{
 "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": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python       : 3.6.6 | packaged by conda-forge | (default, Oct 11 2018, 14:33:06) \n",
      "Geopandas    : 0.4.0+26.g9e584cc\n",
      "GMT          : 0.0.1a0+16.g7004aa0\n",
      "Numpy        : 1.14.5\n",
      "Rasterio     : 1.0.13\n",
      "Scikit-image : 0.14.2\n",
      "Xarray       : 0.11.3\n"
     ]
    }
   ],
   "source": [
    "import glob\n",
    "import hashlib\n",
    "import io\n",
    "import json\n",
    "import os\n",
    "import shutil\n",
    "import sys\n",
    "\n",
    "import requests\n",
    "import tqdm\n",
    "import yaml\n",
    "\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 xarray as xr\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": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "def download_to_path(path: str, url: str):\n",
    "    r\"\"\"\n",
    "    Download from a url to a path\n",
    "\n",
    "    >>> download_to_path(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",
    "    <Response [200]>\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",
    "    # if not os.path.exists(path=path):\n",
    "    r = requests.get(url=url, stream=True)\n",
    "    with open(file=path, mode=\"wb\") as fd:\n",
    "        for chunk in r.iter_content(chunk_size=1024):\n",
    "            fd.write(chunk)\n",
    "    return r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "def check_sha256(path: str):\n",
    "    \"\"\"\n",
    "    Returns SHA256 checksum of a file\n",
    "\n",
    "    >>> download_to_path(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",
    "    <Response [200]>\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.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 Ice Flow](https://doi.org/10.5067/OC7B04ZM9G6Q))"
   ]
  },
  {
   "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\" >MEaSUREs_IceFlowSpeed_450m.tif</td>\n",
       "                        <td id=\"T_miscrow2_col2\" class=\"data row2 col2\" ><a target=\"_blank\" href=\"http://data.pgc.umn.edu/gis/packages/quantarctica/Quantarctica3/Glaciology/MEaSUREs%20Ice%20Flow%20Velocity/MEaSUREs_IceFlowSpeed_450m.tif\">http://data.pgc.umn.edu/gis/packages/quantarctica/Quantarctica3/Glaciology/MEaSUREs%20Ice%20Flow%20Velocity/MEaSUREs_IceFlowSpeed_450m.tif</a></td>\n",
       "                        <td id=\"T_miscrow2_col3\" class=\"data row2 col3\" >4a4efc3a84204c3d67887e8d7fa1186467b51e696451f2832ebbea3ca491c8a8</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": {},
   "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",
       "    </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": {
    "lines_to_next_cell": 2
   },
   "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",
    "    >>> download_to_path(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",
    "    <Response [200]>\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",
    "    j = json.loads(open(pipeline_file).read())\n",
    "    jdf = pd.io.json.json_normalize(j, record_path=\"pipeline\")\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",
    "\n",
    "    path_pattern = os.path.join(os.path.dirname(pipeline_file), reader.filename)\n",
    "    files = [file for file in glob.glob(path_pattern)]\n",
    "    assert len(files) > 0  # check that there are actually files being matched!\n",
    "\n",
    "    df = pd.concat(\n",
    "        pd.read_csv(f, sep=sep, header=skip, names=names, usecols=usecols)\n",
    "        for f in files\n",
    "    )\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.Proj(init=reproject.in_srs)\n",
    "        p2 = pyproj.Proj(init=reproject.out_srs)\n",
    "        reproj_func = lambda x, y: pyproj.transform(p1=p1, p2=p2, x=x, y=y)\n",
    "\n",
    "        x2, y2 = reproj_func(np.array(df[\"x\"]), 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": {},
   "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/bed_WGS84_grid.json pipeline ... 244279 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": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "def get_region(xyz_data: pd.DataFrame) -> str:\n",
    "    \"\"\"\n",
    "    Gets the bounding box region of an xyz pandas.DataFrame in string\n",
    "    format xmin/xmax/ymin/ymax rounded to 5 decimal places.\n",
    "    Used for the -R 'region of interest' parameter in GMT.\n",
    "\n",
    "    >>> xyz_data = pd.DataFrame(np.random.RandomState(seed=42).rand(30).reshape(10, 3))\n",
    "    >>> get_region(xyz_data=xyz_data)\n",
    "    '0.05808/0.83244/0.02058/0.95071'\n",
    "    \"\"\"\n",
    "    xmin, ymin, _ = xyz_data.min(axis=\"rows\")\n",
    "    xmax, ymax, _ = xyz_data.max(axis=\"rows\")\n",
    "    return f\"{xmin:.5f}/{xmax:.5f}/{ymin:.5f}/{ymax:.5f}\""
   ]
  },
  {
   "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 = 1000*pd.DataFrame(np.random.RandomState(seed=42).rand(60).reshape(20, 3))\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, 5, 5)\n",
    "    >>> grid.to_array().values\n",
    "    array([[[403.17618 , 544.92535 , 670.7824  , 980.75055 , 961.47723 ],\n",
    "            [379.0757  , 459.26407 , 314.38297 , 377.78555 , 546.0469  ],\n",
    "            [450.67664 , 343.26    ,  88.391594, 260.10492 , 452.3337  ],\n",
    "            [586.09906 , 469.74008 , 216.8168  , 486.9802  , 642.2116  ],\n",
    "            [451.4794  , 652.7244  , 325.77896 , 879.8973  , 916.7921  ]]],\n",
    "          dtype=float32)\n",
    "    \"\"\"\n",
    "    ## Preprocessing with blockmedian\n",
    "    with gmt.helpers.GMTTempFile(suffix=\".txt\") as tmpfile:\n",
    "        with gmt.clib.Session() as lib:\n",
    "            file_context = lib.virtualfile_from_matrix(matrix=xyz_data.values)\n",
    "            with file_context as infile:\n",
    "                kwargs = {\"V\": \"\", \"R\": region, \"I\": f\"{spacing}+e\"}\n",
    "                arg_str = \" \".join(\n",
    "                    [infile, gmt.helpers.build_arg_string(kwargs), \"->\" + tmpfile.name]\n",
    "                )\n",
    "                lib.call_module(module=\"blockmedian\", args=arg_str)\n",
    "            x, y, z = np.loadtxt(fname=tmpfile.name, unpack=True)\n",
    "\n",
    "    ## XYZ point data to NetCDF grid via GMT surface\n",
    "    grid = gmt.surface(\n",
    "        x=x,\n",
    "        y=y,\n",
    "        z=z,\n",
    "        region=region,\n",
    "        spacing=f\"{spacing}+e\",\n",
    "        T=tension,\n",
    "        V=\"\",\n",
    "        M=f\"{mask_cell_radius}c\",\n",
    "    )\n",
    "\n",
    "    ## Save grid to NetCDF with projection information\n",
    "    if outfile is not None:\n",
    "        grid.to_netcdf(path=outfile)  ##TODO add CRS!!\n",
    "\n",
    "    return grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gridding 2007tx ... done! (1, 266, 74)\n",
      "Gridding 2010tr ... done! (1, 92, 115)\n",
      "Gridding 201x_Antarctica_Basler ... done! (1, 9062, 7437)\n",
      "Gridding 20xx_Antarctica_DC8 ... done! (1, 12388, 15326)\n",
      "Gridding 20xx_Antarctica_TO ... done! (1, 7671, 12287)\n",
      "Gridding bed_WGS84_grid ... done! (1, 123, 163)\n",
      "Gridding istarxx ... done! (1, 552, 377)\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].to_array().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": [
    "## 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, height: int = 32, width: int = 32, step: int = 4\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=(36, 32)),\n",
    "    ...     coords={\"x\": np.arange(1, 37), \"y\": np.arange(1, 33)},\n",
    "    ...     dims=[\"x\", \"y\"],\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",
    "    [(0.5, 4.5, 32.5, 36.5), (0.5, 0.5, 32.5, 32.5)]\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_rasterio(filepath) as dataset:\n",
    "        print(f\"Tiling: {filepath} ... \", end=\"\")\n",
    "        # Vectorized 'loop' along the 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 = mask[0, :, :]  # change to shape (height, width)\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(filled_tiles)  # get x and y index of filled tiles\n",
    "\n",
    "        # Convert x,y tile indexes to bounding box 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(*dataset.transform),\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 ... 164\n",
      "Tiling: highres/201x_Antarctica_Basler.nc ... 961\n",
      "Tiling: highres/20xx_Antarctica_DC8.nc ... 19\n",
      "Tiling: highres/20xx_Antarctica_TO.nc ... 989\n",
      "Tiling: highres/bed_WGS84_grid.nc ... 172\n",
      "Tiling: highres/istarxx.nc ... 175\n",
      "Total number of tiles: 2480\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": [
    "### Show and save tiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa9296b8c18>"
      ]
     },
     "execution_count": 19,
     "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": [
    "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)\n",
    "gdf.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "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": 21,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "def selective_tile(\n",
    "    filepath: str,\n",
    "    window_bounds: list,\n",
    "    padding: int = 0,\n",
    "    out_shape: tuple = None,\n",
    "    gapfill_raster_filepath: str = None,\n",
    ") -> np.ndarray:\n",
    "    \"\"\"\n",
    "    Reads in raster and tiles them selectively.\n",
    "    Tiles will go according to list of window_bounds.\n",
    "    Output shape can be set to e.g. (16,16) to resample input raster to\n",
    "    some desired shape/resolution.\n",
    "\n",
    "    >>> xr.DataArray(\n",
    "    ...     data=np.random.RandomState(seed=42).rand(64).reshape(8, 8),\n",
    "    ...     coords={\"x\": np.arange(8), \"y\": np.arange(8)},\n",
    "    ...     dims=[\"x\", \"y\"],\n",
    "    ... ).to_netcdf(path=\"/tmp/tmp_st.nc\", mode=\"w\")\n",
    "    >>> selective_tile(\n",
    "    ...    filepath=\"/tmp/tmp_st.nc\",\n",
    "    ...    window_bounds=[(1.0, 4.0, 3.0, 6.0), (2.0, 5.0, 4.0, 7.0)],\n",
    "    ... )\n",
    "    Tiling: /tmp/tmp_st.nc\n",
    "    array([[[[0.18485446, 0.96958464],\n",
    "             [0.4951769 , 0.03438852]]],\n",
    "    <BLANKLINE>\n",
    "    <BLANKLINE>\n",
    "           [[[0.04522729, 0.32533032],\n",
    "             [0.96958464, 0.77513283]]]], dtype=float32)\n",
    "    >>> os.remove(\"/tmp/tmp_st.nc\")\n",
    "    \"\"\"\n",
    "    array_list = []\n",
    "\n",
    "    with rasterio.open(filepath) as dataset:\n",
    "        print(f\"Tiling: {filepath}\")\n",
    "        for window_bound in window_bounds:\n",
    "\n",
    "            if padding > 0:\n",
    "                window_bound = (\n",
    "                    window_bound[0] - padding,  # minx\n",
    "                    window_bound[1] - padding,  # miny\n",
    "                    window_bound[2] + padding,  # maxx\n",
    "                    window_bound[3] + padding,  # maxy\n",
    "                )\n",
    "\n",
    "            window = rasterio.windows.from_bounds(\n",
    "                *window_bound, transform=dataset.transform, precision=None\n",
    "            ).round_offsets()\n",
    "\n",
    "            # Read the raster according to the crop window\n",
    "            array = dataset.read(\n",
    "                indexes=list(range(1, dataset.count + 1)),\n",
    "                masked=True,\n",
    "                window=window,\n",
    "                out_shape=out_shape,\n",
    "            )\n",
    "            assert array.ndim == 3  # check that we have shape like (1, height, width)\n",
    "            assert array.shape[0] == 1  # channel-first (assuming only 1 channel)\n",
    "\n",
    "            try:\n",
    "                assert not array.mask.any()  # check that there are no NAN values\n",
    "            except AssertionError:\n",
    "                # Replace pixels from another raster if available, else raise error\n",
    "                if gapfill_raster_filepath is not None:\n",
    "                    with rasterio.open(gapfill_raster_filepath) as dataset2:\n",
    "                        window2 = rasterio.windows.from_bounds(\n",
    "                            *window_bound, transform=dataset2.transform, precision=None\n",
    "                        ).round_offsets()\n",
    "\n",
    "                        array2 = dataset2.read(\n",
    "                            indexes=list(range(1, dataset2.count + 1)),\n",
    "                            masked=True,\n",
    "                            window=window2,\n",
    "                            out_shape=array.shape[1:],\n",
    "                        )\n",
    "\n",
    "                    np.copyto(\n",
    "                        dst=array, src=array2, where=array.mask\n",
    "                    )  # fill in gaps where mask is True\n",
    "                else:\n",
    "                    plt.imshow(array.data[0, :, :])\n",
    "                    plt.show()\n",
    "                    raise ValueError(\n",
    "                        f\"Tile has missing data, try passing in gapfill_raster_filepath\"\n",
    "                    )\n",
    "\n",
    "            # assert array.shape[0] == array.shape[1]  # check that height==width\n",
    "            array_list.append(array.data.astype(dtype=np.float32))\n",
    "\n",
    "    return np.stack(arrays=array_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tiling: highres/2010tr.nc\n",
      "Tiling: highres/201x_Antarctica_Basler.nc\n",
      "Tiling: highres/20xx_Antarctica_DC8.nc\n",
      "Tiling: highres/20xx_Antarctica_TO.nc\n",
      "Tiling: highres/bed_WGS84_grid.nc\n",
      "Tiling: highres/istarxx.nc\n",
      "(2480, 1, 32, 32) float32\n"
     ]
    }
   ],
   "source": [
    "hireses = [\n",
    "    selective_tile(filepath=f\"highres/{f}\", window_bounds=w)\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": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tiling: lowres/bedmap2_bed.tif\n",
      "(2480, 1, 10, 10) 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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tiling: misc/REMA_100m_dem.tif\n",
      "(2480, 1, 100, 100) float32\n"
     ]
    }
   ],
   "source": [
    "rema = selective_tile(\n",
    "    filepath=\"misc/REMA_100m_dem.tif\",\n",
    "    window_bounds=window_bounds_concat,\n",
    "    padding=1000,\n",
    "    gapfill_raster_filepath=\"misc/REMA_200m_dem_filled.tif\",\n",
    ")\n",
    "print(rema.shape, rema.dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tiling: misc/MEaSUREs_IceFlowSpeed_450m.tif\n",
      "(2480, 1, 20, 20) float32\n"
     ]
    }
   ],
   "source": [
    "measuresiceflow = selective_tile(\n",
    "    filepath=\"misc/MEaSUREs_IceFlowSpeed_450m.tif\",\n",
    "    window_bounds=window_bounds_concat,\n",
    "    padding=1000,\n",
    "    out_shape=(20, 20),\n",
    ")\n",
    "print(measuresiceflow.shape, measuresiceflow.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": 27,
   "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=measuresiceflow)\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": 28,
   "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",
      "Failed to launch the browser: Command '['xdg-open', 'https://pkg.quiltdata.com/login']' returned non-zero exit status 3.\n",
      "\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter the code from the webpage:  eyJpZCI6ICIyOWI4YzUyNS1lZmM1LTQ5NTItOGQ4Yy03NzQyYTg1YmI1MmEiLCAiY29kZSI6ICI1Mjk1OTM0ZC1mYjZlLTQzOWEtOWY1Yy0xMjdmNWUxMGE2YWMifQ==\n"
     ]
    }
   ],
   "source": [
    "quilt.login()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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=measuresiceflow)\n",
    "quilt.build(package=\"weiji14/deepbedmap/model/train/X_data\", path=lores)\n",
    "quilt.build(package=\"weiji14/deepbedmap/model/train/Y_data\", path=hires)\n",
    "\n",
    "# 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\", path=\"misc/REMA_100m_dem.tif\"\n",
    ")\n",
    "quilt.build(\n",
    "    package=\"weiji14/deepbedmap/misc/REMA_200m_dem_filled\",\n",
    "    path=\"misc/REMA_200m_dem_filled.tif\",\n",
    ")\n",
    "quilt.build(\n",
    "    package=\"weiji14/deepbedmap/misc/MEaSUREs_IceFlowSpeed_450m\",\n",
    "    path=\"misc/MEaSUREs_IceFlowSpeed_450m.tif\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fetching upload URLs from the registry...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0.00/6.48G [00:00<?, ?B/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Uploading 11 fragments (6480586427 bytes)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 26%|██▌       | 1.69G/6.48G [00:01<190:46:53, 6.97kB/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fragment 28e2ca7656d61b0bc7f8f8c1db41914023e0cab1634e0ee645f38a87d894b416 already uploaded; skipping.\n",
      "Fragment 1f66fe557ce079c063597f0b04d15862f67af2c9dd4f286801851e0c71f0e869 already uploaded; skipping.\n",
      "Fragment ca9c41a8dd56097e40865d2e65c65d299c22fc17608ddb6c604c532a69936307 already uploaded; skipping.\n",
      "Fragment 4a4efc3a84204c3d67887e8d7fa1186467b51e696451f2832ebbea3ca491c8a8 already uploaded; skipping.\n",
      "Fragment f1f660d1287225c30b8b2cbf2a727283d807a1ee443153519cbf407a08937965 already uploaded; skipping.\n",
      "Fragment f750893861a1a268c8ffe0ba7db36c933223bbf5fcbb786ecef3f052b20f9b8a already uploaded; skipping.\n",
      "Fragment 80c9fa41ccc69be1d2cd4a367d56168321d1079e7260a1996089810db25172f6 already uploaded; skipping.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 6.48G/6.48G [00:10<00:00, 635MB/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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