2 Latitude Aligned Frames.ipynb

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    "So, we are using ISCE2 for generating IFGs and we will process the geographic extents of a specified frame. The frames that are generated previously are grouped by *time* - that is we ensure a frame contains all the bursts that were imaged at the same time. However, when processing with ISCE2, we are only able to group bursts *geographically*, specifically, ISCE2 accepts a region of interest. The range direction of the image is constrained by the track. However, the azimuth direction of our frames need to be further constrained by latitude if there is going to be good agreement between our frame definition and what we see in our GUNW.\n",
    "\n",
    "This notebook will generate two additional datasets.\n",
    "\n",
    "1. Latitude aligned frames - these are frames whose North/South boundaries are aligned with a latitude line. This ensures that the GUNW processing will be better aligned with our frame definition.\n",
    "2. Expected GUNW extents for a given frame - the true extent of the GUNW if we have our region of interest dictated by a latitude aligned frame. This will generally be slightly larger than the frame definition.\n",
    "\n",
    "Note the Latitude-aligned frame will be a parallelogram with it's north/south edges aligned with a latitude line. When submitting the bounding box of this frame to ISCE2, we will get all the bursts that overlap this frame, so the expected GUNW will be *larger* than the frame definition. Even though our frames have a specified number of burst overlaps, there will likely be more overlap for this reason. Note the latitude aligned frames north/south edge overlaps its neighboring frame by only $\\approx$1 GUNW pixel ($\\approx$110 meters at the equator). Furthermore, we will want to keep track of both these datasets because the latter will necessary to specify the DEM extents (because we want DEM over all bursts that will be processed). Also, it will be helpful to see how the GUNWs submitted with these frames actually look."
   ]
  },
  {
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   "execution_count": 1,
   "id": "30777f00",
   "metadata": {
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     "start_time": "2023-03-20T23:01:28.042748Z"
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     "name": "stderr",
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     "text": [
      "/var/folders/s6/fbsc26cd0ddffbp3_knts_9c0wcxj1/T/ipykernel_29753/2999978722.py:2: UserWarning: Shapely 2.0 is installed, but because PyGEOS is also installed, GeoPandas will still use PyGEOS by default for now. To force to use and test Shapely 2.0, you have to set the environment variable USE_PYGEOS=0. You can do this before starting the Python process, or in your code before importing geopandas:\n",
      "\n",
      "import os\n",
      "os.environ['USE_PYGEOS'] = '0'\n",
      "import geopandas\n",
      "\n",
      "In a future release, GeoPandas will switch to using Shapely by default. If you are using PyGEOS directly (calling PyGEOS functions on geometries from GeoPandas), this will then stop working and you are encouraged to migrate from PyGEOS to Shapely 2.0 (https://shapely.readthedocs.io/en/latest/migration_pygeos.html).\n",
      "  import geopandas as gpd\n"
     ]
    }
   ],
   "source": [
    "import sqlite3\n",
    "import geopandas as gpd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "import pandas as pd\n",
    "from rasterio.crs import CRS\n",
    "import warnings\n",
    "from shapely import Polygon\n",
    "from zip_io import to_geojson_zip\n",
    "from dateline import dateline_frame_fix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b11f597",
   "metadata": {},
   "source": [
    "# Parameters\n",
    "\n",
    "How the frames will overlap in latitude."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "aaaabd2f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-20T23:01:29.296943Z",
     "start_time": "2023-03-20T23:01:29.294699Z"
    }
   },
   "outputs": [],
   "source": [
    "OVERLAP_DEGREES_LATITUDE = .001\n",
    "SIMPLIFY_TOLERANCE = .001"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b61e8577",
   "metadata": {},
   "source": [
    "# Read Burst Data\n",
    "\n",
    "The data needs to be unzipped first in this directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9deaecbe",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-20T23:01:39.604221Z",
     "start_time": "2023-03-20T23:01:29.298659Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 9.29 s, sys: 934 ms, total: 10.2 s\n",
      "Wall time: 10.3 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# SQL must wrap the geometry in hex(st_asbinary(...))\n",
    "sql = \"SELECT * FROM burst_id_map;\"\n",
    "con = sqlite3.connect(\"S1_burstid_20220530/IW/sqlite/burst_map_IW_000001_375887.sqlite3\")\n",
    "\n",
    "df_burst = gpd.GeoDataFrame.from_postgis(sql, con, geom_col=\"GEOMETRY\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "915654bc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-20T23:01:39.612625Z",
     "start_time": "2023-03-20T23:01:39.606471Z"
    }
   },
   "outputs": [],
   "source": [
    "df_burst['relative_orbit_number_min'] = df_burst['relative_orbit_number']\n",
    "df_burst['relative_orbit_number_max'] = df_burst['relative_orbit_number']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1cd81dce",
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     "end_time": "2023-03-20T23:01:39.628482Z",
     "start_time": "2023-03-20T23:01:39.614454Z"
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       "   OGC_FID                                           GEOMETRY  burst_id  \\\n",
       "0        1  MULTIPOLYGON Z (((3.36758 0.75322 0.00000, 3.7...         1   \n",
       "1        2  MULTIPOLYGON Z (((4.09178 0.97402 0.00000, 4.5...         1   \n",
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       "4        5  MULTIPOLYGON Z (((4.05690 1.14071 0.00000, 4.4...         2   \n",
       "\n",
       "  subswath_name  relative_orbit_number  time_from_anx_sec orbit_pass  \\\n",
       "0           IW1                      1           2.301015  ASCENDING   \n",
       "1           IW2                      1           3.133218  ASCENDING   \n",
       "2           IW3                      1           4.211255  ASCENDING   \n",
       "3           IW1                      1           5.059288  ASCENDING   \n",
       "4           IW2                      1           5.891491  ASCENDING   \n",
       "\n",
       "   relative_orbit_number_min  relative_orbit_number_max  \n",
       "0                          1                          1  \n",
       "1                          1                          1  \n",
       "2                          1                          1  \n",
       "3                          1                          1  \n",
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     "execution_count": 5,
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   "source": [
    "df_burst.head()"
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  {
   "cell_type": "markdown",
   "id": "1164e895",
   "metadata": {},
   "source": [
    "So there is an important caveat in our approach. We want to carry out our geometric manipulations and still have the dateline corrections we had previously applied for our burst aligned frames (it's too hard to re-apply the same corrections without additional accounting/work). Since we perform the intersection using the bursts, we will just copy the bursts to the right and left of the -180/180 marks respectively, creating a slightly expanded dataframe, and use this for intersection."
   ]
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   "execution_count": 6,
   "id": "f06a23da",
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     "end_time": "2023-03-20T23:02:08.185568Z",
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    {
     "name": "stderr",
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     "text": [
      "/var/folders/s6/fbsc26cd0ddffbp3_knts_9c0wcxj1/T/ipykernel_29753/3552873204.py:12: FutureWarning: Currently, index_parts defaults to True, but in the future, it will default to False to be consistent with Pandas. Use `index_parts=True` to keep the current behavior and True/False to silence the warning.\n",
      "  df_burst_large = df_burst_large.explode()\n"
     ]
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       "     OGC_FID  burst_id subswath_name  relative_orbit_number  \\\n",
       "0 0        1         1           IW1                      1   \n",
       "1 0        2         1           IW2                      1   \n",
       "2 0        3         1           IW3                      1   \n",
       "3 0        4         2           IW1                      1   \n",
       "4 0        5         2           IW2                      1   \n",
       "\n",
       "     time_from_anx_sec orbit_pass  relative_orbit_number_min  \\\n",
       "0 0           2.301015  ASCENDING                          1   \n",
       "1 0           3.133218  ASCENDING                          1   \n",
       "2 0           4.211255  ASCENDING                          1   \n",
       "3 0           5.059288  ASCENDING                          1   \n",
       "4 0           5.891491  ASCENDING                          1   \n",
       "\n",
       "     relative_orbit_number_max  \\\n",
       "0 0                          1   \n",
       "1 0                          1   \n",
       "2 0                          1   \n",
       "3 0                          1   \n",
       "4 0                          1   \n",
       "\n",
       "                                              GEOMETRY  \n",
       "0 0  POLYGON ((3.36731 0.75316, 3.32658 0.94794, 3....  \n",
       "1 0  POLYGON ((4.09151 0.97397, 4.05300 1.15801, 4....  \n",
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     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_burst_l = df_burst.cx[170:,:].copy()\n",
    "df_burst_l.geometry = df_burst_l.geometry.translate(xoff=-360, yoff=0)\n",
    "\n",
    "df_burst_r = df_burst.cx[:-170,:].copy()\n",
    "df_burst_r.geometry = df_burst_r.geometry.translate(xoff=360, yoff=0)\n",
    "\n",
    "df_burst_large = pd.concat([df_burst,\n",
    "                            df_burst_l,\n",
    "                            df_burst_r], axis=0).reset_index(drop=True)\n",
    "\n",
    "# Ensures we separate multipolygons across dateline\n",
    "df_burst_large = df_burst_large.explode()\n",
    "# 30 meter buffer so near the dateline, things are slightly smoother when taking their union\n",
    "df_burst_large.GEOMETRY = df_burst_large.GEOMETRY.buffer(0.0002777777777777777775)\n",
    "df_burst_large.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e1b58eb",
   "metadata": {},
   "source": [
    "# Reading our Burst-aligned Frames\n",
    "\n",
    "From the previous notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e46b69ab",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-20T23:02:16.765510Z",
     "start_time": "2023-03-20T23:02:08.187034Z"
    }
   },
   "outputs": [],
   "source": [
    "df_burst_frame = gpd.read_file('s1_frames.geojson.zip')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "872afca4",
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       "      <td>POLYGON Z ((2.84448 3.25558 0.00000, 2.84448 3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>64</td>\n",
       "      <td>87</td>\n",
       "      <td>22</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>ASCENDING</td>\n",
       "      <td>POLYGON Z ((2.60090 4.42328 0.00000, 2.60090 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>85</td>\n",
       "      <td>108</td>\n",
       "      <td>29</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>ASCENDING</td>\n",
       "      <td>POLYGON Z ((2.35832 5.59108 0.00000, 2.35832 5...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>106</td>\n",
       "      <td>129</td>\n",
       "      <td>36</td>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>ASCENDING</td>\n",
       "      <td>POLYGON Z ((2.11654 6.75894 0.00000, 2.11653 6...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   frame_id  OGC_FID_min  OGC_FID_max  burst_id_min  burst_id_max  \\\n",
       "0         0           22           45             8            15   \n",
       "1         1           43           66            15            22   \n",
       "2         2           64           87            22            29   \n",
       "3         3           85          108            29            36   \n",
       "4         4          106          129            36            43   \n",
       "\n",
       "   relative_orbit_number_min  relative_orbit_number_max orbit_direction  \\\n",
       "0                          1                          1       ASCENDING   \n",
       "1                          1                          1       ASCENDING   \n",
       "2                          1                          1       ASCENDING   \n",
       "3                          1                          1       ASCENDING   \n",
       "4                          1                          1       ASCENDING   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON Z ((3.08906 2.08797 0.00000, 3.08720 2...  \n",
       "1  POLYGON Z ((2.84448 3.25558 0.00000, 2.84448 3...  \n",
       "2  POLYGON Z ((2.60090 4.42328 0.00000, 2.60090 4...  \n",
       "3  POLYGON Z ((2.35832 5.59108 0.00000, 2.35832 5...  \n",
       "4  POLYGON Z ((2.11654 6.75894 0.00000, 2.11653 6...  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_burst_frame.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65a18055",
   "metadata": {},
   "source": [
    "# Generating Latitude-aligned Frames\n",
    "\n",
    "The bursts (within an image and our fixed frames) are grouped according to `OGC_FID // 3` (same integer quotient after division by 3). Further, the middle `OGC_FID` of `3n`, `3n+1`, `3n+2` is in the center of the 3 swaths. So, we will take the centroid of this middle burst (`3n+1`) and use that to draw a latitude line at the bottom and top of the frames we previously defined. Our final frame will be an envelope (i.e. axis-aligned rectangle in `shapely` speak) around the frame with latitude top/bottom line. From there, we interesect this box with the frame. We importantly note the burst frames are multipolygons at the dateline. However, we only want the *y* value, which is valued even if the geometries are \"wrapped\" around the dateline. That is the y-value of the centroid will be the same for a burst even it is a multipolygon around the dateline. Therefore, we can use the original bursts directly to look up the centroid. This is still very fast and no extra accounting needed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "dafe023c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-20T23:02:16.822640Z",
     "start_time": "2023-03-20T23:02:16.777834Z"
    }
   },
   "outputs": [],
   "source": [
    "# We only want to lookup from the original burst dataset since we are interested in the y-coord of the centroid\n",
    "# Even if they are multipolygons, \n",
    "df_burst_ogc = df_burst.set_index('OGC_FID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b7662760",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-20T23:02:22.095127Z",
     "start_time": "2023-03-20T23:02:16.826999Z"
    }
   },
   "outputs": [],
   "source": [
    "centroid_min = df_burst_frame.OGC_FID_min.map(lambda ogc_fid: df_burst_ogc.loc[ogc_fid + 1].GEOMETRY.centroid)\n",
    "centroid_max = df_burst_frame.OGC_FID_max.map(lambda ogc_fid: df_burst_ogc.loc[ogc_fid - 1].GEOMETRY.centroid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "46e26be2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-20T23:02:22.546973Z",
     "start_time": "2023-03-20T23:02:22.096832Z"
    }
   },
   "outputs": [],
   "source": [
    "centroid_min_y = centroid_min.map(lambda geo: geo.coords[0][1])\n",
    "centroid_max_y = centroid_max.map(lambda geo: geo.coords[0][1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bdb40149",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-20T23:02:22.551808Z",
     "start_time": "2023-03-20T23:02:22.548650Z"
    }
   },
   "outputs": [],
   "source": [
    "df_burst_frame['centroid_min_y'] = centroid_min_y\n",
    "df_burst_frame['centroid_max_y'] = centroid_max_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "38ad3708",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-20T23:02:22.575261Z",
     "start_time": "2023-03-20T23:02:22.553279Z"
    }
   },
   "outputs": [],
   "source": [
    "df_burst_centroid = df_burst_frame.copy()\n",
    "df_burst_centroid.geometry = centroid_min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5cbdeb39",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-20T23:02:24.125668Z",
     "start_time": "2023-03-20T23:02:22.576901Z"
    }
   },
   "outputs": [],
   "source": [
    "def comp_top(row):\n",
    "    min_y = row['centroid_min_y']\n",
    "    max_y = row['centroid_max_y']\n",
    "    return min_y if min_y >= max_y else max_y\n",
    "\n",
    "def comp_bot(row):\n",
    "    min_y = row['centroid_min_y']\n",
    "    max_y = row['centroid_max_y']\n",
    "    return min_y if min_y <= max_y else max_y\n",
    "\n",
    "df_burst_frame['top_y'] = df_burst_frame.apply(comp_top, axis=1)\n",
    "df_burst_frame['bot_y'] = df_burst_frame.apply(comp_bot, axis=1)\n",
    "df_bounds = df_burst_frame.bounds\n",
    "df_burst_frame['xmin'] = df_bounds.minx - 2\n",
    "df_burst_frame['xmax'] = df_bounds.maxx + 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "895c666f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-20T23:02:29.653309Z",
     "start_time": "2023-03-20T23:02:24.127296Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_yaxis_envelope(row, longitude_buffer=3):\n",
    "    geo = row['geometry']\n",
    "    top_y = row['top_y']\n",
    "    bot_y = row['bot_y']\n",
    "    \n",
    "    xmin = row['xmin']\n",
    "    xmax = row['xmax']\n",
    "    \n",
    "    geo = geo.minimum_rotated_rectangle\n",
    "    center_rect_x = geo.centroid.coords[0][0]\n",
    "    coords = geo.exterior.coords[:]\n",
    "    coords_clipped = [(c[0] - longitude_buffer if c[0] <= center_rect_x else c[0] + longitude_buffer, \n",
    "                       np.clip(c[1], \n",
    "                               bot_y - OVERLAP_DEGREES_LATITUDE, \n",
    "                               top_y + OVERLAP_DEGREES_LATITUDE))\n",
    "                      for c in coords]\n",
    "    return Polygon(coords_clipped).envelope\n",
    "\n",
    "df_burst_frame_envelope = df_burst_frame.copy()\n",
    "df_burst_frame_envelope.geometry = df_burst_frame_envelope.apply(get_yaxis_envelope, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "591791c8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-20T23:02:29.864345Z",
     "start_time": "2023-03-20T23:02:29.654930Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "df_burst_frame_envelope.iloc[:10].plot(alpha=.25, ax=ax)\n",
    "df_burst_frame.iloc[:10].plot(alpha=.1, ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "862b2513",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-20T23:02:59.552963Z",
     "start_time": "2023-03-20T23:02:29.866503Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 29 s, sys: 1.39 s, total: 30.4 s\n",
      "Wall time: 29.7 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "from shapely import STRtree\n",
    "\n",
    "tree_frames = STRtree(df_burst_frame.geometry)\n",
    "tree_bursts = STRtree(df_burst_large.geometry)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7993e1a5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-20T23:05:00.351074Z",
     "start_time": "2023-03-20T23:02:59.554580Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2min, sys: 346 ms, total: 2min\n",
      "Wall time: 2min\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "def intersection_with_bursts(row):\n",
    "    geo = row['geometry']\n",
    "    idxs = tree_bursts.query(geo, predicate=\"intersects\")\n",
    "    df_temp = df_burst_large.iloc[idxs].reset_index(drop=True)\n",
    "    \n",
    "    track_min = row['relative_orbit_number_min']\n",
    "    track_max = row['relative_orbit_number_max']\n",
    "    \n",
    "    ind_min = df_temp.relative_orbit_number >= track_min\n",
    "    ind_max = df_temp.relative_orbit_number <= track_max\n",
    "    df_temp = df_temp[ind_min & ind_max].reset_index(drop=True)\n",
    "    \n",
    "    overlapping_bursts = df_temp.geometry.unary_union\n",
    "    return overlapping_bursts.intersection(geo)\n",
    "\n",
    "df_burst_frame_latitude_aligned = df_burst_frame_envelope.copy()\n",
    "df_burst_frame_latitude_aligned.geometry = df_burst_frame_envelope.apply(intersection_with_bursts, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c7350bef",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-20T23:05:00.582985Z",
     "start_time": "2023-03-20T23:05:00.352673Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_burst_frame_latitude_aligned.boundary.iloc[:3].plot(alpha=.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "bdca3403",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-20T23:05:03.806514Z",
     "start_time": "2023-03-20T23:05:00.585064Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>frame_id</th>\n",
       "      <th>relative_orbit_number_min</th>\n",
       "      <th>relative_orbit_number_max</th>\n",
       "      <th>orbit_direction</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>ASCENDING</td>\n",
       "      <td>POLYGON ((3.01750 2.42121, 2.79603 3.48516, 5....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>ASCENDING</td>\n",
       "      <td>POLYGON ((2.77361 3.58868, 2.55345 4.65139, 4....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>ASCENDING</td>\n",
       "      <td>POLYGON ((2.53163 4.75694, 2.49721 4.92355, 2....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>ASCENDING</td>\n",
       "      <td>POLYGON ((2.28921 5.92475, 2.06990 6.98409, 4....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>ASCENDING</td>\n",
       "      <td>POLYGON ((2.04756 7.09261, 1.82913 8.15065, 4....</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   frame_id  relative_orbit_number_min  relative_orbit_number_max  \\\n",
       "0         0                          1                          1   \n",
       "1         1                          1                          1   \n",
       "2         2                          1                          1   \n",
       "3         3                          1                          1   \n",
       "4         4                          1                          1   \n",
       "\n",
       "  orbit_direction                                           geometry  \n",
       "0       ASCENDING  POLYGON ((3.01750 2.42121, 2.79603 3.48516, 5....  \n",
       "1       ASCENDING  POLYGON ((2.77361 3.58868, 2.55345 4.65139, 4....  \n",
       "2       ASCENDING  POLYGON ((2.53163 4.75694, 2.49721 4.92355, 2....  \n",
       "3       ASCENDING  POLYGON ((2.28921 5.92475, 2.06990 6.98409, 4....  \n",
       "4       ASCENDING  POLYGON ((2.04756 7.09261, 1.82913 8.15065, 4....  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = ['frame_id', \n",
    "           'relative_orbit_number_min',\n",
    "           'relative_orbit_number_max',\n",
    "           'orbit_direction',\n",
    "           'geometry']\n",
    "df_burst_frame_latitude_aligned_final = df_burst_frame_latitude_aligned[columns].copy()\n",
    "# This vastly reduces the filesize of the file - parallelogram geometries are simple as well\n",
    "df_burst_frame_latitude_aligned_final.geometry = df_burst_frame_latitude_aligned_final.geometry.simplify(SIMPLIFY_TOLERANCE)\n",
    "df_burst_frame_latitude_aligned_final = df_burst_frame_latitude_aligned_final.sort_values(by='frame_id')\n",
    "df_burst_frame_latitude_aligned_final = df_burst_frame_latitude_aligned_final.reset_index(drop=True)\n",
    "df_burst_frame_latitude_aligned_final.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb03ba2b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-21T23:51:10.738492Z",
     "start_time": "2023-02-21T23:51:10.704806Z"
    }
   },
   "source": [
    "# Serialize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3644493d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-20T23:05:03.809986Z",
     "start_time": "2023-03-20T23:05:03.808207Z"
    }
   },
   "outputs": [],
   "source": [
    "# df_burst_frame_latitude_aligned_final.to_file('s1_frames_latitude_aligned.geojson', driver='GeoJSON')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f7e7f14",
   "metadata": {},
   "source": [
    "The file above is less than 20 MB as a geojson. So compression not really needed - it's nice to keep things small for version control. Gzip (i.e. `ZIP_DEFLATED`) works as a good compression method. I haven't done any research into comparing compression methods nor am I familiar with the various python standard library compression methods/tools. Got it to work and significantly reduced size to <4 MB. Sticking with this. Moreover, `geopandas` and QGIS can read zip files directly!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6526efb4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-20T23:05:06.767042Z",
     "start_time": "2023-03-20T23:05:03.811556Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'s1_frames_latitude_aligned.geojson.zip'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "to_geojson_zip(df_burst_frame_latitude_aligned_final, 's1_frames_latitude_aligned.geojson.zip')\n",
    "\n",
    "# Illustrating that the above compressed file can be read through geopandas\n",
    "# df_test = gpd.read_file('s1_frames_latitude_aligned.geojson.zip')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3a935d9",
   "metadata": {},
   "source": [
    "# What do our GUNWs look Like?\n",
    "\n",
    "So, during ISCE2 processing, the extent of the GUNW will be determined by taking all bursts that *intersect* the region of interest, which is based on the bounding box around a frame (only option is to specify a bounding box). The top and bottom of the frame will intersect numerous bursts that are not entirely contained in the frame itself and therefore the GUNW extent will go beyond the frame. Further, we should know the expected GUNW extent during the processing because we will want the extent of the DEM to request.\n",
    "\n",
    "## Samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "05bc7a0c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-20T23:05:06.797743Z",
     "start_time": "2023-03-20T23:05:06.768689Z"
    }
   },
   "outputs": [],
   "source": [
    "K = 1000 #19910 #1000 \n",
    "T = 3\n",
    "\n",
    "gunws = []\n",
    "for t in range(T):\n",
    "\n",
    "    sample_frame = df_burst_frame_latitude_aligned.iloc[K + t]\n",
    "    geo_frame = sample_frame.geometry.envelope\n",
    "    # Near the equator, we can have frames with multiple tracks/relative orbit numbers\n",
    "    track_min = sample_frame['relative_orbit_number_min']\n",
    "    track_max = sample_frame['relative_orbit_number_max']\n",
    "    tracks = [track_min, track_max]\n",
    "    \n",
    "    sample_idxs = tree_bursts.query(geo_frame, predicate=\"intersects\")\n",
    "    df_gunw = df_burst_large.iloc[sample_idxs].reset_index(drop=True)\n",
    "    df_gunw = df_gunw[df_gunw.relative_orbit_number.isin(tracks)].copy()\n",
    "    df_gunw = df_gunw.dissolve()\n",
    "    gunws.append(df_gunw)\n",
    "    \n",
    "df_gunw_neighbor = pd.concat(gunws, axis=0).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7f12bd61",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-20T23:05:07.179632Z",
     "start_time": "2023-03-20T23:05:06.799356Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16.605954233322343, 23.906344754079818)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax= plt.subplots()\n",
    "df_gunw_neighbor.plot(alpha=.2, ax=ax)\n",
    "df_burst_centroid.iloc[K: K+T].plot(ax =ax)\n",
    "\n",
    "df_world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))\n",
    "xmin, ymin, xmax, ymax = df_gunw_neighbor.total_bounds\n",
    "df_world.plot(ax=ax, color='green',alpha=.25)\n",
    "\n",
    "\n",
    "df_burst_p = df_burst.cx[xmin: xmax, ymin: ymax]\n",
    "df_burst_p = df_burst_p[df_burst_p.relative_orbit_number_min == track_min]\n",
    "df_burst_p.boundary.plot(ax=ax, alpha=.1)\n",
    "\n",
    "ax.set_ylim(ymin-2, ymax+2)\n",
    "ax.set_xlim(xmin-2, xmax+2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71d2d051",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-21T19:59:53.921355Z",
     "start_time": "2023-02-21T19:59:53.918903Z"
    }
   },
   "source": [
    "## Automate "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "bfb812d3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-20T23:05:07.185359Z",
     "start_time": "2023-03-20T23:05:07.181940Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_slice(k, row):\n",
    "    geo_frame = row['geometry'].envelope\n",
    "    \n",
    "    # Near the equator, we can have frames with multiple tracks/relative orbit numbers\n",
    "    track_min = row['relative_orbit_number_min']\n",
    "    track_max = row['relative_orbit_number_max']\n",
    "    tracks = [track_min, track_max]\n",
    "    frame_id = row['frame_id']\n",
    "    \n",
    "    idxs = tree_bursts.query(geo_frame, predicate=\"intersects\")\n",
    "    df_slice = df_burst_large.iloc[idxs].copy()\n",
    "    \n",
    "    df_slice = df_slice[df_slice.relative_orbit_number.isin(tracks)].reset_index(drop=True)\n",
    "    df_slice['slice_index'] = k\n",
    "    df_slice['frame_id'] = frame_id\n",
    "    return df_slice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "dfa7af9a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-20T23:06:12.145447Z",
     "start_time": "2023-03-20T23:05:07.187204Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███| 27563/27563 [01:04<00:00, 425.20it/s]\n"
     ]
    }
   ],
   "source": [
    "df_slices = [get_slice(k, row) for k, row in tqdm(df_burst_frame_latitude_aligned.iterrows(),\n",
    "                                                  total=df_burst_frame_latitude_aligned.shape[0])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2109a395",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-20T23:06:19.157887Z",
     "start_time": "2023-03-20T23:06:12.147461Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OGC_FID</th>\n",
       "      <th>burst_id</th>\n",
       "      <th>subswath_name</th>\n",
       "      <th>relative_orbit_number</th>\n",
       "      <th>time_from_anx_sec</th>\n",
       "      <th>orbit_pass</th>\n",
       "      <th>relative_orbit_number_min</th>\n",
       "      <th>relative_orbit_number_max</th>\n",
       "      <th>GEOMETRY</th>\n",
       "      <th>slice_index</th>\n",
       "      <th>frame_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>7</td>\n",
       "      <td>IW2</td>\n",
       "      <td>1</td>\n",
       "      <td>19.682856</td>\n",
       "      <td>ASCENDING</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>POLYGON ((3.88264 1.97414, 3.84431 2.15822, 3....</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23</td>\n",
       "      <td>8</td>\n",
       "      <td>IW2</td>\n",
       "      <td>1</td>\n",
       "      <td>22.441128</td>\n",
       "      <td>ASCENDING</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>POLYGON ((3.84792 2.14084, 3.80826 2.32463, 3....</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25</td>\n",
       "      <td>9</td>\n",
       "      <td>IW1</td>\n",
       "      <td>1</td>\n",
       "      <td>24.367199</td>\n",
       "      <td>ASCENDING</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>POLYGON ((3.08693 2.08753, 3.04639 2.28233, 3....</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21</td>\n",
       "      <td>7</td>\n",
       "      <td>IW3</td>\n",
       "      <td>1</td>\n",
       "      <td>20.760893</td>\n",
       "      <td>ASCENDING</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>POLYGON ((4.64669 2.19104, 4.60609 2.38627, 4....</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26</td>\n",
       "      <td>9</td>\n",
       "      <td>IW2</td>\n",
       "      <td>1</td>\n",
       "      <td>25.199402</td>\n",
       "      <td>ASCENDING</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>POLYGON ((3.81187 2.30727, 3.77359 2.49134, 3....</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   OGC_FID  burst_id subswath_name  relative_orbit_number  time_from_anx_sec  \\\n",
       "0       20         7           IW2                      1          19.682856   \n",
       "1       23         8           IW2                      1          22.441128   \n",
       "2       25         9           IW1                      1          24.367199   \n",
       "3       21         7           IW3                      1          20.760893   \n",
       "4       26         9           IW2                      1          25.199402   \n",
       "\n",
       "  orbit_pass  relative_orbit_number_min  relative_orbit_number_max  \\\n",
       "0  ASCENDING                          1                          1   \n",
       "1  ASCENDING                          1                          1   \n",
       "2  ASCENDING                          1                          1   \n",
       "3  ASCENDING                          1                          1   \n",
       "4  ASCENDING                          1                          1   \n",
       "\n",
       "                                            GEOMETRY  slice_index  frame_id  \n",
       "0  POLYGON ((3.88264 1.97414, 3.84431 2.15822, 3....            0         0  \n",
       "1  POLYGON ((3.84792 2.14084, 3.80826 2.32463, 3....            0         0  \n",
       "2  POLYGON ((3.08693 2.08753, 3.04639 2.28233, 3....            0         0  \n",
       "3  POLYGON ((4.64669 2.19104, 4.60609 2.38627, 4....            0         0  \n",
       "4  POLYGON ((3.81187 2.30727, 3.77359 2.49134, 3....            0         0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_slice = pd.concat(df_slices, axis=0)\n",
    "df_slice.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "516fd36e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-20T23:06:19.395537Z",
     "start_time": "2023-03-20T23:06:19.164560Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>slice_index</th>\n",
       "      <th>geometry</th>\n",
       "      <th>frame_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((3.88264 1.97414, 3.84431 2.15822, 3....</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((3.84792 2.14084, 3.80826 2.32463, 3....</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((3.08693 2.08753, 3.04639 2.28233, 3....</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((4.64669 2.19104, 4.60609 2.38627, 4....</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((3.81187 2.30727, 3.77359 2.49134, 3....</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   slice_index                                           geometry  frame_id\n",
       "0            0  POLYGON ((3.88264 1.97414, 3.84431 2.15822, 3....         0\n",
       "1            0  POLYGON ((3.84792 2.14084, 3.80826 2.32463, 3....         0\n",
       "2            0  POLYGON ((3.08693 2.08753, 3.04639 2.28233, 3....         0\n",
       "3            0  POLYGON ((4.64669 2.19104, 4.60609 2.38627, 4....         0\n",
       "4            0  POLYGON ((3.81187 2.30727, 3.77359 2.49134, 3....         0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_slice = df_slice[['slice_index', 'GEOMETRY', 'frame_id']]\n",
    "df_slice = df_slice.rename(columns={'GEOMETRY':'geometry'})\n",
    "df_slice = df_slice.set_geometry(\"geometry\")\n",
    "df_slice = df_slice.set_crs(CRS.from_epsg(4326))\n",
    "df_slice.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e2d13504",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-20T23:06:19.399148Z",
     "start_time": "2023-03-20T23:06:19.397232Z"
    }
   },
   "outputs": [],
   "source": [
    "# %%time\n",
    "# df_frame_gunw_extent = df_slice.dissolve(by='slice_index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "02dc0009",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-20T23:06:20.204094Z",
     "start_time": "2023-03-20T23:06:19.404317Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>slice_index</th>\n",
       "      <th>geometry</th>\n",
       "      <th>frame_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17677</td>\n",
       "      <td>POLYGON ((111.90052 -70.47113, 111.50286 -70.3...</td>\n",
       "      <td>17785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8813</td>\n",
       "      <td>POLYGON ((35.29068 13.36278, 35.25170 13.55831...</td>\n",
       "      <td>8860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17678</td>\n",
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       "      <td>17786</td>\n",
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       "      <th>0</th>\n",
       "      <td>1664</td>\n",
       "      <td>POLYGON ((128.14960 -70.27721, 127.80291 -70.1...</td>\n",
       "      <td>1682</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   slice_index                                           geometry  frame_id\n",
       "0            0  POLYGON ((3.88264 1.97414, 3.84431 2.15822, 3....         0\n",
       "0        17677  POLYGON ((111.90052 -70.47113, 111.50286 -70.3...     17785\n",
       "0         8813  POLYGON ((35.29068 13.36278, 35.25170 13.55831...      8860\n",
       "0        17678  POLYGON ((103.16416 -69.11784, 102.85279 -68.9...     17786\n",
       "0         1664  POLYGON ((128.14960 -70.27721, 127.80291 -70.1...      1682"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import dask_geopandas\n",
    "\n",
    "df_slice_dask = dask_geopandas.from_geopandas(df_slice, npartitions=10)\n",
    "df_slice_dask.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "2801014f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-20T23:06:20.232153Z",
     "start_time": "2023-03-20T23:06:20.206056Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><strong>Dask-GeoPandas GeoDataFrame Structure:</strong></div>\n",
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>frame_id</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>npartitions=1</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>int64</td>\n",
       "      <td>geometry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "<div>Dask Name: set_crs, 5 graph layers</div>"
      ],
      "text/plain": [
       "Dask GeoDataFrame Structure:\n",
       "              frame_id  geometry\n",
       "npartitions=1                   \n",
       "                 int64  geometry\n",
       "                   ...       ...\n",
       "Dask Name: set_crs, 5 graph layers"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_frames_0 = df_slice_dask.dissolve(by='slice_index')\n",
    "df_frames_0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c2765468",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-20T23:07:47.005005Z",
     "start_time": "2023-03-20T23:06:20.233965Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2min 12s, sys: 7.53 s, total: 2min 20s\n",
      "Wall time: 1min 26s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>frame_id</th>\n",
       "      <th>geometry</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((5.38733 2.16933, 5.38734 2.16930, 5....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>POLYGON ((3.60064 3.32495, 3.58372 3.40669, 3....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>POLYGON ((2.60095 4.42301, 2.60093 4.42300, 2....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>POLYGON ((2.28811 5.93010, 2.28344 5.95273, 2....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>POLYGON ((2.07614 6.95375, 2.04756 7.09261, 2....</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   frame_id                                           geometry\n",
       "0         0  POLYGON ((5.38733 2.16933, 5.38734 2.16930, 5....\n",
       "1         1  POLYGON ((3.60064 3.32495, 3.58372 3.40669, 3....\n",
       "2         2  POLYGON ((2.60095 4.42301, 2.60093 4.42300, 2....\n",
       "3         3  POLYGON ((2.28811 5.93010, 2.28344 5.95273, 2....\n",
       "4         4  POLYGON ((2.07614 6.95375, 2.04756 7.09261, 2...."
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "df_frame_gunw_extent = df_frames_0.compute()\n",
    "df_frame_gunw_extent = df_frame_gunw_extent.sort_values(by='frame_id').reset_index(drop=True)\n",
    "df_frame_gunw_extent.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0543f24",
   "metadata": {},
   "source": [
    "# Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d3f5b7c3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-20T23:07:47.008871Z",
     "start_time": "2023-03-20T23:07:47.006665Z"
    }
   },
   "outputs": [],
   "source": [
    "K = 20_000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "502a9410",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-20T23:07:47.019633Z",
     "start_time": "2023-03-20T23:07:47.010543Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>frame_id</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((5.38733 2.16933, 5.38734 2.16930, 5....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>POLYGON ((3.60064 3.32495, 3.58372 3.40669, 3....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>POLYGON ((2.60095 4.42301, 2.60093 4.42300, 2....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>POLYGON ((2.28811 5.93010, 2.28344 5.95273, 2....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>POLYGON ((2.07614 6.95375, 2.04756 7.09261, 2....</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   frame_id                                           geometry\n",
       "0         0  POLYGON ((5.38733 2.16933, 5.38734 2.16930, 5....\n",
       "1         1  POLYGON ((3.60064 3.32495, 3.58372 3.40669, 3....\n",
       "2         2  POLYGON ((2.60095 4.42301, 2.60093 4.42300, 2....\n",
       "3         3  POLYGON ((2.28811 5.93010, 2.28344 5.95273, 2....\n",
       "4         4  POLYGON ((2.07614 6.95375, 2.04756 7.09261, 2...."
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_frame_gunw_extent.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "4b423df4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-20T23:07:47.195638Z",
     "start_time": "2023-03-20T23:07:47.021401Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "s = np.s_[K: K+10]\n",
    "df_frame_gunw_extent.set_index('frame_id').loc[s].plot(alpha=.14, ax=ax)\n",
    "df_burst_frame_latitude_aligned.set_index('frame_id').loc[s].boundary.plot(color='black', ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "23609589",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-20T23:07:47.209110Z",
     "start_time": "2023-03-20T23:07:47.198530Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([20000, 20001, 20002, 20003, 20004, 20005, 20006, 20007, 20008,\n",
       "            20009, 20010],\n",
       "           dtype='int64', name='frame_id')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_frame_gunw_extent.set_index('frame_id').loc[s].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a16692e1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-20T23:07:47.224521Z",
     "start_time": "2023-03-20T23:07:47.211973Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([20000, 20001, 20002, 20003, 20004, 20005, 20006, 20007, 20008,\n",
       "            20009, 20010],\n",
       "           dtype='int64', name='frame_id')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_burst_frame_latitude_aligned.set_index('frame_id').loc[s].index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78c7ca1f",
   "metadata": {},
   "source": [
    "# Serialization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6ce0728",
   "metadata": {},
   "source": [
    "For the GUNW plugin:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e735cc1c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-20T23:08:24.909344Z",
     "start_time": "2023-03-20T23:07:47.227762Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'s1_gunw_frame_footprints.geojson.zip'"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df_frame_gunw_extent.to_file('s1_frames_latitude_aligned_gunw_footprint.geojson')\n",
    "to_geojson_zip(df_frame_gunw_extent, 's1_gunw_frame_footprints.geojson.zip')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b7386d2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "s1-frame-generation",
   "language": "python",
   "name": "s1-frame-generation"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}