><td> (720, 1440) </td> <td> (720, 1440) </td></tr>\n",
       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ssta_notrend</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(478, 720, 1440), meta=np.ndarray&gt;</div><input id='attrs-fb517858-b273-418b-9f2c-e6ce6a6f1c63' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fb517858-b273-418b-9f2c-e6ce6a6f1c63' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e2adcf12-0e78-46bd-adac-1273ab00d0be' class='xr-var-data-in' type='checkbox'><label for='data-e2adcf12-0e78-46bd-adac-1273ab00d0be' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ssta_trend</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(478, 720, 1440), meta=np.ndarray&gt;</div><input id='attrs-bae73530-9e3f-4bc4-b619-30623be788a0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bae73530-9e3f-4bc4-b619-30623be788a0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-77e4d326-ac38-42b7-be34-9ef4f168214e' class='xr-var-data-in' type='checkbox'><label for='data-77e4d326-ac38-42b7-be34-9ef4f168214e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
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      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:           (lat: 720, lon: 1440, time: 478, month: 12)\n",
       "Coordinates:\n",
       "  * time              (time) datetime64[ns] 1981-09-01 1981-10-01 ... 2021-06-01\n",
       "  * lat               (lat) float32 -89.88 -89.62 -89.38 ... 89.38 89.62 89.88\n",
       "  * lon               (lon) float32 0.125 0.375 0.625 ... 359.4 359.6 359.9\n",
       "  * month             (month) int64 1 2 3 4 5 6 7 8 9 10 11 12\n",
       "Data variables:\n",
       "    lsmask            (lat, lon) float32 dask.array<chunksize=(720, 1440), meta=np.ndarray>\n",
       "    ssta_notrend      (time, lat, lon) float64 dask.array<chunksize=(478, 720, 1440), meta=np.ndarray>\n",
       "    ssta_trend        (time, lat, lon) float64 dask.array<chunksize=(478, 720, 1440), meta=np.ndarray>\n",
       "    mean              (time, lat, lon) float64 dask.array<chunksize=(478, 720, 1440), meta=np.ndarray>\n",
       "    trend             (time, lat, lon) float64 dask.array<chunksize=(478, 720, 1440), meta=np.ndarray>\n",
       "    seas              (time, lat, lon) float64 dask.array<chunksize=(478, 720, 1440), meta=np.ndarray>\n",
       "    ssta_stn_notrend  (time, lat, lon) float64 dask.array<chunksize=(478, 720, 1440), meta=np.ndarray>\n",
       "    prct_notrend      (month, lat, lon) float64 dask.array<chunksize=(12, 720, 1440), meta=np.ndarray>\n",
       "    mcs_ssta_notrend  (time, lat, lon) float64 dask.array<chunksize=(478, 720, 1440), meta=np.ndarray>\n",
       "    ssta_stn_trend    (time, lat, lon) float64 dask.array<chunksize=(478, 720, 1440), meta=np.ndarray>\n",
       "    prct_trend        (month, lat, lon) float64 dask.array<chunksize=(12, 720, 1440), meta=np.ndarray>\n",
       "    mcs_ssta_trend    (time, lat, lon) float64 dask.array<chunksize=(478, 720, 1440), meta=np.ndarray>\n",
       "Attributes:\n",
       "    description:  preprocessed monthly averaged OISST v2.1 anomalies"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_path_list = ('/burg/abernathey/users/hillary/oisst_v21/negative_extremes_OISSTv21.nc',\n",
    "                  '/burg/abernathey/users/hillary/oisst_v21/preprocessed_OISSTv21.nc')\n",
    "\n",
    "ds = xr.open_mfdataset(file_path_list)\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### [Optional] Adjust land mask to ignore points poleward of 65ºN and 70ºS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x15552133a220>"
      ]
     },
     "execution_count": 3,
     "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": [
    "mask = ds.lsmask.where((ds.lat<65) & (ds.lat>-70), drop=False, other=0) \n",
    "mask.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run Ocetrac"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set model parameters\n",
    "da = ds.mcs_ssta_trend.load()*-1 # load the DataArray into memory\n",
    "# In the next ocetrac release, you shouldn't have to switch signs of da\n",
    "\n",
    "radius = 8 # radius for structuring element\n",
    "min_size_quartile = 0.75 # threshold for object areas\n",
    "timedim = 'time'\n",
    "xdim = 'lon'\n",
    "ydim = 'lat'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x155520067f70>"
      ]
     },
     "execution_count": 41,
     "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": [
    "da.isel(time=0).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "Tracker =  oce.Tracker(da, mask, radius, min_size_quartile, timedim, xdim, ydim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "minimum area:  2477.0\n",
      "inital objects identified \t 13616\n",
      "final objects tracked \t 506\n",
      "CPU times: user 11min 35s, sys: 3min 40s, total: 15min 16s\n",
      "Wall time: 15min 17s\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "blobs = Tracker.track()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The basic output of `Tracker.track` provides:**\n",
    "- the minimum object area used to filter out MCS that are smaller than this threshold. Area is computed for all objects at each time step. `Ocetrac` looks at the global distribution of object area and finds the size that corresponds to a certain percentile defined by `area_quantile`, which can range from 0.0 to 1.0. Larger values for `area_quantile` exclude more MCS objects and will increase the minimum object area threshold. \n",
    "- inital features identified have undergone size thresholding, however are not connected in time. \n",
    "- final features tracked are the number of unique MCS objects tracked in space and time. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let's make a quick plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x155520902d60>"
      ]
     },
     "execution_count": 38,
     "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": [
    "from matplotlib.colors import ListedColormap\n",
    "maxl = int(np.nanmax(blobs.values))\n",
    "cm = ListedColormap(np.random.random(size=(maxl, 3)).tolist())\n",
    "\n",
    "# MCS identified with Ocetrac\n",
    "blobs.sel(time=('2012-08-01')).plot(cmap= cm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save tracked labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specify path to save output \n",
    "path = '/burg/abernathey/users/hillary/oisst_v21/'\n",
    "\n",
    "ds_out = blobs.to_dataset(name='ocetrac_labels')\n",
    "ds_out.attrs['data'] = 'OISST v2.1 trend'\n",
    "\n",
    "# Save Dataset to netCDF\n",
    "ds_out.to_netcdf(path+'ocetrac_MCS_trend.nc', mode='w')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "marine-heatwaves",
   "language": "python",
   "name": "marine-heatwaves"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}