data_prep.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Preparation\n",
    "\n",
    "Here in this data preparation jupyter notebook, we will prepare our data that will go into a Convolutional Neural Network model later."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 0. Setup parameters and load libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "lines_to_next_cell": 1
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python       : 3.7.3 | packaged by conda-forge | (default, Jul  1 2019, 21:52:21) \n",
      "Geopandas    : 0.5.1\n",
      "GMT          : v0.1.1\n",
      "Numpy        : 1.17.0rc2\n",
      "Rasterio     : 1.0.24\n",
      "Scikit-image : 0.15.0\n",
      "Xarray       : 0.12.3\n"
     ]
    }
   ],
   "source": [
    "import glob\n",
    "import hashlib\n",
    "import json\n",
    "import os\n",
    "import shutil\n",
    "import sys\n",
    "import tarfile\n",
    "import tempfile\n",
    "import urllib\n",
    "import zipfile\n",
    "\n",
    "# need to import before rasterio\n",
    "import xarray as xr\n",
    "import salem\n",
    "\n",
    "import dask\n",
    "import dask.diagnostics\n",
    "import geopandas as gpd\n",
    "import pygmt as gmt\n",
    "import IPython.display\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pyproj\n",
    "import quilt\n",
    "import rasterio\n",
    "import rasterio.mask\n",
    "import rasterio.plot\n",
    "import shapely.geometry\n",
    "import skimage.util.shape\n",
    "import yaml\n",
    "\n",
    "print(\"Python       :\", sys.version.split(\"\\n\")[0])\n",
    "print(\"Geopandas    :\", gpd.__version__)\n",
    "print(\"GMT          :\", gmt.__version__)\n",
    "print(\"Numpy        :\", np.__version__)\n",
    "print(\"Rasterio     :\", rasterio.__version__)\n",
    "print(\"Scikit-image :\", skimage.__version__)\n",
    "print(\"Xarray       :\", xr.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Get Data!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def download_to_path(path: str, url: str):\n",
    "    r\"\"\"\n",
    "    Download from a HTTP or FTP url to a filepath.\n",
    "\n",
    "    >>> d = download_to_path(\n",
    "    ...    path=\"highres/Data_20171204_02.csv\",\n",
    "    ...    url=\"ftp://data.cresis.ku.edu/data/rds/2017_Antarctica_Basler/csv_good/Data_20171204_02.csv\",\n",
    "    ... )\n",
    "    >>> open(\"highres/Data_20171204_02.csv\").readlines()\n",
    "    ['LAT,LON,UTCTIMESOD,THICK,ELEVATION,FRAME,SURFACE,BOTTOM,QUALITY\\n']\n",
    "    >>> os.remove(path=\"highres/Data_20171204_02.csv\")\n",
    "    \"\"\"\n",
    "\n",
    "    folder, filename = os.path.split(p=path)\n",
    "    downloaded_filename = os.path.basename(urllib.parse.urlparse(url=url).path)\n",
    "\n",
    "    # Download file using URL first\n",
    "    if not os.path.exists(os.path.join(folder, downloaded_filename)):\n",
    "        r = urllib.request.urlretrieve(\n",
    "            url=url, filename=os.path.join(folder, downloaded_filename)\n",
    "        )\n",
    "\n",
    "    # If downloaded file is not the final file (e.g. file is in an archive),\n",
    "    # then extract the file from the archive!\n",
    "    if filename != downloaded_filename:\n",
    "        # Extract tar.gz archive file\n",
    "        if downloaded_filename.endswith((\"tgz\", \"tar.gz\")):\n",
    "            archive = tarfile.open(name=f\"{folder}/{downloaded_filename}\")\n",
    "            archive.extract(member=filename, path=folder)\n",
    "        # Extract from .zip archive file\n",
    "        elif downloaded_filename.endswith((\".zip\")):\n",
    "            archive = zipfile.ZipFile(file=f\"{folder}/{downloaded_filename}\")\n",
    "            archive.extract(member=filename, path=folder)\n",
    "        else:\n",
    "            raise ValueError(\n",
    "                f\"Unsupported archive format for downloaded file: {downloaded_filename}\"\n",
    "            )\n",
    "\n",
    "    return os.path.exists(path=path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def check_sha256(path: str):\n",
    "    \"\"\"\n",
    "    Returns SHA256 checksum of a file\n",
    "\n",
    "    >>> d = download_to_path(\n",
    "    ...    path=\"highres/Data_20171204_02.csv\",\n",
    "    ...    url=\"https://data.cresis.ku.edu/data/rds/2017_Antarctica_Basler/csv_good/Data_20171204_02.csv\",\n",
    "    ... )\n",
    "    >>> check_sha256(\"highres/Data_20171204_02.csv\")\n",
    "    '53cef7a0d28ff92b30367514f27e888efbc32b1bda929981b371d2e00d4c671b'\n",
    "    >>> os.remove(path=\"highres/Data_20171204_02.csv\")\n",
    "    \"\"\"\n",
    "    with open(file=path, mode=\"rb\") as afile:\n",
    "        sha = hashlib.sha256(afile.read())\n",
    "\n",
    "    return sha.hexdigest()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parse [data_list.yml](/data_list.yml)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "def parse_datalist(\n",
    "    yaml_file: str = \"data_list.yml\",\n",
    "    record_path: str = \"files\",\n",
    "    schema: list = [\n",
    "        \"citekey\",\n",
    "        \"folder\",\n",
    "        \"location\",\n",
    "        \"resolution\",\n",
    "        [\"doi\", \"dataset\"],\n",
    "        [\"doi\", \"literature\"],\n",
    "    ],\n",
    ") -> pd.DataFrame:\n",
    "\n",
    "    assert yaml_file.endswith((\".yml\", \".yaml\"))\n",
    "\n",
    "    with open(file=yaml_file, mode=\"r\") as yml:\n",
    "        y = yaml.safe_load(stream=yml)\n",
    "\n",
    "    datalist = pd.io.json.json_normalize(\n",
    "        data=y, record_path=record_path, meta=schema, sep=\"_\"\n",
    "    )\n",
    "\n",
    "    return datalist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pretty print table with nice column order and clickable url links\n",
    "pprint_table = lambda df, folder: IPython.display.HTML(\n",
    "    df.query(expr=\"folder == @folder\")\n",
    "    .reindex(columns=[\"folder\", \"filename\", \"url\", \"sha256\"])\n",
    "    .style.format({\"url\": lambda url: f'<a target=\"_blank\" href=\"{url}\">{url}</a>'})\n",
    "    .render(uuid=f\"{folder}\")\n",
    ")\n",
    "dataframe = parse_datalist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Code to autogenerate README.md files in highres/lowres/misc folders from data_list.yml\n",
    "columns = [\"Filename\", \"Location\", \"Resolution\", \"Literature Citation\", \"Data Citation\"]\n",
    "for folder, md_header in [\n",
    "    (\"lowres\", \"Low Resolution\"),\n",
    "    (\"highres\", \"High Resolution\"),\n",
    "    (\"misc\", \"Miscellaneous\"),\n",
    "]:\n",
    "    assert folder in pd.unique(dataframe[\"folder\"])\n",
    "    md_name = f\"{folder}/README.md\"\n",
    "\n",
    "    with open(file=md_name, mode=\"w\") as md_file:\n",
    "        md_file.write(f\"# {md_header} Antarctic datasets\\n\\n\")\n",
    "        md_file.write(\"Note: This file was automatically generated from \")\n",
    "        md_file.write(\"[data_list.yml](/data_list.yml) using \")\n",
    "        md_file.write(\"[data_prep.ipynb](/data_prep.ipynb)\\n\\n\")\n",
    "\n",
    "    md_table = pd.DataFrame(columns=columns)\n",
    "    md_table.loc[0] = [\"---\", \"---\", \"---\", \"---\", \"---\"]\n",
    "\n",
    "    keydf = dataframe.groupby(\"citekey\").aggregate(lambda x: set(x).pop())\n",
    "    for row in keydf.query(expr=\"folder == @folder\").itertuples():\n",
    "        filecount = len(dataframe[dataframe[\"citekey\"] == row.Index])\n",
    "        extension = os.path.splitext(row.filename)[-1]\n",
    "        row_dict = {\n",
    "            \"Filename\": row.filename\n",
    "            if filecount == 1\n",
    "            else f\"{filecount} *{extension} files\",\n",
    "            \"Location\": row.location,\n",
    "            \"Resolution\": row.resolution,\n",
    "            \"Literature Citation\": f\"[{row.Index}]({row.doi_literature})\",\n",
    "            \"Data Citation\": f\"[DOI]({row.doi_dataset})\"\n",
    "            if row.doi_dataset != \"nan\"\n",
    "            else None,\n",
    "        }\n",
    "        md_table = md_table.append(other=row_dict, ignore_index=True)\n",
    "\n",
    "    md_table.to_csv(path_or_buf=md_name, mode=\"a\", sep=\"|\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download Low Resolution bed elevation data (e.g. [BEDMAP2](https://doi.org/10.5194/tc-7-375-2013))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_lowres\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >folder</th>        <th class=\"col_heading level0 col1\" >filename</th>        <th class=\"col_heading level0 col2\" >url</th>        <th class=\"col_heading level0 col3\" >sha256</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_lowreslevel0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_lowresrow0_col0\" class=\"data row0 col0\" >lowres</td>\n",
       "                        <td id=\"T_lowresrow0_col1\" class=\"data row0 col1\" >bedmap2_bed.tif</td>\n",
       "                        <td id=\"T_lowresrow0_col2\" class=\"data row0 col2\" ><a target=\"_blank\" href=\"http://data.pgc.umn.edu/elev/dem/bedmap2/bedmap2_bed.tif\">http://data.pgc.umn.edu/elev/dem/bedmap2/bedmap2_bed.tif</a></td>\n",
       "                        <td id=\"T_lowresrow0_col3\" class=\"data row0 col3\" >28e2ca7656d61b0bc7f8f8c1db41914023e0cab1634e0ee645f38a87d894b416</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in dataframe.query(expr=\"folder == 'lowres'\").itertuples():\n",
    "    path = f\"{dataset.folder}/{dataset.filename}\"  # path to download the file to\n",
    "    if not os.path.exists(path=path):\n",
    "        download_to_path(path=path, url=dataset.url)\n",
    "    assert check_sha256(path=path) == dataset.sha256\n",
    "pprint_table(dataframe, \"lowres\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "with rasterio.open(\"lowres/bedmap2_bed.tif\") as raster_source:\n",
    "    rasterio.plot.show(source=raster_source, cmap=\"BrBG_r\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download miscellaneous data (e.g. [REMA](https://doi.org/10.7910/DVN/SAIK8B), [MEaSUREs phase-based Ice Velocity](https://doi.org/10.5067/PZ3NJ5RXRH10), [Arthern Accumulation](https://doi.org/10.1029/2004JD005667))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_misc\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >folder</th>        <th class=\"col_heading level0 col1\" >filename</th>        <th class=\"col_heading level0 col2\" >url</th>        <th class=\"col_heading level0 col3\" >sha256</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_misclevel0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
       "                        <td id=\"T_miscrow0_col0\" class=\"data row0 col0\" >misc</td>\n",
       "                        <td id=\"T_miscrow0_col1\" class=\"data row0 col1\" >REMA_100m_dem.tif</td>\n",
       "                        <td id=\"T_miscrow0_col2\" class=\"data row0 col2\" ><a target=\"_blank\" href=\"http://data.pgc.umn.edu/elev/dem/setsm/REMA/mosaic/v1.1/100m/REMA_100m_dem.tif\">http://data.pgc.umn.edu/elev/dem/setsm/REMA/mosaic/v1.1/100m/REMA_100m_dem.tif</a></td>\n",
       "                        <td id=\"T_miscrow0_col3\" class=\"data row0 col3\" >80c9fa41ccc69be1d2cd4a367d56168321d1079e7260a1996089810db25172f6</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_misclevel0_row1\" class=\"row_heading level0 row1\" >2</th>\n",
       "                        <td id=\"T_miscrow1_col0\" class=\"data row1 col0\" >misc</td>\n",
       "                        <td id=\"T_miscrow1_col1\" class=\"data row1 col1\" >REMA_200m_dem_filled.tif</td>\n",
       "                        <td id=\"T_miscrow1_col2\" class=\"data row1 col2\" ><a target=\"_blank\" href=\"http://data.pgc.umn.edu/elev/dem/setsm/REMA/mosaic/v1.1/200m/REMA_200m_dem_filled.tif\">http://data.pgc.umn.edu/elev/dem/setsm/REMA/mosaic/v1.1/200m/REMA_200m_dem_filled.tif</a></td>\n",
       "                        <td id=\"T_miscrow1_col3\" class=\"data row1 col3\" >f750893861a1a268c8ffe0ba7db36c933223bbf5fcbb786ecef3f052b20f9b8a</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_misclevel0_row2\" class=\"row_heading level0 row2\" >3</th>\n",
       "                        <td id=\"T_miscrow2_col0\" class=\"data row2 col0\" >misc</td>\n",
       "                        <td id=\"T_miscrow2_col1\" class=\"data row2 col1\" >antarctic_ice_vel_phase_map_v01.nc</td>\n",
       "                        <td id=\"T_miscrow2_col2\" class=\"data row2 col2\" ><a target=\"_blank\" href=\"ftp://ftp.nsidc.org/pub/DATASETS/nsidc0754_MEASURES_antarctic_ice_vel_phase_map_v01/antarctic_ice_vel_phase_map_v01.nc\">ftp://ftp.nsidc.org/pub/DATASETS/nsidc0754_MEASURES_antarctic_ice_vel_phase_map_v01/antarctic_ice_vel_phase_map_v01.nc</a></td>\n",
       "                        <td id=\"T_miscrow2_col3\" class=\"data row2 col3\" >fa0957618b8bd98099f4a419d7dc0e3a2c562d89e9791b4d0ed55e6017f52416</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_misclevel0_row3\" class=\"row_heading level0 row3\" >28</th>\n",
       "                        <td id=\"T_miscrow3_col0\" class=\"data row3 col0\" >misc</td>\n",
       "                        <td id=\"T_miscrow3_col1\" class=\"data row3 col1\" >Arthern_accumulation_bedmap2_grid1.tif</td>\n",
       "                        <td id=\"T_miscrow3_col2\" class=\"data row3 col2\" ><a target=\"_blank\" href=\"https://secure.antarctica.ac.uk/data/bedmap2/resources/Arthern_accumulation/Arthern_accumulation_tif.zip\">https://secure.antarctica.ac.uk/data/bedmap2/resources/Arthern_accumulation/Arthern_accumulation_tif.zip</a></td>\n",
       "                        <td id=\"T_miscrow3_col3\" class=\"data row3 col3\" >e6b139801bf4541f1e4989a8aa8b26ab37eca81bb5eaffa8028b744782455db0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_misclevel0_row4\" class=\"row_heading level0 row4\" >29</th>\n",
       "                        <td id=\"T_miscrow4_col0\" class=\"data row4 col0\" >misc</td>\n",
       "                        <td id=\"T_miscrow4_col1\" class=\"data row4 col1\" >GroundingLine_Antarctica_v2.shp</td>\n",
       "                        <td id=\"T_miscrow4_col2\" class=\"data row4 col2\" ><a target=\"_blank\" href=\"http://data.pgc.umn.edu/gis/packages/quantarctica/Quantarctica3/Glaciology/MEaSUREs%20Antarctic%20Boundaries/GroundingLine_Antarctica_v2.shp\">http://data.pgc.umn.edu/gis/packages/quantarctica/Quantarctica3/Glaciology/MEaSUREs%20Antarctic%20Boundaries/GroundingLine_Antarctica_v2.shp</a></td>\n",
       "                        <td id=\"T_miscrow4_col3\" class=\"data row4 col3\" >2d8f84e301c4e33ad1cb480aa260b8208b3fcaa00df0b68593ffe7d5aa6c9d7e</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_misclevel0_row5\" class=\"row_heading level0 row5\" >30</th>\n",
       "                        <td id=\"T_miscrow5_col0\" class=\"data row5 col0\" >misc</td>\n",
       "                        <td id=\"T_miscrow5_col1\" class=\"data row5 col1\" >GroundingLine_Antarctica_v2.shx</td>\n",
       "                        <td id=\"T_miscrow5_col2\" class=\"data row5 col2\" ><a target=\"_blank\" href=\"http://data.pgc.umn.edu/gis/packages/quantarctica/Quantarctica3/Glaciology/MEaSUREs%20Antarctic%20Boundaries/GroundingLine_Antarctica_v2.shx\">http://data.pgc.umn.edu/gis/packages/quantarctica/Quantarctica3/Glaciology/MEaSUREs%20Antarctic%20Boundaries/GroundingLine_Antarctica_v2.shx</a></td>\n",
       "                        <td id=\"T_miscrow5_col3\" class=\"data row5 col3\" >01eaff4a35b4cd840a3fb1bdb63f7e51b2792c2ccd92d4621cb5d97cb3e365bc</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_misclevel0_row6\" class=\"row_heading level0 row6\" >31</th>\n",
       "                        <td id=\"T_miscrow6_col0\" class=\"data row6 col0\" >misc</td>\n",
       "                        <td id=\"T_miscrow6_col1\" class=\"data row6 col1\" >GroundingLine_Antarctica_v2.dbf</td>\n",
       "                        <td id=\"T_miscrow6_col2\" class=\"data row6 col2\" ><a target=\"_blank\" href=\"http://data.pgc.umn.edu/gis/packages/quantarctica/Quantarctica3/Glaciology/MEaSUREs%20Antarctic%20Boundaries/GroundingLine_Antarctica_v2.dbf\">http://data.pgc.umn.edu/gis/packages/quantarctica/Quantarctica3/Glaciology/MEaSUREs%20Antarctic%20Boundaries/GroundingLine_Antarctica_v2.dbf</a></td>\n",
       "                        <td id=\"T_miscrow6_col3\" class=\"data row6 col3\" >1cfbe90b262a7fb81cbe61af4f75b23e03213a078ed866e66c6467ab422e017e</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in dataframe.query(expr=\"folder == 'misc'\").itertuples():\n",
    "    path = f\"{dataset.folder}/{dataset.filename}\"  # path to download the file to\n",
    "    if not os.path.exists(path=path):\n",
    "        download_to_path(path=path, url=dataset.url)\n",
    "    assert check_sha256(path=path) == dataset.sha256\n",
    "pprint_table(dataframe, \"misc\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download High Resolution bed elevation data (e.g. some-DEM-name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "lines_to_next_cell": 1
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_highres\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >folder</th>        <th class=\"col_heading level0 col1\" >filename</th>        <th class=\"col_heading level0 col2\" >url</th>        <th class=\"col_heading level0 col3\" >sha256</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_highreslevel0_row0\" class=\"row_heading level0 row0\" >4</th>\n",
       "                        <td id=\"T_highresrow0_col0\" class=\"data row0 col0\" >highres</td>\n",
       "                        <td id=\"T_highresrow0_col1\" class=\"data row0 col1\" >bed_WGS84_grid.txt</td>\n",
       "                        <td id=\"T_highresrow0_col2\" class=\"data row0 col2\" ><a target=\"_blank\" href=\"http://ramadda.nerc-bas.ac.uk/repository/entry/get/Polar%20Data%20Centre/DOI/Rutford%20Ice%20Stream%20bed%20elevation%20DEM%20from%20radar%20data/bed_WGS84_grid.txt?entryid=synth%3A54757cbe-0b13-4385-8b31-4dfaa1dab55e%3AL2JlZF9XR1M4NF9ncmlkLnR4dA%3D%3D\">http://ramadda.nerc-bas.ac.uk/repository/entry/get/Polar%20Data%20Centre/DOI/Rutford%20Ice%20Stream%20bed%20elevation%20DEM%20from%20radar%20data/bed_WGS84_grid.txt?entryid=synth%3A54757cbe-0b13-4385-8b31-4dfaa1dab55e%3AL2JlZF9XR1M4NF9ncmlkLnR4dA%3D%3D</a></td>\n",
       "                        <td id=\"T_highresrow0_col3\" class=\"data row0 col3\" >7396e56cda5adb82cecb01f0b3e01294ed0aa6489a9629f3f7e8858ea6cb91cf</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_highreslevel0_row1\" class=\"row_heading level0 row1\" >5</th>\n",
       "                        <td id=\"T_highresrow1_col0\" class=\"data row1 col0\" >highres</td>\n",
       "                        <td id=\"T_highresrow1_col1\" class=\"data row1 col1\" >2007t1.txt</td>\n",
       "                        <td id=\"T_highresrow1_col2\" class=\"data row1 col2\" ><a target=\"_blank\" href=\"nan\">nan</a></td>\n",
       "                        <td id=\"T_highresrow1_col3\" class=\"data row1 col3\" >04bdbd3c8e814cbc8f0d324277e339a46cc90a8dc23434d11815a8966951e766</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_highreslevel0_row2\" class=\"row_heading level0 row2\" >6</th>\n",
       "                        <td id=\"T_highresrow2_col0\" class=\"data row2 col0\" >highres</td>\n",
       "                        <td id=\"T_highresrow2_col1\" class=\"data row2 col1\" >2007tr.txt</td>\n",
       "                        <td id=\"T_highresrow2_col2\" class=\"data row2 col2\" ><a target=\"_blank\" href=\"nan\">nan</a></td>\n",
       "                        <td id=\"T_highresrow2_col3\" class=\"data row2 col3\" >3858a1e58e17b2816920e1b309534cee0391f72a6a0aa68d57777b030e70e9a3</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_highreslevel0_row3\" class=\"row_heading level0 row3\" >7</th>\n",
       "                        <td id=\"T_highresrow3_col0\" class=\"data row3 col0\" >highres</td>\n",
       "                        <td id=\"T_highresrow3_col1\" class=\"data row3 col1\" >2010tr.txt</td>\n",
       "                        <td id=\"T_highresrow3_col2\" class=\"data row3 col2\" ><a target=\"_blank\" href=\"nan\">nan</a></td>\n",
       "                        <td id=\"T_highresrow3_col3\" class=\"data row3 col3\" >751ea56acc5271b3fb54893ed59e05ff485187a6fc5daaedf75946d730805b80</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_highreslevel0_row4\" class=\"row_heading level0 row4\" >8</th>\n",
       "                        <td id=\"T_highresrow4_col0\" class=\"data row4 col0\" >highres</td>\n",
       "                        <td id=\"T_highresrow4_col1\" class=\"data row4 col1\" >istar08.txt</td>\n",
       "                        <td id=\"T_highresrow4_col2\" class=\"data row4 col2\" ><a target=\"_blank\" href=\"nan\">nan</a></td>\n",
       "                        <td id=\"T_highresrow4_col3\" class=\"data row4 col3\" >ed03c64332e8d406371c74a66f3cd21fb3f78ee498ae8408c355879bb89eb13d</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_highreslevel0_row5\" class=\"row_heading level0 row5\" >9</th>\n",
       "                        <td id=\"T_highresrow5_col0\" class=\"data row5 col0\" >highres</td>\n",
       "                        <td id=\"T_highresrow5_col1\" class=\"data row5 col1\" >istar18.txt</td>\n",
       "                        <td id=\"T_highresrow5_col2\" class=\"data row5 col2\" ><a target=\"_blank\" href=\"nan\">nan</a></td>\n",
       "                        <td id=\"T_highresrow5_col3\" class=\"data row5 col3\" >3e69d86f28e26810d29b0b9309090684dcb295c0dd39007fe9ee0d1285c57804</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_highreslevel0_row6\" class=\"row_heading level0 row6\" >10</th>\n",
       "                        <td id=\"T_highresrow6_col0\" class=\"data row6 col0\" >highres</td>\n",
       "                        <td id=\"T_highresrow6_col1\" class=\"data row6 col1\" >istar15.txt</td>\n",
       "                        <td id=\"T_highresrow6_col2\" class=\"data row6 col2\" ><a target=\"_blank\" href=\"nan\">nan</a></td>\n",
       "                        <td id=\"T_highresrow6_col3\" class=\"data row6 col3\" >59c981e8c96f73f3a5bd98be6570e101848b4f67a12d98a577292e7bcf776b17</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_highreslevel0_row7\" class=\"row_heading level0 row7\" >11</th>\n",
       "                        <td id=\"T_highresrow7_col0\" class=\"data row7 col0\" >highres</td>\n",
       "                        <td id=\"T_highresrow7_col1\" class=\"data row7 col1\" >istar13.txt</td>\n",
       "                        <td id=\"T_highresrow7_col2\" class=\"data row7 col2\" ><a target=\"_blank\" href=\"nan\">nan</a></td>\n",
       "                        <td id=\"T_highresrow7_col3\" class=\"data row7 col3\" >f5bcf80c7ea5095e2eabf72b69a264bf36ed56af5cb67976f9428f560e5702a2</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_highreslevel0_row8\" class=\"row_heading level0 row8\" >12</th>\n",
       "                        <td id=\"T_highresrow8_col0\" class=\"data row8 col0\" >highres</td>\n",
       "                        <td id=\"T_highresrow8_col1\" class=\"data row8 col1\" >istar17.txt</td>\n",
       "                        <td id=\"T_highresrow8_col2\" class=\"data row8 col2\" ><a target=\"_blank\" href=\"nan\">nan</a></td>\n",
       "                        <td id=\"T_highresrow8_col3\" class=\"data row8 col3\" >f51a674dc27d6e0b99d199949a706ecf96ea807883c1901fea186efc799a36e8</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_highreslevel0_row9\" class=\"row_heading level0 row9\" >13</th>\n",
       "                        <td id=\"T_highresrow9_col0\" class=\"data row9 col0\" >highres</td>\n",
       "                        <td id=\"T_highresrow9_col1\" class=\"data row9 col1\" >istar07.txt</td>\n",
       "                        <td id=\"T_highresrow9_col2\" class=\"data row9 col2\" ><a target=\"_blank\" href=\"nan\">nan</a></td>\n",
       "                        <td id=\"T_highresrow9_col3\" class=\"data row9 col3\" >c81ec04290433f598ce4368e4aae088adeeabb546913edc44c54a5a5d7593e93</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_highreslevel0_row10\" class=\"row_heading level0 row10\" >14</th>\n",
       "                        <td id=\"T_highresrow10_col0\" class=\"data row10 col0\" >highres</td>\n",
       "                        <td id=\"T_highresrow10_col1\" class=\"data row10 col1\" >2009_Antarctica_DC8.csv</td>\n",
       "                        <td id=\"T_highresrow10_col2\" class=\"data row10 col2\" ><a target=\"_blank\" href=\"https://data.cresis.ku.edu/data/rds/2009_Antarctica_DC8/csv_good/2009_Antarctica_DC8.csv\">https://data.cresis.ku.edu/data/rds/2009_Antarctica_DC8/csv_good/2009_Antarctica_DC8.csv</a></td>\n",
       "                        <td id=\"T_highresrow10_col3\" class=\"data row10 col3\" >1b9fe0faf4ef217794c2a1de9ef8cfa45f5949efdc4e925930d31c0554cf0ca2</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_highreslevel0_row11\" class=\"row_heading level0 row11\" >15</th>\n",
       "                        <td id=\"T_highresrow11_col0\" class=\"data row11 col0\" >highres</td>\n",
       "                        <td id=\"T_highresrow11_col1\" class=\"data row11 col1\" >2009_Antarctica_TO.csv</td>\n",
       "                        <td id=\"T_highresrow11_col2\" class=\"data row11 col2\" ><a target=\"_blank\" href=\"https://data.cresis.ku.edu/data/rds/2009_Antarctica_TO/csv_good/2009_Antarctica_TO.csv\">https://data.cresis.ku.edu/data/rds/2009_Antarctica_TO/csv_good/2009_Antarctica_TO.csv</a></td>\n",
       "                        <td id=\"T_highresrow11_col3\" class=\"data row11 col3\" >7a90c5955fa881b4fb88e45ff11629e60ff9ad045c07bf4c6e3aa1f7d1a9361d</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_highreslevel0_row12\" class=\"row_heading level0 row12\" >16</th>\n",
       "                        <td id=\"T_highresrow12_col0\" class=\"data row12 col0\" >highres</td>\n",
       "                        <td id=\"T_highresrow12_col1\" class=\"data row12 col1\" >2009_Antarctica_TO_Gambit.csv</td>\n",
       "                        <td id=\"T_highresrow12_col2\" class=\"data row12 col2\" ><a target=\"_blank\" href=\"https://data.cresis.ku.edu/data/rds/2009_Antarctica_TO_Gambit/csv_good/2009_Antarctica_TO_Gambit.csv\">https://data.cresis.ku.edu/data/rds/2009_Antarctica_TO_Gambit/csv_good/2009_Antarctica_TO_Gambit.csv</a></td>\n",
       "                        <td id=\"T_highresrow12_col3\" class=\"data row12 col3\" >93da613223733a4850283b700060afdb14f1002fe5613b8d78c6d3be83e34072</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_highreslevel0_row13\" class=\"row_heading level0 row13\" >17</th>\n",
       "                        <td id=\"T_highresrow13_col0\" class=\"data row13 col0\" >highres</td>\n",
       "                        <td id=\"T_highresrow13_col1\" class=\"data row13 col1\" >2010_Antarctica_DC8.csv</td>\n",
       "                        <td id=\"T_highresrow13_col2\" class=\"data row13 col2\" ><a target=\"_blank\" href=\"https://data.cresis.ku.edu/data/rds/2010_Antarctica_DC8/csv_good/2010_Antarctica_DC8.csv\">https://data.cresis.ku.edu/data/rds/2010_Antarctica_DC8/csv_good/2010_Antarctica_DC8.csv</a></td>\n",
       "                        <td id=\"T_highresrow13_col3\" class=\"data row13 col3\" >f725a8dbc21d31601b99ccaf9f5282ecd516f2ff966d268b4e735ea1af2014e6</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_highreslevel0_row14\" class=\"row_heading level0 row14\" >18</th>\n",
       "                        <td id=\"T_highresrow14_col0\" class=\"data row14 col0\" >highres</td>\n",
       "                        <td id=\"T_highresrow14_col1\" class=\"data row14 col1\" >2011_Antarctica_DC8.csv</td>\n",
       "                        <td id=\"T_highresrow14_col2\" class=\"data row14 col2\" ><a target=\"_blank\" href=\"https://data.cresis.ku.edu/data/rds/2011_Antarctica_DC8/csv_good/2011_Antarctica_DC8.csv\">https://data.cresis.ku.edu/data/rds/2011_Antarctica_DC8/csv_good/2011_Antarctica_DC8.csv</a></td>\n",
       "                        <td id=\"T_highresrow14_col3\" class=\"data row14 col3\" >38aba2a39b0d58b72827f25cfcd667fc943f25c0024d3c52cb1b9e65e9e76163</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_highreslevel0_row15\" class=\"row_heading level0 row15\" >19</th>\n",
       "                        <td id=\"T_highresrow15_col0\" class=\"data row15 col0\" >highres</td>\n",
       "                        <td id=\"T_highresrow15_col1\" class=\"data row15 col1\" >2011_Antarctica_TO.csv</td>\n",
       "                        <td id=\"T_highresrow15_col2\" class=\"data row15 col2\" ><a target=\"_blank\" href=\"https://data.cresis.ku.edu/data/rds/2011_Antarctica_TO/csv_good/2011_Antarctica_TO.csv\">https://data.cresis.ku.edu/data/rds/2011_Antarctica_TO/csv_good/2011_Antarctica_TO.csv</a></td>\n",
       "                        <td id=\"T_highresrow15_col3\" class=\"data row15 col3\" >4bf37750b9986ce582c9fd1f3a6ac622fc17f3b3ecb07b7a7132eb3797ee31d1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_highreslevel0_row16\" class=\"row_heading level0 row16\" >20</th>\n",
       "                        <td id=\"T_highresrow16_col0\" class=\"data row16 col0\" >highres</td>\n",
       "                        <td id=\"T_highresrow16_col1\" class=\"data row16 col1\" >2012_Antarctica_DC8.csv</td>\n",
       "                        <td id=\"T_highresrow16_col2\" class=\"data row16 col2\" ><a target=\"_blank\" href=\"https://data.cresis.ku.edu/data/rds/2012_Antarctica_DC8/csv_good/2012_Antarctica_DC8.csv\">https://data.cresis.ku.edu/data/rds/2012_Antarctica_DC8/csv_good/2012_Antarctica_DC8.csv</a></td>\n",
       "                        <td id=\"T_highresrow16_col3\" class=\"data row16 col3\" >5c6701b8c34bd57517b93e8e18f32e4579d6e2f56e4796bd7140b3e338544007</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_highreslevel0_row17\" class=\"row_heading level0 row17\" >21</th>\n",
       "                        <td id=\"T_highresrow17_col0\" class=\"data row17 col0\" >highres</td>\n",
       "                        <td id=\"T_highresrow17_col1\" class=\"data row17 col1\" >2013_Antarctica_Basler.csv</td>\n",
       "                        <td id=\"T_highresrow17_col2\" class=\"data row17 col2\" ><a target=\"_blank\" href=\"https://data.cresis.ku.edu/data/rds/2013_Antarctica_Basler/csv_good/2013_Antarctica_Basler.csv\">https://data.cresis.ku.edu/data/rds/2013_Antarctica_Basler/csv_good/2013_Antarctica_Basler.csv</a></td>\n",
       "                        <td id=\"T_highresrow17_col3\" class=\"data row17 col3\" >56609027b4af04ba078ae093772916341bd1d6ab5f110de11b21294507733cc8</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_highreslevel0_row18\" class=\"row_heading level0 row18\" >22</th>\n",
       "                        <td id=\"T_highresrow18_col0\" class=\"data row18 col0\" >highres</td>\n",
       "                        <td id=\"T_highresrow18_col1\" class=\"data row18 col1\" >2013_Antarctica_P3.csv</td>\n",
       "                        <td id=\"T_highresrow18_col2\" class=\"data row18 col2\" ><a target=\"_blank\" href=\"https://data.cresis.ku.edu/data/rds/2013_Antarctica_P3/csv_good/2013_Antarctica_P3.csv\">https://data.cresis.ku.edu/data/rds/2013_Antarctica_P3/csv_good/2013_Antarctica_P3.csv</a></td>\n",
       "                        <td id=\"T_highresrow18_col3\" class=\"data row18 col3\" >9de95030f49ce0bbf107eb72418db2845c39822872a6c9aa10f023148262f658</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_highreslevel0_row19\" class=\"row_heading level0 row19\" >23</th>\n",
       "                        <td id=\"T_highresrow19_col0\" class=\"data row19 col0\" >highres</td>\n",
       "                        <td id=\"T_highresrow19_col1\" class=\"data row19 col1\" >2014_Antarctica_DC8.csv</td>\n",
       "                        <td id=\"T_highresrow19_col2\" class=\"data row19 col2\" ><a target=\"_blank\" href=\"https://data.cresis.ku.edu/data/rds/2014_Antarctica_DC8/csv_good/2014_Antarctica_DC8.csv\">https://data.cresis.ku.edu/data/rds/2014_Antarctica_DC8/csv_good/2014_Antarctica_DC8.csv</a></td>\n",
       "                        <td id=\"T_highresrow19_col3\" class=\"data row19 col3\" >bd8c8674ba66508c64303725bfe45b3365467d01f69cfa8ec4258a3ced05e5bf</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_highreslevel0_row20\" class=\"row_heading level0 row20\" >24</th>\n",
       "                        <td id=\"T_highresrow20_col0\" class=\"data row20 col0\" >highres</td>\n",
       "                        <td id=\"T_highresrow20_col1\" class=\"data row20 col1\" >2016_Antarctica_DC8.csv</td>\n",
       "                        <td id=\"T_highresrow20_col2\" class=\"data row20 col2\" ><a target=\"_blank\" href=\"https://data.cresis.ku.edu/data/rds/2016_Antarctica_DC8/csv_good/2016_Antarctica_DC8.csv\">https://data.cresis.ku.edu/data/rds/2016_Antarctica_DC8/csv_good/2016_Antarctica_DC8.csv</a></td>\n",
       "                        <td id=\"T_highresrow20_col3\" class=\"data row20 col3\" >ec3b514dfcae265f5b8643eeb3503be8a0a6531e563faf9f12cb67f2b618a741</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_highreslevel0_row21\" class=\"row_heading level0 row21\" >25</th>\n",
       "                        <td id=\"T_highresrow21_col0\" class=\"data row21 col0\" >highres</td>\n",
       "                        <td id=\"T_highresrow21_col1\" class=\"data row21 col1\" >2017_Antarctica_P3.csv</td>\n",
       "                        <td id=\"T_highresrow21_col2\" class=\"data row21 col2\" ><a target=\"_blank\" href=\"https://data.cresis.ku.edu/data/rds/2017_Antarctica_P3/csv_good/2017_Antarctica_P3.csv\">https://data.cresis.ku.edu/data/rds/2017_Antarctica_P3/csv_good/2017_Antarctica_P3.csv</a></td>\n",
       "                        <td id=\"T_highresrow21_col3\" class=\"data row21 col3\" >9208a64fefe2f4a6e7f08d44c0af0c35400cd814590c32b8eb02f1545bfc8bec</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_highreslevel0_row22\" class=\"row_heading level0 row22\" >26</th>\n",
       "                        <td id=\"T_highresrow22_col0\" class=\"data row22 col0\" >highres</td>\n",
       "                        <td id=\"T_highresrow22_col1\" class=\"data row22 col1\" >2017_Antarctica_Basler.csv</td>\n",
       "                        <td id=\"T_highresrow22_col2\" class=\"data row22 col2\" ><a target=\"_blank\" href=\"https://data.cresis.ku.edu/data/rds/2017_Antarctica_Basler/csv_good/2017_Antarctica_Basler.csv\">https://data.cresis.ku.edu/data/rds/2017_Antarctica_Basler/csv_good/2017_Antarctica_Basler.csv</a></td>\n",
       "                        <td id=\"T_highresrow22_col3\" class=\"data row22 col3\" >c97d0d92f3095ee8c3941d915028728423758594cc95e7b819889b51693f0712</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_highreslevel0_row23\" class=\"row_heading level0 row23\" >27</th>\n",
       "                        <td id=\"T_highresrow23_col0\" class=\"data row23 col0\" >highres</td>\n",
       "                        <td id=\"T_highresrow23_col1\" class=\"data row23 col1\" >WISE_ISODYN_RadarByFlight_ASCII.zip</td>\n",
       "                        <td id=\"T_highresrow23_col2\" class=\"data row23 col2\" ><a target=\"_blank\" href=\"https://ramadda.data.bas.ac.uk/repository/entry/get/WISE_ISODYN_RadarByFlight_ASCII.zip?entryid=synth%3A59e5a6f5-e67d-4a05-99af-30f656569401%3AL1dJU0VfSVNPRFlOX1JhZGFyQnlGbGlnaHRfQVNDSUkuemlw\">https://ramadda.data.bas.ac.uk/repository/entry/get/WISE_ISODYN_RadarByFlight_ASCII.zip?entryid=synth%3A59e5a6f5-e67d-4a05-99af-30f656569401%3AL1dJU0VfSVNPRFlOX1JhZGFyQnlGbGlnaHRfQVNDSUkuemlw</a></td>\n",
       "                        <td id=\"T_highresrow23_col3\" class=\"data row23 col3\" >dfb20a4ff133f361f64b5730e7593a46cb3d599ae62b0dc874685794990c8d91</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_highreslevel0_row24\" class=\"row_heading level0 row24\" >32</th>\n",
       "                        <td id=\"T_highresrow24_col0\" class=\"data row24 col0\" >highres</td>\n",
       "                        <td id=\"T_highresrow24_col1\" class=\"data row24 col1\" >bed_depth_below_WGS84_datum.csv</td>\n",
       "                        <td id=\"T_highresrow24_col2\" class=\"data row24 col2\" ><a target=\"_blank\" href=\"nan\">nan</a></td>\n",
       "                        <td id=\"T_highresrow24_col3\" class=\"data row24 col3\" >f84332a824f984ed8dcf291fc64a6ec27177762d546c684d9bde72664bd8511d</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in dataframe.query(expr=\"folder == 'highres'\").itertuples():\n",
    "    path = f\"{dataset.folder}/{dataset.filename}\"  # path to download the file to\n",
    "    if not os.path.exists(path=path):\n",
    "        download_to_path(path=path, url=dataset.url)\n",
    "    assert check_sha256(path=path) == dataset.sha256\n",
    "pprint_table(dataframe, \"highres\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Process high resolution data into grid format\n",
    "\n",
    "Our processing step involves two stages:\n",
    "\n",
    "1) Cleaning up the raw **vector** data, performing necessary calculations and reprojections to EPSG:3031.\n",
    "\n",
    "2) Convert the cleaned vector data table via an interpolation function to a **raster** grid."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 [Raw ASCII Text](https://pdal.io/stages/readers.text.html) to [Clean XYZ table](https://gmt.soest.hawaii.edu/doc/latest/GMT_Docs.html#table-data)\n",
    "\n",
    "![Raw ASCII to Clean Table via pipeline file](https://yuml.me/diagram/scruffy;dir:LR/class/[Raw-ASCII-Text|*.csv/*.txt]->[Pipeline-File|*.json],[Pipeline-File]->[Clean-XYZ-Table|*.xyz])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ascii_to_xyz(pipeline_file: str) -> pd.DataFrame:\n",
    "    \"\"\"\n",
    "    Converts ascii txt/csv files to xyz pandas.DataFrame via\n",
    "    a JSON Pipeline file similar to the one used by PDAL.\n",
    "\n",
    "    >>> os.makedirs(name=\"/tmp/highres\", exist_ok=True)\n",
    "    >>> d = download_to_path(\n",
    "    ...    path=\"/tmp/highres/2011_Antarctica_TO.csv\",\n",
    "    ...    url=\"https://data.cresis.ku.edu/data/rds/2011_Antarctica_TO/csv_good/2011_Antarctica_TO.csv\",\n",
    "    ... )\n",
    "    >>> _ = shutil.copy(src=\"highres/20xx_Antarctica_TO.json\", dst=\"/tmp/highres\")\n",
    "    >>> df = ascii_to_xyz(pipeline_file=\"/tmp/highres/20xx_Antarctica_TO.json\")\n",
    "    >>> df.head(2)\n",
    "                   x             y         z\n",
    "    0  345580.826265 -1.156471e+06 -377.2340\n",
    "    1  345593.322948 -1.156460e+06 -376.6332\n",
    "    >>> shutil.rmtree(path=\"/tmp/highres\")\n",
    "    \"\"\"\n",
    "    assert os.path.exists(pipeline_file)\n",
    "    assert pipeline_file.endswith((\".json\"))\n",
    "\n",
    "    # Read json file first\n",
    "    with open(pipeline_file) as _file:\n",
    "        j = json.loads(_file.read())\n",
    "    jdf = pd.io.json.json_normalize(data=j, record_path=\"pipeline\", max_level=0)\n",
    "    jdf = jdf.set_index(keys=\"type\")\n",
    "    reader = jdf.loc[\"readers.text\"]  # check how to read the file(s)\n",
    "\n",
    "    ## Basic table read\n",
    "    skip = int(reader.skip)  # number of header rows to skip\n",
    "    sep = reader.separator  # delimiter to use\n",
    "    names = reader.header.split(sep=sep)  # header/column names as list\n",
    "    usecols = reader.usecols.split(sep=sep)  # column names to use\n",
    "    na_values = None if not hasattr(reader, \"na_values\") else reader.na_values\n",
    "\n",
    "    path_pattern = os.path.join(os.path.dirname(pipeline_file), reader.filename)\n",
    "    files: list = [file for file in glob.glob(path_pattern)]\n",
    "    assert files  # check that there are actually files being matched!\n",
    "\n",
    "    df = pd.concat(\n",
    "        pd.read_csv(\n",
    "            f, sep=sep, header=skip, names=names, usecols=usecols, na_values=na_values\n",
    "        )\n",
    "        for f in files\n",
    "    )\n",
    "    df.dropna(axis=\"index\", inplace=True)  # drop rows with NaN values\n",
    "    df.reset_index(drop=True, inplace=True)  # reset index after concatenation\n",
    "\n",
    "    ## Advanced table read with conversions\n",
    "    try:\n",
    "        # Perform math operations\n",
    "        newcol, expr = reader.converters.popitem()\n",
    "        df[newcol] = df.eval(expr=expr)\n",
    "        # Drop unneeded columns\n",
    "        dropcols = reader.dropcols.split(sep=sep)\n",
    "        df.drop(columns=dropcols, inplace=True)\n",
    "    except AttributeError:\n",
    "        pass\n",
    "\n",
    "    assert len(df.columns) == 3  # check that we have 3 columns i.e. x, y, z\n",
    "    df.sort_index(axis=\"columns\", inplace=True)  # sort cols alphabetically\n",
    "    df.set_axis(labels=[\"x\", \"y\", \"z\"], axis=\"columns\", inplace=True)  # lower case\n",
    "\n",
    "    ## Reproject x and y coordinates if necessary\n",
    "    try:\n",
    "        reproject = jdf.loc[\"filters.reprojection\"]\n",
    "        p1 = pyproj.CRS.from_string(in_crs_string=reproject.in_srs)\n",
    "        p2 = pyproj.CRS.from_string(in_crs_string=reproject.out_srs)\n",
    "        reprj_func = pyproj.Transformer.from_crs(crs_from=p1, crs_to=p2, always_xy=True)\n",
    "\n",
    "        x2, y2 = reprj_func.transform(xx=np.array(df[\"x\"]), yy=np.array(df[\"y\"]))\n",
    "        df[\"x\"] = pd.Series(x2)\n",
    "        df[\"y\"] = pd.Series(y2)\n",
    "\n",
    "    except KeyError:\n",
    "        pass\n",
    "\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "lines_to_next_cell": 1
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing highres/2007tx.json pipeline ... 42995 datapoints\n",
      "Processing highres/2010tr.json pipeline ... 84922 datapoints\n",
      "Processing highres/201x_Antarctica_Basler.json pipeline ... 2325792 datapoints\n",
      "Processing highres/20xx_Antarctica_DC8.json pipeline ... 12840213 datapoints\n",
      "Processing highres/20xx_Antarctica_TO.json pipeline ... 2895926 datapoints\n",
      "Processing highres/WISE_ISODYN_RadarByFlight.json pipeline ... 2182037 datapoints\n",
      "Processing highres/bed_WGS84_grid.json pipeline ... 244279 datapoints\n",
      "Processing highres/bed_depth_below_WGS84_datum.json pipeline ... 24508 datapoints\n",
      "Processing highres/istarxx.json pipeline ... 396369 datapoints\n"
     ]
    }
   ],
   "source": [
    "xyz_dict = {}\n",
    "for pf in sorted(glob.glob(\"highres/*.json\")):\n",
    "    print(f\"Processing {pf} pipeline\", end=\" ... \")\n",
    "    name = os.path.splitext(os.path.basename(pf))[0]\n",
    "    xyz_dict[name] = ascii_to_xyz(pipeline_file=pf)\n",
    "    print(f\"{len(xyz_dict[name])} datapoints\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 [Clean XYZ table](https://gmt.soest.hawaii.edu/doc/latest/GMT_Docs.html#table-data) to [Raster Grid](https://gmt.soest.hawaii.edu/doc/latest/GMT_Docs.html#grid-files)\n",
    "\n",
    "![Clean XYZ Table to Raster Grid via interpolation function](https://yuml.me/diagram/scruffy;dir:LR/class/[Clean-XYZ-Table|*.xyz]->[Interpolation-Function],[Interpolation-Function]->[Raster-Grid|*.tif/*.nc])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_region(xyz_data: pd.DataFrame, round_increment: int = 250) -> str:\n",
    "    \"\"\"\n",
    "    Gets an extended bounding box region for points in an xyz pandas.DataFrame with\n",
    "    columns x, y, and z. The coordinates will be rounded to values specified by the\n",
    "    round_increment parameter. Implementation uses gmt.info with the -I (increment)\n",
    "    setting, see also https://gmt.soest.hawaii.edu/doc/latest/gmtinfo.html#i\n",
    "\n",
    "    The output region is returned in a string format 'xmin/xmax/ymin/ymax' directly\n",
    "    usable as the -R 'region of interest' parameter in GMT. Indeed, the rounding is\n",
    "    specifically optimized to give grid dimensions for fastest results in programs like\n",
    "    GMT surface.\n",
    "\n",
    "    >>> xyz_data = pd.DataFrame(\n",
    "    ...     10000 * np.random.RandomState(seed=42).rand(30).reshape(10, 3),\n",
    "    ...     columns=[\"x\", \"y\", \"z\"],\n",
    "    ... )\n",
    "    >>> get_region(xyz_data=xyz_data)\n",
    "    '-250/9500/0/9750'\n",
    "    \"\"\"\n",
    "    assert (xyz_data.columns == pd.Index(data=[\"x\", \"y\", \"z\"], dtype=\"object\")).all()\n",
    "\n",
    "    with tempfile.NamedTemporaryFile(suffix=\".csv\") as tmpfile:\n",
    "        xyz_data.to_csv(tmpfile.name, header=False, index=False)\n",
    "        region = gmt.info(fname=tmpfile.name, I=f\"s{round_increment}\").strip()[2:]\n",
    "\n",
    "    return region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "def xyz_to_grid(\n",
    "    xyz_data: pd.DataFrame,\n",
    "    region: str,\n",
    "    spacing: int = 250,\n",
    "    tension: float = 0.35,\n",
    "    outfile: str = None,\n",
    "    mask_cell_radius: int = 3,\n",
    "):\n",
    "    \"\"\"\n",
    "    Performs interpolation of x, y, z point data to a raster grid.\n",
    "\n",
    "    >>> xyz_data = pd.DataFrame(\n",
    "    ...     600 * np.random.RandomState(seed=42).rand(60).reshape(20, 3),\n",
    "    ...     columns=[\"x\", \"y\", \"z\"],\n",
    "    ... )\n",
    "    >>> region = get_region(xyz_data=xyz_data)\n",
    "    >>> grid = xyz_to_grid(xyz_data=xyz_data, region=region, spacing=250)\n",
    "    >>> grid.to_array().shape\n",
    "    (1, 3, 3)\n",
    "    >>> grid.to_array().values\n",
    "    array([[[208.90086, 324.8038 , 515.93726],\n",
    "            [180.06642, 234.68915, 452.8586 ],\n",
    "            [170.60728, 298.23764, 537.49774]]], dtype=float32)\n",
    "    \"\"\"\n",
    "    ## Preprocessing with blockmedian\n",
    "    df = gmt.blockmedian(table=xyz_data, region=region, spacing=f\"{spacing}+e\")\n",
    "\n",
    "    ## XYZ point data to NetCDF grid via GMT surface\n",
    "    grid = gmt.surface(\n",
    "        x=df.x,\n",
    "        y=df.y,\n",
    "        z=df.z,\n",
    "        region=region,\n",
    "        spacing=f\"{spacing}+e\",\n",
    "        T=tension,\n",
    "        V=\"n\",  # normal verbosity: produce only fatal error messages\n",
    "        M=f\"{mask_cell_radius}c\",\n",
    "    )\n",
    "\n",
    "    ## Save grid to NetCDF with projection information\n",
    "    if outfile is not None:\n",
    "        # TODO add CRS!! See https://github.com/pydata/xarray/issues/2288\n",
    "        grid.to_netcdf(path=outfile)\n",
    "\n",
    "    ## Resample grid from gridline to pixel registration\n",
    "    with gmt.helpers.GMTTempFile(suffix=\".nc\") as tmpfile:\n",
    "        with gmt.clib.Session() as lib:\n",
    "            if outfile is not None:  # kind == \"file\"\n",
    "                file_context = gmt.helpers.dummy_context(outfile)\n",
    "            else:  # kind == \"grid\"\n",
    "                file_context = lib.virtualfile_from_grid(grid)\n",
    "                outfile = tmpfile.name\n",
    "            with file_context as infile:\n",
    "                kwargs = {\"T\": \"\", \"G\": f\"{outfile}\"}\n",
    "                arg_str = \" \".join([infile, gmt.helpers.build_arg_string(kwargs)])\n",
    "                lib.call_module(module=\"grdsample\", args=arg_str)\n",
    "            with xr.open_dataset(outfile) as dataset:\n",
    "                grid = dataset.load()\n",
    "\n",
    "    return grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gridding 2007tx ... done! (287, 79)\n",
      "Gridding 2010tr ... done! (95, 119)\n",
      "Gridding 201x_Antarctica_Basler ... done! (9215, 7679)\n",
      "Gridding 20xx_Antarctica_DC8 ... done! (12799, 15359)\n",
      "Gridding 20xx_Antarctica_TO ... done! (7679, 12799)\n",
      "Gridding WISE_ISODYN_RadarByFlight ... done! (5399, 4799)\n",
      "Gridding bed_WGS84_grid ... done! (127, 191)\n",
      "Gridding bed_depth_below_WGS84_datum ... done! (155, 131)\n",
      "Gridding istarxx ... done! (575, 383)\n"
     ]
    }
   ],
   "source": [
    "grid_dict = {}\n",
    "for name in xyz_dict.keys():\n",
    "    print(f\"Gridding {name}\", end=\" ... \")\n",
    "    xyz_data = xyz_dict[name]\n",
    "    region = get_region(xyz_data)\n",
    "    grid_dict[name] = xyz_to_grid(\n",
    "        xyz_data=xyz_data, region=region, outfile=f\"highres/{name}.nc\"\n",
    "    )\n",
    "    print(f\"done! {grid_dict[name].z.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Plot raster grids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grids = sorted(glob.glob(\"highres/*.nc\"))\n",
    "fig, axarr = plt.subplots(\n",
    "    nrows=1 + ((len(grids) - 1) // 3), ncols=3, squeeze=False, figsize=(15, 15)\n",
    ")\n",
    "\n",
    "for i, grid in enumerate(grids):\n",
    "    with rasterio.open(grid) as raster_source:\n",
    "        rasterio.plot.show(\n",
    "            source=raster_source, cmap=\"BrBG_r\", ax=axarr[i // 3, i % 3], title=grid\n",
    "        )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4 Get a proper test region grid\n",
    "\n",
    "If you want to train the model to 'fit' a particular region of Antarctica better,\n",
    "do it here! Just enter the (xmin, xmax, ymin, ymax) bounding box.\n",
    "The example below is for the Thwaites Glacier (THW) region."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !gmt grdcut -R-1524500/-1274500/-650000/-300000 highres/20xx_Antarctica_DC8.nc -Ghighres/20xx_Antarctica_DC8_THW.nc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = gmt.Figure()\n",
    "fig.grdimage(\n",
    "    grid=\"highres/20xx_Antarctica_DC8_THW.nc\",\n",
    "    projection=\"x1:2000000\",\n",
    "    frame=[\"WSne\", \"af\"],\n",
    "    cmap=\"oleron\",\n",
    ")\n",
    "fig.colorbar(S=True, position=\"JMR+n\", frame=\"af\")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Tile data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Big raster to many small square tiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "def get_window_bounds(\n",
    "    filepath: str,\n",
    "    pyproj_srs: str = \"epsg:3031\",\n",
    "    height: int = 36,  # 36 pixels * 250m resolution = 9000m height\n",
    "    width: int = 36,  # 36 pixels * 250m resolution = 9000m width\n",
    "    step: int = 3,  # 3 pixels * 250m resolution = 750m step size\n",
    ") -> list:\n",
    "    \"\"\"\n",
    "    Reads in a raster and finds tiles for them according to a stepped moving window.\n",
    "    Returns a list of bounding box coordinates corresponding to a tile that looks like\n",
    "    [(minx, miny, maxx, maxy), (minx, miny, maxx, maxy), ...]\n",
    "\n",
    "    >>> xr.DataArray(\n",
    "    ...     data=np.zeros(shape=(40, 36)),\n",
    "    ...     coords={\"y\": np.arange(0.5, 40.5), \"x\": np.arange(0.5, 36.5)},\n",
    "    ...     dims=[\"y\", \"x\"],\n",
    "    ... ).to_netcdf(path=\"/tmp/tmp_wb.nc\")\n",
    "    >>> get_window_bounds(filepath=\"/tmp/tmp_wb.nc\")\n",
    "    Tiling: /tmp/tmp_wb.nc ... 2\n",
    "    [(0.0, 4.0, 36.0, 40.0), (0.0, 1.0, 36.0, 37.0)]\n",
    "    >>> os.remove(\"/tmp/tmp_wb.nc\")\n",
    "    \"\"\"\n",
    "    assert height == width  # make sure it's a square!\n",
    "    assert height % 2 == 0  # make sure we are passing in an even number\n",
    "\n",
    "    with xr.open_dataarray(filepath) as dataset:\n",
    "        print(f\"Tiling: {filepath} ... \", end=\"\")\n",
    "\n",
    "        # Use salem to patch projection information into xarray.DataArray\n",
    "        # See also https://salem.readthedocs.io/en/latest/xarray_acc.html\n",
    "        dataset.attrs[\"pyproj_srs\"] = pyproj_srs\n",
    "        sgrid = dataset.salem.grid.corner_grid\n",
    "        assert sgrid.origin == \"lower-left\"  # should be \"lower-left\", not \"upper-left\"\n",
    "\n",
    "        ## Vectorized 'loop' along raster image from top to bottom, and left to right\n",
    "\n",
    "        # Get boolean true/false mask of where the data/nodata pixels lie\n",
    "        mask = dataset.to_masked_array(copy=False).mask\n",
    "        mask = np.ascontiguousarray(a=np.flipud(m=mask))  # flip on y-axis\n",
    "\n",
    "        # Sliding window view of the input geographical raster image\n",
    "        window_views = skimage.util.shape.view_as_windows(\n",
    "            arr_in=mask, window_shape=(height, width), step=step\n",
    "        )\n",
    "        filled_tiles = ~window_views.any(\n",
    "            axis=(-2, -1)\n",
    "        )  # find tiles which are fully filled, i.e. no blank/NODATA pixels\n",
    "        tile_indexes = np.argwhere(a=filled_tiles)  # get x and y index of filled tiles\n",
    "\n",
    "        # Convert x,y tile indexes to bounding box coordinates\n",
    "        # Complicated as xarray uses centre-based coordinates,\n",
    "        # while rasterio uses corner-based coordinates\n",
    "        windows = [\n",
    "            rasterio.windows.Window(\n",
    "                col_off=ulx * step, row_off=uly * step, width=width, height=height\n",
    "            )\n",
    "            for uly, ulx in tile_indexes\n",
    "        ]\n",
    "        window_bounds = [\n",
    "            rasterio.windows.bounds(\n",
    "                window=window,\n",
    "                transform=rasterio.Affine(\n",
    "                    sgrid.dx, 0, sgrid.x0, 0, -sgrid.dy, sgrid.y_coord[-1] + sgrid.dy\n",
    "                ),\n",
    "                width=width,\n",
    "                height=height,\n",
    "            )\n",
    "            for window in windows\n",
    "        ]\n",
    "        print(len(window_bounds))\n",
    "\n",
    "    return window_bounds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tiling: highres/2010tr.nc ... 225\n",
      "Tiling: highres/201x_Antarctica_Basler.nc ... 1831\n",
      "Tiling: highres/20xx_Antarctica_DC8.nc ... 7\n",
      "Tiling: highres/20xx_Antarctica_TO.nc ... 1494\n",
      "Tiling: highres/WISE_ISODYN_RadarByFlight.nc ... 7\n",
      "Tiling: highres/bed_WGS84_grid.nc ... 202\n",
      "Tiling: highres/bed_depth_below_WGS84_datum.nc ... 314\n",
      "Tiling: highres/istarxx.nc ... 190\n",
      "Total number of tiles: 4270\n"
     ]
    }
   ],
   "source": [
    "filepaths = sorted([g for g in glob.glob(\"highres/*.nc\") if g != \"highres/2007tx.nc\"])\n",
    "window_bounds = [get_window_bounds(filepath=grid) for grid in filepaths]\n",
    "window_bounds_concat = np.concatenate([w for w in window_bounds]).tolist()\n",
    "print(f\"Total number of tiles: {len(window_bounds_concat)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Subset tiles to those within grounding line, plot to show, and save"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "tile_gdf = pd.concat(\n",
    "    objs=[\n",
    "        gpd.GeoDataFrame(\n",
    "            pd.Series(\n",
    "                data=len(window_bound) * [os.path.basename(filepath)], name=\"grid_name\"\n",
    "            ),\n",
    "            crs={\"init\": \"epsg:3031\"},\n",
    "            geometry=[shapely.geometry.box(*bound) for bound in window_bound],\n",
    "        )\n",
    "        for filepath, window_bound in zip(filepaths, window_bounds)\n",
    "    ]\n",
    ").reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load grounding line polygon and buffer by 10km\n",
    "gline = gpd.read_file(\"misc/GroundingLine_Antarctica_v2.shp\")\n",
    "gline.crs = {\"init\": \"epsg:3031\"}\n",
    "gline.geometry = gline.geometry.buffer(distance=10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fcce133b710>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Select tiles within the buffered grounding line\n",
    "gdf = gpd.sjoin(left_df=tile_gdf, op=\"within\", right_df=gline, how=\"inner\")\n",
    "gdf = gdf.reset_index()[[\"grid_name\", \"geometry\"]]\n",
    "gdf.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "lines_to_next_cell": 1
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving only 4028 tiles out of 4270\n"
     ]
    }
   ],
   "source": [
    "# Save subsetted tiles to file in both EPSG 3031 and 4326\n",
    "print(f\"Saving only {len(gdf)} tiles out of {len(tile_gdf)}\")\n",
    "gdf.to_file(filename=\"model/train/tiles_3031.geojson\", driver=\"GeoJSON\")\n",
    "gdf.to_crs(crs={\"init\": \"epsg:4326\"}).to_file(\n",
    "    filename=\"model/train/tiles_4326.geojson\", driver=\"GeoJSON\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Do the actual tiling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "def selective_tile(\n",
    "    filepath: str,\n",
    "    window_bounds: list,\n",
    "    padding: int = 0,  # in projected coordinate system units, e.g. 1000 for 1km\n",
    "    resolution: float = None,  # spatial resolution, e.g. 500 for 500m\n",
    "    gapfiller: float = None,  # number to fill in NaNs, e.g. 0\n",
    "    interpolate: bool = True,  # resample grid correct bounds, else just use slicing\n",
    ") -> np.ndarray:\n",
    "    \"\"\"\n",
    "    Reads in a raster and tiles them selectively according to list of window_bounds in\n",
    "    the form of (xmin, ymin, xmax, ymax), optionally extended by some padding length.\n",
    "    The images will be bilinearly interpolated/geo-registered to match the exact bounds\n",
    "    by default, else set interpolate=False to simply use direct slicing, recommended\n",
    "    only if you are confident that the bounding boxes will cut the grid directly.\n",
    "    A resolution can be optionally set to e.g. 500 to resample the raster tiles to some\n",
    "    desired spatial resolution/shape.\n",
    "    Gaps in the main raster can be filled by passing in a number to gapfiller.\n",
    "\n",
    "    >>> xr.DataArray(\n",
    "    ...     data=np.flipud(m=np.diag(v=np.arange(8))).astype(dtype=np.float32),\n",
    "    ...     coords={\"y\": np.linspace(7, 0, 8), \"x\": np.linspace(0, 7, 8)},\n",
    "    ...     dims=[\"y\", \"x\"],\n",
    "    ... ).to_netcdf(path=\"/tmp/tmp_st.nc\", mode=\"w\")\n",
    "    >>> selective_tile(\n",
    "    ...    filepath=\"/tmp/tmp_st.nc\",\n",
    "    ...    window_bounds=[(0.5, 0.5, 2.5, 2.5), (2.5, 1.5, 4.5, 3.5)],\n",
    "    ... )\n",
    "    Tiling: /tmp/tmp_st.nc ... done!\n",
    "    array([[[[0., 2.],\n",
    "             [1., 0.]]],\n",
    "    <BLANKLINE>\n",
    "    <BLANKLINE>\n",
    "           [[[3., 0.],\n",
    "             [0., 0.]]]], dtype=float32)\n",
    "    >>> os.remove(\"/tmp/tmp_st.nc\")\n",
    "    \"\"\"\n",
    "\n",
    "    # Convert list of bounding box tuples to nice rasterio.coords.BoundingBox class\n",
    "    window_bounds = [\n",
    "        rasterio.coords.BoundingBox(\n",
    "            left=x0 - padding, bottom=y0 - padding, right=x1 + padding, top=y1 + padding\n",
    "        )\n",
    "        for x0, y0, x1, y1 in window_bounds  # xmin, ymin, xmax, ymax\n",
    "    ]\n",
    "\n",
    "    # Retrieve tiles from the main raster\n",
    "    with xr.open_rasterio(filepath, chunks={}) as dataset:\n",
    "        print(f\"Tiling: {filepath} ... \", end=\"\")\n",
    "\n",
    "        assert dataset.res[0] == dataset.res[1]  # ensure our pixels are square\n",
    "\n",
    "        if resolution is None:\n",
    "            resolution = dataset.res[0]\n",
    "        halfpix = resolution / 2  # half pixel width used as offset during interpolation\n",
    "\n",
    "        # Use first window_bound to determine size of output tile\n",
    "        assert dataset.y[0] > dataset.y[-1]  # check that y runs from top to bottom\n",
    "        y_length = int((window_bounds[0].top - window_bounds[0].bottom) / resolution)\n",
    "        x_length = int((window_bounds[0].right - window_bounds[0].left) / resolution)\n",
    "\n",
    "        # Subset dataset according to window bound (wb)\n",
    "        if interpolate:\n",
    "            subset_func = dask.delayed(\n",
    "                lambda y, x: dataset.interp(y=y, x=x, method=\"linear\")\n",
    "            )\n",
    "        else:\n",
    "            subset_func = dask.delayed(\n",
    "                lambda y, x: dataset.sel(y=y, x=x, method=\"nearest\", tolerance=0)\n",
    "            )\n",
    "\n",
    "        daarray_list = []\n",
    "        for wb in window_bounds:\n",
    "            # Interpolation (billinear)\n",
    "            new_y = np.linspace(wb.top - halfpix, wb.bottom + halfpix, num=y_length)\n",
    "            new_x = np.linspace(wb.left + halfpix, wb.right - halfpix, num=x_length)\n",
    "            da_subset = subset_func(y=new_y, x=new_x)\n",
    "\n",
    "            # Mask NaN values, if the NaN value is 'nan' itself, we convert to small num\n",
    "            da_masked = dask.delayed(dask.array.ma.masked_values)(\n",
    "                x=da_subset,\n",
    "                value=np.nan_to_num(dataset.nodatavals[0], nan=np.nan_to_num(-np.inf)),\n",
    "            )\n",
    "            daarray_list.append(da_masked)\n",
    "\n",
    "        # Run actual heavy computation\n",
    "        daarray_stack = dask.delayed(dask.array.stack)(daarray_list)\n",
    "        # Stack interpolated grids together, and retrieve the data and mask\n",
    "        daarray_stack = daarray_stack.compute()\n",
    "\n",
    "        assert daarray_stack.ndim == 4  # check that shape is like (m, 1, height, width)\n",
    "        assert daarray_stack.shape[1] == 1  # channel-first (assuming only 1 channel)\n",
    "        assert not 0 in daarray_stack.shape  # ensure no empty dimensions (bad window)\n",
    "\n",
    "        out_tiles = np.asarray(a=dask.array.ma.getdata(daarray_stack), dtype=np.float32)\n",
    "        mask = np.asanyarray(a=dask.array.ma.getmaskarray(daarray_stack))\n",
    "\n",
    "    # Gapfill main raster if there are blank spaces\n",
    "    if mask.any():  # check that there are no NAN values\n",
    "        nan_grid_indexes = np.argwhere(mask.any(axis=(-3, -2, -1))).ravel()\n",
    "\n",
    "        # Replace NaN values with some number, else raise error\n",
    "        if gapfiller is not None:\n",
    "            print(f\"gapfilling ... \", end=\"\")\n",
    "\n",
    "            np.copyto(\n",
    "                dst=out_tiles,\n",
    "                src=np.full_like(a=out_tiles, fill_value=gapfiller),\n",
    "                where=mask,\n",
    "            )\n",
    "\n",
    "        else:\n",
    "            for i in nan_grid_indexes:\n",
    "                daarray_list[i].plot()\n",
    "                plt.show()\n",
    "            print(\n",
    "                f\"WARN: Tiles have missing data, try passing in a number to 'gapfiller'\"\n",
    "            )\n",
    "\n",
    "    print(\"done!\")\n",
    "    return out_tiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "geodataframe = gpd.read_file(\"model/train/tiles_3031.geojson\")\n",
    "filepaths = geodataframe.grid_name.unique()\n",
    "window_bounds = [\n",
    "    [geom.bounds for geom in geodataframe.query(\"grid_name == @filepath\").geometry]\n",
    "    for filepath in filepaths\n",
    "]\n",
    "window_bounds_concat = np.concatenate([w for w in window_bounds]).tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tile High Resolution data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tiling: highres/2010tr.nc ... done!\n",
      "Tiling: highres/201x_Antarctica_Basler.nc ... done!\n",
      "Tiling: highres/20xx_Antarctica_TO.nc ... done!\n",
      "Tiling: highres/WISE_ISODYN_RadarByFlight.nc ... done!\n",
      "Tiling: highres/bed_WGS84_grid.nc ... done!\n",
      "Tiling: highres/bed_depth_below_WGS84_datum.nc ... done!\n",
      "Tiling: highres/istarxx.nc ... done!\n",
      "(4028, 1, 36, 36) float32\n"
     ]
    }
   ],
   "source": [
    "hireses = [\n",
    "    selective_tile(filepath=f\"highres/{f}\", window_bounds=w, interpolate=False)\n",
    "    for f, w in zip(filepaths, window_bounds)\n",
    "]\n",
    "hires = np.concatenate(hireses)\n",
    "print(hires.shape, hires.dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tile low resolution data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "lines_to_next_cell": 1
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tiling: lowres/bedmap2_bed.tif ... done!\n",
      "(4028, 1, 11, 11) float32\n"
     ]
    }
   ],
   "source": [
    "lores = selective_tile(\n",
    "    filepath=\"lowres/bedmap2_bed.tif\", window_bounds=window_bounds_concat, padding=1000\n",
    ")\n",
    "print(lores.shape, lores.dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tile miscellaneous data\n",
    "\n",
    "- REMA (100m) is gapfilled with a 200m_filled version (bilinear interpolated to 100m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "def save_array_to_grid(\n",
    "    outfilepath: str,  # without any extension! Will append .tif and .nc to this name\n",
    "    window_bound: tuple,  # bounding box in format (minx, miny, maxx, maxy)\n",
    "    array: np.ndarray,  # must be in CHW format (channel, height, width)\n",
    "    save_netcdf: bool = False,  # whether to also save a NetCDF file\n",
    "    crs: str = \"+proj=stere +lat_0=-90 +lat_ts=-71 +lon_0=0 +k=1 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs\",  # projected coordinate system to use\n",
    "    dtype: str = None,  # data type to use e.g. np.float32, default inferred from array\n",
    "    nodataval: float = -2000,  # what to use as NaN, hardcoded default to -2000m\n",
    "    tiled: bool = False,  # store data arranged in square tiles, default is False\n",
    "    compression: rasterio.enums.Compression = rasterio.enums.Compression.none.value,\n",
    ") -> xr.DataArray:\n",
    "    \"\"\"\n",
    "    Saves a numpy array to geotiff and netcdf format according to\n",
    "    some bounding box window given as (minx, miny, maxx, maxy).\n",
    "    Script will append \".tif\" and \".nc\" file extension to the outfilepath\n",
    "    for geotiff and netcdf outputs respectively. If save_netcdf=True,\n",
    "    will also return an xarray.DataArray version of the resulting grid.\n",
    "\n",
    "    Optionally set a compression algorithm to compact the geotiff\n",
    "    into a smaller filesize e.g. rasterio.enums.Compression.lzw or zstd.\n",
    "    \"\"\"\n",
    "\n",
    "    assert array.ndim == 3\n",
    "    assert array.shape[0] == 1  # check that there is only one band/channel\n",
    "\n",
    "    transform = rasterio.transform.from_bounds(\n",
    "        *window_bound, height=array.shape[1], width=array.shape[2]\n",
    "    )\n",
    "\n",
    "    # Save array as a GeoTiff first\n",
    "    with rasterio.open(\n",
    "        f\"{outfilepath}.tif\",\n",
    "        mode=\"w\",\n",
    "        driver=\"GTiff\",\n",
    "        height=array.shape[1],\n",
    "        width=array.shape[2],\n",
    "        count=1,\n",
    "        crs=crs,\n",
    "        transform=transform,\n",
    "        dtype=array.dtype if dtype is None else dtype,\n",
    "        nodata=nodataval,\n",
    "        compress=compression,\n",
    "        tiled=tiled,\n",
    "        bigtiff=\"YES\",\n",
    "    ) as new_geotiff:\n",
    "        new_geotiff.write(array[0, :, :], 1)\n",
    "\n",
    "    # Convert deepbedmap3 and cubicbedmap2 from geotiff to netcdf format\n",
    "    if save_netcdf is True:\n",
    "        with xr.open_rasterio(f\"{outfilepath}.tif\") as dataset:\n",
    "            dataset.name: str = \"z\"\n",
    "            dataset.to_netcdf(f\"{outfilepath}.nc\")\n",
    "    else:\n",
    "        dataset = None\n",
    "\n",
    "    return dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Gapfill REMA_100m_dem.tif with REMA_200m_dem_filled.tif\n",
    "if not os.path.exists(\"misc/REMA_100m_dem_filled.tif\"):\n",
    "    window_bound_big = rasterio.coords.BoundingBox(\n",
    "        left=-2_700_000.0, bottom=-2_200_000.0, right=2_800_000.0, top=2_300_000.0\n",
    "    )\n",
    "\n",
    "    # Read in 100m spatial resolution REMA which has data gaps in some areas\n",
    "    with rasterio.open(\"misc/REMA_100m_dem.tif\") as REMA100:\n",
    "        window = rasterio.windows.from_bounds(\n",
    "            *window_bound_big, transform=REMA100.transform, precision=None\n",
    "        ).round_offsets()\n",
    "        array100 = REMA100.read(indexes=1, masked=True, window=window)\n",
    "\n",
    "        # Read in 200m spatial resolution REMA which has complete coverage\n",
    "        # Resample it using bilinear interpolation to match the REMA 100m dataset\n",
    "        with rasterio.open(\"misc/REMA_200m_dem_filled.tif\") as REMA200_filled:\n",
    "            window2 = rasterio.windows.from_bounds(\n",
    "                *window_bound_big, transform=REMA200_filled.transform, precision=None\n",
    "            ).round_offsets()\n",
    "            array200 = REMA200_filled.read(\n",
    "                indexes=1,\n",
    "                masked=True,\n",
    "                window=window2,\n",
    "                out_shape=array100.shape,\n",
    "                resampling=rasterio.enums.Resampling.bilinear,\n",
    "            )\n",
    "\n",
    "    assert array100.shape == array200.shape == (45000, 55000)\n",
    "    # fill in data gaps, i.e. where mask is True\n",
    "    np.copyto(dst=array100, src=array200, where=array100.mask)\n",
    "\n",
    "    save_array_to_grid(\n",
    "        outfilepath=\"misc/REMA_100m_dem_filled\",\n",
    "        window_bound=window_bound_big,\n",
    "        array=np.expand_dims(array100, axis=0),\n",
    "        dtype=array100.dtype,\n",
    "        nodataval=REMA200_filled.nodata,\n",
    "        tiled=True,  # store data arranged as tiles instead of strips for easier tiling\n",
    "        compression=rasterio.enums.Compression.lzw.value,  # Lempel-Ziv-Welch, lossless\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed |  2min 15.5s\n",
      "[########################################] | 100% Completed |  4min 32.0s\n",
      "[########################################] | 100% Completed |  2min 18.6s\n",
      "done!\n",
      "(4028, 1, 110, 110) float32\n"
     ]
    }
   ],
   "source": [
    "with dask.diagnostics.ProgressBar():\n",
    "    rema = selective_tile(\n",
    "        filepath=\"misc/REMA_100m_dem_filled.tif\",\n",
    "        window_bounds=window_bounds_concat,\n",
    "        padding=1000,\n",
    "    )\n",
    "print(rema.shape, rema.dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tiling: netcdf:misc/antarctic_ice_vel_phase_map_v01.nc:VX ... done!\n",
      "Tiling: netcdf:misc/antarctic_ice_vel_phase_map_v01.nc:VY ... done!\n",
      "(4028, 2, 22, 22) float32\n"
     ]
    }
   ],
   "source": [
    "measures_velocity_x = selective_tile(\n",
    "    filepath=\"netcdf:misc/antarctic_ice_vel_phase_map_v01.nc:VX\",\n",
    "    window_bounds=window_bounds_concat,\n",
    "    padding=1000,\n",
    "    resolution=500,\n",
    ")\n",
    "measures_velocity_y = selective_tile(\n",
    "    filepath=\"netcdf:misc/antarctic_ice_vel_phase_map_v01.nc:VY\",\n",
    "    window_bounds=window_bounds_concat,\n",
    "    padding=1000,\n",
    "    resolution=500,\n",
    ")\n",
    "assert measures_velocity_x.shape == measures_velocity_y.shape\n",
    "measuresvelocity = np.concatenate([measures_velocity_x, measures_velocity_y], axis=1)\n",
    "print(measuresvelocity.shape, measuresvelocity.dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tiling: misc/Arthern_accumulation_bedmap2_grid1.tif ... done!\n",
      "(4028, 1, 11, 11) float32\n"
     ]
    }
   ],
   "source": [
    "accumulation = selective_tile(\n",
    "    filepath=\"misc/Arthern_accumulation_bedmap2_grid1.tif\",\n",
    "    window_bounds=window_bounds_concat,\n",
    "    padding=1000,\n",
    ")\n",
    "print(accumulation.shape, accumulation.dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Save the arrays\n",
    "\n",
    "We'll save the numpy arrays to the filesystem first.\n",
    "We label inputs as X (low resolution bed DEMs) and W (miscellaneous).\n",
    "Groundtruth high resolution bed DEMs are labelled as Y.\n",
    "\n",
    "Also, we'll serve the data up on the web using:\n",
    "- [Quilt](https://quiltdata.com/) - Python data versioning\n",
    "- [Dat](https://datproject.org/) - Distributed data sharing (TODO)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.makedirs(name=\"model/train\", exist_ok=True)\n",
    "np.save(file=\"model/train/W1_data.npy\", arr=rema)\n",
    "np.save(file=\"model/train/W2_data.npy\", arr=measuresvelocity)\n",
    "np.save(file=\"model/train/W3_data.npy\", arr=accumulation)\n",
    "np.save(file=\"model/train/X_data.npy\", arr=lores)\n",
    "np.save(file=\"model/train/Y_data.npy\", arr=hires)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quilt\n",
    "\n",
    "Login -> Build -> Push"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Launching a web browser...\n",
      "If that didn't work, please visit the following URL: https://pkg.quiltdata.com/login\n",
      "\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter the code from the webpage:  eyJjb2RlIjogImE2YjU1NWNjLTNjNDctNDU2Zi1iYTRiLTBmNGNmZWVhODRiNCIsICJpZCI6ICIyOWI4YzUyNS1lZmM1LTQ5NTItOGQ4Yy03NzQyYTg1YmI1MmEifQ==\n"
     ]
    }
   ],
   "source": [
    "quilt.login()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Tiled datasets for training neural network\n",
    "quilt.build(package=\"weiji14/deepbedmap/model/train/W1_data\", path=rema)\n",
    "quilt.build(package=\"weiji14/deepbedmap/model/train/W2_data\", path=measuresvelocity)\n",
    "quilt.build(package=\"weiji14/deepbedmap/model/train/W3_data\", path=accumulation)\n",
    "quilt.build(package=\"weiji14/deepbedmap/model/train/X_data\", path=lores)\n",
    "quilt.build(package=\"weiji14/deepbedmap/model/train/Y_data\", path=hires)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Original datasets for neural network predictions on bigger area\n",
    "quilt.build(\n",
    "    package=\"weiji14/deepbedmap/lowres/bedmap2_bed\", path=\"lowres/bedmap2_bed.tif\"\n",
    ")\n",
    "quilt.build(\n",
    "    package=\"weiji14/deepbedmap/misc/REMA_100m_dem_filled\",\n",
    "    path=\"misc/REMA_100m_dem_filled.tif\",\n",
    ")\n",
    "with xr.open_dataset(\"misc/antarctic_ice_vel_phase_map_v01.nc\") as ds:\n",
    "    with tempfile.NamedTemporaryFile(suffix=\".nc\") as tmpfile:\n",
    "        ds[[\"VX\", \"VY\"]].to_netcdf(path=tmpfile.name)  # save only VX, VY variables\n",
    "        quilt.build(\n",
    "            package=\"weiji14/deepbedmap/misc/antarctic_ice_vel_phase_map_v01_VX_VY\",\n",
    "            path=tmpfile.name,\n",
    "        )\n",
    "quilt.build(\n",
    "    package=\"weiji14/deepbedmap/misc/Arthern_accumulation_bedmap2_grid1\",\n",
    "    path=\"misc/Arthern_accumulation_bedmap2_grid1.tif\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fetching upload URLs from the registry...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0.00/15.3G [00:00<?, ?B/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Uploading 15 fragments (15269102922 bytes)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████▉| 15.2G/15.3G [00:01<1:26:34, 7.01kB/s]  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 15.3G/15.3G [00:23<00:00, 659MB/s]   \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Uploading package metadata...\n",
      "Updating the 'latest' tag...\n",
      "Push complete. weiji14/deepbedmap is live:\n",
      "https://quiltdata.com/package/weiji14/deepbedmap\n"
     ]
    }
   ],
   "source": [
    "quilt.push(package=\"weiji14/deepbedmap\", is_public=True)"
   ]
  }
 ],
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