From a079d60eb7802d6f26464b7ec8fdf8449d1480b9 Mon Sep 17 00:00:00 2001 From: Boyd 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) +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 +20200211-18:33:53.148380,-108.227372,39.071901,2979,246.3,238.2,229.3,-108.052331,38.940326,4525.7,2.04,3.53,44.9 +20200211-18:33:53.248380,-108.227372,39.071809,2979,247.8,237.5,227.4,-108.052334,38.940329,4525.7,2.06,3.53,44.9 +20200211-18:33:53.348380,-108.227279,39.071716,2991,247.2,237.5,225.4,-108.052336,38.940331,4525.7,2.05,3.53,44.9 +20200211-18:33:53.448380,-108.227279,39.071716,2991,245.7,237.4,222.6,-108.052339,38.940333,4525.6,2.05,3.53,44.9 +20200211-18:33:53.548380,-108.227279,39.071624,2994,244.6,237.2,221.8,-108.052341,38.940335,4525.6,2.02,3.53,44.9 +20200211-18:33:53.648400,-108.227279,39.071531,3000,245.1,234.6,218.7,-108.052344,38.940337,4525.6,2.01,3.53,44.9 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+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", + "
\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 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
\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": [ + "
\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](#Table-of-Contents)\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..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 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 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 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": [ + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "%%HTML\n", "" + ] + }, + "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": [ + "
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" + ], + "text/plain": [ + " Size: 408MB\n", + "dask.array\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) " + ] + }, + "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": [ + "
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" + ], + "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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"outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "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": [ "


\n", - "## [Warnings](#Table-of-Contents)\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" From 41b86a9d36730d936d29fec74992657a7dd4660f Mon Sep 17 00:00:00 2001 From: Boyd 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": [ - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "%%HTML\n", "" - ] - }, - "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 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "2833b2ee-9460-46ab-8f3b-884e89a91a85", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
<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
" - ], - "text/plain": [ - " Size: 408MB\n", - "dask.array\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) " - ] - }, - "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": [ - "
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UTCLongitude (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)
020200211-18:33:53.048360-108.22737239.0719942971247.1241.2231.2-108.05232938.9403244525.72.023.5344.9
120200211-18:33:53.148380-108.22737239.0719012979246.3238.2229.3-108.05233138.9403264525.72.043.5344.9
220200211-18:33:53.248380-108.22737239.0718092979247.8237.5227.4-108.05233438.9403294525.72.063.5344.9
320200211-18:33:53.348380-108.22727939.0717162991247.2237.5225.4-108.05233638.9403314525.72.053.5344.9
420200211-18:33:53.448380-108.22727939.0717162991245.7237.4222.6-108.05233938.9403334525.62.053.5344.9
\n", - "
" - ], - "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": "", - "text/html": [ - "
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\n", - "" - ], - "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": [ - "
" - ] - }, - "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 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", "
\n", "\n", @@ -169,10 +169,10 @@ "| 36.5 | Ka | Radiometer | 1,000 | H |\n", "\n", "
\n", - "
\n", + "
\n", "
\n", " \n", - "
\n" + "
\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.