From a079d60eb7802d6f26464b7ec8fdf8449d1480b9 Mon Sep 17 00:00:00 2001
From: Boyd <db1950@umd.edu>
Date: Thu, 15 Aug 2024 01:41:19 -0400
Subject: [PATCH 1/6] SWESARR Tutorial Update

Updated SWESARR Tutorial. Includes comparison to SWE values provided by SnowExSQL and a few extra visualizations for comparing SWE and SWESARR data.

The default RAM allocation (2GB) for CryoInTheCloud can't handle the final figure generated by this notebook. This is poor memory management on my part. I've commented out the figure for the tutorial. Currently, this notebook uses 1GB of RAM.

Most libraries are contained by default via CryoInTheCloud, but SnowExSQL isn't. This may be an issue with merging to the main branch.
---
 ...2_13801_20007_000_200211_XKuKa225H_v03.csv | 1458 +++++++++++++++++
 book/tutorials/swesarr/index.md               |    4 +
 book/tutorials/swesarr/swesarr_tut.ipynb      |  840 ++++++++++
 book/tutorials/swesarr/util/helper.py         |  472 ++++++
 4 files changed, 2774 insertions(+)
 create mode 100644 book/tutorials/swesarr/data/SNEX20_SWESARR_TB_GRMCT2_13801_20007_000_200211_XKuKa225H_v03.csv
 create mode 100644 book/tutorials/swesarr/index.md
 create mode 100644 book/tutorials/swesarr/swesarr_tut.ipynb
 create mode 100644 book/tutorials/swesarr/util/helper.py

diff --git a/book/tutorials/swesarr/data/SNEX20_SWESARR_TB_GRMCT2_13801_20007_000_200211_XKuKa225H_v03.csv b/book/tutorials/swesarr/data/SNEX20_SWESARR_TB_GRMCT2_13801_20007_000_200211_XKuKa225H_v03.csv
new file mode 100644
index 0000000..fc129d0
--- /dev/null
+++ b/book/tutorials/swesarr/data/SNEX20_SWESARR_TB_GRMCT2_13801_20007_000_200211_XKuKa225H_v03.csv
@@ -0,0 +1,1458 @@
+UTC,Longitude (deg),Latitude (deg),Elevation (m),TB X (K),TB K (K),TB Ka (K),Antenna Longitude (deg),Antenna Latitude (deg),Antenna Altitude (m),Antenna Yaw (deg),Antenna Pitch (deg),Antenna Look Angle (deg)
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+20200211-18:36:12.652100,-108.153572,39.006065,3086,253.6,235.1,208.2,-108.055877,38.943436,4521.2,2.36,4.34,45.1
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+20200211-18:36:12.852100,-108.153479,39.005972,3085,253.7,235.4,210.5,-108.055882,38.943441,4521.2,2.35,4.34,45.1
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+20200211-18:36:16.652220,-108.15135,39.004306,3085,257.8,248.9,256.3,-108.055978,38.943525,4521.1,2.32,4.36,45.1
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+20200211-18:36:17.152240,-108.151072,39.004028,3085,256.9,251.1,256.5,-108.055991,38.943536,4521.1,2.36,4.36,45.1
+20200211-18:36:17.252240,-108.151072,39.004028,3085,258.6,250.1,256.3,-108.055994,38.943538,4521.1,2.34,4.36,45.1
+20200211-18:36:17.352240,-108.150979,39.003935,3085,257.9,252,256.7,-108.055996,38.943541,4521.1,2.33,4.36,45.1
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+20200211-18:36:17.652240,-108.150887,39.003843,3086,255.2,250.6,255.4,-108.056004,38.943547,4521.1,2.35,4.37,45.1
+20200211-18:36:17.752240,-108.150794,39.00375,3087,257.4,250.9,255.2,-108.056006,38.943549,4521.1,2.35,4.37,45.1
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+20200211-18:36:17.952240,-108.150701,39.003658,3088,255.6,252.6,254.9,-108.056011,38.943554,4521.1,2.34,4.37,45.1
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+20200211-18:36:18.252260,-108.150516,39.003565,3087,255.2,253.7,254.2,-108.056019,38.943561,4521.1,2.33,4.37,45.1
+20200211-18:36:18.352260,-108.150424,39.003565,3087,257.6,252,252.7,-108.056021,38.943563,4521.1,2.35,4.37,45.1
+20200211-18:36:18.452260,-108.150424,39.003472,3086,255.2,250.4,251.8,-108.056024,38.943565,4521.1,2.36,4.37,45.1
+20200211-18:36:18.552260,-108.150331,39.003472,3086,255.3,251,251.5,-108.056027,38.943567,4521.1,2.36,4.37,45.1
+20200211-18:36:18.652260,-108.150331,39.00338,3086,257.5,251.3,251.2,-108.056029,38.943569,4521.1,2.34,4.37,45.1
+20200211-18:36:18.752280,-108.150238,39.00338,3085,256.2,251.1,250.4,-108.056032,38.943572,4521.1,2.33,4.37,45.1
+20200211-18:36:18.852280,-108.150238,39.003287,3085,255.5,248.9,249.7,-108.056034,38.943574,4521.1,2.32,4.37,45.1
+20200211-18:36:18.952280,-108.150146,39.003287,3085,257.3,250.4,249.3,-108.056037,38.943576,4521.1,2.34,4.37,45.1
+20200211-18:36:19.052280,-108.150053,39.003195,3085,255,252.7,248.3,-108.056039,38.943578,4521.1,2.35,4.37,45.1
+20200211-18:36:19.152280,-108.150053,39.003195,3085,254.8,251.1,246.8,-108.056042,38.943581,4521.1,2.35,4.38,45.1
+20200211-18:36:19.252280,-108.149961,39.003102,3085,253.8,249.6,248.5,-108.056044,38.943583,4521.1,2.36,4.37,45.1
+20200211-18:36:19.352280,-108.149961,39.003102,3085,254.5,250.1,248,-108.056047,38.943585,4521.1,2.34,4.38,45.1
+20200211-18:36:19.452300,-108.149868,39.003009,3085,253.9,249.4,248.8,-108.056049,38.943587,4521,2.34,4.37,45.1
+20200211-18:36:19.552280,-108.149775,39.003009,3085,253.1,251.5,248.3,-108.056052,38.943589,4521,2.33,4.38,45.1
\ No newline at end of file
diff --git a/book/tutorials/swesarr/index.md b/book/tutorials/swesarr/index.md
new file mode 100644
index 0000000..100bab1
--- /dev/null
+++ b/book/tutorials/swesarr/index.md
@@ -0,0 +1,4 @@
+# SWESARR Tutorial
+
+```{tableofcontents}
+```
diff --git a/book/tutorials/swesarr/swesarr_tut.ipynb b/book/tutorials/swesarr/swesarr_tut.ipynb
new file mode 100644
index 0000000..d67ed8a
--- /dev/null
+++ b/book/tutorials/swesarr/swesarr_tut.ipynb
@@ -0,0 +1,840 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "9fdd3e66",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "# SWESARR Tutorial"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "56f3be28",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "![NASA](http://www.nasa.gov/sites/all/themes/custom/nasatwo/images/nasa-logo.svg)\n",
+    "\n",
+    "<div>\n",
+    "<img src=\"https://blogs.nasa.gov/swesarr/wp-content/uploads/sites/305/2021/06/swesarr.png\" width=\"1589\"/>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4188e5b9-a513-4b1a-8bc8-9d27ba9eee0b",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "source": [
+    "<div class=\"alert alert-block alert-info\">\n",
+    "<b>Objectives:</b> \n",
+    "    This is a 30-minute tutorial where we will ...\n",
+    "     <ol>\n",
+    "         <li> Introduce SWESARR </li>\n",
+    "         <li> Briefly introduce active and passive microwave remote sensing </li>\n",
+    "         <li> Learn how to access, filter, and visualize SWESARR data </li>\n",
+    "    </ol> \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "63bda652-ba87-4029-8b90-62fbb517a6e7",
+   "metadata": {},
+   "source": [
+    "## Quick References\n",
+    "\n",
+    "<OL>\n",
+    "<LI> <A HREF=\"https://glihtdata.gsfc.nasa.gov/files/radar/SWESARR/prerelease/\">SWESARR SAR Data Pre-release FTP Server</A>    \n",
+    "<LI> <A HREF=\"https://nsidc.org/data/SNEX20_SWESARR_TB/versions/1\"> SWESARR Radiometer Data, SnowEx20, v1</A>\n",
+    "<LI> <A HREF=\"https://nsidc.org/data\">NSIDC Datasets</A>\n",
+    "<LI> <A HREF=\"https://blogs.nasa.gov/swesarr/\">SWESARR Blogspot</A>\n",
+    "<LI> <A HREF=\"https://github.com/db1950/swesarr-tut\">A Version of This Repo That Doesn't Require Access to Amazon Servers </A>\n",
+    "</OL>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ece33f6d-f565-4bcc-b056-79874e84f45c",
+   "metadata": {},
+   "source": [
+    "## What is SWESARR?\n",
+    "<UL>\n",
+    "<LI>  <a href=\"https://www.youtube.com/watch?v=5hVQusosGSg&t=210s\">Description from Batuhan Osmanoglu.</a> \n",
+    "<LI> Airborne sensor system measuring active and passive microwave measurements\n",
+    "<LI> Colocated measurements are taken simultaneously using an ultra-wideband antenna\n",
+    "</UL>\n",
+    "<P>\n",
+    "SWESARR gives us insights on the different ways active and passive signals are influenced by snow over large areas."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2ce50fa9-17de-4718-b463-086d4a1b3173",
+   "metadata": {},
+   "source": [
+    "## Active and Passive? Microwave Remote Sensing?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1fc2036c-5b3c-457b-a9a8-b991892ac54b",
+   "metadata": {},
+   "source": [
+    "\n",
+    "### Passive Systems\n",
+    "\n",
+    "* All materials can naturally emit electromagnetic waves\n",
+    "* What is the cause?\n",
+    "\n",
+    "<div>\n",
+    "<img src=\"https://blogs.nasa.gov/swesarr/wp-content/uploads/sites/305/2021/06/particles.png\" width=\"360\"/>\n",
+    "</div>\n",
+    "<br><br>\n",
+    "\n",
+    "* Material above zero Kelvin will display some vibration or movement of particles\n",
+    "* These moving, charged particles will induce electromagnetic waves\n",
+    "* If we're careful, we can measure these waves with a radio wave measuring tool, or \"radiometer\"\n",
+    "<br>\n",
+    "\n",
+    "<div>\n",
+    "<img src=\"https://blogs.nasa.gov/swesarr/wp-content/uploads/sites/305/2021/06/radiometer.png\" width=\"500\"/>\n",
+    "</div>\n",
+    "\n",
+    "\n",
+    "* Radiometers see emissions from many sources, but they're usually very weak\n",
+    "* It's important to design a radiometer that (1) minimizes side lobes and (2) allows for averaging over the main beam\n",
+    "* For this reason, radiometers often have low spatial resolution\n",
+    "\n",
+    "**✏️  Radiometers allow us to study earth materials through incoherent averaging of naturally emitted signals**\n",
+    "\n",
+    "### Active Systems\n",
+    "* While radiometers generally measure natural electromagnetic waves, radars measure man-made electromagnetic waves\n",
+    "* Transmit your own wave, and listen for the returns\n",
+    "* The return of this signal is dependent on the surface and volume characteristics of the material it contacts\n",
+    "\n",
+    "<div>\n",
+    "<img src=\"https://blogs.nasa.gov/swesarr/wp-content/uploads/sites/305/2021/06/radar.png\" width=\"500\"/>\n",
+    "</div>\n",
+    "\n",
+    "**✏️  Synthetic aperture radar allows for high spatial resolution through processing of coherent signals**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bb939cd3-32f5-47ab-9bd2-e0d08256ea26",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%HTML\n",
+    "<style>\n",
+    "td { font-size: 15px }\n",
+    "th { font-size: 15px }\n",
+    "</style>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "54df5103-646f-4ab8-831c-585181100275",
+   "metadata": {},
+   "source": [
+    "## SWESARR Sensors\n",
+    "<center>\n",
+    "<h1><font size=\"+1\">SWESARR Frequencies, Polarization, and Bandwidth Specification  </font></h1>\n",
+    "</center>\n",
+    "\n",
+    "| Center-Frequency (GHz) | Band       |  Sensor      | Bandwidth (MHz) | Polarization |\n",
+    "| ---------------------- | ---------- | ------------ | --------------- | ------------ |\n",
+    "| 9.65                   |  X         |  SAR         | 200             | VH and VV    |\n",
+    "| 10.65                  |  X         |  Radiometer  | 200             | H            |\n",
+    "| 13.6                   | Ku         |  SAR         | 200             | VH and VV    |\n",
+    "| 17.25                  | Ku         |  SAR         | 200             | VH and VV    |\n",
+    "| 18.7                   |  K         |  Radiometer  | 200             | H            |\n",
+    "| 36.5                   | Ka         |  Radiometer  | 1,000           | H            |\n",
+    "\n",
+    "<br>\n",
+    "<center>\n",
+    "<img src=\"https://blogs.nasa.gov/swesarr/wp-content/uploads/sites/305/wppa/1.jpg\", width=\"400\", title=\"Plane\" /> <br>\n",
+    "    <img src=\"https://blogs.nasa.gov/swesarr/wp-content/uploads/sites/305/wppa/4.jpg\", width=\"400\", title=\"Instrument\" />\n",
+    "</center>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6974a849-5b7b-4deb-b699-926ad4a8d184",
+   "metadata": {},
+   "source": [
+    "## SWESARR Coverage\n",
+    "\n",
+    "\n",
+    "\n",
+    "* Below: radiometer coverage for all passes made between February 10 to February 12, 2020\n",
+    "* SWESARR flights cover many snowpit locations over the Grand Mesa area as shown by the dots in blue\n",
+    "\n",
+    "<div>\n",
+    "<img src=\"https://blogs.nasa.gov/swesarr/wp-content/uploads/sites/305/2021/06/passes.png\", width=\"500\", title=\"SWESARR passes over Grand Mesa in 2020\" />\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "df320f07-895c-45be-9859-706516fd9cea",
+   "metadata": {},
+   "source": [
+    "## Reading SWESARR Data\n",
+    "\n",
+    "- SWESARR's SAR data is organized with a common file naming convention for finding the time, location, and type of data\n",
+    "- [Lets look at the prerelease data on its homepage](https://glihtdata.gsfc.nasa.gov/files/radar/SWESARR/prerelease/)\n",
+    "<div>\n",
+    "<img src=\"https://blogs.nasa.gov/swesarr/wp-content/uploads/sites/305/2020/09/SWESARR-Naming-Convention_recolor_v2.png\" width=\"1000\"/>\n",
+    "</div>\n",
+    "\n",
+    "***\n",
+    "<br><br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c5d5a065-efbf-4735-b514-b259f8ea2822",
+   "metadata": {},
+   "source": [
+    "<CENTER>\n",
+    "<H1 style=\"color:red\">\n",
+    "Accessing Data: SAR\n",
+    "</H1>\n",
+    "</CENTER>\n",
+    "\n",
+    "### SAR Data Example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7bf5bc1a-e9eb-4813-a4aa-5205a8762ebe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import several libraries. \n",
+    "# comments to the right could be useful for local installation on Windows.\n",
+    "\n",
+    "from shapely import speedups      # https://www.lfd.uci.edu/~gohlke/pythonlibs/\n",
+    "#speedups.disable()                # <-- handle a potential error in cartopy\n",
+    "\n",
+    "import requests                   # !conda install -c anaconda requests \n",
+    "\n",
+    "# raster manipulation libraries\n",
+    "import rasterio                   # https://www.lfd.uci.edu/~gohlke/pythonlibs/\n",
+    "from osgeo import gdal            # https://www.lfd.uci.edu/~gohlke/pythonlibs/\n",
+    "import cartopy.crs as ccrs        # https://www.lfd.uci.edu/~gohlke/pythonlibs/\n",
+    "import rioxarray as rxr           # !conda install -c conda-forge rioxarray\n",
+    "import xarray as xr               # !conda install -c conda-forge xarray dask netCDF4 bottleneck\n",
+    "\n",
+    "# SnowEx Pit Data!\n",
+    "from snowexsql.api import LayerMeasurements # pip install snowexsql\n",
+    "\n",
+    "# plotting tools\n",
+    "from matplotlib import pyplot     # !conda install matplotlib\n",
+    "import datashader as ds           # https://www.lfd.uci.edu/~gohlke/pythonlibs/\n",
+    "import hvplot.xarray              # !conda install hvplot\n",
+    "import hvplot.pandas\n",
+    "import holoviews as hv\n",
+    "\n",
+    "# append the subfolders of the current working directory to pythons path\n",
+    "import os, sys, glob\n",
+    "\n",
+    "swesarr_subdirs = [\"data\", \"util\"]\n",
+    "tmp = [sys.path.append(os.getcwd() + \"/\" + sd) for sd in swesarr_subdirs]\n",
+    "del tmp # suppress Jupyter notebook output, delete variable\n",
+    "\n",
+    "# lambda functions!\n",
+    "to_nl = lambda a: 10**(a/10)\n",
+    "to_db = lambda a: 10*np.log10(a)\n",
+    "\n",
+    "# settings to make matplotlib look nicer!\n",
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "plt.rcParams[\"figure.figsize\"] = (8, 4.5) # (w, h)\n",
+    "\n",
+    "#############################################\n",
+    "# Suppress Warnings for Cleaner Output      #\n",
+    "# (Address Warnings in future update)       #\n",
+    "import warnings                             #\n",
+    "warnings.filterwarnings('ignore')           #\n",
+    "#############################################\n",
+    "\n",
+    "from helper import gdal_corners, join_files, join_sar_radiom, filt_pit_to_sar, filt_radiom_points, sar_swe_plot, radiom_swe_plot, rough_radiom_area"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9f066718-3181-4645-92dd-7d55d612cc03",
+   "metadata": {},
+   "source": [
+    "#### Select your data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f36402f2-a63f-4d3d-b874-da53ef328c59",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# select files to download\n",
+    "\n",
+    "# SWESARR data website\n",
+    "source_repo = 'https://glihtdata.gsfc.nasa.gov/files/radar/SWESARR/prerelease/'\n",
+    "\n",
+    "# Example flight line\n",
+    "flight_line = 'GRMCT2_31801_20007_016_200211_225_XX_01/'\n",
+    "\n",
+    "# SAR files within this folder\n",
+    "data_files = [\n",
+    "    'GRMCT2_31801_20007_016_200211_09225VV_XX_01.tif',\n",
+    "    'GRMCT2_31801_20007_016_200211_09225VH_XX_01.tif',\n",
+    "    'GRMCT2_31801_20007_016_200211_13225VV_XX_01.tif',\n",
+    "    'GRMCT2_31801_20007_016_200211_13225VH_XX_01.tif',\n",
+    "    'GRMCT2_31801_20007_016_200211_17225VV_XX_01.tif',\n",
+    "    'GRMCT2_31801_20007_016_200211_17225VH_XX_01.tif'\n",
+    "]\n",
+    "\n",
+    "# store the location of the SAR tiles as they're located on the SWESARR data server\n",
+    "remote_tiles = [source_repo + flight_line + d for d in data_files]\n",
+    "\n",
+    "# create local output data directory\n",
+    "output_dir = os.getcwd() + '/data/'\n",
+    "try:\n",
+    "    os.makedirs(output_dir)\n",
+    "except FileExistsError:\n",
+    "    print('output directory prepared!')\n",
+    "\n",
+    "# store individual TIF files locally on our computer / server\n",
+    "output_paths = [output_dir + d for d in data_files]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "324d06be-dece-4f4f-bd0e-e207f0028142",
+   "metadata": {},
+   "source": [
+    "#### Download SAR data and place into data folder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d4c6cd77-0841-4e66-8199-f6e463a856de",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "##    for each file selected, store the data locally \n",
+    "##\n",
+    "##    only run this block if you want to store data on the current \n",
+    "##    server/hard drive this notebook is located.\n",
+    "##\n",
+    "################################################################\n",
+    "\n",
+    "# Search data directory for all tifs\n",
+    "cur_tifs = glob.glob('./data/*.tif', recursive=True)\n",
+    "\n",
+    "if not cur_tifs:\n",
+    "    for remote_tile, output_path in zip(remote_tiles, output_paths):\n",
+    "        \n",
+    "        # download data\n",
+    "        r = requests.get(remote_tile)\n",
+    "    \n",
+    "        # Store data (~= 65 MB/file)\n",
+    "        if r.status_code == 200:\n",
+    "            with open(output_path, 'wb') as f:\n",
+    "                f.write(r.content)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "782a372e-471e-48e3-af7e-688f0873c3a1",
+   "metadata": {},
+   "source": [
+    "#### Merge SAR datasets into single xarray file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2833b2ee-9460-46ab-8f3b-884e89a91a85",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sar_data = join_files(output_paths)\n",
+    "sar_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "48437341-23ec-4556-a63a-8c68057f0535",
+   "metadata": {},
+   "source": [
+    "#### Get SnowEx Snow Pit Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "36513c9e-438a-4f65-9f74-f3cceb1b6172",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "snow_pits = LayerMeasurements.from_filter(type=\"density\",\n",
+    "                                   site_name=\"Grand Mesa\",\n",
+    "                                   date_greater_equal=\"2020-02-01\",\n",
+    "                                   date_less_equal=\"2020-02-15\",\n",
+    "                                   limit=2000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "842d8a53-0cf3-4438-9a5a-c7b2c284db2d",
+   "metadata": {},
+   "source": [
+    "1.) Find SnowPits within SWESARR swath <br>\n",
+    "2.) Obtain mean SWESARR backscatter in a square about each snowpit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1ffca227-cf79-4ed7-9d24-b54ba1fe3783",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "square_length = 3 # [meters]\n",
+    "[point_swe_filt, swesarr_mean] = filt_pit_to_sar(snow_pits, sar_data, square_length)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1fbf46f0-6a7c-4b3e-a18e-edfd7e3f7f02",
+   "metadata": {},
+   "source": [
+    "#### Plot data with hvplot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6e61bac1-6721-453c-89fb-cafa72ba25c2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set clim directly:\n",
+    "clim=(-20,20)\n",
+    "cmap='gray'\n",
+    "crs = ccrs.UTM(zone='12') #12n\n",
+    "tiles='OSM'\n",
+    "tiles='EsriImagery'\n",
+    "transparent_tile = hv.Tiles('https://tile.openstreetmap.org/{Z}/{X}/{Y}.png', name=\"OSM\").opts(alpha=0)\n",
+    "frame_width  = 600\n",
+    "frame_height = 500\n",
+    "\n",
+    "# create an image for SAR data!\n",
+    "sar_img = sar_data.hvplot.image(x='x',y='y',groupby='band',cmap=cmap,clim=clim,rasterize=True,\n",
+    "                       xlabel='Longitude',ylabel='Latitude',\n",
+    "                       frame_height=frame_height, frame_width=frame_width,\n",
+    "                       xformatter='%.1f',yformatter='%.1f', crs=crs, tiles=tiles, alpha=0.8)\n",
+    "\n",
+    "# create an image for snow pit data!\n",
+    "pit_img = point_swe_filt.hvplot.points('lon', 'lat',  geo=True, color='swe', alpha=0.8,\n",
+    "                        tiles=transparent_tile, frame_height=frame_height, frame_width=frame_width, crs=crs, hover_cols=['site_id'],\n",
+    "                        cmap='Reds')\n",
+    "\n",
+    "# overlay both images!\n",
+    "total_img = sar_img * pit_img  # <- overlay images\n",
+    "\n",
+    "# display the plot!\n",
+    "total_img"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f619edf2-f015-49d5-b35d-5ebfc2b4a980",
+   "metadata": {},
+   "source": [
+    "#### Optional: Plot snowpit SWE against averaged SWESARR data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "acddd149-3e53-4489-b233-b8d8a37e1b11",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sar_swe_fig = sar_swe_plot(point_swe_filt, swesarr_mean)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f45328d8-7d96-4cf3-bf0c-be2033d835d0",
+   "metadata": {},
+   "source": [
+    "**🎉  Congratulations! You now know how to download and display a SWESARR SAR dataset !**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "04764272-38b1-437a-b194-1bfea9ff535c",
+   "metadata": {},
+   "source": [
+    "### Radiometer Data Example\n",
+    "* SWESARR's radiometer data is publicly available at NSIDC\n",
+    "    * Will be available on [SnowExSQL if the fans demand it!](https://github.com/SnowEx/snowexsql/search?q=swesarr)\n",
+    "* [Radiometer Data v1 Available Here](https://nsidc.org/data/SNEX20_SWESARR_TB/versions/1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "493a7452-cc73-4224-9237-ed038d7fa5da",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd      # !conda install pandas\n",
+    "import numpy as np       # !conda install numpy\n",
+    "import xarray as xr      # !conda install -c anaconda xarray \n",
+    "\n",
+    "import hvplot            # !conda install hvplot\n",
+    "import hvplot.pandas\n",
+    "import holoviews as hv   # !conda install -c conda-forge holoviews \n",
+    "from holoviews.operation.datashader import datashade\n",
+    "#from geopy.distance import distance     #!conda install -c conda-forge geopy "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3d9430bc-3eec-440c-81a0-b6940e94d09c",
+   "metadata": {},
+   "source": [
+    "#### Downloading SWESARR Radiometer Data with `wget`\n",
+    "\n",
+    "* If you are running this on the SnowEx Hackweek server, `wget` should be configured.\n",
+    "* If you are using this tutorial on your local machine, you'll need `wget`.\n",
+    "    * Linux Users\n",
+    "        - You should be fine. This is likely baked into your operating systems. Congratulations! You chose correctly.\n",
+    "    * Apple Users\n",
+    "        - The author of this textbox has never used a Mac. There are many command-line suggestions online. `sudo brew install wget`, `sudo port install wget`, etc. Try searching online!\n",
+    "    * Windows Users\n",
+    "        - [Check out this tutorial, page 2](https://blogs.nasa.gov/swesarr/wp-content/uploads/sites/305/2020/10/how_to_download_SWESARR_radar_data.pdf) You'll need to download binaries for `wget`, and you should really make it an environment variable!\n",
+    "        \n",
+    "Be sure to be diligent before installing anything to your computer.\n",
+    "        \n",
+    "Regardless, fill in your NASA Earthdata Login credentials and follow along!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0f29d818-7abc-4ebe-a14a-f067e23b1486",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# !wget --user=USERNAME_HERE --password=PASSWORD_HERE --quiet https://n5eil01u.ecs.nsidc.org/SNOWEX/SNEX20_SWESARR_TB.001/2020.02.12/SNEX20_SWESARR_TB_GRMCT2_13801_20007_000_200211_XKKa225H_v01.csv -O {output_dir}/SNEX20_SWESARR_TB_GRMCT2_13801_20007_000_200211_XKKa225H_v01.csv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4a2dc074-19cb-42bd-9c3f-fed14442429e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# !wget --quiet https://n5eil01u.ecs.nsidc.org/SNOWEX/SNEX20_SWESARR_TB.001/2020.02.12/SNEX20_SWESARR_TB_GRMCT2_13801_20007_000_200211_XKKa225H_v01.csv -O {output_dir}/SNEX20_SWESARR_TB_GRMCT2_13801_20007_000_200211_XKKa225H_v01.csv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a5be5aa-e652-4bea-8bb4-8831209a2b55",
+   "metadata": {},
+   "source": [
+    "#### Select an example radiometer data file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3ba1a0b6-b1f5-4f60-ab99-57444f08b9c4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# use the file we downloaded with wget above\n",
+    "excel_path = f'{output_dir}/SNEX20_SWESARR_TB_GRMCT2_13801_20007_000_200211_XKKa225H_v01.csv'\n",
+    "\n",
+    "# read data\n",
+    "radiom = pd.read_csv(excel_path)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c7157153-9328-463f-ba4e-5d1c00584b49",
+   "metadata": {},
+   "source": [
+    "#### Lets examine the radiometer data files content"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bda67928-c05f-4648-a11b-8403f7c78eb8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "radiom.head()\n",
+    "#radiom.hvplot.table(width=1100) # sortable table in jupyterlab"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0297d88d-a757-4141-ad60-7118b35d4c85",
+   "metadata": {},
+   "source": [
+    "#### Plot radiometer data with hvplot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "750dcc7d-6a05-4036-8241-546b691a3dae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create several series from pandas dataframe\n",
+    "lon_ser = pd.Series( radiom['Longitude (deg)'].to_list() * (3) )\n",
+    "lat_ser = pd.Series( radiom['Latitude (deg)'].to_list()  * (3) )\n",
+    "clim_r=(200,270)\n",
+    "\n",
+    "tb_ser = pd.Series(\n",
+    "    radiom['TB X (K)'].to_list() + \n",
+    "    radiom['TB K (K)'].to_list() + \n",
+    "    radiom['TB Ka (K)'].to_list(), name=\"Tb\"\n",
+    "     )\n",
+    "\n",
+    "# get series length, create IDs for plotting\n",
+    "sl = len(radiom['TB X (K)'])\n",
+    "id_ser = pd.Series(\n",
+    "    ['X-band']*sl + ['K-band']*sl + ['Ka-band']*sl, name=\"ID\"\n",
+    "     )\n",
+    "\n",
+    "frame = {'Longitude (deg)' : lon_ser, 'Latitude (deg)' : lat_ser,\n",
+    "         'TB' : tb_ser, 'ID' : id_ser}\n",
+    "radiom_p = pd.DataFrame(frame)\n",
+    "\n",
+    "del sl, lon_ser, lat_ser, tb_ser, id_ser, frame\n",
+    "\n",
+    "radiom_p.hvplot.points('Longitude (deg)', 'Latitude (deg)', groupby='ID', geo=True, color='TB', alpha=1,\n",
+    "                        tiles='EsriImagery', height=500, width=800, clim=clim_r)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9a7c80c3-3c7e-4f31-997d-22b71853af54",
+   "metadata": {},
+   "source": [
+    "**🎉 Congratulations! You now know how to download and display a SWESARR radiometer dataset !**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e5fe1cf9-4b57-49e5-bea3-aa5a3d01bcdb",
+   "metadata": {},
+   "source": [
+    "#### SWE and Radiometer Data\n",
+    "First, filter the values far away from the radiometer and merge into a single dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ef9bec9d-4ee8-4eee-a28e-6b890b6ef6c8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fp_10m, fp_18m, fp_37m = 496, 282, 144 # maximum field of view for each channel \n",
+    "rad_swe = filt_radiom_points( fp_10m, fp_18m, fp_37m, radiom, point_swe_filt )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5be4087f-cac8-4fd2-9066-c5bf5dede6da",
+   "metadata": {},
+   "source": [
+    "#### Optional: Rough Impression of the Radiometer Footprint Size"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b56904fb-cc6e-4084-9748-36f10f7d8713",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "out_img = rough_radiom_area(radiom, rad_swe, fp_10m, fp_18m, fp_37m)\n",
+    "# Uncomment below to visualize image!\n",
+    "# out_img"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4b2c866c-79dc-40ae-b56b-130670c6d99a",
+   "metadata": {},
+   "source": [
+    "Lets repeat our comparison of snowpits to SWESARR values for the radiometer!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c26131e3-36d2-48f2-a436-e6f41172da2d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rad_fig = radiom_swe_plot(rad_swe)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b3d0d1df-05f0-4b61-bd4b-560b1c826037",
+   "metadata": {},
+   "source": [
+    "## SAR and Radiometer Together"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "21fbbd01-a1ae-4fcd-ad8d-6127010ab703",
+   "metadata": {},
+   "source": [
+    "* The novelty of SWESARR lies in its colocated SAR and radiometer systems\n",
+    "* Lets try filtering the SAR dataset and plotting both datasets together\n",
+    "* For this session, I've made the code a function. We can look at it together by opening `.util/helper.py`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b0855fa7-762b-40ab-afab-84d76e2070a7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# data_p, data_ser = join_sar_radiom(sar_data, radiom)\n",
+    "\n",
+    "# data_p.hvplot.points('Longitude (deg)', 'Latitude (deg)', groupby='ID', geo=True, color='Measurements', alpha=1,\n",
+    "                        # tiles='ESRI', height=400, width=500)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "275c601d-079b-40ef-b172-9bc6f1147b1a",
+   "metadata": {},
+   "source": [
+    "## Exercise"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9ad1a774-9994-4705-b9e0-fbfd7342b518",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-block alert-info\">\n",
+    "<b>Exercise:</b> \n",
+    "     <ol>\n",
+    "         <li>Plot a time-series visualization of the filtered SAR channels from the output of the \n",
+    "             <font face=\"Courier New\" > join_sar_radiom()</font> function\n",
+    "         </li>\n",
+    "         <li>Plot a time-series visualization of the radiometer channels from the output of the \n",
+    "             <font face=\"Courier New\" > join_sar_radiom()</font> function \n",
+    "         </li>\n",
+    "         <li>Hint: the data series variable (<font face=\"Courier New\" > data_ser </font>) is a pandas data series. \n",
+    "             Use some of the methods shown above to read and plot the data!\n",
+    "         </li>\n",
+    "    </ol> \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ea283a7a-28fc-42fc-ab64-f6c9aab2da6b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "### Your Code Here #############################################################################################################\n",
+    "#\n",
+    "# Two of Many Options:\n",
+    "# 1.) Go the matplotlib route\n",
+    "#     a.) Further reading below:\n",
+    "#         https://matplotlib.org/stable/tutorials/introductory/pyplot.html\n",
+    "#\n",
+    "# 2.) Try using hvplot tools if you like\n",
+    "#      a.) Further reading below:\n",
+    "#          https://hvplot.holoviz.org/user_guide/Plotting.html\n",
+    "#\n",
+    "# Remember, if you don't use a library all of the time, you'll end up <search engine of your choice>-ing it. Go crazy!\n",
+    "#\n",
+    "################################################################################################################################\n",
+    "\n",
+    "# configure some inline parameters to make things pretty / readable if you'd like to go with matplotlib\n",
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "plt.rcParams[\"figure.figsize\"] = (16, 9) # (w, h)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8276b584-0564-4054-b2ce-f4a8c385e2da",
+   "metadata": {},
+   "source": [
+    "<br><br><br>\n",
+    "## [Warnings](#Table-of-Contents)\n",
+    "<div class=\"alert alert-block alert-danger\">\n",
+    "<b>Interpreting Data:</b> After the 2019 and 2020 measurement periods for SWESARR, an internal timing error was found in the flight data which affects the spatial precision of the measurements. While we are working to correct this geospatial error, please consider this offset before drawing conclusions from SWESARR data if you are using a dataset prior to this correction. The SWESARR website will announce the update of the geospatially corrected dataset.\n",
+    "</div>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/book/tutorials/swesarr/util/helper.py b/book/tutorials/swesarr/util/helper.py
new file mode 100644
index 0000000..98ea1d4
--- /dev/null
+++ b/book/tutorials/swesarr/util/helper.py
@@ -0,0 +1,472 @@
+import numpy as np
+import matplotlib.pyplot as plt
+
+def cosd(a):
+    return np.cos( a * np.pi/180 )
+def sind(a):
+    return np.sin( a * np.pi/180 )
+def tand(a):
+    return np.tan( a * np.pi/180 )
+
+def gdal_corners(filename):
+    '''
+    a function  that can be used to determine the boundary of a raster / tif file.
+    '''
+    #http://stackoverflow.com/questions/2922532/obtain-latitude-and-longitude-from-a-geotiff-file
+    from osgeo import gdal            # https://www.lfd.uci.edu/~gohlke/pythonlibs/
+    ds = gdal.Open(filename)
+    width = ds.RasterXSize
+    height = ds.RasterYSize
+    gt = ds.GetGeoTransform()
+    minx = gt[0]
+    miny = gt[3] + width*gt[4] + height*gt[5]
+    maxx = gt[0] + width*gt[1] + height*gt[2]
+    maxy = gt[3]
+    return (minx,miny,maxx,maxy)
+                
+def join_files(file_list):
+    
+    '''
+        a method for merging raster/tif data along the band dimension using 
+        rioxarray. this method does not save data to storage.
+    '''
+    
+    import rioxarray as rxr
+    import xarray as xr
+    
+    # initialize band names
+    var_names = []
+    
+    # loop over bands, append to a rasterio xarray
+    for file in file_list:
+        
+        xmin,ymin,xmax,ymax=tuple(gdal_corners(file))
+        cda=rxr.open_rasterio(file,chunks=(1,1200,1200))
+        cda=cda.sel(x=slice(xmin,xmax),y=slice(ymax,ymin))
+        
+        if file == file_list[0]:
+            da = cda
+        else:
+            da = xr.concat([da, cda], "band")
+            
+        # extract frequency / polarization band
+        var_name = file.split('_')[5]
+        var_name = var_name[0:2] + var_name[5:]
+        var_names.append(var_name)
+
+    # change band names for reading
+    da = da.assign_coords({'band' : var_names})
+    return da
+
+
+def join_sar_radiom(da, radiom):
+    ''' 
+    
+    input
+        da - rioxarray containing SWESARR SAR data. 6 channels expected.
+        radiom - pandas array containing radiometer data. 3 channels expected.
+        
+    output
+        data_p - pandas data series intended for plotting with hvplot's "groupby" feature. 
+                sadly, all measurement data is crammed into a single column, making this 
+                difficult to use outside of hvplot functionality.
+        out_data - pandas data series that is readable. intended for use in student exercise.
+        
+    TO-DO / IMPROVEMENTS :
+        implementing this with xarray would be much more RAM efficient. 
+        however, i find pandas arrays to be easier to conceptualize than xarrays.
+        i feel its best to go with pandas arrays for the tutorial.
+            ( with great shame, i admit i find it harder to visualize
+              N-dimensional data rather than 2-dimensional data )
+            
+    '''
+    import numpy as np
+    import pandas as pd
+    import datetime
+    
+    # first, convert the data from the SAR's meter-based, 
+    # universal transverse mercator (UTM) coordinate system
+    # to the radiometer's old-fashioned 
+    # latitude/longitude coordinate system
+    sar_geo = da.rio.reproject("EPSG:4326")
+    
+    # get latidue and longitude from SAR data
+    lat_sar = sar_geo.y.data
+    lon_sar = sar_geo.x.data
+
+    # radiometer latitude/longitude values as numpy arrays
+    lat_rad = radiom['Latitude (deg)'].to_numpy()
+    lon_rad = radiom['Longitude (deg)'].to_numpy()
+
+    # loop over latitude and longitude
+    frames = []
+    k = -1
+    for lat, lon in zip(lat_rad, lon_rad):
+        k+=1
+
+        # get the difference between latitude and longitude pairs
+        # for SAR and radiometer data
+        lat_m = np.abs(lat_sar - lat).tolist()
+        lon_m = np.abs(lon_sar - lon).tolist()
+
+        # use python's built-in functions to find minimum index
+        ind_lat = lat_m.index(min(lat_m))
+        ind_lon = lon_m.index(min(lon_m))
+
+        # write sar lat and lon
+        s_lat = lat_sar[ind_lat]
+        s_lon = lon_sar[ind_lon]
+
+        # get distance between the estimated sar and radiometer center positions
+        # using vincenty's formula from the geopy library
+        dis = distance( (lat, lon), (s_lat, s_lon) ).m
+
+        # construct data dictionary
+        data_d = {'sar_lat': s_lat, 'sar_lon' : s_lon, 
+                  'rad_lat' : lat, 'rad_lon' : lon, 
+                  'ind_lat' : ind_lat, 'ind_lon' : ind_lon,
+                  'dist_m' : dis}
+
+        # make a pandas dataframe based on the above data!
+        df = pd.DataFrame(data_d, index = [k])
+        
+        # throw it in an array for good measure
+        frames.append(df)
+
+    # use "list comprehension" syntax to merge the pandas dataframes together
+    location_data = pd.concat( data for data in frames )
+    del frames, df, data_d
+    
+    # now lets store our SAR data based on our filtered results
+    sar_data = []
+
+    for in1, in2 in zip( location_data['sar_lon'].tolist(), location_data['sar_lat'].tolist()):
+        # access the dask array storing our results
+        d = sar_geo.sel( x=in1, y=in2 ).compute().data.tolist()
+        # append to our list
+        sar_data.append(d)
+
+    # convert both arrays array to a numpy array for easy merging
+    data = np.array(sar_data)
+    radiom_d = radiom.iloc[:,4:7].to_numpy()
+
+    # insert the radiometer data to the SAR data as a column vector
+    for i in range(np.size(radiom_d,1)):
+        data = np.insert( data, np.size(data,1), radiom_d[:,i], axis=1 )
+    del radiom_d
+
+    # the following section is for plotting with hvplot while including a label only.
+    #
+    # use list operations paired with pandas series to repeat the lat/lon data
+    lon_ser = pd.Series( location_data['sar_lon'].to_list() * (6)\
+                        + location_data['rad_lon'].to_list() * (3) )
+    lat_ser = pd.Series( location_data['sar_lat'].to_list() * (6)\
+                        + location_data['rad_lat'].to_list() * (3) )
+    
+    # flatten MATLAB/*F*ortran style. (Default flattens as row vectors)
+    data_ser = data.flatten(order='F')
+
+    # get series length, create IDs for plotting
+    # all series have the same length, so this shouldn't matter.
+    sl = len(radiom['TB X (K)'])
+    id_ser = pd.Series(
+        ['09VV SAR']*sl + ['09VH SAR']*sl + ['13VV SAR']*sl + ['13VH SAR']*sl + \
+            ['17VV SAR']*sl + ['17VH SAR']*sl + \
+        ['X-band Rad']*sl + ['K-band Rad']*sl + ['Ka-band Rad']*sl, name="ID"
+         )
+    
+    # the final frame used only for plotting by groups with hvplot
+    frame = {'Longitude (deg)' : lon_ser, 'Latitude (deg)' : lat_ser,
+             'Measurements' : data_ser, 'ID' : id_ser}
+    data_p = pd.DataFrame(frame)
+    
+    # convert swesarr numpy data to dataframe
+    swesarr_df = pd.DataFrame(data = data, 
+                        columns = ["09VV SAR", "09VH SAR", 
+                                   "13VV SAR", "13VH SAR",
+                                   "17VV SAR", "17VH SAR",
+                                   'X-band Rad', 'Ku-band Rad', 
+                                   'Ka-band Rad'])
+    
+    # Convert UTC string to date time array
+    times = radiom.UTC.to_frame()
+    times = times['UTC'].to_list()
+
+    # convert to datetime
+    times = [datetime.datetime.strptime(time_str, '%Y%m%d-%H:%M:%S.%f') for time_str in times]
+
+    radiom.UTC = times
+
+    
+    # combine time data with swesarr measurements
+    out_data = pd.concat( [radiom.UTC.to_frame(), swesarr_df], axis=1)
+
+    # return the variable used for plotting and its more user-friendly variant.
+    return data_p, out_data
+
+def filt_pit_to_sar(snow_pits, sar_data, box_size):
+    
+    '''
+    input 
+        snow_pits -- DataFrame created from snowexsql LayerMeasurements. (UTM CRS)
+        sar_data  -- xarray.DataArray containing [6,y,x] SWESARR data (UTM CRS)
+        box_size  -- length of the square about each snow pit used for averaging 
+                     obtaining an average swesarr backscatter (numeric)
+    
+    output
+        point_swe_filt -- GeoPandas GeoDataFrame of filtered snow pit data
+        swesarr_mean   -- numpy array of mean SWESARR backscatter values for each filtered snowpit
+    
+    '''
+    
+    # the filtering box will be centered about the swesarr data
+    box_size /= 2
+    
+    import numpy as np
+    import geopandas as gpd
+    
+    to_nl = lambda a: 10**(a/10)
+    to_db = lambda a: 10*np.log10(a)
+    
+
+    snow_pits['value'] = snow_pits['value'].astype(float)
+
+    # Calculate SWE
+    swe_lambda = lambda row: row['value'] * (row['depth'] - row['bottom_depth']) / 100
+    snow_pits['swe'] = snow_pits.apply(swe_lambda, axis=1)
+
+    # Prepare the data to be a single point by summing the SWE by site and date
+    point_swe = gpd.GeoDataFrame(columns=['date', 'swe', 'geometry', 'site_id'])
+
+    sites = snow_pits['site_id'].unique().tolist()
+
+    my_sites = []
+    # Loop over data by site and date
+    for site in sites:
+        if len(site.split()) == 1:
+            ind1 = snow_pits['site_id'] == site
+            dates = snow_pits['date'][ind1].unique().tolist()
+            
+            for date in dates:
+                # Grab all density at this site and date
+                ind2= snow_pits['date'] == date
+                
+                profile = snow_pits[ind1 & ind2]
+            
+                # sum the swe column and assign data to a dictionary
+                data = gpd.pd.DataFrame ({'swe': profile['swe'].sum(), 'geometry': profile['geom'].iloc[0], 'date': date}, index=[0])
+            
+                # Add the data to a dataframe
+                point_swe = gpd.pd.concat([point_swe, data], ignore_index=True)
+                my_sites.append(site)
+    point_swe['site_id'] = my_sites    
+
+    # x and y coordinates as numpy arrays
+    lat_pit = point_swe['geometry'].y.to_numpy()
+    lon_pit = point_swe['geometry'].x.to_numpy()
+
+
+    # get subelements of the primary xarray
+    swesarr_subs = []
+    for cur_lat, cur_lon in zip(lat_pit, lon_pit):
+        # get bounding box about lat / lon
+        lat_box = np.array([cur_lat]) + np.array([-box_size, box_size])
+        lon_box = np.array([cur_lon]) + np.array([-box_size, box_size])
+        
+        # extract points
+        tmp = sar_data.sel(y=slice(lat_box[1], lat_box[0]), x=slice(lon_box[0], lon_box[1]))
+        # add to that thing
+        swesarr_subs.append( tmp )
+        
+    # obtain the mean SAR value for each subelement
+    swesarr_mean = np.zeros([6, len(my_sites)]) * np.nan
+    for sub, ii in zip(swesarr_subs, range(len(my_sites))):
+        for sub_band, jj in zip( sub['band'].values, range(6)):
+            if not any( x == 0 for x in swesarr_subs[ii].shape ) :
+                cur_data = swesarr_subs[ii].sel({'band':sub_band})
+                swesarr_mean[jj,ii] = to_db(np.mean(to_nl(cur_data.values)))
+
+    # filter out any data with nans
+    nan_mask = ~np.isnan(swesarr_mean)
+    nan_mask = np.logical_and.reduce(nan_mask,axis=0)
+    swesarr_mean = swesarr_mean[:,nan_mask]
+    my_sites = [a for a,b in zip(my_sites, nan_mask) if b]
+    lat_pit = lat_pit[nan_mask]
+    lon_pit = lon_pit[nan_mask]
+    point_swe_filt = point_swe[nan_mask]
+    point_swe_filt = point_swe_filt.reset_index(drop=True)
+    point_swe_filt['lat'] = lat_pit
+    point_swe_filt['lon'] = lon_pit
+    
+    return point_swe_filt, swesarr_mean
+
+def filt_radiom_points( fp_10m, fp_18m, fp_37m, radiom, point_swe_filt ):
+    '''
+
+    Parameters
+    ----------
+    fp_10m : int / float (scalar)
+        Semi-major axis of 10 GHz footprint [meter]
+    fp_18m : int / float (scalar)
+        Semi-major axis of 18 GHz footprint [meter]
+    fp_37m : int / float (scalar)
+        Semi-major axis of 37 GHz footprint [meter]
+    radiom : pandas dataframe
+        radiometer data from SWESARR CSV
+    point_swe_filt : geopandas GeoDataFrame
+        filtered SWE data from call to filt_pit_to_sar()
+
+    Returns
+    -------
+    rad_swe : geopandas GeoDataFrame
+        Dataframe of SWE snow pit data with nearest brightness temperature value available
+    '''
+    from pyproj import Transformer
+    rad_lat = np.array( radiom['Latitude (deg)'])
+    rad_lon = np.array( radiom['Longitude (deg)'])
+
+    # Convert latitude and longitude to UTM
+    wgs84_crs = "EPSG:4326"
+    utm_crs   = "EPSG:32612"
+    trnsfmr             = Transformer.from_crs(wgs84_crs, utm_crs)
+    rad_east, rad_north = trnsfmr.transform(rad_lat, rad_lon)
+    
+    lat_pit = np.array( point_swe_filt['lat'] )
+    lon_pit = np.array( point_swe_filt['lon'] )
+
+    rad_pit10 = np.zeros( [ np.shape( lat_pit )[0]]) * np.nan
+    rad_pit18 = np.zeros( [ np.shape( lat_pit )[0]]) * np.nan
+    rad_pit37 = np.zeros( [ np.shape( lat_pit )[0]]) * np.nan
+    for p_lat, p_lon, i in zip(lat_pit, lon_pit, range(len(radiom))): 
+        dif_arr = np.sqrt( (rad_north - p_lat)**2 + (rad_east - p_lon)**2 )
+        dif_val = np.min(dif_arr)
+        dif_ind = np.argmin(dif_arr)
+        if dif_val <= fp_10m:
+            rad_pit10[i] = radiom['TB X (K)'][dif_ind]
+        if dif_val <= fp_18m:
+            rad_pit18[i] = radiom['TB K (K)'][dif_ind]
+        if dif_val <= fp_37m:
+            rad_pit37[i] = radiom['TB Ka (K)'][dif_ind]
+            
+    nan_mask_rad = ~np.isnan(rad_pit10) # filter about the widest footprint
+    rad_swe = point_swe_filt[nan_mask_rad]
+    rad_swe['TB_X'] = rad_pit10[nan_mask_rad]
+    rad_swe['TB_K'] = rad_pit18[nan_mask_rad]
+    rad_swe['TB_Ka'] = rad_pit37[nan_mask_rad]
+    rad_swe = rad_swe.reset_index(drop=True)
+    
+    return rad_swe
+
+def sar_swe_plot(point_swe_filt, swesarr_mean):
+    s_i = np.argsort(point_swe_filt.swe.to_list())
+    
+    # color palette # https://zenodo.org/records/3381072
+    okabe_ito = ["#000000", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7"]
+    sar_labs  = ['09VH', '09VV', '13VH', '13VV', '17VH', '17VV']
+    ## plot avg swe and avg backscatter per site
+    fig, ax1 = plt.subplots()
+    plt.xticks(rotation=70)
+    ax1.plot( point_swe_filt.site_id[s_i], point_swe_filt.swe[s_i], 'd', linewidth=2, label='SWE', color=okabe_ito[-1] )
+    ax1.set_ylabel('SWE [mm]', color=okabe_ito[-1])
+    ax1.set_xlabel('SnowEx20 Pit ID')
+    ax2 = ax1.twinx()
+    for each, lab, pal in zip(swesarr_mean[1:6:2,s_i], sar_labs[1:6:2], okabe_ito):
+        sub_data = each
+        bl1 = (sub_data > -20) & (sub_data < 0)
+        sub_data[~bl1] = np.nan
+        xlab = point_swe_filt.site_id[s_i]
+        ax2.plot( xlab, sub_data, '-o', label=lab, color=pal )
+    plt.legend()
+    ax1.tick_params(axis='y', colors=okabe_ito[-1])
+    ax2.set_ylabel('Backscatter [dB]')
+    return fig
+
+def radiom_swe_plot(rad_swe):
+    '''
+    plot SWE and brightness temperature together
+
+    Parameters
+    ----------
+    rad_swe : geopandas dataframe
+        combined radiometer and swe data
+
+    Returns
+    -------
+    fig : matplotlib.pyplot.figure
+        figure handle for generated image
+    '''
+    # color palette # https://zenodo.org/records/3381072
+    okabe_ito = ["#000000", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7"]
+    TB_ids = ['TB_X', 'TB_K', 'TB_Ka']
+    TB_leg = ['X (10)', 'K (18)', 'Ka (37)']
+    
+    s_i = np.argsort(rad_swe.swe.to_list())
+    fig, ax1 = plt.subplots()
+    plt.xticks(rotation=70)
+    ax1.plot( rad_swe.site_id[s_i], rad_swe.swe[s_i], 'd', linewidth=2, label='SWE', color=okabe_ito[-1] )
+    ax1.set_ylabel('SWE [mm]', color=okabe_ito[-1])
+    ax1.set_xlabel('SnowEx20 Pit ID')
+    ax2 = ax1.twinx()
+    for lab, pal, leg in zip(TB_ids, okabe_ito, TB_leg):
+        ax2.plot( rad_swe.site_id[s_i], rad_swe[lab][s_i], '-o', label=leg, color=pal )
+    plt.legend()
+    ax1.tick_params(axis='y', colors=okabe_ito[-1])
+    ax2.set_ylabel('Brightness Temperature [K]')
+    return fig
+
+
+def rough_radiom_area(radiom, rad_swe, fp_10m, fp_18m, fp_37m):
+    from pyproj import Transformer
+    import holoviews as hv
+    import pandas as pd
+    import cartopy.crs as ccrs
+    
+    crs = ccrs.UTM(zone='12') #12n
+    transparent_tile = hv.Tiles('https://tile.openstreetmap.org/{Z}/{X}/{Y}.png', name="OSM").opts(alpha=0.0)
+    
+    beg_i = radiom['UTC'].argmin()
+    end_i = radiom['UTC'].argmax()
+
+    end_lats = radiom['Latitude (deg)'][[beg_i, end_i]].to_list()
+    end_lons = radiom['Longitude (deg)'][[beg_i, end_i]].to_list()
+
+    wgs84_crs = "EPSG:4326"
+    utm_crs   = "EPSG:32612"
+    trnsfmr             = Transformer.from_crs(wgs84_crs, utm_crs)
+    end_east, end_north = trnsfmr.transform(end_lats, end_lons)
+
+    xd = {}
+    for a, b in zip( [fp_10m, fp_18m, fp_37m], ['10', '18', '37']):
+        xd['x_' + b] = np.array( [ end_east[0] - a, end_east[1] - a, end_east[1] + a, end_east[0] + a, end_east[0] - a] )
+        xd['y_' + b] = np.array( [ end_north[0] + a, end_north[1] - a, end_north[1] - a, end_north[0] + a, end_north[0] + a] )
+
+
+    # Overlay the plot with tiles and rectangle
+    xd_pd = pd.DataFrame(xd)
+
+    rect_data = []
+    for a, b, c in zip( ['blue', 'orange', 'purple'], ['10', '18', '37'], ['EsriImagery', transparent_tile, transparent_tile]):
+        rect_data.append(
+            xd_pd.hvplot.polygons(
+                x='x_' + b, 
+                y='y_' + b, 
+                color=a, 
+                alpha=0.25, 
+                line_width=0,
+                geo=True,
+                crs=utm_crs,
+                tiles=c
+            )
+            )
+
+
+    snow_pit_img = rad_swe.hvplot.points('lon', 'lat',  geo=True, color='swe', alpha=1,
+                            tiles=transparent_tile, height=500, width=800, crs=utm_crs, hover_cols=['site_id'],
+                            cmap='Reds')
+    
+    final_img = rect_data[0]*rect_data[1]*rect_data[2]*snow_pit_img
+    return final_img
+
+    
+if __name__ == '__main__':
+    print('name is main!')
\ No newline at end of file

From 4efdcacde71da557ab51b2d7a1fa14c0d66c3189 Mon Sep 17 00:00:00 2001
From: Boyd <db1950@umd.edu>
Date: Thu, 15 Aug 2024 01:56:06 -0400
Subject: [PATCH 2/6] Table of Contents Update

oops!
---
 book/_toc.yml | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/book/_toc.yml b/book/_toc.yml
index 5d96694..5e0e5b9 100644
--- a/book/_toc.yml
+++ b/book/_toc.yml
@@ -24,6 +24,11 @@ parts:
           title: Albedo
           sections:
             - file: tutorials/albedo/aviris-ng-data
+        - file: tutorials/swesarr/index
+          title: SWESARR
+          sections:
+            - file: tutorials/swesarr/swesarr_tut
+
 - caption: Projects
   chapters:
     - file: projects/index

From 534540636e48d34d926df2b610d6925910bd1b05 Mon Sep 17 00:00:00 2001
From: Boyd <db1950@umd.edu>
Date: Thu, 15 Aug 2024 12:05:43 -0400
Subject: [PATCH 3/6] SnowExSQL install

Looks like the website may not be rendering because snowexsql isn't available. This is definitely an inelegant way of addressing that error, but it's worth a shot at the moment.
---
 book/tutorials/swesarr/swesarr_tut.ipynb | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/book/tutorials/swesarr/swesarr_tut.ipynb b/book/tutorials/swesarr/swesarr_tut.ipynb
index d67ed8a..1b2b869 100644
--- a/book/tutorials/swesarr/swesarr_tut.ipynb
+++ b/book/tutorials/swesarr/swesarr_tut.ipynb
@@ -223,6 +223,16 @@
     "### SAR Data Example"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "54b4ada6-dfd7-4e60-b7aa-d2cd67872650",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install snowexsql"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,

From 3332a53b072be50788fdd54d2dac2a135933b1f7 Mon Sep 17 00:00:00 2001
From: Boyd <db1950@umd.edu>
Date: Thu, 15 Aug 2024 12:27:36 -0400
Subject: [PATCH 4/6] Website Render Tweaks

Version of HVPlot used to render on website seems to have trouble with opacity. Using EsriReference to mitigate some of these issues.

Fixed error with radiometer data reading caused by hanging forward slash.
---
 book/tutorials/swesarr/swesarr_tut.ipynb | 1473 +++++++++++++++++++++-
 1 file changed, 1439 insertions(+), 34 deletions(-)

diff --git a/book/tutorials/swesarr/swesarr_tut.ipynb b/book/tutorials/swesarr/swesarr_tut.ipynb
index 1b2b869..631e223 100644
--- a/book/tutorials/swesarr/swesarr_tut.ipynb
+++ b/book/tutorials/swesarr/swesarr_tut.ipynb
@@ -137,10 +137,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "bb939cd3-32f5-47ab-9bd2-e0d08256ea26",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       "td { font-size: 15px }\n",
+       "th { font-size: 15px }\n",
+       "</style>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "%%HTML\n",
     "<style>\n",
@@ -225,20 +241,612 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "54b4ada6-dfd7-4e60-b7aa-d2cd67872650",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: snowexsql in /srv/conda/envs/notebook/lib/python3.11/site-packages (0.5.0)\n",
+      "Requirement already satisfied: utm<1.0,>=0.5.0 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from snowexsql) (0.7.0)\n",
+      "Requirement already satisfied: geoalchemy2<1.0,>=0.6 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from snowexsql) (0.15.2)\n",
+      "Requirement already satisfied: geopandas<2.0,>=0.7 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from snowexsql) (0.13.2)\n",
+      "Requirement already satisfied: psycopg2-binary<2.10.0,>=2.9.0 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from snowexsql) (2.9.9)\n",
+      "Requirement already satisfied: rasterio>=1.1.5 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from snowexsql) (1.3.10)\n",
+      "Requirement already satisfied: SQLAlchemy>=2.0.0 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from snowexsql) (2.0.30)\n",
+      "Requirement already satisfied: packaging in /srv/conda/envs/notebook/lib/python3.11/site-packages (from geoalchemy2<1.0,>=0.6->snowexsql) (23.2)\n",
+      "Requirement already satisfied: fiona>=1.8.19 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from geopandas<2.0,>=0.7->snowexsql) (1.9.6)\n",
+      "Requirement already satisfied: pandas>=1.1.0 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from geopandas<2.0,>=0.7->snowexsql) (2.2.2)\n",
+      "Requirement already satisfied: pyproj>=3.0.1 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from geopandas<2.0,>=0.7->snowexsql) (3.6.1)\n",
+      "Requirement already satisfied: shapely>=1.7.1 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from geopandas<2.0,>=0.7->snowexsql) (2.0.4)\n",
+      "Requirement already satisfied: affine in /srv/conda/envs/notebook/lib/python3.11/site-packages (from rasterio>=1.1.5->snowexsql) (2.4.0)\n",
+      "Requirement already satisfied: attrs in /srv/conda/envs/notebook/lib/python3.11/site-packages (from rasterio>=1.1.5->snowexsql) (23.2.0)\n",
+      "Requirement already satisfied: certifi in /srv/conda/envs/notebook/lib/python3.11/site-packages (from rasterio>=1.1.5->snowexsql) (2024.2.2)\n",
+      "Requirement already satisfied: click>=4.0 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from rasterio>=1.1.5->snowexsql) (8.1.7)\n",
+      "Requirement already satisfied: cligj>=0.5 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from rasterio>=1.1.5->snowexsql) (0.7.2)\n",
+      "Requirement already satisfied: numpy in /srv/conda/envs/notebook/lib/python3.11/site-packages (from rasterio>=1.1.5->snowexsql) (1.23.5)\n",
+      "Requirement already satisfied: snuggs>=1.4.1 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from rasterio>=1.1.5->snowexsql) (1.4.7)\n",
+      "Requirement already satisfied: click-plugins in /srv/conda/envs/notebook/lib/python3.11/site-packages (from rasterio>=1.1.5->snowexsql) (1.1.1)\n",
+      "Requirement already satisfied: setuptools in /srv/conda/envs/notebook/lib/python3.11/site-packages (from rasterio>=1.1.5->snowexsql) (69.2.0)\n",
+      "Requirement already satisfied: typing-extensions>=4.6.0 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from SQLAlchemy>=2.0.0->snowexsql) (4.10.0)\n",
+      "Requirement already satisfied: greenlet!=0.4.17 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from SQLAlchemy>=2.0.0->snowexsql) (3.0.3)\n",
+      "Requirement already satisfied: six in /srv/conda/envs/notebook/lib/python3.11/site-packages (from fiona>=1.8.19->geopandas<2.0,>=0.7->snowexsql) (1.16.0)\n",
+      "Requirement already satisfied: python-dateutil>=2.8.2 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from pandas>=1.1.0->geopandas<2.0,>=0.7->snowexsql) (2.9.0)\n",
+      "Requirement already satisfied: pytz>=2020.1 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from pandas>=1.1.0->geopandas<2.0,>=0.7->snowexsql) (2024.1)\n",
+      "Requirement already satisfied: tzdata>=2022.7 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from pandas>=1.1.0->geopandas<2.0,>=0.7->snowexsql) (2024.1)\n",
+      "Requirement already satisfied: pyparsing>=2.1.6 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from snuggs>=1.4.1->rasterio>=1.1.5->snowexsql) (3.1.2)\n"
+     ]
+    }
+   ],
    "source": [
     "!pip install snowexsql"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "7bf5bc1a-e9eb-4813-a4aa-5205a8762ebe",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "(function(root) {\n",
+       "  function now() {\n",
+       "    return new Date();\n",
+       "  }\n",
+       "\n",
+       "  var force = true;\n",
+       "  var py_version = '3.2.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n",
+       "  var is_dev = py_version.indexOf(\"+\") !== -1 || py_version.indexOf(\"-\") !== -1;\n",
+       "  var reloading = false;\n",
+       "  var Bokeh = root.Bokeh;\n",
+       "  var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n",
+       "\n",
+       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n",
+       "    root._bokeh_timeout = Date.now() + 5000;\n",
+       "    root._bokeh_failed_load = false;\n",
+       "  }\n",
+       "\n",
+       "  function run_callbacks() {\n",
+       "    try {\n",
+       "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
+       "        if (callback != null)\n",
+       "          callback();\n",
+       "      });\n",
+       "    } finally {\n",
+       "      delete root._bokeh_onload_callbacks;\n",
+       "    }\n",
+       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
+       "  }\n",
+       "\n",
+       "  function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n",
+       "    if (css_urls == null) css_urls = [];\n",
+       "    if (js_urls == null) js_urls = [];\n",
+       "    if (js_modules == null) js_modules = [];\n",
+       "    if (js_exports == null) js_exports = {};\n",
+       "\n",
+       "    root._bokeh_onload_callbacks.push(callback);\n",
+       "\n",
+       "    if (root._bokeh_is_loading > 0) {\n",
+       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
+       "      return null;\n",
+       "    }\n",
+       "    if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n",
+       "      run_callbacks();\n",
+       "      return null;\n",
+       "    }\n",
+       "    if (!reloading) {\n",
+       "      console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
+       "    }\n",
+       "\n",
+       "    function on_load() {\n",
+       "      root._bokeh_is_loading--;\n",
+       "      if (root._bokeh_is_loading === 0) {\n",
+       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
+       "        run_callbacks()\n",
+       "      }\n",
+       "    }\n",
+       "    window._bokeh_on_load = on_load\n",
+       "\n",
+       "    function on_error() {\n",
+       "      console.error(\"failed to load \" + url);\n",
+       "    }\n",
+       "\n",
+       "    var skip = [];\n",
+       "    if (window.requirejs) {\n",
+       "      window.requirejs.config({'packages': {}, 'paths': {'jspanel': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/jspanel', 'jspanel-modal': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal', 'jspanel-tooltip': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip', 'jspanel-hint': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint', 'jspanel-layout': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout', 'jspanel-contextmenu': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu', 'jspanel-dock': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock', 'gridstack': 'https://cdn.jsdelivr.net/npm/gridstack@7.2.3/dist/gridstack-all', 'notyf': 'https://cdn.jsdelivr.net/npm/notyf@3/notyf.min'}, 'shim': {'jspanel': {'exports': 'jsPanel'}, 'gridstack': {'exports': 'GridStack'}}});\n",
+       "      require([\"jspanel\"], function(jsPanel) {\n",
+       "\twindow.jsPanel = jsPanel\n",
+       "\ton_load()\n",
+       "      })\n",
+       "      require([\"jspanel-modal\"], function() {\n",
+       "\ton_load()\n",
+       "      })\n",
+       "      require([\"jspanel-tooltip\"], function() {\n",
+       "\ton_load()\n",
+       "      })\n",
+       "      require([\"jspanel-hint\"], function() {\n",
+       "\ton_load()\n",
+       "      })\n",
+       "      require([\"jspanel-layout\"], function() {\n",
+       "\ton_load()\n",
+       "      })\n",
+       "      require([\"jspanel-contextmenu\"], function() {\n",
+       "\ton_load()\n",
+       "      })\n",
+       "      require([\"jspanel-dock\"], function() {\n",
+       "\ton_load()\n",
+       "      })\n",
+       "      require([\"gridstack\"], function(GridStack) {\n",
+       "\twindow.GridStack = GridStack\n",
+       "\ton_load()\n",
+       "      })\n",
+       "      require([\"notyf\"], function() {\n",
+       "\ton_load()\n",
+       "      })\n",
+       "      root._bokeh_is_loading = css_urls.length + 9;\n",
+       "    } else {\n",
+       "      root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n",
+       "    }\n",
+       "\n",
+       "    var existing_stylesheets = []\n",
+       "    var links = document.getElementsByTagName('link')\n",
+       "    for (var i = 0; i < links.length; i++) {\n",
+       "      var link = links[i]\n",
+       "      if (link.href != null) {\n",
+       "\texisting_stylesheets.push(link.href)\n",
+       "      }\n",
+       "    }\n",
+       "    for (var i = 0; i < css_urls.length; i++) {\n",
+       "      var url = css_urls[i];\n",
+       "      if (existing_stylesheets.indexOf(url) !== -1) {\n",
+       "\ton_load()\n",
+       "\tcontinue;\n",
+       "      }\n",
+       "      const element = document.createElement(\"link\");\n",
+       "      element.onload = on_load;\n",
+       "      element.onerror = on_error;\n",
+       "      element.rel = \"stylesheet\";\n",
+       "      element.type = \"text/css\";\n",
+       "      element.href = url;\n",
+       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
+       "      document.body.appendChild(element);\n",
+       "    }    if (((window['jsPanel'] !== undefined) && (!(window['jsPanel'] instanceof HTMLElement))) || window.requirejs) {\n",
+       "      var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/jspanel.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock.js'];\n",
+       "      for (var i = 0; i < urls.length; i++) {\n",
+       "        skip.push(urls[i])\n",
+       "      }\n",
+       "    }    if (((window['GridStack'] !== undefined) && (!(window['GridStack'] instanceof HTMLElement))) || window.requirejs) {\n",
+       "      var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/gridstack/gridstack@7.2.3/dist/gridstack-all.js'];\n",
+       "      for (var i = 0; i < urls.length; i++) {\n",
+       "        skip.push(urls[i])\n",
+       "      }\n",
+       "    }    if (((window['Notyf'] !== undefined) && (!(window['Notyf'] instanceof HTMLElement))) || window.requirejs) {\n",
+       "      var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/notificationarea/notyf@3/notyf.min.js'];\n",
+       "      for (var i = 0; i < urls.length; i++) {\n",
+       "        skip.push(urls[i])\n",
+       "      }\n",
+       "    }    var existing_scripts = []\n",
+       "    var scripts = document.getElementsByTagName('script')\n",
+       "    for (var i = 0; i < scripts.length; i++) {\n",
+       "      var script = scripts[i]\n",
+       "      if (script.src != null) {\n",
+       "\texisting_scripts.push(script.src)\n",
+       "      }\n",
+       "    }\n",
+       "    for (var i = 0; i < js_urls.length; i++) {\n",
+       "      var url = js_urls[i];\n",
+       "      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n",
+       "\tif (!window.requirejs) {\n",
+       "\t  on_load();\n",
+       "\t}\n",
+       "\tcontinue;\n",
+       "      }\n",
+       "      var element = document.createElement('script');\n",
+       "      element.onload = on_load;\n",
+       "      element.onerror = on_error;\n",
+       "      element.async = false;\n",
+       "      element.src = url;\n",
+       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
+       "      document.head.appendChild(element);\n",
+       "    }\n",
+       "    for (var i = 0; i < js_modules.length; i++) {\n",
+       "      var url = js_modules[i];\n",
+       "      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n",
+       "\tif (!window.requirejs) {\n",
+       "\t  on_load();\n",
+       "\t}\n",
+       "\tcontinue;\n",
+       "      }\n",
+       "      var element = document.createElement('script');\n",
+       "      element.onload = on_load;\n",
+       "      element.onerror = on_error;\n",
+       "      element.async = false;\n",
+       "      element.src = url;\n",
+       "      element.type = \"module\";\n",
+       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
+       "      document.head.appendChild(element);\n",
+       "    }\n",
+       "    for (const name in js_exports) {\n",
+       "      var url = js_exports[name];\n",
+       "      if (skip.indexOf(url) >= 0 || root[name] != null) {\n",
+       "\tif (!window.requirejs) {\n",
+       "\t  on_load();\n",
+       "\t}\n",
+       "\tcontinue;\n",
+       "      }\n",
+       "      var element = document.createElement('script');\n",
+       "      element.onerror = on_error;\n",
+       "      element.async = false;\n",
+       "      element.type = \"module\";\n",
+       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
+       "      element.textContent = `\n",
+       "      import ${name} from \"${url}\"\n",
+       "      window.${name} = ${name}\n",
+       "      window._bokeh_on_load()\n",
+       "      `\n",
+       "      document.head.appendChild(element);\n",
+       "    }\n",
+       "    if (!js_urls.length && !js_modules.length) {\n",
+       "      on_load()\n",
+       "    }\n",
+       "  };\n",
+       "\n",
+       "  function inject_raw_css(css) {\n",
+       "    const element = document.createElement(\"style\");\n",
+       "    element.appendChild(document.createTextNode(css));\n",
+       "    document.body.appendChild(element);\n",
+       "  }\n",
+       "\n",
+       "  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.2.2.min.js\", \"https://cdn.holoviz.org/panel/1.2.3/dist/panel.min.js\"];\n",
+       "  var js_modules = [];\n",
+       "  var js_exports = {};\n",
+       "  var css_urls = [];\n",
+       "  var inline_js = [    function(Bokeh) {\n",
+       "      Bokeh.set_log_level(\"info\");\n",
+       "    },\n",
+       "function(Bokeh) {} // ensure no trailing comma for IE\n",
+       "  ];\n",
+       "\n",
+       "  function run_inline_js() {\n",
+       "    if ((root.Bokeh !== undefined) || (force === true)) {\n",
+       "      for (var i = 0; i < inline_js.length; i++) {\n",
+       "        inline_js[i].call(root, root.Bokeh);\n",
+       "      }\n",
+       "      // Cache old bokeh versions\n",
+       "      if (Bokeh != undefined && !reloading) {\n",
+       "\tvar NewBokeh = root.Bokeh;\n",
+       "\tif (Bokeh.versions === undefined) {\n",
+       "\t  Bokeh.versions = new Map();\n",
+       "\t}\n",
+       "\tif (NewBokeh.version !== Bokeh.version) {\n",
+       "\t  Bokeh.versions.set(NewBokeh.version, NewBokeh)\n",
+       "\t}\n",
+       "\troot.Bokeh = Bokeh;\n",
+       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
+       "      setTimeout(run_inline_js, 100);\n",
+       "    } else if (!root._bokeh_failed_load) {\n",
+       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
+       "      root._bokeh_failed_load = true;\n",
+       "    }\n",
+       "    root._bokeh_is_initializing = false\n",
+       "  }\n",
+       "\n",
+       "  function load_or_wait() {\n",
+       "    // Implement a backoff loop that tries to ensure we do not load multiple\n",
+       "    // versions of Bokeh and its dependencies at the same time.\n",
+       "    // In recent versions we use the root._bokeh_is_initializing flag\n",
+       "    // to determine whether there is an ongoing attempt to initialize\n",
+       "    // bokeh, however for backward compatibility we also try to ensure\n",
+       "    // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n",
+       "    // before older versions are fully initialized.\n",
+       "    if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n",
+       "      root._bokeh_is_initializing = false;\n",
+       "      root._bokeh_onload_callbacks = undefined;\n",
+       "      console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n",
+       "      load_or_wait();\n",
+       "    } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n",
+       "      setTimeout(load_or_wait, 100);\n",
+       "    } else {\n",
+       "      Bokeh = root.Bokeh;\n",
+       "      bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n",
+       "      root._bokeh_is_initializing = true\n",
+       "      root._bokeh_onload_callbacks = []\n",
+       "      if (!reloading && (!bokeh_loaded || is_dev)) {\n",
+       "\troot.Bokeh = undefined;\n",
+       "      }\n",
+       "      load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n",
+       "\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
+       "\trun_inline_js();\n",
+       "      });\n",
+       "    }\n",
+       "  }\n",
+       "  // Give older versions of the autoload script a head-start to ensure\n",
+       "  // they initialize before we start loading newer version.\n",
+       "  setTimeout(load_or_wait, 100)\n",
+       "}(window));"
+      ],
+      "application/vnd.holoviews_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n  var py_version = '3.2.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n  var is_dev = py_version.indexOf(\"+\") !== -1 || py_version.indexOf(\"-\") !== -1;\n  var reloading = false;\n  var Bokeh = root.Bokeh;\n  var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks;\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n    if (js_modules == null) js_modules = [];\n    if (js_exports == null) js_exports = {};\n\n    root._bokeh_onload_callbacks.push(callback);\n\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n      run_callbacks();\n      return null;\n    }\n    if (!reloading) {\n      console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    }\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n    window._bokeh_on_load = on_load\n\n    function on_error() {\n      console.error(\"failed to load \" + url);\n    }\n\n    var skip = [];\n    if (window.requirejs) {\n      window.requirejs.config({'packages': {}, 'paths': {'jspanel': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/jspanel', 'jspanel-modal': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal', 'jspanel-tooltip': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip', 'jspanel-hint': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint', 'jspanel-layout': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout', 'jspanel-contextmenu': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu', 'jspanel-dock': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock', 'gridstack': 'https://cdn.jsdelivr.net/npm/gridstack@7.2.3/dist/gridstack-all', 'notyf': 'https://cdn.jsdelivr.net/npm/notyf@3/notyf.min'}, 'shim': {'jspanel': {'exports': 'jsPanel'}, 'gridstack': {'exports': 'GridStack'}}});\n      require([\"jspanel\"], function(jsPanel) {\n\twindow.jsPanel = jsPanel\n\ton_load()\n      })\n      require([\"jspanel-modal\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-tooltip\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-hint\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-layout\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-contextmenu\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-dock\"], function() {\n\ton_load()\n      })\n      require([\"gridstack\"], function(GridStack) {\n\twindow.GridStack = GridStack\n\ton_load()\n      })\n      require([\"notyf\"], function() {\n\ton_load()\n      })\n      root._bokeh_is_loading = css_urls.length + 9;\n    } else {\n      root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n    }\n\n    var existing_stylesheets = []\n    var links = document.getElementsByTagName('link')\n    for (var i = 0; i < links.length; i++) {\n      var link = links[i]\n      if (link.href != null) {\n\texisting_stylesheets.push(link.href)\n      }\n    }\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      if (existing_stylesheets.indexOf(url) !== -1) {\n\ton_load()\n\tcontinue;\n      }\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }    if (((window['jsPanel'] !== undefined) && (!(window['jsPanel'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/jspanel.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    if (((window['GridStack'] !== undefined) && (!(window['GridStack'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/gridstack/gridstack@7.2.3/dist/gridstack-all.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    if (((window['Notyf'] !== undefined) && (!(window['Notyf'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/notificationarea/notyf@3/notyf.min.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    var existing_scripts = []\n    var scripts = document.getElementsByTagName('script')\n    for (var i = 0; i < scripts.length; i++) {\n      var script = scripts[i]\n      if (script.src != null) {\n\texisting_scripts.push(script.src)\n      }\n    }\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (var i = 0; i < js_modules.length; i++) {\n      var url = js_modules[i];\n      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (const name in js_exports) {\n      var url = js_exports[name];\n      if (skip.indexOf(url) >= 0 || root[name] != null) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onerror = on_error;\n      element.async = false;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      element.textContent = `\n      import ${name} from \"${url}\"\n      window.${name} = ${name}\n      window._bokeh_on_load()\n      `\n      document.head.appendChild(element);\n    }\n    if (!js_urls.length && !js_modules.length) {\n      on_load()\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.2.2.min.js\", \"https://cdn.holoviz.org/panel/1.2.3/dist/panel.min.js\"];\n  var js_modules = [];\n  var js_exports = {};\n  var css_urls = [];\n  var inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n  ];\n\n  function run_inline_js() {\n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }\n      // Cache old bokeh versions\n      if (Bokeh != undefined && !reloading) {\n\tvar NewBokeh = root.Bokeh;\n\tif (Bokeh.versions === undefined) {\n\t  Bokeh.versions = new Map();\n\t}\n\tif (NewBokeh.version !== Bokeh.version) {\n\t  Bokeh.versions.set(NewBokeh.version, NewBokeh)\n\t}\n\troot.Bokeh = Bokeh;\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    }\n    root._bokeh_is_initializing = false\n  }\n\n  function load_or_wait() {\n    // Implement a backoff loop that tries to ensure we do not load multiple\n    // versions of Bokeh and its dependencies at the same time.\n    // In recent versions we use the root._bokeh_is_initializing flag\n    // to determine whether there is an ongoing attempt to initialize\n    // bokeh, however for backward compatibility we also try to ensure\n    // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n    // before older versions are fully initialized.\n    if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n      root._bokeh_is_initializing = false;\n      root._bokeh_onload_callbacks = undefined;\n      console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n      load_or_wait();\n    } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n      setTimeout(load_or_wait, 100);\n    } else {\n      Bokeh = root.Bokeh;\n      bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n      root._bokeh_is_initializing = true\n      root._bokeh_onload_callbacks = []\n      if (!reloading && (!bokeh_loaded || is_dev)) {\n\troot.Bokeh = undefined;\n      }\n      load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n\trun_inline_js();\n      });\n    }\n  }\n  // Give older versions of the autoload script a head-start to ensure\n  // they initialize before we start loading newer version.\n  setTimeout(load_or_wait, 100)\n}(window));"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/javascript": [
+       "\n",
+       "if ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n",
+       "  window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n",
+       "}\n",
+       "\n",
+       "\n",
+       "    function JupyterCommManager() {\n",
+       "    }\n",
+       "\n",
+       "    JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n",
+       "      if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n",
+       "        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n",
+       "        comm_manager.register_target(comm_id, function(comm) {\n",
+       "          comm.on_msg(msg_handler);\n",
+       "        });\n",
+       "      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n",
+       "        window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n",
+       "          comm.onMsg = msg_handler;\n",
+       "        });\n",
+       "      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n",
+       "        google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n",
+       "          var messages = comm.messages[Symbol.asyncIterator]();\n",
+       "          function processIteratorResult(result) {\n",
+       "            var message = result.value;\n",
+       "            console.log(message)\n",
+       "            var content = {data: message.data, comm_id};\n",
+       "            var buffers = []\n",
+       "            for (var buffer of message.buffers || []) {\n",
+       "              buffers.push(new DataView(buffer))\n",
+       "            }\n",
+       "            var metadata = message.metadata || {};\n",
+       "            var msg = {content, buffers, metadata}\n",
+       "            msg_handler(msg);\n",
+       "            return messages.next().then(processIteratorResult);\n",
+       "          }\n",
+       "          return messages.next().then(processIteratorResult);\n",
+       "        })\n",
+       "      }\n",
+       "    }\n",
+       "\n",
+       "    JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n",
+       "      if (comm_id in window.PyViz.comms) {\n",
+       "        return window.PyViz.comms[comm_id];\n",
+       "      } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n",
+       "        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n",
+       "        var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n",
+       "        if (msg_handler) {\n",
+       "          comm.on_msg(msg_handler);\n",
+       "        }\n",
+       "      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n",
+       "        var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n",
+       "        comm.open();\n",
+       "        if (msg_handler) {\n",
+       "          comm.onMsg = msg_handler;\n",
+       "        }\n",
+       "      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n",
+       "        var comm_promise = google.colab.kernel.comms.open(comm_id)\n",
+       "        comm_promise.then((comm) => {\n",
+       "          window.PyViz.comms[comm_id] = comm;\n",
+       "          if (msg_handler) {\n",
+       "            var messages = comm.messages[Symbol.asyncIterator]();\n",
+       "            function processIteratorResult(result) {\n",
+       "              var message = result.value;\n",
+       "              var content = {data: message.data};\n",
+       "              var metadata = message.metadata || {comm_id};\n",
+       "              var msg = {content, metadata}\n",
+       "              msg_handler(msg);\n",
+       "              return messages.next().then(processIteratorResult);\n",
+       "            }\n",
+       "            return messages.next().then(processIteratorResult);\n",
+       "          }\n",
+       "        }) \n",
+       "        var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n",
+       "          return comm_promise.then((comm) => {\n",
+       "            comm.send(data, metadata, buffers, disposeOnDone);\n",
+       "          });\n",
+       "        };\n",
+       "        var comm = {\n",
+       "          send: sendClosure\n",
+       "        };\n",
+       "      }\n",
+       "      window.PyViz.comms[comm_id] = comm;\n",
+       "      return comm;\n",
+       "    }\n",
+       "    window.PyViz.comm_manager = new JupyterCommManager();\n",
+       "    \n",
+       "\n",
+       "\n",
+       "var JS_MIME_TYPE = 'application/javascript';\n",
+       "var HTML_MIME_TYPE = 'text/html';\n",
+       "var EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\n",
+       "var CLASS_NAME = 'output';\n",
+       "\n",
+       "/**\n",
+       " * Render data to the DOM node\n",
+       " */\n",
+       "function render(props, node) {\n",
+       "  var div = document.createElement(\"div\");\n",
+       "  var script = document.createElement(\"script\");\n",
+       "  node.appendChild(div);\n",
+       "  node.appendChild(script);\n",
+       "}\n",
+       "\n",
+       "/**\n",
+       " * Handle when a new output is added\n",
+       " */\n",
+       "function handle_add_output(event, handle) {\n",
+       "  var output_area = handle.output_area;\n",
+       "  var output = handle.output;\n",
+       "  if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
+       "    return\n",
+       "  }\n",
+       "  var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
+       "  var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
+       "  if (id !== undefined) {\n",
+       "    var nchildren = toinsert.length;\n",
+       "    var html_node = toinsert[nchildren-1].children[0];\n",
+       "    html_node.innerHTML = output.data[HTML_MIME_TYPE];\n",
+       "    var scripts = [];\n",
+       "    var nodelist = html_node.querySelectorAll(\"script\");\n",
+       "    for (var i in nodelist) {\n",
+       "      if (nodelist.hasOwnProperty(i)) {\n",
+       "        scripts.push(nodelist[i])\n",
+       "      }\n",
+       "    }\n",
+       "\n",
+       "    scripts.forEach( function (oldScript) {\n",
+       "      var newScript = document.createElement(\"script\");\n",
+       "      var attrs = [];\n",
+       "      var nodemap = oldScript.attributes;\n",
+       "      for (var j in nodemap) {\n",
+       "        if (nodemap.hasOwnProperty(j)) {\n",
+       "          attrs.push(nodemap[j])\n",
+       "        }\n",
+       "      }\n",
+       "      attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n",
+       "      newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n",
+       "      oldScript.parentNode.replaceChild(newScript, oldScript);\n",
+       "    });\n",
+       "    if (JS_MIME_TYPE in output.data) {\n",
+       "      toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n",
+       "    }\n",
+       "    output_area._hv_plot_id = id;\n",
+       "    if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n",
+       "      window.PyViz.plot_index[id] = Bokeh.index[id];\n",
+       "    } else {\n",
+       "      window.PyViz.plot_index[id] = null;\n",
+       "    }\n",
+       "  } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
+       "    var bk_div = document.createElement(\"div\");\n",
+       "    bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
+       "    var script_attrs = bk_div.children[0].attributes;\n",
+       "    for (var i = 0; i < script_attrs.length; i++) {\n",
+       "      toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
+       "    }\n",
+       "    // store reference to server id on output_area\n",
+       "    output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "/**\n",
+       " * Handle when an output is cleared or removed\n",
+       " */\n",
+       "function handle_clear_output(event, handle) {\n",
+       "  var id = handle.cell.output_area._hv_plot_id;\n",
+       "  var server_id = handle.cell.output_area._bokeh_server_id;\n",
+       "  if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n",
+       "  var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n",
+       "  if (server_id !== null) {\n",
+       "    comm.send({event_type: 'server_delete', 'id': server_id});\n",
+       "    return;\n",
+       "  } else if (comm !== null) {\n",
+       "    comm.send({event_type: 'delete', 'id': id});\n",
+       "  }\n",
+       "  delete PyViz.plot_index[id];\n",
+       "  if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n",
+       "    var doc = window.Bokeh.index[id].model.document\n",
+       "    doc.clear();\n",
+       "    const i = window.Bokeh.documents.indexOf(doc);\n",
+       "    if (i > -1) {\n",
+       "      window.Bokeh.documents.splice(i, 1);\n",
+       "    }\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "/**\n",
+       " * Handle kernel restart event\n",
+       " */\n",
+       "function handle_kernel_cleanup(event, handle) {\n",
+       "  delete PyViz.comms[\"hv-extension-comm\"];\n",
+       "  window.PyViz.plot_index = {}\n",
+       "}\n",
+       "\n",
+       "/**\n",
+       " * Handle update_display_data messages\n",
+       " */\n",
+       "function handle_update_output(event, handle) {\n",
+       "  handle_clear_output(event, {cell: {output_area: handle.output_area}})\n",
+       "  handle_add_output(event, handle)\n",
+       "}\n",
+       "\n",
+       "function register_renderer(events, OutputArea) {\n",
+       "  function append_mime(data, metadata, element) {\n",
+       "    // create a DOM node to render to\n",
+       "    var toinsert = this.create_output_subarea(\n",
+       "    metadata,\n",
+       "    CLASS_NAME,\n",
+       "    EXEC_MIME_TYPE\n",
+       "    );\n",
+       "    this.keyboard_manager.register_events(toinsert);\n",
+       "    // Render to node\n",
+       "    var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
+       "    render(props, toinsert[0]);\n",
+       "    element.append(toinsert);\n",
+       "    return toinsert\n",
+       "  }\n",
+       "\n",
+       "  events.on('output_added.OutputArea', handle_add_output);\n",
+       "  events.on('output_updated.OutputArea', handle_update_output);\n",
+       "  events.on('clear_output.CodeCell', handle_clear_output);\n",
+       "  events.on('delete.Cell', handle_clear_output);\n",
+       "  events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n",
+       "\n",
+       "  OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
+       "    safe: true,\n",
+       "    index: 0\n",
+       "  });\n",
+       "}\n",
+       "\n",
+       "if (window.Jupyter !== undefined) {\n",
+       "  try {\n",
+       "    var events = require('base/js/events');\n",
+       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
+       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
+       "      register_renderer(events, OutputArea);\n",
+       "    }\n",
+       "  } catch(err) {\n",
+       "  }\n",
+       "}\n"
+      ],
+      "application/vnd.holoviews_load.v0+json": "\nif ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n  window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n}\n\n\n    function JupyterCommManager() {\n    }\n\n    JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n      if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n        comm_manager.register_target(comm_id, function(comm) {\n          comm.on_msg(msg_handler);\n        });\n      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n        window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n          comm.onMsg = msg_handler;\n        });\n      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n        google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n          var messages = comm.messages[Symbol.asyncIterator]();\n          function processIteratorResult(result) {\n            var message = result.value;\n            console.log(message)\n            var content = {data: message.data, comm_id};\n            var buffers = []\n            for (var buffer of message.buffers || []) {\n              buffers.push(new DataView(buffer))\n            }\n            var metadata = message.metadata || {};\n            var msg = {content, buffers, metadata}\n            msg_handler(msg);\n            return messages.next().then(processIteratorResult);\n          }\n          return messages.next().then(processIteratorResult);\n        })\n      }\n    }\n\n    JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n      if (comm_id in window.PyViz.comms) {\n        return window.PyViz.comms[comm_id];\n      } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n        var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n        if (msg_handler) {\n          comm.on_msg(msg_handler);\n        }\n      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n        var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n        comm.open();\n        if (msg_handler) {\n          comm.onMsg = msg_handler;\n        }\n      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n        var comm_promise = google.colab.kernel.comms.open(comm_id)\n        comm_promise.then((comm) => {\n          window.PyViz.comms[comm_id] = comm;\n          if (msg_handler) {\n            var messages = comm.messages[Symbol.asyncIterator]();\n            function processIteratorResult(result) {\n              var message = result.value;\n              var content = {data: message.data};\n              var metadata = message.metadata || {comm_id};\n              var msg = {content, metadata}\n              msg_handler(msg);\n              return messages.next().then(processIteratorResult);\n            }\n            return messages.next().then(processIteratorResult);\n          }\n        }) \n        var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n          return comm_promise.then((comm) => {\n            comm.send(data, metadata, buffers, disposeOnDone);\n          });\n        };\n        var comm = {\n          send: sendClosure\n        };\n      }\n      window.PyViz.comms[comm_id] = comm;\n      return comm;\n    }\n    window.PyViz.comm_manager = new JupyterCommManager();\n    \n\n\nvar JS_MIME_TYPE = 'application/javascript';\nvar HTML_MIME_TYPE = 'text/html';\nvar EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\nvar CLASS_NAME = 'output';\n\n/**\n * Render data to the DOM node\n */\nfunction render(props, node) {\n  var div = document.createElement(\"div\");\n  var script = document.createElement(\"script\");\n  node.appendChild(div);\n  node.appendChild(script);\n}\n\n/**\n * Handle when a new output is added\n */\nfunction handle_add_output(event, handle) {\n  var output_area = handle.output_area;\n  var output = handle.output;\n  if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n    return\n  }\n  var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n  var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n  if (id !== undefined) {\n    var nchildren = toinsert.length;\n    var html_node = toinsert[nchildren-1].children[0];\n    html_node.innerHTML = output.data[HTML_MIME_TYPE];\n    var scripts = [];\n    var nodelist = html_node.querySelectorAll(\"script\");\n    for (var i in nodelist) {\n      if (nodelist.hasOwnProperty(i)) {\n        scripts.push(nodelist[i])\n      }\n    }\n\n    scripts.forEach( function (oldScript) {\n      var newScript = document.createElement(\"script\");\n      var attrs = [];\n      var nodemap = oldScript.attributes;\n      for (var j in nodemap) {\n        if (nodemap.hasOwnProperty(j)) {\n          attrs.push(nodemap[j])\n        }\n      }\n      attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n      newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n      oldScript.parentNode.replaceChild(newScript, oldScript);\n    });\n    if (JS_MIME_TYPE in output.data) {\n      toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n    }\n    output_area._hv_plot_id = id;\n    if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n      window.PyViz.plot_index[id] = Bokeh.index[id];\n    } else {\n      window.PyViz.plot_index[id] = null;\n    }\n  } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n    var bk_div = document.createElement(\"div\");\n    bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n    var script_attrs = bk_div.children[0].attributes;\n    for (var i = 0; i < script_attrs.length; i++) {\n      toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n    }\n    // store reference to server id on output_area\n    output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n  }\n}\n\n/**\n * Handle when an output is cleared or removed\n */\nfunction handle_clear_output(event, handle) {\n  var id = handle.cell.output_area._hv_plot_id;\n  var server_id = handle.cell.output_area._bokeh_server_id;\n  if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n  var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n  if (server_id !== null) {\n    comm.send({event_type: 'server_delete', 'id': server_id});\n    return;\n  } else if (comm !== null) {\n    comm.send({event_type: 'delete', 'id': id});\n  }\n  delete PyViz.plot_index[id];\n  if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n    var doc = window.Bokeh.index[id].model.document\n    doc.clear();\n    const i = window.Bokeh.documents.indexOf(doc);\n    if (i > -1) {\n      window.Bokeh.documents.splice(i, 1);\n    }\n  }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n  delete PyViz.comms[\"hv-extension-comm\"];\n  window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction handle_update_output(event, handle) {\n  handle_clear_output(event, {cell: {output_area: handle.output_area}})\n  handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n  function append_mime(data, metadata, element) {\n    // create a DOM node to render to\n    var toinsert = this.create_output_subarea(\n    metadata,\n    CLASS_NAME,\n    EXEC_MIME_TYPE\n    );\n    this.keyboard_manager.register_events(toinsert);\n    // Render to node\n    var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n    render(props, toinsert[0]);\n    element.append(toinsert);\n    return toinsert\n  }\n\n  events.on('output_added.OutputArea', handle_add_output);\n  events.on('output_updated.OutputArea', handle_update_output);\n  events.on('clear_output.CodeCell', handle_clear_output);\n  events.on('delete.Cell', handle_clear_output);\n  events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n  OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n    safe: true,\n    index: 0\n  });\n}\n\nif (window.Jupyter !== undefined) {\n  try {\n    var events = require('base/js/events');\n    var OutputArea = require('notebook/js/outputarea').OutputArea;\n    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n      register_renderer(events, OutputArea);\n    }\n  } catch(err) {\n  }\n}\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>*[data-root-id],\n",
+       "*[data-root-id] > * {\n",
+       "  box-sizing: border-box;\n",
+       "  font-family: var(--jp-ui-font-family);\n",
+       "  font-size: var(--jp-ui-font-size1);\n",
+       "  color: var(--vscode-editor-foreground, var(--jp-ui-font-color1));\n",
+       "}\n",
+       "\n",
+       "/* Override VSCode background color */\n",
+       ".cell-output-ipywidget-background:has(\n",
+       "    > .cell-output-ipywidget-background > .lm-Widget > *[data-root-id]\n",
+       "  ),\n",
+       ".cell-output-ipywidget-background:has(> .lm-Widget > *[data-root-id]) {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "</style>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Import several libraries. \n",
     "# comments to the right could be useful for local installation on Windows.\n",
@@ -301,10 +909,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "f36402f2-a63f-4d3d-b874-da53ef328c59",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "output directory prepared!\n"
+     ]
+    }
+   ],
    "source": [
     "# select files to download\n",
     "\n",
@@ -348,7 +964,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "d4c6cd77-0841-4e66-8199-f6e463a856de",
    "metadata": {},
    "outputs": [],
@@ -385,10 +1001,529 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "2833b2ee-9460-46ab-8f3b-884e89a91a85",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body[data-theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (band: 6, y: 4289, x: 3959)&gt; Size: 408MB\n",
+       "dask.array&lt;concatenate, shape=(6, 4289, 3959), dtype=float32, chunksize=(1, 1200, 1200), chunktype=numpy.ndarray&gt;\n",
+       "Coordinates:\n",
+       "  * x            (x) float64 32kB 7.396e+05 7.396e+05 ... 7.475e+05 7.475e+05\n",
+       "  * y            (y) float64 34kB 4.329e+06 4.329e+06 ... 4.32e+06 4.32e+06\n",
+       "    spatial_ref  int64 8B 0\n",
+       "  * band         (band) &lt;U4 96B &#x27;09VV&#x27; &#x27;09VH&#x27; &#x27;13VV&#x27; &#x27;13VH&#x27; &#x27;17VV&#x27; &#x27;17VH&#x27;\n",
+       "Attributes:\n",
+       "    AREA_OR_POINT:  Area\n",
+       "    _FillValue:     nan\n",
+       "    scale_factor:   1.0\n",
+       "    add_offset:     0.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>band</span>: 6</li><li><span class='xr-has-index'>y</span>: 4289</li><li><span class='xr-has-index'>x</span>: 3959</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-fc391482-5ef5-4fcd-b1a1-b4945408127f' class='xr-array-in' type='checkbox' checked><label for='section-fc391482-5ef5-4fcd-b1a1-b4945408127f' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 1200, 1200), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 388.64 MiB </td>\n",
+       "                        <td> 5.49 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (6, 4289, 3959) </td>\n",
+       "                        <td> (1, 1200, 1200) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 96 chunks in 17 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"185\" height=\"184\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"33\" x2=\"24\" y2=\"48\" />\n",
+       "  <line x1=\"10\" y1=\"67\" x2=\"24\" y2=\"82\" />\n",
+       "  <line x1=\"10\" y1=\"100\" x2=\"24\" y2=\"115\" />\n",
+       "  <line x1=\"10\" y1=\"120\" x2=\"24\" y2=\"134\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"122\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"124\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"127\" />\n",
+       "  <line x1=\"19\" y1=\"9\" x2=\"19\" y2=\"129\" />\n",
+       "  <line x1=\"22\" y1=\"12\" x2=\"22\" y2=\"132\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"134\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 24.9485979497544,14.948597949754403 24.9485979497544,134.9485979497544 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"123\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"125\" y2=\"4\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"128\" y2=\"7\" />\n",
+       "  <line x1=\"19\" y1=\"9\" x2=\"130\" y2=\"9\" />\n",
+       "  <line x1=\"22\" y1=\"12\" x2=\"133\" y2=\"12\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"135\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"43\" y1=\"0\" x2=\"58\" y2=\"14\" />\n",
+       "  <line x1=\"77\" y1=\"0\" x2=\"92\" y2=\"14\" />\n",
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+       "  <line x1=\"120\" y1=\"0\" x2=\"135\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 120.76707857309395,0.0 135.71567652284836,14.948597949754403 24.9485979497544,14.948597949754403\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"135\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"24\" y1=\"48\" x2=\"135\" y2=\"48\" />\n",
+       "  <line x1=\"24\" y1=\"82\" x2=\"135\" y2=\"82\" />\n",
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+       "  <line x1=\"24\" y1=\"134\" x2=\"135\" y2=\"134\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"134\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"58\" y1=\"14\" x2=\"58\" y2=\"134\" />\n",
+       "  <line x1=\"92\" y1=\"14\" x2=\"92\" y2=\"134\" />\n",
+       "  <line x1=\"125\" y1=\"14\" x2=\"125\" y2=\"134\" />\n",
+       "  <line x1=\"135\" y1=\"14\" x2=\"135\" y2=\"134\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"24.9485979497544,14.948597949754403 135.71567652284836,14.948597949754403 135.71567652284836,134.9485979497544 24.9485979497544,134.9485979497544\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"80.332137\" y=\"154.948598\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3959</text>\n",
+       "  <text x=\"155.715677\" y=\"74.948598\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,155.715677,74.948598)\">4289</text>\n",
+       "  <text x=\"7.474299\" y=\"147.474299\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,147.474299)\">6</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></div></li><li class='xr-section-item'><input id='section-1160a6ae-2105-4488-baf2-6daa0db77095' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1160a6ae-2105-4488-baf2-6daa0db77095' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>7.396e+05 7.396e+05 ... 7.475e+05</div><input id='attrs-677205ce-c25a-4aad-8741-97392ee1a1e2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-677205ce-c25a-4aad-8741-97392ee1a1e2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-886bebcf-f010-46c5-a6c0-071089608bc0' class='xr-var-data-in' type='checkbox'><label for='data-886bebcf-f010-46c5-a6c0-071089608bc0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([739631.321582, 739633.321582, 739635.321582, ..., 747543.321582,\n",
+       "       747545.321582, 747547.321582])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.329e+06 4.329e+06 ... 4.32e+06</div><input id='attrs-599d57fd-6015-4097-9cc7-f47aa59c649f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-599d57fd-6015-4097-9cc7-f47aa59c649f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-753bae9f-4a72-4cc9-8ae2-aab3980c2178' class='xr-var-data-in' type='checkbox'><label for='data-753bae9f-4a72-4cc9-8ae2-aab3980c2178' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([4328973.426705, 4328971.426705, 4328969.426705, ..., 4320401.426705,\n",
+       "       4320399.426705, 4320397.426705])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-91f50509-45ad-4fd9-9d3c-2bfc34580f28' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-91f50509-45ad-4fd9-9d3c-2bfc34580f28' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4e06be98-6230-473c-b060-a648fa4b68c3' class='xr-var-data-in' type='checkbox'><label for='data-4e06be98-6230-473c-b060-a648fa4b68c3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 12N&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,-111],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32612&quot;]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>horizontal_datum_name :</span></dt><dd>World Geodetic System 1984</dd><dt><span>projected_crs_name :</span></dt><dd>WGS 84 / UTM zone 12N</dd><dt><span>grid_mapping_name :</span></dt><dd>transverse_mercator</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>0.0</dd><dt><span>longitude_of_central_meridian :</span></dt><dd>-111.0</dd><dt><span>false_easting :</span></dt><dd>500000.0</dd><dt><span>false_northing :</span></dt><dd>0.0</dd><dt><span>scale_factor_at_central_meridian :</span></dt><dd>0.9996</dd><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 12N&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,-111],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32612&quot;]]</dd><dt><span>GeoTransform :</span></dt><dd>739630.3215824949 2.0 0.0 4328974.426704684 0.0 -2.0</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>band</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>&lt;U4</div><div class='xr-var-preview xr-preview'>&#x27;09VV&#x27; &#x27;09VH&#x27; ... &#x27;17VV&#x27; &#x27;17VH&#x27;</div><input id='attrs-4bdcd6ee-ee74-4097-bb70-b7aa9894b4e5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4bdcd6ee-ee74-4097-bb70-b7aa9894b4e5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8c00aac0-7287-4632-8762-c864abb59bd0' class='xr-var-data-in' type='checkbox'><label for='data-8c00aac0-7287-4632-8762-c864abb59bd0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;09VV&#x27;, &#x27;09VH&#x27;, &#x27;13VV&#x27;, &#x27;13VH&#x27;, &#x27;17VV&#x27;, &#x27;17VH&#x27;], dtype=&#x27;&lt;U4&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-68b4cb39-9b40-45f3-83c4-9c3f84a01292' class='xr-section-summary-in' type='checkbox'  ><label for='section-68b4cb39-9b40-45f3-83c4-9c3f84a01292' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-96a4b472-3675-40d2-abb1-14949ca3f866' class='xr-index-data-in' type='checkbox'/><label for='index-96a4b472-3675-40d2-abb1-14949ca3f866' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([739631.3215824949, 739633.3215824949, 739635.3215824949,\n",
+       "       739637.3215824949, 739639.3215824949, 739641.3215824949,\n",
+       "       739643.3215824949, 739645.3215824949, 739647.3215824949,\n",
+       "       739649.3215824949,\n",
+       "       ...\n",
+       "       747529.3215824949, 747531.3215824949, 747533.3215824949,\n",
+       "       747535.3215824949, 747537.3215824949, 747539.3215824949,\n",
+       "       747541.3215824949, 747543.3215824949, 747545.3215824949,\n",
+       "       747547.3215824949],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=3959))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-db7e741b-8fda-44a3-b607-dfc050118e74' class='xr-index-data-in' type='checkbox'/><label for='index-db7e741b-8fda-44a3-b607-dfc050118e74' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([4328973.426704684, 4328971.426704684, 4328969.426704684,\n",
+       "       4328967.426704684, 4328965.426704684, 4328963.426704684,\n",
+       "       4328961.426704684, 4328959.426704684, 4328957.426704684,\n",
+       "       4328955.426704684,\n",
+       "       ...\n",
+       "       4320415.426704684, 4320413.426704684, 4320411.426704684,\n",
+       "       4320409.426704684, 4320407.426704684, 4320405.426704684,\n",
+       "       4320403.426704684, 4320401.426704684, 4320399.426704684,\n",
+       "       4320397.426704684],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=4289))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>band</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fcee99cc-9f5a-414e-be25-e1cee826074d' class='xr-index-data-in' type='checkbox'/><label for='index-fcee99cc-9f5a-414e-be25-e1cee826074d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;09VV&#x27;, &#x27;09VH&#x27;, &#x27;13VV&#x27;, &#x27;13VH&#x27;, &#x27;17VV&#x27;, &#x27;17VH&#x27;], dtype=&#x27;object&#x27;, name=&#x27;band&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6e3a2643-cf79-4e4c-85b6-05b9b621fae7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6e3a2643-cf79-4e4c-85b6-05b9b621fae7' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>AREA_OR_POINT :</span></dt><dd>Area</dd><dt><span>_FillValue :</span></dt><dd>nan</dd><dt><span>scale_factor :</span></dt><dd>1.0</dd><dt><span>add_offset :</span></dt><dd>0.0</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray (band: 6, y: 4289, x: 3959)> Size: 408MB\n",
+       "dask.array<concatenate, shape=(6, 4289, 3959), dtype=float32, chunksize=(1, 1200, 1200), chunktype=numpy.ndarray>\n",
+       "Coordinates:\n",
+       "  * x            (x) float64 32kB 7.396e+05 7.396e+05 ... 7.475e+05 7.475e+05\n",
+       "  * y            (y) float64 34kB 4.329e+06 4.329e+06 ... 4.32e+06 4.32e+06\n",
+       "    spatial_ref  int64 8B 0\n",
+       "  * band         (band) <U4 96B '09VV' '09VH' '13VV' '13VH' '17VV' '17VH'\n",
+       "Attributes:\n",
+       "    AREA_OR_POINT:  Area\n",
+       "    _FillValue:     nan\n",
+       "    scale_factor:   1.0\n",
+       "    add_offset:     0.0"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "sar_data = join_files(output_paths)\n",
     "sar_data"
@@ -404,7 +1539,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "36513c9e-438a-4f65-9f74-f3cceb1b6172",
    "metadata": {},
    "outputs": [],
@@ -427,7 +1562,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "1ffca227-cf79-4ed7-9d24-b54ba1fe3783",
    "metadata": {},
    "outputs": [],
@@ -457,7 +1592,7 @@
     "crs = ccrs.UTM(zone='12') #12n\n",
     "tiles='OSM'\n",
     "tiles='EsriImagery'\n",
-    "transparent_tile = hv.Tiles('https://tile.openstreetmap.org/{Z}/{X}/{Y}.png', name=\"OSM\").opts(alpha=0)\n",
+    "transparent_tile = hv.Tiles('https://server.arcgisonline.com/ArcGIS/rest/services/Reference/World_Reference_Overlay/MapServer/tile/{Z}/{Y}/{X}', name=\"EsriReference\").opts(alpha=0.0)\n",
     "frame_width  = 600\n",
     "frame_height = 500\n",
     "\n",
@@ -489,10 +1624,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "acddd149-3e53-4489-b233-b8d8a37e1b11",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x450 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "sar_swe_fig = sar_swe_plot(point_swe_filt, swesarr_mean)"
    ]
@@ -518,7 +1664,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "493a7452-cc73-4224-9237-ed038d7fa5da",
    "metadata": {},
    "outputs": [],
@@ -557,22 +1703,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "id": "0f29d818-7abc-4ebe-a14a-f067e23b1486",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# !wget --user=USERNAME_HERE --password=PASSWORD_HERE --quiet https://n5eil01u.ecs.nsidc.org/SNOWEX/SNEX20_SWESARR_TB.001/2020.02.12/SNEX20_SWESARR_TB_GRMCT2_13801_20007_000_200211_XKKa225H_v01.csv -O {output_dir}/SNEX20_SWESARR_TB_GRMCT2_13801_20007_000_200211_XKKa225H_v01.csv"
+    "# !wget --user=USERNAME_HERE --password=PASSWORD_HERE --quiet https://n5eil01u.ecs.nsidc.org/SNOWEX/SNEX20_SWESARR_TB.001/2020.02.12/SNEX20_SWESARR_TB_GRMCT2_13801_20007_000_200211_XKKa225H_v01.csv -O {output_dir}SNEX20_SWESARR_TB_GRMCT2_13801_20007_000_200211_XKKa225H_v01.csv"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "id": "4a2dc074-19cb-42bd-9c3f-fed14442429e",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# !wget --quiet https://n5eil01u.ecs.nsidc.org/SNOWEX/SNEX20_SWESARR_TB.001/2020.02.12/SNEX20_SWESARR_TB_GRMCT2_13801_20007_000_200211_XKKa225H_v01.csv -O {output_dir}/SNEX20_SWESARR_TB_GRMCT2_13801_20007_000_200211_XKKa225H_v01.csv"
+    "# !wget --quiet https://n5eil01u.ecs.nsidc.org/SNOWEX/SNEX20_SWESARR_TB.001/2020.02.12/SNEX20_SWESARR_TB_GRMCT2_13801_20007_000_200211_XKKa225H_v01.csv -O {output_dir}SNEX20_SWESARR_TB_GRMCT2_13801_20007_000_200211_XKKa225H_v01.csv"
    ]
   },
   {
@@ -585,13 +1731,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "id": "3ba1a0b6-b1f5-4f60-ab99-57444f08b9c4",
    "metadata": {},
    "outputs": [],
    "source": [
     "# use the file we downloaded with wget above\n",
-    "excel_path = f'{output_dir}/SNEX20_SWESARR_TB_GRMCT2_13801_20007_000_200211_XKKa225H_v01.csv'\n",
+    "excel_path = f'{output_dir}SNEX20_SWESARR_TB_GRMCT2_13801_20007_000_200211_XKKa225H_v01.csv'\n",
     "\n",
     "# read data\n",
     "radiom = pd.read_csv(excel_path)"
@@ -607,10 +1753,166 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "id": "bda67928-c05f-4648-a11b-8403f7c78eb8",
    "metadata": {},
-   "outputs": [],
+   "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>UTC</th>\n",
+       "      <th>Longitude (deg)</th>\n",
+       "      <th>Latitude (deg)</th>\n",
+       "      <th>Elevation (m)</th>\n",
+       "      <th>TB X (K)</th>\n",
+       "      <th>TB K (K)</th>\n",
+       "      <th>TB Ka (K)</th>\n",
+       "      <th>Antenna Longitude (deg)</th>\n",
+       "      <th>Antenna Latitude (deg)</th>\n",
+       "      <th>Antenna Altitude (m)</th>\n",
+       "      <th>Antenna Yaw (deg)</th>\n",
+       "      <th>Antenna Pitch (deg)</th>\n",
+       "      <th>Antenna Look Angle (deg)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>20200211-18:33:53.048360</td>\n",
+       "      <td>-108.227372</td>\n",
+       "      <td>39.071994</td>\n",
+       "      <td>2971</td>\n",
+       "      <td>247.1</td>\n",
+       "      <td>241.2</td>\n",
+       "      <td>231.2</td>\n",
+       "      <td>-108.052329</td>\n",
+       "      <td>38.940324</td>\n",
+       "      <td>4525.7</td>\n",
+       "      <td>2.02</td>\n",
+       "      <td>3.53</td>\n",
+       "      <td>44.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>20200211-18:33:53.148380</td>\n",
+       "      <td>-108.227372</td>\n",
+       "      <td>39.071901</td>\n",
+       "      <td>2979</td>\n",
+       "      <td>246.3</td>\n",
+       "      <td>238.2</td>\n",
+       "      <td>229.3</td>\n",
+       "      <td>-108.052331</td>\n",
+       "      <td>38.940326</td>\n",
+       "      <td>4525.7</td>\n",
+       "      <td>2.04</td>\n",
+       "      <td>3.53</td>\n",
+       "      <td>44.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>20200211-18:33:53.248380</td>\n",
+       "      <td>-108.227372</td>\n",
+       "      <td>39.071809</td>\n",
+       "      <td>2979</td>\n",
+       "      <td>247.8</td>\n",
+       "      <td>237.5</td>\n",
+       "      <td>227.4</td>\n",
+       "      <td>-108.052334</td>\n",
+       "      <td>38.940329</td>\n",
+       "      <td>4525.7</td>\n",
+       "      <td>2.06</td>\n",
+       "      <td>3.53</td>\n",
+       "      <td>44.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>20200211-18:33:53.348380</td>\n",
+       "      <td>-108.227279</td>\n",
+       "      <td>39.071716</td>\n",
+       "      <td>2991</td>\n",
+       "      <td>247.2</td>\n",
+       "      <td>237.5</td>\n",
+       "      <td>225.4</td>\n",
+       "      <td>-108.052336</td>\n",
+       "      <td>38.940331</td>\n",
+       "      <td>4525.7</td>\n",
+       "      <td>2.05</td>\n",
+       "      <td>3.53</td>\n",
+       "      <td>44.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>20200211-18:33:53.448380</td>\n",
+       "      <td>-108.227279</td>\n",
+       "      <td>39.071716</td>\n",
+       "      <td>2991</td>\n",
+       "      <td>245.7</td>\n",
+       "      <td>237.4</td>\n",
+       "      <td>222.6</td>\n",
+       "      <td>-108.052339</td>\n",
+       "      <td>38.940333</td>\n",
+       "      <td>4525.6</td>\n",
+       "      <td>2.05</td>\n",
+       "      <td>3.53</td>\n",
+       "      <td>44.9</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                        UTC  Longitude (deg)  Latitude (deg)  Elevation (m)  \\\n",
+       "0  20200211-18:33:53.048360      -108.227372       39.071994           2971   \n",
+       "1  20200211-18:33:53.148380      -108.227372       39.071901           2979   \n",
+       "2  20200211-18:33:53.248380      -108.227372       39.071809           2979   \n",
+       "3  20200211-18:33:53.348380      -108.227279       39.071716           2991   \n",
+       "4  20200211-18:33:53.448380      -108.227279       39.071716           2991   \n",
+       "\n",
+       "   TB X (K)  TB K (K)  TB Ka (K)  Antenna Longitude (deg)  \\\n",
+       "0     247.1     241.2      231.2              -108.052329   \n",
+       "1     246.3     238.2      229.3              -108.052331   \n",
+       "2     247.8     237.5      227.4              -108.052334   \n",
+       "3     247.2     237.5      225.4              -108.052336   \n",
+       "4     245.7     237.4      222.6              -108.052339   \n",
+       "\n",
+       "   Antenna Latitude (deg)  Antenna Altitude (m)  Antenna Yaw (deg)  \\\n",
+       "0               38.940324                4525.7               2.02   \n",
+       "1               38.940326                4525.7               2.04   \n",
+       "2               38.940329                4525.7               2.06   \n",
+       "3               38.940331                4525.7               2.05   \n",
+       "4               38.940333                4525.6               2.05   \n",
+       "\n",
+       "   Antenna Pitch (deg)  Antenna Look Angle (deg)  \n",
+       "0                 3.53                      44.9  \n",
+       "1                 3.53                      44.9  \n",
+       "2                 3.53                      44.9  \n",
+       "3                 3.53                      44.9  \n",
+       "4                 3.53                      44.9  "
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "radiom.head()\n",
     "#radiom.hvplot.table(width=1100) # sortable table in jupyterlab"
@@ -626,10 +1928,102 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "id": "750dcc7d-6a05-4036-8241-546b691a3dae",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {},
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.holoviews_exec.v0+json": "",
+      "text/html": [
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+       "</div>\n",
+       "<script type=\"application/javascript\">(function(root) {\n",
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l9INfIJUkFNshzg7glSQU2yHODuCVJBG0/xiusJUkEbT/GK6wlSQcy28j7oCVJBzLbyPugJUkEM5sfp5AlSQQzmx+nkCVJBnN7JneEJUkGc3smd4QlSQZzeyZ3hCVJBVKCfSN4JUkFUoJ9I3glSQaerdfPaCVJBp6t189oJUkHsfXin1wlSQex9eKfXCVJBtBtPUtQJUkG0G09S1AlSQbQbT1LUCVJB135SBtEJUkHXflIG0QlSQRGvKbHNCVJBEa8psc0JUkHqKAFcyglSQeooAVzKCVJBwWUFEMcJUkHBZQUQxwlSQQxy3brDCVJBDHLdusMJUkHEP+JuwAlSQcQ/4m7ACVJBgd66Gb0JUkGB3roZvQlSQdzGk8S5CVJB3MaTxLkJUkFEbpl4tglSQURumXi2CVJBEOlyI7MJUkEQ6XIjswlSQVchedevCVJBVyF5168JUkGULlOCrAlSQXGFLS2pCVJBcYUtLakJUkFxhS0tqQlSQWaXNOGlCVJBZpc04aUJUkGzgA+MoglSQbOAD4yiCVJBhyMXQJ8JUkGHIxdAnwlSQUKf8uqbCVJBQp/y6psJUkH10vqemAlSQfXS+p6YCVJBHuHWSZUJUkEe4dZJlQlSQec4s/SRCVJB5ziz9JEJUkFHRryojglSQUdGvKiOCVJBfjCZU4sJUkF+MJlTiwlSQX4wmVOLCVJBu86iB4gJUkG7zqIHiAlSQWJLgLKECVJBYkuAsoQJUkGkEV5dgQlSQaQRXl2BCVJBjoloEX4JUkGOiWgRfglSQUDiRrx6CVJBQOJGvHoJUkEF61FwdwlSQQXrUXB3CVJBJNYwG3QJUkHgChDGcAlSQeAKEMZwCVJBUe0bem0JUkFR7Rt6bQlSQXq0+yRqCVJBerT7JGoJUkHGJwjZZglSQcYnCNlmCVJBXIHog2MJUkFcgeiDYwlSQY8kyS5gCVJBjyTJLmAJUkGFcdbiXAlSQYVx1uJcCVJBJqe3jVkJUkEmp7eNWQlSQfSExUFWCVJB9ITFQVYJUkEATafsUglSQQBNp+xSCVJBpl6Jl08JUkGmXomXTwlSQR8WmEtMCVJBHxaYS0wJUkExunr2SAlSQTG6evZICVJBhAKKqkUJUkECOW1VQglSQQI5bVVCCVJBHLlQAD8JUkEX22C0OwlSQRfbYLQ7CVJBme1EXzgJUkGZ7URfOAlSQWugVRM1CVJBa6BVEzUJUkFZRTq+MQlSQVlFOr4xCVJB4zMfaS4JUkHjMx9pLglSQVzAMB0rCVJBXMAwHSsJUkFNQRbIJwlSQU1BFsgnCVJBn14ofCQJUkGfXih8JAlSQftxDichCVJB+3EOJyEJUkHyzvTRHQlSQfLO9NEdCVJB58UHhhoJUkHnxQeGGglSQUi17jAXCVJBSLXuMBcJUkETPQLlEwlSQRM9AuUTCVJB3L7pjxAJUkHcvumPEAlSQT6K0ToNCVJBPorROg0JUkE+itE6DQlSQbDr5e4JCVJBsOvl7gkJUkF5Sc6ZBglSQXlJzpkGCVJBwDvjTQMJUkHAO+NNAwlSQfErzPj/CFJB8SvM+P8IUkG9ZbWj/AhSQb1ltaP8CFJBpzHLV/kIUkGnMctX+QhSQdr9tAL2CFJB2v20AvYIUkGXWsu28ghSQZday7byCFJBMbm1Ye8IUkExubVh7whSQWRhoAzsCFJBZGGgDOwIUkHDl7fA6AhSQcOXt8DoCFJBXdKia+UIUkFd0qJr5QhSQZCZuh/iCFJBkJm6H+IIUkGQZqbK3ghSQZBmpsreCFJBJ32SddsIUkEnfZJ12whSQf0dqynYCFJB/R2rKdgIUkH5xpfU1AhSQfnGl9TUCFJBoviwiNEIUkGi+LCI0QhSQQM0njPOCFJBAzSeM84IUkF89rfnyghSQXz2t+fKCFJBRMSlkscIUkFExKWSxwhSQaTbkz3ECFJBpNuTPcQIUkG7d67xwAhSQbt3rvHACFJBfyGdnL0IUkF/IZ2cvQhSQWpOuFC6CFJBak64ULoIUkGQiqf7tghSQZCKp/u2CFJBURCXprMIUkFREJemswhSQdkWs1qwCFJB2RazWrAIUkH8LqMFrQhSQfwuowWtCFJBU8a/uakIUkFTxr+5qQhSQdlwsGSmCFJB2XCwZKYIUkH2ZKEPowhSQfZkoQ+jCFJB69W+w58IUkHr1b7DnwhSQWpcsG6cCFJBalywbpwIUkEuXs4imQhSQS5eziKZCFJBEHfAzZUIUkEQd8DNlQhSQYnZsniSCFJBidmyeJIIUkHptNEsjwhSQem00SyPCFJBw6nE14sIUkHDqcTXiwhSQfEV5IuICFJB8RXki4gIUkEtndc2hQhSQS2d1zaFCFJBAG7L4YEIUkEAbsvhgQhSQcaz65V+CFJBxrPrlX4IUkH6FuBAewhSQfoW4EB7CFJBju0A9XcIUkGO7QD1dwhSQSPj9Z90CFJBI+P1n3QIUkFNIutKcQhSQU0i60pxCFJBe9IM/20IUkF70gz/bQhSQQakAqpqCFJBBqQCqmoIUkH+5CReZwhSQf7kJF5nCFJB6kgbCWQIUkHqSBsJZAhSQWn2EbRgCFJBafYRtGAIUkH6EDVoXQhSQfoQNWhdCFJB2VAsE1oIUkHZUCwTWghSQTb8T8dWCFJBNvxPx1YIUkFzzkdyUwhSQXPOR3JTCFJBRuo/HVAIUkE7b2TRTAhSQTtvZNFMCFJBax1dfEkIUkEqM4IwRghSQSozgjBGCFJBunN720IIUkG6c3vbQghSQdv9dIY/CFJB2/10hj8IUkEw7Zo6PAhSQTDtmjo8CFJBrwmV5TgIUkGvCZXlOAhSQc6Ju5k1CFJBzom7mTUIUkGsOLZEMghSQaw4tkQyCFJBHjGx7y4IUkEeMbHvLghSQdGK2KMrCFJB0YrYoysIUkGeFdROKAhSQZ4V1E4oCFJBGQD8AiUIUkEZAPwCJQhSQUQd+K0hCFJBRB34rSEIUkEChPRYHghSQQKE9FgeCFJBD0gdDRsIUkEPSB0NGwhSQSpBGrgXCFJBKkEauBcIUkH/lUNsFAhSQf+VQ2wUCFJBdSFBFxEIUkF1IUEXEQhSQXz2PsINCFJBfPY+wg0IUkHmJGl2CghSQeYkaXYKCFJBSYxnIQcIUkFJjGchBwhSQXdLktUDCFJBd0uS1QMIUkE1RZGAAAhSQTVFkYAACFJBK5W8NP0HUkErlbw0/QdSQUUhvN/5B1JBRSG83/kHUkHu9ruK9gdSQe72u4r2B1JBdCDoPvMHUkF0IOg+8wdSQXmI6OnvB1JBeYjo6e8HUkHIQhWe7AdSQchCFZ7sB1JBJT0WSekHUkEUgRf05QdSQRSBF/TlB1JB7RRFqOIHUkHtFEWo4gdSQTfrRlPfB1JBN+tGU98HUkHXD3UH3AdSQdcPdQfcB1JBenh3stgHUkF6eHey2AdSQasqel3VB1JBqyp6XdUHUkHWKKkR0gdSQdYoqRHSB1JBYm2svM4HUkFibay8zgdSQVT823DLB1JBONPfG8gHUkE4098byAdSQarz48bEB1JBqvPjxsQHUkEoXBR7wQdSQShcFHvBB1JB8g4ZJr4HUkHyDhkmvgdSQTMIStq6B1JBMwhK2roHUkFUTU+FtwdSQVRNT4W3B1JBCNxUMLQHUkEI3FQwtAdSQdGuhuSwB1JB0a6G5LAHUkHbz4yPrQdSQdvPjI+tB1JBaDO/Q6oHUkFoM79DqgdSQcfmxe6mB1JBx+bF7qYHUkG148yZowdSQbXjzJmjB1JBzCAATqAHUkHMIABOoAdSQRGwB/mcB1JBEbAH+ZwHUkHpfTutmQdSQel9O62ZB1JBhJ9DWJYHUkGsCkwDkwdSQawKTAOTB1JBrApMA5MHUkENsoC3jwdSQQ2ygLePB1JBiq+JYowHUkGs574WiQdSQaznvhaJB1JBfXfIwYUHUkF9d8jBhQdSQeBQ0myCB1JB4FDSbIIHUkGIYgghfwdSQYhiCCF/B1JBPs4SzHsHUkE+zhLMewdSQaVwSYB4B1JBpXBJgHgHUkGwblQrdQdSQbBuVCt1B1JBRrZf1nEHUkFGtl/WcQdSQTMyl4puB1JBHQyjNWsHUkEdDKM1awdSQcoY2+lnB1JByhjb6WcHUkEGheeUZAdSQQaF55RkB1JB0jr0P2EHUkHSOvQ/YQdSQQQhLfRdB1JBBCEt9F0HUkEhaTqfWgdSQSFpOp9aB1JBEeBzU1cHUkER4HNTVwdSQYC6gf5TB1JBfd6PqVAHUkF93o+pUAdSQe4uyl1NB1JB7i7KXU0HUkE95dgISgdSQT3l2AhKB1JBbMYTvUYHUkFsxhO9RgdSQQwPI2hDB1JBDA8jaEMHUkE4oTITQAdSQTihMhNAB1JB6VtuxzwHUkHpW27HPAdSQWeAfnI5B1JBZ4B+cjkHUkHUy7omNgdSQdTLuiY2B1JBooLL0TIHUkH6gtx8LwdSQfqC3HwvB1JB+oLcfC8HUkHppxkxLAdSQemnGTEsB1JBkTor3CgHUkE88GiQJQdSQTzwaJAlB1JBNBV7OyIHUkE0FXs7IgdSQZtbue8eB1JBm1u57x4HUkHkEsyaGwdSQeQSzJobB1JBtxPfRRgHUkG3E99FGAdSQZszHvoUB1JBmzMe+hQHUkG8xjGlEQdSQbzGMaURB1JBXHdxWQ4HUkFcd3FZDgdSQcuchQQLB1JBy5yFBAsHUkHIC5qvBwdSQcgLmq8HB1JB5JXaYwQHUkHkldpjBAdSQSyX7w4BB1JBLJfvDgEHUkEEsjDD/QZSQQSyMMP9BlJBmkVGbvoGUkGaRUZu+gZSQbwiXBn3BlJBvCJcGfcGUkEOF57N8wZSQQ4Xns3zBlJBDheezfMGUkF7hrR48AZSQXuGtHjwBlJBhgv3LO0GUkFBDQ7Y6QZSQUENDtjpBlJBh1glg+YGUkGHWCWD5gZSQYdYJYPmBlJBDLdoN+MGUkGdlIDi3wZSQZ2UgOLfBlJB3YPEltwGUkHdg8SW3AZSQbjz3EHZBlJBuPPcQdkGUkEerfXs1QZSQR6t9ezVBlJB13U6odIGUkHXdTqh0gZSQYjBU0zPBlJBiMFTTM8GUkH3GpkAzAZSQfcamQDMBlJB8viyq8gGUkHy+LKryAZSQXkgzVbFBlJBeSDNVsUGUkFgUxMLwgZSQWBTEwvCBlJBLw0utr4GUkEvDS62vgZSQc7QdGq7BlJBztB0arsGUkHnHJAVuAZSQecckBW4BlJBi7KrwLQGUkGLsqvAtAZSQZ9P83SxBlJBn0/zdLEGUkGLdw8grgZSQYt3DyCuBlJBVqVX1KoGUkFWpVfUqgZSQYtfdH+nBlJBi190f6cGUkFKY5EqpAZSQUpjkSqkBlJBiGra3qAGUkGIatreoAZSQZAA+ImdBlJBkAD4iZ0GUkGEmEE+mgZSQYSYQT6aBlJB0sBf6ZYGUkHSwF/plgZSQaoyfpSTBlJBqjJ+lJMGUkESpMhIkAZSQRKkyEiQBlJBMajn84wGUkExqOfzjAZSQU2qMqiJBlJBTaoyqIkGUkG0QFJThgZSQbRAUlOGBlJBpCBy/oIGUkGkIHL+ggZSQTD8vbJ/BlJBMPy9sn8GUkFkbt5dfAZSQWRu3l18BlJBp9oqEnkGUkEi30u9dQZSQSLfS711BlJBJi1taHIGUkEmLW1ocgZSQdpyuhxvBlJB2nK6HG8GUkEiU9zHawZSQSJT3MdrBlJBiCkqfGgGUkGIKSp8aAZSQRacTCdlBlJBFpxMJ2UGUkEsWG/SYQZSQQEIvoZeBlJBAQi+hl4GUkFcVuExWwZSQVxW4TFbBlJB45Yw5lcGUkHjljDmVwZSQYF3VJFUBlJBgXdUkVQGUkGpoXg8UQZSQZ+7yPBNBlJBn7vI8E0GUkEIeO2bSgZSQQh47ZtKBlJBryI+UEcGUkGvIj5QRwZSQV1xY/tDBlJBXXFj+0MGUkG0rLSvQAZSQaSN2lo9BlJBpI3aWj0GUkEcuAAGOgZSQRy4AAY6BlJB38xSujYGUkHfzFK6NgZSQZqJeWUzBlJBmol5ZTMGUkEOL8wZMAZSQQ4vzBkwBlJBCn7zxCwGUkEKfvPELAZSQY4WG3ApBlJBa5VuJCYGUkFrlW4kJgZSQTDAls8iBlJBMMCWzyIGUkG+z+qDHwZSQb7P6oMfBlJBxYwTLxwGUkHFjBMvHAZSQU+TPNoYBlJBT5M82hgGUkFJfJGOFQZSQRUVuzkSBlJBFRW7ORIGUkG9jhDuDgZSQb2OEO4OBlJByrk6mQsGUkHKuTqZCwZSQVwuZUQIBlJBXC5lRAgGUkFqgbv4BAZSQWqBu/gEBlJBPYjmowEGUkE9iOajAQZSQfprPVj+BVJB+ms9WP4FUkEMBWkD+wVSQaLnlK73BVJBoueUrvcFUkGi55Su9wVSQcSk7GL0BVJBnRkZDvEFUkGdGRkO8QVSQW1nccLtBVJBbWdxwu0FUkGDbp5t6gVSQYNunm3qBVJBHL/LGOcFUkEcv8sY5wVSQVPmJM3jBVJBK8lSeOAFUkEryVJ44AVSQQ2BrCzdBVJBDYGsLN0FUkEj9trX2QVSQSP22tfZBVJBvbQJg9YFUkG9tAmD1gVSQQNGZDfTBVJB25aT4s8FUkHblpPizwVSQc647pbMBVJBzrjulswFUkHimx5CyQVSQeKbHkLJBVJBechO7cUFUkF5yE7txQVSQc/DqqHCBVJBpILbTL8FUkGkgttMvwVSQaIOOAG8BVJBtF9prLgFUkFI+ppXtQVSQUj6mle1BVJBql/4C7IFUkGqX/gLsgVSQXqMKreuBVJBeowqt64FUkGEgohrqwVSQYSCiGurBVJBj0G7FqgFUkGPQbsWqAVSQR5K7sGkBVJBHkruwaQFUkGKGU12oQVSQYoZTXahBVJBVLSAIZ4FUkFUtIAhngVSQWYU4NWaBVJBakEUgZcFUkFqQRSBlwVSQfC3SCyUBVJB8LdILJQFUkFi8ajgkAVSQWLxqOCQBVJBI/rdi40FUkEj+t2LjQVSQT/EPkCKBVJBP8Q+QIoFUkE/xD5AigVSQThfdOuGBVJBOF9064YFUkGzQ6qWgwVSQbNDqpaDBVJBLecLS4AFUkEt5wtLgAVSQeFdQvZ8BVJB4V1C9nwFUkECkqSqeQVSQe6a21V2BVJB7prbVXYFUkGzXz4KcwVSQbNfPgpzBVJB2vp1tW8FUkHa+nW1bwVSQYLfrWBsBVJBo30RFWkFUkGjfREVaQVSQYH0ScBlBVJBgfRJwGUFUkFKI650YgVSQUojrnRiBVJBSiOudGIFUkFhLOcfXwVSQWEs5x9fBVJB+X4gy1sFUkH5fiDLWwVSQRuHhX9YBVJBG4eFf1gFUkHra78qVQVSQetrvypVBVJBsAQl31EFUkGwBCXfUQVSQbd7X4pOBVJBt3tfik4FUkE9PJo1SwVSQT08mjVLBVJBXq4A6kcFUkFergDqRwVSQRsBPJVEBVJBGwE8lUQFUkHfA6NJQQVSQd8Do0lBBVJB3wOjSUEFUkHR6N70PQVSQdHo3vQ9BVJBQxcboDoFUkFDFxugOgVSQWHzglQ3BVJBYfOCVDcFUkEItL//MwVSQQi0v/8zBVJByCAotDAFUkHIICi0MAVSQaRzZV8tBVJBpHNlXy0FUkECEKMKKgVSQQIQowoqBVJBGFYMvyYFUkEYVgy/JgVSQRhWDL8mBVJBqoRKaiMFUkGqhEpqIwVSQWNbtB4gBVJBY1u0HiAFUkEpHPPJHAVSQSkc88kcBVJBbSYydRkFUkFtJjJ1GQVSQXvWnCkWBVJBe9acKRYFUkH0ctzUEgVSQfRy3NQSBVJBo7NHiQ8FUkGjs0eJDwVSQU7ihzQMBVJBTuKHNAwFUkF4WsjfCAVSQXhayN8IBVJBfXQ0lAUFUkF9dDSUBQVSQX10NJQFBVJB2H51PwIFUkHYfnU/AgVSQYAp4vP+BFJBgCni8/4EUkEPxiOf+wRSQQ/GI5/7BFJBHKxlSvgEUkEcrGVK+ARSQRQw0/70BFJBFDDT/vQEUkFVqBWq8QRSQVWoFarxBFJB7LyDXu4EUkHsvINe7gRSQey8g17uBFJBXsfGCesEUkFex8YJ6wRSQUsbCrXnBFJBSxsKtecEUkE2CXlp5ARSQTYJeWnkBFJBVe+8FOEEUkFV77wU4QRSQd5tLMndBFJB3m0syd0EUkEw5nB02gRSQTDmcHTaBFJB/Ke1H9cEUkH8p7Uf1wRSQdf/JdTTBFJB1/8l1NMEUkHTU2t/0ARSQdNTa3/QBFJBTDzcM80EUkFMPNwzzQRSQUw83DPNBFJBeSIi38kEUkEjUmiKxgRSQSNSaIrGBFJBI1JoisYEUkHrE9o+wwRSQesT2j7DBFJBxdUg6r8EUkHF1SDqvwRSQSook568BFJBKiiTnrwEUkEzfNpJuQRSQTN82km5BFJBtxki9bUEUkG3GSL1tQRSQWpFlamyBFJBakWVqbIEUkEedd1UrwRSQR513VSvBFJBHnXdVK8EUkFtMVEJrARSQW0xUQmsBFJBT/OZtKgEUkFP85m0qARSQTlADmmlBFJBOUAOaaUEUkFJlFcUogRSQUmUVxSiBFJB0zGhv54EUkHTMaG/ngRSQQxYFnSbBFJBDFgWdJsEUkHDh2AfmARSQcOHYB+YBFJBlz7W05QEUkGXPtbTlARSQXsAIX+RBFJBewAhf5EEUkF7ACF/kQRSQdwLbCqOBFJB3AtsKo4EUkH5m+LeigRSQfmb4t6KBFJBhzkuiocEUkE+WqU+hARSQT5apT6EBFJBPlqlPoQEUkH2ifHpgARSQfaJ8emABFJBKgM+lX0EUkEqAz6VfQRSQSz9tUl6BFJBLP21SXoEUkGMCAP1dgRSQYwIA/V2BFJBKJN7qXMEUkEok3upcwRSQbMwyVRwBFJBszDJVHAEUkG3FxcAbQRSQbcXFwBtBFJBnHuQtGkEUkGce5C0aQRSQcv03l9mBFJBy/TeX2YEUkHL9N5fZgRSQUbpWBRjBFJBRulYFGMEUkGh9Ke/XwRSQaH0p79fBFJBd0n3alwEUkF3SfdqXARSQTgXch9ZBFJBOBdyH1kEUkE3/sHKVQRSQTf+wcpVBFJBk1w9f1IEUkGTXD1/UgRSQbnVjSpPBFJBudWNKk8EUkFcmN7VSwRSQVyY3tVLBFJB/M9aikgEUkH8z1qKSARSQcYkrDVFBFJBxiSsNUUEUkH97CjqQQRSQf3sKOpBBFJB8dN6lT4EUkHx03qVPgRSQVsEzUA7BFJBWwTNQDsEUkHZpUr1NwRSQdmlSvU3BFJBbWidoDQEUkFtaJ2gNARSQX+aG1UxBFJBf5obVTEEUkE7724ALgRSQTvvbgAuBFJBco3CqyoEUkFyjcKrKgRSQcWYQWAnBFJBxZhBYCcEUkEiyZULJARSQQ1lFcAgBFJBDWUVwCAEUkENZRXAIARSQY4namsdBFJBjidqax0EUkGKM78WGgRSQbaoP8sWBFJBtqg/yxYEUkHZRpV2EwRSQdlGlXYTBFJB2UaVdhMEUkGaTBYrEARSQeJ8bNYMBFJB4nxs1gwEUkGh9sKBCQRSQaH2woEJBFJBo9VENgYEUkGj1UQ2BgRSQYfhm+ECBFJBh+Gb4QIEUkEdUR6W/wNSQR1RHpb/A1JBJ+91QfwDUkEn73VB/ANSQajWzez4A1JBqNbN7PgDUkF9H1Gh9QNSQX0fUaH1A1JBI5mpTPIDUkGLci0B7wNSQYtyLQHvA1JBVX6GrOsDUkFVfoas6wNSQZjT31foA1JBmNPfV+gDUkE9hmQM5QNSQT2GZAzlA1JBo22+t+EDUkGjbb634QNSQdiwQ2zeA1JB2LBDbN4DUkFhKp4X2wNSQWEqnhfbA1JBYe34wtcDUkFh7fjC1wNSQdYJf3fUA1JB1gl/d9QDUkH3Xtoi0QNSQfde2iLRA1JB/Qth180DUkH9C2HXzQNSQULzvILKA1JBQvO8gsoDUkHWMEQ3xwNSQdYwRDfHA1JBPqqg4sMDUkE+qqDiwwNSQRpt/Y3AA1JBGm39jcADUkHsg4VCvQNSQeyDhUK9A1JB7IOFQr0DUkHq2OLtuQNSQerY4u25A1JBSoBrorYDUkFKgGuitgNSQWpnyU2zA1JBamfJTbMDUkEAmCf5rwNSQQCYJ/mvA1JBmxixrawDUkGbGLGtrANSQVHbD1mpA1JBUdsPWakDUkF57JkNpgNSQXnsmQ2mA1JBUUH5uKIDUkFRQfm4ogNSQZzfWGSfA1JBnN9YZJ8DUkH9yeMYnANSQf3J4xicA1JBavpDxJgDUkFq+kPEmANSQVd1z3iVA1JBV3XPeJUDUkHkNzAkkgNSQeQ3MCSSA1JB5kORz44DUkHmQ5HPjgNSQQyYHYSLA1JBDJgdhIsDUkEqNn8viANSQSo2fy+IA1JB3xoM5IQDUkHfGgzkhANSQR1Lbo+BA1JBHUtuj4EDUkEdS26PgQNSQdDE0Dp+A1JB0MTQOn4DUkG5gl7vegNSQbmCXu96A1JBi47BmncDUkGLjsGadwNSQf7cT090A1JB/txPT3QDUkHverP6cANSQe96s/pwA1JBUmIXpm0DUkFSYhembQNSQQ==\"},\"shape\":[1457],\"dtype\":\"float64\",\"order\":\"little\"}],[\"Longitude_left_parenthesis_deg_right_parenthesis\",{\"type\":\"ndarray\",\"array\":{\"type\":\"bytes\",\"data\":\"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url(\\\"data:image/svg+xml;base64,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\\\");\\n  background-size: auto calc(min(50%, 400px));\\n}\",{\"id\":\"p1161\"},{\"type\":\"object\",\"name\":\"ImportedStyleSheet\",\"id\":\"p1250\",\"attributes\":{\"url\":\"https://cdn.holoviz.org/panel/1.2.3/dist/css/widgetbox.css\"}},{\"id\":\"p1251\"},{\"id\":\"p1159\"},{\"id\":\"p1160\"}],\"margin\":0,\"align\":[\"end\",\"center\"],\"children\":[{\"type\":\"object\",\"name\":\"panel.models.widgets.CustomSelect\",\"id\":\"p1248\",\"attributes\":{\"stylesheets\":[\"\\n:host(.pn-loading.pn-arc):before, .pn-loading.pn-arc:before {\\n  background-image: 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400px));\\n}\",{\"id\":\"p1161\"},{\"type\":\"object\",\"name\":\"ImportedStyleSheet\",\"id\":\"p1247\",\"attributes\":{\"url\":\"https://cdn.holoviz.org/panel/1.2.3/dist/css/select.css\"}},{\"id\":\"p1159\"},{\"id\":\"p1160\"}],\"width\":250,\"min_width\":250,\"margin\":[20,20,20,20],\"align\":\"start\",\"title\":\"ID\",\"options\":[\"X-band\",\"K-band\",\"Ka-band\"],\"value\":\"X-band\"}}]}}]}},{\"type\":\"object\",\"name\":\"panel.models.comm_manager.CommManager\",\"id\":\"p1254\",\"attributes\":{\"plot_id\":\"p1158\",\"comm_id\":\"bdfee2427b974a699262f0ebb50821dd\",\"client_comm_id\":\"03de4eeefcc246a783bb99c4f505aa45\"}}],\"defs\":[{\"type\":\"model\",\"name\":\"ReactiveHTML1\"},{\"type\":\"model\",\"name\":\"FlexBox1\",\"properties\":[{\"name\":\"align_content\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"align_items\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"flex_direction\",\"kind\":\"Any\",\"default\":\"row\"},{\"name\":\"flex_wrap\",\"kind\":\"Any\",\"default\":\"wrap\"},{\"name\":\"justify_content\",\"kind\":\"Any\",\"default\":\"flex-start\"}]},{\"type\":\"model\",\"name\":\"FloatPanel1\",\"properties\":[{\"name\":\"config\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"contained\",\"kind\":\"Any\",\"default\":true},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"right-top\"},{\"name\":\"offsetx\",\"kind\":\"Any\",\"default\":null},{\"name\":\"offsety\",\"kind\":\"Any\",\"default\":null},{\"name\":\"theme\",\"kind\":\"Any\",\"default\":\"primary\"},{\"name\":\"status\",\"kind\":\"Any\",\"default\":\"normalized\"}]},{\"type\":\"model\",\"name\":\"GridStack1\",\"properties\":[{\"name\":\"mode\",\"kind\":\"Any\",\"default\":\"warn\"},{\"name\":\"ncols\",\"kind\":\"Any\",\"default\":null},{\"name\":\"nrows\",\"kind\":\"Any\",\"default\":null},{\"name\":\"allow_resize\",\"kind\":\"Any\",\"default\":true},{\"name\":\"allow_drag\",\"kind\":\"Any\",\"default\":true},{\"name\":\"state\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"drag1\",\"properties\":[{\"name\":\"slider_width\",\"kind\":\"Any\",\"default\":5},{\"name\":\"slider_color\",\"kind\":\"Any\",\"default\":\"black\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":50}]},{\"type\":\"model\",\"name\":\"click1\",\"properties\":[{\"name\":\"terminal_output\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"debug_name\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"clears\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"FastWrapper1\",\"properties\":[{\"name\":\"object\",\"kind\":\"Any\",\"default\":null},{\"name\":\"style\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"NotificationAreaBase1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"NotificationArea1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"notifications\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0},{\"name\":\"types\",\"kind\":\"Any\",\"default\":[{\"type\":\"map\",\"entries\":[[\"type\",\"warning\"],[\"background\",\"#ffc107\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]}]}};\n",
+       "  var render_items = [{\"docid\":\"2658d5f0-a362-4074-a249-3f1f775d1434\",\"roots\":{\"p1158\":\"b9b0a50a-8fbc-428c-9a72-7a03bd05384c\"},\"root_ids\":[\"p1158\"]}];\n",
+       "  var docs = Object.values(docs_json)\n",
+       "  if (!docs) {\n",
+       "    return\n",
+       "  }\n",
+       "  const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
+       "  const is_dev = py_version.indexOf(\"+\") !== -1 || py_version.indexOf(\"-\") !== -1\n",
+       "  function embed_document(root) {\n",
+       "    var Bokeh = get_bokeh(root)\n",
+       "    Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
+       "    for (const render_item of render_items) {\n",
+       "      for (const root_id of render_item.root_ids) {\n",
+       "\tconst id_el = document.getElementById(root_id)\n",
+       "\tif (id_el.children.length && (id_el.children[0].className === 'bk-root')) {\n",
+       "\t  const root_el = id_el.children[0]\n",
+       "\t  root_el.id = root_el.id + '-rendered'\n",
+       "\t}\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "  function get_bokeh(root) {\n",
+       "    if (root.Bokeh === undefined) {\n",
+       "      return null\n",
+       "    } else if (root.Bokeh.version !== py_version && !is_dev) {\n",
+       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
+       "\treturn null\n",
+       "      }\n",
+       "      return root.Bokeh.versions.get(py_version);\n",
+       "    } else if (root.Bokeh.version === py_version) {\n",
+       "      return root.Bokeh\n",
+       "    }\n",
+       "    return null\n",
+       "  }\n",
+       "  function is_loaded(root) {\n",
+       "    var Bokeh = get_bokeh(root)\n",
+       "    return (Bokeh != null && Bokeh.Panel !== undefined)\n",
+       "  }\n",
+       "  if (is_loaded(root)) {\n",
+       "    embed_document(root);\n",
+       "  } else {\n",
+       "    var attempts = 0;\n",
+       "    var timer = setInterval(function(root) {\n",
+       "      if (is_loaded(root)) {\n",
+       "        clearInterval(timer);\n",
+       "        embed_document(root);\n",
+       "      } else if (document.readyState == \"complete\") {\n",
+       "        attempts++;\n",
+       "        if (attempts > 200) {\n",
+       "          clearInterval(timer);\n",
+       "\t  var Bokeh = get_bokeh(root)\n",
+       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
+       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
+       "\t  } else {\n",
+       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
+       "\t    embed_document(root)\n",
+       "\t  }\n",
+       "        }\n",
+       "      }\n",
+       "    }, 25, root)\n",
+       "  }\n",
+       "})(window);</script>"
+      ],
+      "text/plain": [
+       ":DynamicMap   [ID]\n",
+       "   :Overlay\n",
+       "      .Tiles.I  :Tiles   [x,y]\n",
+       "      .Points.I :Points   [Longitude (deg),Latitude (deg)]   (TB)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {
+      "application/vnd.holoviews_exec.v0+json": {
+       "id": "p1158"
+      }
+     },
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# create several series from pandas dataframe\n",
     "lon_ser = pd.Series( radiom['Longitude (deg)'].to_list() * (3) )\n",
@@ -677,7 +2071,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "id": "ef9bec9d-4ee8-4eee-a28e-6b890b6ef6c8",
    "metadata": {},
    "outputs": [],
@@ -696,7 +2090,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "id": "b56904fb-cc6e-4084-9748-36f10f7d8713",
    "metadata": {},
    "outputs": [],
@@ -716,10 +2110,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "id": "c26131e3-36d2-48f2-a436-e6f41172da2d",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x450 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "rad_fig = radiom_swe_plot(rad_swe)"
    ]
@@ -744,7 +2149,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "id": "b0855fa7-762b-40ab-afab-84d76e2070a7",
    "metadata": {},
    "outputs": [],
@@ -786,7 +2191,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "id": "ea283a7a-28fc-42fc-ab64-f6c9aab2da6b",
    "metadata": {},
    "outputs": [],
@@ -819,7 +2224,7 @@
    "metadata": {},
    "source": [
     "<br><br><br>\n",
-    "## [Warnings](#Table-of-Contents)\n",
+    "## [Warnings]\n",
     "<div class=\"alert alert-block alert-danger\">\n",
     "<b>Interpreting Data:</b> After the 2019 and 2020 measurement periods for SWESARR, an internal timing error was found in the flight data which affects the spatial precision of the measurements. While we are working to correct this geospatial error, please consider this offset before drawing conclusions from SWESARR data if you are using a dataset prior to this correction. The SWESARR website will announce the update of the geospatially corrected dataset.\n",
     "</div>\n"

From 41b86a9d36730d936d29fec74992657a7dd4660f Mon Sep 17 00:00:00 2001
From: Boyd <db1950@umd.edu>
Date: Thu, 15 Aug 2024 12:43:16 -0400
Subject: [PATCH 5/6] Incorrect Radiometer Data Reference

Fixing error caused by referencing an incorrect radiometer CSV.

Did not clear notebook outputs before uploading.
---
 book/tutorials/swesarr/swesarr_tut.ipynb | 1475 +---------------------
 1 file changed, 30 insertions(+), 1445 deletions(-)

diff --git a/book/tutorials/swesarr/swesarr_tut.ipynb b/book/tutorials/swesarr/swesarr_tut.ipynb
index 631e223..23f490d 100644
--- a/book/tutorials/swesarr/swesarr_tut.ipynb
+++ b/book/tutorials/swesarr/swesarr_tut.ipynb
@@ -137,26 +137,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "bb939cd3-32f5-47ab-9bd2-e0d08256ea26",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>\n",
-       "td { font-size: 15px }\n",
-       "th { font-size: 15px }\n",
-       "</style>\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%HTML\n",
     "<style>\n",
@@ -241,612 +225,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "54b4ada6-dfd7-4e60-b7aa-d2cd67872650",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Requirement already satisfied: snowexsql in /srv/conda/envs/notebook/lib/python3.11/site-packages (0.5.0)\n",
-      "Requirement already satisfied: utm<1.0,>=0.5.0 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from snowexsql) (0.7.0)\n",
-      "Requirement already satisfied: geoalchemy2<1.0,>=0.6 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from snowexsql) (0.15.2)\n",
-      "Requirement already satisfied: geopandas<2.0,>=0.7 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from snowexsql) (0.13.2)\n",
-      "Requirement already satisfied: psycopg2-binary<2.10.0,>=2.9.0 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from snowexsql) (2.9.9)\n",
-      "Requirement already satisfied: rasterio>=1.1.5 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from snowexsql) (1.3.10)\n",
-      "Requirement already satisfied: SQLAlchemy>=2.0.0 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from snowexsql) (2.0.30)\n",
-      "Requirement already satisfied: packaging in /srv/conda/envs/notebook/lib/python3.11/site-packages (from geoalchemy2<1.0,>=0.6->snowexsql) (23.2)\n",
-      "Requirement already satisfied: fiona>=1.8.19 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from geopandas<2.0,>=0.7->snowexsql) (1.9.6)\n",
-      "Requirement already satisfied: pandas>=1.1.0 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from geopandas<2.0,>=0.7->snowexsql) (2.2.2)\n",
-      "Requirement already satisfied: pyproj>=3.0.1 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from geopandas<2.0,>=0.7->snowexsql) (3.6.1)\n",
-      "Requirement already satisfied: shapely>=1.7.1 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from geopandas<2.0,>=0.7->snowexsql) (2.0.4)\n",
-      "Requirement already satisfied: affine in /srv/conda/envs/notebook/lib/python3.11/site-packages (from rasterio>=1.1.5->snowexsql) (2.4.0)\n",
-      "Requirement already satisfied: attrs in /srv/conda/envs/notebook/lib/python3.11/site-packages (from rasterio>=1.1.5->snowexsql) (23.2.0)\n",
-      "Requirement already satisfied: certifi in /srv/conda/envs/notebook/lib/python3.11/site-packages (from rasterio>=1.1.5->snowexsql) (2024.2.2)\n",
-      "Requirement already satisfied: click>=4.0 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from rasterio>=1.1.5->snowexsql) (8.1.7)\n",
-      "Requirement already satisfied: cligj>=0.5 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from rasterio>=1.1.5->snowexsql) (0.7.2)\n",
-      "Requirement already satisfied: numpy in /srv/conda/envs/notebook/lib/python3.11/site-packages (from rasterio>=1.1.5->snowexsql) (1.23.5)\n",
-      "Requirement already satisfied: snuggs>=1.4.1 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from rasterio>=1.1.5->snowexsql) (1.4.7)\n",
-      "Requirement already satisfied: click-plugins in /srv/conda/envs/notebook/lib/python3.11/site-packages (from rasterio>=1.1.5->snowexsql) (1.1.1)\n",
-      "Requirement already satisfied: setuptools in /srv/conda/envs/notebook/lib/python3.11/site-packages (from rasterio>=1.1.5->snowexsql) (69.2.0)\n",
-      "Requirement already satisfied: typing-extensions>=4.6.0 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from SQLAlchemy>=2.0.0->snowexsql) (4.10.0)\n",
-      "Requirement already satisfied: greenlet!=0.4.17 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from SQLAlchemy>=2.0.0->snowexsql) (3.0.3)\n",
-      "Requirement already satisfied: six in /srv/conda/envs/notebook/lib/python3.11/site-packages (from fiona>=1.8.19->geopandas<2.0,>=0.7->snowexsql) (1.16.0)\n",
-      "Requirement already satisfied: python-dateutil>=2.8.2 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from pandas>=1.1.0->geopandas<2.0,>=0.7->snowexsql) (2.9.0)\n",
-      "Requirement already satisfied: pytz>=2020.1 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from pandas>=1.1.0->geopandas<2.0,>=0.7->snowexsql) (2024.1)\n",
-      "Requirement already satisfied: tzdata>=2022.7 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from pandas>=1.1.0->geopandas<2.0,>=0.7->snowexsql) (2024.1)\n",
-      "Requirement already satisfied: pyparsing>=2.1.6 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from snuggs>=1.4.1->rasterio>=1.1.5->snowexsql) (3.1.2)\n"
-     ]
-    }
-   ],
-   "source": [
-    "!pip install snowexsql"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "7bf5bc1a-e9eb-4813-a4aa-5205a8762ebe",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "(function(root) {\n",
-       "  function now() {\n",
-       "    return new Date();\n",
-       "  }\n",
-       "\n",
-       "  var force = true;\n",
-       "  var py_version = '3.2.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n",
-       "  var is_dev = py_version.indexOf(\"+\") !== -1 || py_version.indexOf(\"-\") !== -1;\n",
-       "  var reloading = false;\n",
-       "  var Bokeh = root.Bokeh;\n",
-       "  var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n",
-       "\n",
-       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n",
-       "    root._bokeh_timeout = Date.now() + 5000;\n",
-       "    root._bokeh_failed_load = false;\n",
-       "  }\n",
-       "\n",
-       "  function run_callbacks() {\n",
-       "    try {\n",
-       "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
-       "        if (callback != null)\n",
-       "          callback();\n",
-       "      });\n",
-       "    } finally {\n",
-       "      delete root._bokeh_onload_callbacks;\n",
-       "    }\n",
-       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
-       "  }\n",
-       "\n",
-       "  function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n",
-       "    if (css_urls == null) css_urls = [];\n",
-       "    if (js_urls == null) js_urls = [];\n",
-       "    if (js_modules == null) js_modules = [];\n",
-       "    if (js_exports == null) js_exports = {};\n",
-       "\n",
-       "    root._bokeh_onload_callbacks.push(callback);\n",
-       "\n",
-       "    if (root._bokeh_is_loading > 0) {\n",
-       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
-       "      return null;\n",
-       "    }\n",
-       "    if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n",
-       "      run_callbacks();\n",
-       "      return null;\n",
-       "    }\n",
-       "    if (!reloading) {\n",
-       "      console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
-       "    }\n",
-       "\n",
-       "    function on_load() {\n",
-       "      root._bokeh_is_loading--;\n",
-       "      if (root._bokeh_is_loading === 0) {\n",
-       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
-       "        run_callbacks()\n",
-       "      }\n",
-       "    }\n",
-       "    window._bokeh_on_load = on_load\n",
-       "\n",
-       "    function on_error() {\n",
-       "      console.error(\"failed to load \" + url);\n",
-       "    }\n",
-       "\n",
-       "    var skip = [];\n",
-       "    if (window.requirejs) {\n",
-       "      window.requirejs.config({'packages': {}, 'paths': {'jspanel': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/jspanel', 'jspanel-modal': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal', 'jspanel-tooltip': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip', 'jspanel-hint': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint', 'jspanel-layout': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout', 'jspanel-contextmenu': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu', 'jspanel-dock': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock', 'gridstack': 'https://cdn.jsdelivr.net/npm/gridstack@7.2.3/dist/gridstack-all', 'notyf': 'https://cdn.jsdelivr.net/npm/notyf@3/notyf.min'}, 'shim': {'jspanel': {'exports': 'jsPanel'}, 'gridstack': {'exports': 'GridStack'}}});\n",
-       "      require([\"jspanel\"], function(jsPanel) {\n",
-       "\twindow.jsPanel = jsPanel\n",
-       "\ton_load()\n",
-       "      })\n",
-       "      require([\"jspanel-modal\"], function() {\n",
-       "\ton_load()\n",
-       "      })\n",
-       "      require([\"jspanel-tooltip\"], function() {\n",
-       "\ton_load()\n",
-       "      })\n",
-       "      require([\"jspanel-hint\"], function() {\n",
-       "\ton_load()\n",
-       "      })\n",
-       "      require([\"jspanel-layout\"], function() {\n",
-       "\ton_load()\n",
-       "      })\n",
-       "      require([\"jspanel-contextmenu\"], function() {\n",
-       "\ton_load()\n",
-       "      })\n",
-       "      require([\"jspanel-dock\"], function() {\n",
-       "\ton_load()\n",
-       "      })\n",
-       "      require([\"gridstack\"], function(GridStack) {\n",
-       "\twindow.GridStack = GridStack\n",
-       "\ton_load()\n",
-       "      })\n",
-       "      require([\"notyf\"], function() {\n",
-       "\ton_load()\n",
-       "      })\n",
-       "      root._bokeh_is_loading = css_urls.length + 9;\n",
-       "    } else {\n",
-       "      root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n",
-       "    }\n",
-       "\n",
-       "    var existing_stylesheets = []\n",
-       "    var links = document.getElementsByTagName('link')\n",
-       "    for (var i = 0; i < links.length; i++) {\n",
-       "      var link = links[i]\n",
-       "      if (link.href != null) {\n",
-       "\texisting_stylesheets.push(link.href)\n",
-       "      }\n",
-       "    }\n",
-       "    for (var i = 0; i < css_urls.length; i++) {\n",
-       "      var url = css_urls[i];\n",
-       "      if (existing_stylesheets.indexOf(url) !== -1) {\n",
-       "\ton_load()\n",
-       "\tcontinue;\n",
-       "      }\n",
-       "      const element = document.createElement(\"link\");\n",
-       "      element.onload = on_load;\n",
-       "      element.onerror = on_error;\n",
-       "      element.rel = \"stylesheet\";\n",
-       "      element.type = \"text/css\";\n",
-       "      element.href = url;\n",
-       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
-       "      document.body.appendChild(element);\n",
-       "    }    if (((window['jsPanel'] !== undefined) && (!(window['jsPanel'] instanceof HTMLElement))) || window.requirejs) {\n",
-       "      var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/jspanel.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock.js'];\n",
-       "      for (var i = 0; i < urls.length; i++) {\n",
-       "        skip.push(urls[i])\n",
-       "      }\n",
-       "    }    if (((window['GridStack'] !== undefined) && (!(window['GridStack'] instanceof HTMLElement))) || window.requirejs) {\n",
-       "      var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/gridstack/gridstack@7.2.3/dist/gridstack-all.js'];\n",
-       "      for (var i = 0; i < urls.length; i++) {\n",
-       "        skip.push(urls[i])\n",
-       "      }\n",
-       "    }    if (((window['Notyf'] !== undefined) && (!(window['Notyf'] instanceof HTMLElement))) || window.requirejs) {\n",
-       "      var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/notificationarea/notyf@3/notyf.min.js'];\n",
-       "      for (var i = 0; i < urls.length; i++) {\n",
-       "        skip.push(urls[i])\n",
-       "      }\n",
-       "    }    var existing_scripts = []\n",
-       "    var scripts = document.getElementsByTagName('script')\n",
-       "    for (var i = 0; i < scripts.length; i++) {\n",
-       "      var script = scripts[i]\n",
-       "      if (script.src != null) {\n",
-       "\texisting_scripts.push(script.src)\n",
-       "      }\n",
-       "    }\n",
-       "    for (var i = 0; i < js_urls.length; i++) {\n",
-       "      var url = js_urls[i];\n",
-       "      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n",
-       "\tif (!window.requirejs) {\n",
-       "\t  on_load();\n",
-       "\t}\n",
-       "\tcontinue;\n",
-       "      }\n",
-       "      var element = document.createElement('script');\n",
-       "      element.onload = on_load;\n",
-       "      element.onerror = on_error;\n",
-       "      element.async = false;\n",
-       "      element.src = url;\n",
-       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
-       "      document.head.appendChild(element);\n",
-       "    }\n",
-       "    for (var i = 0; i < js_modules.length; i++) {\n",
-       "      var url = js_modules[i];\n",
-       "      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n",
-       "\tif (!window.requirejs) {\n",
-       "\t  on_load();\n",
-       "\t}\n",
-       "\tcontinue;\n",
-       "      }\n",
-       "      var element = document.createElement('script');\n",
-       "      element.onload = on_load;\n",
-       "      element.onerror = on_error;\n",
-       "      element.async = false;\n",
-       "      element.src = url;\n",
-       "      element.type = \"module\";\n",
-       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
-       "      document.head.appendChild(element);\n",
-       "    }\n",
-       "    for (const name in js_exports) {\n",
-       "      var url = js_exports[name];\n",
-       "      if (skip.indexOf(url) >= 0 || root[name] != null) {\n",
-       "\tif (!window.requirejs) {\n",
-       "\t  on_load();\n",
-       "\t}\n",
-       "\tcontinue;\n",
-       "      }\n",
-       "      var element = document.createElement('script');\n",
-       "      element.onerror = on_error;\n",
-       "      element.async = false;\n",
-       "      element.type = \"module\";\n",
-       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
-       "      element.textContent = `\n",
-       "      import ${name} from \"${url}\"\n",
-       "      window.${name} = ${name}\n",
-       "      window._bokeh_on_load()\n",
-       "      `\n",
-       "      document.head.appendChild(element);\n",
-       "    }\n",
-       "    if (!js_urls.length && !js_modules.length) {\n",
-       "      on_load()\n",
-       "    }\n",
-       "  };\n",
-       "\n",
-       "  function inject_raw_css(css) {\n",
-       "    const element = document.createElement(\"style\");\n",
-       "    element.appendChild(document.createTextNode(css));\n",
-       "    document.body.appendChild(element);\n",
-       "  }\n",
-       "\n",
-       "  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.2.2.min.js\", \"https://cdn.holoviz.org/panel/1.2.3/dist/panel.min.js\"];\n",
-       "  var js_modules = [];\n",
-       "  var js_exports = {};\n",
-       "  var css_urls = [];\n",
-       "  var inline_js = [    function(Bokeh) {\n",
-       "      Bokeh.set_log_level(\"info\");\n",
-       "    },\n",
-       "function(Bokeh) {} // ensure no trailing comma for IE\n",
-       "  ];\n",
-       "\n",
-       "  function run_inline_js() {\n",
-       "    if ((root.Bokeh !== undefined) || (force === true)) {\n",
-       "      for (var i = 0; i < inline_js.length; i++) {\n",
-       "        inline_js[i].call(root, root.Bokeh);\n",
-       "      }\n",
-       "      // Cache old bokeh versions\n",
-       "      if (Bokeh != undefined && !reloading) {\n",
-       "\tvar NewBokeh = root.Bokeh;\n",
-       "\tif (Bokeh.versions === undefined) {\n",
-       "\t  Bokeh.versions = new Map();\n",
-       "\t}\n",
-       "\tif (NewBokeh.version !== Bokeh.version) {\n",
-       "\t  Bokeh.versions.set(NewBokeh.version, NewBokeh)\n",
-       "\t}\n",
-       "\troot.Bokeh = Bokeh;\n",
-       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
-       "      setTimeout(run_inline_js, 100);\n",
-       "    } else if (!root._bokeh_failed_load) {\n",
-       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
-       "      root._bokeh_failed_load = true;\n",
-       "    }\n",
-       "    root._bokeh_is_initializing = false\n",
-       "  }\n",
-       "\n",
-       "  function load_or_wait() {\n",
-       "    // Implement a backoff loop that tries to ensure we do not load multiple\n",
-       "    // versions of Bokeh and its dependencies at the same time.\n",
-       "    // In recent versions we use the root._bokeh_is_initializing flag\n",
-       "    // to determine whether there is an ongoing attempt to initialize\n",
-       "    // bokeh, however for backward compatibility we also try to ensure\n",
-       "    // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n",
-       "    // before older versions are fully initialized.\n",
-       "    if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n",
-       "      root._bokeh_is_initializing = false;\n",
-       "      root._bokeh_onload_callbacks = undefined;\n",
-       "      console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n",
-       "      load_or_wait();\n",
-       "    } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n",
-       "      setTimeout(load_or_wait, 100);\n",
-       "    } else {\n",
-       "      Bokeh = root.Bokeh;\n",
-       "      bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n",
-       "      root._bokeh_is_initializing = true\n",
-       "      root._bokeh_onload_callbacks = []\n",
-       "      if (!reloading && (!bokeh_loaded || is_dev)) {\n",
-       "\troot.Bokeh = undefined;\n",
-       "      }\n",
-       "      load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n",
-       "\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
-       "\trun_inline_js();\n",
-       "      });\n",
-       "    }\n",
-       "  }\n",
-       "  // Give older versions of the autoload script a head-start to ensure\n",
-       "  // they initialize before we start loading newer version.\n",
-       "  setTimeout(load_or_wait, 100)\n",
-       "}(window));"
-      ],
-      "application/vnd.holoviews_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n  var py_version = '3.2.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n  var is_dev = py_version.indexOf(\"+\") !== -1 || py_version.indexOf(\"-\") !== -1;\n  var reloading = false;\n  var Bokeh = root.Bokeh;\n  var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks;\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n    if (js_modules == null) js_modules = [];\n    if (js_exports == null) js_exports = {};\n\n    root._bokeh_onload_callbacks.push(callback);\n\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n      run_callbacks();\n      return null;\n    }\n    if (!reloading) {\n      console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    }\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n    window._bokeh_on_load = on_load\n\n    function on_error() {\n      console.error(\"failed to load \" + url);\n    }\n\n    var skip = [];\n    if (window.requirejs) {\n      window.requirejs.config({'packages': {}, 'paths': {'jspanel': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/jspanel', 'jspanel-modal': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal', 'jspanel-tooltip': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip', 'jspanel-hint': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint', 'jspanel-layout': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout', 'jspanel-contextmenu': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu', 'jspanel-dock': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock', 'gridstack': 'https://cdn.jsdelivr.net/npm/gridstack@7.2.3/dist/gridstack-all', 'notyf': 'https://cdn.jsdelivr.net/npm/notyf@3/notyf.min'}, 'shim': {'jspanel': {'exports': 'jsPanel'}, 'gridstack': {'exports': 'GridStack'}}});\n      require([\"jspanel\"], function(jsPanel) {\n\twindow.jsPanel = jsPanel\n\ton_load()\n      })\n      require([\"jspanel-modal\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-tooltip\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-hint\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-layout\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-contextmenu\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-dock\"], function() {\n\ton_load()\n      })\n      require([\"gridstack\"], function(GridStack) {\n\twindow.GridStack = GridStack\n\ton_load()\n      })\n      require([\"notyf\"], function() {\n\ton_load()\n      })\n      root._bokeh_is_loading = css_urls.length + 9;\n    } else {\n      root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n    }\n\n    var existing_stylesheets = []\n    var links = document.getElementsByTagName('link')\n    for (var i = 0; i < links.length; i++) {\n      var link = links[i]\n      if (link.href != null) {\n\texisting_stylesheets.push(link.href)\n      }\n    }\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      if (existing_stylesheets.indexOf(url) !== -1) {\n\ton_load()\n\tcontinue;\n      }\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }    if (((window['jsPanel'] !== undefined) && (!(window['jsPanel'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/jspanel.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    if (((window['GridStack'] !== undefined) && (!(window['GridStack'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/gridstack/gridstack@7.2.3/dist/gridstack-all.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    if (((window['Notyf'] !== undefined) && (!(window['Notyf'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/notificationarea/notyf@3/notyf.min.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    var existing_scripts = []\n    var scripts = document.getElementsByTagName('script')\n    for (var i = 0; i < scripts.length; i++) {\n      var script = scripts[i]\n      if (script.src != null) {\n\texisting_scripts.push(script.src)\n      }\n    }\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (var i = 0; i < js_modules.length; i++) {\n      var url = js_modules[i];\n      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (const name in js_exports) {\n      var url = js_exports[name];\n      if (skip.indexOf(url) >= 0 || root[name] != null) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onerror = on_error;\n      element.async = false;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      element.textContent = `\n      import ${name} from \"${url}\"\n      window.${name} = ${name}\n      window._bokeh_on_load()\n      `\n      document.head.appendChild(element);\n    }\n    if (!js_urls.length && !js_modules.length) {\n      on_load()\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.2.2.min.js\", \"https://cdn.holoviz.org/panel/1.2.3/dist/panel.min.js\"];\n  var js_modules = [];\n  var js_exports = {};\n  var css_urls = [];\n  var inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n  ];\n\n  function run_inline_js() {\n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }\n      // Cache old bokeh versions\n      if (Bokeh != undefined && !reloading) {\n\tvar NewBokeh = root.Bokeh;\n\tif (Bokeh.versions === undefined) {\n\t  Bokeh.versions = new Map();\n\t}\n\tif (NewBokeh.version !== Bokeh.version) {\n\t  Bokeh.versions.set(NewBokeh.version, NewBokeh)\n\t}\n\troot.Bokeh = Bokeh;\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    }\n    root._bokeh_is_initializing = false\n  }\n\n  function load_or_wait() {\n    // Implement a backoff loop that tries to ensure we do not load multiple\n    // versions of Bokeh and its dependencies at the same time.\n    // In recent versions we use the root._bokeh_is_initializing flag\n    // to determine whether there is an ongoing attempt to initialize\n    // bokeh, however for backward compatibility we also try to ensure\n    // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n    // before older versions are fully initialized.\n    if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n      root._bokeh_is_initializing = false;\n      root._bokeh_onload_callbacks = undefined;\n      console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n      load_or_wait();\n    } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n      setTimeout(load_or_wait, 100);\n    } else {\n      Bokeh = root.Bokeh;\n      bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n      root._bokeh_is_initializing = true\n      root._bokeh_onload_callbacks = []\n      if (!reloading && (!bokeh_loaded || is_dev)) {\n\troot.Bokeh = undefined;\n      }\n      load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n\trun_inline_js();\n      });\n    }\n  }\n  // Give older versions of the autoload script a head-start to ensure\n  // they initialize before we start loading newer version.\n  setTimeout(load_or_wait, 100)\n}(window));"
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-       "    window.PyViz.comm_manager = new JupyterCommManager();\n",
-       "    \n",
-       "\n",
-       "\n",
-       "var JS_MIME_TYPE = 'application/javascript';\n",
-       "var HTML_MIME_TYPE = 'text/html';\n",
-       "var EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\n",
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-       "\n",
-       "/**\n",
-       " * Render data to the DOM node\n",
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-       "    html_node.innerHTML = output.data[HTML_MIME_TYPE];\n",
-       "    var scripts = [];\n",
-       "    var nodelist = html_node.querySelectorAll(\"script\");\n",
-       "    for (var i in nodelist) {\n",
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-       "\n",
-       "    scripts.forEach( function (oldScript) {\n",
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-       "    output_area._hv_plot_id = id;\n",
-       "    if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n",
-       "      window.PyViz.plot_index[id] = Bokeh.index[id];\n",
-       "    } else {\n",
-       "      window.PyViz.plot_index[id] = null;\n",
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-       "  } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
-       "    var bk_div = document.createElement(\"div\");\n",
-       "    bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
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-       "  delete PyViz.plot_index[id];\n",
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-       "    render(props, toinsert[0]);\n",
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-       "    return toinsert\n",
-       "  }\n",
-       "\n",
-       "  events.on('output_added.OutputArea', handle_add_output);\n",
-       "  events.on('output_updated.OutputArea', handle_update_output);\n",
-       "  events.on('clear_output.CodeCell', handle_clear_output);\n",
-       "  events.on('delete.Cell', handle_clear_output);\n",
-       "  events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n",
-       "\n",
-       "  OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
-       "    safe: true,\n",
-       "    index: 0\n",
-       "  });\n",
-       "}\n",
-       "\n",
-       "if (window.Jupyter !== undefined) {\n",
-       "  try {\n",
-       "    var events = require('base/js/events');\n",
-       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
-       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
-       "      register_renderer(events, OutputArea);\n",
-       "    }\n",
-       "  } catch(err) {\n",
-       "  }\n",
-       "}\n"
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the DOM node\n */\nfunction render(props, node) {\n  var div = document.createElement(\"div\");\n  var script = document.createElement(\"script\");\n  node.appendChild(div);\n  node.appendChild(script);\n}\n\n/**\n * Handle when a new output is added\n */\nfunction handle_add_output(event, handle) {\n  var output_area = handle.output_area;\n  var output = handle.output;\n  if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n    return\n  }\n  var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n  var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n  if (id !== undefined) {\n    var nchildren = toinsert.length;\n    var html_node = toinsert[nchildren-1].children[0];\n    html_node.innerHTML = output.data[HTML_MIME_TYPE];\n    var scripts = [];\n    var nodelist = html_node.querySelectorAll(\"script\");\n    for (var i in nodelist) {\n      if (nodelist.hasOwnProperty(i)) {\n        scripts.push(nodelist[i])\n      }\n    }\n\n    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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<style>*[data-root-id],\n",
-       "*[data-root-id] > * {\n",
-       "  box-sizing: border-box;\n",
-       "  font-family: var(--jp-ui-font-family);\n",
-       "  font-size: var(--jp-ui-font-size1);\n",
-       "  color: var(--vscode-editor-foreground, var(--jp-ui-font-color1));\n",
-       "}\n",
-       "\n",
-       "/* Override VSCode background color */\n",
-       ".cell-output-ipywidget-background:has(\n",
-       "    > .cell-output-ipywidget-background > .lm-Widget > *[data-root-id]\n",
-       "  ),\n",
-       ".cell-output-ipywidget-background:has(> .lm-Widget > *[data-root-id]) {\n",
-       "  background-color: transparent !important;\n",
-       "}\n",
-       "</style>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Import several libraries. \n",
     "# comments to the right could be useful for local installation on Windows.\n",
@@ -909,18 +291,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "f36402f2-a63f-4d3d-b874-da53ef328c59",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "output directory prepared!\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# select files to download\n",
     "\n",
@@ -964,7 +338,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "d4c6cd77-0841-4e66-8199-f6e463a856de",
    "metadata": {},
    "outputs": [],
@@ -1001,529 +375,10 @@
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   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "2833b2ee-9460-46ab-8f3b-884e89a91a85",
    "metadata": {},
-   "outputs": [
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-       "\n",
-       "html[theme=dark],\n",
-       "body[data-theme=dark],\n",
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-       "  --xr-disabled-color: #515151;\n",
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-       "  --xr-background-color-row-odd: #313131;\n",
-       "}\n",
-       "\n",
-       ".xr-wrap {\n",
-       "  display: block !important;\n",
-       "  min-width: 300px;\n",
-       "  max-width: 700px;\n",
-       "}\n",
-       "\n",
-       ".xr-text-repr-fallback {\n",
-       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-header {\n",
-       "  padding-top: 6px;\n",
-       "  padding-bottom: 6px;\n",
-       "  margin-bottom: 4px;\n",
-       "  border-bottom: solid 1px var(--xr-border-color);\n",
-       "}\n",
-       "\n",
-       ".xr-header > div,\n",
-       ".xr-header > ul {\n",
-       "  display: inline;\n",
-       "  margin-top: 0;\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type,\n",
-       ".xr-array-name {\n",
-       "  margin-left: 2px;\n",
-       "  margin-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type {\n",
-       "  color: var(--xr-font-color2);\n",
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-       "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
-       "}\n",
-       "\n",
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-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input {\n",
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-       "\n",
-       ".xr-section-item input + label {\n",
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-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label {\n",
-       "  cursor: pointer;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
-       "  color: var(--xr-font-color0);\n",
-       "}\n",
-       "\n",
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-       "  color: var(--xr-font-color2);\n",
-       "  font-weight: 500;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary > span {\n",
-       "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
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-       "  content: '►';\n",
-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
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-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
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-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-index-preview {\n",
-       "  grid-column: 2 / 5;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data,\n",
-       ".xr-index-data-in:checked ~ .xr-index-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-index-name div,\n",
-       ".xr-index-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (band: 6, y: 4289, x: 3959)&gt; Size: 408MB\n",
-       "dask.array&lt;concatenate, shape=(6, 4289, 3959), dtype=float32, chunksize=(1, 1200, 1200), chunktype=numpy.ndarray&gt;\n",
-       "Coordinates:\n",
-       "  * x            (x) float64 32kB 7.396e+05 7.396e+05 ... 7.475e+05 7.475e+05\n",
-       "  * y            (y) float64 34kB 4.329e+06 4.329e+06 ... 4.32e+06 4.32e+06\n",
-       "    spatial_ref  int64 8B 0\n",
-       "  * band         (band) &lt;U4 96B &#x27;09VV&#x27; &#x27;09VH&#x27; &#x27;13VV&#x27; &#x27;13VH&#x27; &#x27;17VV&#x27; &#x27;17VH&#x27;\n",
-       "Attributes:\n",
-       "    AREA_OR_POINT:  Area\n",
-       "    _FillValue:     nan\n",
-       "    scale_factor:   1.0\n",
-       "    add_offset:     0.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>band</span>: 6</li><li><span class='xr-has-index'>y</span>: 4289</li><li><span class='xr-has-index'>x</span>: 3959</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-fc391482-5ef5-4fcd-b1a1-b4945408127f' class='xr-array-in' type='checkbox' checked><label for='section-fc391482-5ef5-4fcd-b1a1-b4945408127f' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 1200, 1200), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 388.64 MiB </td>\n",
-       "                        <td> 5.49 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (6, 4289, 3959) </td>\n",
-       "                        <td> (1, 1200, 1200) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 96 chunks in 17 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"185\" height=\"184\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"10\" y1=\"33\" x2=\"24\" y2=\"48\" />\n",
-       "  <line x1=\"10\" y1=\"67\" x2=\"24\" y2=\"82\" />\n",
-       "  <line x1=\"10\" y1=\"100\" x2=\"24\" y2=\"115\" />\n",
-       "  <line x1=\"10\" y1=\"120\" x2=\"24\" y2=\"134\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"122\" />\n",
-       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"124\" />\n",
-       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"127\" />\n",
-       "  <line x1=\"19\" y1=\"9\" x2=\"19\" y2=\"129\" />\n",
-       "  <line x1=\"22\" y1=\"12\" x2=\"22\" y2=\"132\" />\n",
-       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"134\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"10.0,0.0 24.9485979497544,14.948597949754403 24.9485979497544,134.9485979497544 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"10\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"12\" y1=\"2\" x2=\"123\" y2=\"2\" />\n",
-       "  <line x1=\"14\" y1=\"4\" x2=\"125\" y2=\"4\" />\n",
-       "  <line x1=\"17\" y1=\"7\" x2=\"128\" y2=\"7\" />\n",
-       "  <line x1=\"19\" y1=\"9\" x2=\"130\" y2=\"9\" />\n",
-       "  <line x1=\"22\" y1=\"12\" x2=\"133\" y2=\"12\" />\n",
-       "  <line x1=\"24\" y1=\"14\" x2=\"135\" y2=\"14\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"43\" y1=\"0\" x2=\"58\" y2=\"14\" />\n",
-       "  <line x1=\"77\" y1=\"0\" x2=\"92\" y2=\"14\" />\n",
-       "  <line x1=\"110\" y1=\"0\" x2=\"125\" y2=\"14\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"135\" y2=\"14\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"10.0,0.0 120.76707857309395,0.0 135.71567652284836,14.948597949754403 24.9485979497544,14.948597949754403\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"24\" y1=\"14\" x2=\"135\" y2=\"14\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"24\" y1=\"48\" x2=\"135\" y2=\"48\" />\n",
-       "  <line x1=\"24\" y1=\"82\" x2=\"135\" y2=\"82\" />\n",
-       "  <line x1=\"24\" y1=\"115\" x2=\"135\" y2=\"115\" />\n",
-       "  <line x1=\"24\" y1=\"134\" x2=\"135\" y2=\"134\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"134\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"58\" y1=\"14\" x2=\"58\" y2=\"134\" />\n",
-       "  <line x1=\"92\" y1=\"14\" x2=\"92\" y2=\"134\" />\n",
-       "  <line x1=\"125\" y1=\"14\" x2=\"125\" y2=\"134\" />\n",
-       "  <line x1=\"135\" y1=\"14\" x2=\"135\" y2=\"134\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"24.9485979497544,14.948597949754403 135.71567652284836,14.948597949754403 135.71567652284836,134.9485979497544 24.9485979497544,134.9485979497544\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"80.332137\" y=\"154.948598\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3959</text>\n",
-       "  <text x=\"155.715677\" y=\"74.948598\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,155.715677,74.948598)\">4289</text>\n",
-       "  <text x=\"7.474299\" y=\"147.474299\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,147.474299)\">6</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></div></li><li class='xr-section-item'><input id='section-1160a6ae-2105-4488-baf2-6daa0db77095' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1160a6ae-2105-4488-baf2-6daa0db77095' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>7.396e+05 7.396e+05 ... 7.475e+05</div><input id='attrs-677205ce-c25a-4aad-8741-97392ee1a1e2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-677205ce-c25a-4aad-8741-97392ee1a1e2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-886bebcf-f010-46c5-a6c0-071089608bc0' class='xr-var-data-in' type='checkbox'><label for='data-886bebcf-f010-46c5-a6c0-071089608bc0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([739631.321582, 739633.321582, 739635.321582, ..., 747543.321582,\n",
-       "       747545.321582, 747547.321582])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.329e+06 4.329e+06 ... 4.32e+06</div><input id='attrs-599d57fd-6015-4097-9cc7-f47aa59c649f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-599d57fd-6015-4097-9cc7-f47aa59c649f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-753bae9f-4a72-4cc9-8ae2-aab3980c2178' class='xr-var-data-in' type='checkbox'><label for='data-753bae9f-4a72-4cc9-8ae2-aab3980c2178' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([4328973.426705, 4328971.426705, 4328969.426705, ..., 4320401.426705,\n",
-       "       4320399.426705, 4320397.426705])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-91f50509-45ad-4fd9-9d3c-2bfc34580f28' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-91f50509-45ad-4fd9-9d3c-2bfc34580f28' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4e06be98-6230-473c-b060-a648fa4b68c3' class='xr-var-data-in' type='checkbox'><label for='data-4e06be98-6230-473c-b060-a648fa4b68c3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 12N&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,-111],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32612&quot;]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>horizontal_datum_name :</span></dt><dd>World Geodetic System 1984</dd><dt><span>projected_crs_name :</span></dt><dd>WGS 84 / UTM zone 12N</dd><dt><span>grid_mapping_name :</span></dt><dd>transverse_mercator</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>0.0</dd><dt><span>longitude_of_central_meridian :</span></dt><dd>-111.0</dd><dt><span>false_easting :</span></dt><dd>500000.0</dd><dt><span>false_northing :</span></dt><dd>0.0</dd><dt><span>scale_factor_at_central_meridian :</span></dt><dd>0.9996</dd><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 12N&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,-111],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32612&quot;]]</dd><dt><span>GeoTransform :</span></dt><dd>739630.3215824949 2.0 0.0 4328974.426704684 0.0 -2.0</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>band</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>&lt;U4</div><div class='xr-var-preview xr-preview'>&#x27;09VV&#x27; &#x27;09VH&#x27; ... &#x27;17VV&#x27; &#x27;17VH&#x27;</div><input id='attrs-4bdcd6ee-ee74-4097-bb70-b7aa9894b4e5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4bdcd6ee-ee74-4097-bb70-b7aa9894b4e5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8c00aac0-7287-4632-8762-c864abb59bd0' class='xr-var-data-in' type='checkbox'><label for='data-8c00aac0-7287-4632-8762-c864abb59bd0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;09VV&#x27;, &#x27;09VH&#x27;, &#x27;13VV&#x27;, &#x27;13VH&#x27;, &#x27;17VV&#x27;, &#x27;17VH&#x27;], dtype=&#x27;&lt;U4&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-68b4cb39-9b40-45f3-83c4-9c3f84a01292' class='xr-section-summary-in' type='checkbox'  ><label for='section-68b4cb39-9b40-45f3-83c4-9c3f84a01292' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-96a4b472-3675-40d2-abb1-14949ca3f866' class='xr-index-data-in' type='checkbox'/><label for='index-96a4b472-3675-40d2-abb1-14949ca3f866' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([739631.3215824949, 739633.3215824949, 739635.3215824949,\n",
-       "       739637.3215824949, 739639.3215824949, 739641.3215824949,\n",
-       "       739643.3215824949, 739645.3215824949, 739647.3215824949,\n",
-       "       739649.3215824949,\n",
-       "       ...\n",
-       "       747529.3215824949, 747531.3215824949, 747533.3215824949,\n",
-       "       747535.3215824949, 747537.3215824949, 747539.3215824949,\n",
-       "       747541.3215824949, 747543.3215824949, 747545.3215824949,\n",
-       "       747547.3215824949],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=3959))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-db7e741b-8fda-44a3-b607-dfc050118e74' class='xr-index-data-in' type='checkbox'/><label for='index-db7e741b-8fda-44a3-b607-dfc050118e74' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([4328973.426704684, 4328971.426704684, 4328969.426704684,\n",
-       "       4328967.426704684, 4328965.426704684, 4328963.426704684,\n",
-       "       4328961.426704684, 4328959.426704684, 4328957.426704684,\n",
-       "       4328955.426704684,\n",
-       "       ...\n",
-       "       4320415.426704684, 4320413.426704684, 4320411.426704684,\n",
-       "       4320409.426704684, 4320407.426704684, 4320405.426704684,\n",
-       "       4320403.426704684, 4320401.426704684, 4320399.426704684,\n",
-       "       4320397.426704684],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=4289))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>band</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fcee99cc-9f5a-414e-be25-e1cee826074d' class='xr-index-data-in' type='checkbox'/><label for='index-fcee99cc-9f5a-414e-be25-e1cee826074d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;09VV&#x27;, &#x27;09VH&#x27;, &#x27;13VV&#x27;, &#x27;13VH&#x27;, &#x27;17VV&#x27;, &#x27;17VH&#x27;], dtype=&#x27;object&#x27;, name=&#x27;band&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6e3a2643-cf79-4e4c-85b6-05b9b621fae7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6e3a2643-cf79-4e4c-85b6-05b9b621fae7' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>AREA_OR_POINT :</span></dt><dd>Area</dd><dt><span>_FillValue :</span></dt><dd>nan</dd><dt><span>scale_factor :</span></dt><dd>1.0</dd><dt><span>add_offset :</span></dt><dd>0.0</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray (band: 6, y: 4289, x: 3959)> Size: 408MB\n",
-       "dask.array<concatenate, shape=(6, 4289, 3959), dtype=float32, chunksize=(1, 1200, 1200), chunktype=numpy.ndarray>\n",
-       "Coordinates:\n",
-       "  * x            (x) float64 32kB 7.396e+05 7.396e+05 ... 7.475e+05 7.475e+05\n",
-       "  * y            (y) float64 34kB 4.329e+06 4.329e+06 ... 4.32e+06 4.32e+06\n",
-       "    spatial_ref  int64 8B 0\n",
-       "  * band         (band) <U4 96B '09VV' '09VH' '13VV' '13VH' '17VV' '17VH'\n",
-       "Attributes:\n",
-       "    AREA_OR_POINT:  Area\n",
-       "    _FillValue:     nan\n",
-       "    scale_factor:   1.0\n",
-       "    add_offset:     0.0"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "sar_data = join_files(output_paths)\n",
     "sar_data"
@@ -1539,7 +394,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "36513c9e-438a-4f65-9f74-f3cceb1b6172",
    "metadata": {},
    "outputs": [],
@@ -1562,7 +417,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "1ffca227-cf79-4ed7-9d24-b54ba1fe3783",
    "metadata": {},
    "outputs": [],
@@ -1624,21 +479,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "acddd149-3e53-4489-b233-b8d8a37e1b11",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x450 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "sar_swe_fig = sar_swe_plot(point_swe_filt, swesarr_mean)"
    ]
@@ -1664,7 +508,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "493a7452-cc73-4224-9237-ed038d7fa5da",
    "metadata": {},
    "outputs": [],
@@ -1703,22 +547,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "0f29d818-7abc-4ebe-a14a-f067e23b1486",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# !wget --user=USERNAME_HERE --password=PASSWORD_HERE --quiet https://n5eil01u.ecs.nsidc.org/SNOWEX/SNEX20_SWESARR_TB.001/2020.02.12/SNEX20_SWESARR_TB_GRMCT2_13801_20007_000_200211_XKKa225H_v01.csv -O {output_dir}SNEX20_SWESARR_TB_GRMCT2_13801_20007_000_200211_XKKa225H_v01.csv"
+    "# !wget --user=USERNAME_HERE --password=PASSWORD_HERE --quiet https://n5eil01u.ecs.nsidc.org/SNOWEX/SNEX20_SWESARR_TB.001/2020.02.12/SNEX20_SWESARR_TB_GRMCT2_13801_20007_000_200211_XKKa225H_v01.csv -O {output_dir}SNEX20_SWESARR_TB_GRMCT2_13801_20007_000_200211_XKuKa225H_v03.csv"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "4a2dc074-19cb-42bd-9c3f-fed14442429e",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# !wget --quiet https://n5eil01u.ecs.nsidc.org/SNOWEX/SNEX20_SWESARR_TB.001/2020.02.12/SNEX20_SWESARR_TB_GRMCT2_13801_20007_000_200211_XKKa225H_v01.csv -O {output_dir}SNEX20_SWESARR_TB_GRMCT2_13801_20007_000_200211_XKKa225H_v01.csv"
+    "# !wget --quiet https://n5eil01u.ecs.nsidc.org/SNOWEX/SNEX20_SWESARR_TB.001/2020.02.12/SNEX20_SWESARR_TB_GRMCT2_13801_20007_000_200211_XKuKa225H_v03.csv -O {output_dir}SNEX20_SWESARR_TB_GRMCT2_13801_20007_000_200211_XKKa225H_v01.csv"
    ]
   },
   {
@@ -1731,13 +575,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "3ba1a0b6-b1f5-4f60-ab99-57444f08b9c4",
    "metadata": {},
    "outputs": [],
    "source": [
     "# use the file we downloaded with wget above\n",
-    "excel_path = f'{output_dir}SNEX20_SWESARR_TB_GRMCT2_13801_20007_000_200211_XKKa225H_v01.csv'\n",
+    "excel_path = f'{output_dir}SNEX20_SWESARR_TB_GRMCT2_13801_20007_000_200211_XKuKa225H_v03.csv'\n",
     "\n",
     "# read data\n",
     "radiom = pd.read_csv(excel_path)"
@@ -1753,166 +597,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "id": "bda67928-c05f-4648-a11b-8403f7c78eb8",
    "metadata": {},
-   "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>UTC</th>\n",
-       "      <th>Longitude (deg)</th>\n",
-       "      <th>Latitude (deg)</th>\n",
-       "      <th>Elevation (m)</th>\n",
-       "      <th>TB X (K)</th>\n",
-       "      <th>TB K (K)</th>\n",
-       "      <th>TB Ka (K)</th>\n",
-       "      <th>Antenna Longitude (deg)</th>\n",
-       "      <th>Antenna Latitude (deg)</th>\n",
-       "      <th>Antenna Altitude (m)</th>\n",
-       "      <th>Antenna Yaw (deg)</th>\n",
-       "      <th>Antenna Pitch (deg)</th>\n",
-       "      <th>Antenna Look Angle (deg)</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>20200211-18:33:53.048360</td>\n",
-       "      <td>-108.227372</td>\n",
-       "      <td>39.071994</td>\n",
-       "      <td>2971</td>\n",
-       "      <td>247.1</td>\n",
-       "      <td>241.2</td>\n",
-       "      <td>231.2</td>\n",
-       "      <td>-108.052329</td>\n",
-       "      <td>38.940324</td>\n",
-       "      <td>4525.7</td>\n",
-       "      <td>2.02</td>\n",
-       "      <td>3.53</td>\n",
-       "      <td>44.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>20200211-18:33:53.148380</td>\n",
-       "      <td>-108.227372</td>\n",
-       "      <td>39.071901</td>\n",
-       "      <td>2979</td>\n",
-       "      <td>246.3</td>\n",
-       "      <td>238.2</td>\n",
-       "      <td>229.3</td>\n",
-       "      <td>-108.052331</td>\n",
-       "      <td>38.940326</td>\n",
-       "      <td>4525.7</td>\n",
-       "      <td>2.04</td>\n",
-       "      <td>3.53</td>\n",
-       "      <td>44.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>20200211-18:33:53.248380</td>\n",
-       "      <td>-108.227372</td>\n",
-       "      <td>39.071809</td>\n",
-       "      <td>2979</td>\n",
-       "      <td>247.8</td>\n",
-       "      <td>237.5</td>\n",
-       "      <td>227.4</td>\n",
-       "      <td>-108.052334</td>\n",
-       "      <td>38.940329</td>\n",
-       "      <td>4525.7</td>\n",
-       "      <td>2.06</td>\n",
-       "      <td>3.53</td>\n",
-       "      <td>44.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>20200211-18:33:53.348380</td>\n",
-       "      <td>-108.227279</td>\n",
-       "      <td>39.071716</td>\n",
-       "      <td>2991</td>\n",
-       "      <td>247.2</td>\n",
-       "      <td>237.5</td>\n",
-       "      <td>225.4</td>\n",
-       "      <td>-108.052336</td>\n",
-       "      <td>38.940331</td>\n",
-       "      <td>4525.7</td>\n",
-       "      <td>2.05</td>\n",
-       "      <td>3.53</td>\n",
-       "      <td>44.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>20200211-18:33:53.448380</td>\n",
-       "      <td>-108.227279</td>\n",
-       "      <td>39.071716</td>\n",
-       "      <td>2991</td>\n",
-       "      <td>245.7</td>\n",
-       "      <td>237.4</td>\n",
-       "      <td>222.6</td>\n",
-       "      <td>-108.052339</td>\n",
-       "      <td>38.940333</td>\n",
-       "      <td>4525.6</td>\n",
-       "      <td>2.05</td>\n",
-       "      <td>3.53</td>\n",
-       "      <td>44.9</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                        UTC  Longitude (deg)  Latitude (deg)  Elevation (m)  \\\n",
-       "0  20200211-18:33:53.048360      -108.227372       39.071994           2971   \n",
-       "1  20200211-18:33:53.148380      -108.227372       39.071901           2979   \n",
-       "2  20200211-18:33:53.248380      -108.227372       39.071809           2979   \n",
-       "3  20200211-18:33:53.348380      -108.227279       39.071716           2991   \n",
-       "4  20200211-18:33:53.448380      -108.227279       39.071716           2991   \n",
-       "\n",
-       "   TB X (K)  TB K (K)  TB Ka (K)  Antenna Longitude (deg)  \\\n",
-       "0     247.1     241.2      231.2              -108.052329   \n",
-       "1     246.3     238.2      229.3              -108.052331   \n",
-       "2     247.8     237.5      227.4              -108.052334   \n",
-       "3     247.2     237.5      225.4              -108.052336   \n",
-       "4     245.7     237.4      222.6              -108.052339   \n",
-       "\n",
-       "   Antenna Latitude (deg)  Antenna Altitude (m)  Antenna Yaw (deg)  \\\n",
-       "0               38.940324                4525.7               2.02   \n",
-       "1               38.940326                4525.7               2.04   \n",
-       "2               38.940329                4525.7               2.06   \n",
-       "3               38.940331                4525.7               2.05   \n",
-       "4               38.940333                4525.6               2.05   \n",
-       "\n",
-       "   Antenna Pitch (deg)  Antenna Look Angle (deg)  \n",
-       "0                 3.53                      44.9  \n",
-       "1                 3.53                      44.9  \n",
-       "2                 3.53                      44.9  \n",
-       "3                 3.53                      44.9  \n",
-       "4                 3.53                      44.9  "
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "radiom.head()\n",
     "#radiom.hvplot.table(width=1100) # sortable table in jupyterlab"
@@ -1928,102 +616,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "id": "750dcc7d-6a05-4036-8241-546b691a3dae",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {},
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.holoviews_exec.v0+json": "",
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-       "</div>\n",
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uiVkGz5ZsEu6JWQbPlmwSh8TEVr+WbBKHxMRWv5ZsEgEAP6aflmwf6RSbJo+WbB/pFJsmj5ZsH4JQBnZ/lmwfglAGdn+WbB1adGH2b5ZsHPO/3TZPlmwc87/dNk+WbByM+ziGP5ZsGlUfpAYvlmwaVR+kBi+WbBn+Ww9WD5ZsGf5bD1YPlmwX1n961f+WbBfWf3rV/5ZsF1+61iXvlmwW+PZBdd+WbBb49kF135ZsFNEavPW/lmwU0Rq89b+WbBR6VhhFr5ZsFHpWGEWvlmwSUnqDxZ+WbBHbte8Vf5ZsEdu17xV/lmwRdPFaZW+WbB9NBbXlX5ZsH00FteVflmwe5kEhNU+WbB7mQSE1T5ZsHM5ljLUvlmwczmWMtS+WbBxHoPgFH5ZsHEeg+AUflmwaT8VThQ+WbBnJAM7U75ZsGckAztTvlmwZQkw6FN+WbBlCTDoU35ZsFypglaTPlmwXKmCVpM+WbBbDrADkv5ZsFKvAbHSflmwUq8BsdJ+WbBRFC9e0j5ZsFEUL17SPlmwTzkczBH+WbBPORzMEf5ZsEbZrroRflmwRtmuuhF+WbBFPpwnUT5ZsEU+nCdRPlmwfF7t1VD+WbB8Xu3VUP5ZsHrD24KQvlmweOjJL9A+WbB46Mkv0D5ZsHDJWt3P/lmwbu5ISw++WbBu7khLD75ZsGaO2jkPPlmwZHPHpk7+WbBkc8emTv5ZsGLY9VNOvlmwYtj1U06+WbBaeUbBjn5ZsFjedK6N/lmwWN50ro3+WbBP/sYczb5ZsE6j88nNflmwTqPzyc1+WbBMyOG3DP5ZsEzI4bcM/lmwRClzJQy+WbBCjmDSTH5ZsEKOYNJMflmwei6yQEw+WbB6LrJATD5ZsHiToC2LvlmweJOgLYu+WbB2uI2ay35ZsG5ZH0jLPlmwblkfSMs+WbBsPgz2Cr5ZsGPenqQKflmwY96epAp+WbBiA4xRSj5ZsGIDjFFKPlmwYKi5/km+WbBgqLn+Sb5ZsFfJC6yJflmwV8kLrIl+WbBWbjkZiT5ZsE3OisfI/lmwTc6Kx8j+WbBL87h0yH5ZsEvzuHTIflmwSlimIgg+WbBB+TeQB/5ZsEH5N5AH/lmwQfk3kAf+WbBAXiV9R35ZsEBeJX1Hflmwd35260c+WbB2I2SYhv5ZsHYjZJiG/lmwdEhSRca+WbB0SFJFxr5ZsGuo4/PGPlmwa6jj88Y+WbBqDdGhBf5ZsGoN0aEF/lmwYa5jDwW+WbBhrmMPBb5ZsF+TUPxFPlmwX5NQ/EU+WbBeOH5pRP5ZsF44fmlE/lmwVZjQF4S+WbBVmNAXhL5ZsFO9/YSEflmwU739hIR+WbBLHk9yw/5ZsEmDfR/DvlmwSYN9H8O+WbBIKGqNA35ZsEgoao0Dflmwf0i8ewL+WbB97anoQr5ZsH3tqehCvlmwdU47lkJ+WbB1TjuWQn5ZsHNzKQOCPlmwc3MpA4I+WbBx2Bbwwb5ZsGl4qF7BflmwaXioXsF+WbBnXZYMAT5ZsGddlgwBPlmwXv4nugC+WbBe/ie6AL5ZsF1jFWdAflmwXWMVZ0B+WbBUw6cVQD5ZsFTDpxVAPlmwUuiUgr/+GbBS6JSCv/4ZsFFNgm//fhmwUU2Cb/9+GbBI7hPd/z4ZsEcTAYs+/hmwRxMBiz7+GbB+s1M5Pn4ZsH6zUzk+fhmwfRhA5n4+GbB7PW5Tff4ZsHs9blN9/hmwcp3AAb2+GbByncABvb4ZsHEC7e69PhmwcQLt7r0+GbBoo39cvP4ZsGijf1y8/hmwZwhtCfy+GbBnCG0J/L4ZsGUtWrc8PhmwXI3sZTv+GbBcjexlO/4ZsFqy2dJ7vhmwUhNrgHt+GbBSE2uAe34ZsFC4WS26/hmwULhZLbr+GbBPHUba+r4ZsE8dRtr6vhmwRn3YSPp+GbBGfdhI+n4ZsETixjY5/hmwROLGNjn+GbB8QxfkOb4ZsHpoBVF5fhmwemgFUXl+GbB4zTM+eP4ZsHBthKy4vhmwcG2ErLi+GbBu0rJZuH4ZsG7Sslm4fhmwZfMDx/g+GbBl8wPH+D4ZsGSYMbT3vhmwZJgxtPe+GbBifR8iN34ZsGJ9HyI3fhmwWh2w0Dc+GbBYQp69dr4ZsFhCnr12vhmwT6MwK3Z+GbBPozArdn4ZsE4IHdi2PhmwTggd2LY+GbBMrQtF9f4ZsEQNnTP1fhmwRA2dM/V+GbBCMoqhNT4ZsEIyiqE1PhmweZLcTzT+GbB5ktxPNP4ZsHg3yfx0fhmweDfJ/HR+GbB2nPepdD4ZsHac96l0Phmwbb1JF7P+GbBsYnbEs74ZsGxidsSzvhmwY4LIsvM+GbBh5/Yf8v4ZsGHn9h/y/hmwYAzjzTK+GbBgDOPNMr4ZsFftdXsyPhmwV+11ezI+GbBV0mMocf4ZsE1y9JZxvhmwTXL0lnG+GbBL1+JDsX4ZsEn8z/Dw/hmwSfzP8PD+GbBBXWGe8L4ZsEFdYZ7wvhmwf8IPTDB+GbB/wg9MMH4ZsHdioPov/hmwd2Kg+i/+GbB1R46nb74ZsHVHjqdvvhmwdCy8FG9+GbB0LLwUb34ZsGuNDcKvPhmwabI7b66+GbBpsjtvrr4ZsGESjR3ufhmwYRKNHe5+GbBft7qK7j4ZsF2cqHgtvhmwXZyoeC2+GbBVPTnmLX4ZsFU9OeYtfhmwU6Ink20+GbBToieTbT4ZsEsCuUFs/hmwSwK5QWz+GbBJJ6burH4ZsEeMlJvsPhmwfyzmCev+GbB/LOYJ6/4ZsH1R0/crfhmwdPJlZSs+GbB08mVlKz4ZsHNXUxJq/hmwc1dTEmr+GbBqd+SAar4ZsGjc0m2qPhmwaNzSbao+GbBnQcAa6f4ZsGdBwBrp/hmwXuJRiOm+GbBcx3916T4ZsFzHf3XpPhmwVGfQ5Cj+GbBUZ9DkKP4ZsFLM/pEovhmwUPHsPmg+GbBQ8ew+aD4ZsEiSfexn/hmwSJJ97Gf+GbBG92tZp74ZsEb3a1mnvhmwfhe9B6d+GbB+F70Hp34ZsHy8qrTm/hmweyGYYia+GbB7IZhiJr4ZsHICKhAmfhmwcKcXvWX+GbBwpxe9Zf4ZsGgHqWtlvhmwaAepa2W+GbBmrJbYpX4ZsGasltilfhmwZRGEheU+GbBlEYSF5T4ZsFwyFjPkvhmwXDIWM+S+GbBa1wPhJH4ZsFH3lU8kPhmwUfeVTyQ+GbBQXIM8Y74ZsFBcgzxjvhmwToGw6WN+GbBF4gJXoz4ZsEXiAlejPhmwREcwBKL+GbBERzAEov4ZsHvnQbLifhmwe+dBsuJ+GbB6TG9f4j4ZsHpMb1/iPhmweHFczSH+GbB4cVzNIf4ZsG/R7rshfhmwbnbcKGE+GbBudtwoYT4ZsGXXbdZg/hmwZddt1mD+GbBj/FtDoL4ZsGP8W0OgvhmwYqFJMOA+GbBZgdre3/4ZsFgmyEwfvhmwWCbITB++GbBPh1o6Hz4ZsE+HWjofPhmwTaxHp17+GbBNrEenXv4ZsEwRdVRevhmwQ7HGwp5+GbBDscbCnn4ZsEIW9K+d/hmwQhb0r53+GbB5NwYd3b4ZsHk3Bh3dvhmwd5wzyt1+GbB3nDPK3X4ZsHYBIbgc/hmwbaGzJhy+GbBtobMmHL4ZsGuGoNNcfhmwY2cyQVw+GbBjZzJBXD4ZsGFMIC6bvhmwYUwgLpu+GbBf8Q2b234ZsF/xDZvbfhmwV1GfSds+GbBV9oz3Gr4ZsFX2jPcavhmwTVcepRp+GbBLfAwSWj4ZsEt8DBJaPhmwSeE5/1m+GbBJ4Tn/Wb4ZsEDBi62ZfhmwQMGLrZl+GbB/ZnkamT4ZsHbGysjY/hmwdsbKyNj+GbB1a/h12H4ZsHVr+HXYfhmwc1DmIxg+GbBrMXeRF/4ZsGsxd5EX/hmwaZZlfld+GbBplmV+V34ZsGC29uxXPhmwXxvkmZb+GbBfG+SZlv4ZsF2A0kbWvhmwXYDSRta+GbBVIWP01j4ZsFUhY/TWPhmwUwZRohX+GbBK5uMQFb4ZsEkL0P1VPhmwSQvQ/VU+GbBAbGJrVP4ZsEBsYmtU/hmwftEQGJS+GbB+0RAYlL4ZsH02PYWUfhmwdFaPc9P+GbB0Vo9z0/4ZsHL7vODTvhmwcvu84NO+GbBqXA6PE34ZsGpcDo8TfhmwaEE8fBL+GbBm5inpUr4ZsF5Gu5dSfhmwXka7l1J+GbBc66kEkj4ZsFzrqQSSPhmwVAw68pG+GbBUDDrykb4ZsFKxKF/RfhmwUNYWDRE+GbBQ1hYNET4ZsEg2p7sQvhmwSDanuxC+GbBGm5VoUH4ZsEablWhQfhmwfjvm1lA+GbB8INSDj/4ZsHwg1IOP/hmweoXCcM9+GbByJlPezz4ZsHImU97PPhmwcAtBjA7+GbBwC0GMDv4ZsGer0zoOfhmwZ6vTOg5+GbBmEMDnTj4ZsGS17lRN/hmwZLXuVE3+GbBb1kACjb4ZsFvWQAKNvhmwWnttr40+GbBR2/9djP4ZsFHb/12M/hmwT8DtCsy+GbBPwO0KzL4ZsE5l2rgMPhmwTmXauAw+GbBFxmxmC/4ZsEXGbGYL/hmwQ+tZ00u+GbB7y6uBS34ZsHvLq4FLfhmwefCZLor+GbB58Jkuiv4ZsHhVhtvKvhmweFWG28q+GbBvdhhJyn4ZsG92GEnKfhmwbdsGNwn+GbBt2wY3Cf4ZsGV7l6UJvhmwZXuXpQm+GbBjoIVSSX4ZsGIFsz9I/hmwYgWzP0j+GbBZpgStiL4ZsFmmBK2IvhmwV4syWoh+GbBPK4PIyD4ZsE8rg8jIPhmwTZCxtce+GbBNkLG1x74ZsEw1nyMHfhmwTDWfIwd+GbBDljDRBz4ZsEOWMNEHPhmwQbsefka+GbB5W3AsRn4ZsHlbcCxGfhmwdwBd2YY+GbB3AF3Zhj4ZsHWlS0bF/hmwbQXdNMV+GbBtBd00xX4ZsGtqyqIFPhmwYstcUAT+GbBiy1xQBP4ZsGFwSf1EfhmwX1V3qkQ+GbBfVXeqRD4ZsFb1yRiD/hmwVVr2xYO+GbBVWvbFg74ZsEz7SHPDPhmwTPtIc8M+GbBLYHYgwv4ZsEJAx88CvhmwQkDHzwK+GbBBJfV8Aj4ZsEEl9XwCPhmwfsqjKUH+GbB+yqMpQf4ZsHarNJdBvhmwdqs0l0G+GbB00CJEgX4ZsHTQIkSBfhmwbLCz8oD+GbBssLPygP4ZsGqVoZ/AvhmwapWhn8C+GbBpOo8NAH4ZsGk6jw0AfhmwYJsg+z/92bBgmyD7P/3ZsF6ADqh/vdmwViCgFn992bBWIKAWf33ZsFSFjcO/PdmwVIWNw7892bBTKrtwvr3ZsFMqu3C+vdmwSgsNHv592bBKCw0e/n3ZsEjwOov+PdmwQFCMej292bBAUIx6Pb3ZsH51eec9fdmwfnV55z192bB82meUfT3ZsHzaZ5R9PdmwdHr5Anz92bB0evkCfP3ZsHJf5u+8fdmwcl/m77x92bBpwHidvD3ZsGnAeJ28PdmwaGVmCvv92bBmSlP4O33ZsGZKU/g7fdmwXerlZjs92bBcT9MTev3ZsFxP0xN6/dmwU/BkgXq92bBT8GSBer3ZsFIVUm66PdmwUhVSbro92bBQun/buf3ZsFC6f9u5/dmwSBrRifm92bBGP/82+T3ZsEY//zb5PdmwfaAQ5Tj92bB9oBDlOP3ZsHwFPpI4vdmwfAU+kji92bB6Kiw/eD3ZsHGKve13/dmwcYq97Xf92bBwL6tat73ZsGeQPQi3fdmwZ5A9CLd92bBltSq19v3ZsGW1KrX2/dmwZbUqtfb92bBkWhhjNr3ZsFu6qdE2fdmwW7qp0TZ92bBZ35e+df3ZsFFAKWx1vdmwUUApbHW92bBP5RbZtX3ZsE/lFtm1fdmwT+UW2bV92bBNygSG9T3ZsEVqljT0vdmwQ8+D4jR92bBDz4PiNH3ZsHtv1VA0Pdmwe2/VUDQ92bB51MM9c73ZsHnUwz1zvdmwd/nwqnN92bBvmkJYsz3ZsG+aQlizPdmwbX9vxbL92bBtf2/Fsv3ZsGUfwbPyfdmwZR/Bs/J92bBjRO9g8j3ZsGGp3M4x/dmwWQpuvDF92bBZCm68MX3ZsFevXClxPdmwV69cKXE92bBPD+3XcP3ZsE0020SwvdmwTTTbRLC92bBLmckx8D3ZsEM6Wp/v/dmwQzpan+/92bBBn0hNL73ZsHj/mfsvPdmweP+Z+y892bB3ZIeobv3ZsHUJtVVuvdmwdQm1VW692bBs6gbDrn3ZsGzqBsOufdmwaw80sK392bBi74Ye7b3ZsGDUs8vtfdmwYNSzy+192bBYdQV6LP3ZsFh1BXos/dmwVtozJyy92bBU/yCUbH3ZsExfskJsPdmwTF+yQmw92bBKxKAvq73ZsEIlMZ2rfdmwQiUxnat92bBAih9K6z3ZsH8uzPgqvdmwfy7M+Cq92bB2j16mKn3ZsHS0TBNqPdmwdLRME2o92bBsFN3Baf3ZsGwU3cFp/dmwarnLbql92bBonvkbqT3ZsGie+RupPdmwYD9Kiej92bBgP0qJ6P3ZsF6keHbofdmwXqR4duh92bBVhMolKD3ZsFWEyiUoPdmwVCn3kif92bBSjuV/Z33ZsFKO5X9nfdmwSi927Wc92bBKL3btZz3ZsEhUZJqm/dmwSFRkmqb92bB/9LYIpr3ZsH5Zo/XmPdmwfH6RYyX92bB8fpFjJf3ZsHPfIxElvdmwckQQ/mU92bByRBD+ZT3ZsGnkomxk/dmwZ8mQGaS92bBmbr2GpH3ZsGZuvYakfdmwXc8PdOP92bBdzw904/3ZsFv0POHjvdmwW/Q84eO92bBTlI6QI33ZsFOUjpAjfdmwUfm8PSL92bBR+bw9Iv3ZsFAeqepivdmwUB6p6mK92bBHvztYYn3ZsEe/O1hifdmwRiQpBaI92bB9BHrzob3ZsH0EevOhvdmwe6loYOF92bB7qWhg4X3ZsHoOVg4hPdmweg5WDiE92bBxrue8IL3ZsHGu57wgvdmwb5PVaWB92bBvk9VpYH3ZsGd0ZtdgPdmwZ3Rm12A92bBlmVSEn/3ZsGO+QjHffdmwY75CMd992bBbXtPf3z3ZsFte09/fPdmwWYPBjR792bBZg8GNHv3ZsFDkUzsefdmwUORTOx592bBPSUDoXj3ZsE3ublVd/dmwTe5uVV392bBEzsADnb3ZsETOwAOdvdmwexQ/Xpz92bB7FD9enP3ZsHl5LMvcvdmweXksy9y92bB3nhq5HD3ZsG8+rCcb/dmwbz6sJxv92bBto5nUW73ZsG2jmdRbvdmwZIQrglt92bBjKRkvmv3ZsGMpGS+a/dmwYU4G3Nq92bBhTgbc2r3ZsFiumErafdmwWK6YStp92bBXE4Y4Gf3ZsFcThjgZ/dmwTrQXphm92bBOtBemGb3ZsE0ZBVNZfdmwTRkFU1l92bBEOZbBWT3ZsEQ5lsFZPdmwQt6Erpi92bBC3oSumL3ZsEEDsluYfdmwQQOyW5h92bB4Y8PJ2D3ZsHhjw8nYPdmwdsjxtte92bB2yPG2173ZsG5pQyUXfdmwbmlDJRd92bBsTnDSFz3ZsGxOcNIXPdmwavNef1a92bBq815/Vr3ZsGJT8C1WfdmwYlPwLVZ92bBiU/AtVn3ZsGD43ZqWPdmwYPjdmpY92bBYWW9Ilf3ZsFhZb0iV/dmwVn5c9dV92bBWflz11X3ZsFTjSqMVPdmwS8PcURT92bBLw9xRFP3ZsEqoyf5UfdmwSqjJ/lR92bBByVusVD3ZsEHJW6xUPdmwQC5JGZP92bBALkkZk/3ZsH6TNsaTvdmwfpM2xpO92bB2M4h00z3ZsHYziHTTPdmwdJi2IdL92bBruQeQEr3ZsGu5B5ASvdmwah41fRI92bBqHjV9Ej3ZsGiDIypR/dmwaIMjKlH92bBgI7SYUb3ZsGAjtJhRvdmwXgiiRZF92bBV6TPzkP3ZsFXpM/OQ/dmwVA4hoNC92bBUDiGg0L3ZsFJzDw4QfdmwSdOg/A/92bBJ06D8D/3ZsEf4jmlPvdmwR/iOaU+92bB/WOAXT33ZsH9Y4BdPfdmwff3NhI892bB9/c2Ejz3ZsHxi+3GOvdmwc0NNH8592bBzQ00fzn3ZsHHoeozOPdmwceh6jM492bBpSMx7Db3ZsGft+egNfdmwZ+356A192bBl0ueVTT3ZsF2zeQNM/dmwXbN5A0z92bBb2GbwjH3ZsFvYZvCMfdmwUzj4Xow92bBTOPhejD3ZsFGd5gvL/dmwT4LT+Qt92bBHI2VnCz3ZsEcjZWcLPdmwRYhTFEr92bBFiFMUSv3ZsH0opIJKvdmwew2Sb4o92bB7DZJvij3ZsHmyv9yJ/dmwcRMRism92bBvuD83yT3ZsG+4PzfJPdmwZtiQ5gj92bBm2JDmCP3ZsGV9vlMIvdmwY6KsAEh92bBjoqwASH3ZsFrDPe5H/dmwWWgrW4e92bBZaCtbh73ZsFDIvQmHfdmwUMi9CYd92bBO7aq2xv3ZsE7tqrbG/dmwTVKYZAa92bBE8ynSBn3ZsETzKdIGfdmwQtgXv0X92bB6eGktRb3ZsHp4aS1FvdmweR1W2oV92bB5HVbahX3ZsHdCRIfFPdmwbqLWNcS92bBuotY1xL3ZsG0Hw+MEfdmwbQfD4wR92bBkqFVRBD3ZsGSoVVEEPdmwYo1DPkO92bBijUM+Q73ZsFot1KxDfdmwWi3UrEN92bBYksJZgz3ZsFa378aC/dmwVrfvxoL92bBOWEG0wn3ZsE5YQbTCfdmwTL1vIcI92bBEXcDQAf3ZsERdwNAB/dmwQgLuvQF92bBA59wqQT3ZsEDn3CpBPdmweAgt2ED92bB4CC3YQP3ZsHZtG0WAvdmwbc2tM4A92bBtza0zgD3ZsGxymqD//ZmwaleITj+9mbBqV4hOP72ZsGH4Gfw/PZmwYfgZ/D89mbBgXQepfv2ZsFf9mRd+vZmwV/2ZF369mbBWIobEvn2ZsFSHtLG9/ZmwVIe0sb39mbBMKAYf/b2ZsEnNM8z9fZmwSc0zzP19mbBBrYV7PP2ZsH/Scyg8vZmwf9JzKDy9mbB+N2CVfH2ZsHWX8kN8PZmwdZfyQ3w9mbB0PN/wu72ZsHQ83/C7vZmwa51xnrt9mbBrnXGeu32ZsGmCX0v7PZmwaCdM+Tq9mbBoJ0z5Or2ZsF+H3qc6fZmwXezMFHo9mbBd7MwUej2ZsFVNXcJ5/ZmwVU1dwnn9mbBT8ktvuX2ZsFPyS2+5fZmwUdd5HLk9mbBJd8qK+P2ZsEl3yor4/ZmwR9z4d/h9mbB/fQnmOD2ZsH99CeY4PZmwfWI3kzf9mbB9YjeTN/2ZsHvHJUB3vZmwc2e27nc9mbBzZ7budz2ZsHFMpJu2/ZmwaO02Cba9mbBo7TYJtr2ZsGdSI/b2PZmwZ1Ij9vY9mbBl9xFkNf2ZsF0XoxI1vZmwXRejEjW9mbBbvJC/dT2ZsFMdIm10/ZmwUx0ibXT9mbBRAhAatL2ZsE+nPYe0fZmwT6c9h7R9mbBHB4918/2ZsEcHj3Xz/ZmwRSy84vO9mbB8jM6RM32ZsHyMzpEzfZmwezH8PjL9mbB5Funrcr2ZsHkW6etyvZmwcLd7WXJ9mbBwt3tZcn2ZsG8caQayPZmwbxxpBrI9mbBmfPq0sb2ZsGTh6GHxfZmwZOHoYfF9mbBjRtYPMT2ZsFrnZ70wvZmwWudnvTC9mbBYzFVqcH2ZsFjMVWpwfZmwUGzm2HA9mbBQbObYcD2ZsE7R1IWv/ZmwTPbCMu99mbBM9sIy732ZsERXU+DvPZmwRFdT4O89mbBC/EFOLv2ZsEL8QU4u/ZmwelyTPC59mbB6XJM8Ln2ZsHhBgOluPZmwcCISV239mbBuBwAErb2ZsG4HAAStvZmwbKwtsa09mbBsrC2xrT2ZsGQMv1+s/ZmwYrGszOy9mbBisazM7L2ZsFmSPrrsPZmwWDcsKCv9mbBYNywoK/2ZsFacGdVrvZmwTjyrQ2t9mbBOPKtDa32ZsExhmTCq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\"},\"shape\":[1457],\"dtype\":\"float64\",\"order\":\"little\"}],[\"Longitude_left_parenthesis_deg_right_parenthesis\",{\"type\":\"ndarray\",\"array\":{\"type\":\"bytes\",\"data\":\"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-       "  var render_items = [{\"docid\":\"2658d5f0-a362-4074-a249-3f1f775d1434\",\"roots\":{\"p1158\":\"b9b0a50a-8fbc-428c-9a72-7a03bd05384c\"},\"root_ids\":[\"p1158\"]}];\n",
-       "  var docs = Object.values(docs_json)\n",
-       "  if (!docs) {\n",
-       "    return\n",
-       "  }\n",
-       "  const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
-       "  const is_dev = py_version.indexOf(\"+\") !== -1 || py_version.indexOf(\"-\") !== -1\n",
-       "  function embed_document(root) {\n",
-       "    var Bokeh = get_bokeh(root)\n",
-       "    Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
-       "    for (const render_item of render_items) {\n",
-       "      for (const root_id of render_item.root_ids) {\n",
-       "\tconst id_el = document.getElementById(root_id)\n",
-       "\tif (id_el.children.length && (id_el.children[0].className === 'bk-root')) {\n",
-       "\t  const root_el = id_el.children[0]\n",
-       "\t  root_el.id = root_el.id + '-rendered'\n",
-       "\t}\n",
-       "      }\n",
-       "    }\n",
-       "  }\n",
-       "  function get_bokeh(root) {\n",
-       "    if (root.Bokeh === undefined) {\n",
-       "      return null\n",
-       "    } else if (root.Bokeh.version !== py_version && !is_dev) {\n",
-       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
-       "\treturn null\n",
-       "      }\n",
-       "      return root.Bokeh.versions.get(py_version);\n",
-       "    } else if (root.Bokeh.version === py_version) {\n",
-       "      return root.Bokeh\n",
-       "    }\n",
-       "    return null\n",
-       "  }\n",
-       "  function is_loaded(root) {\n",
-       "    var Bokeh = get_bokeh(root)\n",
-       "    return (Bokeh != null && Bokeh.Panel !== undefined)\n",
-       "  }\n",
-       "  if (is_loaded(root)) {\n",
-       "    embed_document(root);\n",
-       "  } else {\n",
-       "    var attempts = 0;\n",
-       "    var timer = setInterval(function(root) {\n",
-       "      if (is_loaded(root)) {\n",
-       "        clearInterval(timer);\n",
-       "        embed_document(root);\n",
-       "      } else if (document.readyState == \"complete\") {\n",
-       "        attempts++;\n",
-       "        if (attempts > 200) {\n",
-       "          clearInterval(timer);\n",
-       "\t  var Bokeh = get_bokeh(root)\n",
-       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
-       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
-       "\t  } else {\n",
-       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
-       "\t    embed_document(root)\n",
-       "\t  }\n",
-       "        }\n",
-       "      }\n",
-       "    }, 25, root)\n",
-       "  }\n",
-       "})(window);</script>"
-      ],
-      "text/plain": [
-       ":DynamicMap   [ID]\n",
-       "   :Overlay\n",
-       "      .Tiles.I  :Tiles   [x,y]\n",
-       "      .Points.I :Points   [Longitude (deg),Latitude (deg)]   (TB)"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {
-      "application/vnd.holoviews_exec.v0+json": {
-       "id": "p1158"
-      }
-     },
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# create several series from pandas dataframe\n",
     "lon_ser = pd.Series( radiom['Longitude (deg)'].to_list() * (3) )\n",
@@ -2071,7 +667,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "id": "ef9bec9d-4ee8-4eee-a28e-6b890b6ef6c8",
    "metadata": {},
    "outputs": [],
@@ -2090,7 +686,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "id": "b56904fb-cc6e-4084-9748-36f10f7d8713",
    "metadata": {},
    "outputs": [],
@@ -2110,21 +706,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "id": "c26131e3-36d2-48f2-a436-e6f41172da2d",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x450 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "rad_fig = radiom_swe_plot(rad_swe)"
    ]
@@ -2149,7 +734,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "id": "b0855fa7-762b-40ab-afab-84d76e2070a7",
    "metadata": {},
    "outputs": [],
@@ -2191,7 +776,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "id": "ea283a7a-28fc-42fc-ab64-f6c9aab2da6b",
    "metadata": {},
    "outputs": [],

From 6a8fbe4890042f4bbf6a2295f04cf1e95b5be2ce Mon Sep 17 00:00:00 2001
From: Boyd <db1950@umd.edu>
Date: Fri, 16 Aug 2024 13:02:51 -0400
Subject: [PATCH 6/6] Cosmetic Updates; output_dir = /tmp/ on Linux

* Changed NASA svg location from dead URL to Wikipedia
* Left-aligned two figures instead of centered
* Now using /tmp/ instead of local directory on Linux systems for data read/write
* Minor text updates
* Resized hvplot figures to better render on the website
---
 book/tutorials/swesarr/swesarr_tut.ipynb | 26 ++++++++-----------
 book/tutorials/swesarr/util/helper.py    | 33 ++++++++++++++++++++++++
 2 files changed, 44 insertions(+), 15 deletions(-)

diff --git a/book/tutorials/swesarr/swesarr_tut.ipynb b/book/tutorials/swesarr/swesarr_tut.ipynb
index 23f490d..0746e2f 100644
--- a/book/tutorials/swesarr/swesarr_tut.ipynb
+++ b/book/tutorials/swesarr/swesarr_tut.ipynb
@@ -21,7 +21,7 @@
     }
    },
    "source": [
-    "![NASA](http://www.nasa.gov/sites/all/themes/custom/nasatwo/images/nasa-logo.svg)\n",
+    "![NASA](https://upload.wikimedia.org/wikipedia/commons/e/e5/NASA_logo.svg)\n",
     "\n",
     "<div>\n",
     "<img src=\"https://blogs.nasa.gov/swesarr/wp-content/uploads/sites/305/2021/06/swesarr.png\" width=\"1589\"/>\n",
@@ -169,10 +169,10 @@
     "| 36.5                   | Ka         |  Radiometer  | 1,000           | H            |\n",
     "\n",
     "<br>\n",
-    "<center>\n",
+    "<div>\n",
     "<img src=\"https://blogs.nasa.gov/swesarr/wp-content/uploads/sites/305/wppa/1.jpg\", width=\"400\", title=\"Plane\" /> <br>\n",
     "    <img src=\"https://blogs.nasa.gov/swesarr/wp-content/uploads/sites/305/wppa/4.jpg\", width=\"400\", title=\"Instrument\" />\n",
-    "</center>\n"
+    "</div>\n"
    ]
   },
   {
@@ -278,7 +278,7 @@
     "warnings.filterwarnings('ignore')           #\n",
     "#############################################\n",
     "\n",
-    "from helper import gdal_corners, join_files, join_sar_radiom, filt_pit_to_sar, filt_radiom_points, sar_swe_plot, radiom_swe_plot, rough_radiom_area"
+    "from helper import get_out_dir, gdal_corners, join_files, join_sar_radiom, filt_pit_to_sar, filt_radiom_points, sar_swe_plot, radiom_swe_plot, rough_radiom_area"
    ]
   },
   {
@@ -317,12 +317,8 @@
     "# store the location of the SAR tiles as they're located on the SWESARR data server\n",
     "remote_tiles = [source_repo + flight_line + d for d in data_files]\n",
     "\n",
-    "# create local output data directory\n",
-    "output_dir = os.getcwd() + '/data/'\n",
-    "try:\n",
-    "    os.makedirs(output_dir)\n",
-    "except FileExistsError:\n",
-    "    print('output directory prepared!')\n",
+    "# create, locate, or move file output directory based on operating system\n",
+    "output_dir = get_out_dir()\n",
     "\n",
     "# store individual TIF files locally on our computer / server\n",
     "output_paths = [output_dir + d for d in data_files]"
@@ -351,7 +347,7 @@
     "################################################################\n",
     "\n",
     "# Search data directory for all tifs\n",
-    "cur_tifs = glob.glob('./data/*.tif', recursive=True)\n",
+    "cur_tifs = glob.glob(output_dir + '*.tif', recursive=True)\n",
     "\n",
     "if not cur_tifs:\n",
     "    for remote_tile, output_path in zip(remote_tiles, output_paths):\n",
@@ -370,7 +366,7 @@
    "id": "782a372e-471e-48e3-af7e-688f0873c3a1",
    "metadata": {},
    "source": [
-    "#### Merge SAR datasets into single xarray file"
+    "#### Merge SAR datasets into single xarray object"
    ]
   },
   {
@@ -448,7 +444,7 @@
     "tiles='OSM'\n",
     "tiles='EsriImagery'\n",
     "transparent_tile = hv.Tiles('https://server.arcgisonline.com/ArcGIS/rest/services/Reference/World_Reference_Overlay/MapServer/tile/{Z}/{Y}/{X}', name=\"EsriReference\").opts(alpha=0.0)\n",
-    "frame_width  = 600\n",
+    "frame_width  = 500\n",
     "frame_height = 500\n",
     "\n",
     "# create an image for SAR data!\n",
@@ -603,7 +599,7 @@
    "outputs": [],
    "source": [
     "radiom.head()\n",
-    "#radiom.hvplot.table(width=1100) # sortable table in jupyterlab"
+    "# radiom.hvplot.table(width=1100) # sortable table in jupyterlab"
    ]
   },
   {
@@ -645,7 +641,7 @@
     "del sl, lon_ser, lat_ser, tb_ser, id_ser, frame\n",
     "\n",
     "radiom_p.hvplot.points('Longitude (deg)', 'Latitude (deg)', groupby='ID', geo=True, color='TB', alpha=1,\n",
-    "                        tiles='EsriImagery', height=500, width=800, clim=clim_r)"
+    "                        tiles='EsriImagery', height=frame_height, width=frame_width, clim=clim_r)"
    ]
   },
   {
diff --git a/book/tutorials/swesarr/util/helper.py b/book/tutorials/swesarr/util/helper.py
index 98ea1d4..772cac7 100644
--- a/book/tutorials/swesarr/util/helper.py
+++ b/book/tutorials/swesarr/util/helper.py
@@ -8,6 +8,39 @@ def sind(a):
 def tand(a):
     return np.tan( a * np.pi/180 )
 
+def get_out_dir():
+    '''
+    choose an output directory based on operating system.
+
+    for remote linux servers, the /tmp directory may use SSDs for faster read/write speeds.
+    when running on windows, simply store in the local directory
+    '''
+    import platform, shutil, glob, os
+    
+    # git repo should include a small CSV of data by default. this will either be the
+    # output directory or be copied to /tmp/ if linux
+    output_dir = os.getcwd() + '/data/'
+    try:
+        os.makedirs(output_dir)
+    except FileExistsError:
+        print('output directory prepared!')
+
+    # linux operations
+    cur_sys = platform.system()
+    if cur_sys.lower() == 'linux':
+        tmp_dir = '/tmp/'
+
+        # copy any files that may exist 
+        cur_files = glob.glob(output_dir + '*')
+        for file in cur_files:
+            shutil.copy2(file, tmp_dir)
+        # change output directory to /tmp/
+        output_dir = tmp_dir
+    
+    return output_dir
+        
+        
+
 def gdal_corners(filename):
     '''
     a function  that can be used to determine the boundary of a raster / tif file.