initial_processing.ipynb

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    "# this script takes satellite data from catalog, \n",
    "# parses to place on regularly gridded array, and interpolates nan across desired gaps in space and time\n",
    "\n",
    "import numpy as np\n",
    "import xarray as xr \n",
    "import time \n",
    "from intake import open_catalog\n",
    "from matplotlib.colors import LogNorm\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import interpolate\n",
    "from scipy import integrate\n",
    "from datetime import datetime\n",
    "from tqdm.notebook import tqdm\n",
    "import pickle \n",
    "from altimetry_tools import Haversine, nan_helper, interp_nans, parse_grid_tracks, smooth_tracks, coarsen, specsharp, spectra_slopes\n",
    "import warnings \n",
    "warnings.filterwarnings('ignore')\n",
    "%matplotlib inline\n",
    "\n",
    "# -- main PARAMETERS --- \n",
    "this_sat = 'tp'              # mission to consider \n",
    "hor_grid_spacing = 20        # km, grid to which to interpolate tracks\n",
    "interp_cutoff = 3            # number of acceptable nan gaps in grid cell units across which to interpolate\n",
    "# coarsening_factor0 = 7     # (*hor_grid_spacing = coarsened grid size) this factor should be multiplied by hor_grid_spacing \n",
    "# nyquist_wavelength = np.pi   # factor relative to coarsening factor (what scales do we want to resolve...pi times the grid scale)\n",
    "# f_type = 'sharp'             # filter type (gaussian or sharp)\n",
    "# -----------------------\n",
    "# -- coastline file \n",
    "x4 = xr.open_dataset('/home/jovyan/along-track-altimetry-analysis/misc/coastlines_global.nc')  \n",
    "# -- deformation radius \n",
    "# Chelton et al. 1998 'global variability of the first baroclinic rossby radius of deformation'\n",
    "c98 = xr.open_dataset('/home/jovyan/along-track-altimetry-analysis/misc/global_deformation_radius_chelton_1998.nc')  \n",
    "c98 = c98['values'].data\n",
    "# (http://www-po.coas.oregonstate.edu/research/po/research/rossby_radius/index.html#anchor2)\n",
    "# -- catalog of available satellites \n",
    "cat = open_catalog(\"https://raw.githubusercontent.com/pangeo-data/pangeo-datastore/master/intake-catalogs/ocean/altimetry.yaml\")# load individual satellite \n",
    "# ['al', 'alg', 'c2', 'e1', 'e1g', 'e2', 'en', 'enn', 'g2', 'h2', 'j1', 'j1g', 'j1n', 'j2', 'j2g', 'j2n', 'j3', 's3a', 's3b', 'tp', 'tpn']\n",
    "\n",
    "# -- lat/lon boundaries (permits subsetting to speed processing time)\n",
    "lon_w = 0\n",
    "lon_e = 360\n",
    "lat_s = -65\n",
    "lat_n = 65"
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:         (time: 157384325)\n",
       "Coordinates:\n",
       "    latitude        (time) float64 dask.array&lt;chunksize=(22483475,), meta=np.ndarray&gt;\n",
       "    longitude       (time) float64 dask.array&lt;chunksize=(22483475,), meta=np.ndarray&gt;\n",
       "  * time            (time) datetime64[ns] 1992-12-31T23:20:35.775270016 ... 2...\n",
       "Data variables:\n",
       "    cycle           (time) int16 dask.array&lt;chunksize=(157384325,), meta=np.ndarray&gt;\n",
       "    dac             (time) float64 dask.array&lt;chunksize=(22483475,), meta=np.ndarray&gt;\n",
       "    lwe             (time) float64 dask.array&lt;chunksize=(22483475,), meta=np.ndarray&gt;\n",
       "    mdt             (time) float64 dask.array&lt;chunksize=(22483475,), meta=np.ndarray&gt;\n",
       "    ocean_tide      (time) float64 dask.array&lt;chunksize=(22483475,), meta=np.ndarray&gt;\n",
       "    sla_filtered    (time) float64 dask.array&lt;chunksize=(22483475,), meta=np.ndarray&gt;\n",
       "    sla_unfiltered  (time) float64 dask.array&lt;chunksize=(22483475,), meta=np.ndarray&gt;\n",
       "    track           (time) int16 dask.array&lt;chunksize=(157384325,), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    Conventions:               CF-1.6\n",
       "    Metadata_Conventions:      Unidata Dataset Discovery v1.0\n",
       "    cdm_data_type:             Swath\n",
       "    comment:                   Sea surface height measured by altimeters refe...\n",
       "    contact:                   servicedesk.cmems@mercator-ocean.eu\n",
       "    creator_email:             servicedesk.cmems@mercator-ocean.eu\n",
       "    creator_name:              CMEMS - Sea Level Thematic Assembly Center\n",
       "    creator_url:               http://marine.copernicus.eu\n",
       "    history:                   2019-02-20T16:50:23Z: Creation\n",
       "    institution:               CLS, CNES\n",
       "    keywords:                  Oceans &gt; Ocean Topography &gt; Sea Surface Height\n",
       "    keywords_vocabulary:       NetCDF COARDS Climate and Forecast Standard Names\n",
       "    license:                   http://marine.copernicus.eu/web/27-service-com...\n",
       "    platform:                  Topex/Poseidon\n",
       "    processing_level:          L3\n",
       "    product_version:           2019\n",
       "    project:                   COPERNICUS MARINE ENVIRONMENT MONITORING SERVI...\n",
       "    references:                http://marine.copernicus.eu\n",
       "    software_version:          6.2_DUACS_DT2018_baseline\n",
       "    source:                    Topex/Poseidon measurements\n",
       "    ssalto_duacs_comment:      The reference mission used for the altimeter i...\n",
       "    standard_name_vocabulary:  NetCDF Climate and Forecast (CF) Metadata Conv...\n",
       "    summary:                   SSALTO/DUACS Delayed-Time Level-3 sea surface ...\n",
       "    title:                     DT Topex/Poseidon Global Ocean Along track SSA...</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-625f471e-0261-45be-968b-f81255c78b2f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-625f471e-0261-45be-968b-f81255c78b2f' 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'>time</span>: 157384325</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-63c00616-18a4-4d43-b7c0-a670fec888e8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-63c00616-18a4-4d43-b7c0-a670fec888e8' class='xr-section-summary' >Coordinates: <span>(3)</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>latitude</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(22483475,), meta=np.ndarray&gt;</div><input id='attrs-89330eb4-4ab9-4bb5-bb13-e8add5f8705e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-89330eb4-4ab9-4bb5-bb13-e8add5f8705e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cdb082ba-6b01-4248-8e38-53c3b266cab2' class='xr-var-data-in' type='checkbox'><label for='data-cdb082ba-6b01-4248-8e38-53c3b266cab2' 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'><dt><span>long_name :</span></dt><dd>Latitude of measurement</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(22483475,), meta=np.ndarray&gt;</div><input id='attrs-739d301a-eec0-4e1f-b563-9e4bd46ee988' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-739d301a-eec0-4e1f-b563-9e4bd46ee988' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6b8c56dd-7c1e-4148-81aa-f62ec94e846f' class='xr-var-data-in' type='checkbox'><label for='data-6b8c56dd-7c1e-4148-81aa-f62ec94e846f' 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'><dt><span>long_name :</span></dt><dd>Longitude of measurement</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
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       "  <tbody>\n",
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       "    <tr><th> Count </th><td> 8 Tasks </td><td> 7 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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       "</td>\n",
       "</tr>\n",
       "</table></div></li><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'>1992-12-31T23:20:35.775270016 .....</div><input id='attrs-556d46fa-4643-474e-84a7-cec92c16018e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-556d46fa-4643-474e-84a7-cec92c16018e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-12c63dd3-7928-4527-bcbb-eb7f40b1ac12' class='xr-var-data-in' type='checkbox'><label for='data-12c63dd3-7928-4527-bcbb-eb7f40b1ac12' 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'><dt><span>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>Time of measurement</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1992-12-31T23:20:35.775270016&#x27;, &#x27;1992-12-31T23:20:36.853849984&#x27;,\n",
       "       &#x27;1992-12-31T23:20:37.932429952&#x27;, ..., &#x27;2002-04-24T07:56:07.202327040&#x27;,\n",
       "       &#x27;2002-04-24T07:56:08.280907008&#x27;, &#x27;2002-04-24T07:56:09.359486976&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-830136fc-cdfa-4c20-af56-712cf9b1d612' class='xr-section-summary-in' type='checkbox'  checked><label for='section-830136fc-cdfa-4c20-af56-712cf9b1d612' class='xr-section-summary' >Data variables: <span>(8)</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>cycle</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(157384325,), meta=np.ndarray&gt;</div><input id='attrs-6e94591f-8ea1-40bd-9da8-de81d72b475b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6e94591f-8ea1-40bd-9da8-de81d72b475b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-35f61343-11c2-4c28-96cd-0937d7501749' class='xr-var-data-in' type='checkbox'><label for='data-35f61343-11c2-4c28-96cd-0937d7501749' 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'><dt><span>long_name :</span></dt><dd>Cycle the measurement belongs to</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
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       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 314.77 MB </td> <td> 314.77 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (157384325,) </td> <td> (157384325,) </td></tr>\n",
       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> int16 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dac</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(22483475,), meta=np.ndarray&gt;</div><input id='attrs-a8d39a96-a575-4d73-8fff-5681c33c9b5c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a8d39a96-a575-4d73-8fff-5681c33c9b5c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4472d539-2eb8-4547-bf21-f9c9366bb0dd' class='xr-var-data-in' type='checkbox'><label for='data-4472d539-2eb8-4547-bf21-f9c9366bb0dd' 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'><dt><span>comment :</span></dt><dd>The sla in this file is already corrected for the dac; the uncorrected sla can be computed as follows: [uncorrected sla]=[sla from product]+[dac]; see the product user manual for details</dd><dt><span>long_name :</span></dt><dd>Dynamic Atmospheric Correction</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
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       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 1.26 GB </td> <td> 179.87 MB </td></tr>\n",
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       "    <tr><th> Count </th><td> 8 Tasks </td><td> 7 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lwe</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(22483475,), meta=np.ndarray&gt;</div><input id='attrs-f5509126-7d4a-423e-a039-03f042cea54e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f5509126-7d4a-423e-a039-03f042cea54e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-703d9bff-d13c-452e-9cb6-25897be68b38' class='xr-var-data-in' type='checkbox'><label for='data-703d9bff-d13c-452e-9cb6-25897be68b38' 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'><dt><span>comment :</span></dt><dd>The sla in this file is already corrected for the lwe; the uncorrected sla can be computed as follows: [uncorrected sla]=[sla from product]-[lwe]; see the product user manual for details</dd><dt><span>long_name :</span></dt><dd>Long wavelength error</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
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       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 1.26 GB </td> <td> 179.87 MB </td></tr>\n",
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       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mdt</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(22483475,), meta=np.ndarray&gt;</div><input id='attrs-7dcf8f86-7aba-4270-a662-b5ffee26c6a2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7dcf8f86-7aba-4270-a662-b5ffee26c6a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-16e5f2e1-00b7-4d68-be59-93ec27cfc223' class='xr-var-data-in' type='checkbox'><label for='data-16e5f2e1-00b7-4d68-be59-93ec27cfc223' 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'><dt><span>comment :</span></dt><dd>The mean dynamic topography is the sea surface height above geoid; it is used to compute the absolute dynamic tyopography adt=sla+mdt</dd><dt><span>long_name :</span></dt><dd>Mean dynamic topography</dd><dt><span>standard_name :</span></dt><dd>sea_surface_height_above_geoid</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
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       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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       "  <line x1=\"102\" y1=\"0\" x2=\"102\" y2=\"25\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
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       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ocean_tide</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(22483475,), meta=np.ndarray&gt;</div><input id='attrs-736816b9-0a1b-4fec-9c88-ba0c02437437' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-736816b9-0a1b-4fec-9c88-ba0c02437437' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-19e75a28-5cb4-4a04-87f8-4effd6b29a31' class='xr-var-data-in' type='checkbox'><label for='data-19e75a28-5cb4-4a04-87f8-4effd6b29a31' 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'><dt><span>comment :</span></dt><dd>The sla in this file is already corrected for the ocean_tide; the uncorrected sla can be computed as follows: [uncorrected sla]=[sla from product]+[ocean_tide]; see the product user manual for details</dd><dt><span>long_name :</span></dt><dd>Ocean tide model</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 1.26 GB </td> <td> 179.87 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (157384325,) </td> <td> (22483475,) </td></tr>\n",
       "    <tr><th> Count </th><td> 8 Tasks </td><td> 7 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
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       "  <line x1=\"85\" y1=\"0\" x2=\"85\" y2=\"25\" />\n",
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       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sla_filtered</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(22483475,), meta=np.ndarray&gt;</div><input id='attrs-6f34af8e-ed5e-48c8-a34f-a5e59db63f05' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6f34af8e-ed5e-48c8-a34f-a5e59db63f05' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99e7b045-8ac5-4151-97e3-fd26387fcd74' class='xr-var-data-in' type='checkbox'><label for='data-99e7b045-8ac5-4151-97e3-fd26387fcd74' 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'><dt><span>comment :</span></dt><dd>The sea level anomaly is the sea surface height above mean sea surface height; the uncorrected sla can be computed as follows: [uncorrected sla]=[sla from product]+[dac]+[ocean_tide]-[lwe]; see the product user manual for details</dd><dt><span>long_name :</span></dt><dd>Sea level anomaly filtered not-subsampled with dac, ocean_tide and lwe correction applied</dd><dt><span>standard_name :</span></dt><dd>sea_surface_height_above_sea_level</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 1.26 GB </td> <td> 179.87 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (157384325,) </td> <td> (22483475,) </td></tr>\n",
       "    <tr><th> Count </th><td> 8 Tasks </td><td> 7 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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       "\n",
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       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sla_unfiltered</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(22483475,), meta=np.ndarray&gt;</div><input id='attrs-2c161738-b825-4af8-abd2-43d518940278' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2c161738-b825-4af8-abd2-43d518940278' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae36de78-a0ba-46e1-add3-d93d085527a0' class='xr-var-data-in' type='checkbox'><label for='data-ae36de78-a0ba-46e1-add3-d93d085527a0' 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'><dt><span>comment :</span></dt><dd>The sea level anomaly is the sea surface height above mean sea surface height; the uncorrected sla can be computed as follows: [uncorrected sla]=[sla from product]+[dac]+[ocean_tide]-[lwe]; see the product user manual for details</dd><dt><span>long_name :</span></dt><dd>Sea level anomaly not-filtered not-subsampled with dac, ocean_tide and lwe correction applied</dd><dt><span>standard_name :</span></dt><dd>sea_surface_height_above_sea_level</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 1.26 GB </td> <td> 179.87 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (157384325,) </td> <td> (22483475,) </td></tr>\n",
       "    <tr><th> Count </th><td> 8 Tasks </td><td> 7 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>track</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(157384325,), meta=np.ndarray&gt;</div><input id='attrs-d70c0a9b-07b0-47ea-8458-51f8cbe6adca' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d70c0a9b-07b0-47ea-8458-51f8cbe6adca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e93f21c4-d1a1-48fc-b56a-b01355f10cec' class='xr-var-data-in' type='checkbox'><label for='data-e93f21c4-d1a1-48fc-b56a-b01355f10cec' 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'><dt><span>long_name :</span></dt><dd>Track in cycle the measurement belongs to</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 314.77 MB </td> <td> 314.77 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (157384325,) </td> <td> (157384325,) </td></tr>\n",
       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> int16 </td><td> numpy.ndarray </td></tr>\n",
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       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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       "</td>\n",
       "</tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-3ccfd94e-d897-4e50-94de-4fcf6bdaf67c' class='xr-section-summary-in' type='checkbox'  ><label for='section-3ccfd94e-d897-4e50-94de-4fcf6bdaf67c' class='xr-section-summary' >Attributes: <span>(24)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>Metadata_Conventions :</span></dt><dd>Unidata Dataset Discovery v1.0</dd><dt><span>cdm_data_type :</span></dt><dd>Swath</dd><dt><span>comment :</span></dt><dd>Sea surface height measured by altimeters referenced to the [1993, 2012] period; with additional corrections; the proposed sla is already corrected for dac, ocean_tide and lwe; [uncorrected sla]=[sla from product]+[dac]+[ocean_tide]-[lwe]</dd><dt><span>contact :</span></dt><dd>servicedesk.cmems@mercator-ocean.eu</dd><dt><span>creator_email :</span></dt><dd>servicedesk.cmems@mercator-ocean.eu</dd><dt><span>creator_name :</span></dt><dd>CMEMS - Sea Level Thematic Assembly Center</dd><dt><span>creator_url :</span></dt><dd>http://marine.copernicus.eu</dd><dt><span>history :</span></dt><dd>2019-02-20T16:50:23Z: Creation</dd><dt><span>institution :</span></dt><dd>CLS, CNES</dd><dt><span>keywords :</span></dt><dd>Oceans &gt; Ocean Topography &gt; Sea Surface Height</dd><dt><span>keywords_vocabulary :</span></dt><dd>NetCDF COARDS Climate and Forecast Standard Names</dd><dt><span>license :</span></dt><dd>http://marine.copernicus.eu/web/27-service-commitments-and-licence.php</dd><dt><span>platform :</span></dt><dd>Topex/Poseidon</dd><dt><span>processing_level :</span></dt><dd>L3</dd><dt><span>product_version :</span></dt><dd>2019</dd><dt><span>project :</span></dt><dd>COPERNICUS MARINE ENVIRONMENT MONITORING SERVICE (CMEMS)</dd><dt><span>references :</span></dt><dd>http://marine.copernicus.eu</dd><dt><span>software_version :</span></dt><dd>6.2_DUACS_DT2018_baseline</dd><dt><span>source :</span></dt><dd>Topex/Poseidon measurements</dd><dt><span>ssalto_duacs_comment :</span></dt><dd>The reference mission used for the altimeter inter-calibration processing is Topex/Poseidon between 1993-01-01 and 2002-04-23, Jason-1 between 2002-04-24 and 2008-10-18, OSTM/Jason-2 between 2008-10-19 and 2016-06-25, Jason-3 since 2016-06-25.</dd><dt><span>standard_name_vocabulary :</span></dt><dd>NetCDF Climate and Forecast (CF) Metadata Convention Standard Name Table v37</dd><dt><span>summary :</span></dt><dd>SSALTO/DUACS Delayed-Time Level-3 sea surface height measured by Topex/Poseidon altimetry observations over Global Ocean.</dd><dt><span>title :</span></dt><dd>DT Topex/Poseidon Global Ocean Along track SSALTO/DUACS Sea Surface Height L3 product</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:         (time: 157384325)\n",
       "Coordinates:\n",
       "    latitude        (time) float64 dask.array<chunksize=(22483475,), meta=np.ndarray>\n",
       "    longitude       (time) float64 dask.array<chunksize=(22483475,), meta=np.ndarray>\n",
       "  * time            (time) datetime64[ns] 1992-12-31T23:20:35.775270016 ... 2...\n",
       "Data variables:\n",
       "    cycle           (time) int16 dask.array<chunksize=(157384325,), meta=np.ndarray>\n",
       "    dac             (time) float64 dask.array<chunksize=(22483475,), meta=np.ndarray>\n",
       "    lwe             (time) float64 dask.array<chunksize=(22483475,), meta=np.ndarray>\n",
       "    mdt             (time) float64 dask.array<chunksize=(22483475,), meta=np.ndarray>\n",
       "    ocean_tide      (time) float64 dask.array<chunksize=(22483475,), meta=np.ndarray>\n",
       "    sla_filtered    (time) float64 dask.array<chunksize=(22483475,), meta=np.ndarray>\n",
       "    sla_unfiltered  (time) float64 dask.array<chunksize=(22483475,), meta=np.ndarray>\n",
       "    track           (time) int16 dask.array<chunksize=(157384325,), meta=np.ndarray>\n",
       "Attributes:\n",
       "    Conventions:               CF-1.6\n",
       "    Metadata_Conventions:      Unidata Dataset Discovery v1.0\n",
       "    cdm_data_type:             Swath\n",
       "    comment:                   Sea surface height measured by altimeters refe...\n",
       "    contact:                   servicedesk.cmems@mercator-ocean.eu\n",
       "    creator_email:             servicedesk.cmems@mercator-ocean.eu\n",
       "    creator_name:              CMEMS - Sea Level Thematic Assembly Center\n",
       "    creator_url:               http://marine.copernicus.eu\n",
       "    history:                   2019-02-20T16:50:23Z: Creation\n",
       "    institution:               CLS, CNES\n",
       "    keywords:                  Oceans > Ocean Topography > Sea Surface Height\n",
       "    keywords_vocabulary:       NetCDF COARDS Climate and Forecast Standard Names\n",
       "    license:                   http://marine.copernicus.eu/web/27-service-com...\n",
       "    platform:                  Topex/Poseidon\n",
       "    processing_level:          L3\n",
       "    product_version:           2019\n",
       "    project:                   COPERNICUS MARINE ENVIRONMENT MONITORING SERVI...\n",
       "    references:                http://marine.copernicus.eu\n",
       "    software_version:          6.2_DUACS_DT2018_baseline\n",
       "    source:                    Topex/Poseidon measurements\n",
       "    ssalto_duacs_comment:      The reference mission used for the altimeter i...\n",
       "    standard_name_vocabulary:  NetCDF Climate and Forecast (CF) Metadata Conv...\n",
       "    summary:                   SSALTO/DUACS Delayed-Time Level-3 sea surface ...\n",
       "    title:                     DT Topex/Poseidon Global Ocean Along track SSA..."
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds2_0 = cat[this_sat].to_dask()\n",
    "ds2_0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 17.2 s, sys: 7.74 s, total: 24.9 s\n",
      "Wall time: 9.63 s\n",
      "CPU times: user 624 ms, sys: 796 ms, total: 1.42 s\n",
      "Wall time: 1.42 s\n",
      "tracks in domain\n",
      "[  1.   2.   3.   4.   5.   6.   7.   8.   9.  10.  11.  12.  13.  14.\n",
      "  15.  16.  17.  18.  19.  20.  21.  22.  23.  24.  25.  26.  27.  28.\n",
      "  29.  30.  31.  32.  33.  34.  35.  36.  37.  38.  39.  40.  41.  42.\n",
      "  43.  44.  45.  46.  47.  48.  49.  50.  51.  52.  53.  54.  55.  56.\n",
      "  57.  58.  59.  60.  61.  62.  63.  64.  65.  66.  67.  68.  69.  70.\n",
      "  71.  72.  73.  74.  75.  76.  77.  78.  79.  80.  81.  82.  83.  84.\n",
      "  85.  86.  87.  88.  89.  90.  91.  92.  93.  94.  95.  96.  97.  98.\n",
      "  99. 100. 101. 102. 103. 104. 105. 106. 107. 108. 109. 110. 111. 112.\n",
      " 113. 114. 115. 116. 117. 118. 119. 120. 121. 122. 123. 124. 125. 126.\n",
      " 127. 128. 129. 130. 131. 132. 133. 134. 135. 136. 137. 138. 139. 140.\n",
      " 141. 142. 143. 144. 145. 146. 147. 148. 149. 150. 151. 152. 153. 154.\n",
      " 155. 156. 157. 158. 159. 160. 161. 162. 163. 164. 165. 166. 167. 168.\n",
      " 169. 170. 171. 172. 173. 174. 175. 176. 177. 178. 179. 180. 181. 182.\n",
      " 183. 184. 185. 186. 187. 188. 189. 190. 191. 192. 193. 194. 195. 196.\n",
      " 197. 198. 199. 200. 201. 202. 203. 204. 205. 206. 207. 208. 209. 210.\n",
      " 211. 212. 213. 214. 215. 216. 217. 218. 219. 220. 221. 222. 223. 224.\n",
      " 225. 226. 227. 228. 229. 230. 231. 232. 233. 234. 235. 236. 237. 238.\n",
      " 239. 240. 241. 242. 243. 244. 245. 246. 247. 248. 249. 250. 251. 252.\n",
      " 253. 254.]\n",
      "repeat time = 9 days 22:01:33.581412992\n",
      "nominal grid spacing = 6.162516571411034km\n"
     ]
    }
   ],
   "source": [
    "# ------------------------------------------------------------------------------------------------\n",
    "# RUN ONLY if parsed tracks file doesn't exist (should have a parsed file for each satellite)\n",
    "# ------------------------------------------------------------------------------------------------\n",
    "# load individual sat (your choice) and convert to dataframe from dataset \n",
    "# ds2_0 = cat[this_sat].to_dask()\n",
    "%time ds2 = ds2_0[['latitude', 'longitude', 'sla_filtered', 'track', 'cycle', 'mdt']].reset_coords().astype('f4').load()  # may add 'sla_unfiltered',\n",
    "%time df2 = ds2.to_dataframe()\n",
    "df2_s = df2[(df2['longitude'] > lon_w) & (df2['longitude'] < lon_e) & (df2['latitude'] > lat_s) & (df2['latitude'] < lat_n)]\n",
    "in_tracks2 = np.unique(df2_s['track'])\n",
    "print('tracks in domain')\n",
    "print(in_tracks2)\n",
    "\n",
    "test = df2_s[df2_s['track'] == 11]  # 9 is standard\n",
    "p = test.index\n",
    "ts = (p - np.datetime64('1970-01-01T00:00:00Z')) / np.timedelta64(1, 's')\n",
    "test2 = np.where(np.diff(ts) > 24*60*60)[0]\n",
    "repeat_time = p[test2[3]] - p[test2[2]]\n",
    "print('repeat time = ' + str(repeat_time))\n",
    "\n",
    "test0 = df2_s[df2_s['track'] == in_tracks2[10]]  # 11\n",
    "in_cycles0 = np.unique(test0['cycle'])\n",
    "test1 = test0[test0['cycle'] == in_cycles0[0]]  # 20\n",
    "ii = 5\n",
    "d = Haversine(test1['latitude'][ii], test1['longitude'][ii], test1['latitude'][ii-1], test1['longitude'][ii-1])\n",
    "print('nominal grid spacing = ' + str(d) + 'km')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "model_id": "d41c34bb9861462987b665d1fa122fc2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/254 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 27min 4s, sys: 9.7 s, total: 27min 14s\n",
      "Wall time: 27min 14s\n"
     ]
    }
   ],
   "source": [
    "# ------------------------------------------------------------------------------------------------\n",
    "# RUN ONLY if parsed tracks file doesn't exist (should have a parsed file for each satellite)\n",
    "# ------------------------------------------------------------------------------------------------\n",
    "\n",
    "# -- PARSE_grid_tracks (interpolate nans and group by track) (sla dimensions = [track_number][cycle, along_track_grid])\n",
    "f_v_uf = 1  # if 1, use filtered product from aviso \n",
    "%time lon_t, lat_t, track_t, adt, sla, dist, lon_record, lat_record, time_record, track_record \\\n",
    "    = parse_grid_tracks(in_tracks2, df2_s, hor_grid_spacing, interp_cutoff, f_v_uf)\n",
    "\n",
    "del sla   # make more memory space \n",
    "# del adt     # check which variable I want to export...and change outputs below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ---------------   \n",
    "# -- export to pickle so that we don't have to run/parse everytime \n",
    "# ---------------\n",
    "save_p = 1\n",
    "if save_p > 0:\n",
    "    outputs = {'lon_t': lon_t, 'lat_t': lat_t, 'track_t': track_t, 'adt': adt, \\\n",
    "               'dist': dist, 'lon_record': lon_record, 'lat_record': lat_record, \\\n",
    "               'time_record': time_record, 'track_record': track_record, \\\n",
    "               'repeat_time': repeat_time, 'interp_cutoff': interp_cutoff}\n",
    "    pickle.dump(outputs, open('altimeter/' + this_sat + '/' + this_sat + '_parsed_tracks_adt_x20.p', 'wb'))\n",
    "# ------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bab6bbe489124341915adba0f94501f2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/254 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ------------------------------------------------------------------------------------------------\n",
    "# RUN AFTER parse_grid_tracks is completed  (should have a parsed file for each satellite)\n",
    "# now interpolate in time (input should be output file from above cell)\n",
    "# ------------------------------------------------------------------------------------------------\n",
    "# load_sat = pickle.load(open(this_sat + '/' + this_sat +'_parsed_tracks_adt.p', 'rb'))\n",
    "# adt = load_sat['adt']\n",
    "load_sat = pickle.load(open('altimeter/' + this_sat + '/' + this_sat +'_parsed_tracks_adt_x20.p', 'rb'))\n",
    "adt = load_sat['adt']\n",
    "# -- interpolate in time \n",
    "interp_cutoff_t = 5  # time step to interpolate across (number samples, not a unit of time)\n",
    "adt_time = []\n",
    "sla_time = []\n",
    "# - loop over tracks \n",
    "for i in tqdm(range(len(adt))):\n",
    "    # - loop over along-track gridpoints \n",
    "    # this_interp_t_sla = np.nan * np.ones(np.shape(sla[i]))\n",
    "    this_interp_t_adt = np.nan * np.ones(np.shape(adt[i]))\n",
    "    for j in range(np.shape(adt[i])[1]): \n",
    "        # this_data_1 = sla[i][:, j]\n",
    "        this_data_2 = adt[i][:, j]\n",
    "        # if np.sum(np.isnan(this_data_1)) > 0:\n",
    "        #     nans, x = nan_helper(this_data_1)\n",
    "        #     this_interp_t_sla[:, j] = interp_nans(this_data_1, nans, x, interp_cutoff_t)\n",
    "        # else:\n",
    "        #     this_interp_t_sla[:, j] = this_data_1.copy()\n",
    "        if np.sum(np.isnan(this_data_2)) > 0:\n",
    "            nans, x = nan_helper(this_data_2)\n",
    "            this_interp_t_adt[:, j] = interp_nans(this_data_2, nans, x, interp_cutoff_t)\n",
    "        else:\n",
    "            this_interp_t_adt[:, j] = this_data_2.copy()\n",
    "    # sla_time.append(this_interp_t_sla)\n",
    "    adt_time.append(this_interp_t_adt)\n",
    "    \n",
    "save_p = 1\n",
    "if save_p > 0:\n",
    "    outputs = {'adt': adt_time}\n",
    "    pickle.dump(outputs, open('altimeter/' + this_sat + '/' + this_sat + '_parsed_tracks_adt_x20_interp_t.p', 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "2+2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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