diff --git a/book/_toc.yml b/book/_toc.yml index 0b4e7d2..6584b73 100644 --- a/book/_toc.yml +++ b/book/_toc.yml @@ -20,6 +20,12 @@ parts: - file: tutorials/index sections: - file: tutorials/example/tutorial-notebook + - file: tutorials/snowex-core/index + title: Data Overview + sections: + - file: tutorials/snowex-core/01_snowex_data_overview + - file: tutorials/snowex-core/02_snowex_data_descriptions + - file: tutorials/snowex-core/03_snowex_data_preview - file: tutorials/Data_access/index.md title: Data Access sections: @@ -28,16 +34,14 @@ parts: - file: tutorials/Data_access/earthdata_search.md - file: tutorials/Data_access/earthaccess_snowex.ipynb - file: tutorials/Data_access/earthaccess_icesat2.ipynb + - file: tutorials/swesarr/index + title: SWESARR + sections: + - file: tutorials/swesarr/swesarr_tut - file: tutorials/albedo/index title: Albedo sections: - file: tutorials/albedo/aviris-ng-data - - file: tutorials/snowex-core/index - title: Data Overview - sections: - - file: tutorials/snowex-core/01_snowex_data_overview - - file: tutorials/snowex-core/02_snowex_data_descriptions - - file: tutorials/snowex-core/03_snowex_data_preview - caption: Projects chapters: - file: projects/index 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) +20200211-18:33:53.048360,-108.227372,39.071994,2971,247.1,241.2,231.2,-108.052329,38.940324,4525.7,2.02,3.53,44.9 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+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..0746e2f --- /dev/null +++ b/book/tutorials/swesarr/swesarr_tut.ipynb @@ -0,0 +1,836 @@ +{ + "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](https://upload.wikimedia.org/wikipedia/commons/e/e5/NASA_logo.svg)\n", + "\n", + "
\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "4188e5b9-a513-4b1a-8bc8-9d27ba9eee0b", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "source": [ + "
\n", + "Objectives: \n", + " This is a 30-minute tutorial where we will ...\n", + "
    \n", + "
  1. Introduce SWESARR
    2. \n", + "
  2. Briefly introduce active and passive microwave remote sensing
    3. \n", + "
  3. Learn how to access, filter, and visualize SWESARR data
    4. \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "63bda652-ba87-4029-8b90-62fbb517a6e7", + "metadata": {}, + "source": [ + "## Quick References\n", + "\n", + "
    \n", + "
  1. SWESARR SAR Data Pre-release FTP Server \n", + "
  2. SWESARR Radiometer Data, SnowEx20, v1\n", + "
  3. NSIDC Datasets\n", + "
  4. SWESARR Blogspot\n", + "
  5. A Version of This Repo That Doesn't Require Access to Amazon Servers \n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "ece33f6d-f565-4bcc-b056-79874e84f45c", + "metadata": {}, + "source": [ + "## What is SWESARR?\n", + "\n", + "

\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", + "

\n", + "\n", + "
\n", + "

\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", + "
\n", + "\n", + "
\n", + "\n", + "
\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", + "
\n", + "\n", + "
\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", + "" + ] + }, + { + "cell_type": "markdown", + "id": "54df5103-646f-4ab8-831c-585181100275", + "metadata": {}, + "source": [ + "## SWESARR Sensors\n", + "
\n", + "

SWESARR Frequencies, Polarization, and Bandwidth Specification

\n", + "
\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", + "
\n", + "
\n", + "
\n", + " \n", + "
\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", + "
\n", + "\n", + "
" + ] + }, + { + "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", + "
\n", + "\n", + "
\n", + "\n", + "***\n", + "

" + ] + }, + { + "cell_type": "markdown", + "id": "c5d5a065-efbf-4735-b514-b259f8ea2822", + "metadata": {}, + "source": [ + "
\n", + "

\n", + "Accessing Data: SAR\n", + "

\n", + "
\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 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" + ] + }, + { + "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, 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]" + ] + }, + { + "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(output_dir + '*.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 object" + ] + }, + { + "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
\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://server.arcgisonline.com/ArcGIS/rest/services/Reference/World_Reference_Overlay/MapServer/tile/{Z}/{Y}/{X}', name=\"EsriReference\").opts(alpha=0.0)\n", + "frame_width = 500\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_XKuKa225H_v03.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_XKuKa225H_v03.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_XKuKa225H_v03.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=frame_height, width=frame_width, 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": [ + "
\n", + "Exercise: \n", + "
    \n", + "
  1. Plot a time-series visualization of the filtered SAR channels from the output of the \n", + " join_sar_radiom() function\n", + "
    2. \n", + "
  2. Plot a time-series visualization of the radiometer channels from the output of the \n", + " join_sar_radiom() function \n", + "
    3. \n", + "
  3. Hint: the data series variable ( data_ser ) is a pandas data series. \n", + " Use some of the methods shown above to read and plot the data!\n", + "
    4. \n", + "
\n", + "
" + ] + }, + { + "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 -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": [ + "


\n", + "## [Warnings]\n", + "
\n", + "Interpreting Data: 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", + "
\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..772cac7 --- /dev/null +++ b/book/tutorials/swesarr/util/helper.py @@ -0,0 +1,505 @@ +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 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. + ''' + #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